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master
...
d326a1b09d
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@ -93,17 +93,10 @@ packrat/*
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# exclude data from source control by default
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data/external/*
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!/data/external/empatica/empatica1/E4 Data.zip
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!/data/external/.gitkeep
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!/data/external/stachl_application_genre_catalogue.csv
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!/data/external/timesegments*.csv
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!/data/external/wiki_tz.csv
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!/data/external/main_study_usernames.csv
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!/data/external/timezone.csv
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!/data/external/play_store_application_genre_catalogue.csv
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!/data/external/play_store_categories_count.csv
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data/raw/*
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!/data/raw/.gitkeep
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data/interim/*
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@ -121,12 +114,3 @@ settings.dcf
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tests/fakedata_generation/
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site/
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credentials.yaml
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# Docker container and other files
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.devcontainer
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# Calculating features module
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calculatingfeatures/
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# Temp folder for rapids data/external
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rapids_temp_data/
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188
README.md
188
README.md
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@ -11,191 +11,3 @@
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For more information refer to our [documentation](http://www.rapids.science)
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By [MoSHI](https://www.moshi.pitt.edu/), [University of Pittsburgh](https://www.pitt.edu/)
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## Installation
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For RAPIDS installation refer to to the [documentation](https://www.rapids.science/1.8/setup/installation/)
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### For the installation of the Docker version
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1. Follow the [instructions](https://www.rapids.science/1.8/setup/installation/) to setup RAPIDS via Docker (from scratch).
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2. Delete current contents in /rapids/ folder when in a container session.
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```
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cd ..
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rm -rf rapids/{*,.*}
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cd rapids
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```
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3. Clone RAPIDS workspace from Git and checkout a specific branch.
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```
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git clone "https://repo.ijs.si/junoslukan/rapids.git" .
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git checkout <branch_name>
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```
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4. Install missing “libpq-dev” dependency with bash.
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```
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apt-get update -y
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apt-get install -y libpq-dev
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```
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5. Restore R venv.
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Type R to go to the interactive R session and then:
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```
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renv::restore()
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```
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6. Install cr-features module
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From: https://repo.ijs.si/matjazbostic/calculatingfeatures.git -> branch master.
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Then follow the "cr-features module" section below.
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7. Install all required packages from environment.yml, prune also deletes conda packages not present in environment file.
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```
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conda env update --file environment.yml –prune
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```
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8. If you wish to update your R or Python venvs.
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```
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R in interactive session:
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renv::snapshot()
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Python:
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conda env export --no-builds | sed 's/^.*libgfortran.*$/ - libgfortran/' | sed 's/^.*mkl=.*$/ - mkl/' > environment.yml
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```
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### cr-features module
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This RAPIDS extension uses cr-features library accessible [here](https://repo.ijs.si/matjazbostic/calculatingfeatures).
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To use cr-features library:
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- Follow the installation instructions in the [README.md](https://repo.ijs.si/matjazbostic/calculatingfeatures/-/blob/master/README.md).
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- Copy built calculatingfeatures folder into the RAPIDS workspace.
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- Install the cr-features package by:
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```
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pip install path/to/the/calculatingfeatures/folder
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e.g. pip install ./calculatingfeatures if the folder is copied to main parent directory
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cr-features package has to be built and installed everytime to get the newest version.
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Or an the newest version of the docker image must be used.
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```
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## Updating RAPIDS
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To update RAPIDS, first pull and merge [origin]( https://github.com/carissalow/rapids), such as with:
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```commandline
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git fetch --progress "origin" refs/heads/master
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git merge --no-ff origin/master
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```
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Next, update the conda and R virtual environment.
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```bash
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R -e 'renv::restore(repos = c(CRAN = "https://packagemanager.rstudio.com/all/__linux__/focal/latest"))'
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```
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## Custom configuration
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### Credentials
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As mentioned under [Database in RAPIDS documentation](https://www.rapids.science/1.6/snippets/database/), a `credentials.yaml` file is needed to connect to a database.
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It should contain:
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```yaml
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PSQL_STRAW:
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database: staw
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host: 212.235.208.113
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password: password
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port: 5432
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user: staw_db
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```
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where`password` needs to be specified as well.
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## Possible installation issues
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### Missing dependencies for RPostgres
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To install `RPostgres` R package (used to connect to the PostgreSQL database), an error might occur:
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```text
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------------------------- ANTICONF ERROR ---------------------------
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Configuration failed because libpq was not found. Try installing:
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* deb: libpq-dev (Debian, Ubuntu, etc)
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* rpm: postgresql-devel (Fedora, EPEL)
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* rpm: postgreql8-devel, psstgresql92-devel, postgresql93-devel, or postgresql94-devel (Amazon Linux)
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* csw: postgresql_dev (Solaris)
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* brew: libpq (OSX)
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If libpq is already installed, check that either:
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(i) 'pkg-config' is in your PATH AND PKG_CONFIG_PATH contains a libpq.pc file; or
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(ii) 'pg_config' is in your PATH.
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If neither can detect , you can set INCLUDE_DIR
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and LIB_DIR manually via:
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R CMD INSTALL --configure-vars='INCLUDE_DIR=... LIB_DIR=...'
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--------------------------[ ERROR MESSAGE ]----------------------------
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<stdin>:1:10: fatal error: libpq-fe.h: No such file or directory
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compilation terminated.
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```
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The library requires `libpq` for compiling from source, so install accordingly.
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### Timezone environment variable for tidyverse (relevant for WSL2)
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One of the R packages, `tidyverse` might need access to the `TZ` environment variable during the installation.
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On Ubuntu 20.04 on WSL2 this triggers the following error:
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```text
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> install.packages('tidyverse')
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ERROR: configuration failed for package ‘xml2’
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System has not been booted with systemd as init system (PID 1). Can't operate.
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Failed to create bus connection: Host is down
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Warning in system("timedatectl", intern = TRUE) :
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running command 'timedatectl' had status 1
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Error in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]) :
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namespace ‘xml2’ 1.3.1 is already loaded, but >= 1.3.2 is required
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Calls: <Anonymous> ... namespaceImportFrom -> asNamespace -> loadNamespace
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Execution halted
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ERROR: lazy loading failed for package ‘tidyverse’
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```
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This happens because WSL2 does not use the `timedatectl` service, which provides this variable.
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||||
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```bash
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~$ timedatectl
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System has not been booted with systemd as init system (PID 1). Can't operate.
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Failed to create bus connection: Host is down
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```
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and later
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```bash
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Warning message:
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In system("timedatectl", intern = TRUE) :
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running command 'timedatectl' had status 1
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Execution halted
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||||
```
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||||
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This can be amended by setting the environment variable manually before attempting to install `tidyverse`:
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```bash
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export TZ='Europe/Ljubljana'
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```
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Note: if this is needed to avoid runtime issues, you need to either define this environment variable in each new terminal window or (better) define it in your `~/.bashrc` or `~/.bash_profile`.
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## Possible runtime issues
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### Unix end of line characters
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Upon running rapids, an error might occur:
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```bash
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/usr/bin/env: ‘python3\r’: No such file or directory
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||||
```
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This is due to Windows style end of line characters.
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To amend this, I added a `.gitattributes` files to force `git` to checkout `rapids` using Unix EOL characters.
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If this still fails, `dos2unix` can be used to change them.
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### System has not been booted with systemd as init system (PID 1)
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See [the installation issue above](#Timezone-environment-variable-for-tidyverse-(relevant-for-WSL2)).
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|
|
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34
Snakefile
34
Snakefile
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@ -169,19 +169,9 @@ for provider in config["PHONE_ESM"]["PROVIDERS"].keys():
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files_to_compute.extend(expand("data/raw/{pid}/phone_esm_raw.csv",pid=config["PIDS"]))
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files_to_compute.extend(expand("data/raw/{pid}/phone_esm_with_datetime.csv",pid=config["PIDS"]))
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files_to_compute.extend(expand("data/interim/{pid}/phone_esm_clean.csv",pid=config["PIDS"]))
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files_to_compute.extend(expand("data/interim/{pid}/phone_esm_features/phone_esm_{language}_{provider_key}.csv",pid=config["PIDS"],language=get_script_language(config["PHONE_ESM"]["PROVIDERS"][provider]["SRC_SCRIPT"]),provider_key=provider.lower()))
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files_to_compute.extend(expand("data/processed/features/{pid}/phone_esm.csv", pid=config["PIDS"]))
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# files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv",pid=config["PIDS"]))
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# files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
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for provider in config["PHONE_SPEECH"]["PROVIDERS"].keys():
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if config["PHONE_SPEECH"]["PROVIDERS"][provider]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/phone_speech_raw.csv",pid=config["PIDS"]))
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files_to_compute.extend(expand("data/raw/{pid}/phone_speech_with_datetime.csv",pid=config["PIDS"]))
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files_to_compute.extend(expand("data/interim/{pid}/phone_speech_features/phone_speech_{language}_{provider_key}.csv",pid=config["PIDS"],language=get_script_language(config["PHONE_SPEECH"]["PROVIDERS"][provider]["SRC_SCRIPT"]),provider_key=provider.lower()))
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files_to_compute.extend(expand("data/processed/features/{pid}/phone_speech.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
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files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
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files_to_compute.extend(expand("data/interim/{pid}/phone_esm_features/phone_esm_clean_{provider_key}.csv",pid=config["PIDS"],provider_key=provider.lower()))
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#files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv",pid=config["PIDS"]))
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#files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
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# We can delete these if's as soon as we add feature PROVIDERS to any of these sensors
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if isinstance(config["PHONE_APPLICATIONS_CRASHES"]["PROVIDERS"], dict):
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@ -417,18 +407,10 @@ if config["HEATMAP_FEATURE_CORRELATION_MATRIX"]["PLOT"]:
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# Data Cleaning
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for provider in config["ALL_CLEANING_INDIVIDUAL"]["PROVIDERS"].keys():
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if config["ALL_CLEANING_INDIVIDUAL"]["PROVIDERS"][provider]["COMPUTE"]:
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if provider == "STRAW":
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files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features_cleaned_" + provider.lower() + "_py.csv", pid=config["PIDS"]))
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else:
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files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features_cleaned_" + provider.lower() + "_R.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features_cleaned_" + provider.lower() +".csv", pid=config["PIDS"]))
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for provider in config["ALL_CLEANING_OVERALL"]["PROVIDERS"].keys():
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if config["ALL_CLEANING_OVERALL"]["PROVIDERS"][provider]["COMPUTE"]:
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if provider == "STRAW":
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for target in config["PARAMS_FOR_ANALYSIS"]["TARGET"]["ALL_LABELS"]:
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files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +"_py_(" + target + ").csv"))
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else:
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files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +"_R.csv"))
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files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +".csv"))
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# Baseline features
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if config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["COMPUTE"]:
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@ -437,12 +419,6 @@ if config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["COMPUTE"]:
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files_to_compute.extend(expand("data/interim/{pid}/baseline_questionnaires.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/processed/features/{pid}/baseline_features.csv", pid=config["PIDS"]))
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# Targets (labels)
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if config["PARAMS_FOR_ANALYSIS"]["TARGET"]["COMPUTE"]:
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files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/input.csv", pid=config["PIDS"]))
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for target in config["PARAMS_FOR_ANALYSIS"]["TARGET"]["ALL_LABELS"]:
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files_to_compute.extend(expand("data/processed/models/population_model/input_" + target + ".csv"))
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rule all:
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input:
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files_to_compute
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|
|
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|
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@ -1,57 +0,0 @@
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from pprint import pprint
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import sklearn.metrics
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import autosklearn.regression
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||||
|
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import datetime
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||||
import importlib
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||||
import os
|
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import sys
|
||||
|
||||
import numpy as np
|
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import matplotlib.pyplot as plt
|
||||
import pandas as pd
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import seaborn as sns
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import yaml
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||||
|
||||
from sklearn import linear_model, svm, kernel_ridge, gaussian_process
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from sklearn.model_selection import LeaveOneGroupOut, cross_val_score, train_test_split
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from sklearn.metrics import mean_squared_error, r2_score
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from sklearn.impute import SimpleImputer
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|
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model_input = pd.read_csv("data/processed/models/population_model/input_PANAS_negative_affect_mean.csv") # Standardizirani podatki
|
||||
|
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model_input.dropna(axis=1, how="all", inplace=True)
|
||||
model_input.dropna(axis=0, how="any", subset=["target"], inplace=True)
|
||||
|
||||
categorical_feature_colnames = ["gender", "startlanguage"]
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||||
categorical_feature_colnames += [col for col in model_input.columns if "mostcommonactivity" in col or "homelabel" in col]
|
||||
categorical_features = model_input[categorical_feature_colnames].copy()
|
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mode_categorical_features = categorical_features.mode().iloc[0]
|
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categorical_features = categorical_features.fillna(mode_categorical_features)
|
||||
categorical_features = categorical_features.apply(lambda col: col.astype("category"))
|
||||
if not categorical_features.empty:
|
||||
categorical_features = pd.get_dummies(categorical_features)
|
||||
numerical_features = model_input.drop(categorical_feature_colnames, axis=1)
|
||||
model_in = pd.concat([numerical_features, categorical_features], axis=1)
|
||||
|
||||
index_columns = ["local_segment", "local_segment_label", "local_segment_start_datetime", "local_segment_end_datetime"]
|
||||
model_in.set_index(index_columns, inplace=True)
|
||||
|
||||
X_train, X_test, y_train, y_test = train_test_split(model_in.drop(["target", "pid"], axis=1), model_in["target"], test_size=0.30)
|
||||
|
||||
automl = autosklearn.regression.AutoSklearnRegressor(
|
||||
time_left_for_this_task=7200,
|
||||
per_run_time_limit=120
|
||||
)
|
||||
automl.fit(X_train, y_train, dataset_name='straw')
|
||||
|
||||
print(automl.leaderboard())
|
||||
pprint(automl.show_models(), indent=4)
|
||||
|
||||
train_predictions = automl.predict(X_train)
|
||||
print("Train R2 score:", sklearn.metrics.r2_score(y_train, train_predictions))
|
||||
test_predictions = automl.predict(X_test)
|
||||
print("Test R2 score:", sklearn.metrics.r2_score(y_test, test_predictions))
|
||||
|
||||
import sys
|
||||
sys.exit()
|
||||
187
config.yaml
187
config.yaml
|
|
@ -3,7 +3,7 @@
|
|||
########################################################################################################################
|
||||
|
||||
# See https://www.rapids.science/latest/setup/configuration/#participant-files
|
||||
PIDS: ['p031', 'p032', 'p033', 'p034', 'p035', 'p036', 'p037', 'p038', 'p039', 'p040', 'p042', 'p043', 'p044', 'p045', 'p046', 'p049', 'p050', 'p052', 'p053', 'p054', 'p055', 'p057', 'p058', 'p059', 'p060', 'p061', 'p062', 'p064', 'p067', 'p068', 'p069', 'p070', 'p071', 'p072', 'p073', 'p074', 'p075', 'p076', 'p077', 'p078', 'p079', 'p080', 'p081', 'p082', 'p083', 'p084', 'p085', 'p086', 'p088', 'p089', 'p090', 'p091', 'p092', 'p093', 'p106', 'p107']
|
||||
PIDS: ['p031']
|
||||
|
||||
# See https://www.rapids.science/latest/setup/configuration/#automatic-creation-of-participant-files
|
||||
CREATE_PARTICIPANT_FILES:
|
||||
|
|
@ -16,19 +16,14 @@ CREATE_PARTICIPANT_FILES:
|
|||
ADD: False
|
||||
IGNORED_DEVICE_IDS: []
|
||||
EMPATICA_SECTION:
|
||||
ADD: True
|
||||
ADD: False
|
||||
IGNORED_DEVICE_IDS: []
|
||||
|
||||
# See https://www.rapids.science/latest/setup/configuration/#time-segments
|
||||
TIME_SEGMENTS: &time_segments
|
||||
TYPE: EVENT # FREQUENCY, PERIODIC, EVENT
|
||||
FILE: "data/external/straw_events.csv"
|
||||
TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT
|
||||
FILE: "data/external/timesegments_daily.csv"
|
||||
INCLUDE_PAST_PERIODIC_SEGMENTS: TRUE # Only relevant if TYPE=PERIODIC, see docs
|
||||
TAILORED_EVENTS: # Only relevant if TYPE=EVENT
|
||||
COMPUTE: True
|
||||
SEGMENTING_METHOD: "30_before" # 30_before, 90_before, stress_event
|
||||
INTERVAL_OF_INTEREST: 10 # duration of event of interest [minutes]
|
||||
IOI_ERROR_TOLERANCE: 5 # interval of interest erorr tolerance (before and after IOI) [minutes]
|
||||
|
||||
# See https://www.rapids.science/latest/setup/configuration/#timezone-of-your-study
|
||||
TIMEZONE:
|
||||
|
|
@ -75,6 +70,7 @@ PHONE_ACCELEROMETER:
|
|||
COMPUTE: False
|
||||
FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
|
||||
SRC_SCRIPT: src/features/phone_accelerometer/rapids/main.py
|
||||
|
||||
PANDA:
|
||||
COMPUTE: False
|
||||
VALID_SENSED_MINUTES: False
|
||||
|
|
@ -104,9 +100,9 @@ PHONE_APPLICATIONS_CRASHES:
|
|||
CONTAINER: applications_crashes
|
||||
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/play_store_application_genre_catalogue.csv"
|
||||
UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether 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 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
|
||||
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: # None implemented yet but this sensor can be used in PHONE_DATA_YIELD
|
||||
|
||||
# See https://www.rapids.science/latest/features/phone-applications-foreground/
|
||||
|
|
@ -114,32 +110,24 @@ PHONE_APPLICATIONS_FOREGROUND:
|
|||
CONTAINER: applications
|
||||
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/play_store_application_genre_catalogue.csv"
|
||||
# Refer to data/external/play_store_categories_count.csv for a list of categories (genres) and their frequency.
|
||||
UPDATE_CATALOGUE_FILE: False # if CATALOGUE_SOURCE is equal to FILE, whether 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 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
|
||||
CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv"
|
||||
PACKAGE_NAMES_HASHED: True
|
||||
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
|
||||
INCLUDE_EPISODE_FEATURES: True
|
||||
SINGLE_CATEGORIES: ["Productivity", "Tools", "Communication", "Education", "Social"]
|
||||
SINGLE_CATEGORIES: ["all", "email"]
|
||||
MULTIPLE_CATEGORIES:
|
||||
games: ["Puzzle", "Card", "Casual", "Board", "Strategy", "Trivia", "Word", "Adventure", "Role Playing", "Simulation", "Board, Brain Games", "Racing"]
|
||||
social: ["Communication", "Social", "Dating"]
|
||||
productivity: ["Tools", "Productivity", "Finance", "Education", "News & Magazines", "Business", "Books & Reference"]
|
||||
health: ["Health & Fitness", "Lifestyle", "Food & Drink", "Sports", "Medical", "Parenting"]
|
||||
entertainment: ["Shopping", "Music & Audio", "Entertainment", "Travel & Local", "Photography", "Video Players & Editors", "Personalization", "House & Home", "Art & Design", "Auto & Vehicles", "Entertainment,Music & Video",
|
||||
"Puzzle", "Card", "Casual", "Board", "Strategy", "Trivia", "Word", "Adventure", "Role Playing", "Simulation", "Board, Brain Games", "Racing" # Add all games.
|
||||
]
|
||||
maps_weather: ["Maps & Navigation", "Weather"]
|
||||
social: ["socialnetworks", "socialmediatools"]
|
||||
entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"]
|
||||
CUSTOM_CATEGORIES:
|
||||
SINGLE_APPS: []
|
||||
EXCLUDED_CATEGORIES: ["System", "STRAW"]
|
||||
# Note: A special option here is "is_system_app".
|
||||
# This excludes applications that have is_system_app = TRUE, which is a separate column in the table.
|
||||
# However, all of these applications have been assigned System category.
|
||||
# I will therefore filter by that category, which is a superset and is more complete. JL
|
||||
EXCLUDED_APPS: []
|
||||
social_media: ["com.google.android.youtube", "com.snapchat.android", "com.instagram.android", "com.zhiliaoapp.musically", "com.facebook.katana"]
|
||||
dating: ["com.tinder", "com.relance.happycouple", "com.kiwi.joyride"]
|
||||
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"] # TODO list system apps?
