diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index 1dd11e1f..59e9e709 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -12,10 +12,10 @@ A clear and concise description of what the bug is. **To Reproduce** Steps to reproduce the behavior: -1. Go to '...' -2. Click on '....' -3. Scroll down to '....' -4. See error +1. Enable ... feature provider +2. Setup ... sensor parameters +3. Run RAPIDS +4. etc ... **Expected behavior** A clear and concise description of what you expected to happen. @@ -25,7 +25,8 @@ If applicable, add screenshots to help explain your problem. **Please complete the following information:** - OS: [e.g. MacOS] - - Version [e.g. 22] + - RAPIDS current commit, paste the output of `git rev-parse --short HEAD` + - A link to your `config.yaml` - Type of mobile data you are dealing with (Android/iOS) diff --git a/.github/workflows/docs.yaml b/.github/workflows/docs.yaml new file mode 100644 index 00000000..80d7fb43 --- /dev/null +++ b/.github/workflows/docs.yaml @@ -0,0 +1,23 @@ +name: docs +on: + push: + branches: + - day_segments +jobs: + deploy: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v2 + with: + fetch-depth: 0 + - uses: actions/setup-python@v2 + with: + python-version: 3.x + - run: pip install git+https://${GH_TOKEN}@github.com/carissalow/mkdocs-material-insiders.git + - run: pip install mike + - run: | + git config user.name github-actions + git config user.email github-actions@github.com + mike deploy --push --update-aliases 0.1 latest +env: + GH_TOKEN: ${{ secrets.GH_TOKEN }} diff --git a/.gitignore b/.gitignore index 23992747..60899167 100644 --- a/.gitignore +++ b/.gitignore @@ -95,6 +95,7 @@ packrat/* data/external/* !/data/external/.gitkeep !/data/external/stachl_application_genre_catalogue.csv +!/data/external/timesegments*.csv data/raw/* !/data/raw/.gitkeep data/interim/* @@ -107,5 +108,7 @@ reports/ .RData .Rhistory sn_profile_*/ +!sn_profile_rapids settings.dcf -tests/fakedata_generation/ \ No newline at end of file +tests/fakedata_generation/ +site/ \ No newline at end of file diff --git a/.travis.yml b/.travis.yml index 19d9dedf..98993d3e 100644 --- a/.travis.yml +++ b/.travis.yml @@ -39,38 +39,7 @@ jobs: - "$HOME/.local/share/renv" - "$TRAVIS_BUILD_DIR/renv/library" script: - - bash tests/scripts/run_tests.sh - - name: Python 3.7 on macOS - os: osx - osx_image: xcode11.3 - language: generic - before_install: - - HOMEBREW_NO_AUTO_UPDATE=1 brew install gcc@9 - - HOMEBREW_NO_AUTO_UPDATE=1 brew install https://github.com/Homebrew/homebrew-core/raw/218998d/Formula/r.rb - - R --version - - HOMEBREW_NO_AUTO_UPDATE=1 brew install mysql - - HOMEBREW_NO_AUTO_UPDATE=1 brew services start mysql - - HOMEBREW_NO_AUTO_UPDATE=1 brew cask install miniconda - - eval "$(/opt/miniconda3/condabin/conda shell.bash hook)" - - eval "$(conda shell.bash hook)" - install: - - conda init bash - - conda update -q --all --yes conda - - conda env create -q -n test-environment python=$TRAVIS_PYTHON_VERSION --file - environment.yml - - conda activate test-environment - - snakemake -j1 renv_install - - R -e 'renv::settings$use.cache(FALSE)' - - snakemake -j1 renv_restore - env: - - RENV_PATHS_ROOT="$HOME/renv/cache" - cache: - directories: - - "/usr/local/lib/R" - - "$RENV_PATHS_ROOT" - - "$TRAVIS_BUILD_DIR/renv/library" - script: - - bash tests/scripts/run_tests.sh + - bash tests/scripts/run_tests.sh all test - stage: deploy name: Python 3.7 on Xenial Linux Docker os: linux @@ -83,6 +52,7 @@ jobs: branches: only: - master + - time_segment stages: - name: deploy diff --git a/README.md b/README.md index f9b16765..0596d5f3 100644 --- a/README.md +++ b/README.md @@ -1,6 +1,7 @@ [![Snakemake](https://img.shields.io/badge/snakemake-≥5.7.1-brightgreen.svg?style=flat)](https://snakemake.readthedocs.io) -[![Documentation Status](https://readthedocs.org/projects/rapidspitt/badge/?version=latest)](https://rapidspitt.readthedocs.io/en/latest/?badge=latest) +[![Documentation Status](https://github.com/carissalow/rapids/workflows/docs/badge.svg)](https://www.rapids.science/) [![Build Status](https://travis-ci.com/carissalow/rapids.svg?branch=master)](https://travis-ci.com/carissalow/rapids) +[![Contributor Covenant](https://img.shields.io/badge/Contributor%20Covenant-v2.0%20adopted-ff69b4.svg)](code_of_conduct.md) # RAPIDS diff --git a/Snakefile b/Snakefile index 647db573..04882a79 100644 --- a/Snakefile +++ b/Snakefile @@ -12,164 +12,238 @@ files_to_compute = [] if len(config["PIDS"]) == 0: raise ValueError("Add participants IDs to PIDS in config.yaml. Remember to create their participant files in data/external") -if config["PHONE_VALID_SENSED_BINS"]["COMPUTE"] or config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: # valid sensed bins is necessary for sensed days, so we add these files anyways if sensed days are requested - if len(config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]) == 0: - raise ValueError("If you want to compute PHONE_VALID_SENSED_BINS or PHONE_VALID_SENSED_DAYS, you need to add at least one table to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml") +for provider in config["PHONE_DATA_YIELD"]["PROVIDERS"].keys(): + if config["PHONE_DATA_YIELD"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=map(str.lower, config["PHONE_DATA_YIELD"]["SENSORS"]))) + files_to_compute.extend(expand("data/interim/{pid}/phone_yielded_timestamps.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_data_yield_features/phone_data_yield_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_DATA_YIELD"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_data_yield.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - tables_android = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]] # for android, discard any ios tables that may exist - tables_ios = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist +for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys(): + if config["PHONE_MESSAGES"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_messages_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_messages_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_messages_features/phone_messages_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_MESSAGES"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_messages.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") - for pids,table in zip([pids_android, pids_ios], [tables_android, tables_ios]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) +for provider in config["PHONE_CALLS"]["PROVIDERS"].keys(): + if config["PHONE_CALLS"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_calls_features/phone_calls_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_CALLS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_calls.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_valid_sensed_days_{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins.csv", - pid=config["PIDS"], - min_valid_hours_per_day=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) +for provider in config["PHONE_BLUETOOTH"]["PROVIDERS"].keys(): + if config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_bluetooth_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_bluetooth_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_bluetooth_features/phone_bluetooth_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_bluetooth.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -if config["MESSAGES"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/messages_{messages_type}_{day_segment}.csv", pid=config["PIDS"], messages_type = config["MESSAGES"]["TYPES"], day_segment = config["MESSAGES"]["DAY_SEGMENTS"])) +for provider in config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"].keys(): + if config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_features/phone_activity_recognition_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_activity_recognition.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -if config["CALLS"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/calls_{call_type}_{day_segment}.csv", pid=config["PIDS"], call_type=config["CALLS"]["TYPES"], day_segment = config["CALLS"]["DAY_SEGMENTS"])) +for provider in config["PHONE_BATTERY"]["PROVIDERS"].keys(): + if config["PHONE_BATTERY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_battery_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_features/phone_battery_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_BATTERY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_battery.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -if config["BARNETT_LOCATION"]["COMPUTE"]: - if config["BARNETT_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": - if config["BARNETT_LOCATION"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_resampled.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"])) - else: - raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/location_barnett_{day_segment}.csv", pid=config["PIDS"], day_segment = config["BARNETT_LOCATION"]["DAY_SEGMENTS"])) +for provider in config["PHONE_SCREEN"]["PROVIDERS"].keys(): + if config["PHONE_SCREEN"]["PROVIDERS"][provider]["COMPUTE"]: + # if "PHONE_SCREEN" in config["PHONE_DATA_YIELD"]["SENSORS"]:# not used for now because we took episodepersensedminutes out of the list of supported features + # files_to_compute.extend(expand("data/interim/{pid}/phone_yielded_timestamps.csv", pid=config["PIDS"])) + # else: + # raise ValueError("Error: Add PHONE_SCREEN (and as many PHONE_SENSORS as you have in your database) to [PHONE_DATA_YIELD][SENSORS] in config.yaml. This is necessary to compute phone_yielded_timestamps (time when the smartphone was sensing data)") + files_to_compute.extend(expand("data/raw/{pid}/phone_screen_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_screen_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_screen_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_features/phone_screen_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_SCREEN"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_screen.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -if config["BLUETOOTH"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/bluetooth_{day_segment}.csv", pid=config["PIDS"], day_segment = config["BLUETOOTH"]["DAY_SEGMENTS"])) +for provider in config["PHONE_LIGHT"]["PROVIDERS"].keys(): + if config["PHONE_LIGHT"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_light_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_light_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_light_features/phone_light_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LIGHT"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_light.csv", pid=config["PIDS"],)) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -if config["ACTIVITY_RECOGNITION"]["COMPUTE"]: - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - - for pids,table in zip([pids_android, pids_ios], [config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/processed/{pid}/{sensor}_deltas.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/processed/{pid}/activity_recognition_{day_segment}.csv",pid=config["PIDS"], day_segment = config["ACTIVITY_RECOGNITION"]["DAY_SEGMENTS"])) +for provider in config["PHONE_ACCELEROMETER"]["PROVIDERS"].keys(): + if config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_accelerometer_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_accelerometer_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_accelerometer.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -if config["BATTERY"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/battery_deltas.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/processed/{pid}/battery_{day_segment}.csv", pid = config["PIDS"], day_segment = config["BATTERY"]["DAY_SEGMENTS"])) +for provider in config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys(): + if config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_applications_foreground_features/phone_applications_foreground_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_applications_foreground.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -if config["SCREEN"]["COMPUTE"]: - if config["SCREEN"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) - else: - raise ValueError("Error: Add your screen table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)") - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/screen_deltas.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/processed/{pid}/screen_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SCREEN"]["DAY_SEGMENTS"])) +for provider in config["PHONE_WIFI_VISIBLE"]["PROVIDERS"].keys(): + if config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_visible_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_visible_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_wifi_visible_features/phone_wifi_visible_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_visible.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -if config["LIGHT"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/light_{day_segment}.csv", pid = config["PIDS"], day_segment = config["LIGHT"]["DAY_SEGMENTS"])) +for provider in config["PHONE_WIFI_CONNECTED"]["PROVIDERS"].keys(): + if config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_connected_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_connected_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_wifi_connected_features/phone_wifi_connected_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_connected.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -if config["ACCELEROMETER"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/accelerometer_{day_segment}.csv", pid = config["PIDS"], day_segment = config["ACCELEROMETER"]["DAY_SEGMENTS"])) +for provider in config["PHONE_CONVERSATION"]["PROVIDERS"].keys(): + if config["PHONE_CONVERSATION"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_conversation_features/phone_conversation_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_CONVERSATION"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_conversation.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -if config["APPLICATIONS_FOREGROUND"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor}_with_datetime_with_genre.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/applications_foreground_{day_segment}.csv", pid = config["PIDS"], day_segment = config["APPLICATIONS_FOREGROUND"]["DAY_SEGMENTS"])) +for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): + if config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["COMPUTE"]: + if config["PHONE_LOCATIONS"]["LOCATIONS_TO_USE"] == "FUSED_RESAMPLED": + if "PHONE_LOCATIONS" in config["PHONE_DATA_YIELD"]["SENSORS"]: + files_to_compute.extend(expand("data/interim/{pid}/phone_yielded_timestamps.csv", pid=config["PIDS"])) + else: + raise ValueError("Error: Add PHONE_LOCATIONS (and as many PHONE_SENSORS as you have) to [PHONE_DATA_YIELD][SENSORS] in config.yaml. This is necessary to compute phone_yielded_timestamps (time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") -if config["WIFI"]["COMPUTE"]: - if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/processed/{pid}/wifi_{day_segment}.csv", pid = config["PIDS"], day_segment = config["WIFI"]["DAY_SEGMENTS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_locations_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") - if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/processed/{pid}/wifi_{day_segment}.csv", pid = config["PIDS"], day_segment = config["WIFI"]["DAY_SEGMENTS"])) +for provider in config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"].keys(): + if config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_summary_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_summary_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_summary_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_heartrate_summary_features/fitbit_heartrate_summary_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_heartrate_summary.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -if config["HEARTRATE"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["HEARTRATE"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) - files_to_compute.extend(expand("data/processed/{pid}/fitbit_heartrate_{day_segment}.csv", pid = config["PIDS"], day_segment = config["HEARTRATE"]["DAY_SEGMENTS"])) +for provider in config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"].keys(): + if config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_intraday_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_intraday_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_intraday_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_heartrate_intraday_features/fitbit_heartrate_intraday_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_heartrate_intraday.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -if config["STEP"]["COMPUTE"]: - if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED": - files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["STEP"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_step_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) - files_to_compute.extend(expand("data/processed/{pid}/fitbit_step_{day_segment}.csv", pid = config["PIDS"], day_segment = config["STEP"]["DAY_SEGMENTS"])) +for provider in config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"].keys(): + if config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_summary_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_summary_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_summary_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_sleep_summary_features/fitbit_sleep_summary_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_sleep_summary.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -if config["SLEEP"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SLEEP"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"])) - files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SLEEP"]["DAY_SEGMENTS"])) +# for provider in config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"].keys(): +# if config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]: +# files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_intraday_raw.csv", pid=config["PIDS"])) +# files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_intraday_parsed.csv", pid=config["PIDS"])) +# files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_intraday_parsed_with_datetime.csv", pid=config["PIDS"])) -if config["CONVERSATION"]["COMPUTE"]: - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) +for provider in config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"].keys(): + if config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_summary_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_summary_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_summary_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_steps_summary_features/fitbit_steps_summary_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_steps_summary.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") - for pids,table in zip([pids_android, pids_ios], [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"]]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/processed/{pid}/conversation_{day_segment}.csv",pid=config["PIDS"], day_segment = config["CONVERSATION"]["DAY_SEGMENTS"])) +for provider in config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"].keys(): + if config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_intraday_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_intraday_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_intraday_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_steps_intraday_features/fitbit_steps_intraday_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_steps_intraday.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -if config["DORYAB_LOCATION"]["COMPUTE"]: - if config["DORYAB_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": - if config["DORYAB_LOCATION"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_resampled.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"])) - else: - raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/location_doryab_{segment}.csv", pid=config["PIDS"], segment = config["DORYAB_LOCATION"]["DAY_SEGMENTS"])) +# for provider in config["FITBIT_CALORIES"]["PROVIDERS"].keys(): +# if config["FITBIT_CALORIES"]["PROVIDERS"][provider]["COMPUTE"]: +# files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_CALORIES"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) +# files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) +# files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) +# files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) +# files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -# visualization for data exploration -if config["HEATMAP_FEATURES_CORRELATIONS"]["PLOT"]: - files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_features_correlations.html", min_valid_hours_per_day=config["HEATMAP_FEATURES_CORRELATIONS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) - -if config["HISTOGRAM_VALID_SENSED_HOURS"]["PLOT"]: - files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/histogram_valid_sensed_hours.html", min_valid_hours_per_day=config["HISTOGRAM_VALID_SENSED_HOURS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) +# Visualization for Data Exploration +if config["HISTOGRAM_PHONE_DATA_YIELD"]["PLOT"]: + files_to_compute.append("reports/data_exploration/histogram_phone_data_yield.html") -if config["HEATMAP_DAYS_BY_SENSORS"]["PLOT"]: - files_to_compute.extend(expand("reports/interim/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{pid}/heatmap_days_by_sensors.html", pid=config["PIDS"], min_valid_hours_per_day=config["HEATMAP_DAYS_BY_SENSORS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) - files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_days_by_sensors_all_participants.html", min_valid_hours_per_day=config["HEATMAP_DAYS_BY_SENSORS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) +if config["HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT"]["PLOT"]: + files_to_compute.extend(expand("reports/interim/{pid}/heatmap_sensors_per_minute_per_time_segment.html", pid=config["PIDS"])) + files_to_compute.append("reports/data_exploration/heatmap_sensors_per_minute_per_time_segment.html") -if config["HEATMAP_SENSED_BINS"]["PLOT"]: - files_to_compute.extend(expand("reports/interim/heatmap_sensed_bins/{pid}/heatmap_sensed_bins.html", pid=config["PIDS"])) - files_to_compute.extend(["reports/data_exploration/heatmap_sensed_bins_all_participants.html"]) +if config["HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT"]["PLOT"]: + files_to_compute.extend(expand("reports/interim/{pid}/heatmap_sensor_row_count_per_time_segment.html", pid=config["PIDS"])) + files_to_compute.append("reports/data_exploration/heatmap_sensor_row_count_per_time_segment.html") -if config["OVERALL_COMPLIANCE_HEATMAP"]["PLOT"]: - files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/overall_compliance_heatmap.html", min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) +if config["HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT"]["PLOT"]: + files_to_compute.append("reports/data_exploration/heatmap_phone_data_yield_per_participant_per_time_segment.html") + +if config["HEATMAP_FEATURE_CORRELATION_MATRIX"]["PLOT"]: + files_to_compute.append("reports/data_exploration/heatmap_feature_correlation_matrix.html") rule all: diff --git a/code_of_conduct.md b/code_of_conduct.md new file mode 100644 index 00000000..52f820e6 --- /dev/null +++ b/code_of_conduct.md @@ -0,0 +1,134 @@ + +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, religion, or sexual identity +and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or + advances of any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email + address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +moshi@pitt.edu. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series +of actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or +permanent ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within +the community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.0, available at +[https://www.contributor-covenant.org/version/2/0/code_of_conduct.html][v2.0]. + +Community Impact Guidelines were inspired by +[Mozilla's code of conduct enforcement ladder][Mozilla CoC]. + +For answers to common questions about this code of conduct, see the FAQ at +[https://www.contributor-covenant.org/faq][FAQ]. Translations are available +at [https://www.contributor-covenant.org/translations][translations]. + +[homepage]: https://www.contributor-covenant.org +[v2.0]: https://www.contributor-covenant.org/version/2/0/code_of_conduct.html +[Mozilla CoC]: https://github.com/mozilla/diversity +[FAQ]: https://www.contributor-covenant.org/faq +[translations]: https://www.contributor-covenant.org/translations + diff --git a/config.yaml b/config.yaml index a0cacb3d..014881b4 100644 --- a/config.yaml +++ b/config.yaml @@ -1,244 +1,374 @@ -# Participants to include in the analysis -# You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically -PIDS: [test01] - -# Global var with common day segments -DAY_SEGMENTS: &day_segments - [daily, morning, afternoon, evening, night] - -# Global timezone -# Use codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones -# Double check your code, for example EST is not US Eastern Time. -TIMEZONE: &timezone - America/New_York - +# See https://www.rapids.science/latest/setup/configuration/#database-credentials DATABASE_GROUP: &database_group MY_GROUP -DOWNLOAD_PARTICIPANTS: - IGNORED_DEVICE_IDS: [] # for example "5a1dd68c-6cd1-48fe-ae1e-14344ac5215f" - GROUP: *database_group +# See https://www.rapids.science/latest/setup/configuration/#timezone-of-your-study +TIMEZONE: &timezone + America/New_York -# Download data config -DOWNLOAD_DATASET: - GROUP: *database_group +# See https://www.rapids.science/latest/setup/configuration/#participant-files +PIDS: [test01] -# Readable datetime config -READABLE_DATETIME: - FIXED_TIMEZONE: *timezone +# See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files +CREATE_PARTICIPANT_FILES: + SOURCE: + TYPE: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE + DATABASE_GROUP: *database_group + CSV_FILE_PATH: "data/external/example_participants.csv" # see docs for required format + TIMEZONE: *timezone + PHONE_SECTION: + ADD: TRUE + DEVICE_ID_COLUMN: device_id # column name + IGNORED_DEVICE_IDS: [] + FITBIT_SECTION: + ADD: TRUE + DEVICE_ID_COLUMN: device_id # column name + IGNORED_DEVICE_IDS: [] -PHONE_VALID_SENSED_BINS: - COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features - BIN_SIZE: &bin_size 5 # (in minutes) - # Add as many sensor tables as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS. - # If you are extracting screen or Barnett's location features, screen and locations tables are mandatory. - DB_TABLES: [] +# See https://www.rapids.science/latest/setup/configuration/#time-segments +TIME_SEGMENTS: &time_segments + TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT + FILE: "data/external/timesegments_periodic.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, see docs -PHONE_VALID_SENSED_DAYS: - COMPUTE: False - MIN_VALID_HOURS_PER_DAY: &min_valid_hours_per_day [16] # (out of 24) MIN_HOURS_PER_DAY - MIN_VALID_BINS_PER_HOUR: &min_valid_bins_per_hour [6] # (out of 60min/BIN_SIZE bins) -# Communication SMS features config, TYPES and FEATURES keys need to match -MESSAGES: - COMPUTE: False - DB_TABLE: messages - TYPES : [received, sent] - FEATURES: - received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] - sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] - DAY_SEGMENTS: *day_segments -# Communication call features config, TYPES and FEATURES keys need to match -CALLS: - COMPUTE: False - DB_TABLE: calls - TYPES: [missed, incoming, outgoing] - FEATURES: - missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] - incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] - outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] - DAY_SEGMENTS: *day_segments +######################################################################################################################## +# PHONE # +######################################################################################################################## -APPLICATION_GENRES: - CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) - CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" - UPDATE_CATALOGUE_FILE: false # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE - SCRAPE_MISSING_GENRES: false # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway +# See https://www.rapids.science/latest/setup/configuration/#device-data-source-configuration +PHONE_DATA_CONFIGURATION: + SOURCE: + TYPE: DATABASE + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE + VALUE: *timezone -RESAMPLE_FUSED_LOCATION: - CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold - TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row - TIMEZONE: *timezone +# Sensors ------ -BARNETT_LOCATION: - COMPUTE: False - DB_TABLE: locations - DAY_SEGMENTS: [daily] # These features are only available on a daily basis - FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"] - LOCATIONS_TO_USE: ALL # ALL, ALL_EXCEPT_FUSED OR RESAMPLE_FUSED - ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius - TIMEZONE: *timezone - MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features +# https://www.rapids.science/latest/features/phone-accelerometer/ +PHONE_ACCELEROMETER: + TABLE: accelerometer + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] + SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" + + PANDA: + COMPUTE: False + VALID_SENSED_MINUTES: False + FEATURES: + exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + SRC_FOLDER: "panda" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" -DORYAB_LOCATION: - COMPUTE: False - DB_TABLE: locations - DAY_SEGMENTS: *day_segments - FEATURES: ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy"] - LOCATIONS_TO_USE: ALL # ALL, ALL_EXCEPT_FUSED OR RESAMPLE_FUSED - DBSCAN_EPS: 10 # meters - DBSCAN_MINSAMPLES: 5 - THRESHOLD_STATIC : 1 # km/h - MAXIMUM_GAP_ALLOWED: 300 - MINUTES_DATA_USED: False - SAMPLING_FREQUENCY: 0 - -BLUETOOTH: - COMPUTE: False - DB_TABLE: bluetooth - DAY_SEGMENTS: *day_segments - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - -ACTIVITY_RECOGNITION: - COMPUTE: False - DB_TABLE: +# See https://www.rapids.science/latest/features/phone-activity-recognition/ +PHONE_ACTIVITY_RECOGNITION: + TABLE: ANDROID: plugin_google_activity_recognition IOS: plugin_ios_activity_recognition - DAY_SEGMENTS: *day_segments - FEATURES: ["count","mostcommonactivity","countuniqueactivities","activitychangecount","sumstationary","summobile","sumvehicle"] + EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"] + ACTIVITY_CLASSES: + STATIONARY: ["still", "tilting"] + MOBILE: ["on_foot", "walking", "running", "on_bicycle"] + VEHICLE: ["in_vehicle"] + SRC_FOLDER: "rapids" # inside src/features/phone_activity_recognition + SRC_LANGUAGE: "python" -BATTERY: - COMPUTE: False - DB_TABLE: battery - DAY_SEGMENTS: *day_segments - FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] +# See https://www.rapids.science/latest/features/phone-applications-foreground/ +PHONE_APPLICATIONS_FOREGROUND: + TABLE: applications_foreground + APPLICATION_CATEGORIES: + CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) + CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" + UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE + SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway + PROVIDERS: + RAPIDS: + COMPUTE: False + SINGLE_CATEGORIES: ["all", "email"] + MULTIPLE_CATEGORIES: + social: ["socialnetworks", "socialmediatools"] + entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"] + SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps + EXCLUDED_CATEGORIES: [] + EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] + FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] + SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground + SRC_LANGUAGE: "python" -SCREEN: - COMPUTE: False - DB_TABLE: screen - DAY_SEGMENTS: *day_segments - REFERENCE_HOUR_FIRST_USE: 0 - IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable - IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable - FEATURES_DELTAS: ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] - EPISODE_TYPES: ["unlock"] +# See https://www.rapids.science/latest/features/phone-battery/ +PHONE_BATTERY: + TABLE: battery + EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] + SRC_FOLDER: "rapids" # inside src/features/phone_battery + SRC_LANGUAGE: "python" -LIGHT: - COMPUTE: False - DB_TABLE: light - DAY_SEGMENTS: *day_segments - FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] +# See https://www.rapids.science/latest/features/phone-bluetooth/ +PHONE_BLUETOOTH: + TABLE: bluetooth + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth + SRC_LANGUAGE: "r" -ACCELEROMETER: - COMPUTE: False - DB_TABLE: accelerometer - DAY_SEGMENTS: *day_segments - FEATURES: - MAGNITUDE: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] - EXERTIONAL_ACTIVITY_EPISODE: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] - NONEXERTIONAL_ACTIVITY_EPISODE: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] - VALID_SENSED_MINUTES: False +# See https://www.rapids.science/latest/features/phone-calls/ +PHONE_CALLS: + TABLE: calls + PROVIDERS: + RAPIDS: + COMPUTE: False + CALL_TYPES: [missed, incoming, outgoing] + FEATURES: + missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] + incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] + outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/phone_calls -APPLICATIONS_FOREGROUND: - COMPUTE: False - DB_TABLE: applications_foreground - DAY_SEGMENTS: *day_segments - SINGLE_CATEGORIES: ["all", "email"] - MULTIPLE_CATEGORIES: - social: ["socialnetworks", "socialmediatools"] - entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"] - SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps - EXCLUDED_CATEGORIES: ["system_apps"] - EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] - FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] - -HEARTRATE: - COMPUTE: False - DB_TABLE: fitbit_data - DAY_SEGMENTS: *day_segments - SUMMARY_FEATURES: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. heigh, weight) use with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] - INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] - -STEP: - COMPUTE: False - DB_TABLE: fitbit_data - DAY_SEGMENTS: *day_segments - EXCLUDE_SLEEP: - EXCLUDE: False - TYPE: FIXED # FIXED OR FITBIT_BASED (CONFIGURE FITBIT's SLEEP DB_TABLE) - FIXED: - START: "23:00" - END: "07:00" - FEATURES: - ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] - SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] - ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] - THRESHOLD_ACTIVE_BOUT: 10 # steps - INCLUDE_ZERO_STEP_ROWS: False - -SLEEP: - COMPUTE: False - DB_TABLE: fitbit_data - DAY_SEGMENTS: *day_segments - SLEEP_TYPES: ["main", "nap", "all"] - SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] - -WIFI: - COMPUTE: False - DB_TABLE: - VISIBLE_ACCESS_POINTS: "wifi" # if you only have a CONNECTED_ACCESS_POINTS table, set this value to "" - CONNECTED_ACCESS_POINTS: "sensor_wifi" # if you only have a VISIBLE_ACCESS_POINTS table, set this value to "" - DAY_SEGMENTS: *day_segments - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - -CONVERSATION: - COMPUTE: False - DB_TABLE: +# See https://www.rapids.science/latest/features/phone-conversation/ +PHONE_CONVERSATION: + TABLE: ANDROID: plugin_studentlife_audio_android IOS: plugin_studentlife_audio - DAY_SEGMENTS: *day_segments - FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", "unknownexpectedfraction","countconversation"] - RECORDINGMINUTES: 1 - PAUSEDMINUTES : 3 + RECORDING_MINUTES: 1 + PAUSED_MINUTES : 3 + SRC_FOLDER: "rapids" # inside src/features/phone_conversation + SRC_LANGUAGE: "python" -### Visualizations ################################################################ -HEATMAP_FEATURES_CORRELATIONS: +# See https://www.rapids.science/latest/features/phone-data-yield/ +PHONE_DATA_YIELD: + SENSORS: [] + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours] + MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1 representing the number of minutes with at least + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/phone_data_yield + +# See https://www.rapids.science/latest/features/phone-light/ +PHONE_LIGHT: + TABLE: light + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] + SRC_FOLDER: "rapids" # inside src/features/phone_light + SRC_LANGUAGE: "python" + +# See https://www.rapids.science/latest/features/phone-locations/ +PHONE_LOCATIONS: + TABLE: locations + LOCATIONS_TO_USE: FUSED_RESAMPLED # ALL, GPS OR FUSED_RESAMPLED + FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold + FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row + PROVIDERS: + DORYAB: + COMPUTE: False + FEATURES: ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy"] + DBSCAN_EPS: 10 # meters + DBSCAN_MINSAMPLES: 5 + THRESHOLD_STATIC : 1 # km/h + MAXIMUM_GAP_ALLOWED: 300 + MINUTES_DATA_USED: False + SAMPLING_FREQUENCY: 0 + SRC_FOLDER: "doryab" # inside src/features/phone_locations + SRC_LANGUAGE: "python" + + BARNETT: + COMPUTE: False + FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"] + ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius + TIMEZONE: *timezone + MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features + SRC_FOLDER: "barnett" # inside src/features/phone_locations + SRC_LANGUAGE: "r" + +# See https://www.rapids.science/latest/features/phone-messages/ +PHONE_MESSAGES: + TABLE: messages + PROVIDERS: + RAPIDS: + COMPUTE: False + MESSAGES_TYPES : [received, sent] + FEATURES: + received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/phone_messages + +# See https://www.rapids.science/latest/features/phone-screen/ +PHONE_SCREEN: + TABLE: screen + PROVIDERS: + RAPIDS: + COMPUTE: False + REFERENCE_HOUR_FIRST_USE: 0 + IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable + IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable + FEATURES: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] # "episodepersensedminutes" needs to be added later + EPISODE_TYPES: ["unlock"] + SRC_FOLDER: "rapids" # inside src/features/phone_screen + SRC_LANGUAGE: "python" + +# See https://www.rapids.science/latest/features/phone-wifi-connected/ +PHONE_WIFI_CONNECTED: + TABLE: "sensor_wifi" + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected + SRC_LANGUAGE: "r" + +# See https://www.rapids.science/latest/features/phone-wifi-visible/ +PHONE_WIFI_VISIBLE: + TABLE: "wifi" + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible + SRC_LANGUAGE: "r" + + + +######################################################################################################################## +# FITBIT # +######################################################################################################################## + +# See https://www.rapids.science/latest/setup/configuration/#device-data-source-configuration +FITBIT_DATA_CONFIGURATION: + SOURCE: + TYPE: DATABASE # DATABASE or FILES (set each [FITBIT_SENSOR][TABLE] attribute with a table name or a file path accordingly) + COLUMN_FORMAT: JSON # JSON or PLAIN_TEXT + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # Fitbit devices don't support time zones so we read this data in the timezone indicated by VALUE + VALUE: *timezone + +# Sensors ------ + +# See https://www.rapids.science/latest/features/fitbit-heartrate-summary/ +FITBIT_HEARTRATE_SUMMARY: + TABLE: heartrate_summary + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["maxrestinghr", "minrestinghr", "avgrestinghr", "medianrestinghr", "moderestinghr", "stdrestinghr", "diffmaxmoderestinghr", "diffminmoderestinghr", "entropyrestinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["sumcaloriesoutofrange", "maxcaloriesoutofrange", "mincaloriesoutofrange", "avgcaloriesoutofrange", "mediancaloriesoutofrange", "stdcaloriesoutofrange", "entropycaloriesoutofrange", "sumcaloriesfatburn", "maxcaloriesfatburn", "mincaloriesfatburn", "avgcaloriesfatburn", "mediancaloriesfatburn", "stdcaloriesfatburn", "entropycaloriesfatburn", "sumcaloriescardio", "maxcaloriescardio", "mincaloriescardio", "avgcaloriescardio", "mediancaloriescardio", "stdcaloriescardio", "entropycaloriescardio", "sumcaloriespeak", "maxcaloriespeak", "mincaloriespeak", "avgcaloriespeak", "mediancaloriespeak", "stdcaloriespeak", "entropycaloriespeak"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_summary + SRC_LANGUAGE: "python" + +# See https://www.rapids.science/latest/features/fitbit-heartrate-intraday/ +FITBIT_HEARTRATE_INTRADAY: + TABLE: heartrate_intraday + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_intraday + SRC_LANGUAGE: "python" + +# See https://www.rapids.science/latest/features/fitbit-sleep-summary/ +FITBIT_SLEEP_SUMMARY: + TABLE: sleep_summary + SLEEP_EPISODE_TIMESTAMP: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp. + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"] + SLEEP_TYPES: ["main", "nap", "all"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_sleep_summary + SRC_LANGUAGE: "python" + +# See https://www.rapids.science/latest/features/fitbit-steps-summary/ +FITBIT_STEPS_SUMMARY: + TABLE: steps_summary + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["maxsumsteps", "minsumsteps", "avgsumsteps", "mediansumsteps", "stdsumsteps"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_summary + SRC_LANGUAGE: "python" + +# See https://www.rapids.science/latest/features/fitbit-steps-intraday/ +FITBIT_STEPS_INTRADAY: + TABLE: steps_intraday + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: + STEPS: ["sum", "max", "min", "avg", "std"] + SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + THRESHOLD_ACTIVE_BOUT: 10 # steps + INCLUDE_ZERO_STEP_ROWS: False + SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_intraday + SRC_LANGUAGE: "python" + +# FITBIT_CALORIES: +# TABLE_FORMAT: JSON # JSON or CSV. If your JSON or CSV data are files change [DEVICE_DATA][FITBIT][SOURCE][TYPE] to FILES +# TABLE: +# JSON: fitbit_calories +# CSV: +# SUMMARY: calories_summary +# INTRADAY: calories_intraday +# PROVIDERS: +# RAPIDS: +# COMPUTE: False +# FEATURES: [] + + + +######################################################################################################################## +# PLOTS # +######################################################################################################################## + +# Data quality +HISTOGRAM_PHONE_DATA_YIELD: + PLOT: False + +HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT: + PLOT: False + +HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT: + PLOT: False + +HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT: + PLOT: False + SENSORS: [PHONE_ACCELEROMETER, PHONE_ACTIVITY_RECOGNITION, PHONE_APPLICATIONS_FOREGROUND, PHONE_BATTERY, PHONE_BLUETOOTH, PHONE_CALLS, PHONE_CONVERSATION, PHONE_LIGHT, PHONE_LOCATIONS, PHONE_MESSAGES, PHONE_SCREEN, PHONE_WIFI_CONNECTED, PHONE_WIFI_VISIBLE] + +# Features +HEATMAP_FEATURE_CORRELATION_MATRIX: PLOT: False MIN_ROWS_RATIO: 0.5 - MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour - PHONE_FEATURES: [accelerometer, activity_recognition, applications_foreground, battery, calls_incoming, calls_missed, calls_outgoing, conversation, light, location_doryab, messages_received, messages_sent, screen] - FITBIT_FEATURES: [fitbit_heartrate, fitbit_step, fitbit_sleep] CORR_THRESHOLD: 0.1 CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"} -HISTOGRAM_VALID_SENSED_HOURS: - PLOT: False - MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour - -HEATMAP_DAYS_BY_SENSORS: - PLOT: False - MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour - EXPECTED_NUM_OF_DAYS: -1 - DB_TABLES: [accelerometer, applications_foreground, battery, bluetooth, calls, light, locations, messages, screen, wifi, sensor_wifi, plugin_google_activity_recognition, plugin_ios_activity_recognition, plugin_studentlife_audio_android, plugin_studentlife_audio] - -HEATMAP_SENSED_BINS: - PLOT: False - BIN_SIZE: *bin_size - -OVERALL_COMPLIANCE_HEATMAP: - PLOT: False - ONLY_SHOW_VALID_DAYS: False - EXPECTED_NUM_OF_DAYS: -1 - BIN_SIZE: *bin_size - MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour - diff --git a/data/external/timesegments_default.csv b/data/external/timesegments_default.csv new file mode 100644 index 00000000..28d17a88 --- /dev/null +++ b/data/external/timesegments_default.csv @@ -0,0 +1,2 @@ +label,start_time,length +daily,00:00:00,"23H 59M 59S" diff --git a/data/external/timesegments_event.csv b/data/external/timesegments_event.csv new file mode 100644 index 00000000..9f2a6eac --- /dev/null +++ b/data/external/timesegments_event.csv @@ -0,0 +1,9 @@ +label,event_timestamp,length,shift,shift_direction,device_id +stress,1587661220000,1H,0M,1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +stress,1587747620000,4H,4H,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +stress,1587906020000,3H,0M,1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +stress,1588003220000,7H,4H,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +stress,1588172420000,9H,0,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +mood,1587661220000,1H,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +mood,1587747620000,1D,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 +mood,1587906020000,7D,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 diff --git a/data/external/timesegments_frequency.csv b/data/external/timesegments_frequency.csv new file mode 100644 index 00000000..e56782d1 --- /dev/null +++ b/data/external/timesegments_frequency.csv @@ -0,0 +1,2 @@ +label,length +thirtyminutes,30 \ No newline at end of file diff --git a/data/external/timesegments_periodic.csv b/data/external/timesegments_periodic.csv new file mode 100644 index 00000000..d47a2d2e --- /dev/null +++ b/data/external/timesegments_periodic.csv @@ -0,0 +1,6 @@ +label,start_time,length,repeats_on,repeats_value +daily,00:00:00,23H 59M 59S,every_day,0 +morning,06:00:00,5H 59M 59S,every_day,0 +afternoon,12:00:00,5H 59M 59S,every_day,0 +evening,18:00:00,5H 59M 59S,every_day,0 +night,00:00:00,5H 59M 59S,every_day,0 \ No newline at end of file diff --git a/docs/CNAME b/docs/CNAME new file mode 100644 index 00000000..17ba96bf --- /dev/null +++ b/docs/CNAME @@ -0,0 +1 @@ +www.rapids.science \ No newline at end of file diff --git a/docs/Makefile b/docs/Makefile deleted file mode 100644 index a4da3214..00000000 --- a/docs/Makefile +++ /dev/null @@ -1,153 +0,0 @@ -# Makefile for Sphinx documentation -# - 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The message catalogs are in $(BUILDDIR)/locale." - -changes: - $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes - @echo - @echo "The overview file is in $(BUILDDIR)/changes." - -linkcheck: - $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck - @echo - @echo "Link check complete; look for any errors in the above output " \ - "or in $(BUILDDIR)/linkcheck/output.txt." - -doctest: - $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest - @echo "Testing of doctests in the sources finished, look at the " \ - "results in $(BUILDDIR)/doctest/output.txt." diff --git a/docs/change-log.md b/docs/change-log.md new file mode 100644 index 00000000..df6a6510 --- /dev/null +++ b/docs/change-log.md @@ -0,0 +1,15 @@ +# Change Log + +## v0.1.0 +- New and more consistent docs (this website). The [previous docs](https://rapidspitt.readthedocs.io/en/latest/) are marked as beta +- Consolidate [configuration](../setup/configuration) instructions +- Flexible [time segments](../setup/configuration#time-segments) +- Simplify Fitbit behavioral feature extraction and [documentation](../features/fitbit-heartrate-summary) +- Sensor's configuration and output is more consistent +- Update [visualizations](../visualizations/data-quality-visualizations) to handle flexible day segments +- Create a RAPIDS [execution](../setup/execution) script that allows re-computation of the pipeline after configuration changes +- Add [citation](../citation) guide +- Update [virtual environment](../developers/virtual-environments) guide +- Update analysis workflow [example](../workflow-examples/analysis) +- Add a [Code of Conduct](../code_of_conduct) +- Update [Team](../team) page \ No newline at end of file diff --git a/docs/citation.md b/docs/citation.md new file mode 100644 index 00000000..30d5988f --- /dev/null +++ b/docs/citation.md @@ -0,0 +1,49 @@ +# Cite RAPIDS and providers + +!!! done "RAPIDS and the community" + RAPIDS is a community effort and as such we want to continue recognizing the contributions from other researchers. Besides citing RAPIDS, we ask you to cite any of the authors listed below if you used those sensor providers in your analysis, thank you! + +## RAPIDS + +If you used RAPIDS, please cite [this paper](https://preprints.jmir.org/preprint/23246). + +!!! cite "RAPIDS et al. citation" + Vega J, Li M, Aguillera K, Goel N, Joshi E, Durica KC, Kunta AR, Low CA + RAPIDS: Reproducible Analysis Pipeline for Data Streams Collected with Mobile Devices + JMIR Preprints. 18/08/2020:23246 + DOI: 10.2196/preprints.23246 + URL: https://preprints.jmir.org/preprint/23246 + +## Panda (accelerometer) + +If you computed accelerometer features using the provider `[PHONE_ACCLEROMETER][PANDA]` cite [this paper](https://pubmed.ncbi.nlm.nih.gov/31657854/) in addition to RAPIDS. + +!!! cite "Panda et al. citation" + Panda N, Solsky I, Huang EJ, Lipsitz S, Pradarelli JC, Delisle M, Cusack JC, Gadd MA, Lubitz CC, Mullen JT, Qadan M, Smith BL, Specht M, Stephen AE, Tanabe KK, Gawande AA, Onnela JP, Haynes AB. Using Smartphones to Capture Novel Recovery Metrics After Cancer Surgery. JAMA Surg. 2020 Feb 1;155(2):123-129. doi: 10.1001/jamasurg.2019.4702. PMID: 31657854; PMCID: PMC6820047. + +## Stachl (applications foreground) + +If you computed applications foreground features using the app category (genre) catalogue in `[PHONE_APPLICATIONS_FOREGROUND][RAPIDS]` cite [this paper](https://www.pnas.org/content/117/30/17680) in addition to RAPIDS. + +!!! cite "Stachl et al. citation" + Clemens Stachl, Quay Au, Ramona Schoedel, Samuel D. Gosling, Gabriella M. Harari, Daniel Buschek, Sarah Theres Völkel, Tobias Schuwerk, Michelle Oldemeier, Theresa Ullmann, Heinrich Hussmann, Bernd Bischl, Markus Bühner. Proceedings of the National Academy of Sciences Jul 2020, 117 (30) 17680-17687; DOI: 10.1073/pnas.1920484117 + +## Barnett (locations) + +If you computed locations features using the provider `[PHONE_LOCATIONS][BARNETT]` cite [this paper](https://doi.org/10.1093/biostatistics/kxy059) and [this paper](https://doi.org/10.1145/2750858.2805845) in addition to RAPIDS. + +!!! cite "Barnett et al. citation" + Ian Barnett, Jukka-Pekka Onnela, Inferring mobility measures from GPS traces with missing data, Biostatistics, Volume 21, Issue 2, April 2020, Pages e98–e112, https://doi.org/10.1093/biostatistics/kxy059 + +!!! cite "Canzian et al. citation" + Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '15). Association for Computing Machinery, New York, NY, USA, 1293–1304. DOI:https://doi.org/10.1145/2750858.2805845 + +## Doryab (locations) + +If you computed locations features using the provider `[PHONE_LOCATIONS][DORYAB]` cite [this paper](https://arxiv.org/abs/1812.10394) and [this paper](https://doi.org/10.1145/2750858.2805845) in addition to RAPIDS. + +!!! cite "Doryab et al. citation" + Doryab, A., Chikarsel, P., Liu, X., & Dey, A. K. (2019). Extraction of Behavioral Features from Smartphone and Wearable Data. ArXiv:1812.10394 [Cs, Stat]. http://arxiv.org/abs/1812.10394 + +!!! cite "Canzian et al. citation" + Luca Canzian and Mirco Musolesi. 2015. Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '15). Association for Computing Machinery, New York, NY, USA, 1293–1304. DOI:https://doi.org/10.1145/2750858.2805845 diff --git a/docs/code_of_conduct.md b/docs/code_of_conduct.md new file mode 100644 index 00000000..52f820e6 --- /dev/null +++ b/docs/code_of_conduct.md @@ -0,0 +1,134 @@ + +# Contributor Covenant Code of Conduct + +## Our Pledge + +We as members, contributors, and leaders pledge to make participation in our +community a harassment-free experience for everyone, regardless of age, body +size, visible or invisible disability, ethnicity, sex characteristics, gender +identity and expression, level of experience, education, socio-economic status, +nationality, personal appearance, race, religion, or sexual identity +and orientation. + +We pledge to act and interact in ways that contribute to an open, welcoming, +diverse, inclusive, and healthy community. + +## Our Standards + +Examples of behavior that contributes to a positive environment for our +community include: + +* Demonstrating empathy and kindness toward other people +* Being respectful of differing opinions, viewpoints, and experiences +* Giving and gracefully accepting constructive feedback +* Accepting responsibility and apologizing to those affected by our mistakes, + and learning from the experience +* Focusing on what is best not just for us as individuals, but for the + overall community + +Examples of unacceptable behavior include: + +* The use of sexualized language or imagery, and sexual attention or + advances of any kind +* Trolling, insulting or derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or email + address, without their explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Enforcement Responsibilities + +Community leaders are responsible for clarifying and enforcing our standards of +acceptable behavior and will take appropriate and fair corrective action in +response to any behavior that they deem inappropriate, threatening, offensive, +or harmful. + +Community leaders have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, and will communicate reasons for moderation +decisions when appropriate. + +## Scope + +This Code of Conduct applies within all community spaces, and also applies when +an individual is officially representing the community in public spaces. +Examples of representing our community include using an official e-mail address, +posting via an official social media account, or acting as an appointed +representative at an online or offline event. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported to the community leaders responsible for enforcement at +moshi@pitt.edu. +All complaints will be reviewed and investigated promptly and fairly. + +All community leaders are obligated to respect the privacy and security of the +reporter of any incident. + +## Enforcement Guidelines + +Community leaders will follow these Community Impact Guidelines in determining +the consequences for any action they deem in violation of this Code of Conduct: + +### 1. Correction + +**Community Impact**: Use of inappropriate language or other behavior deemed +unprofessional or unwelcome in the community. + +**Consequence**: A private, written warning from community leaders, providing +clarity around the nature of the violation and an explanation of why the +behavior was inappropriate. A public apology may be requested. + +### 2. Warning + +**Community Impact**: A violation through a single incident or series +of actions. + +**Consequence**: A warning with consequences for continued behavior. No +interaction with the people involved, including unsolicited interaction with +those enforcing the Code of Conduct, for a specified period of time. This +includes avoiding interactions in community spaces as well as external channels +like social media. Violating these terms may lead to a temporary or +permanent ban. + +### 3. Temporary Ban + +**Community Impact**: A serious violation of community standards, including +sustained inappropriate behavior. + +**Consequence**: A temporary ban from any sort of interaction or public +communication with the community for a specified period of time. No public or +private interaction with the people involved, including unsolicited interaction +with those enforcing the Code of Conduct, is allowed during this period. +Violating these terms may lead to a permanent ban. + +### 4. Permanent Ban + +**Community Impact**: Demonstrating a pattern of violation of community +standards, including sustained inappropriate behavior, harassment of an +individual, or aggression toward or disparagement of classes of individuals. + +**Consequence**: A permanent ban from any sort of public interaction within +the community. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], +version 2.0, available at +[https://www.contributor-covenant.org/version/2/0/code_of_conduct.html][v2.0]. + +Community Impact Guidelines were inspired by +[Mozilla's code of conduct enforcement ladder][Mozilla CoC]. + +For answers to common questions about this code of conduct, see the FAQ at +[https://www.contributor-covenant.org/faq][FAQ]. Translations are available +at [https://www.contributor-covenant.org/translations][translations]. + +[homepage]: https://www.contributor-covenant.org +[v2.0]: https://www.contributor-covenant.org/version/2/0/code_of_conduct.html +[Mozilla CoC]: https://github.com/mozilla/diversity +[FAQ]: https://www.contributor-covenant.org/faq +[translations]: https://www.contributor-covenant.org/translations + diff --git a/docs/conf.py b/docs/conf.py deleted file mode 100644 index e5047625..00000000 --- a/docs/conf.py +++ /dev/null @@ -1,244 +0,0 @@ -# -*- coding: utf-8 -*- -# -# RAPIDS documentation build configuration file, created by -# sphinx-quickstart. -# -# This file is execfile()d with the current directory set to its containing dir. -# -# Note that not all possible configuration values are present in this -# autogenerated file. -# -# All configuration values have a default; values that are commented out -# serve to show the default. - -import os -import sys - -# If extensions (or modules to document with autodoc) are in another directory, -# add these directories to sys.path here. If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -# sys.path.insert(0, os.path.abspath('.')) - -# -- General configuration ----------------------------------------------------- - -# If your documentation needs a minimal Sphinx version, state it here. -# needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be extensions -# coming with Sphinx (named 'sphinx.ext.*') or your custom ones. -extensions = [] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# The suffix of source filenames. -source_suffix = '.rst' - -# The encoding of source files. -# source_encoding = 'utf-8-sig' - -# The master toctree document. -master_doc = 'index' - -# General information about the project. -project = u'RAPIDS' - -# The version info for the project you're documenting, acts as replacement for -# |version| and |release|, also used in various other places throughout the -# built documents. -# -# The short X.Y version. -version = '0.1' -# The full version, including alpha/beta/rc tags. -release = '0.1' - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# language = None - -# There are two options for replacing |today|: either, you set today to some -# non-false value, then it is used: -# today = '' -# Else, today_fmt is used as the format for a strftime call. -# today_fmt = '%B %d, %Y' - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -exclude_patterns = ['_build'] - -# The reST default role (used for this markup: `text`) to use for all documents. -# default_role = None - -# If true, '()' will be appended to :func: etc. cross-reference text. -# add_function_parentheses = True - -# If true, the current module name will be prepended to all description -# unit titles (such as .. function::). -# add_module_names = True - -# If true, sectionauthor and moduleauthor directives will be shown in the -# output. They are ignored by default. -# show_authors = False - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' - -# A list of ignored prefixes for module index sorting. -# modindex_common_prefix = [] - - -# -- Options for HTML output --------------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -html_theme = 'sphinx_rtd_theme' - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -# html_theme_options = {} - -# Add any paths that contain custom themes here, relative to this directory. -# html_theme_path = [] - -# The name for this set of Sphinx documents. If None, it defaults to -# " v documentation". -# html_title = None - -# A shorter title for the navigation bar. Default is the same as html_title. -# html_short_title = None - -# The name of an image file (relative to this directory) to place at the top -# of the sidebar. -# html_logo = None - -# The name of an image file (within the static path) to use as favicon of the -# docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 -# pixels large. -# html_favicon = None - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] - -# If not '', a 'Last updated on:' timestamp is inserted at every page bottom, -# using the given strftime format. -# html_last_updated_fmt = '%b %d, %Y' - -# If true, SmartyPants will be used to convert quotes and dashes to -# typographically correct entities. -# html_use_smartypants = True - -# Custom sidebar templates, maps document names to template names. -# html_sidebars = {} - -# Additional templates that should be rendered to pages, maps page names to -# template names. -# html_additional_pages = {} - -# If false, no module index is generated. -# html_domain_indices = True - -# If false, no index is generated. -# html_use_index = True - -# If true, the index is split into individual pages for each letter. -# html_split_index = False - -# If true, links to the reST sources are added to the pages. -# html_show_sourcelink = True - -# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. -# html_show_sphinx = True - -# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. -# html_show_copyright = True - -# If true, an OpenSearch description file will be output, and all pages will -# contain a tag referring to it. The value of this option must be the -# base URL from which the finished HTML is served. -# html_use_opensearch = '' - -# This is the file name suffix for HTML files (e.g. ".xhtml"). -# html_file_suffix = None - -# Output file base name for HTML help builder. -htmlhelp_basename = 'rapidsdoc' - - -# -- Options for LaTeX output -------------------------------------------------- - -latex_elements = { - # The paper size ('letterpaper' or 'a4paper'). - # 'papersize': 'letterpaper', - - # The font size ('10pt', '11pt' or '12pt'). - # 'pointsize': '10pt', - - # Additional stuff for the LaTeX preamble. - # 'preamble': '', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, author, documentclass [howto/manual]). -latex_documents = [ - ('index', - 'rapids.tex', - u'RAPIDS Documentation', - u"RAPIDS", 'manual'), -] - -# The name of an image file (relative to this directory) to place at the top of -# the title page. -# latex_logo = None - -# For "manual" documents, if this is true, then toplevel headings are parts, -# not chapters. -# latex_use_parts = False - -# If true, show page references after internal links. -# latex_show_pagerefs = False - -# If true, show URL addresses after external links. -# latex_show_urls = False - -# Documents to append as an appendix to all manuals. -# latex_appendices = [] - -# If false, no module index is generated. -# latex_domain_indices = True - - -# -- Options for manual page output -------------------------------------------- - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - ('index', 'RAPIDS', u'RAPIDS Documentation', - [u"RAPIDS"], 1) -] - -# If true, show URL addresses after external links. -# man_show_urls = False - - -# -- Options for Texinfo output ------------------------------------------------ - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - ('index', 'RAPIDS', u'RAPIDS Documentation', - u"RAPIDS", 'RAPIDS', - 'Reproducible Analysis Pipeline for Data Streams', 'Miscellaneous'), -] - -# Documents to append as an appendix to all manuals. -# texinfo_appendices = [] - -# If false, no module index is generated. -# texinfo_domain_indices = True - -# How to display URL addresses: 'footnote', 'no', or 'inline'. -# texinfo_show_urls = 'footnote' diff --git a/docs/develop/contributors.rst b/docs/develop/contributors.rst deleted file mode 100644 index d780279f..00000000 --- a/docs/develop/contributors.rst +++ /dev/null @@ -1,83 +0,0 @@ -RAPIDS Contributors -==================== - -Currently, RAPIDS is being developed by the Mobile Sensing + Health Institute (MoSHI) but if you are interested in contributing feel free to submit a pull request or contact us. - - -Julio Vega, PhD -"""""""""""""""""" -**Postdoctoral Associate** - -vegaju@upmc.edu - -Julio Vega is a postdoctoral associate at the Mobile Sensing + Health Institute. He is interested in personalized methodologies to monitor chronic conditions that affect daily human behavior using mobile and wearable data. In the long term, his goal is to explore how we can enable patients to inform, amend, and evaluate their health tracking algorithms to improve disease self-management. - -`Julio Vega Personal Website`_ - - - -Meng Li, MS -""""""""""""" -**Data Scientist** - -lim11@upmc.edu - -Meng Li received her Master of Science degree in Information Science from the University of Pittsburgh. She is interested in applying machine learning algorithms to the medical field. - -`Meng Li Linkedin Profile`_ - -`Meng Li Github Profile`_ - - - - -Kwesi Aguillera, BS -"""""""""""""""""""" -**Intern** - -Kwesi Aguillera is currently in his first year at the University of Pittsburgh pursuing a Master of Sciences in Information Science specializing in Big Data Analytics. He received his Bachelor of Science degree in Computer Science and Management from the University of the West Indies. Kwesi considers himself a full stack developer and looks forward to applying this knowledge to big data analysis. - -`Kwesi Aguillera Linkedin Profile`_ - - -Echhit Joshi, BS -""""""""""""""""" -**Intern** - -Echhit Joshi is a Masters student at the School of Computing and Information at University of Pittsburgh. His areas of interest are Machine/Deep Learning, Data Mining, and Analytics. - -`Echhit Joshi Linkedin Profile`_ - -Nicolas Leo, BS -"""""""""""""""" -**Intern** - -Nicolas is a rising senior studying computer science at the University of Pittsburgh. His academic interests include databases, machine learning, and application development. After completing his undergraduate degree, he plans to attend graduate school for a MS in Computer Science with a focus on Intelligent Systems. - - -Nikunj Goel, BS -"""""""""""""""" -**Intern** - -Nik is a graduate student at the University of Pittsburgh pursuing Master of Science in Information Science. He earned his Bachelor of Technology degree in Information Technology from India. He is a Data Enthusiasts and passionate about finding the meaning out of raw data. In a long term, his goal is to create a breakthrough in Data Science and Deep Learning. - -`Nikunj Goel Linkedin Profile`_ - -Agam Kumar, BS -"""""""""""""""" -**Research Assistant at CMU** - -Agam is a junior at Carnegie Mellon University studying Statistics and Machine Learning and pursuing an additional major in Computer Science. He is a member of the Data Science team in the Health and Human Performance Lab at CMU and has keen interests in software development and data science. His research interests include ML applications in medicine. - -`Agam Kumar Linkedin Profile`_ - -`Agam Kumar Github Profile`_ - -.. _`Julio Vega Personal Website`: https://juliovega.info/ -.. _`Meng Li Linkedin Profile`: https://www.linkedin.com/in/meng-li-57238414a -.. _`Meng Li Github Profile`: https://github.com/Meng6 -.. _`Kwesi Aguillera Linkedin Profile`: https://www.linkedin.com/in/kwesi-aguillera-29529823 -.. _`Echhit Joshi Linkedin Profile`: https://www.linkedin.com/in/echhitjoshi/ -.. _`Nikunj Goel Linkedin Profile`: https://www.linkedin.com/in/nikunjgoel95/ -.. _`Agam Kumar Linkedin Profile`: https://www.linkedin.com/in/agam-kumar -.. _`Agam Kumar Github Profile`: https://github.com/agam-kumar diff --git a/docs/develop/documentation.rst b/docs/develop/documentation.rst deleted file mode 100644 index cd9dc06c..00000000 --- a/docs/develop/documentation.rst +++ /dev/null @@ -1,237 +0,0 @@ -How to Edit Documentation -============================ - -The following is a basic guide for editing the documentation for this project. The documentation is rendered using Sphinx_ documentation builder - -Quick start up ----------------------------------- - -#. Install Sphinx in Mac OS ``brew install sphinx-doc`` or Linux (Ubuntu) ``apt-get install python3-sphinx`` - -#. Go to the docs folder ``cd docs`` - -#. Change any ``.rst`` file you need to modify - -#. To visualise the results locally do ``make dirhtml`` and check the html files in the ``_build/dirhtml`` directory - -#. When you are done, push your changes to the git repo. - - -Sphinx Workspace Structure ----------------------------- - -All of the files concerned with documentation can be found in the ``docs`` directory. At the top level there is the ``conf.py`` file and an ``index.rst`` file among others. There should be no need to change the ``conf.py`` file. The ``index.rst`` file is known as the master document and defines the document structure of the documentation (i.e. Menu Or Table of Contents structure). It contains the root of the “table of contents" tree -or toctree- that is used to connect the multiple files to a single hierarchy of documents. The TOC is defined using the ``toctree`` directive which is used as follows:: - - .. toctree:: - :maxdepth: 2 - :caption: Getting Started - - usage/introduction - usage/installation - -The ``toctree`` inserts a TOC tree at the current location using the individual TOCs of the documents given in the directive command body. In other words if there are ``toctree`` directives in the files listed in the above example it will also be applied to the resulting TOC. Relative document names (not beginning with a slash) are relative to the document the directive occurs in, absolute names are relative to the source directory. Thus in the example above the ``usage`` directory is relative to the ``index.rst`` page . The ``:maxdepth:`` parameter defines the depth of the tree for that particular menu. The ``caption`` parameter is used to give a caption for that menu tree at that level. It should be noted the titles for the links of the menu items under that header would be taken from the titles of the referenced document. For example the menu item title for ``usage/introduction`` is taken from the main header specified in ``introduction.rst`` document in the ``usage`` directory. Also note the document name does not include the extention (i.e. .rst). - -Thus the directory structure for the above example is shown below:: - - ├── index.rst - └── usage - ├── introduction.rst - └── installation.rst - - -Basic reStructuredText Syntax -------------------------------- - -Now we will look at some basic reStructuredText syntax necessary to start editing the .rst files that are used to generate documentation. - -Headers -"""""""" - -**Section Header** - -The following was used to make the header at the top of this page: -:: - - How to Edit Documentation - ========================== - -**Subsection Header** - -The follwoing was used to create the secondary header (e.g. Sphinx Workspace Structure section header) -:: - - Sphinx Workspace structure - ---------------------------- - -..... - - -Lists -"""""" -**Bullets List** -:: - - - This is a bullet - - This is a bullet - -Will produce the following: - -- This is a bullet -- This is a bullet - - -**Numbered List** -:: - - #. This is a numbered list item - #. This is a numbered list item - -Will produce the following: - -#. This is a numbered list item -#. This is a numbered list item - -..... - -Inline Markup -"""""""""""""" -**Emphasis/Italics** -:: - - *This is for emphasis* - -Will produce the following - -*This is for emphasis* - - -**Bold** -:: - - **This is bold text** - -Will produce the following - -**This is bold text** - -..... - -**Code Sample** -:: - - ``Backquotes = code sample`` - -Will produce the following: - -``Backquotes = code sample`` - -**Apostraphies in Text** -:: - - `don't know` - -Will produce the following - -`don't know` - - -**Literal blocks** - -Literal code blocks are introduced by ending a paragraph with the special marker ``::``. The literal block must be indented (and, like all paragraphs, separated from the surrounding ones by blank lines):: - - This is a normal text paragraph. The next paragraph is a code sample:: - - It is not processed in any way, except - that the indentation is removed. - - It can span multiple lines. - - This is a normal text paragraph again. - - -The following is produced: - -..... - -This is a normal text paragraph. The next paragraph is a code sample:: - - It is not processed in any way, except - that the indentation is removed. - - It can span multiple lines. - -This is a normal text paragraph again. - -..... - -**Doctest blocks** - -Doctest blocks are interactive Python sessions cut-and-pasted into docstrings. They do not require the literal blocks syntax. The doctest block must end with a blank line and should not end with with an unused prompt: - ->>> 1 + 1 -2 - -**External links** - -Use ```Link text `_`` for inline web links `Link text `_. If the link text should be the web address, you don’t need special markup at all, the parser finds links and mail addresses in ordinary text. *Important:* There must be a space between the link text and the opening ``<`` for the URL. - -You can also separate the link and the target definition , like this -:: - - This is a paragraph that contains `a link`_. - - .. _a link: https://domain.invalid/ - - -Will produce the following: - -This is a paragraph that contains `a link`_. - -.. _a link: https://domain.invalid/ - - - -**Internal links** - -Internal linking is done via a special reST role provided by Sphinx to cross-reference arbitrary locations. For this to work label names must be unique throughout the entire documentation. There are two ways in which you can refer to labels: - -- If you place a label directly before a section title, you can reference to it with ``:ref:`label-name```. For example:: - - .. _my-reference-label: - - Section to cross-reference - -------------------------- - - This is the text of the section. - - It refers to the section itself, see :ref:`my-reference-label`. - -The ``:ref:`` role would then generate a link to the section, with the link title being “Section to cross-reference”. This works just as well when section and reference are in different source files. The above produces the following: - -..... - -.. _my-reference-label: - -Section to cross-reference -""""""""""""""""""""""""""" - -This is the text of the section. - -It refers to the section itself, see :ref:`my-reference-label`. - -..... - -- Labels that aren’t placed before a section title can still be referenced, but you must give the link an explicit title, using this syntax: ``:ref:`Link title ```. - - -**Comments** - -Every explicit markup block which isn’t a valid markup construct is regarded as a comment. For example:: - - .. This is a comment. - -Go to Sphinx_ for more documentation. - -.. _Sphinx: https://www.sphinx-doc.org -.. _reStructuredText: https://www.sphinx-doc.org/en/master/usage/restructuredtext/index.html - diff --git a/docs/develop/environments.rst b/docs/develop/environments.rst deleted file mode 100644 index 76abcb1a..00000000 --- a/docs/develop/environments.rst +++ /dev/null @@ -1,18 +0,0 @@ -Manage virtual environments -============================= - -**Add new packages** - -Try to install any new package using `conda install my_package`. If a package is not available in one of conda's channels you can install it with pip but make sure your virtual environment is active. - -**Update your conda environment.yaml** - -After installing a new package you can use the following command in your terminal to update your ``environment.yaml`` before publishing your pipeline. Note that we ignore the package version for ``libfortran`` to keep compatibility with Linux: - - ``conda env export --no-builds | sed 's/^.*libgfortran.*$/ - libgfortran/' > environment.yml`` - -**Update and prune your conda environment from a environment.yaml file** - -Execute the following command in your terminal. See https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#updating-an-environment - - ``conda env update --prefix ./env --file environment.yml --prune`` \ No newline at end of file diff --git a/docs/develop/features.rst b/docs/develop/features.rst deleted file mode 100644 index ad5b86e2..00000000 --- a/docs/develop/features.rst +++ /dev/null @@ -1,28 +0,0 @@ -Add new features to RAPIDS -============================ - -Take accelerometer features as an example. - -#. Add your script to accelerometer_ folder - - - Copy the signature of the base_accelerometer_features() function_ for your own feature function - -#. Add any parameters you need for your function - - - Add your parameters to the settings_ of accelerometer sensor in config file - - Add your parameters to the params_ of accelerometer_features rule in features.snakefile - -#. Merge your new features with the existent features - - - Call the function you just created below this line (LINK_) of accelerometer_features.py script - -#. Update config file - - - Add your new feature names to the ``FEATURES`` list for accelerometer in the config_ file - -.. _accelerometer: https://github.com/carissalow/rapids/tree/master/src/features/accelerometer -.. _function: https://github.com/carissalow/rapids/blob/master/src/features/accelerometer/accelerometer_base.py#L35 -.. _settings: https://github.com/carissalow/rapids/blob/master/config.yaml#L100 -.. _params: https://github.com/carissalow/rapids/blob/master/rules/features.snakefile#L146 -.. _LINK: https://github.com/carissalow/rapids/blob/master/src/features/accelerometer_features.py#L10 -.. _config: https://github.com/carissalow/rapids/blob/master/config.yaml#L102 diff --git a/docs/develop/remotesupport.rst b/docs/develop/remotesupport.rst deleted file mode 100644 index 213b1420..00000000 --- a/docs/develop/remotesupport.rst +++ /dev/null @@ -1,16 +0,0 @@ -Remote Support -====================================== - -We use the Live Share extension of Visual Studio Code to debug bugs when sharing data or database credentials is not possible. - -#. Install `Visual Studio Code `_ - -#. Open you rapids folder in a new VSCode window - -#. Open a new Terminal ``Terminal > New terminal`` - -#. Install the `Live Share extension pack `_ - -#. Press ``Ctrl+P``/``Cmd+P`` and run this command ``>live share: start collaboration session`` - -#. Follow the instructions and share the session link you receive \ No newline at end of file diff --git a/docs/develop/test_cases.rst b/docs/develop/test_cases.rst deleted file mode 100644 index 593fdbbb..00000000 --- a/docs/develop/test_cases.rst +++ /dev/null @@ -1,110 +0,0 @@ -.. _test-cases: - -Test Cases ------------ - -Along with the continued development and the addition of new sensors and features to the RAPIDS pipeline, tests for the currently available sensors and features are being implemented. Since this is a Work In Progress this page will be updated with the list of sensors and features for which testing is available. For each of the sensors listed a description of the data used for testing (test cases) are outline. Currently for all intent and testing purposes the ``tests/data/raw/test01/`` contains all the test data files for testing android data formats and ``tests/data/raw/test02/`` contains all the test data files for testing iOS data formats. It follows that the expected (verified output) are contained in the ``tests/data/processed/test01/`` and ``tests/data/processed/test02/`` for Android and iOS respectively. ``tests/data/raw/test03/`` and ``tests/data/raw/test04/`` contain data files for testing empty raw data files for android and iOS respectively. - -List of Sensor with Tests -^^^^^^^^^^^^^^^^^^^^^^^^^^ -The following is a list of the sensors that testing is currently available. - - -Messages (SMS) -""""""""""""""" - - - The raw message data file contains data for 2 separate days. - - The data for the first day contains records 5 records for every ``epoch``. - - The second day's data contains 6 records for each of only 2 ``epoch`` (currently ``morning`` and ``evening``) - - The raw message data contains records for both ``message_types`` (i.e. ``recieved`` and ``sent``) in both days in all epochs. The number records with each ``message_types`` per epoch is randomly distributed There is at least one records with each ``message_types`` per epoch. - - There is one raw message data file each, as described above, for testing both iOS and Android data. - - There is also an additional empty data file for both android and iOS for testing empty data files - -Calls -""""""" - - Due to the difference in the format of the raw call data for iOS and Android (see the **Assumptions/Observations** section of :ref:`Calls`) the following is the expected results the ``calls_with_datetime_unified.csv``. This would give a better idea of the use cases being tested since the ``calls_with_datetime_unified.csv`` would make both the iOS and Android data comparable. - - - The call data would contain data for 2 days. - - The data for the first day contains 6 records for every ``epoch``. - - The second day's data contains 6 records for each of only 2 ``epoch`` (currently ``morning`` and ``evening``) - - The call data contains records for all ``call_types`` (i.e. ``incoming``, ``outgoing`` and ``missed``) in both days in all epochs. The number records with each of the ``call_types`` per epoch is randomly distributed. There is at least one records with each ``call_types`` per epoch. - - There is one call data file each, as described above, for testing both iOS and Android data. - - There is also an additional empty data file for both android and iOS for testing empty data files - -Screen -"""""""" - - Due to the difference in the format of the raw screen data for iOS and Android (see the **Assumptions/Observations** section of :ref:`Screen`) the following is the expected results the ``screen_deltas.csv``. This would give a better idea of the use cases being tested since the ``screen_deltas.csv`` would make both the iOS and Android data comparable. These files are used to calculate the features for the screen sensor. - - - The screen delta data file contains data for 1 day. - - The screen delta data contains 1 record to represent an ``unlock`` episode that falls within an ``epoch`` for every ``epoch``. - - The screen delta data contains 1 record to represent an ``unlock`` episode that falls across the boundary of 2 epochs. Namely the ``unlock`` episode starts in one epoch and ends in the next, thus there is a record for ``unlock`` episodes that fall across ``night`` to ``morning``, ``morning`` to ``afternoon`` and finally ``afternoon`` to ``night`` - - The testing is done for ``unlock`` episode_type. - - There is one screen data file each for testing both iOS and Android data formats. - - There is also an additional empty data file for both android and iOS for testing empty data files - -Battery -""""""""" - - Due to the difference in the format of the raw battery data for iOS and Android as well as versions of iOS (see the **Assumptions/Observations** section of :ref:`Battery`) the following is the expected results the ``battery_deltas.csv``. This would give a better idea of the use cases being tested since the ``battery_deltas.csv`` would make both the iOS and Android data comparable. These files are used to calculate the features for the battery sensor. - - - The battery delta data file contains data for 1 day. - - The battery delta data contains 1 record each for a ``charging`` and ``discharging`` episode that falls within an ``epoch`` for every ``epoch``. Thus, for the ``daily`` epoch there would be multiple ``charging`` and ``discharging`` episodes - - Since either a ``charging`` episode or a ``discharging`` episode and not both can occur across epochs, in order to test episodes that occur across epochs alternating episodes of ``charging`` and ``discharging`` episodes that fall across ``night`` to ``morning``, ``morning`` to ``afternoon`` and finally ``afternoon`` to ``night`` are present in the battery delta data. This starts with a ``discharging`` episode that begins in ``night`` and end in ``morning``. - - There is one battery data file each, for testing both iOS and Android data formats. - - There is also an additional empty data file for both android and iOS for testing empty data files - -Bluetooth -"""""""""" - - - The raw Bluetooth data file contains data for 1 day. - - The raw Bluetooth data contains at least 2 records for each ``epoch``. Each ``epoch`` has a record with a ``timestamp`` for the beginning boundary for that ``epoch`` and a record with a ``timestamp`` for the ending boundary for that ``epoch``. (e.g. For the ``morning`` epoch there is a record with a ``timestamp`` for ``6:00AM`` and another record with a ``timestamp`` for ``11:59:59AM``. These are to test edge cases) - - An option of 5 Bluetooth devices are randomly distributed throughout the data records. - - There is one raw Bluetooth data file each, for testing both iOS and Android data formats. - - There is also an additional empty data file for both android and iOS for testing empty data files. - -WIFI -""""" - - - There are 2 data files (``wifi_raw.csv`` and ``sensor_wifi_raw.csv``) for each fake participant for each phone platform. (see the **Assumptions/Observations** section of :ref:`WIFI`) - - The raw WIFI data files contain data for 1 day. - - The ``sensor_wifi_raw.csv`` data contains at least 2 records for each ``epoch``. Each ``epoch`` has a record with a ``timestamp`` for the beginning boundary for that ``epoch`` and a record with a ``timestamp`` for the ending boundary for that ``epoch``. (e.g. For the ``morning`` epoch there is a record with a ``timestamp`` for ``6:00AM`` and another record with a ``timestamp`` for ``11:59:59AM``. These are to test edge cases) - - The ``wifi_raw.csv`` data contains 3 records with random timestamps for each ``epoch`` to represent visible broadcasting WIFI network. This file is empty for the iOS phone testing data. - - An option of 10 access point devices is randomly distributed throughout the data records. 5 each for ``sensor_wifi_raw.csv`` and ``wifi_raw.csv``. - - There data files for testing both iOS and Android data formats. - - There are also additional empty data files for both android and iOS for testing empty data files. - -Light -""""""" - - - The raw light data file contains data for 1 day. - - The raw light data contains 3 or 4 rows of data for each ``epoch`` except ``night``. The single row of data for ``night`` is for testing features for single values inputs. (Example testing the standard deviation of one input value) - - Since light is only available for Android there is only one file that contains data for Android. All other files (i.e. for iPhone) are empty data files. - -Application Foreground -""""""""""""""""""""""" - - - The raw application foreground data file contains data for 1 day. - - The raw application foreground data contains 7 - 9 rows of data for each ``epoch``. The records for each ``epoch`` contains apps that are randomly selected from a list of apps that are from the ``MULTIPLE_CATEGORIES`` and ``SINGLE_CATEGORIES`` (See `testing_config.yaml`_). There are also records in each epoch that have apps randomly selected from a list of apps that are from the ``EXCLUDED_CATEGORIES`` and ``EXCLUDED_APPS``. This is to test that these apps are actually being excluded from the calculations of features. There are also records to test ``SINGLE_APPS`` calculations. - - Since application foreground is only available for Android there is only one file that contains data for Android. All other files (i.e. for iPhone) are empty data files. - -Activity Recognition -"""""""""""""""""""""" - - - The raw Activity Recognition data file contains data for 1 day. - - The raw Activity Recognition data each ``epoch`` period contains rows that records 2 - 5 different ``activity_types``. The is such that durations of activities can be tested. Additionally, there are records that mimic the duration of an activity over the time boundary of neighboring epochs. (For example, there a set of records that mimic the participant ``in_vehicle`` from ``afternoon`` into ``evening``) - - There is one file each with raw Activity Recognition data for testing both iOS and Android data formats. (plugin_google_activity_recognition_raw.csv for android and plugin_ios_activity_recognition_raw.csv for iOS) - - There is also an additional empty data file for both android and iOS for testing empty data files. - -Conversation -""""""""""""" - - - The raw conversation data file contains data for 2 day. - - The raw conversation data contains records with a sample of both ``datatypes`` (i.e. ``voice/noise`` = ``0``, and ``conversation`` = ``2`` ) as well as rows with for samples of each of the ``inference`` values (i.e. ``silence`` = ``0``, ``noise`` = ``1``, ``voice`` = ``2``, and ``unknown`` = ``3``) for each ``epoch``. The different ``datatype`` and ``inference`` records are randomly distributed throughout the ``epoch``. - - Additionally there are 2 - 5 records for conversations (``datatype`` = 2, and ``inference`` = -1) in each ``epoch`` and for each ``epoch`` except night, there is a conversation record that has a ``double_convo_start`` ``timestamp`` that is from the previous ``epoch``. This is to test the calculations of features across ``epochs``. - - There is a raw conversation data file for both android and iOS platforms (``plugin_studentlife_audio_android_raw.csv`` and ``plugin_studentlife_audio_raw.csv`` respectively). - - Finally, there are also additional empty data files for both android and iOS for testing empty data files - - - .. _`testing_config.yaml`: https://github.com/carissalow/rapids/blob/c498b8d2dfd7cc29d1e4d53e978d30cff6cdf3f2/tests/settings/testing_config.yaml#L70 diff --git a/docs/develop/testing.rst b/docs/develop/testing.rst deleted file mode 100644 index 46d41fc4..00000000 --- a/docs/develop/testing.rst +++ /dev/null @@ -1,67 +0,0 @@ -Testing -========== - -The following is a simple guide to testing RAPIDS. All files necessary for testing are stored in the ``tests`` directory: - -:: - - ├── tests - │ ├── data <- Replica of the project root data directory for testing. - │ │ ├── external <- Contains the fake testing participant files. - │ │ ├── interim <- The expected intermediate data that has been transformed. - │ │ ├── processed <- The expected final data, canonical data sets for modeling used to test/validate feature calculations. - │ │ └── raw <- The specially created raw input datasets (fake data) that will be used for testing. - │ │ - │ ├── scripts <- Scripts for testing. Add test scripts in this directory. - │ │ ├── run_tests.sh <- The shell script to runs RAPIDS pipeline test data and test the results - │ │ ├── test_sensor_features.py <- The default test script for testing RAPIDS builting sensor features. - │ │ └── utils.py <- Contains any helper functions and methods. - │ │ - │ ├── settings <- The directory contains the config and settings files for testing snakemake. - │ │ ├── config.yaml <- Defines the testing profile configurations for running snakemake. - │ │ └── testing_config.yaml <- Contains the actual snakemake configuration settings for testing. - │ │ - │ └── Snakefile <- The Snakefile for testing only. It contains the rules that you would be testing. - │ - - -Steps for Testing -"""""""""""""""""" - -#. To begin testing RAPIDS place the fake raw input data ``csv`` files in ``tests/data/raw/``. The fake participant files should be placed in ``tests/data/external/``. The expected output files of RAPIDS after processing the input data should be placed in ``tests/data/processesd/``. - -#. The Snakemake rule(s) that are to be tested must be placed in the ``tests/Snakemake`` file. The current ``tests/Snakemake`` is a good example of how to define them. (At the time of writing this documentation the snakefile contains rules messages (SMS), calls and screen) - -#. Edit the ``tests/settings/config.yaml``. Add and/or remove the rules to be run for testing from the ``forcerun`` list. - -#. Edit the ``tests/settings/testing_config.yaml`` with the necessary configuration settings for running the rules to be tested. - -#. Add any additional testscripts in ``tests/scripts``. - -#. Uncomment or comment off lines in the testing shell script ``tests/scripts/run_tests.sh``. - -#. Run the testing shell script. - -:: - - $ tests/scripts/run_tests.sh - - -The following is a snippet of the output you should see after running your test. - -:: - - test_sensors_files_exist (test_sensor_features.TestSensorFeatures) ... ok - test_sensors_features_calculations (test_sensor_features.TestSensorFeatures) ... FAIL - - ====================================================================== - FAIL: test_sensors_features_calculations (test_sensor_features.TestSensorFeatures) - ---------------------------------------------------------------------- - -The results above show that the first test ``test_sensors_files_exist`` passed while ``test_sensors_features_calculations`` failed. In addition you should get the traceback of the failure (not shown here). For more information on how to implement test scripts and use unittest please see `Unittest Documentation`_ - -Testing of the RAPIDS sensors and features is a work-in-progess. Please see :ref:`test-cases` for a list of sensors and features that have testing currently available. - -Currently the repository is set up to test a number of senssors out of the box by simply running the ``tests/scripts/run_tests.sh`` command once the RAPIDS python environment is active. - -.. _`Unittest Documentation`: https://docs.python.org/3.7/library/unittest.html#command-line-interface diff --git a/docs/developers/documentation.md b/docs/developers/documentation.md new file mode 100644 index 00000000..1355f13c --- /dev/null +++ b/docs/developers/documentation.md @@ -0,0 +1,41 @@ +# Documentation + +We use [mkdocs](https://www.mkdocs.org/) with the [material theme](https://squidfunk.github.io/mkdocs-material/) to write these docs. Whenever you make any changes, just push them back to the repo and the documentation will be deployed automatically. + +## Set up development environment + +1. Make sure your conda environment is active +2. `pip install mkdocs` +3. `pip install mkdocs-material` + +## Preview + +Run the following command in RAPIDS root folder and go to [http://127.0.0.1:8000](http://127.0.0.1:8000): + +```bash +mkdocs serve +``` + +## File Structure + +The documentation config file is `/mkdocs.yml`, if you are adding new `.md` files to the docs modify the `nav` attribute at the bottom of that file. You can use the hierarchy there to find all the files that appear in the documentation. + +## Reference + +Check this [page](https://squidfunk.github.io/mkdocs-material/reference/abbreviations/) to get familiar with the different visual elements we can use in the docs (admonitions, code blocks, tables, etc.) You can also refer to `/docs/setup/installation.md` and `/docs/setup/configuration.md` to see practical examples of these elements. + +!!! hint + Any links to internal pages should be relative to the current page. For example, any link from this page (documentation) which is inside `./developers` should begin with `../` to go one folder level up like: + ```md + [mylink](../setup/installation.md) + ``` + +## Extras + +You can insert [emojis](https://facelessuser.github.io/pymdown-extensions/extensions/emoji/) using this syntax `:[SOURCE]-[ICON_NAME]` from the following sources: + +- https://materialdesignicons.com/ +- https://fontawesome.com/icons/tasks?style=solid +- https://primer.style/octicons/ + +You can use this [page](https://www.tablesgenerator.com/markdown_tables) to create markdown tables more easily diff --git a/docs/developers/remote-support.md b/docs/developers/remote-support.md new file mode 100644 index 00000000..b4717cbc --- /dev/null +++ b/docs/developers/remote-support.md @@ -0,0 +1,14 @@ +# Remote Support + +We use the Live Share extension of Visual Studio Code to debug bugs when sharing data or database credentials is not possible. + +1. Install [Visual Studio Code](https://code.visualstudio.com/) +2. Open you RAPIDS root folder in a new VSCode window +3. Open a new Terminal `Terminal > New terminal` +4. Install the [Live Share extension pack](https://marketplace.visualstudio.com/items?itemName=MS-vsliveshare.vsliveshare-pack) +5. Press ++ctrl+p++ or ++cmd+p++ and run this command: + + ```bash + >live share: start collaboration session + ``` +6. Follow the instructions and share the session link you receive diff --git a/docs/developers/test-cases.md b/docs/developers/test-cases.md new file mode 100644 index 00000000..5be23f27 --- /dev/null +++ b/docs/developers/test-cases.md @@ -0,0 +1,185 @@ +# Test Cases + +Along with the continued development and the addition of new sensors and features to the RAPIDS pipeline, tests for the currently available sensors and features are being implemented. Since this is a Work In Progress this page will be updated with the list of sensors and features for which testing is available. For each of the sensors listed a description of the data used for testing (test cases) are outline. Currently for all intent and testing purposes the `tests/data/raw/test01/` contains all the test data files for testing android data formats and `tests/data/raw/test02/` contains all the test data files for testing iOS data formats. It follows that the expected (verified output) are contained in the `tests/data/processed/test01/` and `tests/data/processed/test02/` for Android and iOS respectively. `tests/data/raw/test03/` and `tests/data/raw/test04/` contain data files for testing empty raw data files for android and iOS respectively. + +The following is a list of the sensors that testing is currently available. + +## Messages (SMS) + +- The raw message data file contains data for 2 separate days. +- The data for the first day contains records 5 records for every + `epoch`. +- The second day\'s data contains 6 records for each of only 2 + `epoch` (currently `morning` and `evening`) +- The raw message data contains records for both `message_types` + (i.e. `recieved` and `sent`) in both days in all epochs. The + number records with each `message_types` per epoch is randomly + distributed There is at least one records with each + `message_types` per epoch. +- There is one raw message data file each, as described above, for + testing both iOS and Android data. +- There is also an additional empty data file for both android and + iOS for testing empty data files + +## Calls + +Due to the difference in the format of the raw call data for iOS and Android the following is the expected results the `calls_with_datetime_unified.csv`. This would give a better idea of the use cases being tested since the `calls_with_datetime_unified.csv` would make both the iOS and Android data comparable. + +- The call data would contain data for 2 days. +- The data for the first day contains 6 records for every `epoch`. +- The second day\'s data contains 6 records for each of only 2 + `epoch` (currently `morning` and `evening`) +- The call data contains records for all `call_types` (i.e. + `incoming`, `outgoing` and `missed`) in both days in all epochs. + The number records with each of the `call_types` per epoch is + randomly distributed. There is at least one records with each + `call_types` per epoch. +- There is one call data file each, as described above, for testing + both iOS and Android data. +- There is also an additional empty data file for both android and + iOS for testing empty data files + +## Screen + +Due to the difference in the format of the raw screen data for iOS and Android the following is the expected results the `screen_deltas.csv`. This would give a better idea of the use cases being tested since the `screen_eltas.csv` would make both the iOS and Android data comparable These files are used to calculate the features for the screen sensor + +- The screen delta data file contains data for 1 day. +- The screen delta data contains 1 record to represent an `unlock` + episode that falls within an `epoch` for every `epoch`. +- The screen delta data contains 1 record to represent an `unlock` + episode that falls across the boundary of 2 epochs. Namely the + `unlock` episode starts in one epoch and ends in the next, thus + there is a record for `unlock` episodes that fall across `night` + to `morning`, `morning` to `afternoon` and finally `afternoon` to + `night` +- The testing is done for `unlock` episode\_type. +- There is one screen data file each for testing both iOS and + Android data formats. +- There is also an additional empty data file for both android and + iOS for testing empty data files + +## Battery + +Due to the difference in the format of the raw battery data for iOS and Android as well as versions of iOS the following is the expected results the `battery_deltas.csv`. This would give a better idea of the use cases being tested since the `battery_deltas.csv` would make both the iOS and Android data comparable. These files are used to calculate the features for the battery sensor. + +- The battery delta data file contains data for 1 day. +- The battery delta data contains 1 record each for a `charging` and + `discharging` episode that falls within an `epoch` for every + `epoch`. Thus, for the `daily` epoch there would be multiple + `charging` and `discharging` episodes +- Since either a `charging` episode or a `discharging` episode and + not both can occur across epochs, in order to test episodes that + occur across epochs alternating episodes of `charging` and + `discharging` episodes that fall across `night` to `morning`, + `morning` to `afternoon` and finally `afternoon` to `night` are + present in the battery delta data. This starts with a + `discharging` episode that begins in `night` and end in `morning`. +- There is one battery data file each, for testing both iOS and + Android data formats. +- There is also an additional empty data file for both android and + iOS for testing empty data files + +## Bluetooth + +- The raw Bluetooth data file contains data for 1 day. +- The raw Bluetooth data contains at least 2 records for each + `epoch`. Each `epoch` has a record with a `timestamp` for the + beginning boundary for that `epoch` and a record with a + `timestamp` for the ending boundary for that `epoch`. (e.g. For + the `morning` epoch there is a record with a `timestamp` for + `6:00AM` and another record with a `timestamp` for `11:59:59AM`. + These are to test edge cases) +- An option of 5 Bluetooth devices are randomly distributed + throughout the data records. +- There is one raw Bluetooth data file each, for testing both iOS + and Android data formats. +- There is also an additional empty data file for both android and + iOS for testing empty data files. + +## WIFI + +- There are 2 data files (`wifi_raw.csv` and `sensor_wifi_raw.csv`) + for each fake participant for each phone platform. +- The raw WIFI data files contain data for 1 day. +- The `sensor_wifi_raw.csv` data contains at least 2 records for + each `epoch`. Each `epoch` has a record with a `timestamp` for the + beginning boundary for that `epoch` and a record with a + `timestamp` for the ending boundary for that `epoch`. (e.g. For + the `morning` epoch there is a record with a `timestamp` for + `6:00AM` and another record with a `timestamp` for `11:59:59AM`. + These are to test edge cases) +- The `wifi_raw.csv` data contains 3 records with random timestamps + for each `epoch` to represent visible broadcasting WIFI network. + This file is empty for the iOS phone testing data. +- An option of 10 access point devices is randomly distributed + throughout the data records. 5 each for `sensor_wifi_raw.csv` and + `wifi_raw.csv`. +- There data files for testing both iOS and Android data formats. +- There are also additional empty data files for both android and + iOS for testing empty data files. + +## Light + +- The raw light data file contains data for 1 day. +- The raw light data contains 3 or 4 rows of data for each `epoch` + except `night`. The single row of data for `night` is for testing + features for single values inputs. (Example testing the standard + deviation of one input value) +- Since light is only available for Android there is only one file + that contains data for Android. All other files (i.e. for iPhone) + are empty data files. + +## Application Foreground + +- The raw application foreground data file contains data for 1 day. +- The raw application foreground data contains 7 - 9 rows of data + for each `epoch`. The records for each `epoch` contains apps that + are randomly selected from a list of apps that are from the + `MULTIPLE_CATEGORIES` and `SINGLE_CATEGORIES` (See + [testing\_config.yaml]()). There are also records in each epoch + that have apps randomly selected from a list of apps that are from + the `EXCLUDED_CATEGORIES` and `EXCLUDED_APPS`. This is to test + that these apps are actually being excluded from the calculations + of features. There are also records to test `SINGLE_APPS` + calculations. +- Since application foreground is only available for Android there + is only one file that contains data for Android. All other files + (i.e. for iPhone) are empty data files. + +## Activity Recognition + +- The raw Activity Recognition data file contains data for 1 day. +- The raw Activity Recognition data each `epoch` period contains + rows that records 2 - 5 different `activity_types`. The is such + that durations of activities can be tested. Additionally, there + are records that mimic the duration of an activity over the time + boundary of neighboring epochs. (For example, there a set of + records that mimic the participant `in_vehicle` from `afternoon` + into `evening`) +- There is one file each with raw Activity Recognition data for + testing both iOS and Android data formats. + (plugin\_google\_activity\_recognition\_raw.csv for android and + plugin\_ios\_activity\_recognition\_raw.csv for iOS) +- There is also an additional empty data file for both android and + iOS for testing empty data files. + +## Conversation + +- The raw conversation data file contains data for 2 day. +- The raw conversation data contains records with a sample of both + `datatypes` (i.e. `voice/noise` = `0`, and `conversation` = `2` ) + as well as rows with for samples of each of the `inference` values + (i.e. `silence` = `0`, `noise` = `1`, `voice` = `2`, and `unknown` + = `3`) for each `epoch`. The different `datatype` and `inference` + records are randomly distributed throughout the `epoch`. +- Additionally there are 2 - 5 records for conversations (`datatype` + = 2, and `inference` = -1) in each `epoch` and for each `epoch` + except night, there is a conversation record that has a + `double_convo_start` `timestamp` that is from the previous + `epoch`. This is to test the calculations of features across + `epochs`. +- There is a raw conversation data file for both android and iOS + platforms (`plugin_studentlife_audio_android_raw.csv` and + `plugin_studentlife_audio_raw.csv` respectively). +- Finally, there are also additional empty data files for both + android and iOS for testing empty data files diff --git a/docs/developers/testing.md b/docs/developers/testing.md new file mode 100644 index 00000000..afcc6b46 --- /dev/null +++ b/docs/developers/testing.md @@ -0,0 +1,45 @@ +# Testing + +The following is a simple guide to testing RAPIDS. All files necessary for testing are stored in the `/tests` directory + +## Steps for Testing + +1. To begin testing RAPIDS place the fake raw input data `csv` files in + `tests/data/raw/`. The fake participant files should be placed in + `tests/data/external/`. The expected output files of RAPIDS after + processing the input data should be placed in + `tests/data/processesd/`. +2. The Snakemake rule(s) that are to be tested must be placed in the + `tests/Snakemake` file. The current `tests/Snakemake` is a good + example of how to define them. (At the time of writing this + documentation the snakefile contains rules messages (SMS), calls and + screen) +3. Edit the `tests/settings/config.yaml`. Add and/or remove the rules + to be run for testing from the `forcerun` list. +4. Edit the `tests/settings/testing_config.yaml` with the necessary + configuration settings for running the rules to be tested. +5. Add any additional testscripts in `tests/scripts`. +6. Uncomment or comment off lines in the testing shell script + `tests/scripts/run_tests.sh`. +7. Run the testing shell script. + + ```bash + tests/scripts/run_tests.sh + ``` + +The following is a snippet of the output you should see after running your test. + +```bash +test_sensors_files_exist (test_sensor_features.TestSensorFeatures) ... ok +test_sensors_features_calculations (test_sensor_features.TestSensorFeatures) ... FAIL + +====================================================================== +FAIL: test_sensors_features_calculations (test_sensor_features.TestSensorFeatures) +---------------------------------------------------------------------- +``` + +The results above show that the first test `test_sensors_files_exist` passed while `test_sensors_features_calculations` failed. In addition you should get the traceback of the failure (not shown here). For more information on how to implement test scripts and use unittest please see [Unittest Documentation](https://docs.python.org/3.7/library/unittest.html#command-line-interface) + +Testing of the RAPIDS sensors and features is a work-in-progress. Please see `test-cases`{.interpreted-text role="ref"} for a list of sensors and features that have testing currently available. + +Currently the repository is set up to test a number of sensors out of the box by simply running the `tests/scripts/run_tests.sh` command once the RAPIDS python environment is active. diff --git a/docs/developers/virtual-environments.md b/docs/developers/virtual-environments.md new file mode 100644 index 00000000..f0b5eb02 --- /dev/null +++ b/docs/developers/virtual-environments.md @@ -0,0 +1,35 @@ +## Python Virtual Environment + +### Add new packages + +Try to install any new package using `conda install -c CHANNEL PACKAGE_NAME` (you can use `pip` if the package is only available there). Make sure your Python virtual environment is active (`conda activate YOUR_ENV`). + +### Remove packages +Uninstall packages using the same manager you used to install them `conda remove PACKAGE_NAME` or `pip uninstall PACKAGE_NAME` + +### Update your conda `environment.yaml` + +After installing or removing a package you can use the following command in your terminal to update your `environment.yaml` before publishing your pipeline. Note that we ignore the package version for `libfortran` to keep compatibility with Linux: +```bash +conda env export --no-builds | sed 's/^.*libgfortran.*$/ - libgfortran/' > environment.yml +``` + +## R Virtual Environment + +### Add new packages +1. Open your terminal and navigate to RAPIDS' root folder +2. Run `R` to open an R interactive session +3. Run `renv::install("PACKAGE_NAME")` + +### Remove packages +1. Open your terminal and navigate to RAPIDS' root folder +2. Run `R` to open an R interactive session +3. Run `renv::remove("PACKAGE_NAME")` + +### Update your R `renv.lock` +After installing or removing a package you can use the following command in your terminal to update your `renv.lock` before publishing your pipeline. + +1. Open your terminal and navigate to RAPIDS' root folder +2. Run `R` to open an R interactive session +3. Run `renv::snapshot()` (renv will ask you to confirm any updates to this file) + diff --git a/docs/faq.md b/docs/faq.md new file mode 100644 index 00000000..7285fe9e --- /dev/null +++ b/docs/faq.md @@ -0,0 +1,195 @@ +# Frequently Asked Questions + +## Cannot connect to your MySQL server + +???+ failure "Problem" + ```bash + **Error in .local(drv, \...) :** **Failed to connect to database: Error: + Can\'t initialize character set unknown (path: compiled\_in)** : + + Calls: dbConnect -> dbConnect -> .local -> .Call + Execution halted + [Tue Mar 10 19:40:15 2020] + Error in rule download_dataset: + jobid: 531 + output: data/raw/p60/locations_raw.csv + + RuleException: + CalledProcessError in line 20 of /home/ubuntu/rapids/rules/preprocessing.snakefile: + Command 'set -euo pipefail; Rscript --vanilla /home/ubuntu/rapids/.snakemake/scripts/tmp_2jnvqs7.download_dataset.R' returned non-zero exit status 1. + File "/home/ubuntu/rapids/rules/preprocessing.snakefile", line 20, in __rule_download_dataset + File "/home/ubuntu/anaconda3/envs/moshi-env/lib/python3.7/concurrent/futures/thread.py", line 57, in run + Shutting down, this might take some time. + Exiting because a job execution failed. Look above for error message + ``` + +???+ done "Solution" + Please make sure the `DATABASE_GROUP` in `config.yaml` matches your DB credentials group in `.env`. + +--- + +## Cannot start mysql in linux via `brew services start mysql` + +???+ failure "Problem" + Cannot start mysql in linux via `brew services start mysql` + +???+ done "Solution" + Use `mysql.server start` + +--- + +## Every time I run force the download_dataset rule all rules are executed + +???+ failure "Problem" + When running `snakemake -j1 -R download_phone_data` or `./rapids -j1 -R download_phone_data` all the rules and files are re-computed + +???+ done "Solution" + This is expected behavior. The advantage of using `snakemake` under the hood is that every time a file containing data is modified every rule that depends on that file will be re-executed to update their results. In this case, since `download_dataset` updates all the raw data, and you are forcing the rule with the flag `-R` every single rule that depends on those raw files will be executed. +--- + +## Error `Table XXX doesn't exist` while running the `download_phone_data` or `download_fitbit_data` rule. + +???+ failure "Problem" + ```bash + Error in .local(conn, statement, ...) : + could not run statement: Table 'db_name.table_name' doesn't exist + Calls: colnames ... .local -> dbSendQuery -> dbSendQuery -> .local -> .Call + Execution halted + ``` + +???+ done "Solution" + Please make sure the sensors listed in `[PHONE_VALID_SENSED_BINS][PHONE_SENSORS]` and the `[TABLE]` of each sensor you activated in `config.yaml` match your database tables. + +--- +## How do I install RAPIDS on Ubuntu 16.04 + +???+ done "Solution" + 1. Install dependencies (Homebrew - if not installed): + - `sudo apt-get install libmariadb-client-lgpl-dev libxml2-dev libssl-dev` + - Install [brew](https://docs.brew.sh/Homebrew-on-Linux) for linux and add the following line to `~/.bashrc`: `export PATH=$HOME/.linuxbrew/bin:$PATH` + - `source ~/.bashrc` + + 1. Install MySQL + - `brew install mysql` + - `brew services start mysql` + + 2. Install R, pandoc and rmarkdown: + - `brew install r` + - `brew install gcc@6` (needed due to this [bug](https://github.com/Homebrew/linuxbrew-core/issues/17812)) + - `HOMEBREW_CC=gcc-6 brew install pandoc` + + 3. Install miniconda using these [instructions](https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html) + + 4. Clone our repo: + - `git clone https://github.com/carissalow/rapids` + + 5. Create a python virtual environment: + - `cd rapids` + - `conda env create -f environment.yml -n MY_ENV_NAME` + - `conda activate MY_ENV_NAME` + + 6. Install R packages and virtual environment: + - `snakemake renv_install` + - `snakemake renv_init` + - `snakemake renv_restore` + + This step could take several minutes to complete. Please be patient and let it run until completion. +--- + +## `mysql.h` cannot be found + +???+ failure "Problem" + ```bash + --------------------------[ ERROR MESSAGE ]---------------------------- + :1:10: fatal error: mysql.h: No such file or directory + compilation terminated. + ----------------------------------------------------------------------- + ERROR: configuration failed for package 'RMySQL' + ``` + +???+ done "Solution" + ```bash + sudo apt install libmariadbclient-dev + ``` + +--- +## No package `libcurl` found + +???+ failure "Problem" + `libcurl` cannot be found + +???+ done "Solution" + Install `libcurl` + ```bash + sudo apt install libcurl4-openssl-dev + ``` + +--- +## Configuration failed because `openssl` was not found. + +???+ failure "Problem" + `openssl` cannot be found + +???+ done "Solution" + Install `openssl` + ```bash + sudo apt install libssl-dev + ``` +--- +## Configuration failed because `libxml-2.0` was not found + +???+ failure "Problem" + `libxml-2.0` cannot be found + +???+ done "Solution" + Install `libxml-2.0` + ```bash + sudo apt install libxml2-dev + ``` + +--- +## SSL connection error when running RAPIDS + +???+ failure "Problem" + You are getting the following error message when running RAPIDS: + ```bash + Error: Failed to connect: SSL connection error: error:1425F102:SSL routines:ssl_choose_client_version:unsupported protocol. + ``` + +???+ done "Solution" + This is a bug in Ubuntu 20.04 when trying to connect to an old MySQL server with MySQL client 8.0. You should get the same error message if you try to connect from the command line. There you can add the option `--ssl-mode=DISABLED` but we can\'t do this from the R connector. + + If you can\'t update your server, the quickest solution would be to import your database to another server or to a local environment. Alternatively, you could replace `mysql-client` and `libmysqlclient-dev` with `mariadb-client` and `libmariadbclient-dev` and reinstall renv. More info about this issue [here](https://bugs.launchpad.net/ubuntu/+source/mysql-8.0/+bug/1872541) + +--- +## `DB_TABLES` key not found + +???+ failure "Problem" + If you get the following error `KeyError in line 43 of preprocessing.smk: 'PHONE_SENSORS'`, it means that the indentation of the key `[PHONE_SENSORS]` is not matching the other child elements of `PHONE_VALID_SENSED_BINS` + +???+ done "Solution" + You need to add or remove any leading whitespaces as needed on that line. + + ```yaml + PHONE_VALID_SENSED_BINS: + COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features + BIN_SIZE: &bin_size 5 # (in minutes) + PHONE_SENSORS: [] + ``` + +--- +## Error while updating your conda environment in Ubuntu + +???+ failure "Problem" + You get the following error: + ```bash + CondaMultiError: CondaVerificationError: The package for tk located at /home/ubuntu/miniconda2/pkgs/tk-8.6.9-hed695b0_1003 + appears to be corrupted. The path 'include/mysqlStubs.h' + specified in the package manifest cannot be found. + ClobberError: This transaction has incompatible packages due to a shared path. + packages: conda-forge/linux-64::llvm-openmp-10.0.0-hc9558a2_0, anaconda/linux-64::intel-openmp-2019.4-243 + path: 'lib/libiomp5.so' + ``` + +???+ done "Solution" + Reinstall conda \ No newline at end of file diff --git a/docs/features/add-new-features.md b/docs/features/add-new-features.md new file mode 100644 index 00000000..3f1dba8d --- /dev/null +++ b/docs/features/add-new-features.md @@ -0,0 +1,191 @@ +# Add New Features + +!!! hint + We recommend reading the [Behavioral Features Introduction](../feature-introduction/) before reading this page + +!!! hint + You won't have to deal with time zones, dates, times, data cleaning or preprocessing. The data that RAPIDS pipes to your feature extraction code is ready to process. + +## New Features for Existing Sensors + +You can add new features to any existing sensors (see list below) by adding a new provider in three steps: + +1. [Modify](#modify-the-configyaml-file) the `config.yaml` file +2. [Create](#create-a-provider-folder-script-and-function) a provider folder, script and function +3. [Implement](#implement-your-feature-extraction-code) your features extraction code + +As a tutorial, we will add a new provider for `PHONE_ACCELEROMETER` called `VEGA` that extracts `feature1`, `feature2`, `feature3` in Python and that it requires a parameter from the user called `MY_PARAMETER`. + +??? info "Existing Sensors" + An existing sensor is any of the phone or Fitbit sensors with a configuration entry in `config.yaml`: + + - Phone Accelerometer + - Phone Activity Recognition + - Phone Applications Foreground + - Phone Battery + - Phone Bluetooth + - Phone Calls + - Phone Conversation + - Phone Data Yield + - Phone Light + - Phone Locations + - Phone Messages + - Phone Screen + - Phone WiFI Connected + - Phone WiFI Visible + - Fitbit Heart Rate Summary + - Fitbit Heart Rate Intraday + - Fitbit Sleep Summary + - Fitbit Steps Summary + - Fitbit Steps Intraday + + +### Modify the `config.yaml` file + +In this step you need to add your provider configuration section under the relevant sensor in `config.yaml`. See our example for our tutorial's `VEGA` provider for `PHONE_ACCELEROMETER`: + +??? example "Example configuration for a new accelerometer provider `VEGA`" + ```yaml + PHONE_ACCELEROMETER: + TABLE: accelerometer + PROVIDERS: + RAPIDS: + COMPUTE: False + ... + + PANDA: + COMPUTE: False + ... + VEGA: + COMPUTE: False + FEATURES: ["feature1", "feature2", "feature3"] + MY_PARAMTER: a_string + SRC_FOLDER: "vega" + SRC_LANGUAGE: "python" + + ``` + +| Key                          | Description +|---|---| +|`[COMPUTE]`| Flag to activate/deactivate your provider +|`[FEATURES]`| List of features your provider supports. Your provider code should only return the features on this list +|`[MY_PARAMTER]`| An arbitrary parameter that our example provider `VEGA` needs. This can be a boolean, integer, float, string or an array of any of such types. +|`[SRC_LANGUAGE]`| The programming language of your provider script, it can be `python` or `r`, in our example `python` +|`[SRC_FOLDER]`| The name of your provider in lower case, in our example `vega` (this will be the name of your folder in the next step) + +### Create a provider folder, script and function + +In this step you need to add a folder, script and function for your provider. + +5. Create your provider **folder** under `src/feature/DEVICE_SENSOR/YOUR_PROVIDER`, in our example `src/feature/phone_accelerometer/vega` (same as `[SRC_FOLDER]` in the step above). +6. Create your provider **script** inside your provider folder, it can be a Python file called `main.py` or an R file called `main.R`. +7. Add your provider **function** in your provider script. The name of such function should be `[providername]_features`, in our example `vega_features` + + !!! info "Python function" + ```python + def [providername]_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + ``` + + !!! info "R function" + ```r + [providername]_features <- function(sensor_data, time_segment, provider) + ``` + +### Implement your feature extraction code + +The provider function that you created in the step above will receive the following parameters: + +| Parameter                                       | Description +|---|---| +|`sensor_data_files`| Path to the CSV file containing the data of a single participant. This data has been cleaned and preprocessed. Your function will be automatically called for each participant in your study (in the `[PIDS]` array in `config.yaml`) +|`time_segment`| The label of the time segment that should be processed. +|`provider`| The parameters you configured for your provider in `config.yaml` will be available in this variable as a dictionary in Python or a list in R. In our example this dictionary contains `{MY_PARAMETER:"a_string"}` +|`filter_data_by_segment`| Python only. A function that you will use to filter your data. In R this function is already available in the environment. +|`*args`| Python only. Not used for now +|`**kwargs`| Python only. Not used for now + + +The code to extract your behavioral features should be implemented in your provider function and in general terms it will have three stages: + +??? info "1. Read a participant's data by loading the CSV data stored in the file pointed by `sensor_data_files`" + ``` python + acc_data = pd.read_csv(sensor_data_files["sensor_data"]) + ``` + + Note that phone's battery, screen, and activity recognition data is given as episodes instead of event rows (for example, start and end timestamps of the periods the phone screen was on) + + +??? info "2. Filter your data to process only those rows that belong to `time_segment`" + + This step is only one line of code, but to undersand why we need it, keep reading. + ```python + acc_data = filter_data_by_segment(acc_data, time_segment) + ``` + + You should use the `filter_data_by_segment()` function to process and group those rows that belong to each of the [time segments RAPIDS could be configured with](../../setup/configuration/#time-segments). + + Let's understand the `filter_data_by_segment()` function with an example. A RAPIDS user can extract features on any arbitrary [time segment](../../setup/configuration/#time-segments). A time segment is a period of time that has a label and one or more instances. For example, the user (or you) could have requested features on a daily, weekly, and week-end basis for `p01`. The labels are arbritrary and the instances depend on the days a participant was monitored for: + + - the daily segment could be named `my_days` and if `p01` was monitored for 14 days, it would have 14 instances + - the weekly segment could be named `my_weeks` and if `p01` was monitored for 14 days, it would have 2 instances. + - the weekend segment could be named `my_weekends` and if `p01` was monitored for 14 days, it would have 2 instances. + + For this example, RAPIDS will call your provider function three times for `p01`, once where `time_segment` is `my_days`, once where `time_segment` is `my_weeks` and once where `time_segment` is `my_weekends`. In this example not every row in `p01`'s data needs to take part in the feature computation for either segment **and** the rows need to be grouped differently. + + Thus `filter_data_by_segment()` comes in handy, it will return a data frame that contains the rows that were logged during a time segment plus an extra column called `local_segment`. This new column will have as many unique values as time segment instances exist (14, 2, and 2 for our `p01`'s `my_days`, `my_weeks`, and `my_weekends` examples). After filtering, **you should group the data frame by this column and compute any desired features**, for example: + + ```python + acc_features["maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() + ``` + + The reason RAPIDS does not filter the participant's data set for you is because your code might need to compute something based on a participant's complete dataset before computing their features. For example, you might want to identify the number that called a participant the most throughout the study before computing a feature with the number of calls the participant received from this number. + +??? info "3. Return a data frame with your features" + After filtering, grouping your data, and computing your features, your provider function should return a data frame that has: + + - One row per time segment instance (e.g. 14 our `p01`'s `my_days` example) + - The `local_segment` column added by `filter_data_by_segment()` + - One column per feature. By convention the name of your features should only contain letters or numbers (`feature1`). RAPIDS will automatically add the right sensor and provider prefix (`phone_accelerometr_vega_`) + +??? example "`PHONE_ACCELEROMETER` Provider Example" + For your reference, this a short example of our own provider (`RAPIDS`) for `PHONE_ACCELEROMETER` that computes five acceleration features + + ```python + def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + + acc_data = pd.read_csv(sensor_data_files["sensor_data"]) + requested_features = provider["FEATURES"] + # name of the features this function can compute + base_features_names = ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] + # the subset of requested features this function can compute + features_to_compute = list(set(requested_features) & set(base_features_names)) + + acc_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) + if not acc_data.empty: + acc_data = filter_data_by_segment(acc_data, time_segment) + + if not acc_data.empty: + acc_features = pd.DataFrame() + # get magnitude related features: magnitude = sqrt(x^2+y^2+z^2) + magnitude = acc_data.apply(lambda row: np.sqrt(row["double_values_0"] ** 2 + row["double_values_1"] ** 2 + row["double_values_2"] ** 2), axis=1) + acc_data = acc_data.assign(magnitude = magnitude.values) + + if "maxmagnitude" in features_to_compute: + acc_features["maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() + if "minmagnitude" in features_to_compute: + acc_features["minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min() + if "avgmagnitude" in features_to_compute: + acc_features["avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean() + if "medianmagnitude" in features_to_compute: + acc_features["medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median() + if "stdmagnitude" in features_to_compute: + acc_features["stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std() + + acc_features = acc_features.reset_index() + + return acc_features + ``` + +## New Features for Non-Existing Sensors + +If you want to add features for a device or a sensor that we do not support at the moment (those that do not appear in the `"Existing Sensors"` list above), [contact us](../../team) or request it on [Slack](http://awareframework.com:3000/) and we can add the necessary code so you can follow the instructions above. \ No newline at end of file diff --git a/docs/features/extracted.rst b/docs/features/extracted.rst deleted file mode 100644 index 526292b6..00000000 --- a/docs/features/extracted.rst +++ /dev/null @@ -1,1111 +0,0 @@ -.. _rapids_features: - -RAPIDS Features -=============== - -*How do I compute any of these features?* In your ``config.yaml``, go to the sensor section you are interested in and set the corresponding ``COMPUTE`` option to ``TRUE`` as well as ``DB_TABLE`` to the senor's table name in your database (the default table name is the one assigned by Aware), for example -:: - - MESSAGES: - COMPUTE: True - DB_TABLE: messages - ... - -If you want to extract phone_valid_sensed_days.csv, screen features or locaton features based on fused location data don't forget to configure ``[PHONE_VALID_SENSED_BINS][TABLES]`` (see below). - -.. _global-sensor-doc: - -Global Parameters -""""""""""""""""" - -.. _sensor-list: - -.. _pid: - -- ``PIDS`` - The list of participant ids to be included in the analysis. These should match the names of the files created in the ``data/external`` directory (:ref:`see more details`). - -.. _day-segments: - -- ``DAY_SEGMENTS`` - The list of day epochs that features can be segmented into: ``daily``, ``morning`` (6am-12pm), ``afternnon`` (12pm-6pm), ``evening`` (6pm-12am) and ``night`` (12am-6am). This list can be modified globally or on a per sensor basis. See DAY_SEGMENTS_ in ``config`` file. - -.. _timezone: - -- ``TIMEZONE`` - The time zone where data was collected. Use the timezone names from this `List of Timezones`_. Double check your chosen name is correct, for example US Eastern Time is called New America/New_York, not EST. - -.. _database_group: - -- ``DATABASE_GROUP`` - The name of your database credentials group, it should match the one in ``.env`` (:ref:`see the datbase configuration`). - -.. _download-dataset: - -- ``DOWNLOAD_DATASET`` - - - ``GROUP``. Credentials group to connect to the database containing ``SENSORS``. By default it points to ``DATABASE_GROUP``. - -.. _readable-datetime: - -- ``READABLE_DATETIME`` - Configuration to convert UNIX timestamps into readbale date time strings. - - - ``FIXED_TIMEZONE``. See ``TIMEZONE`` above. This assumes that all data of all participants was collected within one time zone. - - Support for multiple time zones for each participant coming soon based on the ``timezone`` table collected by Aware. - -.. _phone-valid-sensed-bins: - -- ``PHONE_VALID_SENSED_BINS`` - Contains three attributes: ``COMPUTE``, ``BIN_SIZE`` and ``TABLES``. See the PHONE_VALID_SENSED_BINS_ section in the ``config.yaml`` file - - Set the ``COMPUTE`` flag to True if you want to get this file (``data/interim/{pid}/phone_sensed_bins``). Phone valid sensed bins is a matrix of days x bins where we divide every hour of every day into N bins of size ``BIN_SIZE`` (in minutes). Each bin contains the number of rows that were recorded in that interval by all the sensors listed in ``TABLES``. Add as many sensor tables to ``TABLES`` as you have in your database because valid sensed bins are used to compute ``PHONE_VALID_SENSED_DAYS``, the ``episodepersensedminutes`` feature of :ref:`Screen` and to resample fused location data if you configure Barnett's/Doryab's location features to use ``RESAMPLE_FUSED``. - - The ``COMPUTE`` flag is automatically ignored (set internally to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features. - -.. _phone-valid-sensed-days: - -- ``PHONE_VALID_SENSED_DAYS``. - - Contains three attributes: ``COMPUTE``, ``MIN_VALID_HOURS_PER_DAY``, ``MIN_VALID_BINS_PER_HOUR``. See the PHONE_VALID_SENSED_DAYS_ section in ``config.yaml``. - - On any given day, Aware could have sensed data only for a few minutes or for 24 hours. Daily estimates of features should be considered more reliable the more hours Aware was running and logging data, for example, 10 calls logged on a day when only one hour of data was recorded is a less reliable feature compared to 10 calls on a day when 23 hours of data were recorded. - - Therefore, we define a valid hour as those that contain a minimum number of valid bins. A valid bin are those that contain at least one row of data from any sensor logged within that period (See ``PHONE_VALID_SENSED_BINS`` above). We mark an hour as valid if contains at least ``MIN_VALID_BINS_PER_HOUR`` (out of the total possible number of bins that can be captured in an hour based on their length i.e. 60min/``BIN_SIZE`` bins). In turn, we mark a day as valid if it has at least ``MIN_VALID_HOURS_PER_DAY``. ``MIN_VALID_HOURS_PER_DAY`` could be a list. For different thresholds, we can get different valid sensed days: ``"data/interim/{pid}/phone_valid_sensed_days_{min_valid_hours_per_day}h.csv"``. - - Note that at the moment RAPIDS *DOES NOT* filter your feature files automatically, you need to do this after your features have been extracted using ``"data/interim/{pid}/phone_valid_sensed_days_{min_valid_hours_per_day}h.csv"``. - -.. _individual-sensor-settings: - - -.. _messages-sensor-doc: - -Messages (SMS) -""""""""""""""" - -See `Messages Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android - -**Rule Chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/features.snakefile/messages_features`` - -.. _messages-parameters: - -**Messages Rule Parameters (messages_features):** - -============== =================== -Name Description -============== =================== -messages_type The particular ``messages_type`` that will be analyzed. The options for this parameter are ``received`` or ``sent``. -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features Features to be computed, see table below -============== =================== - -.. _messages-available-features: - -**Available Message Features** - -========================= ========= ============= -Name Units Description -========================= ========= ============= -count messages Number of messages of type ``messages_type`` that occurred during a particular ``day_segment``. -distinctcontacts contacts Number of distinct contacts that are associated with a particular ``messages_type`` during a particular ``day_segment``. -timefirstmessages minutes Number of minutes between 12:00am (midnight) and the first ``message`` of a particular ``messages_type``. -timelastmessages minutes Number of minutes between 12:00am (midnight) and the last ``message`` of a particular ``messages_type``. -countmostfrequentcontact messages Number of messages from the contact with the most messages of ``messages_type`` during a ``day_segment`` throughout the whole dataset of each participant. -========================= ========= ============= - -**Assumptions/Observations:** - -``TYPES`` and ``FEATURES`` keys in ``config.yaml`` need to match. For example, below the ``TYPE`` ``sent`` matches the ``FEATURES`` key ``sent``:: - - MESSAGES: - ... - TYPES: [sent] - FEATURES: - sent: [count, distinctcontacts, timefirstmessages, timelastmessages, countmostfrequentcontact] - - -.. _call-sensor-doc: - -Calls -"""""" - -See `Call Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android and iOS - -**Rule Chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/features.snakefile/call_features`` - -.. _calls-parameters: - -**Call Rule Parameters (call_features):** - -============ =================== -Name Description -============ =================== -call_type The particular ``call_type`` that will be analyzed. The options for this parameter are ``incoming``, ``outgoing`` or ``missed``. -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features Features to be computed. Note that the same features are available for both ``incoming`` and ``outgoing`` calls, while ``missed`` calls has its own set of features. See :ref:`Available Incoming and Outgoing Call Features ` Table and :ref:`Available Missed Call Features ` Table below. -============ =================== - -.. _available-in-and-out-call-features: - -**Available Incoming and Outgoing Call Features** - -========================= ========= ============= -Name Units Description -========================= ========= ============= -count calls Number of calls of a particular ``call_type`` occurred during a particular ``day_segment``. -distinctcontacts contacts Number of distinct contacts that are associated with a particular ``call_type`` for a particular ``day_segment`` -meanduration seconds The mean duration of all calls of a particular ``call_type`` during a particular ``day_segment``. -sumduration seconds The sum of the duration of all calls of a particular ``call_type`` during a particular ``day_segment``. -minduration seconds The duration of the shortest call of a particular ``call_type`` during a particular ``day_segment``. -maxduration seconds The duration of the longest call of a particular ``call_type`` during a particular ``day_segment``. -stdduration seconds The standard deviation of the duration of all the calls of a particular ``call_type`` during a particular ``day_segment``. -modeduration seconds The mode of the duration of all the calls of a particular ``call_type`` during a particular ``day_segment``. -entropyduration nats The estimate of the Shannon entropy for the the duration of all the calls of a particular ``call_type`` during a particular ``day_segment``. -timefirstcall minutes The time in minutes between 12:00am (midnight) and the first call of ``call_type``. -timelastcall minutes The time in minutes between 12:00am (midnight) and the last call of ``call_type``. -countmostfrequentcontact calls The number of calls of a particular ``call_type`` during a particular ``day_segment`` of the most frequent contact throughout the monitored period. -========================= ========= ============= - -.. _available-missed-call-features: - -**Available Missed Call Features** - -========================= ========= ============= -Name Units Description -========================= ========= ============= -count calls Number of ``missed`` calls that occurred during a particular ``day_segment``. -distinctcontacts contacts Number of distinct contacts that are associated with ``missed`` calls for a particular ``day_segment`` -timefirstcall minutes The time in hours from 12:00am (Midnight) that the first ``missed`` call occurred. -timelastcall minutes The time in hours from 12:00am (Midnight) that the last ``missed`` call occurred. -countmostfrequentcontact calls The number of ``missed`` calls during a particular ``day_segment`` of the most frequent contact throughout the monitored period. -========================= ========= ============= - -**Assumptions/Observations:** - -Traces for iOS calls are unique even for the same contact calling a participant more than once which renders ``countmostfrequentcontact`` meaningless and ``distinctcontacts`` equal to the total number of traces. - -``TYPES`` and ``FEATURES`` keys in ``config.yaml`` need to match. For example, below the ``TYPE`` ``missed`` matches the ``FEATURES`` key ``missed``:: - - CALLS: - ... - TYPES: [missed] - FEATURES: - missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] - -Aware Android client stores call types 1=incoming, 2=outgoing, 3=missed while Aware iOS client stores call status 1=incoming, 2=connected, 3=dialing, 4=disconnected. We extract iOS call types based on call status sequences: (1,2,4)=incoming=1, (3,2,4)=outgoing=2, (1,4) or (3,4)=missed=3. Sometimes (due to a possible bug in Aware) sequences get logged on the exact same timestamp, thus 3-item sequences can be 2,3,4 or 3,2,4. Although iOS stores the duration of ringing/dialing stages for missed calls, we set it to 0 to match Android. - - -.. _bluetooth-sensor-doc: - -Bluetooth -"""""""""" - -See `Bluetooth Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/features.snakefile/bluetooth_features`` - -.. _bluetooth-parameters: - -**Bluetooth Rule Parameters (bluetooth_features):** - -============ =================== -Name Description -============ =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features Features to be computed, see table below -============ =================== - -.. _bluetooth-available-features: - -**Available Bluetooth Features** - -=========================== ========= ============= -Name Units Description -=========================== ========= ============= -countscans devices Number of scanned devices during a ``day_segment``, a device can be detected multiple times over time and these appearances are counted separately -uniquedevices devices Number of unique devices during a ``day_segment`` as identified by their hardware address -countscansmostuniquedevice scans Number of scans of the most scanned device during a ``day_segment`` across the whole monitoring period -=========================== ========= ============= - -**Assumptions/Observations:** N/A - - -.. _wifi-sensor-doc: - -WiFi -"""""""""" - -See `WiFi Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/features.snakefile/wifi_features`` - -.. _wifi-parameters: - -**WiFi Rule Parameters (wifi_features):** - -============ =================== -Name Description -============ =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features Features to be computed, see table below -============ =================== - -.. _wifi-available-features: - -**Available WiFi Features** - -=========================== ========= ============= -Name Units Description -=========================== ========= ============= -countscans devices Number of scanned WiFi access points during a ``day_segment``, an access point can be detected multiple times over time and these appearances are counted separately -uniquedevices devices Number of unique access point during a ``day_segment`` as identified by their hardware address -countscansmostuniquedevice scans Number of scans of the most scanned access point during a ``day_segment`` across the whole monitoring period -=========================== ========= ============= - -**Assumptions/Observations:** -Both phone platforms record the wifi networks a phone is connected to in ``sensor_wifi`` and those networks that are being broadcasted around a phone in ``wifi``. However, iOS cannot record any broadcasting network due to API restrictions, therefore iOS wifi data only exists in ``sensor_wifi``. - - -.. _accelerometer-sensor-doc: - -Accelerometer -"""""""""""""" - -See `Accelerometer Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android and iOS - -**Rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/features.snakefile/accelerometer_features`` - -.. _Accelerometer-parameters: - -**Accelerometer Rule Parameters (accelerometer_features):** - -============ =================== -Name Description -============ =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features Features to be computed, see table below -============ =================== - -.. _accelerometer-available-features: - -**Available Accelerometer Features** - -====================== ============== ============= -Name Units Description -====================== ============== ============= -maxmagnitude m/s\ :sup:`2` The maximum magnitude of acceleration (:math:`\|acceleration\| = \sqrt{x^2 + y^2 + z^2}`). -minmagnitude m/s\ :sup:`2` The minimum magnitude of acceleration. -avgmagnitude m/s\ :sup:`2` The average magnitude of acceleration. -medianmagnitude m/s\ :sup:`2` The median magnitude of acceleration. -stdmagnitude m/s\ :sup:`2` The standard deviation of acceleration. -sumduration minutes Total duration of all exertional or non-exertional activity episodes. -maxduration minutes Longest duration of any exertional or non-exertional activity episode. -minduration minutes Shortest duration of any exertional or non-exertional activity episode. -avgduration minutes Average duration of any exertional or non-exertional activity episode. -medianduration minutes Median duration of any exertional or non-exertional activity episode. -stdduration minutes Standard deviation of the duration of all exertional or non-exertional activity episodes. -====================== ============== ============= - -**Assumptions/Observations:** - -Exertional activity episodes are based on this paper: Panda N, Solsky I, Huang EJ, et al. Using Smartphones to Capture Novel Recovery Metrics After Cancer Surgery. JAMA Surg. 2020;155(2):123–129. doi:10.1001/jamasurg.2019.4702 - - -.. _applications-foreground-sensor-doc: - -Applications Foreground -"""""""""""""""""""""""" - -See `Applications Foreground Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/preprocessing.snakefile/application_genres`` -- Rule ``rules/features.snakefile/applications_foreground_features`` - -.. _applications-foreground-parameters: - -**Applications Foreground Rule Parameters (applications_foreground_features):** - -==================== =================== -Name Description -==================== =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -single_categories App categories to be included in the feature extraction computation. See ``APPLICATION_GENRES`` in this file to add new categories or use the catalogue we provide and read :ref:`Assumtions and Observations ` for more information. -multiple_categories You can group multiple categories into meta categories, for example ``social: ["socialnetworks", "socialmediatools"]``. -single_apps Apps to be included in the feature extraction computation. Use their package name, for example, ``com.google.android.youtube`` or the reserved word ``top1global`` (the most used app by a participant over the whole monitoring study). -excluded_categories App categories to be excluded in the feature extraction computation. See ``APPLICATION_GENRES`` in this file to add new categories or use the catalogue we provide and read :ref:`Assumtions and Observations ` for more information. -excluded_apps Apps to be excluded in the feature extraction computation. Use their package name, for example: ``com.google.android.youtube`` -features Features to be computed, see table below -==================== =================== - -.. _applications-foreground-available-features: - -**Available Applications Foreground Features** - -================== ========= ============= -Name Units Description -================== ========= ============= -count apps Number of times a single app or apps within a category were used (i.e. they were brought to the foreground either by tapping their icon or switching to it from another app). -timeoffirstuse minutes The time in minutes between 12:00am (midnight) and the first use of a single app or apps within a category during a ``day_segment``. -timeoflastuse minutes The time in minutes between 12:00am (midnight) and the last use of a single app or apps within a category during a ``day_segment``. -frequencyentropy nats The entropy of the used apps within a category during a ``day_segment`` (each app is seen as a unique event, the more apps were used, the higher the entropy). This is especially relevant when computed over all apps. Entropy cannot be obtained for a single app. -================== ========= ============= - -.. _applications-foreground-observations: - -**Assumptions/Observations:** - -Features can be computed by app, by apps grouped under a single category (genre) and by multiple categories grouped together (meta categories). For example, we can get features for Facebook, for Social Network Apps (including Facebook and others) or for a meta category called Social formed by Social Network and Social Media Tools categories. - -Apps installed by default like YouTube are considered systems apps on some phones. We do an exact match to exclude apps where "genre" == ``EXCLUDED_CATEGORIES`` or "package_name" == ``EXCLUDED_APPS``. - -We provide three ways of classifying and app within a category (genre): a) by automatically scraping its official category from the Google Play Store, b) by using the catalogue created by Stachl et al. which we provide in RAPIDS (``data/external/``), or c) by manually creating a personalized catalogue. - -The way you choose strategy a, b or c is by modifying ``APPLICATION_GENRES`` keys and values. Set ``CATALOGUE_SOURCE`` to ``FILE`` if you want to use a CSV file as catalogue (strategy b and c) or to ``GOOGLE`` if you want to scrape the genres from the Play Store (strategy a). By default ``CATALOGUE_FILE`` points to the catalogue created by Stachl et al. (strategy b) and you can change this path to your own catalogue that follows the same format (strategy c). In addition, set ``SCRAPE_MISSING_GENRES`` to true if you are using a FILE catalogue and you want to scrape from the Play Store any missing genres and ``UPDATE_CATALOGUE_FILE`` to true if you want to save those scrapped genres back into the FILE. - -The genre catalogue we provide was shared as part of the Supplemental Materials of Stachl, C., Au, Q., Schoedel, R., Buschek, D., Völkel, S., Schuwerk, T., … Bühner, M. (2019, June 12). Behavioral Patterns in Smartphone Usage Predict Big Five Personality Traits. https://doi.org/10.31234/osf.io/ks4vd - -.. _battery-sensor-doc: - -Battery -""""""""" - -See `Battery Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/features.snakefile/battery_deltas`` -- Rule ``rules/features.snakefile/battery_features`` - -.. _battery-parameters: - -**Battery Rule Parameters (battery_features):** - -============ =================== -Name Description -============ =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features Features to be computed, see table below -============ =================== - -.. _battery-available-features: - -**Available Battery Features** - -===================== ================= ============= -Name Units Description -===================== ================= ============= -countdischarge episodes Number of discharging episodes. -sumdurationdischarge minutes The total duration of all discharging episodes. -countcharge episodes Number of battery charging episodes. -sumdurationcharge minutes The total duration of all charging episodes. -avgconsumptionrate episodes/minutes The average of all episodes’ consumption rates. An episode’s consumption rate is defined as the ratio between its battery delta and duration -maxconsumptionrate episodes/minutes The highest of all episodes’ consumption rates. An episode’s consumption rate is defined as the ratio between its battery delta and duration -===================== ================= ============= - -**Assumptions/Observations:** - -For Aware iOS client V1 we swap battery status 3 to 5 and 1 to 3, client V2 does not have this problem. - -.. _activity-recognition-sensor-doc: - - -Activity Recognition -"""""""""""""""""""""""""""" - -See `Activity Recognition Config Code`_ - -**Available Epochs:** daily, morning, afternoon, evening, night - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/preprocessing.snakefile/unify_ios_android`` -- Rule ``rules/features.snakefile/google_activity_recognition_deltas`` -- Rule ``rules/features.snakefile/ios_activity_recognition_deltas`` -- Rule ``rules/features.snakefile/activity_features`` - -.. _activity-recognition-parameters: - -**Rule Parameters (activity_features):** - -============ =================== -Name Description -============ =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features Features to be computed, see table below -============ =================== - -.. _activity-recognition-available-features: - -**Available Activity Recognition Features** - -====================== ============== ============= -Name Units Description -====================== ============== ============= -count rows Number of episodes. -mostcommonactivity activity_type The most common ``activity_type``. If this feature is not unique the first ``activity_type`` of the set of most common ``activity_types`` is selected ordered by ``activity_type``. -countuniqueactivities activity_type Number of unique ``activity_type``. -durationstationary minutes The total duration of episodes of still and tilting (phone) activities. -durationmobile minutes The total duration of episodes of on foot, running, and on bicycle activities -durationvehicle minutes The total duration of episodes of on vehicle activity -====================== ============== ============= - -**Assumptions/Observations:** - -iOS Activity Recognition data labels are unified with Google Activity Recognition labels: "automotive" to "in_vehicle", "cycling" to "on_bicycle", "walking" and "running" to "on_foot", "stationary" to "still". In addition, iOS activity pairs formed by "stationary" and "automotive" labels (driving but stopped at a traffic light) are transformed to "automotive" only. - -In AWARE, Activity Recognition data for Google (Android) and iOS are stored in two different database tables, RAPIDS (via Snakemake) automatically infers what platform each participant belongs to based on their participant file (``data/external/``) which in turn takes this information from the ``aware_device`` table (see ``optional_ar_input`` function in ``rules/features.snakefile``). - -The activties are mapped to activity_types as follows: - -=============== =============== -Activity Name Activity Type -=============== =============== -in_vehicle 0 -on_bicycle 1 -on_foot 2 -still 3 -unknown 4 -tilting 5 -walking 7 -running 8 -=============== =============== - - -.. _light-doc: - -Light -""""""" - -See `Light Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android - -**Rule Chain:** - -- Rule: ``rules/preprocessing.snakefile/download_dataset`` -- Rule: ``rules/preprocessing.snakefile/readable_datetime`` -- Rule: ``rules/features.snakefile/light_features`` - -.. _light-parameters: - -**Light Rule Parameters (light_features):** - -============ =================== -Name Description -============ =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features Features to be computed, see table below -============ =================== - -.. _light-available-features: - -**Available Light Features** - -=========== ========= ============= -Name Units Description -=========== ========= ============= -count rows Number light sensor rows recorded. -maxlux lux The maximum ambient luminance. -minlux lux The minimum ambient luminance. -avglux lux The average ambient luminance. -medianlux lux The median ambient luminance. -stdlux lux The standard deviation of ambient luminance. -=========== ========= ============= - -**Assumptions/Observations:** N/A - - -.. _location-sensor-doc: - -Location (Barnett’s) Features -"""""""""""""""""""""""""""""" -Barnett’s location features are based on the concept of flights and pauses. GPS coordinates are converted into a -sequence of flights (straight line movements) and pauses (time spent stationary). Data is imputed before features -are computed. See Ian Barnett, Jukka-Pekka Onnela, Inferring mobility measures from GPS traces with missing data, Biostatistics, Volume 21, Issue 2, April 2020, Pages e98–e112, https://doi.org/10.1093/biostatistics/kxy059. The code for these features was made open source by Ian Barnett (https://scholar.harvard.edu/ibarnett/software/gpsmobility). - -See `Location (Barnett’s) Config Code`_ - -**Available Epochs (day_segment) :** daily - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/preprocessing.snakefile/phone_sensed_bins`` -- Rule ``rules/preprocessing.snakefile/resample_fused_location`` (only relevant if setting ``location_to_use`` to ````RESAMPLE_FUSED``. -- Rule ``rules/features.snakefile/location_barnett_features`` - -.. _location-parameters: - -**Location Rule Parameters (location_barnett_features):** - -================= =================== -Name Description -================= =================== -location_to_use *Read the Observations section below*. The specifies what type of location data will be use in the analysis. Possible options are ``ALL``, ``ALL_EXCEPT_FUSED`` OR ``RESAMPLE_FUSED`` -accuracy_limit This is in meters. The sensor drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius specified. -timezone The timezone used to calculate location. -minutes_data_used This is NOT a feature. This is just a quality control check, and if set to TRUE, a new column is added to the output file with the number of minutes containing location data that were used to compute all features. The more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. -features Features to be computed, see table below -================= =================== - -.. _location-available-features: - -**Available Location Features** - -Description taken from `Beiwe Summary Statistics`_. - -================ ========= ============= -Name Units Description -================ ========= ============= -hometime minutes Time at home. Time spent at home in minutes. Home is the most visited significant location between 8 pm and 8 am including any pauses within a 200-meter radius. -disttravelled meters Total distance travelled over a day (flights). -rog meters The Radius of Gyration (rog) is a measure in meters of the area covered by a person over a day. A centroid is calculated for all the places (pauses) visited during a day and a weighted distance between all the places and that centroid is computed. The weights are proportional to the time spent in each place. -maxdiam meters The maximum diameter is the largest distance between any two pauses. -maxhomedist meters The maximum distance from home in meters. -siglocsvisited locations The number of significant locations visited during the day. Significant locations are computed using k-means clustering over pauses found in the whole monitoring period. The number of clusters is found iterating k from 1 to 200 stopping until the centroids of two significant locations are within 400 meters of one another. -avgflightlen meters Mean length of all flights. -stdflightlen meters Standard deviation of the length of all flights. -avgflightdur seconds Mean duration of all flights. -stdflightdur seconds The standard deviation of the duration of all flights. -probpause The fraction of a day spent in a pause (as opposed to a flight) -siglocentropy nats Shannon’s entropy measurement based on the proportion of time spent at each significant location visited during a day. -circdnrtn A continuous metric quantifying a person’s circadian routine that can take any value between 0 and 1, where 0 represents a daily routine completely different from any other sensed days and 1 a routine the same as every other sensed day. -wkenddayrtn Same as circdnrtn but computed separately for weekends and weekdays. -================ ========= ============= - -**Assumptions/Observations:** - -*Types of location data to use* - -Aware Android and iOS clients can collect location coordinates through the phone's GPS or Google's fused location API. If your Aware client was ONLY configured to use GPS set ``location_to_use`` to ``ALL``, if your client was configured to use BOTH GPS and fused location you can use ``ALL`` or set ``location_to_use`` to ``ALL_EXCEPT_FUSED`` to ignore fused coordinates, if your client was configured to use fused location only, set ``location_to_use`` to ``RESAMPLE_FUSED``. ``RESAMPLE_FUSED`` takes the original fused location coordinates and replicates each pair forward in time as long as the phone was sensing data as indicated by ``phone_sensed_bins`` (see :ref:`Phone valid sensed days `), this is done because Google's API only logs a new location coordinate pair when it is sufficiently different from the previous one. - -There are two parameters associated with resampling fused location in the ``RESAMPLE_FUSED_LOCATION`` section of the ``config.yaml`` file. ``CONSECUTIVE_THRESHOLD`` (in minutes, default 30) controls the maximum gap between any two coordinate pairs to replicate the last known pair (for example, participant A's phone did not collect data between 10.30am and 10:50am and between 11:05am and 11:40am, the last known coordinate pair will be replicated during the first period but not the second, in other words, we assume that we cannot longer guarantee the participant stayed at the last known location if the phone did not sense data for more than 30 minutes). ``TIME_SINCE_VALID_LOCATION`` (in minutes, default 720 or 12 hours) the last known fused location won't be carried over longer that this threshold even if the phone was sensing data continuously (for example, participant A went home at 9pm and their phone was sensing data without gaps until 11am the next morning, the last known location will only be replicated until 9am). If you have suggestions to modify or improve this imputation, let us know. - -*Barnett's et al features* - -These features are based on a Pause-Flight model. A pause is defined as a mobiity trace (location pings) within a certain duration and distance (by default 300 seconds and 60 meters). A flight is any mobility trace between two pauses. Data is resampled and imputed before the features are computed. See this paper for more information: https://doi.org/10.1093/biostatistics/kxy059. - -In RAPIDS we only expose two parameters for these features (timezone and accuracy). If you wish to change others you can do so in ``src/features/location_barnett/MobilityFeatures.R`` - -*Significant Locations* - -Significant locations are determined using K-means clustering on pauses longer than 10 minutes. The number of clusters (K) is increased until no two clusters are within 400 meters from each other. After this, pauses within a certain range of a cluster (200 meters by default) will count as a visit to that significant location. This description was adapted from the Supplementary Materials of https://doi.org/10.1093/biostatistics/kxy059. - - -*The Circadian Calculation* - -For a detailed description of how this is calculated, see Canzian, L., & Musolesi, M. (2015, September). Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis. In Proceedings of the 2015 ACM international joint conference on pervasive and ubiquitous computing (pp. 1293-1304). Their procedure was followed using 30-min increments as a bin size. Taken from `Beiwe Summary Statistics`_. - - -Location (Doryab's) Features -"""""""""""""""""""""""""""""" -Doryab's location features are based on this paper: Doryab, A., Chikarsel, P., Liu, X., & Dey, A. K. (2019). Extraction of Behavioral Features from Smartphone and Wearable Data. ArXiv:1812.10394 [Cs, Stat]. http://arxiv.org/abs/1812.10394 - -See `Location (Doryab's) Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/preprocessing.snakefile/phone_sensed_bins`` -- Rule ``rules/preprocessing.snakefile/resample_fused_location`` (only relevant if setting ``location_to_use`` to ````RESAMPLE_FUSED``. -- Rule ``rules/features.snakefile/location_doryab_features`` - -.. _location-doryab-parameters: - -**Location Rule Parameters (location_doryab_features):** - -=================== =================== -Name Description -=================== =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -location_to_use *Read the Observations section below*. The specifies what type of location data will be use in the analysis. Possible options are ``ALL``, ``ALL_EXCEPT_FUSED`` OR ``RESAMPLE_FUSED``. -features Features to be computed, see table below. -threshold_static It is the threshold value in km/hr which labels a row as Static or Moving. -dbscan_minsamples The number of samples (or total weight) in a neighborhood for a point to be considered as a core point. This includes the point itself. -dbscan_eps The maximum distance between two samples for one to be considered as in the neighborhood of the other. This is not a maximum bound on the distances of points within a cluster. This is the most important DBSCAN parameter to choose appropriately for your data set and distance function. -maximum_gap_allowed The maximum gap (in seconds) allowed between any two consecutive rows for them to be considered part of the same displacement. If this threshold is too high, it can throw speed and distance calculations off for periods when the the phone was not sensing. -minutes_data_used This is NOT a feature. This is just a quality control check, and if set to TRUE, a new column is added to the output file with the number of minutes containing location data that were used to compute all features. The more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. -sampling_frequency Expected time difference between any two location rows in minutes. If set to '0', the sampling frequency will be inferred automatically as the median of all the differences between any two consecutive row timestamps. This parameter impacts all the time calculations. -=================== =================== - -.. _location-doryab-available-features: - -**Available Location Features** - -============================ ================ ============= -Name Units Description -============================ ================ ============= -locationvariance :math:`meters^2` The sum of the variances of the latitude and longitude columns. -loglocationvariance Log of the sum of the variances of the latitude and longitude columns. -totaldistance meters Total distance travelled in a ``day_segment`` using the haversine formula. -averagespeed km/hr Average speed in a ``day_segment`` considering only the instances labeled as Moving. -varspeed km/hr Speed variance in a ``day_segment`` considering only the instances labeled as Moving. -circadianmovement "It encodes the extent to which a person’s location patterns follow a 24-hour circadian cycle." (Doryab et. al. 2019) -numberofsignificantplaces places Number of significant locations visited. It is calculated using the DBSCAN clustering algorithm which takes in EPS and MIN_SAMPLES as paramters to identify clusters. Each cluster is a significant place. -numberlocationtransitions transitions Number of movements between any two clusters in a ``day_segment``. -radiusgyration meters Quantifies the area covered by a participant -timeattop1location minutes Time spent at the most significant location. -timeattop2location minutes Time spent at the 2nd most significant location. -timeattop3location minutes Time spent at the 3rd most significant location. -movingtostaticratio Ratio between the number of rows labeled Moving versus Static -outlierstimepercent Ratio between the number of rows that belong to non-significant clusters divided by the total number of rows in a ``day_segment``. -maxlengthstayatclusters minutes Maximum time spent in a cluster (significant location). -minlengthstayatclusters minutes Minimum time spent in a cluster (significant location). -meanlengthstayatclusters minutes Average time spent in a cluster (significant location). -stdlengthstayatclusters minutes Standard deviation of time spent in a cluster (significant location). -locationentropy nats Shannon Entropy computed over the row count of each cluster (significant location), it will be higher the more rows belong to a cluster (i.e. the more time a participant spent at a significant location). -normalizedlocationentropy nats Shannon Entropy computed over the row count of each cluster (significant location) divided by the number of clusters, it will be higher the more rows belong to a cluster (i.e. the more time a participant spent at a significant location). -============================ ================ ============= - -**Assumptions/Observations:** - -*Types of location data to use* - -Aware Android and iOS clients can collect location coordinates through the phone's GPS or Google's fused location API. If your Aware client was ONLY configured to use GPS set ``location_to_use`` to ``ALL``, if your client was configured to use BOTH GPS and fused location you can use ``ALL`` or set ``location_to_use`` to ``ALL_EXCEPT_FUSED`` to ignore fused coordinates, if your client was configured to use fused location only, set ``location_to_use`` to ``RESAMPLE_FUSED``. ``RESAMPLE_FUSED`` takes the original fused location coordinates and replicates each pair forward in time as long as the phone was sensing data as indicated by ``phone_sensed_bins`` (see :ref:`Phone valid sensed days `), this is done because Google's API only logs a new location coordinate pair when it is sufficiently different from the previous one. - -There are two parameters associated with resampling fused location in the ``RESAMPLE_FUSED_LOCATION`` section of the ``config.yaml`` file. ``CONSECUTIVE_THRESHOLD`` (in minutes, default 30) controls the maximum gap between any two coordinate pairs to replicate the last known pair (for example, participant A's phone did not collect data between 10.30am and 10:50am and between 11:05am and 11:40am, the last known coordinate pair will be replicated during the first period but not the second, in other words, we assume that we cannot longer guarantee the participant stayed at the last known location if the phone did not sense data for more than 30 minutes). ``TIME_SINCE_VALID_LOCATION`` (in minutes, default 720 or 12 hours) the last known fused location won't be carried over longer that this threshold even if the phone was sensing data continuously (for example, participant A went home at 9pm and their phone was sensing data without gaps until 11am the next morning, the last known location will only be replicated until 9am). If you have suggestions to modify or improve this imputation, let us know. - -*Significant Locations Identified* - -Significant locations are determined using DBSCAN clustering on locations that a patient visit over the course of the period of data collection. - -*Circadian Movement Calculation* - -"Circadian movement (Saeb et al. 2015) is calculated using the Lomb-Scargle method" (Doryab et. al. 2019) - -.. _screen-sensor-doc: - -Screen -"""""""" - -See `Screen Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/preprocessing.snakefile/unify_ios_android`` -- Rule ``rules/features.snakefile/screen_deltas`` -- Rule ``rules/features.snakefile/screen_features`` - -.. _screen-parameters: - -**Screen Rule Parameters (screen_features):** - -============================ =================== -Name Description -============================ =================== -day_segment The particular ``day_segments`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -reference_hour_first_use The reference point from which ``firstuseafter`` is to be computed, default is midnight -ignore_episodes_shorter_than Ignore episodes that are shorter than this threshold (minutes). Set to 0 to disable this filter. -ignore_episodes_longer_than Ignore episodes that are longer than this threshold (minutes). Set to 0 to disable this filter. -features_deltas Features to be computed, see table below -episode_types Currently we only support unlock episodes (from when the phone is unlocked until the screen is off) -============================ =================== - -.. _screen-episodes-available-features: - -**Available Screen Episodes Features** - -========================= ================= ============= -Name Units Description -========================= ================= ============= -sumduration minutes Total duration of all unlock episodes. -maxduration minutes Longest duration of any unlock episode. -minduration minutes Shortest duration of any unlock episode. -avgduration minutes Average duration of all unlock episodes. -stdduration minutes Standard deviation duration of all unlock episodes. -countepisode episodes Number of all unlock episodes -episodepersensedminutes episodes/minute The ratio between the total number of episodes in an epoch divided by the total time (minutes) the phone was sensing data. -firstuseafter minutes Minutes until the first unlock episode. -========================= ================= ============= - -**Assumptions/Observations:** - -In Android, ``lock`` events can happen right after an ``off`` event, after a few seconds of an ``off`` event, or never happen depending on the phone's settings, therefore, an ``unlock`` episode is defined as the time between an ``unlock`` and a ``off`` event. In iOS, ``on`` and ``off`` events do not exist, so an ``unlock`` episode is defined as the time between an ``unlock`` and a ``lock`` event. - -Events in iOS are recorded reliably albeit some duplicated ``lock`` events within milliseconds from each other, so we only keep consecutive unlock/lock pairs. In Android you cand find multiple consecutive ``unlock`` or ``lock`` events, so we only keep consecutive unlock/off pairs. In our experiments these cases are less than 10% of the screen events collected and this happens because ``ACTION_SCREEN_OFF`` and ``ACTION_SCREEN_ON`` are "sent when the device becomes non-interactive which may have nothing to do with the screen turning off". In addition to unlock/off episodes, in Android it is possible to measure the time spent on the lock screen before an ``unlock`` event as well as the total screen time (i.e. ``ON`` to ``OFF``) but these are not implemented at the moment. - -To unify the screen processing and use the same code in RAPIDS, we replace LOCKED episodes with OFF episodes (2 with 0) in iOS. However, as mentioned above this is still computing ``unlock`` to ``lock`` episodes. - -.. _conversation-sensor-doc: - -Conversation -"""""""""""""" - -See `Conversation Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:** Android and iOS - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/readable_datetime`` -- Rule ``rules/features.snakefile/conversation_features`` - -.. _conversation-parameters: - -**Conversation Rule Parameters (conversation_features):** - -========================= =================== -Name Description -========================= =================== -day_segment The particular ``day_segments`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -recordingMinutes Minutes the plugin was recording audio (default 1 min) -pausedMinutes Minutes the plugin was NOT recording audio (default 3 min) -features Features to be computed, see table below -========================= =================== - -.. _conversation-available-features: - -**Available Conversation Features** - -========================= ================= ============= -Name Units Description -========================= ================= ============= -minutessilence minutes Minutes labeled as silence -minutesnoise minutes Minutes labeled as noise -minutesvoice minutes Minutes labeled as voice -minutesunknown minutes Minutes labeled as unknown -sumconversationduration minutes Total duration of all conversations -maxconversationduration minutes Longest duration of all conversations -minconversationduration minutes Shortest duration of all conversations -avgconversationduration minutes Average duration of all conversations -sdconversationduration minutes Standard Deviation of the duration of all conversations -timefirstconversation minutes Minutes since midnight when the first conversation for a day segment was detected -timelastconversation minutes Minutes since midnight when the last conversation for a day segment was detected -noisesumenergy L2-norm Sum of all energy values when inference is noise -noiseavgenergy L2-norm Average of all energy values when inference is noise -noisesdenergy L2-norm Standard Deviation of all energy values when inference is noise -noiseminenergy L2-norm Minimum of all energy values when inference is noise -noisemaxenergy L2-norm Maximum of all energy values when inference is noise -voicesumenergy L2-norm Sum of all energy values when inference is voice -voiceavgenergy L2-norm Average of all energy values when inference is voice -voicesdenergy L2-norm Standard Deviation of all energy values when inference is voice -voiceminenergy L2-norm Minimum of all energy values when inference is voice -voicemaxenergy L2-norm Maximum of all energy values when inference is voice -silencesensedfraction Ratio between minutessilence and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) -noisesensedfraction Ratio between minutesnoise and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) -voicesensedfraction Ratio between minutesvoice and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) -unknownsensedfraction Ratio between minutesunknown and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) -silenceexpectedfraction Ration between minutessilence and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) -noiseexpectedfraction Ration between minutesnoise and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) -voiceexpectedfraction Ration between minutesvoice and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) -unknownexpectedfraction Ration between minutesunknown and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) -========================= ================= ============= - -**Assumptions/Observations:** -N/A - -.. ------------------------------- Begin Fitbit Section ----------------------------------- .. - -.. _fitbit-sleep-sensor-doc: - -Fitbit: Sleep -""""""""""""""""""" - -See `Fitbit: Sleep Config Code`_ - -**Available Epochs (day_segment) :** daily - -**Available Platforms:**: Fitbit - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/fitbit_with_datetime`` -- Rule ``rules/features.snakefile/fitbit_sleep_features`` - -.. _fitbit-sleep-parameters: - -**Fitbit: Sleep Rule Parameters (fitbit_sleep_features):** - -================================== =================== -Name Description -================================== =================== -day_segment The particular ``day_segment`` that will be analyzed. For this sensor only ``daily`` is used. -sleep_types The types of sleep provided by Fitbit: ``main``, ``nap``, ``all``. -daily_features_from_summary_data The sleep features that can be computed based on Fitbit's summary data. See :ref:`Available Fitbit: Sleep Features ` Table below -================================== =================== - -.. _fitbit-sleep-available-features: - -**Available Fitbit: Sleep Features** - -======================== =========== ============= -Name Units Description -======================== =========== ============= -sumdurationtofallasleep minutes Time it took the user to fall asleep for ``sleep_type`` during ``day_segment``. -sumdurationawake minutes Time the user was awake but still in bed for ``sleep_type`` during ``day_segment``. -sumdurationasleep minutes Sleep duration for ``sleep_type`` during ``day_segment``. -sumdurationafterwakeup minutes Time the user stayed in bed after waking up for ``sleep_type`` during ``day_segment``. -sumdurationinbed minutes Total time the user stayed in bed (sumdurationtofallasleep + sumdurationawake + sumdurationasleep + sumdurationafterwakeup) for ``sleep_type`` during ``day_segment``. -avgefficiency scores Sleep efficiency average for ``sleep_type`` during ``day_segment``. -countepisode episodes Number of sleep episodes for ``sleep_type`` during ``day_segment``. -======================== =========== ============= - -**Assumptions/Observations:** - -Only features from summary data are available at the momement. - -The `fitbit_with_datetime` rule will extract Summary data (`fitbit_sleep_summary_with_datetime.csv`) and Intraday data (`fitbit_sleep_intraday_with_datetime.csv`). There are two versions of Fitbit's sleep API (`version 1`_ and `version 1.2`_), and each provides raw sleep data in a different format: - - - Sleep level. In ``v1``, sleep level is an integer with three possible values (1, 2, 3) while in ``v1.2`` is a string. We convert integer levels to strings, ``asleep``, ``restless`` or ``awake`` respectively. - - Count summaries. For Summary data, ``v1`` contains ``count_awake``, ``duration_awake``, ``count_awakenings``, ``count_restless``, and ``duration_restless`` fields for every sleep record while ``v1.2`` does not. - - Types of sleep records. ``v1.2`` has two types of sleep records: ``classic`` and ``stages``. The ``classic`` type contains three sleep levels: ``awake``, ``restless`` and ``asleep``. The ``stages`` type contains four sleep levels: ``wake``, ``deep``, ``light``, and ``rem``. Sleep records from ``v1`` will have the same sleep levels as `v1.2` classic type; therefore we set their type to ``classic``. - - Unified level of sleep. For intraday data, we unify sleep levels of each sleep record with a column named ``unified_level``. Based on `this Fitbit forum post`_ , we merge levels into two categories: - - For the ``classic`` type unified_level is one of {0, 1} where 0 means awake and groups ``awake`` + ``restless``, while 1 means asleep and groups ``asleep``. - - For the ``stages`` type, unified_level is one of {0, 1} where 0 means awake and groups ``wake`` while 1 means asleep and groups ``deep`` + ``light`` + ``rem``. - - Short Data. In ``v1.2``, records of type ``stages`` contain ``shortData`` in addition to ``data``. We merge both to extract intraday data. - - ``data`` contains sleep stages and any wake periods > 3 minutes (180 seconds). - - ``shortData`` contains short wake periods representing physiological awakenings that are <= 3 minutes (180 seconds). - - The following columns of Summary data are not computed by RAPIDS but taken directly from columns with a similar name provided by Fitbit's API: ``efficiency``, ``minutes_after_wakeup``, ``minutes_asleep``, ``minutes_awake``, ``minutes_to_fall_asleep``, ``minutes_in_bed``, ``is_main_sleep`` and ``type`` - - The following columns of Intraday data are not computed by RAPIDS but taken directly from columns with a similar name provided by Fitbit's API: ``original_level``, ``is_main_sleep`` and ``type``. We compute ``unified_level`` as explained above. - -These are examples of intraday and summary data: - -- Intraday data (at 30-second intervals for ``stages`` type or 60-second intervals for ``classic`` type) - -========= ============== ============= ============= ====== =================== ========== =========== ========= ================= ========== ========== ============ ================= -device_id original_level unified_level is_main_sleep type local_date_time local_date local_month local_day local_day_of_week local_time local_hour local_minute local_day_segment -========= ============== ============= ============= ====== =================== ========== =========== ========= ================= ========== ========== ============ ================= -did wake 0 1 stages 2020-05-20 22:13:30 2020-05-20 5 20 2 22:13:30 22 13 evening -did wake 0 1 stages 2020-05-20 22:14:00 2020-05-20 5 20 2 22:14:00 22 14 evening -did light 1 1 stages 2020-05-20 22:14:30 2020-05-20 5 20 2 22:14:30 22 14 evening -did light 1 1 stages 2020-05-20 22:15:00 2020-05-20 5 20 2 22:15:00 22 15 evening -did light 1 1 stages 2020-05-20 22:15:30 2020-05-20 5 20 2 22:15:30 22 15 evening -========= ============== ============= ============= ====== =================== ========== =========== ========= ================= ========== ========== ============ ================= - -- Summary data - -========= ========== ==================== ============== ============= ====================== ============== ============= ====== ===================== =================== ================ ============== ======================= ===================== -device_id efficiency minutes_after_wakeup minutes_asleep minutes_awake minutes_to_fall_asleep minutes_in_bed is_main_sleep type local_start_date_time local_end_date_time local_start_date local_end_date local_start_day_segment local_end_day_segment -========= ========== ==================== ============== ============= ====================== ============== ============= ====== ===================== =================== ================ ============== ======================= ===================== -did 90 0 381 54 0 435 1 stages 2020-05-20 22:12:00 2020-05-21 05:27:00 2020-05-20 2020-05-21 evening night -did 88 0 498 86 0 584 1 stages 2020-05-22 22:03:00 2020-05-23 07:47:03 2020-05-22 2020-05-23 evening morning -========= ========== ==================== ============== ============= ====================== ============== ============= ====== ===================== =================== ================ ============== ======================= ===================== - - -.. _fitbit-heart-rate-sensor-doc: - -Fitbit: Heart Rate -""""""""""""""""""" - -See `Fitbit: Heart Rate Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:**: Fitbit - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/fitbit_with_datetime`` -- Rule ``rules/features.snakefile/fitbit_heartrate_features`` - -.. _fitbit-heart-rate-parameters: - -**Fitbit: Heart Rate Rule Parameters (fitbit_heartrate_features):** - -============ =================== -Name Description -============ =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features The heartrate features that can be computed. See :ref:`Available Fitbit: Heart Rate Features ` Table below -============ =================== - -.. _fitbit-heart-rate-available-features: - -**Available Fitbit: Heart Rate Features** - -================== =========== ============= -Name Units Description -================== =========== ============= -restingheartrate beats/mins The number of times your heart beats per minute when participant is still and well rested for ``daily`` epoch. -calories cals Calories burned during ``heartrate_zone`` for ``daily`` epoch. -maxhr beats/mins The maximum heart rate during ``day_segment`` epoch. -minhr beats/mins The minimum heart rate during ``day_segment`` epoch. -avghr beats/mins The average heart rate during ``day_segment`` epoch. -medianhr beats/mins The median of heart rate during ``day_segment`` epoch. -modehr beats/mins The mode of heart rate during ``day_segment`` epoch. -stdhr beats/mins The standard deviation of heart rate during ``day_segment`` epoch. -diffmaxmodehr beats/mins The difference between the maximum and mode heart rate during ``day_segment`` epoch. -diffminmodehr beats/mins The difference between the mode and minimum heart rate during ``day_segment`` epoch. -entropyhr nats Shannon’s entropy measurement based on heart rate during ``day_segment`` epoch. -minutesonZONE minutes Number of minutes the user's heartrate fell within each ``heartrate_zone`` during ``day_segment`` epoch. -================== =========== ============= - -**Assumptions/Observations:** - -There are four heart rate zones: ``out_of_range``, ``fat_burn``, ``cardio``, and ``peak``. Please refer to `Fitbit documentation`_ for more information about the way they are computed. - -Calories' accuracy depends on the users’ Fitbit profile (weight, height, etc.). - - -.. _fitbit-steps-sensor-doc: - -Fitbit: Steps -""""""""""""""" - -See `Fitbit: Steps Config Code`_ - -**Available Epochs (day_segment) :** daily, morning, afternoon, evening, night - -**Available Platforms:**: Fitbit - -**Snakemake rule chain:** - -- Rule ``rules/preprocessing.snakefile/download_dataset`` -- Rule ``rules/preprocessing.snakefile/fitbit_with_datetime`` -- Rule ``rules/features.snakefile/fitbit_step_features`` - -.. _fitbit-steps-parameters: - -**Fitbit: Steps Rule Parameters (fitbit_step_features):** - -========================== =================== -Name Description -========================== =================== -day_segment The particular ``day_segment`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night`` -features The features that can be computed. See :ref:`Available Fitbit: Steps Features ` Table below -threshold_active_bout Every minute with Fitbit step data wil be labelled as ``sedentary`` if its step count is below this threshold, otherwise, ``active``. -include_zero_step_rows Whether or not to include day segments with a 0 step count -exclude_sleep Whether or not to exclude step rows that happen during sleep -exclude_sleep_type If ``exclude_sleep`` is True, then you can choose between ``FIXED`` or ``FITBIT_BASED``. ``FIXED`` will exclude all step rows that happen between a start and end time (see below). ``FITBIT_BASED`` will exclude step rows that happen during main sleep segments as measured by the Fitbit device (``config[SLEEP][DB_TABLE]`` should be a valid table in your database, it usually is the same table that contains your STEP data) -exclude_sleep_fixed_start Start time of the fixed sleep period to exclude. Only relevant if ``exclude_sleep`` is True and ``exclude_sleep_type`` is ``FIXED`` -exclude_sleep_fixed_end Start time of the fixed sleep period to exclude. Only relevant if ``exclude_sleep`` is True and ``exclude_sleep_type`` is ``FIXED`` -========================== =================== - -.. _fitbit-steps-available-features: - -**Available Fitbit: Steps Features** - -========================== ========= ============= -Name Units Description -========================== ========= ============= -sumallsteps steps The total step count during ``day_segment`` epoch. -maxallsteps steps The maximum step count during ``day_segment`` epoch. -minallsteps steps The minimum step count during ``day_segment`` epoch. -avgallsteps steps The average step count during ``day_segment`` epoch. -stdallsteps steps The standard deviation of step count during ``day_segment`` epoch. -countepisodesedentarybout bouts Number of sedentary bouts during ``day_segment`` epoch. -sumdurationsedentarybout minutes Total duration of all sedentary bouts during ``day_segment`` epoch. -maxdurationsedentarybout minutes The maximum duration of any sedentary bout during ``day_segment`` epoch. -mindurationsedentarybout minutes The minimum duration of any sedentary bout during ``day_segment`` epoch. -avgdurationsedentarybout minutes The average duration of sedentary bouts during ``day_segment`` epoch. -stddurationsedentarybout minutes The standard deviation of the duration of sedentary bouts during ``day_segment`` epoch. -countepisodeactivebout bouts Number of active bouts during ``day_segment`` epoch. -sumdurationactivebout minutes Total duration of all active bouts during ``day_segment`` epoch. -maxdurationactivebout minutes The maximum duration of any active bout during ``day_segment`` epoch. -mindurationactivebout minutes The minimum duration of any active bout during ``day_segment`` epoch. -avgdurationactivebout minutes The average duration of active bouts during ``day_segment`` epoch. -stddurationactivebout minutes The standard deviation of the duration of active bouts during ``day_segment`` epoch. -========================== ========= ============= - -**Assumptions/Observations:** - -Active and sedentary bouts. If the step count per minute is smaller than ``THRESHOLD_ACTIVE_BOUT`` (default value is 10), that minute is labelled as sedentary, otherwise, is labelled as active. Active and sedentary bouts are periods of consecutive minutes labelled as ``active`` or ``sedentary``. - -``validsensedminutes`` feature is not available for Step sensor as we cannot determine the valid minutes based on the raw Fitbit step data. - - -.. -------------------------Links ------------------------------------ .. - -.. _PHONE_VALID_SENSED_BINS: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L30 -.. _`Messages Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L43 -.. _AWARE: https://awareframework.com/what-is-aware/ -.. _`List of Timezones`: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones -.. _DAY_SEGMENTS: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L6 -.. _PHONE_VALID_SENSED_DAYS: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L37 -.. _`Call Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L53 -.. _`WiFi Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L172 -.. _`Bluetooth Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L84 -.. _`Accelerometer Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L118 -.. _`Applications Foreground Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L128 -.. _`Battery Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L98 -.. _`Activity Recognition Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L90 -.. _`Light Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L112 -.. _`Location (Barnett’s) Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L74 -.. _`Location (Doryab's) Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L74 -.. _`Screen Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L104 -.. _`Fitbit: Sleep Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L165 -.. _`version 1`: https://dev.fitbit.com/build/reference/web-api/sleep-v1/ -.. _`version 1.2`: https://dev.fitbit.com/build/reference/web-api/sleep/ -.. _`Conversation Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L191 -.. _`this Fitbit forum post`: https://community.fitbit.com/t5/Alta/What-does-Restless-mean-in-sleep-tracking/td-p/2989011 -.. _shortData: https://dev.fitbit.com/build/reference/web-api/sleep/#interpreting-the-sleep-stage-and-short-data -.. _`Fitbit: Heart Rate Config Code`: https://github.com/carissalow/rapids/blob/4bdc30ffa4e13987b398a2354746d1a1977bef27/config.yaml#L141 -.. _`Fitbit: Steps Config Code`: https://github.com/carissalow/rapids/blob/29b04b0601b62379fbdb76de685f3328b8dde2a2/config.yaml#L148 -.. _`Fitbit documentation`: https://help.fitbit.com/articles/en_US/Help_article/1565 -.. _top1global: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L136 -.. _`Beiwe Summary Statistics`: http://wiki.beiwe.org/wiki/Summary_Statistics -.. _`Pause-Flight Model`: https://academic.oup.com/biostatistics/advance-article/doi/10.1093/biostatistics/kxy059/5145908 diff --git a/docs/features/feature-introduction.md b/docs/features/feature-introduction.md new file mode 100644 index 00000000..f755b34f --- /dev/null +++ b/docs/features/feature-introduction.md @@ -0,0 +1,57 @@ +# Behavioral Features Introduction + +Every phone or Fitbit sensor has a corresponding config section in `config.yaml`, these sections follow a similar structure and we'll use `PHONE_ACCELEROMETER` as an example to explain this structure. + +!!! hint + - We recommend reading this page if you are using RAPIDS for the first time + - All computed sensor features are stored under `/data/processed/features` on files per sensor, per participant and per study (all participants). + - Every time you change any sensor parameters, provider parameters or provider features, all the necessary files will be updated as soon as you execute RAPIDS. + + +!!! example "Config section example for `PHONE_ACCELEROMETER`" + + ```yaml + # 1) Config section + PHONE_ACCELEROMETER: + # 2) Parameters for PHONE_ACCELEROMETER + TABLE: accelerometer + + # 3) Providers for PHONE_ACCELEROMETER + PROVIDERS: + # 4) RAPIDS provider + RAPIDS: + # 4.1) Parameters of RAPIDS provider of PHONE_ACCELEROMETER + COMPUTE: False + # 4.2) Features of RAPIDS provider of PHONE_ACCELEROMETER + FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] + SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" + + # 5) PANDA provider + PANDA: + # 5.1) Parameters of PANDA provider of PHONE_ACCELEROMETER + COMPUTE: False + VALID_SENSED_MINUTES: False + # 5.2) Features of PANDA provider of PHONE_ACCELEROMETER + FEATURES: + exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + SRC_FOLDER: "panda" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" + ``` + +## Sensor Parameters +Each sensor configuration section has a "parameters" subsection (see `#2` in the example). These are parameters that affect different aspects of how the raw data is downloaded, and processed. The `TABLE` parameter exists for every sensor, but some sensors will have extra parameters like [`[PHONE_LOCATIONS]`](../phone-locations/). We explain these parameters in a table at the top of each sensor documentation page. + +## Sensor Providers +Each sensor configuration section can have zero, one or more behavioral feature **providers** (see `#3` in the example). A provider is a script created by the core RAPIDS team or other researchers that extracts behavioral features for that sensor. In this example, accelerometer has two providers: RAPIDS (see `#4`) and PANDA (see `#5`). + +### Provider Parameters +Each provider has parameters that affect the computation of the behavioral features it offers (see `#4.1` or `#5.1` in the example). These parameters will include at least a `[COMPUTE]` flag that you switch to `True` to extract a provider's behavioral features. + +We explain every provider's parameter in a table under the `Parameters description` heading on each provider documentation page. + +### Provider Features +Each provider offers a set of behavioral features (see `#4.2` or `#5.2` in the example). For some providers these features are grouped in an array (like those for `RAPIDS` provider in `#4.2`) but for others they are grouped in a collection of arrays depending on the meaning and purpose of those features (like those for `PANDAS` provider in `#5.2`). In either case, you can delete the features you are not interested in and they will not be included in the sensor's output feature file. + +We explain each behavioral feature in a table under the `Features description` heading on each provider documentation page. diff --git a/docs/features/fitbit-heartrate-intraday.md b/docs/features/fitbit-heartrate-intraday.md new file mode 100644 index 00000000..3dcedc14 --- /dev/null +++ b/docs/features/fitbit-heartrate-intraday.md @@ -0,0 +1,72 @@ +# Fitbit Heart Rate Intraday + +Sensor parameters description for `[FITBIT_HEARTRATE_INTRADAY]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table name or file path where the heart rate intraday data is stored. The configuration keys in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration) control whether this parameter is interpreted as table or file. + +The format of the column(s) containing the Fitbit sensor data can be `JSON` or `PLAIN_TEXT`. The data in `JSON` format is obtained directly from the Fitbit API. We support `PLAIN_TEXT` in case you already parsed your data and don't have access to your participants' Fitbit accounts anymore. If your data is in `JSON` format then summary and intraday data come packed together. + +We provide examples of the input format that RAPIDS expects, note that both examples for `JSON` and `PLAIN_TEXT` are tabular and the actual format difference comes in the `fitbit_data` column (we truncate the `JSON` example for brevity). + +??? example "Example of the structure of source data" + + === "JSON" + + |device_id |fitbit_data | + |---------------------------------------- |--------------------------------------------------------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"activities-heart":[{"dateTime":"2020-10-07","value":{"customHeartRateZones":[],"heartRateZones":[{"caloriesOut":1200.6102,"max":88,"min":31,"minutes":1058,"name":"Out of Range"},{"caloriesOut":760.3020,"max":120,"min":86,"minutes":366,"name":"Fat Burn"},{"caloriesOut":15.2048,"max":146,"min":120,"minutes":2,"name":"Cardio"},{"caloriesOut":0,"max":221,"min":148,"minutes":0,"name":"Peak"}],"restingHeartRate":72}}],"activities-heart-intraday":{"dataset":[{"time":"00:00:00","value":68},{"time":"00:01:00","value":67},{"time":"00:02:00","value":67},...],"datasetInterval":1,"datasetType":"minute"}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"activities-heart":[{"dateTime":"2020-10-08","value":{"customHeartRateZones":[],"heartRateZones":[{"caloriesOut":1100.1120,"max":89,"min":30,"minutes":921,"name":"Out of Range"},{"caloriesOut":660.0012,"max":118,"min":82,"minutes":361,"name":"Fat Burn"},{"caloriesOut":23.7088,"max":142,"min":108,"minutes":3,"name":"Cardio"},{"caloriesOut":0,"max":221,"min":148,"minutes":0,"name":"Peak"}],"restingHeartRate":70}}],"activities-heart-intraday":{"dataset":[{"time":"00:00:00","value":77},{"time":"00:01:00","value":75},{"time":"00:02:00","value":73},...],"datasetInterval":1,"datasetType":"minute"}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"activities-heart":[{"dateTime":"2020-10-09","value":{"customHeartRateZones":[],"heartRateZones":[{"caloriesOut":750.3615,"max":77,"min":30,"minutes":851,"name":"Out of Range"},{"caloriesOut":734.1516,"max":107,"min":77,"minutes":550,"name":"Fat Burn"},{"caloriesOut":131.8579,"max":130,"min":107,"minutes":29,"name":"Cardio"},{"caloriesOut":0,"max":220,"min":130,"minutes":0,"name":"Peak"}],"restingHeartRate":69}}],"activities-heart-intraday":{"dataset":[{"time":"00:00:00","value":90},{"time":"00:01:00","value":89},{"time":"00:02:00","value":88},...],"datasetInterval":1,"datasetType":"minute"}} + + === "PLAIN_TEXT" + + |device_id |local_date_time |heartrate |heartrate_zone | + |-------------------------------------- |---------------------- |--------- |--------------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 00:00:00 |68 |outofrange | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 00:01:00 |67 |outofrange | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 00:02:00 |67 |outofrange | + + +## RAPIDS provider + +!!! info "Available time segments" + - Available for all time segments + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/fitbit_heartrate_intraday_raw.csv + - data/raw/{pid}/fitbit_heartrate_intraday_parsed.csv + - data/raw/{pid}/fitbit_heartrate_intraday_parsed_with_datetime.csv + - data/interim/{pid}/fitbit_heartrate_intraday_features/fitbit_heartrate_intraday_{language}_{provider_key}.csv + - data/processed/features/{pid}/fitbit_heartrate_intraday.csv + ``` + + +Parameters description for `[FITBIT_HEARTRATE_INTRADAY][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]` | Set to `True` to extract `FITBIT_HEARTRATE_INTRADAY` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed from heart rate intraday data, see table below | + + +Features description for `[FITBIT_HEARTRATE_INTRADAY][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |-------------- |---------------------------| +|maxhr |beats/mins |The maximum heart rate during a time segment. +|minhr |beats/mins |The minimum heart rate during a time segment. +|avghr |beats/mins |The average heart rate during a time segment. +|medianhr |beats/mins |The median of heart rate during a time segment. +|modehr |beats/mins |The mode of heart rate during a time segment. +|stdhr |beats/mins |The standard deviation of heart rate during a time segment. +|diffmaxmodehr |beats/mins |The difference between the maximum and mode heart rate during a time segment. +|diffminmodehr |beats/mins |The difference between the mode and minimum heart rate during a time segment. +|entropyhr |nats |Shannon’s entropy measurement based on heart rate during a time segment. +|minutesonZONE |minutes |Number of minutes the user’s heart rate fell within each `heartrate_zone` during a time segment. + +!!! note "Assumptions/Observations" + + 1. There are four heart rate zones (ZONE): ``outofrange``, ``fatburn``, ``cardio``, and ``peak``. Please refer to [Fitbit documentation](https://help.fitbit.com/articles/en_US/Help_article/1565.htm) for more information about the way they are computed. diff --git a/docs/features/fitbit-heartrate-summary.md b/docs/features/fitbit-heartrate-summary.md new file mode 100644 index 00000000..9392dea6 --- /dev/null +++ b/docs/features/fitbit-heartrate-summary.md @@ -0,0 +1,80 @@ +# Fitbit Heart Rate Summary + +Sensor parameters description for `[FITBIT_HEARTRATE_SUMMARY]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table name or file path where the heart rate summary data is stored. The configuration keys in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration) control whether this parameter is interpreted as table or file. + +The format of the column(s) containing the Fitbit sensor data can be `JSON` or `PLAIN_TEXT`. The data in `JSON` format is obtained directly from the Fitbit API. We support `PLAIN_TEXT` in case you already parsed your data and don't have access to your participants' Fitbit accounts anymore. If your data is in `JSON` format then summary and intraday data come packed together. + +We provide examples of the input format that RAPIDS expects, note that both examples for `JSON` and `PLAIN_TEXT` are tabular and the actual format difference comes in the `fitbit_data` column (we truncate the `JSON` example for brevity). + +??? example "Example of the structure of source data" + + === "JSON" + + |device_id |fitbit_data | + |---------------------------------------- |--------------------------------------------------------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"activities-heart":[{"dateTime":"2020-10-07","value":{"customHeartRateZones":[],"heartRateZones":[{"caloriesOut":1200.6102,"max":88,"min":31,"minutes":1058,"name":"Out of Range"},{"caloriesOut":760.3020,"max":120,"min":86,"minutes":366,"name":"Fat Burn"},{"caloriesOut":15.2048,"max":146,"min":120,"minutes":2,"name":"Cardio"},{"caloriesOut":0,"max":221,"min":148,"minutes":0,"name":"Peak"}],"restingHeartRate":72}}],"activities-heart-intraday":{"dataset":[{"time":"00:00:00","value":68},{"time":"00:01:00","value":67},{"time":"00:02:00","value":67},...],"datasetInterval":1,"datasetType":"minute"}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"activities-heart":[{"dateTime":"2020-10-08","value":{"customHeartRateZones":[],"heartRateZones":[{"caloriesOut":1100.1120,"max":89,"min":30,"minutes":921,"name":"Out of Range"},{"caloriesOut":660.0012,"max":118,"min":82,"minutes":361,"name":"Fat Burn"},{"caloriesOut":23.7088,"max":142,"min":108,"minutes":3,"name":"Cardio"},{"caloriesOut":0,"max":221,"min":148,"minutes":0,"name":"Peak"}],"restingHeartRate":70}}],"activities-heart-intraday":{"dataset":[{"time":"00:00:00","value":77},{"time":"00:01:00","value":75},{"time":"00:02:00","value":73},...],"datasetInterval":1,"datasetType":"minute"}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"activities-heart":[{"dateTime":"2020-10-09","value":{"customHeartRateZones":[],"heartRateZones":[{"caloriesOut":750.3615,"max":77,"min":30,"minutes":851,"name":"Out of Range"},{"caloriesOut":734.1516,"max":107,"min":77,"minutes":550,"name":"Fat Burn"},{"caloriesOut":131.8579,"max":130,"min":107,"minutes":29,"name":"Cardio"},{"caloriesOut":0,"max":220,"min":130,"minutes":0,"name":"Peak"}],"restingHeartRate":69}}],"activities-heart-intraday":{"dataset":[{"time":"00:00:00","value":90},{"time":"00:01:00","value":89},{"time":"00:02:00","value":88},...],"datasetInterval":1,"datasetType":"minute"}} + + === "PLAIN_TEXT" + + |device_id |local_date_time |heartrate_daily_restinghr |heartrate_daily_caloriesoutofrange |heartrate_daily_caloriesfatburn |heartrate_daily_caloriescardio |heartrate_daily_caloriespeak | + |-------------------------------------- |----------------- |------- |-------------- |------------- |------------ |-------| + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 |72 |1200.6102 |760.3020 |15.2048 |0 | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-08 |70 |1100.1120 |660.0012 |23.7088 |0 | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-09 |69 |750.3615 |734.1516 |131.8579 |0 | + + +## RAPIDS provider + +!!! info "Available time segments" + - Only available for segments that span 1 or more complete days (e.g. Jan 1st 00:00 to Jan 3rd 23:59) + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/fitbit_heartrate_summary_raw.csv + - data/raw/{pid}/fitbit_heartrate_summary_parsed.csv + - data/raw/{pid}/fitbit_heartrate_summary_parsed_with_datetime.csv + - data/interim/{pid}/fitbit_heartrate_summary_features/fitbit_heartrate_summary_{language}_{provider_key}.csv + - data/processed/features/{pid}/fitbit_heartrate_summary.csv + ``` + + +Parameters description for `[FITBIT_HEARTRATE_SUMMARY][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]` | Set to `True` to extract `FITBIT_HEARTRATE_SUMMARY` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed from heart rate summary data, see table below | + + +Features description for `[FITBIT_HEARTRATE_SUMMARY][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|maxrestinghr |beats/mins |The maximum daily resting heart rate during a time segment. +|minrestinghr |beats/mins |The minimum daily resting heart rate during a time segment. +|avgrestinghr |beats/mins |The average daily resting heart rate during a time segment. +|medianrestinghr |beats/mins |The median of daily resting heart rate during a time segment. +|moderestinghr |beats/mins |The mode of daily resting heart rate during a time segment. +|stdrestinghr |beats/mins |The standard deviation of daily resting heart rate during a time segment. +|diffmaxmoderestinghr |beats/mins |The difference between the maximum and mode daily resting heart rate during a time segment. +|diffminmoderestinghr |beats/mins |The difference between the mode and minimum daily resting heart rate during a time segment. +|entropyrestinghr |nats |Shannon’s entropy measurement based on daily resting heart rate during a time segment. +|sumcaloriesZONE |cals |The total daily calories burned within `heartrate_zone` during a time segment. +|maxcaloriesZONE |cals |The maximum daily calories burned within `heartrate_zone` during a time segment. +|mincaloriesZONE |cals |The minimum daily calories burned within `heartrate_zone` during a time segment. +|avgcaloriesZONE |cals |The average daily calories burned within `heartrate_zone` during a time segment. +|mediancaloriesZONE |cals |The median of daily calories burned within `heartrate_zone` during a time segment. +|stdcaloriesZONE |cals |The standard deviation of daily calories burned within `heartrate_zone` during a time segment. +|entropycaloriesZONE |nats |Shannon’s entropy measurement based on daily calories burned within `heartrate_zone` during a time segment. + +!!! note "Assumptions/Observations" + + 1. There are four heart rate zones (ZONE): ``outofrange``, ``fatburn``, ``cardio``, and ``peak``. Please refer to [Fitbit documentation](https://help.fitbit.com/articles/en_US/Help_article/1565.htm) for more information about the way they are computed. + + 2. Calories' accuracy depends on the users’ Fitbit profile (weight, height, etc.). diff --git a/docs/features/fitbit-sleep-summary.md b/docs/features/fitbit-sleep-summary.md new file mode 100644 index 00000000..9ac79357 --- /dev/null +++ b/docs/features/fitbit-sleep-summary.md @@ -0,0 +1,102 @@ +# Fitbit Sleep Summary + +Sensor parameters description for `[FITBIT_SLEEP_SUMMARY]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table name or file path where the sleep summary data is stored. The configuration keys in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration) control whether this parameter is interpreted as table or file. + +The format of the column(s) containing the Fitbit sensor data can be `JSON` or `PLAIN_TEXT`. The data in `JSON` format is obtained directly from the Fitbit API. We support `PLAIN_TEXT` in case you already parsed your data and don't have access to your participants' Fitbit accounts anymore. If your data is in `JSON` format then summary and intraday data come packed together. + +We provide examples of the input format that RAPIDS expects, note that both examples for `JSON` and `PLAIN_TEXT` are tabular and the actual format difference comes in the `fitbit_data` column (we truncate the `JSON` example for brevity). + +??? example "Example of the structure of source data with Fitbit’s sleep API Version 1" + + === "JSON" + + |device_id |fitbit_data | + |---------------------------------------- |--------------------------------------------------------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep": [{"awakeCount": 2, "awakeDuration": 3, "awakeningsCount": 10, "dateOfSleep": "2020-10-07", "duration": 8100000, "efficiency": 91, "endTime": "2020-10-07T18:10:00.000", "isMainSleep": true, "logId": 14147921940, "minuteData": [{"dateTime": "15:55:00", "value": "3"}, {"dateTime": "15:56:00", "value": "3"}, {"dateTime": "15:57:00", "value": "2"},...], "minutesAfterWakeup": 0, "minutesAsleep": 123, "minutesAwake": 12, "minutesToFallAsleep": 0, "restlessCount": 8, "restlessDuration": 9, "startTime": "2020-10-07T15:55:00.000", "timeInBed": 135}, {"awakeCount": 0, "awakeDuration": 0, "awakeningsCount": 1, "dateOfSleep": "2020-10-07", "duration": 3780000, "efficiency": 100, "endTime": "2020-10-07T10:52:30.000", "isMainSleep": false, "logId": 14144903977, "minuteData": [{"dateTime": "09:49:00", "value": "1"}, {"dateTime": "09:50:00", "value": "1"}, {"dateTime": "09:51:00", "value": "1"},...], "minutesAfterWakeup": 1, "minutesAsleep": 62, "minutesAwake": 0, "minutesToFallAsleep": 0, "restlessCount": 1, "restlessDuration": 1, "startTime": "2020-10-07T09:49:00.000", "timeInBed": 63}], "summary": {"totalMinutesAsleep": 185, "totalSleepRecords": 2, "totalTimeInBed": 198}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep": [{"awakeCount": 3, "awakeDuration": 21, "awakeningsCount": 16, "dateOfSleep": "2020-10-08", "duration": 19260000, "efficiency": 89, "endTime": "2020-10-08T06:01:30.000", "isMainSleep": true, "logId": 14150613895, "minuteData": [{"dateTime": "00:40:00", "value": "3"}, {"dateTime": "00:41:00", "value": "3"}, {"dateTime": "00:42:00", "value": "3"},...], "minutesAfterWakeup": 0, "minutesAsleep": 275, "minutesAwake": 33, "minutesToFallAsleep": 0, "restlessCount": 13, "restlessDuration": 25, "startTime": "2020-10-08T00:40:00.000", "timeInBed": 321}], "summary": {"totalMinutesAsleep": 275, "totalSleepRecords": 1, "totalTimeInBed": 321}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep": [{"awakeCount": 1, "awakeDuration": 3, "awakeningsCount": 8, "dateOfSleep": "2020-10-09", "duration": 19320000, "efficiency": 96, "endTime": "2020-10-09T05:57:30.000", "isMainSleep": true, "logId": 14161136803, "minuteData": [{"dateTime": "00:35:30", "value": "2"}, {"dateTime": "00:36:30", "value": "1"}, {"dateTime": "00:37:30", "value": "1"},...], "minutesAfterWakeup": 0, "minutesAsleep": 309, "minutesAwake": 13, "minutesToFallAsleep": 0, "restlessCount": 7, "restlessDuration": 10, "startTime": "2020-10-09T00:35:30.000", "timeInBed": 322}], "summary": {"totalMinutesAsleep": 309, "totalSleepRecords": 1, "totalTimeInBed": 322}} + + === "PLAIN_TEXT" + + |device_id |local_start_date_time |local_end_date_time |efficiency |minutes_after_wakeup |minutes_asleep |minutes_awake |minutes_to_fall_asleep |minutes_in_bed |is_main_sleep |type |count_awake |duration_awake |count_awakenings |count_restless |duration_restless | + |-------------------------------------- |---------------------- |---------------------- |----------- |--------------------- |--------------- |-------------- |----------------------- |--------------- |-------------- |-------- |----------- |--------------- |----------------- |--------------- |------------------ | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 15:55:00 |2020-10-07 18:10:00 |91 |0 |123 |12 |0 |135 |1 |classic |2 |3 |10 |8 |9 | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 09:49:00 |2020-10-07 10:52:30 |100 |1 |62 |0 |0 |63 |0 |classic |0 |0 |1 |1 |1 | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-08 00:40:00 |2020-10-08 06:01:30 |89 |0 |275 |33 |0 |321 |1 |classic |3 |21 |16 |13 |25 | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-09 00:35:30 |2020-10-09 05:57:30 |96 |0 |309 |13 |0 |322 |1 |classic |1 |3 |8 |7 |10 | + +??? example "Example of the structure of source data with Fitbit’s sleep API Version 1.2" + + === "JSON" + + |device_id |fitbit_data | + |---------------------------------------- |--------------------------------------------------------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep":[{"dateOfSleep":"2020-10-10","duration":3600000,"efficiency":92,"endTime":"2020-10-10T16:37:00.000","infoCode":2,"isMainSleep":false,"levels":{"data":[{"dateTime":"2020-10-10T15:36:30.000","level":"restless","seconds":60},{"dateTime":"2020-10-10T15:37:30.000","level":"asleep","seconds":660},{"dateTime":"2020-10-10T15:48:30.000","level":"restless","seconds":60},...], "summary":{"asleep":{"count":0,"minutes":56},"awake":{"count":0,"minutes":0},"restless":{"count":3,"minutes":4}}},"logId":26315914306,"minutesAfterWakeup":0,"minutesAsleep":55,"minutesAwake":5,"minutesToFallAsleep":0,"startTime":"2020-10-10T15:36:30.000","timeInBed":60,"type":"classic"},{"dateOfSleep":"2020-10-10","duration":22980000,"efficiency":88,"endTime":"2020-10-10T08:10:00.000","infoCode":0,"isMainSleep":true,"levels":{"data":[{"dateTime":"2020-10-10T01:46:30.000","level":"light","seconds":420},{"dateTime":"2020-10-10T01:53:30.000","level":"deep","seconds":1230},{"dateTime":"2020-10-10T02:14:00.000","level":"light","seconds":360},...], "summary":{"deep":{"count":3,"minutes":92,"thirtyDayAvgMinutes":0},"light":{"count":29,"minutes":193,"thirtyDayAvgMinutes":0},"rem":{"count":4,"minutes":33,"thirtyDayAvgMinutes":0},"wake":{"count":28,"minutes":65,"thirtyDayAvgMinutes":0}}},"logId":26311786557,"minutesAfterWakeup":0,"minutesAsleep":318,"minutesAwake":65,"minutesToFallAsleep":0,"startTime":"2020-10-10T01:46:30.000","timeInBed":383,"type":"stages"}],"summary":{"stages":{"deep":92,"light":193,"rem":33,"wake":65},"totalMinutesAsleep":373,"totalSleepRecords":2,"totalTimeInBed":443}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep":[{"dateOfSleep":"2020-10-11","duration":41640000,"efficiency":89,"endTime":"2020-10-11T11:47:00.000","infoCode":0,"isMainSleep":true,"levels":{"data":[{"dateTime":"2020-10-11T00:12:30.000","level":"wake","seconds":450},{"dateTime":"2020-10-11T00:20:00.000","level":"light","seconds":870},{"dateTime":"2020-10-11T00:34:30.000","level":"wake","seconds":780},...], "summary":{"deep":{"count":4,"minutes":52,"thirtyDayAvgMinutes":62},"light":{"count":32,"minutes":442,"thirtyDayAvgMinutes":364},"rem":{"count":6,"minutes":68,"thirtyDayAvgMinutes":58},"wake":{"count":29,"minutes":132,"thirtyDayAvgMinutes":94}}},"logId":26589710670,"minutesAfterWakeup":1,"minutesAsleep":562,"minutesAwake":132,"minutesToFallAsleep":0,"startTime":"2020-10-11T00:12:30.000","timeInBed":694,"type":"stages"}],"summary":{"stages":{"deep":52,"light":442,"rem":68,"wake":132},"totalMinutesAsleep":562,"totalSleepRecords":1,"totalTimeInBed":694}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep":[{"dateOfSleep":"2020-10-12","duration":28980000,"efficiency":93,"endTime":"2020-10-12T09:34:30.000","infoCode":0,"isMainSleep":true,"levels":{"data":[{"dateTime":"2020-10-12T01:31:00.000","level":"wake","seconds":600},{"dateTime":"2020-10-12T01:41:00.000","level":"light","seconds":60},{"dateTime":"2020-10-12T01:42:00.000","level":"deep","seconds":2340},...], "summary":{"deep":{"count":4,"minutes":63,"thirtyDayAvgMinutes":59},"light":{"count":27,"minutes":257,"thirtyDayAvgMinutes":364},"rem":{"count":5,"minutes":94,"thirtyDayAvgMinutes":58},"wake":{"count":24,"minutes":69,"thirtyDayAvgMinutes":95}}},"logId":26589710673,"minutesAfterWakeup":0,"minutesAsleep":415,"minutesAwake":68,"minutesToFallAsleep":0,"startTime":"2020-10-12T01:31:00.000","timeInBed":483,"type":"stages"}],"summary":{"stages":{"deep":63,"light":257,"rem":94,"wake":69},"totalMinutesAsleep":415,"totalSleepRecords":1,"totalTimeInBed":483}} + + === "PLAIN_TEXT" + + |device_id |local_start_date_time |local_end_date_time |efficiency |minutes_after_wakeup |minutes_asleep |minutes_awake |minutes_to_fall_asleep |minutes_in_bed |is_main_sleep |type | + |-------------------------------------- |---------------------- |---------------------- |----------- |--------------------- |--------------- |-------------- |----------------------- |--------------- |-------------- |-------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-10 15:36:30 |2020-10-10 16:37:00 |92 |0 |55 |5 |0 |60 |0 |classic | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-10 01:46:30 |2020-10-10 08:10:00 |88 |0 |318 |65 |0 |383 |1 |stages | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-11 00:12:30 |2020-10-11 11:47:00 |89 |1 |562 |132 |0 |694 |1 |stages | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-12 01:31:00 |2020-10-12 09:34:30 |93 |0 |415 |68 |0 |483 |1 |stages | + + +## RAPIDS provider + +!!! info "Available time segments" + - Only available for segments that span 1 or more complete days (e.g. Jan 1st 00:00 to Jan 3rd 23:59) + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/fitbit_sleep_summary_raw.csv + - data/raw/{pid}/fitbit_sleep_summary_parsed.csv + - data/raw/{pid}/fitbit_sleep_summary_parsed_with_datetime.csv + - data/interim/{pid}/fitbit_sleep_summary_features/fitbit_sleep_summary_{language}_{provider_key}.csv + - data/processed/features/{pid}/fitbit_sleep_summary.csv + ``` + + +Parameters description for `[FITBIT_SLEEP_SUMMARY][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]` | Set to `True` to extract `FITBIT_SLEEP_SUMMARY` features from the `RAPIDS` provider | +|`[SLEEP_TYPES]` | Types of sleep to be included in the feature extraction computation. Fitbit provides 3 types of sleep: `main`, `nap`, `all`. | +|`[FEATURES]` | Features to be computed from sleep summary data, see table below | + + +Features description for `[FITBIT_SLEEP_SUMMARY][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description | +|------------------------------ |---------- |-------------------------------------------- | +|countepisodeTYPE |episodes |Number of sleep episodes for a certain sleep type during a time segment. +|avgefficiencyTYPE |scores |Average sleep efficiency for a certain sleep type during a time segment. +|sumdurationafterwakeupTYPE |minutes |Total duration the user stayed in bed after waking up for a certain sleep type during a time segment. +|sumdurationasleepTYPE |minutes |Total sleep duration for a certain sleep type during a time segment. +|sumdurationawakeTYPE |minutes |Total duration the user stayed awake but still in bed for a certain sleep type during a time segment. +|sumdurationtofallasleepTYPE |minutes |Total duration the user spent to fall asleep for a certain sleep type during a time segment. +|sumdurationinbedTYPE |minutes |Total duration the user stayed in bed (sumdurationtofallasleep + sumdurationawake + sumdurationasleep + sumdurationafterwakeup) for a certain sleep type during a time segment. +|avgdurationafterwakeupTYPE |minutes |Average duration the user stayed in bed after waking up for a certain sleep type during a time segment. +|avgdurationasleepTYPE |minutes |Average sleep duration for a certain sleep type during a time segment. +|avgdurationawakeTYPE |minutes |Average duration the user stayed awake but still in bed for a certain sleep type during a time segment. +|avgdurationtofallasleepTYPE |minutes |Average duration the user spent to fall asleep for a certain sleep type during a time segment. +|avgdurationinbedTYPE |minutes |Average duration the user stayed in bed (sumdurationtofallasleep + sumdurationawake + sumdurationasleep + sumdurationafterwakeup) for a certain sleep type during a time segment. + + + +!!! note "Assumptions/Observations" + + 1. There are three sleep types (TYPE): `main`, `nap`, `all`. The `all` type contains both main sleep and naps. + + 2. There are two versions of Fitbit’s sleep API ([version 1](https://dev.fitbit.com/build/reference/web-api/sleep-v1/) and [version 1.2](https://dev.fitbit.com/build/reference/web-api/sleep/)), and each provides raw sleep data in a different format: + - _Count & duration summaries_. `v1` contains `count_awake`, `duration_awake`, `count_awakenings`, `count_restless`, and `duration_restless` fields for every sleep record but `v1.2` does not. + + 3. _API columns_. Features are computed based on the values provided by Fitbit’s API: `efficiency`, `minutes_after_wakeup`, `minutes_asleep`, `minutes_awake`, `minutes_to_fall_asleep`, `minutes_in_bed`, `is_main_sleep` and `type`. \ No newline at end of file diff --git a/docs/features/fitbit-steps-intraday.md b/docs/features/fitbit-steps-intraday.md new file mode 100644 index 00000000..0dbffe53 --- /dev/null +++ b/docs/features/fitbit-steps-intraday.md @@ -0,0 +1,82 @@ +# Fitbit Steps Intraday + +Sensor parameters description for `[FITBIT_STEPS_INTRADAY]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table name or file path where the steps intraday data is stored. The configuration keys in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration) control whether this parameter is interpreted as table or file. + +The format of the column(s) containing the Fitbit sensor data can be `JSON` or `PLAIN_TEXT`. The data in `JSON` format is obtained directly from the Fitbit API. We support `PLAIN_TEXT` in case you already parsed your data and don't have access to your participants' Fitbit accounts anymore. If your data is in `JSON` format then summary and intraday data come packed together. + +We provide examples of the input format that RAPIDS expects, note that both examples for `JSON` and `PLAIN_TEXT` are tabular and the actual format difference comes in the `fitbit_data` column (we truncate the `JSON` example for brevity). + +??? example "Example of the structure of source data" + + === "JSON" + + |device_id |fitbit_data | + |---------------------------------------- |--------------------------------------------------------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |"activities-steps":[{"dateTime":"2020-10-07","value":"1775"}],"activities-steps-intraday":{"dataset":[{"time":"00:00:00","value":5},{"time":"00:01:00","value":3},{"time":"00:02:00","value":0},...],"datasetInterval":1,"datasetType":"minute"}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |"activities-steps":[{"dateTime":"2020-10-08","value":"3201"}],"activities-steps-intraday":{"dataset":[{"time":"00:00:00","value":14},{"time":"00:01:00","value":11},{"time":"00:02:00","value":10},...],"datasetInterval":1,"datasetType":"minute"}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |"activities-steps":[{"dateTime":"2020-10-09","value":"998"}],"activities-steps-intraday":{"dataset":[{"time":"00:00:00","value":0},{"time":"00:01:00","value":0},{"time":"00:02:00","value":0},...],"datasetInterval":1,"datasetType":"minute"}} + + === "PLAIN_TEXT" + + |device_id |local_date_time |steps | + |-------------------------------------- |---------------------- |--------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 00:00:00 |5 | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 00:01:00 |3 | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 00:02:00 |0 | + + +## RAPIDS provider + +!!! info "Available time segments" + - Available for all time segments + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/fitbit_steps_intraday_raw.csv + - data/raw/{pid}/fitbit_steps_intraday_parsed.csv + - data/raw/{pid}/fitbit_steps_intraday_parsed_with_datetime.csv + - data/interim/{pid}/fitbit_steps_intraday_features/fitbit_steps_intraday_{language}_{provider_key}.csv + - data/processed/features/{pid}/fitbit_steps_intraday.csv + ``` + + +Parameters description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]` | Set to `True` to extract `FITBIT_STEPS_INTRADAY` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed from steps intraday data, see table below | +|`[THRESHOLD_ACTIVE_BOUT]` | Every minute with Fitbit steps data wil be labelled as `sedentary` if its step count is below this threshold, otherwise, `active`. | +|`[INCLUDE_ZERO_STEP_ROWS]` | Whether or not to include time segments with a 0 step count during the whole day. | + + +Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description | +|-------------------------- |-------------- |-------------------------------------------------------------| +|sumsteps |steps |The total step count during a time segment. +|maxsteps |steps |The maximum step count during a time segment. +|minsteps |steps |The minimum step count during a time segment. +|avgsteps |steps |The average step count during a time segment. +|stdsteps |steps |The standard deviation of step count during a time segment. +|countepisodesedentarybout |bouts |Number of sedentary bouts during a time segment. +|sumdurationsedentarybout |minutes |Total duration of all sedentary bouts during a time segment. +|maxdurationsedentarybout |minutes |The maximum duration of any sedentary bout during a time segment. +|mindurationsedentarybout |minutes |The minimum duration of any sedentary bout during a time segment. +|avgdurationsedentarybout |minutes |The average duration of sedentary bouts during a time segment. +|stddurationsedentarybout |minutes |The standard deviation of the duration of sedentary bouts during a time segment. +|countepisodeactivebout |bouts |Number of active bouts during a time segment. +|sumdurationactivebout |minutes |Total duration of all active bouts during a time segment. +|maxdurationactivebout |minutes |The maximum duration of any active bout during a time segment. +|mindurationactivebout |minutes |The minimum duration of any active bout during a time segment. +|avgdurationactivebout |minutes |The average duration of active bouts during a time segment. +|stddurationactivebout |minutes |The standard deviation of the duration of active bouts during a time segment. + +!!! note "Assumptions/Observations" + + 1. _Active and sedentary bouts_. If the step count per minute is smaller than `THRESHOLD_ACTIVE_BOUT` (default value is 10), that minute is labelled as sedentary, otherwise, is labelled as active. Active and sedentary bouts are periods of consecutive minutes labelled as `active` or `sedentary`. + diff --git a/docs/features/fitbit-steps-summary.md b/docs/features/fitbit-steps-summary.md new file mode 100644 index 00000000..af8bb134 --- /dev/null +++ b/docs/features/fitbit-steps-summary.md @@ -0,0 +1,67 @@ +# Fitbit Steps Summary + +Sensor parameters description for `[FITBIT_STEPS_SUMMARY]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table name or file path where the steps summary data is stored. The configuration keys in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration) control whether this parameter is interpreted as table or file. + +The format of the column(s) containing the Fitbit sensor data can be `JSON` or `PLAIN_TEXT`. The data in `JSON` format is obtained directly from the Fitbit API. We support `PLAIN_TEXT` in case you already parsed your data and don't have access to your participants' Fitbit accounts anymore. If your data is in `JSON` format then summary and intraday data come packed together. + +We provide examples of the input format that RAPIDS expects, note that both examples for `JSON` and `PLAIN_TEXT` are tabular and the actual format difference comes in the `fitbit_data` column (we truncate the `JSON` example for brevity). + +??? example "Example of the structure of source data" + + === "JSON" + + |device_id |fitbit_data | + |---------------------------------------- |--------------------------------------------------------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |"activities-steps":[{"dateTime":"2020-10-07","value":"1775"}],"activities-steps-intraday":{"dataset":[{"time":"00:00:00","value":5},{"time":"00:01:00","value":3},{"time":"00:02:00","value":0},...],"datasetInterval":1,"datasetType":"minute"}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |"activities-steps":[{"dateTime":"2020-10-08","value":"3201"}],"activities-steps-intraday":{"dataset":[{"time":"00:00:00","value":14},{"time":"00:01:00","value":11},{"time":"00:02:00","value":10},...],"datasetInterval":1,"datasetType":"minute"}} + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |"activities-steps":[{"dateTime":"2020-10-09","value":"998"}],"activities-steps-intraday":{"dataset":[{"time":"00:00:00","value":0},{"time":"00:01:00","value":0},{"time":"00:02:00","value":0},...],"datasetInterval":1,"datasetType":"minute"}} + + === "PLAIN_TEXT" + + |device_id |local_date_time |steps | + |-------------------------------------- |---------------------- |--------- | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-07 |1775 | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-08 |3201 | + |a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |2020-10-09 |998 | + + +## RAPIDS provider + +!!! info "Available time segments" + - Only available for segments that span 1 or more complete days (e.g. Jan 1st 00:00 to Jan 3rd 23:59) + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/fitbit_steps_summary_raw.csv + - data/raw/{pid}/fitbit_steps_summary_parsed.csv + - data/raw/{pid}/fitbit_steps_summary_parsed_with_datetime.csv + - data/interim/{pid}/fitbit_steps_summary_features/fitbit_steps_summary_{language}_{provider_key}.csv + - data/processed/features/{pid}/fitbit_steps_summary.csv + ``` + + +Parameters description for `[FITBIT_STEPS_SUMMARY][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]` | Set to `True` to extract `FITBIT_STEPS_SUMMARY` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed from steps summary data, see table below | + + +Features description for `[FITBIT_STEPS_SUMMARY][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description | +|-------------------------- |---------- |-------------------------------------------- | +|maxsumsteps |steps |The maximum daily step count during a time segment. +|minsumsteps |steps |The minimum daily step count during a time segment. +|avgsumsteps |steps |The average daily step count during a time segment. +|mediansumsteps |steps |The median of daily step count during a time segment. +|stdsumsteps |steps |The standard deviation of daily step count during a time segment. + +!!! note "Assumptions/Observations" + + NA diff --git a/docs/features/phone-accelerometer.md b/docs/features/phone-accelerometer.md new file mode 100644 index 00000000..64a43380 --- /dev/null +++ b/docs/features/phone-accelerometer.md @@ -0,0 +1,83 @@ +# Phone Accelerometer + +Sensor parameters description for `[PHONE_ACCELEROMETER]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the accelerometer data is stored + +## RAPIDS provider + +!!! info "Available time segments and platforms" + - Available for all time segments + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_accelerometer_raw.csv + - data/raw/{pid}/phone_accelerometer_with_datetime.csv + - data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_accelerometer.csv + ``` + + +Parameters description for `[PHONE_ACCELEROMETER][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_ACCELEROMETER` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below + + +Features description for `[PHONE_ACCELEROMETER][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|maxmagnitude |m/s^2^ |The maximum magnitude of acceleration ($\|acceleration\| = \sqrt{x^2 + y^2 + z^2}$). +|minmagnitude |m/s^2^ |The minimum magnitude of acceleration. +|avgmagnitude |m/s^2^ |The average magnitude of acceleration. +|medianmagnitude |m/s^2^ |The median magnitude of acceleration. +|stdmagnitude |m/s^2^ |The standard deviation of acceleration. + +!!! note "Assumptions/Observations" + 1. Analyzing accelerometer data is a memory intensive task. If RAPIDS crashes is likely because the accelerometer dataset for a participant is to big to fit in memory. We are considering different alternatives to overcome this problem. + +## PANDA provider + +These features are based on the work by [Panda et al](../../citation#panda-accelerometer). + +!!! info "Available time segments and platforms" + - Available for all time segments + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_accelerometer_raw.csv + - data/raw/{pid}/phone_accelerometer_with_datetime.csv + - data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_accelerometer.csv + ``` + + +Parameters description for `[PHONE_ACCELEROMETER][PROVIDERS][PANDA]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_ACCELEROMETER` features from the `PANDA` provider| +|`[FEATURES]` | Features to be computed for exertional and non-exertional activity episodes, see table below + + +Features description for `[PHONE_ACCELEROMETER][PROVIDERS][PANDA]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +| sumduration | minutes | Total duration of all exertional or non-exertional activity episodes. | +| maxduration | minutes | Longest duration of any exertional or non-exertional activity episode. | +| minduration | minutes | Shortest duration of any exertional or non-exertional activity episode. | +| avgduration | minutes | Average duration of any exertional or non-exertional activity episode. | +| medianduration | minutes | Median duration of any exertional or non-exertional activity episode. | +| stdduration | minutes | Standard deviation of the duration of all exertional or non-exertional activity episodes. | + +!!! note "Assumptions/Observations" + 1. Analyzing accelerometer data is a memory intensive task. If RAPIDS crashes is likely because the accelerometer dataset for a participant is to big to fit in memory. We are considering different alternatives to overcome this problem. + 2. See [Panda et al](../../citation#panda-accelerometer) for a definition of exertional and non-exertional activity episodes diff --git a/docs/features/phone-activity-recognition.md b/docs/features/phone-activity-recognition.md new file mode 100644 index 00000000..e2b791b7 --- /dev/null +++ b/docs/features/phone-activity-recognition.md @@ -0,0 +1,64 @@ +# Phone Activity Recognition + +Sensor parameters description for `[PHONE_ACTIVITY_RECOGNITION]`: + +|Key                                                               | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE][ANDROID]`| Database table where the activity data from Android devices is stored (the AWARE client saves this data on different tables for Android and iOS) +|`[TABLE][IOS]`| Database table where the activity data from iOS devices is stored (the AWARE client saves this data on different tables for Android and iOS) +|`[EPISODE_THRESHOLD_BETWEEN_ROWS]` | Difference in minutes between any two rows for them to be considered part of the same activity episode + +## RAPIDS provider + +!!! info "Available time segments and platforms" + - Available for all time segments + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_activity_recognition_raw.csv + - data/raw/{pid}/phone_activity_recognition_with_datetime.csv + - data/raw/{pid}/phone_activity_recognition_with_datetime_unified.csv + - data/interim/{pid}/phone_activity_recognition_episodes.csv + - data/interim/{pid}/phone_activity_recognition_episodes_resampled.csv + - data/interim/{pid}/phone_activity_recognition_episodes_resampled_with_datetime.csv + - data/interim/{pid}/phone_activity_recognition_features/phone_activity_recognition_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_activity_recognition.csv + ``` + + +Parameters description for `[PHONE_ACTIVITY_RECOGNITION][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_ACTIVITY_RECOGNITION` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below +|`[ACTIVITY_CLASSES][STATIONARY]` | An array of the activity labels to be considered in the `STATIONARY` category choose any of `still`, `tilting` +|`[ACTIVITY_CLASSES][MOBILE]` | An array of the activity labels to be considered in the `MOBILE` category choose any of `on_foot`, `walking`, `running`, `on_bicycle` +|`[ACTIVITY_CLASSES][VEHICLE]` | An array of the activity labels to be considered in the `VEHICLE` category choose any of `in_vehicule` + + +Features description for `[PHONE_ACTIVITY_RECOGNITION][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|count |rows | Number of episodes. +|mostcommonactivity |activity type | The most common activity type (e.g. `still`, `on_foot`, etc.). If there is a tie, the first one is chosen. +|countuniqueactivities |activity type | Number of unique activities. +|durationstationary |minutes | The total duration of `[ACTIVITY_CLASSES][STATIONARY]` episodes +|durationmobile |minutes | The total duration of `[ACTIVITY_CLASSES][MOBILE]` episodes of on foot, running, and on bicycle activities +|durationvehicle |minutes | The total duration of `[ACTIVITY_CLASSES][VEHICLE]` episodes of on vehicle activity + +!!! note "Assumptions/Observations" + 1. iOS Activity Recognition names and types are unified with Android labels: + + | iOS Activity Name | Android Activity Name | Android Activity Type | + |----|----|----| + |`walking`| `walking` | `7` + |`running`| `running` | `8` + |`cycling`| `on_bicycle` | `1` + |`automotive`| `in_vehicle` | `0` + |`stationary`| `still` | `3` + |`unknown`| `unknown` | `4` + + 2. In AWARE, Activity Recognition data for Android and iOS are stored in two different database tables, RAPIDS automatically infers what platform each participant belongs to based on their [participant file](../../setup/configuration/#participant-files). \ No newline at end of file diff --git a/docs/features/phone-applications-foreground.md b/docs/features/phone-applications-foreground.md new file mode 100644 index 00000000..53f1aed0 --- /dev/null +++ b/docs/features/phone-applications-foreground.md @@ -0,0 +1,57 @@ +# Phone Applications Foreground + +Sensor parameters description for `[PHONE_APPLICATIONS_FOREGROUND]` (these parameters are used by the only provider available at the moment, RAPIDS): + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the applications foreground data is stored +|`[APPLICATION_CATEGORIES][CATALOGUE_SOURCE]` | `FILE` or `GOOGLE`. If `FILE`, app categories (genres) are read from `[CATALOGUE_FILE]`. If `[GOOGLE]`, app categories (genres) are scrapped from the Play Store +|`[APPLICATION_CATEGORIES][CATALOGUE_FILE]` | CSV file with a `package_name` and `genre` column. By default we provide the catalogue created by [Stachl et al](../../citation#stachl-applications-foreground) in `data/external/stachl_application_genre_catalogue.csv` +|`[APPLICATION_CATEGORIES][UPDATE_CATALOGUE_FILE]` | if `[CATALOGUE_SOURCE]` is equal to `FILE`, this flag signals whether or not to update `[CATALOGUE_FILE]`, if `[CATALOGUE_SOURCE]` is equal to `GOOGLE` all scraped genres will be saved to `[CATALOGUE_FILE]` +|`[APPLICATION_CATEGORIES][SCRAPE_MISSING_CATEGORIES]` | This flag signals whether or not to scrape categories (genres) missing from the `[CATALOGUE_FILE]`. If `[CATALOGUE_SOURCE]` is equal to `GOOGLE`, all genres are scraped anyway (this flag is ignored) + +## RAPIDS provider + +The app category (genre) catalogue used in these features was originally created by [Stachl et al](../../citation#stachl-applications-foreground). + +!!! info "Available time segments and platforms" + - Available for all time segments + - Available for Android only + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_applications_foreground_raw.csv + - data/raw/{pid}/phone_applications_foreground_with_datetime.csv + - data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv + - data/interim/{pid}/phone_applications_foreground_features/phone_applications_foreground_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_applications_foreground.csv + ``` + + +Parameters description for `[PHONE_APPLICATIONS_FOREGROUND][PROVIDERS][RAPIDS]`: + +|Key                                         | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_APPLICATIONS_FOREGROUND` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below +|`[SINGLE_CATEGORIES]` | An array of app categories to be *included* in the feature extraction computation. The special keyword `all` represents a category with all the apps from each participant. By default we use the category catalogue pointed by `[APPLICATION_CATEGORIES][CATALOGUE_FILE]` (see the Sensor parameters description table above) +|`[MULTIPLE_CATEGORIES]` | An array of collections representing meta-categories (a group of categories). They key of each element is the name of the `meta-category` and the value is an array of member app categories. By default we use the category catalogue pointed by `[APPLICATION_CATEGORIES][CATALOGUE_FILE]` (see the Sensor parameters description table above) +|`[SINGLE_APPS]` | An array of apps to be *included* in the feature extraction computation. Use their package name (e.g. `com.google.android.youtube`) or the reserved keyword `top1global` (the most used app by a participant over the whole monitoring study) +|`[EXCLUDED_CATEGORIES]` | An array of app categories to be *excluded* from the feature extraction computation. By default we use the category catalogue pointed by `[APPLICATION_CATEGORIES][CATALOGUE_FILE]` (see the Sensor parameters description table above) +|`[EXCLUDED_APPS]` | An array of apps to be excluded from the feature extraction computation. Use their package name, for example: `com.google.android.youtube` + +Features description for `[PHONE_APPLICATIONS_FOREGROUND][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|count |apps | Number of times a single app or apps within a category were used (i.e. they were brought to the foreground either by tapping their icon or switching to it from another app) +|timeoffirstuse |minutes | The time in minutes between 12:00am (midnight) and the first use of a single app or apps within a category during a `time_segment` +|timeoflastuse |minutes | The time in minutes between 12:00am (midnight) and the last use of a single app or apps within a category during a `time_segment` +|frequencyentropy |nats | The entropy of the used apps within a category during a `time_segment` (each app is seen as a unique event, the more apps were used, the higher the entropy). This is especially relevant when computed over all apps. Entropy cannot be obtained for a single app + +!!! note "Assumptions/Observations" + Features can be computed by app, by apps grouped under a single category (genre) and by multiple categories grouped together (meta-categories). For example, we can get features for `Facebook` (single app), for `Social Network` apps (a category including Facebook and other social media apps) or for `Social` (a meta-category formed by `Social Network` and `Social Media Tools` categories). + + Apps installed by default like YouTube are considered systems apps on some phones. We do an exact match to exclude apps where "genre" == `EXCLUDED_CATEGORIES` or "package_name" == `EXCLUDED_APPS`. + + We provide three ways of classifying and app within a category (genre): a) by automatically scraping its official category from the Google Play Store, b) by using the catalogue created by Stachl et al. which we provide in RAPIDS (`data/external/stachl_application_genre_catalogue.csv`), or c) by manually creating a personalized catalogue. You can choose a, b or c by modifying `[APPLICATION_GENRES]` keys and values (see the Sensor parameters description table above). \ No newline at end of file diff --git a/docs/features/phone-battery.md b/docs/features/phone-battery.md new file mode 100644 index 00000000..8b791537 --- /dev/null +++ b/docs/features/phone-battery.md @@ -0,0 +1,48 @@ +# Phone Battery + +Sensor parameters description for `[PHONE_BATTERY]`: + +|Key                                                               | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the battery data is stored +|`[EPISODE_THRESHOLD_BETWEEN_ROWS]` | Difference in minutes between any two rows for them to be considered part of the same battery charge or discharge episode + +## RAPIDS provider + +!!! info "Available time segments and platforms" + - Available for all time segments + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_battery_raw.csv + - data/interim/{pid}/phone_battery_episodes.csv + - data/interim/{pid}/phone_battery_episodes_resampled.csv + - data/interim/{pid}/phone_battery_episodes_resampled_with_datetime.csv + - data/interim/{pid}/phone_battery_features/phone_battery_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_battery.csv + ``` + + +Parameters description for `[PHONE_BATTERY][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_BATTERY` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below + + +Features description for `[PHONE_BATTERY][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|countdischarge |episodes | Number of discharging episodes. +|sumdurationdischarge |minutes | The total duration of all discharging episodes. +|countcharge |episodes | Number of battery charging episodes. +|sumdurationcharge |minutes | The total duration of all charging episodes. +|avgconsumptionrate |episodes/minutes | The average of all episodes' consumption rates. An episode's consumption rate is defined as the ratio between its battery delta and duration +|maxconsumptionrate |episodes/minutes | The highest of all episodes' consumption rates. An episode's consumption rate is defined as the ratio between its battery delta and duration + +!!! note "Assumptions/Observations" + 1. We convert battery data collected with iOS client v1 (autodetected because battery status `4` do not exist) to match Android battery format: we swap status `3` for `5` and `1` for `3` + 2. We group battery data into discharge or charge episodes considering any contiguous rows with consecutive reductions or increases of the battery level if they are logged within `[EPISODE_THRESHOLD_BETWEEN_ROWS]` minutes from each other. diff --git a/docs/features/phone-bluetooth.md b/docs/features/phone-bluetooth.md new file mode 100644 index 00000000..e37b2628 --- /dev/null +++ b/docs/features/phone-bluetooth.md @@ -0,0 +1,41 @@ +# Phone Bluetooth + +Sensor parameters description for `[PHONE_BLUETOOTH]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the bluetooth data is stored + +## RAPIDS provider + +!!! info "Available time segments and platforms" + - Available for all time segments + - Available for Android only + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_bluetooth_raw.csv + - data/raw/{pid}/phone_bluetooth_with_datetime.csv + - data/interim/{pid}/phone_bluetooth_features/phone_bluetooth_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_bluetooth.csv" + ``` + + +Parameters description for `[PHONE_BLUETOOTH][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_BLUETOOTH` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below + + +Features description for `[PHONE_BLUETOOTH][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +| countscans | devices | Number of scanned devices during a `time_segment`, a device can be detected multiple times over time and these appearances are counted separately | +| uniquedevices | devices | Number of unique devices during a `time_segment` as identified by their hardware (`bt_address`) address | +| countscansmostuniquedevice | scans | Number of scans of the most scanned device during a `time_segment` across the whole monitoring period | + +!!! note "Assumptions/Observations" + NA diff --git a/docs/features/phone-calls.md b/docs/features/phone-calls.md new file mode 100644 index 00000000..f96ef060 --- /dev/null +++ b/docs/features/phone-calls.md @@ -0,0 +1,64 @@ +# Phone Calls + +Sensor parameters description for `[PHONE_CALLS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the calls data is stored + +## RAPIDS Provider + +!!! info "Available time segments and platforms" + - Available for all time segments + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_calls_raw.csv + - data/raw/{pid}/phone_calls_with_datetime.csv + - data/raw/{pid}/phone_calls_with_datetime_unified.csv + - data/interim/{pid}/phone_calls_features/phone_calls_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_calls.csv + ``` + + +Parameters description for `[PHONE_CALLS][PROVIDERS][RAPIDS]`: + +| Key                        | Description | +|-------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +|`[COMPUTE]`| Set to `True` to extract `PHONE_CALLS` features from the `RAPIDS` provider| +| `[CALL_TYPES]` | The particular call_type that will be analyzed. The options for this parameter are incoming, outgoing or missed. | +| `[FEATURES]` | Features to be computed for `outgoing`, `incoming`, and `missed` calls. Note that the same features are available for both incoming and outgoing calls, while missed calls has its own set of features. See the tables below. | + + +Features description for `[PHONE_CALLS][PROVIDERS][RAPIDS]` incoming and outgoing calls: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|count |calls |Number of calls of a particular `call_type` occurred during a particular `time_segment`. +|distinctcontacts |contacts |Number of distinct contacts that are associated with a particular `call_type` for a particular `time_segment` +|meanduration |seconds |The mean duration of all calls of a particular `call_type` during a particular `time_segment`. +|sumduration |seconds |The sum of the duration of all calls of a particular `call_type` during a particular `time_segment`. +|minduration |seconds |The duration of the shortest call of a particular `call_type` during a particular `time_segment`. +|maxduration |seconds |The duration of the longest call of a particular `call_type` during a particular `time_segment`. +|stdduration |seconds |The standard deviation of the duration of all the calls of a particular `call_type` during a particular `time_segment`. +|modeduration |seconds |The mode of the duration of all the calls of a particular `call_type` during a particular `time_segment`. +|entropyduration |nats |The estimate of the Shannon entropy for the the duration of all the calls of a particular `call_type` during a particular `time_segment`. +|timefirstcall |minutes |The time in minutes between 12:00am (midnight) and the first call of `call_type`. +|timelastcall |minutes |The time in minutes between 12:00am (midnight) and the last call of `call_type`. +|countmostfrequentcontact |calls |The number of calls of a particular `call_type` during a particular `time_segment` of the most frequent contact throughout the monitored period. + +Features description for `[PHONE_CALLS][PROVIDERS][RAPIDS]` missed calls: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|count |calls |Number of `missed` calls that occurred during a particular `time_segment`. +|distinctcontacts |contacts |Number of distinct contacts that are associated with `missed` calls for a particular `time_segment` +|timefirstcall |minutes |The time in hours from 12:00am (Midnight) that the first `missed` call occurred. +|timelastcall |minutes |The time in hours from 12:00am (Midnight) that the last `missed` call occurred. +|countmostfrequentcontact |calls |The number of `missed` calls during a particular `time_segment` of the most frequent contact throughout the monitored period. + +!!! note "Assumptions/Observations" + 1. Traces for iOS calls are unique even for the same contact calling a participant more than once which renders `countmostfrequentcontact` meaningless and `distinctcontacts` equal to the total number of traces. + 2. `[CALL_TYPES]` and `[FEATURES]` keys in `config.yaml` need to match. For example, `[CALL_TYPES]` `outgoing` matches the `[FEATURES]` key `outgoing` + 3. iOS calls data is transformed to match Android calls data format. See our [algorithm](algorithms/phone-algorithms.md#phone-calls) diff --git a/docs/features/phone-conversation.md b/docs/features/phone-conversation.md new file mode 100644 index 00000000..d67747aa --- /dev/null +++ b/docs/features/phone-conversation.md @@ -0,0 +1,71 @@ +# Phone Conversation + +Sensor parameters description for `[PHONE_CONVERSATION]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE][ANDROID]`| Database table where the conversation data from Android devices is stored (the AWARE client saves this data on different tables for Android and iOS) +|`[TABLE][IOS]`| Database table where the conversation data from iOS devices is stored (the AWARE client saves this data on different tables for Android and iOS) + +## RAPIDS provider + +!!! info "Available time segments and platforms" + - Available for all time segments + - Available for Android only + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_conversation_raw.csv + - data/raw/{pid}/phone_conversation_with_datetime.csv + - data/raw/{pid}/phone_conversation_with_datetime_unified.csv + - data/interim/{pid}/phone_conversation_features/phone_conversation_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_conversation.csv + ``` + + +Parameters description for `[PHONE_CONVERSATION][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_CONVERSATION` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below +|`[RECORDING_MINUTES]` | Minutes the plugin was recording audio (default 1 min) +|`[PAUSED_MINUTES]` | Minutes the plugin was NOT recording audio (default 3 min) + + +Features description for `[PHONE_CONVERSATION][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +| minutessilence | minutes | Minutes labeled as silence | +| minutesnoise | minutes | Minutes labeled as noise | +| minutesvoice | minutes | Minutes labeled as voice | +| minutesunknown | minutes | Minutes labeled as unknown | +| sumconversationduration | minutes | Total duration of all conversations | +| maxconversationduration | minutes | Longest duration of all conversations | +| minconversationduration | minutes | Shortest duration of all conversations | +| avgconversationduration | minutes | Average duration of all conversations | +| sdconversationduration | minutes | Standard Deviation of the duration of all conversations | +| timefirstconversation | minutes | Minutes since midnight when the first conversation for a time segment was detected | +| timelastconversation | minutes | Minutes since midnight when the last conversation for a time segment was detected | +| noisesumenergy | L2-norm | Sum of all energy values when inference is noise | +| noiseavgenergy | L2-norm | Average of all energy values when inference is noise | +| noisesdenergy | L2-norm | Standard Deviation of all energy values when inference is noise | +| noiseminenergy | L2-norm | Minimum of all energy values when inference is noise | +| noisemaxenergy | L2-norm | Maximum of all energy values when inference is noise | +| voicesumenergy | L2-norm | Sum of all energy values when inference is voice | +| voiceavgenergy | L2-norm | Average of all energy values when inference is voice | +| voicesdenergy | L2-norm | Standard Deviation of all energy values when inference is voice | +| voiceminenergy | L2-norm | Minimum of all energy values when inference is voice | +| voicemaxenergy | L2-norm | Maximum of all energy values when inference is voice | +| silencesensedfraction | - | Ratio between minutessilence and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) | +| noisesensedfraction | - | Ratio between minutesnoise and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) | +| voicesensedfraction | - | Ratio between minutesvoice and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) | +| unknownsensedfraction | - | Ratio between minutesunknown and the sum of (minutessilence, minutesnoise, minutesvoice, minutesunknown) | +| silenceexpectedfraction | - | Ration between minutessilence and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) | +| noiseexpectedfraction | - | Ration between minutesnoise and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) | +| voiceexpectedfraction | - | Ration between minutesvoice and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) | +| unknownexpectedfraction | - | Ration between minutesunknown and the number of minutes that in theory should have been sensed based on the record and pause cycle of the plugin (1440 / recordingMinutes+pausedMinutes) | + +!!! note "Assumptions/Observations" + 1. The timestamp of conversation rows in iOS is in seconds so we convert it to milliseconds to match Android's format diff --git a/docs/features/phone-data-yield.md b/docs/features/phone-data-yield.md new file mode 100644 index 00000000..5327a131 --- /dev/null +++ b/docs/features/phone-data-yield.md @@ -0,0 +1,81 @@ +# Phone Data Yield + +This is a combinatorial sensor which means that we use the data from multiple sensors to extract data yield features. Data yield features can be used to remove rows ([time segments](../../setup/configuration/#time-segments)) that do not contain enough data. You should decide what is your "enough" threshold depending on the type of sensors you collected (frequency vs event based, e.g. acceleroemter vs calls), the length of your study, and the rates of missing data that your analysis could handle. + +!!! hint "Why is data yield important?" + Imagine that you want to extract `PHONE_CALL` features on daily segments (`00:00` to `23:59`). Let's say that on day 1 the phone logged 10 calls and 23 hours of data from other sensors and on day 2 the phone logged 10 calls and only 2 hours of data from other sensors. It's more likely that other calls were placed on the 22 hours of data that you didn't log on day 2 than on the 1 hour of data you didn't log on day 1, and so including day 2 in your analysis could bias your results. + +Sensor parameters description for `[PHONE_DATA_YIELD]`: + +|Key                    | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[SENSORS]`| One or more phone sensor config keys (e.g. `PHONE_MESSAGE`). The more keys you include the more accurately RAPIDS can approximate the time an smartphone was sensing data. The supported phone sensors you can include in this list are outlined below (**do NOT include Fitbit sensors**). + +!!! info "Supported phone sensors for `[PHONE_DATA_YIELD][SENSORS]`" + ```yaml + PHONE_ACCELEROMETER + PHONE_ACTIVITY_RECOGNITION + PHONE_APPLICATIONS_FOREGROUND + PHONE_BATTERY + PHONE_BLUETOOTH + PHONE_CALLS + PHONE_CONVERSATION + PHONE_MESSAGES + PHONE_LIGHT + PHONE_LOCATIONS + PHONE_SCREEN + PHONE_WIFI_VISIBLE + PHONE_WIFI_CONNECTED + ``` + +## RAPIDS provider + +Before explaining the data yield features, let's define the following relevant concepts: + +- A valid minute is any 60 second window when any phone sensor logged at least 1 row of data +- A valid hour is any 60 minute window with at least X valid minutes. The X or threshold is given by `[MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS]` + +The timestamps of all sensors are concatenated and then grouped per time segment. Minute and hour windows are created from the beginning of each time segment instance and these windows are marked as valid based on the definitions above. The duration of each time segment is taken into account to compute the features described below. + +!!! info "Available time segments and platforms" + - Available for all time segments + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/{sensor}_raw.csv # one for every [PHONE_DATA_YIELD][SENSORS] + - data/interim/{pid}/phone_yielded_timestamps.csv + - data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv + - data/interim/{pid}/phone_data_yield_features/phone_data_yield_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_data_yield.csv + ``` + + +Parameters description for `[PHONE_DATA_YIELD][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_DATA_YIELD` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below +|`[MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS]` | The proportion `[0.0 ,1.0]` of valid minutes in a 60-minute window necessary to flag that window as valid. + + +Features description for `[PHONE_DATA_YIELD][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|ratiovalidyieldedminutes |rows | The ratio between the number of valid minutes and the duration in minutes of a time segment. +|ratiovalidyieldedhours |lux | The ratio between the number of valid hours and the duration in hours of a time segment. If the time segment is shorter than 1 hour this feature will always be 1. + + +!!! note "Assumptions/Observations" + 1. We recommend using `ratiovalidyieldedminutes` on time segments that are shorter than two or three hours and `ratiovalidyieldedhours` for longer segments. This is because relying on yielded minutes only can be misleading when a big chunk of those missing minutes are clustered together. + + For example, let's assume we are working with a 24-hour time segment that is missing 12 hours of data. Two extreme cases can occur: + +
    +
  1. the 12 missing hours are from the beginning of the segment or
    2. +
  2. 30 minutes could be missing from every hour (24 * 30 minutes = 12 hours).
    3. +
+ + `ratiovalidyieldedminutes` would be 0.5 for both `a` and `b` (hinting the missing circumstances are similar). However, `ratiovalidyieldedhours` would be 0.5 for `a` and 1.0 for `b` if `[MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS]` is between [0.0 and 0.49] (hinting that the missing circumstances might be more favorable for `b`. In other words, sensed data for `b` is more evenly spread compared to `a`. diff --git a/docs/features/phone-light.md b/docs/features/phone-light.md new file mode 100644 index 00000000..e44b81e8 --- /dev/null +++ b/docs/features/phone-light.md @@ -0,0 +1,44 @@ +# Phone Light + +Sensor parameters description for `[PHONE_LIGHT]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the light data is stored + +## RAPIDS provider + +!!! info "Available time segments and platforms" + - Available for all time segments + - Available for Android only + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_light_raw.csv + - data/raw/{pid}/phone_light_with_datetime.csv + - data/interim/{pid}/phone_light_features/phone_light_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_light.csv + ``` + + +Parameters description for `[PHONE_LIGHT][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_LIGHT` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below + + +Features description for `[PHONE_LIGHT][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|count |rows | Number light sensor rows recorded. +|maxlux |lux | The maximum ambient luminance. +|minlux |lux | The minimum ambient luminance. +|avglux |lux | The average ambient luminance. +|medianlux |lux | The median ambient luminance. +|stdlux |lux | The standard deviation of ambient luminance. + +!!! note "Assumptions/Observations" + NA diff --git a/docs/features/phone-locations.md b/docs/features/phone-locations.md new file mode 100644 index 00000000..646dc9b0 --- /dev/null +++ b/docs/features/phone-locations.md @@ -0,0 +1,142 @@ +# Phone Locations + +Sensor parameters description for `[PHONE_LOCATIONS]`: + +|Key                                                                                        | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the location data is stored +|`[LOCATIONS_TO_USE]`| Type of location data to use, one of `ALL`, `GPS` or `FUSED_RESAMPLED`. This filter is based on the `provider` column of the AWARE locations table, `ALL` includes every row, `GPS` only includes rows where provider is gps, and `FUSED_RESAMPLED` only includes rows where provider is fused after being resampled. +|`[FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD]`| if `FUSED_RESAMPLED` is used, the original fused data has to be resampled, a location row will be resampled to the next valid timestamp (see the Assumptions/Observations below) only if the time difference between them is less or equal than this threshold (in minutes). +|`[FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION]`| if `FUSED_RESAMPLED` is used, the original fused data has to be resampled, a location row will be resampled at most for this long (in minutes) + +!!! note "Assumptions/Observations" + **Types of location data to use** + AWARE Android and iOS clients can collect location coordinates through the phone\'s GPS, the network cellular towers around the phone or Google\'s fused location API. If you want to use only the GPS provider set `[LOCATIONS_TO_USE]` to `GPS`, if you want to use all providers (not recommended due to the difference in accuracy) set `[LOCATIONS_TO_USE]` to `ALL`, if your AWARE client was configured to use fused location only or want to focus only on this provider, set `[LOCATIONS_TO_USE]` to `RESAMPLE_FUSED`. `RESAMPLE_FUSED` takes the original fused location coordinates and replicates each pair forward in time as long as the phone was sensing data as indicated by the joined timestamps of [`[PHONE_DATA_YIELD][SENSORS]`](../phone-data-yield/), this is done because Google\'s API only logs a new location coordinate pair when it is sufficiently different in time or space from the previous one. + + There are two parameters associated with resampling fused location. `FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD` (in minutes, default 30) controls the maximum gap between any two coordinate pairs to replicate the last known pair (for example, participant A\'s phone did not collect data between 10.30am and 10:50am and between 11:05am and 11:40am, the last known coordinate pair will be replicated during the first period but not the second, in other words, we assume that we cannot longer guarantee the participant stayed at the last known location if the phone did not sense data for more than 30 minutes). `FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION` (in minutes, default 720 or 12 hours) stops the last known fused location from being replicated longer that this threshold even if the phone was sensing data continuously (for example, participant A went home at 9pm and their phone was sensing data without gaps until 11am the next morning, the last known location will only be replicated until 9am). If you have suggestions to modify or improve this resampling, let us know. + +## BARNETT provider + +These features are based on the original open-source implementation by [Barnett et al](../../citation#barnett-locations) and some features created by [Canzian et al](../../citation#barnett-locations). + + +!!! info "Available time segments and platforms" + - Available only for segments that start at 00:00:00 and end at 23:59:59 of the same day (daily segments) + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_locations_raw.csv + - data/interim/{pid}/phone_locations_processed.csv + - data/interim/{pid}/phone_locations_processed_with_datetime.csv + - data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_locations.csv + ``` + + +Parameters description for `[PHONE_LOCATIONS][PROVIDERS][BARNETT]`: + +|Key                                          | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_LOCATIONS` features from the `BARNETT` provider| +|`[FEATURES]` | Features to be computed, see table below +|`[ACCURACY_LIMIT]` | An integer in meters, any location rows with an accuracy higher than this will be dropped. This number means there's a 68% probability the true location is within this radius +|`[TIMEZONE]` | Timezone where the location data was collected. By default points to the one defined in the [Configuration](../../setup/configuration#timezone-of-your-study) +|`[MINUTES_DATA_USED]` | Set to `True` to include an extra column in the final location feature file containing the number of minutes used to compute the features on each time segment. Use this for quality control purposes, the more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. + + + +Features description for `[PHONE_LOCATIONS][PROVIDERS][BARNETT]` adapted from [Beiwe Summary Statistics](http://wiki.beiwe.org/wiki/Summary_Statistics): + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|hometime |minutes | Time at home. Time spent at home in minutes. Home is the most visited significant location between 8 pm and 8 am including any pauses within a 200-meter radius. +|disttravelled |meters | Total distance travelled over a day (flights). +|rog |meters | The Radius of Gyration (rog) is a measure in meters of the area covered by a person over a day. A centroid is calculated for all the places (pauses) visited during a day and a weighted distance between all the places and that centroid is computed. The weights are proportional to the time spent in each place. +|maxdiam |meters | The maximum diameter is the largest distance between any two pauses. +|maxhomedist |meters | The maximum distance from home in meters. +|siglocsvisited |locations | The number of significant locations visited during the day. Significant locations are computed using k-means clustering over pauses found in the whole monitoring period. The number of clusters is found iterating k from 1 to 200 stopping until the centroids of two significant locations are within 400 meters of one another. +|avgflightlen |meters | Mean length of all flights. +|stdflightlen |meters | Standard deviation of the length of all flights. +|avgflightdur |seconds | Mean duration of all flights. +|stdflightdur |seconds | The standard deviation of the duration of all flights. +|probpause | - | The fraction of a day spent in a pause (as opposed to a flight) +|siglocentropy |nats | Shannon’s entropy measurement based on the proportion of time spent at each significant location visited during a day. +|circdnrtn | - | A continuous metric quantifying a person’s circadian routine that can take any value between 0 and 1, where 0 represents a daily routine completely different from any other sensed days and 1 a routine the same as every other sensed day. +|wkenddayrtn | - | Same as circdnrtn but computed separately for weekends and weekdays. + +!!! note "Assumptions/Observations" + **Barnett\'s et al features** + These features are based on a Pause-Flight model. A pause is defined as a mobiity trace (location pings) within a certain duration and distance (by default 300 seconds and 60 meters). A flight is any mobility trace between two pauses. Data is resampled and imputed before the features are computed. See [Barnett et al](../../citation#barnett-locations) for more information. In RAPIDS we only expose two parameters for these features (timezone and accuracy limit). You can change other parameters in `src/features/phone_locations/barnett/library/MobilityFeatures.R`. + + **Significant Locations** + Significant locations are determined using K-means clustering on pauses longer than 10 minutes. The number of clusters (K) is increased until no two clusters are within 400 meters from each other. After this, pauses within a certain range of a cluster (200 meters by default) will count as a visit to that significant location. This description was adapted from the Supplementary Materials of [Barnett et al](../../citation#barnett-locations). + + **The Circadian Calculation** + For a detailed description of how this is calculated, see [Canzian et al](../../citation#barnett-locations). + +## DORYAB provider + +These features are based on the original implementation by [Doryab et al.](../../citation#doryab-locations). + + +!!! info "Available time segments and platforms" + - Available for all time segments + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_locations_raw.csv + - data/interim/{pid}/phone_locations_processed.csv + - data/interim/{pid}/phone_locations_processed_with_datetime.csv + - data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_locations.csv + ``` + + +Parameters description for `[PHONE_LOCATIONS][PROVIDERS][BARNETT]`: + +|Key                                          | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_LOCATIONS` features from the `BARNETT` provider| +|`[FEATURES]` | Features to be computed, see table below +| `[DBSCAN_EPS]` | The maximum distance in meters between two samples for one to be considered as in the neighborhood of the other. This is not a maximum bound on the distances of points within a cluster. This is the most important DBSCAN parameter to choose appropriately for your data set and distance function. +| `[DBSCAN_MINSAMPLES]` | The number of samples (or total weight) in a neighborhood for a point to be considered as a core point of a cluster. This includes the point itself. +| `[THRESHOLD_STATIC]` | It is the threshold value in km/hr which labels a row as Static or Moving. +| `[MAXIMUM_GAP_ALLOWED]` | The maximum gap (in seconds) allowed between any two consecutive rows for them to be considered part of the same displacement. If this threshold is too high, it can throw speed and distance calculations off for periods when the the phone was not sensing. +| `[MINUTES_DATA_USED]` | Set to `True` to include an extra column in the final location feature file containing the number of minutes used to compute the features on each time segment. Use this for quality control purposes, the more data minutes exist for a period, the more reliable its features should be. For fused location, a single minute can contain more than one coordinate pair if the participant is moving fast enough. +| `[SAMPLING_FREQUENCY]` | Expected time difference between any two location rows in minutes. If set to `0`, the sampling frequency will be inferred automatically as the median of all the differences between any two consecutive row timestamps (recommended if you are using `FUSED_RESAMPLED` data). This parameter impacts all the time calculations. + + +Features description for `[PHONE_LOCATIONS][PROVIDERS][BARNETT]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|locationvariance |$meters^2$ |The sum of the variances of the latitude and longitude columns. +|loglocationvariance | - | Log of the sum of the variances of the latitude and longitude columns. +|totaldistance |meters |Total distance travelled in a time segment using the haversine formula. +|averagespeed |km/hr |Average speed in a time segment considering only the instances labeled as Moving. +|varspeed |km/hr |Speed variance in a time segment considering only the instances labeled as Moving. +|circadianmovement |- | \"It encodes the extent to which a person's location patterns follow a 24-hour circadian cycle.\" [Doryab et al.](../../citation#doryab-locations). +|numberofsignificantplaces |places |Number of significant locations visited. It is calculated using the DBSCAN clustering algorithm which takes in EPS and MIN_SAMPLES as parameters to identify clusters. Each cluster is a significant place. +|numberlocationtransitions |transitions |Number of movements between any two clusters in a time segment. +|radiusgyration |meters |Quantifies the area covered by a participant +|timeattop1location |minutes |Time spent at the most significant location. +|timeattop2location |minutes |Time spent at the 2nd most significant location. +|timeattop3location |minutes |Time spent at the 3rd most significant location. +|movingtostaticratio | - | Ratio between the number of rows labeled Moving versus Static +|outlierstimepercent | - | Ratio between the number of rows that belong to non-significant clusters divided by the total number of rows in a time segment. +|maxlengthstayatclusters |minutes |Maximum time spent in a cluster (significant location). +|minlengthstayatclusters |minutes |Minimum time spent in a cluster (significant location). +|meanlengthstayatclusters |minutes |Average time spent in a cluster (significant location). +|stdlengthstayatclusters |minutes |Standard deviation of time spent in a cluster (significant location). +|locationentropy |nats |Shannon Entropy computed over the row count of each cluster (significant location), it will be higher the more rows belong to a cluster (i.e. the more time a participant spent at a significant location). +|normalizedlocationentropy |nats |Shannon Entropy computed over the row count of each cluster (significant location) divided by the number of clusters, it will be higher the more rows belong to a cluster (i.e. the more time a participant spent at a significant location). + + +!!! note "Assumptions/Observations" + **Significant Locations Identified** + Significant locations are determined using DBSCAN clustering on locations that a patient visit over the course of the period of data collection. + + **The Circadian Calculation** + For a detailed description of how this is calculated, see [Canzian et al](../../citation#doryab-locations). \ No newline at end of file diff --git a/docs/features/phone-messages.md b/docs/features/phone-messages.md new file mode 100644 index 00000000..2c3d3108 --- /dev/null +++ b/docs/features/phone-messages.md @@ -0,0 +1,46 @@ +# Phone Messages + +Sensor parameters description for `[PHONE_MESSAGES]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the messages data is stored + +## RAPIDS provider + +!!! info "Available time segments and platforms" + - Available for all time segments + - Available for Android only + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_messages_raw.csv + - data/raw/{pid}/phone_messages_with_datetime.csv + - data/interim/{pid}/phone_messages_features/phone_messages_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_messages.csv + ``` + + +Parameters description for `[PHONE_MESSAGES][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_MESSAGES` features from the `RAPIDS` provider| +|`[MESSAGES_TYPES]` | The `messages_type` that will be analyzed. The options for this parameter are `received` or `sent`. +|`[FEATURES]` | Features to be computed, see table below for `[MESSAGES_TYPES]` `received` and `sent` + + +Features description for `[PHONE_MESSAGES][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|count |messages |Number of messages of type `messages_type` that occurred during a particular `time_segment`. +|distinctcontacts |contacts |Number of distinct contacts that are associated with a particular `messages_type` during a particular `time_segment`. +|timefirstmessages |minutes |Number of minutes between 12:00am (midnight) and the first `message` of a particular `messages_type` during a particular `time_segment`. +|timelastmessages |minutes |Number of minutes between 12:00am (midnight) and the last `message` of a particular `messages_type` during a particular `time_segment`. +|countmostfrequentcontact |messages |Number of messages from the contact with the most messages of `messages_type` during a `time_segment` throughout the whole dataset of each participant. + +!!! note "Assumptions/Observations" + 1. `[MESSAGES_TYPES]` and `[FEATURES]` keys in `config.yaml` need to match. For example, `[MESSAGES_TYPES]` `sent` matches the `[FEATURES]` key `sent` + + diff --git a/docs/features/phone-screen.md b/docs/features/phone-screen.md new file mode 100644 index 00000000..e438e328 --- /dev/null +++ b/docs/features/phone-screen.md @@ -0,0 +1,58 @@ +# Phone Screen + +Sensor parameters description for `[PHONE_SCREEN]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the screen data is stored + +## RAPIDS provider + +!!! info "Available time segments and platforms" + - Available for all time segments + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_screen_raw.csv + - data/raw/{pid}/phone_screen_with_datetime.csv + - data/raw/{pid}/phone_screen_with_datetime_unified.csv + - data/interim/{pid}/phone_screen_episodes.csv + - data/interim/{pid}/phone_screen_episodes_resampled.csv + - data/interim/{pid}/phone_screen_episodes_resampled_with_datetime.csv + - data/interim/{pid}/phone_screen_features/phone_screen_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_screen.csv + ``` + + +Parameters description for `[PHONE_SCREEN][PROVIDERS][RAPIDS]`: + +|Key                                                           | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_SCREEN` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below +|`[REFERENCE_HOUR_FIRST_USE]` | The reference point from which `firstuseafter` is to be computed, default is midnight +|`[IGNORE_EPISODES_SHORTER_THAN]` | Ignore episodes that are shorter than this threshold (minutes). Set to 0 to disable this filter. +|`[IGNORE_EPISODES_LONGER_THAN]` | Ignore episodes that are longer than this threshold (minutes). Set to 0 to disable this filter. +|`[EPISODE_TYPES]` | Currently we only support `unlock` episodes (from when the phone is unlocked until the screen is off) + + +Features description for `[PHONE_SCREEN][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +|sumduration |minutes |Total duration of all unlock episodes. +|maxduration |minutes |Longest duration of any unlock episode. +|minduration |minutes |Shortest duration of any unlock episode. +|avgduration |minutes |Average duration of all unlock episodes. +|stdduration |minutes |Standard deviation duration of all unlock episodes. +|countepisode |episodes |Number of all unlock episodes + +|firstuseafter |minutes |Minutes until the first unlock episode. + +!!! note "Assumptions/Observations" + 1. In Android, `lock` events can happen right after an `off` event, after a few seconds of an `off` event, or never happen depending on the phone\'s settings, therefore, an `unlock` episode is defined as the time between an `unlock` and a `off` event. In iOS, `on` and `off` events do not exist, so an `unlock` episode is defined as the time between an `unlock` and a `lock` event. + + 2. Events in iOS are recorded reliably albeit some duplicated `lock` events within milliseconds from each other, so we only keep consecutive unlock/lock pairs. In Android you cand find multiple consecutive `unlock` or `lock` events, so we only keep consecutive unlock/off pairs. In our experiments these cases are less than 10% of the screen events collected and this happens because `ACTION_SCREEN_OFF` and `ACTION_SCREEN_ON` are `sent when the device becomes non-interactive which may have nothing to do with the screen turning off`. In addition to unlock/off episodes, in Android it is possible to measure the time spent on the lock screen before an `unlock` event as well as the total screen time (i.e. `ON` to `OFF`) but these are not implemented at the moment. + + 3. We transform iOS screen events to match Android's format, we replace `lock` episodes with `off` episodes (2 with 0) in iOS. However, as mentioned above this is still computing `unlock` to `lock` episodes. diff --git a/docs/features/phone-wifi-connected.md b/docs/features/phone-wifi-connected.md new file mode 100644 index 00000000..537bfd44 --- /dev/null +++ b/docs/features/phone-wifi-connected.md @@ -0,0 +1,42 @@ +# Phone WiFi Connected + +Sensor parameters description for `[PHONE_WIFI_CONNECTED]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the wifi (connected) data is stored + +## RAPIDS provider + +!!! info "Available time segments and platforms" + - Available for all time segments + - Available for Android and iOS + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_wifi_connected_raw.csv + - data/raw/{pid}/phone_wifi_connected_with_datetime.csv + - data/interim/{pid}/phone_wifi_connected_features/phone_wifi_connected_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_wifi_connected.csv + ``` + + +Parameters description for `[PHONE_WIFI_CONNECTED][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_WIFI_CONNECTED` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below + + +Features description for `[PHONE_WIFI_CONNECTED][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +| countscans | devices | Number of scanned WiFi access points connected during a time_segment, an access point can be detected multiple times over time and these appearances are counted separately | +| uniquedevices | devices | Number of unique access point during a time_segment as identified by their hardware address | +| countscansmostuniquedevice | scans | Number of scans of the most scanned access point during a time_segment across the whole monitoring period | + +!!! note "Assumptions/Observations" + 1. A connected WiFI access point is one that a phone was connected to. + 2. By default AWARE stores this data in the `sensor_wifi` table. diff --git a/docs/features/phone-wifi-visible.md b/docs/features/phone-wifi-visible.md new file mode 100644 index 00000000..60786e07 --- /dev/null +++ b/docs/features/phone-wifi-visible.md @@ -0,0 +1,42 @@ +# Phone WiFi Visible + +Sensor parameters description for `[PHONE_WIFI_VISIBLE]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[TABLE]`| Database table where the wifi (visible) data is stored + +## RAPIDS provider + +!!! info "Available time segments and platforms" + - Available for all time segments + - Available for Android only + +!!! info "File Sequence" + ```bash + - data/raw/{pid}/phone_wifi_visible_raw.csv + - data/raw/{pid}/phone_wifi_visible_with_datetime.csv + - data/interim/{pid}/phone_wifi_visible_features/phone_wifi_visible_{language}_{provider_key}.csv + - data/processed/features/{pid}/phone_wifi_visible.csv + ``` + + +Parameters description for `[PHONE_WIFI_VISIBLE][PROVIDERS][RAPIDS]`: + +|Key                              | Description | +|----------------|----------------------------------------------------------------------------------------------------------------------------------- +|`[COMPUTE]`| Set to `True` to extract `PHONE_WIFI_VISIBLE` features from the `RAPIDS` provider| +|`[FEATURES]` | Features to be computed, see table below + + +Features description for `[PHONE_WIFI_VISIBLE][PROVIDERS][RAPIDS]`: + +|Feature |Units |Description| +|-------------------------- |---------- |---------------------------| +| countscans | devices | Number of scanned WiFi access points visible during a time_segment, an access point can be detected multiple times over time and these appearances are counted separately | +| uniquedevices | devices | Number of unique access point during a time_segment as identified by their hardware address | +| countscansmostuniquedevice | scans | Number of scans of the most scanned access point during a time_segment across the whole monitoring period | + +!!! note "Assumptions/Observations" + 1. A visible WiFI access point is one that a phone sensed around itself but that it was not connected to. Due to API restrictions, this sensor is not available on iOS. + 2. By default AWARE stores this data in the `wifi` table. diff --git a/docs/file-structure.md b/docs/file-structure.md new file mode 100644 index 00000000..767fa2d5 --- /dev/null +++ b/docs/file-structure.md @@ -0,0 +1,20 @@ +# File Structure + +!!! tip + - Read this page if you want to learn more about how RAPIDS is structured. If you want to start using it go to [Installation](../setup/installation/), then to [Configuration](../setup/configuration/), and then to [Execution](../setup/execution/) + - All paths mentioned in this page are relative to RAPIDS' root folder. + +If you want to extract the behavioral features that RAPIDS offers, you will only have to create or modify the [`.env` file](../setup/configuration/#database-credentials), [participants files](../setup/configuration/#participant-files), [time segment files](../setup/configuration/#time-segments), and the `config.yaml` file as instructed in the [Configuration page](../setup/configuration). The `config.yaml` file is the heart of RAPIDS and includes parameters to manage participants, data sources, sensor data, visualizations and more. + + +All data is saved in `data/`. The `data/external/` folder stores any data imported or created by the user, `data/raw/` stores sensor data as imported from your database, `data/interim/` has intermediate files necessary to compute behavioral features from raw data, and `data/processed/` has all the final files with the behavioral features in folders per participant and sensor. + +RAPIDS source code is saved in `src/`. The `src/data/` folder stores scripts to download, clean and pre-process sensor data, `src/features` has scripts to extract behavioral features organized in their respective sensor subfolders , `src/models/` can host any script to create models or statistical analyses with the behavioral features you extract, and `src/visualization/` has scripts to create plots of the raw and processed data. There are other files and folders but only relevant if you are interested in extending RAPIDS (e.g. virtual env files, docs, tests, Dockerfile, the Snakefile, etc.). + +In the figure below, we represent the interactions between users and files. After a user modifies the configuration files mentioned above, the `Snakefile` file will search for and execute the Snakemake rules that contain the Python or R scripts necessary to generate or update the required output files (behavioral features, plots, etc.). + +
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Interaction diagram between the user, and important files in RAPIDS
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At the moment we support smartphone data collected with [AWARE](https://awareframework.com/) and wearable data from Fitbit devices. + +!!! tip + :material-slack: Questions or feedback can be posted on the \#rapids channel in AWARE Framework\'s [slack](http://awareframework.com:3000/). + + :material-github: Bugs and feature requests should be posted on [Github](https://github.com/carissalow/rapids/issues). + + :fontawesome-solid-tasks: Join our discussions on our algorithms and assumptions for feature [processing](https://github.com/carissalow/rapids/issues?q=is%3Aissue+is%3Aopen+label%3Adiscussion). + + :fontawesome-solid-play: Ready to start? Go to [Installation](setup/installation/), then to [Configuration](setup/configuration/), and then to [Execution](setup/execution/) + +## How does it work? + +RAPIDS is formed by R and Python scripts orchestrated by [Snakemake](https://snakemake.readthedocs.io/en/stable/). We suggest you read Snakemake's docs but in short: every link in the analysis chain is atomic and has files as input and output. Behavioral features are processed per sensor and per participant. + +## What are the benefits of using RAPIDS? + +1. **Consistent analysis**. Every participant sensor dataset is analyzed in the exact same way and isolated from each other. +2. **Efficient analysis**. Every analysis step is executed only once. Whenever your data or configuration changes only the affected files are updated. +5. **Parallel execution**. Thanks to Snakemake, your analysis can be executed over multiple cores without changing your code. +6. **Code-free features**. Extract any of the behavioral features offered by RAPIDS without writing any code. +7. **Extensible code**. You can easily add your own behavioral features in R or Python, share them with the community, and keep authorship and citations. +8. **Timezone aware**. Your data is adjusted to the specified timezone (multiple timezones suport *coming soon*). +9. **Flexible time segments**. You can extract behavioral features on time windows of any length (e.g. 5 minutes, 3 hours, 2 days), on every day or particular days (e.g. weekends, Mondays, the 1st of each month, etc.) or around events of interest (e.g. surveys or clinical relapses). +10. **Tested code**. We are constantly adding tests to make sure our behavioral features are correct. +11. **Reproducible code**. If you structure your analysis within RAPIDS, you can be sure your code will run in other computers as intended thanks to R and Python virtual environments. You can share your analysis code along your publications without any overhead. +12. **Private**. All your data is processed locally. + +## How is it organized? + +In broad terms the `config.yaml`, [`.env` file](../setup/configuration/#database-credentials), [participants files](../setup/configuration/#participant-files), and [time segment files](../setup/configuration/#time-segments) are the only ones that you will have to modify. All data is stored in `data/` and all scripts are stored in `src/`. For more information see RAPIDS' [File Structure](file-structure.md). \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst deleted file mode 100644 index 9eb010f7..00000000 --- a/docs/index.rst +++ /dev/null @@ -1,50 +0,0 @@ -.. moshi-aware documentation master file, created by - sphinx-quickstart. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -RAPIDS -====== - -**R**\ eproducible **A**\ nalysis **Pi**\ peline for **D**\ ata **S**\ treams - -Do you want to keep up to date with new functionality or have a question? Join the #rapids channel in AWARE Framework's slack_ - -Contents: - -.. toctree:: - :maxdepth: 2 - :caption: Getting Started - - usage/introduction - usage/installation - usage/quick_rule - usage/example - usage/snakemake_docs - usage/faq - -.. toctree:: - :maxdepth: 2 - :caption: Features - - features/extracted - -.. toctree:: - :maxdepth: 2 - :caption: Visualization - - visualization/data_exploration - -.. toctree:: - :maxdepth: 2 - :caption: Developers - - develop/remotesupport - develop/documentation - develop/features - develop/environments - develop/contributors - develop/testing - develop/test_cases - -.. _slack: http://awareframework.com:3000/ diff --git a/docs/javascripts/extra.js b/docs/javascripts/extra.js new file mode 100644 index 00000000..10faee99 --- /dev/null +++ b/docs/javascripts/extra.js @@ -0,0 +1,14 @@ +window.addEventListener("DOMContentLoaded", function() { + var xhr = new XMLHttpRequest(); + xhr.open("GET", window.location + "../versions.json"); + xhr.onload = function() { + var versions = JSON.parse(this.responseText); + latest_version = "" + for(id in versions) + if(versions[id]["aliases"].length > 0 && versions[id]["aliases"].includes("latest")) + latest_version = "/" + versions[id].version + "/" + if(!window.location.pathname.includes("/latest/") && (latest_version.length > 0 && !window.location.pathname.includes(latest_version))) + document.querySelector("div[data-md-component=announce]").innerHTML = "
You are seeing the docs for a previous version of RAPIDS, click here to go to the latest
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[Install](setup/installation.md) a new copy of RAPIDS (the R and Python virtual environments didn't change so the cached versions will be reused) + 1. Make sure you don't skip a new Installation step to give execution permissions to the RAPIDS script: `chmod +x rapids` +2. Follow the new [Configuration](setup/configuration.md) guide. + 1. You can copy and paste your old `.env` file + 2. You can migrate your old participant files: + ``` + python tools/update_format_participant_files.py + ``` +3. Get familiar with the new way of [Executing](setup/execution.md) RAPIDS +3. You can proceed to reconfigure your `config.yaml`, its structure is more consistent and should be familiar to you. + +!!! info + If you have any questions reach out to us on [Slack](http://awareframework.com:3000/). \ No newline at end of file diff --git a/docs/setup/configuration.md b/docs/setup/configuration.md new file mode 100644 index 00000000..f3116672 --- /dev/null +++ b/docs/setup/configuration.md @@ -0,0 +1,435 @@ + +# Configuration + +You need to follow these steps to configure your RAPIDS deployment before you can extract behavioral features + +1. Add your [database credentials](#database-credentials) +2. Choose the [timezone of your study](#timezone-of-your-study) +3. Create your [participants files](#participant-files) +4. Select what [time segments](#time-segments) you want to extract features on +5. Modify your [device data source configuration](#device-data-source-configuration) +6. Select what [sensors and features](#sensor-and-features-to-process) you want to process + +When you are done with this configuration, go to [executing RAPIDS](setup/execution). + +!!! hint + Every time you see `config["KEY"]` or `[KEY]` in these docs we are referring to the corresponding key in the `config.yaml` file. + +--- +## Database credentials + +1. Create an empty file called `#!bash .env` in your RAPIDS root directory +2. Add the following lines and replace your database-specific credentials (user, password, host, and database): + + ``` yaml + [MY_GROUP] + user=MY_USER + password=MY_PASSWORD + host=MY_HOST + port=3306 + database=MY_DATABASE + ``` + +!!! warning + The label `MY_GROUP` is arbitrary but it has to match the following `config.yaml` key: + + ```yaml + DATABASE_GROUP: &database_group + MY_GROUP + ``` + +!!! note + You can ignore this step if you are only processing Fitbit data in CSV files. +--- + +## Timezone of your study + +### Single timezone + +If your study only happened in a single time zone, select the appropriate code form this [list](https://en.wikipedia.org/wiki/List_of_tz_database_time_zones) and change the following config key. Double check your timezone code pick, for example US Eastern Time is `America/New_York` not `EST` + +``` yaml +TIMEZONE: &timezone + America/New_York +``` + +### Multiple timezones + +Support coming soon. + +--- + +## Participant files + +Participant files link together multiple devices (smartphones and wearables) to specific participants and identify them throughout RAPIDS. You can create these files manually or [automatically](#automatic-creation-of-participant-files). Participant files are stored in `data/external/participant_files/pxx.yaml` and follow a unified [structure](#structure-of-participants-files). + +!!! note + The list `PIDS` in `config.yaml` needs to have the participant file names of the people you want to process. For example, if you created `p01.yaml`, `p02.yaml` and `p03.yaml` files in `/data/external/participant_files/ `, then `PIDS` should be: + ```yaml + PIDS: [p01, p02, p03] + ``` + +!!! tip + Attribute *values* of the `[PHONE]` and `[FITBIT]` sections in every participant file are optional which allows you to analyze data from participants that only carried smartphones, only Fitbit devices, or both. + +??? hint "Optional: Migrating participants files with the old format" + If you were using the pre-release version of RAPIDS with participant files in plain text (as opposed to yaml), you can run the following command and your old files will be converted into yaml files stored in `data/external/participant_files/` + + ```bash + python tools/update_format_participant_files.py + ``` + +### Structure of participants files + +!!! example "Example of the structure of a participant file" + + In this example, the participant used an android phone, an ios phone, and a fitbit device throughout the study between Apr 23rd 2020 and Oct 28th 2020 + + ```yaml + PHONE: + DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524, dsadas-2324-fgsf-sdwr-gdfgs4rfsdf43] + PLATFORMS: [android,ios] + LABEL: test01 + START_DATE: 2020-04-23 + END_DATE: 2020-10-28 + FITBIT: + DEVICE_IDS: [fitbit1] + LABEL: test01 + START_DATE: 2020-04-23 + END_DATE: 2020-10-28 + ``` + +**For `[PHONE]`** + +| Key                      | Description | +|-------------------------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| +| `[DEVICE_IDS]` | An array of the strings that uniquely identify each smartphone, you can have more than one for when participants changed phones in the middle of the study, in this case, data from all their devices will be joined and relabeled with the last 1 on this list. | +| `[PLATFORMS]` | An array that specifies the OS of each smartphone in `[DEVICE_IDS]` , use a combination of `android` or `ios` (we support participants that changed platforms in the middle of your study!). If you have an `aware_device` table in your database you can set `[PLATFORMS]: [multiple]` and RAPIDS will infer them automatically. | +| `[LABEL]` | A string that is used in reports and visualizations. | +| `[START_DATE]` | A string with format `YYY-MM-DD` . Only data collected *after* this date will be included in the analysis | +| `[END_DATE]` | A string with format `YYY-MM-DD` . Only data collected *before* this date will be included in the analysis | + +**For `[FITBIT]`** + +| Key                      | Description | +|------------------|-----------------------------------------------------------------------------------------------------------| +| `[DEVICE_IDS]` | An array of the strings that uniquely identify each Fitbit, you can have more than one in case the participant changed devices in the middle of the study, in this case, data from all devices will be joined and relabeled with the last `device_id` on this list. | +| `[LABEL]` | A string that is used in reports and visualizations. | +| `[START_DATE]` | A string with format `YYY-MM-DD` . Only data collected *after* this date will be included in the analysis | +| `[END_DATE]` | A string with format `YYY-MM-DD` . Only data collected *before* this date will be included in the analysis | + +### Automatic creation of participant files + +You have two options a) use the `aware_device` table in your database or b) use a CSV file. In either case, in your `config.yaml`, set `[PHONE_SECTION][ADD]` or `[FITBIT_SECTION][ADD]` to `TRUE` depending on what devices you used in your study. Set `[DEVICE_ID_COLUMN]` to the name of the column that uniquely identifies each device and include any device ids you want to ignore in `[IGNORED_DEVICE_IDS]`. + +=== "aware_device table" + + Set the following keys in your `config.yaml` + + ```yaml + CREATE_PARTICIPANT_FILES: + SOURCE: + TYPE: AWARE_DEVICE_TABLE + DATABASE_GROUP: *database_group + CSV_FILE_PATH: "" + TIMEZONE: *timezone + PHONE_SECTION: + ADD: TRUE # or FALSE + DEVICE_ID_COLUMN: device_id # column name + IGNORED_DEVICE_IDS: [] + FITBIT_SECTION: + ADD: TRUE # or FALSE + DEVICE_ID_COLUMN: fitbit_id # column name + IGNORED_DEVICE_IDS: [] + ``` + + Then run + + ```bash + snakemake -j1 create_participants_files + ``` + +=== "CSV file" + + Set the following keys in your `config.yaml`. + + ```yaml + CREATE_PARTICIPANT_FILES: + SOURCE: + TYPE: CSV_FILE + DATABASE_GROUP: "" + CSV_FILE_PATH: "your_path/to_your.csv" + TIMEZONE: *timezone + PHONE_SECTION: + ADD: TRUE # or FALSE + DEVICE_ID_COLUMN: device_id # column name + IGNORED_DEVICE_IDS: [] + FITBIT_SECTION: + ADD: TRUE # or FALSE + DEVICE_ID_COLUMN: fitbit_id # column name + IGNORED_DEVICE_IDS: [] + ``` + Your CSV file (`[SOURCE][CSV_FILE_PATH]`) should have the following columns but you can omit any values you don't have on each column: + + | Column | Description | + |------------------|-----------------------------------------------------------------------------------------------------------| + | phone device id | The name of this column has to match `[PHONE_SECTION][DEVICE_ID_COLUMN]`. Separate multiple ids with `;` | + | fitbit device id | The name of this column has to match `[FITBIT_SECTION][DEVICE_ID_COLUMN]`. Separate multiple ids with `;` | + | pid | Unique identifiers with the format pXXX (your participant files will be named with this string | + | platform | Use `android`, `ios` or `multiple` as explained above, separate values with `;` | + | label | A human readable string that is used in reports and visualizations. | + | start_date | A string with format `YYY-MM-DD`. | + | end_date | A string with format `YYY-MM-DD`. | + + !!! example + + ```csv + device_id,pid,label,platform,start_date,end_date,fitbit_id + a748ee1a-1d0b-4ae9-9074-279a2b6ba524;dsadas-2324-fgsf-sdwr-gdfgs4rfsdf43,p01,julio,android;ios,2020-01-01,2021-01-01,fitbit1 + 4c4cf7a1-0340-44bc-be0f-d5053bf7390c,p02,meng,ios,2021-01-01,2022-01-01,fitbit2 + ``` + + Then run + + ```bash + snakemake -j1 create_participants_files + ``` + +--- + +## Time Segments + +Time segments (or epochs) are the time windows on which you want to extract behavioral features. For example, you might want to process data on every day, every morning, or only during weekends. RAPIDS offers three categories of time segments that are flexible enough to cover most use cases: **frequency** (short time windows every day), **periodic** (arbitrary time windows on any day), and **event** (arbitrary time windows around events of interest). See also our [examples](#segment-examples). + +=== "Frequency Segments" + + These segments are computed on every day and all have the same duration (for example 30 minutes). Set the following keys in your `config.yaml` + + ```yaml + TIME_SEGMENTS: &time_segments + TYPE: FREQUENCY + FILE: "data/external/your_frequency_segments.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE + ``` + + The file pointed by `[TIME_SEGMENTS][FILE]` should have the following format and can only have 1 row. + + | Column | Description | + |--------|----------------------------------------------------------------------| + | label | A string that is used as a prefix in the name of your time segments | + | length | An integer representing the duration of your time segments in minutes | + + !!! example + + ```csv + label,length + thirtyminutes,30 + ``` + + This configuration will compute 48 time segments for every day when any data from any participant was sensed. For example: + + ```csv + start_time,length,label + 00:00,30,thirtyminutes0000 + 00:30,30,thirtyminutes0001 + 01:00,30,thirtyminutes0002 + 01:30,30,thirtyminutes0003 + ... + ``` + +=== "Periodic Segments" + + These segments can be computed every day, or on specific days of the week, month, quarter, and year. Their minimum duration is 1 minute but they can be as long as you want. Set the following keys in your `config.yaml`. + + ```yaml + TIME_SEGMENTS: &time_segments + TYPE: PERIODIC + FILE: "data/external/your_periodic_segments.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # or TRUE + ``` + + If `[INCLUDE_PAST_PERIODIC_SEGMENTS]` is set to `TRUE`, RAPIDS will consider instances of your segments back enough in the past as to include the first row of data of each participant. For example, if the first row of data from a participant happened on Saturday March 7th 2020 and the requested segment duration is 7 days starting on every Sunday, the first segment to be considered would start on Sunday March 1st if `[INCLUDE_PAST_PERIODIC_SEGMENTS]` is `TRUE` or on Sunday March 8th if `FALSE`. + + The file pointed by `[TIME_SEGMENTS][FILE]` should have the following format and can have multiple rows. + + | Column | Description | + |---------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | label | A string that is used as a prefix in the name of your time segments. It has to be **unique** between rows | + | start_time | A string with format `HH:MM:SS` representing the starting time of this segment on any day | + | length | A string representing the length of this segment.It can have one or more of the following strings **`XXD XXH XXM XXS`** to represent days, hours, minutes and seconds. For example `7D 23H 59M 59S` | + | repeats_on | One of the follow options `every_day`, `wday`, `qday`, `mday`, and `yday`. The last four represent a week, quarter, month and year day | + | repeats_value | An integer complementing `repeats_on`. If you set `repeats_on` to `every_day` set this to `0`, otherwise `1-7` represent a `wday` starting from Mondays, `1-31` represent a `mday`, `1-91` represent a `qday`, and `1-366` represent a `yday` | + + !!! example + + ```csv + label,start_time,length,repeats_on,repeats_value + daily,00:00:00,23H 59M 59S,every_day,0 + morning,06:00:00,5H 59M 59S,every_day,0 + afternoon,12:00:00,5H 59M 59S,every_day,0 + evening,18:00:00,5H 59M 59S,every_day,0 + night,00:00:00,5H 59M 59S,every_day,0 + ``` + + This configuration will create five segments instances (`daily`, `morning`, `afternoon`, `evening`, `night`) on any given day (`every_day` set to 0). The `daily` segment will start at midnight and will last `23:59:59`, the other four segments will start at 6am, 12pm, 6pm, and 12am respectively and last for `05:59:59`. + +=== "Event segments" + + These segments can be computed before or after an event of interest (defined as any UNIX timestamp). Their minimum duration is 1 minute but they can be as long as you want. The start of each segment can be shifted backwards or forwards from the specified timestamp. Set the following keys in your `config.yaml`. + + ```yaml + TIME_SEGMENTS: &time_segments + TYPE: EVENT + FILE: "data/external/your_event_segments.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # or TRUE + ``` + + The file pointed by `[TIME_SEGMENTS][FILE]` should have the following format and can have multiple rows. + + | Column | Description | + |---------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | label | A string that is used as a prefix in the name of your time segments. If labels are unique, every segment is independent; if two or more segments have the same label, their data will be grouped when computing auxiliary data for features like the `most frequent contact` for calls (the most frequent contact will be computed across all these segments). There cannot be two *overlaping* event segments with the same label (RAPIDS will throw an error) | + | event_timestamp | A UNIX timestamp that represents the moment an event of interest happened (clinical relapse, survey, readmission, etc.). The corresponding time segment will be computed around this moment using `length`, `shift`, and `shift_direction` | + | length | A string representing the length of this segment. It can have one or more of the following keys `XXD XXH XXM XXS` to represent a number of days, hours, minutes, and seconds. For example `7D 23H 59M 59S` | + | shift | A string representing the time shift from `event_timestamp`. It can have one or more of the following keys `XXD XXH XXM XXS` to represent a number of days, hours, minutes and seconds. For example `7D 23H 59M 59S`. Use this value to change the start of a segment with respect to its `event_timestamp`. For example, set this variable to `1H` to create a segment that starts 1 hour from an event of interest (`shift_direction` determines if it's before or after). | + | shift_direction | An integer representing whether the `shift` is before (`-1`) or after (`1`) an `event_timestamp` | + |device_id| The device id (smartphone or fitbit) to whom this segment belongs to. You have to create a line in this event segment file for each event of a participant that you want to analyse. If you have participants with multiple device ids you can choose any of them| + + !!! example + ```csv + label,event_timestamp,length,shift,shift_direction,device_id + stress1,1587661220000,1H,5M,1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + stress2,1587747620000,4H,4H,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + stress3,1587906020000,3H,5M,1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + stress4,1584291600000,7H,4H,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + stress5,1588172420000,9H,5M,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + mood,1587661220000,1H,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + mood,1587747620000,1D,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + mood,1587906020000,7D,0,0,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + ``` + + This example will create eight segments for a single participant (`a748ee1a...`), five independent `stressX` segments with various lengths (1,4,3,7, and 9 hours). Segments `stress1`, `stress3`, and `stress5` are shifted forwards by 5 minutes and `stress2` and `stress4` are shifted backwards by 4 hours (that is, if the `stress4` event happened on March 15th at 1pm EST (`1584291600000`), the time segment will start on that day at 9am and end at 4pm). + + The three `mood` segments are 1 hour, 1 day and 7 days long and have no shift. In addition, these `mood` segments are grouped together, meaning that although RAPIDS will compute features on each one of them, some necessary information to compute a few of such features will be extracted from all three segments, for example the phone contact that called a participant the most or the location clusters visited by a participant. + +### Segment Examples + +=== "5-minutes" + Use the following `Frequency` segment file to create 288 (12 * 60 * 24) 5-minute segments starting from midnight of every day in your study + ```csv + label,length + fiveminutes,5 + ``` +=== "Daily" + Use the following `Periodic` segment file to create daily segments starting from midnight of every day in your study + ```csv + label,start_time,length,repeats_on,repeats_value + daily,00:00:00,23H 59M 59S,every_day,0 + ``` +=== "Morning" + Use the following `Periodic` segment file to create morning segments starting at 06:00:00 and ending at 11:59:59 of every day in your study + ```csv + label,start_time,length,repeats_on,repeats_value + morning,06:00:00,5H 59M 59S,every_day,0 + ``` +=== "Overnight" + Use the following `Periodic` segment file to create overnight segments starting at 20:00:00 and ending at 07:59:59 (next day) of every day in your study + ```csv + label,start_time,length,repeats_on,repeats_value + morning,20:00:00,11H 59M 59S,every_day,0 + ``` +=== "Weekly" + Use the following `Periodic` segment file to create **non-overlapping** weekly segments starting at midnight of every **Monday** in your study + ```csv + label,start_time,length,repeats_on,repeats_value + weekly,00:00:00,6D 23H 59M 59S,wday,1 + ``` + Use the following `Periodic` segment file to create **overlapping** weekly segments starting at midnight of **every day** in your study + ```csv + label,start_time,length,repeats_on,repeats_value + weekly,00:00:00,6D 23H 59M 59S,every_day,0 + ``` +=== "Week-ends" + Use the following `Periodic` segment file to create week-end segments starting at midnight of every **Saturday** in your study + ```csv + label,start_time,length,repeats_on,repeats_value + weekend,00:00:00,1D 23H 59M 59S,wday,6 + ``` +=== "Around surveys" + Use the following `Event` segment file to create two 2-hour segments that start 1 hour before surveys answered by 3 participants + ```csv + label,event_timestamp,length,shift,shift_direction,device_id + survey1,1587661220000,2H,1H,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + survey2,1587747620000,2H,1H,-1,a748ee1a-1d0b-4ae9-9074-279a2b6ba524 + survey1,1587906020000,2H,1H,-1,rqtertsd-43ff-34fr-3eeg-efe4fergregr + survey2,1584291600000,2H,1H,-1,rqtertsd-43ff-34fr-3eeg-efe4fergregr + survey1,1588172420000,2H,1H,-1,klj34oi2-8frk-2343-21kk-324ljklewlr3 + survey2,1584291600000,2H,1H,-1,klj34oi2-8frk-2343-21kk-324ljklewlr3 + ``` +--- +## Device Data Source Configuration + +You might need to modify the following config keys in your `config.yaml` depending on what devices your participants used and where you are storing your data. You can ignore `[PHONE_DATA_CONFIGURATION]` or `[FITBIT_DATA_CONFIGURATION]` if you are not working with either devices. + +=== "Phone" + + The relevant `config.yaml` section looks like this by default: + + ```yaml + PHONE_DATA_CONFIGURATION: + SOURCE: + TYPE: DATABASE + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # SINGLE (MULTIPLE support coming soon) + VALUE: *timezone + + ``` + + **Parameters for `[PHONE_DATA_CONFIGURATION]`** + + | Key | Description | + |---------------------|----------------------------------------------------------------------------------------------------------------------------| + | `[SOURCE] [TYPE]` | Only `DATABASE` is supported (phone data will be pulled from a database) | + | `[SOURCE] [DATABASE_GROUP]` | `*database_group` points to the value defined before in [Database credentials](#database-credentials) | + | `[SOURCE] [DEVICE_ID_COLUMN]` | A column that contains strings that uniquely identify smartphones. For data collected with AWARE this is usually `device_id` | + | `[TIMEZONE] [TYPE]` | Only `SINGLE` is supported for now | + | `[TIMEZONE] [VALUE]` | `*timezone` points to the value defined before in [Timezone of your study](#timezone-of-your-study) | + +=== "Fitbit" + + The relevant `config.yaml` section looks like this by default: + + ```yaml + FITBIT_DATA_CONFIGURATION: + SOURCE: + TYPE: DATABASE # DATABASE or FILES (set each [FITBIT_SENSOR][TABLE] attribute with a table name or a file path accordingly) + COLUMN_FORMAT: JSON # JSON or PLAIN_TEXT + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # Fitbit devices don't support time zones so we read this data in the timezone indicated by VALUE + VALUE: *timezone + + ``` + + **Parameters for For `[FITBIT_DATA_CONFIGURATION]`** + + | Key | Description | + |------------------------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------| + | `[SOURCE]` `[TYPE]` | `DATABASE` or `FILES` (set each `[FITBIT_SENSOR]` `[TABLE]` attribute accordingly with a table name or a file path) | + | `[SOURCE]` `[COLUMN_FORMAT]` | `JSON` or `PLAIN_TEXT`. Column format of the source data. If you pulled your data directly from the Fitbit API the column containing the sensor data will be in `JSON` format | + | `[SOURCE]` `[DATABASE_GROUP]` | `*database_group` points to the value defined before in [Database credentials](#database-credentials). Only used if `[TYPE]` is `DATABASE` . | + | `[SOURCE]` `[DEVICE_ID_COLUMN]` | A column that contains strings that uniquely identify Fitbit devices. | + | `[TIMEZONE]` `[TYPE]` | Only `SINGLE` is supported (Fitbit devices always store data in local time). | + | `[TIMEZONE]` `[VALUE]` | `*timezone` points to the value defined before in [Timezone of your study](#timezone-of-your-study) | + +--- + +## Sensor and Features to Process + +Finally, you need to modify the `config.yaml` section of the sensors you want to extract behavioral features from. All sensors follow the same naming nomenclature (`DEVICE_SENSOR`) and parameter structure which we explain in the [Behavioral Features Introduction](../../features/feature-introduction/). + +!!! done + Head over to [Execution](../execution/) to learn how to execute RAPIDS. \ No newline at end of file diff --git a/docs/setup/execution.md b/docs/setup/execution.md new file mode 100644 index 00000000..8ccf2ef8 --- /dev/null +++ b/docs/setup/execution.md @@ -0,0 +1,36 @@ +# Execution + +After you have [installed](../installation) and [configured](../configuration) RAPIDS, use the following command to execute it. + +```bash +./rapids -j1 +``` + +!!! done "Ready to extract behavioral features" + If you are ready to extract features head over to the [Behavioral Features Introduction](../../features/feature-introduction/) + +!!! info + The script `#!bash ./rapids` is a wrapper around Snakemake so you can pass any parameters that Snakemake accepts (e.g. `-j1`). + +!!! hint "Updating RAPIDS output after modifying `config.yaml`" + Any changes to the `config.yaml` file will be applied automatically and only the relevant files will be updated. This means that after modifying the features list for `PHONE_MESSAGE` for example, RAPIDS will update the output file with the correct features. + +!!! hint "Multi-core" + You can run RAPIDS over multiple cores by modifying the `-j` argument (e.g. use `-j8` to use 8 cores). **However**, take into account that this means multiple sensor datasets for different participants will be load in memory at the same time. If RAPIDS crashes because it ran out of memory reduce the number of cores and try again. + + As reference, we have run RAPIDS over 12 cores and 32 Gb of RAM without problems for a study with 200 participants with 14 days of low-frequency smartphone data (no accelerometer, gyroscope, or magnetometer). + +!!! hint "Forcing a complete rerun" + If you want to update your data from your database or rerun the whole pipeline from scratch run one or both of the following commands depending on the devices you are using: + + ```bash + ./rapids -j1 -R download_phone_data + ./rapids -j1 -R download_fitbit_data + ``` + +!!! hint "Deleting RAPIDS output" + If you want to delete all the output files RAPIDS produces you can execute the following command: + + ```bash + ./rapids -j1 --delete-all-output + ``` diff --git a/docs/setup/installation.md b/docs/setup/installation.md new file mode 100644 index 00000000..91dc7434 --- /dev/null +++ b/docs/setup/installation.md @@ -0,0 +1,203 @@ +# Installation + +You can install RAPIDS using Docker (the fastest), or native instructions for MacOS and Ubuntu + +=== "Docker" + + 1. Install [Docker](https://docs.docker.com/desktop/) + + 2. Pull our RAPIDS container + ``` bash + docker pull agamk/rapids:latest` + ``` + + 3. Run RAPIDS\' container (after this step is done you should see a + prompt in the main RAPIDS folder with its python environment active) + + ``` bash + docker run -it agamk/rapids:latest + ``` + + 4. Pull the latest version of RAPIDS + + ``` bash + git pull + ``` + + 5. Make RAPIDS script executable + ```bash + chmod +x rapids + ``` + + 6. Check that RAPIDS is working + ``` bash + ./rapids -j1 + ``` + 7. *Optional*. You can edit RAPIDS files with `vim` but we recommend using `Visual Studio Code` and its `Remote Containers` extension + + ??? info "How to configure Remote Containers extension" + + - Make sure RAPIDS container is running + - Install the [Remote - Containers extension](https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers) + - Go to the `Remote Explorer` panel on the left hand sidebar + - On the top right dropdown menu choose `Containers` + - Double click on the `agamk/rapids` container in the`CONTAINERS` tree + - A new VS Code session should open on RAPIDS main folder insidethe container. + +=== "MacOS" + We tested these instructions in Catalina + + 1. Install [brew](https://brew.sh/) + + 2. Install MySQL + + ``` bash + brew install mysql + brew services start mysql + ``` + + 3. Install R 4.0, pandoc and rmarkdown. If you have other instances of R, we recommend uninstalling them + + ``` bash + brew install r + brew install pandoc + Rscript --vanilla -e 'install.packages("rmarkdown", repos="http://cran.us.r-project.org")' + ``` + + 4. Install miniconda (restart your terminal afterwards) + + ``` bash + brew cask install miniconda + conda init zsh # (or conda init bash) + ``` + + 5. Clone our repo + + ``` bash + git clone https://github.com/carissalow/rapids + ``` + + 6. Create a python virtual environment + + ``` bash + cd rapids + conda env create -f environment.yml -n rapids + conda activate rapids + ``` + + 7. Install R packages and virtual environment: + + ``` bash + snakemake -j1 renv_install + snakemake -j1 renv_restore + + ``` + + !!! note + This step could take several minutes to complete, especially if you have less than 3Gb of RAM or packages need to be compiled from source. Please be patient and let it run until completion. + + 5. Make RAPIDS script executable + ```bash + chmod +x rapids + ``` + + 8. Check that RAPIDS is working + ``` bash + ./rapids -j1 + ``` + +=== "Ubuntu" + + We tested on Ubuntu 18.04 & 20.04 + + 1. Install dependencies + + ``` bash + sudo apt install libcurl4-openssl-dev + sudo apt install libssl-dev + sudo apt install libxml2-dev + ``` + + 2. Install MySQL + + ``` bash + sudo apt install libmysqlclient-dev + sudo apt install mysql-server + ``` + + 3. Add key for R's repository. + + ``` bash + sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9 + ``` + + 4. Add R's repository + + 1. For 18.04 + ``` bash + sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/' + ``` + + 1. For 20.04 + ``` bash + sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu focal-cran40/' + ``` + + 5. Install R 4.0. If you have other instances of R, we recommend uninstalling them + + ``` bash + sudo apt update + sudo apt install r-base + ``` + + 6. Install Pandoc and rmarkdown + + ``` bash + sudo apt install pandoc + Rscript --vanilla -e 'install.packages("rmarkdown", repos="http://cran.us.r-project.org")' + ``` + + 7. Install git + + ``` bash + sudo apt install git + ``` + + 8. Install [miniconda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html) + + 9. Restart your current shell + + 10. Clone our repo: + + ``` bash + git clone https://github.com/carissalow/rapids + ``` + + 11. Create a python virtual environment: + + ``` bash + cd rapids + conda env create -f environment.yml -n MY_ENV_NAME + conda activate MY_ENV_NAME + ``` + + 7. Install R packages and virtual environment: + + ``` bash + snakemake -j1 renv_install + snakemake -j1 renv_restore + + ``` + + !!! note + This step could take several minutes to complete, especially if you have less than 3Gb of RAM or packages need to be compiled from source. Please be patient and let it run until completion. + + 5. Make RAPIDS script executable + ```bash + chmod +x rapids + ``` + + 8. Check that RAPIDS is working + ``` bash + ./rapids -j1 + ``` diff --git a/docs/stylesheets/extra.css b/docs/stylesheets/extra.css new file mode 100644 index 00000000..83eba024 --- /dev/null +++ b/docs/stylesheets/extra.css @@ -0,0 +1,28 @@ +@media screen and (min-width: 76.25em) { + .md-nav__item--section { + display: block; + margin: 1.75em 0; + } + + .md-nav :not(.md-nav--primary) > .md-nav__list { + padding-left: 7px; + } +} +.md-nav__item .md-nav__link--active { + color: var(--md-typeset-a-color); + background-color: var(--md-code-bg-color); +} + +div[data-md-component=announce] { + background-color: rgba(255,145,0,.1); +} +div[data-md-component=announce]>div#announce-msg{ + color: var(--md-admonition-fg-color); + font-size: .8rem; + text-align: center; + margin: 15px; +} +div[data-md-component=announce]>div#announce-msg>a{ + color: var(--md-typeset-a-color); + text-decoration: underline; +} \ No newline at end of file diff --git a/docs/team.md b/docs/team.md new file mode 100644 index 00000000..67bd59eb --- /dev/null +++ b/docs/team.md @@ -0,0 +1,81 @@ +# RAPIDS Team + +If you are interested in contributing feel free to submit a pull request or contact us. + +## Core Team + +### Julio Vega (Designer and Lead Developer) + +??? abstract "About" + Julio Vega is a postdoctoral associate at the Mobile Sensing + Health Institute. He is interested in personalized methodologies to monitor chronic conditions that affect daily human behavior using mobile and wearable data. + + - *vegaju* at *upmc* . *edu* + - [Personal Website](https://juliovega.info/) + +### Meng Li + +??? abstract "About" + Meng Li received her Master of Science degree in Information Science from the University of Pittsburgh. She is interested in applying machine learning algorithms to the medical field. + + - *lim11* at *upmc* . *edu* + - [Linkedin Profile](https://www.linkedin.com/in/meng-li-57238414a) + - [Github Profile](https://github.com/Meng6) + +### Abhineeth Reddy Kunta + +??? abstract "About" + Abhineeth Reddy Kunta is a Senior Software Engineer with the Mobile Sensing + Health Institute. He is experienced in software development and specializes in building solutions using machine learning. Abhineeth likes exploring ways to leverage technology in advancing medicine and education. Previously he worked as a Computer Programmer at Georgia Department of Public Health. He has a master's degree in Computer Science from George Mason University. + + +### Kwesi Aguillera + +??? abstract "About" + Kwesi Aguillera is currently in his first year at the University of Pittsburgh pursuing a Master of Sciences in Information Science specializing in Big Data Analytics. He received his Bachelor of Science degree in Computer Science and Management from the University of the West Indies. Kwesi considers himself a full stack developer and looks forward to applying this knowledge to big data analysis. + + - [Linkedin Profile](https://www.linkedin.com/in/kwesi-aguillera-29529823) + +### Echhit Joshi + +??? abstract "About" + Echhit Joshi is a Masters student at the School of Computing and Information at University of Pittsburgh. His areas of interest are Machine/Deep Learning, Data Mining, and Analytics. + + - [Linkedin Profile](https://www.linkedin.com/in/echhitjoshi/) + +### Nicolas Leo + +??? abstract "About" + Nicolas is a rising senior studying computer science at the University of Pittsburgh. His academic interests include databases, machine learning, and application development. After completing his undergraduate degree, he plans to attend graduate school for a MS in Computer Science with a focus on Intelligent Systems. + +### Nikunj Goel + +??? abstract "About" + Nik is a graduate student at the University of Pittsburgh pursuing Master of Science in Information Science. He earned his Bachelor of Technology degree in Information Technology from India. He is a Data Enthusiasts and passionate about finding the meaning out of raw data. In a long term, his goal is to create a breakthrough in Data Science and Deep Learning. + + - [Linkedin Profile](https://www.linkedin.com/in/nikunjgoel95/) + +## Community Contributors + +### Agam Kumar + +??? abstract "About" + Agam is a junior at Carnegie Mellon University studying Statistics and Machine Learning and pursuing an additional major in Computer Science. He is a member of the Data Science team in the Health and Human Performance Lab at CMU and has keen interests in software development and data science. His research interests include ML applications in medicine. + + - [Linkedin Profile](https://www.linkedin.com/in/agam-kumar) + - [Github Profile](https://github.com/agam-kumar) + +### Yasaman S. Sefidgar + +??? abstract "About" + - [Linkedin Profile](https://www.linkedin.com/in/ysefidgar/) + +## Advisors + +### Afsaneh Doryab + +??? abstract "About" + - [Personal Website](https://sites.google.com/view/afsanehdoryab) + +### Carissa Low + +??? abstract "About" + - [Profile](https://www.moshi.pitt.edu/people/carissa-low-phd) \ No newline at end of file diff --git a/docs/usage/example.rst b/docs/usage/example.rst deleted file mode 100644 index 79be53ac..00000000 --- a/docs/usage/example.rst +++ /dev/null @@ -1,49 +0,0 @@ -.. _analysis-workflow-example: - -Analysis Workflow Example -========================== - -This is a quick guide for creating and running a simple pipeline to analysis an example dataset with 2 participants. - -#. Install RAPIDS. See :ref:`Installation Section `. - -#. Configure your database credentials (see the example below or step 1 of :ref:`Usage Section ` for more information). - - - Create an ``.env`` file at the root of RAPIDS folder - - Your MySQL user must have write permissions because we will restore our example database - - Name your credentials group ``MY_GROUP``. - - If you are trying to connect to a local MySQL server from our docker container set your host according to this link_. - - You can name your database any way you want, for example ``rapids_example`` - - .. code-block:: bash - - [MY_GROUP] - user=rapids - password=rapids - host=127.0.0.1 - port=3306 - database=rapids_example - -#. Make sure your conda environment is active (the environment is already active in our docker container). See step 6 of :ref:`install-page`. - -#. If you installed RAPIDS from GitHub (did not use docker) you need to download the `example db backup `_ and save it to ``data/external/rapids_example.sql``. - -#. Run the following command to restore database from ``rapids_example.sql`` file:: - - snakemake -j1 restore_sql_file - -#. Create example participants files with the following command:: - - snakemake -j1 create_example_participant_files - -#. Run the following command to analysis the example dataset. - - - Execute over a single core:: - - snakemake -j1 --profile example_profile - - - Execute over multiple cores (here, we use 8 cores):: - - snakemake -j8 --profile example_profile - -.. _link: https://stackoverflow.com/questions/24319662/from-inside-of-a-docker-container-how-do-i-connect-to-the-localhost-of-the-mach diff --git a/docs/usage/faq.rst b/docs/usage/faq.rst deleted file mode 100644 index b1d0f9c7..00000000 --- a/docs/usage/faq.rst +++ /dev/null @@ -1,182 +0,0 @@ -Frequently Asked Questions -============================ - -1. Cannot connect to the MySQL server -""""""""""""""""""""""""""""""""""""""" -**Error in .local(drv, ...) :** -**Failed to connect to database: Error: Can't initialize character set unknown (path: compiled_in)** -:: - - Calls: dbConnect -> dbConnect -> .local -> .Call - Execution halted - [Tue Mar 10 19:40:15 2020] - Error in rule download_dataset: - jobid: 531 - output: data/raw/p60/locations_raw.csv - - RuleException: - CalledProcessError in line 20 of /home/ubuntu/rapids/rules/preprocessing.snakefile: - Command 'set -euo pipefail; Rscript --vanilla /home/ubuntu/rapids/.snakemake/scripts/tmp_2jnvqs7.download_dataset.R' returned non-zero exit status 1. - File "/home/ubuntu/rapids/rules/preprocessing.snakefile", line 20, in __rule_download_dataset - File "/home/ubuntu/anaconda3/envs/moshi-env/lib/python3.7/concurrent/futures/thread.py", line 57, in run - Shutting down, this might take some time. - Exiting because a job execution failed. Look above for error message - -**Solution:** - -Please make sure the ``DATABASE_GROUP`` in ``config.yaml`` matches your DB credentials group in ``.env``. - - - -2. Cannot start mysql in linux via ``brew services start mysql`` -""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" -Use the following command instead: - -``mysql.server start`` - - -3. Every time I run ``snakemake -R download_dataset`` all rules are executed -"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" -This is expected behavior. The advantage of using ``snakemake`` under the hood is that every time a file containing data is modified every rule that depends on that file will be re-executed to update their results. In this case, since ``download_dataset`` updates all the raw data, and you are forcing the rule with the flag ``-R`` every single rule that depends on those raw files will be executed. - - -4. Got an error ``Table XXX doesn't exist`` while running the download_dataset rule. -""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" -:: - - Error in .local(conn, statement, ...) : - could not run statement: Table 'db_name.table_name' doesn't exist - Calls: colnames ... .local -> dbSendQuery -> dbSendQuery -> .local -> .Call - Execution halted - -**Solution:** -Please make sure the sensors listed in ``[PHONE_VALID_SENSED_BINS][TABLES]`` and each sensor section you activated in ``config.yaml`` match your database tables. - - - -5. How do I install on Ubuntu 16.04 -"""""""""""""""""""""""""""""""""""" - -#. Install dependencies (Homebrew - if not installed): - - - ``sudo apt-get install libmariadb-client-lgpl-dev libxml2-dev libssl-dev`` - - Install brew_ for linux and add the following line to ~/.bashrc: ``export PATH=$HOME/.linuxbrew/bin:$PATH`` - - ``source ~/.bashrc`` - -#. Install MySQL - - - ``brew install mysql`` - - ``brew services start mysql`` - -#. Install R, pandoc and rmarkdown: - - - ``brew install r`` - - ``brew install gcc@6`` (needed due to this bug_) - - ``HOMEBREW_CC=gcc-6 brew install pandoc`` - -#. Install miniconda using these instructions_ - -#. Clone our repo: - - - ``git clone https://github.com/carissalow/rapids`` - -#. Create a python virtual environment: - - - ``cd rapids`` - - ``conda env create -f environment.yml -n MY_ENV_NAME`` - - ``conda activate MY_ENV_NAME`` - -#. Install R packages and virtual environment: - - - ``snakemake renv_install`` - - ``snakemake renv_init`` - - ``snakemake renv_restore`` - - This step could take several minutes to complete. Please be patient and let it run until completion. - -#. See :ref:`Usage section `. - - - -6. Configuration failed for package ``RMySQL`` -"""""""""""""""""""""""""""""""""""""""""""""""" -:: - - --------------------------[ ERROR MESSAGE ]---------------------------- - :1:10: fatal error: mysql.h: No such file or directory - compilation terminated. - ----------------------------------------------------------------------- - ERROR: configuration failed for package 'RMySQL' - -Run ``sudo apt install libmariadbclient-dev`` - - - -7. No package ``libcurl`` found -""""""""""""""""""""""""""""""""" - -The ``libcurl`` needs to installed using the following command - -Run ``sudo apt install libcurl4-openssl-dev`` - - - -8. Configuration failed because ``openssl`` was not found. -""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" - -Install the ``openssl`` library using the following command - -Run ``sudo apt install libssl-dev`` - - -9. Configuration failed because ``libxml-2.0`` was not found -""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" - -Install the ``xml`` library using the following command - -Run ``sudo apt install libxml2-dev`` - -10. SSL connection error when running RAPIDS -"""""""""""""""""""""""""""""""""""""""""""""" - -You are getting the following error message when running RAPIDS: - -``Error: Failed to connect: SSL connection error: error:1425F102:SSL routines:ssl_choose_client_version:unsupported protocol``. - -This is a bug in Ubuntu 20.04 when trying to connect to an old MySQL server with MySQL client 8.0. You should get the same error message if you try to connect from the command line. There you can add the option ``--ssl-mode=DISABLED`` but we can't do this from the R connector. - -If you can't update your server, the quickest solution would be to import your database to another server or to a local environment. Alternatively, you could replace ``mysql-client`` and ``libmysqlclient-dev`` with ``mariadb-client`` and ``libmariadbclient-dev`` and reinstall renv. More info about this issue here https://bugs.launchpad.net/ubuntu/+source/mysql-8.0/+bug/1872541 - -11. ``DB_TABLES`` key not found -"""""""""""""""""""""""""""""""" - -If you get the following error ``KeyError in line 43 of preprocessing.smk: 'DB_TABLES'``, means that the indentation of the key ``DB_TABLES`` is not matching the other child elements of ``PHONE_VALID_SENSED_BINS`` and you need to add or remove any leading whitespaces as needed. - -:: - - PHONE_VALID_SENSED_BINS: - COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features - BIN_SIZE: &bin_size 5 # (in minutes) - # Add as many sensor tables as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS. - # If you are extracting screen or Barnett's location features, screen and locations tables are mandatory. - DB_TABLES: [] - -12. Error while updating your conda environment in Ubuntu -""""""""""""""""""""""""""""""""""""""""""""""""""""""""" - -If you get the following error try reinstalling conda. - -:: - - CondaMultiError: CondaVerificationError: The package for tk located at /home/ubuntu/miniconda2/pkgs/tk-8.6.9-hed695b0_1003 - appears to be corrupted. The path 'include/mysqlStubs.h' - specified in the package manifest cannot be found. - ClobberError: This transaction has incompatible packages due to a shared path. - packages: conda-forge/linux-64::llvm-openmp-10.0.0-hc9558a2_0, anaconda/linux-64::intel-openmp-2019.4-243 - path: 'lib/libiomp5.so' - - -.. ------------------------ Links --------------------------- .. - -.. _bug: https://github.com/Homebrew/linuxbrew-core/issues/17812 -.. _instructions: https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html -.. _brew: https://docs.brew.sh/Homebrew-on-Linux diff --git a/docs/usage/installation.rst b/docs/usage/installation.rst deleted file mode 100644 index ff51b3c4..00000000 --- a/docs/usage/installation.rst +++ /dev/null @@ -1,209 +0,0 @@ -.. _install-page: - -Installation -=============== - -These instructions have been tested on macOS (Catalina and Mojave) and Ubuntu 16.04. If you find a problem, please create a GitHub issue or contact us. If you want to test RAPIDS quickly try our docker image or follow the Linux instructions on a virtual machine. - -Docker (the fastest and easiest way) ------------------------------------- - -#. Install docker - -#. Pull RAPIDS' container - - ``docker pull agamk/rapids:latest`` - -#. Run RAPIDS' container (after this step is done you should see a prompt in the main RAPIDS folder with its python environment active) - - ``docker run -it agamk/rapids:latest`` - -#. Pull the latest version of RAPIDS - - ``git pull`` - -#. Optional. You can start editing files with vim but we recommend using Visual Studio Code and its Remote extension - - - Make sure RAPIDS container is running - - Install the Remote - Containers extension: https://marketplace.visualstudio.com/items?itemName=ms-vscode-remote.remote-containers - - Go to the ``Remote Explorer`` panel on the left hand sidebar - - On the top right dropdown menu choose ``Containers`` - - Double click on the ``agamk/rapids`` container in the ``CONTAINERS`` tree - - A new VS Code session should open on RAPIDS main folder inside the container. - -#. See Usage section below. - - -macOS (tested on Catalina 10.15) --------------------------------- - -#. Install dependencies (Homebrew if not installed): - - - Install brew_ for Mac: ``/usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"`` - -#. Install MySQL - - - ``brew install mysql`` - - ``brew services start mysql`` - -#. Install R 4.0, pandoc and rmarkdown. If you have other instances of R, we recommend uninstalling them. - - - ``brew install r`` - - ``brew install pandoc`` - - ``Rscript --vanilla -e 'install.packages("rmarkdown", repos="http://cran.us.r-project.org")'`` - -#. Install miniconda: - - - ``brew cask install miniconda`` - - ``conda init zsh`` or ``conda init bash`` - - Restart terminal if necessary - -#. Clone our repo: - - - ``git clone https://github.com/carissalow/rapids`` - -#. Create a python virtual environment: - - - ``cd rapids`` - - ``conda env create -f environment.yml -n rapids`` - - ``conda activate rapids`` - -#. Install R packages and virtual environment: - - - ``snakemake -j1 renv_install`` - - ``snakemake -j1 renv_restore`` - - - This step could take several minutes to complete, especially if you have less than 3Gb of RAM or packages need to be compiled from source. Please be patient and let it run until completion. - -#. See Usage section below. - - -Linux (tested on Ubuntu 18.04 & 20.04) ---------------------------------------- - -#. Install dependencies : - - - ``sudo apt install libcurl4-openssl-dev`` - - ``sudo apt install libssl-dev`` - - ``sudo apt install libxml2-dev`` - -#. Install MySQL - - - ``sudo apt install libmysqlclient-dev`` - - ``sudo apt install mysql-server`` - - -#. Install R 4.0 . If you have other instances of R, we recommend uninstalling them. - - - ``sudo apt-key adv --keyserver keyserver.ubuntu.com --recv-keys E298A3A825C0D65DFD57CBB651716619E084DAB9`` - - Add R's repository: - - - For 18.04 do: ``sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu bionic-cran40/'`` - - For 20.04 do: ``sudo add-apt-repository 'deb https://cloud.r-project.org/bin/linux/ubuntu focal-cran40/'`` - - ``sudo apt update`` - - ``sudo apt install r-base`` - -#. Install Pandoc and rmarkdown - - - ``sudo apt install pandoc`` - - ``Rscript --vanilla -e 'install.packages("rmarkdown", repos="http://cran.us.r-project.org")'`` - -#. Install GIT - - - ``sudo apt install git`` - -#. Install miniconda using these instructions_ - -#. Restart your current shell - -#. Clone our repo: - - - ``git clone https://github.com/carissalow/rapids`` - -#. Create a python virtual environment: - - - ``cd rapids`` - - ``conda env create -f environment.yml -n MY_ENV_NAME`` - - ``conda activate MY_ENV_NAME`` - -#. Install R packages and virtual environment: - - - ``snakemake -j1 renv_install`` - - ``snakemake -j1 renv_restore`` - - - This step could take several minutes to complete, especially if you have less than 3Gb of RAM or packages need to be compiled from source. Please be patient and let it run until completion. - -#. See Usage section below. - - -.. _usage-section: - -Usage -====== -Once RAPIDS is installed, follow these steps to start processing mobile data. - -.. _db-configuration: - -#. Configure the database connection: - - - Create an empty file called `.env` in the root directory (``rapids/``) - - Add the following lines and replace your database-specific credentials (user, password, host, and database): - - .. code-block:: bash - - [MY_GROUP] - user=MY_USER - password=MY_PASSWORD - host=MY_HOST - port=3306 - database=MY_DATABASE - - .. note:: - - ``MY_GROUP`` is a custom label for your credentials. It has to match ``DATABASE_GROUP`` in the ``config.yaml`` file_. It is not related to your database configuration. - -#. Setup the participants' devices whose data you want to analyze, for this you have two options: - - #. **Automatically**. You can automatically include all devices that are stored in the ``aware_device`` table. If you want to control what devices and dates are included, see the Manual configuration:: - - snakemake -j1 download_participants - - #. **Manually**. Create one file per participant in the ``rapids/data/external/`` directory. The file should NOT have an extension (i.e., no .txt). The name of the file will become the label for that participant in the pipeline. - - - The first line of the file should be the Aware ``device_id`` for that participant. If one participant has multiple device_ids (i.e. Aware had to be re-installed), add all device_ids separated by commas. - - The second line should list the device's operating system (``android`` or ``ios``). If a participant used more than one device (i.e., the participant changed phones and/or platforms mid-study) you can a) list each platform matching the order of the first line (``android,ios``), b) use ``android`` or ``ios`` if all phones belong to the same platform, or c) if you have an ``aware_device`` table in your database, set this line to ``multiple`` and RAPIDS will infer the multiple platforms automatically. - - The third line is an optional human-friendly label that will appear in any plots for that participant. - - The fourth line is optional and contains a start and end date separated by a comma ``YYYYMMDD,YYYYMMDD`` (e.g., ``20201301,20202505``). If these dates are specified, only data within this range will be processed, otherwise, all data from the device(s) will be used. - - For example, let's say participant `p01` had two AWARE device_ids and they were running Android between February 1st 2020 and March 3rd 2020. Their participant file would be named ``p01`` and contain: - - .. code-block:: bash - - 3a7b0d0a-a9ce-4059-ab98-93a7b189da8a,44f20139-50cc-4b13-bdde-0d5a3889e8f9 - android - Participant01 - 2020/02/01,2020/03/03 - -#. Choose what features to extract: - - - See :ref:`Minimal Working Example`. - -#. Execute RAPIDS - - - Standard execution over a single core:: - - snakemake -j1 - - - Standard execution over multiple cores:: - - snakemake -j8 - - - Force a rule (useful if you modify your code and want to update its results):: - - snakemake -j1 -R RULE_NAME - -.. _bug: https://github.com/Homebrew/linuxbrew-core/issues/17812 -.. _instructions: https://docs.conda.io/projects/conda/en/latest/user-guide/install/linux.html -.. _brew: https://docs.brew.sh/Homebrew-on-Linux -.. _AWARE: https://awareframework.com/what-is-aware/ -.. _file: https://github.com/carissalow/rapids/blob/master/config.yaml#L22 diff --git a/docs/usage/introduction.rst b/docs/usage/introduction.rst deleted file mode 100644 index b14d8743..00000000 --- a/docs/usage/introduction.rst +++ /dev/null @@ -1,44 +0,0 @@ -Quick Introduction -================== - -The goal of this pipeline is to standardize the data cleaning, feature extraction, analysis, and evaluation of mobile sensing projects. It leverages Conda_, Cookiecutter_, SciPy_, Snakemake_, Sphinx_, and R_ to create an end-to-end reproducible environment that can be published along with research papers. - -At the moment, mobile data can be collected using different sensing frameworks (AWARE_, Beiwe_) and hardware (Fitbit_). The pipeline is agnostic to these data sources and can unify their analysis. The current implementation only handles data collected with AWARE_ and Fitbit_. However, it can be easily extended to other providers. - -We recommend reading Snakemake_ docs, but the main idea behind the pipeline is that every link in the analysis chain is a rule with an input and an output. Input and output are files, which can be manipulated using any programming language (although Snakemake_ has wrappers for Julia_, Python_, and R_ that can make development slightly more comfortable). Snakemake_ also allows the pipeline rules to be executed in parallel on multiple cores without any code changes. This can drastically reduce the time needed to complete an analysis. - -Do you want to keep up to date with new functionality or have a question? Join the #rapids channel in AWARE Framework's slack_ - -Available features: - -- :ref:`accelerometer-sensor-doc` -- :ref:`applications-foreground-sensor-doc` -- :ref:`battery-sensor-doc` -- :ref:`bluetooth-sensor-doc` -- :ref:`wifi-sensor-doc` -- :ref:`call-sensor-doc` -- :ref:`activity-recognition-sensor-doc` -- :ref:`light-doc` -- :ref:`location-sensor-doc` -- :ref:`screen-sensor-doc` -- :ref:`messages-sensor-doc` -- :ref:`fitbit-sleep-sensor-doc` -- :ref:`fitbit-heart-rate-sensor-doc` -- :ref:`fitbit-steps-sensor-doc` - -We are updating these docs constantly, but if you think something needs clarification, feel free to reach out or submit a pull request on GitHub. - - -.. _Conda: https://docs.conda.io/en/latest/ -.. _Cookiecutter: http://drivendata.github.io/cookiecutter-data-science/ -.. _SciPy: https://www.scipy.org/index.html -.. _Snakemake: https://snakemake.readthedocs.io/en/stable/ -.. _Sphinx: https://www.sphinx-doc.org/en/master/ -.. _R: https://www.r-project.org/ - -.. _AWARE: https://awareframework.com/what-is-aware/ -.. _Beiwe: https://www.beiwe.org/ -.. _Fitbit: https://www.fitbit.com/us/home -.. _Python: https://www.python.org/ -.. _Julia: https://julialang.org/ -.. _slack: http://awareframework.com:3000/ diff --git a/docs/usage/quick_rule.rst b/docs/usage/quick_rule.rst deleted file mode 100644 index 6a2d800e..00000000 --- a/docs/usage/quick_rule.rst +++ /dev/null @@ -1,42 +0,0 @@ -.. _minimal-working-example: - -Minimal Working Example -======================== - -This is a quick guide for creating and running a simple pipeline to extract call features for daily and night epochs of one participant monitored on the US East coast. - -#. Make sure your database connection credentials in ``.env`` are correct. See step 1 of :ref:`Usage Section `. - -#. Create at least one participant file ``p01`` under ``data/external/``. See step 2 of :ref:`Usage Section `. - -#. Make sure your Conda (python) environment is active. See step 6 of :ref:`install-page`. - -#. Modify the following settings in the ``config.yaml`` file with the values shown below (leave all other settings as they are) - -:: - - PIDS: [p01] - - DAY_SEGMENTS: &day_segments - [daily, night] - - TIMEZONE: &timezone - America/New_York - - DATABASE_GROUP: &database_group - MY_GROUP (change this if you added your DB credentials to .env with a different label) - - CALLS: - COMPUTE: True - DB_TABLE: calls (only change DB_TABLE if your database calls table has a different name) - -For more information on the ``calls`` sensor see :ref:`call-sensor-doc` - -#. Run the following command to execute RAPIDS - - :: - - snakemake -j1 - -#. Daily and night call metrics will be found in files under the ``data/processed/p01/`` directory. - diff --git a/docs/usage/snakemake_docs.rst b/docs/usage/snakemake_docs.rst deleted file mode 100644 index c2fed4f1..00000000 --- a/docs/usage/snakemake_docs.rst +++ /dev/null @@ -1,238 +0,0 @@ -.. _rapids-structure: - -RAPIDS Structure -================= - -.. _the-config-file: - -The ``config.yaml`` File ------------------------- - -RAPIDS configuration settings are defined in ``config.yaml`` (See `config.yaml`_). This is the only file that you need to understand in order to compute the features that RAPIDS ships with. - -It has global settings like ``PIDS``, ``DAY_SEGMENTS``, among others (see :ref:`global-sensor-doc` for more information). As well as per sensor settings, for example, for the :ref:`messages-sensor-doc`:: - - | ``MESSAGES:`` - | ``COMPUTE: True`` - | ``DB_TABLE: messages`` - | ``...`` - -.. _the-snakefile-file: - -The ``Snakefile`` File ----------------------- -The ``Snakefile`` file (see the actual `Snakefile`_) pulls the entire system together. The first line in this file identifies the configuration file. Next are a list of included directives that import the rules used to pull, clean, process, analyze and report data. It compiles the list of ``files_to_compute`` by scaning the config file looking for the sensors with a ``COMPUTE`` flag equal to ``True``. Then, the ``all`` rule is called with this list which prompts Snakemake to exectue the pipeline (raw files, intermediate files, feature files, reports, etc). - -.. _includes-section: - -Includes -""""""""" -There are 5 included files in the ``Snakefile`` file. - - - ``renv.smk`` - Rules to create, backup and restore the R renv virtual environment for RAPIDS. (See `renv`_) - - ``preprocessing.smk`` - Rules that are used to pre-preprocess the data such as downloading, cleaning and formatting. (See `preprocessing`_) - - ``features.smk`` - Rules that used for behavioral feature extraction. (See `features`_) - - ``models.smk`` - Rules that are used to build models from features that have been extreacted from the sensor data. (See `models`_) - - ``reports.smk`` - Rules that are used to produce reports and visualizations. (See `reports`_) - -Includes are relative to the root directory. - -.. _rule-all-section: - -``Rule all:`` -""""""""""""" -In RAPIDS the ``all`` rule lists the output files we expect the pipeline to compute. Before the ``all`` rule is called snakemake checks the ``config.yaml`` and adds all the rules for which the sensors ``COMPUTE`` parameter is ``True``. The ``expand`` function allows us to generate a list of file paths that have a common structure except for PIDS or other parameters. Consider the following:: - - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - -If ``pids = ['p01','p02']`` and ``config["MESSAGES"]["DB_TABLE"] = messages`` then the above directive would produce:: - - ["data/raw/p01/messages_raw.csv", "data/raw/p02/messages_raw.csv"] - -Thus, this allows us to define all the desired output files without having to manually list each path for every participant and every sensor. The way Snakemake works is that it looks for the rule that produces the desired output files and then executes that rule. For more information on ``expand`` see `The Expand Function`_ - - -.. _the-env-file: - -The ``.env`` File -------------------- -Your database credentials are stored in the ``.env`` file (See :ref:`install-page`):: - - [MY_GROUP_NAME] - user=MyUSER - password=MyPassword - host=MyIP/DOMAIN - port=3306 - -.. _rules-syntax: - -The ``Rules`` Directory ------------------------- - -The ``rules`` directory contains the ``snakefiles`` that were included in the main ``Snakefile`` file. A short description of these files are given in the :ref:`includes-section` section. - - -Rules -"""""" - -A Snakemake workflow is defined by rules (See the features_ snakefile as an actual example). Rules decompose the workflow into small steps by specifying what output files should be created by running a script on a set of input files. Snakemake automatically determines the dependencies between the rules by matching file names. Thus, a rule can consist of a name, input files, output files, and a command to generate the output from the input. The following is the basic structure of a Snakemake rule:: - - rule NAME: - input: "path/to/inputfile", "path/to/other/inputfile" - output: "path/to/outputfile", "path/to/another/outputfile" - script: "path/to/somescript.R" - - -A sample rule from the RAPIDS source code is shown below:: - - rule messages_features: - input: - expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"]) - params: - messages_type = "{messages_type}", - day_segment = "{day_segment}", - features = lambda wildcards: config["MESSAGES"]["FEATURES"][wildcards.messages_type] - output: - "data/processed/{pid}/messages_{messages_type}_{day_segment}.csv" - script: - "../src/features/messages_features.R" - - -The ``rule`` directive specifies the name of the rule that is being defined. ``params`` defines additional parameters for the rule's script. In the example above, the parameters are passed to the ``messages_features.R`` script as an dictionary. Instead of ``script`` a ``shell`` command call can also be called by replacing the ``script`` directive of the rule and replacing it with:: - - shell: "somecommand {input} {output}" - -It should be noted that rules can be defined without input and output as seen in the ``renv.snakemake``. For more information see `Rules documentation`_ and for an actual example see the `renv`_ snakefile. - -.. _wildcards: - -Wildcards -"""""""""" -There are times when the same rule should be applied to different participants and day segments. For this we use wildcards ``{my_wildcard}``. All wildcards are inferred from the files listed in the ``all` rule of the ``Snakefile`` file and therefore from the output of any rule:: - - rule messages_features: - input: - expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"]) - params: - messages_type = "{messages_type}", - day_segment = "{day_segment}", - features = lambda wildcards: config["MESSAGES"]["FEATURES"][wildcards.messages_type] - output: - "data/processed/{pid}/messages_{messages_type}_{day_segment}.csv" - script: - "../src/features/messages_features.R" - -If the rule’s output matches a requested file, the substrings matched by the wildcards are propagated to the input and params directives. For example, if another rule in the workflow requires the file ``data/processed/p01/messages_sent_daily.csv``, Snakemake recognizes that the above rule is able to produce it by setting ``pid=p01``, ``messages_type=sent`` and ``day_segment=daily``. Thus, it requests the input file ``data/raw/p01/messages_with_datetime.csv`` as input, sets ``messages_type=sent``, ``day_segment=daily`` in the ``params`` directive and executes the script. ``../src/features/messages_features.R``. See the preprocessing_ snakefile for an actual example. - - -.. _the-data-directory: - -The ``data`` Directory ------------------------ - -This directory contains the data files for the project. These directories are as follows: - - - ``external`` - This directory stores the participant `pxxx` files as well as data from third party sources (see :ref:`install-page` page). - - ``raw`` - This directory contains the original, immutable data dump from your database. - - ``interim`` - This directory contains intermediate data that has been transformed but do not represent features. - - ``processed`` - This directory contains all behavioral features. - - -.. _the-src-directory: - -The ``src`` Directory ----------------------- - -The ``src`` directory holds all the scripts used by the pipeline for data manipulation. These scripts can be in any programming language including but not limited to Python_, R_ and Julia_. This directory is organized into the following directories: - - - ``data`` - This directory contains scripts that are used to download and preprocess raw data that will be used in analysis. See `data directory`_ - - ``features`` - This directory contains scripts to extract behavioral features. See `features directory`_ - - ``models`` - This directory contains the scripts for building and training models. See `models directory`_ - - ``visualization`` - This directory contains the scripts to create plots and reports. See `visualization directory`_ - - -.. _RAPIDS_directory_structure: - -:: - - ├── LICENSE - ├── Makefile <- Makefile with commands like `make data` or `make train` - ├── README.md <- The top-level README for developers using this project. - ├── config.yaml <- The configuration settings for the pipeline. - ├── environment.yml <- Environmental settings - channels and dependences that are installed in the env) - ├── data - │ ├── external <- Data from third party sources. - │ ├── interim <- Intermediate data that has been transformed. - │ ├── processed <- The final, canonical data sets for modeling. - │ └── raw <- The original, immutable data dump. - │ - ├── docs <- A default Sphinx project; see sphinx-doc.org for details - │ - ├── models <- Trained and serialized models, model predictions, or model summaries - │ - ├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering), - │ the creator's initials, and a short `-` delimited description, e.g. - │ `1.0-jqp-initial-data-exploration`. - │ - ├── packrat <- Installed R dependences. (Packrat is a dependency management system for R) - │ (Depreciated - replaced by renv) - ├── references <- Data dictionaries, manuals, and all other explanatory materials. - │ - ├── renv.lock <- List of R packages and dependences for that are installed for the pipeline. - │ - ├── reports <- Generated analysis as HTML, PDF, LaTeX, etc. - │ └── figures <- Generated graphics and figures to be used in reporting. - │ - ├── rules - │ ├── features <- Rules to process the feature data pulled in to pipeline. - │ ├── models <- Rules for building models. - │ ├── mystudy <- Rules added by you that are specifically tailored to your project/study. - │ ├── packrat <- Rules for setting up packrat. (Depreciated replaced by renv) - │ ├── preprocessing <- Preprocessing rules to clean data before processing. - │ ├── renv <- Rules for setting up renv and R packages. - │ └── reports <- Snakefile used to produce reports. - │ - ├── setup.py <- makes project pip installable (pip install -e .) so src can be imported - ├── Snakemake <- The root snakemake file (the equivalent of a Makefile) - ├── src <- Source code for use in this project. Can be in any language e.g. Python, - │ │ R, Julia, etc. - │ │ - │ ├── data <- Scripts to download or generate data. Can be in any language e.g. Python, - │ │ R, Julia, etc. - │ │ - │ ├── features <- Scripts to turn raw data into features for modeling. Can be in any language - │ │ e.g. Python, R, Julia, etc. - │ │ - │ ├── models <- Scripts to train models and then use trained models to make prediction. Can - │ │ be in any language e.g. Python, R, Julia, etc. - │ │ - │ └── visualization <- Scripts to create exploratory and results oriented visualizations. Can be - │ in any language e.g. Python, R, Julia, etc. - ├── tests - │ ├── data <- Replication of the project root data directory for testing. - │ ├── scripts <- Scripts for testing. - │ ├── settings <- The config and settings files for running tests. - │ └── Snakefile <- The Snakefile for testing only. - │ - └── tox.ini <- tox file with settings for running tox; see tox.testrun.org - - -.. _Python: https://www.python.org/ -.. _Julia: https://julialang.org/ -.. _R: https://www.r-project.org/ -.. _`List of Timezone`: https://en.wikipedia.org/wiki/List_of_tz_database_time_zones -.. _`The Expand Function`: https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#the-expand-function -.. _`example snakefile`: https://github.com/carissalow/rapids/blob/master/rules/features.snakefile -.. _renv: https://github.com/carissalow/rapids/blob/master/rules/renv.snakefile -.. _preprocessing: https://github.com/carissalow/rapids/blob/master/rules/preprocessing.snakefile -.. _features: https://github.com/carissalow/rapids/blob/master/rules/features.snakefile -.. _models: https://github.com/carissalow/rapids/blob/master/rules/models.snakefile -.. _reports: https://github.com/carissalow/rapids/blob/master/rules/reports.snakefile -.. _mystudy: https://github.com/carissalow/rapids/blob/master/rules/mystudy.snakefile -.. _`Rules documentation`: https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#rules -.. _`data directory`: https://github.com/carissalow/rapids/tree/master/src/data -.. _`features directory`: https://github.com/carissalow/rapids/tree/master/src/features -.. _`models directory`: https://github.com/carissalow/rapids/tree/master/src/models -.. _`visualization directory`: https://github.com/carissalow/rapids/tree/master/src/visualization -.. _`config.yaml`: https://github.com/carissalow/rapids/blob/master/config.yaml -.. _`Snakefile`: https://github.com/carissalow/rapids/blob/master/Snakefile diff --git a/docs/visualization/data_exploration.rst b/docs/visualization/data_exploration.rst deleted file mode 100644 index 89cbbd4b..00000000 --- a/docs/visualization/data_exploration.rst +++ /dev/null @@ -1,216 +0,0 @@ -.. _data_exploration: - -Data Exploration -================ - -These plots are in beta, if you get an error while computing them please let us know. - -.. _histogram-of-valid-sensed-hours: - -Histogram of valid sensed hours -""""""""""""""""""""""""""""""" - -See `Histogram of Valid Sensed Hours Config Code`_ - -**Rule Chain:** - -- Rule: ``rules/preprocessing.smk/download_dataset`` -- Rule: ``rules/preprocessing.smk/readable_datetime`` -- Rule: ``rules/preprocessing.smk/phone_sensed_bins`` -- Rule: ``rules/preprocessing.smk/phone_valid_sensed_days`` -- Rule: ``rules/reports.smk/histogram_valid_sensed_hours`` - -.. _figure1-parameters: - -**Parameters of histogram_valid_sensed_hours Rule:** - -======================= ======================= -Name Description -======================= ======================= -plot Whether the rule is executed or not. The available options are ``True`` and ``False``. -min_valid_bins_per_hour The minimum valid bins an hour should have to be considered valid. A valid bin has at least 1 row of data. It modifies the way we compute phone valid days. Read :ref:`PHONE_VALID_SENSED_BINS` for more information. -min_valid_hours_per_day The minimum valid hours a day should have to be considered valid. It modifies the way we compute phone valid days. Read :ref:`PHONE_VALID_SENSED_DAYS` for more information. -======================= ======================= - -**Observations:** - -This histogram shows the valid sensed hours of all participants processed in RAPIDS (See valid sensed :ref:`bins` and :ref:`days` sections). It can be used as a rough indication of the AWARE client monitoring coverage during a study for all participants. See Figure 1. - -.. figure:: figures/Figure1.png - :scale: 90 % - :align: center - - Figure 1 Histogram of valid sensed hours for all participants - - -.. _heatmap-of-phone-sensed-bins: - -Heatmap of phone sensed bins -"""""""""""""""""""""""""""" - -See `Heatmap of Phone Sensed Bins Config Code`_ - -**Rule Chain:** - -- Rule: ``rules/preprocessing.smk/download_dataset`` -- Rule: ``rules/preprocessing.smk/readable_datetime`` -- Rule: ``rules/preprocessing.smk/phone_sensed_bins`` -- Rule: ``rules/reports.smk/heatmap_sensed_bins`` - -.. _figure2-parameters: - -**Parameters of heatmap_sensed_bins Rule:** - -======================= ======================= -Name Description -======================= ======================= -plot Whether the rule is executed or not. The available options are ``True`` and ``False``. -bin_size Every hour is divided into N bins of size ``BIN_SIZE`` (in minutes). It modifies the way we compute ``data/interim/pXX/phone_sensed_bins.csv`` file. -======================= ======================= - -**Observations:** - -In this heatmap rows are dates, columns are sensed bins for a participant, and cells’ color shows the number of mobile sensors that logged at least one row of data during that bin. This plot shows the periods of time without data for a participant and can be used as a rough indication of whether time-based sensors were following their sensing schedule (e.g. if location was being sensed every 2 minutes). See Figure 2. - -.. figure:: figures/Figure2.png - :scale: 90 % - :align: center - - Figure 2 Heatmap of phone sensed bins for a single participant - - -.. _heatmap-of-days-by-sensors - -Heatmap of days by sensors -"""""""""""""""""""""""""" - -See `Heatmap of Days by Sensors Config Code`_ - -**Rule Chain:** - -- Rule: ``rules/preprocessing.smk/download_dataset`` -- Rule: ``rules/preprocessing.smk/readable_datetime`` -- Rule: ``rules/preprocessing.smk/phone_sensed_bins`` -- Rule: ``rules/preprocessing.smk/phone_valid_sensed_days`` -- Rule: ``rules/reports.smk/heatmap_days_by_sensors`` - -.. _figure3-parameters: - -**Parameters of heatmap_days_by_sensors Rule:** - -======================= ======================= -Name Description -======================= ======================= -plot Whether the rule is executed or not. The available options are ``True`` and ``False``. -min_valid_bins_per_hour The minimum valid bins an hour should have to be considered valid. A valid bin has at least 1 row of data. It modifies the way we compute phone valid days. Read :ref:`PHONE_VALID_SENSED_BINS` for more information. -min_valid_hours_per_day The minimum valid hours a day should have to be considered valid. It modifies the way we compute phone valid days. Read :ref:`PHONE_VALID_SENSED_DAYS` for more information. -expected_num_of_days The number of days of data to show starting from the first day of each participant. -db_tables List of sensor tables to compute valid bins & hours. -======================= ======================= - -**Observations:** - -In this heatmap rows are sensors, columns are days and cells’ color shows the normalized (0 to 1) number of valid sensed hours (See valid sensed :ref:`bins` and :ref:`days` sections) collected by a sensor during a day for a participant. The user can decide how many days of data to show starting from the first day of each participant. This plot can used to judge missing data on a per participant, per sensor basis as well as the number of valid sensed hours (usable data) for each day. See Figure 3. - -.. figure:: figures/Figure3.png - :scale: 90 % - :align: center - - Figure 3 Heatmap of days by sensors for a participant - - -.. _overall-compliance-heatmap - -Overall compliance heatmap -"""""""""""""""""""""""""" - -See `Overall Compliance Heatmap Config Code`_ - -**Rule Chain:** - -- Rule: ``rules/preprocessing.smk/download_dataset`` -- Rule: ``rules/preprocessing.smk/readable_datetime`` -- Rule: ``rules/preprocessing.smk/phone_sensed_bins`` -- Rule: ``rules/preprocessing.smk/phone_valid_sensed_days`` -- Rule: ``rules/reports.smk/overall_compliance_heatmap`` - -.. _figure4-parameters: - -**Parameters of overall_compliance_heatmap Rule:** - -======================= ======================= -Name Description -======================= ======================= -plot Whether the rule is executed or not. The available options are ``True`` and ``False``. -only_show_valid_days Whether the plot only shows valid days or not. The available options are ``True`` and ``False``. -expected_num_of_days The number of days to show before today. -bin_size Every hour is divided into N bins of size ``BIN_SIZE`` (in minutes). It modifies the way we compute ``data/interim/pXX/phone_sensed_bins.csv`` file. -min_valid_bins_per_hour The minimum valid bins an hour should have to be considered valid. A valid bin has at least 1 row of data. It modifies the way we compute phone valid days. Read :ref:`PHONE_VALID_SENSED_BINS` for more information. -min_valid_hours_per_day The minimum valid hours a day should have to be considered valid. It modifies the way we compute phone valid days. Read :ref:`PHONE_VALID_SENSED_DAYS` for more information. -======================= ======================= - -**Observations:** - -In this heatmap rows are participants, columns are days and cells’ color shows the valid sensed hours for a participant during a day (See valid sensed :ref:`bins` and :ref:`days` sections). This plot can be configured to show a certain number of days before today using the ``EXPECTED_NUM_OF_DAYS`` parameter (by default -1 showing all days for every participant). As different participants might join the study on different dates, the x-axis has a day index instead of a date. This plot gives the user a quick overview of the amount of data collected per person and is complementary to the histogram of valid sensed hours as it is broken down per participant and per day. See Figure 4. - -.. figure:: figures/Figure4.png - :scale: 90 % - :align: center - - Figure 4 Overall compliance heatmap for all participants - - -.. _heatmap-of-correlation-matrix-between-features - -Heatmap of correlation matrix between features -"""""""""""""""""""""""""""""""""""""""""""""" - -See `Heatmap of Correlation Matrix Config Code`_ - -**Rule Chain:** - -- Rules to extract features -- Rule: ``rules/preprocessing.smk/download_dataset`` -- Rule: ``rules/preprocessing.smk/readable_datetime`` -- Rule: ``rules/preprocessing.smk/phone_sensed_bins`` -- Rule: ``rules/preprocessing.smk/phone_valid_sensed_days`` -- Rule: ``rules/reports.smk/heatmap_features_correlations`` - -.. _figure5-parameters: - -**Parameters of heatmap_features_correlations Rule:** - -======================= ============== -Name Description -======================= ============== -plot Whether the rule is executed or not. The available options are ``True`` and ``False``. -min_valid_bins_per_hour The minimum valid bins an hour should have to be considered valid. A valid bin has at least 1 row of data. It modifies the way we compute phone valid days. Read :ref:`PHONE_VALID_SENSED_BINS` for more information. -min_valid_hours_per_day The minimum valid hours a day should have to be considered valid. It modifies the way we compute phone valid days. Read :ref:`PHONE_VALID_SENSED_DAYS` for more information. -corr_method Method of correlation. The available options are ``pearson``, ``kendall`` and ``spearman``. -min_rows_ratio Minimum number of observations required per pair of columns to have a valid correlation coefient. Currently, only available for ``pearson`` and ``spearman`` correlation. -phone_features The list of phone features. -fitbit_features The list of Fitbit features. -corr_threshold Only correlation coefficients larger than ``corr_threshold`` can be shown in the heatmap. -======================= ============== - -**Observations:** - -Columns and rows are features computed in RAPIDS, cells’ color represents the correlation coefficient between all days of data for every pair of feature of all participants. The user can specify a minimum number of observations required to compute the correlation between two features using the ``MIN_ROWS_RATIO`` parameter (0.5 by default). In addition, this plot can be configured to only display correlation coefficients above a threshold using the ``CORR_THRESHOLD`` parameter (0.1 by default). See Figure 5. - -.. figure:: figures/Figure5.png - :scale: 90 % - :align: center - - Figure 5 Correlation matrix heatmap for all the data of all participants - - - - - - - -.. _`Histogram of Valid Sensed Hours Config Code`: https://github.com/carissalow/rapids/blob/master/config.yaml#L221 -.. _`Heatmap of Phone Sensed Bins Config Code`: https://github.com/carissalow/rapids/blob/master/config.yaml#L233 -.. _`Heatmap of Days by Sensors Config Code`: https://github.com/carissalow/rapids/blob/master/config.yaml#L226 -.. _`Overall Compliance Heatmap Config Code`: https://github.com/carissalow/rapids/blob/master/config.yaml#L237 -.. _`Heatmap of Correlation Matrix Config Code`: https://github.com/carissalow/rapids/blob/master/config.yaml#L211 diff --git a/docs/visualization/figures/Figure1.png b/docs/visualization/figures/Figure1.png deleted file mode 100644 index c4d47637..00000000 Binary files a/docs/visualization/figures/Figure1.png and /dev/null differ diff --git a/docs/visualization/figures/Figure2.png b/docs/visualization/figures/Figure2.png deleted file mode 100644 index af22ccde..00000000 Binary files a/docs/visualization/figures/Figure2.png and /dev/null differ diff --git a/docs/visualization/figures/Figure3.png b/docs/visualization/figures/Figure3.png deleted file mode 100644 index 09fd60b3..00000000 Binary files a/docs/visualization/figures/Figure3.png and /dev/null differ diff --git a/docs/visualization/figures/Figure4.png b/docs/visualization/figures/Figure4.png deleted file mode 100644 index 6bfeb752..00000000 Binary files a/docs/visualization/figures/Figure4.png and /dev/null differ diff --git a/docs/visualization/figures/Figure5.png b/docs/visualization/figures/Figure5.png deleted file mode 100644 index 93eeeee4..00000000 Binary files a/docs/visualization/figures/Figure5.png and /dev/null differ diff --git a/docs/visualizations/data-quality-visualizations.md b/docs/visualizations/data-quality-visualizations.md new file mode 100644 index 00000000..0871684d --- /dev/null +++ b/docs/visualizations/data-quality-visualizations.md @@ -0,0 +1,64 @@ +# Data Quality Visualizations +We showcase these visualizations with a test study that collected 14 days of smartphone and Fitbit data from two participants (t01 and t02) and extracted behavioral features within five time segments (daily, morning, afternoon, evening, and night). + +!!! note + [Time segments](../../setup/configuration#time-segments) (e.g. `daily`, `morning`, etc.) can have multiple instances (day 1, day 2, or morning 1, morning 2, etc.) + +## 1. Histograms of phone data yield +RAPIDS provides two histograms that show the number of time segment instances that had a certain ratio of valid [yielded minutes and hours](../../features/phone-data-yield/#rapids-provider), respectively. A valid yielded minute has at least 1 row of data from any smartphone sensor and a valid yielded hour contains at least M valid minutes. + +These plots can be used as a rough indication of the smartphone monitoring coverage during a study aggregated across all participants. For example, the figure below shows a valid yielded minutes histogram for daily segments and we can infer that the monitoring coverage was very good since almost all segments contain at least 90 to 100% of the expected sensed minutes. + +!!! example + Click [here](../../img/h-data-yield.html) to see an example of these interactive visualizations in HTML format + +
+ +
Histogram of the data yielded minute ratio for a single participant during five time segments (daily, afternoon, evening, and night)
+
+ +## 2. Heatmaps of overall data yield +These heatmaps are a break down per time segment and per participant of [Visualization 1](#1-histograms-of-phone-data-yield). Heatmap's rows represent participants, columns represent time segment instances and the cells’ color represent the valid yielded minute or hour ratio for a participant during a time segment instance. + +As different participants might join a study on different dates and time segments can be of any length and start on any day, the x-axis is labelled with the time delta between the start of each time segment instance minus the start of the first instance. These plots provide a quick study overview of the monitoring coverage per person and per time segment. + +The figure below shows the heatmap of the valid yielded minute ratio for participants t01 and t02 on daily segments and, as we inferred from the previous histogram, the lighter (yellow) color on most time segment instances (cells) indicate both phones sensed data without interruptions for most days (except for the first and last ones). + +!!! example + Click [here](../../img/hm-data-yield-participants.html) to see an example of these interactive visualizations in HTML format + +
+ +
Overall compliance heatmap for all participants
+
+ +## 3. Heatmap of recorded phone sensors + +In these heatmaps rows represent time segment instances, columns represent minutes since the start of a time segment instance, and cells’ color shows the number of phone sensors that logged at least one row of data during those 1-minute windows. + +RAPIDS creates a plot per participant and per time segment and can be used as a rough indication of whether time-based sensors were following their sensing schedule (e.g. if location was being sensed every 2 minutes). + +The figure below shows this heatmap for phone sensors collected by participant t01 in daily time segments from Apr 23rd 2020 to May 4th 2020. We can infer that for most of the monitoring time, the participant’s phone logged data from at least 8 sensors each minute. + +!!! example + Click [here](../../img/hm-phone-sensors.html) to see an example of these interactive visualizations in HTML format + +
+ +
Heatmap of the recorded phone sensors per minute and per time segment of a single participant
+
+ +## 4. Heatmap of sensor row count +These heatmaps are a per-sensor breakdown of [Visualization 1](#1-histograms-of-phone-data-yield) and [Visualization 2](#2-heatmaps-of-overall-data-yield). Note that the second row (ratio of valid yielded minutes) of this heatmap matches the respective participant (bottom) row the screenshot in Visualization 2. + +In these heatmaps rows represent phone or Fitbit sensors, columns represent time segment instances and cell’s color shows the normalized (0 to 1) row count of each sensor within a time segment instance. RAPIDS creates one heatmap per participant and they can be used to judge missing data on a per participant and per sensor basis. + +The figure below shows data for 16 phone sensors (including data yield) of t01’s daily segments (only half of the sensor names and dates are visible in the screenshot but all can be accessed in the interactive plot). From the top two rows, we can see that the phone was sensing data for most of the monitoring period (as suggested by Figure 3 and Figure 4). We can also infer how phone usage influenced the different sensor streams; there are peaks of screen events during the first day (Apr 23rd), peaks of location coordinates on Apr 26th and Apr 30th, and no sent or received SMS except for Apr 23rd, Apr 29th and Apr 30th (unlabeled row between screen and locations). + +!!! example + Click [here](../../img/hm-sensor_rows.html) to see an example of these interactive visualizations in HTML format + +
+ +
Heatmap of the sensor row count per time segment of a single participant
+
diff --git a/docs/visualizations/feature-visualizations.md b/docs/visualizations/feature-visualizations.md new file mode 100644 index 00000000..db2ce962 --- /dev/null +++ b/docs/visualizations/feature-visualizations.md @@ -0,0 +1,14 @@ +# Feature Visualizations + +## 1. Heatmap Correlation Matrix +Columns and rows are the behavioral features computed in RAPIDS, cells’ color represents the correlation coefficient between all days of data for every pair of features of all participants. + +The user can specify a minimum number of observations ([time segment](../../setup/configuration#time-segments) instances) required to compute the correlation between two features using the `MIN_ROWS_RATIO` parameter (0.5 by default) and the correlation method (Pearson, Spearman or Kendall) with the `CORR_METHOD` parameter. In addition, this plot can be configured to only display correlation coefficients above a threshold using the `CORR_THRESHOLD` parameter (0.1 by default). + +!!! example + Click [here](../../img/hm-feature-correlations.html) to see an example of these interactive visualizations in HTML format + +
+ +
Correlation matrix heatmap for all the features of all participants
+
\ No newline at end of file diff --git a/docs/workflow-examples/analysis.md b/docs/workflow-examples/analysis.md new file mode 100644 index 00000000..fe226f1a --- /dev/null +++ b/docs/workflow-examples/analysis.md @@ -0,0 +1,97 @@ +# Analysis Workflow Example + +!!! info "TL;DR" + - In addition to using RAPIDS to extract behavioral features and create plots, you can structure your data analysis within RAPIDS (i.e. cleaning your features and creating ML/statistical models) + - We include an analysis example in RAPIDS that covers raw data processing, cleaning, feature extraction, machine learning modeling, and evaluation + - Use this example as a guide to structure your own analysis within RAPIDS + - RAPIDS analysis workflows are compatible with your favorite data science tools and libraries + - RAPIDS analysis workflows are reproducible and we encourage you to publish them along with your research papers + +## Why should I integrate my analysis in RAPIDS? +Even though the bulk of RAPIDS current functionality is related to the computation of behavioral features, we recommend RAPIDS as a complementary tool to create a mobile data analysis workflow. This is because the cookiecutter data science file organization guidelines, the use of Snakemake, the provided behavioral features, and the reproducible R and Python development environments allow researchers to divide an analysis workflow into small parts that can be audited, shared in an online repository, reproduced in other computers, and understood by other people as they follow a familiar and consistent structure. We believe these advantages outweigh the time needed to learn how to create these workflows in RAPIDS. + +We clarify that to create analysis workflows in RAPIDS, researchers can still use any data manipulation tools, editors, libraries or languages they are already familiar with. RAPIDS is meant to be the final destination of analysis code that was developed in interactive notebooks or stand-alone scripts. For example, a user can compute call and location features using RAPIDS, then, they can use Jupyter notebooks to explore feature cleaning approaches and once the cleaning code is final, it can be moved to RAPIDS as a new step in the pipeline. In turn, the output of this cleaning step can be used to explore machine learning models and once a model is finished, it can also be transferred to RAPIDS as a step of its own. The idea is that when it is time to publish a piece of research, a RAPIDS workflow can be shared in a public repository as is. + +In the following sections we share an example of how we structured an analysis workflow in RAPIDS. + +## Analysis workflow structure +To accurately reflect the complexity of a real-world modeling scenario, we decided not to oversimplify this example. Importantly, every step in this example follows a basic structure: an input file and parameters are manipulated by an R or Python script that saves the results to an output file. Input files, parameters, output files and scripts are grouped into Snakemake rules that are described on `smk` files in the rules folder (we point the reader to the relevant rule(s) of each step). + +Researchers can use these rules and scripts as a guide to create their own as it is expected every modeling project will have different requirements, data and goals but ultimately most follow a similar chainned pattern. + +!!! hint + The example's config file is `example_profile/example_config.yaml` and its Snakefile is in `example_profile/Snakefile`. The config file is already configured to process the sensor data as explained in [Analysis workflow modules](#analysis-workflow-modules). + +## Description of the study modeled in our analysis workflow example +Our example is based on a hypothetical study that recruited 2 participants that underwent surgery and collected mobile data for at least one week before and one week after the procedure. Participants wore a Fitbit device and installed the AWARE client in their personal Android and iOS smartphones to collect mobile data 24/7. In addition, participants completed daily severity ratings of 12 common symptoms on a scale from 0 to 10 that we summed up into a daily symptom burden score. + +The goal of this workflow is to find out if we can predict the daily symptom burden score of a participant. Thus, we framed this question as a binary classification problem with two classes, high and low symptom burden based on the scores above and below average of each participant. We also want to compare the performance of individual (personalized) models vs a population model. + +In total, our example workflow has nine steps that are in charge of sensor data preprocessing, feature extraction, feature cleaning, machine learning model training and model evaluation (see figure below). We ship this workflow with RAPIDS and share a database with [test data](https://osf.io/skqfv/files/) in an Open Science Framework repository. + +
+ +
Modules of RAPIDS example workflow, from raw data to model evaluation
+
+ + +## Configure and run the analysis workflow example +1. [Install](../../setup/installation) RAPIDS +2. Configure the [user credentials](../../setup/configuration/#database-credentials) of a local or remote MySQL server with writing permissions in your `.env` file. The example config file is at `example_profile/example_config.yaml`. +3. Unzip the [test database](https://osf.io/skqfv/files/) to `data/external/rapids_example.sql` and run: + ```bash + ./rapids -j1 restore_sql_file --profile example_profile + ``` +4. Create the participant files for this example by running: + ```bash + ./rapids -j1 create_example_participant_files + ``` +5. Run the example pipeline with: + ```bash + ./rapids -j1 --profile example_profile + ``` + +## Modules of our analysis workflow example + +??? info "1. Feature extraction" + We extract daily behavioral features for data yield, received and sent messages, missed, incoming and outgoing calls, resample fused location data using Doryab provider, activity recognition, battery, Bluetooth, screen, light, applications foreground, conversations, Wi-Fi connected, Wi-Fi visible, Fitbit heart rate summary and intraday data, Fitbit sleep summary data, and Fitbit step summary and intraday data without excluding sleep periods with an active bout threshold of 10 steps. In total, we obtained 237 daily sensor features over 12 days per participant. + +??? info "2. Extract demographic data." + It is common to have demographic data in addition to mobile and target (ground truth) data. In this example we include participants’ age, gender and the number of days they spent in hospital after their surgery as features in our model. We extract these three columns from the participant_info table of our test database . As these three features remain the same within participants, they are used only on the population model. Refer to the `demographic_features` rule in `rules/models.smk`. + +??? info "3. Create target labels." + The two classes for our machine learning binary classification problem are high and low symptom burden. Target values are already stored in the `participant_target` table of our test database and transferred to a CSV file. A new rule/script can be created if further manipulation is necessary. Refer to the `parse_targets` rule in `rules/models.smk`. + +??? info "4. Feature merging." + These daily features are stored on a CSV file per sensor, a CSV file per participant, and a CSV file including all features from all participants (in every case each column represents a feature and each row represents a day). Refer to the `merge_sensor_features_for_individual_participants` and `merge_features_for_population_model` rules in `rules/features.smk`. + +??? info "5. Data visualization." + At this point the user can use the five plots RAPIDS provides (or implement new ones) to explore and understand the quality of the raw data and extracted features and decide what sensors, days, or participants to include and exclude. Refer to `rules/reports.smk` to find the rules that generate these plots. + +??? info "6. Feature cleaning." + In this stage we perform four steps to clean our sensor feature file. First, we discard days with a data yield hour ratio less than or equal to 0.75, i.e. we include days with at least 18 hours of data. Second, we drop columns (features) with more than 30% of missing rows. Third, we drop columns with zero variance. Fourth, we drop rows (days) with more than 30% of missing columns (features). In this cleaning stage several parameters are created and exposed in `example_profile/example_config.yaml`. + + After this step, we kept 162 features over 11 days for the individual model of p01, 107 features over 12 days for the individual model of p02 and 101 features over 20 days for the population model. Note that the difference in the number of features between p01 and p02 is mostly due to iOS restrictions that stops researchers from collecting the same number of sensors than in Android phones. + + Feature cleaning for the individual models is done in the `clean_sensor_features_for_individual_participants` rule and for the population model in the `clean_sensor_features_for_all_participants` rule in `rules/models.smk`. + +??? info "7. Merge features and targets." + In this step we merge the cleaned features and target labels for our individual models in the `merge_features_and_targets_for_individual_model` rule in `rules/models.smk`. Additionally, we merge the cleaned features, target labels, and demographic features of our two participants for the population model in the `merge_features_and_targets_for_population_model` rule in `rules/models.smk`. These two merged files are the input for our individual and population models. + +??? info "8. Modelling." + This stage has three phases: model building, training and evaluation. + + In the building phase we impute, normalize and oversample our dataset. Missing numeric values in each column are imputed with their mean and we impute missing categorical values with their mode. We normalize each numeric column with one of three strategies (min-max, z-score, and scikit-learn package’s robust scaler) and we one-hot encode each categorial feature as a numerical array. We oversample our imbalanced dataset using SMOTE (Synthetic Minority Over-sampling Technique) or a Random Over sampler from scikit-learn. All these parameters are exposed in `example_profile/example_config.yaml`. + + In the training phase, we create eight models: logistic regression, k-nearest neighbors, support vector machine, decision tree, random forest, gradient boosting classifier, extreme gradient boosting classifier and a light gradient boosting machine. We cross-validate each model with an inner cycle to tune hyper-parameters based on the Macro F1 score and an outer cycle to predict the test set on a model with the best hyper-parameters. Both cross-validation cycles use a leave-one-out strategy. Parameters for each model like weights and learning rates are exposed in `example_profile/example_config.yaml`. + + Finally, in the evaluation phase we compute the accuracy, Macro F1, kappa, area under the curve and per class precision, recall and F1 score of all folds of the outer cross-validation cycle. + + Refer to the `modelling_for_individual_participants` rule for the individual modeling and to the `modelling_for_all_participants` rule for the population modeling, both in `rules/models.smk`. + +??? info "9. Compute model baselines." + We create three baselines to evaluate our classification models. + + First, a majority classifier that labels each test sample with the majority class of our training data. Second, a random weighted classifier that predicts each test observation sampling at random from a binomial distribution based on the ratio of our target labels. Third, a decision tree classifier based solely on the demographic features of each participant. As we do not have demographic features for individual model, this baseline is only available for population model. + + Our baseline metrics (e.g. accuracy, precision, etc.) are saved into a CSV file, ready to be compared to our modeling results. Refer to the `baselines_for_individual_model` rule for the individual model baselines and to the `baselines_for_population_model` rule for population model baselines, both in `rules/models.smk`. diff --git a/docs/workflow-examples/minimal.md b/docs/workflow-examples/minimal.md new file mode 100644 index 00000000..ca9f430e --- /dev/null +++ b/docs/workflow-examples/minimal.md @@ -0,0 +1,88 @@ +Minimal Working Example +======================= + +This is a quick guide for creating and running a simple pipeline to extract missing, outgoing, and incoming call features for `daily` and `night` epochs of one participant monitored on the US East coast. + +1. Install RAPIDS and make sure your `conda` environment is active (see [Installation](../../setup/installation)) +2. Make the changes listed below for the corresponding [Configuration](../../setup/configuration) step (we provide an example of what the relevant sections in your `config.yml` will look like after you are done) + + ??? info "Things to change on each configuration step" + 1\. Setup your database connection credentials in `.env`. We assume your credentials group is called `MY_GROUP`. + + 2\. `America/New_York` should be the default timezone + + 3\. Create a participant file `p01.yaml` based on one of your participants and add `p01` to `[PIDS]` in `config.yaml`. The following would be the content of your `p01.yaml` participant file: + ```yaml + PHONE: + DEVICE_IDS: [aaaaaaaa-1111-bbbb-2222-cccccccccccc] # your participant's AWARE device id + PLATFORMS: [android] # or ios + LABEL: MyTestP01 # any string + START_DATE: 2020-01-01 # this can also be empty + END_DATE: 2021-01-01 # this can also be empty + ``` + + 4\. `[TIME_SEGMENTS][TYPE]` should be the default `PERIODIC`. Change `[TIME_SEGMENTS][FILE]` with the path of a file containing the following lines: + ```csv + label,start_time,length,repeats_on,repeats_value + daily,00:00:00,23H 59M 59S,every_day,0 + night,00:00:00,5H 59M 59S,every_day,0 + ``` + + 5\. If you collected data with AWARE you won't need to modify the attributes of `[DEVICE_DATA][PHONE]` + + 6\. Set `[PHONE_CALLS][PROVIDERS][RAPIDS][COMPUTE]` to `True` + + + ??? example "Example of the `config.yaml` sections after the changes outlined above" + ``` + PIDS: [p01] + + TIMEZONE: &timezone + America/New_York + + DATABASE_GROUP: &database_group + MY_GROUP + + # ... other irrelevant sections + + TIME_SEGMENTS: &time_segments + TYPE: PERIODIC + FILE: "data/external/timesegments_periodic.csv" # make sure the three lines specified above are in the file + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE + + # No need to change this if you collected AWARE data on a database and your credentials are grouped under `MY_GROUP` in `.env` + DEVICE_DATA: + PHONE: + SOURCE: + TYPE: DATABASE + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # SINGLE or MULTIPLE + VALUE: *timezone + + + ############## PHONE ########################################################### + ################################################################################ + + # ... other irrelevant sections + + # Communication call features config, TYPES and FEATURES keys need to match + PHONE_CALLS: + TABLE: calls # change if your calls table has a different name + PROVIDERS: + RAPIDS: + COMPUTE: True # set this to True! + CALL_TYPES: ... + ``` + +3. Run RAPIDS + ```bash + ./rapids -j1 + ``` +4. The call features for daily and morning time segments will be in + ``` + /data/processed/features/p01/phone_calls.csv + ``` + + diff --git a/example_profile/Snakefile b/example_profile/Snakefile index d455f9ff..acb3b1e1 100644 --- a/example_profile/Snakefile +++ b/example_profile/Snakefile @@ -13,272 +13,262 @@ files_to_compute = [] if len(config["PIDS"]) == 0: raise ValueError("Add participants IDs to PIDS in config.yaml. Remember to create their participant files in data/external") -if config["PHONE_VALID_SENSED_BINS"]["COMPUTE"] or config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: # valid sensed bins is necessary for sensed days, so we add these files anyways if sensed days are requested - if len(config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]) == 0: - raise ValueError("If you want to compute PHONE_VALID_SENSED_BINS or PHONE_VALID_SENSED_DAYS, you need to add at least one table to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml") +for provider in config["PHONE_DATA_YIELD"]["PROVIDERS"].keys(): + if config["PHONE_DATA_YIELD"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=map(str.lower, config["PHONE_DATA_YIELD"]["SENSORS"]))) + files_to_compute.extend(expand("data/interim/{pid}/phone_yielded_timestamps.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_data_yield_features/phone_data_yield_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_DATA_YIELD"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_data_yield.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - tables_android = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]] # for android, discard any ios tables that may exist - tables_ios = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist +for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys(): + if config["PHONE_MESSAGES"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_messages_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_messages_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_messages_features/phone_messages_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_MESSAGES"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_messages.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") - for pids,table in zip([pids_android, pids_ios], [tables_android, tables_ios]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) +for provider in config["PHONE_CALLS"]["PROVIDERS"].keys(): + if config["PHONE_CALLS"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_calls_features/phone_calls_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_CALLS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_calls.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") -if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_valid_sensed_days_{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins.csv", +for provider in config["PHONE_BLUETOOTH"]["PROVIDERS"].keys(): + if config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_bluetooth_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_bluetooth_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_bluetooth_features/phone_bluetooth_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_bluetooth.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"].keys(): + if config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_features/phone_activity_recognition_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_activity_recognition.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_BATTERY"]["PROVIDERS"].keys(): + if config["PHONE_BATTERY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_battery_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_features/phone_battery_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_BATTERY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_battery.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_SCREEN"]["PROVIDERS"].keys(): + if config["PHONE_SCREEN"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_screen_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_screen_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_screen_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_features/phone_screen_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_SCREEN"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_screen.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_LIGHT"]["PROVIDERS"].keys(): + if config["PHONE_LIGHT"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_light_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_light_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_light_features/phone_light_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LIGHT"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_light.csv", pid=config["PIDS"],)) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_ACCELEROMETER"]["PROVIDERS"].keys(): + if config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_accelerometer_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_accelerometer_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_accelerometer.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys(): + if config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_applications_foreground_features/phone_applications_foreground_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_applications_foreground.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_WIFI_VISIBLE"]["PROVIDERS"].keys(): + if config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_visible_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_visible_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_wifi_visible_features/phone_wifi_visible_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_visible.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_WIFI_CONNECTED"]["PROVIDERS"].keys(): + if config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_connected_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_connected_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_wifi_connected_features/phone_wifi_connected_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_connected.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_CONVERSATION"]["PROVIDERS"].keys(): + if config["PHONE_CONVERSATION"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_conversation_features/phone_conversation_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_CONVERSATION"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_conversation.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): + if config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["COMPUTE"]: + if config["PHONE_LOCATIONS"]["LOCATIONS_TO_USE"] == "FUSED_RESAMPLED": + if "PHONE_LOCATIONS" in config["PHONE_DATA_YIELD"]["SENSORS"]: + files_to_compute.extend(expand("data/interim/{pid}/phone_yielded_timestamps.csv", pid=config["PIDS"])) + else: + raise ValueError("Error: Add PHONE_LOCATIONS (and as many PHONE_SENSORS as you have) to [PHONE_DATA_YIELD][SENSORS] in config.yaml. This is necessary to compute phone_yielded_timestamps (time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") + + files_to_compute.extend(expand("data/raw/{pid}/phone_locations_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"].keys(): + if config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_summary_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_summary_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_summary_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_heartrate_summary_features/fitbit_heartrate_summary_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_heartrate_summary.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"].keys(): + if config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_intraday_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_intraday_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_intraday_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_heartrate_intraday_features/fitbit_heartrate_intraday_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_heartrate_intraday.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"].keys(): + if config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_summary_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_summary_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_summary_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_sleep_summary_features/fitbit_sleep_summary_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_sleep_summary.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"].keys(): + if config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_summary_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_summary_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_summary_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_steps_summary_features/fitbit_steps_summary_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_steps_summary.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +for provider in config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"].keys(): + if config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_intraday_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_intraday_parsed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_intraday_parsed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/fitbit_steps_intraday_features/fitbit_steps_intraday_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_steps_intraday.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])) + files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv") + +# Visualization for Data Exploration +if config["HISTOGRAM_PHONE_DATA_YIELD"]["PLOT"]: + files_to_compute.append("reports/data_exploration/histogram_phone_data_yield.html") + +if config["HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT"]["PLOT"]: + files_to_compute.extend(expand("reports/interim/{pid}/heatmap_sensors_per_minute_per_time_segment.html", pid=config["PIDS"])) + files_to_compute.append("reports/data_exploration/heatmap_sensors_per_minute_per_time_segment.html") + +if config["HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT"]["PLOT"]: + files_to_compute.extend(expand("reports/interim/{pid}/heatmap_sensor_row_count_per_time_segment.html", pid=config["PIDS"])) + files_to_compute.append("reports/data_exploration/heatmap_sensor_row_count_per_time_segment.html") + +if config["HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT"]["PLOT"]: + files_to_compute.append("reports/data_exploration/heatmap_phone_data_yield_per_participant_per_time_segment.html") + +if config["HEATMAP_FEATURE_CORRELATION_MATRIX"]["PLOT"]: + files_to_compute.append("reports/data_exploration/heatmap_feature_correlation_matrix.html") + +# Analysis Workflow Example +models, scalers = [], [] +for model_name in config["PARAMS_FOR_ANALYSIS"]["MODEL_NAMES"]: + models = models + [model_name] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) + scalers = scalers + config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name] +results = config["PARAMS_FOR_ANALYSIS"]["RESULT_COMPONENTS"] + +# Demographic features +files_to_compute.extend(expand("data/raw/{pid}/participant_info_raw.csv", pid=config["PIDS"])) +files_to_compute.extend(expand("data/processed/features/{pid}/demographic_features.csv", pid=config["PIDS"])) + +# Targets +files_to_compute.extend(expand("data/raw/{pid}/participant_target_raw.csv", pid=config["PIDS"])) +files_to_compute.extend(expand("data/raw/{pid}/participant_target_with_datetime.csv", pid=config["PIDS"])) +files_to_compute.extend(expand("data/processed/targets/{pid}/parsed_targets.csv", pid=config["PIDS"])) + +# Individual model +files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features_cleaned.csv", pid=config["PIDS"])) +files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/input.csv", pid=config["PIDS"])) +files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/output_{cv_method}/baselines.csv", pid=config["PIDS"], cv_method=config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"])) +files_to_compute.extend(expand( + expand("data/processed/models/individual_model/{pid}/output_{cv_method}/{{model}}/{{scaler}}/{result}.csv", pid=config["PIDS"], - min_valid_hours_per_day=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) + cv_method=config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"], + result = results), + zip, + model=models, + scaler=scalers)) -if config["MESSAGES"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/messages_{messages_type}_{day_segment}.csv", pid=config["PIDS"], messages_type = config["MESSAGES"]["TYPES"], day_segment = config["MESSAGES"]["DAY_SEGMENTS"])) +# Population model +files_to_compute.append("data/processed/features/all_participants/all_sensor_features_cleaned.csv") +files_to_compute.append("data/processed/models/population_model/input.csv") +files_to_compute.extend(expand("data/processed/models/population_model/output_{cv_method}/baselines.csv", cv_method=config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"])) +files_to_compute.extend(expand( + expand("data/processed/models/population_model/output_{cv_method}/{{model}}/{{scaler}}/{result}.csv", + cv_method=config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"], + result = results), + zip, + model=models, + scaler=scalers)) -if config["CALLS"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/calls_{call_type}_{day_segment}.csv", pid=config["PIDS"], call_type=config["CALLS"]["TYPES"], day_segment = config["CALLS"]["DAY_SEGMENTS"])) - -if config["BARNETT_LOCATION"]["COMPUTE"]: - if config["BARNETT_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": - if config["BARNETT_LOCATION"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_resampled.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"])) - else: - raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/location_barnett_{day_segment}.csv", pid=config["PIDS"], day_segment = config["BARNETT_LOCATION"]["DAY_SEGMENTS"])) - -if config["BLUETOOTH"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/bluetooth_{day_segment}.csv", pid=config["PIDS"], day_segment = config["BLUETOOTH"]["DAY_SEGMENTS"])) - -if config["ACTIVITY_RECOGNITION"]["COMPUTE"]: - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - - for pids,table in zip([pids_android, pids_ios], [config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/processed/{pid}/{sensor}_deltas.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/processed/{pid}/activity_recognition_{day_segment}.csv",pid=config["PIDS"], day_segment = config["ACTIVITY_RECOGNITION"]["DAY_SEGMENTS"])) - -if config["BATTERY"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/battery_deltas.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/processed/{pid}/battery_{day_segment}.csv", pid = config["PIDS"], day_segment = config["BATTERY"]["DAY_SEGMENTS"])) - -if config["SCREEN"]["COMPUTE"]: - if config["SCREEN"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) - else: - raise ValueError("Error: Add your screen table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)") - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/screen_deltas.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/processed/{pid}/screen_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SCREEN"]["DAY_SEGMENTS"])) - -if config["LIGHT"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/light_{day_segment}.csv", pid = config["PIDS"], day_segment = config["LIGHT"]["DAY_SEGMENTS"])) - -if config["ACCELEROMETER"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/accelerometer_{day_segment}.csv", pid = config["PIDS"], day_segment = config["ACCELEROMETER"]["DAY_SEGMENTS"])) - -if config["APPLICATIONS_FOREGROUND"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor}_with_datetime_with_genre.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/applications_foreground_{day_segment}.csv", pid = config["PIDS"], day_segment = config["APPLICATIONS_FOREGROUND"]["DAY_SEGMENTS"])) - -if config["WIFI"]["COMPUTE"]: - if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/processed/{pid}/wifi_{day_segment}.csv", pid = config["PIDS"], day_segment = config["WIFI"]["DAY_SEGMENTS"])) - - if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/processed/{pid}/wifi_{day_segment}.csv", pid = config["PIDS"], day_segment = config["WIFI"]["DAY_SEGMENTS"])) - -if config["HEARTRATE"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["HEARTRATE"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) - files_to_compute.extend(expand("data/processed/{pid}/fitbit_heartrate_{day_segment}.csv", pid = config["PIDS"], day_segment = config["HEARTRATE"]["DAY_SEGMENTS"])) - -if config["STEP"]["COMPUTE"]: - if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED": - files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["STEP"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_step_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) - files_to_compute.extend(expand("data/processed/{pid}/fitbit_step_{day_segment}.csv", pid = config["PIDS"], day_segment = config["STEP"]["DAY_SEGMENTS"])) - -if config["SLEEP"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SLEEP"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"])) - files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SLEEP"]["DAY_SEGMENTS"])) - -if config["CONVERSATION"]["COMPUTE"]: - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - - for pids,table in zip([pids_android, pids_ios], [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"]]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/processed/{pid}/conversation_{day_segment}.csv",pid=config["PIDS"], day_segment = config["CONVERSATION"]["DAY_SEGMENTS"])) - -if config["DORYAB_LOCATION"]["COMPUTE"]: - if config["DORYAB_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": - if config["DORYAB_LOCATION"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_resampled.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"])) - else: - raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/location_doryab_{segment}.csv", pid=config["PIDS"], segment = config["DORYAB_LOCATION"]["DAY_SEGMENTS"])) - -# visualization for data exploration -if config["HEATMAP_FEATURES_CORRELATIONS"]["PLOT"]: - files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_features_correlations.html", min_valid_hours_per_day=config["HEATMAP_FEATURES_CORRELATIONS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) - -if config["HISTOGRAM_VALID_SENSED_HOURS"]["PLOT"]: - files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/histogram_valid_sensed_hours.html", min_valid_hours_per_day=config["HISTOGRAM_VALID_SENSED_HOURS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) - -if config["HEATMAP_DAYS_BY_SENSORS"]["PLOT"]: - files_to_compute.extend(expand("reports/interim/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{pid}/heatmap_days_by_sensors.html", pid=config["PIDS"], min_valid_hours_per_day=config["HEATMAP_DAYS_BY_SENSORS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) - files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_days_by_sensors_all_participants.html", min_valid_hours_per_day=config["HEATMAP_DAYS_BY_SENSORS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) - -if config["HEATMAP_SENSED_BINS"]["PLOT"]: - files_to_compute.extend(expand("reports/interim/heatmap_sensed_bins/{pid}/heatmap_sensed_bins.html", pid=config["PIDS"])) - files_to_compute.extend(["reports/data_exploration/heatmap_sensed_bins_all_participants.html"]) - -if config["OVERALL_COMPLIANCE_HEATMAP"]["PLOT"]: - files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/overall_compliance_heatmap.html", min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) - -# analysis example -if config["PARAMS_FOR_ANALYSIS"]["COMPUTE"]: - rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"] - cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"] - models, scalers, rows_nan_thresholds, cols_nan_thresholds = [], [], [], [] - for model_name in config["PARAMS_FOR_ANALYSIS"]["MODEL_NAMES"]: - models = models + [model_name] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) * len(rows_nan_threshold) - scalers = scalers + config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name] * len(rows_nan_threshold) - rows_nan_thresholds = rows_nan_thresholds + list(itertools.chain.from_iterable([threshold] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) for threshold in rows_nan_threshold)) - cols_nan_thresholds = cols_nan_thresholds + list(itertools.chain.from_iterable([threshold] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) for threshold in cols_nan_threshold)) - results = config["PARAMS_FOR_ANALYSIS"]["RESULT_COMPONENTS"] + ["merged_population_model_results"] - - files_to_compute.extend(expand("data/processed/{pid}/data_for_individual_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{source}_{day_segment}_original.csv", - pid = config["PIDS"], - min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"], - source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"])) - files_to_compute.extend(expand("data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{source}_{day_segment}_original.csv", - min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"], - source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"])) - files_to_compute.extend(expand( - expand("data/processed/{pid}/data_for_individual_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv", - pid = config["PIDS"], - min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"], - days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], - days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], - cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], - source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]), - zip, - rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], - cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) - files_to_compute.extend(expand( - expand("data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv", - min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"], - days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], - days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], - cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], - source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]), - zip, - rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], - cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) - files_to_compute.extend(expand("data/processed/data_for_population_model/demographic_features.csv")) - files_to_compute.extend(expand("data/processed/data_for_population_model/targets_{summarised}.csv", - summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"])) - files_to_compute.extend(expand( - expand("data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_nancellsratio.csv", - min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"], - days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], - days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], - cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], - source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"]), - zip, - rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], - cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) - files_to_compute.extend(expand( - expand("data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}.csv", - min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"], - days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], - days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], - cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], - source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"], - summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]), - zip, - rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], - cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) - files_to_compute.extend(expand( - expand("data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/baseline/{cv_method}/{source}_{day_segment}_{summarised}.csv", - min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"], - days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], - days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], - cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], - cv_method = config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"], - source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"], - summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]), - zip, - rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], - cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) - files_to_compute.extend(expand( - expand("data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{{model}}/{cv_method}/{source}_{day_segment}_{summarised}_{{scaler}}/{result}.csv", - min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], - min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"], - days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], - days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], - cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], - cv_method = config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"], - source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], - day_segment = config["PARAMS_FOR_ANALYSIS"]["DAY_SEGMENTS"], - summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"], - result = results), - zip, - rows_nan_threshold = rows_nan_thresholds, - cols_nan_threshold = cols_nan_thresholds, - model = models, - scaler = scalers)) rule all: input: diff --git a/example_profile/example_config.yaml b/example_profile/example_config.yaml index ff59e3cb..2ace4951 100644 --- a/example_profile/example_config.yaml +++ b/example_profile/example_config.yaml @@ -1,313 +1,401 @@ -# Participants to include in the analysis -# You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically -PIDS: [example01, example02] - -# Global var with common day segments -DAY_SEGMENTS: &day_segments - [daily] - -# Global timezone -# Use codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones -# Double check your code, for example EST is not US Eastern Time. -TIMEZONE: &timezone - America/New_York - +# See https://www.rapids.science/setup/configuration/#database-credentials DATABASE_GROUP: &database_group MY_GROUP -DOWNLOAD_PARTICIPANTS: - IGNORED_DEVICE_IDS: [] # for example "5a1dd68c-6cd1-48fe-ae1e-14344ac5215f" - GROUP: *database_group +# See https://www.rapids.science/setup/configuration/#timezone-of-your-study +TIMEZONE: &timezone + America/New_York -# Download data config -DOWNLOAD_DATASET: - GROUP: *database_group +# See https://www.rapids.science/setup/configuration/#participant-files +PIDS: [example01, example02] -# Readable datetime config -READABLE_DATETIME: - FIXED_TIMEZONE: *timezone +# See https://www.rapids.science/setup/configuration/#automatic-creation-of-participant-files +CREATE_PARTICIPANT_FILES: + SOURCE: + TYPE: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE + DATABASE_GROUP: *database_group + CSV_FILE_PATH: "data/external/example_participants.csv" # see docs for required format + TIMEZONE: *timezone + PHONE_SECTION: + ADD: TRUE + DEVICE_ID_COLUMN: device_id # column name + IGNORED_DEVICE_IDS: [] + FITBIT_SECTION: + ADD: TRUE + DEVICE_ID_COLUMN: device_id # column name + IGNORED_DEVICE_IDS: [] -PHONE_VALID_SENSED_BINS: - COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features - BIN_SIZE: &bin_size 5 # (in minutes) - # Add as many sensor tables as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS. - # If you are extracting screen or Barnett's location features, screen and locations tables are mandatory. - DB_TABLES: [messages, calls, locations, plugin_google_activity_recognition, plugin_ios_activity_recognition, battery, screen, light, applications_foreground, plugin_studentlife_audio_android, plugin_studentlife_audio, wifi, sensor_wifi, bluetooth, applications_notifications, aware_log, ios_status_monitor, push_notification, significant, timezone, touch, keyboard] +# See https://www.rapids.science/setup/configuration/#time-segments +TIME_SEGMENTS: &time_segments + TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT + FILE: "example_profile/exampleworkflow_timesegments.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, see docs -PHONE_VALID_SENSED_DAYS: - COMPUTE: False - MIN_VALID_HOURS_PER_DAY: &min_valid_hours_per_day [16, 20] # (out of 24) MIN_HOURS_PER_DAY - MIN_VALID_BINS_PER_HOUR: &min_valid_bins_per_hour [12] # (out of 60min/BIN_SIZE bins) -# Communication SMS features config, TYPES and FEATURES keys need to match -MESSAGES: - COMPUTE: True - DB_TABLE: messages - TYPES : [received, sent] - FEATURES: - received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] - sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] - DAY_SEGMENTS: *day_segments -# Communication call features config, TYPES and FEATURES keys need to match -CALLS: - COMPUTE: True - DB_TABLE: calls - TYPES: [missed, incoming, outgoing] - FEATURES: - missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] - incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] - outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] - DAY_SEGMENTS: *day_segments +######################################################################################################################## +# PHONE # +######################################################################################################################## -APPLICATION_GENRES: - CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) - CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" - UPDATE_CATALOGUE_FILE: false # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE - SCRAPE_MISSING_GENRES: false # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway +# See https://www.rapids.science/setup/configuration/#device-data-source-configuration +PHONE_DATA_CONFIGURATION: + SOURCE: + TYPE: DATABASE + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # SINGLE or MULTIPLE + VALUE: *timezone # IF TYPE=SINGLE, see docs -RESAMPLE_FUSED_LOCATION: - CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold - TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row - TIMEZONE: *timezone +# Sensors ------ -BARNETT_LOCATION: - COMPUTE: False - DB_TABLE: locations - DAY_SEGMENTS: [daily] # These features are only available on a daily basis - FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"] - LOCATIONS_TO_USE: ALL # ALL, ALL_EXCEPT_FUSED OR RESAMPLE_FUSED - ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius - TIMEZONE: *timezone - MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features +PHONE_ACCELEROMETER: + TABLE: accelerometer + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] + SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" + + PANDA: + COMPUTE: False + VALID_SENSED_MINUTES: False + FEATURES: + exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + SRC_FOLDER: "panda" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" -DORYAB_LOCATION: - COMPUTE: True - DB_TABLE: locations - DAY_SEGMENTS: *day_segments - FEATURES: ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy"] - LOCATIONS_TO_USE: RESAMPLE_FUSED # ALL, ALL_EXCEPT_FUSED OR RESAMPLE_FUSED - DBSCAN_EPS: 10 # meters - DBSCAN_MINSAMPLES: 5 - THRESHOLD_STATIC : 1 # km/h - MAXIMUM_GAP_ALLOWED: 300 - MINUTES_DATA_USED: False - -BLUETOOTH: - COMPUTE: True - DB_TABLE: bluetooth - DAY_SEGMENTS: *day_segments - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - -ACTIVITY_RECOGNITION: - COMPUTE: True - DB_TABLE: +PHONE_ACTIVITY_RECOGNITION: + TABLE: ANDROID: plugin_google_activity_recognition IOS: plugin_ios_activity_recognition - DAY_SEGMENTS: *day_segments - FEATURES: ["count","mostcommonactivity","countuniqueactivities","activitychangecount","sumstationary","summobile","sumvehicle"] + EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"] + ACTIVITY_CLASSES: + STATIONARY: ["still", "tilting"] + MOBILE: ["on_foot", "walking", "running", "on_bicycle"] + VEHICLE: ["in_vehicle"] + SRC_FOLDER: "rapids" # inside src/features/phone_activity_recognition + SRC_LANGUAGE: "python" -BATTERY: - COMPUTE: True - DB_TABLE: battery - DAY_SEGMENTS: *day_segments - FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] +PHONE_APPLICATIONS_FOREGROUND: + TABLE: applications_foreground + APPLICATION_CATEGORIES: + CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) + CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" + UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE + SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway + PROVIDERS: + RAPIDS: + COMPUTE: True + SINGLE_CATEGORIES: ["all", "email"] + MULTIPLE_CATEGORIES: + social: ["socialnetworks", "socialmediatools"] + entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"] + SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps + EXCLUDED_CATEGORIES: ["system_apps"] + EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] + FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] + SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground + SRC_LANGUAGE: "python" -SCREEN: - COMPUTE: True - DB_TABLE: screen - DAY_SEGMENTS: *day_segments - REFERENCE_HOUR_FIRST_USE: 0 - IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable - IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable - FEATURES_DELTAS: ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] - EPISODE_TYPES: ["unlock"] +PHONE_BATTERY: + TABLE: battery + EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] + SRC_FOLDER: "rapids" # inside src/features/phone_battery + SRC_LANGUAGE: "python" -LIGHT: - COMPUTE: True - DB_TABLE: light - DAY_SEGMENTS: *day_segments - FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] +PHONE_BLUETOOTH: + TABLE: bluetooth + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth + SRC_LANGUAGE: "r" -ACCELEROMETER: - COMPUTE: False - DB_TABLE: accelerometer - DAY_SEGMENTS: *day_segments - FEATURES: - MAGNITUDE: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] - EXERTIONAL_ACTIVITY_EPISODE: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] - NONEXERTIONAL_ACTIVITY_EPISODE: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] - VALID_SENSED_MINUTES: True +PHONE_CALLS: + TABLE: calls + PROVIDERS: + RAPIDS: + COMPUTE: True + CALL_TYPES: [missed, incoming, outgoing] + FEATURES: + missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] + incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] + outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/phone_calls -APPLICATIONS_FOREGROUND: - COMPUTE: True - DB_TABLE: applications_foreground - DAY_SEGMENTS: *day_segments - SINGLE_CATEGORIES: ["all", "email"] - MULTIPLE_CATEGORIES: - social: ["socialnetworks", "socialmediatools"] - entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"] - SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps - EXCLUDED_CATEGORIES: ["system_apps"] - EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] - FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] - -HEARTRATE: - COMPUTE: True - DB_TABLE: fitbit_data - DAY_SEGMENTS: *day_segments - SUMMARY_FEATURES: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. heigh, weight) use with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] - INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] - -STEP: - COMPUTE: True - DB_TABLE: fitbit_data - DAY_SEGMENTS: *day_segments - EXCLUDE_SLEEP: - EXCLUDE: False - TYPE: FIXED # FIXED OR FITBIT_BASED (CONFIGURE FITBIT's SLEEP DB_TABLE) - FIXED: - START: "23:00" - END: "07:00" - FEATURES: - ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] - SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] - ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] - THRESHOLD_ACTIVE_BOUT: 10 # steps - INCLUDE_ZERO_STEP_ROWS: False - -SLEEP: - COMPUTE: True - DB_TABLE: fitbit_data - DAY_SEGMENTS: *day_segments - SLEEP_TYPES: ["main", "nap", "all"] - SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] - -WIFI: - COMPUTE: True - DB_TABLE: - VISIBLE_ACCESS_POINTS: "wifi" # if you only have a CONNECTED_ACCESS_POINTS table, set this value to "" - CONNECTED_ACCESS_POINTS: "sensor_wifi" # if you only have a VISIBLE_ACCESS_POINTS table, set this value to "" - DAY_SEGMENTS: *day_segments - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - -CONVERSATION: - COMPUTE: True - DB_TABLE: +PHONE_CONVERSATION: + TABLE: ANDROID: plugin_studentlife_audio_android IOS: plugin_studentlife_audio - DAY_SEGMENTS: *day_segments - FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", - "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","sumenergy", - "avgenergy","sdenergy","minenergy","maxenergy","silencesensedfraction","noisesensedfraction", + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", + "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", + "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", + "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", "unknownexpectedfraction","countconversation"] - RECORDINGMINUTES: 1 - PAUSEDMINUTES : 3 + RECORDING_MINUTES: 1 + PAUSED_MINUTES : 3 + SRC_FOLDER: "rapids" # inside src/features/phone_conversation + SRC_LANGUAGE: "python" -### Visualizations ################################################################ -HEATMAP_FEATURES_CORRELATIONS: +PHONE_DATA_YIELD: + SENSORS: [PHONE_ACCELEROMETER, PHONE_ACTIVITY_RECOGNITION, PHONE_APPLICATIONS_FOREGROUND, PHONE_BATTERY, PHONE_BLUETOOTH, PHONE_CALLS, PHONE_CONVERSATION, PHONE_LIGHT, PHONE_LOCATIONS, PHONE_MESSAGES, PHONE_SCREEN, PHONE_WIFI_CONNECTED, PHONE_WIFI_VISIBLE] + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours] + MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1 representing the number of minutes with at least + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/phone_data_yield + +PHONE_LIGHT: + TABLE: light + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] + SRC_FOLDER: "rapids" # inside src/features/phone_light + SRC_LANGUAGE: "python" + +PHONE_LOCATIONS: + TABLE: locations + LOCATIONS_TO_USE: FUSED_RESAMPLED # ALL, GPS OR FUSED_RESAMPLED + FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold + FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row + PROVIDERS: + DORYAB: + COMPUTE: True + FEATURES: ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy"] + DBSCAN_EPS: 10 # meters + DBSCAN_MINSAMPLES: 5 + THRESHOLD_STATIC : 1 # km/h + MAXIMUM_GAP_ALLOWED: 300 + MINUTES_DATA_USED: False + SAMPLING_FREQUENCY: 0 + SRC_FOLDER: "doryab" # inside src/features/phone_locations + SRC_LANGUAGE: "python" + + BARNETT: + COMPUTE: False + FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"] + ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius + TIMEZONE: *timezone + MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features + SRC_FOLDER: "barnett" # inside src/features/phone_locations + SRC_LANGUAGE: "r" + +PHONE_MESSAGES: + TABLE: messages + PROVIDERS: + RAPIDS: + COMPUTE: True + MESSAGES_TYPES : [received, sent] + FEATURES: + received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/phone_messages + +PHONE_SCREEN: + TABLE: screen + PROVIDERS: + RAPIDS: + COMPUTE: True + REFERENCE_HOUR_FIRST_USE: 0 + IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable + IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable + FEATURES: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] # "episodepersensedminutes" needs to be added later + EPISODE_TYPES: ["unlock"] + SRC_FOLDER: "rapids" # inside src/features/phone_screen + SRC_LANGUAGE: "python" + +PHONE_WIFI_CONNECTED: + TABLE: "sensor_wifi" + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected + SRC_LANGUAGE: "r" + +PHONE_WIFI_VISIBLE: + TABLE: "wifi" + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible + SRC_LANGUAGE: "r" + + + +######################################################################################################################## +# FITBIT # +######################################################################################################################## + +# See https://www.rapids.science/latest/setup/configuration/#device-data-source-configuration +FITBIT_DATA_CONFIGURATION: + SOURCE: + TYPE: DATABASE # DATABASE or FILES (set each [FITBIT_SENSOR][TABLE] attribute with a table name or a file path accordingly) + COLUMN_FORMAT: JSON # JSON or PLAIN_TEXT + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # Fitbit only supports SINGLE timezones + VALUE: *timezone # see docs + HIDDEN: + SINGLE_FITBIT_TABLE: TRUE + +FITBIT_HEARTRATE_SUMMARY: + TABLE: fitbit_data + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["maxrestinghr", "minrestinghr", "avgrestinghr", "medianrestinghr", "moderestinghr", "stdrestinghr", "diffmaxmoderestinghr", "diffminmoderestinghr", "entropyrestinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["sumcaloriesoutofrange", "maxcaloriesoutofrange", "mincaloriesoutofrange", "avgcaloriesoutofrange", "mediancaloriesoutofrange", "stdcaloriesoutofrange", "entropycaloriesoutofrange", "sumcaloriesfatburn", "maxcaloriesfatburn", "mincaloriesfatburn", "avgcaloriesfatburn", "mediancaloriesfatburn", "stdcaloriesfatburn", "entropycaloriesfatburn", "sumcaloriescardio", "maxcaloriescardio", "mincaloriescardio", "avgcaloriescardio", "mediancaloriescardio", "stdcaloriescardio", "entropycaloriescardio", "sumcaloriespeak", "maxcaloriespeak", "mincaloriespeak", "avgcaloriespeak", "mediancaloriespeak", "stdcaloriespeak", "entropycaloriespeak"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_summary + SRC_LANGUAGE: "python" + +FITBIT_HEARTRATE_INTRADAY: + TABLE: fitbit_data + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate_intraday + SRC_LANGUAGE: "python" + +FITBIT_SLEEP_SUMMARY: + TABLE: fitbit_data + SLEEP_EPISODE_TIMESTAMP: end # summary sleep episodes are considered as events based on either the start timestamp or end timestamp. + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"] + SLEEP_TYPES: ["main", "nap", "all"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_sleep_summary + SRC_LANGUAGE: "python" + +FITBIT_STEPS_SUMMARY: + TABLE: fitbit_data + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: ["maxsumsteps", "minsumsteps", "avgsumsteps", "mediansumsteps", "stdsumsteps"] + SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_summary + SRC_LANGUAGE: "python" + +FITBIT_STEPS_INTRADAY: + TABLE: fitbit_data + PROVIDERS: + RAPIDS: + COMPUTE: True + FEATURES: + STEPS: ["sum", "max", "min", "avg", "std"] + SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + THRESHOLD_ACTIVE_BOUT: 10 # steps + INCLUDE_ZERO_STEP_ROWS: False + SRC_FOLDER: "rapids" # inside src/features/fitbit_steps_intraday + SRC_LANGUAGE: "python" + + + +######################################################################################################################## +# PLOTS # +######################################################################################################################## + +HISTOGRAM_PHONE_DATA_YIELD: PLOT: True + +HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT: + PLOT: True + +HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT: + PLOT: True + SENSORS: [PHONE_ACCELEROMETER, PHONE_ACTIVITY_RECOGNITION, PHONE_APPLICATIONS_FOREGROUND, PHONE_BATTERY, PHONE_BLUETOOTH, PHONE_CALLS, PHONE_CONVERSATION, PHONE_LIGHT, PHONE_LOCATIONS, PHONE_MESSAGES, PHONE_SCREEN, PHONE_WIFI_CONNECTED, PHONE_WIFI_VISIBLE] + +HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT: + PLOT: True + +HEATMAP_FEATURE_CORRELATION_MATRIX: + PLOT: TRUE MIN_ROWS_RATIO: 0.5 - MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour - PHONE_FEATURES: [activity_recognition, applications_foreground, battery, calls_incoming, calls_missed, calls_outgoing, conversation, light, location_doryab, messages_received, messages_sent, screen] - FITBIT_FEATURES: [fitbit_heartrate, fitbit_step, fitbit_sleep] CORR_THRESHOLD: 0.1 CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"} -HISTOGRAM_VALID_SENSED_HOURS: - PLOT: True - MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour - -HEATMAP_DAYS_BY_SENSORS: - PLOT: True - MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour - EXPECTED_NUM_OF_DAYS: -1 - DB_TABLES: [applications_foreground, battery, bluetooth, calls, light, locations, messages, screen, wifi, sensor_wifi, plugin_google_activity_recognition, plugin_ios_activity_recognition, plugin_studentlife_audio_android, plugin_studentlife_audio] -HEATMAP_SENSED_BINS: - PLOT: True - BIN_SIZE: *bin_size +######################################################################################################################## +# Analysis Workflow Example # +######################################################################################################################## -OVERALL_COMPLIANCE_HEATMAP: - PLOT: True - ONLY_SHOW_VALID_DAYS: False - EXPECTED_NUM_OF_DAYS: -1 - BIN_SIZE: *bin_size - MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day - MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour - -### Example Analysis ################################################################ PARAMS_FOR_ANALYSIS: - COMPUTE: True - GROUNDTRUTH_TABLE: participant_info - TARGET_TABLE: participant_target - SOURCES: &sources ["phone_features", "fitbit_features", "phone_fitbit_features"] - DAY_SEGMENTS: *day_segments - PHONE_FEATURES: [activity_recognition, applications_foreground, battery, bluetooth, calls_incoming, calls_missed, calls_outgoing, conversation, light, location_doryab, messages_received, messages_sent, screen, wifi] - FITBIT_FEATURES: [fitbit_heartrate, fitbit_step, fitbit_sleep] - PHONE_FITBIT_FEATURES: "" # This array is merged in the input_merge_features_of_single_participant function in models.snakefile - DEMOGRAPHIC_FEATURES: [age, gender, inpatientdays] - CATEGORICAL_DEMOGRAPHIC_FEATURES: ["gender"] - FEATURES_EXCLUDE_DAY_IDX: True + CATEGORICAL_OPERATORS: [mostcommon] - # Whether or not to include only days with enough valid sensed hours - # logic can be found in rule phone_valid_sensed_days of rules/preprocessing.snakefile - DROP_VALID_SENSED_DAYS: - ENABLED: True - - # Whether or not to include certain days in the analysis, logic can be found in rule days_to_analyse of rules/mystudy.snakefile - # If you want to include all days downloaded for each participant, set ENABLED to False - DAYS_TO_ANALYSE: - ENABLED: True - DAYS_BEFORE_SURGERY: 6 #15 - DAYS_IN_HOSPITAL: F # T or F - DAYS_AFTER_DISCHARGE: 5 #7 + DEMOGRAPHIC: + TABLE: participant_info + FEATURES: [age, gender, inpatientdays] + CATEGORICAL_FEATURES: [gender] + SOURCE: + DATABASE_GROUP: *database_group + TIMEZONE: *timezone + + TARGET: + TABLE: participant_target + SOURCE: + DATABASE_GROUP: *database_group + TIMEZONE: *timezone # Cleaning Parameters - COLS_NAN_THRESHOLD: [0.1, 0.3] + COLS_NAN_THRESHOLD: 0.3 COLS_VAR_THRESHOLD: True - ROWS_NAN_THRESHOLD: [0.1, 0.3] - PARTICIPANT_DAYS_BEFORE_THRESHOLD: 3 - PARTICIPANT_DAYS_AFTER_THRESHOLD: 3 - - # Extract summarised features from daily features with any of the following substrings - NUMERICAL_OPERATORS: ["count", "sum", "length", "avg", "restinghr"] - CATEGORICAL_OPERATORS: ["mostcommon"] - - MODEL_NAMES: ["LogReg", "kNN", "SVM", "DT", "RF", "GB", "XGBoost", "LightGBM"] - CV_METHODS: ["LeaveOneOut"] - SUMMARISED: ["notsummarised"] # "summarised" or "notsummarised" - RESULT_COMPONENTS: ["fold_predictions", "fold_metrics", "overall_results", "fold_feature_importances"] + ROWS_NAN_THRESHOLD: 0.3 + DATA_YIELDED_HOURS_RATIO_THRESHOLD: 0.75 + + MODEL_NAMES: [LogReg, kNN , SVM, DT, RF, GB, XGBoost, LightGBM] + CV_METHODS: [LeaveOneOut] + RESULT_COMPONENTS: [fold_predictions, fold_metrics, overall_results, fold_feature_importances] MODEL_SCALER: - LogReg: ["notnormalized", "minmaxscaler", "standardscaler", "robustscaler"] - kNN: ["minmaxscaler", "standardscaler", "robustscaler"] - SVM: ["minmaxscaler", "standardscaler", "robustscaler"] - DT: ["notnormalized"] - RF: ["notnormalized"] - GB: ["notnormalized"] - XGBoost: ["notnormalized"] - LightGBM: ["notnormalized"] + LogReg: [notnormalized, minmaxscaler, standardscaler, robustscaler] + kNN: [minmaxscaler, standardscaler, robustscaler] + SVM: [minmaxscaler, standardscaler, robustscaler] + DT: [notnormalized] + RF: [notnormalized] + GB: [notnormalized] + XGBoost: [notnormalized] + LightGBM: [notnormalized] MODEL_HYPERPARAMS: LogReg: {"clf__C": [0.01, 0.1, 1, 10, 100], "clf__solver": ["newton-cg", "lbfgs", "liblinear", "saga"], "clf__penalty": ["l2"]} kNN: - {"clf__n_neighbors": [1, 3, 5], "clf__weights": ["uniform", "distance"], "clf__metric": ["euclidean", "manhattan", "minkowski"]} + {"clf__n_neighbors": [3, 5, 7], "clf__weights": ["uniform", "distance"], "clf__metric": ["euclidean", "manhattan", "minkowski"]} SVM: {"clf__C": [0.01, 0.1, 1, 10, 100], "clf__gamma": ["scale", "auto"], "clf__kernel": ["rbf", "poly", "sigmoid"]} DT: - {"clf__criterion": ["gini", "entropy"], "clf__max_depth": [null, 3, 5, 7, 9], "clf__max_features": [null, "auto", "sqrt", "log2"]} + {"clf__criterion": ["gini", "entropy"], "clf__max_depth": [null, 3, 7, 15], "clf__max_features": [null, "auto", "sqrt", "log2"]} RF: - {"clf__n_estimators": [2, 5, 10, 100],"clf__max_depth": [null, 3, 5, 7, 9]} + {"clf__n_estimators": [10, 100, 200],"clf__max_depth": [null, 3, 7, 15]} GB: - {"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [5, 10, 100, 200], "clf__subsample": [0.5, 0.7, 1.0], "clf__max_depth": [3, 5, 7, 9]} + {"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [10, 100, 200], "clf__subsample": [0.5, 0.7, 1.0], "clf__max_depth": [null, 3, 5, 7]} XGBoost: - {"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [5, 10, 100, 200], "clf__num_leaves": [5, 16, 31, 62]} + {"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [10, 100, 200], "clf__max_depth": [3, 5, 7]} LightGBM: - {"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [5, 10, 100, 200], "clf__num_leaves": [5, 16, 31, 62]} + {"clf__learning_rate": [0.01, 0.1, 1], "clf__n_estimators": [10, 100, 200], "clf__num_leaves": [3, 5, 7], "clf__colsample_bytree": [0.6, 0.8, 1]} diff --git a/example_profile/exampleworkflow_timesegments.csv b/example_profile/exampleworkflow_timesegments.csv new file mode 100644 index 00000000..4338e809 --- /dev/null +++ b/example_profile/exampleworkflow_timesegments.csv @@ -0,0 +1,2 @@ +label,start_time,length,repeats_on,repeats_value +daily,00:00:00,23H 59M 59S,every_day,0 \ No newline at end of file diff --git a/mkdocs.yml b/mkdocs.yml new file mode 100644 index 00000000..69feb7ef --- /dev/null +++ b/mkdocs.yml @@ -0,0 +1,119 @@ +site_name: RAPIDS +markdown_extensions: + - toc: + permalink: True + - admonition + - smarty + - wikilinks + - codehilite: + linenums: True + # - urlize # requires: pip install git+https://github.com/r0wb0t/markdown-urlize.git + - pymdownx.arithmatex: + generic: true + - pymdownx.betterem: + smart_enable: all + - pymdownx.caret + - pymdownx.critic + - pymdownx.details + - pymdownx.emoji: + emoji_index: !!python/name:materialx.emoji.twemoji + emoji_generator: !!python/name:materialx.emoji.to_svg + - pymdownx.highlight + - pymdownx.inlinehilite + - pymdownx.magiclink + - pymdownx.mark + - pymdownx.smartsymbols + - pymdownx.superfences + - pymdownx.tabbed + - pymdownx.tasklist: + custom_checkbox: True + - pymdownx.tilde + - attr_list + - pymdownx.keys +extra: + version: + method: mike + social: + - icon: fontawesome/brands/twitter + link: 'https://twitter.com/julio_ui' +extra_javascript: + - https://polyfill.io/v3/polyfill.min.js?features=es6 + - https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-mml-chtml.js + - javascripts/extra.js + +repo_name: 'carissalow/rapids' +repo_url: 'https://github.com/carissalow/rapids' +copyright: 'Released under AGPL' +theme: + name: material + icon: + logo: material/air-filter + palette: + - scheme: default + primary: blue + accent: blue + toggle: + icon: material/toggle-switch + name: Switch to light mode + - scheme: slate + primary: blue + accent: blue + toggle: + icon: material/toggle-switch-off-outline + name: Switch to dark mode + features: + - navigation.sections + - search.suggest + - search.highlight +extra_css: + - stylesheets/extra.css +nav: + - Home: 'index.md' + - Setup: + - File Structure: file-structure.md + - Installation: 'setup/installation.md' + - Configuration: setup/configuration.md + - Execution: setup/execution.md + - Example Workflows: + - Minimal: workflow-examples/minimal.md + - Analysis: workflow-examples/analysis.md + - Behavioral Features: + - Introduction: features/feature-introduction.md + - Phone: + - Phone Accelerometer: features/phone-accelerometer.md + - Phone Activity Recognition: features/phone-activity-recognition.md + - Phone Applications Foreground: features/phone-applications-foreground.md + - Phone Battery: features/phone-battery.md + - Phone Bluetooth: features/phone-bluetooth.md + - Phone Calls: features/phone-calls.md + - Phone Conversation: features/phone-conversation.md + - Phone Data Yield: features/phone-data-yield.md + - Phone Light: features/phone-light.md + - Phone Locations: features/phone-locations.md + - Phone Messages: features/phone-messages.md + - Phone Screen: features/phone-screen.md + - Phone WiFI Connected: features/phone-wifi-connected.md + - Phone WiFI Visible: features/phone-wifi-visible.md + - Fitbit: + - Fitbit Heart Rate Summary: features/fitbit-heartrate-summary.md + - Fitbit Heart Rate Intraday: features/fitbit-heartrate-intraday.md + - Fitbit Sleep Summary: features/fitbit-sleep-summary.md + - Fitbit Steps Summary: features/fitbit-steps-summary.md + - Fitbit Steps Intraday: features/fitbit-steps-intraday.md + - Add New Features: features/add-new-features.md + - Visualizations: + - Data Quality: visualizations/data-quality-visualizations.md + - Features: visualizations/feature-visualizations.md + - Developers: + - Remote Support: developers/remote-support.md + - Virtual Environments: developers/virtual-environments.md + - Documentation: developers/documentation.md + - Testing: developers/testing.md + - Test cases: developers/test-cases.md + - Others: + - Migrating from beta: migrating-from-old-versions.md + - Code of Conduct: code_of_conduct.md + - FAQ: faq.md + - Team: team.md + - Change Log: change-log.md + - Citation: citation.md diff --git a/rapids b/rapids new file mode 100755 index 00000000..ac244b93 --- /dev/null +++ b/rapids @@ -0,0 +1,4 @@ +#!/usr/bin/env python +from sys import argv +import subprocess +subprocess.run(" ".join(["snakemake", "-R", "`snakemake --list-params-changes`"] + argv[1:]), shell=True) \ No newline at end of file diff --git a/renv.lock b/renv.lock index 61cc6db0..e20ff9f0 100644 --- a/renv.lock +++ b/renv.lock @@ -86,12 +86,12 @@ "Repository": "CRAN", "Hash": "e031418365a7f7a766181ab5a41a5716" }, - "RMySQL": { - "Package": "RMySQL", - "Version": "0.10.20", + "RMariaDB": { + "Package": "RMariaDB", + "Version": "1.0.10", "Source": "Repository", "Repository": "CRAN", - "Hash": "022066398851453f187950137e21daad" + "Hash": "d149479ea03e5cbb1e788449ef5e04b4" }, "Rcpp": { "Package": "Rcpp", @@ -156,6 +156,20 @@ "Repository": "CRAN", "Hash": "543776ae6848fde2f48ff3816d0628bc" }, + "bit": { + "Package": "bit", + "Version": "4.0.4", + "Source": "Repository", + "Repository": "CRAN", + "Hash": "f36715f14d94678eea9933af927bc15d" + }, + "bit64": { + "Package": "bit64", + "Version": "4.0.5", + "Source": "Repository", + "Repository": "CRAN", + "Hash": "9fe98599ca456d6552421db0d6772d8f" + }, "boot": { "Package": "boot", "Version": "1.3-24", diff --git a/renv/activate.R b/renv/activate.R index c2fe5e92..f71b9e85 100644 --- a/renv/activate.R +++ b/renv/activate.R @@ -1,6 +1,8 @@ local({ + options(tidyverse.quiet = TRUE) + # the requested version of renv version <- "0.10.0" @@ -302,7 +304,7 @@ local({ renv_bootstrap_validate_version(version) # load the project - renv::load(project) + renv::load(project, quiet = TRUE) TRUE diff --git a/rules/common.smk b/rules/common.smk index bebbac44..ef9af1ca 100644 --- a/rules/common.smk +++ b/rules/common.smk @@ -1,62 +1,10 @@ -# Common.smk ########################################################################################################## - -def infer_participant_platform(participant_file): - with open(participant_file, encoding="ISO-8859-1") as external_file: - external_file_content = external_file.readlines() - platforms = external_file_content[1].strip().split(",") - if platforms[0] == "multiple" or (len(platforms) > 1 and "android" in platforms and "ios" in platforms): - platform = "android" - else: - platform = platforms[0] - - if platform not in ["android", "ios"]: - raise ValueError("Platform (line 2) in a participant file should be 'android', 'ios', or 'multiple'. You typed '" + platforms + "'") - - return platform - -# Preprocessing.smk #################################################################################################### - -def optional_phone_sensed_bins_input(wildcards): - platform = infer_participant_platform("data/external/"+wildcards.pid) - - if platform == "android": - tables_platform = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]] # for android, discard any ios tables that may exist - elif platform == "ios": - tables_platform = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist - - return expand("data/raw/{{pid}}/{table}_with_datetime.csv", table = tables_platform) - # Features.smk ######################################################################################################### - -def optional_ar_input(wildcards): - platform = infer_participant_platform("data/external/"+wildcards.pid) - - if platform == "android": - return ["data/raw/{pid}/" + config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"] + "_with_datetime_unified.csv", - "data/processed/{pid}/" + config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"] + "_deltas.csv"] - elif platform == "ios": - return ["data/raw/{pid}/"+config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]+"_with_datetime_unified.csv", - "data/processed/{pid}/"+config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]+"_deltas.csv"] - -def optional_conversation_input(wildcards): - platform = infer_participant_platform("data/external/"+wildcards.pid) - - if platform == "android": - return ["data/raw/{pid}/" + config["CONVERSATION"]["DB_TABLE"]["ANDROID"] + "_with_datetime_unified.csv"] - elif platform == "ios": - return ["data/raw/{pid}/" + config["CONVERSATION"]["DB_TABLE"]["IOS"] + "_with_datetime_unified.csv"] - -def optional_location_barnett_input(wildcards): - if config["BARNETT_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": - return expand("data/raw/{{pid}}/{sensor}_resampled.csv", sensor=config["BARNETT_LOCATION"]["DB_TABLE"]) - else: - return expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BARNETT_LOCATION"]["DB_TABLE"]) - -def optional_location_doryab_input(wildcards): - if config["DORYAB_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": - return expand("data/raw/{{pid}}/{sensor}_resampled.csv", sensor=config["DORYAB_LOCATION"]["DB_TABLE"]) - else: - return expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["DORYAB_LOCATION"]["DB_TABLE"]) +def find_features_files(wildcards): + feature_files = [] + for provider_key, provider in config[(wildcards.sensor_key).upper()]["PROVIDERS"].items(): + if provider["COMPUTE"]: + feature_files.extend(expand("data/interim/{{pid}}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", sensor_key=wildcards.sensor_key.lower(), language=provider["SRC_LANGUAGE"].lower(), provider_key=provider_key.lower())) + return(feature_files) def optional_steps_sleep_input(wildcards): if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED": @@ -64,50 +12,13 @@ def optional_steps_sleep_input(wildcards): else: return [] -def optional_wifi_input(wildcards): - if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0 and len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) == 0: - return {"visible_access_points": expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])} - elif len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) == 0 and len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: - return {"connected_access_points": expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])} - elif len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0 and len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: - return {"visible_access_points": expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]), "connected_access_points": expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])} - else: - raise ValueError("If you are computing WIFI features you need to provide either VISIBLE_ACCESS_POINTS, CONNECTED_ACCESS_POINTS or both") +def input_merge_sensor_features_for_individual_participants(wildcards): + feature_files = [] + for config_key in config.keys(): + if config_key.startswith(("PHONE", "FITBIT")) and "PROVIDERS" in config[config_key]: + for provider_key, provider in config[config_key]["PROVIDERS"].items(): + if "COMPUTE" in provider.keys() and provider["COMPUTE"]: + feature_files.append("data/processed/features/{pid}/" + config_key.lower() + ".csv") + break + return feature_files - -# Models.smk ########################################################################################################### - -def input_merge_features_of_single_participant(wildcards): - if wildcards.source == "phone_fitbit_features": - return expand("data/processed/{pid}/{features}_{day_segment}.csv", pid=wildcards.pid, features=config["PARAMS_FOR_ANALYSIS"]["PHONE_FEATURES"] + config["PARAMS_FOR_ANALYSIS"]["FITBIT_FEATURES"], day_segment=wildcards.day_segment) - else: - return expand("data/processed/{pid}/{features}_{day_segment}.csv", pid=wildcards.pid, features=config["PARAMS_FOR_ANALYSIS"][wildcards.source.upper()], day_segment=wildcards.day_segment) - -def optional_input_days_to_include(wildcards): - if config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["ENABLED"]: - # This input automatically trigers the rule days_to_analyse in mystudy.snakefile - return ["data/interim/{pid}/days_to_analyse" + \ - "_" + str(config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["DAYS_BEFORE_SURGERY"]) + \ - "_" + str(config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["DAYS_IN_HOSPITAL"]) + \ - "_" + str(config["PARAMS_FOR_ANALYSIS"]["DAYS_TO_ANALYSE"]["DAYS_AFTER_DISCHARGE"]) + ".csv"] - else: - return [] - -def optional_input_valid_sensed_days(wildcards): - if config["PARAMS_FOR_ANALYSIS"]["DROP_VALID_SENSED_DAYS"]["ENABLED"]: - # This input automatically trigers the rule phone_valid_sensed_days in preprocessing.snakefile - return ["data/interim/{pid}/phone_valid_sensed_days_{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins.csv"] - else: - return [] - -# Reports.smk ########################################################################################################### - -def optional_heatmap_days_by_sensors_input(wildcards): - platform = infer_participant_platform("data/external/"+wildcards.pid) - - if platform == "android": - tables_platform = [table for table in config["HEATMAP_DAYS_BY_SENSORS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]] # for android, discard any ios tables that may exist - elif platform == "ios": - tables_platform = [table for table in config["HEATMAP_DAYS_BY_SENSORS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist - - return expand("data/raw/{{pid}}/{table}_with_datetime.csv", table = tables_platform) diff --git a/rules/features.smk b/rules/features.smk index 4a55059d..c106912f 100644 --- a/rules/features.smk +++ b/rules/features.smk @@ -1,248 +1,547 @@ -rule messages_features: - input: - expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["MESSAGES"]["DB_TABLE"]) - params: - messages_type = "{messages_type}", - day_segment = "{day_segment}", - features = lambda wildcards: config["MESSAGES"]["FEATURES"][wildcards.messages_type] - output: - "data/processed/{pid}/messages_{messages_type}_{day_segment}.csv" - script: - "../src/features/messages_features.R" - -rule call_features: - input: - expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["CALLS"]["DB_TABLE"]) - params: - call_type = "{call_type}", - day_segment = "{day_segment}", - features = lambda wildcards: config["CALLS"]["FEATURES"][wildcards.call_type] - output: - "data/processed/{pid}/calls_{call_type}_{day_segment}.csv" - script: - "../src/features/call_features.R" - -rule battery_deltas: +rule join_features_from_providers: input: - expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["BATTERY"]["DB_TABLE"]) + sensor_features = find_features_files + wildcard_constraints: + sensor_key = '(phone|fitbit).*' output: - "data/processed/{pid}/battery_deltas.csv" + "data/processed/features/{pid}/{sensor_key}.csv" script: - "../src/features/battery_deltas.R" + "../src/features/utils/join_features_from_providers.R" -rule screen_deltas: +rule phone_data_yield_python_features: input: - screen = expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["SCREEN"]["DB_TABLE"]) - output: - "data/processed/{pid}/screen_deltas.csv" - script: - "../src/features/screen_deltas.R" - -rule google_activity_recognition_deltas: - input: - expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]) - output: - expand("data/processed/{{pid}}/{sensor}_deltas.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]) - script: - "../src/features/activity_recognition_deltas.R" - -rule ios_activity_recognition_deltas: - input: - expand("data/raw/{{pid}}/{sensor}_with_datetime_unified.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]) - output: - expand("data/processed/{{pid}}/{sensor}_deltas.csv", sensor=config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]) - script: - "../src/features/activity_recognition_deltas.R" - -rule location_barnett_features: - input: - locations = optional_location_barnett_input + sensor_data = "data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: - features = config["BARNETT_LOCATION"]["FEATURES"], - locations_to_use = config["BARNETT_LOCATION"]["LOCATIONS_TO_USE"], - accuracy_limit = config["BARNETT_LOCATION"]["ACCURACY_LIMIT"], - timezone = config["BARNETT_LOCATION"]["TIMEZONE"], - minutes_data_used = config["BARNETT_LOCATION"]["MINUTES_DATA_USED"], - day_segment = "{day_segment}" + provider = lambda wildcards: config["PHONE_DATA_YIELD"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_data_yield" output: - "data/processed/{pid}/location_barnett_{day_segment}.csv" + "data/interim/{pid}/phone_data_yield_features/phone_data_yield_python_{provider_key}.csv" script: - "../src/features/location_barnett_features.R" + "../src/features/entry.py" -rule location_doryab_features: +rule phone_data_yield_r_features: input: - locations = optional_location_doryab_input + sensor_data = "data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: - features = config["DORYAB_LOCATION"]["FEATURES"], - day_segment = "{day_segment}", - dbscan_eps = config["DORYAB_LOCATION"]["DBSCAN_EPS"], - dbscan_minsamples = config["DORYAB_LOCATION"]["DBSCAN_MINSAMPLES"], - threshold_static = config["DORYAB_LOCATION"]["THRESHOLD_STATIC"], - maximum_gap_allowed = config["DORYAB_LOCATION"]["MAXIMUM_GAP_ALLOWED"], - minutes_data_used = config["DORYAB_LOCATION"]["MINUTES_DATA_USED"], - sampling_frequency = config["DORYAB_LOCATION"]["SAMPLING_FREQUENCY"] + provider = lambda wildcards: config["PHONE_DATA_YIELD"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_data_yield" output: - "data/processed/{pid}/location_doryab_{day_segment}.csv" + "data/interim/{pid}/phone_data_yield_features/phone_data_yield_r_{provider_key}.csv" script: - "../src/features/location_doryab_features.py" + "../src/features/entry.R" -rule bluetooth_features: - input: - expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["BLUETOOTH"]["DB_TABLE"]) - params: - day_segment = "{day_segment}", - features = config["BLUETOOTH"]["FEATURES"] - output: - "data/processed/{pid}/bluetooth_{day_segment}.csv" - script: - "../src/features/bluetooth_features.R" - -rule activity_features: +rule phone_accelerometer_python_features: input: - optional_ar_input + sensor_data = "data/raw/{pid}/phone_accelerometer_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: - segment = "{day_segment}", - features = config["ACTIVITY_RECOGNITION"]["FEATURES"] + provider = lambda wildcards: config["PHONE_ACCELEROMETER"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_accelerometer" output: - "data/processed/{pid}/activity_recognition_{day_segment}.csv" + "data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_python_{provider_key}.csv" script: - "../src/features/activity_recognition.py" + "../src/features/entry.py" -rule battery_features: +rule phone_accelerometer_r_features: input: - "data/processed/{pid}/battery_deltas.csv" + sensor_data = "data/raw/{pid}/phone_accelerometer_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: - day_segment = "{day_segment}", - features = config["BATTERY"]["FEATURES"] + provider = lambda wildcards: config["PHONE_ACCELEROMETER"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_accelerometer" output: - "data/processed/{pid}/battery_{day_segment}.csv" + "data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_r_{provider_key}.csv" script: - "../src/features/battery_features.py" + "../src/features/entry.R" -rule screen_features: +rule activity_recognition_episodes: input: - screen_deltas = "data/processed/{pid}/screen_deltas.csv", - phone_sensed_bins = "data/interim/{pid}/phone_sensed_bins.csv" + sensor_data = "data/raw/{pid}/phone_activity_recognition_with_datetime_unified.csv" params: - day_segment = "{day_segment}", - reference_hour_first_use = config["SCREEN"]["REFERENCE_HOUR_FIRST_USE"], - features_deltas = config["SCREEN"]["FEATURES_DELTAS"], - episode_types = config["SCREEN"]["EPISODE_TYPES"], - ignore_episodes_shorter_than = config["SCREEN"]["IGNORE_EPISODES_SHORTER_THAN"], - ignore_episodes_longer_than = config["SCREEN"]["IGNORE_EPISODES_LONGER_THAN"], - bin_size = config["PHONE_VALID_SENSED_BINS"]["BIN_SIZE"] + episode_threshold_between_rows = config["PHONE_BATTERY"]["EPISODE_THRESHOLD_BETWEEN_ROWS"] output: - "data/processed/{pid}/screen_{day_segment}.csv" + "data/interim/{pid}/phone_activity_recognition_episodes.csv" script: - "../src/features/screen_features.py" + "../src/features/phone_activity_recognition/episodes/activity_recognition_episodes.R" -rule light_features: +rule phone_activity_recognition_python_features: input: - expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["LIGHT"]["DB_TABLE"]), + sensor_episodes = "data/interim/{pid}/phone_activity_recognition_episodes_resampled_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: - day_segment = "{day_segment}", - features = config["LIGHT"]["FEATURES"], + provider = lambda wildcards: config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_activity_recognition" output: - "data/processed/{pid}/light_{day_segment}.csv" + "data/interim/{pid}/phone_activity_recognition_features/phone_activity_recognition_python_{provider_key}.csv" script: - "../src/features/light_features.py" + "../src/features/entry.py" -rule conversation_features: +rule phone_activity_recognition_r_features: input: - optional_conversation_input + sensor_episodes = "data/interim/{pid}/phone_activity_recognition_episodes_resampled_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: - day_segment = "{day_segment}", - features = config["CONVERSATION"]["FEATURES"], - recordingMinutes = config["CONVERSATION"]["RECORDINGMINUTES"], - pausedMinutes = config["CONVERSATION"]["PAUSEDMINUTES"], + provider = lambda wildcards: config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_activity_recognition" output: - "data/processed/{pid}/conversation_{day_segment}.csv" + "data/interim/{pid}/phone_activity_recognition_features/phone_activity_recognition_r_{provider_key}.csv" script: - "../src/features/conversation_features.py" + "../src/features/entry.R" -rule accelerometer_features: +rule phone_applications_foreground_python_features: input: - expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor=config["ACCELEROMETER"]["DB_TABLE"]), + sensor_data = "data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: - day_segment = "{day_segment}", - magnitude = config["ACCELEROMETER"]["FEATURES"]["MAGNITUDE"], - exertional_activity_episode = config["ACCELEROMETER"]["FEATURES"]["EXERTIONAL_ACTIVITY_EPISODE"], - nonexertional_activity_episode = config["ACCELEROMETER"]["FEATURES"]["NONEXERTIONAL_ACTIVITY_EPISODE"], - valid_sensed_minutes = config["ACCELEROMETER"]["FEATURES"]["VALID_SENSED_MINUTES"], + provider = lambda wildcards: config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_applications_foreground" output: - "data/processed/{pid}/accelerometer_{day_segment}.csv" + "data/interim/{pid}/phone_applications_foreground_features/phone_applications_foreground_python_{provider_key}.csv" script: - "../src/features/accelerometer_features.py" + "../src/features/entry.py" -rule applications_foreground_features: +rule phone_applications_foreground_r_features: input: - expand("data/interim/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]) + sensor_data = "data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: - day_segment = "{day_segment}", - single_categories = config["APPLICATIONS_FOREGROUND"]["SINGLE_CATEGORIES"], - multiple_categories = config["APPLICATIONS_FOREGROUND"]["MULTIPLE_CATEGORIES"], - single_apps = config["APPLICATIONS_FOREGROUND"]["SINGLE_APPS"], - excluded_categories = config["APPLICATIONS_FOREGROUND"]["EXCLUDED_CATEGORIES"], - excluded_apps = config["APPLICATIONS_FOREGROUND"]["EXCLUDED_APPS"], - features = config["APPLICATIONS_FOREGROUND"]["FEATURES"], + provider = lambda wildcards: config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_applications_foreground" output: - "data/processed/{pid}/applications_foreground_{day_segment}.csv" + "data/interim/{pid}/phone_applications_foreground_features/phone_applications_foreground_r_{provider_key}.csv" script: - "../src/features/applications_foreground_features.py" + "../src/features/entry.R" -rule wifi_features: - input: - unpack(optional_wifi_input) - params: - day_segment = "{day_segment}", - features = config["WIFI"]["FEATURES"] - output: - "data/processed/{pid}/wifi_{day_segment}.csv" - script: - "../src/features/wifi_features.R" - -rule fitbit_heartrate_features: +rule battery_episodes: input: - heartrate_summary_data = "data/raw/{pid}/fitbit_heartrate_summary_with_datetime.csv", - heartrate_intraday_data = "data/raw/{pid}/fitbit_heartrate_intraday_with_datetime.csv" + "data/raw/{pid}/phone_battery_raw.csv" params: - day_segment = "{day_segment}", - summary_features = config["HEARTRATE"]["SUMMARY_FEATURES"], - intraday_features = config["HEARTRATE"]["INTRADAY_FEATURES"] + episode_threshold_between_rows = config["PHONE_BATTERY"]["EPISODE_THRESHOLD_BETWEEN_ROWS"] output: - "data/processed/{pid}/fitbit_heartrate_{day_segment}.csv" + "data/interim/{pid}/phone_battery_episodes.csv" script: - "../src/features/fitbit_heartrate_features.py" + "../src/features/phone_battery/episodes/battery_episodes.R" -rule fitbit_step_features: +rule phone_battery_python_features: input: - step_data = "data/raw/{pid}/fitbit_step_intraday_with_datetime.csv", - sleep_data = optional_steps_sleep_input + sensor_episodes = "data/interim/{pid}/phone_battery_episodes_resampled_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: - day_segment = "{day_segment}", - features_all_steps = config["STEP"]["FEATURES"]["ALL_STEPS"], - features_sedentary_bout = config["STEP"]["FEATURES"]["SEDENTARY_BOUT"], - features_active_bout = config["STEP"]["FEATURES"]["ACTIVE_BOUT"], - threshold_active_bout = config["STEP"]["THRESHOLD_ACTIVE_BOUT"], - include_zero_step_rows = config["STEP"]["INCLUDE_ZERO_STEP_ROWS"], - exclude_sleep = config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"], - exclude_sleep_type = config["STEP"]["EXCLUDE_SLEEP"]["TYPE"], - exclude_sleep_fixed_start = config["STEP"]["EXCLUDE_SLEEP"]["FIXED"]["START"], - exclude_sleep_fixed_end = config["STEP"]["EXCLUDE_SLEEP"]["FIXED"]["END"], + provider = lambda wildcards: config["PHONE_BATTERY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_battery" output: - "data/processed/{pid}/fitbit_step_{day_segment}.csv" + "data/interim/{pid}/phone_battery_features/phone_battery_python_{provider_key}.csv" script: - "../src/features/fitbit_step_features.py" + "../src/features/entry.py" -rule fitbit_sleep_features: +rule phone_battery_r_features: input: - sleep_summary_data = "data/raw/{pid}/fitbit_sleep_summary_with_datetime.csv", - sleep_intraday_data = "data/raw/{pid}/fitbit_sleep_intraday_with_datetime.csv" + sensor_episodes = "data/interim/{pid}/phone_battery_episodes_resampled_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: - day_segment = "{day_segment}", - summary_features = config["SLEEP"]["SUMMARY_FEATURES"], - sleep_types = config["SLEEP"]["SLEEP_TYPES"] + provider = lambda wildcards: config["PHONE_BATTERY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_battery" output: - "data/processed/{pid}/fitbit_sleep_{day_segment}.csv" + "data/interim/{pid}/phone_battery_features/phone_battery_r_{provider_key}.csv" script: - "../src/features/fitbit_sleep_features.py" + "../src/features/entry.R" + +rule phone_bluetooth_python_features: + input: + sensor_data = "data/raw/{pid}/phone_bluetooth_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_BLUETOOTH"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_bluetooth" + output: + "data/interim/{pid}/phone_bluetooth_features/phone_bluetooth_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule phone_bluetooth_r_features: + input: + sensor_data = "data/raw/{pid}/phone_bluetooth_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_BLUETOOTH"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_bluetooth" + output: + "data/interim/{pid}/phone_bluetooth_features/phone_bluetooth_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule calls_python_features: + input: + sensor_data = "data/raw/{pid}/phone_calls_with_datetime_unified.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_CALLS"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_calls" + output: + "data/interim/{pid}/phone_calls_features/phone_calls_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule calls_r_features: + input: + sensor_data = "data/raw/{pid}/phone_calls_with_datetime_unified.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_CALLS"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_calls" + output: + "data/interim/{pid}/phone_calls_features/phone_calls_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule conversation_python_features: + input: + sensor_data = "data/raw/{pid}/phone_conversation_with_datetime_unified.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_CONVERSATION"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_conversation" + output: + "data/interim/{pid}/phone_conversation_features/phone_conversation_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule conversation_r_features: + input: + sensor_data = "data/raw/{pid}/phone_conversation_with_datetime_unified.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_CONVERSATION"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_conversation" + output: + "data/interim/{pid}/phone_conversation_features/phone_conversation_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule phone_light_python_features: + input: + sensor_data = "data/raw/{pid}/phone_light_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_LIGHT"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_light" + output: + "data/interim/{pid}/phone_light_features/phone_light_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule phone_light_r_features: + input: + sensor_data = "data/raw/{pid}/phone_light_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_LIGHT"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_light" + output: + "data/interim/{pid}/phone_light_features/phone_light_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule phone_locations_python_features: + input: + sensor_data = "data/interim/{pid}/phone_locations_processed_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_LOCATIONS"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_locations" + output: + "data/interim/{pid}/phone_locations_features/phone_locations_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule phone_locations_r_features: + input: + sensor_data = "data/interim/{pid}/phone_locations_processed_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_LOCATIONS"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_locations" + output: + "data/interim/{pid}/phone_locations_features/phone_locations_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule phone_messages_python_features: + input: + sensor_data = "data/raw/{pid}/phone_messages_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_MESSAGES"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_messages" + output: + "data/interim/{pid}/phone_messages_features/phone_messages_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule phone_messages_r_features: + input: + sensor_data = "data/raw/{pid}/phone_messages_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_MESSAGES"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_messages" + output: + "data/interim/{pid}/phone_messages_features/phone_messages_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule screen_episodes: + input: + screen = "data/raw/{pid}/phone_screen_with_datetime_unified.csv" + output: + "data/interim/{pid}/phone_screen_episodes.csv" + script: + "../src/features/phone_screen/episodes/screen_episodes.R" + +rule phone_screen_python_features: + input: + sensor_episodes = "data/interim/{pid}/phone_screen_episodes_resampled_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_SCREEN"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_screen" + output: + "data/interim/{pid}/phone_screen_features/phone_screen_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule phone_screen_r_features: + input: + sensor_episodes = "data/interim/{pid}/phone_screen_episodes_resampled_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_SCREEN"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_screen" + output: + "data/interim/{pid}/phone_screen_features/phone_screen_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule phone_wifi_connected_python_features: + input: + sensor_data = "data/raw/{pid}/phone_wifi_connected_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_wifi_connected" + output: + "data/interim/{pid}/phone_wifi_connected_features/phone_wifi_connected_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule phone_wifi_connected_r_features: + input: + sensor_data = "data/raw/{pid}/phone_wifi_connected_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_wifi_connected" + output: + "data/interim/{pid}/phone_wifi_connected_features/phone_wifi_connected_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule phone_wifi_visible_python_features: + input: + sensor_data = "data/raw/{pid}/phone_wifi_visible_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_wifi_visible" + output: + "data/interim/{pid}/phone_wifi_visible_features/phone_wifi_visible_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule phone_wifi_visible_r_features: + input: + sensor_data = "data/raw/{pid}/phone_wifi_visible_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "phone_wifi_visible" + output: + "data/interim/{pid}/phone_wifi_visible_features/phone_wifi_visible_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule fitbit_heartrate_summary_python_features: + input: + sensor_data = "data/raw/{pid}/fitbit_heartrate_summary_parsed_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_heartrate_summary" + output: + "data/interim/{pid}/fitbit_heartrate_summary_features/fitbit_heartrate_summary_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule fitbit_heartrate_summary_r_features: + input: + sensor_data = "data/raw/{pid}/fitbit_heartrate_summary_parsed_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_HEARTRATE_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_heartrate_summary" + output: + "data/interim/{pid}/fitbit_heartrate_summary_features/fitbit_heartrate_summary_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule fitbit_heartrate_intraday_python_features: + input: + sensor_data = "data/raw/{pid}/fitbit_heartrate_intraday_parsed_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_heartrate_intraday" + output: + "data/interim/{pid}/fitbit_heartrate_intraday_features/fitbit_heartrate_intraday_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule fitbit_heartrate_intraday_r_features: + input: + sensor_data = "data/raw/{pid}/fitbit_heartrate_intraday_parsed_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_HEARTRATE_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_heartrate_intraday" + output: + "data/interim/{pid}/fitbit_heartrate_intraday_features/fitbit_heartrate_intraday_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule fitbit_steps_summary_python_features: + input: + sensor_data = "data/raw/{pid}/fitbit_steps_summary_parsed_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_steps_summary" + output: + "data/interim/{pid}/fitbit_steps_summary_features/fitbit_steps_summary_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule fitbit_steps_summary_r_features: + input: + sensor_data = "data/raw/{pid}/fitbit_steps_summary_parsed_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_steps_summary" + output: + "data/interim/{pid}/fitbit_steps_summary_features/fitbit_steps_summary_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule fitbit_steps_intraday_python_features: + input: + sensor_data = "data/raw/{pid}/fitbit_steps_intraday_parsed_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_steps_intraday" + output: + "data/interim/{pid}/fitbit_steps_intraday_features/fitbit_steps_intraday_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule fitbit_steps_intraday_r_features: + input: + sensor_data = "data/raw/{pid}/fitbit_steps_intraday_parsed_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_STEPS_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_steps_intraday" + output: + "data/interim/{pid}/fitbit_steps_intraday_features/fitbit_steps_intraday_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule fitbit_sleep_summary_python_features: + input: + sensor_data = "data/raw/{pid}/fitbit_sleep_summary_parsed_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_sleep_summary" + output: + "data/interim/{pid}/fitbit_sleep_summary_features/fitbit_sleep_summary_python_{provider_key}.csv" + script: + "../src/features/entry.py" + +rule fitbit_sleep_summary_r_features: + input: + sensor_data = "data/raw/{pid}/fitbit_sleep_summary_parsed_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" + params: + provider = lambda wildcards: config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"][wildcards.provider_key.upper()], + provider_key = "{provider_key}", + sensor_key = "fitbit_sleep_summary" + output: + "data/interim/{pid}/fitbit_sleep_summary_features/fitbit_sleep_summary_r_{provider_key}.csv" + script: + "../src/features/entry.R" + +rule merge_sensor_features_for_individual_participants: + input: + feature_files = input_merge_sensor_features_for_individual_participants + output: + "data/processed/features/{pid}/all_sensor_features.csv" + script: + "../src/features/utils/merge_sensor_features_for_individual_participants.R" + +rule merge_sensor_features_for_all_participants: + input: + feature_files = expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]) + output: + "data/processed/features/all_participants/all_sensor_features.csv" + script: + "../src/features/utils/merge_sensor_features_for_all_participants.R" diff --git a/rules/models.smk b/rules/models.smk index ce6fcba6..1b09663b 100644 --- a/rules/models.smk +++ b/rules/models.smk @@ -1,174 +1,165 @@ -ruleorder: nan_cells_ratio_of_cleaned_features > merge_features_and_targets - -rule days_to_analyse: +rule download_demographic_data: input: - participant_info = "data/raw/{pid}/" + config["PARAMS_FOR_ANALYSIS"]["GROUNDTRUTH_TABLE"] + "_raw.csv" + participant_file = "data/external/participant_files/{pid}.yaml" params: - days_before_surgery = "{days_before_surgery}", - days_in_hospital = "{days_in_hospital}", - days_after_discharge= "{days_after_discharge}" + source = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["SOURCE"], + table = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["TABLE"], output: - "data/interim/{pid}/days_to_analyse_{days_before_surgery}_{days_in_hospital}_{days_after_discharge}.csv" + "data/raw/{pid}/participant_info_raw.csv" script: - "../src/models/select_days_to_analyse.py" - -rule targets: - input: - participant_info = "data/raw/{pid}/" + config["PARAMS_FOR_ANALYSIS"]["TARGET_TABLE"] + "_raw.csv" - params: - pid = "{pid}", - summarised = "{summarised}" - output: - "data/processed/{pid}/targets_{summarised}.csv" - script: - "../src/models/targets.py" + "../src/data/workflow_example/download_demographic_data.R" rule demographic_features: input: - participant_info = "data/raw/{pid}/" + config["PARAMS_FOR_ANALYSIS"]["GROUNDTRUTH_TABLE"] + "_raw.csv" + participant_info = "data/raw/{pid}/participant_info_raw.csv" params: pid = "{pid}", - features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC_FEATURES"] + features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["FEATURES"] output: - "data/processed/{pid}/demographic_features.csv" + "data/processed/features/{pid}/demographic_features.csv" script: - "../src/features/demographic_features.py" + "../src/features/workflow_example/demographic_features.py" -rule merge_features_for_individual_model: +rule download_target_data: input: - feature_files = input_merge_features_of_single_participant, - phone_valid_sensed_days = optional_input_valid_sensed_days, - days_to_include = optional_input_days_to_include + participant_file = "data/external/participant_files/{pid}.yaml" params: - source = "{source}" + source = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["SOURCE"], + table = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["TABLE"], output: - "data/processed/{pid}/data_for_individual_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{source}_{day_segment}_original.csv" + "data/raw/{pid}/participant_target_raw.csv" script: - "../src/models/merge_features_for_individual_model.R" + "../src/data/workflow_example/download_target_data.R" -rule merge_features_for_population_model: +rule target_readable_datetime: input: - feature_files = expand("data/processed/{pid}/data_for_individual_model/{{min_valid_hours_per_day}}hours_{{min_valid_bins_per_hour}}bins/{{source}}_{{day_segment}}_original.csv", pid=config["PIDS"]) - output: - "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{source}_{day_segment}_original.csv" - script: - "../src/models/merge_features_for_population_model.R" - -rule merge_demographicfeatures_for_population_model: - input: - data_files = expand("data/processed/{pid}/demographic_features.csv", pid=config["PIDS"]) - output: - "data/processed/data_for_population_model/demographic_features.csv" - script: - "../src/models/merge_data_for_population_model.py" - -rule merge_targets_for_population_model: - input: - data_files = expand("data/processed/{pid}/targets_{{summarised}}.csv", pid=config["PIDS"]) - output: - "data/processed/data_for_population_model/targets_{summarised}.csv" - script: - "../src/models/merge_data_for_population_model.py" - -rule clean_features_for_individual_model: - input: - rules.merge_features_for_individual_model.output + sensor_input = "data/raw/{pid}/participant_target_raw.csv", + time_segments = "data/interim/time_segments/{pid}_time_segments.csv" params: - features_exclude_day_idx = config["PARAMS_FOR_ANALYSIS"]["FEATURES_EXCLUDE_DAY_IDX"], - cols_nan_threshold = "{cols_nan_threshold}", - cols_var_threshold = "{cols_var_threshold}", - days_before_threshold = "{days_before_threshold}", - days_after_threshold = "{days_after_threshold}", - rows_nan_threshold = "{rows_nan_threshold}", + fixed_timezone = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["SOURCE"]["TIMEZONE"], + time_segments_type = config["TIME_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: - "data/processed/{pid}/data_for_individual_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv" + "data/raw/{pid}/participant_target_with_datetime.csv" script: - "../src/models/clean_features_for_model.R" + "../src/data/readable_datetime.R" -rule clean_features_for_population_model: +rule parse_targets: input: - rules.merge_features_for_population_model.output - params: - features_exclude_day_idx = config["PARAMS_FOR_ANALYSIS"]["FEATURES_EXCLUDE_DAY_IDX"], - cols_nan_threshold = "{cols_nan_threshold}", - cols_var_threshold = "{cols_var_threshold}", - days_before_threshold = "{days_before_threshold}", - days_after_threshold = "{days_after_threshold}", - rows_nan_threshold = "{rows_nan_threshold}", + targets = "data/raw/{pid}/participant_target_with_datetime.csv", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" output: - "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv" + "data/processed/targets/{pid}/parsed_targets.csv" script: - "../src/models/clean_features_for_model.R" + "../src/models/workflow_example/parse_targets.py" -rule nan_cells_ratio_of_cleaned_features: +rule clean_sensor_features_for_individual_participants: input: - cleaned_features = "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv" - output: - "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_nancellsratio.csv" - script: - "../src/models/nan_cells_ratio_of_cleaned_features.py" - -rule merge_features_and_targets: - input: - cleaned_features = "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_clean.csv", - demographic_features = "data/processed/data_for_population_model/demographic_features.csv", - targets = "data/processed/data_for_population_model/targets_{summarised}.csv", + rules.merge_sensor_features_for_individual_participants.output params: - summarised = "{summarised}", - cols_var_threshold = "{cols_var_threshold}", - numerical_operators = config["PARAMS_FOR_ANALYSIS"]["NUMERICAL_OPERATORS"], - categorical_operators = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_OPERATORS"], - features_exclude_day_idx = config["PARAMS_FOR_ANALYSIS"]["FEATURES_EXCLUDE_DAY_IDX"], + cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"], + cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], + rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], + data_yielded_hours_ratio_threshold = config["PARAMS_FOR_ANALYSIS"]["DATA_YIELDED_HOURS_RATIO_THRESHOLD"], output: - "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}.csv" + "data/processed/features/{pid}/all_sensor_features_cleaned.csv" script: - "../src/models/merge_features_and_targets.py" - -rule baseline: + "../src/models/workflow_example/clean_sensor_features.R" + +rule clean_sensor_features_for_all_participants: input: - "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}.csv" + rules.merge_sensor_features_for_all_participants.output + params: + cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"], + cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], + rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], + data_yielded_hours_ratio_threshold = config["PARAMS_FOR_ANALYSIS"]["DATA_YIELDED_HOURS_RATIO_THRESHOLD"], + output: + "data/processed/features/all_participants/all_sensor_features_cleaned.csv" + script: + "../src/models/workflow_example/clean_sensor_features.R" + +rule merge_features_and_targets_for_individual_model: + input: + cleaned_sensor_features = "data/processed/features/{pid}/all_sensor_features_cleaned.csv", + targets = "data/processed/targets/{pid}/parsed_targets.csv", + output: + "data/processed/models/individual_model/{pid}/input.csv" + script: + "../src/models/workflow_example/merge_features_and_targets_for_individual_model.py" + +rule merge_features_and_targets_for_population_model: + input: + cleaned_sensor_features = "data/processed/features/all_participants/all_sensor_features_cleaned.csv", + demographic_features = expand("data/processed/features/{pid}/demographic_features.csv", pid=config["PIDS"]), + targets = expand("data/processed/targets/{pid}/parsed_targets.csv", pid=config["PIDS"]), + output: + "data/processed/models/population_model/input.csv" + script: + "../src/models/workflow_example/merge_features_and_targets_for_population_model.py" + +rule baselines_for_individual_model: + input: + "data/processed/models/individual_model/{pid}/input.csv" params: cv_method = "{cv_method}", - rowsnan_colsnan_days_colsvar_threshold = "{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}", - demographic_features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC_FEATURES"] + colnames_demographic_features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["FEATURES"], output: - "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/baseline/{cv_method}/{source}_{day_segment}_{summarised}.csv" + "data/processed/models/individual_model/{pid}/output_{cv_method}/baselines.csv" log: - "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/baseline/{cv_method}/{source}_{day_segment}_{summarised}_notes.log" + "data/processed/models/individual_model/{pid}/output_{cv_method}/baselines_notes.log" script: - "../src/models/baseline.py" - - -rule modeling: + "../src/models/workflow_example/baselines.py" + +rule baselines_for_population_model: input: - data = "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_{summarised}.csv" + "data/processed/models/population_model/input.csv" + params: + cv_method = "{cv_method}", + colnames_demographic_features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["FEATURES"], + output: + "data/processed/models/population_model/output_{cv_method}/baselines.csv" + log: + "data/processed/models/population_model/output_{cv_method}/baselines_notes.log" + script: + "../src/models/workflow_example/baselines.py" + +rule modelling_for_individual_participants: + input: + data = "data/processed/models/individual_model/{pid}/input.csv" params: model = "{model}", cv_method = "{cv_method}", - source = "{source}", - day_segment = "{day_segment}", - summarised = "{summarised}", scaler = "{scaler}", categorical_operators = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_OPERATORS"], - categorical_demographic_features = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_DEMOGRAPHIC_FEATURES"], + categorical_demographic_features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["CATEGORICAL_FEATURES"], model_hyperparams = config["PARAMS_FOR_ANALYSIS"]["MODEL_HYPERPARAMS"], - rowsnan_colsnan_days_colsvar_threshold = "{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}" output: - fold_predictions = "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/fold_predictions.csv", - fold_metrics = "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/fold_metrics.csv", - overall_results = "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/overall_results.csv", - fold_feature_importances = "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/fold_feature_importances.csv" + fold_predictions = "data/processed/models/individual_model/{pid}/output_{cv_method}/{model}/{scaler}/fold_predictions.csv", + fold_metrics = "data/processed/models/individual_model/{pid}/output_{cv_method}/{model}/{scaler}/fold_metrics.csv", + overall_results = "data/processed/models/individual_model/{pid}/output_{cv_method}/{model}/{scaler}/overall_results.csv", + fold_feature_importances = "data/processed/models/individual_model/{pid}/output_{cv_method}/{model}/{scaler}/fold_feature_importances.csv" log: - "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/notes.log" + "data/processed/models/individual_model/{pid}/output_{cv_method}/{model}/{scaler}/notes.log" script: - "../src/models/modeling.py" + "../src/models/workflow_example/modelling.py" -rule merge_population_model_results: +rule modelling_for_all_participants: input: - overall_results = "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/overall_results.csv", - nan_cells_ratio = "data/processed/data_for_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{day_segment}_nancellsratio.csv", - baseline = "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/baseline/{cv_method}/{source}_{day_segment}_{summarised}.csv" + data = "data/processed/models/population_model/input.csv" + params: + model = "{model}", + cv_method = "{cv_method}", + scaler = "{scaler}", + categorical_operators = config["PARAMS_FOR_ANALYSIS"]["CATEGORICAL_OPERATORS"], + categorical_demographic_features = config["PARAMS_FOR_ANALYSIS"]["DEMOGRAPHIC"]["CATEGORICAL_FEATURES"], + model_hyperparams = config["PARAMS_FOR_ANALYSIS"]["MODEL_HYPERPARAMS"], output: - "data/processed/output_population_model/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{rows_nan_threshold}|{cols_nan_threshold}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{model}/{cv_method}/{source}_{day_segment}_{summarised}_{scaler}/merged_population_model_results.csv" + fold_predictions = "data/processed/models/population_model/output_{cv_method}/{model}/{scaler}/fold_predictions.csv", + fold_metrics = "data/processed/models/population_model/output_{cv_method}/{model}/{scaler}/fold_metrics.csv", + overall_results = "data/processed/models/population_model/output_{cv_method}/{model}/{scaler}/overall_results.csv", + fold_feature_importances = "data/processed/models/population_model/output_{cv_method}/{model}/{scaler}/fold_feature_importances.csv" + log: + "data/processed/models/population_model/output_{cv_method}/{model}/{scaler}/notes.log" script: - "../src/models/merge_population_model_results.py" + "../src/models/workflow_example/modelling.py" diff --git a/rules/preprocessing.smk b/rules/preprocessing.smk index fdcc4f3f..15713020 100644 --- a/rules/preprocessing.smk +++ b/rules/preprocessing.smk @@ -3,7 +3,7 @@ rule restore_sql_file: sql_file = "data/external/rapids_example.sql", db_credentials = ".env" params: - group = config["DOWNLOAD_PARTICIPANTS"]["GROUP"] + group = config["DATABASE_GROUP"] output: touch("data/interim/restore_sql_file.done") script: @@ -11,148 +11,234 @@ rule restore_sql_file: rule create_example_participant_files: output: - expand("data/external/{pid}", pid = ["example01", "example02"]) + expand("data/external/participant_files/{pid}.yaml", pid = ["example01", "example02"]) shell: - "echo 'a748ee1a-1d0b-4ae9-9074-279a2b6ba524\nandroid\ntest01\n2020/04/23,2020/05/04\n' >> ./data/external/example01 && echo '13dbc8a3-dae3-4834-823a-4bc96a7d459d\nios\ntest02\n2020/04/23,2020/05/04\n' >> ./data/external/example02" + "echo 'PHONE:\n DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524]\n PLATFORMS: [android]\n LABEL: test01\n START_DATE: 2020-04-23\n END_DATE: 2020-05-04\nFITBIT:\n DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524]\n LABEL: test01\n START_DATE: 2020-04-23\n END_DATE: 2020-05-04\n' >> ./data/external/participant_files/example01.yaml && echo 'PHONE:\n DEVICE_IDS: [13dbc8a3-dae3-4834-823a-4bc96a7d459d]\n PLATFORMS: [ios]\n LABEL: test02\n START_DATE: 2020-04-23\n END_DATE: 2020-05-04\nFITBIT:\n DEVICE_IDS: [13dbc8a3-dae3-4834-823a-4bc96a7d459d]\n LABEL: test02\n START_DATE: 2020-04-23\n END_DATE: 2020-05-04\n' >> ./data/external/participant_files/example02.yaml" -rule download_participants: - params: - group = config["DOWNLOAD_PARTICIPANTS"]["GROUP"], - ignored_device_ids = config["DOWNLOAD_PARTICIPANTS"]["IGNORED_DEVICE_IDS"], - timezone = config["TIMEZONE"] - priority: 1 - script: - "../src/data/download_participants.R" - -rule download_dataset: +rule create_participants_files: input: - "data/external/{pid}" + participants_file = [] if config["CREATE_PARTICIPANT_FILES"]["SOURCE"]["TYPE"] == "AWARE_DEVICE_TABLE" else config["CREATE_PARTICIPANT_FILES"]["SOURCE"]["CSV_FILE_PATH"] params: - group = config["DOWNLOAD_DATASET"]["GROUP"], - table = "{sensor}", - timezone = config["TIMEZONE"], - aware_multiplatform_tables = config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"] + "," + config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"] + "," + config["CONVERSATION"]["DB_TABLE"]["ANDROID"] + "," + config["CONVERSATION"]["DB_TABLE"]["IOS"], - unifiable_sensors = {"calls": config["CALLS"]["DB_TABLE"], "battery": config["BATTERY"]["DB_TABLE"], "screen": config["SCREEN"]["DB_TABLE"], "ios_activity_recognition": config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"], "ios_conversation": config["CONVERSATION"]["DB_TABLE"]["IOS"]} - output: - "data/raw/{pid}/{sensor}_raw.csv" + config = config["CREATE_PARTICIPANT_FILES"] script: - "../src/data/download_dataset.R" + "../src/data/create_participants_files.R" -PHONE_SENSORS = [] -PHONE_SENSORS.extend([config["MESSAGES"]["DB_TABLE"], config["CALLS"]["DB_TABLE"], config["BARNETT_LOCATION"]["DB_TABLE"], config["DORYAB_LOCATION"]["DB_TABLE"], config["BLUETOOTH"]["DB_TABLE"], config["BATTERY"]["DB_TABLE"], config["SCREEN"]["DB_TABLE"], config["LIGHT"]["DB_TABLE"], config["ACCELEROMETER"]["DB_TABLE"], config["APPLICATIONS_FOREGROUND"]["DB_TABLE"], config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]) -PHONE_SENSORS.extend(config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]) - -if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0: - PHONE_SENSORS.append(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) -if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: - PHONE_SENSORS.append(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) - - -rule readable_datetime: +rule download_phone_data: input: - sensor_input = rules.download_dataset.output + "data/external/participant_files/{pid}.yaml" params: - timezones = None, - fixed_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"] - wildcard_constraints: - sensor = '.*(' + '|'.join([re.escape(x) for x in PHONE_SENSORS]) + ').*' # only process smartphone sensors, not fitbit + source = config["PHONE_DATA_CONFIGURATION"]["SOURCE"], + sensor = "phone_" + "{sensor}", + table = lambda wildcards: config["PHONE_" + str(wildcards.sensor).upper()]["TABLE"], + timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], + aware_multiplatform_tables = config["PHONE_ACTIVITY_RECOGNITION"]["TABLE"]["ANDROID"] + "," + config["PHONE_ACTIVITY_RECOGNITION"]["TABLE"]["IOS"] + "," + config["PHONE_CONVERSATION"]["TABLE"]["ANDROID"] + "," + config["PHONE_CONVERSATION"]["TABLE"]["IOS"], output: - "data/raw/{pid}/{sensor}_with_datetime.csv" + "data/raw/{pid}/phone_{sensor}_raw.csv" + script: + "../src/data/download_phone_data.R" + +rule download_fitbit_data: + input: + participant_file = "data/external/participant_files/{pid}.yaml", + input_file = [] if config["FITBIT_DATA_CONFIGURATION"]["SOURCE"]["TYPE"] == "DATABASE" else lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"] + params: + data_configuration = config["FITBIT_DATA_CONFIGURATION"], + sensor = "fitbit_" + "{sensor}", + table = lambda wildcards: config["FITBIT_" + str(wildcards.sensor).upper()]["TABLE"], + output: + "data/raw/{pid}/fitbit_{sensor}_raw.csv" + script: + "../src/data/download_fitbit_data.R" + +rule compute_time_segments: + input: + config["TIME_SEGMENTS"]["FILE"], + "data/external/participant_files/{pid}.yaml" + params: + time_segments_type = config["TIME_SEGMENTS"]["TYPE"], + pid = "{pid}" + output: + segments_file = "data/interim/time_segments/{pid}_time_segments.csv", + segments_labels_file = "data/interim/time_segments/{pid}_time_segments_labels.csv", + script: + "../src/data/compute_time_segments.py" + +rule phone_readable_datetime: + input: + sensor_input = "data/raw/{pid}/phone_{sensor}_raw.csv", + time_segments = "data/interim/time_segments/{pid}_time_segments.csv" + params: + timezones = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["TYPE"], + fixed_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], + time_segments_type = config["TIME_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] + output: + "data/raw/{pid}/phone_{sensor}_with_datetime.csv" script: "../src/data/readable_datetime.R" -rule phone_sensed_bins: +rule phone_yielded_timestamps: input: - all_sensors = optional_phone_sensed_bins_input + all_sensors = expand("data/raw/{{pid}}/{sensor}_raw.csv", sensor = map(str.lower, config["PHONE_DATA_YIELD"]["SENSORS"])) params: - bin_size = config["PHONE_VALID_SENSED_BINS"]["BIN_SIZE"] + sensors = config["PHONE_DATA_YIELD"]["SENSORS"] # not used but needed so the rule is triggered if this array changes output: - "data/interim/{pid}/phone_sensed_bins.csv" + "data/interim/{pid}/phone_yielded_timestamps.csv" script: - "../src/data/phone_sensed_bins.R" + "../src/data/phone_yielded_timestamps.R" -rule phone_valid_sensed_days: +rule phone_yielded_timestamps_with_datetime: input: - phone_sensed_bins = "data/interim/{pid}/phone_sensed_bins.csv" + sensor_input = "data/interim/{pid}/phone_yielded_timestamps.csv", + time_segments = "data/interim/time_segments/{pid}_time_segments.csv" params: - min_valid_hours_per_day = "{min_valid_hours_per_day}", - min_valid_bins_per_hour = "{min_valid_bins_per_hour}" + timezones = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["TYPE"], + fixed_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], + time_segments_type = config["TIME_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: - "data/interim/{pid}/phone_valid_sensed_days_{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins.csv" + "data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv" script: - "../src/data/phone_valid_sensed_days.R" - + "../src/data/readable_datetime.R" rule unify_ios_android: input: sensor_data = "data/raw/{pid}/{sensor}_with_datetime.csv", - participant_info = "data/external/{pid}" + participant_info = "data/external/participant_files/{pid}.yaml" params: sensor = "{sensor}", - unifiable_sensors = {"calls": config["CALLS"]["DB_TABLE"], "battery": config["BATTERY"]["DB_TABLE"], "screen": config["SCREEN"]["DB_TABLE"], "ios_activity_recognition": config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"], "ios_conversation": config["CONVERSATION"]["DB_TABLE"]["IOS"]} output: "data/raw/{pid}/{sensor}_with_datetime_unified.csv" script: "../src/data/unify_ios_android.R" -rule resample_fused_location: +rule process_phone_locations_types: input: - locations = "data/raw/{pid}/{sensor}_raw.csv", - phone_sensed_bins = rules.phone_sensed_bins.output + locations = "data/raw/{pid}/phone_locations_raw.csv", + phone_sensed_timestamps = "data/interim/{pid}/phone_yielded_timestamps.csv", params: - bin_size = config["PHONE_VALID_SENSED_BINS"]["BIN_SIZE"], - timezone = config["RESAMPLE_FUSED_LOCATION"]["TIMEZONE"], - consecutive_threshold = config["RESAMPLE_FUSED_LOCATION"]["CONSECUTIVE_THRESHOLD"], - time_since_valid_location = config["RESAMPLE_FUSED_LOCATION"]["TIME_SINCE_VALID_LOCATION"] + consecutive_threshold = config["PHONE_LOCATIONS"]["FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD"], + time_since_valid_location = config["PHONE_LOCATIONS"]["FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION"], + locations_to_use = config["PHONE_LOCATIONS"]["LOCATIONS_TO_USE"] output: - "data/raw/{pid}/{sensor}_resampled.csv" + "data/interim/{pid}/phone_locations_processed.csv" script: - "../src/data/resample_fused_location.R" + "../src/data/process_location_types.R" -rule application_genres: +rule phone_locations_processed_with_datetime: input: - "data/raw/{pid}/{sensor}_with_datetime.csv" + sensor_input = "data/interim/{pid}/phone_locations_processed.csv", + time_segments = "data/interim/time_segments/{pid}_time_segments.csv" params: - catalogue_source = config["APPLICATION_GENRES"]["CATALOGUE_SOURCE"], - catalogue_file = config["APPLICATION_GENRES"]["CATALOGUE_FILE"], - update_catalogue_file = config["APPLICATION_GENRES"]["UPDATE_CATALOGUE_FILE"], - scrape_missing_genres = config["APPLICATION_GENRES"]["SCRAPE_MISSING_GENRES"] + timezones = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["TYPE"], + fixed_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], + time_segments_type = config["TIME_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: - "data/interim/{pid}/{sensor}_with_datetime_with_genre.csv" + "data/interim/{pid}/phone_locations_processed_with_datetime.csv" script: - "../src/data/application_genres.R" + "../src/data/readable_datetime.R" -rule fitbit_heartrate_with_datetime: +rule resample_episodes: input: - expand("data/raw/{{pid}}/{fitbit_table}_raw.csv", fitbit_table=config["HEARTRATE"]["DB_TABLE"]) - params: - local_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], - fitbit_sensor = "heartrate" + "data/interim/{pid}/{sensor}_episodes.csv" output: - summary_data = "data/raw/{pid}/fitbit_heartrate_summary_with_datetime.csv", - intraday_data = "data/raw/{pid}/fitbit_heartrate_intraday_with_datetime.csv" + "data/interim/{pid}/{sensor}_episodes_resampled.csv" script: - "../src/data/fitbit_readable_datetime.py" + "../src/features/utils/resample_episodes.R" -rule fitbit_step_with_datetime: +rule resample_episodes_with_datetime: input: - expand("data/raw/{{pid}}/{fitbit_table}_raw.csv", fitbit_table=config["STEP"]["DB_TABLE"]) + sensor_input = "data/interim/{pid}/{sensor}_episodes_resampled.csv", + time_segments = "data/interim/time_segments/{pid}_time_segments.csv" params: - local_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], - fitbit_sensor = "steps" + timezones = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["TYPE"], + fixed_timezone = config["PHONE_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], + time_segments_type = config["TIME_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] output: - intraday_data = "data/raw/{pid}/fitbit_step_intraday_with_datetime.csv" + "data/interim/{pid}/{sensor}_episodes_resampled_with_datetime.csv" script: - "../src/data/fitbit_readable_datetime.py" + "../src/data/readable_datetime.R" -rule fitbit_sleep_with_datetime: +rule phone_application_categories: input: - expand("data/raw/{{pid}}/{fitbit_table}_raw.csv", fitbit_table=config["SLEEP"]["DB_TABLE"]) + "data/raw/{pid}/phone_applications_foreground_with_datetime.csv" params: - local_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], - fitbit_sensor = "sleep" + catalogue_source = config["PHONE_APPLICATIONS_FOREGROUND"]["APPLICATION_CATEGORIES"]["CATALOGUE_SOURCE"], + catalogue_file = config["PHONE_APPLICATIONS_FOREGROUND"]["APPLICATION_CATEGORIES"]["CATALOGUE_FILE"], + update_catalogue_file = config["PHONE_APPLICATIONS_FOREGROUND"]["APPLICATION_CATEGORIES"]["UPDATE_CATALOGUE_FILE"], + scrape_missing_genres = config["PHONE_APPLICATIONS_FOREGROUND"]["APPLICATION_CATEGORIES"]["SCRAPE_MISSING_CATEGORIES"] output: - summary_data = "data/raw/{pid}/fitbit_sleep_summary_with_datetime.csv", - intraday_data = "data/raw/{pid}/fitbit_sleep_intraday_with_datetime.csv" + "data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv" script: - "../src/data/fitbit_readable_datetime.py" + "../src/data/application_categories.R" + +rule fitbit_parse_heartrate: + input: + participant_file = "data/external/participant_files/{pid}.yaml", + raw_data = "data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_raw.csv" + params: + timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], + table = lambda wildcards: config["FITBIT_HEARTRATE_"+str(wildcards.fitbit_data_type).upper()]["TABLE"], + column_format = config["FITBIT_DATA_CONFIGURATION"]["SOURCE"]["COLUMN_FORMAT"], + fitbit_data_type = "{fitbit_data_type}" + output: + "data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_parsed.csv" + script: + "../src/data/fitbit_parse_heartrate.py" + +rule fitbit_parse_steps: + input: + participant_file = "data/external/participant_files/{pid}.yaml", + raw_data = "data/raw/{pid}/fitbit_steps_{fitbit_data_type}_raw.csv" + params: + timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], + table = lambda wildcards: config["FITBIT_STEPS_"+str(wildcards.fitbit_data_type).upper()]["TABLE"], + column_format = config["FITBIT_DATA_CONFIGURATION"]["SOURCE"]["COLUMN_FORMAT"], + fitbit_data_type = "{fitbit_data_type}" + output: + "data/raw/{pid}/fitbit_steps_{fitbit_data_type}_parsed.csv" + script: + "../src/data/fitbit_parse_steps.py" + +rule fitbit_parse_sleep: + input: + participant_file = "data/external/participant_files/{pid}.yaml", + raw_data = "data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_raw.csv" + params: + timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], + table = lambda wildcards: config["FITBIT_SLEEP_"+str(wildcards.fitbit_data_type).upper()]["TABLE"], + column_format = config["FITBIT_DATA_CONFIGURATION"]["SOURCE"]["COLUMN_FORMAT"], + fitbit_data_type = "{fitbit_data_type}", + sleep_episode_timestamp = config["FITBIT_SLEEP_SUMMARY"]["SLEEP_EPISODE_TIMESTAMP"] + output: + "data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_parsed.csv" + script: + "../src/data/fitbit_parse_sleep.py" + +# rule fitbit_parse_calories: +# input: +# data = expand("data/raw/{{pid}}/fitbit_calories_{fitbit_data_type}_raw.csv", fitbit_data_type = (["json"] if config["FITBIT_CALORIES"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"])) +# params: +# timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], +# table = config["FITBIT_CALORIES"]["TABLE"], +# table_format = config["FITBIT_CALORIES"]["TABLE_FORMAT"] +# output: +# summary_data = "data/raw/{pid}/fitbit_calories_summary_parsed.csv", +# intraday_data = "data/raw/{pid}/fitbit_calories_intraday_parsed.csv" +# script: +# "../src/data/fitbit_parse_calories.py" + +rule fitbit_readable_datetime: + input: + sensor_input = "data/raw/{pid}/fitbit_{sensor}_{fitbit_data_type}_parsed.csv", + time_segments = "data/interim/time_segments/{pid}_time_segments.csv" + params: + fixed_timezone = config["FITBIT_DATA_CONFIGURATION"]["TIMEZONE"]["VALUE"], + time_segments_type = config["TIME_SEGMENTS"]["TYPE"], + include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"] + output: + "data/raw/{pid}/fitbit_{sensor}_{fitbit_data_type}_parsed_with_datetime.csv" + script: + "../src/data/readable_datetime.R" diff --git a/rules/reports.smk b/rules/reports.smk index 13064a02..9e30ed35 100644 --- a/rules/reports.smk +++ b/rules/reports.smk @@ -1,76 +1,71 @@ -rule heatmap_features_correlations: +rule histogram_phone_data_yield: input: - features = expand("data/processed/{pid}/{sensor}_{day_segment}.csv", pid=config["PIDS"], sensor=config["HEATMAP_FEATURES_CORRELATIONS"]["PHONE_FEATURES"]+config["HEATMAP_FEATURES_CORRELATIONS"]["FITBIT_FEATURES"], day_segment=config["DAY_SEGMENTS"]), - phone_valid_sensed_days = expand("data/interim/{pid}/phone_valid_sensed_days_{{min_valid_hours_per_day}}hours_{{min_valid_bins_per_hour}}bins.csv", pid=config["PIDS"]) + "data/processed/features/all_participants/all_sensor_features.csv" + output: + "reports/data_exploration/histogram_phone_data_yield.html" + script: + "../src/visualization/histogram_phone_data_yield.py" + +rule heatmap_sensors_per_minute_per_time_segment: + input: + phone_data_yield = "data/interim/{pid}/phone_yielded_timestamps_with_datetime.csv", + participant_file = "data/external/participant_files/{pid}.yaml", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: - min_rows_ratio = config["HEATMAP_FEATURES_CORRELATIONS"]["MIN_ROWS_RATIO"], - corr_threshold = config["HEATMAP_FEATURES_CORRELATIONS"]["CORR_THRESHOLD"], - corr_method = config["HEATMAP_FEATURES_CORRELATIONS"]["CORR_METHOD"] + pid = "{pid}" output: - "reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_features_correlations.html" + "reports/interim/{pid}/heatmap_sensors_per_minute_per_time_segment.html" script: - "../src/visualization/heatmap_features_correlations.py" + "../src/visualization/heatmap_sensors_per_minute_per_time_segment.py" -rule histogram_valid_sensed_hours: +rule merge_heatmap_sensors_per_minute_per_time_segment: input: - phone_valid_sensed_days = expand("data/interim/{pid}/phone_valid_sensed_days_{{min_valid_hours_per_day}}hours_{{min_valid_bins_per_hour}}bins.csv", pid=config["PIDS"]) + heatmap_sensors_per_minute_per_time_segment = expand("reports/interim/{pid}/heatmap_sensors_per_minute_per_time_segment.html", pid=config["PIDS"]) output: - "reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/histogram_valid_sensed_hours.html" + "reports/data_exploration/heatmap_sensors_per_minute_per_time_segment.html" script: - "../src/visualization/histogram_valid_sensed_hours.py" + "../src/visualization/merge_heatmap_sensors_per_minute_per_time_segment.Rmd" -rule heatmap_days_by_sensors: +rule heatmap_sensor_row_count_per_time_segment: input: - sensors = optional_heatmap_days_by_sensors_input, - phone_valid_sensed_days = "data/interim/{pid}/phone_valid_sensed_days_{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins.csv" + all_sensors = expand("data/raw/{{pid}}/{sensor}_with_datetime.csv", sensor = map(str.lower, config["HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT"]["SENSORS"])), + phone_data_yield = "data/processed/features/{pid}/phone_data_yield.csv", + participant_file = "data/external/participant_files/{pid}.yaml", + time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv" params: - pid = "{pid}", - expected_num_of_days = config["HEATMAP_DAYS_BY_SENSORS"]["EXPECTED_NUM_OF_DAYS"] + pid = "{pid}" output: - "reports/interim/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{pid}/heatmap_days_by_sensors.html" + "reports/interim/{pid}/heatmap_sensor_row_count_per_time_segment.html" script: - "../src/visualization/heatmap_days_by_sensors.py" + "../src/visualization/heatmap_sensor_row_count_per_time_segment.py" -rule heatmap_days_by_sensors_all_participants: +rule merge_heatmap_sensor_row_count_per_time_segment: input: - heatmap_rows = expand("reports/interim/{{min_valid_hours_per_day}}hours_{{min_valid_bins_per_hour}}bins/{pid}/heatmap_days_by_sensors.html", pid=config["PIDS"]) + heatmap_sensor_row_count_per_time_segment = expand("reports/interim/{pid}/heatmap_sensor_row_count_per_time_segment.html", pid=config["PIDS"]) output: - "reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_days_by_sensors_all_participants.html" + "reports/data_exploration/heatmap_sensor_row_count_per_time_segment.html" script: - "../src/visualization/heatmap_days_by_sensors_all_participants.Rmd" + "../src/visualization/merge_heatmap_sensor_row_count_per_time_segment.Rmd" -rule heatmap_sensed_bins: +rule heatmap_phone_data_yield_per_participant_per_time_segment: input: - sensor = "data/interim/{pid}/phone_sensed_bins.csv", - pid_file = "data/external/{pid}" + phone_data_yield = expand("data/processed/features/{pid}/phone_data_yield.csv", pid=config["PIDS"]), + participant_file = expand("data/external/participant_files/{pid}.yaml", pid=config["PIDS"]), + time_segments_labels = expand("data/interim/time_segments/{pid}_time_segments_labels.csv", pid=config["PIDS"]) + output: + "reports/data_exploration/heatmap_phone_data_yield_per_participant_per_time_segment.html" + script: + "../src/visualization/heatmap_phone_data_yield_per_participant_per_time_segment.py" + +rule heatmap_feature_correlation_matrix: + input: + all_sensor_features = "data/processed/features/all_participants/all_sensor_features.csv" # before data cleaning params: - pid = "{pid}", - bin_size = config["HEATMAP_SENSED_BINS"]["BIN_SIZE"] + min_rows_ratio = config["HEATMAP_FEATURE_CORRELATION_MATRIX"]["MIN_ROWS_RATIO"], + corr_threshold = config["HEATMAP_FEATURE_CORRELATION_MATRIX"]["CORR_THRESHOLD"], + corr_method = config["HEATMAP_FEATURE_CORRELATION_MATRIX"]["CORR_METHOD"] output: - "reports/interim/heatmap_sensed_bins/{pid}/heatmap_sensed_bins.html" + "reports/data_exploration/heatmap_feature_correlation_matrix.html" script: - "../src/visualization/heatmap_sensed_bins.py" + "../src/visualization/heatmap_feature_correlation_matrix.py" -rule heatmap_sensed_bins_all_participants: - input: - heatmap_sensed_bins = expand("reports/interim/heatmap_sensed_bins/{pid}/heatmap_sensed_bins.html", pid=config["PIDS"]) - output: - "reports/data_exploration/heatmap_sensed_bins_all_participants.html" - script: - "../src/visualization/heatmap_sensed_bins_all_participants.Rmd" - -rule overall_compliance_heatmap: - input: - phone_sensed_bins = expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"]), - phone_valid_sensed_days = expand("data/interim/{pid}/phone_valid_sensed_days_{{min_valid_hours_per_day}}hours_{{min_valid_bins_per_hour}}bins.csv", pid=config["PIDS"]), - pid_files = expand("data/external/{pid}", pid=config["PIDS"]) - params: - only_show_valid_days = config["OVERALL_COMPLIANCE_HEATMAP"]["ONLY_SHOW_VALID_DAYS"], - local_timezone = config["READABLE_DATETIME"]["FIXED_TIMEZONE"], - expected_num_of_days = config["OVERALL_COMPLIANCE_HEATMAP"]["EXPECTED_NUM_OF_DAYS"], - bin_size = config["OVERALL_COMPLIANCE_HEATMAP"]["BIN_SIZE"], - min_bins_per_hour = "{min_valid_bins_per_hour}" - output: - "reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/overall_compliance_heatmap.html" - script: - "../src/visualization/overall_compliance_heatmap.py" diff --git a/sn_profile_rapids/Snakefile b/sn_profile_rapids/Snakefile new file mode 100644 index 00000000..18a469a5 --- /dev/null +++ b/sn_profile_rapids/Snakefile @@ -0,0 +1,231 @@ +import itertools +import hashlib +import collections + +configfile: "config.yaml" +include: "../rules/common.smk" +include: "../rules/renv.snakefile" +include: "../rules/preprocessing.snakefile" +include: "../rules/features.snakefile" +include: "../rules/models.snakefile" +include: "../rules/reports.snakefile" +include: "../rules/mystudy.snakefile" # You can add snakfiles with rules tailored to your project + + + +if len(config["PIDS"]) == 0: + raise ValueError("Add participants IDs to PIDS in config.yaml. Remember to create their participant files in data/external") + +files_to_compute = [] + +if config["PHONE_VALID_SENSED_BINS"]["COMPUTE"]: + if len(config["PHONE_VALID_SENSED_BINS"]["TABLES"]) == 0: + raise ValueError("If you want to compute PHONE_VALID_SENSED_BINS, you need to add at least one table to [PHONE_VALID_SENSED_BINS][TABLES] in config.yaml") + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + +if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: + if len(config["PHONE_VALID_SENSED_BINS"]["TABLES"]) == 0: + raise ValueError("If you want to compute PHONE_VALID_SENSED_DAYS, you need to add at least one table to [PHONE_VALID_SENSED_BINS][TABLES] in config.yaml") + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_valid_sensed_days.csv", pid=config["PIDS"])) + +if config["MESSAGES"]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/messages_{messages_type}_{time_segment}.csv", pid=config["PIDS"], messages_type = config["MESSAGES"]["TYPES"], time_segment = config["MESSAGES"]["TIME_SEGMENTS"])) + +if config["CALLS"]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/calls_{call_type}_{time_segment}.csv", pid=config["PIDS"], call_type=config["CALLS"]["TYPES"], time_segment = config["CALLS"]["TIME_SEGMENTS"])) + +if config["BARNETT_LOCATION"]["COMPUTE"]: + # TODO add files_to_compute.extend(optional_location_input(None)) + if config["BARNETT_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": + if config["BARNETT_LOCATION"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["TABLES"]: + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + else: + raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BARNETT_LOCATION"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/location_barnett_{time_segment}.csv", pid=config["PIDS"], time_segment = config["BARNETT_LOCATION"]["TIME_SEGMENTS"])) + +if config["BLUETOOTH"]["COMPUTE"]: + files_to_compute.extend(expand("data/interim/{sensor}_time_segments.csv", sensor=config["BLUETOOTH"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/bluetooth_features.csv", pid=config["PIDS"] )) + +if config["ACTIVITY_RECOGNITION"]["COMPUTE"]: + # TODO add files_to_compute.extend(optional_ar_input(None)), the Android or iOS table gets processed depending on each participant + files_to_compute.extend(expand("data/processed/{pid}/activity_recognition_{time_segment}.csv",pid=config["PIDS"], time_segment = config["ACTIVITY_RECOGNITION"]["TIME_SEGMENTS"])) + +if config["BATTERY"]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/battery_deltas.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/{pid}/battery_{time_segment}.csv", pid = config["PIDS"], time_segment = config["BATTERY"]["TIME_SEGMENTS"])) + +if config["SCREEN"]["COMPUTE"]: + if config["SCREEN"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["TABLES"]: + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + else: + raise ValueError("Error: Add your screen table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)") + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/screen_deltas.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/processed/{pid}/screen_{time_segment}.csv", pid = config["PIDS"], time_segment = config["SCREEN"]["TIME_SEGMENTS"])) + +if config["LIGHT"]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/light_{time_segment}.csv", pid = config["PIDS"], time_segment = config["LIGHT"]["TIME_SEGMENTS"])) + +if config["ACCELEROMETER"]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/accelerometer_{time_segment}.csv", pid = config["PIDS"], time_segment = config["ACCELEROMETER"]["TIME_SEGMENTS"])) + +if config["APPLICATIONS_FOREGROUND"]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) + files_to_compute.extend(expand("data/interim/{pid}/{sensor}_with_datetime_with_genre.