|
||||
FEATURES:
|
||||
APP_EVENTS: ["countevent", "timeoffirstuse", "timeoflastuse", "frequencyentropy"]
|
||||
APP_EPISODES: ["countepisode", "minduration", "maxduration", "meanduration", "sumduration"]
|
||||
|
|
@ -172,7 +160,7 @@ PHONE_BLUETOOTH:
|
|||
CONTAINER: bluetooth
|
||||
PROVIDERS:
|
||||
RAPIDS:
|
||||
COMPUTE: False
|
||||
COMPUTE: True
|
||||
FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
|
||||
SRC_SCRIPT: src/features/phone_bluetooth/rapids/main.R
|
||||
|
||||
|
|
@ -251,8 +239,7 @@ PHONE_ESM:
|
|||
PROVIDERS:
|
||||
STRAW:
|
||||
COMPUTE: True
|
||||
SCALES: ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support",
|
||||
"appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]
|
||||
SCALES: ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support"]
|
||||
FEATURES: [mean]
|
||||
SRC_SCRIPT: src/features/phone_esm/straw/main.py
|
||||
|
||||
|
|
@ -337,15 +324,6 @@ PHONE_SCREEN:
|
|||
EPISODE_TYPES: ["unlock"]
|
||||
SRC_SCRIPT: src/features/phone_screen/rapids/main.py
|
||||
|
||||
# Custom added sensor
|
||||
PHONE_SPEECH:
|
||||
CONTAINER: speech
|
||||
PROVIDERS:
|
||||
STRAW:
|
||||
COMPUTE: True
|
||||
FEATURES: ["meanspeech", "stdspeech", "nlargest", "nsmallest", "medianspeech"]
|
||||
SRC_SCRIPT: src/features/phone_speech/straw/main.py
|
||||
|
||||
# See https://www.rapids.science/latest/features/phone-wifi-connected/
|
||||
PHONE_WIFI_CONNECTED:
|
||||
CONTAINER: sensor_wifi
|
||||
|
|
@ -463,6 +441,7 @@ FITBIT_SLEEP_INTRADAY:
|
|||
UNIFIED: [awake, asleep]
|
||||
SLEEP_TYPES: [main, nap, all]
|
||||
SRC_SCRIPT: src/features/fitbit_sleep_intraday/rapids/main.py
|
||||
|
||||
PRICE:
|
||||
COMPUTE: False
|
||||
FEATURES: [avgduration, avgratioduration, avgstarttimeofepisodemain, avgendtimeofepisodemain, avgmidpointofepisodemain, stdstarttimeofepisodemain, stdendtimeofepisodemain, stdmidpointofepisodemain, socialjetlag, rmssdmeanstarttimeofepisodemain, rmssdmeanendtimeofepisodemain, rmssdmeanmidpointofepisodemain, rmssdmedianstarttimeofepisodemain, rmssdmedianendtimeofepisodemain, rmssdmedianmidpointofepisodemain]
|
||||
|
|
@ -506,7 +485,6 @@ FITBIT_STEPS_INTRADAY:
|
|||
INCLUDE_ZERO_STEP_ROWS: False
|
||||
SRC_SCRIPT: src/features/fitbit_steps_intraday/rapids/main.py
|
||||
|
||||
|
||||
########################################################################################################################
|
||||
# EMPATICA #
|
||||
########################################################################################################################
|
||||
|
|
@ -528,15 +506,6 @@ EMPATICA_ACCELEROMETER:
|
|||
COMPUTE: False
|
||||
FEATURES: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude"]
|
||||
SRC_SCRIPT: src/features/empatica_accelerometer/dbdp/main.py
|
||||
CR:
|
||||
COMPUTE: True
|
||||
FEATURES: ["totalMagnitudeBand", "absoluteMeanBand", "varianceBand"] # Acc features
|
||||
WINDOWS:
|
||||
COMPUTE: True
|
||||
WINDOW_LENGTH: 15 # specify window length in seconds
|
||||
SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows']
|
||||
SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py
|
||||
|
||||
|
||||
# See https://www.rapids.science/latest/features/empatica-heartrate/
|
||||
EMPATICA_HEARTRATE:
|
||||
|
|
@ -555,15 +524,6 @@ EMPATICA_TEMPERATURE:
|
|||
COMPUTE: False
|
||||
FEATURES: ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp", "diffminmodetemp", "entropytemp"]
|
||||
SRC_SCRIPT: src/features/empatica_temperature/dbdp/main.py
|
||||
CR:
|
||||
COMPUTE: True
|
||||
FEATURES: ["maximum", "minimum", "meanAbsChange", "longestStrikeAboveMean", "longestStrikeBelowMean",
|
||||
"stdDev", "median", "meanChange", "sumSquared", "squareSumOfComponent", "sumOfSquareComponents"]
|
||||
WINDOWS:
|
||||
COMPUTE: True
|
||||
WINDOW_LENGTH: 300 # specify window length in seconds
|
||||
SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows']
|
||||
SRC_SCRIPT: src/features/empatica_temperature/cr/main.py
|
||||
|
||||
# See https://www.rapids.science/latest/features/empatica-electrodermal-activity/
|
||||
EMPATICA_ELECTRODERMAL_ACTIVITY:
|
||||
|
|
@ -573,19 +533,6 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
|
|||
COMPUTE: False
|
||||
FEATURES: ["maxeda", "mineda", "avgeda", "medianeda", "modeeda", "stdeda", "diffmaxmodeeda", "diffminmodeeda", "entropyeda"]
|
||||
SRC_SCRIPT: src/features/empatica_electrodermal_activity/dbdp/main.py
|
||||
CR:
|
||||
COMPUTE: True
|
||||
FEATURES: ['mean', 'std', 'q25', 'q75', 'qd', 'deriv', 'power', 'numPeaks', 'ratePeaks', 'powerPeaks', 'sumPosDeriv', 'propPosDeriv', 'derivTonic',
|
||||
'sigTonicDifference', 'freqFeats','maxPeakAmplitudeChangeBefore', 'maxPeakAmplitudeChangeAfter', 'avgPeakAmplitudeChangeBefore',
|
||||
'avgPeakAmplitudeChangeAfter', 'avgPeakChangeRatio', 'maxPeakIncreaseTime', 'maxPeakDecreaseTime', 'maxPeakDuration', 'maxPeakChangeRatio',
|
||||
'avgPeakIncreaseTime', 'avgPeakDecreaseTime', 'avgPeakDuration', 'signalOverallChange', 'changeDuration', 'changeRate', 'significantIncrease',
|
||||
'significantDecrease']
|
||||
WINDOWS:
|
||||
COMPUTE: True
|
||||
WINDOW_LENGTH: 60 # specify window length in seconds
|
||||
SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', count_windows, eda_num_peaks_non_zero]
|
||||
IMPUTE_NANS: True
|
||||
SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py
|
||||
|
||||
# See https://www.rapids.science/latest/features/empatica-blood-volume-pulse/
|
||||
EMPATICA_BLOOD_VOLUME_PULSE:
|
||||
|
|
@ -595,15 +542,6 @@ EMPATICA_BLOOD_VOLUME_PULSE:
|
|||
COMPUTE: False
|
||||
FEATURES: ["maxbvp", "minbvp", "avgbvp", "medianbvp", "modebvp", "stdbvp", "diffmaxmodebvp", "diffminmodebvp", "entropybvp"]
|
||||
SRC_SCRIPT: src/features/empatica_blood_volume_pulse/dbdp/main.py
|
||||
CR:
|
||||
COMPUTE: False
|
||||
FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features
|
||||
'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features
|
||||
WINDOWS:
|
||||
COMPUTE: True
|
||||
WINDOW_LENGTH: 300 # specify window length in seconds
|
||||
SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows', 'hrv_num_windows_non_nan']
|
||||
SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py
|
||||
|
||||
# See https://www.rapids.science/latest/features/empatica-inter-beat-interval/
|
||||
EMPATICA_INTER_BEAT_INTERVAL:
|
||||
|
|
@ -613,16 +551,6 @@ EMPATICA_INTER_BEAT_INTERVAL:
|
|||
COMPUTE: False
|
||||
FEATURES: ["maxibi", "minibi", "avgibi", "medianibi", "modeibi", "stdibi", "diffmaxmodeibi", "diffminmodeibi", "entropyibi"]
|
||||
SRC_SCRIPT: src/features/empatica_inter_beat_interval/dbdp/main.py
|
||||
CR:
|
||||
COMPUTE: True
|
||||
FEATURES: ['meanHr', 'ibi', 'sdnn', 'sdsd', 'rmssd', 'pnn20', 'pnn50', 'sd', 'sd2', 'sd1/sd2', 'numRR', # Time features
|
||||
'VLF', 'LF', 'LFnorm', 'HF', 'HFnorm', 'LF/HF', 'fullIntegral'] # Freq features
|
||||
PATCH_WITH_BVP: True
|
||||
WINDOWS:
|
||||
COMPUTE: True
|
||||
WINDOW_LENGTH: 300 # specify window length in seconds
|
||||
SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows', 'hrv_num_windows_non_nan']
|
||||
SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py
|
||||
|
||||
# See https://www.rapids.science/latest/features/empatica-tags/
|
||||
EMPATICA_TAGS:
|
||||
|
|
@ -638,7 +566,7 @@ EMPATICA_TAGS:
|
|||
|
||||
# See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#1-histograms-of-phone-data-yield
|
||||
HISTOGRAM_PHONE_DATA_YIELD:
|
||||
PLOT: False
|
||||
PLOT: True
|
||||
|
||||
# See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#2-heatmaps-of-overall-data-yield
|
||||
HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT:
|
||||
|
|
@ -647,7 +575,7 @@ HEATMAP_PHONE_DATA_YIELD_PER_PARTICIPANT_PER_TIME_SEGMENT:
|
|||
|
||||
# See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#3-heatmap-of-recorded-phone-sensors
|
||||
HEATMAP_SENSORS_PER_MINUTE_PER_TIME_SEGMENT:
|
||||
PLOT: False
|
||||
PLOT: True
|
||||
|
||||
# See https://www.rapids.science/latest/visualizations/data-quality-visualizations/#4-heatmap-of-sensor-row-count
|
||||
HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT:
|
||||
|
|
@ -658,7 +586,7 @@ HEATMAP_SENSOR_ROW_COUNT_PER_TIME_SEGMENT:
|
|||
|
||||
# See https://www.rapids.science/latest/visualizations/feature-visualizations/#1-heatmap-correlation-matrix
|
||||
HEATMAP_FEATURE_CORRELATION_MATRIX:
|
||||
PLOT: False
|
||||
PLOT: True
|
||||
MIN_ROWS_RATIO: 0.5
|
||||
CORR_THRESHOLD: 0.1
|
||||
CORR_METHOD: "pearson" # choose from {"pearson", "kendall", "spearman"}
|
||||
|
|
@ -671,88 +599,55 @@ HEATMAP_FEATURE_CORRELATION_MATRIX:
|
|||
ALL_CLEANING_INDIVIDUAL:
|
||||
PROVIDERS:
|
||||
RAPIDS:
|
||||
COMPUTE: False
|
||||
COMPUTE: True
|
||||
IMPUTE_SELECTED_EVENT_FEATURES:
|
||||
COMPUTE: False
|
||||
COMPUTE: True
|
||||
MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
|
||||
COLS_NAN_THRESHOLD: 1 # set to 1 to disable
|
||||
COLS_NAN_THRESHOLD: 0.3 # set to 1 to disable
|
||||
COLS_VAR_THRESHOLD: True
|
||||
ROWS_NAN_THRESHOLD: 1 # set to 1 to disable
|
||||
DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
|
||||
DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
|
||||
DATA_YIELD_RATIO_THRESHOLD: 0.3 # set to 0 to disable
|
||||
DROP_HIGHLY_CORRELATED_FEATURES:
|
||||
COMPUTE: True
|
||||
COMPUTE: False
|
||||
MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
|
||||
CORR_THRESHOLD: 0.95
|
||||
SRC_SCRIPT: src/features/all_cleaning_individual/rapids/main.R
|
||||
STRAW:
|
||||
COMPUTE: True
|
||||
PHONE_DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_MINUTES # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
|
||||
PHONE_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
|
||||
EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
|
||||
ROWS_NAN_THRESHOLD: 0.33 # set to 1 to disable
|
||||
COLS_NAN_THRESHOLD: 0.9 # set to 1 to remove only columns that contains all (100% of) NaN
|
||||
COLS_VAR_THRESHOLD: True
|
||||
DROP_HIGHLY_CORRELATED_FEATURES:
|
||||
COMPUTE: True
|
||||
MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
|
||||
CORR_THRESHOLD: 0.95
|
||||
STANDARDIZATION: True
|
||||
SRC_SCRIPT: src/features/all_cleaning_individual/straw/main.py
|
||||
|
||||
ALL_CLEANING_OVERALL:
|
||||
PROVIDERS:
|
||||
RAPIDS:
|
||||
COMPUTE: False
|
||||
COMPUTE: True
|
||||
IMPUTE_SELECTED_EVENT_FEATURES:
|
||||
COMPUTE: False
|
||||
COMPUTE: True
|
||||
MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
|
||||
COLS_NAN_THRESHOLD: 1 # set to 1 to disable
|
||||
COLS_NAN_THRESHOLD: 0.3 # set to 1 to disable
|
||||
COLS_VAR_THRESHOLD: True
|
||||
ROWS_NAN_THRESHOLD: 1 # set to 1 to disable
|
||||
DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_HOURS # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
|
||||
DATA_YIELD_RATIO_THRESHOLD: 0 # set to 0 to disable
|
||||
DATA_YIELD_RATIO_THRESHOLD: 0.3 # set to 0 to disable
|
||||
DROP_HIGHLY_CORRELATED_FEATURES:
|
||||
COMPUTE: True
|
||||
COMPUTE: False
|
||||
MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
|
||||
CORR_THRESHOLD: 0.95
|
||||
SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R
|
||||
STRAW:
|
||||
COMPUTE: True
|
||||
PHONE_DATA_YIELD_FEATURE: RATIO_VALID_YIELDED_MINUTES # RATIO_VALID_YIELDED_HOURS or RATIO_VALID_YIELDED_MINUTES
|
||||
PHONE_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
|
||||
EMPATICA_DATA_YIELD_RATIO_THRESHOLD: 0.5 # set to 0 to disable
|
||||
ROWS_NAN_THRESHOLD: 0.33 # set to 1 to disable
|
||||
COLS_NAN_THRESHOLD: 0.8 # set to 1 to remove only columns that contains all (100% of) NaN
|
||||
COLS_VAR_THRESHOLD: True
|
||||
DROP_HIGHLY_CORRELATED_FEATURES:
|
||||
COMPUTE: True
|
||||
MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
|
||||
CORR_THRESHOLD: 0.95
|
||||
STANDARDIZATION: True
|
||||
TARGET_STANDARDIZATION: False
|
||||
SRC_SCRIPT: src/features/all_cleaning_overall/straw/main.py
|
||||
|
||||
|
||||
########################################################################################################################
|
||||
# Baseline #
|
||||
# Analysis Workflow Example #
|
||||
########################################################################################################################
|
||||
|
||||
PARAMS_FOR_ANALYSIS:
|
||||
BASELINE:
|
||||
COMPUTE: True
|
||||
COMPUTE: False
|
||||
FOLDER: data/external/baseline
|
||||
CONTAINER: [results-survey637813_final.csv, # Slovenia
|
||||
results-survey358134_final.csv, # Belgium 1
|
||||
results-survey413767_final.csv # Belgium 2
|
||||
]
|
||||
QUESTION_LIST: survey637813+question_text.csv
|
||||
FEATURES: [age, gender, startlanguage, limesurvey_demand, limesurvey_control, limesurvey_demand_control_ratio, limesurvey_demand_control_ratio_quartile]
|
||||
FEATURES: [age, gender, startlanguage, demand, control, demand_control_ratio]
|
||||
CATEGORICAL_FEATURES: [gender]
|
||||
|
||||
TARGET:
|
||||
COMPUTE: True
|
||||
LABEL: appraisal_stressfulness_event_mean
|
||||
ALL_LABELS: [PANAS_positive_affect_mean, PANAS_negative_affect_mean, JCQ_job_demand_mean, JCQ_job_control_mean, JCQ_supervisor_support_mean, JCQ_coworker_support_mean, appraisal_stressfulness_period_mean]
|
||||
# PANAS_positive_affect_mean, PANAS_negative_affect_mean, JCQ_job_demand_mean, JCQ_job_control_mean, JCQ_supervisor_support_mean,
|
||||
# JCQ_coworker_support_mean, appraisal_stressfulness_period_mean, appraisal_stressfulness_event_mean, appraisal_threat_mean, appraisal_challenge_mean
|
||||
SCALE: [positive_affect, negative_affect]
|
||||
|
||||
|
|
|
|||
|
|
@ -1,9 +0,0 @@
|
|||
"_id","timestamp","device_id","call_type","call_duration","trace"
|
||||
1,1587663260695,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",2,14,"d5e84f8af01b2728021d4f43f53a163c0c90000c"
|
||||
2,1587739118007,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",3,0,"47c125dc7bd163b8612cdea13724a814917b6e93"
|
||||
5,1587746544891,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",2,95,"9cc793ffd6e88b1d850ce540b5d7e000ef5650d4"
|
||||
6,1587911379859,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",2,63,"51fb9344e988049a3fec774c7ca622358bf80264"
|
||||
7,1587992647361,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",3,0,"2a862a7730cfdfaf103a9487afe3e02935fd6e02"
|
||||
8,1588020039448,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",1,11,"a2c53f6a086d98622c06107780980cf1bb4e37bd"
|
||||
11,1588176189024,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",2,65,"56589df8c830c70e330b644921ed38e08d8fd1f3"
|
||||
12,1588197745079,"a748ee1a-1d0b-4ae9-9074-279a2b6ba524",3,0,"cab458018a8ed3b626515e794c70b6f415318adc"
|
||||
|
Binary file not shown.
|
|
@ -1,57 +0,0 @@
|
|||
label,empatica_id
|
||||
uploader_79170,A0245B
|
||||
uploader_89788,A02731
|
||||
uploader_68294,A02705
|
||||
uploader_92856,A024AF
|
||||
uploader_23726,A0231C
|
||||
uploader_66620,A02305
|
||||
uploader_58435,A026B5
|
||||
uploader_87801,A022A8
|
||||
uploader_96055,A027BA
|
||||
uploader_69549,A0226C
|
||||
uploader_26363,A0263D
|
||||
uploader_72010,A023FA
|
||||
uploader_13997,A024AF
|
||||
uploader_31156,A02305
|
||||
uploader_63187,A027BA
|
||||
uploader_94821,A022A8
|
||||
uploader_65413,A023F1;A023FA
|
||||
uploader_36488,A02713
|
||||
uploader_91087,A0231C
|
||||
uploader_35174,A025D1
|
||||
uploader_73880,A02705
|
||||
uploader_78650,A02731
|
||||
uploader_70578,A0245B
|
||||
uploader_88313,A02736
|
||||
uploader_58482,A0261A
|
||||
uploader_80601,A027BA
|
||||
uploader_93729,A0226C
|
||||
uploader_61663,A0245B
|
||||
uploader_80848,A025D1
|
||||
uploader_57312,A023F9;A02361;A027A0
|
||||
uploader_52087,A02666
|
||||
uploader_98770,A02953
|
||||
uploader_51327,A0245F
|
||||
uploader_11737,A02732
|
||||
uploader_77440,A0264E
|
||||
uploader_57277,A02422
|
||||
uploader_13098,A026E5
|
||||
uploader_80719,A023C8
|
||||
uploader_54698,A02953
|
||||
uploader_95571,A02853
|
||||
uploader_21880,A024DC
|
||||
uploader_92905,A02920
|
||||
uploader_12108,A023F4
|
||||
uploader_17436,A026E5
|
||||
uploader_58440,A0273F
|
||||
uploader_22172,A0245F
|
||||
uploader_39250,A02422
|
||||
uploader_15311,A023F9
|
||||
uploader_45766,A02920
|
||||
uploader_23096,A02361
|
||||
uploader_78243,A02422
|
||||
uploader_58777,A0245F
|
||||
uploader_82941,A02666
|
||||
uploader_89606,A023F4
|
||||
uploader_82969,A023C8
|
||||
uploader_53573,A024DC;A02361
|
||||
|
|
|
@ -1,11 +0,0 @@
|
|||
PHONE:
|
||||
DEVICE_IDS: [4b62a655-cbf0-4ac0-a448-06726f45b56a]
|
||||
PLATFORMS: [android]
|
||||
LABEL: uploader_53573
|
||||
START_DATE: 2021-05-21 09:21:24
|
||||
END_DATE: 2021-07-12 17:32:07
|
||||
EMPATICA:
|
||||
DEVICE_IDS: [uploader_53573]
|
||||
LABEL: uploader_53573
|
||||
START_DATE: 2021-05-21 09:21:24
|
||||
END_DATE: 2021-07-12 17:32:07
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -1,45 +0,0 @@
|
|||
genre,n
|
||||
System,261
|
||||
Tools,96
|
||||
Productivity,71
|
||||
Health & Fitness,60
|
||||
Finance,54
|
||||
Communication,39
|
||||
Music & Audio,39
|
||||
Shopping,38
|
||||
Lifestyle,33
|
||||
Education,28
|
||||
News & Magazines,24
|
||||
Maps & Navigation,23
|
||||
Entertainment,21
|
||||
Business,18
|
||||
Travel & Local,18
|
||||
Books & Reference,16
|
||||
Social,16
|
||||
Weather,16
|
||||
Food & Drink,14
|
||||
Sports,14
|
||||
Other,13
|
||||
Photography,13
|
||||
Puzzle,13
|
||||
Video Players & Editors,12
|
||||
Card,9
|
||||
Casual,9
|
||||
Personalization,8
|
||||
Medical,7
|
||||
Board,5
|
||||
Strategy,4
|
||||
House & Home,3
|
||||
Trivia,3
|
||||
Word,3
|
||||
Adventure,2
|
||||
Art & Design,2
|
||||
Auto & Vehicles,2
|
||||
Dating,2
|
||||
Role Playing,2
|
||||
STRAW,2
|
||||
Simulation,2
|
||||
"Board,Brain Games",1
|
||||
"Entertainment,Music & Video",1
|
||||
Parenting,1
|
||||
Racing,1
|
||||
|
|
|
@ -1,3 +1,2 @@
|
|||
label,start_time,length,repeats_on,repeats_value
|
||||
daily,04:00:00,23H 59M 59S,every_day,0
|
||||
working_day,04:00:00,18H 00M 00S,every_day,0
|
||||
|
|
|
|||
|
|
|
@ -1,2 +1,2 @@
|
|||
label,length
|
||||
fiveminutes,5
|
||||
thirtyminutes,30
|
||||
|
|
|
@ -1,2 +1,9 @@
|
|||
label,start_time,length,repeats_on,repeats_value
|
||||
threeday,00:00:00,2D 23H 59M 59S,every_day,0
|
||||
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
|
||||
two_weeks_overlapping,00:00:00,13D 23H 59M 59S,every_day,0
|
||||
weekends,00:00:00,2D 23H 59M 59S,wday,5
|
||||
|
|
|
|||
|
File diff suppressed because it is too large
Load Diff
|
|
@ -1,9 +1,4 @@
|
|||
# Change Log
|
||||
## v1.8.0
|
||||
- Add data stream for AWARE Micro server
|
||||
- Fix the NA bug in PHONE_LOCATIONS BARNETT provider
|
||||
- Fix the bug of data type for call_duration field
|
||||
- Fix the index bug of heatmap_sensors_per_minute_per_time_segment
|
||||
## v1.7.1
|
||||
- Update docs for Git Flow section
|
||||
- Update RAPIDS paper information
|
||||
|
|
|
|||
|
|
@ -1,15 +0,0 @@
|
|||
# `aware_micro_mysql`
|
||||
|
||||
This [data stream](../../datastreams/data-streams-introduction) handles iOS and Android sensor data collected with the [AWARE Framework's](https://awareframework.com/) [AWARE Micro](https://github.com/denzilferreira/aware-micro) server and stored in a MySQL database.
|
||||
|
||||
## Container
|
||||
A MySQL database with a table per sensor, each containing the data for all participants. Sensor data is stored in a JSON field within each table called `data`
|
||||
|
||||
The script to connect and download data from this container is at:
|
||||
```bash
|
||||
src/data/streams/aware_micro_mysql/container.R
|
||||
```
|
||||
|
||||
## Format
|
||||
|
||||
--8<---- "docs/snippets/aware_format.md"
|
||||
|
|
@ -16,7 +16,6 @@ For reference, these are the data streams we currently support:
|
|||
| Data Stream | Device | Format | Container | Docs
|
||||
|--|--|--|--|--|
|
||||
| `aware_mysql`| Phone | AWARE app | MySQL | [link](../aware-mysql)
|
||||
| `aware_micro_mysql`| Phone | AWARE Micro server | MySQL | [link](../aware-micro-mysql)
|
||||
| `aware_csv`| Phone | AWARE app | CSV files | [link](../aware-csv)
|
||||
| `aware_influxdb` (beta)| Phone | AWARE app | InfluxDB | [link](../aware-influxdb)
|
||||
| `fitbitjson_mysql`| Fitbit | JSON (per [Fitbit's API](https://dev.fitbit.com/build/reference/web-api/)) | MySQL | [link](../fitbitjson-mysql)
|
||||
|
|
|
|||
|
|
@ -1,39 +0,0 @@
|
|||
"""
|
||||
Please do not make any changes, as RAPIDS is running on tmux server ...
|
||||
"""
|
||||
# !
|
||||
# !
|
||||
"""
|
||||
Please do not make any changes, as RAPIDS is running on tmux server ...
|
||||
"""
|
||||
# !
|
||||
# !
|
||||
"""
|
||||
Please do not make any changes, as RAPIDS is running on tmux server ...
|
||||
"""
|
||||
# !
|
||||
# !
|
||||
"""
|
||||
Please do not make any changes, as RAPIDS is running on tmux server ...
|
||||
"""
|
||||
# !
|
||||
# !
|
||||
"""
|
||||
Please do not make any changes, as RAPIDS is running on tmux server ...
|
||||
"""
|
||||
# !
|
||||
# !
|
||||
"""
|
||||
Please do not make any changes, as RAPIDS is running on tmux server ...
|
||||
"""
|
||||
# !
|
||||
# !
|
||||
"""
|
||||
Please do not make any changes, as RAPIDS is running on tmux server ...
|
||||
"""
|
||||
# !
|
||||
# !
|
||||
"""
|
||||
Please do not make any changes, as RAPIDS is running on tmux server ...
|
||||
"""
|
||||
# !
|
||||
136
environment.yml
136
environment.yml
|
|
@ -1,30 +1,116 @@
|
|||
name: rapids
|
||||
channels:
|
||||
- conda-forge
|
||||
- defaults
|
||||
dependencies:
|
||||
- auto-sklearn
|
||||
- hmmlearn
|
||||
- imbalanced-learn
|
||||
- jsonschema
|
||||
- lightgbm
|
||||
- matplotlib
|
||||
- numpy
|
||||
- pandas
|
||||
- peakutils
|
||||
- pip
|
||||
- plotly
|
||||
- python-dateutil
|
||||
- pytz
|
||||
- pywavelets
|
||||
- pyyaml
|
||||
- scikit-learn
|
||||
- scipy
|
||||
- seaborn
|
||||
- setuptools
|
||||
- bioconda::snakemake
|
||||
- bioconda::snakemake-minimal
|
||||
- tqdm
|
||||
- xgboost
|
||||
- _py-xgboost-mutex=2.0
|
||||
- appdirs=1.4.*
|
||||
- arrow=0.16.0
|
||||
- asn1crypto=1.4.*
|
||||
- astropy=4.2.*
|
||||
- attrs=20.3.*
|
||||
- binaryornot=0.4.*
|
||||
- blas=1.0
|
||||
- brotlipy=0.7.*
|
||||
- bzip2=1.0.*
|
||||
- ca-certificates
|
||||
- certifi
|
||||
- cffi=1.14.4
|
||||
- chardet=3.0.*
|
||||
- click=7.1.*
|
||||
- cookiecutter=1.6.*
|
||||
- cryptography=3.3.*
|
||||
- datrie=0.8.*
|
||||
- docutils=0.16
|
||||
- future=0.18.2
|
||||
- gitdb=4.0.*
|
||||
- gitdb2=4.0.*
|
||||
- gitpython=3.1.*
|
||||
- idna=2.10
|
||||
- imbalanced-learn=0.6.*
|
||||
- importlib-metadata=2.0.*
|
||||
- importlib_metadata=2.0.*
|
||||
- intel-openmp=2019.4
|
||||
- jinja2=2.11.2
|
||||
- jinja2-time=0.2.*
|
||||
- joblib=1.0.*
|
||||
- jsonschema=3.2.*
|
||||
- libblas=3.8.*
|
||||
- libcblas=3.8.*
|
||||
- libcxx=10.0.*
|
||||
- libedit=3.1.*
|
||||
- libffi=3.3
|
||||
- libgfortran
|
||||
- liblapack=3.8.*
|
||||
- libopenblas=0.3.*
|
||||
- libxgboost=0.90
|
||||
- lightgbm=3.1.*
|
||||
- llvm-openmp=10.0.*
|
||||
- markupsafe=1.1.*
|
||||
- mkl
|
||||
- mkl-service=2.3.*
|
||||
- mkl_fft=1.2.*
|
||||
- mkl_random=1.1.*
|
||||
- more-itertools=8.6.*
|
||||
- ncurses=6.2
|
||||
- numpy=1.19.2
|
||||
- numpy-base=1.19.2
|
||||
- openblas=0.3.*
|
||||
- openssl
|
||||
- pandas=1.1.*
|
||||
- pbr=5.5.*
|
||||
- pip=20.3.*
|
||||
- plotly=4.14.1
|
||||
- poyo=0.5.*
|
||||
- psutil=5.7.*
|
||||
- psycopg2
|
||||
- py-xgboost=0.90
|
||||
- pycparser=2.20
|
||||
- pyerfa=1.7.*
|
||||
- pyopenssl=20.0.*
|
||||
- pyprojroot
|
||||
- pysocks=1.7.*
|
||||
- python=3.7.*
|
||||
- python-dateutil=2.8.*
|
||||
- python-dotenv
|
||||
- python_abi=3.7
|
||||
- pytz=2020.4
|
||||
- pyyaml=5.3.*
|
||||
- readline=8.0
|
||||
- requests=2.25.0
|
||||
- retrying=1.3.*
|
||||
- scikit-learn=0.23.2
|
||||
- scipy=1.5.*
|
||||
- setuptools=51.0.*
|
||||
- six=1.15.0
|
||||
- smmap=3.0.*
|
||||
- smmap2=3.0.*
|
||||
- sqlalchemy
|
||||
- sqlite=3.33.0
|
||||
- threadpoolctl=2.1.*
|
||||
- tk=8.6.*
|
||||
- tqdm=4.62.0
|
||||
- urllib3=1.25.11
|
||||
- wheel=0.36.2
|
||||
- whichcraft=0.6.*
|
||||
- wrapt=1.12.1
|
||||
- xgboost=0.90
|
||||
- xz=5.2.*
|
||||
- yaml=0.2.*
|
||||
- zipp=3.4.*
|
||||
- zlib=1.2.*
|
||||
- pip:
|
||||
- biosppy
|
||||
- cr_features>=0.2
|
||||
- amply==0.1.*
|
||||
- configargparse==0.15.1
|
||||
- decorator==4.4.*
|
||||
- ipython-genutils==0.2.*
|
||||
- jupyter-core==4.6.*
|
||||
- nbformat==5.0.*
|
||||
- pulp==2.4
|
||||
- pyparsing==2.4.*
|
||||
- pyrsistent==0.15.5
|
||||
- ratelimiter==1.2.*
|
||||
- snakemake==5.30.2
|
||||
- toposort==1.5
|
||||
- traitlets==4.3.*
|
||||
prefix: /usr/local/Caskroom/miniconda/base/envs/rapids202108
|
||||
|
|
|
|||
|
|
@ -85,7 +85,6 @@ nav:
|
|||
- Introduction: datastreams/data-streams-introduction.md
|
||||
- Phone:
|
||||
- aware_mysql: datastreams/aware-mysql.md
|
||||
- aware_micro_mysql: datastreams/aware-micro-mysql.md
|
||||
- aware_csv: datastreams/aware-csv.md
|
||||
- aware_influxdb (beta): datastreams/aware-influxdb.md
|
||||
- Mandatory Phone Format: datastreams/mandatory-phone-format.md
|
||||
|
|
|
|||
|
|
@ -15,6 +15,9 @@ local({
|
|||
Sys.setenv("RENV_R_INITIALIZING" = "true")
|
||||
on.exit(Sys.unsetenv("RENV_R_INITIALIZING"), add = TRUE)
|
||||
|
||||
if(grepl("Darwin", Sys.info()["sysname"], fixed = TRUE) & grepl("ARM64", Sys.info()["version"], fixed = TRUE)) # M1 Macs
|
||||
Sys.setenv("TZDIR" = file.path(R.home(), "share", "zoneinfo"))
|
||||
|
||||
# signal that we've consented to use renv
|
||||
options(renv.consent = TRUE)
|
||||
|
||||
|
|
|
|||
|
|
@ -40,17 +40,6 @@ def find_features_files(wildcards):
|
|||
feature_files.extend(expand("data/interim/{{pid}}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", sensor_key=wildcards.sensor_key.lower(), language=get_script_language(provider["SRC_SCRIPT"]), provider_key=provider_key.lower()))
|
||||
return(feature_files)
|
||||
|
||||
def find_joint_non_empatica_sensor_files(wildcards):
|
||||
joined_files = []
|
||||
for config_key in config.keys():
|
||||
if config_key.startswith(("PHONE", "FITBIT")) and "PROVIDERS" in config[config_key] and isinstance(config[config_key]["PROVIDERS"], dict):
|
||||
for provider_key, provider in config[config_key]["PROVIDERS"].items():
|
||||
if "COMPUTE" in provider.keys() and provider["COMPUTE"]:
|
||||
joined_files.append("data/processed/features/{pid}/" + config_key.lower() + ".csv")
|
||||
break
|
||||
return joined_files
|
||||
|
||||
|
||||
def optional_steps_sleep_input(wildcards):
|
||||
if config["FITBIT_STEPS_INTRADAY"]["EXCLUDE_SLEEP"]["FITBIT_BASED"]["EXCLUDE"]:
|
||||
return "data/raw/{pid}/fitbit_sleep_summary_raw.csv"
|
||||
|
|
|
|||
|
|
@ -341,20 +341,7 @@ rule esm_features:
|
|||
provider_key = "{provider_key}",
|
||||
sensor_key = "phone_esm",
|
||||
scales=lambda wildcards: config["PHONE_ESM"]["PROVIDERS"][wildcards.provider_key.upper()]["SCALES"]
|
||||
output: "data/interim/{pid}/phone_esm_features/phone_esm_python_{provider_key}.csv"
|
||||
script:
|
||||
"../src/features/entry.py"
|
||||
|
||||
rule phone_speech_python_features:
|
||||
input:
|
||||
sensor_data = "data/raw/{pid}/phone_speech_with_datetime.csv",
|
||||
time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
|
||||
params:
|
||||
provider = lambda wildcards: config["PHONE_SPEECH"]["PROVIDERS"][wildcards.provider_key.upper()],
|
||||
provider_key = "{provider_key}",
|
||||
sensor_key = "phone_speech"
|
||||
output:
|
||||
"data/interim/{pid}/phone_speech_features/phone_speech_python_{provider_key}.csv"
|
||||
output: "data/interim/{pid}/phone_esm_features/phone_esm_clean_{provider_key}.csv"
|
||||
script:
|
||||
"../src/features/entry.py"
|
||||
|
||||
|
|
@ -804,8 +791,7 @@ rule empatica_accelerometer_python_features:
|
|||
provider_key = "{provider_key}",
|
||||
sensor_key = "empatica_accelerometer"
|
||||
output:
|
||||
"data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}.csv",
|
||||
"data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows.csv"
|
||||
"data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}.csv"
|
||||
script:
|
||||
"../src/features/entry.py"
|
||||
|
||||
|
|
@ -831,8 +817,7 @@ rule empatica_heartrate_python_features:
|
|||
provider_key = "{provider_key}",
|
||||
sensor_key = "empatica_heartrate"
|
||||
output:
|
||||
"data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}.csv",
|
||||
"data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}_windows.csv"
|
||||
"data/interim/{pid}/empatica_heartrate_features/empatica_heartrate_python_{provider_key}.csv"
|
||||
script:
|
||||
"../src/features/entry.py"
|
||||
|
||||
|
|
@ -858,8 +843,7 @@ rule empatica_temperature_python_features:
|
|||
provider_key = "{provider_key}",
|
||||
sensor_key = "empatica_temperature"
|
||||
output:
|
||||
"data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}.csv",
|
||||
"data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows.csv"
|
||||
"data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}.csv"
|
||||
script:
|
||||
"../src/features/entry.py"
|
||||
|
||||
|
|
@ -885,8 +869,7 @@ rule empatica_electrodermal_activity_python_features:
|
|||
provider_key = "{provider_key}",
|
||||
sensor_key = "empatica_electrodermal_activity"
|
||||
output:
|
||||
"data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}.csv",
|
||||
"data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows.csv"
|
||||
"data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}.csv"
|
||||
script:
|
||||
"../src/features/entry.py"
|
||||
|
||||
|
|
@ -912,8 +895,7 @@ rule empatica_blood_volume_pulse_python_features:
|
|||
provider_key = "{provider_key}",
|
||||
sensor_key = "empatica_blood_volume_pulse"
|
||||
output:
|
||||
"data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}.csv",
|
||||
"data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows.csv"
|
||||
"data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}.csv"
|
||||
script:
|
||||
"../src/features/entry.py"
|
||||
|
||||
|
|
@ -939,8 +921,7 @@ rule empatica_inter_beat_interval_python_features:
|
|||
provider_key = "{provider_key}",
|
||||
sensor_key = "empatica_inter_beat_interval"
|
||||
output:
|
||||