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/applications_foreground_{time_segment}.csv", pid = config["PIDS"], time_segment = config["APPLICATIONS_FOREGROUND"]["TIME_SEGMENTS"])) + +if config["WIFI"]["COMPUTE"]: + files_to_compute.extend(expand("data/interim/{sensor}_time_segments.csv", sensor=config["WIFI"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/wifi_features.csv", pid = config["PIDS"], time_segment = config["WIFI"]["TIME_SEGMENTS"])) + +if config["HEARTRATE"]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["HEARTRATE"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + files_to_compute.extend(expand("data/processed/{pid}/fitbit_heartrate_{time_segment}.csv", pid = config["PIDS"], time_segment = config["HEARTRATE"]["TIME_SEGMENTS"])) + +if config["STEP"]["COMPUTE"]: + if config["STEP"]["EXCLUDE_SLEEP"]["EXCLUDE"] == True and config["STEP"]["EXCLUDE_SLEEP"]["TYPE"] == "FITBIT_BASED": + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["STEP"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_step_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) + files_to_compute.extend(expand("data/processed/{pid}/fitbit_step_{time_segment}.csv", pid = config["PIDS"], time_segment = config["STEP"]["TIME_SEGMENTS"])) + +if config["SLEEP"]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SLEEP"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday", "summary"])) + files_to_compute.extend(expand("data/processed/{pid}/fitbit_sleep_{time_segment}.csv", pid = config["PIDS"], time_segment = config["SLEEP"]["TIME_SEGMENTS"])) + +if config["CONVERSATION"]["COMPUTE"]: + # TODO add files_to_compute.extend(optional_conversation_input(None)), the Android or iOS table gets processed depending on each participant + files_to_compute.extend(expand("data/processed/{pid}/conversation_{time_segment}.csv",pid=config["PIDS"], time_segment = config["CONVERSATION"]["TIME_SEGMENTS"])) + +if config["DORYAB_LOCATION"]["COMPUTE"]: + if config["DORYAB_LOCATION"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": + if config["DORYAB_LOCATION"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["TABLES"]: + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + else: + raise ValueError("Error: Add your locations table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"])) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["DORYAB_LOCATION"]["DB_TABLE"])) + files_to_compute.extend(expand("data/processed/{pid}/location_doryab_{segment}.csv", pid=config["PIDS"], segment = config["DORYAB_LOCATION"]["TIME_SEGMENTS"])) + +if config["PARAMS_FOR_ANALYSIS"]["COMPUTE"]: + rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"] + cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"] + models, scalers, rows_nan_thresholds, cols_nan_thresholds = [], [], [], [] + for model_name in config["PARAMS_FOR_ANALYSIS"]["MODEL_NAMES"]: + models = models + [model_name] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) * len(rows_nan_threshold) + scalers = scalers + config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name] * len(rows_nan_threshold) + rows_nan_thresholds = rows_nan_thresholds + list(itertools.chain.from_iterable([threshold] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) for threshold in rows_nan_threshold)) + cols_nan_thresholds = cols_nan_thresholds + list(itertools.chain.from_iterable([threshold] * len(config["PARAMS_FOR_ANALYSIS"]["MODEL_SCALER"][model_name]) for threshold in cols_nan_threshold)) + results = config["PARAMS_FOR_ANALYSIS"]["RESULT_COMPONENTS"] + ["merged_population_model_results"] + + files_to_compute.extend(expand("data/processed/{pid}/data_for_individual_model/{source}_{time_segment}_original.csv", + pid = config["PIDS"], + source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], + time_segment = config["PARAMS_FOR_ANALYSIS"]["TIME_SEGMENTS"])) + files_to_compute.extend(expand("data/processed/data_for_population_model/{source}_{time_segment}_original.csv", + source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], + time_segment = config["PARAMS_FOR_ANALYSIS"]["TIME_SEGMENTS"])) + files_to_compute.extend(expand( + expand("data/processed/{pid}/data_for_individual_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{time_segment}_clean.csv", + pid = config["PIDS"], + days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], + days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], + cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], + source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], + time_segment = config["PARAMS_FOR_ANALYSIS"]["TIME_SEGMENTS"]), + zip, + rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], + cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) + files_to_compute.extend(expand( + expand("data/processed/data_for_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{time_segment}_clean.csv", + days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], + days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], + cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], + source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], + time_segment = config["PARAMS_FOR_ANALYSIS"]["TIME_SEGMENTS"]), + zip, + rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], + cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) + files_to_compute.extend(expand("data/processed/data_for_population_model/demographic_features.csv")) + files_to_compute.extend(expand("data/processed/data_for_population_model/targets_{summarised}.csv", + summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"])) + files_to_compute.extend(expand( + expand("data/processed/data_for_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{time_segment}_nancellsratio.csv", + days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], + days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], + cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], + source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], + time_segment = config["PARAMS_FOR_ANALYSIS"]["TIME_SEGMENTS"]), + zip, + rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], + cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) + files_to_compute.extend(expand( + expand("data/processed/data_for_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{time_segment}_{summarised}.csv", + days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], + days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], + cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], + source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], + time_segment = config["PARAMS_FOR_ANALYSIS"]["TIME_SEGMENTS"], + summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]), + zip, + rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], + cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) + files_to_compute.extend(expand( + expand("data/processed/output_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{source}_{time_segment}_{summarised}_{cv_method}_baseline.csv", + days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], + days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], + cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], + cv_method = config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"], + source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], + time_segment = config["PARAMS_FOR_ANALYSIS"]["TIME_SEGMENTS"], + summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"]), + zip, + rows_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["ROWS_NAN_THRESHOLD"], + cols_nan_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_NAN_THRESHOLD"])) + files_to_compute.extend(expand( + expand("data/processed/output_population_model/{{rows_nan_threshold}}|{{cols_nan_threshold}}_{days_before_threshold}|{days_after_threshold}_{cols_var_threshold}/{{model}}/{cv_method}/{source}_{time_segment}_{summarised}_{{scaler}}/{result}.csv", + days_before_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_BEFORE_THRESHOLD"], + days_after_threshold = config["PARAMS_FOR_ANALYSIS"]["PARTICIPANT_DAYS_AFTER_THRESHOLD"], + cols_var_threshold = config["PARAMS_FOR_ANALYSIS"]["COLS_VAR_THRESHOLD"], + cv_method = config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"], + source = config["PARAMS_FOR_ANALYSIS"]["SOURCES"], + time_segment = config["PARAMS_FOR_ANALYSIS"]["TIME_SEGMENTS"], + summarised = config["PARAMS_FOR_ANALYSIS"]["SUMMARISED"], + result = results), + zip, + rows_nan_threshold = rows_nan_thresholds, + cols_nan_threshold = cols_nan_thresholds, + model = models, + scaler = scalers)) +rule all: + input: + files_to_compute + +rule clean: + shell: + "rm -rf data/raw/* && rm -rf data/interim/* && rm -rf data/processed/* && rm -rf reports/figures/* && rm -rf reports/*.zip && rm -rf reports/compliance/*" \ No newline at end of file diff --git a/sn_profile_rapids/config.yaml b/sn_profile_rapids/config.yaml new file mode 100644 index 00000000..a68742c2 --- /dev/null +++ b/sn_profile_rapids/config.yaml @@ -0,0 +1,5 @@ +configfile: ./sn_profile_rapids/pipeline_config.yaml +directory: ./ +snakefile: ./sn_profile_rapids/Snakefile +cores: 1 +# forcerun: compute_time_segments \ No newline at end of file diff --git a/sn_profile_rapids/pipeline_config.yaml b/sn_profile_rapids/pipeline_config.yaml new file mode 100644 index 00000000..cbacf8e2 --- /dev/null +++ b/sn_profile_rapids/pipeline_config.yaml @@ -0,0 +1,8 @@ +PIDS: [t01] +DOWNLOAD_DATASET: + GROUP: RAPIDS +BLUETOOTH: + COMPUTE: True + TIME_SEGMENTS: "data/external/timesegments_bluetooth.csv" +WIFI: + COMPUTE: True \ No newline at end of file diff --git a/src/data/application_genres.R b/src/data/application_categories.R similarity index 98% rename from src/data/application_genres.R rename to src/data/application_categories.R index 08944fae..55d8f66b 100644 --- a/src/data/application_genres.R +++ b/src/data/application_categories.R @@ -1,7 +1,7 @@ source("renv/activate.R") library(tidyr) -library(dplyr) +library("dplyr", warn.conflicts = F) library(stringr) library("rvest") diff --git a/src/data/assign_to_time_segment.R b/src/data/assign_to_time_segment.R new file mode 100644 index 00000000..43df1f12 --- /dev/null +++ b/src/data/assign_to_time_segment.R @@ -0,0 +1,175 @@ +library("tidyverse") +library("lubridate", warn.conflicts = F) +options(scipen=999) + +day_type_delay <- function(day_type, include_past_periodic_segments){ + delay <- time_segments %>% mutate(length_duration = duration(length)) %>% filter(repeats_on == day_type) %>% arrange(-length_duration) %>% pull(length_duration) %>% first() + return(if_else(is.na(delay) | include_past_periodic_segments == FALSE, duration("0days"), delay)) +} + +get_segment_dates <- function(data, local_timezone, day_type, delay){ + dates <- data %>% + distinct(local_date) %>% + mutate(local_date_obj = date(lubridate::ymd(local_date, tz = local_timezone))) %>% + complete(local_date_obj = seq(date(min(local_date_obj) - delay), max(local_date_obj), by="days")) %>% + mutate(local_date = replace_na(as.character(date(local_date_obj)))) + + if(day_type == "every_day") + dates <- dates %>% mutate(every_day = 0) + else if (day_type == "wday") + dates <- dates %>% mutate(wday = wday(local_date_obj, week_start = 1)) + else if (day_type == "mday") + dates <- dates %>% mutate(mday = mday(local_date_obj)) + else if (day_type == "qday") + dates <- dates %>% mutate(qday = qday(local_date_obj)) + else if (day_type == "yday") + dates <- dates %>% mutate(yday = yday(local_date_obj)) + return(dates) +} + +assign_rows_to_segments <- function(nested_data, nested_inferred_time_segments){ + nested_data <- nested_data %>% mutate(assigned_segments = "") + for(i in 1:nrow(nested_inferred_time_segments)) { + segment <- nested_inferred_time_segments[i,] + nested_data$assigned_segments <- ifelse(segment$segment_start_ts<= nested_data$timestamp & segment$segment_end_ts >= nested_data$timestamp, + stringi::stri_c(nested_data$assigned_segments, segment$segment_id, sep = "|"), nested_data$assigned_segments) + } + nested_data$assigned_segments <- substring(nested_data$assigned_segments, 2) + return(nested_data) +} + +assign_rows_to_segments_frequency <- function(nested_data, nested_timezone, time_segments){ + for(i in 1:nrow(time_segments)) { + segment <- time_segments[i,] + nested_data$assigned_segments <- ifelse(segment$segment_start_ts<= nested_data$local_time_obj & segment$segment_end_ts >= nested_data$local_time_obj, + # The segment_id is assambled on the fly because it depends on each row's local_date and timezone + stringi::stri_c("[", + segment[["label"]], "#", + nested_data$local_date, " ", + segment[["segment_id_start_time"]], ",", + nested_data$local_date, " ", + segment[["segment_id_end_time"]], ";", + as.numeric(lubridate::as_datetime(stringi::stri_c(nested_data$local_date, segment$segment_id_start_time), tz = nested_timezone)) * 1000, ",", + as.numeric(lubridate::as_datetime(stringi::stri_c(nested_data$local_date, segment$segment_id_end_time), tz = nested_timezone)) * 1000 + 999, + "]"), + nested_data$assigned_segments) + } + return(nested_data) +} + +assign_to_time_segment <- function(sensor_data, time_segments, time_segments_type, include_past_periodic_segments){ + + if(nrow(sensor_data) == 0 || nrow(time_segments) == 0) + return(sensor_data %>% mutate(assigned_segments = NA)) + + if(time_segments_type == "FREQUENCY"){ + + time_segments <- time_segments %>% mutate(start_time = lubridate::hm(start_time), + end_time = start_time + minutes(length) - seconds(1), + segment_id_start_time = paste(str_pad(hour(start_time),2, pad="0"), str_pad(minute(start_time),2, pad="0"), str_pad(second(start_time),2, pad="0"),sep =":"), + segment_id_end_time = paste(str_pad(hour(ymd("1970-01-01") + end_time),2, pad="0"), str_pad(minute(ymd("1970-01-01") + end_time),2, pad="0"), str_pad(second(ymd("1970-01-01") + end_time),2, pad="0"),sep =":"), # add ymd("1970-01-01") to get a real time instead of duration + segment_start_ts = as.numeric(start_time), + segment_end_ts = as.numeric(end_time)) + + sensor_data <- sensor_data %>% mutate(local_time_obj = as.numeric(lubridate::hms(local_time)), + assigned_segments = "") + + sensor_data <- sensor_data %>% + group_by(local_timezone) %>% + nest() %>% + mutate(data = map2(data, local_timezone, assign_rows_to_segments_frequency, time_segments)) %>% + unnest(cols = data) %>% + arrange(timestamp) %>% + select(-local_time_obj) + + return(sensor_data) + + + } else if (time_segments_type == "PERIODIC"){ + + # We need to take into account segment start dates that could include the first day of data + time_segments <- time_segments %>% mutate(length_duration = duration(length)) + every_day_delay <- duration("0days") + wday_delay <- day_type_delay("wday", include_past_periodic_segments) + mday_delay <- day_type_delay("mday", include_past_periodic_segments) + qday_delay <- day_type_delay("qday", include_past_periodic_segments) + yday_delay <- day_type_delay("yday", include_past_periodic_segments) + + sensor_data <- sensor_data %>% + group_by(local_timezone) %>% + nest() %>% + # get existent days that we need to start segments from + mutate(every_date = map2(data, local_timezone, get_segment_dates, "every_day", every_day_delay), + week_dates = map2(data, local_timezone, get_segment_dates, "wday", wday_delay), + month_dates = map2(data, local_timezone, get_segment_dates, "mday", mday_delay), + quarter_dates = map2(data, local_timezone, get_segment_dates, "qday", qday_delay), + year_dates = map2(data, local_timezone, get_segment_dates, "yday", yday_delay), + existent_dates = pmap(list(every_date, week_dates, month_dates, quarter_dates, year_dates), + function(every_date, week_dates, month_dates, quarter_dates, year_dates) reduce(list(every_date, week_dates,month_dates, quarter_dates, year_dates), .f=full_join)), + # build the actual time segments taking into account the users requested length and repeat schedule + inferred_time_segments = map(existent_dates, + ~ crossing(time_segments, .x) %>% + pivot_longer(cols = c(every_day,wday, mday, qday, yday), names_to = "day_type", values_to = "day_value") %>% + filter(repeats_on == day_type & repeats_value == day_value) %>% + # The segment ids (segment_id_start and segment_id_end) are computed in UTC to avoid having different labels for instances of a segment that happen in different timezones + mutate(segment_id_start = lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM")), + segment_id_end = segment_id_start + lubridate::duration(length), + # The actual segments are computed using timestamps taking into account the timezone + segment_start_ts = as.numeric(lubridate::parse_date_time(paste(local_date, start_time), orders = c("Ymd HMS", "Ymd HM"), tz = local_timezone)) * 1000, + segment_end_ts = segment_start_ts + as.numeric(lubridate::duration(length)) * 1000 + 999, + segment_id = paste0("[", + paste0(label,"#", + paste0(lubridate::date(segment_id_start), " ", + paste(str_pad(hour(segment_id_start),2, pad="0"), str_pad(minute(segment_id_start),2, pad="0"), str_pad(second(segment_id_start),2, pad="0"),sep =":"), ",", + lubridate::date(segment_id_end), " ", + paste(str_pad(hour(segment_id_end),2, pad="0"), str_pad(minute(segment_id_end),2, pad="0"), str_pad(second(segment_id_end),2, pad="0"),sep =":")),";", + paste0(segment_start_ts, ",", segment_end_ts)), + "]")) %>% + # drop time segments with an invalid start or end time (mostly due to daylight saving changes, e.g. 2020-03-08 02:00:00 EST does not exist, clock jumps from 01:59am to 03:00am) + drop_na(segment_start_ts, segment_end_ts)), + data = map2(data, inferred_time_segments, assign_rows_to_segments) + ) %>% + select(-existent_dates, -inferred_time_segments, -every_date, -week_dates, -month_dates, -quarter_dates, -year_dates) %>% + unnest(cols = data) %>% + arrange(timestamp) + + } else if ( time_segments_type == "EVENT"){ + + sensor_data <- sensor_data %>% + group_by(local_timezone) %>% + nest() %>% + mutate(inferred_time_segments = map(local_timezone, function(tz){ + inferred <- time_segments %>% + mutate(shift = ifelse(shift == "0", "0seconds", shift), + segment_start_ts = event_timestamp + (as.integer(seconds(lubridate::duration(shift))) * ifelse(shift_direction >= 0, 1, -1) * 1000), + segment_end_ts = segment_start_ts + (as.integer(seconds(lubridate::duration(length))) * 1000), + # these start and end datetime objects are for labeling only + segment_id_start = lubridate::as_datetime(segment_start_ts/1000, tz = tz), + segment_id_end = lubridate::as_datetime(segment_end_ts/1000, tz = tz), + segment_end_ts = segment_end_ts + 999, + segment_id = paste0("[", + paste0(label,"#", + paste0(lubridate::date(segment_id_start), " ", + paste(str_pad(hour(segment_id_start),2, pad="0"), str_pad(minute(segment_id_start),2, pad="0"), str_pad(second(segment_id_start),2, pad="0"),sep =":"), ",", + lubridate::date(segment_id_end), " ", + paste(str_pad(hour(segment_id_end),2, pad="0"), str_pad(minute(segment_id_end),2, pad="0"), str_pad(second(segment_id_end),2, pad="0"),sep =":")),";", + paste0(segment_start_ts, ",", segment_end_ts)), + "]")) + # Check that for overlapping segments (not allowed because our resampling episode algorithm would have to have a second instead of minute granularity that increases storage and computation time) + overlapping <- inferred %>% group_by(label) %>% arrange(segment_start_ts) %>% + mutate(overlaps = if_else(segment_start_ts <= lag(segment_end_ts), TRUE, FALSE), + overlapping_segments = paste(paste(lag(label), lag(event_timestamp), lag(length), lag(shift), lag(shift_direction), lag(device_id), sep = ","),"and", + paste(label, event_timestamp, length, shift, shift_direction, device_id, sep = ","))) + if(any(overlapping$overlaps, na.rm = TRUE)){ + stop(paste0("\n\nOne or more event time segments overlap for ",overlapping$device_id[[1]],", modify their lengths so they don't:\n", paste0(overlapping %>% filter(overlaps == TRUE) %>% pull(overlapping_segments), collapse = "\n"), "\n\n")) + } else{ + return(inferred) + }}), + data = map2(data, inferred_time_segments, assign_rows_to_segments)) %>% + select(-inferred_time_segments) %>% + unnest(data) %>% + arrange(timestamp) + } + + return(sensor_data) +} \ No newline at end of file diff --git a/src/data/compute_time_segments.py b/src/data/compute_time_segments.py new file mode 100644 index 00000000..adf50958 --- /dev/null +++ b/src/data/compute_time_segments.py @@ -0,0 +1,216 @@ +import pandas as pd +import warnings +import yaml + +def is_valid_frequency_segments(time_segments, time_segments_file): + """ + returns true if time_segment has the expected structure for generating frequency segments; + raises ValueError exception otherwise. + """ + + valid_columns = ["label", "length"] + if set(time_segments.columns) != set(valid_columns): + error_message = 'The FREQUENCY time segments file in [TIME_SEGMENTS][FILE] must have two columns: label, and length ' \ + 'but instead we found {}. Modify {}'.format(list(time_segments.columns), time_segments_file) + raise ValueError(error_message) + + if time_segments.shape[0] > 1: + message = 'The FREQUENCY time segments file in [TIME_SEGMENTS][FILE] can only have 1 row.' \ + 'Modify {}'.format(time_segments_file) + raise ValueError(message) + + if not pd.api.types.is_integer_dtype(time_segments.dtypes['length']): + message = 'The column length in the FREQUENCY time segments file in [TIME_SEGMENTS][FILE] must be integer but instead is ' \ + '{}. . This usually means that not all values in this column are formed by digits. Modify {}'.format(time_segments.dtypes['length'], time_segments_file) + raise ValueError(message) + + if time_segments.iloc[0].loc['length'] < 0: + message = 'The value in column length in the FREQUENCY time segments file in [TIME_SEGMENTS][FILE] must be positive but instead is ' \ + '{}. Modify {}'.format(time_segments.iloc[0].loc['length'], time_segments_file) + raise ValueError(message) + if time_segments.iloc[0].loc['length'] >= 1440: + message = 'The column length in the FREQUENCY time segments file in [TIME_SEGMENTS][FILE] must be shorter than a day in minutes (1440) but instead is ' \ + '{}. Modify {}'.format(time_segments.iloc[0].loc['length'], time_segments_file) + raise ValueError(message) + + return True + +def is_valid_periodic_segments(time_segments, time_segments_file): + time_segments = time_segments.copy(deep=True) + + valid_columns = ["label", "start_time", "length", "repeats_on", "repeats_value"] + if set(time_segments.columns) != set(valid_columns): + error_message = 'The PERIODIC time segments file in [TIME_SEGMENTS][FILE] must have five columns: label, start_time, length, repeats_on, repeats_value ' \ + 'but instead we found {}. Modify {}'.format(list(time_segments.columns), time_segments_file) + raise ValueError(error_message) + + valid_repeats_on = ["every_day", "wday", "mday", "qday", "yday"] + if len(list(set(time_segments["repeats_on"]) - set(valid_repeats_on))) > 0: + error_message = 'The column repeats_on in the PERIODIC time segments file in [TIME_SEGMENTS][FILE] can only accept: "every_day", "wday", "mday", "qday", or "yday" ' \ + 'but instead we found {}. Modify {}'.format(list(set(time_segments["repeats_on"])), time_segments_file) + raise ValueError(error_message) + + if not pd.api.types.is_integer_dtype(time_segments.dtypes['repeats_value']): + message = 'The column repeats_value in the PERIODIC time segments file in [TIME_SEGMENTS][FILE] must be integer but instead is ' \ + '{}. . This usually means that not all values in this column are formed by digits. Modify {}'.format(time_segments.dtypes['repeats_value'], time_segments_file) + raise ValueError(message) + + invalid_time_segments = time_segments.query("repeats_on == 'every_day' and repeats_value != 0") + if invalid_time_segments.shape[0] > 0: + message = 'Every row with repeats_on=every_day must have a repeats_value=0 in the PERIODIC time segments file in [TIME_SEGMENTS][FILE].' \ + ' Modify row(s) of segment(s) {} of {}'.format(invalid_time_segments["label"].to_numpy(), time_segments_file) + raise ValueError(message) + + invalid_time_segments = time_segments.query("repeats_on == 'wday' and (repeats_value < 1 | repeats_value > 7)") + if invalid_time_segments.shape[0] > 0: + message = 'Every row with repeats_on=wday must have a repeats_value=[1,7] in the PERIODIC time segments file in [TIME_SEGMENTS][FILE].' \ + ' Modify row(s) of segment(s) {} of {}'.format(invalid_time_segments["label"].to_numpy(), time_segments_file) + raise ValueError(message) + + invalid_time_segments = time_segments.query("repeats_on == 'mday' and (repeats_value < 1 | repeats_value > 31)") + if invalid_time_segments.shape[0] > 0: + message = 'Every row with repeats_on=mday must have a repeats_value=[1,31] in the PERIODIC time segments file in [TIME_SEGMENTS][FILE].' \ + ' Modify row(s) of segment(s) {} of {}'.format(invalid_time_segments["label"].to_numpy(), time_segments_file) + raise ValueError(message) + + invalid_time_segments = time_segments.query("repeats_on == 'qday' and (repeats_value < 1 | repeats_value > 92)") + if invalid_time_segments.shape[0] > 0: + message = 'Every row with repeats_on=qday must have a repeats_value=[1,92] in the PERIODIC time segments file in [TIME_SEGMENTS][FILE].' \ + ' Modify row(s) of segment(s) {} of {}'.format(invalid_time_segments["label"].to_numpy(), time_segments_file) + raise ValueError(message) + + invalid_time_segments = time_segments.query("repeats_on == 'yday' and (repeats_value < 1 | repeats_value > 366)") + if invalid_time_segments.shape[0] > 0: + message = 'Every row with repeats_on=yday must have a repeats_value=[1,366] in the PERIODIC time segments file in [TIME_SEGMENTS][FILE].' \ + ' Modify row(s) of segment(s) {} of {}'.format(invalid_time_segments["label"].to_numpy(), time_segments_file) + raise ValueError(message) + + try: + time_segments["start_time"] = pd.to_datetime(time_segments["start_time"]) + except ValueError as err: + raise ValueError("At least one start_time in the PERIODIC time segments file in [TIME_SEGMENTS][FILE] has an invalid format, it should be HH:MM:SS in 24hr clock({}). Modify {}".format(err, time_segments_file)) + + if(time_segments.shape[0] != time_segments.drop_duplicates().shape[0]): + error_message = 'The PERIODIC time segments file in [TIME_SEGMENTS][FILE] has two or more rows that are identical. ' \ + 'Modify {}'.format(time_segments_file) + raise ValueError(error_message) + + duplicated_labels = time_segments[time_segments["label"].duplicated()] + if(duplicated_labels.shape[0] > 0): + error_message = 'Segements labels must be unique. The PERIODIC time segments file in [TIME_SEGMENTS][FILE] has {} row(s) with the same label {}. ' \ + 'Modify {}'.format(duplicated_labels.shape[0], duplicated_labels["label"].to_numpy(), time_segments_file) + raise ValueError(error_message) + + # TODO Validate string format for lubridate + + return True + +def is_valid_event_segments(time_segments, time_segments_file): + time_segments = time_segments.copy(deep=True) + + valid_columns = ["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"] + if set(time_segments.columns) != set(valid_columns): + error_message = 'The EVENT time segments file in [TIME_SEGMENTS][FILE] must have six columns: label, event_timestamp, length, shift, shift_direction and device_id ' \ + 'but instead we found {}. Modify {}'.format(list(time_segments.columns), time_segments_file) + raise ValueError(error_message) + + if not pd.api.types.is_integer_dtype(time_segments.dtypes['event_timestamp']): + message = 'The column event_timestamp in the EVENT time segments file in [TIME_SEGMENTS][FILE] must be integer but instead is ' \ + '{}. This usually means that not all values in this column are formed by digits. Modify {}'.format(time_segments.dtypes['event_timestamp'], time_segments_file) + raise ValueError(message) + + valid_shift_direction_values = [1, -1, 0] + provided_values = time_segments["shift_direction"].unique() + if len(list(set(provided_values) - set(valid_shift_direction_values))) > 0: + error_message = 'The values of shift_direction column in the EVENT time segments file in [TIME_SEGMENTS][FILE] can only be 1, -1 or 0 ' \ + 'but instead we found {}. Modify {}'.format(provided_values, time_segments_file) + raise ValueError(error_message) + + if(time_segments.shape[0] != time_segments.drop_duplicates().shape[0]): + error_message = 'The EVENT time segments file in [TIME_SEGMENTS][FILE] has two or more rows that are identical. ' \ + 'Modify {}'.format(time_segments_file) + raise ValueError(error_message) + + # TODO Validate string format for lubridate of length and shift + # TODO validate unique labels per participant + return True + + +def parse_frequency_segments(time_segments: pd.DataFrame) -> pd.DataFrame: + """ + returns a table with rows identifying start and end of time slots with frequency freq (in minutes). For example, + for freq = 10 it outputs: + bin_id start end label + 0 00:00 00:10 epoch_0000 + 1 00:10 00:20 epoch_0001 + 2 00:20 00:30 epoch_0002 + ... + 143 23:50 00:00 epoch_0143 + time_segments argument is expected to have the following structure: + label length + epoch 10 + """ + freq = time_segments.iloc[0].loc['length'] + slots = pd.date_range(start='2020-01-01', end='2020-01-02', freq='{}min'.format(freq)) + slots = ['{:02d}:{:02d}'.format(x.hour, x.minute) for x in slots] + + table = pd.DataFrame(slots, columns=['start_time']) + table['length'] = time_segments.iloc[0].loc['length'] + table = table.iloc[:-1, :] + + label = time_segments.loc[0, 'label'] + table['label'] = range(0, table.shape[0]) + table['label'] = table['label'].apply(lambda x: '{}{:04}'.format(label, x)) + + return table[['start_time', 'length', 'label']] + +def parse_periodic_segments(time_segments): + time_segments.loc[time_segments["repeats_on"] == "every_day", "repeats_value"] = 0 + return time_segments + +def parse_event_segments(time_segments, device_ids): + return time_segments.query("device_id == @device_ids") + +def parse_time_segments(time_segments_file, segments_type, device_ids): + # Add code to validate and parse frequencies, intervals, and events + # Expected formats: + # Frequency: label, length columns (e.g. my_prefix, 5) length has to be in minutes (int) + # Interval: label, start, end columns (e.g. daily, 00:00, 23:59) start and end should be valid hours in 24 hour format + # Event: label, timestamp, length, shift (e.g., survey1, 1532313215463, 60, -30), timestamp is a UNIX timestamp in ms (we could take a date time string instead), length is in minutes (int), shift is in minutes (+/-int) and is added/substracted from timestamp + # Our output should have local_date, start_time, end_time, label. In the readable_datetime script, If local_date has the same value for all rows, every segment will be applied for all days, otherwise each segment will be applied only to its local_date + time_segments = pd.read_csv(time_segments_file) + + if time_segments is None: + message = 'The time segments file in [TIME_SEGMENTS][FILE] is None. Modify {}'.format(time_segments_file) + raise ValueError(message) + + if time_segments.shape[0] == 0: + message = 'The time segments file in [TIME_SEGMENTS][FILE] is empty. Modify {}'.format(time_segments_file) + raise ValueError(message) + + if(segments_type not in ["FREQUENCY", "PERIODIC", "EVENT"]): + raise ValueError("[TIME_SEGMENTS][TYPE] can only be FREQUENCY, PERIODIC, or EVENT") + + if(segments_type == "FREQUENCY" and is_valid_frequency_segments(time_segments, time_segments_file)): + time_segments = parse_frequency_segments(time_segments) + elif(segments_type == "PERIODIC" and is_valid_periodic_segments(time_segments, time_segments_file)): + time_segments = parse_periodic_segments(time_segments) + elif(segments_type == "EVENT" and is_valid_event_segments(time_segments, time_segments_file)): + time_segments = parse_event_segments(time_segments, device_ids) + else: + raise ValueError("{} does not have a format compatible with frequency, periodic or event time segments. Please refer to [LINK]".format(time_segments_file)) + return time_segments + +participant_file = yaml.load(open(snakemake.input[1], 'r'), Loader=yaml.FullLoader) +device_ids = [] +for key in participant_file.keys(): + if "DEVICE_IDS" in participant_file[key]: + device_ids = device_ids + participant_file[key]["DEVICE_IDS"] + +final_time_segments = parse_time_segments(snakemake.input[0], snakemake.params["time_segments_type"], device_ids) + +if snakemake.params["time_segments_type"] == "EVENT" and final_time_segments.shape[0] == 0: + warnings.warn("There are no event time segments for {}. Check your time segment file {}".format(snakemake.params["pid"], snakemake.input[0])) + +final_time_segments.to_csv(snakemake.output["segments_file"], index=False) +pd.DataFrame({"label" : final_time_segments["label"].unique()}).to_csv(snakemake.output["segments_labels_file"], index=False) \ No newline at end of file diff --git a/src/data/create_participants_files.R b/src/data/create_participants_files.R new file mode 100644 index 00000000..0aba554f --- /dev/null +++ b/src/data/create_participants_files.R @@ -0,0 +1,83 @@ +source("renv/activate.R") + +library(RMariaDB) +library(stringr) +library(purrr) +library(readr) +library("dplyr", warn.conflicts = F) + +config <- snakemake@params[["config"]] +group <- config$SOURCE$DATABASE_GROUP +timezone <- config$SOURCE$TIMEZONE +phone_device_id_column = config$PHONE_SECTION$DEVICE_ID_COLUMN +fitbit_device_id_column = config$FITBIT_SECTION$DEVICE_ID_COLUMN +add_fitbit_section = config$PHONE_SECTION$ADD +add_phone_section = config$FITBIT_SECTION$ADD +phone_ignored = config$PHONE_SECTION$IGNORED_DEVICE_IDS +fitbit_ignored = config$FITBIT_SECTION$IGNORED_DEVICE_IDS + +rmysql.settingsfile <- "./.env" + +if(config$SOURCE$TYPE == "AWARE_DEVICE_TABLE"){ + database <- dbConnect(MariaDB(), default.file = rmysql.settingsfile, group = group) + if(config$FITBIT_SECTION$ADD == TRUE){ + query <- paste("SELECT",phone_device_id_column, ",",fitbit_device_id_column," as _temp_fitbit_id, brand, label, timestamp FROM aware_device order by timestamp asc") + fitbit_device_id_column <- "_temp_fitbit_id" + } + else + query <- paste("SELECT ",phone_device_id_column,", brand, label, timestamp FROM aware_device order by timestamp asc") + participants <- dbGetQuery(database, query) + dbDisconnect(database) + participants <- participants %>% + mutate(pid = if_else(row_number()<10, paste0("p","0",row_number()), paste0("p", row_number())), + platform = if_else(brand == "iPhone", "ios", "android"), brand = NULL, + label = iconv(if_else(label == "", "EMPTY_LABEL", label), from = "UTF-8", to = "UTF-8", sub=''), + start_date = format(as.POSIXct(timestamp / 1000, origin = "1970-01-01", tz = timezone), "%Y-%m-%d"), + end_date = format(Sys.Date(), "%Y-%m-%d"), + !!phone_device_id_column := if_else(!!rlang::sym(phone_device_id_column) %in% phone_ignored, NA_character_, !!rlang::sym(phone_device_id_column)), + !!fitbit_device_id_column := if_else(!!rlang::sym(fitbit_device_id_column) %in% fitbit_ignored, NA_character_, !!rlang::sym(fitbit_device_id_column))) + +} else if(config$SOURCE$TYPE == "CSV_FILE"){ + participants <- read_csv(config$SOURCE$CSV_FILE_PATH, col_types=cols_only(device_id="c",pid="c",label="c",platform="c", + start_date=col_date(format = "%Y-%m-%d"),end_date=col_date(format = "%Y-%m-%d"),fitbit_id="c")) + participants <- participants %>% + mutate(!!phone_device_id_column := str_replace(!!rlang::sym(phone_device_id_column), ";",","), + platform = str_replace(platform, ";",","), + !!phone_device_id_column := if_else(!!rlang::sym(phone_device_id_column) %in% phone_ignored, NA_character_, !!rlang::sym(phone_device_id_column)), + !!fitbit_device_id_column := if_else(!!rlang::sym(fitbit_device_id_column) %in% fitbit_ignored, NA_character_, !!rlang::sym(fitbit_device_id_column))) +} + +dir.create(file.path("./data/external/participant_files/")) + +participants %>% + pwalk(function(add_phone_section, add_fitbit_section, phone_device_id_column, fitbit_device_id_column, ...) { + empty_phone <- c("PHONE:", " DEVICE_IDS:", " PLATFORMS:"," LABEL:", " START_DATE:", " END_DATE:") + empty_fitbit <- c("FITBIT:", " DEVICE_IDS:", " LABEL:", " START_DATE:", " END_DATE:") + row <- tibble(...) + lines <- c() + + if(add_phone_section == TRUE && !is.na(row[phone_device_id_column])){ + lines <- append(lines, c("PHONE:", paste0(" DEVICE_IDS: [",row[phone_device_id_column],"]"), paste0(" PLATFORMS: [",row$platform,"]"), + paste(" LABEL:",row$label), paste(" START_DATE:", row$start_date), paste(" END_DATE:", row$end_date))) + }else + lines <- append(lines, empty_phone) + + if(add_fitbit_section == TRUE && !is.na(row[fitbit_device_id_column])){ + lines <- append(lines, c("FITBIT:", paste0(" DEVICE_IDS: [",row[fitbit_device_id_column],"]"), + paste(" LABEL:",row$label), paste(" START_DATE:", row$start_date), paste(" END_DATE:", row$end_date))) + } else + lines <- append(lines, empty_fitbit) + + file_connection <- file(paste0("./data/external/participant_files/", row$pid, ".yaml")) + writeLines(lines, file_connection) + close(file_connection) + + }, add_phone_section, add_fitbit_section, phone_device_id_column, fitbit_device_id_column) + +file_lines <-readLines("./config.yaml") +for (i in 1:length(file_lines)){ + if(startsWith(file_lines[i], "PIDS:")){ + file_lines[i] <- paste0("PIDS: [", paste(participants$pid, collapse = ", "), "]") + } +} +writeLines(file_lines, con = "./config.yaml") \ No newline at end of file diff --git a/src/data/download_dataset.R b/src/data/download_dataset.R deleted file mode 100644 index 7eadbf41..00000000 --- a/src/data/download_dataset.R +++ /dev/null @@ -1,87 +0,0 @@ -source("renv/activate.R") -source("src/data/unify_utils.R") -library(RMySQL) -library(stringr) -library(dplyr) -library(readr) - -validate_deviceid_platforms <- function(device_ids, platforms){ - if(length(device_ids) == 1){ - if(length(platforms) > 1 || (platforms != "android" && platforms != "ios")) - stop(paste0("If you have 1 device_id, its platform should be 'android' or 'ios' but you typed: '", paste0(platforms, collapse = ","), "'. Participant file: ", participant)) - } else if(length(device_ids) > 1 && length(platforms) == 1){ - if(platforms != "android" && platforms != "ios" && platforms != "multiple") - stop(paste0("If you have more than 1 device_id, platform should be 'android', 'ios' OR 'multiple' but you typed: '", paste0(platforms, collapse = "s,"), "'. Participant file: ", participant)) - } else if(length(device_ids) > 1 && length(platforms) > 1){ - if(length(device_ids) != length(platforms)) - stop(paste0("The number of device_ids should match the number of platforms. Participant file:", participant)) - if(all(intersect(c("android", "ios"), unique(platforms)) != c("android", "ios"))) - stop(paste0("If you have more than 1 device_id and more than 1 platform, the platforms should be a mix of 'android' AND 'ios' but you typed: '", paste0(platforms, collapse = ","), "'. Participant file: ", participant)) - } -} - -is_multiplaform_participant <- function(dbEngine, device_ids, platforms){ - # Multiple android and ios platforms or the same platform (android, ios) for multiple devices - if((length(device_ids) > 1 && length(platforms) > 1) || (length(device_ids) > 1 && length(platforms) == 1 && (platforms == "android" || platforms == "ios"))){ - return(TRUE) - } - # Multiple platforms for multiple devices, we search the platform for every device in the aware_device table - if(length(device_ids) > 1 && length(platforms) == 1 && platforms == "multiple"){ - devices_platforms <- dbGetQuery(dbEngine, paste0("SELECT device_id,brand FROM aware_device WHERE device_id IN ('", paste0(device_ids, collapse = "','"), "')")) - platforms <- devices_platforms %>% distinct(brand) %>% pull(brand) - # Android phones have different brands so we check that we got at least two different platforms and one of them is iPhone - if(length(platforms) > 1 && "iPhone" %in% platforms){ - return(TRUE) - } - } - return(FALSE) -} - -participant <- snakemake@input[[1]] -group <- snakemake@params[["group"]] -table <- snakemake@params[["table"]] -timezone <- snakemake@params[["timezone"]] -aware_multiplatform_tables <- str_split(snakemake@params[["aware_multiplatform_tables"]], ",")[[1]] -unifiable_tables = snakemake@params[["unifiable_sensors"]] -sensor_file <- snakemake@output[[1]] - -device_ids <- strsplit(readLines(participant, n=1), ",")[[1]] -unified_device_id <- tail(device_ids, 1) -platforms <- strsplit(readLines(participant, n=2)[[2]], ",")[[1]] -validate_deviceid_platforms(device_ids, platforms) - -# Read start and end date from the participant file to filter data within that range -start_date <- strsplit(readLines(participant, n=4)[4], ",")[[1]][1] -end_date <- strsplit(readLines(participant, n=4)[4], ",")[[1]][2] -start_datetime_utc = format(as.POSIXct(paste0(start_date, " 00:00:00"),format="%Y/%m/%d %H:%M:%S",origin="1970-01-01",tz=timezone), tz="UTC") -end_datetime_utc = format(as.POSIXct(paste0(end_date, " 23:59:59"),format="%Y/%m/%d %H:%M:%S",origin="1970-01-01",tz=timezone), tz="UTC") - -dbEngine <- dbConnect(MySQL(), default.file = "./.env", group = group) - -# Get existent columns in table -available_columns <- colnames(dbGetQuery(dbEngine, paste0("SELECT * FROM ", table, " LIMIT 1"))) - -if("device_id" %in% available_columns){ - if(is_multiplaform_participant(dbEngine, device_ids, platforms)){ - sensor_data <- unify_raw_data(dbEngine, table, start_datetime_utc, end_datetime_utc, aware_multiplatform_tables, unifiable_tables, device_ids, platforms) - }else { - query <- paste0("SELECT * FROM ", table, " WHERE device_id IN ('", paste0(device_ids, collapse = "','"), "')") - if("timestamp" %in% available_columns && !(is.na(start_datetime_utc)) && !(is.na(end_datetime_utc)) && start_datetime_utc < end_datetime_utc) - query <- paste0(query, "AND timestamp BETWEEN 1000*UNIX_TIMESTAMP('", start_datetime_utc, "') AND 1000*UNIX_TIMESTAMP('", end_datetime_utc, "')") - sensor_data <- dbGetQuery(dbEngine, query) - } - - if("timestamp" %in% available_columns) - sensor_data <- sensor_data %>% arrange(timestamp) - - # Unify device_id - sensor_data <- sensor_data %>% mutate(device_id = unified_device_id) - - # Droping duplicates on all columns except for _id or id - sensor_data <- sensor_data %>% distinct(!!!syms(setdiff(names(sensor_data), c("_id", "id")))) - -} else - stop(paste0("Table ", table, "does not have a device_id column (Aware ID) to link its data to a participant")) - -write_csv(sensor_data, sensor_file) -dbDisconnect(dbEngine) \ No newline at end of file diff --git a/src/data/download_fitbit_data.R b/src/data/download_fitbit_data.R new file mode 100644 index 00000000..54ac4346 --- /dev/null +++ b/src/data/download_fitbit_data.R @@ -0,0 +1,46 @@ +source("renv/activate.R") +library(RMariaDB) +library("dplyr", warn.conflicts = F) +library(readr) +library(stringr) +library(yaml) + + +participant_file <- snakemake@input[["participant_file"]] +input_file <- snakemake@input[["input_file"]] +data_configuration <- snakemake@params[["data_configuration"]] +source <- data_configuration$SOURCE +sensor <- snakemake@params[["sensor"]] +table <- snakemake@params[["table"]] +sensor_file <- snakemake@output[[1]] + +participant <- read_yaml(participant_file) +if(! "FITBIT" %in% names(participant)){ + stop(paste("The following participant file does not have a FITBIT section, create one manually or automatically (see the docs):", participant_file)) +} +device_ids <- participant$FITBIT$DEVICE_IDS +unified_device_id <- tail(device_ids, 1) +# As opposed to phone data, we dont' filter by date here because data can still be in JSON format, we need to parse it first + +if(source$TYPE == "DATABASE"){ + dbEngine <- dbConnect(MariaDB(), default.file = "./.env", group = source$DATABASE_GROUP) + query <- paste0("SELECT * FROM ", table, " WHERE ",source$DEVICE_ID_COLUMN," IN ('", paste0(device_ids, collapse = "','"), "')") + sensor_data <- dbGetQuery(dbEngine, query) + dbDisconnect(dbEngine) +} else if(source$TYPE == "FILES"){ + sensor_data <- read_csv_chunked(input_file, callback = DataFrameCallback$new(function(x, pos) subset(x,x[[source$DEVICE_ID_COLUMN]] %in% device_ids)), progress = T, chunk_size = 50000) + if(is.null(sensor_data)) # emtpy file + sensor_data <- read.csv(input_file) +} + +sensor_data <- sensor_data %>% + rename(device_id = source$DEVICE_ID_COLUMN) %>% + mutate(device_id = unified_device_id) # Unify device_id + +if("HIDDEN" %in% names(data_configuration) && data_configuration$HIDDEN$SINGLE_FITBIT_TABLE == TRUE) # For MoSHI use, we didn't split fitbit sensors into different tables + sensor_data <- sensor_data %>% filter(fitbit_data_type == str_split(sensor, "_", simplify = TRUE)[[2]]) + +# Droping duplicates on all columns except for _id or id +sensor_data <- sensor_data %>% distinct(!!!syms(setdiff(names(sensor_data), c("_id", "id")))) + +write_csv(sensor_data, sensor_file) \ No newline at end of file diff --git a/src/data/download_participants.R b/src/data/download_participants.R deleted file mode 100644 index d3299804..00000000 --- a/src/data/download_participants.R +++ /dev/null @@ -1,38 +0,0 @@ -source("renv/activate.R") - -library(RMySQL) - -group <- snakemake@params[["group"]] -ignored_device_ids <- snakemake@params[["ignored_device_ids"]] -timezone <- snakemake@params[["timezone"]] -rmysql.settingsfile <- "./.env" - -stopDB <- dbConnect(MySQL(), default.file = rmysql.settingsfile, group = group) -query <- "SELECT device_id, brand, label, timestamp FROM aware_device order by timestamp asc" -participants <- dbGetQuery(stopDB, query) -pids <- c() - -end_date <- format(Sys.Date(), "%Y/%m/%d") - -for(id in 1:nrow(participants)){ - device_id <- participants$device_id[[id]] - brand <- ifelse(participants$brand[[id]] == "iPhone", "ios", "android") - label <- ifelse(participants$label[[id]] == "", "EMPTY_LABEL", participants$label[[id]]) - label <- iconv(label, from = "UTF-8", to = "UTF-8", sub='') - start_date <- format(as.POSIXct(participants$timestamp[[id]] / 1000, origin = "1970-01-01", tz = timezone), "%Y/%m/%d") - if(!(device_id %in% ignored_device_ids)){ - pid <- paste0("p", ifelse(id < 10, paste0("0", id), id)) - pids <- append(pids, pid) - file_connection <- file(paste0("./data/external/", pid)) - writeLines(c(device_id, brand, label, paste0(start_date, ",", end_date)), file_connection) - close(file_connection) - } -} - -file_lines <-readLines("./config.yaml") -for (i in 1:length(file_lines)){ - if(startsWith(file_lines[i], "PIDS:")){ - file_lines[i] <- paste0("PIDS: [", paste(pids, collapse = ", "), "]") - } -} -writeLines(file_lines, con = "./config.yaml") \ No newline at end of file diff --git a/src/data/download_phone_data.R b/src/data/download_phone_data.R new file mode 100644 index 00000000..331da881 --- /dev/null +++ b/src/data/download_phone_data.R @@ -0,0 +1,107 @@ +source("renv/activate.R") +source("src/data/unify_utils.R") +library(RMariaDB) +library(stringr) +library("dplyr", warn.conflicts = F) +library(readr) +library(yaml) +library(lubridate) +options(scipen=999) + +validate_deviceid_platforms <- function(device_ids, platforms){ + if(length(device_ids) == 1){ + if(length(platforms) > 1 || (platforms != "android" && platforms != "ios")) + stop(paste0("If you have 1 device_id, its platform should be 'android' or 'ios' but you typed: '", paste0(platforms, collapse = ","), "'. Participant file: ", participant)) + } else if(length(device_ids) > 1 && length(platforms) == 1){ + if(platforms != "android" && platforms != "ios" && platforms != "multiple") + stop(paste0("If you have more than 1 device_id, platform should be 'android', 'ios' OR 'multiple' but you typed: '", paste0(platforms, collapse = "s,"), "'. Participant file: ", participant)) + } else if(length(device_ids) > 1 && length(platforms) > 1){ + if(length(device_ids) != length(platforms)) + stop(paste0("The number of device_ids should match the number of platforms. Participant file:", participant)) + if(all(intersect(c("android", "ios"), unique(platforms)) != c("android", "ios"))) + stop(paste0("If you have more than 1 device_id and more than 1 platform, the platforms should be a mix of 'android' AND 'ios' but you typed: '", paste0(platforms, collapse = ","), "'. Participant file: ", participant)) + } +} + +is_multiplaform_participant <- function(dbEngine, device_ids, platforms){ + # Multiple android and ios platforms or the same platform (android, ios) for multiple devices + if((length(device_ids) > 1 && length(platforms) > 1) || (length(device_ids) > 1 && length(platforms) == 1 && (platforms == "android" || platforms == "ios"))){ + return(TRUE) + } + # Multiple platforms for multiple devices, we search the platform for every device in the aware_device table + if(length(device_ids) > 1 && length(platforms) == 1 && platforms == "multiple"){ + devices_platforms <- dbGetQuery(dbEngine, paste0("SELECT device_id,brand FROM aware_device WHERE device_id IN ('", paste0(device_ids, collapse = "','"), "')")) + platforms <- devices_platforms %>% distinct(brand) %>% pull(brand) + # Android phones have different brands so we check that we got at least two different platforms and one of them is iPhone + if(length(platforms) > 1 && "iPhone" %in% platforms){ + return(TRUE) + } + } + return(FALSE) +} + +get_timestamp_filter <- function(device_ids, participant, timezone){ + # Read start and end date from the participant file to filter data within that range + start_date <- ymd_hms(paste(participant$PHONE$START_DATE,"00:00:00"), tz=timezone, quiet=TRUE) + end_date <- ymd_hms(paste(participant$PHONE$END_DATE, "23:59:59"), tz=timezone, quiet=TRUE) + start_timestamp = as.numeric(start_date) * 1000 + end_timestamp = as.numeric(end_date) * 1000 + if(is.na(start_timestamp)){ + message(paste("PHONE[START_DATE] was not provided or failed to parse (", participant$PHONE$START_DATE,"), all data for", paste0(device_ids, collapse=","),"is returned")) + return("") + }else if(is.na(end_timestamp)){ + message(paste("PHONE[END_DATE] was not provided or failed to parse (", participant$PHONE$END_DATE,"), all data for", paste0(device_ids, collapse=","),"is returned")) + return("") + } else if(start_timestamp > end_timestamp){ + stop(paste("Start date has to be before end date in PHONE[TIME_SPAN] (",start_date,",", date(end_date),"), all data for", paste0(device_ids, collapse=","),"is returned")) + return("") + } else { + message(paste("Filtering data between", start_date, "and", end_date, "in", timezone, "for",paste0(device_ids, collapse=","))) + return(paste0("AND timestamp BETWEEN ", start_timestamp, " AND ", end_timestamp)) + } +} + +participant_file <- snakemake@input[[1]] +source <- snakemake@params[["source"]] +group <- source$DATABASE_GROUP +table <- snakemake@params[["table"]] +sensor <- snakemake@params[["sensor"]] +timezone <- snakemake@params[["timezone"]] +aware_multiplatform_tables <- str_split(snakemake@params[["aware_multiplatform_tables"]], ",")[[1]] +sensor_file <- snakemake@output[[1]] + +participant <- read_yaml(participant_file) +if(! "PHONE" %in% names(participant)){ + stop(paste("The following participant file does not have a PHONE section, create one manually or automatically (see the docs):", participant_file)) +} +device_ids <- participant$PHONE$DEVICE_IDS +unified_device_id <- tail(device_ids, 1) +platforms <- participant$PHONE$PLATFORMS +validate_deviceid_platforms(device_ids, platforms) +timestamp_filter <- get_timestamp_filter(device_ids, participant, timezone) + +dbEngine <- dbConnect(MariaDB(), default.file = "./.env", group = group) + +if(is_multiplaform_participant(dbEngine, device_ids, platforms)){ + sensor_data <- unify_raw_data(dbEngine, table, sensor, timestamp_filter, aware_multiplatform_tables, device_ids, platforms) +}else { + # table has two elements for conversation and activity recognition (they store data on a different table for ios and android) + if(length(table) > 1) + table <- table[[toupper(platforms[1])]] + query <- paste0("SELECT * FROM ", table, " WHERE ",source$DEVICE_ID_COLUMN," IN ('", paste0(device_ids, collapse = "','"), "')", timestamp_filter) + sensor_data <- dbGetQuery(dbEngine, query) %>% + rename(device_id = source$DEVICE_ID_COLUMN) +} + +sensor_data <- sensor_data %>% arrange(timestamp) + +# Unify device_id +sensor_data <- sensor_data %>% mutate(device_id = unified_device_id) + +# Removing blob_feature conversation column (it's loaded as a list column that crashes write_csv) +sensor_data <- sensor_data %>% select(-any_of("blob_feature")) +# Droping duplicates on all columns except for _id or id +sensor_data <- sensor_data %>% distinct(!!!syms(setdiff(names(sensor_data), c("_id", "id")))) + +write_csv(sensor_data, sensor_file) +dbDisconnect(dbEngine) \ No newline at end of file diff --git a/src/data/fitbit_parse_calories.py b/src/data/fitbit_parse_calories.py new file mode 100644 index 00000000..059006bd --- /dev/null +++ b/src/data/fitbit_parse_calories.py @@ -0,0 +1,50 @@ +import json +import numpy as np +import pandas as pd +from datetime import datetime + + +CALORIES_INTRADAY_COLUMNS = ("device_id", + "level", "mets", "value", + "local_date_time", "timestamp") + +def parseCaloriesData(calories_data): + if calories_data.empty: + return pd.DataFrame(), pd.DataFrame(columns=CALORIES_INTRADAY_COLUMNS) + device_id = calories_data["device_id"].iloc[0] + records_intraday = [] + # Parse JSON into individual records + for record in calories_data.fitbit_data: + record = json.loads(record) # Parse text into JSON + curr_date = datetime.strptime( + record["activities-calories"][0]["dateTime"], "%Y-%m-%d") + dataset = record["activities-calories-intraday"]["dataset"] + for data in dataset: + d_time = datetime.strptime(data["time"], '%H:%M:%S').time() + d_datetime = datetime.combine(curr_date, d_time) + + row_intraday = (device_id, + data["level"], data["mets"], data["value"], + d_datetime, 0) + + records_intraday.append(row_intraday) + + return pd.DataFrame(data=[], columns=["local_date_time", "timestamp"]), pd.DataFrame(data=records_intraday, columns=CALORIES_INTRADAY_COLUMNS) + +table_format = snakemake.params["table_format"] +timezone = snakemake.params["timezone"] + +if table_format == "JSON": + json_raw = pd.read_csv(snakemake.input[0]) + summary, intraday = parseCaloriesData(json_raw) +elif table_format == "CSV": + summary = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) + intraday = pd.read_csv(snakemake.input[1], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) + +if summary.shape[0] > 0: + summary["timestamp"] = summary["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 +if intraday.shape[0] > 0: + intraday["timestamp"] = intraday["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 + +summary.to_csv(snakemake.output["summary_data"], index=False) +intraday.to_csv(snakemake.output["intraday_data"], index=False) \ No newline at end of file diff --git a/src/data/fitbit_parse_sensors/fitbit_parse_heartrate.py b/src/data/fitbit_parse_heartrate.py similarity index 59% rename from src/data/fitbit_parse_sensors/fitbit_parse_heartrate.py rename to src/data/fitbit_parse_heartrate.py index 9271025c..33e9c484 100644 --- a/src/data/fitbit_parse_sensors/fitbit_parse_heartrate.py +++ b/src/data/fitbit_parse_heartrate.py @@ -1,10 +1,13 @@ -import json +import yaml, json, sys import pandas as pd -from datetime import datetime +import numpy as np +from datetime import datetime, timezone +from math import trunc HR_SUMMARY_COLUMNS = ("device_id", - "local_date", + "local_date_time", + "timestamp", "heartrate_daily_restinghr", "heartrate_daily_caloriesoutofrange", "heartrate_daily_caloriesfatburn", @@ -12,10 +15,10 @@ HR_SUMMARY_COLUMNS = ("device_id", "heartrate_daily_caloriespeak") HR_INTRADAY_COLUMNS = ("device_id", - "heartrate", "heartrate_zone", - "local_date_time", "local_date", "local_month", "local_day", - "local_day_of_week", "local_time", "local_hour", "local_minute", - "local_day_segment") + "heartrate", + "heartrate_zone", + "local_date_time", + "timestamp") def parseHeartrateZones(heartrate_data): # Get the range of heartrate zones: outofrange, fatburn, cardio, peak @@ -58,6 +61,7 @@ def parseHeartrateSummaryData(record_summary, device_id, curr_date): row_summary = (device_id, curr_date, + 0, d_resting_heartrate, d_calories_outofrange, d_calories_fatburn, @@ -68,7 +72,7 @@ def parseHeartrateSummaryData(record_summary, device_id, curr_date): -def parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date, heartrate_zones_range, HOUR2EPOCH): +def parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date, heartrate_zones_range): for data in dataset: d_time = datetime.strptime(data["time"], '%H:%M:%S').time() d_datetime = datetime.combine(curr_date, d_time) @@ -83,15 +87,15 @@ def parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date, row_intraday = (device_id, d_hr, d_hrzone, - d_datetime, d_datetime.date(), d_datetime.month, d_datetime.day, - d_datetime.weekday(), d_datetime.time(), d_datetime.hour, d_datetime.minute, - HOUR2EPOCH[d_datetime.hour]) + d_datetime, + 0) records_intraday.append(row_intraday) return records_intraday -def parseHeartrateData(heartrate_data, HOUR2EPOCH): + +def parseHeartrateData(heartrate_data, fitbit_data_type): if heartrate_data.empty: return pd.DataFrame(columns=HR_SUMMARY_COLUMNS), pd.DataFrame(columns=HR_INTRADAY_COLUMNS) device_id = heartrate_data["device_id"].iloc[0] @@ -104,11 +108,46 @@ def parseHeartrateData(heartrate_data, HOUR2EPOCH): record = json.loads(record) # Parse text into JSON curr_date = datetime.strptime(record["activities-heart"][0]["dateTime"], "%Y-%m-%d") - record_summary = record["activities-heart"][0] - row_summary = parseHeartrateSummaryData(record_summary, device_id, curr_date) - records_summary.append(row_summary) + if fitbit_data_type == "summary": + record_summary = record["activities-heart"][0] + row_summary = parseHeartrateSummaryData(record_summary, device_id, curr_date) + records_summary.append(row_summary) - dataset = record["activities-heart-intraday"]["dataset"] - records_intraday = parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date, heartrate_zones_range, HOUR2EPOCH) + if fitbit_data_type == "intraday": + dataset = record["activities-heart-intraday"]["dataset"] + records_intraday = parseHeartrateIntradayData(records_intraday, dataset, device_id, curr_date, heartrate_zones_range) + + if fitbit_data_type == "summary": + parsed_data = pd.DataFrame(data=records_summary, columns=HR_SUMMARY_COLUMNS) + elif fitbit_data_type == "intraday": + parsed_data = pd.DataFrame(data=records_intraday, columns=HR_INTRADAY_COLUMNS) + else: + raise ValueError("fitbit_data_type can only be one of ['summary', 'intraday'].") + return parsed_data + - return pd.DataFrame(data=records_summary, columns=HR_SUMMARY_COLUMNS), pd.DataFrame(data=records_intraday, columns=HR_INTRADAY_COLUMNS) + +timezone = snakemake.params["timezone"] +column_format = snakemake.params["column_format"] +fitbit_data_type = snakemake.params["fitbit_data_type"] + +with open(snakemake.input["participant_file"], "r", encoding="utf-8") as f: + participant_file = yaml.safe_load(f) +local_start_date = pd.Timestamp(participant_file["FITBIT"]["START_DATE"]) +local_end_date = pd.Timestamp(participant_file["FITBIT"]["END_DATE"]) + pd.DateOffset(1) + +if column_format == "JSON": + json_raw = pd.read_csv(snakemake.input["raw_data"]) + parsed_data = parseHeartrateData(json_raw, fitbit_data_type) +elif column_format == "PLAIN_TEXT": + parsed_data = pd.read_csv(snakemake.input["raw_data"], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) +else: + raise ValueError("column_format can only be one of ['JSON', 'PLAIN_TEXT'].") + +# Only keep dates in the range of [local_start_date, local_end_date) +parsed_data = parsed_data.loc[(parsed_data["local_date_time"] >= local_start_date) & (parsed_data["local_date_time"] < local_end_date)] + +if parsed_data.shape[0] > 0: + parsed_data["timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 + +parsed_data.to_csv(snakemake.output[0], index=False) diff --git a/src/data/fitbit_parse_sensors/fitbit_parse_calories.py b/src/data/fitbit_parse_sensors/fitbit_parse_calories.py deleted file mode 100644 index 08a0ed53..00000000 --- a/src/data/fitbit_parse_sensors/fitbit_parse_calories.py +++ /dev/null @@ -1,35 +0,0 @@ -import json -import pandas as pd -from datetime import datetime - - -CALORIES_INTRADAY_COLUMNS = ("device_id", - "level", "mets", "value", - "local_date_time", "local_date", "local_month", "local_day", - "local_day_of_week", "local_time", "local_hour", "local_minute", - "local_day_segment") - -def parseCaloriesData(calories_data, HOUR2EPOCH): - if calories_data.empty: - return pd.DataFrame(), pd.DataFrame(columns=CALORIES_INTRADAY_COLUMNS) - device_id = calories_data["device_id"].iloc[0] - records_intraday = [] - # Parse JSON into individual records - for record in calories_data.fitbit_data: - record = json.loads(record) # Parse text into JSON - curr_date = datetime.strptime( - record["activities-calories"][0]["dateTime"], "%Y-%m-%d") - dataset = record["activities-calories-intraday"]["dataset"] - for data in dataset: - d_time = datetime.strptime(data["time"], '%H:%M:%S').time() - d_datetime = datetime.combine(curr_date, d_time) - - row_intraday = (device_id, - data["level"], data["mets"], data["value"], - d_datetime, d_datetime.date(), d_datetime.month, d_datetime.day, - d_datetime.weekday(), d_datetime.time(), d_datetime.hour, d_datetime.minute, - HOUR2EPOCH[d_datetime.hour]) - - records_intraday.append(row_intraday) - - return pd.DataFrame(), pd.DataFrame(data=records_intraday, columns=CALORIES_INTRADAY_COLUMNS) diff --git a/src/data/fitbit_parse_sensors/fitbit_parse_steps.py b/src/data/fitbit_parse_sensors/fitbit_parse_steps.py deleted file mode 100644 index f4bfeef3..00000000 --- a/src/data/fitbit_parse_sensors/fitbit_parse_steps.py +++ /dev/null @@ -1,35 +0,0 @@ -import json -import pandas as pd -from datetime import datetime - -STEPS_INTRADAY_COLUMNS = ("device_id", - "steps", - "local_date_time", "local_date", "local_month", "local_day", - "local_day_of_week", "local_time", "local_hour", "local_minute", - "local_day_segment") - - -def parseStepsData(steps_data, HOUR2EPOCH): - if steps_data.empty: - return pd.DataFrame(), pd.DataFrame(columns=STEPS_INTRADAY_COLUMNS) - device_id = steps_data["device_id"].iloc[0] - records_intraday = [] - # Parse JSON into individual records - for record in steps_data.fitbit_data: - record = json.loads(record) # Parse text into JSON - curr_date = datetime.strptime( - record["activities-steps"][0]["dateTime"], "%Y-%m-%d") - dataset = record["activities-steps-intraday"]["dataset"] - for data in dataset: - d_time = datetime.strptime(data["time"], '%H:%M:%S').time() - d_datetime = datetime.combine(curr_date, d_time) - - row_intraday = (device_id, - data["value"], - d_datetime, d_datetime.date(), d_datetime.month, d_datetime.day, - d_datetime.weekday(), d_datetime.time(), d_datetime.hour, d_datetime.minute, - HOUR2EPOCH[d_datetime.hour]) - - records_intraday.append(row_intraday) - - return pd.DataFrame(), pd.DataFrame(data=records_intraday, columns=STEPS_INTRADAY_COLUMNS) diff --git a/src/data/fitbit_parse_sensors/fitbit_parse_sleep.py b/src/data/fitbit_parse_sleep.py similarity index 52% rename from src/data/fitbit_parse_sensors/fitbit_parse_sleep.py rename to src/data/fitbit_parse_sleep.py index 8f1450c4..a5f49d81 100644 --- a/src/data/fitbit_parse_sensors/fitbit_parse_sleep.py +++ b/src/data/fitbit_parse_sleep.py @@ -1,9 +1,8 @@ -import json +import json, yaml import pandas as pd -from datetime import datetime import numpy as np +from datetime import datetime, timedelta import dateutil.parser -from datetime import timedelta SLEEP_CODE2LEVEL = ["asleep", "restless", "awake"] @@ -12,14 +11,13 @@ SLEEP_SUMMARY_COLUMNS_V1_2 = ("device_id", "efficiency", "minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed", "is_main_sleep", "type", "local_start_date_time", "local_end_date_time", - "local_start_date", "local_end_date", - "local_start_day_segment", "local_end_day_segment") + "timestamp") SLEEP_SUMMARY_COLUMNS_V1 = SLEEP_SUMMARY_COLUMNS_V1_2 + ("count_awake", "duration_awake", "count_awakenings", "count_restless", "duration_restless") SLEEP_INTRADAY_COLUMNS = ("device_id", # For "classic" type, original_level is one of {"awake", "restless", "asleep"} # For "stages" type, original_level is one of {"wake", "deep", "light", "rem"} - "original_level", + "level", # For "classic" type, unified_level is one of {0, 1} where 0: awake {"awake" + "restless"}, 1: asleep {"asleep"} # For "stages" type, unified_level is one of {0, 1} where 0: awake {"wake"}, 1: asleep {"deep" + "light" + "rem"} "unified_level", @@ -27,9 +25,8 @@ SLEEP_INTRADAY_COLUMNS = ("device_id", "is_main_sleep", # one of {"classic", "stages"} "type", - "local_date_time", "local_date", "local_month", "local_day", - "local_day_of_week", "local_time", "local_hour", "local_minute", - "local_day_segment") + "local_date_time", + "timestamp") def mergeLongAndShortData(data_summary): longData = pd.DataFrame(columns=['dateTime', 'level', 'seconds']) @@ -73,78 +70,75 @@ def classicData1min(data_summary): newRow = {'dateTime':dateutil.parser.parse(origEntry['dateTime'])+timedelta(seconds=counter*timeDuration),'level':origEntry['level'],'seconds':timeDuration} dataList.append(newRow) counter = counter + 1 - # print(dataList) return dataList -# Parse one record for sleep API version 1 -def parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday, HOUR2EPOCH): - # Summary data +# Parse one record for sleep API version 1 +def parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type): + sleep_record_type = "classic" d_start_datetime = datetime.strptime(record["startTime"][:18], "%Y-%m-%dT%H:%M:%S") d_end_datetime = datetime.strptime(record["endTime"][:18], "%Y-%m-%dT%H:%M:%S") - row_summary = (device_id, record["efficiency"], - record["minutesAfterWakeup"], record["minutesAsleep"], record["minutesAwake"], record["minutesToFallAsleep"], record["timeInBed"], - d_is_main_sleep, sleep_record_type, - d_start_datetime, d_end_datetime, - d_start_datetime.date(), d_end_datetime.date(), - HOUR2EPOCH[d_start_datetime.hour], HOUR2EPOCH[d_end_datetime.hour], - record["awakeCount"], record["awakeDuration"], record["awakeningsCount"], - record["restlessCount"], record["restlessDuration"]) - - records_summary.append(row_summary) + # Summary data + if fitbit_data_type == "summary": + row_summary = (device_id, record["efficiency"], + record["minutesAfterWakeup"], record["minutesAsleep"], record["minutesAwake"], record["minutesToFallAsleep"], record["timeInBed"], + d_is_main_sleep, sleep_record_type, + d_start_datetime, d_end_datetime, + 0, + record["awakeCount"], record["awakeDuration"], record["awakeningsCount"], + record["restlessCount"], record["restlessDuration"]) + + records_summary.append(row_summary) # Intraday data - start_date = d_start_datetime.date() - end_date = d_end_datetime.date() - is_before_midnight = True - curr_date = start_date - for data in record["minuteData"]: - # For overnight episodes, use end_date once we are over midnight - d_time = datetime.strptime(data["dateTime"], '%H:%M:%S').time() - if is_before_midnight and d_time.hour == 0: - curr_date = end_date - d_datetime = datetime.combine(curr_date, d_time) + if fitbit_data_type == "intraday": + start_date = d_start_datetime.date() + end_date = d_end_datetime.date() + is_before_midnight = True + curr_date = start_date + for data in record["minuteData"]: + # For overnight episodes, use end_date once we are over midnight + d_time = datetime.strptime(data["dateTime"], '%H:%M:%S').time() + if is_before_midnight and d_time.hour == 0: + curr_date = end_date + d_datetime = datetime.combine(curr_date, d_time) - # API 1.2 stores original_level as strings, so we convert original_levels of API 1 to strings too - # (1: "asleep", 2: "restless", 3: "awake") - d_original_level = SLEEP_CODE2LEVEL[int(data["value"])-1] + # API 1.2 stores original_level as strings, so we convert original_levels of API 1 to strings too + # (1: "asleep", 2: "restless", 3: "awake") + d_original_level = SLEEP_CODE2LEVEL[int(data["value"])-1] - # unified_level summarises original_level (we came up with this classification) - # 0 is awake, 1 is asleep - # {"awake" + "restless"} are set to 0 and {"asleep"} is set to 1 - d_unified_level = 0 if d_original_level == "awake" or d_original_level == "restless" else 1 - row_intraday = (device_id, - d_original_level, d_unified_level, d_is_main_sleep, sleep_record_type, - d_datetime, d_datetime.date(), d_datetime.month, d_datetime.day, - d_datetime.weekday(), d_datetime.time(), d_datetime.hour, d_datetime.minute, - HOUR2EPOCH[d_datetime.hour]) + row_intraday = (device_id, + d_original_level, -1, d_is_main_sleep, sleep_record_type, + d_datetime, 0) - records_intraday.append(row_intraday) + records_intraday.append(row_intraday) return records_summary, records_intraday # Parse one record for sleep API version 1.2 -def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, records_intraday, HOUR2EPOCH): +def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type): - # Summary data sleep_record_type = record['type'] d_start_datetime = datetime.strptime(record["startTime"][:18], "%Y-%m-%dT%H:%M:%S") d_end_datetime = datetime.strptime(record["endTime"][:18], "%Y-%m-%dT%H:%M:%S") - row_summary = (device_id, record["efficiency"], - record["minutesAfterWakeup"], record["minutesAsleep"], record["minutesAwake"], record["minutesToFallAsleep"], record["timeInBed"], - d_is_main_sleep, sleep_record_type, - d_start_datetime, d_end_datetime, - d_start_datetime.date(), d_end_datetime.date(), - HOUR2EPOCH[d_start_datetime.hour], HOUR2EPOCH[d_end_datetime.hour]) + # Summary data + if fitbit_data_type == "summary": + row_summary = (device_id, record["efficiency"], + record["minutesAfterWakeup"], record["minutesAsleep"], record["minutesAwake"], record["minutesToFallAsleep"], record["timeInBed"], + d_is_main_sleep, sleep_record_type, + d_start_datetime, d_end_datetime, + 0) + + records_summary.append(row_summary) - records_summary.append(row_summary) - if sleep_record_type == 'classic': - # Intraday data + # Intraday data + if fitbit_data_type == "intraday": + if sleep_record_type == 'classic': start_date = d_start_datetime.date() end_date = d_end_datetime.date() is_before_midnight = True @@ -160,16 +154,12 @@ def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, re d_original_level = data["level"] - d_unified_level = 0 if d_original_level == "awake" or d_original_level == "restless" else 1 - row_intraday = (device_id, - d_original_level, d_unified_level, d_is_main_sleep, sleep_record_type, - d_datetime, d_datetime.date(), d_datetime.month, d_datetime.day, - d_datetime.weekday(), d_datetime.time(), d_datetime.hour, d_datetime.minute, - HOUR2EPOCH[d_datetime.hour]) + d_original_level, -1, d_is_main_sleep, sleep_record_type, + d_datetime, 0) records_intraday.append(row_intraday) - else: - ## for sleep type "stages" + else: + # For sleep type "stages" start_date = d_start_datetime.date() end_date = d_end_datetime.date() is_before_midnight = True @@ -185,13 +175,9 @@ def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, re d_original_level = data[1] - d_unified_level = 1 if d_original_level == "deep" or d_original_level == "light" or d_original_level == "rem" else 0 - row_intraday = (device_id, - d_original_level, d_unified_level, d_is_main_sleep, sleep_record_type, - d_datetime, d_datetime.date(), d_datetime.month, d_datetime.day, - d_datetime.weekday(), d_datetime.time(), d_datetime.hour, d_datetime.minute, - HOUR2EPOCH[d_datetime.hour]) + d_original_level, -1, d_is_main_sleep, sleep_record_type, + d_datetime, 0) records_intraday.append(row_intraday) @@ -199,7 +185,7 @@ def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, re -def parseSleepData(sleep_data, HOUR2EPOCH): +def parseSleepData(sleep_data, fitbit_data_type): SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1_2 if sleep_data.empty: return pd.DataFrame(columns=SLEEP_SUMMARY_COLUMNS), pd.DataFrame(columns=SLEEP_INTRADAY_COLUMNS) @@ -214,10 +200,66 @@ def parseSleepData(sleep_data, HOUR2EPOCH): # For sleep API version 1 if "awakeCount" in record: SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1 - records_summary, records_intraday = parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday, HOUR2EPOCH) + records_summary, records_intraday = parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type) # For sleep API version 1.2 else: SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1_2 - records_summary, records_intraday = parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, records_intraday, HOUR2EPOCH) + records_summary, records_intraday = parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type) - return pd.DataFrame(data=records_summary, columns=SLEEP_SUMMARY_COLUMNS), pd.DataFrame(data=records_intraday, columns=SLEEP_INTRADAY_COLUMNS) + if fitbit_data_type == "summary": + parsed_data = pd.DataFrame(data=records_summary, columns=SLEEP_SUMMARY_COLUMNS) + elif fitbit_data_type == "intraday": + parsed_data = pd.DataFrame(data=records_intraday, columns=SLEEP_INTRADAY_COLUMNS) + else: + raise ValueError("fitbit_data_type can only be one of ['summary', 'intraday'].") + + return parsed_data + + + +timezone = snakemake.params["timezone"] +column_format = snakemake.params["column_format"] +fitbit_data_type = snakemake.params["fitbit_data_type"] +sleep_episode_timestamp = snakemake.params["sleep_episode_timestamp"] + +with open(snakemake.input["participant_file"], "r", encoding="utf-8") as f: + participant_file = yaml.safe_load(f) +local_start_date = pd.Timestamp(participant_file["FITBIT"]["START_DATE"]) +local_end_date = pd.Timestamp(participant_file["FITBIT"]["END_DATE"]) + pd.DateOffset(1) + +if column_format == "JSON": + json_raw = pd.read_csv(snakemake.input["raw_data"]) + parsed_data = parseSleepData(json_raw, fitbit_data_type) +elif column_format == "PLAIN_TEXT": + if fitbit_data_type == "summary": + parsed_data = pd.read_csv(snakemake.input["raw_data"], parse_dates=["local_start_date_time", "local_end_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) + elif fitbit_data_type == "intraday": + parsed_data = pd.read_csv(snakemake.input["raw_data"], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) + else: + raise ValueError("fitbit_data_type can only be one of ['summary', 'intraday'].") +else: + raise ValueError("column_format can only be one of ['JSON', 'PLAIN_TEXT'].") + +if parsed_data.shape[0] > 0 and fitbit_data_type == "summary": + + if sleep_episode_timestamp != "start" and sleep_episode_timestamp != "end": + raise ValueError("SLEEP_EPISODE_TIMESTAMP can only be one of ['start', 'end'].") + + # Column name to be considered as the event datetime + datetime_column = "local_" + sleep_episode_timestamp + "_date_time" + # Only keep dates in the range of [local_start_date, local_end_date) + parsed_data = parsed_data.loc[(parsed_data[datetime_column] >= local_start_date) & (parsed_data[datetime_column] < local_end_date)] + # Convert datetime to timestamp + parsed_data["timestamp"] = parsed_data[datetime_column].dt.tz_localize(timezone).astype(np.int64) // 10**6 + # Drop useless columns: local_start_date_time and local_end_date_time + parsed_data.drop(["local_start_date_time", "local_end_date_time"], axis = 1, inplace=True) + +if parsed_data.shape[0] > 0 and fitbit_data_type == "intraday": + # Only keep dates in the range of [local_start_date, local_end_date) + parsed_data = parsed_data.loc[(parsed_data["local_date_time"] >= local_start_date) & (parsed_data["local_date_time"] < local_end_date)] + # Convert datetime to timestamp + parsed_data["timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 + # Unifying level + parsed_data["unified_level"] = np.where(parsed_data["level"].isin(["awake", "wake", "restless"]), 0, 1) + +parsed_data.to_csv(snakemake.output[0], index=False) diff --git a/src/data/fitbit_parse_steps.py b/src/data/fitbit_parse_steps.py new file mode 100644 index 00000000..b6f32eb7 --- /dev/null +++ b/src/data/fitbit_parse_steps.py @@ -0,0 +1,79 @@ +import json, yaml +import pandas as pd +import numpy as np +from datetime import datetime, timezone +from math import trunc + +STEPS_COLUMNS = ("device_id", "steps", "local_date_time", "timestamp") + + +def parseStepsData(steps_data, fitbit_data_type): + if steps_data.empty: + return pd.DataFrame(), pd.DataFrame(columns=STEPS_INTRADAY_COLUMNS) + device_id = steps_data["device_id"].iloc[0] + records_summary, records_intraday = [], [] + + # Parse JSON into individual records + for record in steps_data.fitbit_data: + record = json.loads(record) # Parse text into JSON + curr_date = datetime.strptime(record["activities-steps"][0]["dateTime"], "%Y-%m-%d") + + # Parse summary data + if fitbit_data_type == "summary": + + row_summary = (device_id, + record["activities-steps"][0]["value"], + curr_date, + 0) + + records_summary.append(row_summary) + + # Parse intraday data + if fitbit_data_type == "intraday": + dataset = record["activities-steps-intraday"]["dataset"] + for data in dataset: + d_time = datetime.strptime(data["time"], '%H:%M:%S').time() + d_datetime = datetime.combine(curr_date, d_time) + + row_intraday = (device_id, + data["value"], + d_datetime, + 0) + + records_intraday.append(row_intraday) + + if fitbit_data_type == "summary": + parsed_data = pd.DataFrame(data=records_summary, columns=STEPS_COLUMNS) + elif fitbit_data_type == "intraday": + parsed_data = pd.DataFrame(data=records_intraday, columns=STEPS_COLUMNS) + else: + raise ValueError("fitbit_data_type can only be one of ['summary', 'intraday'].") + + return parsed_data + + + +timezone = snakemake.params["timezone"] +column_format = snakemake.params["column_format"] +fitbit_data_type = snakemake.params["fitbit_data_type"] + +with open(snakemake.input["participant_file"], "r", encoding="utf-8") as f: + participant_file = yaml.safe_load(f) +local_start_date = pd.Timestamp(participant_file["FITBIT"]["START_DATE"]) +local_end_date = pd.Timestamp(participant_file["FITBIT"]["END_DATE"]) + pd.DateOffset(1) + +if column_format == "JSON": + json_raw = pd.read_csv(snakemake.input["raw_data"]) + parsed_data = parseStepsData(json_raw, fitbit_data_type) +elif column_format == "PLAIN_TEXT": + parsed_data = pd.read_csv(snakemake.input["raw_data"], parse_dates=["local_date_time"], date_parser=lambda col: pd.to_datetime(col).tz_localize(None)) +else: + raise ValueError("column_format can only be one of ['JSON', 'PLAIN_TEXT'].") + +# Only keep dates in the range of [local_start_date, local_end_date) +parsed_data = parsed_data.loc[(parsed_data["local_date_time"] >= local_start_date) & (parsed_data["local_date_time"] < local_end_date)] + +if parsed_data.shape[0] > 0: + parsed_data["timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone).astype(np.int64) // 10**6 + +parsed_data.to_csv(snakemake.output[0], index=False) diff --git a/src/data/phone_sensed_bins.R b/src/data/phone_sensed_bins.R deleted file mode 100644 index 58d76dad..00000000 --- a/src/data/phone_sensed_bins.R +++ /dev/null @@ -1,40 +0,0 @@ -source("renv/activate.R") - -library(dplyr) -library(tidyr) -library(lubridate) - -all_sensors <- snakemake@input[["all_sensors"]] -bin_size <- snakemake@params[["bin_size"]] -output_file <- snakemake@output[[1]] - -# Load all sensors and extract timestamps -all_sensor_data <- data.frame(timestamp = c()) -for(sensor in all_sensors){ - sensor_data <- read.csv(sensor, stringsAsFactors = F) %>% - select(local_date, local_hour, local_minute) %>% - mutate(sensor = basename(sensor)) - all_sensor_data <- rbind(all_sensor_data, sensor_data) -} - -if(nrow(all_sensor_data) == 0){ - bins = seq(0, 59, by = bin_size) - hours = seq(0, 23, 1) - write.csv(crossing(hours, bins) %>% unite("hour_bin",hours, bins, sep = "_") %>% mutate(value = NA, local_date = NA) %>% pivot_wider(names_from = hour_bin, values_from=value) %>% head(0), output_file, row.names = FALSE) -} else{ - phone_sensed_bins <- all_sensor_data %>% - mutate(bin = (local_minute %/% bin_size) * bin_size) %>% # bin rows into bin_size-minute bins - group_by(local_date, local_hour, bin) %>% - summarise(sensor_count = n_distinct(sensor)) %>% - ungroup() %>% - mutate(local_date = lubridate::ymd(local_date)) %>% - complete(local_date = seq.Date(min(local_date), max(local_date), by="day"), - fill = list(local_hour = 0, bin = 0, sensor_count = 0)) %>% - complete(nesting(local_date), - local_hour = seq(0, 23, 1), - bin = seq(0, 59, bin_size), - fill = list(sensor_count=0)) %>% - pivot_wider(names_from = c(local_hour, bin), values_from = sensor_count) - - write.csv(phone_sensed_bins, output_file, row.names = FALSE) -} diff --git a/src/data/phone_valid_sensed_days.R b/src/data/phone_valid_sensed_days.R deleted file mode 100644 index 450baf70..00000000 --- a/src/data/phone_valid_sensed_days.R +++ /dev/null @@ -1,18 +0,0 @@ -source("renv/activate.R") -library("dplyr") -library("tidyr") - -phone_sensed_bins <- read.csv(snakemake@input[["phone_sensed_bins"]]) -min_valid_hours_per_day <- as.integer(snakemake@params[["min_valid_hours_per_day"]]) -min_valid_bins_per_hour <- as.integer(snakemake@params[["min_valid_bins_per_hour"]]) -output_file <- snakemake@output[[1]] - -phone_valid_sensed_days <- phone_sensed_bins %>% - pivot_longer(cols = -local_date, names_to = c("hour", "bin"), names_sep = "_") %>% - group_by(local_date, hour) %>% - summarise(valid_bins = sum(value > 0)) %>% - group_by(local_date) %>% - summarise(valid_sensed_hours = sum(valid_bins >= min_valid_bins_per_hour)) %>% - mutate(is_valid_sensed_day = ifelse(valid_sensed_hours >= min_valid_hours_per_day, TRUE, FALSE)) - -write.csv(phone_valid_sensed_days, output_file, row.names = FALSE) diff --git a/src/data/phone_yielded_timestamps.R b/src/data/phone_yielded_timestamps.R new file mode 100644 index 00000000..c8bb6495 --- /dev/null +++ b/src/data/phone_yielded_timestamps.R @@ -0,0 +1,18 @@ +source("renv/activate.R") +library("dplyr", warn.conflicts = F) +library(readr) +library(tidyr) +library(purrr) + +all_sensors = snakemake@input[["all_sensors"]] + +sensor_timestamps <- tibble(files = all_sensors) %>% + mutate(timestamps = map(files,~ read_csv(.,col_types = cols_only(timestamp = col_double()))), + sensor = row_number(), + files = NULL) %>% + unnest(timestamps) %>% + mutate(timestamp = (timestamp %/% 1000) * 1000) %>% + distinct(timestamp, .keep_all = TRUE) %>% + arrange(timestamp) + +write.csv(sensor_timestamps, snakemake@output[[1]], row.names = FALSE) \ No newline at end of file diff --git a/src/data/process_location_types.R b/src/data/process_location_types.R new file mode 100644 index 00000000..141ae417 --- /dev/null +++ b/src/data/process_location_types.R @@ -0,0 +1,61 @@ +source("renv/activate.R") +library("dplyr", warn.conflicts = F) +library(readr) +library(tidyr) + +consecutive_threshold <- snakemake@params[["consecutive_threshold"]] +time_since_valid_location <- snakemake@params[["time_since_valid_location"]] +locations_to_use <- snakemake@params[["locations_to_use"]] + +phone_sensed_timestamps <- read_csv(snakemake@input[["phone_sensed_timestamps"]], col_types = cols_only(timestamp = col_double())) +locations <- read.csv(snakemake@input[["locations"]]) %>% + filter(double_latitude != 0 & double_longitude != 0) %>% + drop_na(double_longitude, double_latitude) + +if(!locations_to_use %in% c("ALL", "FUSED_RESAMPLED", "GPS")){ + print("Unkown location filter, provide one of the following three: ALL, GPS, or FUSED_RESAMPLED") + quit(save = "no", status = 1, runLast = FALSE) + } + + +if(locations_to_use == "ALL"){ + processed_locations <- locations +} else if(locations_to_use == "GPS"){ + processed_locations <- locations %>% filter(provider == "gps") +} else if(locations_to_use == "FUSED_RESAMPLED"){ + locations <- locations %>% filter(provider == "fused") + if(nrow(locations) > 0){ + processed_locations <- locations %>% + # TODO filter repeated location rows based on the accurcy + distinct(timestamp, .keep_all = TRUE) %>% + bind_rows(phone_sensed_timestamps) %>% + arrange(timestamp) %>% + # We group and therefore, fill in, missing rows that appear after a valid fused location record and exist + # within consecutive_threshold minutes from each other + mutate(consecutive_time_diff = c(1, diff(timestamp)), + resample_group = cumsum(!is.na(double_longitude) | consecutive_time_diff > (1000 * 60 * consecutive_threshold))) %>% + group_by(resample_group) %>% + # Filter those rows that are further away than time_since_valid_location since the last fused location + mutate(time_from_fused = timestamp - first(timestamp)) %>% + filter(provider == "fused" | (time_from_fused < (1000 * 60 * time_since_valid_location))) %>% + # Summarise the period to resample for + summarise(limit = max(timestamp), timestamp = first(timestamp), double_latitude = first(double_latitude), double_longitude = first(double_longitude), + double_bearing=first(double_bearing), double_speed = first(double_speed), double_altitude=first(double_altitude), provider=first(provider), + accuracy=first(accuracy), label=first(label)) %>% + # the limit will be equal to the next timestamp-1 or the last binded timestamp (limit) plus the consecutive_threshold buffer + # you can think of consecutive_threshold as the period a location row is valid for + mutate(limit = pmin(lead(timestamp, default = 9999999999999) - 1, limit + (1000 * 60 * consecutive_threshold)), + n_resample = (limit - timestamp)%/%60001, + n_resample = if_else(n_resample == 0, 1, n_resample)) %>% + drop_na(double_longitude, double_latitude) %>% + uncount(weights = n_resample, .id = "id") %>% + mutate(provider = if_else(id > 1, "resampled", provider), + id = id -1, + timestamp = timestamp + (id * 60000)) %>% + ungroup() %>% + select(-resample_group, -limit, -id) + } else { + processed_locations <- locations + } +} +write.csv(processed_locations,snakemake@output[[1]], row.names = F) diff --git a/src/data/readable_datetime.R b/src/data/readable_datetime.R index f3cb0ebc..10a78209 100644 --- a/src/data/readable_datetime.R +++ b/src/data/readable_datetime.R @@ -1,41 +1,49 @@ source("renv/activate.R") - library("tidyverse") -library(readr) +library("readr") -input <- read.csv(snakemake@input[[1]]) %>% arrange(timestamp) +source("src/data/assign_to_time_segment.R") + +input <- read.csv(snakemake@input[["sensor_input"]]) %>% arrange(timestamp) +time_segments <- read.csv(snakemake@input[["time_segments"]]) +time_segments_type <- snakemake@params[["time_segments_type"]] sensor_output <- snakemake@output[[1]] timezone_periods <- snakemake@params[["timezone_periods"]] fixed_timezone <- snakemake@params[["fixed_timezone"]] +include_past_periodic_segments <- snakemake@params[["include_past_periodic_segments"]] -split_local_date_time <- function(data){ - return(data %>% - separate(local_date_time, c("local_date","local_time"), "\\s", remove = FALSE) %>% - separate(local_time, c("local_hour", "local_minute"), ":", remove = FALSE, extra = "drop") %>% - mutate(local_hour = as.numeric(local_hour), - local_minute = as.numeric(local_minute), - local_day_segment = case_when(local_hour %in% 0:5 ~ "night", - local_hour %in% 6:11 ~ "morning", - local_hour %in% 12:17 ~ "afternoon", - local_hour %in% 18:23 ~ "evening"))) +split_local_date_time <- function(data, time_segments){ + split_data <- data %>% + separate(local_date_time, c("local_date","local_time"), "\\s", remove = FALSE) %>% + separate(local_time, c("local_hour", "local_minute"), ":", remove = FALSE, extra = "drop") %>% + mutate(local_hour = as.numeric(local_hour), + local_minute = as.numeric(local_minute)) + + return(split_data) } + if(!is.null(timezone_periods)){ - timezones <- read_csv(timezone_periods) - tz_starts <- timezones$start - output <- input %>% - mutate(timezone = findInterval(timestamp / 1000, tz_starts), # Set an interval ID based on timezones' start column - timezone = ifelse(timezone == 0, 1, timezone), # Correct the first timezone ID - timezone = recode(timezone, !!! timezones$timezone), # Swap IDs for text labels - timezone = as.character(timezone)) %>% - rowwise() %>% - mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"), - local_date_time = format(utc_date_time, tz = timezone, usetz = T)) - output <- split_local_date_time(output) - write.csv(output, sensor_output) + # TODO: Not active yet + # timezones <- read_csv(timezone_periods) + # tz_starts <- timezones$start + # output <- input %>% + # mutate(timezone = findInterval(timestamp / 1000, tz_starts), # Set an interval ID based on timezones' start column + # timezone = ifelse(timezone == 0, 1, timezone), # Correct the first timezone ID + # timezone = recode(timezone, !!! timezones$timezone), # Swap IDs for text labels + # timezone = as.character(timezone)) %>% + # rowwise() %>% + # mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"), + # local_date_time = format(utc_date_time, tz = timezone, usetz = T, "%Y-%m-%d %H:%M:%S")) + # output <- split_local_date_time(output, time_segments) + # TODO: Implement time segment assigment with support for multiple timezones + # output <- assign_to_time_segment(output, time_segments, time_segments_type, fixed_timezone) + # write.csv(output, sensor_output) } else if(!is.null(fixed_timezone)){ - output <- input %>% - mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"), - local_date_time = format(utc_date_time, tz = fixed_timezone, usetz = F)) - output <- split_local_date_time(output) - write_csv(output, sensor_output) + output <- input %>% + mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"), + local_timezone = fixed_timezone, + local_date_time = format(utc_date_time, tz = fixed_timezone, "%Y-%m-%d %H:%M:%S")) + output <- split_local_date_time(output, time_segments) + output <- assign_to_time_segment(output, time_segments, time_segments_type, include_past_periodic_segments) + write_csv(output, sensor_output) } diff --git a/src/data/resample_fused_location.R b/src/data/resample_fused_location.R deleted file mode 100644 index ccff8a05..00000000 --- a/src/data/resample_fused_location.R +++ /dev/null @@ -1,59 +0,0 @@ -source("renv/activate.R") - -library(dplyr) -library(readr) -library(tidyr) - -bin_size <- snakemake@params[["bin_size"]] -timezone <- snakemake@params[["timezone"]] -consecutive_threshold <- snakemake@params[["consecutive_threshold"]] -time_since_valid_location <- snakemake@params[["time_since_valid_location"]] - -locations <- read_csv(snakemake@input[["locations"]], col_types = cols()) %>% filter(provider == "fused") -phone_sensed_bins <- read_csv(snakemake@input[["phone_sensed_bins"]], col_types = cols(local_date = col_character())) - -if(nrow(locations) > 0){ - sensed_minute_bins <- phone_sensed_bins %>% - pivot_longer(-local_date, names_to = c("hour", "bin"), names_sep = "_", values_to = "sensor_count") %>% - mutate(hour = as.integer(hour), bin = as.integer(bin)) %>% - complete(nesting(local_date, hour), bin = seq(0, 59,1)) %>% - fill(sensor_count) %>% - mutate(timestamp = as.numeric(as.POSIXct(paste0(local_date, " ", hour,":", bin,":00"), format = "%Y-%m-%d %H:%M:%S", tz = timezone)) * 1000 ) %>% - filter(sensor_count > 0) %>% - select(timestamp) - - resampled_locations <- locations %>% - bind_rows(sensed_minute_bins) %>% - mutate(provider = replace_na(provider, "resampled")) %>% - arrange(timestamp) %>% - # We group and therefore, fill in, missing rows that appear after a valid fused location record and exist - # within consecutive_threshold minutes from each other - mutate(consecutive_time_diff = c(1, diff(timestamp)), - resample_group = cumsum(!is.na(double_longitude) | consecutive_time_diff > (1000 * 60 * consecutive_threshold))) %>% - group_by(resample_group) %>% - # drop rows that are logged after time_since_valid_location minutes from the last valid fused location - filter((timestamp - first(timestamp) < (1000 * 60 * time_since_valid_location))) %>% - fill(-timestamp, -resample_group) %>% - select(-consecutive_time_diff) %>% - drop_na(double_longitude, double_latitude, accuracy) %>% - # Add local date_time - mutate(utc_date_time = as.POSIXct(timestamp/1000, origin="1970-01-01", tz="UTC"), - local_date_time = format(utc_date_time, tz = timezone, usetz = F)) %>% - separate(local_date_time, c("local_date","local_time"), "\\s", remove = FALSE) %>% - separate(local_time, c("local_hour", "local_minute"), ":", remove = FALSE, extra = "drop") %>% - mutate(local_hour = as.numeric(local_hour), - local_minute = as.numeric(local_minute), - local_day_segment = case_when(local_hour %in% 0:5 ~ "night", - local_hour %in% 6:11 ~ "morning", - local_hour %in% 12:17 ~ "afternoon", - local_hour %in% 18:23 ~ "evening")) %>% - # Delete resampled rows that exist in the same minute as other original (fused) rows - group_by(local_date, local_hour, local_minute) %>% - mutate(n = n()) %>% - filter(n == 1 | (n > 1 & provider == "fused")) %>% - select(-n) - - write.csv(resampled_locations,snakemake@output[[1]], row.names = F) -} else { - write.csv(locations,snakemake@output[[1]], row.names = F) -} diff --git a/src/data/unify_ios_android.R b/src/data/unify_ios_android.R index ca4211c5..48ed3efc 100644 --- a/src/data/unify_ios_android.R +++ b/src/data/unify_ios_android.R @@ -1,14 +1,15 @@ source("renv/activate.R") source("src/data/unify_utils.R") +library(yaml) sensor_data <- read.csv(snakemake@input[["sensor_data"]], stringsAsFactors = FALSE) participant_info <- snakemake@input[["participant_info"]] sensor <- snakemake@params[["sensor"]] -unifiable_sensors = snakemake@params[["unifiable_sensors"]] -platforms <- strsplit(readLines(participant_info, n=2)[[2]], ",")[[1]] +participant <- read_yaml(participant_info) +platforms <- participant$PHONE$PLATFORMS platform <- ifelse(platforms[1] == "multiple" | (length(platforms) > 1 & "android" %in% platforms & "ios" %in% platforms), "android", platforms[1]) -sensor_data <- unify_data(sensor_data, sensor, platform, unifiable_sensors) +sensor_data <- unify_data(sensor_data, sensor, platform) write.csv(sensor_data, snakemake@output[[1]], row.names = FALSE) diff --git a/src/data/unify_utils.R b/src/data/unify_utils.R index f373c55f..c5da2de0 100644 --- a/src/data/unify_utils.R +++ b/src/data/unify_utils.R @@ -1,4 +1,4 @@ -library(dplyr) +library("dplyr", warn.conflicts = F) library(stringr) unify_ios_screen <- function(ios_screen){ @@ -54,7 +54,8 @@ unify_ios_calls <- function(ios_calls){ local_time = first(local_time), local_hour = first(local_hour), local_minute = first(local_minute), - local_day_segment = first(local_day_segment)) + local_timezone = first(local_timezone), + assigned_segments = first(assigned_segments)) } else { ios_calls <- ios_calls %>% summarise(call_type_sequence = paste(call_type, collapse = ","), call_duration = sum(call_duration), timestamp = first(timestamp)) @@ -100,7 +101,7 @@ clean_ios_activity_column <- function(ios_gar){ return(ios_gar) } -unify_ios_gar <- function(ios_gar){ +unify_ios_activity_recognition <- function(ios_gar){ # We only need to unify Google Activity Recognition data for iOS # discard rows where activities column is blank ios_gar <- ios_gar[-which(ios_gar$activities == ""), ] @@ -111,11 +112,13 @@ unify_ios_gar <- function(ios_gar){ ios_gar <- ios_gar %>% mutate(activity_name = case_when(activities == "automotive" ~ "in_vehicle", activities == "cycling" ~ "on_bicycle", - activities == "walking" | activities == "running" ~ "on_foot", + activities == "walking" ~ "walking", + activities == "running" ~ "running", activities == "stationary" ~ "still"), activity_type = case_when(activities == "automotive" ~ 0, activities == "cycling" ~ 1, - activities == "walking" | activities == "running" ~ 2, + activities == "walking" ~ 7, + activities == "running" ~ 8, activities == "stationary" ~ 3, activities == "unknown" ~ 4)) @@ -137,7 +140,7 @@ unify_ios_conversation <- function(conversation){ } # This function is used in download_dataset.R -unify_raw_data <- function(dbEngine, table, start_datetime_utc, end_datetime_utc, aware_multiplatform_tables, unifiable_tables, device_ids, platforms){ +unify_raw_data <- function(dbEngine, sensor_table, sensor, timestamp_filter, aware_multiplatform_tables, device_ids, platforms){ # If platforms is 'multiple', fetch each device_id's platform from aware_device, otherwise, use those given by the user if(length(platforms) == 1 && platforms == "multiple") devices_platforms <- dbGetQuery(dbEngine, paste0("SELECT device_id,brand FROM aware_device WHERE device_id IN ('", paste0(device_ids, collapse = "','"), "')")) %>% @@ -146,8 +149,9 @@ unify_raw_data <- function(dbEngine, table, start_datetime_utc, end_datetime_utc devices_platforms <- data.frame(device_id = device_ids, platform = platforms) # Get existent tables in database - available_tables_in_db <- dbGetQuery(dbEngine, paste0("SELECT table_name FROM information_schema.tables WHERE table_type = 'base table' AND table_schema='", dbGetInfo(dbEngine)$dbname,"'")) %>% pull(table_name) - + available_tables_in_db <- dbGetQuery(dbEngine, paste0("SELECT table_name FROM information_schema.tables WHERE table_schema='", dbGetInfo(dbEngine)$dbname,"'"))[[1]] + if(!any(sensor_table %in% available_tables_in_db)) + stop(paste0("You requested data from these table(s) ", paste0(sensor_table, collapse=", "), " but they don't exist in your database ", dbGetInfo(dbEngine)$dbname)) # Parse the table names for activity recognition and conversation plugins because they are different between android and ios ar_tables <- setNames(aware_multiplatform_tables[1:2], c("android", "ios")) conversation_tables <- setNames(aware_multiplatform_tables[3:4], c("android", "ios")) @@ -159,17 +163,16 @@ unify_raw_data <- function(dbEngine, table, start_datetime_utc, end_datetime_utc platform <- row$platform # Handle special cases when tables for the same sensor have different names for Android and iOS (AR and conversation) - if(table %in% ar_tables) + if(length(sensor_table) == 1) + table <- sensor_table + else if(all(sensor_table == ar_tables)) table <- ar_tables[[platform]] - else if(table %in% conversation_tables) + else if(all(sensor_table == conversation_tables)) table <- conversation_tables[[platform]] if(table %in% available_tables_in_db){ - query <- paste0("SELECT * FROM ", table, " WHERE device_id IN ('", device_id, "')") - if("timestamp" %in% available_columns && !(is.na(start_datetime_utc)) && !(is.na(end_datetime_utc)) && start_datetime_utc < end_datetime_utc){ - query <- paste0(query, "AND timestamp BETWEEN 1000*UNIX_TIMESTAMP('", start_datetime_utc, "') AND 1000*UNIX_TIMESTAMP('", end_datetime_utc, "')") - } - sensor_data <- unify_data(dbGetQuery(dbEngine, query), table, platform, unifiable_tables) + query <- paste0("SELECT * FROM ", table, " WHERE device_id IN ('", device_id, "')", timestamp_filter) + sensor_data <- unify_data(dbGetQuery(dbEngine, query), sensor, platform) participants_sensordata <- append(participants_sensordata, list(sensor_data)) }else{ warning(paste0("Missing ", table, " table. We unified the data from ", paste0(devices_platforms$device_id, collapse = " and "), " but without records from this missing table for ", device_id)) @@ -181,25 +184,16 @@ unify_raw_data <- function(dbEngine, table, start_datetime_utc, end_datetime_utc } # This function is used in unify_ios_android.R and unify_raw_data function -unify_data <- function(sensor_data, sensor, platform, unifiable_sensors){ - if(sensor == unifiable_sensors$calls){ - if(platform == "ios"){ - sensor_data = unify_ios_calls(sensor_data) - } - # android calls remain unchanged - } else if(sensor == unifiable_sensors$battery){ - if(platform == "ios"){ - sensor_data = unify_ios_battery(sensor_data) - } - # android battery remains unchanged - } else if(sensor == unifiable_sensors$ios_activity_recognition){ - sensor_data = unify_ios_gar(sensor_data) - } else if(sensor == unifiable_sensors$screen){ - if(platform == "ios"){ - sensor_data = unify_ios_screen(sensor_data) - } - # android screen remains unchanged - } else if(sensor == unifiable_sensors$ios_conversation){ +unify_data <- function(sensor_data, sensor, platform){ + if(sensor == "phone_calls" & platform == "ios"){ + sensor_data = unify_ios_calls(sensor_data) + } else if(sensor == "phone_battery" & platform == "ios"){ + sensor_data = unify_ios_battery(sensor_data) + } else if(sensor == "phone_activity_recognition" & platform == "ios"){ + sensor_data = unify_ios_activity_recognition(sensor_data) + } else if(sensor == "phone_screen" & platform == "ios"){ + sensor_data = unify_ios_screen(sensor_data) + } else if(sensor == "phone_conversation" & platform == "ios"){ sensor_data = unify_ios_conversation(sensor_data) } return(sensor_data) diff --git a/src/data/workflow_example/download_demographic_data.R b/src/data/workflow_example/download_demographic_data.R new file mode 100644 index 00000000..0adb1086 --- /dev/null +++ b/src/data/workflow_example/download_demographic_data.R @@ -0,0 +1,22 @@ +source("renv/activate.R") +library(RMariaDB) +library("dplyr", warn.conflicts = F) +library(readr) +library(stringr) +library(yaml) + + +participant_file <- snakemake@input[["participant_file"]] +source <- snakemake@params[["source"]] +table <- snakemake@params[["table"]] +sensor_file <- snakemake@output[[1]] + +participant <- read_yaml(participant_file) +record_id <- participant$PHONE$LABEL + +dbEngine <- dbConnect(MariaDB(), default.file = "./.env", group = source$DATABASE_GROUP) +query <- paste0("SELECT * FROM ", table, " WHERE record_id = '", record_id, "'") +sensor_data <- dbGetQuery(dbEngine, query) +dbDisconnect(dbEngine) + +write_csv(sensor_data, sensor_file) diff --git a/src/data/workflow_example/download_target_data.R b/src/data/workflow_example/download_target_data.R new file mode 100644 index 00000000..19ffa8d9 --- /dev/null +++ b/src/data/workflow_example/download_target_data.R @@ -0,0 +1,26 @@ +source("renv/activate.R") +library(RMariaDB) +library("dplyr", warn.conflicts = F) +library(readr) +library(stringr) +library(yaml) +library(lubridate) + + +participant_file <- snakemake@input[["participant_file"]] +source <- snakemake@params[["source"]] +table <- snakemake@params[["table"]] +sensor_file <- snakemake@output[[1]] + +participant <- read_yaml(participant_file) +record_id <- participant$PHONE$LABEL + +dbEngine <- dbConnect(MariaDB(), default.file = "./.env", group = source$DATABASE_GROUP) +query <- paste0("SELECT * FROM ", table, " WHERE record_id = '", record_id, "'") +sensor_data <- dbGetQuery(dbEngine, query) +dbDisconnect(dbEngine) + +# generate timestamp based on local_date +sensor_data$timestamp <- as.numeric(ymd_hms(paste(sensor_data$local_date, "00:00:00"), tz=source$TIMEZONE, quiet=TRUE)) * 1000 + +write_csv(sensor_data, sensor_file) diff --git a/src/features/__init__.py b/src/features/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/features/accelerometer/accelerometer_base.py b/src/features/accelerometer/accelerometer_base.py deleted file mode 100644 index 230584ce..00000000 --- a/src/features/accelerometer/accelerometer_base.py +++ /dev/null @@ -1,111 +0,0 @@ -import pandas as pd -import numpy as np - -def getActivityEpisodes(acc_minute): - # rebuild local date time for resampling - acc_minute["local_datetime"] = pd.to_datetime(acc_minute["local_date"].dt.strftime("%Y-%m-%d") + \ - " " + acc_minute["local_hour"].apply(str) + ":" + acc_minute["local_minute"].apply(str) + ":00") - - # resample the data into 1 minute bins, set "isexertionalactivity" column to be NA if it is missing - resampled_acc_minute = pd.DataFrame(acc_minute.resample("1T", on="local_datetime")["isexertionalactivity"].sum(min_count=1)) - - # group rows by consecutive values of "isexertionalactivity" column - group = pd.DataFrame(resampled_acc_minute["isexertionalactivity"] != resampled_acc_minute["isexertionalactivity"].shift()).cumsum().rename(columns={"isexertionalactivity": "group_idx"}) - - # combine resampled_acc_minute and group column - resampled_acc_minute = pd.concat([resampled_acc_minute, group], axis=1) - - # drop rows where "isexertionalactivity" column is missing and reset the index - resampled_acc_minute.dropna(subset=["isexertionalactivity"], inplace=True) - resampled_acc_minute.reset_index(inplace=True) - resampled_acc_minute.loc[:, "local_date"] = resampled_acc_minute["local_datetime"].dt.date - - # duration column contains the number of minutes (rows) of exertional and nonexertional activity for each episode - activity_episode = resampled_acc_minute.groupby(["isexertionalactivity", "group_idx", "local_date"]).count().rename(columns={"local_datetime": "duration"}).reset_index() - - return activity_episode - -def dropRowsWithCertainThreshold(data, threshold): - data_grouped = data.groupby(["local_date", "local_hour", "local_minute"]).count() - drop_dates = data_grouped[data_grouped["timestamp"] == threshold].index - data.set_index(["local_date", "local_hour", "local_minute"], inplace = True) - if not drop_dates.empty: - data.drop(drop_dates, axis = 0, inplace = True) - return data.reset_index() - -def statsFeatures(acc_data, day_segment, features_to_compute, features_type, acc_features): - if features_type == "magnitude": - col_name = features_type - elif features_type == "durationexertionalactivityepisode" or features_type == "durationnonexertionalactivityepisode": - col_name = "duration" - else: - raise ValueError("features_type can only be one of ['magnitude', 'durationexertionalactivityepisode', 'durationnonexertionalactivityepisode'].") - - if "sum" + features_type in features_to_compute: - acc_features["acc_" + day_segment + "_sum" + features_type] = acc_data.groupby(["local_date"])[col_name].sum() - if "max" + features_type in features_to_compute: - acc_features["acc_" + day_segment + "_max" + features_type] = acc_data.groupby(["local_date"])[col_name].max() - if "min" + features_type in features_to_compute: - acc_features["acc_" + day_segment + "_min" + features_type] = acc_data.groupby(["local_date"])[col_name].min() - if "avg" + features_type in features_to_compute: - acc_features["acc_" + day_segment + "_avg" + features_type] = acc_data.groupby(["local_date"])[col_name].mean() - if "median" + features_type in features_to_compute: - acc_features["acc_" + day_segment + "_median" + features_type] = acc_data.groupby(["local_date"])[col_name].median() - if "std" + features_type in features_to_compute: - acc_features["acc_" + day_segment + "_std" + features_type] = acc_data.groupby(["local_date"])[col_name].std() - - return acc_features - - - -def base_accelerometer_features(acc_data, day_segment, requested_features, valid_sensed_minutes): - # name of the features this function can compute - base_features_names_magnitude = ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] - base_features_names_exertionalactivityepisode = ["sumdurationexertionalactivityepisode", "maxdurationexertionalactivityepisode", "mindurationexertionalactivityepisode", "avgdurationexertionalactivityepisode", "mediandurationexertionalactivityepisode", "stddurationexertionalactivityepisode"] - base_features_names_nonexertionalactivityepisode = ["sumdurationnonexertionalactivityepisode", "maxdurationnonexertionalactivityepisode", "mindurationnonexertionalactivityepisode", "avgdurationnonexertionalactivityepisode", "mediandurationnonexertionalactivityepisode", "stddurationnonexertionalactivityepisode"] - # the subset of requested features this function can compute - features_to_compute_magnitude = list(set(requested_features["magnitude"]) & set(base_features_names_magnitude)) - features_to_compute_exertionalactivityepisode = list(set(requested_features["exertional_activity_episode"]) & set(base_features_names_exertionalactivityepisode)) - features_to_compute_nonexertionalactivityepisode = list(set(requested_features["nonexertional_activity_episode"]) & set(base_features_names_nonexertionalactivityepisode)) - - features_to_compute = features_to_compute_magnitude + features_to_compute_exertionalactivityepisode + features_to_compute_nonexertionalactivityepisode + (["validsensedminutes"] if valid_sensed_minutes else []) - - acc_features = pd.DataFrame(columns=["local_date"] + ["acc_" + day_segment + "_" + x for x in features_to_compute]) - if not acc_data.empty: - if day_segment != "daily": - acc_data = acc_data[acc_data["local_day_segment"] == day_segment] - - if not acc_data.empty: - acc_features = pd.DataFrame() - # get magnitude related features: magnitude = sqrt(x^2+y^2+z^2) - magnitude = acc_data.apply(lambda row: np.sqrt(row["double_values_0"] ** 2 + row["double_values_1"] ** 2 + row["double_values_2"] ** 2), axis=1) - acc_data = acc_data.assign(magnitude = magnitude.values) - acc_features = statsFeatures(acc_data, day_segment, features_to_compute_magnitude, "magnitude", acc_features) - - - # get extertional activity features - # reference: https://jamanetwork.com/journals/jamasurgery/fullarticle/2753807 - - # drop rows where we only have one row per minute (no variance) - acc_data = dropRowsWithCertainThreshold(acc_data, 1) - if not acc_data.empty: - # check if the participant performs exertional activity for each minute - acc_minute = pd.DataFrame() - acc_minute["isexertionalactivity"] = (acc_data.groupby(["local_date", "local_hour", "local_minute"])["double_values_0"].var() + acc_data.groupby(["local_date", "local_hour", "local_minute"])["double_values_1"].var() + acc_data.groupby(["local_date", "local_hour", "local_minute"])["double_values_2"].var()).apply(lambda x: 1 if x > 0.15 * (9.807 ** 2) else 0) - acc_minute.reset_index(inplace=True) - - if valid_sensed_minutes: - acc_features["acc_" + day_segment + "_validsensedminutes"] = acc_minute.groupby(["local_date"])["isexertionalactivity"].count() - - activity_episode = getActivityEpisodes(acc_minute) - exertionalactivity_episodes = activity_episode[activity_episode["isexertionalactivity"] == 1] - acc_features = statsFeatures(exertionalactivity_episodes, day_segment, features_to_compute_exertionalactivityepisode, "durationexertionalactivityepisode", acc_features) - - nonexertionalactivity_episodes = activity_episode[activity_episode["isexertionalactivity"] == 0] - acc_features = statsFeatures(nonexertionalactivity_episodes, day_segment, features_to_compute_nonexertionalactivityepisode, "durationnonexertionalactivityepisode", acc_features) - - acc_features[[colname for colname in acc_features.columns if "std" not in colname]] = acc_features[[colname for colname in acc_features.columns if "std" not in colname]].fillna(0) - - acc_features = acc_features.reset_index() - - return acc_features diff --git a/src/features/accelerometer_features.py b/src/features/accelerometer_features.py deleted file mode 100644 index f4461c1d..00000000 --- a/src/features/accelerometer_features.py +++ /dev/null @@ -1,22 +0,0 @@ -import numpy as np -import pandas as pd -from accelerometer.accelerometer_base import base_accelerometer_features - - -acc_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"]) -day_segment = snakemake.params["day_segment"] - -requested_features = {} -requested_features["magnitude"] = snakemake.params["magnitude"] -requested_features["exertional_activity_episode"] = [feature + "exertionalactivityepisode" for feature in snakemake.params["exertional_activity_episode"]] -requested_features["nonexertional_activity_episode"] = [feature + "nonexertionalactivityepisode" for feature in snakemake.params["nonexertional_activity_episode"]] - -valid_sensed_minutes = snakemake.params["valid_sensed_minutes"] - -acc_features = pd.DataFrame(columns=["local_date"]) - -acc_features = acc_features.merge(base_accelerometer_features(acc_data, day_segment, requested_features, valid_sensed_minutes), on="local_date", how="outer") - -assert np.sum([len(x) for x in requested_features.values()]) + (1 if valid_sensed_minutes else 0) + 1 == acc_features.shape[1], "The number of features in the output dataframe (=" + str(acc_features.shape[1]) + ") does not match the expected value (=" + str(np.sum([len(x) for x in requested_features.values()]) + (1 if valid_sensed_minutes else 0)) + " + 1). Verify your accelerometer feature extraction functions" - -acc_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/activity_recognition.py b/src/features/activity_recognition.py deleted file mode 100644 index 5a3d7117..00000000 --- a/src/features/activity_recognition.py +++ /dev/null @@ -1,15 +0,0 @@ -import pandas as pd -from ar.ar_base import base_ar_features - -ar_data = pd.read_csv(snakemake.input[0],parse_dates=["local_date_time"]) -ar_deltas = pd.read_csv(snakemake.input[1],parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"]) -day_segment = snakemake.params["segment"] -requested_features = snakemake.params["features"] -ar_features = pd.DataFrame(columns=["local_date"]) - - -ar_features = ar_features.merge(base_ar_features(ar_data, ar_deltas, day_segment, requested_features), on="local_date", how="outer") - -assert len(requested_features) + 1 == ar_features.shape[1], "The number of features in the output dataframe (=" + str(ar_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your activity recognition feature extraction functions" - -ar_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/activity_recognition_deltas.R b/src/features/activity_recognition_deltas.R deleted file mode 100644 index 78739e16..00000000 --- a/src/features/activity_recognition_deltas.R +++ /dev/null @@ -1,33 +0,0 @@ -source("renv/activate.R") - -library("tidyverse") - -gar <- read.csv(snakemake@input[[1]]) - -if(nrow(gar) > 0){ - activity_episodes <- - gar %>% - mutate(activity_episode = cumsum(c(1, head(activity_type, -1) != tail(activity_type, -1)))) %>% - group_by(activity_episode) %>% - filter(n() > 1) %>% - summarize(activity = first(activity_name), - time_diff = (last(timestamp) - first(timestamp)) / (1000 * 60), - local_start_date_time = first(local_date_time), - local_end_date_time = last(local_date_time), - local_start_date = first(local_date), - local_end_date = last(local_date), - local_start_day_segment = first(local_day_segment), - local_end_day_segment = last(local_day_segment)) %>% - select(-activity_episode) -} else { - activity_episodes <- data.frame(activity = character(), - time_diff = numeric(), - local_start_date_time = character(), - local_end_date_time = character(), - local_start_date = character(), - local_end_date = character(), - local_start_day_segment = character(), - local_end_day_segment = character()) -} - -write.csv(activity_episodes, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/applications_foreground/applications_foreground_base.py b/src/features/applications_foreground/applications_foreground_base.py deleted file mode 100644 index 5fb5e7f2..00000000 --- a/src/features/applications_foreground/applications_foreground_base.py +++ /dev/null @@ -1,74 +0,0 @@ -import pandas as pd -import itertools -from scipy.stats import entropy - - -def compute_features(filtered_data, apps_type, requested_features, apps_features, day_segment): - # There is the rare occasion that filtered_data is empty (found in testing) - if "timeoffirstuse" in requested_features: - time_first_event = filtered_data.sort_values(by="timestamp", ascending=True).drop_duplicates(subset="local_date", keep="first").set_index("local_date") - if time_first_event.empty: - apps_features["apps_" + day_segment + "_timeoffirstuse" + apps_type] = 'NA' - else: - apps_features["apps_" + day_segment + "_timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"] - if "timeoflastuse" in requested_features: - time_last_event = filtered_data.sort_values(by="timestamp", ascending=False).drop_duplicates(subset="local_date", keep="first").set_index("local_date") - if time_last_event.empty: - apps_features["apps_" + day_segment + "_timeoflastuse" + apps_type] = 'NA' - else: - apps_features["apps_" + day_segment + "_timeoflastuse" + apps_type] = time_last_event["local_hour"] * 60 + time_last_event["local_minute"] - if "frequencyentropy" in requested_features: - apps_with_count = filtered_data.groupby(["local_date","application_name"]).count().sort_values(by="timestamp", ascending=False).reset_index() - if (len(apps_with_count.index) < 2 ): - apps_features["apps_" + day_segment + "_frequencyentropy" + apps_type] = 'NA' - else: - apps_features["apps_" + day_segment + "_frequencyentropy" + apps_type] = apps_with_count.groupby("local_date")["timestamp"].agg(entropy) - if "count" in requested_features: - apps_features["apps_" + day_segment + "_count" + apps_type] = filtered_data.groupby(["local_date"]).count()["timestamp"] - apps_features.fillna(value={"apps_" + day_segment + "_count" + apps_type: 0}, inplace=True) - return apps_features - - -def base_applications_foreground_features(apps_data, day_segment, requested_features, params): - multiple_categories_with_genres = params["multiple_categories_with_genres"] - single_categories = params["single_categories"] - multiple_categories = params["multiple_categories"] - apps = params["apps"] - - # deep copy the apps_data for the top1global computation - apps_data_global = apps_data.copy() - - apps_features = pd.DataFrame(columns=["local_date"] + ["apps_" + day_segment + "_" + x for x in ["".join(feature) for feature in itertools.product(requested_features, single_categories + multiple_categories + apps)]]) - if not apps_data.empty: - if day_segment != "daily": - apps_data =apps_data[apps_data["local_day_segment"] == day_segment] - - if not apps_data.empty: - apps_features = pd.DataFrame() - # single category - single_categories.sort() - for sc in single_categories: - if sc == "all": - apps_features = compute_features(apps_data, "all", requested_features, apps_features, day_segment) - else: - filtered_data = apps_data[apps_data["genre"].isin([sc])] - apps_features = compute_features(filtered_data, sc, requested_features, apps_features, day_segment) - # multiple category - for mc in multiple_categories: - filtered_data = apps_data[apps_data["genre"].isin(multiple_categories_with_genres[mc])] - apps_features = compute_features(filtered_data, mc, requested_features, apps_features, day_segment) - # single apps - for app in apps: - col_name = app - if app == "top1global": - # get the most used app - apps_with_count = apps_data_global.groupby(["local_date","package_name"]).count().sort_values(by="timestamp", ascending=False).reset_index() - app = apps_with_count.iloc[0]["package_name"] - col_name = "top1global" - - filtered_data = apps_data[apps_data["package_name"].isin([app])] - apps_features = compute_features(filtered_data, col_name, requested_features, apps_features, day_segment) - - apps_features = apps_features.reset_index() - - return apps_features diff --git a/src/features/applications_foreground_features.py b/src/features/applications_foreground_features.py deleted file mode 100644 index 9863381c..00000000 --- a/src/features/applications_foreground_features.py +++ /dev/null @@ -1,36 +0,0 @@ -import pandas as pd -from applications_foreground.applications_foreground_base import base_applications_foreground_features - -apps_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"], encoding="ISO-8859-1") -day_segment = snakemake.params["day_segment"] -single_categories = snakemake.params["single_categories"] -multiple_categories_with_genres = snakemake.params["multiple_categories"] -single_apps = snakemake.params["single_apps"] -excluded_categories = snakemake.params["excluded_categories"] -excluded_apps = snakemake.params["excluded_apps"] -requested_features = snakemake.params["features"] -apps_features = pd.DataFrame(columns=["local_date"]) - -single_categories = list(set(single_categories) - set(excluded_categories)) -multiple_categories = list(multiple_categories_with_genres.keys() - set(excluded_categories)) -apps = list(set(single_apps) - set(excluded_apps)) -type_count = len(single_categories) + len(multiple_categories) + len(apps) - -params = {} -params["multiple_categories_with_genres"] = multiple_categories_with_genres -params["single_categories"] = single_categories -params["multiple_categories"] = multiple_categories -params["apps"] = apps - -# exclude categories in the excluded_categories list -if "system_apps" in excluded_categories: - apps_data = apps_data[apps_data["is_system_app"] == 0] -apps_data = apps_data[~apps_data["genre"].isin(excluded_categories)] -# exclude apps in the excluded_apps list -apps_data = apps_data[~apps_data["package_name"].isin(excluded_apps)] - -apps_features = apps_features.merge(base_applications_foreground_features(apps_data, day_segment, requested_features, params), on="local_date", how="outer") - -assert len(requested_features) * type_count + 1 == apps_features.shape[1], "The number of features in the output dataframe (=" + str(apps_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your application foreground feature extraction functions" - -apps_features.to_csv(snakemake.output[0], index=False) diff --git a/src/features/ar/ar_base.py b/src/features/ar/ar_base.py deleted file mode 100644 index 0508545a..00000000 --- a/src/features/ar/ar_base.py +++ /dev/null @@ -1,63 +0,0 @@ -import pandas as pd -import numpy as np -import scipy.stats as stats -from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes - -def base_ar_features(ar_data, ar_deltas, day_segment, requested_features): - # name of the features this function can compute - base_features_names = ["count","mostcommonactivity","countuniqueactivities","activitychangecount","sumstationary","summobile","sumvehicle"] - # the subset of requested features this function can compute - features_to_compute = list(set(requested_features) & set(base_features_names)) - - ar_features = pd.DataFrame(columns = ["local_date"] + ["ar_" + day_segment + "_" + x for x in features_to_compute]) - if not ar_data.empty: - ar_deltas = splitOvernightEpisodes(ar_deltas, [],["activity"]) - - if day_segment != "daily": - ar_deltas = splitMultiSegmentEpisodes(ar_deltas, day_segment, []) - - ar_data.local_date_time = pd.to_datetime(ar_data.local_date_time) - resampledData = ar_data.set_index(ar_data.local_date_time) - resampledData.drop(columns=["local_date_time"], inplace=True) - - if day_segment != "daily": - resampledData = resampledData.loc[resampledData["local_day_segment"] == day_segment] - - if not resampledData.empty: - ar_features = pd.DataFrame() - - # finding the count of samples of the day - if "count" in features_to_compute: - ar_features["ar_" + day_segment + "_count"] = resampledData["activity_type"].resample("D").count() - - # finding most common activity of the day - if "mostcommonactivity" in features_to_compute: - ar_features["ar_" + day_segment + "_mostcommonactivity"] = resampledData["activity_type"].resample("D").apply(lambda x: stats.mode(x)[0] if len(stats.mode(x)[0]) != 0 else None) - - # finding different number of activities during a day - if "countuniqueactivities" in features_to_compute: - ar_features["ar_" + day_segment + "_countuniqueactivities"] = resampledData["activity_type"].resample("D").nunique() - - # finding Number of times activity changed - if "activitychangecount" in features_to_compute: - resampledData["activity_type_shift"] = resampledData["activity_type"].shift().fillna(resampledData["activity_type"].head(1)) - resampledData["different_activity"] = np.where(resampledData["activity_type"]!=resampledData["activity_type_shift"],1,0) - ar_features["ar_" + day_segment + "_activitychangecount"] = resampledData["different_activity"].resample("D").sum() - - - deltas_features = {"sumstationary":["still","tilting"], - "summobile":["on_foot","walking","running","on_bicycle"], - "sumvehicle":["in_vehicle"]} - - for column, activity_labels in deltas_features.items(): - if column in features_to_compute: - filtered_data = ar_deltas[ar_deltas["activity"].isin(pd.Series(activity_labels))] - if not filtered_data.empty: - ar_features["ar_" + day_segment + "_" + column] = ar_deltas[ar_deltas["activity"].isin(pd.Series(activity_labels))].groupby(["local_start_date"])["time_diff"].sum().fillna(0) - else: - ar_features["ar_" + day_segment + "_" + column] = 0 - - ar_features.index.names = ["local_date"] - ar_features = ar_features.reset_index() - - return ar_features diff --git a/src/features/battery/battery_base.py b/src/features/battery/battery_base.py deleted file mode 100644 index c6cdedf6..00000000 --- a/src/features/battery/battery_base.py +++ /dev/null @@ -1,48 +0,0 @@ -import pandas as pd -from datetime import datetime, timedelta, time -from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes - - -def base_battery_features(battery_data, day_segment, requested_features): - # name of the features this function can compute - base_features_names = ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] - # the subset of requested features this function can compute - features_to_compute = list(set(requested_features) & set(base_features_names)) - - battery_features = pd.DataFrame(columns=["local_date"] + ["battery_" + day_segment + "_" + x for x in features_to_compute]) - if not battery_data.empty: - battery_data = splitOvernightEpisodes(battery_data, ["battery_diff"], []) - - if day_segment != "daily": - battery_data = splitMultiSegmentEpisodes(battery_data, day_segment, ["battery_diff"]) - - if not battery_data.empty: - battery_data["battery_consumption_rate"] = battery_data["battery_diff"] / battery_data["time_diff"] - - # for battery_data_discharge: - battery_data_discharge = battery_data[battery_data["battery_diff"] > 0] - battery_discharge_features = pd.DataFrame() - if "countdischarge" in features_to_compute: - battery_discharge_features["battery_"+day_segment+"_countdischarge"] = battery_data_discharge.groupby(["local_start_date"])["local_start_date"].count() - if "sumdurationdischarge" in features_to_compute: - battery_discharge_features["battery_"+day_segment+"_sumdurationdischarge"] = battery_data_discharge.groupby(["local_start_date"])["time_diff"].sum() - if "avgconsumptionrate" in features_to_compute: - battery_discharge_features["battery_"+day_segment+"_avgconsumptionrate"] = battery_data_discharge.groupby(["local_start_date"])["battery_consumption_rate"].mean() - if "maxconsumptionrate" in features_to_compute: - battery_discharge_features["battery_"+day_segment+"_maxconsumptionrate"] = battery_data_discharge.groupby(["local_start_date"])["battery_consumption_rate"].max() - - # for battery_data_charge: - battery_data_charge = battery_data[battery_data["battery_diff"] <= 0] - battery_charge_features = pd.DataFrame() - if "countcharge" in features_to_compute: - battery_charge_features["battery_"+day_segment+"_countcharge"] = battery_data_charge.groupby(["local_start_date"])["local_start_date"].count() - if "sumdurationcharge" in features_to_compute: - battery_charge_features["battery_"+day_segment+"_sumdurationcharge"] = battery_data_charge.groupby(["local_start_date"])["time_diff"].sum() - - # combine discharge features and charge features; fill the missing values with ZERO - battery_features = pd.concat([battery_discharge_features, battery_charge_features], axis=1, sort=True).fillna(0) - - battery_features.index.rename("local_date", inplace=True) - battery_features = battery_features.reset_index() - - return battery_features diff --git a/src/features/battery_deltas.R b/src/features/battery_deltas.R deleted file mode 100644 index 488eaa52..00000000 --- a/src/features/battery_deltas.R +++ /dev/null @@ -1,34 +0,0 @@ -source("renv/activate.R") - -library("tidyverse") - -battery <- read.csv(snakemake@input[[1]]) - -if(nrow(battery) > 0){ - consumption <- battery %>% - mutate(group = ifelse(lag(battery_status) != battery_status, 1, 0) %>% coalesce(0), - group_id = cumsum(group) + 1) %>% - filter(battery_status == 2 | battery_status == 3) %>% - group_by(group_id) %>% - summarize(battery_diff = first(battery_level) - last(battery_level), - time_diff = (last(timestamp) - first(timestamp)) / (1000 * 60), - local_start_date_time = first(local_date_time), - local_end_date_time = last(local_date_time), - local_start_date = first(local_date), - local_end_date = last(local_date), - local_start_day_segment = first(local_day_segment), - local_end_day_segment = last(local_day_segment)) %>% - select(-group_id) %>% - filter(time_diff > 6) # Avoids including quick cycles -} else { - consumption <- data.frame(battery_diff = numeric(), - time_diff = numeric(), - local_start_date_time = character(), - local_end_date_time = character(), - local_start_date = character(), - local_end_date = character(), - local_start_day_segment = character(), - local_end_day_segment = character()) -} - -write.csv(consumption, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/battery_features.py b/src/features/battery_features.py deleted file mode 100644 index 0a9c21c6..00000000 --- a/src/features/battery_features.py +++ /dev/null @@ -1,13 +0,0 @@ -import pandas as pd -from battery.battery_base import base_battery_features - -battery_data = pd.read_csv(snakemake.input[0], parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"]) -day_segment = snakemake.params["day_segment"] -requested_features = snakemake.params["features"] -battery_features = pd.DataFrame(columns=["local_date"]) - -battery_features = battery_features.merge(base_battery_features(battery_data, day_segment, requested_features), on="local_date", how="outer") - -assert len(requested_features) + 1 == battery_features.shape[1], "The number of features in the output dataframe (=" + str(battery_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your battery feature extraction functions" - -battery_features.to_csv(snakemake.output[0], index=False) diff --git a/src/features/bluetooth/bluetooth_base.R b/src/features/bluetooth/bluetooth_base.R deleted file mode 100644 index e525b723..00000000 --- a/src/features/bluetooth/bluetooth_base.R +++ /dev/null @@ -1,53 +0,0 @@ -library(dplyr) -library(tidyr) - -filter_by_day_segment <- function(data, day_segment) { - if(day_segment %in% c("morning", "afternoon", "evening", "night")) - data <- data %>% filter(local_day_segment == day_segment) - - return(data %>% group_by(local_date)) -} - -compute_bluetooth_feature <- function(data, feature, day_segment){ - data <- data %>% filter_by_day_segment(day_segment) - if(feature %in% c("countscans", "uniquedevices")){ - data <- switch(feature, - "countscans" = data %>% summarise(!!paste("bluetooth", day_segment, feature, sep = "_") := n()), - "uniquedevices" = data %>% summarise(!!paste("bluetooth", day_segment, feature, sep = "_") := n_distinct(bt_address))) - return(data) - } else if(feature == "countscansmostuniquedevice"){ - # Get the most scanned device - mostuniquedevice <- data %>% - group_by(bt_address) %>% - mutate(N=n()) %>% - ungroup() %>% - filter(N == max(N)) %>% - head(1) %>% # if there are multiple device with the same amount of scans pick the first one only - pull(bt_address) - return(data %>% - filter(bt_address == mostuniquedevice) %>% - group_by(local_date) %>% - summarise(!!paste("bluetooth", day_segment, feature, sep = "_") := n()) %>% - replace(is.na(.), 0)) - } -} - -base_bluetooth_features <- function(bluetooth_data, day_segment, requested_features){ - # Output dataframe - features = data.frame(local_date = character(), stringsAsFactors = FALSE) - - # The name of the features this function can compute - base_features_names <- c("countscans", "uniquedevices", "countscansmostuniquedevice") - - # The subset of requested features this function can compute - features_to_compute <- intersect(base_features_names, requested_features) - - for(feature_name in features_to_compute){ - feature <- compute_bluetooth_feature(bluetooth_data, feature_name, day_segment) - features <- merge(features, feature, by="local_date", all = TRUE) - } - - features <- features %>% mutate_at(vars(contains("countscansmostuniquedevice")), list( ~ replace_na(., 0))) - - return(features) -} \ No newline at end of file diff --git a/src/features/bluetooth_features.R b/src/features/bluetooth_features.R deleted file mode 100644 index ee181852..00000000 --- a/src/features/bluetooth_features.R +++ /dev/null @@ -1,18 +0,0 @@ -source("renv/activate.R") -source("src/features/bluetooth/bluetooth_base.R") -library(dplyr) -library(tidyr) - -bluetooth_data <- read.csv(snakemake@input[[1]], stringsAsFactors = FALSE) -day_segment <- snakemake@params[["day_segment"]] -requested_features <- snakemake@params[["features"]] -features = data.frame(local_date = character(), stringsAsFactors = FALSE) - -# Compute base bluetooth features -features <- merge(features, base_bluetooth_features(bluetooth_data, day_segment, requested_features), by="local_date", all = TRUE) - -if(ncol(features) != length(requested_features) + 1) - stop(paste0("The number of features in the output dataframe (=", ncol(features),") does not match the expected value (=", length(requested_features)," + 1). Verify your bluetooth feature extraction functions")) - - -write.csv(features, snakemake@output[[1]], row.names = FALSE) \ No newline at end of file diff --git a/src/features/build_features.py b/src/features/build_features.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/features/call/call_base.R b/src/features/call/call_base.R deleted file mode 100644 index b5a01025..00000000 --- a/src/features/call/call_base.R +++ /dev/null @@ -1,77 +0,0 @@ -library('tidyr') - -filter_by_day_segment <- function(data, day_segment) { - if(day_segment %in% c("morning", "afternoon", "evening", "night")) - data <- data %>% filter(local_day_segment == day_segment) - else if(day_segment == "daily") - return(data) - else - return(data %>% head(0)) -} - -Mode <- function(v) { - uniqv <- unique(v) - uniqv[which.max(tabulate(match(v, uniqv)))] -} - -base_call_features <- function(calls, call_type, day_segment, requested_features){ - # Output dataframe - features = data.frame(local_date = character(), stringsAsFactors = FALSE) - - # The name of the features this function can compute - base_features_names <- c("count", "distinctcontacts", "meanduration", "sumduration", "minduration", "maxduration", "stdduration", "modeduration", "entropyduration", "timefirstcall", "timelastcall", "countmostfrequentcontact") - - # The subset of requested features this function can compute - features_to_compute <- intersect(base_features_names, requested_features) - - # Filter rows that belong to the calls type and day segment of interest - call_type_label = ifelse(call_type == "incoming", "1", ifelse(call_type == "outgoing", "2", ifelse(call_type == "missed", "3", NA))) - if(is.na(call_type_label)) - stop(paste("Call type can online be incoming, outgoing or missed but instead you typed: ", call_type)) - calls <- calls %>% filter(call_type == call_type_label) %>% filter_by_day_segment(day_segment) - - # If there are not features or data to work with, return an empty df with appropiate columns names - if(length(features_to_compute) == 0) - return(features) - if(nrow(calls) < 1) - return(cbind(features, read.csv(text = paste(paste("call", call_type, day_segment, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE))) - - for(feature_name in features_to_compute){ - if(feature_name == "countmostfrequentcontact"){ - # Get the number of messages for the most frequent contact throughout the study - mostfrequentcontact <- calls %>% - group_by(trace) %>% - mutate(N=n()) %>% - ungroup() %>% - filter(N == max(N)) %>% - head(1) %>% # if there are multiple contacts with the same amount of messages pick the first one only - pull(trace) - feature <- calls %>% - filter(trace == mostfrequentcontact) %>% - group_by(local_date) %>% - summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := n()) %>% - replace(is.na(.), 0) - features <- merge(features, feature, by="local_date", all = TRUE) - } else { - feature <- calls %>% - group_by(local_date) - - feature <- switch(feature_name, - "count" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := n()), - "distinctcontacts" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := n_distinct(trace)), - "meanduration" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := mean(call_duration)), - "sumduration" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := sum(call_duration)), - "minduration" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := min(call_duration)), - "maxduration" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := max(call_duration)), - "stdduration" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := sd(call_duration)), - "modeduration" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := Mode(call_duration)), - "entropyduration" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := entropy.MillerMadow(call_duration)), - "timefirstcall" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)), - "timelastcall" = feature %>% summarise(!!paste("call", call_type, day_segment, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute))) - - features <- merge(features, feature, by="local_date", all = TRUE) - } - } - features <- features %>% mutate_at(vars(contains("countmostfrequentcontact")), list( ~ replace_na(., 0))) - return(features) -} \ No newline at end of file diff --git a/src/features/call_features.R b/src/features/call_features.R deleted file mode 100644 index 324dc24c..00000000 --- a/src/features/call_features.R +++ /dev/null @@ -1,18 +0,0 @@ -source("renv/activate.R") -source("src/features/call/call_base.R") -library(dplyr) -library(entropy) - -calls <- read.csv(snakemake@input[[1]], stringsAsFactors = FALSE) -day_segment <- snakemake@params[["day_segment"]] -requested_features <- snakemake@params[["features"]] -call_type <- snakemake@params[["call_type"]] -features = data.frame(local_date = character(), stringsAsFactors = FALSE) - -# Compute base Call features -features <- merge(features, base_call_features(calls, call_type, day_segment, requested_features), by="local_date", all = TRUE) - -if(ncol(features) != length(requested_features) + 1) - stop(paste0("The number of features in the output dataframe (=", ncol(features),") does not match the expected value (=", length(requested_features)," + 1). Verify your Call feature extraction functions")) - -write.csv(features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/conversation/conversation_base.py b/src/features/conversation/conversation_base.py deleted file mode 100644 index 578024ca..00000000 --- a/src/features/conversation/conversation_base.py +++ /dev/null @@ -1,132 +0,0 @@ -import pandas as pd - -def base_conversation_features(conversation_data, day_segment, requested_features,recordingMinutes,pausedMinutes,expectedMinutes): - # name of the features this function can compute - base_features_names = ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", - "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", - "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", - "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", - "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", - "unknownexpectedfraction","countconversation"] - - # the subset of requested features this function can compute - features_to_compute = list(set(requested_features) & set(base_features_names)) - - conversation_features = pd.DataFrame(columns=["local_date"] + ["conversation_" + day_segment + "_" + x for x in features_to_compute]) - if not conversation_data.empty: - if day_segment != "daily": - conversation_data = conversation_data[conversation_data["local_day_segment"] == day_segment] - - if not conversation_data.empty: - conversation_features = pd.DataFrame() - - conversation_data = conversation_data.drop_duplicates(subset=['local_date','local_time'], keep="first") - - if "minutessilence" in features_to_compute: - conversation_features["conversation_" + day_segment + "_minutessilence"] = conversation_data[conversation_data['inference']==0].groupby(["local_date"])['inference'].count()/60 - - if "minutesnoise" in features_to_compute: - conversation_features["conversation_" + day_segment + "_minutesnoise"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])['inference'].count()/60 - - if "minutesvoice" in features_to_compute: - conversation_features["conversation_" + day_segment + "_minutesvoice"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])['inference'].count()/60 - - if "minutesunknown" in features_to_compute: - conversation_features["conversation_" + day_segment + "_minutesunknown"] = conversation_data[conversation_data['inference']==3].groupby(["local_date"])['inference'].count()/60 - - if "countconversation" in features_to_compute: - conversation_features["conversation_" + day_segment + "_countconversation"] = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_date"])['double_convo_start'].nunique() - - conv_duration = (conversation_data['double_convo_end']/1000 - conversation_data['double_convo_start']/1000)/60 - conversation_data = conversation_data.assign(conv_duration = conv_duration.values) - - conv_totalDuration = conversation_data[(conversation_data['inference'] >= 0) & (conversation_data['inference'] < 4)].groupby(["local_date"])['inference'].count()/60 - - if "silencesensedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_silencesensedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_date"])['inference'].count()/60)/ conv_totalDuration - - if "noisesensedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_noisesensedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_date"])['inference'].count()/60)/ conv_totalDuration - - if "voicesensedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_voicesensedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_date"])['inference'].count()/60)/ conv_totalDuration - - if "unknownsensedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_unknownsensedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_date"])['inference'].count()/60)/ conv_totalDuration - - if "silenceexpectedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_silenceexpectedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_date"])['inference'].count()/60)/ expectedMinutes - - if "noiseexpectedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_noiseexpectedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_date"])['inference'].count()/60)/ expectedMinutes - - if "voiceexpectedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_voiceexpectedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_date"])['inference'].count()/60)/ expectedMinutes - - if "unknownexpectedfraction" in features_to_compute: - conversation_features["conversation_" + day_segment + "_unknownexpectedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_date"])['inference'].count()/60)/ expectedMinutes - - if "sumconversationduration" in features_to_compute: - conversation_features["conversation_" + day_segment + "_sumconversationduration"] = conversation_data.groupby(["local_date"])["conv_duration"].sum() - - if "avgconversationduration" in features_to_compute: - conversation_features["conversation_" + day_segment + "_avgconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_date"])["conv_duration"].mean() - - if "sdconversationduration" in features_to_compute: - conversation_features["conversation_" + day_segment + "_sdconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_date"])["conv_duration"].std() - - if "minconversationduration" in features_to_compute: - conversation_features["conversation_" + day_segment + "_minconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_date"])["conv_duration"].min() - - if "maxconversationduration" in features_to_compute: - conversation_features["conversation_" + day_segment + "_maxconversationduration"] = conversation_data.groupby(["local_date"])["conv_duration"].max() - - if "timefirstconversation" in features_to_compute: - timeFirstConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_date"])['local_time'].min() - if len(list(timeFirstConversation.index)) > 0: - for date in list(timeFirstConversation.index): - conversation_features.loc[date,"conversation_" + day_segment + "_timefirstconversation"] = int(timeFirstConversation.loc[date].split(':')[0])*60 + int(timeFirstConversation.loc[date].split(':')[1]) - else: - conversation_features["conversation_" + day_segment + "_timefirstconversation"] = 0 - - if "timelastconversation" in features_to_compute: - timeLastConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_date"])['local_time'].max() - if len(list(timeLastConversation.index)) > 0: - for date in list(timeLastConversation.index): - conversation_features.loc[date,"conversation_" + day_segment + "_timelastconversation"] = int(timeLastConversation.loc[date].split(':')[0])*60 + int(timeLastConversation.loc[date].split(':')[1]) - else: - conversation_features["conversation_" + day_segment + "_timelastconversation"] = 0 - - if "noisesumenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_noisesumenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])["double_energy"].sum() - - if "noiseavgenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_noiseavgenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])["double_energy"].mean() - - if "noisesdenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_noisesdenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])["double_energy"].std() - - if "noiseminenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_noiseminenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])["double_energy"].min() - - if "noisemaxenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_noisemaxenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_date"])["double_energy"].max() - - if "voicesumenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_voicesumenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])["double_energy"].sum() - - if "voiceavgenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_voiceavgenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])["double_energy"].mean() - - if "voicesdenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_voicesdenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])["double_energy"].std() - - if "voiceminenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_voiceminenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])["double_energy"].min() - - if "voicemaxenergy" in features_to_compute: - conversation_features["conversation_" + day_segment + "_voicemaxenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_date"])["double_energy"].max() - - conversation_features = conversation_features.reset_index() - - return conversation_features \ No newline at end of file diff --git a/src/features/conversation_features.py b/src/features/conversation_features.py deleted file mode 100644 index 4b15a38a..00000000 --- a/src/features/conversation_features.py +++ /dev/null @@ -1,16 +0,0 @@ -import pandas as pd -from conversation.conversation_base import base_conversation_features - -conversation_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"]) -day_segment = snakemake.params["day_segment"] -requested_features = snakemake.params["features"] -recordingMinutes = snakemake.params["recordingMinutes"] -pausedMinutes = snakemake.params["pausedMinutes"] -expectedMinutes = 1440 / (recordingMinutes + pausedMinutes) -conversation_features = pd.DataFrame(columns=["local_date"]) - - -conversation_features = conversation_features.merge(base_conversation_features(conversation_data, day_segment, requested_features,recordingMinutes,pausedMinutes,expectedMinutes), on="local_date", how="outer") -assert len(requested_features) + 1 == conversation_features.shape[1], "The number of features in the output dataframe (=" + str(conversation_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your conversation feature extraction functions" - -conversation_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/entry.R b/src/features/entry.R new file mode 100644 index 00000000..51131c0f --- /dev/null +++ b/src/features/entry.R @@ -0,0 +1,16 @@ +source("renv/activate.R") +source("src/features/utils/utils.R") +library("dplyr",warn.conflicts = F) +library("tidyr") + +sensor_data_files <- snakemake@input +sensor_data_files$time_segments_labels <- NULL +time_segments_file <- snakemake@input[["time_segments_labels"]] + +provider <- snakemake@params["provider"][["provider"]] +provider_key <- snakemake@params["provider_key"] +sensor_key <- snakemake@params["sensor_key"] + +sensor_features <- fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file) + +write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE) \ No newline at end of file diff --git a/src/features/entry.py b/src/features/entry.py new file mode 100644 index 00000000..e05ee9d6 --- /dev/null +++ b/src/features/entry.py @@ -0,0 +1,14 @@ +import pandas as pd +from utils.utils import fetch_provider_features + +sensor_data_files = dict(snakemake.input) +del sensor_data_files["time_segments_labels"] +time_segments_file = snakemake.input["time_segments_labels"] + +provider = snakemake.params["provider"] +provider_key = snakemake.params["provider_key"] +sensor_key = snakemake.params["sensor_key"] + +sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file) + +sensor_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/features_utils.py b/src/features/features_utils.py deleted file mode 100644 index f824402b..00000000 --- a/src/features/features_utils.py +++ /dev/null @@ -1,85 +0,0 @@ -import pandas as pd -from datetime import datetime, timedelta, time - -SEGMENT = {"night": 0, "morning": 1, "afternoon": 2, "evening": 3} -EPOCH_TIMES = {"night": [0,5], "morning": [6,11], "afternoon": [12,17], "evening": [18,23]} - -def truncateTime(df, segment_column, new_day_segment, datetime_column, date_column, new_time): - df.loc[:, segment_column] = new_day_segment - df.loc[:, datetime_column] = df[date_column].apply(lambda date: datetime.combine(date, new_time)) - return df - -# calculate truncated time differences and truncated extra_cols if it is not empty -def computeTruncatedDifferences(df, extra_cols): - df["truncated_time_diff"] = df["local_end_date_time"] - df["local_start_date_time"] - df["truncated_time_diff"] = df["truncated_time_diff"].apply(lambda time: time.total_seconds()/60) - if extra_cols: - for extra_col in extra_cols: - df[extra_col] = df[extra_col] * (df["truncated_time_diff"] / df["time_diff"]) - del df["time_diff"] - df.rename(columns={"truncated_time_diff": "time_diff"}, inplace=True) - return df - -def splitOvernightEpisodes(sensor_deltas, extra_cols, fixed_cols): - overnight = sensor_deltas[(sensor_deltas["local_start_date"] + timedelta(days=1)) == sensor_deltas["local_end_date"]] - not_overnight = sensor_deltas[sensor_deltas["local_start_date"] == sensor_deltas["local_end_date"]] - - if not overnight.empty: - today = overnight[extra_cols + fixed_cols + ["time_diff", "local_start_date_time", "local_start_date", "local_start_day_segment"]].copy() - tomorrow = overnight[extra_cols + fixed_cols + ["time_diff", "local_end_date_time", "local_end_date", "local_end_day_segment"]].copy() - - # truncate the end time of all overnight periods to midnight - today = truncateTime(today, "local_end_day_segment", "evening", "local_end_date_time", "local_start_date", time(23,59,59)) - today["local_end_date"] = overnight["local_start_date"] - - # set the start time of all periods after midnight to midnight - tomorrow = truncateTime(tomorrow, "local_start_day_segment", "night", "local_start_date_time", "local_end_date", time(0,0,0)) - tomorrow["local_start_date"] = overnight["local_end_date"] - - overnight = pd.concat([today, tomorrow], axis=0, sort=False) - - # calculate new time_diff and extra_cols for split overnight periods - overnight = computeTruncatedDifferences(overnight, extra_cols) - - # sort by local_start_date_time and reset the index - days = pd.concat([not_overnight, overnight], axis=0, sort=False) - days = days.sort_values(by=['local_start_date_time']).reset_index(drop=True) - - return days - -def splitMultiSegmentEpisodes(sensor_deltas, day_segment, extra_cols): - # extract episodes that start and end at the same epochs - exact_segments = sensor_deltas.query("local_start_day_segment == local_end_day_segment and local_start_day_segment == @day_segment").copy() - - # extract episodes that start and end at different epochs - across_segments = sensor_deltas.query("local_start_day_segment != local_end_day_segment").copy() - # 1) if start time is in current day_segment - start_segment = across_segments[across_segments["local_start_day_segment"] == day_segment].copy() - if not start_segment.empty: - start_segment = truncateTime(start_segment, "local_end_day_segment", day_segment, "local_end_date_time", "local_end_date", time(EPOCH_TIMES[day_segment][1],59,59)) - # 2) if end time is in current day_segment - end_segment = across_segments[across_segments["local_end_day_segment"] == day_segment].copy() - if not end_segment.empty: - end_segment = truncateTime(end_segment, "local_start_day_segment", day_segment, "local_start_date_time", "local_start_date", time(EPOCH_TIMES[day_segment][0],0,0)) - # 3) if current episode comtains day_segment - across_segments.loc[:,"start_segment"] = across_segments["local_start_day_segment"].apply(lambda seg: SEGMENT[seg]) - across_segments.loc[:,"end_segment"] = across_segments["local_end_day_segment"].apply(lambda seg: SEGMENT[seg]) - day_segment_num = SEGMENT[day_segment] - within_segments = across_segments.query("start_segment < @day_segment_num and end_segment > @day_segment_num") - del across_segments["start_segment"], across_segments["end_segment"] - del within_segments["start_segment"], within_segments["end_segment"] - - if not within_segments.empty: - within_segments = truncateTime(within_segments, "local_start_day_segment", day_segment, "local_start_date_time", "local_start_date", time(EPOCH_TIMES[day_segment][0],0,0)) - within_segments = truncateTime(within_segments, "local_end_day_segment", day_segment, "local_end_date_time", "local_end_date", time(EPOCH_TIMES[day_segment][1],59,59)) - - across_segments = pd.concat([start_segment, end_segment, within_segments], axis=0, sort=False) - - if not across_segments.empty: - accross_segments = computeTruncatedDifferences(across_segments, extra_cols) - - # sort by local_start_date_time and reset the index - segments = pd.concat([exact_segments, across_segments], axis=0, sort=False) - segments = segments.sort_values(by=['local_start_date_time']).reset_index(drop=True) - - return segments \ No newline at end of file diff --git a/src/features/fitbit_heartrate/fitbit_heartrate_base.py b/src/features/fitbit_heartrate/fitbit_heartrate_base.py deleted file mode 100644 index 2952fa57..00000000 --- a/src/features/fitbit_heartrate/fitbit_heartrate_base.py +++ /dev/null @@ -1,76 +0,0 @@ -import pandas as pd -from scipy.stats import entropy - -def extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features): - heartrate_summary_features = pd.DataFrame() - if "restinghr" in summary_features: - heartrate_summary_features["heartrate_daily_restinghr"] = heartrate_summary_data["heartrate_daily_restinghr"] - # calories features might be inaccurate: they depend on users' fitbit profile (weight, height, etc.) - if "caloriesoutofrange" in summary_features: - heartrate_summary_features["heartrate_daily_caloriesoutofrange"] = heartrate_summary_data["heartrate_daily_caloriesoutofrange"] - if "caloriesfatburn" in summary_features: - heartrate_summary_features["heartrate_daily_caloriesfatburn"] = heartrate_summary_data["heartrate_daily_caloriesfatburn"] - if "caloriescardio" in summary_features: - heartrate_summary_features["heartrate_daily_caloriescardio"] = heartrate_summary_data["heartrate_daily_caloriescardio"] - if "caloriespeak" in summary_features: - heartrate_summary_features["heartrate_daily_caloriespeak"] = heartrate_summary_data["heartrate_daily_caloriespeak"] - heartrate_summary_features.reset_index(inplace=True) - - return heartrate_summary_features - -def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, day_segment): - heartrate_intraday_features = pd.DataFrame(columns=["local_date"] + ["heartrate_" + day_segment + "_" + x for x in features]) - if not heartrate_intraday_data.empty: - device_id = heartrate_intraday_data["device_id"][0] - num_rows_per_minute = heartrate_intraday_data.groupby(["local_date", "local_hour", "local_minute"]).count().mean()["device_id"] - if day_segment != "daily": - heartrate_intraday_data = heartrate_intraday_data[heartrate_intraday_data["local_day_segment"] == day_segment] - - if not heartrate_intraday_data.empty: - heartrate_intraday_features = pd.DataFrame() - - # get stats of heartrate - if "maxhr" in features: - heartrate_intraday_features["heartrate_" + day_segment + "_maxhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].max() - if "minhr" in features: - heartrate_intraday_features["heartrate_" + day_segment + "_minhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].min() - if "avghr" in features: - heartrate_intraday_features["heartrate_" + day_segment + "_avghr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].mean() - if "medianhr" in features: - heartrate_intraday_features["heartrate_" + day_segment + "_medianhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].median() - if "modehr" in features: - heartrate_intraday_features["heartrate_" + day_segment + "_modehr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0]) - if "stdhr" in features: - heartrate_intraday_features["heartrate_" + day_segment + "_stdhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].std() - if "diffmaxmodehr" in features: - heartrate_intraday_features["heartrate_" + day_segment + "_diffmaxmodehr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].max() - heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0]) - if "diffminmodehr" in features: - heartrate_intraday_features["heartrate_" + day_segment + "_diffminmodehr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].min() - if "entropyhr" in features: - heartrate_intraday_features["heartrate_" + day_segment + "_entropyhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].agg(entropy) - - # get number of minutes in each heart rate zone - for feature_name in list(set(["minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]) & set(features)): - heartrate_zone = heartrate_intraday_data[heartrate_intraday_data["heartrate_zone"] == feature_name[9:-4]] - heartrate_intraday_features["heartrate_" + day_segment + "_" + feature_name] = heartrate_zone.groupby(["local_date"])["device_id"].count() / num_rows_per_minute - heartrate_intraday_features.fillna(value={"heartrate_" + day_segment + "_" + feature_name: 0}, inplace=True) - heartrate_intraday_features.reset_index(inplace=True) - - return heartrate_intraday_features - -def base_fitbit_heartrate_features(heartrate_summary_data, heartrate_intraday_data, day_segment, requested_summary_features, requested_intraday_features): - # name of the features this function can compute - base_summary_features_names = ["restinghr", "caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] - base_intraday_features_names = ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] - # the subset of requested features this function can compute - summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features_names)) - intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) - - heartrate_intraday_features = extractHRFeaturesFromIntradayData(heartrate_intraday_data, intraday_features_to_compute, day_segment) - if not heartrate_summary_data.empty and day_segment == "daily" and summary_features_to_compute != []: - heartrate_summary_features = extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features_to_compute) - heartrate_features = heartrate_intraday_features.merge(heartrate_summary_features, on=["local_date"], how="outer") - else: - heartrate_features = heartrate_intraday_features - - return heartrate_features diff --git a/src/features/fitbit_heartrate_features.py b/src/features/fitbit_heartrate_features.py deleted file mode 100644 index 61bad1ee..00000000 --- a/src/features/fitbit_heartrate_features.py +++ /dev/null @@ -1,16 +0,0 @@ -import pandas as pd -from fitbit_heartrate.fitbit_heartrate_base import base_fitbit_heartrate_features - -heartrate_summary_data = pd.read_csv(snakemake.input["heartrate_summary_data"], index_col=["local_date"], parse_dates=["local_date"]) -heartrate_intraday_data = pd.read_csv(snakemake.input["heartrate_intraday_data"], parse_dates=["local_date_time", "local_date"]) -day_segment = snakemake.params["day_segment"] -requested_summary_features = snakemake.params["summary_features"] -requested_intraday_features = snakemake.params["intraday_features"] -heartrate_features = pd.DataFrame(columns=["local_date"]) - -heartrate_features = heartrate_features.merge(base_fitbit_heartrate_features(heartrate_summary_data, heartrate_intraday_data, day_segment, requested_summary_features, requested_intraday_features), on="local_date", how="outer") - -requested_features = requested_summary_features + requested_intraday_features if day_segment == "daily" else requested_intraday_features -assert len(requested_features) + 1 == heartrate_features.shape[1], "The number of features in the output dataframe (=" + str(heartrate_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your fitbit heartrate feature extraction functions" - -heartrate_features.to_csv(snakemake.output[0], index=False) diff --git a/src/features/fitbit_heartrate_intraday/rapids/main.py b/src/features/fitbit_heartrate_intraday/rapids/main.py new file mode 100644 index 00000000..1a853722 --- /dev/null +++ b/src/features/fitbit_heartrate_intraday/rapids/main.py @@ -0,0 +1,79 @@ +import pandas as pd +from scipy.stats import entropy + +def statsFeatures(heartrate_data, features, features_type, heartrate_features): + + if features_type == "hr": + col_name = "heartrate" + elif features_type == "restinghr": + col_name = "heartrate_daily_restinghr" + elif features_type == "caloriesoutofrange": + col_name = "heartrate_daily_caloriesoutofrange" + elif features_type == "caloriesfatburn": + col_name = "heartrate_daily_caloriesfatburn" + elif features_type == "caloriescardio": + col_name = "heartrate_daily_caloriescardio" + elif features_type == "caloriespeak": + col_name = "heartrate_daily_caloriespeak" + else: + raise ValueError("features_type can only be one of ['hr', 'restinghr', 'caloriesoutofrange', 'caloriesfatburn', 'caloriescardio', 'caloriespeak'].") + + if "sum" + features_type in features: + heartrate_features["sum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum() + if "max" + features_type in features: + heartrate_features["max" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() + if "min" + features_type in features: + heartrate_features["min" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() + if "avg" + features_type in features: + heartrate_features["avg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean() + if "median" + features_type in features: + heartrate_features["median" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median() + if "mode" + features_type in features: + heartrate_features["mode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) + if "std" + features_type in features: + heartrate_features["std" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std() + if "diffmaxmode" + features_type in features: + heartrate_features["diffmaxmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) + if "diffminmode" + features_type in features: + heartrate_features["diffminmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() + if "entropy" + features_type in features: + heartrate_features["entropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy) + + return heartrate_features + +def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, time_segment, filter_data_by_segment): + heartrate_intraday_features = pd.DataFrame(columns=["local_segment"] + features) + if not heartrate_intraday_data.empty: + num_rows_per_minute = heartrate_intraday_data.groupby(["local_date", "local_hour", "local_minute"]).count().mean()["device_id"] + heartrate_intraday_data = filter_data_by_segment(heartrate_intraday_data, time_segment) + + if not heartrate_intraday_data.empty: + heartrate_intraday_features = pd.DataFrame() + + # get stats of heartrate + heartrate_intraday_features = statsFeatures(heartrate_intraday_data, features, "hr", heartrate_intraday_features) + + # get number of minutes in each heart rate zone + for feature_name in list(set(["minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]) & set(features)): + heartrate_zone = heartrate_intraday_data[heartrate_intraday_data["heartrate_zone"] == feature_name[17:-4]] + heartrate_intraday_features[feature_name] = heartrate_zone.groupby(["local_segment"])["device_id"].count() / num_rows_per_minute + heartrate_intraday_features.fillna(value={feature_name: 0}, inplace=True) + heartrate_intraday_features.reset_index(inplace=True) + + return heartrate_intraday_features + + +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + + heartrate_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) + + requested_intraday_features = provider["FEATURES"] + # name of the features this function can compute + base_intraday_features_names = ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] + # the subset of requested features this function can compute + intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) + + # extract features from intraday data + heartrate_intraday_features = extractHRFeaturesFromIntradayData(heartrate_intraday_data, intraday_features_to_compute, time_segment, filter_data_by_segment) + + return heartrate_intraday_features diff --git a/src/features/fitbit_heartrate_summary/rapids/main.py b/src/features/fitbit_heartrate_summary/rapids/main.py new file mode 100644 index 00000000..e236c38f --- /dev/null +++ b/src/features/fitbit_heartrate_summary/rapids/main.py @@ -0,0 +1,88 @@ +import pandas as pd +from scipy.stats import entropy + +def statsFeatures(heartrate_data, features, features_type, heartrate_features): + + if features_type == "hr": + col_name = "heartrate" + elif features_type == "restinghr": + col_name = "heartrate_daily_restinghr" + elif features_type == "caloriesoutofrange": + col_name = "heartrate_daily_caloriesoutofrange" + elif features_type == "caloriesfatburn": + col_name = "heartrate_daily_caloriesfatburn" + elif features_type == "caloriescardio": + col_name = "heartrate_daily_caloriescardio" + elif features_type == "caloriespeak": + col_name = "heartrate_daily_caloriespeak" + else: + raise ValueError("features_type can only be one of ['hr', 'restinghr', 'caloriesoutofrange', 'caloriesfatburn', 'caloriescardio', 'caloriespeak'].") + + if "sum" + features_type in features: + heartrate_features["sum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum() + if "max" + features_type in features: + heartrate_features["max" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() + if "min" + features_type in features: + heartrate_features["min" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() + if "avg" + features_type in features: + heartrate_features["avg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean() + if "median" + features_type in features: + heartrate_features["median" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median() + if "mode" + features_type in features: + heartrate_features["mode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: None if len(pd.Series.mode(x)) == 0 else pd.Series.mode(x)[0]) + if "std" + features_type in features: + heartrate_features["std" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std() + if "diffmaxmode" + features_type in features: + heartrate_features["diffmaxmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: None if len(pd.Series.mode(x)) == 0 else pd.Series.mode(x)[0]) + if "diffminmode" + features_type in features: + heartrate_features["diffminmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: None if len(pd.Series.mode(x)) == 0 else pd.Series.mode(x)[0]) - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() + if "entropy" + features_type in features: + heartrate_features["entropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy) + + return heartrate_features + +def extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features): + heartrate_summary_features = pd.DataFrame() + + # get stats of resting heartrate + heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "restinghr", heartrate_summary_features) + + # get stats of calories features + # calories features might be inaccurate: they depend on users' fitbit profile (weight, height, etc.) + heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "caloriesoutofrange", heartrate_summary_features) + heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "caloriesfatburn", heartrate_summary_features) + heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "caloriescardio", heartrate_summary_features) + heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "caloriespeak", heartrate_summary_features) + + heartrate_summary_features.reset_index(inplace=True) + + return heartrate_summary_features + + +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + + heartrate_summary_data = pd.read_csv(sensor_data_files["sensor_data"]) + + requested_summary_features = provider["FEATURES"] + # name of the features this function can compute + base_summary_features_names = ["maxrestinghr", "minrestinghr", "avgrestinghr", "medianrestinghr", "moderestinghr", "stdrestinghr", "diffmaxmoderestinghr", "diffminmoderestinghr", "entropyrestinghr", "sumcaloriesoutofrange", "maxcaloriesoutofrange", "mincaloriesoutofrange", "avgcaloriesoutofrange", "mediancaloriesoutofrange", "stdcaloriesoutofrange", "entropycaloriesoutofrange", "sumcaloriesfatburn", "maxcaloriesfatburn", "mincaloriesfatburn", "avgcaloriesfatburn", "mediancaloriesfatburn", "stdcaloriesfatburn", "entropycaloriesfatburn", "sumcaloriescardio", "maxcaloriescardio", "mincaloriescardio", "avgcaloriescardio", "mediancaloriescardio", "stdcaloriescardio", "entropycaloriescardio", "sumcaloriespeak", "maxcaloriespeak", "mincaloriespeak", "avgcaloriespeak", "mediancaloriespeak", "stdcaloriespeak", "entropycaloriespeak"] + # the subset of requested features this function can compute + summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features_names)) + + # extract features from summary data + heartrate_summary_features = pd.DataFrame(columns=["local_segment"] + summary_features_to_compute) + if not heartrate_summary_data.empty: + heartrate_summary_data = filter_data_by_segment(heartrate_summary_data, time_segment) + + if not heartrate_summary_data.empty: + # only keep the segments start at 00:00:00 and end at 23:59:59 + datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00" + datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59" + + segment_regex = "{}#{},{}".format(time_segment, datetime_start_regex, datetime_end_regex) + heartrate_summary_data = heartrate_summary_data[heartrate_summary_data["local_segment"].str.match(segment_regex)] + + if not heartrate_summary_data.empty: + heartrate_summary_features = extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features_to_compute) + + return heartrate_summary_features diff --git a/src/features/fitbit_sleep/fitbit_sleep_base.py b/src/features/fitbit_sleep/fitbit_sleep_base.py deleted file mode 100644 index d4eca912..00000000 --- a/src/features/fitbit_sleep/fitbit_sleep_base.py +++ /dev/null @@ -1,70 +0,0 @@ -import pandas as pd -import itertools - - - -def dailyFeaturesFromSummaryData(sleep_daily_features, sleep_summary_data, summary_features, sleep_type): - if sleep_type == "main": - sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 1] - elif sleep_type == "nap": - sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 0] - elif sleep_type == "all": - pass - else: - raise ValueError("sleep_type can only be one of ['main', 'nap', 'all'].") - - features_sum = sleep_summary_data[["minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed", "local_end_date"]].groupby(["local_end_date"]).sum() - features_sum.index.rename("local_date", inplace=True) - if "sumdurationafterwakeup" in summary_features: - sleep_daily_features = sleep_daily_features.join(features_sum[["minutes_after_wakeup"]], how="outer").rename(columns={"minutes_after_wakeup": "sleep_daily_sumdurationafterwakeup" + sleep_type}) - if "sumdurationasleep" in summary_features: - sleep_daily_features = sleep_daily_features.join(features_sum[["minutes_asleep"]], how="outer").rename(columns={"minutes_asleep": "sleep_daily_sumdurationasleep" + sleep_type}) - if "sumdurationawake" in summary_features: - sleep_daily_features = sleep_daily_features.join(features_sum[["minutes_awake"]], how="outer").rename(columns={"minutes_awake": "sleep_daily_sumdurationawake" + sleep_type}) - if "sumdurationtofallasleep" in summary_features: - sleep_daily_features = sleep_daily_features.join(features_sum[["minutes_to_fall_asleep"]], how="outer").rename(columns={"minutes_to_fall_asleep": "sleep_daily_sumdurationtofallasleep" + sleep_type}) - if "sumdurationinbed" in summary_features: - sleep_daily_features = sleep_daily_features.join(features_sum[["minutes_in_bed"]], how="outer").rename(columns={"minutes_in_bed": "sleep_daily_sumdurationinbed" + sleep_type}) - - features_avg = sleep_summary_data[["efficiency", "local_end_date"]].groupby(["local_end_date"]).mean() - features_avg.index.rename("local_date", inplace=True) - if "avgefficiency" in summary_features: - sleep_daily_features = sleep_daily_features.join(features_avg[["efficiency"]], how="outer").rename(columns={"efficiency": "sleep_daily_avgefficiency" + sleep_type}) - - features_count = sleep_summary_data[["local_start_date_time", "local_end_date"]].groupby(["local_end_date"]).count() - features_count.index.rename("local_date", inplace=True) - if "countepisode" in summary_features: - sleep_daily_features = sleep_daily_features.join(features_count[["local_start_date_time"]], how="outer").rename(columns={"local_start_date_time": "sleep_daily_countepisode" + sleep_type}) - - return sleep_daily_features - -def base_fitbit_sleep_features(sleep_summary_data, day_segment, requested_summary_features, requested_sleep_type): - if not day_segment == "daily": - return pd.DataFrame(columns=["local_date"]) - else: - # name of the features this function can compute - base_summary_features_names = ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] - base_sleep_type = ["main", "nap", "all"] - # the subset of requested features this function can compute - summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features_names)) - sleep_type_to_compute = list(set(requested_sleep_type) & set(base_sleep_type)) - # full names - features_fullnames_to_compute = ["".join(feature) for feature in itertools.product(summary_features_to_compute, sleep_type_to_compute)] - - colnames_can_be_zero = ["sleep_daily_" + x for x in [col for col in features_fullnames_to_compute if "avgefficiency" not in col]] - - if sleep_summary_data.empty: - sleep_summary_features = pd.DataFrame(columns=["local_date"] + ["sleep_daily_" + x for x in features_fullnames_to_compute]) - else: - - sleep_summary_features = pd.DataFrame() - - for sleep_type in sleep_type_to_compute: - sleep_summary_features = dailyFeaturesFromSummaryData(sleep_summary_features, sleep_summary_data, summary_features_to_compute, sleep_type) - - sleep_summary_features[colnames_can_be_zero] = sleep_summary_features[colnames_can_be_zero].fillna(0) - - sleep_summary_features = sleep_summary_features.reset_index() - - return sleep_summary_features - diff --git a/src/features/fitbit_sleep_features.py b/src/features/fitbit_sleep_features.py deleted file mode 100644 index 314c13d5..00000000 --- a/src/features/fitbit_sleep_features.py +++ /dev/null @@ -1,18 +0,0 @@ -import pandas as pd -from fitbit_sleep.fitbit_sleep_base import base_fitbit_sleep_features -import itertools - -sleep_summary_data = pd.read_csv(snakemake.input["sleep_summary_data"]) -requested_summary_features = snakemake.params["summary_features"] -requested_sleep_type = snakemake.params["sleep_types"] -day_segment = snakemake.params["day_segment"] -sleep_features = pd.DataFrame(columns=["local_date"]) - -sleep_features = sleep_features.merge(base_fitbit_sleep_features(sleep_summary_data, day_segment, requested_summary_features, requested_sleep_type), on="local_date", how="outer") - -requested_features = ["".join(feature) for feature in itertools.product(requested_summary_features, requested_sleep_type)] if day_segment == "daily" else [] - -assert len(requested_features) + 1 == sleep_features.shape[1], "The number of features in the output dataframe (=" + str(sleep_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your fitbit sleep feature extraction functions" - -sleep_features.to_csv(snakemake.output[0], index=False) - diff --git a/src/features/fitbit_sleep_summary/rapids/main.py b/src/features/fitbit_sleep_summary/rapids/main.py new file mode 100644 index 00000000..048baa20 --- /dev/null +++ b/src/features/fitbit_sleep_summary/rapids/main.py @@ -0,0 +1,91 @@ +import pandas as pd +import itertools + +def extractSleepFeaturesFromSummaryData(sleep_summary_data, summary_features, sleep_type, sleep_summary_features): + if sleep_type == "main": + sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 1] + elif sleep_type == "nap": + sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 0] + elif sleep_type == "all": + pass + else: + raise ValueError("sleep_type can only be one of ['main', 'nap', 'all'].") + + features_sum = sleep_summary_data[["local_segment", "minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed"]].groupby(["local_segment"]).sum() + + if "sumdurationafterwakeup" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_after_wakeup"]], how="outer").rename(columns={"minutes_after_wakeup": "sumdurationafterwakeup" + sleep_type}) + if "sumdurationasleep" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_asleep"]], how="outer").rename(columns={"minutes_asleep": "sumdurationasleep" + sleep_type}) + if "sumdurationawake" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_awake"]], how="outer").rename(columns={"minutes_awake": "sumdurationawake" + sleep_type}) + if "sumdurationtofallasleep" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_to_fall_asleep"]], how="outer").rename(columns={"minutes_to_fall_asleep": "sumdurationtofallasleep" + sleep_type}) + if "sumdurationinbed" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_sum[["minutes_in_bed"]], how="outer").rename(columns={"minutes_in_bed": "sumdurationinbed" + sleep_type}) + + features_avg = sleep_summary_data[["local_segment", "efficiency", "minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed"]].groupby(["local_segment"]).mean() + + if "avgefficiency" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_avg[["efficiency"]], how="outer").rename(columns={"efficiency": "avgefficiency" + sleep_type}) + if "avgdurationafterwakeup" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_after_wakeup"]], how="outer").rename(columns={"minutes_after_wakeup": "avgdurationafterwakeup" + sleep_type}) + if "avgdurationasleep" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_asleep"]], how="outer").rename(columns={"minutes_asleep": "avgdurationasleep" + sleep_type}) + if "avgdurationawake" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_awake"]], how="outer").rename(columns={"minutes_awake": "avgdurationawake" + sleep_type}) + if "avgdurationtofallasleep" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_to_fall_asleep"]], how="outer").rename(columns={"minutes_to_fall_asleep": "avgdurationtofallasleep" + sleep_type}) + if "avgdurationinbed" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_avg[["minutes_in_bed"]], how="outer").rename(columns={"minutes_in_bed": "avgdurationinbed" + sleep_type}) + + features_count = sleep_summary_data[["local_segment", "timestamp"]].groupby(["local_segment"]).count() + + if "countepisode" in summary_features: + sleep_summary_features = sleep_summary_features.join(features_count[["timestamp"]], how="outer").rename(columns={"timestamp": "countepisode" + sleep_type}) + + return sleep_summary_features + + +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + + sleep_summary_data = pd.read_csv(sensor_data_files["sensor_data"]) + + requested_summary_features = provider["FEATURES"] + requested_sleep_types = provider["SLEEP_TYPES"] + + # name of the features this function can compute + base_summary_features = ["countepisode", "avgefficiency", "sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgdurationafterwakeup", "avgdurationasleep", "avgdurationawake", "avgdurationtofallasleep", "avgdurationinbed"] + base_sleep_types = ["main", "nap", "all"] + # the subset of requested features this function can compute + summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features)) + sleep_types_to_compute = list(set(requested_sleep_types) & set(base_sleep_types)) + # full names + features_fullnames_to_compute = ["".join(feature) for feature in itertools.product(summary_features_to_compute, sleep_types_to_compute)] + + colnames_can_be_zero = [col for col in features_fullnames_to_compute if "avgefficiency" not in col] + + # extract features from summary data + sleep_summary_features = pd.DataFrame(columns=["local_segment"] + features_fullnames_to_compute) + if not sleep_summary_data.empty: + sleep_summary_data = filter_data_by_segment(sleep_summary_data, time_segment) + + if not sleep_summary_data.empty: + # only keep the segments start at 00:00:00 and end at 23:59:59 + datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00" + datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59" + + segment_regex = "{}#{},{}".format(time_segment, datetime_start_regex, datetime_end_regex) + sleep_summary_data = sleep_summary_data[sleep_summary_data["local_segment"].str.match(segment_regex)] + + if not sleep_summary_data.empty: + sleep_summary_features = pd.DataFrame() + + for sleep_type in sleep_types_to_compute: + sleep_summary_features = extractSleepFeaturesFromSummaryData(sleep_summary_data, summary_features_to_compute, sleep_type, sleep_summary_features) + + sleep_summary_features[colnames_can_be_zero] = sleep_summary_features[colnames_can_be_zero].fillna(0) + + sleep_summary_features = sleep_summary_features.reset_index() + + return sleep_summary_features diff --git a/src/features/fitbit_step/fitbit_step_base.py b/src/features/fitbit_step/fitbit_step_base.py deleted file mode 100644 index ba08a43a..00000000 --- a/src/features/fitbit_step/fitbit_step_base.py +++ /dev/null @@ -1,103 +0,0 @@ -import pandas as pd -import numpy as np - -def getBouts(step_data, time_interval): - # resample the data into time_interval minute bins, set "isactivebout" column to be NA if it is missing - resampled_step_minute = pd.DataFrame(step_data.resample(str(time_interval) + "T", on="local_date_time")["isactivebout"].sum(min_count=1)) - - # group rows by consecutive values of "isactivebout" column - group = pd.DataFrame(resampled_step_minute["isactivebout"] != resampled_step_minute["isactivebout"].shift()).cumsum().rename(columns={"isactivebout": "group_idx"}) - - # combine resampled_acc_minute and group column - resampled_step_minute = pd.concat([resampled_step_minute, group], axis=1) - - # drop rows where "isactivebout" column is missing and reset the index - resampled_step_minute.dropna(subset=["isactivebout"], inplace=True) - resampled_step_minute.reset_index(inplace=True) - resampled_step_minute.loc[:, "local_date"] = resampled_step_minute["local_date_time"].dt.date - - # duration column contains the number of minutes (rows) of active and sedentary bout - bouts = resampled_step_minute.groupby(["isactivebout", "group_idx", "local_date"]).count().rename(columns={"local_date_time": "duration"}).reset_index() - bouts["duration"] = bouts["duration"] * time_interval - - return bouts - -def statsFeatures(step_data, day_segment, features_to_compute, features_type, step_features): - if features_type == "allsteps": - col_name = "steps" - elif features_type == "durationsedentarybout" or features_type == "durationactivebout": - col_name = "duration" - else: - raise ValueError("features_type can only be one of ['allsteps', 'durationsedentarybout', 'durationactivebout'].") - - if "count" + features_type.replace("duration", "episode") in features_to_compute: - step_features["step_" + day_segment + "_count" + features_type.replace("duration", "episode")] = step_data.groupby(["local_date"])[col_name].count() - if "sum" + features_type in features_to_compute: - step_features["step_" + day_segment + "_sum" + features_type] = step_data.groupby(["local_date"])[col_name].sum() - if "max" + features_type in features_to_compute: - step_features["step_" + day_segment + "_max" + features_type] = step_data.groupby(["local_date"])[col_name].max() - if "min" + features_type in features_to_compute: - step_features["step_" + day_segment + "_min" + features_type] = step_data.groupby(["local_date"])[col_name].min() - if "avg" + features_type in features_to_compute: - step_features["step_" + day_segment + "_avg" + features_type] = step_data.groupby(["local_date"])[col_name].mean() - if "median" + features_type in features_to_compute: - step_features["step_" + day_segment + "_median" + features_type] = step_data.groupby(["local_date"])[col_name].median() - if "std" + features_type in features_to_compute: - step_features["step_" + day_segment + "_std" + features_type] = step_data.groupby(["local_date"])[col_name].std() - - return step_features - -def base_fitbit_step_features(step_data, day_segment, requested_features, threshold_active_bout, include_zero_step_rows): - requested_features_allsteps = requested_features["features_all_steps"] - requested_features_sedentarybout = requested_features["features_sedentary_bout"] - requested_features_activebout = requested_features["features_active_bout"] - - # name of the features this function can compute - base_features_allsteps = ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] - base_features_sedentarybout = ["countepisodesedentarybout", "sumdurationsedentarybout", "maxdurationsedentarybout", "mindurationsedentarybout", "avgdurationsedentarybout", "stddurationsedentarybout"] - base_features_activebout = ["countepisodeactivebout", "sumdurationactivebout", "maxdurationactivebout", "mindurationactivebout", "avgdurationactivebout", "stddurationactivebout"] - # the subset of requested features this function can compute - features_to_compute_allsteps = list(set(requested_features_allsteps) & set(base_features_allsteps)) - features_to_compute_sedentarybout = list(set(requested_features_sedentarybout) & set(base_features_sedentarybout)) - features_to_compute_activebout = list(set(requested_features_activebout) & set(base_features_activebout)) - - features_to_compute = features_to_compute_allsteps + features_to_compute_sedentarybout + features_to_compute_activebout - - step_features = pd.DataFrame(columns=["local_date"] + ["step_" + day_segment + "_" + x for x in features_to_compute]) - if not step_data.empty: - if day_segment != "daily": - step_data =step_data[step_data["local_day_segment"] == day_segment] - - if not step_data.empty: - step_features = pd.DataFrame() - - # statistics features of step count - step_features = statsFeatures(step_data, day_segment, features_to_compute_allsteps, "allsteps", step_features) - - # calculate time interval between two records in minutes - time_interval = step_data["local_date_time"].diff().min().total_seconds() / 60 - - # sedentary bout: less than THRESHOLD_ACTIVE_BOUT (default: 10) steps in a minute - # active bout: greater or equal to THRESHOLD_ACTIVE_BOUT (default: 10) steps in a minute - isactivebout = np.where(step_data["steps"] < int(threshold_active_bout) * time_interval, 0, 1) - step_data = step_data.assign(isactivebout = isactivebout) - - bouts = getBouts(step_data, time_interval) - - # statistics features of sedentary bout - sedentary_bout = bouts[bouts["isactivebout"] == 0] - step_features = statsFeatures(sedentary_bout, day_segment, features_to_compute_sedentarybout, "durationsedentarybout", step_features) - - # statistics features of active bout - active_bout = bouts[bouts["isactivebout"] == 1] - step_features = statsFeatures(active_bout, day_segment, features_to_compute_activebout, "durationactivebout", step_features) - - # exclude data when the total step count is ZERO during the whole epoch - if not include_zero_step_rows: - step_features["sumallsteps_aux"] = step_data.groupby(["local_date"])["steps"].sum() - step_features = step_features.query("sumallsteps_aux != 0") - del step_features["sumallsteps_aux"] - - step_features = step_features.reset_index() - - return step_features diff --git a/src/features/fitbit_step_features.py b/src/features/fitbit_step_features.py deleted file mode 100644 index e0ca3523..00000000 --- a/src/features/fitbit_step_features.py +++ /dev/null @@ -1,66 +0,0 @@ -import pandas as pd -import numpy as np -import time -from fitbit_step.fitbit_step_base import base_fitbit_step_features - -def isInvalidTime(str_time): - try: - time.strptime(str_time, '%H:%M') - return False - except ValueError: - return True - -def isInMainSleep(local_date_time, sleep): - # sleep_period_container = sleep.query("local_start_date_time <= @local_date_time <= local_end_date_time") - sleep_period_container = sleep[(sleep["local_start_date_time"] <= local_date_time) & (local_date_time <= sleep["local_end_date_time"])] - if sleep_period_container.shape[0] >= 1: - return True - else: - return False - -def getStepsOutsideFitbitMainSleep(sleep, steps): - steps['inMainSleep'] = steps.apply(lambda row : isInMainSleep(row['local_date_time'], sleep), axis = 1) - return steps[steps['inMainSleep'] == False] - - -def getStepsOutsideFixedMainSleep(sleepStart, sleepEnd, steps): - steps = steps.set_index('local_date_time') - steps['inMainSleep'] = False - steps.loc[steps.between_time(sleepStart, sleepEnd).index, 'inMainSleep'] = True - steps.reset_index(level=0, inplace=True) - return steps[steps['inMainSleep'] == False] - -step_data = pd.read_csv(snakemake.input["step_data"], parse_dates=["local_date_time", "local_date"]) -day_segment = snakemake.params["day_segment"] -threshold_active_bout = snakemake.params["threshold_active_bout"] -include_zero_step_rows = snakemake.params["include_zero_step_rows"] -exclude_sleep = snakemake.params["exclude_sleep"] -exclude_sleep_type = snakemake.params["exclude_sleep_type"] -exclude_sleep_fixed_start = snakemake.params["exclude_sleep_fixed_start"] -exclude_sleep_fixed_end = snakemake.params["exclude_sleep_fixed_end"] - -step_features = pd.DataFrame(columns=["local_date"]) -requested_features = {} -requested_features["features_all_steps"] = snakemake.params["features_all_steps"] -requested_features["features_sedentary_bout"] = [feature + "sedentarybout" for feature in snakemake.params["features_sedentary_bout"]] -requested_features["features_active_bout"] = [feature + "activebout" for feature in snakemake.params["features_active_bout"]] - -if exclude_sleep == True: - if exclude_sleep_type == "FIXED": - if isInvalidTime(exclude_sleep_fixed_start): - raise ValueError("Your fixed start time has an invalid format in your config.yml file") - if isInvalidTime(exclude_sleep_fixed_end): - raise ValueError("Your fixed end time has an invalid format in your config.yml file") - step_data = getStepsOutsideFixedMainSleep(exclude_sleep_fixed_start, exclude_sleep_fixed_end, step_data) - elif exclude_sleep_type == "FITBIT_BASED": - sleep_data = pd.read_csv(snakemake.input["sleep_data"], parse_dates=["local_start_date_time", "local_end_date_time"]) - step_data = getStepsOutsideFitbitMainSleep(sleep_data, step_data) - else: - raise ValueError("We only support FIXED or FITBIT_BASED to filter step data based on sleep data. You typed " + exclude_sleep_type + ", Check your config.yaml file for typos") - -step_features = step_features.merge(base_fitbit_step_features(step_data, day_segment, requested_features, threshold_active_bout, include_zero_step_rows), on="local_date", how="outer") - - -assert np.sum([len(x) for x in requested_features.values()]) + 1 == step_features.shape[1], "The number of features in the output dataframe (=" + str(step_features.shape[1]) + ") does not match the expected value (=" + str(np.sum([len(x) for x in requested_features.values()])) + " + 1). Verify your fitbit step feature extraction functions" - -step_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/fitbit_steps_intraday/rapids/main.py b/src/features/fitbit_steps_intraday/rapids/main.py new file mode 100644 index 00000000..ee9d0dd3 --- /dev/null +++ b/src/features/fitbit_steps_intraday/rapids/main.py @@ -0,0 +1,108 @@ +import pandas as pd +import numpy as np + +def statsFeatures(steps_data, features_to_compute, features_type, steps_features): + if features_type == "steps" or features_type == "sumsteps": + col_name = "steps" + elif features_type == "durationsedentarybout" or features_type == "durationactivebout": + col_name = "duration" + else: + raise ValueError("features_type can only be one of ['steps', 'sumsteps', 'durationsedentarybout', 'durationactivebout'].") + + if "count" + features_type.replace("duration", "episode") in features_to_compute: + steps_features["count" + features_type.replace("duration", "episode")] = steps_data.groupby(["local_segment"])[col_name].count() + if "sum" + features_type in features_to_compute: + steps_features["sum" + features_type] = steps_data.groupby(["local_segment"])[col_name].sum() + if "max" + features_type in features_to_compute: + steps_features["max" + features_type] = steps_data.groupby(["local_segment"])[col_name].max() + if "min" + features_type in features_to_compute: + steps_features["min" + features_type] = steps_data.groupby(["local_segment"])[col_name].min() + if "avg" + features_type in features_to_compute: + steps_features["avg" + features_type] = steps_data.groupby(["local_segment"])[col_name].mean() + if "median" + features_type in features_to_compute: + steps_features["median" + features_type] = steps_data.groupby(["local_segment"])[col_name].median() + if "std" + features_type in features_to_compute: + steps_features["std" + features_type] = steps_data.groupby(["local_segment"])[col_name].std() + + return steps_features + +def getBouts(steps_data): + + # put consecutive rows into the same group if they have the same values of "isactivebout", "local_timezone", and "local_segment" + steps_data["group_idx"] = (steps_data[["isactivebout", "local_timezone", "local_segment"]].shift() != steps_data[["isactivebout", "local_timezone", "local_segment"]]).any(axis=1).cumsum() + + # get bouts: duration column contains the number of minutes (rows) of sedentary and active activity for each episode + grouped = steps_data.groupby("group_idx") + bouts = grouped["local_segment"].agg(duration="count") + bouts[["local_segment", "isactivebout"]] = grouped[["local_segment", "isactivebout"]].first() + + return bouts + +def extractStepsFeaturesFromIntradayData(steps_intraday_data, threshold_active_bout, intraday_features_to_compute_steps, intraday_features_to_compute_sedentarybout, intraday_features_to_compute_activebout, steps_intraday_features): + steps_intraday_features = pd.DataFrame() + + # statistics features of steps count + steps_intraday_features = statsFeatures(steps_intraday_data, intraday_features_to_compute_steps, "steps", steps_intraday_features) + + # sedentary bout: less than THRESHOLD_ACTIVE_BOUT (default: 10) steps in a minute + # active bout: greater or equal to THRESHOLD_ACTIVE_BOUT (default: 10) steps in a minute + isactivebout = np.where(steps_intraday_data["steps"] < int(threshold_active_bout), 0, 1) + steps_intraday_data = steps_intraday_data.assign(isactivebout = isactivebout) + bouts = getBouts(steps_intraday_data) + + # statistics features of sedentary bout + sedentary_bout = bouts[bouts["isactivebout"] == 0] + steps_intraday_features = statsFeatures(sedentary_bout, intraday_features_to_compute_sedentarybout, "durationsedentarybout", steps_intraday_features) + + # statistics features of active bout + active_bout = bouts[bouts["isactivebout"] == 1] + steps_intraday_features = statsFeatures(active_bout, intraday_features_to_compute_activebout, "durationactivebout", steps_intraday_features) + + steps_intraday_features.reset_index(inplace=True) + + return steps_intraday_features + + + +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + + threshold_active_bout = provider["THRESHOLD_ACTIVE_BOUT"] + include_zero_step_rows = provider["INCLUDE_ZERO_STEP_ROWS"] + + steps_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) + + requested_intraday_features = provider["FEATURES"] + + requested_intraday_features_steps = [x + "steps" for x in requested_intraday_features["STEPS"]] + requested_intraday_features_sedentarybout = [x + "sedentarybout" for x in requested_intraday_features["SEDENTARY_BOUT"]] + requested_intraday_features_activebout = [x + "activebout" for x in requested_intraday_features["ACTIVE_BOUT"]] + # name of the features this function can compute + base_intraday_features_steps = ["sumsteps", "maxsteps", "minsteps", "avgsteps", "stdsteps"] + base_intraday_features_sedentarybout = ["countepisodesedentarybout", "sumdurationsedentarybout", "maxdurationsedentarybout", "mindurationsedentarybout", "avgdurationsedentarybout", "stddurationsedentarybout"] + base_intraday_features_activebout = ["countepisodeactivebout", "sumdurationactivebout", "maxdurationactivebout", "mindurationactivebout", "avgdurationactivebout", "stddurationactivebout"] + # the subset of requested features this function can compute + intraday_features_to_compute_steps = list(set(requested_intraday_features_steps) & set(base_intraday_features_steps)) + intraday_features_to_compute_sedentarybout = list(set(requested_intraday_features_sedentarybout) & set(base_intraday_features_sedentarybout)) + intraday_features_to_compute_activebout = list(set(requested_intraday_features_activebout) & set(base_intraday_features_activebout)) + + intraday_features_to_compute = intraday_features_to_compute_steps + intraday_features_to_compute_sedentarybout + intraday_features_to_compute_activebout + + # extract features from intraday features + steps_intraday_features = pd.DataFrame(columns=["local_segment"] + intraday_features_to_compute) + if not steps_intraday_data.empty: + steps_intraday_data = filter_data_by_segment(steps_intraday_data, time_segment) + + if not steps_intraday_data.empty: + steps_intraday_features = extractStepsFeaturesFromIntradayData(steps_intraday_data, threshold_active_bout, intraday_features_to_compute_steps, intraday_features_to_compute_sedentarybout, intraday_features_to_compute_activebout, steps_intraday_features) + + # exclude rows when the total step count is ZERO during the whole day + if not include_zero_step_rows: + steps_intraday_features.index = steps_intraday_features["local_segment"].apply(lambda segment: segment.split("#")[1][:10]) + + steps_intraday_features["dailycountstep"] = steps_intraday_data.groupby(["local_date"])["steps"].sum() + steps_intraday_features = steps_intraday_features.query("dailycountstep != 0") + + del steps_intraday_features["dailycountstep"] + steps_intraday_features.reset_index(drop=True, inplace=True) + + return steps_intraday_features diff --git a/src/features/fitbit_steps_summary/rapids/main.py b/src/features/fitbit_steps_summary/rapids/main.py new file mode 100644 index 00000000..5db8bc52 --- /dev/null +++ b/src/features/fitbit_steps_summary/rapids/main.py @@ -0,0 +1,67 @@ +import pandas as pd +import numpy as np + +def statsFeatures(steps_data, features_to_compute, features_type, steps_features): + if features_type == "steps" or features_type == "sumsteps": + col_name = "steps" + elif features_type == "durationsedentarybout" or features_type == "durationactivebout": + col_name = "duration" + else: + raise ValueError("features_type can only be one of ['steps', 'sumsteps', 'durationsedentarybout', 'durationactivebout'].") + + if "count" + features_type.replace("duration", "episode") in features_to_compute: + steps_features["count" + features_type.replace("duration", "episode")] = steps_data.groupby(["local_segment"])[col_name].count() + if "sum" + features_type in features_to_compute: + steps_features["sum" + features_type] = steps_data.groupby(["local_segment"])[col_name].sum() + if "max" + features_type in features_to_compute: + steps_features["max" + features_type] = steps_data.groupby(["local_segment"])[col_name].max() + if "min" + features_type in features_to_compute: + steps_features["min" + features_type] = steps_data.groupby(["local_segment"])[col_name].min() + if "avg" + features_type in features_to_compute: + steps_features["avg" + features_type] = steps_data.groupby(["local_segment"])[col_name].mean() + if "median" + features_type in features_to_compute: + steps_features["median" + features_type] = steps_data.groupby(["local_segment"])[col_name].median() + if "std" + features_type in features_to_compute: + steps_features["std" + features_type] = steps_data.groupby(["local_segment"])[col_name].std() + + return steps_features + +def extractStepsFeaturesFromSummaryData(steps_summary_data, summary_features_to_compute): + steps_summary_features = pd.DataFrame() + + # statistics features of daily steps count + steps_summary_features = statsFeatures(steps_summary_data, summary_features_to_compute, "sumsteps", steps_summary_features) + + steps_summary_features.reset_index(inplace=True) + + return steps_summary_features + + + +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + + steps_summary_data = pd.read_csv(sensor_data_files["sensor_data"]) + requested_summary_features = provider["FEATURES"] + + # name of the features this function can compute + base_summary_features = ["maxsumsteps", "minsumsteps", "avgsumsteps", "mediansumsteps", "stdsumsteps"] + # the subset of requested features this function can compute + summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features)) + + # extract features from summary data + steps_summary_features = pd.DataFrame(columns=["local_segment"] + summary_features_to_compute) + if not steps_summary_data.empty: + steps_summary_data = filter_data_by_segment(steps_summary_data, time_segment) + + if not steps_summary_data.empty: + # only keep the segments start at 00:00:00 and end at 23:59:59 + datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00" + datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59" + + segment_regex = "{}#{},{}".format(time_segment, datetime_start_regex, datetime_end_regex) + steps_summary_data = steps_summary_data[steps_summary_data["local_segment"].str.match(segment_regex)] + + if not steps_summary_data.empty: + steps_summary_features = extractStepsFeaturesFromSummaryData(steps_summary_data, summary_features_to_compute) + + return steps_summary_features diff --git a/src/features/light/light_base.py b/src/features/light/light_base.py deleted file mode 100644 index 54450000..00000000 --- a/src/features/light/light_base.py +++ /dev/null @@ -1,34 +0,0 @@ -import pandas as pd -import numpy as np - -def base_light_features(light_data, day_segment, requested_features): - # name of the features this function can compute - base_features_names = ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] - # the subset of requested features this function can compute - features_to_compute = list(set(requested_features) & set(base_features_names)) - - light_features = pd.DataFrame(columns=["local_date"] + ["light_" + day_segment + "_" + x for x in features_to_compute]) - if not light_data.empty: - if day_segment != "daily": - light_data =light_data[light_data["local_day_segment"] == day_segment] - - if not light_data.empty: - light_features = pd.DataFrame() - if "count" in features_to_compute: - light_features["light_" + day_segment + "_count"] = light_data.groupby(["local_date"]).count()["timestamp"] - - # get light ambient luminance related features - if "maxlux" in features_to_compute: - light_features["light_" + day_segment + "_maxlux"] = light_data.groupby(["local_date"])["double_light_lux"].max() - if "minlux" in features_to_compute: - light_features["light_" + day_segment + "_minlux"] = light_data.groupby(["local_date"])["double_light_lux"].min() - if "avglux" in features_to_compute: - light_features["light_" + day_segment + "_avglux"] = light_data.groupby(["local_date"])["double_light_lux"].mean() - if "medianlux" in features_to_compute: - light_features["light_" + day_segment + "_medianlux"] = light_data.groupby(["local_date"])["double_light_lux"].median() - if "stdlux" in features_to_compute: - light_features["light_" + day_segment + "_stdlux"] = light_data.groupby(["local_date"])["double_light_lux"].std().fillna('NA') - - light_features = light_features.reset_index() - - return light_features \ No newline at end of file diff --git a/src/features/light_features.py b/src/features/light_features.py deleted file mode 100644 index fd6f06ee..00000000 --- a/src/features/light_features.py +++ /dev/null @@ -1,13 +0,0 @@ -import pandas as pd -from light.light_base import base_light_features - -light_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"]) -day_segment = snakemake.params["day_segment"] -requested_features = snakemake.params["features"] -light_features = pd.DataFrame(columns=["local_date"]) - -light_features = light_features.merge(base_light_features(light_data, day_segment, requested_features), on="local_date", how="outer") - -assert len(requested_features) + 1 == light_features.shape[1], "The number of features in the output dataframe (=" + str(light_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your light feature extraction functions" - -light_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/location_barnett_features.R b/src/features/location_barnett_features.R deleted file mode 100644 index b90aa039..00000000 --- a/src/features/location_barnett_features.R +++ /dev/null @@ -1,93 +0,0 @@ -source("renv/activate.R") -# Load Ian Barnett's code. Taken from https://scholar.harvard.edu/ibarnett/software/gpsmobility -file.sources = list.files(c("src/features/location_barnett"), pattern="*.R$", full.names=TRUE, ignore.case=TRUE) -sapply(file.sources,source,.GlobalEnv) - -library(dplyr) - -write_empty_file <- function(file_path, requested_features){ - write.csv(data.frame(local_date= character(), - location_barnett_hometime= numeric(), - location_barnett_disttravelled= numeric(), - location_barnett_rog= numeric(), - location_barnett_maxdiam= numeric(), - location_barnett_maxhomedist= numeric(), - location_barnett_siglocsvisited= numeric(), - location_barnett_avgflightlen= numeric(), - location_barnett_stdflightlen= numeric(), - location_barnett_avgflightdur= numeric(), - location_barnett_stdflightdur= numeric(), - location_barnett_probpause= numeric(), - location_barnett_siglocentropy= numeric(), - location_barnett_minsmissing= numeric(), - location_barnett_circdnrtn= numeric(), - location_barnett_wkenddayrtn= numeric(), - minutes_data_used= numeric() - ) %>% select(requested_features), file_path, row.names = F) -} - -location <- read.csv(snakemake@input[["locations"]], stringsAsFactors = F) -# The choice between RESAMPLE_FUSED and the original location data happens at the rule level in the function -# optional_location_input in features.snakefile -locations_to_use <- snakemake@params[["locations_to_use"]] -accuracy_limit <- snakemake@params[["accuracy_limit"]] -timezone <- snakemake@params[["timezone"]] -minutes_data_used <- snakemake@params[["minutes_data_used"]] -requested_features <- intersect(unlist(snakemake@params["features"], use.names = F), - c("hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","minsmissing","circdnrtn","wkenddayrtn")) -requested_features <- c("local_date", paste("location_barnett", requested_features, sep = "_")) -if(minutes_data_used) - requested_features <- c(requested_features, "minutes_data_used") - -if(!locations_to_use %in% c("ALL_EXCEPT_FUSED", "RESAMPLE_FUSED", "ALL")){ - print("Unkown filter, provide one of the following three: ALL, ALL_EXCEPT_FUSED, or RESAMPLE_FUSED") - quit(save = "no", status = 1, runLast = FALSE) -} - - # excludes fused and resample -if(locations_to_use == "ALL_EXCEPT_FUSED") - location <- location %>% filter(provider == "gps") - -# Remove 0,0 location coordinates -location <- location %>% filter(double_latitude != 0 & double_longitude != 0) - -# Excludes datasets with less than 24 hours of data -if(max(location$timestamp) - min(location$timestamp) < 86400000) - location <- head(location, 0) - -if (nrow(location) > 1){ - - # Count how many minutes of data we use to get location features - # Some minutes have multiple fused rows - location_minutes_used <- location %>% - group_by(local_date, local_hour) %>% - summarise(n_minutes = n_distinct(local_minute)) %>% - group_by(local_date) %>% - summarise(minutes_data_used = sum(n_minutes)) %>% - select(local_date, minutes_data_used) - - location <- location %>% - select(timestamp, latitude = double_latitude, longitude = double_longitude, altitude = double_altitude, accuracy) - if(nrow(location %>% filter(accuracy < accuracy_limit)) > 1){ - outputMobility <- MobilityFeatures(location, ACCURACY_LIM = accuracy_limit, tz = timezone) - } else { - print(paste("Cannot compute location features because there are no rows with an accuracy value lower than ACCURACY_LIMIT", accuracy_limit)) - outputMobility <- NULL - } - - if(is.null(outputMobility)){ - write_empty_file(snakemake@output[[1]], requested_features) - } else{ - # Copy index (dates) as a column - features <- cbind(rownames(outputMobility$featavg), outputMobility$featavg) - features <- as.data.frame(features) - features[-1] <- lapply(lapply(features[-1], as.character), as.numeric) - colnames(features)=c("local_date",tolower(paste("location_barnett", colnames(outputMobility$featavg), sep = "_"))) - # Add the minute count column - features <- left_join(features, location_minutes_used, by = "local_date") - write.csv(features %>% select(requested_features), snakemake@output[[1]], row.names = F) - } - -} else { - write_empty_file(snakemake@output[[1]], requested_features) -} diff --git a/src/features/location_doryab_features.py b/src/features/location_doryab_features.py deleted file mode 100644 index 8d749483..00000000 --- a/src/features/location_doryab_features.py +++ /dev/null @@ -1,24 +0,0 @@ -import pandas as pd -from location_doryab.location_base import base_location_features - -location_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"]) -day_segment = snakemake.params["day_segment"] -requested_features = snakemake.params["features"] -location_features = pd.DataFrame(columns=["local_date"]) -dbscan_eps = snakemake.params["dbscan_eps"] -dbscan_minsamples = snakemake.params["dbscan_minsamples"] -threshold_static = snakemake.params["threshold_static"] -maximum_gap_allowed = snakemake.params["maximum_gap_allowed"] -minutes_data_used = snakemake.params["minutes_data_used"] -sampling_frequency = snakemake.params["sampling_frequency"] - -if(minutes_data_used): - requested_features.append("minutesdataused") - -base_features = base_location_features(location_data, day_segment, requested_features, dbscan_eps, dbscan_minsamples,threshold_static,maximum_gap_allowed,sampling_frequency) - -location_features = location_features.merge(base_features, on="local_date", how="outer") - -assert len(requested_features) + 1 == location_features.shape[1], "The number of features in the output dataframe (=" + str(location_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your location feature extraction functions" - -location_features.to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/features/messages/messages_base.R b/src/features/messages/messages_base.R deleted file mode 100644 index a784a213..00000000 --- a/src/features/messages/messages_base.R +++ /dev/null @@ -1,63 +0,0 @@ -library('tidyr') - -filter_by_day_segment <- function(data, day_segment) { - if(day_segment %in% c("morning", "afternoon", "evening", "night")) - data <- data %>% filter(local_day_segment == day_segment) - else if(day_segment == "daily") - return(data) - else - return(data %>% head(0)) -} - -base_messages_features <- function(messages, messages_type, day_segment, requested_features){ - # Output dataframe - features = data.frame(local_date = character(), stringsAsFactors = FALSE) - - # The name of the features this function can compute - base_features_names <- c("countmostfrequentcontact", "count", "distinctcontacts", "timefirstmessage", "timelastmessage") - - # The subset of requested features this function can compute - features_to_compute <- intersect(base_features_names, requested_features) - - # Filter rows that belong to the message type and day segment of interest - messages <- messages %>% filter(message_type == ifelse(messages_type == "received", "1", ifelse(messages_type == "sent", 2, NA))) %>% - filter_by_day_segment(day_segment) - - # If there are not features or data to work with, return an empty df with appropiate columns names - if(length(features_to_compute) == 0) - return(features) - if(nrow(messages) < 1) - return(cbind(features, read.csv(text = paste(paste("messages", messages_type, day_segment, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE))) - - for(feature_name in features_to_compute){ - if(feature_name == "countmostfrequentcontact"){ - # Get the number of messages for the most frequent contact throughout the study - mostfrequentcontact <- messages %>% - group_by(trace) %>% - mutate(N=n()) %>% - ungroup() %>% - filter(N == max(N)) %>% - head(1) %>% # if there are multiple contacts with the same amount of messages pick the first one only - pull(trace) - feature <- messages %>% - filter(trace == mostfrequentcontact) %>% - group_by(local_date) %>% - summarise(!!paste("messages", messages_type, day_segment, feature_name, sep = "_") := n()) %>% - replace(is.na(.), 0) - features <- merge(features, feature, by="local_date", all = TRUE) - } else { - feature <- messages %>% - group_by(local_date) - - feature <- switch(feature_name, - "count" = feature %>% summarise(!!paste("messages", messages_type, day_segment, feature_name, sep = "_") := n()), - "distinctcontacts" = feature %>% summarise(!!paste("messages", messages_type, day_segment, feature_name, sep = "_") := n_distinct(trace)), - "timefirstmessage" = feature %>% summarise(!!paste("messages", messages_type, day_segment, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)), - "timelastmessage" = feature %>% summarise(!!paste("messages", messages_type, day_segment, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute))) - - features <- merge(features, feature, by="local_date", all = TRUE) - } - } - features <- features %>% mutate_at(vars(contains("countmostfrequentcontact")), list( ~ replace_na(., 0))) - return(features) -} \ No newline at end of file diff --git a/src/features/messages_features.R b/src/features/messages_features.R deleted file mode 100644 index 1f8cabde..00000000 --- a/src/features/messages_features.R +++ /dev/null @@ -1,20 +0,0 @@ -# If you want to implement extra features, source(..) a new file and duplicate the line "features <- merge(...)", then -# swap base_sms_features(...) for your own function - -source("renv/activate.R") -source("src/features/messages/messages_base.R") -library(dplyr, warn.conflicts = FALSE) - -messages <- read.csv(snakemake@input[[1]]) -day_segment <- snakemake@params[["day_segment"]] -requested_features <- snakemake@params[["features"]] -messages_type <- snakemake@params[["messages_type"]] -features <- data.frame(local_date = character(), stringsAsFactors = FALSE) - -# Compute base SMS features -features <- merge(features, base_messages_features(messages, messages_type, day_segment, requested_features), by="local_date", all = TRUE) - -if(ncol(features) != length(requested_features) + 1) - stop(paste0("The number of features in the output dataframe (=", ncol(features),") does not match the expected value (=", length(requested_features)," + 1). Verify your Messages (SMS) feature extraction functions")) - -write.csv(features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/phone_accelerometer/panda/main.py b/src/features/phone_accelerometer/panda/main.py new file mode 100644 index 00000000..be52a8b6 --- /dev/null +++ b/src/features/phone_accelerometer/panda/main.py @@ -0,0 +1,89 @@ +import pandas as pd +import numpy as np + +def dropRowsWithCertainThreshold(data, threshold): + data_grouped = data.groupby(["local_timezone", "local_segment", "local_date", "local_hour", "local_minute"]) + data_cleaned = data_grouped.filter(lambda x: x["timestamp"].count() > threshold) + return data_cleaned + +def getActivityEpisodes(acc_minute): + # rebuild local date time for resampling + acc_minute["local_datetime"] = pd.to_datetime(acc_minute["local_date"] + \ + " " + acc_minute["local_hour"].apply(str) + ":" + acc_minute["local_minute"].apply(str) + ":00") + + # compute time interval between consecutive rows in minutes + acc_minute["rows_interval"] = round(acc_minute["local_datetime"].diff().dt.total_seconds() / 60, 0) + + # put consecutive rows into the same group if (1) the interval between two rows is 1 minute and (2) have the same values of "isexertionalactivity", "local_timezone", and "local_segment" + acc_minute["group_idx"] = ((acc_minute[["isexertionalactivity", "local_timezone", "local_segment"]].shift() != acc_minute[["isexertionalactivity", "local_timezone", "local_segment"]]).any(axis=1) | (acc_minute["rows_interval"] != 1)).cumsum() + + # get activity episodes: duration column contains the number of minutes (rows) of exertional and nonexertional activity for each episode + grouped = acc_minute.groupby("group_idx") + activity_episodes = grouped["local_segment"].agg(duration="count") + activity_episodes[["local_segment", "isexertionalactivity"]] = grouped[["local_segment", "isexertionalactivity"]].first() + + return activity_episodes + +def statsFeatures(acc_data, features_to_compute, features_type, acc_features): + if "sum" + features_type in features_to_compute: + acc_features["sum" + features_type] = acc_data.groupby(["local_segment"])["duration"].sum() + if "max" + features_type in features_to_compute: + acc_features["max" + features_type] = acc_data.groupby(["local_segment"])["duration"].max() + if "min" + features_type in features_to_compute: + acc_features["min" + features_type] = acc_data.groupby(["local_segment"])["duration"].min() + if "avg" + features_type in features_to_compute: + acc_features["avg" + features_type] = acc_data.groupby(["local_segment"])["duration"].mean() + if "median" + features_type in features_to_compute: + acc_features["median" + features_type] = acc_data.groupby(["local_segment"])["duration"].median() + if "std" + features_type in features_to_compute: + acc_features["std" + features_type] = acc_data.groupby(["local_segment"])["duration"].std() + + return acc_features + + + +def panda_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + + acc_data = pd.read_csv(sensor_data_files["sensor_data"]) + requested_features = provider["FEATURES"] + valid_sensed_minutes = provider["VALID_SENSED_MINUTES"] + # name of the features this function can compute + base_features_names_exertionalactivityepisode = ["sumdurationexertionalactivityepisode", "maxdurationexertionalactivityepisode", "mindurationexertionalactivityepisode", "avgdurationexertionalactivityepisode", "mediandurationexertionalactivityepisode", "stddurationexertionalactivityepisode"] + base_features_names_nonexertionalactivityepisode = ["sumdurationnonexertionalactivityepisode", "maxdurationnonexertionalactivityepisode", "mindurationnonexertionalactivityepisode", "avgdurationnonexertionalactivityepisode", "mediandurationnonexertionalactivityepisode", "stddurationnonexertionalactivityepisode"] + # the subset of requested features this function can compute + features_to_compute_exertionalactivityepisode = list(set([x + "exertionalactivityepisode" for x in requested_features["exertional_activity_episode"]]) & set(base_features_names_exertionalactivityepisode)) + features_to_compute_nonexertionalactivityepisode = list(set([ x + "nonexertionalactivityepisode" for x in requested_features["nonexertional_activity_episode"]]) & set(base_features_names_nonexertionalactivityepisode)) + + features_to_compute = features_to_compute_exertionalactivityepisode + features_to_compute_nonexertionalactivityepisode + (["validsensedminutes"] if valid_sensed_minutes else []) + + acc_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) + if not acc_data.empty: + acc_data = filter_data_by_segment(acc_data, time_segment) + + if not acc_data.empty: + acc_features = pd.DataFrame() + # drop rows where we only have one row per minute (no variance) + acc_data = dropRowsWithCertainThreshold(acc_data, 1) + + if not acc_data.empty: + # check if the participant performs exertional activity for each minute + acc_minute = pd.DataFrame() + acc_minute["isexertionalactivity"] = (acc_data.groupby(["local_timezone", "local_segment", "local_date", "local_hour", "local_minute"])["double_values_0"].var() + acc_data.groupby(["local_timezone", "local_segment", "local_date", "local_hour", "local_minute"])["double_values_1"].var() + acc_data.groupby(["local_timezone", "local_segment", "local_date", "local_hour", "local_minute"])["double_values_2"].var()).apply(lambda x: 1 if x > 0.15 * (9.807 ** 2) else 0) + acc_minute.reset_index(inplace=True) + + if valid_sensed_minutes: + acc_features["validsensedminutes"] = acc_minute.groupby(["local_segment"])["isexertionalactivity"].count() + + activity_episodes = getActivityEpisodes(acc_minute) + # compute exertional episodes features + exertionalactivity_episodes = activity_episodes[activity_episodes["isexertionalactivity"] == 1] + acc_features = statsFeatures(exertionalactivity_episodes, features_to_compute_exertionalactivityepisode, "durationexertionalactivityepisode", acc_features) + # compute non-exertional episodes features + nonexertionalactivity_episodes = activity_episodes[activity_episodes["isexertionalactivity"] == 0] + acc_features = statsFeatures(nonexertionalactivity_episodes, features_to_compute_nonexertionalactivityepisode, "durationnonexertionalactivityepisode", acc_features) + + acc_features[[colname for colname in acc_features.columns if "std" not in colname]] = acc_features[[colname for colname in acc_features.columns if "std" not in colname]].fillna(0) + + acc_features = acc_features.reset_index() + + return acc_features diff --git a/src/features/phone_accelerometer/rapids/main.py b/src/features/phone_accelerometer/rapids/main.py new file mode 100644 index 00000000..7fc10918 --- /dev/null +++ b/src/features/phone_accelerometer/rapids/main.py @@ -0,0 +1,36 @@ +import pandas as pd +import numpy as np + +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + + acc_data = pd.read_csv(sensor_data_files["sensor_data"]) + requested_features = provider["FEATURES"] + # name of the features this function can compute + base_features_names = ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] + # the subset of requested features this function can compute + features_to_compute = list(set(requested_features) & set(base_features_names)) + + acc_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) + if not acc_data.empty: + acc_data = filter_data_by_segment(acc_data, time_segment) + + if not acc_data.empty: + acc_features = pd.DataFrame() + # get magnitude related features: magnitude = sqrt(x^2+y^2+z^2) + magnitude = acc_data.apply(lambda row: np.sqrt(row["double_values_0"] ** 2 + row["double_values_1"] ** 2 + row["double_values_2"] ** 2), axis=1) + acc_data = acc_data.assign(magnitude = magnitude.values) + + if "maxmagnitude" in features_to_compute: + acc_features["maxmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].max() + if "minmagnitude" in features_to_compute: + acc_features["minmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].min() + if "avgmagnitude" in features_to_compute: + acc_features["avgmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].mean() + if "medianmagnitude" in features_to_compute: + acc_features["medianmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].median() + if "stdmagnitude" in features_to_compute: + acc_features["stdmagnitude"] = acc_data.groupby(["local_segment"])["magnitude"].std() + + acc_features = acc_features.reset_index() + + return acc_features diff --git a/src/features/phone_activity_recognition/episodes/activity_recognition_episodes.R b/src/features/phone_activity_recognition/episodes/activity_recognition_episodes.R new file mode 100644 index 00000000..3018264e --- /dev/null +++ b/src/features/phone_activity_recognition/episodes/activity_recognition_episodes.R @@ -0,0 +1,29 @@ +source("renv/activate.R") +library("dplyr", warn.conflicts = F) + +activity_recognition <- read.csv(snakemake@input[[1]]) +episode_threshold_between_rows <- snakemake@params[["episode_threshold_between_rows"]] + +if(nrow(activity_recognition) > 0){ + episode_threshold_between_rows = episode_threshold_between_rows * 60000 + + ar_episodes <- activity_recognition %>% + mutate(start_timestamp = timestamp, # a battery level starts as soon as is logged + time_diff = (lead(timestamp) - start_timestamp), # lead diff + # we assume the current activity existed until the next row only if that row is logged within [episode_threshold_between_rows] minutes + end_timestamp = if_else(is.na(time_diff) | time_diff > (episode_threshold_between_rows), start_timestamp + (episode_threshold_between_rows), lead(timestamp) - 1), + time_diff = c(1, diff(start_timestamp)), # lag diff + type_diff = c(1, diff(activity_type)), + episode_id = cumsum(type_diff != 0 | time_diff > (episode_threshold_between_rows))) %>% + group_by(episode_id) %>% + summarise(activity_name = first(activity_name), activity_type = first(activity_type), start_timestamp=first(start_timestamp), end_timestamp = last(end_timestamp)) + +} else { + ar_episodes <- data.frame(start_timestamp = numeric(), + end_timestamp = numeric(), + episode_id = numeric(), + activity_type = numeric(), + activity_name = character()) +} + +write.csv(ar_episodes, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/phone_activity_recognition/rapids/main.py b/src/features/phone_activity_recognition/rapids/main.py new file mode 100644 index 00000000..d504e6c5 --- /dev/null +++ b/src/features/phone_activity_recognition/rapids/main.py @@ -0,0 +1,41 @@ +import pandas as pd +import numpy as np + +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + + ar_episodes = pd.read_csv(sensor_data_files["sensor_episodes"]) + activity_classes = provider["ACTIVITY_CLASSES"] + + # name of the features this function can compute + base_features_names = ["count","mostcommonactivity","countuniqueactivities","durationstationary","durationmobile","durationvehicle"] + # the subset of requested features this function can compute + requested_features = provider["FEATURES"] + features_to_compute = list(set(requested_features) & set(base_features_names)) + + ar_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) + if not ar_episodes.empty: + ar_episodes = filter_data_by_segment(ar_episodes, time_segment) + + if not ar_episodes.empty: + ar_features = pd.DataFrame() + + if "count" in features_to_compute: + ar_features["count"] = ar_episodes.groupby(["local_segment"]).count()["episode_id"] + if "mostcommonactivity" in features_to_compute: + ar_features["mostcommonactivity"] = ar_episodes.groupby(["local_segment"])["activity_type"].agg(lambda x: pd.Series.mode(x)[0]) + if "countuniqueactivities" in features_to_compute: + ar_features["countuniqueactivities"] = ar_episodes.groupby(["local_segment"])["activity_type"].nunique() + + # duration features + for column, activity_labels in activity_classes.items(): + if "duration" + column.lower() in features_to_compute: + filtered_data = ar_episodes[ar_episodes["activity_name"].isin(pd.Series(activity_labels))] + if not filtered_data.empty: + ar_features["duration" + column.lower()] = ar_episodes[ar_episodes["activity_name"].isin(pd.Series(activity_labels))].groupby(["local_segment"])["duration"].sum().fillna(0) + else: + ar_features["duration" + column.lower()] = 0 + + ar_features.index.names = ["local_segment"] + ar_features = ar_features.reset_index() + + return ar_features diff --git a/src/features/phone_applications_foreground/rapids/main.py b/src/features/phone_applications_foreground/rapids/main.py new file mode 100644 index 00000000..970e5c55 --- /dev/null +++ b/src/features/phone_applications_foreground/rapids/main.py @@ -0,0 +1,90 @@ +import pandas as pd +import numpy as np +import itertools +from scipy.stats import entropy + + +def compute_features(filtered_data, apps_type, requested_features, apps_features, time_segment): + # There is the rare occasion that filtered_data is empty (found in testing) + if "timeoffirstuse" in requested_features: + time_first_event = filtered_data.sort_values(by="timestamp", ascending=True).drop_duplicates(subset="local_segment", keep="first").set_index("local_segment") + if time_first_event.empty: + apps_features["timeoffirstuse" + apps_type] = np.nan + else: + apps_features["timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"] + if "timeoflastuse" in requested_features: + time_last_event = filtered_data.sort_values(by="timestamp", ascending=False).drop_duplicates(subset="local_segment", keep="first").set_index("local_segment") + if time_last_event.empty: + apps_features["timeoflastuse" + apps_type] = np.nan + else: + apps_features["timeoflastuse" + apps_type] = time_last_event["local_hour"] * 60 + time_last_event["local_minute"] + if "frequencyentropy" in requested_features: + apps_with_count = filtered_data.groupby(["local_segment","application_name"]).count().sort_values(by="timestamp", ascending=False).reset_index() + if (len(apps_with_count.index) < 2 ): + apps_features["frequencyentropy" + apps_type] = np.nan + else: + apps_features["frequencyentropy" + apps_type] = apps_with_count.groupby("local_segment")["timestamp"].agg(entropy) + if "count" in requested_features: + apps_features["count" + apps_type] = filtered_data.groupby(["local_segment"]).count()["timestamp"] + apps_features.fillna(value={"count" + apps_type: 0}, inplace=True) + return apps_features + + +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + + apps_data = pd.read_csv(sensor_data_files["sensor_data"]) + + requested_features = provider["FEATURES"] + excluded_categories = provider["EXCLUDED_CATEGORIES"] + excluded_apps = provider["EXCLUDED_APPS"] + multiple_categories_with_genres = provider["MULTIPLE_CATEGORIES"] + single_categories = provider["SINGLE_CATEGORIES"] + multiple_categories = provider["MULTIPLE_CATEGORIES"] + single_apps = provider["SINGLE_APPS"] + + single_categories = list(set(single_categories) - set(excluded_categories)) + multiple_categories = list(multiple_categories_with_genres.keys() - set(excluded_categories)) + single_apps = list(set(single_apps) - set(excluded_apps)) + + # exclude categories in the excluded_categories list + if "system_apps" in excluded_categories: + apps_data = apps_data[apps_data["is_system_app"] == 0] + apps_data = apps_data[~apps_data["genre"].isin(excluded_categories)] + # exclude apps in the excluded_apps list + apps_data = apps_data[~apps_data["package_name"].isin(excluded_apps)] + + apps_features = pd.DataFrame(columns=["local_segment"] + ["".join(feature) for feature in itertools.product(requested_features, single_categories + multiple_categories + single_apps)]) + if not apps_data.empty: + # deep copy the apps_data for the top1global computation + apps_data_global = apps_data.copy() + + apps_data = filter_data_by_segment(apps_data, time_segment) + + if not apps_data.empty: + apps_features = pd.DataFrame() + # single category + single_categories.sort() + for sc in single_categories: + if sc == "all": + apps_features = compute_features(apps_data, "all", requested_features, apps_features, time_segment) + else: + filtered_data = apps_data[apps_data["genre"].isin([sc])] + apps_features = compute_features(filtered_data, sc, requested_features, apps_features, time_segment) + # multiple category + for mc in multiple_categories: + filtered_data = apps_data[apps_data["genre"].isin(multiple_categories_with_genres[mc])] + apps_features = compute_features(filtered_data, mc, requested_features, apps_features, time_segment) + # single apps + for app in single_apps: + col_name = app + if app == "top1global": + # get the most used app + apps_with_count = apps_data_global.groupby(["package_name"]).count().sort_values(by="timestamp", ascending=False).reset_index() + app = apps_with_count.iloc[0]["package_name"] + col_name = "top1global" + filtered_data = apps_data[apps_data["package_name"].isin([app])] + apps_features = compute_features(filtered_data, col_name, requested_features, apps_features, time_segment) + + apps_features = apps_features.reset_index() + + return apps_features diff --git a/src/features/phone_battery/episodes/battery_episodes.R b/src/features/phone_battery/episodes/battery_episodes.R new file mode 100644 index 00000000..6e0d9db4 --- /dev/null +++ b/src/features/phone_battery/episodes/battery_episodes.R @@ -0,0 +1,31 @@ +source("renv/activate.R") +library("dplyr", warn.conflicts = F) + +battery <- read.csv(snakemake@input[[1]]) +episode_threshold_between_rows <- snakemake@params[["episode_threshold_between_rows"]] + +if(nrow(battery) > 0){ + episode_threshold_between_rows = episode_threshold_between_rows * 60000 + + battery_episodes <- battery %>% + filter(battery_status >= 2 ) %>% # discard unknown states + mutate(start_timestamp = timestamp, # a battery level starts as soon as is logged + end_timestamp = lead(timestamp) - 1, # a battery level ends as soon as a new one is logged + time_diff = (end_timestamp - start_timestamp), + # we assume the current level existed until the next row only if that row is logged within [episode_threshold_between_rows] minutes + end_timestamp = if_else(is.na(time_diff) | time_diff > (episode_threshold_between_rows), start_timestamp + (episode_threshold_between_rows), end_timestamp)) %>% + mutate(time_diff = c(1, diff(start_timestamp)), + level_diff = c(1, diff(battery_level)), + status_diff = c(1, diff(battery_status)), + episode_id = cumsum(level_diff != 0 | status_diff != 0 | time_diff > (episode_threshold_between_rows))) %>% + group_by(episode_id) %>% + summarise(battery_level = first(battery_level), battery_status = first(battery_status), start_timestamp=first(start_timestamp), end_timestamp = last(end_timestamp)) +} else { + battery_episodes <- data.frame(episode_id = numeric(), + start_timestamp = numeric(), + end_timestamp = character(), + battery_level = character(), + battery_status = character()) +} + +write.csv(battery_episodes, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/phone_battery/rapids/main.py b/src/features/phone_battery/rapids/main.py new file mode 100644 index 00000000..52b5199e --- /dev/null +++ b/src/features/phone_battery/rapids/main.py @@ -0,0 +1,53 @@ +import pandas as pd +from datetime import datetime, timedelta, time + +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + + battery_data = pd.read_csv(sensor_data_files["sensor_episodes"]) + + # name of the features this function can compute + base_features_names = ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] + # the subset of requested features this function can compute + requested_features = provider["FEATURES"] + features_to_compute = list(set(requested_features) & set(base_features_names)) + + battery_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) + if not battery_data.empty: + battery_data = filter_data_by_segment(battery_data, time_segment) + + if not battery_data.empty: + + battery_data["episode_id"] = ((battery_data.battery_status != battery_data.battery_status.shift()) | (battery_data.start_timestamp - battery_data.end_timestamp.shift() > 1)).cumsum() + grouped = battery_data.groupby(by=["local_segment", "episode_id", "battery_status"]) + battery_episodes= grouped[["duration"]].sum() + battery_episodes["battery_diff"] = grouped["battery_level"].first() - grouped["battery_level"].last() + battery_episodes["battery_consumption_rate"] = battery_episodes["battery_diff"] / battery_episodes["duration"] + battery_episodes.reset_index(inplace=True) + + # for discharge episodes + battery_discharge_episodes = battery_episodes[(battery_episodes["battery_status"] == 3) | (battery_episodes["battery_status"] == 4)] + battery_discharge_features = pd.DataFrame() + if "countdischarge" in features_to_compute: + battery_discharge_features["countdischarge"] = battery_discharge_episodes.groupby(["local_segment"])["episode_id"].count() + if "sumdurationdischarge" in features_to_compute: + battery_discharge_features["sumdurationdischarge"] = battery_discharge_episodes.groupby(["local_segment"])["duration"].sum() + if "avgconsumptionrate" in features_to_compute: + battery_discharge_features["avgconsumptionrate"] = battery_discharge_episodes.groupby(["local_segment"])["battery_consumption_rate"].mean() + if "maxconsumptionrate" in features_to_compute: + battery_discharge_features["maxconsumptionrate"] = battery_discharge_episodes.groupby(["local_segment"])["battery_consumption_rate"].max() + + # for charge episodes + battery_charge_episodes = battery_episodes[(battery_episodes["battery_status"] == 2) | (battery_episodes["battery_status"] == 5)] + battery_charge_features = pd.DataFrame() + if "countcharge" in features_to_compute: + battery_charge_features["countcharge"] = battery_charge_episodes.groupby(["local_segment"])["episode_id"].count() + if "sumdurationcharge" in features_to_compute: + battery_charge_features["sumdurationcharge"] = battery_charge_episodes.groupby(["local_segment"])["duration"].sum() + + # combine discharge features and charge features; fill the missing values with ZERO + battery_features = pd.concat([battery_discharge_features, battery_charge_features], axis=1, sort=True).fillna(0) + + battery_features.index.rename("local_segment", inplace=True) + battery_features = battery_features.reset_index() + + return battery_features diff --git a/src/features/phone_bluetooth/rapids/main.R b/src/features/phone_bluetooth/rapids/main.R new file mode 100644 index 00000000..a8aa1ace --- /dev/null +++ b/src/features/phone_bluetooth/rapids/main.R @@ -0,0 +1,52 @@ +library("dplyr", warn.conflicts = F) +library(tidyr) + +compute_bluetooth_feature <- function(data, feature, time_segment){ + data <- data %>% filter_data_by_segment(time_segment) + if(feature %in% c("countscans", "uniquedevices")){ + data <- data %>% group_by(local_segment) + data <- switch(feature, + "countscans" = data %>% summarise(!!feature := n()), + "uniquedevices" = data %>% summarise(!!feature := n_distinct(bt_address))) + return(data) + } else if(feature == "countscansmostuniquedevice"){ + # Get the most scanned device + mostuniquedevice <- data %>% + group_by(bt_address) %>% + mutate(N=n()) %>% + ungroup() %>% + filter(N == max(N)) %>% + head(1) %>% # if there are multiple device with the same amount of scans pick the first one only + pull(bt_address) + mostuniquedevice + return(data %>% + filter(bt_address == mostuniquedevice) %>% + group_by(local_segment) %>% + summarise(!!feature := n()) %>% + replace(is.na(.), 0)) + } +} + +rapids_features <- function(sensor_data_files, time_segment, provider){ + + bluetooth_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE) + requested_features <- provider[["FEATURES"]] + + # Output dataframe + features = data.frame(local_segment = character(), stringsAsFactors = FALSE) + + # The name of the features this function can compute + base_features_names <- c("countscans", "uniquedevices", "countscansmostuniquedevice") + + # The subset of requested features this function can compute + features_to_compute <- intersect(base_features_names, requested_features) + + for(feature_name in features_to_compute){ + feature <- compute_bluetooth_feature(bluetooth_data, feature_name, time_segment) + features <- merge(features, feature, by="local_segment", all = TRUE) + } + + features <- features %>% mutate_at(vars(contains("countscansmostuniquedevice")), list( ~ replace_na(., 0))) + + return(features) +} \ No newline at end of file diff --git a/src/features/phone_calls/rapids/main.R b/src/features/phone_calls/rapids/main.R new file mode 100644 index 00000000..39cdfc45 --- /dev/null +++ b/src/features/phone_calls/rapids/main.R @@ -0,0 +1,82 @@ +library('tidyr') +library('stringr') +library('entropy') + +Mode <- function(v) { + uniqv <- unique(v) + uniqv[which.max(tabulate(match(v, uniqv)))] +} + +call_features_of_type <- function(calls, call_type, time_segment, requested_features){ + # Output dataframe + features = data.frame(local_segment = character(), stringsAsFactors = FALSE) + + # The name of the features this function can compute + base_features_names <- c("count", "distinctcontacts", "meanduration", "sumduration", "minduration", "maxduration", "stdduration", "modeduration", "entropyduration", "timefirstcall", "timelastcall", "countmostfrequentcontact") + # The subset of requested features this function can compute + features_to_compute <- intersect(base_features_names, requested_features) + + # If there are not features or data to work with, return an empty df with appropiate columns names + if(length(features_to_compute) == 0) + return(features) + if(nrow(calls) < 1) + return(cbind(features, read.csv(text = paste(paste(call_type, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE))) + + for(feature_name in features_to_compute){ + if(feature_name == "countmostfrequentcontact"){ + # Get the number of messages for the most frequent contact throughout the study + mostfrequentcontact <- calls %>% + group_by(trace) %>% + mutate(N=n()) %>% + ungroup() %>% + filter(N == max(N)) %>% + head(1) %>% # if there are multiple contacts with the same amount of messages pick the first one only + pull(trace) + feature <- calls %>% + group_by(local_segment) %>% + summarise(!!paste(call_type, feature_name, sep = "_") := sum(trace == mostfrequentcontact)) + features <- merge(features, feature, by="local_segment", all = TRUE) + } else { + feature <- calls %>% + group_by(local_segment) + + feature <- switch(feature_name, + "count" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := n()), + "distinctcontacts" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := n_distinct(trace)), + "meanduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := mean(call_duration)), + "sumduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := sum(call_duration)), + "minduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := min(call_duration)), + "maxduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := max(call_duration)), + "stdduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := sd(call_duration)), + "modeduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := Mode(call_duration)), + "entropyduration" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := entropy.MillerMadow(call_duration)), + "timefirstcall" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)), + "timelastcall" = feature %>% summarise(!!paste(call_type, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute))) + + features <- merge(features, feature, by="local_segment", all = TRUE) + } + } + return(features) +} + +rapids_features <- function(sensor_data_files, time_segment, provider){ + calls_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE) + calls_data <- calls_data %>% filter_data_by_segment(time_segment) + call_types = provider[["CALL_TYPES"]] + call_features <- setNames(data.frame(matrix(ncol=1, nrow=0)), c("local_segment")) + + for(call_type in call_types){ + # Filter rows that belong to the calls type and time segment of interest + call_type_label = ifelse(call_type == "incoming", "1", ifelse(call_type == "outgoing", "2", ifelse(call_type == "missed", "3", NA))) + if(is.na(call_type_label)) + stop(paste("Call type can online be incoming, outgoing or missed but instead you typed: ", call_type, " in config[CALLS][CALL_TYPES]")) + + requested_features <- provider[["FEATURES"]][[call_type]] + calls_of_type <- calls_data %>% filter(call_type == call_type_label) + + features <- call_features_of_type(calls_of_type, call_type, time_segment, requested_features) + call_features <- merge(call_features, features, all=TRUE) + } + call_features <- call_features %>% mutate_at(vars(contains("countmostfrequentcontact") | contains("distinctcontacts") | contains("count")), list( ~ replace_na(., 0))) + return(call_features) +} \ No newline at end of file diff --git a/src/features/phone_conversation/rapids/main.py b/src/features/phone_conversation/rapids/main.py new file mode 100644 index 00000000..902cad69 --- /dev/null +++ b/src/features/phone_conversation/rapids/main.py @@ -0,0 +1,145 @@ +import pandas as pd +import numpy as np + +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + + conversation_data = pd.read_csv(sensor_data_files["sensor_data"]) + + requested_features = provider["FEATURES"] + recordingMinutes = provider["RECORDING_MINUTES"] + pausedMinutes = provider["PAUSED_MINUTES"] + expectedMinutes = 1440 / (recordingMinutes + pausedMinutes) + + # name of the features this function can compute + base_features_names = ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", + "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", + "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", + "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", + "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", + "unknownexpectedfraction","countconversation"] + + # the subset of requested features this function can compute + features_to_compute = list(set(requested_features) & set(base_features_names)) + + conversation_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) + if not conversation_data.empty: + conversation_data = filter_data_by_segment(conversation_data, time_segment) + + if not conversation_data.empty: + conversation_features = pd.DataFrame() + + conversation_data = conversation_data.drop_duplicates(subset=["local_date", "local_time"], keep="first") + + if "minutessilence" in features_to_compute: + conversation_features["minutessilence"] = conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60 + + if "minutesnoise" in features_to_compute: + conversation_features["minutesnoise"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60 + + if "minutesvoice" in features_to_compute: + conversation_features["minutesvoice"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60 + + if "minutesunknown" in features_to_compute: + conversation_features["minutesunknown"] = conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60 + + if "countconversation" in features_to_compute: + conversation_features["countconversation"] = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['double_convo_start'].nunique() + + conv_duration = (conversation_data['double_convo_end']/1000 - conversation_data['double_convo_start']/1000)/60 + conversation_data = conversation_data.assign(conv_duration = conv_duration.values) + + conv_totalDuration = conversation_data[(conversation_data['inference'] >= 0) & (conversation_data['inference'] < 4)].groupby(["local_segment"])['inference'].count()/60 + + if "silencesensedfraction" in features_to_compute: + conversation_features["silencesensedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration + + if "noisesensedfraction" in features_to_compute: + conversation_features["noisesensedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration + + if "voicesensedfraction" in features_to_compute: + conversation_features["voicesensedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration + + if "unknownsensedfraction" in features_to_compute: + conversation_features["unknownsensedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60)/ conv_totalDuration + + if "silenceexpectedfraction" in features_to_compute: + conversation_features["silenceexpectedfraction"] = (conversation_data[conversation_data['inference']==0].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes + + if "noiseexpectedfraction" in features_to_compute: + conversation_features["noiseexpectedfraction"] = (conversation_data[conversation_data['inference']==1].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes + + if "voiceexpectedfraction" in features_to_compute: + conversation_features["voiceexpectedfraction"] = (conversation_data[conversation_data['inference']==2].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes + + if "unknownexpectedfraction" in features_to_compute: + conversation_features["unknownexpectedfraction"] = (conversation_data[conversation_data['inference']==3].groupby(["local_segment"])['inference'].count()/60)/ expectedMinutes + + if "sumconversationduration" in features_to_compute: + conversation_features["sumconversationduration"] = conversation_data.groupby(["local_segment"])["conv_duration"].sum() + + if "avgconversationduration" in features_to_compute: + conversation_features["avgconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].mean() + + if "sdconversationduration" in features_to_compute: + conversation_features["sdconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].std() + + if "minconversationduration" in features_to_compute: + conversation_features["minconversationduration"] = conversation_data[conversation_data["conv_duration"] > 0].groupby(["local_segment"])["conv_duration"].min() + + if "maxconversationduration" in features_to_compute: + conversation_features["maxconversationduration"] = conversation_data.groupby(["local_segment"])["conv_duration"].max() + + if "timefirstconversation" in features_to_compute: + timestampsLastConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['timestamp'].min() + if len(list(timestampsLastConversation.index)) > 0: + for date in list(timestampsLastConversation.index): + lastimestamp = timestampsLastConversation.loc[date] + lasttime = (conversation_data.query('timestamp == @lastimestamp', inplace = False))['local_time'].iat[0] + conversation_features.loc[date,"timefirstconversation"] = int(lasttime.split(':')[0])*60 + int(lasttime.split(':')[1]) + else: + conversation_features["timefirstconversation"] = np.nan + + if "timelastconversation" in features_to_compute: + timestampsLastConversation = conversation_data[conversation_data["double_convo_start"] > 0].groupby(["local_segment"])['timestamp'].max() + if len(list(timestampsLastConversation.index)) > 0: + for date in list(timestampsLastConversation.index): + lastimestamp = timestampsLastConversation.loc[date] + lasttime = (conversation_data.query('timestamp == @lastimestamp', inplace = False))['local_time'].iat[0] + conversation_features.loc[date,"timelastconversation"] = int(lasttime.split(':')[0])*60 + int(lasttime.split(':')[1]) + else: + conversation_features["timelastconversation"] = np.nan + + if "noisesumenergy" in features_to_compute: + conversation_features["noisesumenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].sum() + + if "noiseavgenergy" in features_to_compute: + conversation_features["noiseavgenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].mean() + + if "noisesdenergy" in features_to_compute: + conversation_features["noisesdenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].std() + + if "noiseminenergy" in features_to_compute: + conversation_features["noiseminenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].min() + + if "noisemaxenergy" in features_to_compute: + conversation_features["noisemaxenergy"] = conversation_data[conversation_data['inference']==1].groupby(["local_segment"])["double_energy"].max() + + if "voicesumenergy" in features_to_compute: + conversation_features["voicesumenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].sum() + + if "voiceavgenergy" in features_to_compute: + conversation_features["voiceavgenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].mean() + + if "voicesdenergy" in features_to_compute: + conversation_features["voicesdenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].std() + + if "voiceminenergy" in features_to_compute: + conversation_features["voiceminenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].min() + + if "voicemaxenergy" in features_to_compute: + conversation_features["voicemaxenergy"] = conversation_data[conversation_data['inference']==2].groupby(["local_segment"])["double_energy"].max() + + + conversation_features = conversation_features.reset_index() + + return conversation_features \ No newline at end of file diff --git a/src/features/phone_data_yield/rapids/main.R b/src/features/phone_data_yield/rapids/main.R new file mode 100644 index 00000000..57473e98 --- /dev/null +++ b/src/features/phone_data_yield/rapids/main.R @@ -0,0 +1,47 @@ +library("dplyr", warn.conflicts = F) +library(tidyr) +library(readr) + +compute_data_yield_features <- function(data, feature_name, time_segment, provider){ + data <- data %>% filter_data_by_segment(time_segment) + features <- data %>% + separate(timestamps_segment, into = c("start_timestamp", "end_timestamp"), convert = T, sep = ",") %>% + mutate(duration_minutes = (end_timestamp - start_timestamp) / 60000, + timestamp_since_segment_start = timestamp - start_timestamp, + minute_bin = timestamp_since_segment_start %/% 60000, # 60 * 1000 + hour_bin = timestamp_since_segment_start %/% 3600000) %>% # (60 * 60 * 1000) + group_by(local_segment, hour_bin) %>% + summarise(minute_count = n_distinct(minute_bin), + duration_minutes = first(duration_minutes), + valid_hour = (minute_count/min(duration_minutes, 60)) > provider$MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS) %>% + group_by(local_segment) %>% + summarise(valid_yielded_minutes = sum(minute_count), + valid_yielded_hours = sum(valid_hour == TRUE) / 1.0, + duration_minutes = first(duration_minutes), + duration_hours = duration_minutes / 60.0, + ratiovalidyieldedminutes = valid_yielded_minutes / duration_minutes, + ratiovalidyieldedhours = if_else(duration_hours > 1, valid_yielded_hours / duration_hours, valid_yielded_hours)) + return(features) +} + + + +rapids_features <- function(sensor_data_files, time_segment, provider){ + + yield_data <- read_csv(sensor_data_files[["sensor_data"]], col_types = cols_only(timestamp ="d", assigned_segments = "c")) + requested_features <- provider[["FEATURES"]] + + # Output dataframe + features = data.frame(local_segment = character(), stringsAsFactors = FALSE) + + # The name of the features this function can compute + base_features_names <- c("ratiovalidyieldedminutes", "ratiovalidyieldedhours") + + # The subset of requested features this function can compute + features_to_compute <- intersect(base_features_names, requested_features) + + features <- compute_data_yield_features(yield_data, feature_name, time_segment, provider) %>% + select(c("local_segment", features_to_compute)) + + return(features) +} \ No newline at end of file diff --git a/src/features/phone_light/rapids/main.py b/src/features/phone_light/rapids/main.py new file mode 100644 index 00000000..32df47ef --- /dev/null +++ b/src/features/phone_light/rapids/main.py @@ -0,0 +1,36 @@ +import pandas as pd +import numpy as np + +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + + light_data = pd.read_csv(sensor_data_files["sensor_data"]) + requested_features = provider["FEATURES"] + # name of the features this function can compute + base_features_names = ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] + # the subset of requested features this function can compute + features_to_compute = list(set(requested_features) & set(base_features_names)) + + light_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) + if not light_data.empty: + light_data = filter_data_by_segment(light_data, time_segment) + + if not light_data.empty: + light_features = pd.DataFrame() + if "count" in features_to_compute: + light_features["count"] = light_data.groupby(["local_segment"]).count()["timestamp"] + + # get light ambient luminance related features + if "maxlux" in features_to_compute: + light_features["maxlux"] = light_data.groupby(["local_segment"])["double_light_lux"].max() + if "minlux" in features_to_compute: + light_features["minlux"] = light_data.groupby(["local_segment"])["double_light_lux"].min() + if "avglux" in features_to_compute: + light_features["avglux"] = light_data.groupby(["local_segment"])["double_light_lux"].mean() + if "medianlux" in features_to_compute: + light_features["medianlux"] = light_data.groupby(["local_segment"])["double_light_lux"].median() + if "stdlux" in features_to_compute: + light_features["stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std() + + light_features = light_features.reset_index() + + return light_features \ No newline at end of file diff --git a/src/features/location_barnett/AvgFlightDur.R b/src/features/phone_locations/barnett/library/AvgFlightDur.R similarity index 100% rename from src/features/location_barnett/AvgFlightDur.R rename to src/features/phone_locations/barnett/library/AvgFlightDur.R diff --git a/src/features/location_barnett/AvgFlightLen.R b/src/features/phone_locations/barnett/library/AvgFlightLen.R similarity index 100% rename from src/features/location_barnett/AvgFlightLen.R rename to src/features/phone_locations/barnett/library/AvgFlightLen.R diff --git a/src/features/location_barnett/Collapse2Pause.R b/src/features/phone_locations/barnett/library/Collapse2Pause.R similarity index 100% rename from src/features/location_barnett/Collapse2Pause.R rename to src/features/phone_locations/barnett/library/Collapse2Pause.R diff --git a/src/features/location_barnett/DailyMobilityPlots.R b/src/features/phone_locations/barnett/library/DailyMobilityPlots.R similarity index 100% rename from src/features/location_barnett/DailyMobilityPlots.R rename to src/features/phone_locations/barnett/library/DailyMobilityPlots.R diff --git a/src/features/location_barnett/DailyRoutineIndex.R b/src/features/phone_locations/barnett/library/DailyRoutineIndex.R similarity index 100% rename from src/features/location_barnett/DailyRoutineIndex.R rename to src/features/phone_locations/barnett/library/DailyRoutineIndex.R diff --git a/src/features/location_barnett/DayDist.R b/src/features/phone_locations/barnett/library/DayDist.R similarity index 100% rename from src/features/location_barnett/DayDist.R rename to src/features/phone_locations/barnett/library/DayDist.R diff --git a/src/features/location_barnett/DistanceTravelled.R b/src/features/phone_locations/barnett/library/DistanceTravelled.R similarity index 100% rename from src/features/location_barnett/DistanceTravelled.R rename to src/features/phone_locations/barnett/library/DistanceTravelled.R diff --git a/src/features/location_barnett/ExtractFlights.R b/src/features/phone_locations/barnett/library/ExtractFlights.R similarity index 100% rename from src/features/location_barnett/ExtractFlights.R rename to src/features/phone_locations/barnett/library/ExtractFlights.R diff --git a/src/features/location_barnett/ExtractTimePeriod.R b/src/features/phone_locations/barnett/library/ExtractTimePeriod.R similarity index 100% rename from src/features/location_barnett/ExtractTimePeriod.R rename to src/features/phone_locations/barnett/library/ExtractTimePeriod.R diff --git a/src/features/location_barnett/GPS2MobMat.R b/src/features/phone_locations/barnett/library/GPS2MobMat.R similarity index 100% rename from src/features/location_barnett/GPS2MobMat.R rename to src/features/phone_locations/barnett/library/GPS2MobMat.R diff --git a/src/features/location_barnett/GPSmobility-internal.R b/src/features/phone_locations/barnett/library/GPSmobility-internal.R similarity index 100% rename from src/features/location_barnett/GPSmobility-internal.R rename to src/features/phone_locations/barnett/library/GPSmobility-internal.R diff --git a/src/features/location_barnett/GetMobilityFeaturesMat.R b/src/features/phone_locations/barnett/library/GetMobilityFeaturesMat.R similarity index 100% rename from src/features/location_barnett/GetMobilityFeaturesMat.R rename to src/features/phone_locations/barnett/library/GetMobilityFeaturesMat.R diff --git a/src/features/location_barnett/GuessPause.R b/src/features/phone_locations/barnett/library/GuessPause.R similarity index 100% rename from src/features/location_barnett/GuessPause.R rename to src/features/phone_locations/barnett/library/GuessPause.R diff --git a/src/features/location_barnett/Hometime.R b/src/features/phone_locations/barnett/library/Hometime.R similarity index 100% rename from src/features/location_barnett/Hometime.R rename to src/features/phone_locations/barnett/library/Hometime.R diff --git a/src/features/location_barnett/InitializeParams.R b/src/features/phone_locations/barnett/library/InitializeParams.R similarity index 100% rename from src/features/location_barnett/InitializeParams.R rename to src/features/phone_locations/barnett/library/InitializeParams.R diff --git a/src/features/location_barnett/IsFlight.R b/src/features/phone_locations/barnett/library/IsFlight.R similarity index 100% rename from src/features/location_barnett/IsFlight.R rename to src/features/phone_locations/barnett/library/IsFlight.R diff --git a/src/features/location_barnett/LatLong2XY.R b/src/features/phone_locations/barnett/library/LatLong2XY.R similarity index 100% rename from src/features/location_barnett/LatLong2XY.R rename to src/features/phone_locations/barnett/library/LatLong2XY.R diff --git a/src/features/location_barnett/LocationAt.R b/src/features/phone_locations/barnett/library/LocationAt.R similarity index 100% rename from src/features/location_barnett/LocationAt.R rename to src/features/phone_locations/barnett/library/LocationAt.R diff --git a/src/features/location_barnett/MaxDiam.R b/src/features/phone_locations/barnett/library/MaxDiam.R similarity index 100% rename from src/features/location_barnett/MaxDiam.R rename to src/features/phone_locations/barnett/library/MaxDiam.R diff --git a/src/features/location_barnett/MaxDistBetweenTrajectories.R b/src/features/phone_locations/barnett/library/MaxDistBetweenTrajectories.R similarity index 100% rename from src/features/location_barnett/MaxDistBetweenTrajectories.R rename to src/features/phone_locations/barnett/library/MaxDistBetweenTrajectories.R diff --git a/src/features/location_barnett/MaxHomeDist.R b/src/features/phone_locations/barnett/library/MaxHomeDist.R similarity index 100% rename from src/features/location_barnett/MaxHomeDist.R rename to src/features/phone_locations/barnett/library/MaxHomeDist.R diff --git a/src/features/location_barnett/MaxRadius.R b/src/features/phone_locations/barnett/library/MaxRadius.R similarity index 100% rename from src/features/location_barnett/MaxRadius.R rename to src/features/phone_locations/barnett/library/MaxRadius.R diff --git a/src/features/location_barnett/MinsMissing.R b/src/features/phone_locations/barnett/library/MinsMissing.R similarity index 100% rename from src/features/location_barnett/MinsMissing.R rename to src/features/phone_locations/barnett/library/MinsMissing.R diff --git a/src/features/location_barnett/MobilityFeatures.R b/src/features/phone_locations/barnett/library/MobilityFeatures.R similarity index 100% rename from src/features/location_barnett/MobilityFeatures.R rename to src/features/phone_locations/barnett/library/MobilityFeatures.R diff --git a/src/features/location_barnett/MobmatQualityOK.R b/src/features/phone_locations/barnett/library/MobmatQualityOK.R similarity index 100% rename from src/features/location_barnett/MobmatQualityOK.R rename to src/features/phone_locations/barnett/library/MobmatQualityOK.R diff --git a/src/features/location_barnett/ProbPause.R b/src/features/phone_locations/barnett/library/ProbPause.R similarity index 100% rename from src/features/location_barnett/ProbPause.R rename to src/features/phone_locations/barnett/library/ProbPause.R diff --git a/src/features/location_barnett/ProgressBar.R b/src/features/phone_locations/barnett/library/ProgressBar.R similarity index 100% rename from src/features/location_barnett/ProgressBar.R rename to src/features/phone_locations/barnett/library/ProgressBar.R diff --git a/src/features/location_barnett/RadiusOfGyration.R b/src/features/phone_locations/barnett/library/RadiusOfGyration.R similarity index 100% rename from src/features/location_barnett/RadiusOfGyration.R rename to src/features/phone_locations/barnett/library/RadiusOfGyration.R diff --git a/src/features/location_barnett/RandomBridge.R b/src/features/phone_locations/barnett/library/RandomBridge.R similarity index 100% rename from src/features/location_barnett/RandomBridge.R rename to src/features/phone_locations/barnett/library/RandomBridge.R diff --git a/src/features/location_barnett/SigLocEntropy.R b/src/features/phone_locations/barnett/library/SigLocEntropy.R similarity index 100% rename from src/features/location_barnett/SigLocEntropy.R rename to src/features/phone_locations/barnett/library/SigLocEntropy.R diff --git a/src/features/location_barnett/SigLocs.R b/src/features/phone_locations/barnett/library/SigLocs.R similarity index 100% rename from src/features/location_barnett/SigLocs.R rename to src/features/phone_locations/barnett/library/SigLocs.R diff --git a/src/features/location_barnett/SigLocsVisited.R b/src/features/phone_locations/barnett/library/SigLocsVisited.R similarity index 100% rename from src/features/location_barnett/SigLocsVisited.R rename to src/features/phone_locations/barnett/library/SigLocsVisited.R diff --git a/src/features/location_barnett/SimulateMobilityGaps.R b/src/features/phone_locations/barnett/library/SimulateMobilityGaps.R similarity index 100% rename from src/features/location_barnett/SimulateMobilityGaps.R rename to src/features/phone_locations/barnett/library/SimulateMobilityGaps.R diff --git a/src/features/location_barnett/StdFlightDur.R b/src/features/phone_locations/barnett/library/StdFlightDur.R similarity index 100% rename from src/features/location_barnett/StdFlightDur.R rename to src/features/phone_locations/barnett/library/StdFlightDur.R diff --git a/src/features/location_barnett/StdFlightLen.R b/src/features/phone_locations/barnett/library/StdFlightLen.R similarity index 100% rename from src/features/location_barnett/StdFlightLen.R rename to src/features/phone_locations/barnett/library/StdFlightLen.R diff --git a/src/features/location_barnett/WriteSurveyAnswers2File.R b/src/features/phone_locations/barnett/library/WriteSurveyAnswers2File.R similarity index 100% rename from src/features/location_barnett/WriteSurveyAnswers2File.R rename to src/features/phone_locations/barnett/library/WriteSurveyAnswers2File.R diff --git a/src/features/location_barnett/plot.flights.R b/src/features/phone_locations/barnett/library/plot.flights.R similarity index 100% rename from src/features/location_barnett/plot.flights.R rename to src/features/phone_locations/barnett/library/plot.flights.R diff --git a/src/features/location_barnett/plotlimits.R b/src/features/phone_locations/barnett/library/plotlimits.R similarity index 100% rename from src/features/location_barnett/plotlimits.R rename to src/features/phone_locations/barnett/library/plotlimits.R diff --git a/src/features/phone_locations/barnett/main.R b/src/features/phone_locations/barnett/main.R new file mode 100644 index 00000000..b305be20 --- /dev/null +++ b/src/features/phone_locations/barnett/main.R @@ -0,0 +1,107 @@ +source("renv/activate.R") +library("dplyr", warn.conflicts = F) +library("stringr") + +# Load Ian Barnett's code. Taken from https://scholar.harvard.edu/ibarnett/software/gpsmobility +file.sources = list.files(c("src/features/phone_locations/barnett/library"), pattern="*.R$", full.names=TRUE, ignore.case=TRUE) +sapply(file.sources,source,.GlobalEnv) + +create_empty_file <- function(requested_features){ + return(data.frame(local_segment= character(), + hometime= numeric(), + disttravelled= numeric(), + rog= numeric(), + maxdiam= numeric(), + maxhomedist= numeric(), + siglocsvisited= numeric(), + avgflightlen= numeric(), + stdflightlen= numeric(), + avgflightdur= numeric(), + stdflightdur= numeric(), + probpause= numeric(), + siglocentropy= numeric(), + minsmissing= numeric(), + circdnrtn= numeric(), + wkenddayrtn= numeric(), + minutes_data_used= numeric() + ) %>% select(all_of(requested_features))) +} + +barnett_features <- function(sensor_data_files, time_segment, params){ + + location_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE) + location_features <- NULL + + location <- location_data + accuracy_limit <- params[["ACCURACY_LIMIT"]] + timezone <- params[["TIMEZONE"]] + minutes_data_used <- params[["MINUTES_DATA_USED"]] + + # Compute what features were requested + available_features <- c("hometime","disttravelled","rog","maxdiam", "maxhomedist","siglocsvisited","avgflightlen", "stdflightlen", + "avgflightdur","stdflightdur", "probpause","siglocentropy","minsmissing", "circdnrtn","wkenddayrtn") + requested_features <- intersect(unlist(params["FEATURES"], use.names = F), available_features) + requested_features <- c("local_segment", requested_features) + if(minutes_data_used) + requested_features <- c(requested_features, "minutes_data_used") + + # Excludes datasets with less than 24 hours of data + if(max(location$timestamp) - min(location$timestamp) < 86400000) + location <- head(location, 0) + + if (nrow(location) > 1){ + # Filter by segment and skipping any non-daily segment + location <- location %>% filter_data_by_segment(time_segment) + + datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00" + datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59" + location <- location %>% mutate(is_daily = str_detect(local_segment, paste0(time_segment, "#", datetime_start_regex, ",", datetime_end_regex))) + + if(!all(location$is_daily)){ + message(paste("Barnett's location features cannot be computed for time segmentes that are not daily (cover 00:00:00 to 23:59:59 of every day). Skipping ", time_segment)) + location_features <- create_empty_file(requested_features) + } else { + # Count how many minutes of data we use to get location features + # Some minutes have multiple fused rows + location_minutes_used <- location %>% + group_by(local_date, local_hour) %>% + summarise(n_minutes = n_distinct(local_minute)) %>% + group_by(local_date) %>% + summarise(minutes_data_used = sum(n_minutes)) %>% + select(local_date, minutes_data_used) + + # Save time segment to attach it later + location_dates_segments <- location %>% select(local_date, local_segment) %>% distinct(local_date, .keep_all = TRUE) + + # Select only the columns that the algorithm needs + location <- location %>% select(timestamp, latitude = double_latitude, longitude = double_longitude, altitude = double_altitude, accuracy) + if(nrow(location %>% filter(accuracy < accuracy_limit)) > 1){ + outputMobility <- MobilityFeatures(location, ACCURACY_LIM = accuracy_limit, tz = timezone) + } else { + print(paste("Cannot compute location features because there are no rows with an accuracy value lower than ACCURACY_LIMIT", accuracy_limit)) + outputMobility <- NULL + } + + if(is.null(outputMobility)){ + location_features <- create_empty_file(requested_features) + } else{ + # Copy index (dates) as a column + features <- cbind(rownames(outputMobility$featavg), outputMobility$featavg) + features <- as.data.frame(features) + features[-1] <- lapply(lapply(features[-1], as.character), as.numeric) + colnames(features)=c("local_date",tolower(colnames(outputMobility$featavg))) + # Add the minute count column + features <- left_join(features, location_minutes_used, by = "local_date") + # Add the time segment column for consistency + features <- left_join(features, location_dates_segments, by = "local_date") + location_features <- features %>% select(all_of(requested_features)) + } + } + } else { + location_features <- create_empty_file(requested_features) + } + + if(ncol(location_features) != length(requested_features)) + stop(paste0("The number of features in the output dataframe (=", ncol(location_features),") does not match the expected value (=", length(requested_features),"). Verify your barnett location features")) + return(location_features) +} \ No newline at end of file diff --git a/src/features/location_doryab/location_base.py b/src/features/phone_locations/doryab/main.py similarity index 69% rename from src/features/location_doryab/location_base.py rename to src/features/phone_locations/doryab/main.py index af5866c6..a29efc37 100644 --- a/src/features/location_doryab/location_base.py +++ b/src/features/phone_locations/doryab/main.py @@ -4,21 +4,34 @@ from astropy.timeseries import LombScargle from sklearn.cluster import DBSCAN from math import radians, cos, sin, asin, sqrt -def base_location_features(location_data, day_segment, requested_features, dbscan_eps, dbscan_minsamples, threshold_static, maximum_gap_allowed,sampling_frequency): +def doryab_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + + location_data = pd.read_csv(sensor_data_files["sensor_data"]) + requested_features = provider["FEATURES"] + dbscan_eps = provider["DBSCAN_EPS"] + dbscan_minsamples = provider["DBSCAN_MINSAMPLES"] + threshold_static = provider["THRESHOLD_STATIC"] + maximum_gap_allowed = provider["MAXIMUM_GAP_ALLOWED"] + sampling_frequency = provider["SAMPLING_FREQUENCY"] + + minutes_data_used = provider["MINUTES_DATA_USED"] + if(minutes_data_used): + requested_features.append("minutesdataused") + # name of the features this function can compute base_features_names = ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy","minutesdataused"] # the subset of requested features this function can compute features_to_compute = list(set(requested_features) & set(base_features_names)) - dataEmptyFlag = 0 + + if location_data.empty: - location_features = pd.DataFrame(columns=["local_date"] + ["location_" + day_segment + "_" + x for x in features_to_compute]) + location_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) else: - if day_segment != "daily": - location_data = location_data[location_data["local_day_segment"] == day_segment] + location_data = filter_data_by_segment(location_data, time_segment) if location_data.empty: - location_features = pd.DataFrame(columns=["local_date"] + ["location_" + day_segment + "_" + x for x in features_to_compute]) + location_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) else: location_features = pd.DataFrame() @@ -26,114 +39,114 @@ def base_location_features(location_data, day_segment, requested_features, dbsca sampling_frequency = getSamplingFrequency(location_data) if "minutesdataused" in features_to_compute: - for localDate in location_data["local_date"].unique(): - location_features.loc[localDate,"location_" + day_segment + "_minutesdataused"] = getMinutesData(location_data[location_data["local_date"]==localDate]) + for localDate in location_data["local_segment"].unique(): + location_features.loc[localDate,"minutesdataused"] = getMinutesData(location_data[location_data["local_segment"]==localDate]) - location_features.index.name = 'local_date' + location_features.index.name = 'local_segment' location_data = location_data[(location_data['double_latitude']!=0.0) & (location_data['double_longitude']!=0.0)] if location_data.empty: - location_features = pd.DataFrame(columns=["local_date"] + ["location_" + day_segment + "_" + x for x in features_to_compute]) + location_features = pd.DataFrame(columns=["local_date"] + ["location_" + time_segment + "_" + x for x in features_to_compute]) location_features = location_features.reset_index(drop=True) return location_features if "locationvariance" in features_to_compute: - location_features["location_" + day_segment + "_locationvariance"] = location_data.groupby(['local_date'])['double_latitude'].var() + location_data.groupby(['local_date'])['double_longitude'].var() + location_features["locationvariance"] = location_data.groupby(['local_segment'])['double_latitude'].var() + location_data.groupby(['local_segment'])['double_longitude'].var() if "loglocationvariance" in features_to_compute: - location_features["location_" + day_segment + "_loglocationvariance"] = (location_data.groupby(['local_date'])['double_latitude'].var() + location_data.groupby(['local_date'])['double_longitude'].var()).apply(lambda x: np.log10(x) if x > 0 else None) + location_features["loglocationvariance"] = (location_data.groupby(['local_segment'])['double_latitude'].var() + location_data.groupby(['local_segment'])['double_longitude'].var()).apply(lambda x: np.log10(x) if x > 0 else None) preComputedDistanceandSpeed = pd.DataFrame() - for localDate in location_data['local_date'].unique(): - distance, speeddf = get_all_travel_distances_meters_speed(location_data[location_data['local_date']==localDate],threshold_static,maximum_gap_allowed) + for localDate in location_data['local_segment'].unique(): + distance, speeddf = get_all_travel_distances_meters_speed(location_data[location_data['local_segment']==localDate],threshold_static,maximum_gap_allowed) preComputedDistanceandSpeed.loc[localDate,"distance"] = distance.sum() preComputedDistanceandSpeed.loc[localDate,"avgspeed"] = speeddf[speeddf['speedTag'] == 'Moving']['speed'].mean() preComputedDistanceandSpeed.loc[localDate,"varspeed"] = speeddf[speeddf['speedTag'] == 'Moving']['speed'].var() if "totaldistance" in features_to_compute: - for localDate in location_data['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_totaldistance"] = preComputedDistanceandSpeed.loc[localDate,"distance"] + for localDate in location_data['local_segment'].unique(): + location_features.loc[localDate,"totaldistance"] = preComputedDistanceandSpeed.loc[localDate,"distance"] if "averagespeed" in features_to_compute: - for localDate in location_data['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_averagespeed"] = preComputedDistanceandSpeed.loc[localDate,"avgspeed"] + for localDate in location_data['local_segment'].unique(): + location_features.loc[localDate,"averagespeed"] = preComputedDistanceandSpeed.loc[localDate,"avgspeed"] if "varspeed" in features_to_compute: - for localDate in location_data['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_varspeed"] = preComputedDistanceandSpeed.loc[localDate,"varspeed"] + for localDate in location_data['local_segment'].unique(): + location_features.loc[localDate,"varspeed"] = preComputedDistanceandSpeed.loc[localDate,"varspeed"] if "circadianmovement" in features_to_compute: - for localDate in location_data['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_circadianmovement"] = circadian_movement(location_data[location_data['local_date']==localDate]) + for localDate in location_data['local_segment'].unique(): + location_features.loc[localDate,"circadianmovement"] = circadian_movement(location_data[location_data['local_segment']==localDate]) newLocationData = cluster_and_label(location_data, eps= distance_to_degrees(dbscan_eps), min_samples=dbscan_minsamples) if "numberofsignificantplaces" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_numberofsignificantplaces"] = number_of_significant_places(newLocationData[newLocationData['local_date']==localDate]) + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"numberofsignificantplaces"] = number_of_significant_places(newLocationData[newLocationData['local_segment']==localDate]) if "numberlocationtransitions" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_numberlocationtransitions"] = number_location_transitions(newLocationData[newLocationData['local_date']==localDate]) - + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"numberlocationtransitions"] = number_location_transitions(newLocationData[newLocationData['local_segment']==localDate]) + if "radiusgyration" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_radiusgyration"] = radius_of_gyration(newLocationData[newLocationData['local_date']==localDate],sampling_frequency) + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"radiusgyration"] = radius_of_gyration(newLocationData[newLocationData['local_segment']==localDate],sampling_frequency) if "timeattop1location" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_timeattop1location"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_date']==localDate],1,sampling_frequency) + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"timeattop1"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_segment']==localDate],1,sampling_frequency) if "timeattop2location" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_timeattop2location"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_date']==localDate],2,sampling_frequency) - + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"timeattop2"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_segment']==localDate],2,sampling_frequency) + if "timeattop3location" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_timeattop3location"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_date']==localDate],3,sampling_frequency) + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"timeattop3"] = time_at_topn_clusters_in_group(newLocationData[newLocationData['local_segment']==localDate],3,sampling_frequency) if "movingtostaticratio" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_movingtostaticratio"] = (newLocationData[newLocationData['local_date']==localDate].shape[0]*sampling_frequency) / (location_data[location_data['local_date']==localDate].shape[0] * sampling_frequency) - + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"movingtostaticratio"] = (newLocationData[newLocationData['local_segment']==localDate].shape[0]*sampling_frequency) / (location_data[location_data['local_segment']==localDate].shape[0] * sampling_frequency) + if "outlierstimepercent" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_outlierstimepercent"] = outliers_time_percent(newLocationData[newLocationData['local_date']==localDate],sampling_frequency) + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"outlierstimepercent"] = outliers_time_percent(newLocationData[newLocationData['local_segment']==localDate],sampling_frequency) preComputedmaxminCluster = pd.DataFrame() - for localDate in newLocationData['local_date'].unique(): - smax, smin, sstd,smean = len_stay_at_clusters_in_minutes(newLocationData[newLocationData['local_date']==localDate],sampling_frequency) - preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_maxlengthstayatclusters"] = smax - preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_minlengthstayatclusters"] = smin - preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_stdlengthstayatclusters"] = sstd - preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_meanlengthstayatclusters"] = smean + for localDate in newLocationData['local_segment'].unique(): + smax, smin, sstd,smean = len_stay_at_clusters_in_minutes(newLocationData[newLocationData['local_segment']==localDate],sampling_frequency) + preComputedmaxminCluster.loc[localDate,"maxlengthstayatclusters"] = smax + preComputedmaxminCluster.loc[localDate,"minlengthstayatclusters"] = smin + preComputedmaxminCluster.loc[localDate,"stdlengthstayatclusters"] = sstd + preComputedmaxminCluster.loc[localDate,"meanlengthstayatclusters"] = smean if "maxlengthstayatclusters" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_maxlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_maxlengthstayatclusters"] - + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"maxlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"maxlengthstayatclusters"] + if "minlengthstayatclusters" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_minlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_minlengthstayatclusters"] + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"minlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"minlengthstayatclusters"] if "stdlengthstayatclusters" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_stdlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_stdlengthstayatclusters"] + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"stdlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"stdlengthstayatclusters"] if "meanlengthstayatclusters" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_meanlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"location_" + day_segment + "_meanlengthstayatclusters"] + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"meanlengthstayatclusters"] = preComputedmaxminCluster.loc[localDate,"meanlengthstayatclusters"] if "locationentropy" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_locationentropy"] = location_entropy(newLocationData[newLocationData['local_date']==localDate]) + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"locationentropy"] = location_entropy(newLocationData[newLocationData['local_segment']==localDate]) if "normalizedlocationentropy" in features_to_compute: - for localDate in newLocationData['local_date'].unique(): - location_features.loc[localDate,"location_" + day_segment + "_normalizedlocationentropy"] = location_entropy_normalized(newLocationData[newLocationData['local_date']==localDate]) - + for localDate in newLocationData['local_segment'].unique(): + location_features.loc[localDate,"normalizedlocationentropy"] = location_entropy_normalized(newLocationData[newLocationData['local_segment']==localDate]) + location_features = location_features.reset_index() return location_features diff --git a/src/features/phone_messages/rapids/main.R b/src/features/phone_messages/rapids/main.R new file mode 100644 index 00000000..b92769fd --- /dev/null +++ b/src/features/phone_messages/rapids/main.R @@ -0,0 +1,70 @@ +library('tidyr') +library('stringr') + +message_features_of_type <- function(messages, messages_type, time_segment, requested_features){ + # Output dataframe + features = data.frame(local_segment = character(), stringsAsFactors = FALSE) + + # The name of the features this function can compute + base_features_names <- c("countmostfrequentcontact", "count", "distinctcontacts", "timefirstmessage", "timelastmessage") + + # The subset of requested features this function can compute + features_to_compute <- intersect(base_features_names, requested_features) + + # If there are not features or data to work with, return an empty df with appropiate columns names + if(length(features_to_compute) == 0) + return(features) + if(nrow(messages) < 1) + return(cbind(features, read.csv(text = paste(paste(messages_type, features_to_compute, sep = "_"), collapse = ","), stringsAsFactors = FALSE))) + + for(feature_name in features_to_compute){ + if(feature_name == "countmostfrequentcontact"){ + # Get the number of messages for the most frequent contact throughout the study + mostfrequentcontact <- messages %>% + group_by(trace) %>% + mutate(N=n()) %>% + ungroup() %>% + filter(N == max(N)) %>% + head(1) %>% # if there are multiple contacts with the same amount of messages pick the first one only + pull(trace) + feature <- messages %>% + group_by(local_segment) %>% + summarise(!!paste(messages_type, feature_name, sep = "_") := sum(trace == mostfrequentcontact)) + features <- merge(features, feature, by="local_segment", all = TRUE) + } else { + feature <- messages %>% + group_by(local_segment) + + feature <- switch(feature_name, + "count" = feature %>% summarise(!!paste(messages_type, feature_name, sep = "_") := n()), + "distinctcontacts" = feature %>% summarise(!!paste(messages_type, feature_name, sep = "_") := n_distinct(trace)), + "timefirstmessage" = feature %>% summarise(!!paste(messages_type, feature_name, sep = "_") := first(local_hour) * 60 + first(local_minute)), + "timelastmessage" = feature %>% summarise(!!paste(messages_type, feature_name, sep = "_") := last(local_hour) * 60 + last(local_minute))) + + features <- merge(features, feature, by="local_segment", all = TRUE) + } + } + return(features) +} + +rapids_features <- function(sensor_data_files, time_segment, provider){ + messages_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE) + messages_data <- messages_data %>% filter_data_by_segment(time_segment) + messages_types = provider[["MESSAGES_TYPES"]] + messages_features <- setNames(data.frame(matrix(ncol=1, nrow=0)), c("local_segment")) + + for(message_type in messages_types){ + # Filter rows that belong to the message type and time segment of interest + message_type_label = ifelse(message_type == "received", "1", ifelse(message_type == "sent", "2", NA)) + if(is.na(message_type_label)) + stop(paste("Message type can online be received or sent but instead you typed: ", message_type, " in config[PHONE_MESSAGES][MESSAGES_TYPES]")) + + requested_features <- provider[["FEATURES"]][[message_type]] + messages_of_type <- messages_data %>% filter(message_type == message_type_label) + + features <- message_features_of_type(messages_of_type, message_type, time_segment, requested_features) + messages_features <- merge(messages_features, features, all=TRUE) + } + messages_features <- messages_features %>% mutate_at(vars(contains("countmostfrequentcontact") | contains("distinctcontacts") | contains("count")), list( ~ replace_na(., 0))) + return(messages_features) +} \ No newline at end of file diff --git a/src/features/screen_deltas.R b/src/features/phone_screen/episodes/screen_episodes.R similarity index 77% rename from src/features/screen_deltas.R rename to src/features/phone_screen/episodes/screen_episodes.R index 85e33da7..3f254b14 100644 --- a/src/features/screen_deltas.R +++ b/src/features/phone_screen/episodes/screen_episodes.R @@ -1,6 +1,6 @@ source("renv/activate.R") -library(dplyr) +library("dplyr", warn.conflicts = F) library(tidyr) library(stringr) @@ -44,13 +44,8 @@ get_screen_episodes <- function(screen){ filter( (screen_status == 3 & lead(screen_status) == 0) | (screen_status == 0 & lag(screen_status) == 3) ) %>% summarise(episode = "unlock", screen_sequence = toString(screen_status), - time_diff = (last(timestamp) - first(timestamp)) / (1000 * 60), - local_start_date_time = first(local_date_time), - local_end_date_time = last(local_date_time), - local_start_date = first(local_date), - local_end_date = last(local_date), - local_start_day_segment = first(local_day_segment), - local_end_day_segment = last(local_day_segment)) %>% + start_timestamp = first(timestamp), + end_timestamp = last(timestamp)) %>% filter(str_detect(screen_sequence, paste0("^(", paste(c(3), collapse = "|"), # Filter sequences that start with 3 (UNLOCK) AND @@ -61,13 +56,9 @@ get_screen_episodes <- function(screen){ if(nrow(screen) < 2){ episodes <- data.frame(episode = character(), - time_diff = numeric(), - local_start_date_time = character(), - local_end_date_time = character(), - local_start_date = character(), - local_end_date = character(), - local_start_day_segment = character(), - local_end_day_segment = character()) + screen_sequence = character(), + start_timestamp = character(), + end_timestamp = character()) } else { episodes <- get_screen_episodes(screen) } diff --git a/src/features/phone_screen/rapids/main.py b/src/features/phone_screen/rapids/main.py new file mode 100644 index 00000000..9a99342c --- /dev/null +++ b/src/features/phone_screen/rapids/main.py @@ -0,0 +1,68 @@ +import pandas as pd +import itertools + +def getEpisodeDurationFeatures(screen_data, time_segment, episode, features, reference_hour_first_use): + screen_data_episode = screen_data[screen_data["episode"] == episode] + duration_helper = pd.DataFrame() + if "countepisode" in features: + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].count().rename(columns = {"duration": "countepisode" + episode})], axis = 1) + if "sumduration" in features: + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].sum().rename(columns = {"duration": "sumduration" + episode})], axis = 1) + if "maxduration" in features: + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].max().rename(columns = {"duration": "maxduration" + episode})], axis = 1) + if "minduration" in features: + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].min().rename(columns = {"duration": "minduration" + episode})], axis = 1) + if "avgduration" in features: + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].mean().rename(columns = {"duration":"avgduration" + episode})], axis = 1) + if "stdduration" in features: + duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].std().rename(columns = {"duration":"stdduration" + episode})], axis = 1) + if "firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) in features: + screen_data_episode_after_hour = screen_data_episode.copy() + screen_data_episode_after_hour["hour"] = pd.to_datetime(screen_data_episode["local_start_date_time"]).dt.hour + screen_data_episode_after_hour = screen_data_episode_after_hour[screen_data_episode_after_hour["hour"] >= reference_hour_first_use] + + duration_helper = pd.concat([duration_helper, pd.DataFrame(screen_data_episode_after_hour.groupby(["local_segment"])[["local_start_date_time"]].min().local_start_date_time.apply(lambda x: (x.to_pydatetime().hour - reference_hour_first_use) * 60 + x.to_pydatetime().minute + (x.to_pydatetime().second / 60))).rename(columns = {"local_start_date_time":"firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) + episode})], axis = 1) + return duration_helper + + +def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs): + + screen_data = pd.read_csv(sensor_data_files["sensor_episodes"]) + + reference_hour_first_use = provider["REFERENCE_HOUR_FIRST_USE"] + requested_features_episodes = provider["FEATURES"] + requested_episode_types = provider["EPISODE_TYPES"] + ignore_episodes_shorter_than = provider["IGNORE_EPISODES_SHORTER_THAN"] + ignore_episodes_longer_than = provider["IGNORE_EPISODES_LONGER_THAN"] + + # name of the features this function can compute + base_features_episodes = ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] + base_episode_type = ["unlock"] + # the subset of requested features this function can compute + features_episodes_to_compute = list(set(requested_features_episodes) & set(base_features_episodes)) + episode_type_to_compute = list(set(requested_episode_types) & set(base_episode_type)) + + features_episodes_to_compute = ["firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) if feature_name == "firstuseafter" else feature_name for feature_name in features_episodes_to_compute] + features_to_compute = ["".join(feature) for feature in itertools.product(features_episodes_to_compute, episode_type_to_compute)] + + screen_features = pd.DataFrame(columns=["local_segment"] + features_to_compute) + if not screen_data.empty: + + screen_data = filter_data_by_segment(screen_data, time_segment) + + if not screen_data.empty: + if ignore_episodes_shorter_than > 0: + screen_data = screen_data.query('@ignore_episodes_shorter_than <= duration') + if ignore_episodes_longer_than > 0: + screen_data = screen_data.query('duration <= @ignore_episodes_longer_than') + + if not screen_data.empty: + screen_features = pd.DataFrame() + for episode in episode_type_to_compute: + screen_features = pd.concat([screen_features, getEpisodeDurationFeatures(screen_data, time_segment, episode, features_episodes_to_compute, reference_hour_first_use)], axis=1) + + if not screen_features.empty: + screen_features = screen_features.reset_index() + + return screen_features + diff --git a/src/features/phone_wifi_connected/rapids/main.R b/src/features/phone_wifi_connected/rapids/main.R new file mode 100644 index 00000000..7ab44c6e --- /dev/null +++ b/src/features/phone_wifi_connected/rapids/main.R @@ -0,0 +1,46 @@ +library("dplyr", warn.conflicts = F) + +compute_wifi_feature <- function(data, feature, time_segment){ + data <- data %>% filter_data_by_segment(time_segment) + if(feature %in% c("countscans", "uniquedevices")){ + data <- data %>% group_by(local_segment) + data <- switch(feature, + "countscans" = data %>% summarise(!!feature := n()), + "uniquedevices" = data %>% summarise(!!feature := n_distinct(bssid))) + return(data) + } else if(feature == "countscansmostuniquedevice"){ + # Get the most scanned device + mostuniquedevice <- data %>% + group_by(bssid) %>% + mutate(N=n()) %>% + ungroup() %>% + filter(N == max(N)) %>% + head(1) %>% # if there are multiple device with the same amount of scans pick the first one only + pull(bssid) + return(data %>% + filter(bssid == mostuniquedevice) %>% + group_by(local_segment) %>% + summarise(!!feature := n()) %>% + replace(is.na(.), 0)) + } +} + +rapids_features <- function(sensor_data_files, time_segment, provider){ + wifi_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE) + requested_features <- provider[["FEATURES"]] + # Output dataframe + features = data.frame(local_segment = character(), stringsAsFactors = FALSE) + + # The name of the features this function can compute + base_features_names <- c("countscans", "uniquedevices", "countscansmostuniquedevice") + + # The subset of requested features this function can compute + features_to_compute <- intersect(base_features_names, requested_features) + + for(feature_name in features_to_compute){ + feature <- compute_wifi_feature(wifi_data, feature_name, time_segment) + features <- merge(features, feature, by="local_segment", all = TRUE) + } + + return(features) +} diff --git a/src/features/phone_wifi_visible/rapids/main.R b/src/features/phone_wifi_visible/rapids/main.R new file mode 100644 index 00000000..7ab44c6e --- /dev/null +++ b/src/features/phone_wifi_visible/rapids/main.R @@ -0,0 +1,46 @@ +library("dplyr", warn.conflicts = F) + +compute_wifi_feature <- function(data, feature, time_segment){ + data <- data %>% filter_data_by_segment(time_segment) + if(feature %in% c("countscans", "uniquedevices")){ + data <- data %>% group_by(local_segment) + data <- switch(feature, + "countscans" = data %>% summarise(!!feature := n()), + "uniquedevices" = data %>% summarise(!!feature := n_distinct(bssid))) + return(data) + } else if(feature == "countscansmostuniquedevice"){ + # Get the most scanned device + mostuniquedevice <- data %>% + group_by(bssid) %>% + mutate(N=n()) %>% + ungroup() %>% + filter(N == max(N)) %>% + head(1) %>% # if there are multiple device with the same amount of scans pick the first one only + pull(bssid) + return(data %>% + filter(bssid == mostuniquedevice) %>% + group_by(local_segment) %>% + summarise(!!feature := n()) %>% + replace(is.na(.), 0)) + } +} + +rapids_features <- function(sensor_data_files, time_segment, provider){ + wifi_data <- read.csv(sensor_data_files[["sensor_data"]], stringsAsFactors = FALSE) + requested_features <- provider[["FEATURES"]] + # Output dataframe + features = data.frame(local_segment = character(), stringsAsFactors = FALSE) + + # The name of the features this function can compute + base_features_names <- c("countscans", "uniquedevices", "countscansmostuniquedevice") + + # The subset of requested features this function can compute + features_to_compute <- intersect(base_features_names, requested_features) + + for(feature_name in features_to_compute){ + feature <- compute_wifi_feature(wifi_data, feature_name, time_segment) + features <- merge(features, feature, by="local_segment", all = TRUE) + } + + return(features) +} diff --git a/src/features/screen/screen_base.py b/src/features/screen/screen_base.py deleted file mode 100644 index c7df2169..00000000 --- a/src/features/screen/screen_base.py +++ /dev/null @@ -1,83 +0,0 @@ -import pandas as pd -import itertools -from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes - -EPOCH2HOUR = {"night": ("0_", "1_", "2_", "3_", "4_", "5_"), - "morning": ("6_", "7_", "8_", "9_", "10_", "11_"), - "afternoon": ("12_", "13_", "14_", "15_", "16_", "17_"), - "evening": ("18_", "19_", "20_", "21_", "22_", "23_")} - -def getEpisodeDurationFeatures(screen_data, day_segment, episode, features, phone_sensed_bins, bin_size, reference_hour_first_use): - screen_data_episode = screen_data[screen_data["episode"] == episode] - duration_helper = pd.DataFrame() - if "countepisode" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode[["time_diff"]].groupby(["local_start_date"]).count().rename(columns = {"time_diff": "screen_" + day_segment + "_countepisode" + episode})], axis = 1) - if "episodepersensedminutes" in features: - for date, row in screen_data_episode[["time_diff"]].groupby(["local_start_date"]).count().iterrows(): - - try: - if day_segment == "daily": - sensed_minutes = phone_sensed_bins.loc[date, :].sum() * bin_size - else: - sensed_minutes = phone_sensed_bins.loc[date, phone_sensed_bins.columns.str.startswith(EPOCH2HOUR[day_segment])].sum() * bin_size - except: - raise ValueError("You need to include the screen sensor in the list for phone_sensed_bins.") - - episode_per_sensedminutes = row["time_diff"] / (1 if sensed_minutes == 0 else sensed_minutes) - duration_helper.loc[date, "screen_" + day_segment + "_episodepersensedminutes" + episode] = episode_per_sensedminutes - if "sumduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode[["time_diff"]].groupby(["local_start_date"]).sum().rename(columns = {"time_diff": "screen_" + day_segment + "_sumduration" + episode})], axis = 1) - if "maxduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode[["time_diff"]].groupby(["local_start_date"]).max().rename(columns = {"time_diff": "screen_" + day_segment + "_maxduration" + episode})], axis = 1) - if "minduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode[["time_diff"]].groupby(["local_start_date"]).min().rename(columns = {"time_diff": "screen_" + day_segment + "_minduration" + episode})], axis = 1) - if "avgduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode[["time_diff"]].groupby(["local_start_date"]).mean().rename(columns = {"time_diff":"screen_" + day_segment + "_avgduration" + episode})], axis = 1) - if "stdduration" in features: - duration_helper = pd.concat([duration_helper, screen_data_episode[["time_diff"]].groupby(["local_start_date"]).std().rename(columns = {"time_diff":"screen_" + day_segment + "_stdduration" + episode})], axis = 1) - if "firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) in features: - duration_helper = pd.concat([duration_helper, pd.DataFrame(screen_data_episode.groupby(["local_start_date"]).first()[["local_start_date_time"]].local_start_date_time.apply(lambda x: (x.to_pydatetime().hour - reference_hour_first_use) * 60 + x.to_pydatetime().minute + (x.to_pydatetime().second / 60))).rename(columns = {"local_start_date_time":"screen_" + day_segment + "_firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) + episode})], axis = 1) - return duration_helper - - -def base_screen_features(screen_data, phone_sensed_bins, day_segment, params): - - reference_hour_first_use = params["reference_hour_first_use"] - bin_size = params["bin_size"] - requested_features_deltas = params["features_deltas"] - requested_episode_types = params["episode_types"] - ignore_episodes_shorter_than = params["ignore_episodes_shorter_than"] - ignore_episodes_longer_than = params["ignore_episodes_longer_than"] - - # name of the features this function can compute - base_features_deltas = ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] - base_episode_type = ["unlock"] - # the subset of requested features this function can compute - features_deltas_to_compute = list(set(requested_features_deltas) & set(base_features_deltas)) - episode_type_to_compute = list(set(requested_episode_types) & set(base_episode_type)) - - features_deltas_to_compute = ["firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) if feature_name == "firstuseafter" else feature_name for feature_name in features_deltas_to_compute] - features_to_compute = ["".join(feature) for feature in itertools.product(features_deltas_to_compute, episode_type_to_compute)] - - screen_features = pd.DataFrame(columns=["local_date"]+["screen_" + day_segment + "_" + x for x in features_to_compute]) - if not screen_data.empty: - # preprocess day_segment and episodes - screen_data = splitOvernightEpisodes(screen_data, [], ["episode"]) - if (not screen_data.empty) and (day_segment != "daily"): - screen_data = splitMultiSegmentEpisodes(screen_data, day_segment, []) - screen_data.set_index(["local_start_date"],inplace=True) - - if ignore_episodes_shorter_than > 0: - screen_data = screen_data.query('@ignore_episodes_shorter_than <= time_diff') - if ignore_episodes_longer_than > 0: - screen_data = screen_data.query('time_diff <= @ignore_episodes_longer_than') - - if not screen_data.empty: - screen_features = pd.DataFrame() - for episode in episode_type_to_compute: - screen_features = pd.concat([screen_features, getEpisodeDurationFeatures(screen_data, day_segment, episode, features_deltas_to_compute, phone_sensed_bins, bin_size, reference_hour_first_use)], axis=1) - - if not screen_features.empty: - screen_features = screen_features.rename_axis("local_date").reset_index() - - return screen_features diff --git a/src/features/screen_features.py b/src/features/screen_features.py deleted file mode 100644 index ef417a1e..00000000 --- a/src/features/screen_features.py +++ /dev/null @@ -1,18 +0,0 @@ -import pandas as pd -import itertools -from screen.screen_base import base_screen_features - -screen_data = pd.read_csv(snakemake.input["screen_deltas"], parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"]) -phone_sensed_bins = pd.read_csv(snakemake.input["phone_sensed_bins"], parse_dates=["local_date"], index_col="local_date") -phone_sensed_bins[phone_sensed_bins > 0] = 1 -day_segment = snakemake.params["day_segment"] -screen_features = pd.DataFrame(columns=["local_date"]) - -requested_features_deltas = ["firstuseafter" + "{0:0=2d}".format(snakemake.params["reference_hour_first_use"]) if feature_name == "firstuseafter" else feature_name for feature_name in snakemake.params["features_deltas"]] -requested_features = ["".join(feature) for feature in itertools.product(requested_features_deltas, snakemake.params["episode_types"])] - -screen_features = screen_features.merge(base_screen_features(screen_data, phone_sensed_bins, day_segment, snakemake.params), on="local_date", how="outer") - -assert len(requested_features) + 1 == screen_features.shape[1], "The number of features in the output dataframe (=" + str(screen_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your screen feature extraction functions" - -screen_features.to_csv(snakemake.output[0], index=False) diff --git a/src/features/utils/join_features_from_providers.R b/src/features/utils/join_features_from_providers.R new file mode 100644 index 00000000..bbbc18b3 --- /dev/null +++ b/src/features/utils/join_features_from_providers.R @@ -0,0 +1,14 @@ +source("renv/activate.R") + +library("tidyr") +library("dplyr", warn.conflicts = F) + +location_features_files <- snakemake@input[["sensor_features"]] +location_features <- setNames(data.frame(matrix(ncol = 1, nrow = 0)), c("local_segment")) + + +for(location_features_file in location_features_files){ + location_features <- merge(location_features, read.csv(location_features_file), all = TRUE) +} + +write.csv(location_features, snakemake@output[[1]], row.names = FALSE) \ No newline at end of file diff --git a/src/models/merge_features_for_population_model.R b/src/features/utils/merge_sensor_features_for_all_participants.R similarity index 67% rename from src/models/merge_features_for_population_model.R rename to src/features/utils/merge_sensor_features_for_all_participants.R index abb36d6a..972e56d3 100644 --- a/src/models/merge_features_for_population_model.R +++ b/src/features/utils/merge_sensor_features_for_all_participants.R @@ -2,14 +2,14 @@ source("renv/activate.R") library(tidyr) library(purrr) -library(dplyr) +library("dplyr", warn.conflicts = F) library(stringr) feature_files <- snakemake@input[["feature_files"]] features_of_all_participants <- tibble(filename = feature_files) %>% # create a data frame - mutate(file_contents = map(filename, ~ read.csv(., stringsAsFactors = F, colClasses = c(local_date = "character"))), + mutate(file_contents = map(filename, ~ read.csv(., stringsAsFactors = F, colClasses = c(local_segment = "character", local_segment_label = "character", local_segment_start_datetime="character", local_segment_end_datetime="character"))), pid = str_match(filename, ".*/([a-zA-Z]+?[0-9]+?)/.*")[,2]) %>% unnest(cols = c(file_contents)) %>% select(-filename) diff --git a/src/features/utils/merge_sensor_features_for_individual_participants.R b/src/features/utils/merge_sensor_features_for_individual_participants.R new file mode 100644 index 00000000..e1264e1e --- /dev/null +++ b/src/features/utils/merge_sensor_features_for_individual_participants.R @@ -0,0 +1,22 @@ +source("renv/activate.R") + +library(tidyr) +library(purrr) +library("dplyr", warn.conflicts = F) +library("methods") +library("mgm") +library("qgraph") +library("dplyr", warn.conflicts = F) +library("scales") +library("ggplot2") +library("purrr") +library("tidyr") +library("reshape2") + +feature_files <- snakemake@input[["feature_files"]] + +features_for_individual_model <- feature_files %>% + map(read.csv, stringsAsFactors = F, colClasses = c(local_segment = "character", local_segment_label = "character", local_segment_start_datetime="character", local_segment_end_datetime="character")) %>% + reduce(full_join, by=c("local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime")) + +write.csv(features_for_individual_model, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/utils/resample_episodes.R b/src/features/utils/resample_episodes.R new file mode 100644 index 00000000..9db05b91 --- /dev/null +++ b/src/features/utils/resample_episodes.R @@ -0,0 +1,27 @@ +source("renv/activate.R") +library("tibble") +library("dplyr", warn.conflicts = F) +library("tidyr") +library("tibble") +options(scipen=999) + +# Using mostly indeixng instead of tidyr because is faster +resampled_episodes <- read.csv(snakemake@input[[1]]) +resampled_episodes["n_resamples"] <- 1 + (resampled_episodes["end_timestamp"] - resampled_episodes["start_timestamp"]) %/% 60001 +resampled_episodes <- resampled_episodes %>% uncount(n_resamples, .id = "nrow") + +resampled_episodes["nrow"] <- (resampled_episodes["nrow"] - 1) * 60000 +resampled_episodes["start_timestamp"] <- resampled_episodes["start_timestamp"] + resampled_episodes["nrow"] +# Use +59999 because each resampled minute should not overlap with each other +resampled_episodes["end_timestamp"] <- pmin(resampled_episodes["start_timestamp"] + 59999, resampled_episodes["end_timestamp"]) +resampled_episodes <- resampled_episodes %>% select(-nrow) +resampled_episodes <- resampled_episodes %>% uncount(2, .id = "end_flag") + +resampled_episodes <- resampled_episodes %>% add_column(timestamp = NA_real_) +if(nrow(resampled_episodes) > 0){ + resampled_episodes[resampled_episodes$end_flag ==1, "timestamp"] = resampled_episodes[resampled_episodes$end_flag ==1, "start_timestamp"] + resampled_episodes[resampled_episodes$end_flag ==2, "timestamp"] = resampled_episodes[resampled_episodes$end_flag ==2, "end_timestamp"] +} +resampled_episodes <- resampled_episodes %>% select(-end_flag) + +write.csv(resampled_episodes, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/utils/utils.R b/src/features/utils/utils.R new file mode 100644 index 00000000..11d25557 --- /dev/null +++ b/src/features/utils/utils.R @@ -0,0 +1,79 @@ +library("stringr") + +rapids_log_tag <- "RAPIDS:" + +filter_data_by_segment <- function(data, time_segment){ + # Filter the rows that belong to time_segment, and put the segment full name in a new column for grouping + datetime_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} [0-9]{2}:[0-9]{2}:[0-9]{2}" + timestamp_regex = "[0-9]{13}" + data <- data %>% + filter(grepl(paste0("\\[", time_segment, "#"), assigned_segments)) %>% + mutate(local_segment = str_extract(assigned_segments, paste0("\\[", time_segment, "#", datetime_regex, ",", datetime_regex, ";", timestamp_regex, ",", timestamp_regex, "\\]"))) %>% + extract(local_segment, into = c("local_segment", "timestamps_segment"), paste0("\\[(", time_segment, "#", datetime_regex, ",", datetime_regex, ");(", timestamp_regex, ",", timestamp_regex, ")\\]")) %>% + select(-assigned_segments) + return(data) +} + +chunk_episodes <- function(sensor_episodes){ + columns_to_drop <- c("^timestamp$", "utc_date_time", "local_date_time", "local_date", "local_time", "local_hour", "local_minute", "segment_start", "segment_end" ) + + chunked_episodes <- sensor_episodes %>% + separate(col = timestamps_segment, + into = c("segment_start_timestamp", "segment_end_timestamp"), + sep = ",", convert = TRUE, remove = TRUE) %>% + group_by(local_timezone) %>% + nest() %>% + mutate(data = map(data, ~.x %>% + distinct(start_timestamp, end_timestamp, local_segment, .keep_all = TRUE) %>% + mutate(start_timestamp = pmax(start_timestamp, segment_start_timestamp), + end_timestamp = pmin(end_timestamp, segment_end_timestamp), + duration = (end_timestamp - start_timestamp) / (1000 * 60)) %>% + select(-matches(columns_to_drop)) %>% + group_by_at(vars(setdiff(colnames(.), c("start_timestamp", "end_timestamp", "duration")))) %>% + summarize(start_timestamp = first(start_timestamp), + end_timestamp = last(end_timestamp), + duration = sum(duration)) %>% + mutate(local_start_date_time = format(lubridate::as_datetime(start_timestamp / 1000, tz = local_timezone), "%Y-%m-%d %H:%M:%S"), + local_end_date_time = format(lubridate::as_datetime(end_timestamp / 1000, tz = local_timezone), "%Y-%m-%d %H:%M:%S")) %>% + ungroup()) + ) %>% + unnest(data) %>% + ungroup() %>% + select(-local_timezone) + + return(chunked_episodes) +} + +fetch_provider_features <- function(provider, provider_key, sensor_key, sensor_data_files, time_segments_file){ + sensor_features <- data.frame(local_segment = character(), stringsAsFactors = FALSE) + + time_segments_labels <- read.csv(time_segments_file, stringsAsFactors = FALSE) + + if(!"FEATURES" %in% names(provider)) + stop(paste0("Provider config[", sensor_key,"][PROVIDERS][", provider_key,"] is missing a FEATURES attribute in config.yaml")) + + if(provider[["COMPUTE"]] == TRUE){ + code_path <- paste0("src/features/", sensor_key,"/", provider[["SRC_FOLDER"]], "/main.R") + source(code_path) + features_function <- match.fun(paste0(provider[["SRC_FOLDER"]], "_features")) + time_segments <- time_segments_labels %>% pull(label) + for (time_segment in time_segments){ + print(paste(rapids_log_tag,"Processing", sensor_key, provider_key, time_segment)) + + features <- features_function(sensor_data_files, time_segment, provider) + if(!"local_segment" %in% colnames(features)) + stop(paste0("The dataframe returned by the ",sensor_key," provider '", provider_key,"' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (", code_path,")\n The 'local_segment' column is used to index a provider's features (each row corresponds to a different time segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)")) + features <- features %>% rename_at(vars(!matches("local_segment")), ~ paste(sensor_key, provider_key, ., sep = "_")) + sensor_features <- merge(sensor_features, features, all = TRUE) + } + } else { # This is redundant, if COMPUTE is FALSE this script will be never executed + for(feature in provider[["FEATURES"]]) + sensor_features[,feature] <- NA + } + + sensor_features <- sensor_features %>% extract(col = local_segment, + into = c("local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"), + "(.*)#(.*),(.*)", + remove = FALSE) + return(sensor_features) +} \ No newline at end of file diff --git a/src/features/utils/utils.py b/src/features/utils/utils.py new file mode 100644 index 00000000..6938f875 --- /dev/null +++ b/src/features/utils/utils.py @@ -0,0 +1,104 @@ +rapids_log_tag = "RAPIDS:" + +def filter_data_by_segment(data, time_segment): + datetime_regex = "[0-9]{4}[\-|\/][0-9]{2}[\-|\/][0-9]{2} [0-9]{2}:[0-9]{2}:[0-9]{2}" + timestamps_regex = "[0-9]{13}" + segment_regex = "\[({}#{},{};{},{})\]".format(time_segment, datetime_regex, datetime_regex, timestamps_regex, timestamps_regex) + data["local_segment"] = data["assigned_segments"].str.extract(segment_regex, expand=True) + data = data.drop(columns=["assigned_segments"]) + data = data.dropna(subset = ["local_segment"]) + if(data.shape[0] == 0): # there are no rows belonging to time_segment + data["timestamps_segment"] = None + else: + data[["local_segment","timestamps_segment"]] = data["local_segment"].str.split(pat =";",n=1, expand=True) + + # chunk episodes + if (not data.empty) and ("start_timestamp" in data.columns) and ("end_timestamp" in data.columns): + data = chunk_episodes(data) + + return data + +# Each minute could fall into two segments. +# Firstly, we generate two rows for each resampled minute via resample_episodes rule: +# the first row's timestamp column is the start_timestamp, while the second row's timestamp column is the end_timestamp. +# Then, we check if the segments of start_timestamp are the same as the segments of end_timestamp: +# if they are the same (only fall into one segment), we will discard the second row; +# otherwise (fall into two segments), we will keep both. +def chunk_episodes(sensor_episodes): + import copy + import pandas as pd + + # Deduplicate episodes + # Drop rows where segments of start_timestamp and end_timestamp are the same + sensor_episodes = sensor_episodes.drop_duplicates(subset=["start_timestamp", "end_timestamp", "local_segment"], keep="first") + + # Delete useless columns + for drop_col in ["utc_date_time", "local_date_time", "local_date", "local_time", "local_hour", "local_minute"]: + del sensor_episodes[drop_col] + + # Avoid SettingWithCopyWarning + sensor_episodes = sensor_episodes.copy() + + # Unix timestamp for current segment in milliseconds + sensor_episodes[["segment_start_timestamp", "segment_end_timestamp"]] = sensor_episodes["timestamps_segment"].str.split(",", expand=True).astype(int) + + # Compute chunked timestamp + sensor_episodes["chunked_start_timestamp"] = sensor_episodes[["start_timestamp", "segment_start_timestamp"]].max(axis=1) + sensor_episodes["chunked_end_timestamp"] = sensor_episodes[["end_timestamp", "segment_end_timestamp"]].min(axis=1) + + # Compute duration: intersection of current row and segment + sensor_episodes["duration"] = (sensor_episodes["chunked_end_timestamp"] - sensor_episodes["chunked_start_timestamp"]) / (1000 * 60) + + # Merge episodes + cols_for_groupby = [col for col in sensor_episodes.columns if col not in ["timestamps_segment", "timestamp", "assigned_segments", "start_datetime", "end_datetime", "start_timestamp", "end_timestamp", "duration", "segment_start_timestamp", "segment_end_timestamp", "chunked_start_timestamp", "chunked_end_timestamp"]] + + sensor_episodes_grouped = sensor_episodes.groupby(by=cols_for_groupby) + merged_sensor_episodes = sensor_episodes_grouped[["duration"]].sum() + + merged_sensor_episodes["start_timestamp"] = sensor_episodes_grouped["chunked_start_timestamp"].first() + merged_sensor_episodes["end_timestamp"] = sensor_episodes_grouped["chunked_end_timestamp"].last() + + merged_sensor_episodes.reset_index(inplace=True) + + # Compute datetime + merged_sensor_episodes["local_start_date_time"] = pd.to_datetime(merged_sensor_episodes["start_timestamp"], unit="ms", utc=True) + merged_sensor_episodes["local_start_date_time"] = pd.concat([data["local_start_date_time"].dt.tz_convert(tz) for tz, data in merged_sensor_episodes.groupby("local_timezone")]).dt.tz_localize(None).apply(lambda x: x.replace(microsecond=0)) + + merged_sensor_episodes["local_end_date_time"] = pd.to_datetime(merged_sensor_episodes["end_timestamp"], unit="ms", utc=True) + merged_sensor_episodes["local_end_date_time"] = pd.concat([data["local_end_date_time"].dt.tz_convert(tz) for tz, data in merged_sensor_episodes.groupby("local_timezone")]).dt.tz_localize(None).apply(lambda x: x.replace(microsecond=0)) + + return merged_sensor_episodes + +def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file): + import pandas as pd + from importlib import import_module, util + + sensor_features = pd.DataFrame(columns=["local_segment"]) + time_segments_labels = pd.read_csv(time_segments_file, header=0) + if "FEATURES" not in provider: + raise ValueError("Provider config[{}][PROVIDERS][{}] is missing a FEATURES attribute in config.yaml".format(sensor_key.upper(), provider_key.upper())) + + if provider["COMPUTE"] == True: + + code_path = sensor_key + "." + provider["SRC_FOLDER"] + ".main" + feature_module = import_module(code_path) + feature_function = getattr(feature_module, provider["SRC_FOLDER"] + "_features") + + for time_segment in time_segments_labels["label"]: + print("{} Processing {} {} {}".format(rapids_log_tag, sensor_key, provider_key, time_segment)) + features = feature_function(sensor_data_files, time_segment, provider, filter_data_by_segment=filter_data_by_segment, chunk_episodes=chunk_episodes) + if not "local_segment" in features.columns: + raise ValueError("The dataframe returned by the " + sensor_key + " provider '" + provider_key + "' is missing the 'local_segment' column added by the 'filter_data_by_segment()' function. Check the provider script is using such function and is not removing 'local_segment' by accident (" + code_path + ")\n The 'local_segment' column is used to index a provider's features (each row corresponds to a different time segment instance (e.g. 2020-01-01, 2020-01-02, 2020-01-03, etc.)") + features.columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features.columns] + sensor_features = sensor_features.merge(features, how="outer") + else: + for feature in provider["FEATURES"]: + sensor_features[feature] = None + segment_colums = pd.DataFrame() + split_segemnt_columns = sensor_features["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True) + new_segment_columns = split_segemnt_columns.iloc[:,1:4] if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_segment_start_datetime","local_segment_end_datetime"]) + segment_colums[["local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]] = new_segment_columns + for i in range(segment_colums.shape[1]): + sensor_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]]) + + return sensor_features diff --git a/src/features/wifi/wifi_base.R b/src/features/wifi/wifi_base.R deleted file mode 100644 index 4ee2e3fe..00000000 --- a/src/features/wifi/wifi_base.R +++ /dev/null @@ -1,50 +0,0 @@ -library(dplyr) - -filter_by_day_segment <- function(data, day_segment) { - if(day_segment %in% c("morning", "afternoon", "evening", "night")) - data <- data %>% filter(local_day_segment == day_segment) - - return(data %>% group_by(local_date)) -} - -compute_wifi_feature <- function(data, feature, day_segment){ - data <- data %>% filter_by_day_segment(day_segment) - if(feature %in% c("countscans", "uniquedevices")){ - data <- switch(feature, - "countscans" = data %>% summarise(!!paste("wifi", day_segment, feature, sep = "_") := n()), - "uniquedevices" = data %>% summarise(!!paste("wifi", day_segment, feature, sep = "_") := n_distinct(bssid))) - return(data) - } else if(feature == "countscansmostuniquedevice"){ - # Get the most scanned device - mostuniquedevice <- data %>% - group_by(bssid) %>% - mutate(N=n()) %>% - ungroup() %>% - filter(N == max(N)) %>% - head(1) %>% # if there are multiple device with the same amount of scans pick the first one only - pull(bssid) - return(data %>% - filter(bssid == mostuniquedevice) %>% - group_by(local_date) %>% - summarise(!!paste("wifi", day_segment, feature, sep = "_") := n()) %>% - replace(is.na(.), 0)) - } -} - -base_wifi_features <- function(wifi_data, day_segment, requested_features){ - # Output dataframe - features = data.frame(local_date = character(), stringsAsFactors = FALSE) - - # The name of the features this function can compute - base_features_names <- c("countscans", "uniquedevices", "countscansmostuniquedevice") - - # The subset of requested features this function can compute - features_to_compute <- intersect(base_features_names, requested_features) - - for(feature_name in features_to_compute){ - feature <- compute_wifi_feature(wifi_data, feature_name, day_segment) - features <- merge(features, feature, by="local_date", all = TRUE) - } - - return(features) -} diff --git a/src/features/wifi_features.R b/src/features/wifi_features.R deleted file mode 100644 index 54fcc99e..00000000 --- a/src/features/wifi_features.R +++ /dev/null @@ -1,29 +0,0 @@ -source("renv/activate.R") -source("src/features/wifi/wifi_base.R") -library("dplyr") - -if(!is.null(snakemake@input[["visible_access_points"]]) && is.null(snakemake@input[["connected_access_points"]])){ - wifi_data <- read.csv(snakemake@input[["visible_access_points"]], stringsAsFactors = FALSE) - wifi_data <- wifi_data %>% mutate(connected = 0) -} else if(is.null(snakemake@input[["visible_access_points"]]) && !is.null(snakemake@input[["connected_access_points"]])){ - wifi_data <- read.csv(snakemake@input[["connected_access_points"]], stringsAsFactors = FALSE) - wifi_data <- wifi_data %>% mutate(connected = 1) -} else if(!is.null(snakemake@input[["visible_access_points"]]) && !is.null(snakemake@input[["connected_access_points"]])){ - visible_access_points <- read.csv(snakemake@input[["visible_access_points"]], stringsAsFactors = FALSE) - visible_access_points <- visible_access_points %>% mutate(connected = 0) - connected_access_points <- read.csv(snakemake@input[["connected_access_points"]], stringsAsFactors = FALSE) - connected_access_points <- connected_access_points %>% mutate(connected = 1) - wifi_data <- bind_rows(visible_access_points, connected_access_points) %>% arrange(timestamp) -} - -day_segment <- snakemake@params[["day_segment"]] -requested_features <- snakemake@params[["features"]] -features = data.frame(local_date = character(), stringsAsFactors = FALSE) - -# Compute base wifi features -features <- merge(features, base_wifi_features(wifi_data, day_segment, requested_features), by="local_date", all = TRUE) - -if(ncol(features) != length(requested_features) + 1) - stop(paste0("The number of features in the output dataframe (=", ncol(features),") does not match the expected value (=", length(requested_features)," + 1). Verify your wifi feature extraction functions")) - -write.csv(features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/features/demographic_features.py b/src/features/workflow_example/demographic_features.py similarity index 86% rename from src/features/demographic_features.py rename to src/features/workflow_example/demographic_features.py index 63718350..8768e214 100644 --- a/src/features/demographic_features.py +++ b/src/features/workflow_example/demographic_features.py @@ -2,10 +2,9 @@ import pandas as pd pid = snakemake.params["pid"] requested_features = snakemake.params["features"] -demographic_features = pd.DataFrame(columns=["pid"] + requested_features) +demographic_features = pd.DataFrame(columns=requested_features) participant_info = pd.read_csv(snakemake.input["participant_info"], parse_dates=["surgery_date", "discharge_date"]) -demographic_features.loc[0, "pid"] = pid if not participant_info.empty: if "age" in requested_features: demographic_features.loc[0, "age"] = participant_info.loc[0, "age"] diff --git a/src/models/__init__.py b/src/models/__init__.py deleted file mode 100644 index e69de29b..00000000 diff --git a/src/models/clean_features_for_model.R b/src/models/clean_features_for_model.R deleted file mode 100644 index a11a86b8..00000000 --- a/src/models/clean_features_for_model.R +++ /dev/null @@ -1,61 +0,0 @@ -source("renv/activate.R") -library(tidyr) -library(dplyr) - -filter_participant_without_enough_days <- function(clean_features, days_before_threshold, days_after_threshold){ - clean_features$day_type <- ifelse(clean_features$day_idx < 0, -1, ifelse(clean_features$day_idx > 0, 1, 0)) - if("pid" %in% colnames(clean_features)){ - clean_features <- clean_features %>% - group_by(pid) %>% - add_count(pid, day_type) # this adds a new column "n" - } else { - clean_features <- clean_features %>% add_count(day_type < 0) - } - - # Only keep participants with enough days before surgery and after discharge - clean_features <- clean_features %>% - mutate(count_before = ifelse(day_type == -1, n, NA), # before surgery - count_after = ifelse(day_type == 1, n, NA)) %>% # after discharge - fill(count_before, .direction = "downup") %>% - fill(count_after, .direction = "downup") %>% - filter(count_before >= days_before_threshold & count_after >= days_after_threshold) %>% - select(-n, -count_before, -count_after, -day_type) %>% - ungroup() - - return(clean_features) -} - -clean_features <- read.csv(snakemake@input[[1]]) -cols_nan_threshold <- as.numeric(snakemake@params[["cols_nan_threshold"]]) -drop_zero_variance_columns <- as.logical(snakemake@params[["cols_var_threshold"]]) -rows_nan_threshold <- as.numeric(snakemake@params[["rows_nan_threshold"]]) -days_before_threshold <- as.numeric(snakemake@params[["days_before_threshold"]]) -days_after_threshold <- as.numeric(snakemake@params[["days_after_threshold"]]) -features_exclude_day_idx <- as.logical(snakemake@params[["features_exclude_day_idx"]]) - - -# We have to do this before and after dropping rows, that's why is duplicated -clean_features <- filter_participant_without_enough_days(clean_features, days_before_threshold, days_after_threshold) - -# drop columns with a percentage of NA values above cols_nan_threshold -if(nrow(clean_features)) - clean_features <- clean_features %>% select_if(~ sum(is.na(.)) / length(.) <= cols_nan_threshold ) - -if(drop_zero_variance_columns) - clean_features <- clean_features %>% select_if(grepl("pid|local_date",names(.)) | sapply(., n_distinct, na.rm = T) > 1) - -# drop rows with a percentage of NA values above rows_nan_threshold -clean_features <- clean_features %>% - mutate(percentage_na = rowSums(is.na(.)) / ncol(.)) %>% - filter(percentage_na < rows_nan_threshold) %>% - select(-percentage_na) - -if(nrow(clean_features) != 0){ - clean_features <- filter_participant_without_enough_days(clean_features, days_before_threshold, days_after_threshold) - - # include "day_idx" as features or not - if(features_exclude_day_idx) - clean_features <- clean_features %>% select(-day_idx) -} - -write.csv(clean_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/models/merge_data_for_population_model.py b/src/models/merge_data_for_population_model.py deleted file mode 100644 index 376b43a8..00000000 --- a/src/models/merge_data_for_population_model.py +++ /dev/null @@ -1,8 +0,0 @@ -import pandas as pd - -data_all_participants = pd.DataFrame() -for data_file in snakemake.input["data_files"]: - data_single_participant = pd.read_csv(data_file) - data_all_participants = pd.concat([data_all_participants, data_single_participant], axis=0) - -data_all_participants.to_csv(snakemake.output[0], index=False) diff --git a/src/models/merge_features_and_targets.py b/src/models/merge_features_and_targets.py deleted file mode 100644 index 6a8cac97..00000000 --- a/src/models/merge_features_and_targets.py +++ /dev/null @@ -1,66 +0,0 @@ -import pandas as pd -import numpy as np -from modeling_utils import getMatchingColNames, dropZeroVarianceCols - - -def summarisedNumericalFeatures(col_names, features): - numerical_features = features.groupby(["pid"])[col_names].var() - numerical_features.columns = numerical_features.columns.str.replace("daily", "overallvar") - return numerical_features - -def summarisedCategoricalFeatures(col_names, features): - categorical_features = features.groupby(["pid"])[col_names].agg(lambda x: int(pd.Series.mode(x)[0])) - categorical_features.columns = categorical_features.columns.str.replace("daily", "overallmode") - return categorical_features - -def summariseFeatures(features, numerical_operators, categorical_operators, cols_var_threshold): - numerical_col_names = getMatchingColNames(numerical_operators, features) - categorical_col_names = getMatchingColNames(categorical_operators, features) - numerical_features = summarisedNumericalFeatures(numerical_col_names, features) - categorical_features = summarisedCategoricalFeatures(categorical_col_names, features) - features = pd.concat([numerical_features, categorical_features], axis=1) - if cols_var_threshold == "True": # double check the categorical features - features = dropZeroVarianceCols(features) - elif cols_var_threshold == "Flase": - pass - else: - ValueError("COLS_VAR_THRESHOLD parameter in config.yaml can only be 'True' or 'False'") - return features - - -summarised = snakemake.params["summarised"] -cols_var_threshold = snakemake.params["cols_var_threshold"] -numerical_operators = snakemake.params["numerical_operators"] -categorical_operators = snakemake.params["categorical_operators"] -features_exclude_day_idx = snakemake.params["features_exclude_day_idx"] - - -# Extract summarised features based on daily features: -# for categorical features: calculate variance across all days -# for numerical features: calculate mode across all days -if summarised == "summarised": - - features = pd.read_csv(snakemake.input["cleaned_features"], parse_dates=["local_date"]) - demographic_features = pd.read_csv(snakemake.input["demographic_features"], index_col=["pid"]) - targets = pd.read_csv(snakemake.input["targets"], index_col=["pid"]) - - features = summariseFeatures(features, numerical_operators, categorical_operators, cols_var_threshold) - data = pd.concat([features, demographic_features, targets], axis=1, join="inner") - -elif summarised == "notsummarised": - - features = pd.read_csv(snakemake.input["cleaned_features"]) - demographic_features = pd.read_csv(snakemake.input["demographic_features"]) - - features = features.merge(demographic_features, on="pid", how="left").set_index(["pid", "local_date"]) - targets = pd.read_csv(snakemake.input["targets"], index_col=["pid", "local_date"]) - data = pd.concat([features, targets], axis=1, join="inner") - -else: - raise ValueError("SUMMARISED parameter in config.yaml can only be 'summarised' or 'notsummarised'") - -if features_exclude_day_idx and ("day_idx" in data.columns): - del data["day_idx"] - -data.to_csv(snakemake.output[0], index=True) - diff --git a/src/models/merge_features_for_individual_model.R b/src/models/merge_features_for_individual_model.R deleted file mode 100644 index ea99055a..00000000 --- a/src/models/merge_features_for_individual_model.R +++ /dev/null @@ -1,35 +0,0 @@ -source("renv/activate.R") - -library(tidyr) -library(purrr) -library(dplyr) -library("methods") -library("mgm") -library("qgraph") -library("dplyr") -library("scales") -library("ggplot2") -library("purrr") -library("tidyr") -library("reshape2") - -feature_files <- snakemake@input[["feature_files"]] -phone_valid_sensed_days <- snakemake@input[["phone_valid_sensed_days"]] -days_to_include <- snakemake@input[["days_to_include"]] -source <- snakemake@params[["source"]] - -features_for_individual_model <- feature_files %>% - map(read.csv, stringsAsFactors = F, colClasses = c(local_date = "character")) %>% - reduce(full_join, by="local_date") - -if(!is.null(phone_valid_sensed_days) && source %in% c("phone_features", "phone_fitbit_features")){ - valid_days <- read.csv(phone_valid_sensed_days) - valid_days <- valid_days[valid_days$is_valid_sensed_day == TRUE, ] - features_for_individual_model <- merge(features_for_individual_model, valid_days, by="local_date") %>% select(-valid_sensed_hours, -is_valid_sensed_day) -} - -if(!is.null(days_to_include)){ - features_for_individual_model <- merge(features_for_individual_model, read.csv(days_to_include), by="local_date") -} - -write.csv(features_for_individual_model, snakemake@output[[1]], row.names = FALSE) \ No newline at end of file diff --git a/src/models/merge_population_model_results.py b/src/models/merge_population_model_results.py deleted file mode 100644 index d81cc596..00000000 --- a/src/models/merge_population_model_results.py +++ /dev/null @@ -1,16 +0,0 @@ -import pandas as pd - -overall_results = pd.read_csv(snakemake.input["overall_results"]) -nan_cells_ratio = pd.read_csv(snakemake.input["nan_cells_ratio"]) -baseline = pd.read_csv(snakemake.input["baseline"], index_col=["method"]) - -# add nan cells ratio -overall_results.insert(3, "nan_cells_ratio", nan_cells_ratio["nan_cells_ratio"]) - -# add baseline -baseline = baseline.stack().to_frame().T -baseline.columns = ['{}_{}'.format(*col) for col in baseline.columns] -baseline = baseline.add_prefix('b_') -results = pd.concat([overall_results, baseline], axis=1) - -results.to_csv(snakemake.output[0], index=False) diff --git a/src/models/nan_cells_ratio_of_cleaned_features.py b/src/models/nan_cells_ratio_of_cleaned_features.py deleted file mode 100644 index de06a0c2..00000000 --- a/src/models/nan_cells_ratio_of_cleaned_features.py +++ /dev/null @@ -1,8 +0,0 @@ -import pandas as pd - -features = pd.read_csv(snakemake.input["cleaned_features"], parse_dates=["local_date"]) - -# Compute the proportion of missing value cells among all features -nan_cells_ratio = features.isnull().sum().sum() / (features.shape[0] * features.shape[1]) - -pd.DataFrame({"nan_cells_ratio": [nan_cells_ratio]}).to_csv(snakemake.output[0], index=False) \ No newline at end of file diff --git a/src/models/select_days_to_analyse.py b/src/models/select_days_to_analyse.py deleted file mode 100644 index 5a1370b0..00000000 --- a/src/models/select_days_to_analyse.py +++ /dev/null @@ -1,43 +0,0 @@ -import numpy as np -import pandas as pd -from datetime import timedelta - -def appendDaysInRange(days_to_analyse, start_date, end_date, day_type): - num_of_days = (end_date - start_date).days - if np.isnan(num_of_days): - return days_to_analyse - - for day in range(num_of_days + 1): - - if day_type == -1: - day_idx = (num_of_days - day + 1) * day_type - elif day_type == 1: - day_idx = day + 1 - else: - day_idx = 0 - - days_to_analyse = days_to_analyse.append({"local_date": start_date + timedelta(days = day), "day_idx": day_idx}, ignore_index=True) - - return days_to_analyse - -days_before_surgery = int(snakemake.params["days_before_surgery"]) -days_in_hospital = str(snakemake.params["days_in_hospital"]) -days_after_discharge = int(snakemake.params["days_after_discharge"]) -participant_info = pd.read_csv(snakemake.input["participant_info"], parse_dates=["surgery_date", "discharge_date"]) -days_to_analyse = pd.DataFrame(columns = ["local_date", "day_idx"]) - -try: - surgery_date, discharge_date = participant_info["surgery_date"].iloc[0].date(), participant_info["discharge_date"].iloc[0].date() -except: - pass -else: - start_date = surgery_date - timedelta(days = days_before_surgery) - end_date = discharge_date + timedelta(days = days_after_discharge) - - # days before surgery: -1; in hospital: 0; after discharge: 1 - days_to_analyse = appendDaysInRange(days_to_analyse, start_date, surgery_date - timedelta(days = 1), -1) - if days_in_hospital == "T": - days_to_analyse = appendDaysInRange(days_to_analyse, surgery_date, discharge_date, 0) - days_to_analyse = appendDaysInRange(days_to_analyse, discharge_date + timedelta(days = 1), end_date, 1) - -days_to_analyse.to_csv(snakemake.output[0], index=False) diff --git a/src/models/targets.py b/src/models/targets.py deleted file mode 100644 index e12794d4..00000000 --- a/src/models/targets.py +++ /dev/null @@ -1,18 +0,0 @@ -import pandas as pd -import numpy as np - -pid = snakemake.params["pid"] -summarised = snakemake.params["summarised"] -participant_info = pd.read_csv(snakemake.input["participant_info"]) - -if summarised == "summarised": - raise ValueError("Do not support summarised features for example dataset.") - -elif summarised == "notsummarised": - targets = participant_info[["local_date", "target"]] - targets.insert(0, "pid", pid) - -else: - raise ValueError("SUMMARISED parameter in config.yaml can only be 'summarised' or 'notsummarised'") - -targets.to_csv(snakemake.output[0], index=False) diff --git a/src/models/baseline.py b/src/models/workflow_example/baselines.py similarity index 53% rename from src/models/baseline.py rename to src/models/workflow_example/baselines.py index faa5bfeb..9c1ae564 100644 --- a/src/models/baseline.py +++ b/src/models/workflow_example/baselines.py @@ -1,7 +1,7 @@ import numpy as np import pandas as pd from statistics import mean -from modeling_utils import getMetrics, createPipeline +from modelling_utils import getMetrics, createPipeline from sklearn.model_selection import LeaveOneOut @@ -10,18 +10,18 @@ from sklearn.model_selection import LeaveOneOut def baselineAccuracyOfMajorityClassClassifier(targets): majority_class = targets["target"].value_counts().idxmax() pred_y = [majority_class] * targets.shape[0] - pred_y_prob = pred_y - metrics = getMetrics(pred_y, pred_y_prob, targets["target"].values.ravel().tolist()) + pred_y_proba = pred_y + metrics = getMetrics(pred_y, pred_y_proba, targets["target"].values.ravel().tolist()) return metrics, majority_class def baselineMetricsOfRandomWeightedClassifier(targets, majority_ratio, majority_class, iter_times): - metrics_all_iters = {"accuracy": [], "precision0":[], "recall0": [], "f10": [], "precision1": [], "recall1": [], "f11": [], "auc": [], "kappa": []} + metrics_all_iters = {"accuracy": [], "precision0":[], "recall0": [], "f10": [], "precision1": [], "recall1": [], "f11": [], "f1_macro": [], "auc": [], "kappa": []} probabilities = [0, 0] probabilities[majority_class], probabilities[1 - majority_class] = majority_ratio, 1 - majority_ratio for i in range(iter_times): pred_y = np.random.RandomState(i).multinomial(1, probabilities, targets.shape[0])[:,1].tolist() - pred_y_prob = pred_y - metrics = getMetrics(pred_y, pred_y_prob, targets["target"].values.ravel().tolist()) + pred_y_proba = pred_y + metrics = getMetrics(pred_y, pred_y_proba, targets["target"].values.ravel().tolist()) for key in metrics_all_iters.keys(): metrics_all_iters[key].append(metrics[key].item()) # Calculate average metrics across all iterations @@ -38,21 +38,25 @@ def baselineMetricsOfDTWithDemographicFeatures(cv_method, data_x, data_y, oversa clf = createPipeline("DT", oversampler_type) clf.fit(train_x, train_y.values.ravel()) pred_y = pred_y + clf.predict(test_x).ravel().tolist() - pred_y_prob = pred_y + pred_y_proba = pred_y true_y = true_y + test_y.values.ravel().tolist() - return getMetrics(pred_y, pred_y_prob, true_y) + return getMetrics(pred_y, pred_y_proba, true_y) cv_method = globals()[snakemake.params["cv_method"]]() -colnames_demographic_features = snakemake.params["demographic_features"] -rowsnan_colsnan_days_colsvar_threshold = snakemake.params["rowsnan_colsnan_days_colsvar_threshold"] +colnames_demographic_features = snakemake.params["colnames_demographic_features"] + +data = pd.read_csv(snakemake.input[0]) +index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"] +if "pid" in data.columns: + index_columns.append("pid") +data.set_index(index_columns, inplace=True) -data = pd.read_csv(snakemake.input[0], index_col=["pid"]) data_x, data_y = data.drop("target", axis=1), data[["target"]] targets_value_counts = data_y["target"].value_counts() -baseline_metrics = pd.DataFrame(columns=["method", "fullMethodName", "accuracy", "precision0", "recall0", "f10", "precision1", "recall1", "f11", "auc", "kappa"]) +baseline_metrics = pd.DataFrame(columns=["method", "fullMethodName", "accuracy", "precision0", "recall0", "f10", "precision1", "recall1", "f11", "f1_macro", "auc", "kappa"]) if len(targets_value_counts) < 2: fout = open(snakemake.log[0], "w") fout.write(targets_value_counts.to_string()) @@ -69,21 +73,33 @@ else: majority_ratio = baseline1_metrics["accuracy"] # Baseline 2: random weighted classifier => random classifier with binomial distribution baseline2_metrics = baselineMetricsOfRandomWeightedClassifier(data_y, majority_ratio, majority_class, 1000) - # Baseline 3: decision tree with demographic features - baseline3_metrics = baselineMetricsOfDTWithDemographicFeatures(cv_method, data_x[colnames_demographic_features], data_y, oversampler_type) - baselines = [baseline1_metrics, baseline2_metrics, baseline3_metrics] + if "pid" in index_columns: + # Baseline 3: decision tree with demographic features + baseline3_metrics = baselineMetricsOfDTWithDemographicFeatures(cv_method, data_x[colnames_demographic_features], data_y, oversampler_type) + + baselines = [baseline1_metrics, baseline2_metrics, baseline3_metrics] + methods = ["majority", "rwc", "dt"] + fullMethodNames = ["MajorityClassClassifier", "RandomWeightedClassifier", "DecisionTreeWithDemographicFeatures"] + + else: + # Only have 2 baselines + baselines = [baseline1_metrics, baseline2_metrics] + methods = ["majority", "rwc"] + fullMethodNames = ["MajorityClassClassifier", "RandomWeightedClassifier"] + + baseline_metrics = pd.DataFrame({"method": methods, + "fullMethodName": fullMethodNames, + "accuracy": [baseline["accuracy"] for baseline in baselines], + "precision0": [baseline["precision0"] for baseline in baselines], + "recall0": [baseline["recall0"] for baseline in baselines], + "f10": [baseline["f10"] for baseline in baselines], + "precision1": [baseline["precision1"] for baseline in baselines], + "recall1": [baseline["recall1"] for baseline in baselines], + "f11": [baseline["f11"] for baseline in baselines], + "f1_macro": [baseline["f1_macro"] for baseline in baselines], + "auc": [baseline["auc"] for baseline in baselines], + "kappa": [baseline["kappa"] for baseline in baselines]}) - baseline_metrics = pd.DataFrame({"method": ["majority", "rwc", "dt"], - "fullMethodName": ["MajorityClassClassifier", "RandomWeightedClassifier", "DecisionTreeWithDemographicFeatures"], - "accuracy": [baseline["accuracy"] for baseline in baselines], - "precision0": [baseline["precision0"] for baseline in baselines], - "recall0": [baseline["recall0"] for baseline in baselines], - "f10": [baseline["f10"] for baseline in baselines], - "precision1": [baseline["precision1"] for baseline in baselines], - "recall1": [baseline["recall1"] for baseline in baselines], - "f11": [baseline["f11"] for baseline in baselines], - "auc": [baseline["auc"] for baseline in baselines], - "kappa": [baseline["kappa"] for baseline in baselines]}) baseline_metrics.to_csv(snakemake.output[0], index=False) diff --git a/src/models/workflow_example/clean_sensor_features.R b/src/models/workflow_example/clean_sensor_features.R new file mode 100644 index 00000000..57b9b285 --- /dev/null +++ b/src/models/workflow_example/clean_sensor_features.R @@ -0,0 +1,29 @@ +source("renv/activate.R") +library(tidyr) +library("dplyr", warn.conflicts = F) + + +clean_features <- read.csv(snakemake@input[[1]]) +cols_nan_threshold <- as.numeric(snakemake@params[["cols_nan_threshold"]]) +drop_zero_variance_columns <- as.logical(snakemake@params[["cols_var_threshold"]]) +rows_nan_threshold <- as.numeric(snakemake@params[["rows_nan_threshold"]]) +data_yielded_hours_ratio_threshold <- as.numeric(snakemake@params[["data_yielded_hours_ratio_threshold"]]) + +# drop rows with the value of "phone_data_yield_rapids_ratiovalidyieldedhours" column less than data_yielded_hours_ratio_threshold +clean_features <- clean_features %>% + filter(phone_data_yield_rapids_ratiovalidyieldedhours > data_yielded_hours_ratio_threshold) + +# drop columns with a percentage of NA values above cols_nan_threshold +if(nrow(clean_features)) + clean_features <- clean_features %>% select_if(~ sum(is.na(.)) / length(.) <= cols_nan_threshold ) + +if(drop_zero_variance_columns) + clean_features <- clean_features %>% select_if(grepl("pid|local_segment|local_segment_label|local_segment_start_datetime|local_segment_end_datetime",names(.)) | sapply(., n_distinct, na.rm = T) > 1) + +# drop rows with a percentage of NA values above rows_nan_threshold +clean_features <- clean_features %>% + mutate(percentage_na = rowSums(is.na(.)) / ncol(.)) %>% + filter(percentage_na < rows_nan_threshold) %>% + select(-percentage_na) + +write.csv(clean_features, snakemake@output[[1]], row.names = FALSE) diff --git a/src/models/workflow_example/merge_features_and_targets_for_individual_model.py b/src/models/workflow_example/merge_features_and_targets_for_individual_model.py new file mode 100644 index 00000000..95ce9041 --- /dev/null +++ b/src/models/workflow_example/merge_features_and_targets_for_individual_model.py @@ -0,0 +1,10 @@ +import pandas as pd +import numpy as np + +index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"] +sensor_features = pd.read_csv(snakemake.input["cleaned_sensor_features"], index_col=index_columns) +targets = pd.read_csv(snakemake.input["targets"], index_col=index_columns) + +data = pd.concat([sensor_features, targets[["target"]]], axis=1, join="inner") + +data.to_csv(snakemake.output[0], index=True) diff --git a/src/models/workflow_example/merge_features_and_targets_for_population_model.py b/src/models/workflow_example/merge_features_and_targets_for_population_model.py new file mode 100644 index 00000000..69c4cca7 --- /dev/null +++ b/src/models/workflow_example/merge_features_and_targets_for_population_model.py @@ -0,0 +1,27 @@ +import pandas as pd +import numpy as np + +merge_keys = ["pid", "local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"] +sensor_features = pd.read_csv(snakemake.input["cleaned_sensor_features"]) + +all_demographic_features = pd.DataFrame() +for demographic_features_path in snakemake.input["demographic_features"]: + pid = demographic_features_path.split("/")[3] + demographic_features = pd.read_csv(demographic_features_path) + demographic_features = demographic_features.assign(pid=pid) + all_demographic_features = pd.concat([all_demographic_features, demographic_features], axis=0) + +# merge sensor features and demographic features +features = sensor_features.merge(all_demographic_features, on="pid", how="left") + +all_targets = pd.DataFrame() +for targets_path in snakemake.input["targets"]: + pid = targets_path.split("/")[3] + targets = pd.read_csv(targets_path) + targets = targets.assign(pid=pid) + all_targets = pd.concat([all_targets, targets], axis=0) + +# merge features and targets +data = features.merge(all_targets[["target"] + merge_keys], on=merge_keys, how="inner") + +data.to_csv(snakemake.output[0], index=False) diff --git a/src/models/modeling.py b/src/models/workflow_example/modelling.py similarity index 75% rename from src/models/modeling.py rename to src/models/workflow_example/modelling.py index 57a50eb7..a83ee0c5 100644 --- a/src/models/modeling.py +++ b/src/models/workflow_example/modelling.py @@ -1,7 +1,7 @@ import pandas as pd import numpy as np -from modeling_utils import getMatchingColNames, dropZeroVarianceCols, getNormAllParticipantsScaler, getMetrics, getFeatureImportances, createPipeline -from sklearn.model_selection import train_test_split, LeaveOneOut, GridSearchCV, cross_val_score, KFold +from modelling_utils import getMatchingColNames, getNormAllParticipantsScaler, getMetrics, getFeatureImportances, createPipeline +from sklearn.model_selection import LeaveOneOut, GridSearchCV @@ -25,7 +25,8 @@ def preprocessCategoricalFeatures(categorical_features, mode_categorical_feature categorical_features = categorical_features.fillna(mode_categorical_features) # one-hot encoding categorical_features = categorical_features.apply(lambda col: col.astype("category")) - categorical_features = pd.get_dummies(categorical_features) + if not categorical_features.empty: + categorical_features = pd.get_dummies(categorical_features) return categorical_features def splitNumericalCategoricalFeatures(features, categorical_feature_colnames): @@ -48,32 +49,32 @@ def preprocesFeatures(train_numerical_features, test_numerical_features, categor # Step 4. Save results, parameters, and metrics to CSV files ############################################################## - +# For reproducibility +np.random.seed(0) # Step 1. Read parameters and data # Read parameters model = snakemake.params["model"] -source = snakemake.params["source"] -summarised = snakemake.params["summarised"] -day_segment = snakemake.params["day_segment"] scaler = snakemake.params["scaler"] cv_method = snakemake.params["cv_method"] categorical_operators = snakemake.params["categorical_operators"] categorical_colnames_demographic_features = snakemake.params["categorical_demographic_features"] model_hyperparams = snakemake.params["model_hyperparams"][model] -rowsnan_colsnan_days_colsvar_threshold = snakemake.params["rowsnan_colsnan_days_colsvar_threshold"] # thresholds for data cleaning # Read data and split -if summarised == "summarised": - data = pd.read_csv(snakemake.input["data"], index_col=["pid"]) -elif summarised == "notsummarised": - data = pd.read_csv(snakemake.input["data"], index_col=["pid", "local_date"]) -else: - raise ValueError("SUMMARISED parameter in config.yaml can only be 'summarised' or 'notsummarised'") +data = pd.read_csv(snakemake.input["data"]) +index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"] +if "pid" in data.columns: + index_columns.append("pid") +data.set_index(index_columns, inplace=True) data_x, data_y = data.drop("target", axis=1), data[["target"]] -categorical_feature_colnames = categorical_colnames_demographic_features + getMatchingColNames(categorical_operators, data_x) + +if "pid" in index_columns: + categorical_feature_colnames = categorical_colnames_demographic_features + getMatchingColNames(categorical_operators, data_x) +else: + categorical_feature_colnames = getMatchingColNames(categorical_operators, data_x) @@ -82,7 +83,7 @@ cv_class = globals()[cv_method] inner_cv = cv_class() outer_cv = cv_class() -fold_id, pid, best_params, true_y, pred_y, pred_y_prob = [], [], [], [], [], [] +fold_id, pid, best_params, true_y, pred_y, pred_y_proba = [], [], [], [], [], [] feature_importances_all_folds = pd.DataFrame() fold_count = 1 @@ -99,7 +100,7 @@ for train_index, test_index in outer_cv.split(data_x): mode_categorical_features = train_categorical_features.mode().iloc[0] train_x = preprocesFeatures(train_numerical_features, None, train_categorical_features, mode_categorical_features, scaler, "train") test_x = preprocesFeatures(train_numerical_features, test_numerical_features, test_categorical_features, mode_categorical_features, scaler, "test") - train_x, test_x = train_x.align(test_x, join='outer', axis=1, fill_value=0) # in case we get rid off categorical columns + train_x, test_x = train_x.align(test_x, join="outer", axis=1, fill_value=0) # in case we get rid off categorical columns # Compute number of participants and features # values do not change between folds @@ -129,7 +130,7 @@ for train_index, test_index in outer_cv.split(data_x): pred_y = pred_y + cur_fold_pred proba_of_two_categories = clf.predict_proba(test_x).tolist() - pred_y_prob = pred_y_prob + [probabilities[clf.classes_.tolist().index(1)] for probabilities in proba_of_two_categories] + pred_y_proba = pred_y_proba + [probabilities[clf.classes_.tolist().index(1)] for probabilities in proba_of_two_categories] true_y = true_y + test_y.values.ravel().tolist() pid = pid + test_y.index.tolist() # each test partition (fold) in the outer cv is a participant (LeaveOneOut cv) @@ -140,16 +141,16 @@ for train_index, test_index in outer_cv.split(data_x): # Step 3. Model evaluation if len(pred_y) > 1: - metrics = getMetrics(pred_y, pred_y_prob, true_y) + metrics = getMetrics(pred_y, pred_y_proba, true_y) else: - metrics = {"accuracy": None, "precision0": None, "recall0": None, "f10": None, "precision1": None, "recall1": None, "f11": None, "auc": None, "kappa": None} + metrics = {"accuracy": None, "precision0": None, "recall0": None, "f10": None, "precision1": None, "recall1": None, "f11": None, "f1_macro": None, "auc": None, "kappa": None} # Step 4. Save results, parameters, and metrics to CSV files -fold_predictions = pd.DataFrame({"fold_id": fold_id, "pid": pid, "hyperparameters": best_params, "true_y": true_y, "pred_y": pred_y, "pred_y_prob": pred_y_prob}) -fold_metrics = pd.DataFrame({"fold_id":[], "accuracy":[], "precision0": [], "recall0": [], "f10": [], "precision1": [], "recall1": [], "f11": [], "auc": [], "kappa": []}) -overall_results = pd.DataFrame({"num_of_rows": [num_of_rows], "num_of_features": [num_of_features], "rowsnan_colsnan_days_colsvar_threshold": [rowsnan_colsnan_days_colsvar_threshold], "model": [model], "cv_method": [cv_method], "source": [source], "scaler": [scaler], "day_segment": [day_segment], "summarised": [summarised], "accuracy": [metrics["accuracy"]], "precision0": [metrics["precision0"]], "recall0": [metrics["recall0"]], "f10": [metrics["f10"]], "precision1": [metrics["precision1"]], "recall1": [metrics["recall1"]], "f11": [metrics["f11"]], "auc": [metrics["auc"]], "kappa": [metrics["kappa"]]}) -feature_importances_all_folds.insert(loc=0, column='fold_id', value=fold_id) -feature_importances_all_folds.insert(loc=1, column='pid', value=pid) +fold_predictions = pd.DataFrame({"fold_id": fold_id, "pid": pid, "hyperparameters": best_params, "true_y": true_y, "pred_y": pred_y, "pred_y_proba": pred_y_proba}) +fold_metrics = pd.DataFrame({"fold_id":[], "accuracy":[], "precision0": [], "recall0": [], "f10": [], "precision1": [], "recall1": [], "f11": [], "f1_macro": [], "auc": [], "kappa": []}) +overall_results = pd.DataFrame({"num_of_rows": [num_of_rows], "num_of_features": [num_of_features], "model": [model], "cv_method": [cv_method], "scaler": [scaler], "accuracy": [metrics["accuracy"]], "precision0": [metrics["precision0"]], "recall0": [metrics["recall0"]], "f10": [metrics["f10"]], "precision1": [metrics["precision1"]], "recall1": [metrics["recall1"]], "f11": [metrics["f11"]], "f1_macro": [metrics["f1_macro"]], "auc": [metrics["auc"]], "kappa": [metrics["kappa"]]}) +feature_importances_all_folds.insert(loc=0, column="fold_id", value=fold_id) +feature_importances_all_folds.insert(loc=1, column="pid", value=pid) fold_predictions.to_csv(snakemake.output["fold_predictions"], index=False) fold_metrics.to_csv(snakemake.output["fold_metrics"], index=False) diff --git a/src/models/modeling_utils.py b/src/models/workflow_example/modelling_utils.py similarity index 83% rename from src/models/modeling_utils.py rename to src/models/workflow_example/modelling_utils.py index 1ba17c50..5d11c995 100644 --- a/src/models/modeling_utils.py +++ b/src/models/workflow_example/modelling_utils.py @@ -1,4 +1,5 @@ import pandas as pd +import numpy as np from sklearn.preprocessing import MinMaxScaler, StandardScaler, RobustScaler from sklearn.metrics import accuracy_score, f1_score, precision_score, recall_score, classification_report, confusion_matrix from sklearn.metrics import precision_recall_fscore_support @@ -44,24 +45,24 @@ def getNormAllParticipantsScaler(features, scaler_flag): scaler.fit(features) return scaler -# get metrics: accuracy, precision1, recall1, f11, auc, kappa -def getMetrics(pred_y, pred_y_prob, true_y): +# get metrics: accuracy, precision0, recall0, f10, precision1, recall1, f11, f1_macro, auc, kappa +def getMetrics(pred_y, pred_y_proba, true_y): metrics = {} + count = len(np.unique(true_y)) + label= np.unique(true_y)[0] # metrics for all categories metrics["accuracy"] = accuracy_score(true_y, pred_y) - try: - metrics["auc"] = roc_auc_score(true_y, pred_y_prob) - except: - metrics["auc"] = None + metrics["f1_macro"] = f1_score(true_y, pred_y, average="macro") # unweighted mean + metrics["auc"] = np.nan if count == 1 else roc_auc_score(true_y, pred_y_proba) metrics["kappa"] = cohen_kappa_score(true_y, pred_y) # metrics for label 0 - metrics["precision0"] = precision_score(true_y, pred_y, average=None, labels=[0,1], zero_division=0)[0] - metrics["recall0"] = recall_score(true_y, pred_y, average=None, labels=[0,1])[0] - metrics["f10"] = f1_score(true_y, pred_y, average=None, labels=[0,1])[0] + metrics["precision0"] = np.nan if (count == 1 and label == 1) else precision_score(true_y, pred_y, average=None, labels=[0,1], zero_division=0)[0] + metrics["recall0"] = np.nan if (count == 1 and label == 1) else recall_score(true_y, pred_y, average=None, labels=[0,1])[0] + metrics["f10"] = np.nan if (count == 1 and label == 1) else f1_score(true_y, pred_y, average=None, labels=[0,1])[0] # metrics for label 1 - metrics["precision1"] = precision_score(true_y, pred_y, average=None, labels=[0,1], zero_division=0)[1] - metrics["recall1"] = recall_score(true_y, pred_y, average=None, labels=[0,1])[1] - metrics["f11"] = f1_score(true_y, pred_y, average=None, labels=[0,1])[1] + metrics["precision1"] = np.nan if (count == 1 and label == 0) else precision_score(true_y, pred_y, average=None, labels=[0,1], zero_division=0)[1] + metrics["recall1"] = np.nan if (count == 1 and label == 0) else recall_score(true_y, pred_y, average=None, labels=[0,1])[1] + metrics["f11"] = np.nan if (count == 1 and label == 0) else f1_score(true_y, pred_y, average=None, labels=[0,1])[1] return metrics diff --git a/src/models/workflow_example/parse_targets.py b/src/models/workflow_example/parse_targets.py new file mode 100644 index 00000000..73ec542b --- /dev/null +++ b/src/models/workflow_example/parse_targets.py @@ -0,0 +1,28 @@ +import pandas as pd +import numpy as np +from importlib import import_module, util +from pathlib import Path + + +# import filter_data_by_segment from src/features/utils/utils.py +spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent.parent / "features" / "utils" / "utils.py")) +mod = util.module_from_spec(spec) +spec.loader.exec_module(mod) +filter_data_by_segment = getattr(mod, "filter_data_by_segment") + +targets = pd.read_csv(snakemake.input["targets"]) +time_segments_labels = pd.read_csv(snakemake.input["time_segments_labels"], header=0) + +all_targets = pd.DataFrame(columns=["local_segment"]) +for time_segment in time_segments_labels["label"]: + filtered_targets = filter_data_by_segment(targets, time_segment) + all_targets = all_targets.merge(filtered_targets, how="outer") + +segment_colums = pd.DataFrame() +split_segemnt_columns = all_targets["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True) +new_segment_columns = split_segemnt_columns.iloc[:,1:4] if split_segemnt_columns.shape[1] == 5 else pd.DataFrame(columns=["local_segment_label", "local_segment_start_datetime","local_segment_end_datetime"]) +segment_colums[["local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]] = new_segment_columns +for i in range(segment_colums.shape[1]): + all_targets.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]]) + +all_targets.to_csv(snakemake.output[0], index=False) diff --git a/src/visualization/battery_consumption_rates_barchart.py b/src/visualization/battery_consumption_rates_barchart.py deleted file mode 100644 index 148b76ef..00000000 --- a/src/visualization/battery_consumption_rates_barchart.py +++ /dev/null @@ -1,34 +0,0 @@ -import pandas as pd -import datetime -import plotly.io as pio -import plotly.graph_objects as go - -def getBatteryConsumptionRatesBarChart(battery_data, pid): - plot = go.Figure(go.Bar( - x=battery_data["battery_daily_avgconsumptionrate"], - y=battery_data["local_date"].apply(lambda x: x.strftime("%Y/%m/%d")).tolist(), - orientation='h')) - plot.update_layout(title="Daily battery consumption rates bar chart for " + pid + "
Label: " + label + ", device_id: " + device_id, - xaxis_title="battery drains % per hour", - ) - return plot - - - -battery_data = pd.read_csv(snakemake.input["sensor"], parse_dates=["local_date"]) -pid = snakemake.params["pid"] - -with open(snakemake.input["pid_file"], encoding="ISO-8859-1") as external_file: - external_file_content = external_file.readlines() -device_id = external_file_content[0].split(",")[-1] -label = external_file_content[2] - -if battery_data.empty: - empty_html = open(snakemake.output[0], "w") - empty_html.write("There is no battery data for " + pid + "
Label: " + label + ", device_id: " + device_id) - empty_html.close() -else: - battery_data.set_index(["local_date"], inplace=True) - battery_data = battery_data.resample("1D").asfreq().fillna(0).reset_index() - plot = getBatteryConsumptionRatesBarChart(battery_data, pid) - pio.write_html(plot, file=snakemake.output[0], auto_open=False, include_plotlyjs="cdn") \ No newline at end of file diff --git a/src/visualization/compliance_report.Rmd b/src/visualization/compliance_report.Rmd deleted file mode 100644 index 2717875d..00000000 --- a/src/visualization/compliance_report.Rmd +++ /dev/null @@ -1,39 +0,0 @@ ---- -title: "Compliance Report" -author: - - "MoSHI Pipeline" -date: "`r format(Sys.time(), '%d %B, %Y')`" -params: - rmd: "compliance_report.Rmd" -output: - html_document: - highlight: tango - number_sections: no - theme: default - toc: yes - toc_depth: 3 - toc_float: - collapsed: no - smooth_scroll: yes ---- - -```{r include=FALSE} -source("renv/activate.R") -``` - -## Overall phone compliance - -```{r, echo=FALSE} -htmltools::includeHTML(snakemake@input[["compliance_heatmap"]]) -``` - -## Per sensor compliance -```{r, echo=FALSE} -heatmaps <- snakemake@input[["sensor_heatmaps"]] -heatmaps.html <- vector(mode="list", length(heatmaps)) - -for(sensor_id in 1:length(heatmaps)){ - heatmaps.html[[sensor_id]] <- htmltools::includeHTML(heatmaps[sensor_id]) -} -htmltools::tagList(heatmaps.html) -``` diff --git a/src/visualization/heatmap_days_by_sensors.py b/src/visualization/heatmap_days_by_sensors.py deleted file mode 100644 index f1ab53c0..00000000 --- a/src/visualization/heatmap_days_by_sensors.py +++ /dev/null @@ -1,74 +0,0 @@ -import numpy as np -import pandas as pd -import plotly.io as pio -import plotly.graph_objects as go -from datetime import datetime, timedelta - -def getRowCountHeatmap(row_count_sensors_normalized, row_count_sensors, pid, output_path): - plot = go.Figure(data=go.Heatmap(z=row_count_sensors_normalized.T.values.tolist(), - x=[datetime.strftime(idx[0], "%Y/%m/%d")+"("+str(idx[1])+")" for idx in row_count_sensors.index], - y=row_count_sensors.columns.tolist(), - hovertext=row_count_sensors.T.values.tolist(), - hovertemplate="Date: %{x}
Sensor: %{y}
Row count: %{hovertext}", - colorscale="Viridis")) - plot.update_layout(title="Row count heatmap for " + pid) - pio.write_html(plot, file=output_path, auto_open=False, include_plotlyjs="cdn") - - - -phone_valid_sensed_days = pd.read_csv(snakemake.input["phone_valid_sensed_days"], parse_dates=["local_date"], index_col=["local_date"]) -phone_valid_sensed_days = phone_valid_sensed_days[phone_valid_sensed_days["is_valid_sensed_day"] == True] - -row_count_sensors = pd.DataFrame() -for sensor_path in snakemake.input["sensors"]: - sensor_name = sensor_path.split("/")[-1].replace("_with_datetime.csv", "") - # plugin_studentlife_audio_android or plugin_studentlife_audio => conversion; plugin_google_activity_recognition or plugin_ios_activity_recognition => AR; applications_foreground => apps - sensor_name = sensor_name.replace("plugin_studentlife_audio_android", "conversion").replace("plugin_studentlife_audio", "conversion") \ - .replace("plugin_google_activity_recognition", "AR").replace("plugin_ios_activity_recognition", "AR") \ - .replace("applications_foreground", "apps") - - sensor_data = pd.read_csv(sensor_path, encoding="ISO-8859-1", parse_dates=["local_date"], dtype={"label": str}) - if sensor_data.empty: - row_count_sensor = pd.DataFrame(columns=[sensor_name]) - else: - row_count_sensor = sensor_data[["timestamp", "local_date"]].groupby(["local_date"]).count().rename(columns={"timestamp": sensor_name}) - row_count_sensors = row_count_sensors.join(row_count_sensor, how="outer") - -row_count_sensors.index = pd.to_datetime(row_count_sensors.index) -row_count_sensors = row_count_sensors.join(phone_valid_sensed_days[["valid_sensed_hours"]], how="outer") - -if row_count_sensors.empty: - empty_html = open(snakemake.output[0], "w") - empty_html.write("There are no records of sensors in database.") - empty_html.close() -else: - # set date_idx based on the first date - reference_date = row_count_sensors.index.min() - last_date = row_count_sensors.index.max() - row_count_sensors["date_idx"] = (row_count_sensors.index - reference_date).days - row_count_sensors["local_date"] = row_count_sensors.index - row_count_sensors.set_index(["local_date", "date_idx"], inplace=True) - - - expected_num_of_days = int(snakemake.params["expected_num_of_days"]) - if expected_num_of_days < -1: - raise ValueError("EXPECTED_NUM_OF_DAYS of HEATMAP_DAYS_BY_SENSORS section in config.yaml must be larger or equal to -1.") - # if expected_num_of_days = -1, return all dates - expected_num_of_days = (last_date - reference_date).days if expected_num_of_days == -1 else expected_num_of_days - - # add empty rows to make sure different participants have the same date_idx range - date_idx_range = [idx for idx in range(expected_num_of_days)] - date_range = [reference_date + timedelta(days=idx) for idx in date_idx_range] - all_dates = pd.DataFrame({"local_date": date_range, "date_idx": date_idx_range}) - all_dates.set_index(["local_date", "date_idx"], inplace=True) - - row_count_sensors = row_count_sensors.merge(all_dates, left_index=True, right_index=True, how="right") - - # normalize each sensor (column) - if row_count_sensors.count().max() > 1: - row_count_sensors_normalized = row_count_sensors.fillna(np.nan).apply(lambda x: (x - np.nanmin(x)) / (np.nanmax(x) - np.nanmin(x)) if np.nanmax(x) != np.nanmin(x) else (x / np.nanmin(x)), axis=0) - else: - row_count_sensors_normalized = row_count_sensors - - pid = sensor_path.split("/")[2] - getRowCountHeatmap(row_count_sensors_normalized, row_count_sensors, pid, snakemake.output[0]) diff --git a/src/visualization/heatmap_feature_correlation_matrix.py b/src/visualization/heatmap_feature_correlation_matrix.py new file mode 100644 index 00000000..8fb01934 --- /dev/null +++ b/src/visualization/heatmap_feature_correlation_matrix.py @@ -0,0 +1,48 @@ +import numpy as np +import pandas as pd +import plotly.graph_objects as go + + +def getCorrMatrixHeatmap(corr_matrix, time_segment, html_file): + + feature_names = corr_matrix.columns + + fig = go.Figure(data=go.Heatmap(z=corr_matrix.values.tolist(), + x=feature_names, + y=feature_names, + colorscale="Viridis")) + + fig.update_layout(title="Correlation matrix of features of " + time_segment + " segments.") + + html_file.write(fig.to_html(full_html=False, include_plotlyjs="cdn")) + + + +min_rows_ratio = snakemake.params["min_rows_ratio"] +corr_threshold = snakemake.params["corr_threshold"] +corr_method = snakemake.params["corr_method"] + +features = pd.read_csv(snakemake.input["all_sensor_features"]) +time_segments = set(features["local_segment_label"]) + +html_file = open(snakemake.output[0], "a", encoding="utf-8") +if features.empty: + html_file.write("There are no features for any participant.") +else: + + for time_segment in time_segments: + features_per_segment = features[features["local_segment_label"] == time_segment] + if features_per_segment.empty: + html_file.write("There are no features for " + time_segment + " segments.