"data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}.csv",
|
||||
"data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows.csv"
|
||||
"data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}.csv"
|
||||
script:
|
||||
"../src/features/entry.py"
|
||||
|
||||
|
|
@ -1007,12 +988,11 @@ rule clean_sensor_features_for_individual_participants:
|
|||
params:
|
||||
provider = lambda wildcards: config["ALL_CLEANING_INDIVIDUAL"]["PROVIDERS"][wildcards.provider_key.upper()],
|
||||
provider_key = "{provider_key}",
|
||||
script_extension = "{script_extension}",
|
||||
sensor_key = "all_cleaning_individual"
|
||||
output:
|
||||
"data/processed/features/{pid}/all_sensor_features_cleaned_{provider_key}_{script_extension}.csv"
|
||||
"data/processed/features/{pid}/all_sensor_features_cleaned_{provider_key}.csv"
|
||||
script:
|
||||
"../src/features/entry.{params.script_extension}"
|
||||
"../src/features/entry.R"
|
||||
|
||||
rule clean_sensor_features_for_all_participants:
|
||||
input:
|
||||
|
|
@ -1020,10 +1000,9 @@ rule clean_sensor_features_for_all_participants:
|
|||
params:
|
||||
provider = lambda wildcards: config["ALL_CLEANING_OVERALL"]["PROVIDERS"][wildcards.provider_key.upper()],
|
||||
provider_key = "{provider_key}",
|
||||
script_extension = "{script_extension}",
|
||||
sensor_key = "all_cleaning_overall",
|
||||
target = "{target}"
|
||||
sensor_key = "all_cleaning_overall"
|
||||
output:
|
||||
"data/processed/features/all_participants/all_sensor_features_cleaned_{provider_key}_{script_extension}_({target}).csv"
|
||||
"data/processed/features/all_participants/all_sensor_features_cleaned_{provider_key}.csv"
|
||||
script:
|
||||
"../src/features/entry.{params.script_extension}"
|
||||
"../src/features/entry.R"
|
||||
|
||||
|
|
|
|||
|
|
@ -27,26 +27,3 @@ rule baseline_features:
|
|||
features="data/processed/features/{pid}/baseline_features.csv"
|
||||
script:
|
||||
"../src/data/baseline_features.py"
|
||||
|
||||
rule select_target:
|
||||
input:
|
||||
cleaned_sensor_features = "data/processed/features/{pid}/all_sensor_features_cleaned_straw_py.csv"
|
||||
params:
|
||||
target_variable = config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
|
||||
output:
|
||||
"data/processed/models/individual_model/{pid}/input.csv"
|
||||
script:
|
||||
"../src/models/select_targets.py"
|
||||
|
||||
rule merge_features_and_targets_for_population_model:
|
||||
input:
|
||||
cleaned_sensor_features = "data/processed/features/all_participants/all_sensor_features_cleaned_straw_py_({target}).csv",
|
||||
demographic_features = expand("data/processed/features/{pid}/baseline_features.csv", pid=config["PIDS"]),
|
||||
params:
|
||||
target_variable="{target}"
|
||||
output:
|
||||
"data/processed/models/population_model/input_{target}.csv"
|
||||
script:
|
||||
"../src/models/merge_features_and_targets_for_population_model.py"
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -4,14 +4,14 @@ rule create_example_participant_files:
|
|||
shell:
|
||||
"echo 'PHONE:\n DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524]\n PLATFORMS: [android]\n LABEL: test-01\n START_DATE: 2020-04-23 00:00:00\n END_DATE: 2020-05-04 23:59:59\nFITBIT:\n DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524]\n LABEL: test-01\n START_DATE: 2020-04-23 00:00:00\n END_DATE: 2020-05-04 23:59:59\n' >> ./data/external/participant_files/example01.yaml && echo 'PHONE:\n DEVICE_IDS: [13dbc8a3-dae3-4834-823a-4bc96a7d459d]\n PLATFORMS: [ios]\n LABEL: test-02\n START_DATE: 2020-04-23 00:00:00\n END_DATE: 2020-05-04 23:59:59\nFITBIT:\n DEVICE_IDS: [13dbc8a3-dae3-4834-823a-4bc96a7d459d]\n LABEL: test-02\n START_DATE: 2020-04-23 00:00:00\n END_DATE: 2020-05-04 23:59:59\n' >> ./data/external/participant_files/example02.yaml"
|
||||
|
||||
# rule query_usernames_device_empatica_ids:
|
||||
# params:
|
||||
# baseline_folder = "/mnt/e/STRAWbaseline/"
|
||||
# output:
|
||||
# usernames_file = config["CREATE_PARTICIPANT_FILES"]["USERNAMES_CSV"],
|
||||
# timezone_file = config["TIMEZONE"]["MULTIPLE"]["TZ_FILE"]
|
||||
# script:
|
||||
# "../../participants/prepare_usernames_file.py"
|
||||
rule query_usernames_device_empatica_ids:
|
||||
params:
|
||||
baseline_folder = "/mnt/e/STRAWbaseline/"
|
||||
output:
|
||||
usernames_file = config["CREATE_PARTICIPANT_FILES"]["USERNAMES_CSV"],
|
||||
timezone_file = config["TIMEZONE"]["MULTIPLE"]["TZ_FILE"]
|
||||
script:
|
||||
"../../participants/prepare_usernames_file.py"
|
||||
|
||||
rule prepare_tzcodes_file:
|
||||
input:
|
||||
|
|
@ -247,33 +247,5 @@ rule empatica_readable_datetime:
|
|||
include_past_periodic_segments = config["TIME_SEGMENTS"]["INCLUDE_PAST_PERIODIC_SEGMENTS"]
|
||||
output:
|
||||
"data/raw/{pid}/empatica_{sensor}_with_datetime.csv"
|
||||
resources:
|
||||
mem_mb=50000
|
||||
script:
|
||||
"../src/data/datetime/readable_datetime.R"
|
||||
|
||||
|
||||
rule extract_event_information_from_esm:
|
||||
input:
|
||||
esm_raw_input = "data/raw/{pid}/phone_esm_raw.csv",
|
||||
pid_file = "data/external/participant_files/{pid}.yaml"
|
||||
params:
|
||||
stage = "extract",
|
||||
pid = "{pid}"
|
||||
output:
|
||||
"data/raw/ers/{pid}_ers.csv",
|
||||
"data/raw/ers/{pid}_stress_event_targets.csv"
|
||||
script:
|
||||
"../src/features/phone_esm/straw/process_user_event_related_segments.py"
|
||||
|
||||
rule merge_event_related_segments_files:
|
||||
input:
|
||||
ers_files = expand("data/raw/ers/{pid}_ers.csv", pid=config["PIDS"]),
|
||||
se_files = expand("data/raw/ers/{pid}_stress_event_targets.csv", pid=config["PIDS"])
|
||||
params:
|
||||
stage = "merge"
|
||||
output:
|
||||
"data/external/straw_events.csv",
|
||||
"data/external/stress_event_targets.csv"
|
||||
script:
|
||||
"../src/features/phone_esm/straw/process_user_event_related_segments.py"
|
||||
|
|
@ -29,17 +29,24 @@ get_genre <- function(apps){
|
|||
apps <- read.csv(snakemake@input[[1]], stringsAsFactors = F)
|
||||
genre_catalogue <- data.frame()
|
||||
catalogue_source <- snakemake@params[["catalogue_source"]]
|
||||
package_names_hashed <- snakemake@params[["package_names_hashed"]]
|
||||
update_catalogue_file <- snakemake@params[["update_catalogue_file"]]
|
||||
scrape_missing_genres <- snakemake@params[["scrape_missing_genres"]]
|
||||
apps_with_genre <- data.frame(matrix(ncol=length(colnames(apps)) + 1,nrow=0, dimnames=list(NULL, c(colnames(apps), "genre"))))
|
||||
|
||||
if (length(package_names_hashed) == 0) {package_names_hashed <- FALSE}
|
||||
|
||||
if(nrow(apps) > 0){
|
||||
if(catalogue_source == "GOOGLE"){
|
||||
apps_with_genre <- apps %>% mutate(genre = NA_character_)
|
||||
} else if(catalogue_source == "FILE"){
|
||||
genre_catalogue <- read.csv(snakemake@params[["catalogue_file"]], colClasses = c("character", "character"))
|
||||
if (package_names_hashed) {
|
||||
apps_with_genre <- left_join(apps, genre_catalogue, by = "package_hash")
|
||||
} else {
|
||||
apps_with_genre <- left_join(apps, genre_catalogue, by = "package_name")
|
||||
}
|
||||
}
|
||||
|
||||
if(catalogue_source == "GOOGLE" || (catalogue_source == "FILE" && scrape_missing_genres)){
|
||||
apps_without_genre <- (apps_with_genre %>% filter(is.na(genre)) %>% distinct(package_name))$package_name
|
||||
|
|
|
|||
|
|
@ -60,15 +60,15 @@ if not participant_info.empty:
|
|||
0, "startlanguage"
|
||||
]
|
||||
if (
|
||||
("limesurvey_demand" in requested_features)
|
||||
or ("limesurvey_control" in requested_features)
|
||||
or ("limesurvey_demand_control_ratio" in requested_features)
|
||||
("demand" in requested_features)
|
||||
or ("control" in requested_features)
|
||||
or ("demand_control_ratio" in requested_features)
|
||||
):
|
||||
participant_info_t = participant_info.T
|
||||
rows_baseline = participant_info_t.index
|
||||
|
||||
if ("limesurvey_demand" in requested_features) or (
|
||||
"limesurvey_demand_control_ratio" in requested_features
|
||||
if ("demand" in requested_features) or (
|
||||
"demand_control_ratio" in requested_features
|
||||
):
|
||||
# Find questions about demand, but disregard time (duration of filling in questionnaire)
|
||||
rows_demand = rows_baseline.str.startswith(
|
||||
|
|
@ -95,13 +95,13 @@ if not participant_info.empty:
|
|||
- limesurvey_demand.loc[rows_demand_reverse, "score_original"]
|
||||
)
|
||||
baseline_interim = pd.concat([baseline_interim, limesurvey_demand], axis=0, ignore_index=True)
|
||||
if "limesurvey_demand" in requested_features:
|
||||
baseline_features.loc[0, "limesurvey_demand"] = limesurvey_demand[
|
||||
if "demand" in requested_features:
|
||||
baseline_features.loc[0, "demand"] = limesurvey_demand[
|
||||
"score"
|
||||
].sum()
|
||||
|
||||
if ("limesurvey_control" in requested_features) or (
|
||||
"limesurvey_demand_control_ratio" in requested_features
|
||||
if ("control" in requested_features) or (
|
||||
"demand_control_ratio" in requested_features
|
||||
):
|
||||
# Find questions about control, but disregard time (duration of filling in questionnaire)
|
||||
rows_control = rows_baseline.str.startswith(
|
||||
|
|
@ -130,18 +130,15 @@ if not participant_info.empty:
|
|||
|
||||
baseline_interim = pd.concat([baseline_interim, limesurvey_control], axis=0, ignore_index=True)
|
||||
|
||||
if "limesurvey_control" in requested_features:
|
||||
baseline_features.loc[0, "limesurvey_control"] = limesurvey_control[
|
||||
if "control" in requested_features:
|
||||
baseline_features.loc[0, "control"] = limesurvey_control[
|
||||
"score"
|
||||
].sum()
|
||||
|
||||
if "limesurvey_demand_control_ratio" in requested_features:
|
||||
if limesurvey_control["score"].sum():
|
||||
if "demand_control_ratio" in requested_features:
|
||||
limesurvey_demand_control_ratio = (
|
||||
limesurvey_demand["score"].sum() / limesurvey_control["score"].sum()
|
||||
)
|
||||
else:
|
||||
limesurvey_demand_control_ratio = 0
|
||||
if (
|
||||
JCQ_NORMS[participant_info.loc[0, "gender"]][0]
|
||||
<= limesurvey_demand_control_ratio
|
||||
|
|
@ -170,10 +167,10 @@ if not participant_info.empty:
|
|||
limesurvey_quartile = np.nan
|
||||
|
||||
baseline_features.loc[
|
||||
0, "limesurvey_demand_control_ratio"
|
||||
0, "demand_control_ratio"
|
||||
] = limesurvey_demand_control_ratio
|
||||
baseline_features.loc[
|
||||
0, "limesurvey_demand_control_ratio_quartile"
|
||||
0, "demand_control_ratio_quartile"
|
||||
] = limesurvey_quartile
|
||||
|
||||
if not baseline_interim.empty:
|
||||
|
|
|
|||
|
|
@ -58,7 +58,7 @@ participants %>%
|
|||
lines <- append(lines, empty_fitbit)
|
||||
|
||||
if(add_empatica_section == TRUE && !is.na(row[empatica_device_id_column])){
|
||||
lines <- append(lines, c("EMPATICA:", paste0(" DEVICE_IDS: [",row$label,"]"),
|
||||
lines <- append(lines, c("EMPATICA:", paste0(" DEVICE_IDS: [",row[empatica_device_id_column],"]"),
|
||||
paste(" LABEL:",row$label), paste(" START_DATE:", start_date), paste(" END_DATE:", end_date)))
|
||||
} else
|
||||
lines <- append(lines, empty_empatica)
|
||||
|
|
|
|||
|
|
@ -5,16 +5,13 @@ options(scipen=999)
|
|||
|
||||
assign_rows_to_segments <- function(data, segments){
|
||||
# This function is used by all segment types, we use data.tables because they are fast
|
||||
|
||||
data <- data.table::as.data.table(data)
|
||||
data[, assigned_segments := ""]
|
||||
for(i in seq_len(nrow(segments))) {
|
||||
segment <- segments[i,]
|
||||
|
||||
data[segment$segment_start_ts<= timestamp & segment$segment_end_ts >= timestamp,
|
||||
assigned_segments := stringi::stri_c(assigned_segments, segment$segment_id, sep = "|")]
|
||||
}
|
||||
|
||||
data[,assigned_segments:=substring(assigned_segments, 2)]
|
||||
data
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,85 +0,0 @@
|
|||
# if you need a new package, you should add it with renv::install(package) so your renv venv is updated
|
||||
library(RMariaDB)
|
||||
library(yaml)
|
||||
|
||||
#' @description
|
||||
#' Auxiliary function to parse the connection credentials from a specifc group in ./credentials.yaml
|
||||
#' You can reause most of this function if you are connection to a DB or Web API.
|
||||
#' It's OK to delete this function if you don't need credentials, e.g., you are pulling data from a CSV for example.
|
||||
#' @param group the yaml key containing the credentials to connect to a database
|
||||
#' @preturn dbEngine a database engine (connection) ready to perform queries
|
||||
get_db_engine <- function(group){
|
||||
# The working dir is aways RAPIDS root folder, so your credentials file is always /credentials.yaml
|
||||
credentials <- read_yaml("./credentials.yaml")
|
||||
if(!group %in% names(credentials))
|
||||
stop(paste("The credentials group",group, "does not exist in ./credentials.yaml. The only groups that exist in that file are:", paste(names(credentials), collapse = ","), ". Did you forget to set the group in [PHONE_DATA_STREAMS][aware_mysql][DATABASE_GROUP] in config.yaml?"))
|
||||
dbEngine <- dbConnect(MariaDB(), db = credentials[[group]][["database"]],
|
||||
username = credentials[[group]][["user"]],
|
||||
password = credentials[[group]][["password"]],
|
||||
host = credentials[[group]][["host"]],
|
||||
port = credentials[[group]][["port"]])
|
||||
return(dbEngine)
|
||||
}
|
||||
|
||||
# This file gets executed for each PHONE_SENSOR of each participant
|
||||
# If you are connecting to a database the env file containing its credentials is available at "./.env"
|
||||
# If you are reading a CSV file instead of a DB table, the @param sensor_container wil contain the file path as set in config.yaml
|
||||
# You are not bound to databases or files, you can query a web API or whatever data source you need.
|
||||
|
||||
#' @description
|
||||
#' RAPIDS allows users to use the keyword "infer" (previously "multiple") to automatically infer the mobile Operative System a device was running.
|
||||
#' If you have a way to infer the OS of a device ID, implement this function. For example, for AWARE data we use the "aware_device" table.
|
||||
#'
|
||||
#' If you don't have a way to infer the OS, call stop("Error Message") so other users know they can't use "infer" or the inference failed,
|
||||
#' and they have to assign the OS manually in the participant file
|
||||
#'
|
||||
#' @param stream_parameters The PHONE_STREAM_PARAMETERS key in config.yaml. If you need specific parameters add them there.
|
||||
#' @param device A device ID string
|
||||
#' @return The OS the device ran, "android" or "ios"
|
||||
|
||||
infer_device_os <- function(stream_parameters, device){
|
||||
dbEngine <- get_db_engine(stream_parameters$DATABASE_GROUP)
|
||||
query <- paste0("SELECT device_id,brand FROM aware_device WHERE device_id = '", device, "'")
|
||||
message(paste0("Executing the following query to infer phone OS: ", query))
|
||||
os <- dbGetQuery(dbEngine, query)
|
||||
dbDisconnect(dbEngine)
|
||||
|
||||
if(nrow(os) > 0)
|
||||
return(os %>% mutate(os = ifelse(brand == "iPhone", "ios", "android")) %>% pull(os))
|
||||
else
|
||||
stop(paste("We cannot infer the OS of the following device id because it does not exist in the aware_device table:", device))
|
||||
|
||||
return(os)
|
||||
}
|
||||
|
||||
#' @description
|
||||
#' Gets the sensor data for a specific device id from a database table, file or whatever source you want to query
|
||||
#'
|
||||
#' @param stream_parameters The PHONE_STREAM_PARAMETERS key in config.yaml. If you need specific parameters add them there.
|
||||
#' @param device A device ID string
|
||||
#' @param sensor_container database table or file containing the sensor data for all participants. This is the PHONE_SENSOR[CONTAINER] key in config.yaml
|
||||
#' @param columns the columns needed from this sensor (we recommend to only return these columns instead of every column in sensor_container)
|
||||
#' @return A dataframe with the sensor data for device
|
||||
|
||||
pull_data <- function(stream_parameters, device, sensor, sensor_container, columns){
|
||||
dbEngine <- get_db_engine(stream_parameters$DATABASE_GROUP)
|
||||
|
||||
select_items <- c()
|
||||
for (column in columns) {
|
||||
select_items <- append(select_items, paste0("data->>'$.", column, "' ", column))
|
||||
}
|
||||
|
||||
query <- paste0("SELECT ", paste(select_items, collapse = ",")," FROM ", sensor_container, " WHERE ", columns$DEVICE_ID ," = '", device,"'")
|
||||
|
||||
# Letting the user know what we are doing
|
||||
message(paste0("Executing the following query to download data: ", query))
|
||||
sensor_data <- dbGetQuery(dbEngine, query)
|
||||
|
||||
dbDisconnect(dbEngine)
|
||||
|
||||
if(nrow(sensor_data) == 0)
|
||||
warning(paste("The device '", device,"' did not have data in ", sensor_container))
|
||||
|
||||
return(sensor_data)
|
||||
}
|
||||
|
||||
|
|
@ -1,337 +0,0 @@
|
|||
PHONE_ACCELEROMETER:
|
||||
ANDROID:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
DOUBLE_VALUES_0: double_values_0
|
||||
DOUBLE_VALUES_1: double_values_1
|
||||
DOUBLE_VALUES_2: double_values_2
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
IOS:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
DOUBLE_VALUES_0: double_values_0
|
||||
DOUBLE_VALUES_1: double_values_1
|
||||
DOUBLE_VALUES_2: double_values_2
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
|
||||
PHONE_ACTIVITY_RECOGNITION:
|
||||
ANDROID:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
ACTIVITY_NAME: activity_name
|
||||
ACTIVITY_TYPE: activity_type
|
||||
CONFIDENCE: confidence
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
IOS:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
ACTIVITY_NAME: FLAG_TO_MUTATE
|
||||
ACTIVITY_TYPE: FLAG_TO_MUTATE
|
||||
CONFIDENCE: FLAG_TO_MUTATE
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
ACTIVITIES: activities
|
||||
CONFIDENCE: confidence
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
- "src/data/streams/mutations/phone/aware/activity_recogniton_ios_unification.R"
|
||||
|
||||
PHONE_APPLICATIONS_CRASHES:
|
||||
ANDROID:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
PACKAGE_NAME: package_name
|
||||
APPLICATION_NAME: application_name
|
||||
APPLICATION_VERSION: application_version
|
||||
ERROR_SHORT: error_short
|
||||
ERROR_LONG: error_long
|
||||
ERROR_CONDITION: error_condition
|
||||
IS_SYSTEM_APP: is_system_app
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
|
||||
PHONE_APPLICATIONS_FOREGROUND:
|
||||
ANDROID:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
PACKAGE_NAME: package_name
|
||||
APPLICATION_NAME: application_name
|
||||
IS_SYSTEM_APP: is_system_app
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
|
||||
PHONE_APPLICATIONS_NOTIFICATIONS:
|
||||
ANDROID:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
PACKAGE_NAME: package_name
|
||||
APPLICATION_NAME: application_name
|
||||
TEXT: text
|
||||
SOUND: sound
|
||||
VIBRATE: vibrate
|
||||
DEFAULTS: defaults
|
||||
FLAGS: flags
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
|
||||
PHONE_BATTERY:
|
||||
ANDROID:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
BATTERY_STATUS: battery_status
|
||||
BATTERY_LEVEL: battery_level
|
||||
BATTERY_SCALE: battery_scale
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
IOS:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
BATTERY_STATUS: FLAG_TO_MUTATE
|
||||
BATTERY_LEVEL: battery_level
|
||||
BATTERY_SCALE: battery_scale
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
BATTERY_STATUS: battery_status
|
||||
SCRIPTS:
|
||||
- "src/data/streams/mutations/phone/aware/battery_ios_unification.R"
|
||||
|
||||
PHONE_BLUETOOTH:
|
||||
ANDROID:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
BT_ADDRESS: bt_address
|
||||
BT_NAME: bt_name
|
||||
BT_RSSI: bt_rssi
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
IOS:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
BT_ADDRESS: bt_address
|
||||
BT_NAME: bt_name
|
||||
BT_RSSI: bt_rssi
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
|
||||
PHONE_CALLS:
|
||||
ANDROID:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
CALL_TYPE: call_type
|
||||
CALL_DURATION: call_duration
|
||||
TRACE: trace
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
IOS:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
CALL_TYPE: FLAG_TO_MUTATE
|
||||
CALL_DURATION: call_duration
|
||||
TRACE: trace
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
CALL_TYPE: call_type
|
||||
SCRIPTS:
|
||||
- "src/data/streams/mutations/phone/aware/calls_ios_unification.R"
|
||||
|
||||
PHONE_CONVERSATION:
|
||||
ANDROID:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
DOUBLE_ENERGY: double_energy
|
||||
INFERENCE: inference
|
||||
DOUBLE_CONVO_START: double_convo_start
|
||||
DOUBLE_CONVO_END: double_convo_end
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
IOS:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
DOUBLE_ENERGY: double_energy
|
||||
INFERENCE: inference
|
||||
DOUBLE_CONVO_START: double_convo_start
|
||||
DOUBLE_CONVO_END: double_convo_end
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
- "src/data/streams/mutations/phone/aware/conversation_ios_timestamp.R"
|
||||
|
||||
PHONE_KEYBOARD:
|
||||
ANDROID:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
PACKAGE_NAME: package_name
|
||||
BEFORE_TEXT: before_text
|
||||
CURRENT_TEXT: current_text
|
||||
IS_PASSWORD: is_password
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
|
||||
PHONE_LIGHT:
|
||||
ANDROID:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
DOUBLE_LIGHT_LUX: double_light_lux
|
||||
ACCURACY: accuracy
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
|
||||
PHONE_LOCATIONS:
|
||||
ANDROID:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
DOUBLE_LATITUDE: double_latitude
|
||||
DOUBLE_LONGITUDE: double_longitude
|
||||
DOUBLE_BEARING: double_bearing
|
||||
DOUBLE_SPEED: double_speed
|
||||
DOUBLE_ALTITUDE: double_altitude
|
||||
PROVIDER: provider
|
||||
ACCURACY: accuracy
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
IOS:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
DOUBLE_LATITUDE: double_latitude
|
||||
DOUBLE_LONGITUDE: double_longitude
|
||||
DOUBLE_BEARING: double_bearing
|
||||
DOUBLE_SPEED: double_speed
|
||||
DOUBLE_ALTITUDE: double_altitude
|
||||
PROVIDER: provider
|
||||
ACCURACY: accuracy
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
|
||||
PHONE_LOG:
|
||||
ANDROID:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
LOG_MESSAGE: log_message
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
IOS:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
LOG_MESSAGE: log_message
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
|
||||
PHONE_MESSAGES:
|
||||
ANDROID:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
MESSAGE_TYPE: message_type
|
||||
TRACE: trace
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
|
||||
PHONE_SCREEN:
|
||||
ANDROID:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
SCREEN_STATUS: screen_status
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
IOS:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
SCREEN_STATUS: FLAG_TO_MUTATE
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCREEN_STATUS: screen_status
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
- "src/data/streams/mutations/phone/aware/screen_ios_unification.R"
|
||||
|
||||
PHONE_WIFI_CONNECTED:
|
||||
ANDROID:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
MAC_ADDRESS: mac_address
|
||||
SSID: ssid
|
||||
BSSID: bssid
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
IOS:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
MAC_ADDRESS: mac_address
|
||||
SSID: ssid
|
||||
BSSID: bssid
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
|
||||
PHONE_WIFI_VISIBLE:
|
||||
ANDROID:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
SSID: ssid
|
||||
BSSID: bssid
|
||||
SECURITY: security
|
||||
FREQUENCY: frequency
|
||||
RSSI: rssi
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
IOS:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
SSID: ssid
|
||||
BSSID: bssid
|
||||
SECURITY: security
|
||||
FREQUENCY: frequency
|
||||
RSSI: rssi
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
|
||||
|
|
@ -349,24 +349,3 @@ PHONE_WIFI_VISIBLE:
|
|||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
|
||||
PHONE_SPEECH:
|
||||
ANDROID:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
SPEECH_PROPORTION: speech_proportion
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
IOS:
|
||||
RAPIDS_COLUMN_MAPPINGS:
|
||||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
SPEECH_PROPORTION: speech_proportion
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -2,16 +2,11 @@ from zipfile import ZipFile
|
|||
import warnings
|
||||
from pathlib import Path
|
||||
import pandas as pd
|
||||
import numpy as np
|
||||
from pandas.core import indexing
|
||||
import yaml
|
||||
import csv
|
||||
from collections import OrderedDict
|
||||
from io import BytesIO, StringIO
|
||||
import sys, os
|
||||
|
||||
from cr_features.hrv import get_HRV_features, get_patched_ibi_with_bvp
|
||||
from cr_features.helper_functions import empatica1d_to_array, empatica2d_to_array
|
||||
|
||||
def processAcceleration(x, y, z):
|
||||
x = float(x)
|
||||
|
|
@ -57,8 +52,6 @@ def extract_empatica_data(data, sensor):
|
|||
df = pd.DataFrame.from_dict(ddict, orient='index', columns=[column])
|
||||
df[column] = df[column].astype(float)
|
||||
df.index.name = 'timestamp'
|
||||
if df.empty:
|
||||
return df
|
||||
|
||||
elif sensor == 'EMPATICA_ACCELEROMETER':
|
||||
ddict = readFile(sensor_data_file, sensor)
|
||||
|
|
@ -67,16 +60,9 @@ def extract_empatica_data(data, sensor):
|
|||
df['y'] = df['y'].astype(float)
|
||||
df['z'] = df['z'].astype(float)
|
||||
df.index.name = 'timestamp'
|
||||
if df.empty:
|
||||
return df
|
||||
|
||||
elif sensor == 'EMPATICA_INTER_BEAT_INTERVAL':
|
||||
|
||||
df = pd.read_csv(sensor_data_file, names=['timings', column], header=None)
|
||||
df['timestamp'] = df['timings']
|
||||
if df.empty:
|
||||
df = df.set_index('timestamp')
|
||||
return df
|
||||
df = pd.read_csv(sensor_data_file, names=['timestamp', column], header=None)
|
||||
timestampstart = float(df['timestamp'][0])
|
||||
df['timestamp'] = (df['timestamp'][1:len(df)]).astype(float) + timestampstart
|
||||
df = df.drop([0])
|
||||
|
|
@ -98,10 +84,6 @@ def pull_data(data_configuration, device, sensor, container, columns_to_download
|
|||
participant_data = pd.DataFrame(columns=columns_to_download.values())
|
||||
participant_data.set_index('timestamp', inplace=True)
|
||||
|
||||
with open('config.yaml', 'r') as stream:
|
||||
config = yaml.load(stream, Loader=yaml.FullLoader)
|
||||
cr_ibi_provider = config['EMPATICA_INTER_BEAT_INTERVAL']['PROVIDERS']['CR']
|
||||
|
||||
available_zipfiles = list((Path(data_configuration["FOLDER"]) / Path(device)).rglob("*.zip"))
|
||||
if len(available_zipfiles) == 0:
|
||||
warnings.warn("There were no zip files in: {}. If you were expecting data for this participant the [EMPATICA][DEVICE_IDS] key in their participant file is missing the pid".format((Path(data_configuration["FOLDER"]) / Path(device))))
|
||||
|
|
@ -112,13 +94,7 @@ def pull_data(data_configuration, device, sensor, container, columns_to_download
|
|||
listOfFileNames = zipFile.namelist()
|
||||
for fileName in listOfFileNames:
|
||||
if fileName == sensor_csv:
|
||||
if sensor == "EMPATICA_INTER_BEAT_INTERVAL" and cr_ibi_provider.get('PATCH_WITH_BVP', False):
|
||||
participant_data = \
|
||||
pd.concat([participant_data, patch_ibi_with_bvp(zipFile.read('IBI.csv'), zipFile.read('BVP.csv'))], axis=0)
|
||||
#print("patch with ibi")
|
||||
else:
|
||||
participant_data = pd.concat([participant_data, extract_empatica_data(zipFile.read(fileName), sensor)], axis=0)
|
||||
#print("no patching")
|
||||
warning = False
|
||||
if warning:
|
||||
warnings.warn("We could not find a zipped file for {} in {} (we tried to find {})".format(sensor, zipFile, sensor_csv))
|
||||
|
|
@ -129,54 +105,4 @@ def pull_data(data_configuration, device, sensor, container, columns_to_download
|
|||
participant_data["device_id"] = device
|
||||
return(participant_data)
|
||||
|
||||
def patch_ibi_with_bvp(ibi_data, bvp_data):
|
||||
ibi_data_file = BytesIO(ibi_data).getvalue().decode('utf-8')
|
||||
ibi_data_file = StringIO(ibi_data_file)
|
||||
|
||||
# Begin with the cr-features part
|
||||
try:
|
||||
ibi_data, ibi_start_timestamp = empatica2d_to_array(ibi_data_file)
|
||||
except (IndexError, KeyError) as e:
|
||||
# Checks whether IBI.csv is empty
|
||||
# It may raise a KeyError if df is empty here: startTimeStamp = df.time[0]
|
||||
df_test = pd.read_csv(ibi_data_file, names=['timings', 'inter_beat_interval'], header=None)
|
||||
if df_test.empty:
|
||||
df_test['timestamp'] = df_test['timings']
|
||||
df_test = df_test.set_index('timestamp')
|
||||
return df_test
|
||||
else:
|
||||
raise IndexError("Something went wrong with indices. Error that was previously caught:\n", repr(e))
|
||||
|
||||
bvp_data_file = BytesIO(bvp_data).getvalue().decode('utf-8')
|
||||
bvp_data_file = StringIO(bvp_data_file)
|
||||
|
||||
bvp_data, bvp_start_timestamp, sample_rate = empatica1d_to_array(bvp_data_file)
|
||||
|
||||
hrv_time_and_freq_features, sample, bvp_rr, bvp_timings, peak_indx = \
|
||||
get_HRV_features(bvp_data, ma=False,
|
||||
detrend=False, m_deternd=False, low_pass=False, winsorize=True,
|
||||
winsorize_value=25, hampel_fiter=False, median_filter=False,
|
||||
mod_z_score_filter=True, sampling=64, feature_names=['meanHr'])
|
||||
|
||||
ibi_timings, ibi_rr = get_patched_ibi_with_bvp(ibi_data[0], ibi_data[1], bvp_timings, bvp_rr)
|
||||
|
||||
df = \
|
||||
pd.DataFrame(np.array([ibi_timings, ibi_rr]).transpose(), columns=['timestamp', 'inter_beat_interval'])
|
||||
df.loc[-1] = [ibi_start_timestamp, 'IBI'] # adding a row
|
||||
df.index = df.index + 1 # shifting index
|
||||
df = df.sort_index() # sorting by index
|
||||
|
||||
# Repeated as in extract_empatica_data for IBI
|
||||
df['timings'] = df['timestamp']
|
||||
timestampstart = float(df['timestamp'][0])
|
||||
df['timestamp'] = (df['timestamp'][1:len(df)]).astype(float) + timestampstart
|
||||
df = df.drop([0])
|
||||
df['inter_beat_interval'] = df['inter_beat_interval'].astype(float)
|
||||
df = df.set_index('timestamp')
|
||||
|
||||
# format timestamps
|
||||
df.index *= 1000
|
||||
df.index = df.index.astype(int)
|
||||
return(df)
|
||||
|
||||
# print(pull_data({'FOLDER': 'data/external/empatica'}, "e01", "EMPATICA_accelerometer", {'TIMESTAMP': 'timestamp', 'DEVICE_ID': 'device_id', 'DOUBLE_VALUES_0': 'x', 'DOUBLE_VALUES_1': 'y', 'DOUBLE_VALUES_2': 'z'}))
|
||||
|
|
@ -50,7 +50,6 @@ EMPATICA_INTER_BEAT_INTERVAL:
|
|||
TIMESTAMP: timestamp
|
||||
DEVICE_ID: device_id
|
||||
INTER_BEAT_INTERVAL: inter_beat_interval
|
||||
TIMINGS: timings
|
||||
MUTATION:
|
||||
COLUMN_MAPPINGS:
|
||||
SCRIPTS: # List any python or r scripts that mutate your raw data
|
||||
|
|
|
|||
|
|
@ -39,7 +39,7 @@ unify_ios_calls <- function(ios_calls){
|
|||
assigned_segments = first(assigned_segments))
|
||||
}
|
||||
else {
|
||||
ios_calls <- ios_calls %>% summarise(call_type_sequence = paste(call_type, collapse = ","), call_duration = sum(as.numeric(call_duration)), timestamp = first(timestamp), device_id = first(device_id))
|
||||
ios_calls <- ios_calls %>% summarise(call_type_sequence = paste(call_type, collapse = ","), call_duration = sum(call_duration), timestamp = first(timestamp), device_id = first(device_id))
|
||||
}
|
||||
ios_calls <- ios_calls %>% mutate(call_type = case_when(
|
||||
call_type_sequence == "1,2,4" | call_type_sequence == "2,1,4" ~ 1, # incoming
|
||||
|
|
|
|||
|
|
@ -118,11 +118,6 @@ PHONE_SCREEN:
|
|||
- DEVICE_ID
|
||||
- SCREEN_STATUS
|
||||
|
||||
PHONE_SPEECH:
|
||||
- TIMESTAMP
|
||||
- DEVICE_ID
|
||||
- SPEECH_PROPORTION
|
||||
|
||||
PHONE_WIFI_CONNECTED:
|
||||
- TIMESTAMP
|
||||
- DEVICE_ID
|
||||
|
|
@ -232,7 +227,6 @@ EMPATICA_INTER_BEAT_INTERVAL:
|
|||
- TIMESTAMP
|
||||
- DEVICE_ID
|
||||
- INTER_BEAT_INTERVAL
|
||||
- TIMINGS
|
||||
|
||||
EMPATICA_TAGS:
|
||||
- TIMESTAMP
|
||||
|
|
|
|||
|
|
@ -39,18 +39,16 @@ rapids_cleaning <- function(sensor_data_files, provider){
|
|||
if(!data_yield_column %in% colnames(clean_features)){
|
||||
stop(paste0("Error: RAPIDS provider needs to clean data based on ", data_yield_column, " column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded", data_yield_unit, "' in [FEATURES]."))
|
||||
}
|
||||
if (data_yield_ratio_threshold > 0) {
|
||||
clean_features <- clean_features %>%
|
||||
filter(.[[data_yield_column]] >= data_yield_ratio_threshold)
|
||||
}
|
||||
|
||||
# Drop columns with a percentage of NA values above cols_nan_threshold
|
||||
if(nrow(clean_features))
|
||||
clean_features <- clean_features %>% select(where(~ sum(is.na(.)) / length(.) <= cols_nan_threshold ), starts_with("phone_esm"))
|
||||
clean_features <- clean_features %>% select_if(~ sum(is.na(.)) / length(.) <= cols_nan_threshold )
|
||||
|
||||
# Drop columns with zero variance
|
||||
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|phone_esm",names(.)) | sapply(., n_distinct, na.rm = T) > 1)
|
||||
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 highly correlated features
|
||||
if(as.logical(drop_highly_correlated_features$COMPUTE)){
|
||||
|
|
|
|||
|
|
@ -1,180 +0,0 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
import math, sys, random
|
||||
import yaml
|
||||
|
||||
from sklearn.impute import KNNImputer
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
|
||||
sys.path.append('/rapids/')
|
||||
from src.features import empatica_data_yield as edy
|
||||
|
||||
pd.set_option('display.max_columns', 20)
|
||||
|
||||
def straw_cleaning(sensor_data_files, provider):
|
||||
|
||||
features = pd.read_csv(sensor_data_files["sensor_data"][0])
|
||||
|
||||
esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
|
||||
|
||||
with open('config.yaml', 'r') as stream:
|
||||
config = yaml.load(stream, Loader=yaml.FullLoader)
|
||||
|
||||
excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
|
||||
|
||||
# (1) FILTER_OUT THE ROWS THAT DO NOT HAVE THE TARGET COLUMN AVAILABLE
|
||||
if config['PARAMS_FOR_ANALYSIS']['TARGET']['COMPUTE']:
|
||||
target = config['PARAMS_FOR_ANALYSIS']['TARGET']['LABEL'] # get target label from config
|
||||
if 'phone_esm_straw_' + target in features:
|
||||
features = features[features['phone_esm_straw_' + target].notna()].reset_index(drop=True)
|
||||
else:
|
||||
return features
|
||||
|
||||
# (2.1) QUALITY CHECK (DATA YIELD COLUMN) deletes the rows where E4 or phone data is low quality
|
||||
phone_data_yield_unit = provider["PHONE_DATA_YIELD_FEATURE"].split("_")[3].lower()
|
||||
phone_data_yield_column = "phone_data_yield_rapids_ratiovalidyielded" + phone_data_yield_unit
|
||||
|
||||
if features.empty:
|
||||
return features
|
||||
|
||||
features = edy.calculate_empatica_data_yield(features)
|
||||
|
||||
if not phone_data_yield_column in features.columns and not "empatica_data_yield" in features.columns:
|
||||
raise KeyError(f"RAPIDS provider needs to clean the selected event features based on {phone_data_yield_column} and empatica_data_yield columns. For phone data yield, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded{data_yield_unit}' in [FEATURES].")