") + else: + # drop useless columns + features_per_segment = features_per_segment.drop(["pid", "local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"], axis=1).astype(float) + # get correlation matrix + corr_matrix = features_per_segment.corr(method=corr_method, min_periods=min_rows_ratio * features_per_segment.shape[0]) + # replace correlation coefficients less than corr_threshold with NA + corr_matrix[(corr_matrix > -corr_threshold) & (corr_matrix < corr_threshold)] = np.nan + + # plot heatmap + getCorrMatrixHeatmap(corr_matrix, time_segment, html_file) + +html_file.close() diff --git a/src/visualization/heatmap_features_correlations.py b/src/visualization/heatmap_features_correlations.py deleted file mode 100644 index 8093a9db..00000000 --- a/src/visualization/heatmap_features_correlations.py +++ /dev/null @@ -1,59 +0,0 @@ -import numpy as np -import pandas as pd -import plotly.io as pio -import plotly.graph_objects as go - - -def getCorrMatrixHeatmap(corr_matrix, output_path): - colnames = corr_matrix.columns - plot = go.Figure(data=go.Heatmap(z=corr_matrix.values.tolist(), - x=colnames, - y=colnames, - colorscale="Viridis")) - plot.update_layout(title="Correlation Matrix Heatmap") - pio.write_html(plot, file=output_path, auto_open=False, include_plotlyjs="cdn") - - -min_rows_ratio = snakemake.params["min_rows_ratio"] -corr_threshold = snakemake.params["corr_threshold"] - -# merge features -features, features_all_sensors = pd.DataFrame(columns=["local_date"]), pd.DataFrame(columns=["local_date"]) -pids = set() -last_pid = None -for path in snakemake.input["features"]: - pid = path.split("/")[2] - if pid not in pids: - pids.add(pid) - features_all_sensors["pid"] = last_pid - features = pd.concat([features, features_all_sensors], axis=0, ignore_index=True, sort=False) - features_all_sensors = pd.DataFrame(columns=["local_date"]) - features_per_sensor = pd.read_csv(path) - features_all_sensors = features_all_sensors.merge(features_per_sensor, on="local_date", how="outer") - last_pid = pid - -features_all_sensors["pid"] = last_pid -features = pd.concat([features, features_all_sensors], axis=0, ignore_index=True, sort=False) -features.set_index(["pid", "local_date"], inplace=True) - -# select days based on the input of "phone_valid_sensed_days" -selected_participants_and_days = pd.DataFrame() -for path in snakemake.input["phone_valid_sensed_days"]: - pid = path.split("/")[2] - phone_valid_sensed_days = pd.read_csv(path) - phone_valid_sensed_days = phone_valid_sensed_days[phone_valid_sensed_days["is_valid_sensed_day"] == True] - phone_valid_sensed_days["pid"] = pid - selected_participants_and_days = pd.concat([selected_participants_and_days, phone_valid_sensed_days], axis=0) - -selected_participants_and_days.set_index(["pid", "local_date"], inplace=True) -features = features.loc[features.index.intersection(selected_participants_and_days.index), :] - -# get correlation matrix -features = features.astype(float) -corr_matrix = features.corr(method=snakemake.params["corr_method"], min_periods=min_rows_ratio * features.shape[0]) - -# replace correlation coefficients less than corr_threshold with NA -corr_matrix[(corr_matrix > -corr_threshold) & (corr_matrix < corr_threshold)] = np.nan - -# plot heatmap -getCorrMatrixHeatmap(corr_matrix, snakemake.output[0]) diff --git a/src/visualization/heatmap_phone_data_yield_per_participant_per_time_segment.py b/src/visualization/heatmap_phone_data_yield_per_participant_per_time_segment.py new file mode 100644 index 00000000..fd9595c5 --- /dev/null +++ b/src/visualization/heatmap_phone_data_yield_per_participant_per_time_segment.py @@ -0,0 +1,85 @@ +import pandas as pd +import numpy as np +import plotly.graph_objects as go +import yaml + + + + + +def getPhoneDataYieldHeatmap(data_for_plot, y_axis_labels, time_segment, type, html_file): + + fig = go.Figure(data=go.Heatmap(z=data_for_plot.values.tolist(), + x=data_for_plot.columns.tolist(), + y=y_axis_labels, + hovertext=data_for_plot.values.tolist(), + hovertemplate="Time since first segment: %{x}
Participant: %{y}
Ratiovalidyielded" + type + ": %{z}", + zmin=0, zmax=1, + colorscale="Viridis")) + + fig.update_layout(title="Heatmap of valid yielded " + type + " ratio for " + time_segment + " segments.
y-axis shows participant information (format: pid.label).
x-axis shows the time since their first segment.
z-axis (color) shows valid yielded " + type + " ratio during a segment instance.") + + fig["layout"]["xaxis"].update(side="bottom") + fig["layout"].update(xaxis_title="Time Since First Segment") + fig["layout"].update(margin=dict(t=160)) + + html_file.write(fig.to_html(full_html=False, include_plotlyjs="cdn")) + + + + + + + +y_axis_labels, phone_data_yield_minutes, phone_data_yield_hours = [], {}, {} +for phone_data_yield_path, participant_file_path, time_segments_path in zip(snakemake.input["phone_data_yield"], snakemake.input["participant_file"], snakemake.input["time_segments_labels"]): + + # set pid.label as y_axis_label + pid = phone_data_yield_path.split("/")[3] + time_segments = pd.read_csv(time_segments_path, header=0)["label"] + + with open(participant_file_path, "r", encoding="utf-8") as f: + participant_file = yaml.safe_load(f) + label = participant_file["PHONE"]["LABEL"] + + y_axis_label = pid + "." + label + y_axis_labels.append(y_axis_label) + + + phone_data_yield = pd.read_csv(phone_data_yield_path, index_col=["local_segment_start_datetime"], parse_dates=["local_segment_start_datetime"]) + # make sure the phone_data_yield file contains "phone_data_yield_rapids_ratiovalidyieldedminutes" and "phone_data_yield_rapids_ratiovalidyieldedhours" columns + if ("phone_data_yield_rapids_ratiovalidyieldedminutes" not in phone_data_yield.columns) or ("phone_data_yield_rapids_ratiovalidyieldedhours" not in phone_data_yield.columns): + raise ValueError("Please make sure [PHONE_DATA_YIELD][RAPIDS][COMPUTE] is True AND [PHONE_DATA_YIELD][RAPIDS][FEATURES] contains [ratiovalidyieldedminutes, ratiovalidyieldedhours].") + + if not phone_data_yield.empty: + + for time_segment in time_segments: + phone_data_yield_per_segment = phone_data_yield[phone_data_yield["local_segment_label"] == time_segment] + + if not phone_data_yield_per_segment.empty: + + # set number of minutes after the first start date time of local segments as x_axis_label + phone_data_yield_per_segment.index = phone_data_yield_per_segment.index - phone_data_yield_per_segment.index.min() + + phone_data_yield_minutes_per_segment = phone_data_yield_per_segment[["phone_data_yield_rapids_ratiovalidyieldedminutes"]].rename(columns={"phone_data_yield_rapids_ratiovalidyieldedminutes": y_axis_label}) + phone_data_yield_hours_per_segment = phone_data_yield_per_segment[["phone_data_yield_rapids_ratiovalidyieldedhours"]].rename(columns={"phone_data_yield_rapids_ratiovalidyieldedhours": y_axis_label}) + + if time_segment not in phone_data_yield_minutes.keys(): + phone_data_yield_minutes[time_segment] = phone_data_yield_minutes_per_segment + phone_data_yield_hours[time_segment] = phone_data_yield_hours_per_segment + else: + phone_data_yield_minutes[time_segment] = pd.concat([phone_data_yield_minutes[time_segment], phone_data_yield_minutes_per_segment], axis=1, sort=True) + phone_data_yield_hours[time_segment] = pd.concat([phone_data_yield_hours[time_segment], phone_data_yield_hours_per_segment], axis=1, sort=True) + + +html_file = open(snakemake.output[0], "a", encoding="utf-8") +if len(phone_data_yield_minutes.keys()) == 0: + html_file.write("There is no sensor data for the sensors in [PHONE_DATA_YIELD][SENSORS].") +for time_segment in phone_data_yield_minutes.keys(): + minutes_data_for_plot = phone_data_yield_minutes[time_segment].transpose().reindex(pd.Index(y_axis_labels)).round(3) + hours_data_for_plot = phone_data_yield_hours[time_segment].transpose().reindex(pd.Index(y_axis_labels)).round(3) + + getPhoneDataYieldHeatmap(minutes_data_for_plot, y_axis_labels, time_segment, "minutes", html_file) + getPhoneDataYieldHeatmap(hours_data_for_plot, y_axis_labels, time_segment, "hours", html_file) + +html_file.close() diff --git a/src/visualization/heatmap_rows.py b/src/visualization/heatmap_rows.py deleted file mode 100644 index 764478ec..00000000 --- a/src/visualization/heatmap_rows.py +++ /dev/null @@ -1,68 +0,0 @@ -import pandas as pd -import numpy as np -import plotly.io as pio -import plotly.graph_objects as go -import datetime - -def getComplianceMatrix(dates, compliance_bins): - compliance_matrix = [] - for date in dates: - date_bins = compliance_bins[compliance_bins["local_date"] == date]["count"].tolist() - compliance_matrix.append(date_bins) - return compliance_matrix - - -def getRowCountHeatmap(dates, row_count_per_bin, sensor_name, pid, output_path, bin_size): - bins_per_hour = int(60 / bin_size) - x_axis_labels = ["{0:0=2d}".format(x // bins_per_hour) + ":" + \ - "{0:0=2d}".format(x % bins_per_hour * bin_size) for x in range(24 * bins_per_hour)] - plot = go.Figure(data=go.Heatmap(z=row_count_per_bin, - x=x_axis_labels, - y=[datetime.datetime.strftime(date, '%Y/%m/%d') for date in dates], - colorscale="Viridis")) - plot.update_layout(title="Row count heatmap for " + sensor_name + " of " + pid + "
Label: " + label + ", device_id: " + device_id) - pio.write_html(plot, file=output_path, auto_open=False, include_plotlyjs="cdn") - - - -sensor_data = pd.read_csv(snakemake.input["sensor"], encoding="ISO-8859-1") -sensor_name = snakemake.params["table"] -pid = snakemake.params["pid"] -bin_size = snakemake.params["bin_size"] - -with open(snakemake.input["pid_file"], encoding="ISO-8859-1") as external_file: - external_file_content = external_file.readlines() -device_id = external_file_content[0].split(",")[-1] -label = external_file_content[2] - - -# check if we have sensor data -if sensor_data.empty: - empty_html = open(snakemake.output[0], "w") - empty_html.write("There is no " + sensor_name + " data for " + pid + "
Label: " + label + ", device_id: " + device_id) - empty_html.close() -else: - start_date = sensor_data["local_date"][0] - end_date = sensor_data.at[sensor_data.index[-1],"local_date"] - - sensor_data["local_date_time"] = pd.to_datetime(sensor_data["local_date_time"]) - sensor_data = sensor_data[["local_date_time"]] - sensor_data["count"] = 1 - - # Add first and last day boundaries for resampling - sensor_data = sensor_data.append([pd.Series([datetime.datetime.strptime(start_date + " 00:00:00", "%Y-%m-%d %H:%M:%S"), 0], sensor_data.columns), - pd.Series([datetime.datetime.strptime(end_date + " 23:59:59", "%Y-%m-%d %H:%M:%S"), 0], sensor_data.columns)]) - - # Resample into bins with the size of bin_size - resampled_bins = pd.DataFrame(sensor_data.resample(str(bin_size) + "T", on="local_date_time")["count"].sum()) - - # Extract list of dates for creating the heatmap - resampled_bins.reset_index(inplace=True) - resampled_bins["local_date"] = resampled_bins["local_date_time"].dt.date - dates = resampled_bins["local_date"].drop_duplicates().tolist() - - # Create heatmap - row_count_per_bin = getComplianceMatrix(dates, resampled_bins) - row_count_per_bin = np.asarray(row_count_per_bin) - row_count_per_bin = np.where(row_count_per_bin == 0, np.nan, row_count_per_bin) - getRowCountHeatmap(dates, row_count_per_bin, sensor_name, pid, snakemake.output[0], bin_size) diff --git a/src/visualization/heatmap_sensed_bins.py b/src/visualization/heatmap_sensed_bins.py deleted file mode 100644 index 26639400..00000000 --- a/src/visualization/heatmap_sensed_bins.py +++ /dev/null @@ -1,50 +0,0 @@ -import pandas as pd -import numpy as np -import plotly.io as pio -import plotly.graph_objects as go -import datetime - -def getDatesComplianceMatrix(phone_sensed_bins): - dates = phone_sensed_bins.index - compliance_matrix = [] - for date in dates: - compliance_matrix.append(phone_sensed_bins.loc[date, :].tolist()) - return dates, compliance_matrix - -def getComplianceHeatmap(dates, compliance_matrix, pid, output_path, bin_size): - bins_per_hour = int(60 / bin_size) - x_axis_labels = ["{0:0=2d}".format(x // bins_per_hour) + ":" + \ - "{0:0=2d}".format(x % bins_per_hour * bin_size) for x in range(24 * bins_per_hour)] - plot = go.Figure(data=go.Heatmap(z=compliance_matrix, - x=x_axis_labels, - y=[datetime.datetime.strftime(date, '%Y/%m/%d') for date in dates], - colorscale='Viridis', - colorbar={'tick0': 0,'dtick': 1})) - plot.update_layout(title="Heatmap sensed bins.
Five-minute bins showing how many sensors logged at least one row of data in that period for " + pid + "
Label: " + label + ", device_id: " + device_id) - pio.write_html(plot, file=output_path, auto_open=False, include_plotlyjs="cdn") - -# get current patient id -pid = snakemake.params["pid"] -bin_size = snakemake.params["bin_size"] - -with open(snakemake.input["pid_file"], encoding="ISO-8859-1") as external_file: - external_file_content = external_file.readlines() -device_id = external_file_content[0].split(",")[-1] -label = external_file_content[2] - -phone_sensed_bins = pd.read_csv(snakemake.input["sensor"], parse_dates=["local_date"], index_col="local_date") - -if phone_sensed_bins.empty: - empty_html = open(snakemake.output[0], "w", encoding="ISO-8859-1") - empty_html.write("There is no sensor data for " + pid + "
Label: " + label + ", device_id: " + device_id) - empty_html.close() -else: - # resample to impute missing dates - phone_sensed_bins = phone_sensed_bins.resample("1D").asfreq().fillna(0) - # get dates and compliance_matrix - dates, compliance_matrix = getDatesComplianceMatrix(phone_sensed_bins) - # convert compliance_matrix from list to np.array and replace 0 with np.nan - compliance_matrix = np.asarray(compliance_matrix) - compliance_matrix = np.where(compliance_matrix == 0, np.nan, compliance_matrix) - # get heatmap - getComplianceHeatmap(dates, compliance_matrix, pid, snakemake.output[0], bin_size) \ No newline at end of file diff --git a/src/visualization/heatmap_sensor_row_count_per_time_segment.py b/src/visualization/heatmap_sensor_row_count_per_time_segment.py new file mode 100644 index 00000000..6b62e6e1 --- /dev/null +++ b/src/visualization/heatmap_sensor_row_count_per_time_segment.py @@ -0,0 +1,89 @@ +import pandas as pd +import numpy as np +import plotly.graph_objects as go +from importlib import util +from pathlib import Path +import yaml + + +def getRowCountHeatmap(data_for_plot, scaled_data_for_plot, pid, time_segment, html_file): + + fig = go.Figure(data=go.Heatmap(z=scaled_data_for_plot.values.tolist(), + x=data_for_plot.columns, + y=data_for_plot.index, + hovertext=data_for_plot.values.tolist(), + hovertemplate="Segment start: %{x}
Sensor: %{y}
Row count: %{hovertext}", + zmin=0, zmax=1, + colorscale='Viridis')) + + fig.update_layout(title="Heatmap of sensor row count for " + time_segment + " segments. Pid: " + pid +". Label: " + label + "
y-axis shows the included sensors.
x-axis shows the start (date and time) of a time segment.
z-axis (color) shows row count per sensor per segment instance.") + fig["layout"].update(margin=dict(t=160)) + + html_file.write(fig.to_html(full_html=False, include_plotlyjs="cdn")) + + + + +# import filter_data_by_segment from src/features/utils/utils.py +spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "features" / "utils" / "utils.py")) +mod = util.module_from_spec(spec) +spec.loader.exec_module(mod) +filter_data_by_segment = getattr(mod, "filter_data_by_segment") + + + + + +phone_data_yield = pd.read_csv(snakemake.input["phone_data_yield"], index_col=["local_segment_start_datetime"], parse_dates=["local_segment_start_datetime"]) +# make sure the phone_data_yield file contains "phone_data_yield_rapids_ratiovalidyieldedminutes" and "phone_data_yield_rapids_ratiovalidyieldedhours" columns +if ("phone_data_yield_rapids_ratiovalidyieldedminutes" not in phone_data_yield.columns) or ("phone_data_yield_rapids_ratiovalidyieldedhours" not in phone_data_yield.columns): + raise ValueError("Please make sure [PHONE_DATA_YIELD][RAPIDS][COMPUTE] is True AND [PHONE_DATA_YIELD][RAPIDS][FEATURES] contains [ratiovalidyieldedminutes, ratiovalidyieldedhours].") +phone_data_yield = phone_data_yield[["local_segment_label", "phone_data_yield_rapids_ratiovalidyieldedminutes", "phone_data_yield_rapids_ratiovalidyieldedhours"]] + +time_segments = pd.read_csv(snakemake.input["time_segments_labels"], header=0)["label"] +pid = snakemake.params["pid"] + +with open(snakemake.input["participant_file"], "r", encoding="utf-8") as f: + participant_file = yaml.safe_load(f) +label = participant_file["PHONE"]["LABEL"] + +sensor_names = [] +sensors_row_count = dict(zip(time_segments, [pd.DataFrame()] * len(time_segments))) + +for sensor_path in snakemake.input["all_sensors"]: + sensor_data = pd.read_csv(sensor_path, usecols=["assigned_segments"]) + sensor_name = sensor_path.split("/")[-1].replace("_with_datetime.csv", "") + sensor_names.append(sensor_name) + + if not sensor_data.empty: + for time_segment in time_segments: + sensor_data_per_segment = filter_data_by_segment(sensor_data, time_segment) + + if not sensor_data_per_segment.empty: + # extract local start datetime of the segment from "local_segment" column + sensor_data_per_segment["local_segment_start_datetime"] = pd.to_datetime(sensor_data_per_segment["local_segment"].apply(lambda x: x.split("#")[1].split(",")[0])) + sensor_row_count = sensor_data_per_segment.groupby("local_segment_start_datetime")[["local_segment"]].count().rename(columns={"local_segment": sensor_name}) + sensors_row_count[time_segment] = pd.concat([sensors_row_count[time_segment], sensor_row_count], axis=1, sort=False) + +# add phone data yield features and plot heatmap +html_file = open(snakemake.output[0], "a", encoding="utf-8") +sensor_names.extend(["ratiovalidyieldedminutes", "ratiovalidyieldedhours"]) +for time_segment in time_segments: + if not phone_data_yield.empty: + phone_data_yield_per_segment = phone_data_yield[phone_data_yield["local_segment_label"] == time_segment].rename(columns={"phone_data_yield_rapids_ratiovalidyieldedminutes": "ratiovalidyieldedminutes","phone_data_yield_rapids_ratiovalidyieldedhours": "ratiovalidyieldedhours"}).round(3) + if not phone_data_yield_per_segment.empty: + sensors_row_count[time_segment] = pd.concat([sensors_row_count[time_segment], phone_data_yield_per_segment], axis=1, sort=True) + + # consider all the sensors + data_for_plot = sensors_row_count[time_segment].transpose().reindex(pd.Index(sensor_names)) + + if data_for_plot.empty: + html_file.write("There are no records of selected sensors in database for " + time_segment + " segments. Pid: " + pid + ". Label: " + label + ".
") + else: + # except for phone data yield sensor, scale each sensor (row) to the range of [0, 1] + scaled_data_for_plot = data_for_plot.copy() + scaled_data_for_plot.loc[sensor_names[:-2]] = scaled_data_for_plot.fillna(np.nan).loc[sensor_names[:-2]].apply(lambda x: (x - np.nanmin(x)) / (np.nanmax(x) - np.nanmin(x)) if np.nanmax(x) != np.nanmin(x) else (x / np.nanmin(x)), axis=1) + + getRowCountHeatmap(data_for_plot, scaled_data_for_plot, pid, time_segment, html_file) + +html_file.close() diff --git a/src/visualization/heatmap_sensors_per_minute_per_time_segment.py b/src/visualization/heatmap_sensors_per_minute_per_time_segment.py new file mode 100644 index 00000000..dd524322 --- /dev/null +++ b/src/visualization/heatmap_sensors_per_minute_per_time_segment.py @@ -0,0 +1,100 @@ +import pandas as pd +import numpy as np +import plotly.graph_objects as go +from importlib import util +from pathlib import Path +import yaml + + +def colors2colorscale(colors): + colorscale = [] + length = len(colors) + for i in range(length): + if i != length - 1: + colorscale = colorscale + [[i/(length-1), colors[i]], [(i+1)/(length-1), colors[i]]] + else: + colorscale.append([1, colors[i]]) + return colorscale + +def getSensorsPerMinPerSegmentHeatmap(phone_data_yield, pid, time_segment, html_file): + + x_axis_labels = [pd.Timedelta(minutes=x) for x in phone_data_yield.columns] + + fig = go.Figure(data=go.Heatmap(z=phone_data_yield.values.tolist(), + x=x_axis_labels, + y=phone_data_yield.index, + zmin=0, zmax=16, + colorscale=colors2colorscale(colors), + colorbar=dict(thickness=25, tickvals=[1/2 + x for x in range(16)],ticktext=[x for x in range(16)]))) + + fig.update_layout(title="Number of sensors with any data per minute for " + time_segment + " segments. Pid: "+pid+". Label: " + label + "
y-axis shows the start (date and time) of a time segment.
x-axis shows the time since the start of the time segment.
z-axis (color) shows how many sensors logged at least one row of data per minute.") + fig["layout"].update(margin=dict(t=160)) + + html_file.write(fig.to_html(full_html=False, include_plotlyjs="cdn")) + + + + + +# import filter_data_by_segment from src/features/utils/utils.py +spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "features" / "utils" / "utils.py")) +mod = util.module_from_spec(spec) +spec.loader.exec_module(mod) +filter_data_by_segment = getattr(mod, "filter_data_by_segment") + + + + + + + + + + +colors = ["red", "#3D0751", "#423176", "#414381", "#3F5688", "#42678B", "#42768C", "#45868B", "#4A968A", "#53A485", "#5FB57E", "#76C170", "#91CF63", "#B4DA55", "#D9E152", "#F8E755", "#DEE00F"] +pid = snakemake.params["pid"] +time_segments_labels = pd.read_csv(snakemake.input["time_segments_labels"], header=0) + +with open(snakemake.input["participant_file"], "r", encoding="utf-8") as f: + participant_file = yaml.safe_load(f) +label = participant_file["PHONE"]["LABEL"] + +phone_data_yield = pd.read_csv(snakemake.input["phone_data_yield"], parse_dates=["local_date_time"]) + +html_file = open(snakemake.output[0], "a", encoding="utf-8") +if phone_data_yield.empty: + html_file.write("There is no sensor data for " + pid + " (pid) and " + label + " (label).") +else: + for time_segment in time_segments_labels["label"]: + phone_data_yield_per_segment = filter_data_by_segment(phone_data_yield, time_segment) + + if phone_data_yield_per_segment.empty: + html_file.write("There is no sensor data of " + time_segment + " segments for " + pid + " (pid) and " + label + " (label).
") + else: + # calculate the length (in minute) of per segment instance + phone_data_yield_per_segment["length"] = phone_data_yield_per_segment["timestamps_segment"].str.split(",").apply(lambda x: int((int(x[1])-int(x[0])) / (1000 * 60))) + # calculate the number of sensors logged at least one row of data per minute. + phone_data_yield_per_segment = phone_data_yield_per_segment.groupby(["local_segment", "length", "local_date", "local_hour", "local_minute"])[["sensor", "local_date_time"]].max().reset_index() + # extract local start datetime of the segment from "local_segment" column + phone_data_yield_per_segment["local_segment_start_datetimes"] = pd.to_datetime(phone_data_yield_per_segment["local_segment"].apply(lambda x: x.split("#")[1].split(",")[0])) + # calculate the number of minutes after local start datetime of the segment + phone_data_yield_per_segment["minutes_after_segment_start"] = ((phone_data_yield_per_segment["local_date_time"] - phone_data_yield_per_segment["local_segment_start_datetimes"]) / pd.Timedelta(minutes=1)).astype("int") + + # impute missing rows with 0 + columns_for_full_index = phone_data_yield_per_segment[["local_segment_start_datetimes", "length"]].drop_duplicates(keep="first") + columns_for_full_index = columns_for_full_index.apply(lambda row: [[row["local_segment_start_datetimes"], x] for x in range(row["length"] + 1)], axis=1) + full_index = [] + for columns in columns_for_full_index: + full_index = full_index + columns + full_index = pd.MultiIndex.from_tuples(full_index, names=("local_segment_start_datetimes", "minutes_after_segment_start")) + phone_data_yield_per_segment = phone_data_yield_per_segment.set_index(["local_segment_start_datetimes", "minutes_after_segment_start"]).reindex(full_index).reset_index().fillna(0) + + # transpose the dataframe per local start datetime of the segment and discard the useless index layer + phone_data_yield_per_segment = phone_data_yield_per_segment.groupby("local_segment_start_datetimes")[["minutes_after_segment_start", "sensor"]].apply(lambda x: x.set_index("minutes_after_segment_start").transpose()) + phone_data_yield_per_segment.index = phone_data_yield_per_segment.index.get_level_values("local_segment_start_datetimes") + + # get heatmap + getSensorsPerMinPerSegmentHeatmap(phone_data_yield_per_segment, pid, time_segment, html_file) + + +html_file.close() diff --git a/src/visualization/histogram_phone_data_yield.py b/src/visualization/histogram_phone_data_yield.py new file mode 100644 index 00000000..cd15ec8d --- /dev/null +++ b/src/visualization/histogram_phone_data_yield.py @@ -0,0 +1,25 @@ +import pandas as pd +import plotly.express as px + + +phone_data_yield = pd.read_csv(snakemake.input[0]) + +# make sure the input file contains "phone_data_yield_rapids_ratiovalidyieldedminutes" and "phone_data_yield_rapids_ratiovalidyieldedhours" columns +if ("phone_data_yield_rapids_ratiovalidyieldedminutes" not in phone_data_yield.columns) or ("phone_data_yield_rapids_ratiovalidyieldedhours" not in phone_data_yield.columns): + raise ValueError("Please make sure [PHONE_DATA_YIELD][RAPIDS][COMPUTE] is True AND [PHONE_DATA_YIELD][RAPIDS][FEATURES] contains [ratiovalidyieldedminutes, ratiovalidyieldedhours].") + +html_file = open(snakemake.output[0], "a", encoding="utf-8") +if phone_data_yield.empty: + html_file.write("There is no sensor data for the sensors in [PHONE_DATA_YIELD][SENSORS].") +else: + # plot ratio valid yielded minutes histogram + fig_ratiovalidyieldedminutes = px.histogram(phone_data_yield, x="phone_data_yield_rapids_ratiovalidyieldedminutes", color="local_segment_label") + fig_ratiovalidyieldedminutes.update_layout(title="Histogram of valid yielded minutes ratio per time segment.") + html_file.write(fig_ratiovalidyieldedminutes.to_html(full_html=False, include_plotlyjs="cdn")) + + # plot ratio valid yielded hours histogram + fig_ratiovalidyieldedhours = px.histogram(phone_data_yield, x="phone_data_yield_rapids_ratiovalidyieldedhours", color="local_segment_label") + fig_ratiovalidyieldedhours.update_layout(title="Histogram of valid yielded hours ratio per time segment.") + html_file.write(fig_ratiovalidyieldedhours.to_html(full_html=False, include_plotlyjs="cdn")) + +html_file.close() diff --git a/src/visualization/histogram_valid_sensed_hours.py b/src/visualization/histogram_valid_sensed_hours.py deleted file mode 100644 index cb5c904b..00000000 --- a/src/visualization/histogram_valid_sensed_hours.py +++ /dev/null @@ -1,16 +0,0 @@ -import pandas as pd -import plotly.express as px -import plotly.io as pio - - -# merge "phone_valid_sensed_days" for all participants -selected_participants_and_days = pd.DataFrame() -for path in snakemake.input["phone_valid_sensed_days"]: - phone_valid_sensed_days = pd.read_csv(path) - phone_valid_sensed_days = phone_valid_sensed_days[phone_valid_sensed_days["is_valid_sensed_day"] == True] - selected_participants_and_days = pd.concat([selected_participants_and_days, phone_valid_sensed_days], axis=0) - -# plot histogram -fig = px.histogram(selected_participants_and_days, x="valid_sensed_hours") -fig.update_layout(title="Phone Valid Hours Histogram") -pio.write_html(fig, file=snakemake.output[0], auto_open=False, include_plotlyjs="cdn") \ No newline at end of file diff --git a/src/visualization/heatmap_sensed_bins_all_participants.Rmd b/src/visualization/merge_heatmap_sensor_row_count_per_time_segment.Rmd similarity index 63% rename from src/visualization/heatmap_sensed_bins_all_participants.Rmd rename to src/visualization/merge_heatmap_sensor_row_count_per_time_segment.Rmd index e6dbdbbf..b6c8463c 100644 --- a/src/visualization/heatmap_sensed_bins_all_participants.Rmd +++ b/src/visualization/merge_heatmap_sensor_row_count_per_time_segment.Rmd @@ -1,10 +1,10 @@ --- -title: "Heatmap Sensed Bins Report" +title: "Sensor Row Count per Time Segment For All Participants" author: - - "MoSHI Pipeline" + - "RAPIDS" date: "`r format(Sys.time(), '%d %B, %Y')`" params: - rmd: "heatmap_sensed_bins_all_participants.Rmd" + rmd: "merge_heatmap_sensor_row_count_per_time_segment.Rmd" output: html_document: highlight: tango @@ -17,14 +17,17 @@ output: smooth_scroll: yes --- + + ```{r include=FALSE} source("renv/activate.R") ``` -## All phone sensors ```{r, echo=FALSE} -heatmaps <- snakemake@input[["heatmap_sensed_bins"]] +heatmaps <- snakemake@input[["heatmap_sensor_row_count_per_time_segment"]] heatmaps.html <- vector(mode="list", length(heatmaps)) for(pid in 1:length(heatmaps)){ diff --git a/src/visualization/heatmap_days_by_sensors_all_participants.Rmd b/src/visualization/merge_heatmap_sensors_per_minute_per_time_segment.Rmd similarity index 63% rename from src/visualization/heatmap_days_by_sensors_all_participants.Rmd rename to src/visualization/merge_heatmap_sensors_per_minute_per_time_segment.Rmd index cb4303c2..2e1143e0 100644 --- a/src/visualization/heatmap_days_by_sensors_all_participants.Rmd +++ b/src/visualization/merge_heatmap_sensors_per_minute_per_time_segment.Rmd @@ -1,10 +1,10 @@ --- -title: "Heatmap Rows Report" +title: "Sensors per Minute per Time Segment for All Participants" author: - - "MoSHI Pipeline" + - "RAPIDS" date: "`r format(Sys.time(), '%d %B, %Y')`" params: - rmd: "heatmap_days_by_sensors_all_participants.Rmd" + rmd: "merge_heatmap_sensors_per_minute_per_time_segment.Rmd" output: html_document: highlight: tango @@ -17,14 +17,17 @@ output: smooth_scroll: yes --- + + ```{r include=FALSE} source("renv/activate.R") ``` -## All phone sensors ```{r, echo=FALSE} -heatmaps <- snakemake@input[["heatmap_rows"]] +heatmaps <- snakemake@input[["heatmap_sensors_per_minute_per_time_segment"]] heatmaps.html <- vector(mode="list", length(heatmaps)) for(pid in 1:length(heatmaps)){ diff --git a/src/visualization/overall_compliance_heatmap.py b/src/visualization/overall_compliance_heatmap.py deleted file mode 100644 index 877ab0d2..00000000 --- a/src/visualization/overall_compliance_heatmap.py +++ /dev/null @@ -1,102 +0,0 @@ -import pandas as pd -import numpy as np -import plotly.io as pio -import plotly.graph_objects as go -from dateutil import tz -import datetime - -def getOneRow(data_per_participant, last_certain_dates, col_name, row, expected_num_of_days, only_show_valid_days): - - data = pd.read_csv(data_per_participant, index_col=["local_date"]) - - if col_name == "num_sensors": - data["num_sensors"] = data.max(axis=1) - - if only_show_valid_days and col_name == "valid_sensed_hours": - # replace invalid days' valid sensed hours with np.nan to let our heatmap only shows valid days - data.loc[data[data["is_valid_sensed_day"] == False].index, "valid_sensed_hours"] = np.nan - - if expected_num_of_days == -1: - # show all days - data.index = pd.to_datetime(data.index) - start_date = data.index.min() - # upsample data into one day bins - data = data.resample("1D").sum() - data["date_idx"] = (data.index - start_date).days - data.set_index("date_idx", inplace=True, drop=True) - row = row + data[col_name].tolist() - else: - # only show last certain days - for date in last_certain_dates: - if date in data.index: - row.append(data.loc[date][col_name]) - else: - row.append(0) - - return row - -def getOverallComplianceHeatmap(sensors_with_data, valid_sensed_hours, last_certain_dates, bin_size, min_bins_per_hour, expected_num_of_days, output_path): - plot = go.Figure(data=go.Heatmap(z=valid_sensed_hours[last_certain_dates].values, - x=[date.replace("-", "/") for date in last_certain_dates] if expected_num_of_days != -1 else last_certain_dates, - y=[pid + "." + label for pid, label in zip(sensors_with_data["pid"].to_list(), sensors_with_data["label"].to_list())], - text=sensors_with_data[last_certain_dates].values, - hovertemplate="Date: %{x}
Participant: %{y}
Valid sensed hours: %{z}
Number of sensors with data: %{text}" if expected_num_of_days != -1 else "Day index: %{x}
Participant: %{y}
Valid sensed hours: %{z}
Number of sensors with data: %{text}", - colorscale="Viridis", - colorbar={"tick0": 0,"dtick": 1}, - showscale=True)) - if expected_num_of_days != -1: - plot.update_layout(title="Overall compliance heatmap for last " + str(expected_num_of_days) + " days.
Bin's color shows valid sensed hours for that day.
A valid hour has at least one row of any sensor in "+ str(min_bins_per_hour) +" out of " + str(int(60 / bin_size)) + " bins of " + str(bin_size) + " minutes.
You can hover over every day to see the number of sensors with data in that day.") - else: - plot.update_layout(title="Overall compliance heatmap for all days.
Bin's color shows valid sensed hours for that day.
A valid hour has at least one row of any sensor in "+ str(min_bins_per_hour) +" out of " + str(int(60 / bin_size)) + " bins of " + str(bin_size) + " minutes.
You can hover over every day to see the number of sensors with data in that day.") - - plot["layout"]["xaxis"].update(side="bottom") - plot["layout"].update(xaxis_title="Day indexes") - plot["layout"].update(margin=dict(t=160)) - pio.write_html(plot, file=output_path, auto_open=False, include_plotlyjs="cdn") - - -phone_sensed_bins = snakemake.input["phone_sensed_bins"] -phone_valid_sensed_days = snakemake.input["phone_valid_sensed_days"] -pid_files = snakemake.input["pid_files"] -only_show_valid_days = snakemake.params["only_show_valid_days"] -local_timezone = snakemake.params["local_timezone"] -bin_size = snakemake.params["bin_size"] -min_bins_per_hour = snakemake.params["min_bins_per_hour"] -expected_num_of_days = int(snakemake.params["expected_num_of_days"]) - -if expected_num_of_days < -1: - raise ValueError("EXPECTED_NUM_OF_DAYS of OVERALL_COMPLIANCE_HEATMAP section in config.yaml must be larger or equal to -1.") - -last_certain_dates = [] -if expected_num_of_days != -1: - # get the list of dates to show - cur_date = datetime.datetime.now().astimezone(tz.gettz(local_timezone)).date() - for date_offset in range(expected_num_of_days-1, -1, -1): - last_certain_dates.append((cur_date - datetime.timedelta(days=date_offset)).strftime("%Y-%m-%d")) - -sensors_with_data_records, valid_sensed_hours_records = [], [] -for sensors_with_data_individual, valid_sensed_hours_individual, pid_file in zip(phone_sensed_bins, phone_valid_sensed_days, pid_files): - - with open(pid_file, encoding="ISO-8859-1") as external_file: - external_file_content = external_file.readlines() - device_id = external_file_content[0].split(",")[-1].strip() - label = external_file_content[2].strip() - pid = pid_file.split("/")[-1] - - sensors_with_data_records.append(getOneRow(sensors_with_data_individual, last_certain_dates, "num_sensors", [pid, label, device_id], expected_num_of_days, only_show_valid_days)) - valid_sensed_hours_records.append(getOneRow(valid_sensed_hours_individual, last_certain_dates, "valid_sensed_hours", [pid, label, device_id], expected_num_of_days, only_show_valid_days)) - -if expected_num_of_days == -1: - # get the date_idx of all days - total_num_of_days = max([len(x) for x in sensors_with_data_records]) - 3 - last_certain_dates = [date_idx for date_idx in range(total_num_of_days)] - -sensors_with_data = pd.DataFrame(data=sensors_with_data_records, columns=["pid", "label", "device_id"] + last_certain_dates).replace(0, np.nan) -valid_sensed_hours = pd.DataFrame(data=valid_sensed_hours_records, columns=["pid", "label", "device_id"] + last_certain_dates).replace(0, np.nan) - -if sensors_with_data.empty: - empty_html = open(snakemake.output[0], "w") - empty_html.write("There is no sensor data for all participants") - empty_html.close() -else: - getOverallComplianceHeatmap(sensors_with_data, valid_sensed_hours, last_certain_dates, bin_size, min_bins_per_hour, expected_num_of_days, snakemake.output[0]) diff --git a/src/visualization/visualize.py b/src/visualization/visualize.py deleted file mode 100644 index e69de29b..00000000 diff --git a/tests/Snakefile b/tests/Snakefile index e8c10bca..e4e8574c 100644 --- a/tests/Snakefile +++ b/tests/Snakefile @@ -5,24 +5,21 @@ include: "../rules/preprocessing.smk" include: "../rules/features.smk" include: "../rules/reports.smk" +import itertools + files_to_compute = [] if len(config["PIDS"]) == 0: raise ValueError("Add participants IDs to PIDS in config.yaml. Remember to create their participant files in data/external") if config["PHONE_VALID_SENSED_BINS"]["COMPUTE"] or config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: # valid sensed bins is necessary for sensed days, so we add these files anyways if sensed days are requested - if len(config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]) == 0: - raise ValueError("If you want to compute PHONE_VALID_SENSED_BINS or PHONE_VALID_SENSED_DAYS, you need to add at least one table to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml") + if len(config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]) == 0: + raise ValueError("If you want to compute PHONE_VALID_SENSED_BINS or PHONE_VALID_SENSED_DAYS, you need to add at least one PHONE_SENSOR to [PHONE_VALID_SENSED_BINS][PHONE_SENSORS] in config.yaml") - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - tables_android = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["IOS"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]] # for android, discard any ios tables that may exist - tables_ios = [table for table in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"] if table not in [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"]]] # for ios, discard any android tables that may exist - - for pids,table in zip([pids_android, pids_ios], [tables_android, tables_ios]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=map(str.lower, config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]))) + files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=map(str.lower, config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]))) files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_timestamps.csv", pid=config["PIDS"])) if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: files_to_compute.extend(expand("data/interim/{pid}/phone_valid_sensed_days_{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins.csv", @@ -30,82 +27,177 @@ if config["PHONE_VALID_SENSED_DAYS"]["COMPUTE"]: min_valid_hours_per_day=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) -if config["MESSAGES"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["MESSAGES"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/messages_{messages_type}_{day_segment}.csv", pid=config["PIDS"], messages_type = config["MESSAGES"]["TYPES"], day_segment = config["MESSAGES"]["DAY_SEGMENTS"])) +for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys(): + if config["PHONE_MESSAGES"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_messages_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_messages_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_messages_features/phone_messages_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_MESSAGES"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_messages.csv", pid=config["PIDS"])) -if config["CALLS"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["CALLS"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/calls_{call_type}_{segment}.csv", pid=config["PIDS"], call_type=config["CALLS"]["TYPES"], segment = config["CALLS"]["DAY_SEGMENTS"])) +for provider in config["PHONE_CALLS"]["PROVIDERS"].keys(): + if config["PHONE_CALLS"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_calls_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_calls_features/phone_calls_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_CALLS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_calls.csv", pid=config["PIDS"])) -if config["BLUETOOTH"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BLUETOOTH"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/bluetooth_{segment}.csv", pid=config["PIDS"], segment = config["BLUETOOTH"]["DAY_SEGMENTS"])) +for provider in config["PHONE_BLUETOOTH"]["PROVIDERS"].keys(): + if config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_bluetooth_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_bluetooth_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_bluetooth_features/phone_bluetooth_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_bluetooth.csv", pid=config["PIDS"])) -if config["ACTIVITY_RECOGNITION"]["COMPUTE"]: - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) +for provider in config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"].keys(): + if config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_activity_recognition_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_activity_recognition_features/phone_activity_recognition_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_activity_recognition.csv", pid=config["PIDS"])) + + +for provider in config["PHONE_BATTERY"]["PROVIDERS"].keys(): + if config["PHONE_BATTERY"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_battery_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_battery_features/phone_battery_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_BATTERY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_battery.csv", pid=config["PIDS"])) + + +for provider in config["PHONE_SCREEN"]["PROVIDERS"].keys(): + if config["PHONE_SCREEN"]["PROVIDERS"][provider]["COMPUTE"]: + if "PHONE_SCREEN" in config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]: + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + else: + raise ValueError("Error: Add PHONE_SCREEN (and as many phone sensor as you have in your database) to [PHONE_VALID_SENSED_BINS][PHONE_SENSORS] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)") + files_to_compute.extend(expand("data/raw/{pid}/phone_screen_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_screen_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_screen_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes_resampled.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_episodes_resampled_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_screen_features/phone_screen_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_SCREEN"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_screen.csv", pid=config["PIDS"])) + +for provider in config["PHONE_LIGHT"]["PROVIDERS"].keys(): + if config["PHONE_LIGHT"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_light_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_light_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_light_features/phone_light_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LIGHT"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_light.csv", pid=config["PIDS"],)) + +for provider in config["PHONE_ACCELEROMETER"]["PROVIDERS"].keys(): + if config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_accelerometer_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_accelerometer_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_accelerometer_features/phone_accelerometer_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_accelerometer.csv", pid=config["PIDS"])) + +for provider in config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys(): + if config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_applications_foreground_with_datetime_with_categories.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_applications_foreground_features/phone_applications_foreground_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_applications_foreground.csv", pid=config["PIDS"])) + +for provider in config["PHONE_WIFI_VISIBLE"]["PROVIDERS"].keys(): + if config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_visible_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_visible_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_wifi_visible_features/phone_wifi_visible_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_visible.csv", pid=config["PIDS"])) + +for provider in config["PHONE_WIFI_CONNECTED"]["PROVIDERS"].keys(): + if config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_connected_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_wifi_connected_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_wifi_connected_features/phone_wifi_connected_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_connected.csv", pid=config["PIDS"])) + +for provider in config["PHONE_CONVERSATION"]["PROVIDERS"].keys(): + if config["PHONE_CONVERSATION"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/raw/{pid}/phone_conversation_with_datetime_unified.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_conversation_features/phone_conversation_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_CONVERSATION"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_conversation.csv", pid=config["PIDS"])) + +for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys(): + if config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["COMPUTE"]: + if config["PHONE_LOCATIONS"]["LOCATIONS_TO_USE"] == "RESAMPLE_FUSED": + if "PHONE_LOCATIONS" in config["PHONE_VALID_SENSED_BINS"]["PHONE_SENSORS"]: + files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) + else: + raise ValueError("Error: Add PHONE_LOCATIONS (and as many PHONE_SENSORS as you have) to [PHONE_VALID_SENSED_BINS][PHONE_SENSORS] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data) which is used to resample fused location data (RESAMPLED_FUSED)") + + files_to_compute.extend(expand("data/raw/{pid}/phone_locations_raw.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_locations_processed_with_datetime.csv", pid=config["PIDS"])) + files_to_compute.extend(expand("data/interim/{pid}/phone_locations_features/phone_locations_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower())) + files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"])) + +if config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: + raise ValueError("config['FITBIT_HEARTRATE']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_HEARTRATE"]["TABLE_FORMAT"]) + +if config["FITBIT_STEPS"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: + raise ValueError("config['FITBIT_STEPS']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_STEPS"]["TABLE_FORMAT"]) + +if config["FITBIT_CALORIES"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: + raise ValueError("config['FITBIT_CALORIES']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_CALORIES"]["TABLE_FORMAT"]) + +if config["FITBIT_SLEEP"]["TABLE_FORMAT"] not in ["JSON", "CSV"]: + raise ValueError("config['FITBIT_SLEEP']['TABLE_FORMAT'] should be JSON or CSV but you typed" + config["FITBIT_SLEEP"]["TABLE_FORMAT"]) + +for provider in config["FITBIT_HEARTRATE"]["PROVIDERS"].keys(): + if config["FITBIT_HEARTRATE"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_HEARTRATE"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_heartrate_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + +for provider in config["FITBIT_STEPS"]["PROVIDERS"].keys(): + if config["FITBIT_STEPS"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_STEPS"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_steps_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + +for provider in config["FITBIT_CALORIES"]["PROVIDERS"].keys(): + if config["FITBIT_CALORIES"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_CALORIES"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_calories_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["summary", "intraday"])) + +for provider in config["FITBIT_SLEEP"]["PROVIDERS"].keys(): + if config["FITBIT_SLEEP"]["PROVIDERS"][provider]["COMPUTE"]: + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_raw.csv", pid=config["PIDS"], fitbit_data_type=(["json"] if config["FITBIT_SLEEP"]["TABLE_FORMAT"] == "JSON" else ["summary", "intraday"]))) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_parsed_episodes.csv", pid=config["PIDS"], fitbit_data_type=["summary"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_parsed.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) + files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_{fitbit_data_type}_parsed_with_datetime.csv", pid=config["PIDS"], fitbit_data_type=["intraday"])) + +# visualization for data exploration +if config["HEATMAP_FEATURES_CORRELATIONS"]["PLOT"]: + files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_features_correlations.html", min_valid_hours_per_day=config["HEATMAP_FEATURES_CORRELATIONS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) - for pids,table in zip([pids_android, pids_ios], [config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["ANDROID"], config["ACTIVITY_RECOGNITION"]["DB_TABLE"]["IOS"]]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/processed/{pid}/{sensor}_deltas.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/processed/{pid}/activity_recognition_{day_segment}.csv",pid=config["PIDS"], day_segment = config["ACTIVITY_RECOGNITION"]["DAY_SEGMENTS"])) +if config["HISTOGRAM_VALID_SENSED_HOURS"]["PLOT"]: + files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/histogram_valid_sensed_hours.html", min_valid_hours_per_day=config["HISTOGRAM_VALID_SENSED_HOURS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) -if config["BATTERY"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["BATTERY"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/battery_deltas.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/processed/{pid}/battery_{day_segment}.csv", pid = config["PIDS"], day_segment = config["BATTERY"]["DAY_SEGMENTS"])) +if config["HEATMAP_DAYS_BY_SENSORS"]["PLOT"]: + files_to_compute.extend(expand("reports/interim/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/{pid}/heatmap_days_by_sensors.html", pid=config["PIDS"], min_valid_hours_per_day=config["HEATMAP_DAYS_BY_SENSORS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) + files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/heatmap_days_by_sensors_all_participants.html", min_valid_hours_per_day=config["HEATMAP_DAYS_BY_SENSORS"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) -if config["SCREEN"]["COMPUTE"]: - if config["SCREEN"]["DB_TABLE"] in config["PHONE_VALID_SENSED_BINS"]["DB_TABLES"]: - files_to_compute.extend(expand("data/interim/{pid}/phone_sensed_bins.csv", pid=config["PIDS"])) - else: - raise ValueError("Error: Add your screen table (and as many sensor tables as you have) to [PHONE_VALID_SENSED_BINS][DB_TABLES] in config.yaml. This is necessary to compute phone_sensed_bins (bins of time when the smartphone was sensing data)") - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=config["PIDS"], sensor=config["SCREEN"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/screen_deltas.csv", pid=config["PIDS"])) - files_to_compute.extend(expand("data/processed/{pid}/screen_{day_segment}.csv", pid = config["PIDS"], day_segment = config["SCREEN"]["DAY_SEGMENTS"])) +if config["HEATMAP_SENSED_BINS"]["PLOT"]: + files_to_compute.extend(expand("reports/interim/heatmap_sensed_bins/{pid}/heatmap_sensed_bins.html", pid=config["PIDS"])) + files_to_compute.extend(["reports/data_exploration/heatmap_sensed_bins_all_participants.html"]) -if config["LIGHT"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["LIGHT"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/light_{day_segment}.csv", pid = config["PIDS"], day_segment = config["LIGHT"]["DAY_SEGMENTS"])) - -if config["APPLICATIONS_FOREGROUND"]["COMPUTE"]: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/interim/{pid}/{sensor}_with_datetime_with_genre.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])) - files_to_compute.extend(expand("data/processed/{pid}/applications_foreground_{day_segment}.csv", pid = config["PIDS"], day_segment = config["APPLICATIONS_FOREGROUND"]["DAY_SEGMENTS"])) - -if config["WIFI"]["COMPUTE"]: - if len(config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"]) > 0: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["VISIBLE_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/processed/{pid}/wifi_{day_segment}.csv", pid = config["PIDS"], day_segment = config["WIFI"]["DAY_SEGMENTS"])) - - if len(config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"]) > 0: - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["WIFI"]["DB_TABLE"]["CONNECTED_ACCESS_POINTS"])) - files_to_compute.extend(expand("data/processed/{pid}/wifi_{day_segment}.csv", pid = config["PIDS"], day_segment = config["WIFI"]["DAY_SEGMENTS"])) - -if config["CONVERSATION"]["COMPUTE"]: - pids_android = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "android", config["PIDS"])) - pids_ios = list(filter(lambda pid: infer_participant_platform("data/external/" + pid) == "ios", config["PIDS"])) - - for pids,table in zip([pids_android, pids_ios], [config["CONVERSATION"]["DB_TABLE"]["ANDROID"], config["CONVERSATION"]["DB_TABLE"]["IOS"]]): - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_unified.csv", pid=pids, sensor=table)) - files_to_compute.extend(expand("data/processed/{pid}/conversation_{day_segment}.csv",pid=config["PIDS"], day_segment = config["CONVERSATION"]["DAY_SEGMENTS"])) +if config["OVERALL_COMPLIANCE_HEATMAP"]["PLOT"]: + files_to_compute.extend(expand("reports/data_exploration/{min_valid_hours_per_day}hours_{min_valid_bins_per_hour}bins/overall_compliance_heatmap.html", min_valid_hours_per_day=config["OVERALL_COMPLIANCE_HEATMAP"]["MIN_VALID_HOURS_PER_DAY"], min_valid_bins_per_hour=config["PHONE_VALID_SENSED_DAYS"]["MIN_VALID_BINS_PER_HOUR"])) rule all: diff --git a/tests/data/external/participant_files/test01.yaml b/tests/data/external/participant_files/test01.yaml new file mode 100644 index 00000000..22910e36 --- /dev/null +++ b/tests/data/external/participant_files/test01.yaml @@ -0,0 +1,11 @@ +PHONE: + DEVICE_IDS: [wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun] + PLATFORMS: [android] + LABEL: test01 android + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 +FITBIT: + DEVICE_IDS: [wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun] + LABEL: test01 android + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 diff --git a/tests/data/external/participant_files/test02.yaml b/tests/data/external/participant_files/test02.yaml new file mode 100644 index 00000000..34cf92e7 --- /dev/null +++ b/tests/data/external/participant_files/test02.yaml @@ -0,0 +1,11 @@ +PHONE: + DEVICE_IDS: [7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w] + PLATFORMS: [ios] + LABEL: test02 ios + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 +FITBIT: + DEVICE_IDS: [7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w] + LABEL: test02 ios + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 diff --git a/tests/data/external/participant_files/test03.yaml b/tests/data/external/participant_files/test03.yaml new file mode 100644 index 00000000..d9b44c27 --- /dev/null +++ b/tests/data/external/participant_files/test03.yaml @@ -0,0 +1,11 @@ +PHONE: + DEVICE_IDS: [bU6WEnGU-bjBN-HhUh-7XNT-ZnrLJnOTW9Or] + PLATFORMS: [android] + LABEL: test03 android + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 +FITBIT: + DEVICE_IDS: [bU6WEnGU-bjBN-HhUh-7XNT-ZnrLJnOTW9Or] + LABEL: test03 android + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 diff --git a/tests/data/external/participant_files/test04.yaml b/tests/data/external/participant_files/test04.yaml new file mode 100644 index 00000000..b4930d28 --- /dev/null +++ b/tests/data/external/participant_files/test04.yaml @@ -0,0 +1,11 @@ +PHONE: + DEVICE_IDS: [dGhYuH4N-8D8J-mL6l-9uQA-ArIzHIjBiJxU] + PLATFORMS: [ios] + LABEL: test04 ios + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 +FITBIT: + DEVICE_IDS: [dGhYuH4N-8D8J-mL6l-9uQA-ArIzHIjBiJxU] + LABEL: test04 ios + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 diff --git a/tests/data/external/participant_files/test05.yaml b/tests/data/external/participant_files/test05.yaml new file mode 100644 index 00000000..1fb61d16 --- /dev/null +++ b/tests/data/external/participant_files/test05.yaml @@ -0,0 +1,11 @@ +PHONE: + DEVICE_IDS: [tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun] + PLATFORMS: [android] + LABEL: test05 android + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 +FITBIT: + DEVICE_IDS: [tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun] + LABEL: test05 android + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 diff --git a/tests/data/external/participant_files/test06.yaml b/tests/data/external/participant_files/test06.yaml new file mode 100644 index 00000000..cbbdde72 --- /dev/null +++ b/tests/data/external/participant_files/test06.yaml @@ -0,0 +1,11 @@ +PHONE: + DEVICE_IDS: [3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w] + PLATFORMS: [ios] + LABEL: test06 ios + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 +FITBIT: + DEVICE_IDS: [3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w] + LABEL: test06 ios + START_DATE: 2020/01/01 + END_DATE: 2020/06/01 diff --git a/tests/data/external/test05 b/tests/data/external/test05 new file mode 100644 index 00000000..d08c6039 --- /dev/null +++ b/tests/data/external/test05 @@ -0,0 +1,4 @@ +tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun +android +test05 android +2020/01/01,2020/06/01 diff --git a/tests/data/external/test06 b/tests/data/external/test06 new file mode 100644 index 00000000..f3032309 --- /dev/null +++ b/tests/data/external/test06 @@ -0,0 +1,4 @@ +3jAbRIXO-xKTC-0bhC-PZXC-7yKzcQm4sf5w +ios +test06 ios +2020/01/01,2020/06/01 \ No newline at end of file diff --git a/tests/data/processed/features/frequency/test03/phone_applications_foreground.csv b/tests/data/processed/features/frequency/test03/phone_applications_foreground.csv new file mode 100644 index 00000000..c2f63594 --- /dev/null +++ b/tests/data/processed/features/frequency/test03/phone_applications_foreground.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","apps_rapids_countemail","apps_rapids_countall","apps_rapids_countentertainment","apps_rapids_countsocial","apps_rapids_counttop1global","apps_rapids_countcom.facebook.moments","apps_rapids_timeoffirstuseemail","apps_rapids_timeoffirstuseall","apps_rapids_timeoffirstuseentertainment","apps_rapids_timeoffirstusesocial","apps_rapids_timeoffirstusetop1global","apps_rapids_timeoffirstusecom.facebook.moments","apps_rapids_timeoflastuseemail","apps_rapids_timeoflastuseall","apps_rapids_timeoflastuseentertainment","apps_rapids_timeoflastusesocial","apps_rapids_timeoflastusetop1global","apps_rapids_timeoflastusecom.facebook.moments","apps_rapids_frequencyentropyemail","apps_rapids_frequencyentropyall","apps_rapids_frequencyentropyentertainment","apps_rapids_frequencyentropysocial","apps_rapids_frequencyentropytop1global","apps_rapids_frequencyentropycom.facebook.moments" diff --git a/tests/data/processed/features/frequency/test03/phone_bluetooth.csv b/tests/data/processed/features/frequency/test03/phone_bluetooth.csv new file mode 100644 index 00000000..2e48244e --- /dev/null +++ b/tests/data/processed/features/frequency/test03/phone_bluetooth.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/frequency/test03/phone_calls.csv b/tests/data/processed/features/frequency/test03/phone_calls.csv new file mode 100644 index 00000000..3de7ca65 --- /dev/null +++ b/tests/data/processed/features/frequency/test03/phone_calls.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" diff --git a/tests/data/processed/features/frequency/test03/phone_conversation.csv b/tests/data/processed/features/frequency/test03/phone_conversation.csv new file mode 100644 index 00000000..40da4df7 --- /dev/null +++ b/tests/data/processed/features/frequency/test03/phone_conversation.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","conversation_rapids_unknownexpectedfraction","conversation_rapids_voicemaxenergy","conversation_rapids_minutesvoice","conversation_rapids_avgconversationduration","conversation_rapids_silencesensedfraction","conversation_rapids_noiseminenergy","conversation_rapids_unknownsensedfraction","conversation_rapids_silenceexpectedfraction","conversation_rapids_countconversation","conversation_rapids_noiseavgenergy","conversation_rapids_timelastconversation","conversation_rapids_voicesumenergy","conversation_rapids_voicesdenergy","conversation_rapids_sumconversationduration","conversation_rapids_timefirstconversation","conversation_rapids_sdconversationduration","conversation_rapids_voiceavgenergy","conversation_rapids_minutesunknown","conversation_rapids_minutessilence","conversation_rapids_noisesumenergy","conversation_rapids_noiseexpectedfraction","conversation_rapids_voiceminenergy","conversation_rapids_minconversationduration","conversation_rapids_minutesnoise","conversation_rapids_voiceexpectedfraction","conversation_rapids_noisemaxenergy","conversation_rapids_noisesdenergy","conversation_rapids_noisesensedfraction","conversation_rapids_maxconversationduration","conversation_rapids_voicesensedfraction" diff --git a/tests/data/processed/features/frequency/test03/phone_light.csv b/tests/data/processed/features/frequency/test03/phone_light.csv new file mode 100644 index 00000000..b7e343e4 --- /dev/null +++ b/tests/data/processed/features/frequency/test03/phone_light.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","light_rapids__minlux","light_rapids__stdlux","light_rapids__count","light_rapids__avglux","light_rapids__medianlux","light_rapids__maxlux" diff --git a/tests/data/processed/features/frequency/test03/phone_messages.csv b/tests/data/processed/features/frequency/test03/phone_messages.csv new file mode 100644 index 00000000..ea11fbdb --- /dev/null +++ b/tests/data/processed/features/frequency/test03/phone_messages.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" diff --git a/tests/data/processed/features/frequency/test03/phone_wifi_connected.csv b/tests/data/processed/features/frequency/test03/phone_wifi_connected.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/frequency/test03/phone_wifi_connected.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/frequency/test03/phone_wifi_visible.csv b/tests/data/processed/features/frequency/test03/phone_wifi_visible.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/frequency/test03/phone_wifi_visible.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/frequency/test04/phone_applications_foreground.csv b/tests/data/processed/features/frequency/test04/phone_applications_foreground.csv new file mode 100644 index 00000000..c2f63594 --- /dev/null +++ b/tests/data/processed/features/frequency/test04/phone_applications_foreground.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","apps_rapids_countemail","apps_rapids_countall","apps_rapids_countentertainment","apps_rapids_countsocial","apps_rapids_counttop1global","apps_rapids_countcom.facebook.moments","apps_rapids_timeoffirstuseemail","apps_rapids_timeoffirstuseall","apps_rapids_timeoffirstuseentertainment","apps_rapids_timeoffirstusesocial","apps_rapids_timeoffirstusetop1global","apps_rapids_timeoffirstusecom.facebook.moments","apps_rapids_timeoflastuseemail","apps_rapids_timeoflastuseall","apps_rapids_timeoflastuseentertainment","apps_rapids_timeoflastusesocial","apps_rapids_timeoflastusetop1global","apps_rapids_timeoflastusecom.facebook.moments","apps_rapids_frequencyentropyemail","apps_rapids_frequencyentropyall","apps_rapids_frequencyentropyentertainment","apps_rapids_frequencyentropysocial","apps_rapids_frequencyentropytop1global","apps_rapids_frequencyentropycom.facebook.moments" diff --git a/tests/data/processed/features/frequency/test04/phone_bluetooth.csv b/tests/data/processed/features/frequency/test04/phone_bluetooth.csv new file mode 100644 index 00000000..2e48244e --- /dev/null +++ b/tests/data/processed/features/frequency/test04/phone_bluetooth.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/frequency/test04/phone_calls.csv b/tests/data/processed/features/frequency/test04/phone_calls.csv new file mode 100644 index 00000000..3de7ca65 --- /dev/null +++ b/tests/data/processed/features/frequency/test04/phone_calls.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" diff --git a/tests/data/processed/features/frequency/test04/phone_conversation.csv b/tests/data/processed/features/frequency/test04/phone_conversation.csv new file mode 100644 index 00000000..40da4df7 --- /dev/null +++ b/tests/data/processed/features/frequency/test04/phone_conversation.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","conversation_rapids_unknownexpectedfraction","conversation_rapids_voicemaxenergy","conversation_rapids_minutesvoice","conversation_rapids_avgconversationduration","conversation_rapids_silencesensedfraction","conversation_rapids_noiseminenergy","conversation_rapids_unknownsensedfraction","conversation_rapids_silenceexpectedfraction","conversation_rapids_countconversation","conversation_rapids_noiseavgenergy","conversation_rapids_timelastconversation","conversation_rapids_voicesumenergy","conversation_rapids_voicesdenergy","conversation_rapids_sumconversationduration","conversation_rapids_timefirstconversation","conversation_rapids_sdconversationduration","conversation_rapids_voiceavgenergy","conversation_rapids_minutesunknown","conversation_rapids_minutessilence","conversation_rapids_noisesumenergy","conversation_rapids_noiseexpectedfraction","conversation_rapids_voiceminenergy","conversation_rapids_minconversationduration","conversation_rapids_minutesnoise","conversation_rapids_voiceexpectedfraction","conversation_rapids_noisemaxenergy","conversation_rapids_noisesdenergy","conversation_rapids_noisesensedfraction","conversation_rapids_maxconversationduration","conversation_rapids_voicesensedfraction" diff --git a/tests/data/processed/features/frequency/test04/phone_light.csv b/tests/data/processed/features/frequency/test04/phone_light.csv new file mode 100644 index 00000000..90111cce --- /dev/null +++ b/tests/data/processed/features/frequency/test04/phone_light.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","light_rapids__medianlux","light_rapids__minlux","light_rapids__avglux","light_rapids__maxlux","light_rapids__count","light_rapids__stdlux" diff --git a/tests/data/processed/features/frequency/test04/phone_messages.csv b/tests/data/processed/features/frequency/test04/phone_messages.csv new file mode 100644 index 00000000..ea11fbdb --- /dev/null +++ b/tests/data/processed/features/frequency/test04/phone_messages.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" diff --git a/tests/data/processed/features/frequency/test04/phone_wifi_connected.csv b/tests/data/processed/features/frequency/test04/phone_wifi_connected.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/frequency/test04/phone_wifi_connected.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/frequency/test04/phone_wifi_visible.csv b/tests/data/processed/features/frequency/test04/phone_wifi_visible.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/frequency/test04/phone_wifi_visible.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/frequency/test05/phone_applications_foreground.csv b/tests/data/processed/features/frequency/test05/phone_applications_foreground.csv new file mode 100644 index 00000000..82514a32 --- /dev/null +++ b/tests/data/processed/features/frequency/test05/phone_applications_foreground.csv @@ -0,0 +1,7 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","apps_rapids_timeoffirstuseall","apps_rapids_timeoflastuseall","apps_rapids_frequencyentropyall","apps_rapids_countall","apps_rapids_timeoffirstuseemail","apps_rapids_timeoflastuseemail","apps_rapids_frequencyentropyemail","apps_rapids_countemail","apps_rapids_timeoffirstusesocial","apps_rapids_timeoflastusesocial","apps_rapids_frequencyentropysocial","apps_rapids_countsocial","apps_rapids_timeoffirstuseentertainment","apps_rapids_timeoflastuseentertainment","apps_rapids_frequencyentropyentertainment","apps_rapids_countentertainment","apps_rapids_timeoffirstusetop1global","apps_rapids_timeoflastusetop1global","apps_rapids_frequencyentropytop1global","apps_rapids_counttop1global","apps_rapids_timeoffirstusecom.facebook.moments","apps_rapids_timeoflastusecom.facebook.moments","apps_rapids_frequencyentropycom.facebook.moments","apps_rapids_countcom.facebook.moments" +"thirtyminutes0000#2020-10-12 00:00:00,2020-10-12 00:29:59","thirtyminutes0000","2020-10-12 00:00:00","2020-10-12 00:29:59",0,29,1.32966134885476,6,NA,NA,NA,0,13,13,NA,1,0,29,0.693147180559945,4,13,13,NA,1,0,0,NA,1 +"thirtyminutes0001#2020-10-12 00:30:00,2020-10-12 00:59:59","thirtyminutes0001","2020-10-12 00:30:00","2020-10-12 00:59:59",30,59,1.27703425946614,7,30,59,NA,3,NA,NA,NA,0,30,59,0.693147180559945,2,NA,NA,NA,0,30,46,0,2 +"thirtyminutes0001#2020-10-13 00:30:00,2020-10-13 00:59:59","thirtyminutes0001","2020-10-13 00:30:00","2020-10-13 00:59:59",31,31,0,1,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,NA,NA,NA,0,31,31,0,1 +"thirtyminutes0002#2020-10-12 01:00:00,2020-10-12 01:29:59","thirtyminutes0002","2020-10-12 01:00:00","2020-10-12 01:29:59",60,88,0.693147180559945,4,60,60,NA,2,NA,NA,NA,0,88,88,NA,2,NA,NA,NA,0,NA,NA,NA,0 +"thirtyminutes0046#2020-10-12 23:00:00,2020-10-12 23:29:59","thirtyminutes0046","2020-10-12 23:00:00","2020-10-12 23:29:59",1380,1409,NA,6,NA,NA,NA,0,1380,1409,NA,6,NA,NA,NA,0,1380,1409,NA,6,NA,NA,NA,0 +"thirtyminutes0047#2020-10-12 23:30:00,2020-10-12 23:59:59","thirtyminutes0047","2020-10-12 23:30:00","2020-10-12 23:59:59",1410,1439,0.693147180559945,4,NA,NA,NA,0,NA,NA,NA,0,1410,1439,NA,2,NA,NA,NA,0,1410,1439,NA,2 diff --git a/tests/data/processed/features/frequency/test05/phone_bluetooth.csv b/tests/data/processed/features/frequency/test05/phone_bluetooth.csv new file mode 100644 index 00000000..cfd4b23b --- /dev/null +++ b/tests/data/processed/features/frequency/test05/phone_bluetooth.csv @@ -0,0 +1,14 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" +"thirtyminutes0000#2020-07-02 00:00:00,2020-07-02 00:29:59","thirtyminutes0000","2020-07-02 00:00:00","2020-07-02 00:29:59",1,1,1 +"thirtyminutes0001#2020-07-02 00:30:00,2020-07-02 00:59:59","thirtyminutes0001","2020-07-02 00:30:00","2020-07-02 00:59:59",1,1,1 +"thirtyminutes0007#2020-07-02 03:30:00,2020-07-02 03:59:59","thirtyminutes0007","2020-07-02 03:30:00","2020-07-02 03:59:59",1,1,1 +"thirtyminutes0011#2020-07-02 05:30:00,2020-07-02 05:59:59","thirtyminutes0011","2020-07-02 05:30:00","2020-07-02 05:59:59",1,1,1 +"thirtyminutes0012#2020-07-02 06:00:00,2020-07-02 06:29:59","thirtyminutes0012","2020-07-02 06:00:00","2020-07-02 06:29:59",1,1,1 +"thirtyminutes0014#2020-07-02 07:00:00,2020-07-02 07:29:59","thirtyminutes0014","2020-07-02 07:00:00","2020-07-02 07:29:59",1,1,1 +"thirtyminutes0023#2020-07-02 11:30:00,2020-07-02 11:59:59","thirtyminutes0023","2020-07-02 11:30:00","2020-07-02 11:59:59",1,1,1 +"thirtyminutes0024#2020-07-02 12:00:00,2020-07-02 12:29:59","thirtyminutes0024","2020-07-02 12:00:00","2020-07-02 12:29:59",1,1,1 +"thirtyminutes0035#2020-07-02 17:30:00,2020-07-02 17:59:59","thirtyminutes0035","2020-07-02 17:30:00","2020-07-02 17:59:59",1,1,1 +"thirtyminutes0036#2020-07-02 18:00:00,2020-07-02 18:29:59","thirtyminutes0036","2020-07-02 18:00:00","2020-07-02 18:29:59",1,1,1 +"thirtyminutes0039#2020-07-02 19:30:00,2020-07-02 19:59:59","thirtyminutes0039","2020-07-02 19:30:00","2020-07-02 19:59:59",1,1,1 +"thirtyminutes0042#2020-07-02 21:00:00,2020-07-02 21:29:59","thirtyminutes0042","2020-07-02 21:00:00","2020-07-02 21:29:59",1,1,1 +"thirtyminutes0047#2020-07-02 23:30:00,2020-07-02 23:59:59","thirtyminutes0047","2020-07-02 23:30:00","2020-07-02 23:59:59",2,2,1 diff --git a/tests/data/processed/features/frequency/test05/phone_calls.csv b/tests/data/processed/features/frequency/test05/phone_calls.csv new file mode 100644 index 00000000..99e72614 --- /dev/null +++ b/tests/data/processed/features/frequency/test05/phone_calls.csv @@ -0,0 +1,19 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" +"thirtyminutes0012#2020-10-01 06:00:00,2020-10-01 06:29:59","thirtyminutes0012","2020-10-01 06:00:00","2020-10-01 06:29:59",2,1,381,388,2,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,1421,1421,1421,1421,NA,1421,0,373,373,1 +"thirtyminutes0014#2020-10-01 07:00:00,2020-10-01 07:29:59","thirtyminutes0014","2020-10-01 07:00:00","2020-10-01 07:29:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,1186,1186,1186,1186,NA,1186,0,423,423,1 +"thirtyminutes0015#2020-10-01 07:30:00,2020-10-01 07:59:59","thirtyminutes0015","2020-10-01 07:30:00","2020-10-01 07:59:59",1,1,472,472,1,1,1,1331,1331,1331,1331,NA,1331,0,462,462,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0017#2020-10-01 08:30:00,2020-10-01 08:59:59","thirtyminutes0017","2020-10-01 08:30:00","2020-10-01 08:59:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,759,759,759,759,NA,759,0,529,529,1 +"thirtyminutes0018#2020-10-01 09:00:00,2020-10-01 09:29:59","thirtyminutes0018","2020-10-01 09:00:00","2020-10-01 09:29:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,2,2,1240.5,2481,1060,1421,255.265548008344,1060,0.682725062547722,555,561,1 +"thirtyminutes0019#2020-10-01 09:30:00,2020-10-01 09:59:59","thirtyminutes0019","2020-10-01 09:30:00","2020-10-01 09:59:59",1,1,584,584,1,1,1,213,213,213,213,NA,213,0,598,598,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0020#2020-10-01 10:00:00,2020-10-01 10:29:59","thirtyminutes0020","2020-10-01 10:00:00","2020-10-01 10:29:59",0,0,NA,NA,0,2,2,945.5,1891,667,1224,393.858477120907,667,0.649380675519282,609,617,1,1,1,970,970,970,970,NA,970,0,602,602,1 +"thirtyminutes0022#2020-10-01 11:00:00,2020-10-01 11:29:59","thirtyminutes0022","2020-10-01 11:00:00","2020-10-01 11:29:59",0,0,NA,NA,0,1,1,1299,1299,1299,1299,NA,1299,0,665,665,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0025#2020-10-01 12:30:00,2020-10-01 12:59:59","thirtyminutes0025","2020-10-01 12:30:00","2020-10-01 12:59:59",1,1,760,760,1,1,1,1157,1157,1157,1157,NA,1157,0,767,767,1,1,1,759,759,759,759,NA,759,0,767,767,1 +"thirtyminutes0026#2020-10-01 13:00:00,2020-10-01 13:29:59","thirtyminutes0026","2020-10-01 13:00:00","2020-10-01 13:29:59",1,1,793,793,1,2,2,909.5,1819,662,1157,350.017856687341,662,0.655924265162771,794,807,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0027#2020-10-01 13:30:00,2020-10-01 13:59:59","thirtyminutes0027","2020-10-01 13:30:00","2020-10-01 13:59:59",0,0,NA,NA,0,2,2,746,1492,439,1053,434.16356364854,1053,0.606236134907757,814,831,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0028#2020-10-01 14:00:00,2020-10-01 14:29:59","thirtyminutes0028","2020-10-01 14:00:00","2020-10-01 14:29:59",0,0,NA,NA,0,1,1,1719,1719,1719,1719,NA,1719,0,850,850,1,1,1,1116,1116,1116,1116,NA,1116,0,862,862,1 +"thirtyminutes0029#2020-10-01 14:30:00,2020-10-01 14:59:59","thirtyminutes0029","2020-10-01 14:30:00","2020-10-01 14:59:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,1289,1289,1289,1289,NA,1289,0,882,882,1 +"thirtyminutes0030#2020-10-01 15:00:00,2020-10-01 15:29:59","thirtyminutes0030","2020-10-01 15:00:00","2020-10-01 15:29:59",0,0,NA,NA,0,2,2,1509,3018,1299,1719,296.98484809835,1719,0.683597902114995,918,919,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0031#2020-10-01 15:30:00,2020-10-01 15:59:59","thirtyminutes0031","2020-10-01 15:30:00","2020-10-01 15:59:59",1,1,956,956,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0033#2020-10-01 16:30:00,2020-10-01 16:59:59","thirtyminutes0033","2020-10-01 16:30:00","2020-10-01 16:59:59",1,1,1015,1015,1,1,1,1224,1224,1224,1224,NA,1224,0,1000,1000,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0034#2020-10-01 17:00:00,2020-10-01 17:29:59","thirtyminutes0034","2020-10-01 17:00:00","2020-10-01 17:29:59",0,0,NA,NA,0,1,1,667,667,667,667,NA,667,0,1046,1046,1,2,2,1329.5,2659,1116,1543,301.934595566656,1116,0.680385180743283,1031,1047,1 +"thirtyminutes0035#2020-10-01 17:30:00,2020-10-01 17:59:59","thirtyminutes0035","2020-10-01 17:30:00","2020-10-01 17:59:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,2,2,1301.5,2603,1060,1543,341.532575313102,1543,0.676023738060706,1066,1076,1 diff --git a/tests/data/processed/features/frequency/test05/phone_conversation.csv b/tests/data/processed/features/frequency/test05/phone_conversation.csv new file mode 100644 index 00000000..d12b6265 --- /dev/null +++ b/tests/data/processed/features/frequency/test05/phone_conversation.csv @@ -0,0 +1,26 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","conversation_rapids_minutessilence","conversation_rapids_minutesnoise","conversation_rapids_minutesvoice","conversation_rapids_minutesunknown","conversation_rapids_countconversation","conversation_rapids_silencesensedfraction","conversation_rapids_noisesensedfraction","conversation_rapids_voicesensedfraction","conversation_rapids_unknownsensedfraction","conversation_rapids_silenceexpectedfraction","conversation_rapids_noiseexpectedfraction","conversation_rapids_voiceexpectedfraction","conversation_rapids_unknownexpectedfraction","conversation_rapids_sumconversationduration","conversation_rapids_avgconversationduration","conversation_rapids_sdconversationduration","conversation_rapids_minconversationduration","conversation_rapids_maxconversationduration","conversation_rapids_timefirstconversation","conversation_rapids_timelastconversation","conversation_rapids_noisesumenergy","conversation_rapids_noiseavgenergy","conversation_rapids_noisesdenergy","conversation_rapids_noiseminenergy","conversation_rapids_noisemaxenergy","conversation_rapids_voicesumenergy","conversation_rapids_voiceavgenergy","conversation_rapids_voicesdenergy","conversation_rapids_voiceminenergy","conversation_rapids_voicemaxenergy" +"thirtyminutes0002#2020-07-07 01:00:00,2020-07-07 01:29:59","thirtyminutes0002","2020-07-07 01:00:00","2020-07-07 01:29:59",0.05,0.683333333333333,0.183333333333333,0.0666666666666667,1,0.0508474576271186,0.694915254237288,0.186440677966102,0.0677966101694915,0.000138888888888889,0.00189814814814815,0.000509259259259259,0.000185185185185185,1666.66666666667,1666.66666666667,NA,1666.66666666667,1666.66666666667,73,73,254984,6219.12195121951,3611.99537094887,559,11769,29248,2658.90909090909,1686.32716010538,61,4981 +"thirtyminutes0002#2020-07-08 01:00:00,2020-07-08 01:29:59","thirtyminutes0002","2020-07-08 01:00:00","2020-07-08 01:29:59",0.133333333333333,2.06666666666667,0.6,0.183333333333333,1,0.0446927374301676,0.692737430167598,0.201117318435754,0.0614525139664804,0.00037037037037037,0.00574074074074074,0.00166666666666667,0.000509259259259259,2.28333333333333,2.28333333333333,NA,2.28333333333333,2.28333333333333,77,77,738382,5954.6935483871,3538.5841001422,267,11967,116954,3248.72222222222,1789.91462862835,69,5859 +"thirtyminutes0005#2020-07-08 02:30:00,2020-07-08 02:59:59","thirtyminutes0005","2020-07-08 02:30:00","2020-07-08 02:59:59",0.133333333333333,2.13333333333333,0.583333333333333,0.133333333333333,1,0.0446927374301676,0.715083798882682,0.195530726256983,0.0446927374301676,0.00037037037037037,0.00592592592592593,0.00162037037037037,0.00037037037037037,1.4,1.4,NA,1.4,1.4,160,160,817385,6385.8203125,3632.45674751624,99,11936,112712,3220.34285714286,1755.06022928085,275,5979 +"thirtyminutes0011#2020-07-07 05:30:00,2020-07-07 05:59:59","thirtyminutes0011","2020-07-07 05:30:00","2020-07-07 05:59:59",0.65,9.61666666666667,2.66666666666667,0.75,1,0.0475030450669915,0.702801461632156,0.194884287454324,0.0548112058465286,0.00180555555555556,0.026712962962963,0.00740740740740741,0.00208333333333333,10.05,10.05,NA,10.05,10.05,359,359,3570056,6187.27209705373,3541.42211319418,31,11976,470594,2941.2125,1751.29396685232,54,5982 +"thirtyminutes0011#2020-07-08 05:30:00,2020-07-08 05:59:59","thirtyminutes0011","2020-07-08 05:30:00","2020-07-08 05:59:59",0.816666666666667,11.5333333333333,2.53333333333333,0.983333333333333,1,0.0514705882352941,0.726890756302521,0.159663865546218,0.0619747899159664,0.00226851851851852,0.032037037037037,0.00703703703703704,0.00273148148148148,14.2666666666667,14.2666666666667,NA,14.2666666666667,14.2666666666667,359,359,3977612,5747.99421965318,3407.17956872983,49,11981,485171,3191.91447368421,1753.97448743435,15,5983 +"thirtyminutes0012#2020-07-07 06:00:00,2020-07-07 06:29:59","thirtyminutes0012","2020-07-07 06:00:00","2020-07-07 06:29:59",0.0166666666666667,0.55,0.133333333333333,0.0333333333333333,NA,0.0227272727272727,0.75,0.181818181818182,0.0454545454545455,4.62962962962963e-05,0.00152777777777778,0.00037037037037037,9.25925925925926e-05,0,NA,NA,NA,0,NA,NA,205237,6219.30303030303,3486.62605971776,470,11468,18261,2282.625,1954.05621629179,18,5494 +"thirtyminutes0012#2020-07-08 06:00:00,2020-07-08 06:29:59","thirtyminutes0012","2020-07-08 06:00:00","2020-07-08 06:29:59",0.35,3.56666666666667,0.966666666666667,0.316666666666667,NA,0.0673076923076923,0.685897435897436,0.185897435897436,0.0608974358974359,0.000972222222222222,0.00990740740740741,0.00268518518518519,0.00087962962962963,0,NA,NA,NA,0,NA,NA,1329661,6213.36915887851,3654.61971833146,32,11986,193236,3331.65517241379,1766.04139102042,241,5858 +"thirtyminutes0013#2020-07-07 06:30:00,2020-07-07 06:59:59","thirtyminutes0013","2020-07-07 06:30:00","2020-07-07 06:59:59",0.133333333333333,1.25,0.533333333333333,0.0666666666666667,1,0.0672268907563025,0.630252100840336,0.26890756302521,0.0336134453781513,0.00037037037037037,0.00347222222222222,0.00148148148148148,0.000185185185185185,1.33333333333333,1.33333333333333,NA,1.33333333333333,1.33333333333333,392,392,459919,6132.25333333333,3609.22901886686,222,11920,98678,3083.6875,1557.76443284144,648,5974 +"thirtyminutes0016#2020-07-08 08:00:00,2020-07-08 08:29:59","thirtyminutes0016","2020-07-08 08:00:00","2020-07-08 08:29:59",0.1,0.716666666666667,0.15,0.0166666666666667,1,0.101694915254237,0.728813559322034,0.152542372881356,0.0169491525423729,0.000277777777777778,0.00199074074074074,0.000416666666666667,4.62962962962963e-05,0.283333333333333,0.283333333333333,NA,0.283333333333333,0.283333333333333,481,481,286820,6670.23255813954,3577.58151108239,381,11839,33052,3672.44444444444,1379.26475985497,1910,5866 +"thirtyminutes0017#2020-07-07 08:30:00,2020-07-07 08:59:59","thirtyminutes0017","2020-07-07 08:30:00","2020-07-07 08:59:59",0.15,2.81666666666667,0.783333333333333,0.216666666666667,2,0.0378151260504202,0.710084033613445,0.197478991596639,0.0546218487394958,0.000416666666666667,0.00782407407407408,0.00217592592592593,0.000601851851851852,1.6,0.8,0.0707106781186548,0.75,0.85,516,535,938565,5553.63905325444,3370.70343882599,134,11938,139223,2962.1914893617,1794.68495041061,69,5959 +"thirtyminutes0021#2020-07-07 10:30:00,2020-07-07 10:59:59","thirtyminutes0021","2020-07-07 10:30:00","2020-07-07 10:59:59",0.116666666666667,1.35,0.333333333333333,0.183333333333333,1,0.0588235294117647,0.680672268907563,0.168067226890756,0.092436974789916,0.000324074074074074,0.00375,0.000925925925925926,0.000509259259259259,1.66666666666667,1.66666666666667,NA,1.66666666666667,1.66666666666667,656,656,474758,5861.20987654321,3158.11819251611,13,11825,63061,3153.05,1392.35402617672,381,5031 +"thirtyminutes0023#2020-07-07 11:30:00,2020-07-07 11:59:59","thirtyminutes0023","2020-07-07 11:30:00","2020-07-07 11:59:59",0.0833333333333333,2.3,0.65,0.15,NA,0.0261780104712042,0.722513089005236,0.204188481675393,0.0471204188481675,0.000231481481481481,0.00638888888888889,0.00180555555555556,0.000416666666666667,0,NA,NA,NA,0,NA,NA,939738,6809.69565217391,3246.64083920798,348,11985,120596,3092.20512820513,1859.21142281072,154,5822 +"thirtyminutes0023#2020-07-08 11:30:00,2020-07-08 11:59:59","thirtyminutes0023","2020-07-08 11:30:00","2020-07-08 11:59:59",0.366666666666667,4.68333333333333,1.36666666666667,0.233333333333333,2,0.0551378446115288,0.704260651629073,0.205513784461153,0.0350877192982456,0.00101851851851852,0.0130092592592593,0.0037962962962963,0.000648148148148148,5.15,2.575,0.0824957911384304,2.51666666666667,2.63333333333333,697,719,1711922,6092.24911032028,3386.07934815594,21,11935,258424,3151.51219512195,1750.14532901955,39,5972 +"thirtyminutes0024#2020-07-07 12:00:00,2020-07-07 12:29:59","thirtyminutes0024","2020-07-07 12:00:00","2020-07-07 12:29:59",0.7,9.85,2.88333333333333,0.45,1,0.0504201680672269,0.709483793517407,0.207683073229292,0.0324129651860744,0.00194444444444444,0.0273611111111111,0.00800925925925926,0.00125,1.78333333333333,1.78333333333333,NA,1.78333333333333,1.78333333333333,733,733,3527573,5968.820642978,3557.82238983441,29,11997,517505,2991.35838150289,1791.95776038444,18,5998 +"thirtyminutes0024#2020-07-08 12:00:00,2020-07-08 12:29:59","thirtyminutes0024","2020-07-08 12:00:00","2020-07-08 12:29:59",0.316666666666667,4.35,1.03333333333333,0.25,1,0.0532212885154062,0.73109243697479,0.173669467787115,0.0420168067226891,0.00087962962962963,0.0120833333333333,0.00287037037037037,0.000694444444444444,5.36666666666667,5.36666666666667,NA,5.36666666666667,5.36666666666667,725,725,1618598,6201.52490421456,3426.75049881198,32,11990,175378,2828.67741935484,1651.7847453756,26,5753 +"thirtyminutes0025#2020-07-07 12:30:00,2020-07-07 12:59:59","thirtyminutes0025","2020-07-07 12:30:00","2020-07-07 12:59:59",0.283333333333333,1.78333333333333,0.783333333333333,0.133333333333333,1,0.0949720670391061,0.597765363128492,0.262569832402235,0.0446927374301676,0.000787037037037037,0.0049537037037037,0.00217592592592593,0.00037037037037037,2.21666666666667,2.21666666666667,NA,2.21666666666667,2.21666666666667,768,768,651511,6088.88785046729,3361.1700745905,117,11613,147681,3142.14893617021,1650.99227052576,113,5839 +"thirtyminutes0033#2020-07-07 16:30:00,2020-07-07 16:59:59","thirtyminutes0033","2020-07-07 16:30:00","2020-07-07 16:59:59",0.383333333333333,4.45,1.2,0.366666666666667,1,0.0598958333333333,0.6953125,0.1875,0.0572916666666667,0.00106481481481481,0.0123611111111111,0.00333333333333333,0.00101851851851852,1.96666666666667,1.96666666666667,NA,1.96666666666667,1.96666666666667,1010,1010,1558080,5835.50561797753,3361.14808904221,51,11957,219689,3051.23611111111,1839.39286135288,5,5973 +"thirtyminutes0033#2020-07-08 16:30:00,2020-07-08 16:59:59","thirtyminutes0033","2020-07-08 16:30:00","2020-07-08 16:59:59",0.1,1.96666666666667,0.616666666666667,0.166666666666667,NA,0.0350877192982456,0.690058479532164,0.216374269005848,0.0584795321637427,0.000277777777777778,0.00546296296296296,0.00171296296296296,0.000462962962962963,0,NA,NA,NA,0,NA,NA,705058,5975.06779661017,3287.01941917461,130,11994,106715,2884.18918918919,1589.3816903619,155,5896 +"thirtyminutes0034#2020-07-07 17:00:00,2020-07-07 17:29:59","thirtyminutes0034","2020-07-07 17:00:00","2020-07-07 17:29:59",0.766666666666667,11.3333333333333,3.4,0.85,2,0.0468909276248726,0.693170234454638,0.207951070336391,0.0519877675840979,0.00212962962962963,0.0314814814814815,0.00944444444444444,0.00236111111111111,6.45,3.225,1.04887505876005,2.48333333333333,3.96666666666667,1032,1038,4205272,6184.22352941176,3480.06016995185,15,11869,621932,3048.6862745098,1727.26067460511,37,5928 +"thirtyminutes0034#2020-07-08 17:00:00,2020-07-08 17:29:59","thirtyminutes0034","2020-07-08 17:00:00","2020-07-08 17:29:59",0.2,1.83333333333333,0.583333333333333,0.183333333333333,1,0.0714285714285714,0.654761904761905,0.208333333333333,0.0654761904761905,0.000555555555555556,0.00509259259259259,0.00162037037037037,0.000509259259259259,5.23333333333333,5.23333333333333,NA,5.23333333333333,5.23333333333333,1022,1022,706913,6426.48181818182,3597.07282557782,155,11984,112200,3205.71428571429,1996.67760176203,26,5965 +"thirtyminutes0035#2020-07-08 17:30:00,2020-07-08 17:59:59","thirtyminutes0035","2020-07-08 17:30:00","2020-07-08 17:59:59",0.0333333333333333,0.733333333333333,0.2,0.0166666666666667,1,0.0338983050847458,0.745762711864407,0.203389830508475,0.0169491525423729,9.25925925925926e-05,0.00203703703703704,0.000555555555555556,4.62962962962963e-05,0.35,0.35,NA,0.35,0.35,1079,1079,269590,6127.04545454545,3796.70145670873,137,11971,39917,3326.41666666667,1785.64887408335,541,5590 +"thirtyminutes0039#2020-07-08 19:30:00,2020-07-08 19:59:59","thirtyminutes0039","2020-07-08 19:30:00","2020-07-08 19:59:59",0.1,2.1,0.716666666666667,0.0666666666666667,NA,0.0335195530726257,0.70391061452514,0.240223463687151,0.0223463687150838,0.000277777777777778,0.00583333333333333,0.00199074074074074,0.000185185185185185,0,NA,NA,NA,0,NA,NA,737899,5856.34126984127,3586.7682415516,65,11872,138265,3215.46511627907,1685.61227181609,344,5791 +"thirtyminutes0040#2020-07-07 20:00:00,2020-07-07 20:29:59","thirtyminutes0040","2020-07-07 20:00:00","2020-07-07 20:29:59",0.0833333333333333,1.3,0.5,0.1,1,0.0420168067226891,0.65546218487395,0.252100840336134,0.0504201680672269,0.000231481481481481,0.00361111111111111,0.00138888888888889,0.000277777777777778,1.35,1.35,NA,1.35,1.35,1204,1204,505175,6476.60256410256,3468.7579061419,255,11958,89106,2970.2,1714.36781149888,116,5801 +"thirtyminutes0046#2020-07-07 23:00:00,2020-07-07 23:29:59","thirtyminutes0046","2020-07-07 23:00:00","2020-07-07 23:29:59",0.133333333333333,1.36666666666667,0.366666666666667,0.116666666666667,1,0.0672268907563025,0.689075630252101,0.184873949579832,0.0588235294117647,0.00037037037037037,0.0037962962962963,0.00101851851851852,0.000324074074074074,1.38333333333333,1.38333333333333,NA,1.38333333333333,1.38333333333333,1407,1407,451528,5506.43902439024,3323.384672502,449,11997,69623,3164.68181818182,1700.61863775576,128,5807 +"thirtyminutes0047#2020-07-07 23:30:00,2020-07-07 23:59:59","thirtyminutes0047","2020-07-07 23:30:00","2020-07-07 23:59:59",0.15,1.98333333333333,0.65,0.2,1,0.0502793296089385,0.664804469273743,0.217877094972067,0.0670391061452514,0.000416666666666667,0.00550925925925926,0.00180555555555556,0.000555555555555556,2.15,2.15,NA,2.15,2.15,1436,1436,696959,5856.79831932773,3410.93125754917,117,11685,129548,3321.74358974359,1756.63003800412,116,5961 diff --git a/tests/data/processed/features/frequency/test05/phone_light.csv b/tests/data/processed/features/frequency/test05/phone_light.csv new file mode 100644 index 00000000..962ccd5c --- /dev/null +++ b/tests/data/processed/features/frequency/test05/phone_light.csv @@ -0,0 +1,7 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","light_rapids_count","light_rapids_maxlux","light_rapids_minlux","light_rapids_avglux","light_rapids_medianlux","light_rapids_stdlux" +"thirtyminutes0000#2020-09-27 00:00:00,2020-09-27 00:29:59","thirtyminutes0000","2020-09-27 00:00:00","2020-09-27 00:29:59",2,15258,1469,8363.5,8363.5,9750.2954057813 +"thirtyminutes0002#2020-09-27 01:00:00,2020-09-27 01:29:59","thirtyminutes0002","2020-09-27 01:00:00","2020-09-27 01:29:59",2,0,0,0,0,0 +"thirtyminutes0039#2020-09-27 19:30:00,2020-09-27 19:59:59","thirtyminutes0039","2020-09-27 19:30:00","2020-09-27 19:59:59",1,472.52,472.52,472.52,472.52,NA +"thirtyminutes0040#2020-09-26 20:00:00,2020-09-26 20:29:59","thirtyminutes0040","2020-09-26 20:00:00","2020-09-26 20:29:59",4,114615,37252,84955.25,93977,33392.3886474648 +"thirtyminutes0041#2020-09-26 20:30:00,2020-09-26 20:59:59","thirtyminutes0041","2020-09-26 20:30:00","2020-09-26 20:59:59",2,90625,24414,57519.5,57519.5,46818.2470891425 +"thirtyminutes0047#2020-09-26 23:30:00,2020-09-26 23:59:59","thirtyminutes0047","2020-09-26 23:30:00","2020-09-26 23:59:59",1,10351,10351,10351,10351,NA diff --git a/tests/data/processed/features/frequency/test05/phone_messages.csv b/tests/data/processed/features/frequency/test05/phone_messages.csv new file mode 100644 index 00000000..2d9e939f --- /dev/null +++ b/tests/data/processed/features/frequency/test05/phone_messages.csv @@ -0,0 +1,8 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" +"thirtyminutes0000#2020-10-12 00:00:00,2020-10-12 00:29:59","thirtyminutes0000","2020-10-12 00:00:00","2020-10-12 00:29:59",1,3,3,0,13,1,1,1,29,29 +"thirtyminutes0000#2020-10-13 00:00:00,2020-10-13 00:29:59","thirtyminutes0000","2020-10-13 00:00:00","2020-10-13 00:29:59",1,1,1,0,0,0,0,0,NA,NA +"thirtyminutes0001#2020-10-12 00:30:00,2020-10-12 00:59:59","thirtyminutes0001","2020-10-12 00:30:00","2020-10-12 00:59:59",1,2,2,46,59,2,3,2,30,59 +"thirtyminutes0001#2020-10-13 00:30:00,2020-10-13 00:59:59","thirtyminutes0001","2020-10-13 00:30:00","2020-10-13 00:59:59",0,0,0,NA,NA,0,1,1,30,30 +"thirtyminutes0002#2020-10-12 01:00:00,2020-10-12 01:29:59","thirtyminutes0002","2020-10-12 01:00:00","2020-10-12 01:29:59",1,1,1,60,60,1,2,2,60,88 +"thirtyminutes0046#2020-10-12 23:00:00,2020-10-12 23:29:59","thirtyminutes0046","2020-10-12 23:00:00","2020-10-12 23:29:59",4,4,1,1380,1409,1,1,1,1393,1393 +"thirtyminutes0047#2020-10-12 23:30:00,2020-10-12 23:59:59","thirtyminutes0047","2020-10-12 23:30:00","2020-10-12 23:59:59",2,2,1,1426,1439,1,1,1,1410,1410 diff --git a/tests/data/processed/features/frequency/test05/phone_wifi_connected.csv b/tests/data/processed/features/frequency/test05/phone_wifi_connected.csv new file mode 100644 index 00000000..b19b20b6 --- /dev/null +++ b/tests/data/processed/features/frequency/test05/phone_wifi_connected.csv @@ -0,0 +1,8 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" +"thirtyminutes0000#2020-10-12 00:00:00,2020-10-12 00:29:59","thirtyminutes0000","2020-10-12 00:00:00","2020-10-12 00:29:59",6,4,2 +"thirtyminutes0000#2020-10-13 00:00:00,2020-10-13 00:29:59","thirtyminutes0000","2020-10-13 00:00:00","2020-10-13 00:29:59",2,1,2 +"thirtyminutes0001#2020-10-12 00:30:00,2020-10-12 00:59:59","thirtyminutes0001","2020-10-12 00:30:00","2020-10-12 00:59:59",6,6,1 +"thirtyminutes0001#2020-10-13 00:30:00,2020-10-13 00:59:59","thirtyminutes0001","2020-10-13 00:30:00","2020-10-13 00:59:59",2,2,1 +"thirtyminutes0002#2020-10-12 01:00:00,2020-10-12 01:29:59","thirtyminutes0002","2020-10-12 01:00:00","2020-10-12 01:29:59",4,4,1 +"thirtyminutes0046#2020-10-12 23:00:00,2020-10-12 23:29:59","thirtyminutes0046","2020-10-12 23:00:00","2020-10-12 23:29:59",6,4,2 +"thirtyminutes0047#2020-10-12 23:30:00,2020-10-12 23:59:59","thirtyminutes0047","2020-10-12 23:30:00","2020-10-12 23:59:59",6,4,2 diff --git a/tests/data/processed/features/frequency/test05/phone_wifi_visible.csv b/tests/data/processed/features/frequency/test05/phone_wifi_visible.csv new file mode 100644 index 00000000..b19b20b6 --- /dev/null +++ b/tests/data/processed/features/frequency/test05/phone_wifi_visible.csv @@ -0,0 +1,8 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" +"thirtyminutes0000#2020-10-12 00:00:00,2020-10-12 00:29:59","thirtyminutes0000","2020-10-12 00:00:00","2020-10-12 00:29:59",6,4,2 +"thirtyminutes0000#2020-10-13 00:00:00,2020-10-13 00:29:59","thirtyminutes0000","2020-10-13 00:00:00","2020-10-13 00:29:59",2,1,2 +"thirtyminutes0001#2020-10-12 00:30:00,2020-10-12 00:59:59","thirtyminutes0001","2020-10-12 00:30:00","2020-10-12 00:59:59",6,6,1 +"thirtyminutes0001#2020-10-13 00:30:00,2020-10-13 00:59:59","thirtyminutes0001","2020-10-13 00:30:00","2020-10-13 00:59:59",2,2,1 +"thirtyminutes0002#2020-10-12 01:00:00,2020-10-12 01:29:59","thirtyminutes0002","2020-10-12 01:00:00","2020-10-12 01:29:59",4,4,1 +"thirtyminutes0046#2020-10-12 23:00:00,2020-10-12 23:29:59","thirtyminutes0046","2020-10-12 23:00:00","2020-10-12 23:29:59",6,4,2 +"thirtyminutes0047#2020-10-12 23:30:00,2020-10-12 23:59:59","thirtyminutes0047","2020-10-12 23:30:00","2020-10-12 23:59:59",6,4,2 diff --git a/tests/data/processed/features/frequency/test06/phone_applications_foreground.csv b/tests/data/processed/features/frequency/test06/phone_applications_foreground.csv new file mode 100644 index 00000000..c2f63594 --- /dev/null +++ b/tests/data/processed/features/frequency/test06/phone_applications_foreground.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","apps_rapids_countemail","apps_rapids_countall","apps_rapids_countentertainment","apps_rapids_countsocial","apps_rapids_counttop1global","apps_rapids_countcom.facebook.moments","apps_rapids_timeoffirstuseemail","apps_rapids_timeoffirstuseall","apps_rapids_timeoffirstuseentertainment","apps_rapids_timeoffirstusesocial","apps_rapids_timeoffirstusetop1global","apps_rapids_timeoffirstusecom.facebook.moments","apps_rapids_timeoflastuseemail","apps_rapids_timeoflastuseall","apps_rapids_timeoflastuseentertainment","apps_rapids_timeoflastusesocial","apps_rapids_timeoflastusetop1global","apps_rapids_timeoflastusecom.facebook.moments","apps_rapids_frequencyentropyemail","apps_rapids_frequencyentropyall","apps_rapids_frequencyentropyentertainment","apps_rapids_frequencyentropysocial","apps_rapids_frequencyentropytop1global","apps_rapids_frequencyentropycom.facebook.moments" diff --git a/tests/data/processed/features/frequency/test06/phone_bluetooth.csv b/tests/data/processed/features/frequency/test06/phone_bluetooth.csv new file mode 100644 index 00000000..0f8bec90 --- /dev/null +++ b/tests/data/processed/features/frequency/test06/phone_bluetooth.csv @@ -0,0 +1,14 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" +"thirtyminutes0000#2020-07-02 00:00:00,2020-07-02 00:29:59","thirtyminutes0000","2020-07-02 00:00:00","2020-07-02 00:29:59",1,1,1 +"thirtyminutes0001#2020-07-02 00:30:00,2020-07-02 00:59:59","thirtyminutes0001","2020-07-02 00:30:00","2020-07-02 00:59:59",1,1,1 +"thirtyminutes0007#2020-07-02 03:30:00,2020-07-02 03:59:59","thirtyminutes0007","2020-07-02 03:30:00","2020-07-02 03:59:59",1,1,1 +"thirtyminutes0011#2020-07-02 05:30:00,2020-07-02 05:59:59","thirtyminutes0011","2020-07-02 05:30:00","2020-07-02 05:59:59",1,1,1 +"thirtyminutes0012#2020-07-02 06:00:00,2020-07-02 06:29:59","thirtyminutes0012","2020-07-02 06:00:00","2020-07-02 06:29:59",1,1,1 +"thirtyminutes0014#2020-07-02 07:00:00,2020-07-02 07:29:59","thirtyminutes0014","2020-07-02 07:00:00","2020-07-02 07:29:59",1,1,1 +"thirtyminutes0023#2020-07-02 11:30:00,2020-07-02 11:59:59","thirtyminutes0023","2020-07-02 11:30:00","2020-07-02 11:59:59",1,1,1 +"thirtyminutes0024#2020-07-02 12:00:00,2020-07-02 12:29:59","thirtyminutes0024","2020-07-02 12:00:00","2020-07-02 12:29:59",1,1,1 +"thirtyminutes0035#2020-07-02 17:30:00,2020-07-02 17:59:59","thirtyminutes0035","2020-07-02 17:30:00","2020-07-02 17:59:59",1,1,1 +"thirtyminutes0036#2020-07-02 18:00:00,2020-07-02 18:29:59","thirtyminutes0036","2020-07-02 18:00:00","2020-07-02 18:29:59",1,1,1 +"thirtyminutes0039#2020-07-02 19:30:00,2020-07-02 19:59:59","thirtyminutes0039","2020-07-02 19:30:00","2020-07-02 19:59:59",1,1,1 +"thirtyminutes0042#2020-07-02 21:00:00,2020-07-02 21:29:59","thirtyminutes0042","2020-07-02 21:00:00","2020-07-02 21:29:59",1,1,1 +"thirtyminutes0047#2020-07-02 23:30:00,2020-07-02 23:59:59","thirtyminutes0047","2020-07-02 23:30:00","2020-07-02 23:59:59",2,1,2 diff --git a/tests/data/processed/features/frequency/test06/phone_calls.csv b/tests/data/processed/features/frequency/test06/phone_calls.csv new file mode 100644 index 00000000..5cfb1f0f --- /dev/null +++ b/tests/data/processed/features/frequency/test06/phone_calls.csv @@ -0,0 +1,19 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" +"thirtyminutes0012#2020-10-01 06:00:00,2020-10-01 06:29:59","thirtyminutes0012","2020-10-01 06:00:00","2020-10-01 06:29:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,3,3,976.666666666667,2930,0,1530,848.312049503798,1400,0.692333219466401,373,388,1 +"thirtyminutes0014#2020-10-01 07:00:00,2020-10-01 07:29:59","thirtyminutes0014","2020-10-01 07:00:00","2020-10-01 07:29:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,1170,1170,1170,1170,NA,1170,0,423,423,1 +"thirtyminutes0015#2020-10-01 07:30:00,2020-10-01 07:59:59","thirtyminutes0015","2020-10-01 07:30:00","2020-10-01 07:59:59",1,1,462,462,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,1410,1410,1410,1410,NA,1410,0,472,472,1 +"thirtyminutes0017#2020-10-01 08:30:00,2020-10-01 08:59:59","thirtyminutes0017","2020-10-01 08:30:00","2020-10-01 08:59:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,742,742,742,742,NA,742,0,529,529,1 +"thirtyminutes0018#2020-10-01 09:00:00,2020-10-01 09:29:59","thirtyminutes0018","2020-10-01 09:00:00","2020-10-01 09:29:59",0,0,NA,NA,0,1,1,1140,1140,1140,1140,NA,1140,0,561,561,1,1,1,1040,1040,1040,1040,NA,1040,0,555,555,1 +"thirtyminutes0019#2020-10-01 09:30:00,2020-10-01 09:59:59","thirtyminutes0019","2020-10-01 09:30:00","2020-10-01 09:59:59",0,0,NA,NA,0,1,1,198,198,198,198,NA,198,0,598,598,1,1,1,0,0,0,0,NA,0,NA,584,584,1 +"thirtyminutes0020#2020-10-01 10:00:00,2020-10-01 10:29:59","thirtyminutes0020","2020-10-01 10:00:00","2020-10-01 10:29:59",0,0,NA,NA,0,2,2,1515,3030,1320,1710,275.771644662754,1320,0.685005670382782,602,617,1,1,1,1050,1050,1050,1050,NA,1050,0,609,609,1 +"thirtyminutes0022#2020-10-01 11:00:00,2020-10-01 11:29:59","thirtyminutes0022","2020-10-01 11:00:00","2020-10-01 11:29:59",0,0,NA,NA,0,1,1,1279,1279,1279,1279,NA,1279,0,665,665,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0025#2020-10-01 12:30:00,2020-10-01 12:59:59","thirtyminutes0025","2020-10-01 12:30:00","2020-10-01 12:59:59",0,0,NA,NA,0,1,1,1140,1140,1140,1140,NA,1140,0,767,767,1,2,2,1030,2060,960,1100,98.9949493661167,960,0.691078758022212,760,767,1 +"thirtyminutes0026#2020-10-01 13:00:00,2020-10-01 13:29:59","thirtyminutes0026","2020-10-01 13:00:00","2020-10-01 13:29:59",1,1,793,793,1,1,1,650,650,650,650,NA,650,0,794,794,1,1,1,750,750,750,750,NA,750,0,807,807,1 +"thirtyminutes0027#2020-10-01 13:30:00,2020-10-01 13:59:59","thirtyminutes0027","2020-10-01 13:30:00","2020-10-01 13:59:59",0,0,NA,NA,0,1,1,1040,1040,1040,1040,NA,1040,0,814,814,1,1,1,0,0,0,0,NA,0,NA,831,831,1 +"thirtyminutes0028#2020-10-01 14:00:00,2020-10-01 14:29:59","thirtyminutes0028","2020-10-01 14:00:00","2020-10-01 14:29:59",0,0,NA,NA,0,2,2,1495,2990,1290,1700,289.913780286484,1700,0.68388325746694,850,862,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0029#2020-10-01 14:30:00,2020-10-01 14:59:59","thirtyminutes0029","2020-10-01 14:30:00","2020-10-01 14:59:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,1279,1279,1279,1279,NA,1279,0,882,882,1 +"thirtyminutes0030#2020-10-01 15:00:00,2020-10-01 15:29:59","thirtyminutes0030","2020-10-01 15:00:00","2020-10-01 15:29:59",0,0,NA,NA,0,2,2,540,1080,420,660,169.705627484771,660,0.668711440627502,918,919,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 +"thirtyminutes0031#2020-10-01 15:30:00,2020-10-01 15:59:59","thirtyminutes0031","2020-10-01 15:30:00","2020-10-01 15:59:59",0,0,NA,NA,0,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,1530,1530,1530,1530,NA,1530,0,956,956,1 +"thirtyminutes0033#2020-10-01 16:30:00,2020-10-01 16:59:59","thirtyminutes0033","2020-10-01 16:30:00","2020-10-01 16:59:59",1,1,1015,1015,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0,1,1,0,0,0,0,NA,0,NA,1000,1000,1 +"thirtyminutes0034#2020-10-01 17:00:00,2020-10-01 17:29:59","thirtyminutes0034","2020-10-01 17:00:00","2020-10-01 17:29:59",0,0,NA,NA,0,2,2,933.5,1867,657,1210,391.030049996161,657,0.648883578550163,1046,1047,1,1,1,1096,1096,1096,1096,NA,1096,0,1031,1031,1 +"thirtyminutes0035#2020-10-01 17:30:00,2020-10-01 17:59:59","thirtyminutes0035","2020-10-01 17:30:00","2020-10-01 17:59:59",1,1,1076,1076,1,1,1,1210,1210,1210,1210,NA,1210,0,1066,1066,1,0,0,NA,NA,NA,NA,NA,NA,NA,NA,NA,0 diff --git a/tests/data/processed/features/frequency/test06/phone_conversation.csv b/tests/data/processed/features/frequency/test06/phone_conversation.csv new file mode 100644 index 00000000..d12b6265 --- /dev/null +++ b/tests/data/processed/features/frequency/test06/phone_conversation.csv @@ -0,0 +1,26 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","conversation_rapids_minutessilence","conversation_rapids_minutesnoise","conversation_rapids_minutesvoice","conversation_rapids_minutesunknown","conversation_rapids_countconversation","conversation_rapids_silencesensedfraction","conversation_rapids_noisesensedfraction","conversation_rapids_voicesensedfraction","conversation_rapids_unknownsensedfraction","conversation_rapids_silenceexpectedfraction","conversation_rapids_noiseexpectedfraction","conversation_rapids_voiceexpectedfraction","conversation_rapids_unknownexpectedfraction","conversation_rapids_sumconversationduration","conversation_rapids_avgconversationduration","conversation_rapids_sdconversationduration","conversation_rapids_minconversationduration","conversation_rapids_maxconversationduration","conversation_rapids_timefirstconversation","conversation_rapids_timelastconversation","conversation_rapids_noisesumenergy","conversation_rapids_noiseavgenergy","conversation_rapids_noisesdenergy","conversation_rapids_noiseminenergy","conversation_rapids_noisemaxenergy","conversation_rapids_voicesumenergy","conversation_rapids_voiceavgenergy","conversation_rapids_voicesdenergy","conversation_rapids_voiceminenergy","conversation_rapids_voicemaxenergy" +"thirtyminutes0002#2020-07-07 01:00:00,2020-07-07 01:29:59","thirtyminutes0002","2020-07-07 01:00:00","2020-07-07 01:29:59",0.05,0.683333333333333,0.183333333333333,0.0666666666666667,1,0.0508474576271186,0.694915254237288,0.186440677966102,0.0677966101694915,0.000138888888888889,0.00189814814814815,0.000509259259259259,0.000185185185185185,1666.66666666667,1666.66666666667,NA,1666.66666666667,1666.66666666667,73,73,254984,6219.12195121951,3611.99537094887,559,11769,29248,2658.90909090909,1686.32716010538,61,4981 +"thirtyminutes0002#2020-07-08 01:00:00,2020-07-08 01:29:59","thirtyminutes0002","2020-07-08 01:00:00","2020-07-08 01:29:59",0.133333333333333,2.06666666666667,0.6,0.183333333333333,1,0.0446927374301676,0.692737430167598,0.201117318435754,0.0614525139664804,0.00037037037037037,0.00574074074074074,0.00166666666666667,0.000509259259259259,2.28333333333333,2.28333333333333,NA,2.28333333333333,2.28333333333333,77,77,738382,5954.6935483871,3538.5841001422,267,11967,116954,3248.72222222222,1789.91462862835,69,5859 +"thirtyminutes0005#2020-07-08 02:30:00,2020-07-08 02:59:59","thirtyminutes0005","2020-07-08 02:30:00","2020-07-08 02:59:59",0.133333333333333,2.13333333333333,0.583333333333333,0.133333333333333,1,0.0446927374301676,0.715083798882682,0.195530726256983,0.0446927374301676,0.00037037037037037,0.00592592592592593,0.00162037037037037,0.00037037037037037,1.4,1.4,NA,1.4,1.4,160,160,817385,6385.8203125,3632.45674751624,99,11936,112712,3220.34285714286,1755.06022928085,275,5979 +"thirtyminutes0011#2020-07-07 05:30:00,2020-07-07 05:59:59","thirtyminutes0011","2020-07-07 05:30:00","2020-07-07 05:59:59",0.65,9.61666666666667,2.66666666666667,0.75,1,0.0475030450669915,0.702801461632156,0.194884287454324,0.0548112058465286,0.00180555555555556,0.026712962962963,0.00740740740740741,0.00208333333333333,10.05,10.05,NA,10.05,10.05,359,359,3570056,6187.27209705373,3541.42211319418,31,11976,470594,2941.2125,1751.29396685232,54,5982 +"thirtyminutes0011#2020-07-08 05:30:00,2020-07-08 05:59:59","thirtyminutes0011","2020-07-08 05:30:00","2020-07-08 05:59:59",0.816666666666667,11.5333333333333,2.53333333333333,0.983333333333333,1,0.0514705882352941,0.726890756302521,0.159663865546218,0.0619747899159664,0.00226851851851852,0.032037037037037,0.00703703703703704,0.00273148148148148,14.2666666666667,14.2666666666667,NA,14.2666666666667,14.2666666666667,359,359,3977612,5747.99421965318,3407.17956872983,49,11981,485171,3191.91447368421,1753.97448743435,15,5983 +"thirtyminutes0012#2020-07-07 06:00:00,2020-07-07 06:29:59","thirtyminutes0012","2020-07-07 06:00:00","2020-07-07 06:29:59",0.0166666666666667,0.55,0.133333333333333,0.0333333333333333,NA,0.0227272727272727,0.75,0.181818181818182,0.0454545454545455,4.62962962962963e-05,0.00152777777777778,0.00037037037037037,9.25925925925926e-05,0,NA,NA,NA,0,NA,NA,205237,6219.30303030303,3486.62605971776,470,11468,18261,2282.625,1954.05621629179,18,5494 +"thirtyminutes0012#2020-07-08 06:00:00,2020-07-08 06:29:59","thirtyminutes0012","2020-07-08 06:00:00","2020-07-08 06:29:59",0.35,3.56666666666667,0.966666666666667,0.316666666666667,NA,0.0673076923076923,0.685897435897436,0.185897435897436,0.0608974358974359,0.000972222222222222,0.00990740740740741,0.00268518518518519,0.00087962962962963,0,NA,NA,NA,0,NA,NA,1329661,6213.36915887851,3654.61971833146,32,11986,193236,3331.65517241379,1766.04139102042,241,5858 +"thirtyminutes0013#2020-07-07 06:30:00,2020-07-07 06:59:59","thirtyminutes0013","2020-07-07 06:30:00","2020-07-07 06:59:59",0.133333333333333,1.25,0.533333333333333,0.0666666666666667,1,0.0672268907563025,0.630252100840336,0.26890756302521,0.0336134453781513,0.00037037037037037,0.00347222222222222,0.00148148148148148,0.000185185185185185,1.33333333333333,1.33333333333333,NA,1.33333333333333,1.33333333333333,392,392,459919,6132.25333333333,3609.22901886686,222,11920,98678,3083.6875,1557.76443284144,648,5974 +"thirtyminutes0016#2020-07-08 08:00:00,2020-07-08 08:29:59","thirtyminutes0016","2020-07-08 08:00:00","2020-07-08 08:29:59",0.1,0.716666666666667,0.15,0.0166666666666667,1,0.101694915254237,0.728813559322034,0.152542372881356,0.0169491525423729,0.000277777777777778,0.00199074074074074,0.000416666666666667,4.62962962962963e-05,0.283333333333333,0.283333333333333,NA,0.283333333333333,0.283333333333333,481,481,286820,6670.23255813954,3577.58151108239,381,11839,33052,3672.44444444444,1379.26475985497,1910,5866 +"thirtyminutes0017#2020-07-07 08:30:00,2020-07-07 08:59:59","thirtyminutes0017","2020-07-07 08:30:00","2020-07-07 08:59:59",0.15,2.81666666666667,0.783333333333333,0.216666666666667,2,0.0378151260504202,0.710084033613445,0.197478991596639,0.0546218487394958,0.000416666666666667,0.00782407407407408,0.00217592592592593,0.000601851851851852,1.6,0.8,0.0707106781186548,0.75,0.85,516,535,938565,5553.63905325444,3370.70343882599,134,11938,139223,2962.1914893617,1794.68495041061,69,5959 +"thirtyminutes0021#2020-07-07 10:30:00,2020-07-07 10:59:59","thirtyminutes0021","2020-07-07 10:30:00","2020-07-07 10:59:59",0.116666666666667,1.35,0.333333333333333,0.183333333333333,1,0.0588235294117647,0.680672268907563,0.168067226890756,0.092436974789916,0.000324074074074074,0.00375,0.000925925925925926,0.000509259259259259,1.66666666666667,1.66666666666667,NA,1.66666666666667,1.66666666666667,656,656,474758,5861.20987654321,3158.11819251611,13,11825,63061,3153.05,1392.35402617672,381,5031 +"thirtyminutes0023#2020-07-07 11:30:00,2020-07-07 11:59:59","thirtyminutes0023","2020-07-07 11:30:00","2020-07-07 11:59:59",0.0833333333333333,2.3,0.65,0.15,NA,0.0261780104712042,0.722513089005236,0.204188481675393,0.0471204188481675,0.000231481481481481,0.00638888888888889,0.00180555555555556,0.000416666666666667,0,NA,NA,NA,0,NA,NA,939738,6809.69565217391,3246.64083920798,348,11985,120596,3092.20512820513,1859.21142281072,154,5822 +"thirtyminutes0023#2020-07-08 11:30:00,2020-07-08 11:59:59","thirtyminutes0023","2020-07-08 11:30:00","2020-07-08 11:59:59",0.366666666666667,4.68333333333333,1.36666666666667,0.233333333333333,2,0.0551378446115288,0.704260651629073,0.205513784461153,0.0350877192982456,0.00101851851851852,0.0130092592592593,0.0037962962962963,0.000648148148148148,5.15,2.575,0.0824957911384304,2.51666666666667,2.63333333333333,697,719,1711922,6092.24911032028,3386.07934815594,21,11935,258424,3151.51219512195,1750.14532901955,39,5972 +"thirtyminutes0024#2020-07-07 12:00:00,2020-07-07 12:29:59","thirtyminutes0024","2020-07-07 12:00:00","2020-07-07 12:29:59",0.7,9.85,2.88333333333333,0.45,1,0.0504201680672269,0.709483793517407,0.207683073229292,0.0324129651860744,0.00194444444444444,0.0273611111111111,0.00800925925925926,0.00125,1.78333333333333,1.78333333333333,NA,1.78333333333333,1.78333333333333,733,733,3527573,5968.820642978,3557.82238983441,29,11997,517505,2991.35838150289,1791.95776038444,18,5998 +"thirtyminutes0024#2020-07-08 12:00:00,2020-07-08 12:29:59","thirtyminutes0024","2020-07-08 12:00:00","2020-07-08 12:29:59",0.316666666666667,4.35,1.03333333333333,0.25,1,0.0532212885154062,0.73109243697479,0.173669467787115,0.0420168067226891,0.00087962962962963,0.0120833333333333,0.00287037037037037,0.000694444444444444,5.36666666666667,5.36666666666667,NA,5.36666666666667,5.36666666666667,725,725,1618598,6201.52490421456,3426.75049881198,32,11990,175378,2828.67741935484,1651.7847453756,26,5753 +"thirtyminutes0025#2020-07-07 12:30:00,2020-07-07 12:59:59","thirtyminutes0025","2020-07-07 12:30:00","2020-07-07 12:59:59",0.283333333333333,1.78333333333333,0.783333333333333,0.133333333333333,1,0.0949720670391061,0.597765363128492,0.262569832402235,0.0446927374301676,0.000787037037037037,0.0049537037037037,0.00217592592592593,0.00037037037037037,2.21666666666667,2.21666666666667,NA,2.21666666666667,2.21666666666667,768,768,651511,6088.88785046729,3361.1700745905,117,11613,147681,3142.14893617021,1650.99227052576,113,5839 +"thirtyminutes0033#2020-07-07 16:30:00,2020-07-07 16:59:59","thirtyminutes0033","2020-07-07 16:30:00","2020-07-07 16:59:59",0.383333333333333,4.45,1.2,0.366666666666667,1,0.0598958333333333,0.6953125,0.1875,0.0572916666666667,0.00106481481481481,0.0123611111111111,0.00333333333333333,0.00101851851851852,1.96666666666667,1.96666666666667,NA,1.96666666666667,1.96666666666667,1010,1010,1558080,5835.50561797753,3361.14808904221,51,11957,219689,3051.23611111111,1839.39286135288,5,5973 +"thirtyminutes0033#2020-07-08 16:30:00,2020-07-08 16:59:59","thirtyminutes0033","2020-07-08 16:30:00","2020-07-08 16:59:59",0.1,1.96666666666667,0.616666666666667,0.166666666666667,NA,0.0350877192982456,0.690058479532164,0.216374269005848,0.0584795321637427,0.000277777777777778,0.00546296296296296,0.00171296296296296,0.000462962962962963,0,NA,NA,NA,0,NA,NA,705058,5975.06779661017,3287.01941917461,130,11994,106715,2884.18918918919,1589.3816903619,155,5896 +"thirtyminutes0034#2020-07-07 17:00:00,2020-07-07 17:29:59","thirtyminutes0034","2020-07-07 17:00:00","2020-07-07 17:29:59",0.766666666666667,11.3333333333333,3.4,0.85,2,0.0468909276248726,0.693170234454638,0.207951070336391,0.0519877675840979,0.00212962962962963,0.0314814814814815,0.00944444444444444,0.00236111111111111,6.45,3.225,1.04887505876005,2.48333333333333,3.96666666666667,1032,1038,4205272,6184.22352941176,3480.06016995185,15,11869,621932,3048.6862745098,1727.26067460511,37,5928 +"thirtyminutes0034#2020-07-08 17:00:00,2020-07-08 17:29:59","thirtyminutes0034","2020-07-08 17:00:00","2020-07-08 17:29:59",0.2,1.83333333333333,0.583333333333333,0.183333333333333,1,0.0714285714285714,0.654761904761905,0.208333333333333,0.0654761904761905,0.000555555555555556,0.00509259259259259,0.00162037037037037,0.000509259259259259,5.23333333333333,5.23333333333333,NA,5.23333333333333,5.23333333333333,1022,1022,706913,6426.48181818182,3597.07282557782,155,11984,112200,3205.71428571429,1996.67760176203,26,5965 +"thirtyminutes0035#2020-07-08 17:30:00,2020-07-08 17:59:59","thirtyminutes0035","2020-07-08 17:30:00","2020-07-08 17:59:59",0.0333333333333333,0.733333333333333,0.2,0.0166666666666667,1,0.0338983050847458,0.745762711864407,0.203389830508475,0.0169491525423729,9.25925925925926e-05,0.00203703703703704,0.000555555555555556,4.62962962962963e-05,0.35,0.35,NA,0.35,0.35,1079,1079,269590,6127.04545454545,3796.70145670873,137,11971,39917,3326.41666666667,1785.64887408335,541,5590 +"thirtyminutes0039#2020-07-08 19:30:00,2020-07-08 19:59:59","thirtyminutes0039","2020-07-08 19:30:00","2020-07-08 19:59:59",0.1,2.1,0.716666666666667,0.0666666666666667,NA,0.0335195530726257,0.70391061452514,0.240223463687151,0.0223463687150838,0.000277777777777778,0.00583333333333333,0.00199074074074074,0.000185185185185185,0,NA,NA,NA,0,NA,NA,737899,5856.34126984127,3586.7682415516,65,11872,138265,3215.46511627907,1685.61227181609,344,5791 +"thirtyminutes0040#2020-07-07 20:00:00,2020-07-07 20:29:59","thirtyminutes0040","2020-07-07 20:00:00","2020-07-07 20:29:59",0.0833333333333333,1.3,0.5,0.1,1,0.0420168067226891,0.65546218487395,0.252100840336134,0.0504201680672269,0.000231481481481481,0.00361111111111111,0.00138888888888889,0.000277777777777778,1.35,1.35,NA,1.35,1.35,1204,1204,505175,6476.60256410256,3468.7579061419,255,11958,89106,2970.2,1714.36781149888,116,5801 +"thirtyminutes0046#2020-07-07 23:00:00,2020-07-07 23:29:59","thirtyminutes0046","2020-07-07 23:00:00","2020-07-07 23:29:59",0.133333333333333,1.36666666666667,0.366666666666667,0.116666666666667,1,0.0672268907563025,0.689075630252101,0.184873949579832,0.0588235294117647,0.00037037037037037,0.0037962962962963,0.00101851851851852,0.000324074074074074,1.38333333333333,1.38333333333333,NA,1.38333333333333,1.38333333333333,1407,1407,451528,5506.43902439024,3323.384672502,449,11997,69623,3164.68181818182,1700.61863775576,128,5807 +"thirtyminutes0047#2020-07-07 23:30:00,2020-07-07 23:59:59","thirtyminutes0047","2020-07-07 23:30:00","2020-07-07 23:59:59",0.15,1.98333333333333,0.65,0.2,1,0.0502793296089385,0.664804469273743,0.217877094972067,0.0670391061452514,0.000416666666666667,0.00550925925925926,0.00180555555555556,0.000555555555555556,2.15,2.15,NA,2.15,2.15,1436,1436,696959,5856.79831932773,3410.93125754917,117,11685,129548,3321.74358974359,1756.63003800412,116,5961 diff --git a/tests/data/processed/features/frequency/test06/phone_light.csv b/tests/data/processed/features/frequency/test06/phone_light.csv new file mode 100644 index 00000000..20530380 --- /dev/null +++ b/tests/data/processed/features/frequency/test06/phone_light.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","light_rapids_minlux","light_rapids_stdlux","light_rapids_count","light_rapids_avglux","light_rapids_medianlux","light_rapids_maxlux" diff --git a/tests/data/processed/features/frequency/test06/phone_messages.csv b/tests/data/processed/features/frequency/test06/phone_messages.csv new file mode 100644 index 00000000..ea11fbdb --- /dev/null +++ b/tests/data/processed/features/frequency/test06/phone_messages.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" diff --git a/tests/data/processed/features/frequency/test06/phone_wifi_connected.csv b/tests/data/processed/features/frequency/test06/phone_wifi_connected.csv new file mode 100644 index 00000000..b19b20b6 --- /dev/null +++ b/tests/data/processed/features/frequency/test06/phone_wifi_connected.csv @@ -0,0 +1,8 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" +"thirtyminutes0000#2020-10-12 00:00:00,2020-10-12 00:29:59","thirtyminutes0000","2020-10-12 00:00:00","2020-10-12 00:29:59",6,4,2 +"thirtyminutes0000#2020-10-13 00:00:00,2020-10-13 00:29:59","thirtyminutes0000","2020-10-13 00:00:00","2020-10-13 00:29:59",2,1,2 +"thirtyminutes0001#2020-10-12 00:30:00,2020-10-12 00:59:59","thirtyminutes0001","2020-10-12 00:30:00","2020-10-12 00:59:59",6,6,1 +"thirtyminutes0001#2020-10-13 00:30:00,2020-10-13 00:59:59","thirtyminutes0001","2020-10-13 00:30:00","2020-10-13 00:59:59",2,2,1 +"thirtyminutes0002#2020-10-12 01:00:00,2020-10-12 01:29:59","thirtyminutes0002","2020-10-12 01:00:00","2020-10-12 01:29:59",4,4,1 +"thirtyminutes0046#2020-10-12 23:00:00,2020-10-12 23:29:59","thirtyminutes0046","2020-10-12 23:00:00","2020-10-12 23:29:59",6,4,2 +"thirtyminutes0047#2020-10-12 23:30:00,2020-10-12 23:59:59","thirtyminutes0047","2020-10-12 23:30:00","2020-10-12 23:59:59",6,4,2 diff --git a/tests/data/processed/features/frequency/test06/phone_wifi_visible.csv b/tests/data/processed/features/frequency/test06/phone_wifi_visible.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/frequency/test06/phone_wifi_visible.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/periodic/test01/phone_applications_foreground.csv b/tests/data/processed/features/periodic/test01/phone_applications_foreground.csv new file mode 100644 index 00000000..aa1e7c53 --- /dev/null +++ b/tests/data/processed/features/periodic/test01/phone_applications_foreground.csv @@ -0,0 +1,6 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","apps_rapids_timeoffirstuseall","apps_rapids_timeoflastuseall","apps_rapids_frequencyentropyall","apps_rapids_countall","apps_rapids_timeoffirstuseemail","apps_rapids_timeoflastuseemail","apps_rapids_frequencyentropyemail","apps_rapids_countemail","apps_rapids_timeoffirstuseentertainment","apps_rapids_timeoflastuseentertainment","apps_rapids_frequencyentropyentertainment","apps_rapids_countentertainment","apps_rapids_timeoffirstusesocial","apps_rapids_timeoflastusesocial","apps_rapids_frequencyentropysocial","apps_rapids_countsocial","apps_rapids_timeoffirstusetop1global","apps_rapids_timeoflastusetop1global","apps_rapids_frequencyentropytop1global","apps_rapids_counttop1global","apps_rapids_timeoffirstusecom.facebook.moments","apps_rapids_timeoflastusecom.facebook.moments","apps_rapids_frequencyentropycom.facebook.moments","apps_rapids_countcom.facebook.moments" +"afternoon#2020-07-05 12:00:00,2020-07-05 17:59:59","afternoon","2020-07-05 12:00:00","2020-07-05 17:59:59",721,889,1.03972077083992,4,889,889,NA,1,721,721,NA,1,NA,NA,NA,0,NA,NA,NA,0,798,877,NA,2 +"daily#2020-07-05 00:00:00,2020-07-05 23:59:59","daily","2020-07-05 00:00:00","2020-07-05 23:59:59",17,1359,1.54438198091684,17,889,1308,NA,2,195,721,0.598269588585257,7,302,1359,NA,4,195,719,NA,5,17,877,NA,4 +"evening#2020-07-05 18:00:00,2020-07-05 23:59:59","evening","2020-07-05 18:00:00","2020-07-05 23:59:59",1168,1359,0.636514168294813,3,1308,1308,NA,1,NA,NA,NA,0,1168,1359,NA,2,NA,NA,NA,0,NA,NA,NA,0 +"morning#2020-07-05 06:00:00,2020-07-05 11:59:59","morning","2020-07-05 06:00:00","2020-07-05 11:59:59",412,719,0.950270539233235,5,NA,NA,NA,0,412,719,0.562335144618808,4,427,427,NA,1,412,719,NA,3,NA,NA,NA,0 +"night#2020-07-05 00:00:00,2020-07-05 05:59:59","night","2020-07-05 00:00:00","2020-07-05 05:59:59",17,359,1.05492016798614,5,NA,NA,NA,0,195,359,NA,2,302,302,NA,1,195,359,NA,2,17,59,NA,2 diff --git a/tests/data/processed/features/periodic/test01/phone_bluetooth.csv b/tests/data/processed/features/periodic/test01/phone_bluetooth.csv new file mode 100644 index 00000000..ab777e3c --- /dev/null +++ b/tests/data/processed/features/periodic/test01/phone_bluetooth.csv @@ -0,0 +1,6 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" +"afternoon#2020-07-02 12:00:00,2020-07-02 17:59:59","afternoon","2020-07-02 12:00:00","2020-07-02 17:59:59",2,2,1 +"daily#2020-07-02 00:00:00,2020-07-02 23:59:59","daily","2020-07-02 00:00:00","2020-07-02 23:59:59",14,5,4 +"evening#2020-07-02 18:00:00,2020-07-02 23:59:59","evening","2020-07-02 18:00:00","2020-07-02 23:59:59",5,4,2 +"morning#2020-07-02 06:00:00,2020-07-02 11:59:59","morning","2020-07-02 06:00:00","2020-07-02 11:59:59",3,2,2 +"night#2020-07-02 00:00:00,2020-07-02 05:59:59","night","2020-07-02 00:00:00","2020-07-02 05:59:59",4,4,1 diff --git a/tests/data/processed/features/periodic/test01/phone_calls.csv b/tests/data/processed/features/periodic/test01/phone_calls.csv new file mode 100644 index 00000000..7aa01af9 --- /dev/null +++ b/tests/data/processed/features/periodic/test01/phone_calls.csv @@ -0,0 +1,9 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" +"afternoon#2020-06-01 12:00:00,2020-06-01 17:59:59","afternoon","2020-06-01 12:00:00","2020-06-01 17:59:59",1,1,874,874,1,3,2,642.666666666667,1928,213,1053,420.333597673721,1053,0.941278069255821,753,921,2,2,2,1237.5,2475,1186,1289,72.8319984622144,1289,0.692482998176928,869,1051,1 +"daily#2020-06-01 00:00:00,2020-06-01 23:59:59","daily","2020-06-01 00:00:00","2020-06-01 23:59:59",6,3,13,1167,4,10,6,976.4,9764,213,1719,465.141603289913,439,2.18820020272087,163,1331,5,8,6,1168,9344,759,1543,250.842010607702,970,2.05922274128194,172,1277,3 +"daily#2020-06-02 00:00:00,2020-06-02 23:59:59","daily","2020-06-02 00:00:00","2020-06-02 23:59:59",2,2,589,1167,1,5,5,1213.2,6066,667,1719,375.767481296612,667,1.56941860966338,519,1331,1,5,5,1179.8,5899,759,1543,310.478179587552,1116,1.58127936063292,418,1277,1 +"evening#2020-06-01 18:00:00,2020-06-01 23:59:59","evening","2020-06-01 18:00:00","2020-06-01 23:59:59",1,1,1167,1167,1,3,3,1366.66666666667,4100,1157,1719,306.963081384934,1157,1.08260122332248,1144,1331,1,2,2,1482,2964,1421,1543,86.2670273047588,1421,0.692468534923961,1156,1277,1 +"evening#2020-06-02 18:00:00,2020-06-02 23:59:59","evening","2020-06-02 18:00:00","2020-06-02 23:59:59",1,1,1167,1167,1,3,3,1366.66666666667,4100,1157,1719,306.963081384934,1157,1.08260122332248,1144,1331,1,2,2,1482,2964,1421,1543,86.2670273047588,1421,0.692468534923961,1156,1277,1 +"morning#2020-06-01 06:00:00,2020-06-01 11:59:59","morning","2020-06-01 06:00:00","2020-06-01 11:59:59",1,1,589,589,1,2,2,983,1966,667,1299,446.891485709898,667,0.640802774623272,519,600,1,3,3,978.333333333333,2935,759,1116,192.000868053593,1116,1.08558305836162,418,687,1 +"morning#2020-06-02 06:00:00,2020-06-02 11:59:59","morning","2020-06-02 06:00:00","2020-06-02 11:59:59",1,1,589,589,1,2,2,983,1966,667,1299,446.891485709898,667,0.640802774623272,519,600,1,3,3,978.333333333333,2935,759,1116,192.000868053593,1116,1.08558305836162,418,687,1 +"night#2020-06-01 00:00:00,2020-06-01 05:59:59","night","2020-06-01 00:00:00","2020-06-01 05:59:59",3,1,13,257,3,2,1,885,1770,439,1331,630.7392488184,439,0.560434787927257,163,257,2,1,1,970,970,970,970,NA,970,0,172,172,1 diff --git a/tests/data/processed/features/periodic/test01/phone_conversation.csv b/tests/data/processed/features/periodic/test01/phone_conversation.csv new file mode 100644 index 00000000..ea198d8b --- /dev/null +++ b/tests/data/processed/features/periodic/test01/phone_conversation.csv @@ -0,0 +1,11 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","conversation_rapids_minutessilence","conversation_rapids_minutesnoise","conversation_rapids_minutesvoice","conversation_rapids_minutesunknown","conversation_rapids_countconversation","conversation_rapids_silencesensedfraction","conversation_rapids_noisesensedfraction","conversation_rapids_voicesensedfraction","conversation_rapids_unknownsensedfraction","conversation_rapids_silenceexpectedfraction","conversation_rapids_noiseexpectedfraction","conversation_rapids_voiceexpectedfraction","conversation_rapids_unknownexpectedfraction","conversation_rapids_sumconversationduration","conversation_rapids_avgconversationduration","conversation_rapids_sdconversationduration","conversation_rapids_minconversationduration","conversation_rapids_maxconversationduration","conversation_rapids_timefirstconversation","conversation_rapids_timelastconversation","conversation_rapids_noisesumenergy","conversation_rapids_noiseavgenergy","conversation_rapids_noisesdenergy","conversation_rapids_noiseminenergy","conversation_rapids_noisemaxenergy","conversation_rapids_voicesumenergy","conversation_rapids_voiceavgenergy","conversation_rapids_voicesdenergy","conversation_rapids_voiceminenergy","conversation_rapids_voicemaxenergy" +"afternoon#2020-07-07 12:00:00,2020-07-07 17:59:59","afternoon","2020-07-07 12:00:00","2020-07-07 17:59:59",2.13333333333333,27.4166666666667,8.26666666666667,1.8,5,0.0538493899873791,0.692048801009676,0.208666386201094,0.0454354228018511,0.00592592592592593,0.0761574074074074,0.022962962962963,0.005,12.4379500031471,2.48759000062943,0.869095100149537,1.7806666692098,3.96938333511353,733,1038,9942436,6044.03404255319,3481.15148889292,15,11997,1506807,3037.91733870968,1754.92051540106,5,5998 +"afternoon#2020-07-08 12:00:00,2020-07-08 17:59:59","afternoon","2020-07-08 12:00:00","2020-07-08 17:59:59",0.65,8.88333333333333,2.43333333333333,0.616666666666667,3,0.0516556291390728,0.705960264900662,0.193377483443709,0.0490066225165563,0.00180555555555556,0.0246759259259259,0.00675925925925926,0.00171296296296296,10.9306666652362,3.64355555507872,2.86167904998861,0.340166664123535,5.36541666984558,725,1079,3300159,6191.66791744841,3457.36750549971,32,11994,434210,2974.04109589041,1728.00992524573,26,5965 +"daily#2020-07-07 00:00:00,2020-07-07 23:59:59","daily","2020-07-07 00:00:00","2020-07-07 23:59:59",3.7,50.6333333333333,15.0666666666667,3.68333333333333,14,0.0506271379703535,0.692816419612315,0.206157354618016,0.0503990877993159,0.0102777777777778,0.140648148148148,0.0418518518518518,0.0102314814814815,32.6724333286285,2.33374523775918,2.37483782469835,0.718016664187113,10.0464333335559,73,1436,18439355,6069.57044107966,3468.92936877742,13,11997,2734745,3025.16039823009,1740.00534289166,5,5998 +"daily#2020-07-08 00:00:00,2020-07-08 23:59:59","daily","2020-07-08 00:00:00","2020-07-08 23:59:59",2.65,35.6833333333333,9.35,2.55,9,0.0527538155275382,0.710351692103517,0.186131386861314,0.0507631055076311,0.00736111111111111,0.0991203703703704,0.0259722222222222,0.00708333333333333,34.3204833308856,3.81338703676506,4.31994487064644,0.290083332856496,14.2620333313942,77,1079,12899840,6025.14712751051,3479.79867896124,21,11994,1772024,3158.688057041,1734.43700437943,15,5983 +"evening#2020-07-07 18:00:00,2020-07-07 23:59:59","evening","2020-07-07 18:00:00","2020-07-07 23:59:59",0.366666666666667,4.65,1.51666666666667,0.416666666666667,3,0.052757793764988,0.669064748201439,0.218225419664269,0.0599520383693046,0.00101851851851852,0.0129166666666667,0.00421296296296296,0.00115740740740741,4.88070000012716,1.62690000004239,0.447887881340764,1.35104999939601,2.14368333419164,1204,1436,1653662,5927.10394265233,3410.01360257296,117,11997,288277,3167.87912087912,1716.97773773935,116,5961 +"evening#2020-07-08 18:00:00,2020-07-08 23:59:59","evening","2020-07-08 18:00:00","2020-07-08 23:59:59",0.1,2.1,0.716666666666667,0.0666666666666667,NA,0.0335195530726257,0.70391061452514,0.240223463687151,0.0223463687150838,0.000277777777777778,0.00583333333333333,0.00199074074074074,0.000185185185185185,0,NA,NA,NA,0,NA,NA,737899,5856.34126984127,3586.7682415516,65,11872,138265,3215.46511627907,1685.61227181609,344,5791 +"morning#2020-07-07 06:00:00,2020-07-07 11:59:59","morning","2020-07-07 06:00:00","2020-07-07 11:59:59",0.5,8.26666666666667,2.43333333333333,0.65,4,0.0421940928270042,0.69760900140647,0.205344585091421,0.0548523206751055,0.00138888888888889,0.022962962962963,0.00675925925925926,0.00180555555555556,4.58933332761129,1.14733333190282,0.42892803694051,0.749316664536794,1.66509999831518,392,656,3018217,6085.11491935484,3372.11073498322,13,11985,439819,3012.45890410959,1708.97915132947,18,5974 +"morning#2020-07-08 06:00:00,2020-07-08 11:59:59","morning","2020-07-08 06:00:00","2020-07-08 11:59:59",0.816666666666667,8.96666666666667,2.48333333333333,0.566666666666667,3,0.0636363636363636,0.698701298701299,0.193506493506494,0.0441558441558441,0.00226851851851852,0.0249074074074074,0.00689814814814815,0.00157407407407407,5.44793333212535,1.81597777737512,1.32280782143747,0.290083332856496,2.63854999939601,481,719,3328403,6186.62267657993,3507.24281146226,21,11986,484712,3253.10067114094,1731.83020181106,39,5972 +"night#2020-07-07 00:00:00,2020-07-07 05:59:59","night","2020-07-07 00:00:00","2020-07-07 05:59:59",0.7,10.3,2.85,0.816666666666667,2,0.0477272727272727,0.702272727272727,0.194318181818182,0.0556818181818182,0.00194444444444444,0.0286111111111111,0.00791666666666667,0.00226851851851852,10.764449997743,5.38222499887148,6.59618668464427,0.718016664187113,10.0464333335559,73,359,3825040,6189.38511326861,3543.18152922148,31,11976,499842,2923.05263157895,1743.75373754829,54,5982 +"night#2020-07-08 00:00:00,2020-07-08 05:59:59","night","2020-07-08 00:00:00","2020-07-08 05:59:59",1.08333333333333,15.7333333333333,3.71666666666667,1.3,3,0.049618320610687,0.720610687022901,0.170229007633588,0.0595419847328244,0.00300925925925926,0.0437037037037037,0.0103240740740741,0.00361111111111111,17.9418833335241,5.98062777784136,7.18578728163615,1.39351666768392,14.2620333313942,77,359,5533379,5861.63029661017,3459.01595983081,49,11981,714837,3205.54708520179,1752.09560659939,15,5983 diff --git a/tests/data/processed/features/periodic/test01/phone_light.csv b/tests/data/processed/features/periodic/test01/phone_light.csv new file mode 100644 index 00000000..8eaa366d --- /dev/null +++ b/tests/data/processed/features/periodic/test01/phone_light.csv @@ -0,0 +1,6 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","light_rapids_count","light_rapids_maxlux","light_rapids_minlux","light_rapids_avglux","light_rapids_medianlux","light_rapids_stdlux" +"afternoon#2020-07-04 12:00:00,2020-07-04 17:59:59","afternoon","2020-07-04 12:00:00","2020-07-04 17:59:59",4,97656,10351,55761.5,57519.5,44778.6855136831 +"daily#2020-07-04 00:00:00,2020-07-04 23:59:59","daily","2020-07-04 00:00:00","2020-07-04 23:59:59",12,114615,0.065,40207.8950833333,19836,44686.694225665 +"evening#2020-07-04 18:00:00,2020-07-04 23:59:59","evening","2020-07-04 18:00:00","2020-07-04 23:59:59",3,15258,84.156,5603.71866666667,1469,8389.47610244199 +"morning#2020-07-04 06:00:00,2020-07-04 11:59:59","morning","2020-07-04 06:00:00","2020-07-04 11:59:59",4,114615,472.52,60659.38,63775,51510.5823438666 +"night#2020-07-04 00:00:00,2020-07-04 05:59:59","night","2020-07-04 00:00:00","2020-07-04 05:59:59",1,0.065,0.065,0.065,0.065,NA diff --git a/tests/data/processed/features/periodic/test01/phone_messages.csv b/tests/data/processed/features/periodic/test01/phone_messages.csv new file mode 100644 index 00000000..e7ca6e93 --- /dev/null +++ b/tests/data/processed/features/periodic/test01/phone_messages.csv @@ -0,0 +1,9 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" +"afternoon#2020-05-28 12:00:00,2020-05-28 17:59:59","afternoon","2020-05-28 12:00:00","2020-05-28 17:59:59",1,2,2,830,949,1,3,3,722,979 +"daily#2020-05-28 00:00:00,2020-05-28 23:59:59","daily","2020-05-28 00:00:00","2020-05-28 23:59:59",7,12,6,6,1382,3,8,6,219,1401 +"daily#2020-05-29 00:00:00,2020-05-29 23:59:59","daily","2020-05-29 00:00:00","2020-05-29 23:59:59",3,6,4,401,1382,1,4,4,388,1401 +"evening#2020-05-28 18:00:00,2020-05-28 23:59:59","evening","2020-05-28 18:00:00","2020-05-28 23:59:59",2,3,2,1173,1382,1,2,2,1218,1401 +"evening#2020-05-29 18:00:00,2020-05-29 23:59:59","evening","2020-05-29 18:00:00","2020-05-29 23:59:59",2,3,2,1173,1382,1,2,2,1218,1401 +"morning#2020-05-28 06:00:00,2020-05-28 11:59:59","morning","2020-05-28 06:00:00","2020-05-28 11:59:59",1,3,3,401,660,1,2,2,388,654 +"morning#2020-05-29 06:00:00,2020-05-29 11:59:59","morning","2020-05-29 06:00:00","2020-05-29 11:59:59",1,3,3,401,660,1,2,2,388,654 +"night#2020-05-28 00:00:00,2020-05-28 05:59:59","night","2020-05-28 00:00:00","2020-05-28 05:59:59",4,4,1,6,312,1,1,1,219,219 diff --git a/tests/data/processed/features/periodic/test01/phone_wifi_connected.csv b/tests/data/processed/features/periodic/test01/phone_wifi_connected.csv new file mode 100644 index 00000000..ff2fe035 --- /dev/null +++ b/tests/data/processed/features/periodic/test01/phone_wifi_connected.csv @@ -0,0 +1,6 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" +"afternoon#2020-07-03 12:00:00,2020-07-03 17:59:59","afternoon","2020-07-03 12:00:00","2020-07-03 17:59:59",2,2,1 +"daily#2020-07-03 00:00:00,2020-07-03 23:59:59","daily","2020-07-03 00:00:00","2020-07-03 23:59:59",12,5,4 +"evening#2020-07-03 18:00:00,2020-07-03 23:59:59","evening","2020-07-03 18:00:00","2020-07-03 23:59:59",4,3,2 +"morning#2020-07-03 06:00:00,2020-07-03 11:59:59","morning","2020-07-03 06:00:00","2020-07-03 11:59:59",3,2,2 +"night#2020-07-03 00:00:00,2020-07-03 05:59:59","night","2020-07-03 00:00:00","2020-07-03 05:59:59",3,2,2 diff --git a/tests/data/processed/features/periodic/test01/phone_wifi_visible.csv b/tests/data/processed/features/periodic/test01/phone_wifi_visible.csv new file mode 100644 index 00000000..b602f6d3 --- /dev/null +++ b/tests/data/processed/features/periodic/test01/phone_wifi_visible.csv @@ -0,0 +1,6 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" +"afternoon#2020-07-03 12:00:00,2020-07-03 17:59:59","afternoon","2020-07-03 12:00:00","2020-07-03 17:59:59",2,2,1 +"daily#2020-07-03 00:00:00,2020-07-03 23:59:59","daily","2020-07-03 00:00:00","2020-07-03 23:59:59",14,5,6 +"evening#2020-07-03 18:00:00,2020-07-03 23:59:59","evening","2020-07-03 18:00:00","2020-07-03 23:59:59",3,3,1 +"morning#2020-07-03 06:00:00,2020-07-03 11:59:59","morning","2020-07-03 06:00:00","2020-07-03 11:59:59",4,3,2 +"night#2020-07-03 00:00:00,2020-07-03 05:59:59","night","2020-07-03 00:00:00","2020-07-03 05:59:59",5,4,2 diff --git a/tests/data/processed/features/periodic/test02/phone_applications_foreground.csv b/tests/data/processed/features/periodic/test02/phone_applications_foreground.csv new file mode 100644 index 00000000..4f9c1881 --- /dev/null +++ b/tests/data/processed/features/periodic/test02/phone_applications_foreground.