|
||||
|
||||
# Drop rows where phone data yield is less then given threshold
|
||||
if provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]:
|
||||
features = features[features[phone_data_yield_column] >= provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
|
||||
|
||||
# Drop rows where empatica data yield is less then given threshold
|
||||
if provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]:
|
||||
features = features[features["empatica_data_yield"] >= provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
|
||||
|
||||
if features.empty:
|
||||
return features
|
||||
|
||||
# (2.2) DO THE ROWS CONSIST OF ENOUGH NON-NAN VALUES?
|
||||
min_count = math.ceil((1 - provider["ROWS_NAN_THRESHOLD"]) * features.shape[1]) # minimal not nan values in row
|
||||
features.dropna(axis=0, thresh=min_count, inplace=True) # Thresh => at least this many not-nans
|
||||
|
||||
# (3) REMOVE COLS IF THEIR NAN THRESHOLD IS PASSED (should be <= if even all NaN columns must be preserved - this solution now drops columns with all NaN rows)
|
||||
esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
|
||||
|
||||
features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]
|
||||
|
||||
# Preserve esm cols if deleted (has to come after drop cols operations)
|
||||
for esm in esm_cols:
|
||||
if esm not in features:
|
||||
features[esm] = esm_cols[esm]
|
||||
|
||||
# (4) CONTEXTUAL IMPUTATION
|
||||
|
||||
# Impute selected phone features with a high number
|
||||
impute_w_hn = [col for col in features.columns if \
|
||||
"timeoffirstuse" in col or
|
||||
"timeoflastuse" in col or
|
||||
"timefirstcall" in col or
|
||||
"timelastcall" in col or
|
||||
"firstuseafter" in col or
|
||||
"timefirstmessages" in col or
|
||||
"timelastmessages" in col]
|
||||
features[impute_w_hn] = features[impute_w_hn].fillna(1500)
|
||||
|
||||
|
||||
# Impute special case (mostcommonactivity) and (homelabel)
|
||||
impute_w_sn = [col for col in features.columns if "mostcommonactivity" in col]
|
||||
features[impute_w_sn] = features[impute_w_sn].fillna(4) # Special case of imputation - nominal/ordinal value
|
||||
|
||||
impute_w_sn2 = [col for col in features.columns if "homelabel" in col]
|
||||
features[impute_w_sn2] = features[impute_w_sn2].fillna(1) # Special case of imputation - nominal/ordinal value
|
||||
|
||||
impute_w_sn3 = [col for col in features.columns if "loglocationvariance" in col]
|
||||
features[impute_w_sn2] = features[impute_w_sn2].fillna(-1000000) # Special case of imputation - nominal/ordinal value
|
||||
|
||||
|
||||
# Impute selected phone features with 0
|
||||
impute_zero = [col for col in features if \
|
||||
col.startswith('phone_applications_foreground_rapids_') or
|
||||
col.startswith('phone_battery_rapids_') or
|
||||
col.startswith('phone_bluetooth_rapids_') or
|
||||
col.startswith('phone_light_rapids_') or
|
||||
col.startswith('phone_calls_rapids_') or
|
||||
col.startswith('phone_messages_rapids_') or
|
||||
col.startswith('phone_screen_rapids_') or
|
||||
col.startswith('phone_wifi_visible')]
|
||||
|
||||
features[impute_zero+list(esm_cols.columns)] = features[impute_zero+list(esm_cols.columns)].fillna(0)
|
||||
|
||||
## (5) STANDARDIZATION
|
||||
if provider["STANDARDIZATION"]:
|
||||
features.loc[:, ~features.columns.isin(excluded_columns)] = StandardScaler().fit_transform(features.loc[:, ~features.columns.isin(excluded_columns)])
|
||||
|
||||
# (6) IMPUTATION: IMPUTE DATA WITH KNN METHOD
|
||||
impute_cols = [col for col in features.columns if col not in excluded_columns]
|
||||
features.reset_index(drop=True, inplace=True)
|
||||
features[impute_cols] = impute(features[impute_cols], method="knn")
|
||||
|
||||
# (7) REMOVE COLS WHERE VARIANCE IS 0
|
||||
esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')]
|
||||
|
||||
if provider["COLS_VAR_THRESHOLD"]:
|
||||
features.drop(features.std(numeric_only=True)[features.std(numeric_only=True) == 0].index.values, axis=1, inplace=True)
|
||||
|
||||
fe5 = features.copy()
|
||||
|
||||
# (8) DROP HIGHLY CORRELATED FEATURES
|
||||
drop_corr_features = provider["DROP_HIGHLY_CORRELATED_FEATURES"]
|
||||
if drop_corr_features["COMPUTE"] and features.shape[0]: # If small amount of segments (rows) is present, do not execute correlation check
|
||||
|
||||
numerical_cols = features.select_dtypes(include=np.number).columns.tolist()
|
||||
|
||||
# Remove columns where NaN count threshold is passed
|
||||
valid_features = features[numerical_cols].loc[:, features[numerical_cols].isna().sum() < drop_corr_features['MIN_OVERLAP_FOR_CORR_THRESHOLD'] * features[numerical_cols].shape[0]]
|
||||
|
||||
corr_matrix = valid_features.corr().abs()
|
||||
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))
|
||||
to_drop = [column for column in upper.columns if any(upper[column] > drop_corr_features["CORR_THRESHOLD"])]
|
||||
|
||||
features.drop(to_drop, axis=1, inplace=True)
|
||||
|
||||
# Preserve esm cols if deleted (has to come after drop cols operations)
|
||||
for esm in esm_cols:
|
||||
if esm not in features:
|
||||
features[esm] = esm_cols[esm]
|
||||
|
||||
# (9) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
|
||||
if features.isna().any().any():
|
||||
raise ValueError("There are still some NaNs present in the dataframe. Please check for implementation errors.")
|
||||
|
||||
return features
|
||||
|
||||
|
||||
def k_nearest(df):
|
||||
pd.set_option('display.max_columns', None)
|
||||
imputer = KNNImputer(n_neighbors=3)
|
||||
return pd.DataFrame(imputer.fit_transform(df), columns=df.columns)
|
||||
|
||||
|
||||
def impute(df, method='zero'):
|
||||
|
||||
return {
|
||||
'zero': df.fillna(0),
|
||||
'high_number': df.fillna(1500),
|
||||
'mean': df.fillna(df.mean()),
|
||||
'median': df.fillna(df.median()),
|
||||
'knn': k_nearest(df)
|
||||
}[method]
|
||||
|
||||
|
||||
def graph_bf_af(features, phase_name, plt_flag=False):
|
||||
if plt_flag:
|
||||
sns.set(rc={"figure.figsize":(16, 8)})
|
||||
sns.heatmap(features.isna(), cbar=False) #features.select_dtypes(include=np.number)
|
||||
plt.savefig(f'features_overall_nans_{phase_name}.png', bbox_inches='tight')
|
||||
|
||||
print(f"\n-------------{phase_name}-------------")
|
||||
print("Rows number:", features.shape[0])
|
||||
print("Columns number:", len(features.columns))
|
||||
print("---------------------------------------------\n")
|
||||
|
|
@ -39,18 +39,16 @@ rapids_cleaning <- function(sensor_data_files, provider){
|
|||
if(!data_yield_column %in% colnames(clean_features)){
|
||||
stop(paste0("Error: RAPIDS provider needs to clean data based on ", data_yield_column, " column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded", data_yield_unit, "' in [FEATURES]."))
|
||||
}
|
||||
if (data_yield_ratio_threshold > 0) {
|
||||
clean_features <- clean_features %>%
|
||||
filter(.[[data_yield_column]] >= data_yield_ratio_threshold)
|
||||
}
|
||||
|
||||
# Drop columns with a percentage of NA values above cols_nan_threshold
|
||||
if(nrow(clean_features))
|
||||
clean_features <- clean_features %>% select(where(~ sum(is.na(.)) / length(.) <= cols_nan_threshold ), starts_with("phone_esm"))
|
||||
clean_features <- clean_features %>% select_if(~ sum(is.na(.)) / length(.) <= cols_nan_threshold )
|
||||
|
||||
# Drop columns with zero variance
|
||||
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|phone_esm",names(.)) | sapply(., n_distinct, na.rm = T) > 1)
|
||||
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 highly correlated features
|
||||
if(as.logical(drop_highly_correlated_features$COMPUTE)){
|
||||
|
|
|
|||
|
|
@ -1,275 +0,0 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
import math, sys, random, warnings, yaml
|
||||
|
||||
from sklearn.impute import KNNImputer
|
||||
from sklearn.preprocessing import StandardScaler, minmax_scale
|
||||
import matplotlib.pyplot as plt
|
||||
import seaborn as sns
|
||||
|
||||
sys.path.append('/rapids/')
|
||||
from src.features import empatica_data_yield as edy
|
||||
|
||||
def straw_cleaning(sensor_data_files, provider, target):
|
||||
|
||||
features = pd.read_csv(sensor_data_files["sensor_data"][0])
|
||||
|
||||
with open('config.yaml', 'r') as stream:
|
||||
config = yaml.load(stream, Loader=yaml.FullLoader)
|
||||
|
||||
esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
|
||||
|
||||
excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
|
||||
|
||||
graph_bf_af(features, "1target_rows_before")
|
||||
|
||||
# (1.0) OVERRIDE STRESSFULNESS EVENT TARGETS IF ERS SEGMENTING_METHOD IS "STRESS_EVENT"
|
||||
if config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["SEGMENTING_METHOD"] == "stress_event":
|
||||
|
||||
stress_events_targets = pd.read_csv("data/external/stress_event_targets.csv")
|
||||
|
||||
if "appraisal_stressfulness_event_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
|
||||
features.drop(columns=['phone_esm_straw_appraisal_stressfulness_event_mean'], inplace=True)
|
||||
features = features.merge(stress_events_targets[["label", "appraisal_stressfulness_event"]] \
|
||||
.rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
|
||||
.rename(columns={'appraisal_stressfulness_event': 'phone_esm_straw_appraisal_stressfulness_event_mean'})
|
||||
|
||||
if "appraisal_threat_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
|
||||
features.drop(columns=['phone_esm_straw_appraisal_threat_mean'], inplace=True)
|
||||
features = features.merge(stress_events_targets[["label", "appraisal_threat"]] \
|
||||
.rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
|
||||
.rename(columns={'appraisal_threat': 'phone_esm_straw_appraisal_threat_mean'})
|
||||
|
||||
if "appraisal_challenge_mean" in config['PARAMS_FOR_ANALYSIS']['TARGET']['ALL_LABELS']:
|
||||
features.drop(columns=['phone_esm_straw_appraisal_challenge_mean'], inplace=True)
|
||||
features = features.merge(stress_events_targets[["label", "appraisal_challenge"]] \
|
||||
.rename(columns={'label': 'local_segment_label'}), on=['local_segment_label'], how='inner') \
|
||||
.rename(columns={'appraisal_challenge': 'phone_esm_straw_appraisal_challenge_mean'})
|
||||
|
||||
esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
|
||||
|
||||
# (1.1) FILTER_OUT THE ROWS THAT DO NOT HAVE THE TARGET COLUMN AVAILABLE
|
||||
if config['PARAMS_FOR_ANALYSIS']['TARGET']['COMPUTE']:
|
||||
features = features[features['phone_esm_straw_' + target].notna()].reset_index(drop=True)
|
||||
|
||||
if features.empty:
|
||||
return pd.DataFrame(columns=excluded_columns)
|
||||
|
||||
graph_bf_af(features, "2target_rows_after")
|
||||
|
||||
# (2) QUALITY CHECK (DATA YIELD COLUMN) drops the rows where E4 or phone data is low quality
|
||||
phone_data_yield_unit = provider["PHONE_DATA_YIELD_FEATURE"].split("_")[3].lower()
|
||||
phone_data_yield_column = "phone_data_yield_rapids_ratiovalidyielded" + phone_data_yield_unit
|
||||
|
||||
features = edy.calculate_empatica_data_yield(features)
|
||||
|
||||
if not phone_data_yield_column in features.columns and not "empatica_data_yield" in features.columns:
|
||||
raise KeyError(f"RAPIDS provider needs to clean the selected event features based on {phone_data_yield_column} and empatica_data_yield columns. For phone data yield, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyielded{data_yield_unit}' in [FEATURES].")
|
||||
|
||||
hist = features[["empatica_data_yield", phone_data_yield_column]].hist()
|
||||
plt.savefig(f'phone_E4_histogram.png', bbox_inches='tight')
|
||||
|
||||
# Drop rows where phone data yield is less then given threshold
|
||||
if provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]:
|
||||
hist = features[phone_data_yield_column].hist(bins=5)
|
||||
plt.close()
|
||||
features = features[features[phone_data_yield_column] >= provider["PHONE_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
|
||||
|
||||
# Drop rows where empatica data yield is less then given threshold
|
||||
if provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]:
|
||||
features = features[features["empatica_data_yield"] >= provider["EMPATICA_DATA_YIELD_RATIO_THRESHOLD"]].reset_index(drop=True)
|
||||
|
||||
if features.empty:
|
||||
return pd.DataFrame(columns=excluded_columns)
|
||||
|
||||
graph_bf_af(features, "3data_yield_drop_rows")
|
||||
|
||||
if features.empty:
|
||||
return pd.DataFrame(columns=excluded_columns)
|
||||
|
||||
|
||||
# (3) CONTEXTUAL IMPUTATION
|
||||
|
||||
# Impute selected phone features with a high number
|
||||
impute_w_hn = [col for col in features.columns if \
|
||||
"timeoffirstuse" in col or
|
||||
"timeoflastuse" in col or
|
||||
"timefirstcall" in col or
|
||||
"timelastcall" in col or
|
||||
"firstuseafter" in col or
|
||||
"timefirstmessages" in col or
|
||||
"timelastmessages" in col]
|
||||
features[impute_w_hn] = features[impute_w_hn].fillna(1500)
|
||||
|
||||
# Impute special case (mostcommonactivity) and (homelabel)
|
||||
impute_w_sn = [col for col in features.columns if "mostcommonactivity" in col]
|
||||
features[impute_w_sn] = features[impute_w_sn].fillna(4) # Special case of imputation - nominal/ordinal value
|
||||
|
||||
impute_w_sn2 = [col for col in features.columns if "homelabel" in col]
|
||||
features[impute_w_sn2] = features[impute_w_sn2].fillna(1) # Special case of imputation - nominal/ordinal value
|
||||
|
||||
impute_w_sn3 = [col for col in features.columns if "loglocationvariance" in col]
|
||||
features[impute_w_sn3] = features[impute_w_sn3].fillna(-1000000) # Special case of imputation - loglocation
|
||||
|
||||
# Impute location features
|
||||
impute_locations = [col for col in features \
|
||||
if col.startswith('phone_locations_doryab_') and
|
||||
'radiusgyration' not in col
|
||||
]
|
||||
|
||||
# Impute selected phone, location, and esm features with 0
|
||||
impute_zero = [col for col in features if \
|
||||
col.startswith('phone_applications_foreground_rapids_') or
|
||||
col.startswith('phone_activity_recognition_') or
|
||||
col.startswith('phone_battery_rapids_') or
|
||||
col.startswith('phone_bluetooth_rapids_') or
|
||||
col.startswith('phone_light_rapids_') or
|
||||
col.startswith('phone_calls_rapids_') or
|
||||
col.startswith('phone_messages_rapids_') or
|
||||
col.startswith('phone_screen_rapids_') or
|
||||
col.startswith('phone_bluetooth_doryab_') or
|
||||
col.startswith('phone_wifi_visible')
|
||||
]
|
||||
|
||||
features[impute_zero+impute_locations+list(esm_cols.columns)] = features[impute_zero+impute_locations+list(esm_cols.columns)].fillna(0)
|
||||
|
||||
pd.set_option('display.max_rows', None)
|
||||
|
||||
graph_bf_af(features, "4context_imp")
|
||||
|
||||
# (4) REMOVE COLS IF THEIR NAN THRESHOLD IS PASSED (should be <= if even all NaN columns must be preserved - this solution now drops columns with all NaN rows)
|
||||
esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
|
||||
|
||||
features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]
|
||||
|
||||
graph_bf_af(features, "5too_much_nans_cols")
|
||||
# (5) REMOVE COLS WHERE VARIANCE IS 0
|
||||
|
||||
if provider["COLS_VAR_THRESHOLD"]:
|
||||
features.drop(features.std(numeric_only=True)[features.std(numeric_only=True) == 0].index.values, axis=1, inplace=True)
|
||||
|
||||
graph_bf_af(features, "6variance_drop")
|
||||
|
||||
# Preserve esm cols if deleted (has to come after drop cols operations)
|
||||
for esm in esm_cols:
|
||||
if esm not in features:
|
||||
features[esm] = esm_cols[esm]
|
||||
|
||||
# (6) DO THE ROWS CONSIST OF ENOUGH NON-NAN VALUES?
|
||||
min_count = math.ceil((1 - provider["ROWS_NAN_THRESHOLD"]) * features.shape[1]) # minimal not nan values in row
|
||||
features.dropna(axis=0, thresh=min_count, inplace=True) # Thresh => at least this many not-nans
|
||||
|
||||
graph_bf_af(features, "7too_much_nans_rows")
|
||||
|
||||
if features.empty:
|
||||
return pd.DataFrame(columns=excluded_columns)
|
||||
|
||||
# (7) STANDARDIZATION
|
||||
if provider["STANDARDIZATION"]:
|
||||
nominal_cols = [col for col in features.columns if "mostcommonactivity" in col or "homelabel" in col] # Excluded nominal features
|
||||
# Expected warning within this code block
|
||||
with warnings.catch_warnings():
|
||||
warnings.simplefilter("ignore", category=RuntimeWarning)
|
||||
if provider["TARGET_STANDARDIZATION"]:
|
||||
features.loc[:, ~features.columns.isin(excluded_columns + ["pid"] + nominal_cols)] = \
|
||||
features.loc[:, ~features.columns.isin(excluded_columns + nominal_cols)].groupby('pid').transform(lambda x: StandardScaler().fit_transform(x.values[:,np.newaxis]).ravel())
|
||||
else:
|
||||
features.loc[:, ~features.columns.isin(excluded_columns + ["pid"] + nominal_cols + ['phone_esm_straw_' + target])] = \
|
||||
features.loc[:, ~features.columns.isin(excluded_columns + nominal_cols + ['phone_esm_straw_' + target])].groupby('pid').transform(lambda x: StandardScaler().fit_transform(x.values[:,np.newaxis]).ravel())
|
||||
|
||||
graph_bf_af(features, "8standardization")
|
||||
|
||||
# (8) IMPUTATION: IMPUTE DATA WITH KNN METHOD
|
||||
features.reset_index(drop=True, inplace=True)
|
||||
impute_cols = [col for col in features.columns if col not in excluded_columns and col != "pid"]
|
||||
|
||||
features[impute_cols] = impute(features[impute_cols], method="knn")
|
||||
|
||||
graph_bf_af(features, "9knn_after")
|
||||
|
||||
|
||||
# (9) DROP HIGHLY CORRELATED FEATURES
|
||||
esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')]
|
||||
|
||||
drop_corr_features = provider["DROP_HIGHLY_CORRELATED_FEATURES"]
|
||||
if drop_corr_features["COMPUTE"] and features.shape[0] > 5: # If small amount of segments (rows) is present, do not execute correlation check
|
||||
|
||||
numerical_cols = features.select_dtypes(include=np.number).columns.tolist()
|
||||
|
||||
# Remove columns where NaN count threshold is passed
|
||||
valid_features = features[numerical_cols].loc[:, features[numerical_cols].isna().sum() < drop_corr_features['MIN_OVERLAP_FOR_CORR_THRESHOLD'] * features[numerical_cols].shape[0]]
|
||||
|
||||
corr_matrix = valid_features.corr().abs()
|
||||
upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(bool))
|
||||
to_drop = [column for column in upper.columns if any(upper[column] > drop_corr_features["CORR_THRESHOLD"])]
|
||||
|
||||
# sns.heatmap(corr_matrix, cmap="YlGnBu")
|
||||
# plt.savefig(f'correlation_matrix.png', bbox_inches='tight')
|
||||
# plt.close()
|
||||
|
||||
# s = corr_matrix.unstack()
|
||||
# so = s.sort_values(ascending=False)
|
||||
|
||||
# pd.set_option('display.max_rows', None)
|
||||
# sorted_upper = upper.unstack().sort_values(ascending=False)
|
||||
# print(sorted_upper[sorted_upper > drop_corr_features["CORR_THRESHOLD"]])
|
||||
|
||||
features.drop(to_drop, axis=1, inplace=True)
|
||||
|
||||
# Preserve esm cols if deleted (has to come after drop cols operations)
|
||||
for esm in esm_cols:
|
||||
if esm not in features:
|
||||
features[esm] = esm_cols[esm]
|
||||
|
||||
graph_bf_af(features, "10correlation_drop")
|
||||
|
||||
# Transform categorical columns to category dtype
|
||||
|
||||
cat1 = [col for col in features.columns if "mostcommonactivity" in col]
|
||||
if cat1: # Transform columns to category dtype (mostcommonactivity)
|
||||
features[cat1] = features[cat1].astype(int).astype('category')
|
||||
|
||||
cat2 = [col for col in features.columns if "homelabel" in col]
|
||||
if cat2: # Transform columns to category dtype (homelabel)
|
||||
features[cat2] = features[cat2].astype(int).astype('category')
|
||||
|
||||
# (10) DROP ALL WINDOW RELATED COLUMNS
|
||||
win_count_cols = [col for col in features if "SO_windowsCount" in col]
|
||||
if win_count_cols:
|
||||
features.drop(columns=win_count_cols, inplace=True)
|
||||
|
||||
# (11) VERIFY IF THERE ARE ANY NANS LEFT IN THE DATAFRAME
|
||||
if features.isna().any().any():
|
||||
raise ValueError("There are still some NaNs present in the dataframe. Please check for implementation errors.")
|
||||
|
||||
|
||||
return features
|
||||
|
||||
|
||||
def k_nearest(df):
|
||||
imputer = KNNImputer(n_neighbors=3)
|
||||
return pd.DataFrame(imputer.fit_transform(df), columns=df.columns)
|
||||
|
||||
|
||||
def impute(df, method='zero'):
|
||||
|
||||
return {
|
||||
'zero': df.fillna(0),
|
||||
'high_number': df.fillna(1500),
|
||||
'mean': df.fillna(df.mean()),
|
||||
'median': df.fillna(df.median()),
|
||||
'knn': k_nearest(df)
|
||||
}[method]
|
||||
|
||||
|
||||
def graph_bf_af(features, phase_name, plt_flag=False):
|
||||
if plt_flag:
|
||||
sns.set(rc={"figure.figsize":(16, 8)})
|
||||
sns.heatmap(features.isna(), cbar=False) #features.select_dtypes(include=np.number)
|
||||
plt.savefig(f'features_overall_nans_{phase_name}.png', bbox_inches='tight')
|
||||
|
||||
print(f"\n-------------{phase_name}-------------")
|
||||
print("Rows number:", features.shape[0])
|
||||
print("Columns number:", len(features.columns))
|
||||
print("NaN values:", features.isna().sum().sum())
|
||||
print("---------------------------------------------\n")
|
||||
|
|
@ -1,59 +0,0 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
import math as m
|
||||
|
||||
import sys
|
||||
|
||||
def extract_second_order_features(intraday_features, so_features_names, prefix=""):
|
||||
|
||||
if prefix:
|
||||
groupby_cols = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
|
||||
else:
|
||||
groupby_cols = ['local_segment']
|
||||
|
||||
if not intraday_features.empty:
|
||||
so_features = pd.DataFrame()
|
||||
#print(intraday_features.drop("level_1", axis=1).groupby(["local_segment"]).nsmallest())
|
||||
if "mean" in so_features_names:
|
||||
so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).mean(numeric_only=True).add_suffix("_SO_mean")], axis=1)
|
||||
|
||||
if "median" in so_features_names:
|
||||
so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).median(numeric_only=True).add_suffix("_SO_median")], axis=1)
|
||||
|
||||
if "sd" in so_features_names:
|
||||
so_features = pd.concat([so_features, intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols).std(numeric_only=True).fillna(0).add_suffix("_SO_sd")], axis=1)
|
||||
|
||||
if "nlargest" in so_features_names: # largest 5 -- maybe there is a faster groupby solution?
|
||||
for column in intraday_features.loc[:, ~intraday_features.columns.isin(groupby_cols+[prefix+"level_1"])]:
|
||||
so_features[column+"_SO_nlargest"] = intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols)[column].apply(lambda x: x.nlargest(5).mean())
|
||||
|
||||
if "nsmallest" in so_features_names: # smallest 5 -- maybe there is a faster groupby solution?
|
||||
for column in intraday_features.loc[:, ~intraday_features.columns.isin(groupby_cols+[prefix+"level_1"])]:
|
||||
so_features[column+"_SO_nsmallest"] = intraday_features.drop(prefix+"level_1", axis=1).groupby(groupby_cols)[column].apply(lambda x: x.nsmallest(5).mean())
|
||||
|
||||
if "count_windows" in so_features_names:
|
||||
so_features["SO_windowsCount"] = intraday_features.groupby(groupby_cols).count()[prefix+"level_1"]
|
||||
|
||||
# numPeaksNonZero specialized for EDA sensor
|
||||
if "eda_num_peaks_non_zero" in so_features_names and prefix+"numPeaks" in intraday_features.columns:
|
||||
so_features[prefix+"SO_numPeaksNonZero"] = intraday_features.groupby(groupby_cols)[prefix+"numPeaks"].apply(lambda x: (x!=0).sum())
|
||||
|
||||
# numWindowsNonZero specialized for BVP and IBI sensors
|
||||
if "hrv_num_windows_non_nan" in so_features_names and prefix+"meanHr" in intraday_features.columns:
|
||||
so_features[prefix+"SO_numWindowsNonNaN"] = intraday_features.groupby(groupby_cols)[prefix+"meanHr"].apply(lambda x: (~np.isnan(x)).sum())
|
||||
|
||||
so_features.reset_index(inplace=True)
|
||||
|
||||
else:
|
||||
so_features = pd.DataFrame(columns=groupby_cols)
|
||||
|
||||
return so_features
|
||||
|
||||
def get_sample_rate(data): # To-Do get the sample rate information from the file's metadata
|
||||
try:
|
||||
timestamps_diff = data['timestamp'].diff().dropna().mean()
|
||||
print("Timestamp diff:", timestamps_diff)
|
||||
except:
|
||||
raise Exception("Error occured while trying to get the mean sample rate from the data.")
|
||||
|
||||
return m.ceil(1000/timestamps_diff)
|
||||
|
|
@ -1,75 +0,0 @@
|
|||
import pandas as pd
|
||||
from scipy.stats import entropy
|
||||
|
||||
from cr_features.helper_functions import convert_to2d, accelerometer_features, frequency_features
|
||||
from cr_features.calculate_features_old import calculateFeatures
|
||||
from cr_features.calculate_features import calculate_features
|
||||
from cr_features_helper_methods import extract_second_order_features
|
||||
|
||||
import sys
|
||||
|
||||
def extract_acc_features_from_intraday_data(acc_intraday_data, features, window_length, time_segment, filter_data_by_segment):
|
||||
acc_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
|
||||
|
||||
if not acc_intraday_data.empty:
|
||||
sample_rate = 32
|
||||
|
||||
acc_intraday_data = filter_data_by_segment(acc_intraday_data, time_segment)
|
||||
|
||||
if not acc_intraday_data.empty:
|
||||
|
||||
acc_intraday_features = pd.DataFrame()
|
||||
|
||||
# apply methods from calculate features module
|
||||
if window_length is None:
|
||||
acc_intraday_features = \
|
||||
acc_intraday_data.groupby('local_segment').apply(lambda x: calculate_features( \
|
||||
convert_to2d(x['double_values_0'], x.shape[0]), \
|
||||
convert_to2d(x['double_values_1'], x.shape[0]), \
|
||||
convert_to2d(x['double_values_2'], x.shape[0]), \
|
||||
fs=sample_rate, feature_names=features, show_progress=False))
|
||||
else:
|
||||
acc_intraday_features = \
|
||||
acc_intraday_data.groupby('local_segment').apply(lambda x: calculate_features( \
|
||||
convert_to2d(x['double_values_0'], window_length*sample_rate), \
|
||||
convert_to2d(x['double_values_1'], window_length*sample_rate), \
|
||||
convert_to2d(x['double_values_2'], window_length*sample_rate), \
|
||||
fs=sample_rate, feature_names=features, show_progress=False))
|
||||
|
||||
acc_intraday_features.reset_index(inplace=True)
|
||||
|
||||
return acc_intraday_features
|
||||
|
||||
|
||||
|
||||
def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
|
||||
|
||||
data_types = {'local_timezone': 'str', 'device_id': 'str', 'timestamp': 'int64', 'double_values_0': 'float64',
|
||||
'double_values_1': 'float64', 'double_values_2': 'float64', 'local_date_time': 'str', 'local_date': "str",
|
||||
'local_time': "str", 'local_hour': "str", 'local_minute': "str", 'assigned_segments': "str"}
|
||||
acc_intraday_data = pd.read_csv(sensor_data_files["sensor_data"], dtype=data_types)
|
||||
|
||||
requested_intraday_features = provider["FEATURES"]
|
||||
|
||||
calc_windows = kwargs.get('calc_windows', False)
|
||||
|
||||
if provider["WINDOWS"]["COMPUTE"] and calc_windows:
|
||||
requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
|
||||
else:
|
||||
requested_window_length = None
|
||||
|
||||
# name of the features this function can compute
|
||||
base_intraday_features_names = accelerometer_features + frequency_features
|
||||
# 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
|
||||
acc_intraday_features = extract_acc_features_from_intraday_data(acc_intraday_data, intraday_features_to_compute,
|
||||
requested_window_length, time_segment, filter_data_by_segment)
|
||||
|
||||
if calc_windows:
|
||||
so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
|
||||
acc_second_order_features = extract_second_order_features(acc_intraday_features, so_features_names)
|
||||
return acc_intraday_features, acc_second_order_features
|
||||
|
||||
return acc_intraday_features
|
||||
|
|
@ -1,73 +0,0 @@
|
|||
import pandas as pd
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
|
||||
from cr_features.helper_functions import convert_to2d, hrv_features
|
||||
from cr_features.hrv import extract_hrv_features_2d_wrapper
|
||||
from cr_features_helper_methods import extract_second_order_features
|
||||
|
||||
import sys
|
||||
|
||||
# pd.set_option('display.max_rows', 1000)
|
||||
pd.set_option('display.max_columns', None)
|
||||
|
||||
def extract_bvp_features_from_intraday_data(bvp_intraday_data, features, window_length, time_segment, filter_data_by_segment):
|
||||
bvp_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
|
||||
|
||||
if not bvp_intraday_data.empty:
|
||||
sample_rate = 64
|
||||
|
||||
bvp_intraday_data = filter_data_by_segment(bvp_intraday_data, time_segment)
|
||||
|
||||
if not bvp_intraday_data.empty:
|
||||
|
||||
bvp_intraday_features = pd.DataFrame()
|
||||
|
||||
# apply methods from calculate features module
|
||||
if window_length is None:
|
||||
bvp_intraday_features = \
|
||||
bvp_intraday_data.groupby('local_segment').apply(\
|
||||
lambda x:
|
||||
extract_hrv_features_2d_wrapper(
|
||||
convert_to2d(x['blood_volume_pulse'], x.shape[0]),
|
||||
sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features))
|
||||
|
||||
else:
|
||||
bvp_intraday_features = \
|
||||
bvp_intraday_data.groupby('local_segment').apply(\
|
||||
lambda x:
|
||||
extract_hrv_features_2d_wrapper(
|
||||
convert_to2d(x['blood_volume_pulse'], window_length*sample_rate),
|
||||
sampling=sample_rate, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features))
|
||||
|
||||
bvp_intraday_features.reset_index(inplace=True)
|
||||
|
||||
return bvp_intraday_features
|
||||
|
||||
|
||||
def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
|
||||
bvp_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
|
||||
|
||||
requested_intraday_features = provider["FEATURES"]
|
||||
|
||||
calc_windows = kwargs.get('calc_windows', False)
|
||||
|
||||
if provider["WINDOWS"]["COMPUTE"] and calc_windows:
|
||||
requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
|
||||
else:
|
||||
requested_window_length = None
|
||||
|
||||
# name of the features this function can compute
|
||||
base_intraday_features_names = hrv_features
|
||||
# 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
|
||||
bvp_intraday_features = extract_bvp_features_from_intraday_data(bvp_intraday_data, intraday_features_to_compute,
|
||||
requested_window_length, time_segment, filter_data_by_segment)
|
||||
|
||||
if calc_windows:
|
||||
so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
|
||||
bvp_second_order_features = extract_second_order_features(bvp_intraday_features, so_features_names)
|
||||
return bvp_intraday_features, bvp_second_order_features
|
||||
|
||||
return bvp_intraday_features
|
||||
|
|
@ -1,32 +0,0 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
from datetime import datetime
|
||||
|
||||
import sys, yaml
|
||||
|
||||
def calculate_empatica_data_yield(features): # TODO
|
||||
|
||||
# Get time segment duration in seconds from all segments in features dataframe
|
||||
datetime_start = pd.to_datetime(features['local_segment_start_datetime'], format='%Y-%m-%d %H:%M:%S')
|
||||
datetime_end = pd.to_datetime(features['local_segment_end_datetime'], format='%Y-%m-%d %H:%M:%S')
|
||||
tseg_duration = (datetime_end - datetime_start).dt.total_seconds()
|
||||
|
||||
with open('config.yaml', 'r') as stream:
|
||||
config = yaml.load(stream, Loader=yaml.FullLoader)
|
||||
|
||||
sensors = ["EMPATICA_ACCELEROMETER", "EMPATICA_TEMPERATURE", "EMPATICA_ELECTRODERMAL_ACTIVITY", "EMPATICA_INTER_BEAT_INTERVAL"]
|
||||
for sensor in sensors:
|
||||
features[f"{sensor.lower()}_data_yield"] = \
|
||||
(features[f"{sensor.lower()}_cr_SO_windowsCount"] * config[sensor]["PROVIDERS"]["CR"]["WINDOWS"]["WINDOW_LENGTH"]) / tseg_duration \
|
||||
if f'{sensor.lower()}_cr_SO_windowsCount' in features else 0
|
||||
|
||||
empatica_data_yield_cols = [sensor.lower() + "_data_yield" for sensor in sensors]
|
||||
pd.set_option('display.max_rows', None)
|
||||
|
||||
# Assigns 1 to values that are over 1 (in case of windows not being filled fully)
|
||||
features[empatica_data_yield_cols] = features[empatica_data_yield_cols].apply(lambda x: [y if y <= 1 or np.isnan(y) else 1 for y in x])
|
||||
|
||||
features["empatica_data_yield"] = features[empatica_data_yield_cols].mean(axis=1, numeric_only=True).fillna(0)
|
||||
features.drop(empatica_data_yield_cols, axis=1, inplace=True) # In case of if the advanced operations will later not be needed (e.g., weighted average)
|
||||
|
||||
return features
|
||||
|
|
@ -1,82 +0,0 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
from scipy.stats import entropy
|
||||
|
||||
from cr_features.helper_functions import convert_to2d, gsr_features
|
||||
from cr_features.calculate_features import calculate_features
|
||||
from cr_features.gsr import extractGsrFeatures2D
|
||||
from cr_features_helper_methods import extract_second_order_features
|
||||
|
||||
import sys
|
||||
|
||||
#pd.set_option('display.max_columns', None)
|
||||
#pd.set_option('display.max_rows', None)
|
||||
#np.seterr(invalid='ignore')
|
||||
|
||||
|
||||
def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_length, time_segment, filter_data_by_segment):
|
||||
eda_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
|
||||
|
||||
if not eda_intraday_data.empty:
|
||||
sample_rate = 4
|
||||
|
||||
eda_intraday_data = filter_data_by_segment(eda_intraday_data, time_segment)
|
||||
|
||||
if not eda_intraday_data.empty:
|
||||
|
||||
eda_intraday_features = pd.DataFrame()
|
||||
|
||||
# apply methods from calculate features module
|
||||
if window_length is None:
|
||||
eda_intraday_features = \
|
||||
eda_intraday_data.groupby('local_segment').apply(\
|
||||
lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], x.shape[0]), sampleRate=sample_rate, featureNames=features,
|
||||
threshold=.01, offset=1, riseTime=5, decayTime=15))
|
||||
else:
|
||||
eda_intraday_features = \
|
||||
eda_intraday_data.groupby('local_segment').apply(\
|
||||
lambda x: extractGsrFeatures2D(convert_to2d(x['electrodermal_activity'], window_length*sample_rate), sampleRate=sample_rate, featureNames=features,
|
||||
threshold=.01, offset=1, riseTime=5, decayTime=15))
|
||||
|
||||
eda_intraday_features.reset_index(inplace=True)
|
||||
|
||||
return eda_intraday_features
|
||||
|
||||
|
||||
def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
|
||||
|
||||
data_types = {'local_timezone': 'str', 'device_id': 'str', 'timestamp': 'int64', 'electrodermal_activity': 'float64', 'local_date_time': 'str',
|
||||
'local_date': "str", 'local_time': "str", 'local_hour': "str", 'local_minute': "str", 'assigned_segments': "str"}
|
||||
|
||||
eda_intraday_data = pd.read_csv(sensor_data_files["sensor_data"], dtype=data_types)
|
||||
|
||||
requested_intraday_features = provider["FEATURES"]
|
||||
|
||||
calc_windows = kwargs.get('calc_windows', False)
|
||||
|
||||
if provider["WINDOWS"]["COMPUTE"] and calc_windows:
|
||||
requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
|
||||
else:
|
||||
requested_window_length = None
|
||||
|
||||
# name of the features this function can compute
|
||||
base_intraday_features_names = gsr_features
|
||||
# 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
|
||||
eda_intraday_features = extract_eda_features_from_intraday_data(eda_intraday_data, intraday_features_to_compute,
|
||||
requested_window_length, time_segment, filter_data_by_segment)
|
||||
|
||||
if calc_windows:
|
||||
if provider["WINDOWS"]["IMPUTE_NANS"]:
|
||||
eda_intraday_features[eda_intraday_features["numPeaks"] == 0] = \
|
||||
eda_intraday_features[eda_intraday_features["numPeaks"] == 0].fillna(0)
|
||||
pd.set_option('display.max_columns', None)
|
||||
|
||||
so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
|
||||
eda_second_order_features = extract_second_order_features(eda_intraday_features, so_features_names)
|
||||
|
||||
return eda_intraday_features, eda_second_order_features
|
||||
|
||||
return eda_intraday_features
|
||||
|
|
@ -1,83 +0,0 @@
|
|||
import pandas as pd
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
import numpy as np
|
||||
|
||||
from cr_features.helper_functions import convert_ibi_to2d_time, hrv_features
|
||||
from cr_features.hrv import extract_hrv_features_2d_wrapper, get_HRV_features
|
||||
from cr_features_helper_methods import extract_second_order_features
|
||||
|
||||
import math
|
||||
import sys
|
||||
|
||||
# pd.set_option('display.max_rows', 1000)
|
||||
pd.set_option('display.max_columns', None)
|
||||
|
||||
|
||||
def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_length, time_segment, filter_data_by_segment):
|
||||
ibi_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
|
||||
|
||||
if not ibi_intraday_data.empty:
|
||||
|
||||
ibi_intraday_data = filter_data_by_segment(ibi_intraday_data, time_segment)
|
||||
|
||||
if not ibi_intraday_data.empty:
|
||||
|
||||
ibi_intraday_features = pd.DataFrame()
|
||||
|
||||
# apply methods from calculate features module
|
||||
if window_length is None:
|
||||
ibi_intraday_features = \
|
||||
ibi_intraday_data.groupby('local_segment').apply(\
|
||||
lambda x:
|
||||
extract_hrv_features_2d_wrapper(
|
||||
signal_2D = \
|
||||
convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[0],
|
||||
ibi_timings = \
|
||||
convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], math.ceil(x['timings'].iloc[-1]))[1],
|
||||
sampling=None, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features))
|
||||
else:
|
||||
ibi_intraday_features = \
|
||||
ibi_intraday_data.groupby('local_segment').apply(\
|
||||
lambda x:
|
||||
extract_hrv_features_2d_wrapper(
|
||||
signal_2D = convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[0],
|
||||
ibi_timings = convert_ibi_to2d_time(x[['timings', 'inter_beat_interval']], window_length)[1],
|
||||
sampling=None, hampel_fiter=False, median_filter=False, mod_z_score_filter=True, feature_names=features))
|
||||
|
||||
ibi_intraday_features.reset_index(inplace=True)
|
||||
|
||||
return ibi_intraday_features
|
||||
|
||||
|
||||
def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
|
||||
|
||||
data_types = {'local_timezone': 'str', 'device_id': 'str', 'timestamp': 'int64', 'inter_beat_interval': 'float64', 'timings': 'float64', 'local_date_time': 'str',
|
||||
'local_date': "str", 'local_time': "str", 'local_hour': "str", 'local_minute': "str", 'assigned_segments': "str"}
|
||||
|
||||
ibi_intraday_data = pd.read_csv(sensor_data_files["sensor_data"], dtype=data_types)
|
||||
|
||||
requested_intraday_features = provider["FEATURES"]
|
||||
|
||||
calc_windows = kwargs.get('calc_windows', False)
|
||||
|
||||
if provider["WINDOWS"]["COMPUTE"] and calc_windows:
|
||||
requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
|
||||
else:
|
||||
requested_window_length = None
|
||||
|
||||
# name of the features this function can compute
|
||||
base_intraday_features_names = hrv_features
|
||||
# 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
|
||||
ibi_intraday_features = extract_ibi_features_from_intraday_data(ibi_intraday_data, intraday_features_to_compute,
|
||||
requested_window_length, time_segment, filter_data_by_segment)
|
||||
|
||||
if calc_windows:
|
||||
so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
|
||||