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","apps_rapids_timeoffirstuseall","apps_rapids_timeoflastuseall","apps_rapids_frequencyentropyall","apps_rapids_countall","apps_rapids_timeoffirstuseemail","apps_rapids_timeoflastuseemail","apps_rapids_frequencyentropyemail","apps_rapids_countemail","apps_rapids_timeoffirstuseentertainment","apps_rapids_timeoflastuseentertainment","apps_rapids_frequencyentropyentertainment","apps_rapids_countentertainment","apps_rapids_timeoffirstusesocial","apps_rapids_timeoflastusesocial","apps_rapids_frequencyentropysocial","apps_rapids_countsocial","apps_rapids_timeoffirstusetop1global","apps_rapids_timeoflastusetop1global","apps_rapids_frequencyentropytop1global","apps_rapids_counttop1global","apps_rapids_timeoffirstusecom.facebook.moments","apps_rapids_timeoflastusecom.facebook.moments","apps_rapids_frequencyentropycom.facebook.moments","apps_rapids_countcom.facebook.moments" \ No newline at end of file diff --git a/tests/data/processed/features/periodic/test02/phone_bluetooth.csv b/tests/data/processed/features/periodic/test02/phone_bluetooth.csv new file mode 100644 index 00000000..15b8e071 --- /dev/null +++ b/tests/data/processed/features/periodic/test02/phone_bluetooth.csv @@ -0,0 +1,6 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" +"afternoon#2020-07-02 12:00:00,2020-07-02 17:59:59","afternoon","2020-07-02 12:00:00","2020-07-02 17:59:59",2,2,1 +"daily#2020-07-02 00:00:00,2020-07-02 23:59:59","daily","2020-07-02 00:00:00","2020-07-02 23:59:59",14,5,5 +"evening#2020-07-02 18:00:00,2020-07-02 23:59:59","evening","2020-07-02 18:00:00","2020-07-02 23:59:59",5,3,3 +"morning#2020-07-02 06:00:00,2020-07-02 11:59:59","morning","2020-07-02 06:00:00","2020-07-02 11:59:59",3,3,1 +"night#2020-07-02 00:00:00,2020-07-02 05:59:59","night","2020-07-02 00:00:00","2020-07-02 05:59:59",4,2,2 diff --git a/tests/data/processed/features/periodic/test02/phone_calls.csv b/tests/data/processed/features/periodic/test02/phone_calls.csv new file mode 100644 index 00000000..56ba3439 --- /dev/null +++ b/tests/data/processed/features/periodic/test02/phone_calls.csv @@ -0,0 +1,9 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" +"afternoon#2020-06-01 12:00:00,2020-06-01 17:59:59","afternoon","2020-06-01 12:00:00","2020-06-01 17:59:59",0,0,NA,NA,0,3,3,629.333333333333,1888,198,1040,421.38027164704,1040,0.932593293857646,753,921,1,3,3,816.333333333333,2449,0,1279,709.062996731132,1279,0.692360538889388,869,1051,1 +"daily#2020-06-01 00:00:00,2020-06-01 23:59:59","daily","2020-06-01 00:00:00","2020-06-01 23:59:59",3,3,13,1167,1,10,10,961.4,9614,198,1700,464.326800624062,420,2.18431625877259,163,1331,1,11,11,837.909090909091,9217,0,1530,577.720772440364,0,2.0585997872903,57,1277,1 +"daily#2020-06-02 00:00:00,2020-06-02 23:59:59","daily","2020-06-02 00:00:00","2020-06-02 23:59:59",1,1,589,589,0,5,5,1202,6010,660,1710,375.326524508993,660,1.56885323664414,519,1331,0,6,6,973.333333333333,5840,0,1530,551.313582878807,1100,1.58092421564451,418,1277,0 +"evening#2020-06-01 18:00:00,2020-06-01 23:59:59","evening","2020-06-01 18:00:00","2020-06-01 23:59:59",1,1,1167,1167,1,3,3,1350,4050,1140,1700,305.122926047847,1140,1.08239814505043,1144,1331,1,2,2,1465,2930,1400,1530,91.9238815542512,1400,0.692333219466401,1156,1277,1 +"evening#2020-06-02 18:00:00,2020-06-02 23:59:59","evening","2020-06-02 18:00:00","2020-06-02 23:59:59",0,0,NA,NA,0,3,3,1353.33333333333,4060,1140,1710,310.859024854247,1140,1.08186609729599,1144,1331,0,3,3,980,2940,0,1530,850.823130856232,1410,0.692484030888463,1156,1277,0 +"morning#2020-06-01 06:00:00,2020-06-01 11:59:59","morning","2020-06-01 06:00:00","2020-06-01 11:59:59",1,1,589,589,1,2,2,968,1936,657,1279,439.820417898033,657,0.640867990789142,519,600,1,3,3,959.333333333333,2878,742,1096,190.287501779106,1096,1.08529592058152,418,687,1 +"morning#2020-06-02 06:00:00,2020-06-02 11:59:59","morning","2020-06-02 06:00:00","2020-06-02 11:59:59",1,1,589,589,0,2,2,975,1950,660,1290,445.477272147525,660,0.640266157864261,519,600,0,3,3,966.666666666667,2900,750,1100,189.296944860009,1100,1.08563855912214,418,687,0 +"night#2020-06-01 00:00:00,2020-06-01 05:59:59","night","2020-06-01 00:00:00","2020-06-01 05:59:59",1,1,13,13,1,2,2,870,1740,420,1320,636.396103067893,420,0.552951978816239,163,257,1,3,3,320,960,0,960,554.256258422041,0,0,57,257,1 diff --git a/tests/data/processed/features/periodic/test02/phone_conversation.csv b/tests/data/processed/features/periodic/test02/phone_conversation.csv new file mode 100644 index 00000000..a0a9e4e0 --- /dev/null +++ b/tests/data/processed/features/periodic/test02/phone_conversation.csv @@ -0,0 +1,11 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","conversation_rapids_minutessilence","conversation_rapids_minutesnoise","conversation_rapids_minutesvoice","conversation_rapids_minutesunknown","conversation_rapids_countconversation","conversation_rapids_silencesensedfraction","conversation_rapids_noisesensedfraction","conversation_rapids_voicesensedfraction","conversation_rapids_unknownsensedfraction","conversation_rapids_silenceexpectedfraction","conversation_rapids_noiseexpectedfraction","conversation_rapids_voiceexpectedfraction","conversation_rapids_unknownexpectedfraction","conversation_rapids_sumconversationduration","conversation_rapids_avgconversationduration","conversation_rapids_sdconversationduration","conversation_rapids_minconversationduration","conversation_rapids_maxconversationduration","conversation_rapids_timefirstconversation","conversation_rapids_timelastconversation","conversation_rapids_noisesumenergy","conversation_rapids_noiseavgenergy","conversation_rapids_noisesdenergy","conversation_rapids_noiseminenergy","conversation_rapids_noisemaxenergy","conversation_rapids_voicesumenergy","conversation_rapids_voiceavgenergy","conversation_rapids_voicesdenergy","conversation_rapids_voiceminenergy","conversation_rapids_voicemaxenergy" +"afternoon#2020-07-07 12:00:00,2020-07-07 17:59:59","afternoon","2020-07-07 12:00:00","2020-07-07 17:59:59",2.13333333333333,27.4166666666667,8.26666666666667,1.8,5,0.0538493899873791,0.692048801009676,0.208666386201094,0.0454354228018511,0.00592592592592593,0.0761574074074074,0.022962962962963,0.005,12.4166666666667,2.48333333333333,0.870105357605235,1.78333333333333,3.96666666666667,733,1038,9942436,6044.03404255319,3481.15148889292,15,11997,1506807,3037.91733870968,1754.92051540106,5,5998 +"afternoon#2020-07-08 12:00:00,2020-07-08 17:59:59","afternoon","2020-07-08 12:00:00","2020-07-08 17:59:59",0.65,8.88333333333333,2.43333333333333,0.616666666666667,3,0.0516556291390728,0.705960264900662,0.193377483443709,0.0490066225165563,0.00180555555555556,0.0246759259259259,0.00675925925925926,0.00171296296296296,10.95,3.65,2.85866130285566,0.35,5.36666666666667,725,1079,3300159,6191.66791744841,3457.36750549971,32,11994,434210,2974.04109589041,1728.00992524573,26,5965 +"daily#2020-07-07 00:00:00,2020-07-07 23:59:59","daily","2020-07-07 00:00:00","2020-07-07 23:59:59",3.7,50.6333333333333,15.0666666666667,3.68333333333333,14,0.0506271379703535,0.692816419612315,0.206157354618016,0.0503990877993159,0.0102777777777778,0.140648148148148,0.0418518518518518,0.0102314814814815,32.6666666666667,2.33333333333333,2.37545317313934,0.716666666666667,10.05,73,1436,18439355,6069.57044107966,3468.92936877742,13,11997,2734745,3025.16039823009,1740.00534289166,5,5998 +"daily#2020-07-08 00:00:00,2020-07-08 23:59:59","daily","2020-07-08 00:00:00","2020-07-08 23:59:59",2.65,35.6833333333333,9.35,2.55,10,0.0527538155275382,0.710351692103517,0.186131386861314,0.0507631055076311,0.00736111111111111,0.0991203703703704,0.0259722222222222,0.00708333333333333,35.95,3.595,4.13312462137333,0.283333333333333,14.2666666666667,77,1198,12899840,6025.14712751051,3479.79867896124,21,11994,1772024,3158.688057041,1734.43700437943,15,5983 +"evening#2020-07-07 18:00:00,2020-07-07 23:59:59","evening","2020-07-07 18:00:00","2020-07-07 23:59:59",0.366666666666667,4.65,1.51666666666667,0.416666666666667,3,0.052757793764988,0.669064748201439,0.218225419664269,0.0599520383693046,0.00101851851851852,0.0129166666666667,0.00421296296296296,0.00115740740740741,4.88333333333333,1.62777777777778,0.452564707875918,1.35,2.15,1204,1436,1653662,5927.10394265233,3410.01360257296,117,11997,288277,3167.87912087912,1716.97773773935,116,5961 +"evening#2020-07-08 18:00:00,2020-07-08 23:59:59","evening","2020-07-08 18:00:00","2020-07-08 23:59:59",0.1,2.1,0.716666666666667,0.0666666666666667,1,0.0335195530726257,0.70391061452514,0.240223463687151,0.0223463687150838,0.000277777777777778,0.00583333333333333,0.00199074074074074,0.000185185185185185,1.61666666666667,1.61666666666667,NA,1.61666666666667,1.61666666666667,1198,1198,737899,5856.34126984127,3586.7682415516,65,11872,138265,3215.46511627907,1685.61227181609,344,5791 +"morning#2020-07-07 06:00:00,2020-07-07 11:59:59","morning","2020-07-07 06:00:00","2020-07-07 11:59:59",0.5,8.26666666666667,2.43333333333333,0.65,4,0.0421940928270042,0.69760900140647,0.205344585091421,0.0548523206751055,0.00138888888888889,0.022962962962963,0.00675925925925926,0.00180555555555556,4.6,1.15,0.4283906144146,0.75,1.66666666666667,392,656,3018217,6085.11491935484,3372.11073498322,13,11985,439819,3012.45890410959,1708.97915132947,18,5974 +"morning#2020-07-08 06:00:00,2020-07-08 11:59:59","morning","2020-07-08 06:00:00","2020-07-08 11:59:59",0.816666666666667,8.96666666666667,2.48333333333333,0.566666666666667,3,0.0636363636363636,0.698701298701299,0.193506493506494,0.0441558441558441,0.00226851851851852,0.0249074074074074,0.00689814814814815,0.00157407407407407,5.43333333333333,1.81111111111111,1.32437965911648,0.283333333333333,2.63333333333333,481,719,3328403,6186.62267657993,3507.24281146226,21,11986,484712,3253.10067114094,1731.83020181106,39,5972 +"night#2020-07-07 00:00:00,2020-07-07 05:59:59","night","2020-07-07 00:00:00","2020-07-07 05:59:59",0.7,10.3,2.85,0.816666666666667,2,0.0477272727272727,0.702272727272727,0.194318181818182,0.0556818181818182,0.00194444444444444,0.0286111111111111,0.00791666666666667,0.00226851851851852,10.7666666666667,5.38333333333333,6.59966329107444,0.716666666666667,10.05,73,359,3825040,6189.38511326861,3543.18152922148,31,11976,499842,2923.05263157895,1743.75373754829,54,5982 +"night#2020-07-08 00:00:00,2020-07-08 05:59:59","night","2020-07-08 00:00:00","2020-07-08 05:59:59",1.08333333333333,15.7333333333333,3.71666666666667,1.3,3,0.049618320610687,0.720610687022901,0.170229007633588,0.0595419847328244,0.00300925925925926,0.0437037037037037,0.0103240740740741,0.00361111111111111,17.95,5.98333333333333,7.18716062000689,1.4,14.2666666666667,77,359,5533379,5861.63029661017,3459.01595983081,49,11981,714837,3205.54708520179,1752.09560659939,15,5983 diff --git a/tests/data/processed/features/periodic/test02/phone_light.csv b/tests/data/processed/features/periodic/test02/phone_light.csv new file mode 100644 index 00000000..8b95e935 --- /dev/null +++ b/tests/data/processed/features/periodic/test02/phone_light.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","light_rapids_count","light_rapids_maxlux","light_rapids_minlux","light_rapids_avglux","light_rapids_medianlux","light_rapids_stdlux" \ No newline at end of file diff --git a/tests/data/processed/features/periodic/test02/phone_messages.csv b/tests/data/processed/features/periodic/test02/phone_messages.csv new file mode 100644 index 00000000..a3337318 --- /dev/null +++ b/tests/data/processed/features/periodic/test02/phone_messages.csv @@ -0,0 +1,9 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" +"afternoon#2020-05-28 12:00:00,2020-05-28 17:59:59","afternoon","2020-05-28 12:00:00","2020-05-28 17:59:59",1,2,2,830,949,1,3,3,722,979 +"daily#2020-05-28 00:00:00,2020-05-28 23:59:59","daily","2020-05-28 00:00:00","2020-05-28 23:59:59",1,12,12,6,1382,1,8,8,219,1401 +"daily#2020-05-29 00:00:00,2020-05-29 23:59:59","daily","2020-05-29 00:00:00","2020-05-29 23:59:59",0,6,6,401,1382,0,4,4,388,1401 +"evening#2020-05-28 18:00:00,2020-05-28 23:59:59","evening","2020-05-28 18:00:00","2020-05-28 23:59:59",1,3,3,1173,1382,1,2,2,1218,1401 +"evening#2020-05-29 18:00:00,2020-05-29 23:59:59","evening","2020-05-29 18:00:00","2020-05-29 23:59:59",0,3,3,1173,1382,0,2,2,1218,1401 +"morning#2020-05-28 06:00:00,2020-05-28 11:59:59","morning","2020-05-28 06:00:00","2020-05-28 11:59:59",1,3,3,401,660,1,2,2,388,654 +"morning#2020-05-29 06:00:00,2020-05-29 11:59:59","morning","2020-05-29 06:00:00","2020-05-29 11:59:59",0,3,3,401,660,0,2,2,388,654 +"night#2020-05-28 00:00:00,2020-05-28 05:59:59","night","2020-05-28 00:00:00","2020-05-28 05:59:59",1,4,4,6,312,1,1,1,219,219 diff --git a/tests/data/processed/features/periodic/test02/phone_wifi_connected.csv b/tests/data/processed/features/periodic/test02/phone_wifi_connected.csv new file mode 100644 index 00000000..88d7b453 --- /dev/null +++ b/tests/data/processed/features/periodic/test02/phone_wifi_connected.csv @@ -0,0 +1,6 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" +"afternoon#2020-07-03 12:00:00,2020-07-03 17:59:59","afternoon","2020-07-03 12:00:00","2020-07-03 17:59:59",2,2,1 +"daily#2020-07-03 00:00:00,2020-07-03 23:59:59","daily","2020-07-03 00:00:00","2020-07-03 23:59:59",14,5,4 +"evening#2020-07-03 18:00:00,2020-07-03 23:59:59","evening","2020-07-03 18:00:00","2020-07-03 23:59:59",5,4,2 +"morning#2020-07-03 06:00:00,2020-07-03 11:59:59","morning","2020-07-03 06:00:00","2020-07-03 11:59:59",3,2,2 +"night#2020-07-03 00:00:00,2020-07-03 05:59:59","night","2020-07-03 00:00:00","2020-07-03 05:59:59",4,4,1 diff --git a/tests/data/processed/features/periodic/test02/phone_wifi_visible.csv b/tests/data/processed/features/periodic/test02/phone_wifi_visible.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/periodic/test02/phone_wifi_visible.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/periodic/test03/phone_applications_foreground.csv b/tests/data/processed/features/periodic/test03/phone_applications_foreground.csv new file mode 100644 index 00000000..4f9c1881 --- /dev/null +++ b/tests/data/processed/features/periodic/test03/phone_applications_foreground.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","apps_rapids_timeoffirstuseall","apps_rapids_timeoflastuseall","apps_rapids_frequencyentropyall","apps_rapids_countall","apps_rapids_timeoffirstuseemail","apps_rapids_timeoflastuseemail","apps_rapids_frequencyentropyemail","apps_rapids_countemail","apps_rapids_timeoffirstuseentertainment","apps_rapids_timeoflastuseentertainment","apps_rapids_frequencyentropyentertainment","apps_rapids_countentertainment","apps_rapids_timeoffirstusesocial","apps_rapids_timeoflastusesocial","apps_rapids_frequencyentropysocial","apps_rapids_countsocial","apps_rapids_timeoffirstusetop1global","apps_rapids_timeoflastusetop1global","apps_rapids_frequencyentropytop1global","apps_rapids_counttop1global","apps_rapids_timeoffirstusecom.facebook.moments","apps_rapids_timeoflastusecom.facebook.moments","apps_rapids_frequencyentropycom.facebook.moments","apps_rapids_countcom.facebook.moments" \ No newline at end of file diff --git a/tests/data/processed/features/periodic/test03/phone_bluetooth.csv b/tests/data/processed/features/periodic/test03/phone_bluetooth.csv new file mode 100644 index 00000000..2e48244e --- /dev/null +++ b/tests/data/processed/features/periodic/test03/phone_bluetooth.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/periodic/test03/phone_calls.csv b/tests/data/processed/features/periodic/test03/phone_calls.csv new file mode 100644 index 00000000..3de7ca65 --- /dev/null +++ b/tests/data/processed/features/periodic/test03/phone_calls.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" diff --git a/tests/data/processed/features/periodic/test03/phone_conversation.csv b/tests/data/processed/features/periodic/test03/phone_conversation.csv new file mode 100644 index 00000000..58d2a540 --- /dev/null +++ b/tests/data/processed/features/periodic/test03/phone_conversation.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","conversation_rapids_sdconversationduration","conversation_rapids_minutesunknown","conversation_rapids_minutesvoice","conversation_rapids_timefirstconversation","conversation_rapids_silencesensedfraction","conversation_rapids_minutesnoise","conversation_rapids_minconversationduration","conversation_rapids_timelastconversation","conversation_rapids_voicemaxenergy","conversation_rapids_noiseminenergy","conversation_rapids_voiceavgenergy","conversation_rapids_voiceexpectedfraction","conversation_rapids_noiseexpectedfraction","conversation_rapids_voicesdenergy","conversation_rapids_noisesumenergy","conversation_rapids_noiseavgenergy","conversation_rapids_avgconversationduration","conversation_rapids_silenceexpectedfraction","conversation_rapids_sumconversationduration","conversation_rapids_voiceminenergy","conversation_rapids_noisesensedfraction","conversation_rapids_voicesensedfraction","conversation_rapids_noisemaxenergy","conversation_rapids_unknownexpectedfraction","conversation_rapids_minutessilence","conversation_rapids_voicesumenergy","conversation_rapids_countconversation","conversation_rapids_noisesdenergy","conversation_rapids_unknownsensedfraction","conversation_rapids_maxconversationduration" diff --git a/tests/data/processed/features/periodic/test03/phone_light.csv b/tests/data/processed/features/periodic/test03/phone_light.csv new file mode 100644 index 00000000..68b9e1a6 --- /dev/null +++ b/tests/data/processed/features/periodic/test03/phone_light.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","light_rapids__minlux","light_rapids__avglux","light_rapids__count","light_rapids__medianlux","light_rapids__maxlux","light_rapids__stdlux" diff --git a/tests/data/processed/features/periodic/test03/phone_messages.csv b/tests/data/processed/features/periodic/test03/phone_messages.csv new file mode 100644 index 00000000..ea11fbdb --- /dev/null +++ b/tests/data/processed/features/periodic/test03/phone_messages.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" diff --git a/tests/data/processed/features/periodic/test03/phone_wifi_connected.csv b/tests/data/processed/features/periodic/test03/phone_wifi_connected.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/periodic/test03/phone_wifi_connected.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/periodic/test03/phone_wifi_visible.csv b/tests/data/processed/features/periodic/test03/phone_wifi_visible.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/periodic/test03/phone_wifi_visible.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/periodic/test04/phone_applications_foreground.csv b/tests/data/processed/features/periodic/test04/phone_applications_foreground.csv new file mode 100644 index 00000000..4f9c1881 --- /dev/null +++ b/tests/data/processed/features/periodic/test04/phone_applications_foreground.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","apps_rapids_timeoffirstuseall","apps_rapids_timeoflastuseall","apps_rapids_frequencyentropyall","apps_rapids_countall","apps_rapids_timeoffirstuseemail","apps_rapids_timeoflastuseemail","apps_rapids_frequencyentropyemail","apps_rapids_countemail","apps_rapids_timeoffirstuseentertainment","apps_rapids_timeoflastuseentertainment","apps_rapids_frequencyentropyentertainment","apps_rapids_countentertainment","apps_rapids_timeoffirstusesocial","apps_rapids_timeoflastusesocial","apps_rapids_frequencyentropysocial","apps_rapids_countsocial","apps_rapids_timeoffirstusetop1global","apps_rapids_timeoflastusetop1global","apps_rapids_frequencyentropytop1global","apps_rapids_counttop1global","apps_rapids_timeoffirstusecom.facebook.moments","apps_rapids_timeoflastusecom.facebook.moments","apps_rapids_frequencyentropycom.facebook.moments","apps_rapids_countcom.facebook.moments" \ No newline at end of file diff --git a/tests/data/processed/features/periodic/test04/phone_bluetooth.csv b/tests/data/processed/features/periodic/test04/phone_bluetooth.csv new file mode 100644 index 00000000..2e48244e --- /dev/null +++ b/tests/data/processed/features/periodic/test04/phone_bluetooth.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","bluetooth_rapids_countscans","bluetooth_rapids_uniquedevices","bluetooth_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/periodic/test04/phone_calls.csv b/tests/data/processed/features/periodic/test04/phone_calls.csv new file mode 100644 index 00000000..3de7ca65 --- /dev/null +++ b/tests/data/processed/features/periodic/test04/phone_calls.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","calls_rapids_missed_count","calls_rapids_missed_distinctcontacts","calls_rapids_missed_timefirstcall","calls_rapids_missed_timelastcall","calls_rapids_missed_countmostfrequentcontact","calls_rapids_incoming_count","calls_rapids_incoming_distinctcontacts","calls_rapids_incoming_meanduration","calls_rapids_incoming_sumduration","calls_rapids_incoming_minduration","calls_rapids_incoming_maxduration","calls_rapids_incoming_stdduration","calls_rapids_incoming_modeduration","calls_rapids_incoming_entropyduration","calls_rapids_incoming_timefirstcall","calls_rapids_incoming_timelastcall","calls_rapids_incoming_countmostfrequentcontact","calls_rapids_outgoing_count","calls_rapids_outgoing_distinctcontacts","calls_rapids_outgoing_meanduration","calls_rapids_outgoing_sumduration","calls_rapids_outgoing_minduration","calls_rapids_outgoing_maxduration","calls_rapids_outgoing_stdduration","calls_rapids_outgoing_modeduration","calls_rapids_outgoing_entropyduration","calls_rapids_outgoing_timefirstcall","calls_rapids_outgoing_timelastcall","calls_rapids_outgoing_countmostfrequentcontact" diff --git a/tests/data/processed/features/periodic/test04/phone_conversation.csv b/tests/data/processed/features/periodic/test04/phone_conversation.csv new file mode 100644 index 00000000..2217de9e --- /dev/null +++ b/tests/data/processed/features/periodic/test04/phone_conversation.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","conversation_rapids_minconversationduration","conversation_rapids_voicesensedfraction","conversation_rapids_silenceexpectedfraction","conversation_rapids_unknownexpectedfraction","conversation_rapids_countconversation","conversation_rapids_voicemaxenergy","conversation_rapids_avgconversationduration","conversation_rapids_voicesumenergy","conversation_rapids_minutessilence","conversation_rapids_noiseexpectedfraction","conversation_rapids_maxconversationduration","conversation_rapids_sumconversationduration","conversation_rapids_noiseavgenergy","conversation_rapids_voiceexpectedfraction","conversation_rapids_noisesdenergy","conversation_rapids_timefirstconversation","conversation_rapids_unknownsensedfraction","conversation_rapids_voiceminenergy","conversation_rapids_silencesensedfraction","conversation_rapids_minutesnoise","conversation_rapids_sdconversationduration","conversation_rapids_voicesdenergy","conversation_rapids_minutesvoice","conversation_rapids_noisemaxenergy","conversation_rapids_noiseminenergy","conversation_rapids_minutesunknown","conversation_rapids_voiceavgenergy","conversation_rapids_noisesensedfraction","conversation_rapids_timelastconversation","conversation_rapids_noisesumenergy" diff --git a/tests/data/processed/features/periodic/test04/phone_light.csv b/tests/data/processed/features/periodic/test04/phone_light.csv new file mode 100644 index 00000000..e6ce1932 --- /dev/null +++ b/tests/data/processed/features/periodic/test04/phone_light.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","light_rapids__count","light_rapids__avglux","light_rapids__maxlux","light_rapids__minlux","light_rapids__stdlux","light_rapids__medianlux" diff --git a/tests/data/processed/features/periodic/test04/phone_messages.csv b/tests/data/processed/features/periodic/test04/phone_messages.csv new file mode 100644 index 00000000..ea11fbdb --- /dev/null +++ b/tests/data/processed/features/periodic/test04/phone_messages.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","messages_rapids_received_countmostfrequentcontact","messages_rapids_received_count","messages_rapids_received_distinctcontacts","messages_rapids_received_timefirstmessage","messages_rapids_received_timelastmessage","messages_rapids_sent_countmostfrequentcontact","messages_rapids_sent_count","messages_rapids_sent_distinctcontacts","messages_rapids_sent_timefirstmessage","messages_rapids_sent_timelastmessage" diff --git a/tests/data/processed/features/periodic/test04/phone_wifi_connected.csv b/tests/data/processed/features/periodic/test04/phone_wifi_connected.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/periodic/test04/phone_wifi_connected.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/features/periodic/test04/phone_wifi_visible.csv b/tests/data/processed/features/periodic/test04/phone_wifi_visible.csv new file mode 100644 index 00000000..bdd5ff23 --- /dev/null +++ b/tests/data/processed/features/periodic/test04/phone_wifi_visible.csv @@ -0,0 +1 @@ +"local_segment","local_segment_label","local_segment_start_datetime","local_segment_end_datetime","wifi_rapids_countscans","wifi_rapids_uniquedevices","wifi_rapids_countscansmostuniquedevice" diff --git a/tests/data/processed/test01/activity_recognition_afternoon.csv b/tests/data/processed/test01/activity_recognition_afternoon.csv deleted file mode 100644 index a9fa65eb..00000000 --- a/tests/data/processed/test01/activity_recognition_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_afternoon_count,ar_afternoon_mostcommonactivity,ar_afternoon_countuniqueactivities,ar_afternoon_activitychangecount,ar_afternoon_sumstationary,ar_afternoon_summobile,ar_afternoon_sumvehicle -2020-07-06,11,0,3,2,91.4326,60.46666666666667,25.433333333333334 diff --git a/tests/data/processed/test01/activity_recognition_daily.csv b/tests/data/processed/test01/activity_recognition_daily.csv deleted file mode 100644 index 12418f75..00000000 --- a/tests/data/processed/test01/activity_recognition_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_daily_count,ar_daily_mostcommonactivity,ar_daily_countuniqueactivities,ar_daily_activitychangecount,ar_daily_sumstationary,ar_daily_summobile,ar_daily_sumvehicle -2020-07-06,55,3,8,14,316.39519999999965,286.15086666666673,70.3941833333333 diff --git a/tests/data/processed/test01/activity_recognition_evening.csv b/tests/data/processed/test01/activity_recognition_evening.csv deleted file mode 100644 index 326bf7f0..00000000 --- a/tests/data/processed/test01/activity_recognition_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_evening_count,ar_evening_mostcommonactivity,ar_evening_countuniqueactivities,ar_evening_activitychangecount,ar_evening_sumstationary,ar_evening_summobile,ar_evening_sumvehicle -2020-07-06,17,1,6,5,7.609966666666669,120.1363,13.033333333333333 diff --git a/tests/data/processed/test01/activity_recognition_morning.csv b/tests/data/processed/test01/activity_recognition_morning.csv deleted file mode 100644 index e81eeb79..00000000 --- a/tests/data/processed/test01/activity_recognition_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_morning_count,ar_morning_mostcommonactivity,ar_morning_countuniqueactivities,ar_morning_activitychangecount,ar_morning_sumstationary,ar_morning_summobile,ar_morning_sumvehicle -2020-07-06,17,0,5,4,46.0,105.53365000000008,31.9119 diff --git a/tests/data/processed/test01/activity_recognition_night.csv b/tests/data/processed/test01/activity_recognition_night.csv deleted file mode 100644 index 51bafc29..00000000 --- a/tests/data/processed/test01/activity_recognition_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_night_count,ar_night_mostcommonactivity,ar_night_countuniqueactivities,ar_night_activitychangecount,ar_night_sumstationary,ar_night_summobile,ar_night_sumvehicle -2020-07-06,10,3,3,3,171.33763333333334,0,0 diff --git a/tests/data/processed/test01/applications_foreground_afternoon.csv b/tests/data/processed/test01/applications_foreground_afternoon.csv deleted file mode 100644 index b80fb405..00000000 --- a/tests/data/processed/test01/applications_foreground_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,apps_afternoon_timeoffirstuseall,apps_afternoon_timeoflastuseall,apps_afternoon_frequencyentropyall,apps_afternoon_countall,apps_afternoon_timeoffirstuseemail,apps_afternoon_timeoflastuseemail,apps_afternoon_frequencyentropyemail,apps_afternoon_countemail,apps_afternoon_timeoffirstusesocial,apps_afternoon_timeoflastusesocial,apps_afternoon_frequencyentropysocial,apps_afternoon_countsocial,apps_afternoon_timeoffirstuseentertainment,apps_afternoon_timeoflastuseentertainment,apps_afternoon_frequencyentropyentertainment,apps_afternoon_countentertainment,apps_afternoon_timeoffirstusetop1global,apps_afternoon_timeoflastusetop1global,apps_afternoon_frequencyentropytop1global,apps_afternoon_counttop1global,apps_afternoon_timeoffirstusecom.facebook.moments,apps_afternoon_timeoflastusecom.facebook.moments,apps_afternoon_frequencyentropycom.facebook.moments,apps_afternoon_countcom.facebook.moments,apps_afternoon_timeoffirstusecom.google.android.youtube,apps_afternoon_timeoflastusecom.google.android.youtube,apps_afternoon_frequencyentropycom.google.android.youtube,apps_afternoon_countcom.google.android.youtube -2020-07-05,721,889,1.0397207708399179,4,889,889,NA,1,NA,NA,NA,0.0,721,721,NA,1,NA,NA,NA,0.0,798,877,NA,2,NA,NA,NA,0.0 diff --git a/tests/data/processed/test01/applications_foreground_daily.csv b/tests/data/processed/test01/applications_foreground_daily.csv deleted file mode 100644 index 49c7f0db..00000000 --- a/tests/data/processed/test01/applications_foreground_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,apps_daily_timeoffirstuseall,apps_daily_timeoflastuseall,apps_daily_frequencyentropyall,apps_daily_countall,apps_daily_timeoffirstuseemail,apps_daily_timeoflastuseemail,apps_daily_frequencyentropyemail,apps_daily_countemail,apps_daily_timeoffirstuseentertainment,apps_daily_timeoflastuseentertainment,apps_daily_frequencyentropyentertainment,apps_daily_countentertainment,apps_daily_timeoffirstusesocial,apps_daily_timeoflastusesocial,apps_daily_frequencyentropysocial,apps_daily_countsocial,apps_daily_timeoffirstusetop1global,apps_daily_timeoflastusetop1global,apps_daily_frequencyentropytop1global,apps_daily_counttop1global,apps_daily_timeoffirstusecom.google.android.youtube,apps_daily_timeoflastusecom.google.android.youtube,apps_daily_frequencyentropycom.google.android.youtube,apps_daily_countcom.google.android.youtube,apps_daily_timeoffirstusecom.facebook.moments,apps_daily_timeoflastusecom.facebook.moments,apps_daily_frequencyentropycom.facebook.moments,apps_daily_countcom.facebook.moments -2020-07-05,17,1359,1.5443819809168389,17,889,1308,NA,2,195,721,0.5982695885852573,7,302,1359,NA,4,195,719,NA,5,NA,NA,NA,0.0,17,877,NA,4 diff --git a/tests/data/processed/test01/applications_foreground_evening.csv b/tests/data/processed/test01/applications_foreground_evening.csv deleted file mode 100644 index c6a08487..00000000 --- a/tests/data/processed/test01/applications_foreground_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,apps_evening_timeoffirstuseall,apps_evening_timeoflastuseall,apps_evening_frequencyentropyall,apps_evening_countall,apps_evening_timeoffirstuseemail,apps_evening_timeoflastuseemail,apps_evening_frequencyentropyemail,apps_evening_countemail,apps_evening_timeoffirstusesocial,apps_evening_timeoflastusesocial,apps_evening_frequencyentropysocial,apps_evening_countsocial,apps_evening_timeoffirstuseentertainment,apps_evening_timeoflastuseentertainment,apps_evening_frequencyentropyentertainment,apps_evening_countentertainment,apps_evening_timeoffirstusecom.google.android.youtube,apps_evening_timeoflastusecom.google.android.youtube,apps_evening_frequencyentropycom.google.android.youtube,apps_evening_countcom.google.android.youtube,apps_evening_timeoffirstusetop1global,apps_evening_timeoflastusetop1global,apps_evening_frequencyentropytop1global,apps_evening_counttop1global,apps_evening_timeoffirstusecom.facebook.moments,apps_evening_timeoflastusecom.facebook.moments,apps_evening_frequencyentropycom.facebook.moments,apps_evening_countcom.facebook.moments -2020-07-05,1168,1359,0.6365141682948128,3,1308,1308,NA,1,1168,1359,NA,2,NA,NA,NA,0.0,NA,NA,NA,0.0,NA,NA,NA,0.0,NA,NA,NA,0.0 diff --git a/tests/data/processed/test01/applications_foreground_morning.csv b/tests/data/processed/test01/applications_foreground_morning.csv deleted file mode 100644 index a768590d..00000000 --- a/tests/data/processed/test01/applications_foreground_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,apps_morning_timeoffirstuseall,apps_morning_timeoflastuseall,apps_morning_frequencyentropyall,apps_morning_countall,apps_morning_timeoffirstuseemail,apps_morning_timeoflastuseemail,apps_morning_frequencyentropyemail,apps_morning_countemail,apps_morning_timeoffirstusesocial,apps_morning_timeoflastusesocial,apps_morning_frequencyentropysocial,apps_morning_countsocial,apps_morning_timeoffirstuseentertainment,apps_morning_timeoflastuseentertainment,apps_morning_frequencyentropyentertainment,apps_morning_countentertainment,apps_morning_timeoffirstusecom.facebook.moments,apps_morning_timeoflastusecom.facebook.moments,apps_morning_frequencyentropycom.facebook.moments,apps_morning_countcom.facebook.moments,apps_morning_timeoffirstusetop1global,apps_morning_timeoflastusetop1global,apps_morning_frequencyentropytop1global,apps_morning_counttop1global,apps_morning_timeoffirstusecom.google.android.youtube,apps_morning_timeoflastusecom.google.android.youtube,apps_morning_frequencyentropycom.google.android.youtube,apps_morning_countcom.google.android.youtube -2020-07-05,412,719,0.9502705392332347,5,NA,NA,NA,0.0,427,427,NA,1,412,719,0.5623351446188083,4,NA,NA,NA,0.0,412,719,NA,3,NA,NA,NA,0.0 diff --git a/tests/data/processed/test01/applications_foreground_night.csv b/tests/data/processed/test01/applications_foreground_night.csv deleted file mode 100644 index 2f086c94..00000000 --- a/tests/data/processed/test01/applications_foreground_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,apps_night_timeoffirstuseall,apps_night_timeoflastuseall,apps_night_frequencyentropyall,apps_night_countall,apps_night_timeoffirstuseemail,apps_night_timeoflastuseemail,apps_night_frequencyentropyemail,apps_night_countemail,apps_night_timeoffirstusesocial,apps_night_timeoflastusesocial,apps_night_frequencyentropysocial,apps_night_countsocial,apps_night_timeoffirstuseentertainment,apps_night_timeoflastuseentertainment,apps_night_frequencyentropyentertainment,apps_night_countentertainment,apps_night_timeoffirstusecom.facebook.moments,apps_night_timeoflastusecom.facebook.moments,apps_night_frequencyentropycom.facebook.moments,apps_night_countcom.facebook.moments,apps_night_timeoffirstusecom.google.android.youtube,apps_night_timeoflastusecom.google.android.youtube,apps_night_frequencyentropycom.google.android.youtube,apps_night_countcom.google.android.youtube,apps_night_timeoffirstusetop1global,apps_night_timeoflastusetop1global,apps_night_frequencyentropytop1global,apps_night_counttop1global -2020-07-05,17,359,1.0549201679861442,5,NA,NA,NA,0.0,302,302,NA,1,195,359,NA,2,17,59,NA,2,NA,NA,NA,0.0,195,359,NA,2 diff --git a/tests/data/processed/test01/battery_afternoon.csv b/tests/data/processed/test01/battery_afternoon.csv deleted file mode 100644 index 4544acf8..00000000 --- a/tests/data/processed/test01/battery_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_afternoon_countdischarge,battery_afternoon_sumdurationdischarge,battery_afternoon_avgconsumptionrate,battery_afternoon_maxconsumptionrate,battery_afternoon_countcharge,battery_afternoon_sumdurationcharge -2020-07-01,2,12.141000000000004,0.3148499579639538,0.3655119197498273,2,19.457666666666668 diff --git a/tests/data/processed/test01/battery_daily.csv b/tests/data/processed/test01/battery_daily.csv deleted file mode 100644 index b69d3523..00000000 --- a/tests/data/processed/test01/battery_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_daily_countdischarge,battery_daily_sumdurationdischarge,battery_daily_avgconsumptionrate,battery_daily_maxconsumptionrate,battery_daily_countcharge,battery_daily_sumdurationcharge -2020-07-01,6,54.820549999999955,0.3388682207181443,0.4262039670118132,5,49.705000000000034 diff --git a/tests/data/processed/test01/battery_deltas.csv b/tests/data/processed/test01/battery_deltas.csv deleted file mode 100644 index 71e63cf7..00000000 --- a/tests/data/processed/test01/battery_deltas.csv +++ /dev/null @@ -1,12 +0,0 @@ -"battery_diff","time_diff","local_start_date_time","local_end_date_time","local_start_date","local_end_date","local_start_day_segment","local_end_day_segment" -3,9.46978333333333,"2020-07-01 00:08:10","2020-07-01 00:17:38","2020-07-01","2020-07-01","night","night" --3,10.091,"2020-07-01 03:20:30","2020-07-01 03:30:35","2020-07-01","2020-07-01","night","night" -3,7.03888333333333,"2020-07-01 05:57:30","2020-07-01 06:04:32","2020-07-01","2020-07-01","night","morning" --3,8.591,"2020-07-01 08:04:32","2020-07-01 08:13:07","2020-07-01","2020-07-01","morning","morning" -3,11.0243333333333,"2020-07-01 10:23:17","2020-07-01 10:34:18","2020-07-01","2020-07-01","morning","morning" --3,12.1576666666667,"2020-07-01 11:55:58","2020-07-01 12:08:07","2020-07-01","2020-07-01","morning","afternoon" -3,8.20766666666667,"2020-07-01 13:52:07","2020-07-01 14:00:19","2020-07-01","2020-07-01","afternoon","afternoon" --3,11.341,"2020-07-01 16:13:27","2020-07-01 16:24:47","2020-07-01","2020-07-01","afternoon","afternoon" -3,11.35555,"2020-07-01 17:56:03","2020-07-01 18:07:24","2020-07-01","2020-07-01","afternoon","evening" --3,7.52433333333333,"2020-07-01 20:03:03","2020-07-01 20:10:34","2020-07-01","2020-07-01","evening","evening" -3,7.72433333333333,"2020-07-01 21:30:04","2020-07-01 21:37:47","2020-07-01","2020-07-01","evening","evening" diff --git a/tests/data/processed/test01/battery_evening.csv b/tests/data/processed/test01/battery_evening.csv deleted file mode 100644 index d8dd2c3c..00000000 --- a/tests/data/processed/test01/battery_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_evening_countdischarge,battery_evening_sumdurationdischarge,battery_evening_avgconsumptionrate,battery_evening_maxconsumptionrate,battery_evening_countcharge,battery_evening_sumdurationcharge -2020-07-01,2,15.12433333333333,0.3262855140774751,0.3883830319768698,1,7.52433333333333 diff --git a/tests/data/processed/test01/battery_morning.csv b/tests/data/processed/test01/battery_morning.csv deleted file mode 100644 index 1bc5aa67..00000000 --- a/tests/data/processed/test01/battery_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_morning_countdischarge,battery_morning_sumdurationdischarge,battery_morning_avgconsumptionrate,battery_morning_maxconsumptionrate,battery_morning_countcharge,battery_morning_sumdurationcharge -2020-07-01,2,15.557666666666634,0.3491646327968694,0.4262039670118131,2,12.607666666666667 diff --git a/tests/data/processed/test01/battery_night.csv b/tests/data/processed/test01/battery_night.csv deleted file mode 100644 index 304297ec..00000000 --- a/tests/data/processed/test01/battery_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_night_countdischarge,battery_night_sumdurationdischarge,battery_night_avgconsumptionrate,battery_night_maxconsumptionrate,battery_night_countcharge,battery_night_sumdurationcharge -2020-07-01,2,11.953116666666663,0.37150053891108137,0.4262039670118132,1,10.091000000000001 diff --git a/tests/data/processed/test01/bluetooth_afternoon.csv b/tests/data/processed/test01/bluetooth_afternoon.csv deleted file mode 100644 index 931e4dc4..00000000 --- a/tests/data/processed/test01/bluetooth_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_afternoon_countscans","bluetooth_afternoon_uniquedevices","bluetooth_afternoon_countscansmostuniquedevice" -"2020-07-02",2,2,1 diff --git a/tests/data/processed/test01/bluetooth_daily.csv b/tests/data/processed/test01/bluetooth_daily.csv deleted file mode 100644 index ff814b87..00000000 --- a/tests/data/processed/test01/bluetooth_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_daily_countscans","bluetooth_daily_uniquedevices","bluetooth_daily_countscansmostuniquedevice" -"2020-07-02",14,5,4 diff --git a/tests/data/processed/test01/bluetooth_evening.csv b/tests/data/processed/test01/bluetooth_evening.csv deleted file mode 100644 index 28032ac8..00000000 --- a/tests/data/processed/test01/bluetooth_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_evening_countscans","bluetooth_evening_uniquedevices","bluetooth_evening_countscansmostuniquedevice" -"2020-07-02",5,4,2 diff --git a/tests/data/processed/test01/bluetooth_morning.csv b/tests/data/processed/test01/bluetooth_morning.csv deleted file mode 100644 index 09299dca..00000000 --- a/tests/data/processed/test01/bluetooth_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_morning_countscans","bluetooth_morning_uniquedevices","bluetooth_morning_countscansmostuniquedevice" -"2020-07-02",3,2,2 diff --git a/tests/data/processed/test01/bluetooth_night.csv b/tests/data/processed/test01/bluetooth_night.csv deleted file mode 100644 index 59486b18..00000000 --- a/tests/data/processed/test01/bluetooth_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_night_countscans","bluetooth_night_uniquedevices","bluetooth_night_countscansmostuniquedevice" -"2020-07-02",4,4,1 diff --git a/tests/data/processed/test01/calls_incoming_afternoon.csv b/tests/data/processed/test01/calls_incoming_afternoon.csv deleted file mode 100644 index 8c1e502a..00000000 --- a/tests/data/processed/test01/calls_incoming_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_incoming_afternoon_count","call_incoming_afternoon_distinctcontacts","call_incoming_afternoon_meanduration","call_incoming_afternoon_sumduration","call_incoming_afternoon_minduration","call_incoming_afternoon_maxduration","call_incoming_afternoon_stdduration","call_incoming_afternoon_modeduration","call_incoming_afternoon_entropyduration","call_incoming_afternoon_timefirstcall","call_incoming_afternoon_timelastcall","call_incoming_afternoon_countmostfrequentcontact" -"2020-06-01",3,2,642.666666666667,1928,213,1053,420.333597673721,1053,0.941278069255821,753,921,2 diff --git a/tests/data/processed/test01/calls_incoming_daily.csv b/tests/data/processed/test01/calls_incoming_daily.csv deleted file mode 100644 index f16631a0..00000000 --- a/tests/data/processed/test01/calls_incoming_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_incoming_daily_count","call_incoming_daily_distinctcontacts","call_incoming_daily_meanduration","call_incoming_daily_sumduration","call_incoming_daily_minduration","call_incoming_daily_maxduration","call_incoming_daily_stdduration","call_incoming_daily_modeduration","call_incoming_daily_entropyduration","call_incoming_daily_timefirstcall","call_incoming_daily_timelastcall","call_incoming_daily_countmostfrequentcontact" -"2020-06-01",10,6,976.4,9764,213,1719,465.141603289913,439,2.18820020272087,163,1331,5 -"2020-06-02",5,5,1213.2,6066,667,1719,375.767481296612,667,1.56941860966338,519,1331,1 diff --git a/tests/data/processed/test01/calls_incoming_evening.csv b/tests/data/processed/test01/calls_incoming_evening.csv deleted file mode 100644 index 1ec1c738..00000000 --- a/tests/data/processed/test01/calls_incoming_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_incoming_evening_count","call_incoming_evening_distinctcontacts","call_incoming_evening_meanduration","call_incoming_evening_sumduration","call_incoming_evening_minduration","call_incoming_evening_maxduration","call_incoming_evening_stdduration","call_incoming_evening_modeduration","call_incoming_evening_entropyduration","call_incoming_evening_timefirstcall","call_incoming_evening_timelastcall","call_incoming_evening_countmostfrequentcontact" -"2020-06-01",3,3,1366.66666666667,4100,1157,1719,306.963081384934,1157,1.08260122332248,1144,1331,1 -"2020-06-02",3,3,1366.66666666667,4100,1157,1719,306.963081384934,1157,1.08260122332248,1144,1331,1 diff --git a/tests/data/processed/test01/calls_incoming_morning.csv b/tests/data/processed/test01/calls_incoming_morning.csv deleted file mode 100644 index bc94caa7..00000000 --- a/tests/data/processed/test01/calls_incoming_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_incoming_morning_count","call_incoming_morning_distinctcontacts","call_incoming_morning_meanduration","call_incoming_morning_sumduration","call_incoming_morning_minduration","call_incoming_morning_maxduration","call_incoming_morning_stdduration","call_incoming_morning_modeduration","call_incoming_morning_entropyduration","call_incoming_morning_timefirstcall","call_incoming_morning_timelastcall","call_incoming_morning_countmostfrequentcontact" -"2020-06-01",2,2,983,1966,667,1299,446.891485709898,667,0.640802774623272,519,600,1 -"2020-06-02",2,2,983,1966,667,1299,446.891485709898,667,0.640802774623272,519,600,1 diff --git a/tests/data/processed/test01/calls_incoming_night.csv b/tests/data/processed/test01/calls_incoming_night.csv deleted file mode 100644 index d3a404c2..00000000 --- a/tests/data/processed/test01/calls_incoming_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_incoming_night_count","call_incoming_night_distinctcontacts","call_incoming_night_meanduration","call_incoming_night_sumduration","call_incoming_night_minduration","call_incoming_night_maxduration","call_incoming_night_stdduration","call_incoming_night_modeduration","call_incoming_night_entropyduration","call_incoming_night_timefirstcall","call_incoming_night_timelastcall","call_incoming_night_countmostfrequentcontact" -"2020-06-01",2,1,885,1770,439,1331,630.7392488184,439,0.560434787927257,163,257,2 diff --git a/tests/data/processed/test01/calls_missed_afternoon.csv b/tests/data/processed/test01/calls_missed_afternoon.csv deleted file mode 100644 index 48a33476..00000000 --- a/tests/data/processed/test01/calls_missed_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_missed_afternoon_count","call_missed_afternoon_distinctcontacts","call_missed_afternoon_timefirstcall","call_missed_afternoon_timelastcall","call_missed_afternoon_countmostfrequentcontact" -"2020-06-01",1,1,874,874,1 diff --git a/tests/data/processed/test01/calls_missed_daily.csv b/tests/data/processed/test01/calls_missed_daily.csv deleted file mode 100644 index 77d7e8ee..00000000 --- a/tests/data/processed/test01/calls_missed_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_missed_daily_count","call_missed_daily_distinctcontacts","call_missed_daily_timefirstcall","call_missed_daily_timelastcall","call_missed_daily_countmostfrequentcontact" -"2020-06-01",6,3,13,1167,4 -"2020-06-02",2,2,589,1167,1 diff --git a/tests/data/processed/test01/calls_missed_evening.csv b/tests/data/processed/test01/calls_missed_evening.csv deleted file mode 100644 index c636f34c..00000000 --- a/tests/data/processed/test01/calls_missed_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_missed_evening_count","call_missed_evening_distinctcontacts","call_missed_evening_timefirstcall","call_missed_evening_timelastcall","call_missed_evening_countmostfrequentcontact" -"2020-06-01",1,1,1167,1167,1 -"2020-06-02",1,1,1167,1167,1 diff --git a/tests/data/processed/test01/calls_missed_morning.csv b/tests/data/processed/test01/calls_missed_morning.csv deleted file mode 100644 index 36b37224..00000000 --- a/tests/data/processed/test01/calls_missed_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_missed_morning_count","call_missed_morning_distinctcontacts","call_missed_morning_timefirstcall","call_missed_morning_timelastcall","call_missed_morning_countmostfrequentcontact" -"2020-06-01",1,1,589,589,1 -"2020-06-02",1,1,589,589,1 diff --git a/tests/data/processed/test01/calls_missed_night.csv b/tests/data/processed/test01/calls_missed_night.csv deleted file mode 100644 index 8359fafe..00000000 --- a/tests/data/processed/test01/calls_missed_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_missed_night_count","call_missed_night_distinctcontacts","call_missed_night_timefirstcall","call_missed_night_timelastcall","call_missed_night_countmostfrequentcontact" -"2020-06-01",3,1,13,257,3 diff --git a/tests/data/processed/test01/calls_outgoing_afternoon.csv b/tests/data/processed/test01/calls_outgoing_afternoon.csv deleted file mode 100644 index 0e8c6fa1..00000000 --- a/tests/data/processed/test01/calls_outgoing_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_outgoing_afternoon_count","call_outgoing_afternoon_distinctcontacts","call_outgoing_afternoon_meanduration","call_outgoing_afternoon_sumduration","call_outgoing_afternoon_minduration","call_outgoing_afternoon_maxduration","call_outgoing_afternoon_stdduration","call_outgoing_afternoon_modeduration","call_outgoing_afternoon_entropyduration","call_outgoing_afternoon_timefirstcall","call_outgoing_afternoon_timelastcall","call_outgoing_afternoon_countmostfrequentcontact" -"2020-06-01",2,2,1237.5,2475,1186,1289,72.8319984622144,1289,0.692482998176928,869,1051,1 diff --git a/tests/data/processed/test01/calls_outgoing_daily.csv b/tests/data/processed/test01/calls_outgoing_daily.csv deleted file mode 100644 index fe3a2fac..00000000 --- a/tests/data/processed/test01/calls_outgoing_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_outgoing_daily_count","call_outgoing_daily_distinctcontacts","call_outgoing_daily_meanduration","call_outgoing_daily_sumduration","call_outgoing_daily_minduration","call_outgoing_daily_maxduration","call_outgoing_daily_stdduration","call_outgoing_daily_modeduration","call_outgoing_daily_entropyduration","call_outgoing_daily_timefirstcall","call_outgoing_daily_timelastcall","call_outgoing_daily_countmostfrequentcontact" -"2020-06-01",8,6,1168,9344,759,1543,250.842010607702,970,2.05922274128194,172,1277,3 -"2020-06-02",5,5,1179.8,5899,759,1543,310.478179587552,1116,1.58127936063292,418,1277,1 diff --git a/tests/data/processed/test01/calls_outgoing_evening.csv b/tests/data/processed/test01/calls_outgoing_evening.csv deleted file mode 100644 index 64aee2fe..00000000 --- a/tests/data/processed/test01/calls_outgoing_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_outgoing_evening_count","call_outgoing_evening_distinctcontacts","call_outgoing_evening_meanduration","call_outgoing_evening_sumduration","call_outgoing_evening_minduration","call_outgoing_evening_maxduration","call_outgoing_evening_stdduration","call_outgoing_evening_modeduration","call_outgoing_evening_entropyduration","call_outgoing_evening_timefirstcall","call_outgoing_evening_timelastcall","call_outgoing_evening_countmostfrequentcontact" -"2020-06-01",2,2,1482,2964,1421,1543,86.2670273047588,1421,0.692468534923961,1156,1277,1 -"2020-06-02",2,2,1482,2964,1421,1543,86.2670273047588,1421,0.692468534923961,1156,1277,1 diff --git a/tests/data/processed/test01/calls_outgoing_morning.csv b/tests/data/processed/test01/calls_outgoing_morning.csv deleted file mode 100644 index b4a3bdf7..00000000 --- a/tests/data/processed/test01/calls_outgoing_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_outgoing_morning_count","call_outgoing_morning_distinctcontacts","call_outgoing_morning_meanduration","call_outgoing_morning_sumduration","call_outgoing_morning_minduration","call_outgoing_morning_maxduration","call_outgoing_morning_stdduration","call_outgoing_morning_modeduration","call_outgoing_morning_entropyduration","call_outgoing_morning_timefirstcall","call_outgoing_morning_timelastcall","call_outgoing_morning_countmostfrequentcontact" -"2020-06-01",3,3,978.333333333333,2935,759,1116,192.000868053593,1116,1.08558305836162,418,687,1 -"2020-06-02",3,3,978.333333333333,2935,759,1116,192.000868053593,1116,1.08558305836162,418,687,1 diff --git a/tests/data/processed/test01/calls_outgoing_night.csv b/tests/data/processed/test01/calls_outgoing_night.csv deleted file mode 100644 index cb0303cb..00000000 --- a/tests/data/processed/test01/calls_outgoing_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_outgoing_night_count","call_outgoing_night_distinctcontacts","call_outgoing_night_meanduration","call_outgoing_night_sumduration","call_outgoing_night_minduration","call_outgoing_night_maxduration","call_outgoing_night_stdduration","call_outgoing_night_modeduration","call_outgoing_night_entropyduration","call_outgoing_night_timefirstcall","call_outgoing_night_timelastcall","call_outgoing_night_countmostfrequentcontact" -"2020-06-01",1,1,970,970,970,970,NA,970,0,172,172,1 diff --git a/tests/data/processed/test01/conversation_afternoon.csv b/tests/data/processed/test01/conversation_afternoon.csv deleted file mode 100644 index e35713fb..00000000 --- a/tests/data/processed/test01/conversation_afternoon.csv +++ /dev/null @@ -1,3 +0,0 @@ -local_date,conversation_afternoon_minutessilence,conversation_afternoon_minutesnoise,conversation_afternoon_minutesvoice,conversation_afternoon_minutesunknown,conversation_afternoon_countconversation,conversation_afternoon_silencesensedfraction,conversation_afternoon_noisesensedfraction,conversation_afternoon_voicesensedfraction,conversation_afternoon_unknownsensedfraction,conversation_afternoon_silenceexpectedfraction,conversation_afternoon_noiseexpectedfraction,conversation_afternoon_voiceexpectedfraction,conversation_afternoon_unknownexpectedfraction,conversation_afternoon_sumconversationduration,conversation_afternoon_avgconversationduration,conversation_afternoon_sdconversationduration,conversation_afternoon_minconversationduration,conversation_afternoon_maxconversationduration,conversation_afternoon_timefirstconversation,conversation_afternoon_timelastconversation,conversation_afternoon_noisesumenergy,conversation_afternoon_noiseavgenergy,conversation_afternoon_noisesdenergy,conversation_afternoon_noiseminenergy,conversation_afternoon_noisemaxenergy,conversation_afternoon_voicesumenergy,conversation_afternoon_voiceavgenergy,conversation_afternoon_voicesdenergy,conversation_afternoon_voiceminenergy,conversation_afternoon_voicemaxenergy -2020-07-07,2.1333333333333333,27.416666666666668,8.266666666666667,1.8,5,0.05384938998737905,0.692048801009676,0.20866638620109385,0.045435422801851075,0.005925925925925926,0.07615740740740741,0.022962962962962966,0.005,12.437950003147126,2.4875900006294254,0.8690951001495368,1.780666669209798,3.9693833351135255,733.0,1038.0,9942436,6044.034042553191,3481.1514888929237,15,11997,1506807,3037.9173387096776,1754.9205154010617,5,5998 -2020-07-08,0.65,8.883333333333333,2.433333333333333,0.6166666666666667,3,0.051655629139072845,0.7059602649006622,0.1933774834437086,0.04900662251655629,0.0018055555555555557,0.024675925925925924,0.006759259259259258,0.001712962962962963,10.930666665236155,3.643555555078718,2.8616790499886138,0.34016666412353513,5.365416669845581,725.0,1079.0,3300159,6191.667917448405,3457.367505499712,32,11994,434210,2974.041095890411,1728.009925245731,26,5965 diff --git a/tests/data/processed/test01/conversation_daily.csv b/tests/data/processed/test01/conversation_daily.csv deleted file mode 100644 index c8ffccfb..00000000 --- a/tests/data/processed/test01/conversation_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -local_date,conversation_daily_minutessilence,conversation_daily_minutesnoise,conversation_daily_minutesvoice,conversation_daily_minutesunknown,conversation_daily_countconversation,conversation_daily_silencesensedfraction,conversation_daily_noisesensedfraction,conversation_daily_voicesensedfraction,conversation_daily_unknownsensedfraction,conversation_daily_silenceexpectedfraction,conversation_daily_noiseexpectedfraction,conversation_daily_voiceexpectedfraction,conversation_daily_unknownexpectedfraction,conversation_daily_sumconversationduration,conversation_daily_avgconversationduration,conversation_daily_sdconversationduration,conversation_daily_minconversationduration,conversation_daily_maxconversationduration,conversation_daily_timefirstconversation,conversation_daily_timelastconversation,conversation_daily_noisesumenergy,conversation_daily_noiseavgenergy,conversation_daily_noisesdenergy,conversation_daily_noiseminenergy,conversation_daily_noisemaxenergy,conversation_daily_voicesumenergy,conversation_daily_voiceavgenergy,conversation_daily_voicesdenergy,conversation_daily_voiceminenergy,conversation_daily_voicemaxenergy -2020-07-07,3.7,50.63333333333333,15.066666666666666,3.683333333333333,14,0.05062713797035348,0.6928164196123148,0.206157354618016,0.05039908779931585,0.010277777777777778,0.14064814814814813,0.04185185185185185,0.01023148148148148,32.67243332862854,2.3337452377591816,2.374837824698354,0.7180166641871134,10.046433333555857,73.0,1436.0,18439355,6069.570441079658,3468.929368777422,13,11997,2734745,3025.1603982300885,1740.005342891663,5,5998 -2020-07-08,2.65,35.68333333333333,9.35,2.55,9,0.052753815527538155,0.7103516921035169,0.18613138686131386,0.05076310550763105,0.007361111111111111,0.09912037037037036,0.025972222222222223,0.007083333333333333,34.320483330885565,3.8133870367650626,4.319944870646438,0.2900833328564962,14.262033331394196,77.0,1079.0,12899840,6025.147127510509,3479.798678961238,21,11994,1772024,3158.688057040998,1734.437004379429,15,5983 diff --git a/tests/data/processed/test01/conversation_evening.csv b/tests/data/processed/test01/conversation_evening.csv deleted file mode 100644 index 79256ecf..00000000 --- a/tests/data/processed/test01/conversation_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -local_date,conversation_evening_minutessilence,conversation_evening_minutesnoise,conversation_evening_minutesvoice,conversation_evening_minutesunknown,conversation_evening_countconversation,conversation_evening_silencesensedfraction,conversation_evening_noisesensedfraction,conversation_evening_voicesensedfraction,conversation_evening_unknownsensedfraction,conversation_evening_silenceexpectedfraction,conversation_evening_noiseexpectedfraction,conversation_evening_voiceexpectedfraction,conversation_evening_unknownexpectedfraction,conversation_evening_sumconversationduration,conversation_evening_avgconversationduration,conversation_evening_sdconversationduration,conversation_evening_minconversationduration,conversation_evening_maxconversationduration,conversation_evening_timefirstconversation,conversation_evening_timelastconversation,conversation_evening_noisesumenergy,conversation_evening_noiseavgenergy,conversation_evening_noisesdenergy,conversation_evening_noiseminenergy,conversation_evening_noisemaxenergy,conversation_evening_voicesumenergy,conversation_evening_voiceavgenergy,conversation_evening_voicesdenergy,conversation_evening_voiceminenergy,conversation_evening_voicemaxenergy -2020-07-07,0.36666666666666664,4.65,1.5166666666666666,0.4166666666666667,3.0,0.05275779376498801,0.6690647482014389,0.21822541966426856,0.05995203836930456,0.0010185185185185184,0.012916666666666668,0.004212962962962963,0.0011574074074074076,4.880700000127156,1.6269000000423854,0.44788788134076357,1.3510499993960063,2.1436833341916404,1204.0,1436.0,1653662,5927.103942652329,3410.0136025729585,117,11997,288277,3167.879120879121,1716.9777377393546,116,5961 -2020-07-08,0.1,2.1,0.7166666666666667,0.06666666666666667,,0.0335195530726257,0.7039106145251397,0.24022346368715083,0.0223463687150838,0.0002777777777777778,0.005833333333333334,0.001990740740740741,0.00018518518518518518,0.0,,,,0.0,,,737899,5856.341269841269,3586.7682415516033,65,11872,138265,3215.4651162790697,1685.612271816093,344,5791 diff --git a/tests/data/processed/test01/conversation_morning.csv b/tests/data/processed/test01/conversation_morning.csv deleted file mode 100644 index c08b3ca7..00000000 --- a/tests/data/processed/test01/conversation_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -local_date,conversation_morning_minutessilence,conversation_morning_minutesnoise,conversation_morning_minutesvoice,conversation_morning_minutesunknown,conversation_morning_countconversation,conversation_morning_silencesensedfraction,conversation_morning_noisesensedfraction,conversation_morning_voicesensedfraction,conversation_morning_unknownsensedfraction,conversation_morning_silenceexpectedfraction,conversation_morning_noiseexpectedfraction,conversation_morning_voiceexpectedfraction,conversation_morning_unknownexpectedfraction,conversation_morning_sumconversationduration,conversation_morning_avgconversationduration,conversation_morning_sdconversationduration,conversation_morning_minconversationduration,conversation_morning_maxconversationduration,conversation_morning_timefirstconversation,conversation_morning_timelastconversation,conversation_morning_noisesumenergy,conversation_morning_noiseavgenergy,conversation_morning_noisesdenergy,conversation_morning_noiseminenergy,conversation_morning_noisemaxenergy,conversation_morning_voicesumenergy,conversation_morning_voiceavgenergy,conversation_morning_voicesdenergy,conversation_morning_voiceminenergy,conversation_morning_voicemaxenergy -2020-07-07,0.5,8.266666666666667,2.433333333333333,0.65,4,0.04219409282700422,0.6976090014064699,0.20534458509142053,0.05485232067510549,0.001388888888888889,0.022962962962962966,0.006759259259259258,0.0018055555555555557,4.589333327611287,1.1473333319028218,0.42892803694050957,0.749316664536794,1.6650999983151753,392.0,656.0,3018217,6085.114919354839,3372.11073498322,13,11985,439819,3012.458904109589,1708.9791513294683,18,5974 -2020-07-08,0.8166666666666667,8.966666666666667,2.4833333333333334,0.5666666666666667,3,0.06363636363636363,0.6987012987012987,0.19350649350649352,0.04415584415584415,0.0022685185185185187,0.02490740740740741,0.006898148148148148,0.001574074074074074,5.447933332125346,1.8159777773751153,1.3228078214374743,0.2900833328564962,2.638549999396006,481.0,719.0,3328403,6186.622676579926,3507.2428114622626,21,11986,484712,3253.1006711409395,1731.8302018110644,39,5972 diff --git a/tests/data/processed/test01/conversation_night.csv b/tests/data/processed/test01/conversation_night.csv deleted file mode 100644 index a0cb8334..00000000 --- a/tests/data/processed/test01/conversation_night.csv +++ /dev/null @@ -1,3 +0,0 @@ -local_date,conversation_night_minutessilence,conversation_night_minutesnoise,conversation_night_minutesvoice,conversation_night_minutesunknown,conversation_night_countconversation,conversation_night_silencesensedfraction,conversation_night_noisesensedfraction,conversation_night_voicesensedfraction,conversation_night_unknownsensedfraction,conversation_night_silenceexpectedfraction,conversation_night_noiseexpectedfraction,conversation_night_voiceexpectedfraction,conversation_night_unknownexpectedfraction,conversation_night_sumconversationduration,conversation_night_avgconversationduration,conversation_night_sdconversationduration,conversation_night_minconversationduration,conversation_night_maxconversationduration,conversation_night_timefirstconversation,conversation_night_timelastconversation,conversation_night_noisesumenergy,conversation_night_noiseavgenergy,conversation_night_noisesdenergy,conversation_night_noiseminenergy,conversation_night_noisemaxenergy,conversation_night_voicesumenergy,conversation_night_voiceavgenergy,conversation_night_voicesdenergy,conversation_night_voiceminenergy,conversation_night_voicemaxenergy -2020-07-07,0.7,10.3,2.85,0.8166666666666667,2,0.04772727272727273,0.7022727272727274,0.19431818181818183,0.055681818181818186,0.0019444444444444444,0.02861111111111111,0.007916666666666667,0.0022685185185185187,10.76444999774297,5.382224998871485,6.596186684644267,0.7180166641871134,10.046433333555857,73.0,359.0,3825040,6189.385113268609,3543.1815292214824,31,11976,499842,2923.0526315789475,1743.7537375482877,54,5982 -2020-07-08,1.0833333333333333,15.733333333333333,3.716666666666667,1.3,3,0.04961832061068702,0.7206106870229008,0.1702290076335878,0.05954198473282443,0.0030092592592592593,0.0437037037037037,0.010324074074074074,0.0036111111111111114,17.941883333524068,5.980627777841356,7.185787281636153,1.3935166676839192,14.262033331394196,77.0,359.0,5533379,5861.630296610169,3459.015959830812,49,11981,714837,3205.547085201794,1752.0956065993914,15,5983 diff --git a/tests/data/processed/test01/light_afternoon.csv b/tests/data/processed/test01/light_afternoon.csv deleted file mode 100644 index 1ba407f5..00000000 --- a/tests/data/processed/test01/light_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,light_afternoon_count,light_afternoon_maxlux,light_afternoon_minlux,light_afternoon_avglux,light_afternoon_medianlux,light_afternoon_stdlux -2020-07-04,4,97656.0,10351.0,55761.5,57519.5,44778.68551368311 diff --git a/tests/data/processed/test01/light_daily.csv b/tests/data/processed/test01/light_daily.csv deleted file mode 100644 index de186382..00000000 --- a/tests/data/processed/test01/light_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,light_daily_count,light_daily_maxlux,light_daily_minlux,light_daily_avglux,light_daily_medianlux,light_daily_stdlux -2020-07-04,12,114615.0,0.065,40207.89508333333,19836.0,44686.69422566498 diff --git a/tests/data/processed/test01/light_evening.csv b/tests/data/processed/test01/light_evening.csv deleted file mode 100644 index 93f958a7..00000000 --- a/tests/data/processed/test01/light_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,light_evening_count,light_evening_maxlux,light_evening_minlux,light_evening_avglux,light_evening_medianlux,light_evening_stdlux -2020-07-04,3,15258.0,84.156,5603.718666666667,1469.0,8389.476102441995 diff --git a/tests/data/processed/test01/light_morning.csv b/tests/data/processed/test01/light_morning.csv deleted file mode 100644 index 867c142b..00000000 --- a/tests/data/processed/test01/light_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,light_morning_count,light_morning_maxlux,light_morning_minlux,light_morning_avglux,light_morning_medianlux,light_morning_stdlux -2020-07-04,4,114615.0,472.52,60659.38,63775.0,51510.58234386665 diff --git a/tests/data/processed/test01/light_night.csv b/tests/data/processed/test01/light_night.csv deleted file mode 100644 index ef38d120..00000000 --- a/tests/data/processed/test01/light_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,light_night_count,light_night_maxlux,light_night_minlux,light_night_avglux,light_night_medianlux,light_night_stdlux -2020-07-04,1,0.065,0.065,0.065,0.065,NA diff --git a/tests/data/processed/test01/messages_received_afternoon.csv b/tests/data/processed/test01/messages_received_afternoon.csv deleted file mode 100644 index 10344957..00000000 --- a/tests/data/processed/test01/messages_received_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","messages_received_afternoon_countmostfrequentcontact","messages_received_afternoon_count","messages_received_afternoon_distinctcontacts","messages_received_afternoon_timefirstmessage","messages_received_afternoon_timelastmessage" -"2020-05-28",1,2,2,830,949 diff --git a/tests/data/processed/test01/messages_received_daily.csv b/tests/data/processed/test01/messages_received_daily.csv deleted file mode 100644 index b3db335e..00000000 --- a/tests/data/processed/test01/messages_received_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_received_daily_countmostfrequentcontact","messages_received_daily_count","messages_received_daily_distinctcontacts","messages_received_daily_timefirstmessage","messages_received_daily_timelastmessage" -"2020-05-28",7,12,6,6,1382 -"2020-05-29",3,6,4,401,1382 diff --git a/tests/data/processed/test01/messages_received_evening.csv b/tests/data/processed/test01/messages_received_evening.csv deleted file mode 100644 index 2791e326..00000000 --- a/tests/data/processed/test01/messages_received_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_received_evening_countmostfrequentcontact","messages_received_evening_count","messages_received_evening_distinctcontacts","messages_received_evening_timefirstmessage","messages_received_evening_timelastmessage" -"2020-05-28",2,3,2,1173,1382 -"2020-05-29",2,3,2,1173,1382 diff --git a/tests/data/processed/test01/messages_received_morning.csv b/tests/data/processed/test01/messages_received_morning.csv deleted file mode 100644 index 2aab40a6..00000000 --- a/tests/data/processed/test01/messages_received_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_received_morning_countmostfrequentcontact","messages_received_morning_count","messages_received_morning_distinctcontacts","messages_received_morning_timefirstmessage","messages_received_morning_timelastmessage" -"2020-05-28",1,3,3,401,660 -"2020-05-29",1,3,3,401,660 diff --git a/tests/data/processed/test01/messages_received_night.csv b/tests/data/processed/test01/messages_received_night.csv deleted file mode 100644 index 84a53e1b..00000000 --- a/tests/data/processed/test01/messages_received_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","messages_received_night_countmostfrequentcontact","messages_received_night_count","messages_received_night_distinctcontacts","messages_received_night_timefirstmessage","messages_received_night_timelastmessage" -"2020-05-28",4,4,1,6,312 diff --git a/tests/data/processed/test01/messages_sent_afternoon.csv b/tests/data/processed/test01/messages_sent_afternoon.csv deleted file mode 100644 index 1ac41cd2..00000000 --- a/tests/data/processed/test01/messages_sent_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","messages_sent_afternoon_countmostfrequentcontact","messages_sent_afternoon_count","messages_sent_afternoon_distinctcontacts","messages_sent_afternoon_timefirstmessage","messages_sent_afternoon_timelastmessage" -"2020-05-28",1,3,3,722,979 diff --git a/tests/data/processed/test01/messages_sent_daily.csv b/tests/data/processed/test01/messages_sent_daily.csv deleted file mode 100644 index b6d423ea..00000000 --- a/tests/data/processed/test01/messages_sent_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_sent_daily_countmostfrequentcontact","messages_sent_daily_count","messages_sent_daily_distinctcontacts","messages_sent_daily_timefirstmessage","messages_sent_daily_timelastmessage" -"2020-05-28",3,8,6,219,1401 -"2020-05-29",1,4,4,388,1401 diff --git a/tests/data/processed/test01/messages_sent_evening.csv b/tests/data/processed/test01/messages_sent_evening.csv deleted file mode 100644 index b5a38e27..00000000 --- a/tests/data/processed/test01/messages_sent_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_sent_evening_countmostfrequentcontact","messages_sent_evening_count","messages_sent_evening_distinctcontacts","messages_sent_evening_timefirstmessage","messages_sent_evening_timelastmessage" -"2020-05-28",1,2,2,1218,1401 -"2020-05-29",1,2,2,1218,1401 diff --git a/tests/data/processed/test01/messages_sent_morning.csv b/tests/data/processed/test01/messages_sent_morning.csv deleted file mode 100644 index a3d3fb5e..00000000 --- a/tests/data/processed/test01/messages_sent_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_sent_morning_countmostfrequentcontact","messages_sent_morning_count","messages_sent_morning_distinctcontacts","messages_sent_morning_timefirstmessage","messages_sent_morning_timelastmessage" -"2020-05-28",1,2,2,388,654 -"2020-05-29",1,2,2,388,654 diff --git a/tests/data/processed/test01/messages_sent_night.csv b/tests/data/processed/test01/messages_sent_night.csv deleted file mode 100644 index bbabc406..00000000 --- a/tests/data/processed/test01/messages_sent_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","messages_sent_night_countmostfrequentcontact","messages_sent_night_count","messages_sent_night_distinctcontacts","messages_sent_night_timefirstmessage","messages_sent_night_timelastmessage" -"2020-05-28",1,1,1,219,219 diff --git a/tests/data/processed/test01/screen_afternoon.csv b/tests/data/processed/test01/screen_afternoon.csv deleted file mode 100644 index 868d0364..00000000 --- a/tests/data/processed/test01/screen_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_afternoon_countepisodeunlock,screen_afternoon_episodepersensedminutesunlock,screen_afternoon_sumdurationunlock,screen_afternoon_maxdurationunlock,screen_afternoon_mindurationunlock,screen_afternoon_avgdurationunlock,screen_afternoon_stddurationunlock,screen_afternoon_firstuseafter00unlock -2020-06-01,3,0.06,30.08298333333333,12.55,8.533333333333333,10.02766111111111,2.1968176781889985,720.0 diff --git a/tests/data/processed/test01/screen_daily.csv b/tests/data/processed/test01/screen_daily.csv deleted file mode 100644 index 393d7794..00000000 --- a/tests/data/processed/test01/screen_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_daily_countepisodeunlock,screen_daily_episodepersensedminutesunlock,screen_daily_sumdurationunlock,screen_daily_maxdurationunlock,screen_daily_mindurationunlock,screen_daily_avgdurationunlock,screen_daily_stddurationunlock,screen_daily_firstuseafter00unlock -2020-06-01,7,0.04,94.60658333333333,21.6598833333333,5.9910166666666695,13.51522619047619,6.19241804679586,170.5 diff --git a/tests/data/processed/test01/screen_deltas.csv b/tests/data/processed/test01/screen_deltas.csv deleted file mode 100644 index c4d8eb2e..00000000 --- a/tests/data/processed/test01/screen_deltas.csv +++ /dev/null @@ -1,8 +0,0 @@ -"episode_id","episode","screen_sequence","time_diff","local_start_date_time","local_end_date_time","local_start_date","local_end_date","local_start_day_segment","local_end_day_segment" -3,"unlock","3, 0",5.99101666666667,"2020-06-01 02:50:30","2020-06-01 02:56:30","2020-06-01","2020-06-01","night","night" -7,"unlock","3, 0",6.99016666666667,"2020-06-01 05:56:51","2020-06-01 06:03:51","2020-06-01","2020-06-01","night","morning" -11,"unlock","3, 0",21.6598833333333,"2020-06-01 10:00:24","2020-06-01 10:22:04","2020-06-01","2020-06-01","morning","morning" -15,"unlock","3, 0",14.9953833333333,"2020-06-01 11:57:33","2020-06-01 12:12:33","2020-06-01","2020-06-01","morning","afternoon" -19,"unlock","3, 0",8.99965,"2020-06-01 14:51:02","2020-06-01 15:00:02","2020-06-01","2020-06-01","afternoon","afternoon" -23,"unlock","3, 0",16.9834166666667,"2020-06-01 17:51:27","2020-06-01 18:08:26","2020-06-01","2020-06-01","afternoon","evening" -27,"unlock","3, 0",18.9870666666667,"2020-06-01 20:42:58","2020-06-01 21:01:58","2020-06-01","2020-06-01","evening","evening" diff --git a/tests/data/processed/test01/screen_evening.csv b/tests/data/processed/test01/screen_evening.csv deleted file mode 100644 index f23670da..00000000 --- a/tests/data/processed/test01/screen_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_evening_countepisodeunlock,screen_evening_episodepersensedminutesunlock,screen_evening_sumdurationunlock,screen_evening_maxdurationunlock,screen_evening_mindurationunlock,screen_evening_avgdurationunlock,screen_evening_stddurationunlock,screen_evening_firstuseafter00unlock -2020-06-01,2,0.044444444444444446,27.420400000000033,18.9870666666667,8.433333333333334,13.710200000000016,7.462616406834528,1080.0 diff --git a/tests/data/processed/test01/screen_morning.csv b/tests/data/processed/test01/screen_morning.csv deleted file mode 100644 index 9da7a42e..00000000 --- a/tests/data/processed/test01/screen_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_morning_countepisodeunlock,screen_morning_episodepersensedminutesunlock,screen_morning_sumdurationunlock,screen_morning_maxdurationunlock,screen_morning_mindurationunlock,screen_morning_avgdurationunlock,screen_morning_stddurationunlock,screen_morning_firstuseafter00unlock -2020-06-01,3,0.06666666666666667,27.943216666666636,21.6598833333333,2.433333333333333,9.314405555555545,10.714935943920263,360.0 diff --git a/tests/data/processed/test01/screen_night.csv b/tests/data/processed/test01/screen_night.csv deleted file mode 100644 index f4ba3ad6..00000000 --- a/tests/data/processed/test01/screen_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_night_countepisodeunlock,screen_night_episodepersensedminutesunlock,screen_night_sumdurationunlock,screen_night_maxdurationunlock,screen_night_mindurationunlock,screen_night_avgdurationunlock,screen_night_stddurationunlock,screen_night_firstuseafter00unlock -2020-06-01,2,0.05714285714285714,9.124350000000003,5.9910166666666695,3.1333333333333333,4.562175000000002,2.0206872634837794,170.5 diff --git a/tests/data/processed/test01/wifi_afternoon.csv b/tests/data/processed/test01/wifi_afternoon.csv deleted file mode 100644 index 2c670e59..00000000 --- a/tests/data/processed/test01/wifi_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_afternoon_countscans","wifi_afternoon_uniquedevices","wifi_afternoon_countscansmostuniquedevice" -"2020-07-03",4,4,1 diff --git a/tests/data/processed/test01/wifi_daily.csv b/tests/data/processed/test01/wifi_daily.csv deleted file mode 100644 index 66cb5e2a..00000000 --- a/tests/data/processed/test01/wifi_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_daily_countscans","wifi_daily_uniquedevices","wifi_daily_countscansmostuniquedevice" -"2020-07-03",26,10,6 diff --git a/tests/data/processed/test01/wifi_evening.csv b/tests/data/processed/test01/wifi_evening.csv deleted file mode 100644 index e63ca233..00000000 --- a/tests/data/processed/test01/wifi_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_evening_countscans","wifi_evening_uniquedevices","wifi_evening_countscansmostuniquedevice" -"2020-07-03",7,6,2 diff --git a/tests/data/processed/test01/wifi_morning.csv b/tests/data/processed/test01/wifi_morning.csv deleted file mode 100644 index 2359f24e..00000000 --- a/tests/data/processed/test01/wifi_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_morning_countscans","wifi_morning_uniquedevices","wifi_morning_countscansmostuniquedevice" -"2020-07-03",7,5,2 diff --git a/tests/data/processed/test01/wifi_night.csv b/tests/data/processed/test01/wifi_night.csv deleted file mode 100644 index 89fef7fb..00000000 --- a/tests/data/processed/test01/wifi_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_night_countscans","wifi_night_uniquedevices","wifi_night_countscansmostuniquedevice" -"2020-07-03",8,6,2 diff --git a/tests/data/processed/test02/activity_recognition_afternoon.csv b/tests/data/processed/test02/activity_recognition_afternoon.csv deleted file mode 100644 index a9fa65eb..00000000 --- a/tests/data/processed/test02/activity_recognition_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_afternoon_count,ar_afternoon_mostcommonactivity,ar_afternoon_countuniqueactivities,ar_afternoon_activitychangecount,ar_afternoon_sumstationary,ar_afternoon_summobile,ar_afternoon_sumvehicle -2020-07-06,11,0,3,2,91.4326,60.46666666666667,25.433333333333334 diff --git a/tests/data/processed/test02/activity_recognition_daily.csv b/tests/data/processed/test02/activity_recognition_daily.csv deleted file mode 100644 index 73ae19db..00000000 --- a/tests/data/processed/test02/activity_recognition_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_daily_count,ar_daily_mostcommonactivity,ar_daily_countuniqueactivities,ar_daily_activitychangecount,ar_daily_sumstationary,ar_daily_summobile,ar_daily_sumvehicle -2020-07-06,49,2,4,9,501.1613166666666,334.0321000000003,70.3941833333333 diff --git a/tests/data/processed/test02/activity_recognition_evening.csv b/tests/data/processed/test02/activity_recognition_evening.csv deleted file mode 100644 index ee8ecd91..00000000 --- a/tests/data/processed/test02/activity_recognition_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_evening_count,ar_evening_mostcommonactivity,ar_evening_countuniqueactivities,ar_evening_activitychangecount,ar_evening_sumstationary,ar_evening_summobile,ar_evening_sumvehicle -2020-07-06,16,2,4,4,7.609966666666669,120.1363,13.033333333333333 diff --git a/tests/data/processed/test02/activity_recognition_morning.csv b/tests/data/processed/test02/activity_recognition_morning.csv deleted file mode 100644 index e58fb2b0..00000000 --- a/tests/data/processed/test02/activity_recognition_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_morning_count,ar_morning_mostcommonactivity,ar_morning_countuniqueactivities,ar_morning_activitychangecount,ar_morning_sumstationary,ar_morning_summobile,ar_morning_sumvehicle -2020-07-06,17,2,3,3,46.0,153.41488333333368,31.9119 diff --git a/tests/data/processed/test02/activity_recognition_night.csv b/tests/data/processed/test02/activity_recognition_night.csv deleted file mode 100644 index 7743c271..00000000 --- a/tests/data/processed/test02/activity_recognition_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,ar_night_count,ar_night_mostcommonactivity,ar_night_countuniqueactivities,ar_night_activitychangecount,ar_night_sumstationary,ar_night_summobile,ar_night_sumvehicle -2020-07-06,5,3,1,0,356.1,0,0 diff --git a/tests/data/processed/test02/applications_foreground_afternoon.csv b/tests/data/processed/test02/applications_foreground_afternoon.csv deleted file mode 100644 index 36c73188..00000000 --- a/tests/data/processed/test02/applications_foreground_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_afternoon_countemail,apps_afternoon_countall,apps_afternoon_countentertainment,apps_afternoon_countsocial,apps_afternoon_countcom.facebook.moments,apps_afternoon_countcom.google.android.youtube,apps_afternoon_counttop1global,apps_afternoon_timeoffirstuseemail,apps_afternoon_timeoffirstuseall,apps_afternoon_timeoffirstuseentertainment,apps_afternoon_timeoffirstusesocial,apps_afternoon_timeoffirstusecom.facebook.moments,apps_afternoon_timeoffirstusecom.google.android.youtube,apps_afternoon_timeoffirstusetop1global,apps_afternoon_timeoflastuseemail,apps_afternoon_timeoflastuseall,apps_afternoon_timeoflastuseentertainment,apps_afternoon_timeoflastusesocial,apps_afternoon_timeoflastusecom.facebook.moments,apps_afternoon_timeoflastusecom.google.android.youtube,apps_afternoon_timeoflastusetop1global,apps_afternoon_frequencyentropyemail,apps_afternoon_frequencyentropyall,apps_afternoon_frequencyentropyentertainment,apps_afternoon_frequencyentropysocial,apps_afternoon_frequencyentropycom.facebook.moments,apps_afternoon_frequencyentropycom.google.android.youtube,apps_afternoon_frequencyentropytop1global diff --git a/tests/data/processed/test02/applications_foreground_daily.csv b/tests/data/processed/test02/applications_foreground_daily.csv deleted file mode 100644 index 444dcbd7..00000000 --- a/tests/data/processed/test02/applications_foreground_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_daily_countall,apps_daily_countemail,apps_daily_countentertainment,apps_daily_countsocial,apps_daily_countcom.facebook.moments,apps_daily_countcom.google.android.youtube,apps_daily_counttop1global,apps_daily_timeoffirstuseall,apps_daily_timeoffirstuseemail,apps_daily_timeoffirstuseentertainment,apps_daily_timeoffirstusesocial,apps_daily_timeoffirstusecom.facebook.moments,apps_daily_timeoffirstusecom.google.android.youtube,apps_daily_timeoffirstusetop1global,apps_daily_timeoflastuseall,apps_daily_timeoflastuseemail,apps_daily_timeoflastuseentertainment,apps_daily_timeoflastusesocial,apps_daily_timeoflastusecom.facebook.moments,apps_daily_timeoflastusecom.google.android.youtube,apps_daily_timeoflastusetop1global,apps_daily_frequencyentropyall,apps_daily_frequencyentropyemail,apps_daily_frequencyentropyentertainment,apps_daily_frequencyentropysocial,apps_daily_frequencyentropycom.facebook.moments,apps_daily_frequencyentropycom.google.android.youtube,apps_daily_frequencyentropytop1global diff --git a/tests/data/processed/test02/applications_foreground_evening.csv b/tests/data/processed/test02/applications_foreground_evening.csv deleted file mode 100644 index 3a6bc42e..00000000 --- a/tests/data/processed/test02/applications_foreground_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_evening_countemail,apps_evening_countall,apps_evening_countsocial,apps_evening_countentertainment,apps_evening_counttop1global,apps_evening_countcom.facebook.moments,apps_evening_countcom.google.android.youtube,apps_evening_timeoffirstuseemail,apps_evening_timeoffirstuseall,apps_evening_timeoffirstusesocial,apps_evening_timeoffirstuseentertainment,apps_evening_timeoffirstusetop1global,apps_evening_timeoffirstusecom.facebook.moments,apps_evening_timeoffirstusecom.google.android.youtube,apps_evening_timeoflastuseemail,apps_evening_timeoflastuseall,apps_evening_timeoflastusesocial,apps_evening_timeoflastuseentertainment,apps_evening_timeoflastusetop1global,apps_evening_timeoflastusecom.facebook.moments,apps_evening_timeoflastusecom.google.android.youtube,apps_evening_frequencyentropyemail,apps_evening_frequencyentropyall,apps_evening_frequencyentropysocial,apps_evening_frequencyentropyentertainment,apps_evening_frequencyentropytop1global,apps_evening_frequencyentropycom.facebook.moments,apps_evening_frequencyentropycom.google.android.youtube diff --git a/tests/data/processed/test02/applications_foreground_morning.csv b/tests/data/processed/test02/applications_foreground_morning.csv deleted file mode 100644 index 12158188..00000000 --- a/tests/data/processed/test02/applications_foreground_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_morning_countemail,apps_morning_countall,apps_morning_countentertainment,apps_morning_countsocial,apps_morning_counttop1global,apps_morning_countcom.google.android.youtube,apps_morning_countcom.facebook.moments,apps_morning_timeoffirstuseemail,apps_morning_timeoffirstuseall,apps_morning_timeoffirstuseentertainment,apps_morning_timeoffirstusesocial,apps_morning_timeoffirstusetop1global,apps_morning_timeoffirstusecom.google.android.youtube,apps_morning_timeoffirstusecom.facebook.moments,apps_morning_timeoflastuseemail,apps_morning_timeoflastuseall,apps_morning_timeoflastuseentertainment,apps_morning_timeoflastusesocial,apps_morning_timeoflastusetop1global,apps_morning_timeoflastusecom.google.android.youtube,apps_morning_timeoflastusecom.facebook.moments,apps_morning_frequencyentropyemail,apps_morning_frequencyentropyall,apps_morning_frequencyentropyentertainment,apps_morning_frequencyentropysocial,apps_morning_frequencyentropytop1global,apps_morning_frequencyentropycom.google.android.youtube,apps_morning_frequencyentropycom.facebook.moments diff --git a/tests/data/processed/test02/applications_foreground_night.csv b/tests/data/processed/test02/applications_foreground_night.csv deleted file mode 100644 index 2b589972..00000000 --- a/tests/data/processed/test02/applications_foreground_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_night_countall,apps_night_countemail,apps_night_countentertainment,apps_night_countsocial,apps_night_countcom.google.android.youtube,apps_night_counttop1global,apps_night_countcom.facebook.moments,apps_night_timeoffirstuseall,apps_night_timeoffirstuseemail,apps_night_timeoffirstuseentertainment,apps_night_timeoffirstusesocial,apps_night_timeoffirstusecom.google.android.youtube,apps_night_timeoffirstusetop1global,apps_night_timeoffirstusecom.facebook.moments,apps_night_timeoflastuseall,apps_night_timeoflastuseemail,apps_night_timeoflastuseentertainment,apps_night_timeoflastusesocial,apps_night_timeoflastusecom.google.android.youtube,apps_night_timeoflastusetop1global,apps_night_timeoflastusecom.facebook.moments,apps_night_frequencyentropyall,apps_night_frequencyentropyemail,apps_night_frequencyentropyentertainment,apps_night_frequencyentropysocial,apps_night_frequencyentropycom.google.android.youtube,apps_night_frequencyentropytop1global,apps_night_frequencyentropycom.facebook.moments diff --git a/tests/data/processed/test02/battery_afternoon.csv b/tests/data/processed/test02/battery_afternoon.csv deleted file mode 100644 index 4544acf8..00000000 --- a/tests/data/processed/test02/battery_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_afternoon_countdischarge,battery_afternoon_sumdurationdischarge,battery_afternoon_avgconsumptionrate,battery_afternoon_maxconsumptionrate,battery_afternoon_countcharge,battery_afternoon_sumdurationcharge -2020-07-01,2,12.141000000000004,0.3148499579639538,0.3655119197498273,2,19.457666666666668 diff --git a/tests/data/processed/test02/battery_daily.csv b/tests/data/processed/test02/battery_daily.csv deleted file mode 100644 index b69d3523..00000000 --- a/tests/data/processed/test02/battery_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_daily_countdischarge,battery_daily_sumdurationdischarge,battery_daily_avgconsumptionrate,battery_daily_maxconsumptionrate,battery_daily_countcharge,battery_daily_sumdurationcharge -2020-07-01,6,54.820549999999955,0.3388682207181443,0.4262039670118132,5,49.705000000000034 diff --git a/tests/data/processed/test02/battery_deltas.csv b/tests/data/processed/test02/battery_deltas.csv deleted file mode 100644 index 71e63cf7..00000000 --- a/tests/data/processed/test02/battery_deltas.csv +++ /dev/null @@ -1,12 +0,0 @@ -"battery_diff","time_diff","local_start_date_time","local_end_date_time","local_start_date","local_end_date","local_start_day_segment","local_end_day_segment" -3,9.46978333333333,"2020-07-01 00:08:10","2020-07-01 00:17:38","2020-07-01","2020-07-01","night","night" --3,10.091,"2020-07-01 03:20:30","2020-07-01 03:30:35","2020-07-01","2020-07-01","night","night" -3,7.03888333333333,"2020-07-01 05:57:30","2020-07-01 06:04:32","2020-07-01","2020-07-01","night","morning" --3,8.591,"2020-07-01 08:04:32","2020-07-01 08:13:07","2020-07-01","2020-07-01","morning","morning" -3,11.0243333333333,"2020-07-01 10:23:17","2020-07-01 10:34:18","2020-07-01","2020-07-01","morning","morning" --3,12.1576666666667,"2020-07-01 11:55:58","2020-07-01 12:08:07","2020-07-01","2020-07-01","morning","afternoon" -3,8.20766666666667,"2020-07-01 13:52:07","2020-07-01 14:00:19","2020-07-01","2020-07-01","afternoon","afternoon" --3,11.341,"2020-07-01 16:13:27","2020-07-01 16:24:47","2020-07-01","2020-07-01","afternoon","afternoon" -3,11.35555,"2020-07-01 17:56:03","2020-07-01 18:07:24","2020-07-01","2020-07-01","afternoon","evening" --3,7.52433333333333,"2020-07-01 20:03:03","2020-07-01 20:10:34","2020-07-01","2020-07-01","evening","evening" -3,7.72433333333333,"2020-07-01 21:30:04","2020-07-01 21:37:47","2020-07-01","2020-07-01","evening","evening" diff --git a/tests/data/processed/test02/battery_evening.csv b/tests/data/processed/test02/battery_evening.csv deleted file mode 100644 index d8dd2c3c..00000000 --- a/tests/data/processed/test02/battery_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_evening_countdischarge,battery_evening_sumdurationdischarge,battery_evening_avgconsumptionrate,battery_evening_maxconsumptionrate,battery_evening_countcharge,battery_evening_sumdurationcharge -2020-07-01,2,15.12433333333333,0.3262855140774751,0.3883830319768698,1,7.52433333333333 diff --git a/tests/data/processed/test02/battery_morning.csv b/tests/data/processed/test02/battery_morning.csv deleted file mode 100644 index 1bc5aa67..00000000 --- a/tests/data/processed/test02/battery_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_morning_countdischarge,battery_morning_sumdurationdischarge,battery_morning_avgconsumptionrate,battery_morning_maxconsumptionrate,battery_morning_countcharge,battery_morning_sumdurationcharge -2020-07-01,2,15.557666666666634,0.3491646327968694,0.4262039670118131,2,12.607666666666667 diff --git a/tests/data/processed/test02/battery_night.csv b/tests/data/processed/test02/battery_night.csv deleted file mode 100644 index 304297ec..00000000 --- a/tests/data/processed/test02/battery_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,battery_night_countdischarge,battery_night_sumdurationdischarge,battery_night_avgconsumptionrate,battery_night_maxconsumptionrate,battery_night_countcharge,battery_night_sumdurationcharge -2020-07-01,2,11.953116666666663,0.37150053891108137,0.4262039670118132,1,10.091000000000001 diff --git a/tests/data/processed/test02/bluetooth_afternoon.csv b/tests/data/processed/test02/bluetooth_afternoon.csv deleted file mode 100644 index 931e4dc4..00000000 --- a/tests/data/processed/test02/bluetooth_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_afternoon_countscans","bluetooth_afternoon_uniquedevices","bluetooth_afternoon_countscansmostuniquedevice" -"2020-07-02",2,2,1 diff --git a/tests/data/processed/test02/bluetooth_daily.csv b/tests/data/processed/test02/bluetooth_daily.csv deleted file mode 100644 index c1538fd7..00000000 --- a/tests/data/processed/test02/bluetooth_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_daily_countscans","bluetooth_daily_uniquedevices","bluetooth_daily_countscansmostuniquedevice" -"2020-07-02",14,5,5 diff --git a/tests/data/processed/test02/bluetooth_evening.csv b/tests/data/processed/test02/bluetooth_evening.csv deleted file mode 100644 index 4b8d6c55..00000000 --- a/tests/data/processed/test02/bluetooth_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_evening_countscans","bluetooth_evening_uniquedevices","bluetooth_evening_countscansmostuniquedevice" -"2020-07-02",5,3,3 diff --git a/tests/data/processed/test02/bluetooth_morning.csv b/tests/data/processed/test02/bluetooth_morning.csv deleted file mode 100644 index b2d37fd0..00000000 --- a/tests/data/processed/test02/bluetooth_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_morning_countscans","bluetooth_morning_uniquedevices","bluetooth_morning_countscansmostuniquedevice" -"2020-07-02",3,3,1 diff --git a/tests/data/processed/test02/bluetooth_night.csv b/tests/data/processed/test02/bluetooth_night.csv deleted file mode 100644 index 4887dbaa..00000000 --- a/tests/data/processed/test02/bluetooth_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","bluetooth_night_countscans","bluetooth_night_uniquedevices","bluetooth_night_countscansmostuniquedevice" -"2020-07-02",4,2,2 diff --git a/tests/data/processed/test02/calls_incoming_afternoon.csv b/tests/data/processed/test02/calls_incoming_afternoon.csv deleted file mode 100644 index 9d4635bf..00000000 --- a/tests/data/processed/test02/calls_incoming_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_incoming_afternoon_count","call_incoming_afternoon_distinctcontacts","call_incoming_afternoon_meanduration","call_incoming_afternoon_sumduration","call_incoming_afternoon_minduration","call_incoming_afternoon_maxduration","call_incoming_afternoon_stdduration","call_incoming_afternoon_modeduration","call_incoming_afternoon_entropyduration","call_incoming_afternoon_timefirstcall","call_incoming_afternoon_timelastcall","call_incoming_afternoon_countmostfrequentcontact" -"2020-06-01",3,3,629.333333333333,1888,198,1040,421.38027164704,1040,0.932593293857646,753,921,1 diff --git a/tests/data/processed/test02/calls_incoming_daily.csv b/tests/data/processed/test02/calls_incoming_daily.csv deleted file mode 100644 index 994563cf..00000000 --- a/tests/data/processed/test02/calls_incoming_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_incoming_daily_count","call_incoming_daily_distinctcontacts","call_incoming_daily_meanduration","call_incoming_daily_sumduration","call_incoming_daily_minduration","call_incoming_daily_maxduration","call_incoming_daily_stdduration","call_incoming_daily_modeduration","call_incoming_daily_entropyduration","call_incoming_daily_timefirstcall","call_incoming_daily_timelastcall","call_incoming_daily_countmostfrequentcontact" -"2020-06-01",10,10,961.4,9614,198,1700,464.326800624062,420,2.18431625877259,163,1331,1 -"2020-06-02",5,5,1202,6010,660,1710,375.326524508993,660,1.56885323664414,519,1331,0 diff --git a/tests/data/processed/test02/calls_incoming_evening.csv b/tests/data/processed/test02/calls_incoming_evening.csv deleted file mode 100644 index 37331ec6..00000000 --- a/tests/data/processed/test02/calls_incoming_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_incoming_evening_count","call_incoming_evening_distinctcontacts","call_incoming_evening_meanduration","call_incoming_evening_sumduration","call_incoming_evening_minduration","call_incoming_evening_maxduration","call_incoming_evening_stdduration","call_incoming_evening_modeduration","call_incoming_evening_entropyduration","call_incoming_evening_timefirstcall","call_incoming_evening_timelastcall","call_incoming_evening_countmostfrequentcontact" -"2020-06-01",3,3,1350,4050,1140,1700,305.122926047847,1140,1.08239814505043,1144,1331,1 -"2020-06-02",3,3,1353.33333333333,4060,1140,1710,310.859024854247,1140,1.08186609729599,1144,1331,0 diff --git a/tests/data/processed/test02/calls_incoming_morning.csv b/tests/data/processed/test02/calls_incoming_morning.csv deleted file mode 100644 index 413e0506..00000000 --- a/tests/data/processed/test02/calls_incoming_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_incoming_morning_count","call_incoming_morning_distinctcontacts","call_incoming_morning_meanduration","call_incoming_morning_sumduration","call_incoming_morning_minduration","call_incoming_morning_maxduration","call_incoming_morning_stdduration","call_incoming_morning_modeduration","call_incoming_morning_entropyduration","call_incoming_morning_timefirstcall","call_incoming_morning_timelastcall","call_incoming_morning_countmostfrequentcontact" -"2020-06-01",2,2,968,1936,657,1279,439.820417898033,657,0.640867990789142,519,600,1 -"2020-06-02",2,2,975,1950,660,1290,445.477272147525,660,0.640266157864261,519,600,0 diff --git a/tests/data/processed/test02/calls_incoming_night.csv b/tests/data/processed/test02/calls_incoming_night.csv deleted file mode 100644 index a73428ad..00000000 --- a/tests/data/processed/test02/calls_incoming_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_incoming_night_count","call_incoming_night_distinctcontacts","call_incoming_night_meanduration","call_incoming_night_sumduration","call_incoming_night_minduration","call_incoming_night_maxduration","call_incoming_night_stdduration","call_incoming_night_modeduration","call_incoming_night_entropyduration","call_incoming_night_timefirstcall","call_incoming_night_timelastcall","call_incoming_night_countmostfrequentcontact" -"2020-06-01",2,2,870,1740,420,1320,636.396103067893,420,0.552951978816239,163,257,1 diff --git a/tests/data/processed/test02/calls_missed_afternoon.csv b/tests/data/processed/test02/calls_missed_afternoon.csv deleted file mode 100644 index 7b1d275f..00000000 --- a/tests/data/processed/test02/calls_missed_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_afternoon_count","call_missed_afternoon_distinctcontacts","call_missed_afternoon_timefirstcall","call_missed_afternoon_timelastcall","call_missed_afternoon_countmostfrequentcontact" diff --git a/tests/data/processed/test02/calls_missed_daily.csv b/tests/data/processed/test02/calls_missed_daily.csv deleted file mode 100644 index 677ee442..00000000 --- a/tests/data/processed/test02/calls_missed_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_missed_daily_count","call_missed_daily_distinctcontacts","call_missed_daily_timefirstcall","call_missed_daily_timelastcall","call_missed_daily_countmostfrequentcontact" -"2020-06-01",3,3,13,1167,1 -"2020-06-02",1,1,589,589,0 diff --git a/tests/data/processed/test02/calls_missed_evening.csv b/tests/data/processed/test02/calls_missed_evening.csv deleted file mode 100644 index 290cf82b..00000000 --- a/tests/data/processed/test02/calls_missed_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_missed_evening_count","call_missed_evening_distinctcontacts","call_missed_evening_timefirstcall","call_missed_evening_timelastcall","call_missed_evening_countmostfrequentcontact" -"2020-06-01",1,1,1167,1167,1 diff --git a/tests/data/processed/test02/calls_missed_morning.csv b/tests/data/processed/test02/calls_missed_morning.csv deleted file mode 100644 index 4d35ed4b..00000000 --- a/tests/data/processed/test02/calls_missed_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_missed_morning_count","call_missed_morning_distinctcontacts","call_missed_morning_timefirstcall","call_missed_morning_timelastcall","call_missed_morning_countmostfrequentcontact" -"2020-06-01",1,1,589,589,1 -"2020-06-02",1,1,589,589,0 diff --git a/tests/data/processed/test02/calls_missed_night.csv b/tests/data/processed/test02/calls_missed_night.csv deleted file mode 100644 index ac1c9e25..00000000 --- a/tests/data/processed/test02/calls_missed_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_missed_night_count","call_missed_night_distinctcontacts","call_missed_night_timefirstcall","call_missed_night_timelastcall","call_missed_night_countmostfrequentcontact" -"2020-06-01",1,1,13,13,1 diff --git a/tests/data/processed/test02/calls_outgoing_afternoon.csv b/tests/data/processed/test02/calls_outgoing_afternoon.csv deleted file mode 100644 index 5f91c20c..00000000 --- a/tests/data/processed/test02/calls_outgoing_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_outgoing_afternoon_count","call_outgoing_afternoon_distinctcontacts","call_outgoing_afternoon_meanduration","call_outgoing_afternoon_sumduration","call_outgoing_afternoon_minduration","call_outgoing_afternoon_maxduration","call_outgoing_afternoon_stdduration","call_outgoing_afternoon_modeduration","call_outgoing_afternoon_entropyduration","call_outgoing_afternoon_timefirstcall","call_outgoing_afternoon_timelastcall","call_outgoing_afternoon_countmostfrequentcontact" -"2020-06-01",3,3,816.333333333333,2449,0,1279,709.062996731132,1279,0.692360538889388,869,1051,1 diff --git a/tests/data/processed/test02/calls_outgoing_daily.csv b/tests/data/processed/test02/calls_outgoing_daily.csv deleted file mode 100644 index e4fa7771..00000000 --- a/tests/data/processed/test02/calls_outgoing_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_outgoing_daily_count","call_outgoing_daily_distinctcontacts","call_outgoing_daily_meanduration","call_outgoing_daily_sumduration","call_outgoing_daily_minduration","call_outgoing_daily_maxduration","call_outgoing_daily_stdduration","call_outgoing_daily_modeduration","call_outgoing_daily_entropyduration","call_outgoing_daily_timefirstcall","call_outgoing_daily_timelastcall","call_outgoing_daily_countmostfrequentcontact" -"2020-06-01",11,11,837.909090909091,9217,0,1530,577.720772440364,0,2.0585997872903,57,1277,1 -"2020-06-02",6,6,973.333333333333,5840,0,1530,551.313582878807,1100,1.58092421564451,418,1277,0 diff --git a/tests/data/processed/test02/calls_outgoing_evening.csv b/tests/data/processed/test02/calls_outgoing_evening.csv deleted file mode 100644 index 7d675f85..00000000 --- a/tests/data/processed/test02/calls_outgoing_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_outgoing_evening_count","call_outgoing_evening_distinctcontacts","call_outgoing_evening_meanduration","call_outgoing_evening_sumduration","call_outgoing_evening_minduration","call_outgoing_evening_maxduration","call_outgoing_evening_stdduration","call_outgoing_evening_modeduration","call_outgoing_evening_entropyduration","call_outgoing_evening_timefirstcall","call_outgoing_evening_timelastcall","call_outgoing_evening_countmostfrequentcontact" -"2020-06-01",2,2,1465,2930,1400,1530,91.9238815542512,1400,0.692333219466401,1156,1277,1 -"2020-06-02",3,3,980,2940,0,1530,850.823130856232,1410,0.692484030888463,1156,1277,0 diff --git a/tests/data/processed/test02/calls_outgoing_morning.csv b/tests/data/processed/test02/calls_outgoing_morning.csv deleted file mode 100644 index c036931a..00000000 --- a/tests/data/processed/test02/calls_outgoing_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","call_outgoing_morning_count","call_outgoing_morning_distinctcontacts","call_outgoing_morning_meanduration","call_outgoing_morning_sumduration","call_outgoing_morning_minduration","call_outgoing_morning_maxduration","call_outgoing_morning_stdduration","call_outgoing_morning_modeduration","call_outgoing_morning_entropyduration","call_outgoing_morning_timefirstcall","call_outgoing_morning_timelastcall","call_outgoing_morning_countmostfrequentcontact" -"2020-06-01",3,3,959.333333333333,2878,742,1096,190.287501779106,1096,1.08529592058152,418,687,1 -"2020-06-02",3,3,966.666666666667,2900,750,1100,189.296944860009,1100,1.08563855912214,418,687,0 diff --git a/tests/data/processed/test02/calls_outgoing_night.csv b/tests/data/processed/test02/calls_outgoing_night.csv deleted file mode 100644 index 20593c0e..00000000 --- a/tests/data/processed/test02/calls_outgoing_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","call_outgoing_night_count","call_outgoing_night_distinctcontacts","call_outgoing_night_meanduration","call_outgoing_night_sumduration","call_outgoing_night_minduration","call_outgoing_night_maxduration","call_outgoing_night_stdduration","call_outgoing_night_modeduration","call_outgoing_night_entropyduration","call_outgoing_night_timefirstcall","call_outgoing_night_timelastcall","call_outgoing_night_countmostfrequentcontact" -"2020-06-01",3,3,320,960,0,960,554.256258422041,0,0,57,257,1 diff --git a/tests/data/processed/test02/conversation_afternoon.csv b/tests/data/processed/test02/conversation_afternoon.csv deleted file mode 100644 index aabc359e..00000000 --- a/tests/data/processed/test02/conversation_afternoon.csv +++ /dev/null @@ -1,3 +0,0 @@ -local_date,conversation_afternoon_minutessilence,conversation_afternoon_minutesnoise,conversation_afternoon_minutesvoice,conversation_afternoon_minutesunknown,conversation_afternoon_countconversation,conversation_afternoon_silencesensedfraction,conversation_afternoon_noisesensedfraction,conversation_afternoon_voicesensedfraction,conversation_afternoon_unknownsensedfraction,conversation_afternoon_silenceexpectedfraction,conversation_afternoon_noiseexpectedfraction,conversation_afternoon_voiceexpectedfraction,conversation_afternoon_unknownexpectedfraction,conversation_afternoon_sumconversationduration,conversation_afternoon_avgconversationduration,conversation_afternoon_sdconversationduration,conversation_afternoon_minconversationduration,conversation_afternoon_maxconversationduration,conversation_afternoon_timefirstconversation,conversation_afternoon_timelastconversation,conversation_afternoon_noisesumenergy,conversation_afternoon_noiseavgenergy,conversation_afternoon_noisesdenergy,conversation_afternoon_noiseminenergy,conversation_afternoon_noisemaxenergy,conversation_afternoon_voicesumenergy,conversation_afternoon_voiceavgenergy,conversation_afternoon_voicesdenergy,conversation_afternoon_voiceminenergy,conversation_afternoon_voicemaxenergy -2020-07-07,2.1333333333333333,27.416666666666668,8.266666666666667,1.8,5,0.05384938998737905,0.692048801009676,0.20866638620109385,0.045435422801851075,0.005925925925925926,0.07615740740740741,0.022962962962962966,0.005,12.416666666666668,2.4833333333333334,0.8701053576052348,1.7833333333333334,3.966666666666667,733.0,1038.0,9942436,6044.034042553191,3481.1514888929237,15,11997,1506807,3037.9173387096776,1754.9205154010617,5,5998 -2020-07-08,0.65,8.883333333333333,2.433333333333333,0.6166666666666667,3,0.051655629139072845,0.7059602649006622,0.1933774834437086,0.04900662251655629,0.0018055555555555557,0.024675925925925924,0.006759259259259258,0.001712962962962963,10.95,3.65,2.858661302855664,0.35,5.366666666666666,725.0,1079.0,3300159,6191.667917448405,3457.367505499712,32,11994,434210,2974.041095890411,1728.009925245731,26,5965 diff --git a/tests/data/processed/test02/conversation_daily.csv b/tests/data/processed/test02/conversation_daily.csv deleted file mode 100644 index aa1ddb97..00000000 --- a/tests/data/processed/test02/conversation_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -local_date,conversation_daily_minutessilence,conversation_daily_minutesnoise,conversation_daily_minutesvoice,conversation_daily_minutesunknown,conversation_daily_countconversation,conversation_daily_silencesensedfraction,conversation_daily_noisesensedfraction,conversation_daily_voicesensedfraction,conversation_daily_unknownsensedfraction,conversation_daily_silenceexpectedfraction,conversation_daily_noiseexpectedfraction,conversation_daily_voiceexpectedfraction,conversation_daily_unknownexpectedfraction,conversation_daily_sumconversationduration,conversation_daily_avgconversationduration,conversation_daily_sdconversationduration,conversation_daily_minconversationduration,conversation_daily_maxconversationduration,conversation_daily_timefirstconversation,conversation_daily_timelastconversation,conversation_daily_noisesumenergy,conversation_daily_noiseavgenergy,conversation_daily_noisesdenergy,conversation_daily_noiseminenergy,conversation_daily_noisemaxenergy,conversation_daily_voicesumenergy,conversation_daily_voiceavgenergy,conversation_daily_voicesdenergy,conversation_daily_voiceminenergy,conversation_daily_voicemaxenergy -2020-07-07,3.7,50.63333333333333,15.066666666666666,3.683333333333333,14,0.05062713797035348,0.6928164196123148,0.206157354618016,0.05039908779931585,0.010277777777777778,0.14064814814814813,0.04185185185185185,0.01023148148148148,32.666666666666664,2.333333333333333,2.375453173139344,0.7166666666666667,10.05,73.0,1436.0,18439355,6069.570441079658,3468.929368777422,13,11997,2734745,3025.1603982300885,1740.005342891663,5,5998 -2020-07-08,2.65,35.68333333333333,9.35,2.55,10,0.052753815527538155,0.7103516921035169,0.18613138686131386,0.05076310550763105,0.007361111111111111,0.09912037037037036,0.025972222222222223,0.007083333333333333,35.95,3.595,4.133124621373335,0.2833333333333333,14.266666666666667,77.0,1198.0,12899840,6025.147127510509,3479.798678961238,21,11994,1772024,3158.688057040998,1734.437004379429,15,5983 diff --git a/tests/data/processed/test02/conversation_evening.csv b/tests/data/processed/test02/conversation_evening.csv deleted file mode 100644 index 3272910b..00000000 --- a/tests/data/processed/test02/conversation_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -local_date,conversation_evening_minutessilence,conversation_evening_minutesnoise,conversation_evening_minutesvoice,conversation_evening_minutesunknown,conversation_evening_countconversation,conversation_evening_silencesensedfraction,conversation_evening_noisesensedfraction,conversation_evening_voicesensedfraction,conversation_evening_unknownsensedfraction,conversation_evening_silenceexpectedfraction,conversation_evening_noiseexpectedfraction,conversation_evening_voiceexpectedfraction,conversation_evening_unknownexpectedfraction,conversation_evening_sumconversationduration,conversation_evening_avgconversationduration,conversation_evening_sdconversationduration,conversation_evening_minconversationduration,conversation_evening_maxconversationduration,conversation_evening_timefirstconversation,conversation_evening_timelastconversation,conversation_evening_noisesumenergy,conversation_evening_noiseavgenergy,conversation_evening_noisesdenergy,conversation_evening_noiseminenergy,conversation_evening_noisemaxenergy,conversation_evening_voicesumenergy,conversation_evening_voiceavgenergy,conversation_evening_voicesdenergy,conversation_evening_voiceminenergy,conversation_evening_voicemaxenergy -2020-07-07,0.36666666666666664,4.65,1.5166666666666666,0.4166666666666667,3,0.05275779376498801,0.6690647482014389,0.21822541966426856,0.05995203836930456,0.0010185185185185184,0.012916666666666668,0.004212962962962963,0.0011574074074074076,4.883333333333333,1.6277777777777775,0.4525647078759177,1.35,2.15,1204.0,1436.0,1653662,5927.103942652329,3410.0136025729585,117,11997,288277,3167.879120879121,1716.9777377393546,116,5961 -2020-07-08,0.1,2.1,0.7166666666666667,0.06666666666666667,1,0.0335195530726257,0.7039106145251397,0.24022346368715083,0.0223463687150838,0.0002777777777777778,0.005833333333333334,0.001990740740740741,0.00018518518518518518,1.6166666666666667,1.6166666666666667,,1.6166666666666667,1.6166666666666667,1198.0,1198.0,737899,5856.341269841269,3586.7682415516033,65,11872,138265,3215.4651162790697,1685.612271816093,344,5791 diff --git a/tests/data/processed/test02/conversation_morning.csv b/tests/data/processed/test02/conversation_morning.csv deleted file mode 100644 index fde02072..00000000 --- a/tests/data/processed/test02/conversation_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -local_date,conversation_morning_minutessilence,conversation_morning_minutesnoise,conversation_morning_minutesvoice,conversation_morning_minutesunknown,conversation_morning_countconversation,conversation_morning_silencesensedfraction,conversation_morning_noisesensedfraction,conversation_morning_voicesensedfraction,conversation_morning_unknownsensedfraction,conversation_morning_silenceexpectedfraction,conversation_morning_noiseexpectedfraction,conversation_morning_voiceexpectedfraction,conversation_morning_unknownexpectedfraction,conversation_morning_sumconversationduration,conversation_morning_avgconversationduration,conversation_morning_sdconversationduration,conversation_morning_minconversationduration,conversation_morning_maxconversationduration,conversation_morning_timefirstconversation,conversation_morning_timelastconversation,conversation_morning_noisesumenergy,conversation_morning_noiseavgenergy,conversation_morning_noisesdenergy,conversation_morning_noiseminenergy,conversation_morning_noisemaxenergy,conversation_morning_voicesumenergy,conversation_morning_voiceavgenergy,conversation_morning_voicesdenergy,conversation_morning_voiceminenergy,conversation_morning_voicemaxenergy -2020-07-07,0.5,8.266666666666667,2.433333333333333,0.65,4,0.04219409282700422,0.6976090014064699,0.20534458509142053,0.05485232067510549,0.001388888888888889,0.022962962962962966,0.006759259259259258,0.0018055555555555557,4.6,1.15,0.4283906144146001,0.75,1.6666666666666667,392.0,656.0,3018217,6085.114919354839,3372.11073498322,13,11985,439819,3012.458904109589,1708.9791513294683,18,5974 -2020-07-08,0.8166666666666667,8.966666666666667,2.4833333333333334,0.5666666666666667,3,0.06363636363636363,0.6987012987012987,0.19350649350649352,0.04415584415584415,0.0022685185185185187,0.02490740740740741,0.006898148148148148,0.001574074074074074,5.433333333333334,1.8111111111111111,1.3243796591164791,0.2833333333333333,2.6333333333333333,481.0,719.0,3328403,6186.622676579926,3507.2428114622626,21,11986,484712,3253.1006711409395,1731.8302018110644,39,5972 diff --git a/tests/data/processed/test02/conversation_night.csv b/tests/data/processed/test02/conversation_night.csv deleted file mode 100644 index 1d18eda1..00000000 --- a/tests/data/processed/test02/conversation_night.csv +++ /dev/null @@ -1,3 +0,0 @@ -local_date,conversation_night_minutessilence,conversation_night_minutesnoise,conversation_night_minutesvoice,conversation_night_minutesunknown,conversation_night_countconversation,conversation_night_silencesensedfraction,conversation_night_noisesensedfraction,conversation_night_voicesensedfraction,conversation_night_unknownsensedfraction,conversation_night_silenceexpectedfraction,conversation_night_noiseexpectedfraction,conversation_night_voiceexpectedfraction,conversation_night_unknownexpectedfraction,conversation_night_sumconversationduration,conversation_night_avgconversationduration,conversation_night_sdconversationduration,conversation_night_minconversationduration,conversation_night_maxconversationduration,conversation_night_timefirstconversation,conversation_night_timelastconversation,conversation_night_noisesumenergy,conversation_night_noiseavgenergy,conversation_night_noisesdenergy,conversation_night_noiseminenergy,conversation_night_noisemaxenergy,conversation_night_voicesumenergy,conversation_night_voiceavgenergy,conversation_night_voicesdenergy,conversation_night_voiceminenergy,conversation_night_voicemaxenergy -2020-07-07,0.7,10.3,2.85,0.8166666666666667,2,0.04772727272727273,0.7022727272727274,0.19431818181818183,0.055681818181818186,0.0019444444444444444,0.02861111111111111,0.007916666666666667,0.0022685185185185187,10.766666666666667,5.383333333333334,6.599663291074444,0.7166666666666667,10.05,73.0,359.0,3825040,6189.385113268609,3543.1815292214824,31,11976,499842,2923.0526315789475,1743.7537375482877,54,5982 -2020-07-08,1.0833333333333333,15.733333333333333,3.716666666666667,1.3,3,0.04961832061068702,0.7206106870229008,0.1702290076335878,0.05954198473282443,0.0030092592592592593,0.0437037037037037,0.010324074074074074,0.0036111111111111114,17.95,5.983333333333333,7.187160620006888,1.4,14.266666666666667,77.0,359.0,5533379,5861.630296610169,3459.015959830812,49,11981,714837,3205.547085201794,1752.0956065993914,15,5983 diff --git a/tests/data/processed/test02/light_afternoon.csv b/tests/data/processed/test02/light_afternoon.csv deleted file mode 100644 index 71584288..00000000 --- a/tests/data/processed/test02/light_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_afternoon_maxlux,light_afternoon_medianlux,light_afternoon_avglux,light_afternoon_stdlux,light_afternoon_minlux,light_afternoon_count diff --git a/tests/data/processed/test02/light_daily.csv b/tests/data/processed/test02/light_daily.csv deleted file mode 100644 index 5162a82f..00000000 --- a/tests/data/processed/test02/light_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_daily_maxlux,light_daily_count,light_daily_minlux,light_daily_stdlux,light_daily_avglux,light_daily_medianlux diff --git a/tests/data/processed/test02/light_evening.csv b/tests/data/processed/test02/light_evening.csv deleted file mode 100644 index 993b04b1..00000000 --- a/tests/data/processed/test02/light_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_evening_count,light_evening_avglux,light_evening_medianlux,light_evening_minlux,light_evening_maxlux,light_evening_stdlux diff --git a/tests/data/processed/test02/light_morning.csv b/tests/data/processed/test02/light_morning.csv deleted file mode 100644 index b4c1fc12..00000000 --- a/tests/data/processed/test02/light_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_morning_count,light_morning_stdlux,light_morning_minlux,light_morning_maxlux,light_morning_medianlux,light_morning_avglux diff --git a/tests/data/processed/test02/light_night.csv b/tests/data/processed/test02/light_night.csv deleted file mode 100644 index 8a222bf8..00000000 --- a/tests/data/processed/test02/light_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_night_stdlux,light_night_maxlux,light_night_avglux,light_night_count,light_night_medianlux,light_night_minlux diff --git a/tests/data/processed/test02/messages_received_afternoon.csv b/tests/data/processed/test02/messages_received_afternoon.csv deleted file mode 100644 index 10344957..00000000 --- a/tests/data/processed/test02/messages_received_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","messages_received_afternoon_countmostfrequentcontact","messages_received_afternoon_count","messages_received_afternoon_distinctcontacts","messages_received_afternoon_timefirstmessage","messages_received_afternoon_timelastmessage" -"2020-05-28",1,2,2,830,949 diff --git a/tests/data/processed/test02/messages_received_daily.csv b/tests/data/processed/test02/messages_received_daily.csv deleted file mode 100644 index a6143606..00000000 --- a/tests/data/processed/test02/messages_received_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_received_daily_countmostfrequentcontact","messages_received_daily_count","messages_received_daily_distinctcontacts","messages_received_daily_timefirstmessage","messages_received_daily_timelastmessage" -"2020-05-28",1,12,12,6,1382 -"2020-05-29",0,6,6,401,1382 diff --git a/tests/data/processed/test02/messages_received_evening.csv b/tests/data/processed/test02/messages_received_evening.csv deleted file mode 100644 index 01058f5a..00000000 --- a/tests/data/processed/test02/messages_received_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_received_evening_countmostfrequentcontact","messages_received_evening_count","messages_received_evening_distinctcontacts","messages_received_evening_timefirstmessage","messages_received_evening_timelastmessage" -"2020-05-28",1,3,3,1173,1382 -"2020-05-29",0,3,3,1173,1382 diff --git a/tests/data/processed/test02/messages_received_morning.csv b/tests/data/processed/test02/messages_received_morning.csv deleted file mode 100644 index 57bab4a1..00000000 --- a/tests/data/processed/test02/messages_received_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_received_morning_countmostfrequentcontact","messages_received_morning_count","messages_received_morning_distinctcontacts","messages_received_morning_timefirstmessage","messages_received_morning_timelastmessage" -"2020-05-28",1,3,3,401,660 -"2020-05-29",0,3,3,401,660 diff --git a/tests/data/processed/test02/messages_received_night.csv b/tests/data/processed/test02/messages_received_night.csv deleted file mode 100644 index 574edc88..00000000 --- a/tests/data/processed/test02/messages_received_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","messages_received_night_countmostfrequentcontact","messages_received_night_count","messages_received_night_distinctcontacts","messages_received_night_timefirstmessage","messages_received_night_timelastmessage" -"2020-05-28",1,4,4,6,312 diff --git a/tests/data/processed/test02/messages_sent_afternoon.csv b/tests/data/processed/test02/messages_sent_afternoon.csv deleted file mode 100644 index 1ac41cd2..00000000 --- a/tests/data/processed/test02/messages_sent_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","messages_sent_afternoon_countmostfrequentcontact","messages_sent_afternoon_count","messages_sent_afternoon_distinctcontacts","messages_sent_afternoon_timefirstmessage","messages_sent_afternoon_timelastmessage" -"2020-05-28",1,3,3,722,979 diff --git a/tests/data/processed/test02/messages_sent_daily.csv b/tests/data/processed/test02/messages_sent_daily.csv deleted file mode 100644 index a14ba553..00000000 --- a/tests/data/processed/test02/messages_sent_daily.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_sent_daily_countmostfrequentcontact","messages_sent_daily_count","messages_sent_daily_distinctcontacts","messages_sent_daily_timefirstmessage","messages_sent_daily_timelastmessage" -"2020-05-28",1,8,8,219,1401 -"2020-05-29",0,4,4,388,1401 diff --git a/tests/data/processed/test02/messages_sent_evening.csv b/tests/data/processed/test02/messages_sent_evening.csv deleted file mode 100644 index 7a6edd68..00000000 --- a/tests/data/processed/test02/messages_sent_evening.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_sent_evening_countmostfrequentcontact","messages_sent_evening_count","messages_sent_evening_distinctcontacts","messages_sent_evening_timefirstmessage","messages_sent_evening_timelastmessage" -"2020-05-28",1,2,2,1218,1401 -"2020-05-29",0,2,2,1218,1401 diff --git a/tests/data/processed/test02/messages_sent_morning.csv b/tests/data/processed/test02/messages_sent_morning.csv deleted file mode 100644 index 0a7a3e5f..00000000 --- a/tests/data/processed/test02/messages_sent_morning.csv +++ /dev/null @@ -1,3 +0,0 @@ -"local_date","messages_sent_morning_countmostfrequentcontact","messages_sent_morning_count","messages_sent_morning_distinctcontacts","messages_sent_morning_timefirstmessage","messages_sent_morning_timelastmessage" -"2020-05-28",1,2,2,388,654 -"2020-05-29",0,2,2,388,654 diff --git a/tests/data/processed/test02/messages_sent_night.csv b/tests/data/processed/test02/messages_sent_night.csv deleted file mode 100644 index bbabc406..00000000 --- a/tests/data/processed/test02/messages_sent_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","messages_sent_night_countmostfrequentcontact","messages_sent_night_count","messages_sent_night_distinctcontacts","messages_sent_night_timefirstmessage","messages_sent_night_timelastmessage" -"2020-05-28",1,1,1,219,219 diff --git a/tests/data/processed/test02/screen_afternoon.csv b/tests/data/processed/test02/screen_afternoon.csv deleted file mode 100644 index 602bf31b..00000000 --- a/tests/data/processed/test02/screen_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_afternoon_countepisodeunlock,screen_afternoon_episodepersensedminutesunlock,screen_afternoon_sumdurationunlock,screen_afternoon_maxdurationunlock,screen_afternoon_mindurationunlock,screen_afternoon_avgdurationunlock,screen_afternoon_stddurationunlock,screen_afternoon_firstuseafter00unlock -2020-06-01,3,0.05,30.09043333333333,12.55,8.533333333333333,10.030144444444444,2.1950780690579683,720.0 diff --git a/tests/data/processed/test02/screen_daily.csv b/tests/data/processed/test02/screen_daily.csv deleted file mode 100644 index 28c0f43d..00000000 --- a/tests/data/processed/test02/screen_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_daily_countepisodeunlock,screen_daily_episodepersensedminutesunlock,screen_daily_sumdurationunlock,screen_daily_maxdurationunlock,screen_daily_mindurationunlock,screen_daily_avgdurationunlock,screen_daily_stddurationunlock,screen_daily_firstuseafter00unlock -2020-06-01,7,0.029166666666666667,94.6528166666666,21.6598833333333,6.00145,13.521830952380942,6.189885966253717,170.5 diff --git a/tests/data/processed/test02/screen_deltas.csv b/tests/data/processed/test02/screen_deltas.csv deleted file mode 100644 index 7cdedb62..00000000 --- a/tests/data/processed/test02/screen_deltas.csv +++ /dev/null @@ -1,8 +0,0 @@ -"episode_id","episode","screen_sequence","time_diff","local_start_date_time","local_end_date_time","local_start_date","local_end_date","local_start_day_segment","local_end_day_segment" -2,"unlock","3, 2",6.00145,"2020-06-01 02:50:30","2020-06-01 02:56:30","2020-06-01","2020-06-01","night","night" -4,"unlock","3, 2",6.9984,"2020-06-01 05:56:51","2020-06-01 06:03:51","2020-06-01","2020-06-01","night","morning" -6,"unlock","3, 2",21.6598833333333,"2020-06-01 10:00:24","2020-06-01 10:22:04","2020-06-01","2020-06-01","morning","morning" -8,"unlock","3, 2",15.00205,"2020-06-01 11:57:33","2020-06-01 12:12:33","2020-06-01","2020-06-01","morning","afternoon" -10,"unlock","3, 2",9.0071,"2020-06-01 14:51:02","2020-06-01 15:00:02","2020-06-01","2020-06-01","afternoon","afternoon" -12,"unlock","3, 2",16.98925,"2020-06-01 17:51:27","2020-06-01 18:08:26","2020-06-01","2020-06-01","afternoon","evening" -14,"unlock","3, 2",18.9946833333333,"2020-06-01 20:42:58","2020-06-01 21:01:58","2020-06-01","2020-06-01","evening","evening" diff --git a/tests/data/processed/test02/screen_evening.csv b/tests/data/processed/test02/screen_evening.csv deleted file mode 100644 index f9c8fc01..00000000 --- a/tests/data/processed/test02/screen_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_evening_countepisodeunlock,screen_evening_episodepersensedminutesunlock,screen_evening_sumdurationunlock,screen_evening_maxdurationunlock,screen_evening_mindurationunlock,screen_evening_avgdurationunlock,screen_evening_stddurationunlock,screen_evening_firstuseafter00unlock -2020-06-01,2,0.02857142857142857,27.428016666666636,18.994683333333302,8.433333333333334,13.714008333333318,7.468002203484522,1080.0 diff --git a/tests/data/processed/test02/screen_morning.csv b/tests/data/processed/test02/screen_morning.csv deleted file mode 100644 index 6c9566a4..00000000 --- a/tests/data/processed/test02/screen_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_morning_countepisodeunlock,screen_morning_episodepersensedminutesunlock,screen_morning_sumdurationunlock,screen_morning_maxdurationunlock,screen_morning_mindurationunlock,screen_morning_avgdurationunlock,screen_morning_stddurationunlock,screen_morning_firstuseafter00unlock -2020-06-01,3,0.046153846153846156,27.943216666666636,21.6598833333333,2.433333333333333,9.314405555555545,10.714935943920263,360.0 diff --git a/tests/data/processed/test02/screen_night.csv b/tests/data/processed/test02/screen_night.csv deleted file mode 100644 index cebc41bb..00000000 --- a/tests/data/processed/test02/screen_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -local_date,screen_night_countepisodeunlock,screen_night_episodepersensedminutesunlock,screen_night_sumdurationunlock,screen_night_maxdurationunlock,screen_night_mindurationunlock,screen_night_avgdurationunlock,screen_night_stddurationunlock,screen_night_firstuseafter00unlock -2020-06-01,2,0.044444444444444446,9.134783333333333,6.00145,3.1333333333333333,4.5673916666666665,2.0280647442341566,170.5 diff --git a/tests/data/processed/test02/wifi_afternoon.csv b/tests/data/processed/test02/wifi_afternoon.csv deleted file mode 100644 index f89b0613..00000000 --- a/tests/data/processed/test02/wifi_afternoon.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_afternoon_countscans","wifi_afternoon_uniquedevices","wifi_afternoon_countscansmostuniquedevice" -"2020-07-03",2,2,1 diff --git a/tests/data/processed/test02/wifi_daily.csv b/tests/data/processed/test02/wifi_daily.csv deleted file mode 100644 index f8ff9e1c..00000000 --- a/tests/data/processed/test02/wifi_daily.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_daily_countscans","wifi_daily_uniquedevices","wifi_daily_countscansmostuniquedevice" -"2020-07-03",14,5,4 diff --git a/tests/data/processed/test02/wifi_evening.csv b/tests/data/processed/test02/wifi_evening.csv deleted file mode 100644 index ae6ff422..00000000 --- a/tests/data/processed/test02/wifi_evening.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_evening_countscans","wifi_evening_uniquedevices","wifi_evening_countscansmostuniquedevice" -"2020-07-03",5,4,2 diff --git a/tests/data/processed/test02/wifi_morning.csv b/tests/data/processed/test02/wifi_morning.csv deleted file mode 100644 index 9af5aa95..00000000 --- a/tests/data/processed/test02/wifi_morning.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_morning_countscans","wifi_morning_uniquedevices","wifi_morning_countscansmostuniquedevice" -"2020-07-03",3,2,2 diff --git a/tests/data/processed/test02/wifi_night.csv b/tests/data/processed/test02/wifi_night.csv deleted file mode 100644 index 9bdc0ffe..00000000 --- a/tests/data/processed/test02/wifi_night.csv +++ /dev/null @@ -1,2 +0,0 @@ -"local_date","wifi_night_countscans","wifi_night_uniquedevices","wifi_night_countscansmostuniquedevice" -"2020-07-03",4,4,1 diff --git a/tests/data/processed/test03/activity_recognition_afternoon.csv b/tests/data/processed/test03/activity_recognition_afternoon.csv deleted file mode 100644 index cbcbc070..00000000 --- a/tests/data/processed/test03/activity_recognition_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_afternoon_summobile,ar_afternoon_countuniqueactivities,ar_afternoon_count,ar_afternoon_sumstationary,ar_afternoon_sumvehicle,ar_afternoon_mostcommonactivity,ar_afternoon_activitychangecount diff --git a/tests/data/processed/test03/activity_recognition_daily.csv b/tests/data/processed/test03/activity_recognition_daily.csv deleted file mode 100644 index be7af81a..00000000 --- a/tests/data/processed/test03/activity_recognition_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_daily_sumvehicle,ar_daily_countuniqueactivities,ar_daily_mostcommonactivity,ar_daily_activitychangecount,ar_daily_sumstationary,ar_daily_summobile,ar_daily_count diff --git a/tests/data/processed/test03/activity_recognition_evening.csv b/tests/data/processed/test03/activity_recognition_evening.csv deleted file mode 100644 index 9886f6ab..00000000 --- a/tests/data/processed/test03/activity_recognition_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_evening_count,ar_evening_summobile,ar_evening_activitychangecount,ar_evening_sumstationary,ar_evening_sumvehicle,ar_evening_countuniqueactivities,ar_evening_mostcommonactivity diff --git a/tests/data/processed/test03/activity_recognition_morning.csv b/tests/data/processed/test03/activity_recognition_morning.csv deleted file mode 100644 index 577b6ba0..00000000 --- a/tests/data/processed/test03/activity_recognition_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_morning_mostcommonactivity,ar_morning_activitychangecount,ar_morning_countuniqueactivities,ar_morning_summobile,ar_morning_count,ar_morning_sumvehicle,ar_morning_sumstationary diff --git a/tests/data/processed/test03/activity_recognition_night.csv b/tests/data/processed/test03/activity_recognition_night.csv deleted file mode 100644 index 966c0ace..00000000 --- a/tests/data/processed/test03/activity_recognition_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_night_summobile,ar_night_sumstationary,ar_night_countuniqueactivities,ar_night_mostcommonactivity,ar_night_activitychangecount,ar_night_count,ar_night_sumvehicle diff --git a/tests/data/processed/test03/applications_foreground_afternoon.csv b/tests/data/processed/test03/applications_foreground_afternoon.csv deleted file mode 100644 index a16fad0d..00000000 --- a/tests/data/processed/test03/applications_foreground_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_afternoon_countemail,apps_afternoon_countall,apps_afternoon_countsocial,apps_afternoon_countentertainment,apps_afternoon_counttop1global,apps_afternoon_countcom.facebook.moments,apps_afternoon_countcom.google.android.youtube,apps_afternoon_timeoffirstuseemail,apps_afternoon_timeoffirstuseall,apps_afternoon_timeoffirstusesocial,apps_afternoon_timeoffirstuseentertainment,apps_afternoon_timeoffirstusetop1global,apps_afternoon_timeoffirstusecom.facebook.moments,apps_afternoon_timeoffirstusecom.google.android.youtube,apps_afternoon_timeoflastuseemail,apps_afternoon_timeoflastuseall,apps_afternoon_timeoflastusesocial,apps_afternoon_timeoflastuseentertainment,apps_afternoon_timeoflastusetop1global,apps_afternoon_timeoflastusecom.facebook.moments,apps_afternoon_timeoflastusecom.google.android.youtube,apps_afternoon_frequencyentropyemail,apps_afternoon_frequencyentropyall,apps_afternoon_frequencyentropysocial,apps_afternoon_frequencyentropyentertainment,apps_afternoon_frequencyentropytop1global,apps_afternoon_frequencyentropycom.facebook.moments,apps_afternoon_frequencyentropycom.google.android.youtube diff --git a/tests/data/processed/test03/applications_foreground_daily.csv b/tests/data/processed/test03/applications_foreground_daily.csv deleted file mode 100644 index 3d9c0e6f..00000000 --- a/tests/data/processed/test03/applications_foreground_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_daily_countall,apps_daily_countemail,apps_daily_countsocial,apps_daily_countentertainment,apps_daily_counttop1global,apps_daily_countcom.google.android.youtube,apps_daily_countcom.facebook.moments,apps_daily_timeoffirstuseall,apps_daily_timeoffirstuseemail,apps_daily_timeoffirstusesocial,apps_daily_timeoffirstuseentertainment,apps_daily_timeoffirstusetop1global,apps_daily_timeoffirstusecom.google.android.youtube,apps_daily_timeoffirstusecom.facebook.moments,apps_daily_timeoflastuseall,apps_daily_timeoflastuseemail,apps_daily_timeoflastusesocial,apps_daily_timeoflastuseentertainment,apps_daily_timeoflastusetop1global,apps_daily_timeoflastusecom.google.android.youtube,apps_daily_timeoflastusecom.facebook.moments,apps_daily_frequencyentropyall,apps_daily_frequencyentropyemail,apps_daily_frequencyentropysocial,apps_daily_frequencyentropyentertainment,apps_daily_frequencyentropytop1global,apps_daily_frequencyentropycom.google.android.youtube,apps_daily_frequencyentropycom.facebook.moments diff --git a/tests/data/processed/test03/applications_foreground_evening.csv b/tests/data/processed/test03/applications_foreground_evening.csv deleted file mode 100644 index ec977e5b..00000000 --- a/tests/data/processed/test03/applications_foreground_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_evening_countall,apps_evening_countemail,apps_evening_countsocial,apps_evening_countentertainment,apps_evening_counttop1global,apps_evening_countcom.facebook.moments,apps_evening_countcom.google.android.youtube,apps_evening_timeoffirstuseall,apps_evening_timeoffirstuseemail,apps_evening_timeoffirstusesocial,apps_evening_timeoffirstuseentertainment,apps_evening_timeoffirstusetop1global,apps_evening_timeoffirstusecom.facebook.moments,apps_evening_timeoffirstusecom.google.android.youtube,apps_evening_timeoflastuseall,apps_evening_timeoflastuseemail,apps_evening_timeoflastusesocial,apps_evening_timeoflastuseentertainment,apps_evening_timeoflastusetop1global,apps_evening_timeoflastusecom.facebook.moments,apps_evening_timeoflastusecom.google.android.youtube,apps_evening_frequencyentropyall,apps_evening_frequencyentropyemail,apps_evening_frequencyentropysocial,apps_evening_frequencyentropyentertainment,apps_evening_frequencyentropytop1global,apps_evening_frequencyentropycom.facebook.moments,apps_evening_frequencyentropycom.google.android.youtube diff --git a/tests/data/processed/test03/applications_foreground_morning.csv b/tests/data/processed/test03/applications_foreground_morning.csv deleted file mode 100644 index f86a5c30..00000000 --- a/tests/data/processed/test03/applications_foreground_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_morning_countall,apps_morning_countemail,apps_morning_countsocial,apps_morning_countentertainment,apps_morning_countcom.facebook.moments,apps_morning_counttop1global,apps_morning_countcom.google.android.youtube,apps_morning_timeoffirstuseall,apps_morning_timeoffirstuseemail,apps_morning_timeoffirstusesocial,apps_morning_timeoffirstuseentertainment,apps_morning_timeoffirstusecom.facebook.moments,apps_morning_timeoffirstusetop1global,apps_morning_timeoffirstusecom.google.android.youtube,apps_morning_timeoflastuseall,apps_morning_timeoflastuseemail,apps_morning_timeoflastusesocial,apps_morning_timeoflastuseentertainment,apps_morning_timeoflastusecom.facebook.moments,apps_morning_timeoflastusetop1global,apps_morning_timeoflastusecom.google.android.youtube,apps_morning_frequencyentropyall,apps_morning_frequencyentropyemail,apps_morning_frequencyentropysocial,apps_morning_frequencyentropyentertainment,apps_morning_frequencyentropycom.facebook.moments,apps_morning_frequencyentropytop1global,apps_morning_frequencyentropycom.google.android.youtube diff --git a/tests/data/processed/test03/applications_foreground_night.csv b/tests/data/processed/test03/applications_foreground_night.csv deleted file mode 100644 index 2409c9ac..00000000 --- a/tests/data/processed/test03/applications_foreground_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_night_countemail,apps_night_countall,apps_night_countsocial,apps_night_countentertainment,apps_night_counttop1global,apps_night_countcom.facebook.moments,apps_night_countcom.google.android.youtube,apps_night_timeoffirstuseemail,apps_night_timeoffirstuseall,apps_night_timeoffirstusesocial,apps_night_timeoffirstuseentertainment,apps_night_timeoffirstusetop1global,apps_night_timeoffirstusecom.facebook.moments,apps_night_timeoffirstusecom.google.android.youtube,apps_night_timeoflastuseemail,apps_night_timeoflastuseall,apps_night_timeoflastusesocial,apps_night_timeoflastuseentertainment,apps_night_timeoflastusetop1global,apps_night_timeoflastusecom.facebook.moments,apps_night_timeoflastusecom.google.android.youtube,apps_night_frequencyentropyemail,apps_night_frequencyentropyall,apps_night_frequencyentropysocial,apps_night_frequencyentropyentertainment,apps_night_frequencyentropytop1global,apps_night_frequencyentropycom.facebook.moments,apps_night_frequencyentropycom.google.android.youtube diff --git a/tests/data/processed/test03/battery_afternoon.csv b/tests/data/processed/test03/battery_afternoon.csv deleted file mode 100644 index a95b9366..00000000 --- a/tests/data/processed/test03/battery_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_afternoon_countdischarge,battery_afternoon_maxconsumptionrate,battery_afternoon_countcharge,battery_afternoon_avgconsumptionrate,battery_afternoon_sumdurationcharge,battery_afternoon_sumdurationdischarge diff --git a/tests/data/processed/test03/battery_daily.csv b/tests/data/processed/test03/battery_daily.csv deleted file mode 100644 index ef6083bb..00000000 --- a/tests/data/processed/test03/battery_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_daily_sumdurationcharge,battery_daily_sumdurationdischarge,battery_daily_countdischarge,battery_daily_maxconsumptionrate,battery_daily_countcharge,battery_daily_avgconsumptionrate diff --git a/tests/data/processed/test03/battery_deltas.csv b/tests/data/processed/test03/battery_deltas.csv deleted file mode 100644 index a3714a6e..00000000 --- a/tests/data/processed/test03/battery_deltas.csv +++ /dev/null @@ -1 +0,0 @@ -"battery_diff","time_diff","local_start_date_time","local_end_date_time","local_start_date","local_end_date","local_start_day_segment","local_end_day_segment" diff --git a/tests/data/processed/test03/battery_evening.csv b/tests/data/processed/test03/battery_evening.csv deleted file mode 100644 index 65b9c934..00000000 --- a/tests/data/processed/test03/battery_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_evening_sumdurationcharge,battery_evening_countdischarge,battery_evening_avgconsumptionrate,battery_evening_countcharge,battery_evening_maxconsumptionrate,battery_evening_sumdurationdischarge diff --git a/tests/data/processed/test03/battery_morning.csv b/tests/data/processed/test03/battery_morning.csv deleted file mode 100644 index 2d4ee156..00000000 --- a/tests/data/processed/test03/battery_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_morning_sumdurationcharge,battery_morning_sumdurationdischarge,battery_morning_maxconsumptionrate,battery_morning_countcharge,battery_morning_avgconsumptionrate,battery_morning_countdischarge diff --git a/tests/data/processed/test03/battery_night.csv b/tests/data/processed/test03/battery_night.csv deleted file mode 100644 index e978126f..00000000 --- a/tests/data/processed/test03/battery_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_night_maxconsumptionrate,battery_night_countcharge,battery_night_sumdurationdischarge,battery_night_avgconsumptionrate,battery_night_sumdurationcharge,battery_night_countdischarge diff --git a/tests/data/processed/test03/bluetooth_afternoon.csv b/tests/data/processed/test03/bluetooth_afternoon.csv deleted file mode 100644 index c043ce0b..00000000 --- a/tests/data/processed/test03/bluetooth_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_afternoon_countscans","bluetooth_afternoon_uniquedevices","bluetooth_afternoon_countscansmostuniquedevice" diff --git a/tests/data/processed/test03/bluetooth_daily.csv b/tests/data/processed/test03/bluetooth_daily.csv deleted file mode 100644 index 4c1eac01..00000000 --- a/tests/data/processed/test03/bluetooth_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_daily_countscans","bluetooth_daily_uniquedevices","bluetooth_daily_countscansmostuniquedevice" diff --git a/tests/data/processed/test03/bluetooth_evening.csv b/tests/data/processed/test03/bluetooth_evening.csv deleted file mode 100644 index cba45ebf..00000000 --- a/tests/data/processed/test03/bluetooth_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_evening_countscans","bluetooth_evening_uniquedevices","bluetooth_evening_countscansmostuniquedevice" diff --git a/tests/data/processed/test03/bluetooth_morning.csv b/tests/data/processed/test03/bluetooth_morning.csv deleted file mode 100644 index 47d4d8ed..00000000 --- a/tests/data/processed/test03/bluetooth_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_morning_countscans","bluetooth_morning_uniquedevices","bluetooth_morning_countscansmostuniquedevice" diff --git a/tests/data/processed/test03/bluetooth_night.csv b/tests/data/processed/test03/bluetooth_night.csv deleted file mode 100644 index 27c31a0c..00000000 --- a/tests/data/processed/test03/bluetooth_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_night_countscans","bluetooth_night_uniquedevices","bluetooth_night_countscansmostuniquedevice" diff --git a/tests/data/processed/test03/calls_incoming_afternoon.csv b/tests/data/processed/test03/calls_incoming_afternoon.csv deleted file mode 100644 index 7700f26f..00000000 --- a/tests/data/processed/test03/calls_incoming_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_afternoon_count","call_incoming_afternoon_distinctcontacts","call_incoming_afternoon_meanduration","call_incoming_afternoon_sumduration","call_incoming_afternoon_minduration","call_incoming_afternoon_maxduration","call_incoming_afternoon_stdduration","call_incoming_afternoon_modeduration","call_incoming_afternoon_entropyduration","call_incoming_afternoon_timefirstcall","call_incoming_afternoon_timelastcall","call_incoming_afternoon_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_incoming_daily.csv b/tests/data/processed/test03/calls_incoming_daily.csv deleted file mode 100644 index 15a7ab61..00000000 --- a/tests/data/processed/test03/calls_incoming_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_daily_count","call_incoming_daily_distinctcontacts","call_incoming_daily_meanduration","call_incoming_daily_sumduration","call_incoming_daily_minduration","call_incoming_daily_maxduration","call_incoming_daily_stdduration","call_incoming_daily_modeduration","call_incoming_daily_entropyduration","call_incoming_daily_timefirstcall","call_incoming_daily_timelastcall","call_incoming_daily_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_incoming_evening.csv b/tests/data/processed/test03/calls_incoming_evening.csv deleted file mode 100644 index 8b52a987..00000000 --- a/tests/data/processed/test03/calls_incoming_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_evening_count","call_incoming_evening_distinctcontacts","call_incoming_evening_meanduration","call_incoming_evening_sumduration","call_incoming_evening_minduration","call_incoming_evening_maxduration","call_incoming_evening_stdduration","call_incoming_evening_modeduration","call_incoming_evening_entropyduration","call_incoming_evening_timefirstcall","call_incoming_evening_timelastcall","call_incoming_evening_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_incoming_morning.csv b/tests/data/processed/test03/calls_incoming_morning.csv deleted file mode 100644 index 5e6c2b05..00000000 --- a/tests/data/processed/test03/calls_incoming_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_morning_count","call_incoming_morning_distinctcontacts","call_incoming_morning_meanduration","call_incoming_morning_sumduration","call_incoming_morning_minduration","call_incoming_morning_maxduration","call_incoming_morning_stdduration","call_incoming_morning_modeduration","call_incoming_morning_entropyduration","call_incoming_morning_timefirstcall","call_incoming_morning_timelastcall","call_incoming_morning_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_incoming_night.csv b/tests/data/processed/test03/calls_incoming_night.csv deleted file mode 100644 index d5718bda..00000000 --- a/tests/data/processed/test03/calls_incoming_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_night_count","call_incoming_night_distinctcontacts","call_incoming_night_meanduration","call_incoming_night_sumduration","call_incoming_night_minduration","call_incoming_night_maxduration","call_incoming_night_stdduration","call_incoming_night_modeduration","call_incoming_night_entropyduration","call_incoming_night_timefirstcall","call_incoming_night_timelastcall","call_incoming_night_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_missed_afternoon.csv b/tests/data/processed/test03/calls_missed_afternoon.csv deleted file mode 100644 index 7b1d275f..00000000 --- a/tests/data/processed/test03/calls_missed_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_afternoon_count","call_missed_afternoon_distinctcontacts","call_missed_afternoon_timefirstcall","call_missed_afternoon_timelastcall","call_missed_afternoon_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_missed_daily.csv b/tests/data/processed/test03/calls_missed_daily.csv deleted file mode 100644 index 52bccdee..00000000 --- a/tests/data/processed/test03/calls_missed_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_daily_count","call_missed_daily_distinctcontacts","call_missed_daily_timefirstcall","call_missed_daily_timelastcall","call_missed_daily_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_missed_evening.csv b/tests/data/processed/test03/calls_missed_evening.csv deleted file mode 100644 index 5d1d36c7..00000000 --- a/tests/data/processed/test03/calls_missed_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_evening_count","call_missed_evening_distinctcontacts","call_missed_evening_timefirstcall","call_missed_evening_timelastcall","call_missed_evening_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_missed_morning.csv b/tests/data/processed/test03/calls_missed_morning.csv deleted file mode 100644 index 573c74f8..00000000 --- a/tests/data/processed/test03/calls_missed_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_morning_count","call_missed_morning_distinctcontacts","call_missed_morning_timefirstcall","call_missed_morning_timelastcall","call_missed_morning_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_missed_night.csv b/tests/data/processed/test03/calls_missed_night.csv deleted file mode 100644 index 61557e4e..00000000 --- a/tests/data/processed/test03/calls_missed_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_night_count","call_missed_night_distinctcontacts","call_missed_night_timefirstcall","call_missed_night_timelastcall","call_missed_night_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_outgoing_afternoon.csv b/tests/data/processed/test03/calls_outgoing_afternoon.csv deleted file mode 100644 index 3430c380..00000000 --- a/tests/data/processed/test03/calls_outgoing_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_afternoon_count","call_outgoing_afternoon_distinctcontacts","call_outgoing_afternoon_meanduration","call_outgoing_afternoon_sumduration","call_outgoing_afternoon_minduration","call_outgoing_afternoon_maxduration","call_outgoing_afternoon_stdduration","call_outgoing_afternoon_modeduration","call_outgoing_afternoon_entropyduration","call_outgoing_afternoon_timefirstcall","call_outgoing_afternoon_timelastcall","call_outgoing_afternoon_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_outgoing_daily.csv b/tests/data/processed/test03/calls_outgoing_daily.csv deleted file mode 100644 index ed6ee7fa..00000000 --- a/tests/data/processed/test03/calls_outgoing_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_daily_count","call_outgoing_daily_distinctcontacts","call_outgoing_daily_meanduration","call_outgoing_daily_sumduration","call_outgoing_daily_minduration","call_outgoing_daily_maxduration","call_outgoing_daily_stdduration","call_outgoing_daily_modeduration","call_outgoing_daily_entropyduration","call_outgoing_daily_timefirstcall","call_outgoing_daily_timelastcall","call_outgoing_daily_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_outgoing_evening.csv b/tests/data/processed/test03/calls_outgoing_evening.csv deleted file mode 100644 index a459636a..00000000 --- a/tests/data/processed/test03/calls_outgoing_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_evening_count","call_outgoing_evening_distinctcontacts","call_outgoing_evening_meanduration","call_outgoing_evening_sumduration","call_outgoing_evening_minduration","call_outgoing_evening_maxduration","call_outgoing_evening_stdduration","call_outgoing_evening_modeduration","call_outgoing_evening_entropyduration","call_outgoing_evening_timefirstcall","call_outgoing_evening_timelastcall","call_outgoing_evening_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_outgoing_morning.csv b/tests/data/processed/test03/calls_outgoing_morning.csv deleted file mode 100644 index 0beb5827..00000000 --- a/tests/data/processed/test03/calls_outgoing_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_morning_count","call_outgoing_morning_distinctcontacts","call_outgoing_morning_meanduration","call_outgoing_morning_sumduration","call_outgoing_morning_minduration","call_outgoing_morning_maxduration","call_outgoing_morning_stdduration","call_outgoing_morning_modeduration","call_outgoing_morning_entropyduration","call_outgoing_morning_timefirstcall","call_outgoing_morning_timelastcall","call_outgoing_morning_countmostfrequentcontact" diff --git a/tests/data/processed/test03/calls_outgoing_night.csv b/tests/data/processed/test03/calls_outgoing_night.csv deleted file mode 100644 index fa763f36..00000000 --- a/tests/data/processed/test03/calls_outgoing_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_night_count","call_outgoing_night_distinctcontacts","call_outgoing_night_meanduration","call_outgoing_night_sumduration","call_outgoing_night_minduration","call_outgoing_night_maxduration","call_outgoing_night_stdduration","call_outgoing_night_modeduration","call_outgoing_night_entropyduration","call_outgoing_night_timefirstcall","call_outgoing_night_timelastcall","call_outgoing_night_countmostfrequentcontact" diff --git a/tests/data/processed/test03/conversation_afternoon.csv b/tests/data/processed/test03/conversation_afternoon.csv deleted file mode 100644 index 2b1a72b9..00000000 --- a/tests/data/processed/test03/conversation_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_afternoon_minutessilence,conversation_afternoon_voicesumenergy,conversation_afternoon_unknownsensedfraction,conversation_afternoon_voiceexpectedfraction,conversation_afternoon_timelastconversation,conversation_afternoon_sdconversationduration,conversation_afternoon_noisesdenergy,conversation_afternoon_voicesensedfraction,conversation_afternoon_unknownexpectedfraction,conversation_afternoon_sumconversationduration,conversation_afternoon_noisemaxenergy,conversation_afternoon_silenceexpectedfraction,conversation_afternoon_timefirstconversation,conversation_afternoon_noiseexpectedfraction,conversation_afternoon_avgconversationduration,conversation_afternoon_countconversation,conversation_afternoon_maxconversationduration,conversation_afternoon_voicemaxenergy,conversation_afternoon_noisesumenergy,conversation_afternoon_voicesdenergy,conversation_afternoon_minutesunknown,conversation_afternoon_noisesensedfraction,conversation_afternoon_noiseavgenergy,conversation_afternoon_minutesnoise,conversation_afternoon_voiceminenergy,conversation_afternoon_noiseminenergy,conversation_afternoon_minutesvoice,conversation_afternoon_voiceavgenergy,conversation_afternoon_minconversationduration,conversation_afternoon_silencesensedfraction diff --git a/tests/data/processed/test03/conversation_daily.csv b/tests/data/processed/test03/conversation_daily.csv deleted file mode 100644 index cbcc9c07..00000000 --- a/tests/data/processed/test03/conversation_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_daily_timelastconversation,conversation_daily_noisesumenergy,conversation_daily_voiceminenergy,conversation_daily_noisesdenergy,conversation_daily_voiceexpectedfraction,conversation_daily_sdconversationduration,conversation_daily_noisemaxenergy,conversation_daily_noiseavgenergy,conversation_daily_minutesvoice,conversation_daily_voicesensedfraction,conversation_daily_countconversation,conversation_daily_voicesumenergy,conversation_daily_minutesunknown,conversation_daily_noisesensedfraction,conversation_daily_unknownexpectedfraction,conversation_daily_silencesensedfraction,conversation_daily_minutesnoise,conversation_daily_silenceexpectedfraction,conversation_daily_avgconversationduration,conversation_daily_minconversationduration,conversation_daily_noiseexpectedfraction,conversation_daily_voiceavgenergy,conversation_daily_maxconversationduration,conversation_daily_sumconversationduration,conversation_daily_voicesdenergy,conversation_daily_noiseminenergy,conversation_daily_voicemaxenergy,conversation_daily_minutessilence,conversation_daily_timefirstconversation,conversation_daily_unknownsensedfraction diff --git a/tests/data/processed/test03/conversation_evening.csv b/tests/data/processed/test03/conversation_evening.csv deleted file mode 100644 index 4fb15162..00000000 --- a/tests/data/processed/test03/conversation_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_evening_sumconversationduration,conversation_evening_sdconversationduration,conversation_evening_countconversation,conversation_evening_voicemaxenergy,conversation_evening_voicesensedfraction,conversation_evening_timefirstconversation,conversation_evening_unknownexpectedfraction,conversation_evening_timelastconversation,conversation_evening_noisesumenergy,conversation_evening_minutessilence,conversation_evening_voicesumenergy,conversation_evening_noisesensedfraction,conversation_evening_minconversationduration,conversation_evening_minutesvoice,conversation_evening_maxconversationduration,conversation_evening_minutesnoise,conversation_evening_noisemaxenergy,conversation_evening_noisesdenergy,conversation_evening_noiseexpectedfraction,conversation_evening_noiseminenergy,conversation_evening_noiseavgenergy,conversation_evening_unknownsensedfraction,conversation_evening_silencesensedfraction,conversation_evening_voiceavgenergy,conversation_evening_silenceexpectedfraction,conversation_evening_voicesdenergy,conversation_evening_voiceminenergy,conversation_evening_avgconversationduration,conversation_evening_minutesunknown,conversation_evening_voiceexpectedfraction diff --git a/tests/data/processed/test03/conversation_morning.csv b/tests/data/processed/test03/conversation_morning.csv deleted file mode 100644 index 43724c07..00000000 --- a/tests/data/processed/test03/conversation_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_morning_sumconversationduration,conversation_morning_voicesumenergy,conversation_morning_noisesensedfraction,conversation_morning_timelastconversation,conversation_morning_noiseexpectedfraction,conversation_morning_minutesnoise,conversation_morning_voiceminenergy,conversation_morning_maxconversationduration,conversation_morning_avgconversationduration,conversation_morning_voicesdenergy,conversation_morning_noiseavgenergy,conversation_morning_unknownsensedfraction,conversation_morning_noisesdenergy,conversation_morning_minutessilence,conversation_morning_timefirstconversation,conversation_morning_minutesvoice,conversation_morning_minconversationduration,conversation_morning_silencesensedfraction,conversation_morning_unknownexpectedfraction,conversation_morning_noisemaxenergy,conversation_morning_voicesensedfraction,conversation_morning_countconversation,conversation_morning_silenceexpectedfraction,conversation_morning_noiseminenergy,conversation_morning_voiceexpectedfraction,conversation_morning_voicemaxenergy,conversation_morning_noisesumenergy,conversation_morning_minutesunknown,conversation_morning_sdconversationduration,conversation_morning_voiceavgenergy diff --git a/tests/data/processed/test03/conversation_night.csv b/tests/data/processed/test03/conversation_night.csv deleted file mode 100644 index 818479f6..00000000 --- a/tests/data/processed/test03/conversation_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_night_minutessilence,conversation_night_minutesnoise,conversation_night_voiceminenergy,conversation_night_minconversationduration,conversation_night_voicesumenergy,conversation_night_voicemaxenergy,conversation_night_noiseavgenergy,conversation_night_avgconversationduration,conversation_night_unknownsensedfraction,conversation_night_silenceexpectedfraction,conversation_night_noiseminenergy,conversation_night_noisesensedfraction,conversation_night_unknownexpectedfraction,conversation_night_countconversation,conversation_night_minutesunknown,conversation_night_noisesumenergy,conversation_night_noisemaxenergy,conversation_night_timefirstconversation,conversation_night_sdconversationduration,conversation_night_maxconversationduration,conversation_night_noiseexpectedfraction,conversation_night_minutesvoice,conversation_night_silencesensedfraction,conversation_night_sumconversationduration,conversation_night_timelastconversation,conversation_night_voicesdenergy,conversation_night_voicesensedfraction,conversation_night_noisesdenergy,conversation_night_voiceexpectedfraction,conversation_night_voiceavgenergy diff --git a/tests/data/processed/test03/light_afternoon.csv b/tests/data/processed/test03/light_afternoon.csv deleted file mode 100644 index ee008597..00000000 --- a/tests/data/processed/test03/light_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_afternoon_maxlux,light_afternoon_medianlux,light_afternoon_minlux,light_afternoon_stdlux,light_afternoon_count,light_afternoon_avglux diff --git a/tests/data/processed/test03/light_daily.csv b/tests/data/processed/test03/light_daily.csv deleted file mode 100644 index e1791592..00000000 --- a/tests/data/processed/test03/light_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_daily_maxlux,light_daily_avglux,light_daily_count,light_daily_minlux,light_daily_medianlux,light_daily_stdlux diff --git a/tests/data/processed/test03/light_evening.csv b/tests/data/processed/test03/light_evening.csv deleted file mode 100644 index bea84344..00000000 --- a/tests/data/processed/test03/light_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_evening_stdlux,light_evening_medianlux,light_evening_maxlux,light_evening_count,light_evening_minlux,light_evening_avglux diff --git a/tests/data/processed/test03/light_morning.csv b/tests/data/processed/test03/light_morning.csv deleted file mode 100644 index 79cd0bc5..00000000 --- a/tests/data/processed/test03/light_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_morning_stdlux,light_morning_medianlux,light_morning_maxlux,light_morning_minlux,light_morning_count,light_morning_avglux diff --git a/tests/data/processed/test03/light_night.csv b/tests/data/processed/test03/light_night.csv deleted file mode 100644 index 02300377..00000000 --- a/tests/data/processed/test03/light_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_night_maxlux,light_night_medianlux,light_night_minlux,light_night_stdlux,light_night_count,light_night_avglux diff --git a/tests/data/processed/test03/messages_received_afternoon.csv b/tests/data/processed/test03/messages_received_afternoon.csv deleted file mode 100644 index 640096b5..00000000 --- a/tests/data/processed/test03/messages_received_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_afternoon_countmostfrequentcontact","messages_received_afternoon_count","messages_received_afternoon_distinctcontacts","messages_received_afternoon_timefirstmessage","messages_received_afternoon_timelastmessage" diff --git a/tests/data/processed/test03/messages_received_daily.csv b/tests/data/processed/test03/messages_received_daily.csv deleted file mode 100644 index de5543b9..00000000 --- a/tests/data/processed/test03/messages_received_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_daily_countmostfrequentcontact","messages_received_daily_count","messages_received_daily_distinctcontacts","messages_received_daily_timefirstmessage","messages_received_daily_timelastmessage" diff --git a/tests/data/processed/test03/messages_received_evening.csv b/tests/data/processed/test03/messages_received_evening.csv deleted file mode 100644 index 7325e9a9..00000000 --- a/tests/data/processed/test03/messages_received_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_evening_countmostfrequentcontact","messages_received_evening_count","messages_received_evening_distinctcontacts","messages_received_evening_timefirstmessage","messages_received_evening_timelastmessage" diff --git a/tests/data/processed/test03/messages_received_morning.csv b/tests/data/processed/test03/messages_received_morning.csv deleted file mode 100644 index bbd446c3..00000000 --- a/tests/data/processed/test03/messages_received_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_morning_countmostfrequentcontact","messages_received_morning_count","messages_received_morning_distinctcontacts","messages_received_morning_timefirstmessage","messages_received_morning_timelastmessage" diff --git a/tests/data/processed/test03/messages_received_night.csv b/tests/data/processed/test03/messages_received_night.csv deleted file mode 100644 index 2d0ae76f..00000000 --- a/tests/data/processed/test03/messages_received_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_night_countmostfrequentcontact","messages_received_night_count","messages_received_night_distinctcontacts","messages_received_night_timefirstmessage","messages_received_night_timelastmessage" diff --git a/tests/data/processed/test03/messages_sent_afternoon.csv b/tests/data/processed/test03/messages_sent_afternoon.csv deleted file mode 100644 index ab84aaba..00000000 --- a/tests/data/processed/test03/messages_sent_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_afternoon_countmostfrequentcontact","messages_sent_afternoon_count","messages_sent_afternoon_distinctcontacts","messages_sent_afternoon_timefirstmessage","messages_sent_afternoon_timelastmessage" diff --git a/tests/data/processed/test03/messages_sent_daily.csv b/tests/data/processed/test03/messages_sent_daily.csv deleted file mode 100644 index f827b72f..00000000 --- a/tests/data/processed/test03/messages_sent_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_daily_countmostfrequentcontact","messages_sent_daily_count","messages_sent_daily_distinctcontacts","messages_sent_daily_timefirstmessage","messages_sent_daily_timelastmessage" diff --git a/tests/data/processed/test03/messages_sent_evening.csv b/tests/data/processed/test03/messages_sent_evening.csv deleted file mode 100644 index ddfed962..00000000 --- a/tests/data/processed/test03/messages_sent_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_evening_countmostfrequentcontact","messages_sent_evening_count","messages_sent_evening_distinctcontacts","messages_sent_evening_timefirstmessage","messages_sent_evening_timelastmessage" diff --git a/tests/data/processed/test03/messages_sent_morning.csv b/tests/data/processed/test03/messages_sent_morning.csv deleted file mode 100644 index 80235f96..00000000 --- a/tests/data/processed/test03/messages_sent_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_morning_countmostfrequentcontact","messages_sent_morning_count","messages_sent_morning_distinctcontacts","messages_sent_morning_timefirstmessage","messages_sent_morning_timelastmessage" diff --git a/tests/data/processed/test03/messages_sent_night.csv b/tests/data/processed/test03/messages_sent_night.csv deleted file mode 100644 index 1b4351e0..00000000 --- a/tests/data/processed/test03/messages_sent_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_night_countmostfrequentcontact","messages_sent_night_count","messages_sent_night_distinctcontacts","messages_sent_night_timefirstmessage","messages_sent_night_timelastmessage" diff --git a/tests/data/processed/test03/screen_afternoon.csv b/tests/data/processed/test03/screen_afternoon.csv deleted file mode 100644 index 1bcf3206..00000000 --- a/tests/data/processed/test03/screen_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_afternoon_countepisodeunlock,screen_afternoon_stddurationunlock,screen_afternoon_avgdurationunlock,screen_afternoon_episodepersensedminutesunlock,screen_afternoon_firstuseafter00unlock,screen_afternoon_mindurationunlock,screen_afternoon_maxdurationunlock,screen_afternoon_sumdurationunlock diff --git a/tests/data/processed/test03/screen_daily.csv b/tests/data/processed/test03/screen_daily.csv deleted file mode 100644 index d06a5734..00000000 --- a/tests/data/processed/test03/screen_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_daily_episodepersensedminutesunlock,screen_daily_stddurationunlock,screen_daily_avgdurationunlock,screen_daily_sumdurationunlock,screen_daily_mindurationunlock,screen_daily_firstuseafter00unlock,screen_daily_countepisodeunlock,screen_daily_maxdurationunlock diff --git a/tests/data/processed/test03/screen_deltas.csv b/tests/data/processed/test03/screen_deltas.csv deleted file mode 100644 index 8209e74c..00000000 --- a/tests/data/processed/test03/screen_deltas.csv +++ /dev/null @@ -1 +0,0 @@ -"episode","time_diff","local_start_date_time","local_end_date_time","local_start_date","local_end_date","local_start_day_segment","local_end_day_segment" diff --git a/tests/data/processed/test03/screen_evening.csv b/tests/data/processed/test03/screen_evening.csv deleted file mode 100644 index 184b7b44..00000000 --- a/tests/data/processed/test03/screen_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_evening_stddurationunlock,screen_evening_maxdurationunlock,screen_evening_mindurationunlock,screen_evening_sumdurationunlock,screen_evening_countepisodeunlock,screen_evening_avgdurationunlock,screen_evening_firstuseafter00unlock,screen_evening_episodepersensedminutesunlock diff --git a/tests/data/processed/test03/screen_morning.csv b/tests/data/processed/test03/screen_morning.csv deleted file mode 100644 index 14809678..00000000 --- a/tests/data/processed/test03/screen_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_morning_maxdurationunlock,screen_morning_countepisodeunlock,screen_morning_stddurationunlock,screen_morning_firstuseafter00unlock,screen_morning_mindurationunlock,screen_morning_sumdurationunlock,screen_morning_avgdurationunlock,screen_morning_episodepersensedminutesunlock diff --git a/tests/data/processed/test03/screen_night.csv b/tests/data/processed/test03/screen_night.csv deleted file mode 100644 index a577efa3..00000000 --- a/tests/data/processed/test03/screen_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_night_mindurationunlock,screen_night_avgdurationunlock,screen_night_sumdurationunlock,screen_night_firstuseafter00unlock,screen_night_stddurationunlock,screen_night_episodepersensedminutesunlock,screen_night_countepisodeunlock,screen_night_maxdurationunlock diff --git a/tests/data/processed/test03/wifi_afternoon.csv b/tests/data/processed/test03/wifi_afternoon.csv deleted file mode 100644 index e23766ae..00000000 --- a/tests/data/processed/test03/wifi_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_afternoon_countscans","wifi_afternoon_uniquedevices","wifi_afternoon_countscansmostuniquedevice" diff --git a/tests/data/processed/test03/wifi_daily.csv b/tests/data/processed/test03/wifi_daily.csv deleted file mode 100644 index 569ec612..00000000 --- a/tests/data/processed/test03/wifi_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_daily_countscans","wifi_daily_uniquedevices","wifi_daily_countscansmostuniquedevice" diff --git a/tests/data/processed/test03/wifi_evening.csv b/tests/data/processed/test03/wifi_evening.csv deleted file mode 100644 index d2879ba8..00000000 --- a/tests/data/processed/test03/wifi_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_evening_countscans","wifi_evening_uniquedevices","wifi_evening_countscansmostuniquedevice" diff --git a/tests/data/processed/test03/wifi_morning.csv b/tests/data/processed/test03/wifi_morning.csv deleted file mode 100644 index f34385b1..00000000 --- a/tests/data/processed/test03/wifi_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_morning_countscans","wifi_morning_uniquedevices","wifi_morning_countscansmostuniquedevice" diff --git a/tests/data/processed/test03/wifi_night.csv b/tests/data/processed/test03/wifi_night.csv deleted file mode 100644 index c9660149..00000000 --- a/tests/data/processed/test03/wifi_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_night_countscans","wifi_night_uniquedevices","wifi_night_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/activity_recognition_afternoon.csv b/tests/data/processed/test04/activity_recognition_afternoon.csv deleted file mode 100644 index aebb430a..00000000 --- a/tests/data/processed/test04/activity_recognition_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_afternoon_activitychangecount,ar_afternoon_mostcommonactivity,ar_afternoon_sumvehicle,ar_afternoon_countuniqueactivities,ar_afternoon_summobile,ar_afternoon_sumstationary,ar_afternoon_count diff --git a/tests/data/processed/test04/activity_recognition_daily.csv b/tests/data/processed/test04/activity_recognition_daily.csv deleted file mode 100644 index 104f6dac..00000000 --- a/tests/data/processed/test04/activity_recognition_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_daily_count,ar_daily_sumstationary,ar_daily_countuniqueactivities,ar_daily_summobile,ar_daily_sumvehicle,ar_daily_mostcommonactivity,ar_daily_activitychangecount diff --git a/tests/data/processed/test04/activity_recognition_evening.csv b/tests/data/processed/test04/activity_recognition_evening.csv deleted file mode 100644 index 5708f362..00000000 --- a/tests/data/processed/test04/activity_recognition_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_evening_sumvehicle,ar_evening_activitychangecount,ar_evening_sumstationary,ar_evening_summobile,ar_evening_mostcommonactivity,ar_evening_count,ar_evening_countuniqueactivities diff --git a/tests/data/processed/test04/activity_recognition_morning.csv b/tests/data/processed/test04/activity_recognition_morning.csv deleted file mode 100644 index 24122ea7..00000000 --- a/tests/data/processed/test04/activity_recognition_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_morning_mostcommonactivity,ar_morning_sumvehicle,ar_morning_countuniqueactivities,ar_morning_sumstationary,ar_morning_count,ar_morning_summobile,ar_morning_activitychangecount diff --git a/tests/data/processed/test04/activity_recognition_night.csv b/tests/data/processed/test04/activity_recognition_night.csv deleted file mode 100644 index 0d143f6f..00000000 --- a/tests/data/processed/test04/activity_recognition_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,ar_night_count,ar_night_countuniqueactivities,ar_night_sumstationary,ar_night_sumvehicle,ar_night_activitychangecount,ar_night_summobile,ar_night_mostcommonactivity diff --git a/tests/data/processed/test04/applications_foreground_afternoon.csv b/tests/data/processed/test04/applications_foreground_afternoon.csv deleted file mode 100644 index 36c73188..00000000 --- a/tests/data/processed/test04/applications_foreground_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_afternoon_countemail,apps_afternoon_countall,apps_afternoon_countentertainment,apps_afternoon_countsocial,apps_afternoon_countcom.facebook.moments,apps_afternoon_countcom.google.android.youtube,apps_afternoon_counttop1global,apps_afternoon_timeoffirstuseemail,apps_afternoon_timeoffirstuseall,apps_afternoon_timeoffirstuseentertainment,apps_afternoon_timeoffirstusesocial,apps_afternoon_timeoffirstusecom.facebook.moments,apps_afternoon_timeoffirstusecom.google.android.youtube,apps_afternoon_timeoffirstusetop1global,apps_afternoon_timeoflastuseemail,apps_afternoon_timeoflastuseall,apps_afternoon_timeoflastuseentertainment,apps_afternoon_timeoflastusesocial,apps_afternoon_timeoflastusecom.facebook.moments,apps_afternoon_timeoflastusecom.google.android.youtube,apps_afternoon_timeoflastusetop1global,apps_afternoon_frequencyentropyemail,apps_afternoon_frequencyentropyall,apps_afternoon_frequencyentropyentertainment,apps_afternoon_frequencyentropysocial,apps_afternoon_frequencyentropycom.facebook.moments,apps_afternoon_frequencyentropycom.google.android.youtube,apps_afternoon_frequencyentropytop1global diff --git a/tests/data/processed/test04/applications_foreground_daily.csv b/tests/data/processed/test04/applications_foreground_daily.csv deleted file mode 100644 index 660db104..00000000 --- a/tests/data/processed/test04/applications_foreground_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_daily_countall,apps_daily_countemail,apps_daily_countsocial,apps_daily_countentertainment,apps_daily_countcom.facebook.moments,apps_daily_counttop1global,apps_daily_countcom.google.android.youtube,apps_daily_timeoffirstuseall,apps_daily_timeoffirstuseemail,apps_daily_timeoffirstusesocial,apps_daily_timeoffirstuseentertainment,apps_daily_timeoffirstusecom.facebook.moments,apps_daily_timeoffirstusetop1global,apps_daily_timeoffirstusecom.google.android.youtube,apps_daily_timeoflastuseall,apps_daily_timeoflastuseemail,apps_daily_timeoflastusesocial,apps_daily_timeoflastuseentertainment,apps_daily_timeoflastusecom.facebook.moments,apps_daily_timeoflastusetop1global,apps_daily_timeoflastusecom.google.android.youtube,apps_daily_frequencyentropyall,apps_daily_frequencyentropyemail,apps_daily_frequencyentropysocial,apps_daily_frequencyentropyentertainment,apps_daily_frequencyentropycom.facebook.moments,apps_daily_frequencyentropytop1global,apps_daily_frequencyentropycom.google.android.youtube diff --git a/tests/data/processed/test04/applications_foreground_evening.csv b/tests/data/processed/test04/applications_foreground_evening.csv deleted file mode 100644 index 680f8a20..00000000 --- a/tests/data/processed/test04/applications_foreground_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_evening_countemail,apps_evening_countall,apps_evening_countentertainment,apps_evening_countsocial,apps_evening_counttop1global,apps_evening_countcom.google.android.youtube,apps_evening_countcom.facebook.moments,apps_evening_timeoffirstuseemail,apps_evening_timeoffirstuseall,apps_evening_timeoffirstuseentertainment,apps_evening_timeoffirstusesocial,apps_evening_timeoffirstusetop1global,apps_evening_timeoffirstusecom.google.android.youtube,apps_evening_timeoffirstusecom.facebook.moments,apps_evening_timeoflastuseemail,apps_evening_timeoflastuseall,apps_evening_timeoflastuseentertainment,apps_evening_timeoflastusesocial,apps_evening_timeoflastusetop1global,apps_evening_timeoflastusecom.google.android.youtube,apps_evening_timeoflastusecom.facebook.moments,apps_evening_frequencyentropyemail,apps_evening_frequencyentropyall,apps_evening_frequencyentropyentertainment,apps_evening_frequencyentropysocial,apps_evening_frequencyentropytop1global,apps_evening_frequencyentropycom.google.android.youtube,apps_evening_frequencyentropycom.facebook.moments diff --git a/tests/data/processed/test04/applications_foreground_morning.csv b/tests/data/processed/test04/applications_foreground_morning.csv deleted file mode 100644 index 8b10a1ab..00000000 --- a/tests/data/processed/test04/applications_foreground_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_morning_countemail,apps_morning_countall,apps_morning_countsocial,apps_morning_countentertainment,apps_morning_countcom.google.android.youtube,apps_morning_countcom.facebook.moments,apps_morning_counttop1global,apps_morning_timeoffirstuseemail,apps_morning_timeoffirstuseall,apps_morning_timeoffirstusesocial,apps_morning_timeoffirstuseentertainment,apps_morning_timeoffirstusecom.google.android.youtube,apps_morning_timeoffirstusecom.facebook.moments,apps_morning_timeoffirstusetop1global,apps_morning_timeoflastuseemail,apps_morning_timeoflastuseall,apps_morning_timeoflastusesocial,apps_morning_timeoflastuseentertainment,apps_morning_timeoflastusecom.google.android.youtube,apps_morning_timeoflastusecom.facebook.moments,apps_morning_timeoflastusetop1global,apps_morning_frequencyentropyemail,apps_morning_frequencyentropyall,apps_morning_frequencyentropysocial,apps_morning_frequencyentropyentertainment,apps_morning_frequencyentropycom.google.android.youtube,apps_morning_frequencyentropycom.facebook.moments,apps_morning_frequencyentropytop1global diff --git a/tests/data/processed/test04/applications_foreground_night.csv b/tests/data/processed/test04/applications_foreground_night.csv deleted file mode 100644 index 2b589972..00000000 --- a/tests/data/processed/test04/applications_foreground_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,apps_night_countall,apps_night_countemail,apps_night_countentertainment,apps_night_countsocial,apps_night_countcom.google.android.youtube,apps_night_counttop1global,apps_night_countcom.facebook.moments,apps_night_timeoffirstuseall,apps_night_timeoffirstuseemail,apps_night_timeoffirstuseentertainment,apps_night_timeoffirstusesocial,apps_night_timeoffirstusecom.google.android.youtube,apps_night_timeoffirstusetop1global,apps_night_timeoffirstusecom.facebook.moments,apps_night_timeoflastuseall,apps_night_timeoflastuseemail,apps_night_timeoflastuseentertainment,apps_night_timeoflastusesocial,apps_night_timeoflastusecom.google.android.youtube,apps_night_timeoflastusetop1global,apps_night_timeoflastusecom.facebook.moments,apps_night_frequencyentropyall,apps_night_frequencyentropyemail,apps_night_frequencyentropyentertainment,apps_night_frequencyentropysocial,apps_night_frequencyentropycom.google.android.youtube,apps_night_frequencyentropytop1global,apps_night_frequencyentropycom.facebook.moments diff --git a/tests/data/processed/test04/battery_afternoon.csv b/tests/data/processed/test04/battery_afternoon.csv deleted file mode 100644 index db64a165..00000000 --- a/tests/data/processed/test04/battery_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_afternoon_countcharge,battery_afternoon_sumdurationcharge,battery_afternoon_sumdurationdischarge,battery_afternoon_maxconsumptionrate,battery_afternoon_avgconsumptionrate,battery_afternoon_countdischarge diff --git a/tests/data/processed/test04/battery_daily.csv b/tests/data/processed/test04/battery_daily.csv deleted file mode 100644 index 033d0b06..00000000 --- a/tests/data/processed/test04/battery_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_daily_maxconsumptionrate,battery_daily_countdischarge,battery_daily_countcharge,battery_daily_avgconsumptionrate,battery_daily_sumdurationdischarge,battery_daily_sumdurationcharge diff --git a/tests/data/processed/test04/battery_deltas.csv b/tests/data/processed/test04/battery_deltas.csv deleted file mode 100644 index a3714a6e..00000000 --- a/tests/data/processed/test04/battery_deltas.csv +++ /dev/null @@ -1 +0,0 @@ -"battery_diff","time_diff","local_start_date_time","local_end_date_time","local_start_date","local_end_date","local_start_day_segment","local_end_day_segment" diff --git a/tests/data/processed/test04/battery_evening.csv b/tests/data/processed/test04/battery_evening.csv deleted file mode 100644 index 5dd6b96b..00000000 --- a/tests/data/processed/test04/battery_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_evening_sumdurationdischarge,battery_evening_sumdurationcharge,battery_evening_maxconsumptionrate,battery_evening_avgconsumptionrate,battery_evening_countdischarge,battery_evening_countcharge diff --git a/tests/data/processed/test04/battery_morning.csv b/tests/data/processed/test04/battery_morning.csv deleted file mode 100644 index 23a19699..00000000 --- a/tests/data/processed/test04/battery_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_morning_countcharge,battery_morning_avgconsumptionrate,battery_morning_countdischarge,battery_morning_sumdurationdischarge,battery_morning_maxconsumptionrate,battery_morning_sumdurationcharge diff --git a/tests/data/processed/test04/battery_night.csv b/tests/data/processed/test04/battery_night.csv deleted file mode 100644 index c9bb058a..00000000 --- a/tests/data/processed/test04/battery_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,battery_night_countdischarge,battery_night_maxconsumptionrate,battery_night_sumdurationdischarge,battery_night_countcharge,battery_night_sumdurationcharge,battery_night_avgconsumptionrate diff --git a/tests/data/processed/test04/bluetooth_afternoon.csv b/tests/data/processed/test04/bluetooth_afternoon.csv deleted file mode 100644 index c043ce0b..00000000 --- a/tests/data/processed/test04/bluetooth_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_afternoon_countscans","bluetooth_afternoon_uniquedevices","bluetooth_afternoon_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/bluetooth_daily.csv b/tests/data/processed/test04/bluetooth_daily.csv deleted file mode 100644 index 4c1eac01..00000000 --- a/tests/data/processed/test04/bluetooth_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_daily_countscans","bluetooth_daily_uniquedevices","bluetooth_daily_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/bluetooth_evening.csv b/tests/data/processed/test04/bluetooth_evening.csv deleted file mode 100644 index cba45ebf..00000000 --- a/tests/data/processed/test04/bluetooth_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_evening_countscans","bluetooth_evening_uniquedevices","bluetooth_evening_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/bluetooth_morning.csv b/tests/data/processed/test04/bluetooth_morning.csv deleted file mode 100644 index 47d4d8ed..00000000 --- a/tests/data/processed/test04/bluetooth_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_morning_countscans","bluetooth_morning_uniquedevices","bluetooth_morning_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/bluetooth_night.csv b/tests/data/processed/test04/bluetooth_night.csv deleted file mode 100644 index 27c31a0c..00000000 --- a/tests/data/processed/test04/bluetooth_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","bluetooth_night_countscans","bluetooth_night_uniquedevices","bluetooth_night_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/calls_incoming_afternoon.csv b/tests/data/processed/test04/calls_incoming_afternoon.csv deleted file mode 100644 index 7700f26f..00000000 --- a/tests/data/processed/test04/calls_incoming_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_afternoon_count","call_incoming_afternoon_distinctcontacts","call_incoming_afternoon_meanduration","call_incoming_afternoon_sumduration","call_incoming_afternoon_minduration","call_incoming_afternoon_maxduration","call_incoming_afternoon_stdduration","call_incoming_afternoon_modeduration","call_incoming_afternoon_entropyduration","call_incoming_afternoon_timefirstcall","call_incoming_afternoon_timelastcall","call_incoming_afternoon_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_incoming_daily.csv b/tests/data/processed/test04/calls_incoming_daily.csv deleted file mode 100644 index 15a7ab61..00000000 --- a/tests/data/processed/test04/calls_incoming_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_daily_count","call_incoming_daily_distinctcontacts","call_incoming_daily_meanduration","call_incoming_daily_sumduration","call_incoming_daily_minduration","call_incoming_daily_maxduration","call_incoming_daily_stdduration","call_incoming_daily_modeduration","call_incoming_daily_entropyduration","call_incoming_daily_timefirstcall","call_incoming_daily_timelastcall","call_incoming_daily_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_incoming_evening.csv b/tests/data/processed/test04/calls_incoming_evening.csv deleted file mode 100644 index 8b52a987..00000000 --- a/tests/data/processed/test04/calls_incoming_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_evening_count","call_incoming_evening_distinctcontacts","call_incoming_evening_meanduration","call_incoming_evening_sumduration","call_incoming_evening_minduration","call_incoming_evening_maxduration","call_incoming_evening_stdduration","call_incoming_evening_modeduration","call_incoming_evening_entropyduration","call_incoming_evening_timefirstcall","call_incoming_evening_timelastcall","call_incoming_evening_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_incoming_morning.csv b/tests/data/processed/test04/calls_incoming_morning.csv deleted file mode 100644 index 5e6c2b05..00000000 --- a/tests/data/processed/test04/calls_incoming_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_morning_count","call_incoming_morning_distinctcontacts","call_incoming_morning_meanduration","call_incoming_morning_sumduration","call_incoming_morning_minduration","call_incoming_morning_maxduration","call_incoming_morning_stdduration","call_incoming_morning_modeduration","call_incoming_morning_entropyduration","call_incoming_morning_timefirstcall","call_incoming_morning_timelastcall","call_incoming_morning_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_incoming_night.csv b/tests/data/processed/test04/calls_incoming_night.csv deleted file mode 100644 index d5718bda..00000000 --- a/tests/data/processed/test04/calls_incoming_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_incoming_night_count","call_incoming_night_distinctcontacts","call_incoming_night_meanduration","call_incoming_night_sumduration","call_incoming_night_minduration","call_incoming_night_maxduration","call_incoming_night_stdduration","call_incoming_night_modeduration","call_incoming_night_entropyduration","call_incoming_night_timefirstcall","call_incoming_night_timelastcall","call_incoming_night_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_missed_afternoon.csv b/tests/data/processed/test04/calls_missed_afternoon.csv deleted file mode 100644 index 7b1d275f..00000000 --- a/tests/data/processed/test04/calls_missed_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_afternoon_count","call_missed_afternoon_distinctcontacts","call_missed_afternoon_timefirstcall","call_missed_afternoon_timelastcall","call_missed_afternoon_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_missed_daily.csv b/tests/data/processed/test04/calls_missed_daily.csv deleted file mode 100644 index 52bccdee..00000000 --- a/tests/data/processed/test04/calls_missed_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_daily_count","call_missed_daily_distinctcontacts","call_missed_daily_timefirstcall","call_missed_daily_timelastcall","call_missed_daily_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_missed_evening.csv b/tests/data/processed/test04/calls_missed_evening.csv deleted file mode 100644 index 5d1d36c7..00000000 --- a/tests/data/processed/test04/calls_missed_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_evening_count","call_missed_evening_distinctcontacts","call_missed_evening_timefirstcall","call_missed_evening_timelastcall","call_missed_evening_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_missed_morning.csv b/tests/data/processed/test04/calls_missed_morning.csv deleted file mode 100644 index 573c74f8..00000000 --- a/tests/data/processed/test04/calls_missed_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_morning_count","call_missed_morning_distinctcontacts","call_missed_morning_timefirstcall","call_missed_morning_timelastcall","call_missed_morning_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_missed_night.csv b/tests/data/processed/test04/calls_missed_night.csv deleted file mode 100644 index 61557e4e..00000000 --- a/tests/data/processed/test04/calls_missed_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_missed_night_count","call_missed_night_distinctcontacts","call_missed_night_timefirstcall","call_missed_night_timelastcall","call_missed_night_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_outgoing_afternoon.csv b/tests/data/processed/test04/calls_outgoing_afternoon.csv deleted file mode 100644 index 3430c380..00000000 --- a/tests/data/processed/test04/calls_outgoing_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_afternoon_count","call_outgoing_afternoon_distinctcontacts","call_outgoing_afternoon_meanduration","call_outgoing_afternoon_sumduration","call_outgoing_afternoon_minduration","call_outgoing_afternoon_maxduration","call_outgoing_afternoon_stdduration","call_outgoing_afternoon_modeduration","call_outgoing_afternoon_entropyduration","call_outgoing_afternoon_timefirstcall","call_outgoing_afternoon_timelastcall","call_outgoing_afternoon_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_outgoing_daily.csv b/tests/data/processed/test04/calls_outgoing_daily.csv deleted file mode 100644 index ed6ee7fa..00000000 --- a/tests/data/processed/test04/calls_outgoing_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_daily_count","call_outgoing_daily_distinctcontacts","call_outgoing_daily_meanduration","call_outgoing_daily_sumduration","call_outgoing_daily_minduration","call_outgoing_daily_maxduration","call_outgoing_daily_stdduration","call_outgoing_daily_modeduration","call_outgoing_daily_entropyduration","call_outgoing_daily_timefirstcall","call_outgoing_daily_timelastcall","call_outgoing_daily_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_outgoing_evening.csv b/tests/data/processed/test04/calls_outgoing_evening.csv deleted file mode 100644 index a459636a..00000000 --- a/tests/data/processed/test04/calls_outgoing_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_evening_count","call_outgoing_evening_distinctcontacts","call_outgoing_evening_meanduration","call_outgoing_evening_sumduration","call_outgoing_evening_minduration","call_outgoing_evening_maxduration","call_outgoing_evening_stdduration","call_outgoing_evening_modeduration","call_outgoing_evening_entropyduration","call_outgoing_evening_timefirstcall","call_outgoing_evening_timelastcall","call_outgoing_evening_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_outgoing_morning.csv b/tests/data/processed/test04/calls_outgoing_morning.csv deleted file mode 100644 index 0beb5827..00000000 --- a/tests/data/processed/test04/calls_outgoing_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_morning_count","call_outgoing_morning_distinctcontacts","call_outgoing_morning_meanduration","call_outgoing_morning_sumduration","call_outgoing_morning_minduration","call_outgoing_morning_maxduration","call_outgoing_morning_stdduration","call_outgoing_morning_modeduration","call_outgoing_morning_entropyduration","call_outgoing_morning_timefirstcall","call_outgoing_morning_timelastcall","call_outgoing_morning_countmostfrequentcontact" diff --git a/tests/data/processed/test04/calls_outgoing_night.csv b/tests/data/processed/test04/calls_outgoing_night.csv deleted file mode 100644 index fa763f36..00000000 --- a/tests/data/processed/test04/calls_outgoing_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","call_outgoing_night_count","call_outgoing_night_distinctcontacts","call_outgoing_night_meanduration","call_outgoing_night_sumduration","call_outgoing_night_minduration","call_outgoing_night_maxduration","call_outgoing_night_stdduration","call_outgoing_night_modeduration","call_outgoing_night_entropyduration","call_outgoing_night_timefirstcall","call_outgoing_night_timelastcall","call_outgoing_night_countmostfrequentcontact" diff --git a/tests/data/processed/test04/conversation_afternoon.csv b/tests/data/processed/test04/conversation_afternoon.csv deleted file mode 100644 index a018ba8e..00000000 --- a/tests/data/processed/test04/conversation_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_afternoon_minutessilence,conversation_afternoon_sumconversationduration,conversation_afternoon_sdconversationduration,conversation_afternoon_voicesensedfraction,conversation_afternoon_timelastconversation,conversation_afternoon_minutesvoice,conversation_afternoon_avgconversationduration,conversation_afternoon_unknownsensedfraction,conversation_afternoon_voiceexpectedfraction,conversation_afternoon_voicesdenergy,conversation_afternoon_noiseminenergy,conversation_afternoon_unknownexpectedfraction,conversation_afternoon_minconversationduration,conversation_afternoon_noisesensedfraction,conversation_afternoon_voiceavgenergy,conversation_afternoon_voicemaxenergy,conversation_afternoon_timefirstconversation,conversation_afternoon_noiseavgenergy,conversation_afternoon_silenceexpectedfraction,conversation_afternoon_minutesunknown,conversation_afternoon_voiceminenergy,conversation_afternoon_noisemaxenergy,conversation_afternoon_noisesumenergy,conversation_afternoon_voicesumenergy,conversation_afternoon_maxconversationduration,conversation_afternoon_noisesdenergy,conversation_afternoon_noiseexpectedfraction,conversation_afternoon_silencesensedfraction,conversation_afternoon_minutesnoise,conversation_afternoon_countconversation diff --git a/tests/data/processed/test04/conversation_daily.csv b/tests/data/processed/test04/conversation_daily.csv deleted file mode 100644 index 55b23684..00000000 --- a/tests/data/processed/test04/conversation_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_daily_avgconversationduration,conversation_daily_timefirstconversation,conversation_daily_noiseminenergy,conversation_daily_minutessilence,conversation_daily_voicemaxenergy,conversation_daily_voicesensedfraction,conversation_daily_minutesvoice,conversation_daily_voiceavgenergy,conversation_daily_sdconversationduration,conversation_daily_maxconversationduration,conversation_daily_noisesumenergy,conversation_daily_noisesdenergy,conversation_daily_minutesnoise,conversation_daily_noiseexpectedfraction,conversation_daily_noisesensedfraction,conversation_daily_voicesumenergy,conversation_daily_silenceexpectedfraction,conversation_daily_noiseavgenergy,conversation_daily_voiceexpectedfraction,conversation_daily_noisemaxenergy,conversation_daily_minutesunknown,conversation_daily_minconversationduration,conversation_daily_voiceminenergy,conversation_daily_unknownexpectedfraction,conversation_daily_sumconversationduration,conversation_daily_silencesensedfraction,conversation_daily_voicesdenergy,conversation_daily_unknownsensedfraction,conversation_daily_timelastconversation,conversation_daily_countconversation diff --git a/tests/data/processed/test04/conversation_evening.csv b/tests/data/processed/test04/conversation_evening.csv deleted file mode 100644 index 6a6d988f..00000000 --- a/tests/data/processed/test04/conversation_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_evening_maxconversationduration,conversation_evening_voiceexpectedfraction,conversation_evening_countconversation,conversation_evening_avgconversationduration,conversation_evening_noisesensedfraction,conversation_evening_unknownsensedfraction,conversation_evening_timelastconversation,conversation_evening_voicesdenergy,conversation_evening_sdconversationduration,conversation_evening_minutessilence,conversation_evening_minutesnoise,conversation_evening_silenceexpectedfraction,conversation_evening_silencesensedfraction,conversation_evening_voicesumenergy,conversation_evening_noiseminenergy,conversation_evening_voiceminenergy,conversation_evening_minutesunknown,conversation_evening_noisemaxenergy,conversation_evening_noisesdenergy,conversation_evening_voicesensedfraction,conversation_evening_sumconversationduration,conversation_evening_minconversationduration,conversation_evening_timefirstconversation,conversation_evening_noiseexpectedfraction,conversation_evening_unknownexpectedfraction,conversation_evening_voiceavgenergy,conversation_evening_voicemaxenergy,conversation_evening_noisesumenergy,conversation_evening_noiseavgenergy,conversation_evening_minutesvoice diff --git a/tests/data/processed/test04/conversation_morning.csv b/tests/data/processed/test04/conversation_morning.csv deleted file mode 100644 index 9038a1e1..00000000 --- a/tests/data/processed/test04/conversation_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_morning_voicesensedfraction,conversation_morning_noiseminenergy,conversation_morning_noisemaxenergy,conversation_morning_unknownsensedfraction,conversation_morning_sumconversationduration,conversation_morning_countconversation,conversation_morning_noisesumenergy,conversation_morning_voicesumenergy,conversation_morning_voicesdenergy,conversation_morning_minutesnoise,conversation_morning_noiseavgenergy,conversation_morning_maxconversationduration,conversation_morning_noisesdenergy,conversation_morning_voicemaxenergy,conversation_morning_noiseexpectedfraction,conversation_morning_silencesensedfraction,conversation_morning_minutesunknown,conversation_morning_sdconversationduration,conversation_morning_voiceminenergy,conversation_morning_voiceexpectedfraction,conversation_morning_minconversationduration,conversation_morning_silenceexpectedfraction,conversation_morning_timelastconversation,conversation_morning_unknownexpectedfraction,conversation_morning_avgconversationduration,conversation_morning_noisesensedfraction,conversation_morning_timefirstconversation,conversation_morning_minutesvoice,conversation_morning_voiceavgenergy,conversation_morning_minutessilence diff --git a/tests/data/processed/test04/conversation_night.csv b/tests/data/processed/test04/conversation_night.csv deleted file mode 100644 index 8aff3aed..00000000 --- a/tests/data/processed/test04/conversation_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,conversation_night_countconversation,conversation_night_avgconversationduration,conversation_night_voiceexpectedfraction,conversation_night_voicemaxenergy,conversation_night_noisesumenergy,conversation_night_noiseexpectedfraction,conversation_night_silenceexpectedfraction,conversation_night_minconversationduration,conversation_night_voiceavgenergy,conversation_night_minutessilence,conversation_night_timelastconversation,conversation_night_unknownsensedfraction,conversation_night_timefirstconversation,conversation_night_sdconversationduration,conversation_night_silencesensedfraction,conversation_night_noiseavgenergy,conversation_night_minutesunknown,conversation_night_voicesumenergy,conversation_night_noiseminenergy,conversation_night_minutesvoice,conversation_night_unknownexpectedfraction,conversation_night_maxconversationduration,conversation_night_noisesensedfraction,conversation_night_noisesdenergy,conversation_night_voicesdenergy,conversation_night_voiceminenergy,conversation_night_noisemaxenergy,conversation_night_voicesensedfraction,conversation_night_minutesnoise,conversation_night_sumconversationduration diff --git a/tests/data/processed/test04/light_afternoon.csv b/tests/data/processed/test04/light_afternoon.csv deleted file mode 100644 index da1c58e3..00000000 --- a/tests/data/processed/test04/light_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_afternoon_avglux,light_afternoon_count,light_afternoon_maxlux,light_afternoon_medianlux,light_afternoon_minlux,light_afternoon_stdlux diff --git a/tests/data/processed/test04/light_daily.csv b/tests/data/processed/test04/light_daily.csv deleted file mode 100644 index a455b944..00000000 --- a/tests/data/processed/test04/light_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_daily_avglux,light_daily_stdlux,light_daily_medianlux,light_daily_minlux,light_daily_maxlux,light_daily_count diff --git a/tests/data/processed/test04/light_evening.csv b/tests/data/processed/test04/light_evening.csv deleted file mode 100644 index c13d87f9..00000000 --- a/tests/data/processed/test04/light_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_evening_avglux,light_evening_minlux,light_evening_maxlux,light_evening_medianlux,light_evening_stdlux,light_evening_count diff --git a/tests/data/processed/test04/light_morning.csv b/tests/data/processed/test04/light_morning.csv deleted file mode 100644 index bb3b6662..00000000 --- a/tests/data/processed/test04/light_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_morning_minlux,light_morning_avglux,light_morning_stdlux,light_morning_medianlux,light_morning_count,light_morning_maxlux diff --git a/tests/data/processed/test04/light_night.csv b/tests/data/processed/test04/light_night.csv deleted file mode 100644 index 1c4159e5..00000000 --- a/tests/data/processed/test04/light_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,light_night_count,light_night_stdlux,light_night_minlux,light_night_medianlux,light_night_maxlux,light_night_avglux diff --git a/tests/data/processed/test04/messages_received_afternoon.csv b/tests/data/processed/test04/messages_received_afternoon.csv deleted file mode 100644 index 640096b5..00000000 --- a/tests/data/processed/test04/messages_received_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_afternoon_countmostfrequentcontact","messages_received_afternoon_count","messages_received_afternoon_distinctcontacts","messages_received_afternoon_timefirstmessage","messages_received_afternoon_timelastmessage" diff --git a/tests/data/processed/test04/messages_received_daily.csv b/tests/data/processed/test04/messages_received_daily.csv deleted file mode 100644 index de5543b9..00000000 --- a/tests/data/processed/test04/messages_received_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_daily_countmostfrequentcontact","messages_received_daily_count","messages_received_daily_distinctcontacts","messages_received_daily_timefirstmessage","messages_received_daily_timelastmessage" diff --git a/tests/data/processed/test04/messages_received_evening.csv b/tests/data/processed/test04/messages_received_evening.csv deleted file mode 100644 index 7325e9a9..00000000 --- a/tests/data/processed/test04/messages_received_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_evening_countmostfrequentcontact","messages_received_evening_count","messages_received_evening_distinctcontacts","messages_received_evening_timefirstmessage","messages_received_evening_timelastmessage" diff --git a/tests/data/processed/test04/messages_received_morning.csv b/tests/data/processed/test04/messages_received_morning.csv deleted file mode 100644 index bbd446c3..00000000 --- a/tests/data/processed/test04/messages_received_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_morning_countmostfrequentcontact","messages_received_morning_count","messages_received_morning_distinctcontacts","messages_received_morning_timefirstmessage","messages_received_morning_timelastmessage" diff --git a/tests/data/processed/test04/messages_received_night.csv b/tests/data/processed/test04/messages_received_night.csv deleted file mode 100644 index 2d0ae76f..00000000 --- a/tests/data/processed/test04/messages_received_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_received_night_countmostfrequentcontact","messages_received_night_count","messages_received_night_distinctcontacts","messages_received_night_timefirstmessage","messages_received_night_timelastmessage" diff --git a/tests/data/processed/test04/messages_sent_afternoon.csv b/tests/data/processed/test04/messages_sent_afternoon.csv deleted file mode 100644 index ab84aaba..00000000 --- a/tests/data/processed/test04/messages_sent_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_afternoon_countmostfrequentcontact","messages_sent_afternoon_count","messages_sent_afternoon_distinctcontacts","messages_sent_afternoon_timefirstmessage","messages_sent_afternoon_timelastmessage" diff --git a/tests/data/processed/test04/messages_sent_daily.csv b/tests/data/processed/test04/messages_sent_daily.csv deleted file mode 100644 index f827b72f..00000000 --- a/tests/data/processed/test04/messages_sent_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_daily_countmostfrequentcontact","messages_sent_daily_count","messages_sent_daily_distinctcontacts","messages_sent_daily_timefirstmessage","messages_sent_daily_timelastmessage" diff --git a/tests/data/processed/test04/messages_sent_evening.csv b/tests/data/processed/test04/messages_sent_evening.csv deleted file mode 100644 index ddfed962..00000000 --- a/tests/data/processed/test04/messages_sent_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_evening_countmostfrequentcontact","messages_sent_evening_count","messages_sent_evening_distinctcontacts","messages_sent_evening_timefirstmessage","messages_sent_evening_timelastmessage" diff --git a/tests/data/processed/test04/messages_sent_morning.csv b/tests/data/processed/test04/messages_sent_morning.csv deleted file mode 100644 index 80235f96..00000000 --- a/tests/data/processed/test04/messages_sent_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_morning_countmostfrequentcontact","messages_sent_morning_count","messages_sent_morning_distinctcontacts","messages_sent_morning_timefirstmessage","messages_sent_morning_timelastmessage" diff --git a/tests/data/processed/test04/messages_sent_night.csv b/tests/data/processed/test04/messages_sent_night.csv deleted file mode 100644 index 1b4351e0..00000000 --- a/tests/data/processed/test04/messages_sent_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","messages_sent_night_countmostfrequentcontact","messages_sent_night_count","messages_sent_night_distinctcontacts","messages_sent_night_timefirstmessage","messages_sent_night_timelastmessage" diff --git a/tests/data/processed/test04/screen_afternoon.csv b/tests/data/processed/test04/screen_afternoon.csv deleted file mode 100644 index b1d57552..00000000 --- a/tests/data/processed/test04/screen_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_afternoon_sumdurationunlock,screen_afternoon_countepisodeunlock,screen_afternoon_mindurationunlock,screen_afternoon_episodepersensedminutesunlock,screen_afternoon_maxdurationunlock,screen_afternoon_stddurationunlock,screen_afternoon_firstuseafter00unlock,screen_afternoon_avgdurationunlock diff --git a/tests/data/processed/test04/screen_daily.csv b/tests/data/processed/test04/screen_daily.csv deleted file mode 100644 index e0180dc6..00000000 --- a/tests/data/processed/test04/screen_daily.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_daily_firstuseafter00unlock,screen_daily_sumdurationunlock,screen_daily_maxdurationunlock,screen_daily_countepisodeunlock,screen_daily_stddurationunlock,screen_daily_episodepersensedminutesunlock,screen_daily_mindurationunlock,screen_daily_avgdurationunlock diff --git a/tests/data/processed/test04/screen_deltas.csv b/tests/data/processed/test04/screen_deltas.csv deleted file mode 100644 index 8209e74c..00000000 --- a/tests/data/processed/test04/screen_deltas.csv +++ /dev/null @@ -1 +0,0 @@ -"episode","time_diff","local_start_date_time","local_end_date_time","local_start_date","local_end_date","local_start_day_segment","local_end_day_segment" diff --git a/tests/data/processed/test04/screen_evening.csv b/tests/data/processed/test04/screen_evening.csv deleted file mode 100644 index bf8fbea6..00000000 --- a/tests/data/processed/test04/screen_evening.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_evening_episodepersensedminutesunlock,screen_evening_stddurationunlock,screen_evening_mindurationunlock,screen_evening_countepisodeunlock,screen_evening_firstuseafter00unlock,screen_evening_sumdurationunlock,screen_evening_avgdurationunlock,screen_evening_maxdurationunlock diff --git a/tests/data/processed/test04/screen_morning.csv b/tests/data/processed/test04/screen_morning.csv deleted file mode 100644 index d5c03303..00000000 --- a/tests/data/processed/test04/screen_morning.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_morning_episodepersensedminutesunlock,screen_morning_maxdurationunlock,screen_morning_countepisodeunlock,screen_morning_avgdurationunlock,screen_morning_sumdurationunlock,screen_morning_mindurationunlock,screen_morning_firstuseafter00unlock,screen_morning_stddurationunlock diff --git a/tests/data/processed/test04/screen_night.csv b/tests/data/processed/test04/screen_night.csv deleted file mode 100644 index a3d8eb07..00000000 --- a/tests/data/processed/test04/screen_night.csv +++ /dev/null @@ -1 +0,0 @@ -local_date,screen_night_episodepersensedminutesunlock,screen_night_countepisodeunlock,screen_night_maxdurationunlock,screen_night_mindurationunlock,screen_night_sumdurationunlock,screen_night_stddurationunlock,screen_night_firstuseafter00unlock,screen_night_avgdurationunlock diff --git a/tests/data/processed/test04/wifi_afternoon.csv b/tests/data/processed/test04/wifi_afternoon.csv deleted file mode 100644 index e23766ae..00000000 --- a/tests/data/processed/test04/wifi_afternoon.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_afternoon_countscans","wifi_afternoon_uniquedevices","wifi_afternoon_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/wifi_daily.csv b/tests/data/processed/test04/wifi_daily.csv deleted file mode 100644 index 569ec612..00000000 --- a/tests/data/processed/test04/wifi_daily.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_daily_countscans","wifi_daily_uniquedevices","wifi_daily_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/wifi_evening.csv b/tests/data/processed/test04/wifi_evening.csv deleted file mode 100644 index d2879ba8..00000000 --- a/tests/data/processed/test04/wifi_evening.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_evening_countscans","wifi_evening_uniquedevices","wifi_evening_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/wifi_morning.csv b/tests/data/processed/test04/wifi_morning.csv deleted file mode 100644 index f34385b1..00000000 --- a/tests/data/processed/test04/wifi_morning.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_morning_countscans","wifi_morning_uniquedevices","wifi_morning_countscansmostuniquedevice" diff --git a/tests/data/processed/test04/wifi_night.csv b/tests/data/processed/test04/wifi_night.csv deleted file mode 100644 index c9660149..00000000 --- a/tests/data/processed/test04/wifi_night.csv +++ /dev/null @@ -1 +0,0 @@ -"local_date","wifi_night_countscans","wifi_night_uniquedevices","wifi_night_countscansmostuniquedevice" diff --git a/tests/data/raw/test01/applications_foreground_raw.csv b/tests/data/raw/test01/phone_applications_foreground_raw.csv similarity index 100% rename from tests/data/raw/test01/applications_foreground_raw.csv rename to tests/data/raw/test01/phone_applications_foreground_raw.csv diff --git a/tests/data/raw/test01/battery_raw.csv b/tests/data/raw/test01/phone_battery_raw.csv similarity index 100% rename from tests/data/raw/test01/battery_raw.csv rename to tests/data/raw/test01/phone_battery_raw.csv diff --git a/tests/data/raw/test01/bluetooth_raw.csv b/tests/data/raw/test01/phone_bluetooth_raw.csv similarity index 100% rename from tests/data/raw/test01/bluetooth_raw.csv rename to tests/data/raw/test01/phone_bluetooth_raw.csv diff --git a/tests/data/raw/test01/calls_raw.csv b/tests/data/raw/test01/phone_calls_raw.csv similarity index 100% rename from tests/data/raw/test01/calls_raw.csv rename to tests/data/raw/test01/phone_calls_raw.csv diff --git a/tests/data/raw/test01/plugin_studentlife_audio_android_raw.csv b/tests/data/raw/test01/phone_conversation_raw.csv similarity index 98% rename from tests/data/raw/test01/plugin_studentlife_audio_android_raw.csv rename to tests/data/raw/test01/phone_conversation_raw.csv index c65e581b..bb86abe5 100644 --- a/tests/data/raw/test01/plugin_studentlife_audio_android_raw.csv +++ b/tests/data/raw/test01/phone_conversation_raw.csv @@ -6023,7 +6023,6 @@ timestamp,device_id,datatype,double_energy,inference,blod_feature,double_convo_s 1594202709319,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,5109,2,NA,0,0 1594202710281,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,10047,1,NA,0,0 1594202711198,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,10897,1,NA,0,0 - 1594209643489,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,7660,1,NA,0,0 1594209644584,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,3429,2,NA,0,0 1594209645144,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,9353,1,NA,0,0 @@ -6451,245 +6450,125 @@ timestamp,device_id,datatype,double_energy,inference,blod_feature,double_convo_s 1594223963838,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,2171,2,NA,0,0 1594223964753,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,3855,2,NA,0,0 1594223965042,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,9340,1,NA,0,0 -1594223965949,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,0,0,NA,0,0 1594223966049,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,7175,1,NA,0,0 -1594223966096,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,9339,1,NA,0,0 1594223967033,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,725,2,NA,0,0 -1594223967969,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,9242,1,NA,0,0 1594223968533,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,1618,2,NA,0,0 -1594223968544,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,4867,1,NA,0,0 1594223969122,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,11576,1,NA,0,0 -1594223969713,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,42,1,NA,0,0 1594223970437,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,4050,2,NA,0,0 -1594223970734,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,3493,1,NA,0,0 1594223971039,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,4378,2,NA,0,0 -1594223971870,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,3971,2,NA,0,0 1594223972134,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,3965,1,NA,0,0 -1594223972438,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,2044,3,NA,0,0 1594223973168,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,11081,1,NA,0,0 -1594223973261,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,8986,1,NA,0,0 1594223974315,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,3470,1,NA,0,0 -1594223974979,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,11941,1,NA,0,0 1594223975283,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,3966,1,NA,0,0 -1594223975582,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,11816,1,NA,0,0 1594223976689,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,5733,1,NA,0,0 -1594223976710,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,0,9028,1,NA,0,0 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tests/data/raw/test01/phone_messages_raw.csv diff --git a/tests/data/raw/test01/screen_raw.csv b/tests/data/raw/test01/phone_screen_raw.csv similarity index 100% rename from tests/data/raw/test01/screen_raw.csv rename to tests/data/raw/test01/phone_screen_raw.csv diff --git a/tests/data/raw/test01/sensor_wifi_raw.csv b/tests/data/raw/test01/phone_wifi_connected_raw.csv similarity index 100% rename from tests/data/raw/test01/sensor_wifi_raw.csv rename to tests/data/raw/test01/phone_wifi_connected_raw.csv diff --git a/tests/data/raw/test01/wifi_raw.csv b/tests/data/raw/test01/phone_wifi_visible_raw.csv similarity index 100% rename from tests/data/raw/test01/wifi_raw.csv rename to tests/data/raw/test01/phone_wifi_visible_raw.csv diff --git a/tests/data/raw/test02/applications_foreground_raw.csv b/tests/data/raw/test02/phone_applications_foreground_raw.csv similarity index 100% rename from tests/data/raw/test02/applications_foreground_raw.csv rename to tests/data/raw/test02/phone_applications_foreground_raw.csv diff --git a/tests/data/raw/test02/battery_raw.csv b/tests/data/raw/test02/phone_battery_raw.csv similarity index 100% rename from tests/data/raw/test02/battery_raw.csv rename to tests/data/raw/test02/phone_battery_raw.csv diff --git a/tests/data/raw/test02/bluetooth_raw.csv b/tests/data/raw/test02/phone_bluetooth_raw.csv similarity index 100% rename from tests/data/raw/test02/bluetooth_raw.csv rename to tests/data/raw/test02/phone_bluetooth_raw.csv diff --git a/tests/data/raw/test02/calls_raw.csv b/tests/data/raw/test02/phone_calls_raw.csv similarity index 100% rename from tests/data/raw/test02/calls_raw.csv rename to tests/data/raw/test02/phone_calls_raw.csv diff --git a/tests/data/raw/test02/plugin_studentlife_audio_raw.csv b/tests/data/raw/test02/phone_conversation_raw.csv similarity index 71% rename from tests/data/raw/test02/plugin_studentlife_audio_raw.csv rename to tests/data/raw/test02/phone_conversation_raw.csv index 989c42f8..0cc5961e 100644 --- a/tests/data/raw/test02/plugin_studentlife_audio_raw.csv +++ b/tests/data/raw/test02/phone_conversation_raw.csv @@ -7541,4 +7541,3028 @@ timestamp,device_id,datatype,double_energy,inference,blod_feature,double_convo_s 1594252735232,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,10261,1,NA,0,0 1594252736221,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,6935,1,NA,0,0 1594252737495,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,2696,1,NA,0,0 +1594252738721,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,0,0,NA,0,0 +1594185306551,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,1967,1,NA,0,0 +1594185307601,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,11957,1,NA,0,0 +1594185308785,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,6973,1,NA,0,0 +1594185309022,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,4297,2,NA,0,0 +1594185310956,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,3064,2,NA,0,0 +1594185311595,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,10988,1,NA,0,0 +1594185312155,7yKzcQm4-xKTC-0bhC-PZXC-3jAbRIXOsf5w,0,10351,1,NA,0,0 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b/tests/data/raw/test02/phone_screen_raw.csv similarity index 100% rename from tests/data/raw/test02/screen_raw.csv rename to tests/data/raw/test02/phone_screen_raw.csv diff --git a/tests/data/raw/test02/sensor_wifi_raw.csv b/tests/data/raw/test02/phone_wifi_connected_raw.csv similarity index 100% rename from tests/data/raw/test02/sensor_wifi_raw.csv rename to tests/data/raw/test02/phone_wifi_connected_raw.csv diff --git a/tests/data/raw/test02/wifi_raw.csv b/tests/data/raw/test02/phone_wifi_visible_raw.csv similarity index 100% rename from tests/data/raw/test02/wifi_raw.csv rename to tests/data/raw/test02/phone_wifi_visible_raw.csv diff --git a/tests/data/raw/test03/applications_foreground_raw.csv b/tests/data/raw/test03/phone_applications_foreground_raw.csv similarity index 100% rename from tests/data/raw/test03/applications_foreground_raw.csv rename to tests/data/raw/test03/phone_applications_foreground_raw.csv diff --git a/tests/data/raw/test03/battery_raw.csv b/tests/data/raw/test03/phone_battery_raw.csv similarity index 100% rename from tests/data/raw/test03/battery_raw.csv rename to tests/data/raw/test03/phone_battery_raw.csv diff --git a/tests/data/raw/test03/bluetooth_raw.csv b/tests/data/raw/test03/phone_bluetooth_raw.csv similarity index 100% rename from tests/data/raw/test03/bluetooth_raw.csv rename to tests/data/raw/test03/phone_bluetooth_raw.csv diff --git a/tests/data/raw/test03/calls_raw.csv b/tests/data/raw/test03/phone_calls_raw.csv similarity index 100% rename from tests/data/raw/test03/calls_raw.csv rename to tests/data/raw/test03/phone_calls_raw.csv diff --git a/tests/data/raw/test03/plugin_studentlife_audio_android_raw.csv b/tests/data/raw/test03/phone_conversation_raw.csv similarity index 100% rename from tests/data/raw/test03/plugin_studentlife_audio_android_raw.csv rename to tests/data/raw/test03/phone_conversation_raw.csv diff --git a/tests/data/raw/test03/light_raw.csv b/tests/data/raw/test03/phone_light_raw.csv similarity index 100% rename from tests/data/raw/test03/light_raw.csv rename to tests/data/raw/test03/phone_light_raw.csv diff --git a/tests/data/raw/test03/messages_raw.csv b/tests/data/raw/test03/phone_messages_raw.csv similarity index 100% rename from tests/data/raw/test03/messages_raw.csv rename to tests/data/raw/test03/phone_messages_raw.csv diff --git a/tests/data/raw/test03/screen_raw.csv b/tests/data/raw/test03/phone_screen_raw.csv similarity index 100% rename from tests/data/raw/test03/screen_raw.csv rename to tests/data/raw/test03/phone_screen_raw.csv diff --git a/tests/data/raw/test03/sensor_wifi_raw.csv b/tests/data/raw/test03/phone_wifi_connected_raw.csv similarity index 100% rename from tests/data/raw/test03/sensor_wifi_raw.csv rename to tests/data/raw/test03/phone_wifi_connected_raw.csv diff --git a/tests/data/raw/test03/wifi_raw.csv b/tests/data/raw/test03/phone_wifi_visible_raw.csv similarity index 100% rename from tests/data/raw/test03/wifi_raw.csv rename to tests/data/raw/test03/phone_wifi_visible_raw.csv diff --git a/tests/data/raw/test04/applications_foreground_raw.csv b/tests/data/raw/test04/phone_applications_foreground_raw.csv similarity index 100% rename from tests/data/raw/test04/applications_foreground_raw.csv rename to tests/data/raw/test04/phone_applications_foreground_raw.csv diff --git a/tests/data/raw/test04/battery_raw.csv b/tests/data/raw/test04/phone_battery_raw.csv similarity index 100% rename from tests/data/raw/test04/battery_raw.csv rename to tests/data/raw/test04/phone_battery_raw.csv diff --git a/tests/data/raw/test04/bluetooth_raw.csv b/tests/data/raw/test04/phone_bluetooth_raw.csv similarity index 100% rename from tests/data/raw/test04/bluetooth_raw.csv rename to tests/data/raw/test04/phone_bluetooth_raw.csv diff --git a/tests/data/raw/test04/calls_raw.csv b/tests/data/raw/test04/phone_calls_raw.csv similarity index 100% rename from tests/data/raw/test04/calls_raw.csv rename to tests/data/raw/test04/phone_calls_raw.csv diff --git a/tests/data/raw/test04/plugin_studentlife_audio_raw.csv b/tests/data/raw/test04/phone_conversation_raw.csv similarity index 100% rename from tests/data/raw/test04/plugin_studentlife_audio_raw.csv rename to tests/data/raw/test04/phone_conversation_raw.csv diff --git a/tests/data/raw/test04/light_raw.csv b/tests/data/raw/test04/phone_light_raw.csv similarity index 100% rename from tests/data/raw/test04/light_raw.csv rename to tests/data/raw/test04/phone_light_raw.csv diff --git a/tests/data/raw/test04/messages_raw.csv b/tests/data/raw/test04/phone_messages_raw.csv similarity index 100% rename from tests/data/raw/test04/messages_raw.csv rename to tests/data/raw/test04/phone_messages_raw.csv diff --git a/tests/data/raw/test04/screen_raw.csv b/tests/data/raw/test04/phone_screen_raw.csv similarity index 100% rename from tests/data/raw/test04/screen_raw.csv rename to tests/data/raw/test04/phone_screen_raw.csv diff --git a/tests/data/raw/test04/sensor_wifi_raw.csv b/tests/data/raw/test04/phone_wifi_connected_raw.csv similarity index 100% rename from tests/data/raw/test04/sensor_wifi_raw.csv rename to tests/data/raw/test04/phone_wifi_connected_raw.csv diff --git a/tests/data/raw/test04/wifi_raw.csv b/tests/data/raw/test04/phone_wifi_visible_raw.csv similarity index 100% rename from tests/data/raw/test04/wifi_raw.csv rename to tests/data/raw/test04/phone_wifi_visible_raw.csv diff --git a/tests/data/raw/test05/phone_applications_foreground_raw.csv b/tests/data/raw/test05/phone_applications_foreground_raw.csv new file mode 100644 index 00000000..aed098cc --- /dev/null +++ b/tests/data/raw/test05/phone_applications_foreground_raw.csv @@ -0,0 +1,53 @@ +timestamp,device_id,package_name,application_name,is_system_app +1602475200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,tv.twitch.android.app,Twitch,0 +1602475200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.netflix.mediaclient,Netflix,0 +1602475200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gms,Google,1 +1602475200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.facebook.moments,Facebook Moments,0 +1602475999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,tv.twitch.android.app,Twitch,0 +1602475999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.apps.youtube.creator,Youtube Video Creater,0 +1602476999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.netflix.mediaclient,Netflix,0 +1602476999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.supercell.clashofclans,Clash of Clans,0 +1602476999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.netflix.mediaclient,Netflix,0 +1602476999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.supercell.clashofclans,Clash of Clans,0 +1602477000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,tv.twitch.android.app,Twitch,0 +1602477000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.facebook.moments,Facebook Moments,0 +1602477000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gm,Gmail,0 +1602478000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.facebook.moments,Facebook Moments,0 +1602478000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.fitbit.FitbitMobile,Fitbit,0 +1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gm,Gmail,0 +1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.netflix.mediaclient,Netflix,0 +1602478000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.fitbit.FitbitMobile,Fitbit,0 +1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gm,Gmail,0 +1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.supercell.clashofclans,Clash of Clans,0 +1602478799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.netflix.mediaclient,Netflix,0 +1602478800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gm,Gmail,0 +1602478800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.fitbit.FitbitMobile,Fitbit,0 +1602478800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gm,Gmail,0 +1602478800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.fitbit.FitbitMobile,Fitbit,0 +1602480500000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.netflix.mediaclient,Netflix,0 +1602480500000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.supercell.clashofclans,Clash of Clans,0 +1602480500000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.supercell.clashofclans,Clash of Clans,0 +1602558000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.youtube,Youtube,0 +1602558000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.youtube,Youtube,0 +1602558000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.apps.youtube.creator,Youtube Video Creater,0 +1602558799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gms,Google,1 +1602558799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.apps.youtube.creator,Youtube Video Creater,0 +1602558000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.apps.youtube.creator,Youtube Video Creater,0 +1602558799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gms,Google,1 +1602558799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.apps.youtube.creator,Youtube Video Creater,0 +1602559799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.aware.plugin.upmc.cancer,AWARE,0 +1602559799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.apps.youtube.creator,Youtube Video Creater,0 +1602559799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.aware.plugin.upmc.cancer,AWARE,0 +1602559799000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.apps.youtube.creator,Youtube Video Creater,0 +1602559800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,tv.twitch.android.app,Twitch,0 +1602559800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.facebook.moments,Facebook Moments,0 +1602560800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gms,Google,1 +1602560800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.youtube,Youtube,0 +1602560800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gms,Google,1 +1602560800000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.youtube,Youtube,0 +1602561599000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.facebook.moments,Facebook Moments,0 +1602561599000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,tv.twitch.android.app,Twitch,0 +1602561600000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gms,Google,1 +1602561600000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gms,Google,1 +1602563400000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.google.android.gms,Google,1 +1602563500000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,com.facebook.moments,Facebook Moments,0 \ No newline at end of file diff --git a/tests/data/raw/test05/phone_bluetooth_raw.csv b/tests/data/raw/test05/phone_bluetooth_raw.csv new file mode 100644 index 00000000..e0dcd746 --- /dev/null +++ b/tests/data/raw/test05/phone_bluetooth_raw.csv @@ -0,0 +1,15 @@ +timestamp,device_id,bt_address,bt_name,bt_rssi,label +1593684000123,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,A6:93:AF:8C:36,Phone,-92,1593684000123 +1593705599321,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,A6:93:AF:8C:36,Phone,-79,1593684000123 +1593687638456,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,7E:D1:4E:80:2B,,-96,1593684000123 +1593705600654,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,7E:D1:4E:80:2B,,-92,1593705600654 +1593727199546,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,4B:2C:AA:88:1B,Smart TV,-95,1593705600654 +1593727200564,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,F5:F6:EC:8D:6C,Laptop,-88,1593727200564 +1593748799465,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,A6:93:AF:8C:36,Phone,-91,1593727200564 +1593738619645,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,10:6F:61:E4:A9,Speakers,-90,1593727200564 +1593747525789,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,7E:D1:4E:80:2B,,-84,1593727200564 +1593733518987,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,A6:93:AF:8C:36,Phone,-78,1593727200564 +1593662400798,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,7E:D1:4E:80:2B,,-76,1593662400798 +1593683999978,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,F5:F6:EC:8D:6C,Laptop,-97,1593662400798 +1593675299879,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,10:6F:61:E4:A9,Speakers,-93,1593662400798 +1593665344132,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,4B:2C:AA:88:1B,Smart TV,-94,1593662400798 \ No newline at end of file diff --git a/tests/data/raw/test05/phone_calls_raw.csv b/tests/data/raw/test05/phone_calls_raw.csv new file mode 100644 index 00000000..2d7f6a6b --- /dev/null +++ b/tests/data/raw/test05/phone_calls_raw.csv @@ -0,0 +1,37 @@ +timestamp,device_id,call_type,call_duration,trace +1601564701000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,1,1299,OwyykjwekUUwhaCHEODWV5lZICBzdyLPnTE2wVL5 +1601572412000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,3,0,OwyykjwekUUwhaCHEODWV5lZICBzdyLPnTE2wVL5 +1601586664000,tOmCKs02-4GfR-G5I6-7iKL-wYESbVwIMBun,2,1116,ciXg3DYB9bzl0KXxPcvxi50Z4NqSk0WVf7dLniNP 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+timestamp,device_id,message_type,trace \ No newline at end of file diff --git a/tests/data/raw/test06/phone_wifi_connected_raw.csv b/tests/data/raw/test06/phone_wifi_connected_raw.csv new file mode 100644 index 00000000..9e2df5ec --- /dev/null +++ b/tests/data/raw/test06/phone_wifi_connected_raw.csv @@ -0,0 +1,33 @@ +timestamp,device_id,mac_address,ssid,bssid +1602475200000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,HOME-1234,E0:A9:8A:1E:48:1B +1602475999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,HOME-1234,E0:A9:8A:1E:48:1B +1602476999000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Stewart Family,0D:FB:75:AA:43:0A +1602477000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,NETGEAR07,EF:E7:56:4A:F4:1D +1602478000000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,Fios-H4S9a,48:6B:8C:51:08:F2 +1602480500000,wYESbVwI-4GfR-G5I6-7iKL-tOmCKs02MBun,af:13:50:8b:5f:ab,HOME-1234,E0:A9:8A:1E:48:1B 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f6297a9b..9a8958b3 100755 --- a/tests/scripts/run_tests.sh +++ b/tests/scripts/run_tests.sh @@ -3,32 +3,149 @@ echo Setting up for testing... -# Uncomment the section below if neccessary to remove old files when testing locally -# echo deleting old data... -# rm -rf data/raw/* -# rm -rf data/processed/* -# rm -rf data/interim/* -# rm -rf data/external/test* +clean_old_data() { + + echo deleting old data... + rm -rf data/processed/* + rm -rf data/interim/* + + echo Backing up preprocessing... + cp rules/preprocessing.smk bak +} + +run_periodic_pipeline() { + + echo Running RAPIDS Pipeline periodic segment on testdata... + snakemake --profile tests/settings/periodic/ + + echo Moving produced data from previous pipeline run ... + mkdir data/processed/features/periodic + mv data/processed/features/test* data/processed/features/periodic/ + rm -rf data/interim/* +} + +run_frequency_pipeline() { + + echo Running RAPIDS Pipeline frequency segment on testdata... + snakemake --profile tests/settings/frequency/ + + echo Moving produced data from previous pipeline run... + mkdir data/processed/features/frequency + mv data/processed/features/test* data/processed/features/frequency/ +} + +run_periodic_test() { + + echo Re-writing the config file being loaded for testing + sed -e 's/tests\/settings\/[a-z]*\/testing_config\.yaml/tests\/settings\/periodic\/testing_config\.yaml/' tests/scripts/test_sensor_features.py > test_tmp + mv test_tmp tests/scripts/test_sensor_features.py + + echo Running tests on periodic data produced... + python -m unittest discover tests/scripts/ -v +} + +run_frequency_test() { + + echo Re-writing the config file being loaded for testing + sed -e 's/tests\/settings\/[a-z]*\/testing_config\.yaml/tests\/settings\/frequency\/testing_config\.yaml/' tests/scripts/test_sensor_features.py > test_tmp + mv test_tmp tests/scripts/test_sensor_features.py + + echo Running tests on frequency data produced... + python -m unittest discover tests/scripts/ -v +} + +display_usage() { + + echo "Usage: run_test.sh [-l] all | periodic | frequency [test]" +} echo Copying files... cp -r tests/data/raw/* data/raw -cp tests/data/external/* data/external - -# Uncomment the section below to backup snakemake file when testing locally -# echo Backing up preprocessing... -# cp rules/preprocessing.smk bak +cp -r tests/data/external/* data/external echo Disabling downloading of dataset... -sed -e '27,39 s/^/#/' -e 's/rules.download_dataset.output/"data\/raw\/\{pid\}\/\{sensor\}_raw\.csv"/' rules/preprocessing.smk > tmp -cp tmp rules/preprocessing.smk +sed -e '27,53 s/^/#/' -e 's/rules.download_dataset.output/"data\/raw\/\{pid\}\/\{sensor\}_raw\.csv"/' rules/preprocessing.smk > tmp +mv tmp rules/preprocessing.smk -echo Running RAPIDS Pipeline on testdata... -snakemake --profile tests/settings - -echo Running tests on data produced... -python -m unittest discover tests/scripts/ -v - -# Uncomment to return snakemake back to the original version when testing locally -# echo Cleaning up... -# mv bak rules/preprocessing.smk -# rm tmp \ No newline at end of file +if [ $# -eq 1 ] +then + if [ $1 == '-l' ] || [ $1 == 'test' ] + then + display_usage + elif [ $1 == 'all' ] + then + run_periodic_pipeline && run_frequency_pipeline + elif [ $1 == 'periodic' ] + then + run_periodic_pipeline + elif [ $1 == 'frequency' ] + then + run_frequency_pipeline + else + display_usage + fi +elif [ $# -gt 1 ] +then + if [ $1 == '-l' ] + then + clean_old_data + if [ $2 == 'all' ] + then + run_periodic_pipeline && run_frequency_pipeline + if [ $# -gt 2 ] && [ $3 == 'test' ] + then + run_periodic_test + run_frequency_test + fi + elif [ $2 == 'periodic' ] + then + run_periodic_pipeline + if [ $# -gt 2 ] && [ $3 == 'test' ] + then + run_periodic_test + fi + elif [ $2 == 'frequency' ] + then + run_frequency_pipeline + if [ $# -gt 2 ] && [ $3 == 'test' ] + then + run_frequency_test + fi + else + display_usage + fi + mv bak rules/preprocessing.smk + elif [ $1 == 'all' ] + then + run_periodic_pipeline + run_frequency_pipeline + if [ $2 == 'test' ] + then + run_periodic_test && run_frequency_test + else + display_usage + fi + elif [ $1 == 'periodic' ] + then + run_periodic_pipeline + if [ $2 == 'test' ] + then + run_periodic_test + else + display_usage + fi + elif [ $1 == 'frequency' ] + then + run_frequency_pipeline + if [ $2 == 'test' ] + then + run_frequency_test + else + display_usage + fi + else + display_usage + fi +else + display_usage +fi diff --git a/tests/scripts/test_sensor_features.py b/tests/scripts/test_sensor_features.py index 6219437a..1354cd37 100644 --- a/tests/scripts/test_sensor_features.py +++ b/tests/scripts/test_sensor_features.py @@ -1,8 +1,10 @@ +from snakemake.io import expand import unittest import hashlib import pandas as pd import utils import yaml +import sys import os class TestSensorFeatures(unittest.TestCase): @@ -14,23 +16,26 @@ class TestSensorFeatures(unittest.TestCase): def setUpClass(cls): # Runs once to Setup env global configs - with open(r'tests/settings/testing_config.yaml') as file: + with open(r'tests/settings/frequency/testing_config.yaml') as file: configs = yaml.full_load(file) def test_sensors_files_exist(self): # Loop through the file_list dictionary and check if the files exist. - + + #print("Testing existance of files") file_lists = utils.generate_sensor_file_lists(configs) for each in file_lists: - for out_file, _ in file_lists[each]: - self.assertEqual(os.path.exists(out_file), 1) + #for out_file, _ in file_lists[each]: + self.assertEqual(os.path.exists( each[0]), 1) def test_sensors_features_calculations(self): - calc_files = utils.generate_sensor_file_lists(configs) - for each in calc_files: - for act_result, exp_result in calc_files[each]: + + # print("Testing calculations..") + sensor_file_list = utils.generate_sensor_file_lists(configs) + for each in sensor_file_list: + for act_result, exp_result in sensor_file_list: df_act = pd.read_csv(act_result) df_exp = pd.read_csv(exp_result) if df_act.empty: @@ -45,4 +50,5 @@ class TestSensorFeatures(unittest.TestCase): if __name__ == '__main__': - unittest.main() \ No newline at end of file + unittest.main() + diff --git a/tests/scripts/utils.py b/tests/scripts/utils.py index e2b0c7c4..e5db9cda 100644 --- a/tests/scripts/utils.py +++ b/tests/scripts/utils.py @@ -49,11 +49,11 @@ def generate_file_list(configs, sensor): # i.e. The sensor passed into the function. # Initialize string of file path for both expected and actual metric values - act_str = "data/processed/{pid}/{sensor}_{sensor_type}{day_segment}.csv" - exp_str = "tests/data/processed/{pid}/{sensor}_{sensor_type}{day_segment}.csv" + act_str = "data/processed/features/{pid}/{sensor}_{sensor_type}{time_segment}.csv" + exp_str = "tests/data/processed/features/period/{pid}/{sensor}_{sensor_type}{time_segment}.csv" sensor_cap = sensor.upper() - if 'DAY_SEGMENTS' and 'FEATURES' in configs[sensor_cap]: + if 'TIME_SEGMENTS' and 'FEATURES' in configs[sensor_cap]: sensor_type = [] if 'TYPES' in configs[sensor_cap]: for each in configs[sensor_cap]['TYPES']: @@ -62,64 +62,42 @@ def generate_file_list(configs, sensor): act_file_list = expand(act_str,pid=configs["PIDS"], sensor = sensor, sensor_type = sensor_type, - day_segment = configs[sensor_cap]["DAY_SEGMENTS"]) + time_segment = configs[sensor_cap]["TIME_SEGMENTS"]) exp_file_list = expand(exp_str,pid=configs["PIDS"], sensor = sensor, sensor_type = sensor_type, - day_segment = configs[sensor_cap]["DAY_SEGMENTS"]) + time_segment = configs[sensor_cap]["TIME_SEGMENTS"]) return zip(act_file_list, exp_file_list) -def generate_sensor_file_lists(config): +def generate_sensor_file_lists(configs): # Go through the configs and select those sensors with COMPUTE = True. - # Also get DAY_SEGMENTS, and optionally TYPES then create expected + # Also get TIME_SEGMENTS, and optionally TYPES then create expected # files. Return dictionary with list of file paths of expected and # actual files for each sensor listed in the config file. Added for Travis. # Initialize string of file path for both expected and actual metric values - act_str = "data/processed/{pid}/{sensor}_{sensor_type}{day_segment}.csv" - exp_str = "tests/data/processed/{pid}/{sensor}_{sensor_type}{day_segment}.csv" + segment = configs['TIME_SEGMENTS']['TYPE'].lower() + print(segment) + act_str = "data/processed/features/"+segment+"/{pid}/{sensor_key}.csv" + exp_str = "tests/data/processed/features/"+segment+"/{pid}/{sensor_key}.csv" # List of available sensors that can be tested by the testing suite - TESTABLE_SENSORS = ['MESSAGES', 'CALLS', 'SCREEN', 'BATTERY', 'BLUETOOTH', 'WIFI', 'LIGHT', 'APPLICATIONS_FOREGROUND', 'ACTIVITY_RECOGNITION', 'CONVERSATION'] + TESTABLE_SENSORS = ['PHONE_MESSAGES', 'PHONE_CALLS', 'PHONE_SCREEN', 'PHONE_BATTERY', 'PHONE_BLUETOOTH', 'PHONE_WIFI_VISIBLE', 'PHONE_WIFI_CONNECTED', 'PHONE_LIGHT', 'PHONE_APPLICATIONS_FOREGROUND', 'PHONE_ACTIVITY_RECOGNITION', 'PHONE_CONVERSATION'] # Build list of sensors to be tested. sensors = [] for sensor in TESTABLE_SENSORS: - if config[sensor]["COMPUTE"] == True: - sensors.append(sensor) + if sensor in configs.keys(): + for provider in configs[sensor]["PROVIDERS"]: + if configs[sensor]["PROVIDERS"][provider]["COMPUTE"]: + sensors.append(sensor.lower()) - sensor_file_lists = {} - - # Loop though all sensors and create the actual and expected file paths - for sensor in sensors: - if 'DAY_SEGMENTS' in config[sensor]: - sensor_type = [] - if 'TYPES' in config[sensor]: - for each in config[sensor]['TYPES']: - sensor_type.append(each+'_') - lower_sensor = sensor.lower() - if sensor_type: - act_file_list = expand(act_str, pid=config["PIDS"], - sensor = lower_sensor, - sensor_type = sensor_type, - day_segment = config[sensor]["DAY_SEGMENTS"]) - exp_file_list = expand(exp_str, pid=config["PIDS"], - sensor = lower_sensor, - sensor_type = sensor_type, - day_segment = config[sensor]["DAY_SEGMENTS"]) - else: - act_file_list = expand(act_str, pid=config["PIDS"], - sensor = lower_sensor, - sensor_type = '', - day_segment = config[sensor]["DAY_SEGMENTS"]) - exp_file_list = expand(exp_str, pid=config["PIDS"], - sensor = lower_sensor, - sensor_type = '', - day_segment = config[sensor]["DAY_SEGMENTS"]) - - sensor_file_lists[sensor] = list(zip(act_file_list,exp_file_list)) + act_file_list = expand(act_str,pid=configs["PIDS"],sensor_key = sensors) + exp_file_list = expand(exp_str, pid=configs["PIDS"],sensor_key = sensors) + sensor_file_lists = list(zip(act_file_list,exp_file_list)) + #sensor_file_lists[sensor] = list(zip(act_file_list,exp_file_list)) return sensor_file_lists \ No newline at end of file diff --git a/tests/settings/config.yaml b/tests/settings/config.yaml deleted file mode 100644 index ab817e0b..00000000 --- a/tests/settings/config.yaml +++ /dev/null @@ -1,5 +0,0 @@ -directory: ./ -configfile: ./tests/settings/testing_config.yaml -snakefile: ./tests/Snakefile -cores: 1 -forcerun: [messages_features, call_features, bluetooth_features, activity_features, battery_features, screen_features, light_features, applications_foreground_features, wifi_features, conversation_features] \ No newline at end of file diff --git a/tests/settings/frequency/config.yaml b/tests/settings/frequency/config.yaml new file mode 100644 index 00000000..ca2ae708 --- /dev/null +++ b/tests/settings/frequency/config.yaml @@ -0,0 +1,5 @@ +directory: ./ +configfile: ./tests/settings/frequency/testing_config.yaml +snakefile: ./tests/Snakefile +cores: 1 +forcerun: [compute_time_segments, join_features_from_providers] \ No newline at end of file diff --git a/tests/settings/frequency/testing_config.yaml b/tests/settings/frequency/testing_config.yaml new file mode 100644 index 00000000..54d2e379 --- /dev/null +++ b/tests/settings/frequency/testing_config.yaml @@ -0,0 +1,353 @@ +# Participants to include in the analysis +# You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically +PIDS: [test03, test04,test05, test06] + +# Global var with common time segments +TIME_SEGMENTS: &time_segments + TYPE: FREQUENCY # FREQUENCY, PERIODIC, EVENT + FILE: "data/external/timesegments_frequency.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, if set to TRUE we consider time segments back enough in the past as to include the first day of data + +# Use tz codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones. Double check your code, for example EST is not US Eastern Time. +TIMEZONE: &timezone + America/New_York + +DATABASE_GROUP: &database_group + MY_GROUP + +# config section for the script that creates participant files automatically +PARTICIPANT_FILES: # run snakemake -j1 -R parse_participant_files + PHONE_SECTION: + ADD: FALSE + PARSED_FROM: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE + PARSED_SOURCE: *database_group # DB credentials group or CSV file path. If CSV file, it should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) + IGNORED_DEVICE_IDS: [] + FITBIT_SECTION: + ADD: FALSE + SAME_AS_PHONE: FALSE # If TRUE, all config below is ignored + PARSED_FROM: CSV_FILE + PARSED_SOURCE: "external/my_fitbit_participants.csv" # CSV file should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) + +DEVICE_DATA: + PHONE: + SOURCE: + TYPE: DATABASE # Phone only supports DATABASE for now + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # SINGLE or MULTIPLE + VALUE: *timezone # IF TYPE=SINGLE, timezone code (e.g. America/New_York, see attribute TIMEZONE above). If TYPE=MULTIPLE, a table in your database with two columns (timestamp, timezone) where timestamp is a unix timestamp and timezone is one of https://en.wikipedia.org/wiki/List_of_tz_database_time_zones + FITBIT: + SOURCE: + TYPE: DATABASE # DATABASE or FILES (set each FITBIT_SENSOR TABLE attribute accordingly with a table name or a file path) + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # Fitbit only supports SINGLE timezones + VALUE: *timezone # timezone code (e.g. America/New_York, see attribute TIMEZONE above and https://en.wikipedia.org/wiki/List_of_tz_database_time_zones) + +PHONE_VALID_SENSED_BINS: + COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features + BIN_SIZE: &bin_size 5 # (in minutes) + # Add as many PHONE sensors as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS. + # If you are extracting screen or Barnett/Doryab location features, PHONE_SCREEN and PHONE_LOCATIONS tables are mandatory. + # You can choose any of the keys shown below, just make sure its TABLE exists in your database! + # PHONE_MESSAGES, PHONE_CALLS, PHONE_LOCATIONS, PHONE_BLUETOOTH, PHONE_ACTIVITY_RECOGNITION, PHONE_BATTERY, PHONE_SCREEN, PHONE_LIGHT, + # PHONE_ACCELEROMETER, PHONE_APPLICATIONS_FOREGROUND, PHONE_WIFI_VISIBLE, PHONE_WIFI_CONNECTED, PHONE_CONVERSATION + PHONE_SENSORS: [] + +PHONE_VALID_SENSED_DAYS: + COMPUTE: False + MIN_VALID_HOURS_PER_DAY: &min_valid_hours_per_day [16] # (out of 24) MIN_HOURS_PER_DAY + MIN_VALID_BINS_PER_HOUR: &min_valid_bins_per_hour [6] # (out of 60min/BIN_SIZE bins) + +# Communication SMS features config, TYPES and FEATURES keys need to match +PHONE_MESSAGES: + TABLE: messages + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + MESSAGES_TYPES : [received, sent] + FEATURES: + received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/phone_messages + +# Communication call features config, TYPES and FEATURES keys need to match +PHONE_CALLS: + TABLE: calls + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + CALL_TYPES: [missed, incoming, outgoing] + FEATURES: + missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] + incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] + outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/phone_calls + +PHONE_LOCATIONS: + TABLE: locations + LOCATIONS_TO_USE: FUSED_RESAMPLED # ALL, GPS OR FUSED_RESAMPLED + FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold + FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row + PROVIDERS: + DORYAB: + COMPUTE: False + FEATURES: ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy"] + DBSCAN_EPS: 10 # meters + DBSCAN_MINSAMPLES: 5 + THRESHOLD_STATIC : 1 # km/h + MAXIMUM_GAP_ALLOWED: 300 + MINUTES_DATA_USED: False + SAMPLING_FREQUENCY: 0 + SRC_FOLDER: "doryab" # inside src/features/phone_locations + SRC_LANGUAGE: "python" + + BARNETT: + COMPUTE: False + FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"] + ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius + TIMEZONE: *timezone + MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features + SRC_FOLDER: "barnett" # inside src/features/phone_locations + SRC_LANGUAGE: "r" + +PHONE_BLUETOOTH: + TABLE: bluetooth + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth + SRC_LANGUAGE: "r" + + +PHONE_ACTIVITY_RECOGNITION: + TABLE: + ANDROID: plugin_google_activity_recognition + IOS: plugin_ios_activity_recognition + EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"] + ACTIVITY_CLASSES: + STATIONARY: ["still", "tilting"] + MOBILE: ["on_foot", "walking", "running", "on_bicycle"] + VEHICLE: ["in_vehicle"] + SRC_FOLDER: "rapids" # inside src/features/phone_activity_recognition + SRC_LANGUAGE: "python" + +PHONE_BATTERY: + TABLE: battery + EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] + SRC_FOLDER: "rapids" # inside src/features/phone_battery + SRC_LANGUAGE: "python" + +PHONE_SCREEN: + TABLE: screen + PROVIDERS: + RAPIDS: + COMPUTE: False + REFERENCE_HOUR_FIRST_USE: 0 + IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable + IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable + FEATURES: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] # "episodepersensedminutes" needs to be added later + EPISODE_TYPES: ["unlock"] + SRC_FOLDER: "rapids" # inside src/features/phone_screen + SRC_LANGUAGE: "python" + +PHONE_LIGHT: + TABLE: light + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] + SRC_FOLDER: "rapids" # inside src/features/phone_light + SRC_LANGUAGE: "python" + +PHONE_ACCELEROMETER: + TABLE: accelerometer + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] + SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" + + PANDA: + COMPUTE: False + VALID_SENSED_MINUTES: False + FEATURES: + exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + SRC_FOLDER: "panda" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" + +PHONE_APPLICATIONS_FOREGROUND: + TABLE: applications_foreground + APPLICATION_CATEGORIES: + CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) + CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" + UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE + SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + SINGLE_CATEGORIES: ["all", "email"] + MULTIPLE_CATEGORIES: + social: ["socialnetworks", "socialmediatools"] + entertainment: ["entertainment", "gamingstrategy"] + SINGLE_APPS: ["top1global", "com.facebook.moments"] # There's no entropy for single apps + EXCLUDED_CATEGORIES: ["systemapp", "tvvideoapps"] + EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] + FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] + SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground + SRC_LANGUAGE: "python" + +PHONE_WIFI_VISIBLE: + TABLE: "wifi" + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible + SRC_LANGUAGE: "r" + +PHONE_WIFI_CONNECTED: + TABLE: "sensor_wifi" + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected + SRC_LANGUAGE: "r" + +PHONE_CONVERSATION: + TABLE: + ANDROID: plugin_studentlife_audio_android + IOS: plugin_studentlife_audio + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", + "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", + "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", + "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", + "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", + "unknownexpectedfraction","countconversation"] + RECORDING_MINUTES: 1 + PAUSED_MINUTES : 3 + SRC_FOLDER: "rapids" # inside src/features/phone_conversation + SRC_LANGUAGE: "python" + +############## FITBIT ########################################################## +################################################################################ + +FITBIT_HEARTRATE: + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_heartrate + CSV: + SUMMARY: heartrate_summary.csv + INTRADAY: heartrate_intraday.csv + PROVIDERS: + RAPIDS: + COMPUTE: False + SUMMARY_FEATURES: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] + INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] + + +FITBIT_STEPS: + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_steps + CSV: + SUMMARY: steps_summary.csv + INTRADAY: steps_intraday.csv + EXCLUDE_SLEEP: # you can exclude sleep periods from the step features computation + EXCLUDE: False + TYPE: FIXED # FIXED OR FITBIT_BASED (configure FITBIT_SLEEP section) + FIXED: + START: "23:00" + END: "07:00" + + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: + ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] + SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + THRESHOLD_ACTIVE_BOUT: 10 # steps + INCLUDE_ZERO_STEP_ROWS: False + +FITBIT_SLEEP: + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_sleep + CSV: + SUMMARY: sleep_summary.csv + INTRADAY: sleep_intraday.csv + PROVIDERS: + RAPIDS: + COMPUTE: False + SLEEP_TYPES: ["main", "nap", "all"] + SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] + +FITBIT_CALORIES: + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_calories + CSV: + SUMMARY: calories_summary.csv + INTRADAY: calories_intraday.csv + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: [] + +### Visualizations ############################################################# +################################################################################ + +HEATMAP_FEATURES_CORRELATIONS: + PLOT: False + MIN_ROWS_RATIO: 0.5 + MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + PHONE_FEATURES: [accelerometer, activity_recognition, applications_foreground, battery, calls_incoming, calls_missed, calls_outgoing, conversation, light, location_doryab, messages_received, messages_sent, screen] + FITBIT_FEATURES: [fitbit_heartrate, fitbit_step, fitbit_sleep] + CORR_THRESHOLD: 0.1 + CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"} + +HISTOGRAM_VALID_SENSED_HOURS: + PLOT: False + MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + +HEATMAP_DAYS_BY_SENSORS: + PLOT: False + MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + EXPECTED_NUM_OF_DAYS: -1 + DB_TABLES: [accelerometer, applications_foreground, battery, bluetooth, calls, light, locations, messages, screen, wifi, sensor_wifi, plugin_google_activity_recognition, plugin_ios_activity_recognition, plugin_studentlife_audio_android, plugin_studentlife_audio] + +HEATMAP_SENSED_BINS: + PLOT: False + BIN_SIZE: *bin_size + +OVERALL_COMPLIANCE_HEATMAP: + PLOT: False + ONLY_SHOW_VALID_DAYS: False + EXPECTED_NUM_OF_DAYS: -1 + BIN_SIZE: *bin_size + MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + diff --git a/tests/settings/periodic/config.yaml b/tests/settings/periodic/config.yaml new file mode 100644 index 00000000..739120f0 --- /dev/null +++ b/tests/settings/periodic/config.yaml @@ -0,0 +1,5 @@ +directory: ./ +configfile: ./tests/settings/periodic/testing_config.yaml +snakefile: ./tests/Snakefile +cores: 1 +forcerun: [compute_time_segments, join_features_from_providers] \ No newline at end of file diff --git a/tests/settings/periodic/testing_config.yaml b/tests/settings/periodic/testing_config.yaml new file mode 100644 index 00000000..5fe9c839 --- /dev/null +++ b/tests/settings/periodic/testing_config.yaml @@ -0,0 +1,353 @@ +# Participants to include in the analysis +# You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically +PIDS: [test01, test02, test03, test04] + +# Global var with common time segments +TIME_SEGMENTS: &time_segments + TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT + FILE: "data/external/timesegments_periodic.csv" + INCLUDE_PAST_PERIODIC_SEGMENTS: FALSE # Only relevant if TYPE=PERIODIC, if set to TRUE we consider time segments back enough in the past as to include the first day of data + +# Use tz codes from https://en.wikipedia.org/wiki/List_of_tz_database_time_zones. Double check your code, for example EST is not US Eastern Time. +TIMEZONE: &timezone + America/New_York + +DATABASE_GROUP: &database_group + MY_GROUP + +# config section for the script that creates participant files automatically +PARTICIPANT_FILES: # run snakemake -j1 -R parse_participant_files + PHONE_SECTION: + ADD: FALSE + PARSED_FROM: AWARE_DEVICE_TABLE #AWARE_DEVICE_TABLE or CSV_FILE + PARSED_SOURCE: *database_group # DB credentials group or CSV file path. If CSV file, it should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) + IGNORED_DEVICE_IDS: [] + FITBIT_SECTION: + ADD: FALSE + SAME_AS_PHONE: FALSE # If TRUE, all config below is ignored + PARSED_FROM: CSV_FILE + PARSED_SOURCE: "external/my_fitbit_participants.csv" # CSV file should have: device_id, pid (optional), label (optional), start_date (optional), end_date (optional) + +DEVICE_DATA: + PHONE: + SOURCE: + TYPE: DATABASE # Phone only supports DATABASE for now + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # SINGLE or MULTIPLE + VALUE: *timezone # IF TYPE=SINGLE, timezone code (e.g. America/New_York, see attribute TIMEZONE above). If TYPE=MULTIPLE, a table in your database with two columns (timestamp, timezone) where timestamp is a unix timestamp and timezone is one of https://en.wikipedia.org/wiki/List_of_tz_database_time_zones + FITBIT: + SOURCE: + TYPE: DATABASE # DATABASE or FILES (set each FITBIT_SENSOR TABLE attribute accordingly with a table name or a file path) + DATABASE_GROUP: *database_group + DEVICE_ID_COLUMN: device_id # column name + TIMEZONE: + TYPE: SINGLE # Fitbit only supports SINGLE timezones + VALUE: *timezone # timezone code (e.g. America/New_York, see attribute TIMEZONE above and https://en.wikipedia.org/wiki/List_of_tz_database_time_zones) + +PHONE_VALID_SENSED_BINS: + COMPUTE: False # This flag is automatically ignored (set to True) if you are extracting PHONE_VALID_SENSED_DAYS or screen or Barnett's location features + BIN_SIZE: &bin_size 5 # (in minutes) + # Add as many PHONE sensors as you have, they all improve the computation of PHONE_VALID_SENSED_BINS and PHONE_VALID_SENSED_DAYS. + # If you are extracting screen or Barnett/Doryab location features, PHONE_SCREEN and PHONE_LOCATIONS tables are mandatory. + # You can choose any of the keys shown below, just make sure its TABLE exists in your database! + # PHONE_MESSAGES, PHONE_CALLS, PHONE_LOCATIONS, PHONE_BLUETOOTH, PHONE_ACTIVITY_RECOGNITION, PHONE_BATTERY, PHONE_SCREEN, PHONE_LIGHT, + # PHONE_ACCELEROMETER, PHONE_APPLICATIONS_FOREGROUND, PHONE_WIFI_VISIBLE, PHONE_WIFI_CONNECTED, PHONE_CONVERSATION + PHONE_SENSORS: [] + +PHONE_VALID_SENSED_DAYS: + COMPUTE: False + MIN_VALID_HOURS_PER_DAY: &min_valid_hours_per_day [16] # (out of 24) MIN_HOURS_PER_DAY + MIN_VALID_BINS_PER_HOUR: &min_valid_bins_per_hour [6] # (out of 60min/BIN_SIZE bins) + +# Communication SMS features config, TYPES and FEATURES keys need to match +PHONE_MESSAGES: + TABLE: messages + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + MESSAGES_TYPES : [received, sent] + FEATURES: + received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/phone_messages + +# Communication call features config, TYPES and FEATURES keys need to match +PHONE_CALLS: + TABLE: calls + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + CALL_TYPES: [missed, incoming, outgoing] + FEATURES: + missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] + incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] + outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] + SRC_LANGUAGE: "r" + SRC_FOLDER: "rapids" # inside src/features/phone_calls + +PHONE_LOCATIONS: + TABLE: locations + LOCATIONS_TO_USE: FUSED_RESAMPLED # ALL, GPS OR FUSED_RESAMPLED + FUSED_RESAMPLED_CONSECUTIVE_THRESHOLD: 30 # minutes, only replicate location samples to the next sensed bin if the phone did not stop collecting data for more than this threshold + FUSED_RESAMPLED_TIME_SINCE_VALID_LOCATION: 720 # minutes, only replicate location samples to consecutive sensed bins if they were logged within this threshold after a valid location row + PROVIDERS: + DORYAB: + COMPUTE: False + FEATURES: ["locationvariance","loglocationvariance","totaldistance","averagespeed","varspeed","circadianmovement","numberofsignificantplaces","numberlocationtransitions","radiusgyration","timeattop1location","timeattop2location","timeattop3location","movingtostaticratio","outlierstimepercent","maxlengthstayatclusters","minlengthstayatclusters","meanlengthstayatclusters","stdlengthstayatclusters","locationentropy","normalizedlocationentropy"] + DBSCAN_EPS: 10 # meters + DBSCAN_MINSAMPLES: 5 + THRESHOLD_STATIC : 1 # km/h + MAXIMUM_GAP_ALLOWED: 300 + MINUTES_DATA_USED: False + SAMPLING_FREQUENCY: 0 + SRC_FOLDER: "doryab" # inside src/features/phone_locations + SRC_LANGUAGE: "python" + + BARNETT: + COMPUTE: False + FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"] + ACCURACY_LIMIT: 51 # meters, drops location coordinates with an accuracy higher than this. This number means there's a 68% probability the true location is within this radius + TIMEZONE: *timezone + MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features + SRC_FOLDER: "barnett" # inside src/features/phone_locations + SRC_LANGUAGE: "r" + +PHONE_BLUETOOTH: + TABLE: bluetooth + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_bluetooth + SRC_LANGUAGE: "r" + + +PHONE_ACTIVITY_RECOGNITION: + TABLE: + ANDROID: plugin_google_activity_recognition + IOS: plugin_ios_activity_recognition + EPISODE_THRESHOLD_BETWEEN_ROWS: 5 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["count", "mostcommonactivity", "countuniqueactivities", "durationstationary", "durationmobile", "durationvehicle"] + ACTIVITY_CLASSES: + STATIONARY: ["still", "tilting"] + MOBILE: ["on_foot", "walking", "running", "on_bicycle"] + VEHICLE: ["in_vehicle"] + SRC_FOLDER: "rapids" # inside src/features/phone_activity_recognition + SRC_LANGUAGE: "python" + +PHONE_BATTERY: + TABLE: battery + EPISODE_THRESHOLD_BETWEEN_ROWS: 30 # minutes. Max time difference for two consecutive rows to be considered within the same battery episode. + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] + SRC_FOLDER: "rapids" # inside src/features/phone_battery + SRC_LANGUAGE: "python" + +PHONE_SCREEN: + TABLE: screen + PROVIDERS: + RAPIDS: + COMPUTE: False + REFERENCE_HOUR_FIRST_USE: 0 + IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable + IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable + FEATURES: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] # "episodepersensedminutes" needs to be added later + EPISODE_TYPES: ["unlock"] + SRC_FOLDER: "rapids" # inside src/features/phone_screen + SRC_LANGUAGE: "python" + +PHONE_LIGHT: + TABLE: light + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] + SRC_FOLDER: "rapids" # inside src/features/phone_light + SRC_LANGUAGE: "python" + +PHONE_ACCELEROMETER: + TABLE: accelerometer + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"] + SRC_FOLDER: "rapids" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" + + PANDA: + COMPUTE: False + VALID_SENSED_MINUTES: False + FEATURES: + exertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + nonexertional_activity_episode: ["sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"] + SRC_FOLDER: "panda" # inside src/features/phone_accelerometer + SRC_LANGUAGE: "python" + +PHONE_APPLICATIONS_FOREGROUND: + TABLE: applications_foreground + APPLICATION_CATEGORIES: + CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store) + CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv" + UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE + SCRAPE_MISSING_CATEGORIES: False # whether or not to scrape missing genres, only effective if CATALOGUE_SOURCE is equal to FILE. If CATALOGUE_SOURCE is equal to GOOGLE, all genres are scraped anyway + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + SINGLE_CATEGORIES: ["all", "email"] + MULTIPLE_CATEGORIES: + social: ["socialnetworks", "socialmediatools"] + entertainment: ["entertainment", "gamingstrategy"] + SINGLE_APPS: ["top1global", "com.facebook.moments"] # There's no entropy for single apps + EXCLUDED_CATEGORIES: ["systemapp", "tvvideoapps"] + EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] + FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] + SRC_FOLDER: "rapids" # inside src/features/phone_applications_foreground + SRC_LANGUAGE: "python" + +PHONE_WIFI_VISIBLE: + TABLE: "wifi" + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_wifi_visible + SRC_LANGUAGE: "r" + +PHONE_WIFI_CONNECTED: + TABLE: "sensor_wifi" + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] + SRC_FOLDER: "rapids" # inside src/features/phone_wifi_connected + SRC_LANGUAGE: "r" + +PHONE_CONVERSATION: + TABLE: + ANDROID: plugin_studentlife_audio_android + IOS: plugin_studentlife_audio + PROVIDERS: + RAPIDS: + COMPUTE: TRUE + FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", + "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", + "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", + "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", + "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", + "unknownexpectedfraction","countconversation"] + RECORDING_MINUTES: 1 + PAUSED_MINUTES : 3 + SRC_FOLDER: "rapids" # inside src/features/phone_conversation + SRC_LANGUAGE: "python" + +############## FITBIT ########################################################## +################################################################################ + +FITBIT_HEARTRATE: + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_heartrate + CSV: + SUMMARY: heartrate_summary.csv + INTRADAY: heartrate_intraday.csv + PROVIDERS: + RAPIDS: + COMPUTE: False + SUMMARY_FEATURES: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"] + INTRADAY_FEATURES: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] + + +FITBIT_STEPS: + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_steps + CSV: + SUMMARY: steps_summary.csv + INTRADAY: steps_intraday.csv + EXCLUDE_SLEEP: # you can exclude sleep periods from the step features computation + EXCLUDE: False + TYPE: FIXED # FIXED OR FITBIT_BASED (configure FITBIT_SLEEP section) + FIXED: + START: "23:00" + END: "07:00" + + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: + ALL_STEPS: ["sumallsteps", "maxallsteps", "minallsteps", "avgallsteps", "stdallsteps"] + SEDENTARY_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + ACTIVE_BOUT: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"] + THRESHOLD_ACTIVE_BOUT: 10 # steps + INCLUDE_ZERO_STEP_ROWS: False + +FITBIT_SLEEP: + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_sleep + CSV: + SUMMARY: sleep_summary.csv + INTRADAY: sleep_intraday.csv + PROVIDERS: + RAPIDS: + COMPUTE: False + SLEEP_TYPES: ["main", "nap", "all"] + SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"] + +FITBIT_CALORIES: + TABLE_FORMAT: JSON # JSON or CSV + TABLE: + JSON: fitbit_calories + CSV: + SUMMARY: calories_summary.csv + INTRADAY: calories_intraday.csv + PROVIDERS: + RAPIDS: + COMPUTE: False + FEATURES: [] + +### Visualizations ############################################################# +################################################################################ + +HEATMAP_FEATURES_CORRELATIONS: + PLOT: False + MIN_ROWS_RATIO: 0.5 + MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + PHONE_FEATURES: [accelerometer, activity_recognition, applications_foreground, battery, calls_incoming, calls_missed, calls_outgoing, conversation, light, location_doryab, messages_received, messages_sent, screen] + FITBIT_FEATURES: [fitbit_heartrate, fitbit_step, fitbit_sleep] + CORR_THRESHOLD: 0.1 + CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"} + +HISTOGRAM_VALID_SENSED_HOURS: + PLOT: False + MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + +HEATMAP_DAYS_BY_SENSORS: + PLOT: False + MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + EXPECTED_NUM_OF_DAYS: -1 + DB_TABLES: [accelerometer, applications_foreground, battery, bluetooth, calls, light, locations, messages, screen, wifi, sensor_wifi, plugin_google_activity_recognition, plugin_ios_activity_recognition, plugin_studentlife_audio_android, plugin_studentlife_audio] + +HEATMAP_SENSED_BINS: + PLOT: False + BIN_SIZE: *bin_size + +OVERALL_COMPLIANCE_HEATMAP: + PLOT: False + ONLY_SHOW_VALID_DAYS: False + EXPECTED_NUM_OF_DAYS: -1 + BIN_SIZE: *bin_size + MIN_VALID_HOURS_PER_DAY: *min_valid_hours_per_day + MIN_VALID_BINS_PER_HOUR: *min_valid_bins_per_hour + diff --git a/tests/settings/testing_config.yaml b/tests/settings/testing_config.yaml deleted file mode 100644 index 4e03d9c4..00000000 --- a/tests/settings/testing_config.yaml +++ /dev/null @@ -1,104 +0,0 @@ -# Participants to include in the analysis -# You must create a file for each participant named pXXX containing their device_id. This can be done manually or automatically -# PIDS: [test01, test02, test03, test04] -PIDS: [test01, test02, test03, test04] - -# Global var with common day segments -DAY_SEGMENTS: &day_segments - [daily, morning, afternoon, evening, night] - -PHONE_VALID_SENSED_BINS: - DB_TABLES: [messages, calls, screen, battery, bluetooth, wifi, light, applications_foreground] - -# Communication SMS features config, TYPES and FEATURES keys need to match -MESSAGES: - COMPUTE: True - DB_TABLE: messages - TYPES : [received, sent] - FEATURES: - received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] - sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact] - DAY_SEGMENTS: *day_segments - -# Communication call features config, TYPES and FEATURES keys need to match -CALLS: - COMPUTE: True - DB_TABLE: calls - TYPES: [missed, incoming, outgoing] - FEATURES: - missed: [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact] - incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] - outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact] - DAY_SEGMENTS: *day_segments - -BLUETOOTH: - COMPUTE: True - DB_TABLE: bluetooth - DAY_SEGMENTS: *day_segments - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - -ACTIVITY_RECOGNITION: - COMPUTE: True - DB_TABLE: - ANDROID: plugin_google_activity_recognition - IOS: plugin_ios_activity_recognition - DAY_SEGMENTS: *day_segments - FEATURES: ["count","mostcommonactivity","countuniqueactivities","activitychangecount","sumstationary","summobile","sumvehicle"] - -BATTERY: - COMPUTE: True - DB_TABLE: battery - DAY_SEGMENTS: *day_segments - FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"] - -SCREEN: - COMPUTE: True - DB_TABLE: screen - DAY_SEGMENTS: *day_segments - REFERENCE_HOUR_FIRST_USE: 0 - IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable - IGNORE_EPISODES_LONGER_THAN: 0 # in minutes, set to 0 to disable - FEATURES_DELTAS: ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] - EPISODE_TYPES: ["unlock"] - -LIGHT: - COMPUTE: True - DB_TABLE: light - DAY_SEGMENTS: *day_segments - FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"] - -APPLICATIONS_FOREGROUND: - COMPUTE: True - DB_TABLE: applications_foreground - DAY_SEGMENTS: *day_segments - SINGLE_CATEGORIES: ["all", "email"] - MULTIPLE_CATEGORIES: - social: ["socialnetworks", "socialmediatools"] - entertainment: ["entertainment", "gamingstrategy"] - SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube"] # There's no entropy for single apps - EXCLUDED_CATEGORIES: ["systemapp", "tvvideoapps"] - EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] - FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"] - -WIFI: - COMPUTE: True - DB_TABLE: - VISIBLE_ACCESS_POINTS: "wifi" # if you only have a CONNECTED_ACCESS_POINTS table, set this value to "" - CONNECTED_ACCESS_POINTS: "sensor_wifi" # if you only have a VISIBLE_ACCESS_POINTS table, set this value to "" - DAY_SEGMENTS: *day_segments - FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"] - -CONVERSATION: - COMPUTE: True - DB_TABLE: - ANDROID: plugin_studentlife_audio_android - IOS: plugin_studentlife_audio - DAY_SEGMENTS: *day_segments - FEATURES: ["minutessilence", "minutesnoise", "minutesvoice", "minutesunknown","sumconversationduration","avgconversationduration", - "sdconversationduration","minconversationduration","maxconversationduration","timefirstconversation","timelastconversation","noisesumenergy", - "noiseavgenergy","noisesdenergy","noiseminenergy","noisemaxenergy","voicesumenergy", - "voiceavgenergy","voicesdenergy","voiceminenergy","voicemaxenergy","silencesensedfraction","noisesensedfraction", - "voicesensedfraction","unknownsensedfraction","silenceexpectedfraction","noiseexpectedfraction","voiceexpectedfraction", - "unknownexpectedfraction","countconversation"] - RECORDINGMINUTES: 1 - PAUSEDMINUTES : 3 \ No newline at end of file diff --git a/tools/update_format_participant_files.py b/tools/update_format_participant_files.py new file mode 100644 index 00000000..3c6a0934 --- /dev/null +++ b/tools/update_format_participant_files.py @@ -0,0 +1,37 @@ +#!/usr/bin/python + +from pathlib import Path +import yaml, os +import sys +p = Path(r'data/external/').glob('*') +files = [x for x in p if x.is_file() and x.suffix == "" and "." not in x.stem] +for file in files: + reader = open(file, 'r') + phone = {"DEVICES_IDS" :"", "PLATFORMS" :"", "LABEL" :"", "START_DATE" :"", "END_DATE" :""} + lines = reader.read().splitlines() + if(len(lines) >=1 and len(lines[0]) > 0): + phone["DEVICE_IDS"] = lines[0] + if(len(lines) >=2 and len(lines[1]) > 0): + phone["PLATFORMS"] = lines[1] + if(len(lines) >=3 and len(lines[2]) > 0): + phone["LABEL"] = lines[2] + if(len(lines) >=4 and len(lines[3]) > 0): + phone["START_DATE"] = lines[3].split(",")[0] + phone["END_DATE"] = lines[3].split(",")[1] + new_participant_file = Path(r'data/external/participant_files/') / (file.stem + ".yaml") + os.makedirs(os.path.dirname(new_participant_file), exist_ok=True) + with open(new_participant_file, 'w') as writer: + writer.write("PHONE:\n") + writer.write(" DEVICE_IDS: [{}]\n".format(phone["DEVICE_IDS"])) + writer.write(" PLATFORMS: [{}]\n".format(phone["PLATFORMS"])) + writer.write(" LABEL: {}\n".format(phone["LABEL"])) + writer.write(" START_DATE: {}\n".format(phone["START_DATE"])) + writer.write(" END_DATE: {}\n".format(phone["END_DATE"])) + + writer.write("FITBIT:\n") + writer.write(" DEVICE_IDS: [{}]\n".format(phone["DEVICE_IDS"])) + writer.write(" LABEL: {}\n".format(phone["LABEL"])) + writer.write(" START_DATE: {}\n".format(phone["START_DATE"])) + writer.write(" END_DATE: {}\n".format(phone["END_DATE"])) +print("Processed files:") +print(list(map(str, files))) \ No newline at end of file