ibi_second_order_features = extract_second_order_features(ibi_intraday_features, so_features_names)
|
||||
|
||||
return ibi_intraday_features, ibi_second_order_features
|
||||
|
||||
return ibi_intraday_features
|
||||
|
|
@ -1,68 +0,0 @@
|
|||
import pandas as pd
|
||||
from scipy.stats import entropy
|
||||
|
||||
from cr_features.helper_functions import convert_to2d, generic_features
|
||||
from cr_features.calculate_features_old import calculateFeatures
|
||||
from cr_features.calculate_features import calculate_features
|
||||
from cr_features_helper_methods import extract_second_order_features
|
||||
|
||||
import sys
|
||||
|
||||
def extract_temp_features_from_intraday_data(temperature_intraday_data, features, window_length, time_segment, filter_data_by_segment):
|
||||
temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
|
||||
|
||||
if not temperature_intraday_data.empty:
|
||||
sample_rate = 4
|
||||
|
||||
temperature_intraday_data = filter_data_by_segment(temperature_intraday_data, time_segment)
|
||||
|
||||
if not temperature_intraday_data.empty:
|
||||
|
||||
temperature_intraday_features = pd.DataFrame()
|
||||
|
||||
# apply methods from calculate features module
|
||||
if window_length is None:
|
||||
temperature_intraday_features = \
|
||||
temperature_intraday_data.groupby('local_segment').apply(\
|
||||
lambda x: calculate_features(convert_to2d(x['temperature'], x.shape[0]), fs=sample_rate, feature_names=features, show_progress=False))
|
||||
else:
|
||||
temperature_intraday_features = \
|
||||
temperature_intraday_data.groupby('local_segment').apply(\
|
||||
lambda x: calculate_features(convert_to2d(x['temperature'], window_length*sample_rate), fs=sample_rate, feature_names=features, show_progress=False))
|
||||
|
||||
|
||||
temperature_intraday_features.reset_index(inplace=True)
|
||||
|
||||
return temperature_intraday_features
|
||||
|
||||
|
||||
def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
|
||||
data_types = {'local_timezone': 'str', 'device_id': 'str', 'timestamp': 'int64', 'temperature': 'float64', 'local_date_time': 'str',
|
||||
'local_date': "str", 'local_time': "str", 'local_hour': "str", 'local_minute': "str", 'assigned_segments': "str"}
|
||||
|
||||
temperature_intraday_data = pd.read_csv(sensor_data_files["sensor_data"], dtype=data_types)
|
||||
|
||||
requested_intraday_features = provider["FEATURES"]
|
||||
|
||||
calc_windows = kwargs.get('calc_windows', False)
|
||||
|
||||
if provider["WINDOWS"]["COMPUTE"] and calc_windows:
|
||||
requested_window_length = provider["WINDOWS"]["WINDOW_LENGTH"]
|
||||
else:
|
||||
requested_window_length = None
|
||||
|
||||
# name of the features this function can compute
|
||||
base_intraday_features_names = generic_features
|
||||
# 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
|
||||
temperature_intraday_features = extract_temp_features_from_intraday_data(temperature_intraday_data, intraday_features_to_compute,
|
||||
requested_window_length, time_segment, filter_data_by_segment)
|
||||
|
||||
if calc_windows:
|
||||
so_features_names = provider["WINDOWS"]["SECOND_ORDER_FEATURES"]
|
||||
temperature_second_order_features = extract_second_order_features(temperature_intraday_features, so_features_names)
|
||||
return temperature_intraday_features, temperature_second_order_features
|
||||
|
||||
return temperature_intraday_features
|
||||
|
|
@ -1,38 +1,19 @@
|
|||
import pandas as pd
|
||||
from utils.utils import fetch_provider_features, run_provider_cleaning_script
|
||||
|
||||
import sys
|
||||
|
||||
sensor_data_files = dict(snakemake.input)
|
||||
|
||||
provider = snakemake.params["provider"]
|
||||
provider_key = snakemake.params["provider_key"]
|
||||
sensor_key = snakemake.params["sensor_key"]
|
||||
|
||||
calc_windows = True if (provider.get("WINDOWS", False) and provider["WINDOWS"].get("COMPUTE", False)) else False
|
||||
|
||||
if sensor_key == "all_cleaning_individual" or sensor_key == "all_cleaning_overall":
|
||||
# Data cleaning
|
||||
if "overall" in sensor_key:
|
||||
sensor_features = run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files, snakemake.params["target"])
|
||||
else:
|
||||
sensor_features = run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files)
|
||||
else:
|
||||
# Extract sensor features
|
||||
del sensor_data_files["time_segments_labels"]
|
||||
time_segments_file = snakemake.input["time_segments_labels"]
|
||||
sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file)
|
||||
|
||||
if calc_windows:
|
||||
window_features, second_order_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=True)
|
||||
|
||||
window_features.to_csv(snakemake.output[1], index=False)
|
||||
second_order_features.to_csv(snakemake.output[0], index=False)
|
||||
|
||||
elif "empatica" in sensor_key:
|
||||
pd.DataFrame().to_csv(snakemake.output[1], index=False)
|
||||
|
||||
if not calc_windows:
|
||||
sensor_features = fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=False)
|
||||
|
||||
if not calc_windows:
|
||||
sensor_features.to_csv(snakemake.output[0], index=False)
|
||||
sensor_features.to_csv(snakemake.output[0], index=False)
|
||||
|
|
@ -37,6 +37,6 @@ def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_se
|
|||
ar_features.index.names = ["local_segment"]
|
||||
ar_features = ar_features.reset_index()
|
||||
|
||||
ar_features.fillna(value={"count": 0, "countuniqueactivities": 0, "durationstationary": 0, "durationmobile": 0, "durationvehicle": 0, "mostcommonactivity": 4}, inplace=True)
|
||||
ar_features.fillna(value={"count": 0, "countuniqueactivities": 0, "durationstationary": 0, "durationmobile": 0, "durationvehicle": 0}, inplace=True)
|
||||
|
||||
return ar_features
|
||||
|
|
|
|||
|
|
@ -9,19 +9,19 @@ def compute_features(filtered_data, apps_type, requested_features, apps_features
|
|||
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] = 1500 # np.nan
|
||||
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] = 1500 # np.nan
|
||||
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] = 0 # np.nan
|
||||
apps_features["frequencyentropy" + apps_type] = np.nan
|
||||
else:
|
||||
apps_features["frequencyentropy" + apps_type] = apps_with_count.groupby("local_segment")["timestamp"].agg(entropy)
|
||||
if "countevent" in requested_features:
|
||||
|
|
@ -43,7 +43,6 @@ def compute_features(filtered_data, apps_type, requested_features, apps_features
|
|||
apps_features["sumduration" + apps_type] = filtered_data.groupby(by = ["local_segment"])["duration"].sum()
|
||||
|
||||
apps_features.index.names = ["local_segment"]
|
||||
|
||||
return apps_features
|
||||
|
||||
def process_app_features(data, requested_features, time_segment, provider, filter_data_by_segment):
|
||||
|
|
|
|||
|
|
@ -15,7 +15,7 @@ def deviceFeatures(devices, ownership, common_devices, features_to_compute, feat
|
|||
if "meanscans" in features_to_compute:
|
||||
features = features.join(device_value_counts.groupby("local_segment")["scans"].mean().to_frame("meanscans" + ownership), how="outer")
|
||||
if "stdscans" in features_to_compute:
|
||||
features = features.join(device_value_counts.groupby("local_segment")["scans"].std().to_frame("stdscans" + ownership).fillna(0), how="outer")
|
||||
features = features.join(device_value_counts.groupby("local_segment")["scans"].std().to_frame("stdscans" + ownership), how="outer")
|
||||
# Most frequent device within segments, across segments, and across dataset
|
||||
if "countscansmostfrequentdevicewithinsegments" in features_to_compute:
|
||||
features = features.join(device_value_counts.groupby("local_segment")["scans"].max().to_frame("countscansmostfrequentdevicewithinsegments" + ownership), how="outer")
|
||||
|
|
|
|||
|
|
@ -88,16 +88,6 @@ rapids_features <- function(sensor_data_files, time_segment, provider){
|
|||
features <- call_features_of_type(calls_of_type, features_type, call_type, time_segment, requested_features)
|
||||
call_features <- merge(call_features, features, all=TRUE)
|
||||
}
|
||||
|
||||
# Fill seleted columns with a high number
|
||||
time_cols <- select(call_features, contains("timefirstcall") | contains("timelastcall")) %>%
|
||||
colnames(.)
|
||||
|
||||
call_features <- call_features %>%
|
||||
mutate_at(., time_cols, ~replace(., is.na(.), 1500))
|
||||
|
||||
# Fill NA values with 0
|
||||
call_features <- call_features %>% mutate_all(~replace(., is.na(.), 0))
|
||||
|
||||
call_features <- call_features %>% mutate_at(vars(contains("countmostfrequentcontact") | contains("distinctcontacts") | contains("count") | contains("sumduration") | contains("minduration") | contains("maxduration") | contains("meanduration") | contains("modeduration")), list( ~ replace_na(., 0)))
|
||||
return(call_features)
|
||||
}
|
||||
|
|
@ -3,11 +3,9 @@ library(tidyr)
|
|||
library(readr)
|
||||
|
||||
compute_data_yield_features <- function(data, feature_name, time_segment, provider){
|
||||
|
||||
data <- data %>% filter_data_by_segment(time_segment)
|
||||
if(nrow(data) == 0){
|
||||
if(nrow(data) == 0)
|
||||
return(tibble(local_segment = character(), ratiovalidyieldedminutes = numeric(), ratiovalidyieldedhours = numeric()))
|
||||
}
|
||||
features <- data %>%
|
||||
separate(timestamps_segment, into = c("start_timestamp", "end_timestamp"), convert = T, sep = ",") %>%
|
||||
mutate(duration_minutes = (end_timestamp - start_timestamp) / 60000,
|
||||
|
|
|
|||
|
|
@ -1,274 +0,0 @@
|
|||
from collections.abc import Collection
|
||||
|
||||
import numpy as np
|
||||
import pandas as pd
|
||||
from pytz import timezone
|
||||
import datetime, json
|
||||
|
||||
# from config.models import ESM, Participant
|
||||
# from features import helper
|
||||
|
||||
ESM_STATUS_ANSWERED = 2
|
||||
|
||||
GROUP_SESSIONS_BY = ["device_id", "esm_session"] # 'participant_id
|
||||
|
||||
SESSION_STATUS_UNANSWERED = "ema_unanswered"
|
||||
SESSION_STATUS_DAY_FINISHED = "day_finished"
|
||||
SESSION_STATUS_COMPLETE = "ema_completed"
|
||||
|
||||
ANSWER_DAY_FINISHED = "DayFinished3421"
|
||||
ANSWER_DAY_OFF = "DayOff3421"
|
||||
ANSWER_SET_EVENING = "DayFinishedSetEvening"
|
||||
|
||||
MAX_MORNING_LENGTH = 3
|
||||
# When the participants was not yet at work at the time of the first (morning) EMA,
|
||||
# only three items were answered.
|
||||
# Two sleep related items and one indicating NOT starting work yet.
|
||||
# Daytime EMAs are all longer, in fact they always consist of at least 6 items.
|
||||
|
||||
|
||||
TZ_LJ = timezone("Europe/Ljubljana")
|
||||
COLUMN_TIMESTAMP = "timestamp"
|
||||
COLUMN_TIMESTAMP_ESM = "double_esm_user_answer_timestamp"
|
||||
|
||||
|
||||
def get_date_from_timestamp(df_aware) -> pd.DataFrame:
|
||||
"""
|
||||
Transform a UNIX timestamp into a datetime (with Ljubljana timezone).
|
||||
Additionally, extract only the date part, where anything until 4 AM is considered the same day.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df_aware: pd.DataFrame
|
||||
Any AWARE-type data as defined in models.py.
|
||||
|
||||
Returns
|
||||
-------
|
||||
df_aware: pd.DataFrame
|
||||
The same dataframe with datetime_lj and date_lj columns added.
|
||||
|
||||
"""
|
||||
if COLUMN_TIMESTAMP_ESM in df_aware:
|
||||
column_timestamp = COLUMN_TIMESTAMP_ESM
|
||||
else:
|
||||
column_timestamp = COLUMN_TIMESTAMP
|
||||
|
||||
df_aware["datetime_lj"] = df_aware[column_timestamp].apply(
|
||||
lambda x: datetime.datetime.fromtimestamp(x / 1000.0, tz=TZ_LJ)
|
||||
)
|
||||
df_aware = df_aware.assign(
|
||||
date_lj=lambda x: (x.datetime_lj - datetime.timedelta(hours=4)).dt.date
|
||||
)
|
||||
# Since daytime EMAs could *theoretically* last beyond midnight, but never after 4 AM,
|
||||
# the datetime is first translated to 4 h earlier.
|
||||
|
||||
return df_aware
|
||||
|
||||
|
||||
def preprocess_esm(df_esm: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
Convert timestamps into human-readable datetimes and dates
|
||||
and expand the JSON column into several Pandas DF columns.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df_esm: pd.DataFrame
|
||||
A dataframe of esm data.
|
||||
|
||||
Returns
|
||||
-------
|
||||
df_esm_preprocessed: pd.DataFrame
|
||||
A dataframe with added columns: datetime in Ljubljana timezone and all fields from ESM_JSON column.
|
||||
"""
|
||||
df_esm = get_date_from_timestamp(df_esm)
|
||||
|
||||
df_esm_json = df_esm["esm_json"].apply(json.loads)
|
||||
df_esm_json = pd.json_normalize(df_esm_json).drop(
|
||||
columns=["esm_trigger"]
|
||||
) # The esm_trigger column is already present in the main df.
|
||||
return df_esm.join(df_esm_json)
|
||||
|
||||
|
||||
def classify_sessions_by_completion(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
For each distinct EMA session, determine how the participant responded to it.
|
||||
Possible outcomes are: SESSION_STATUS_UNANSWERED, SESSION_STATUS_DAY_FINISHED, and SESSION_STATUS_COMPLETE
|
||||
|
||||
This is done in three steps.
|
||||
|
||||
First, the esm_status is considered.
|
||||
If any of the ESMs in a session has a status *other than* "answered", then this session is taken as unfinished.
|
||||
|
||||
Second, the sessions which do not represent full questionnaires are identified.
|
||||
These are sessions where participants only marked they are finished with the day or have not yet started working.
|
||||
|
||||
Third, the sessions with only one item are marked with their trigger.
|
||||
We never offered questionnaires with single items, so we can be sure these are unfinished.
|
||||
|
||||
Finally, all sessions that remain are marked as completed.
|
||||
By going through different possibilities in expl_esm_adherence.ipynb, this turned out to be a reasonable option.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df_esm_preprocessed: pd.DataFrame
|
||||
A preprocessed dataframe of esm data, which must include the session ID (esm_session).
|
||||
|
||||
Returns
|
||||
-------
|
||||
df_session_counts: pd.Dataframe
|
||||
A dataframe of all sessions (grouped by GROUP_SESSIONS_BY) with their statuses and the number of items.
|
||||
"""
|
||||
sessions_grouped = df_esm_preprocessed.groupby(GROUP_SESSIONS_BY)
|
||||
|
||||
# 0. First, assign all session statuses as NaN.
|
||||
df_session_counts = pd.DataFrame(sessions_grouped.count()["timestamp"]).rename(
|
||||
columns={"timestamp": "esm_session_count"}
|
||||
)
|
||||
df_session_counts["session_response"] = np.nan
|
||||
|
||||
# 1. Identify all ESMs with status other than answered.
|
||||
esm_not_answered = sessions_grouped.apply(
|
||||
lambda x: (x.esm_status != ESM_STATUS_ANSWERED).any()
|
||||
)
|
||||
df_session_counts.loc[
|
||||
esm_not_answered, "session_response"
|
||||
] = SESSION_STATUS_UNANSWERED
|
||||
|
||||
# 2. Identify non-sessions, i.e. answers about the end of the day.
|
||||
non_session = sessions_grouped.apply(
|
||||
lambda x: (
|
||||
(x.esm_user_answer == ANSWER_DAY_FINISHED) # I finished working for today.
|
||||
| (x.esm_user_answer == ANSWER_DAY_OFF) # I am not going to work today.
|
||||
| (
|
||||
x.esm_user_answer == ANSWER_SET_EVENING
|
||||
) # When would you like to answer the evening EMA?
|
||||
).any()
|
||||
)
|
||||
df_session_counts.loc[non_session, "session_response"] = SESSION_STATUS_DAY_FINISHED
|
||||
|
||||
# 3. Identify sessions appearing only once, as those were not true EMAs for sure.
|
||||
singleton_sessions = (df_session_counts.esm_session_count == 1) & (
|
||||
df_session_counts.session_response.isna()
|
||||
)
|
||||
df_session_1 = df_session_counts[singleton_sessions]
|
||||
df_esm_unique_session = df_session_1.join(
|
||||
df_esm_preprocessed.set_index(GROUP_SESSIONS_BY), how="left"
|
||||
)
|
||||
df_esm_unique_session = df_esm_unique_session.assign(
|
||||
session_response=lambda x: x.esm_trigger
|
||||
)["session_response"]
|
||||
df_session_counts.loc[
|
||||
df_esm_unique_session.index, "session_response"
|
||||
] = df_esm_unique_session
|
||||
|
||||
# 4. Mark the remaining sessions as completed.
|
||||
df_session_counts.loc[
|
||||
df_session_counts.session_response.isna(), "session_response"
|
||||
] = SESSION_STATUS_COMPLETE
|
||||
|
||||
return df_session_counts
|
||||
|
||||
|
||||
def classify_sessions_by_time(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
|
||||
"""
|
||||
For each EMA session, determine the time of the first user answer and its time type (morning, workday, or evening.)
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df_esm_preprocessed: pd.DataFrame
|
||||
A preprocessed dataframe of esm data, which must include the session ID (esm_session).
|
||||
|
||||
Returns
|
||||
-------
|
||||
df_session_time: pd.DataFrame
|
||||
A dataframe of all sessions (grouped by GROUP_SESSIONS_BY) with their time type and timestamp of first answer.
|
||||
"""
|
||||
df_session_time = (
|
||||
df_esm_preprocessed.sort_values(["datetime_lj"]) # "participant_id"
|
||||
.groupby(GROUP_SESSIONS_BY)
|
||||
.first()[["time", "datetime_lj"]]
|
||||
)
|
||||
return df_session_time
|
||||
|
||||
|
||||
def classify_sessions_by_completion_time(
|
||||
df_esm_preprocessed: pd.DataFrame,
|
||||
) -> pd.DataFrame:
|
||||
"""
|
||||
The point of this function is to not only classify sessions by using the previously defined functions.
|
||||
It also serves to "correct" the time type of some EMA sessions.
|
||||
|
||||
A morning questionnaire could seamlessly transition into a daytime questionnaire,
|
||||
if the participant was already at work.
|
||||
In this case, the "time" label changed mid-session.
|
||||
Because of the way classify_sessions_by_time works, this questionnaire was classified as "morning".
|
||||
But for all intents and purposes, it can be treated as a "daytime" EMA.
|
||||
|
||||
The way this scenario is differentiated from a true "morning" questionnaire,
|
||||
where the participants NOT yet at work, is by considering their length.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
df_esm_preprocessed: pd.DataFrame
|
||||
A preprocessed dataframe of esm data, which must include the session ID (esm_session).
|
||||
|
||||
Returns
|
||||
-------
|
||||
df_session_counts_time: pd.DataFrame
|
||||
A dataframe of all sessions (grouped by GROUP_SESSIONS_BY) with statuses, the number of items,
|
||||
their time type (with some morning EMAs reclassified) and timestamp of first answer.
|
||||
|
||||
"""
|
||||
df_session_counts = classify_sessions_by_completion(df_esm_preprocessed)
|
||||
df_session_time = classify_sessions_by_time(df_esm_preprocessed)
|
||||
|
||||
df_session_counts_time = df_session_time.join(df_session_counts)
|
||||
|
||||
morning_transition_to_daytime = (df_session_counts_time.time == "morning") & (
|
||||
df_session_counts_time.esm_session_count > MAX_MORNING_LENGTH
|
||||
)
|
||||
|
||||
df_session_counts_time.loc[morning_transition_to_daytime, "time"] = "daytime"
|
||||
|
||||
return df_session_counts_time
|
||||
|
||||
|
||||
# def clean_up_esm(df_esm_preprocessed: pd.DataFrame) -> pd.DataFrame:
|
||||
# """
|
||||
# This function eliminates invalid ESM responses.
|
||||
# It removes unanswered ESMs and those that indicate end of work and similar.
|
||||
# It also extracts a numeric answer from strings such as "4 - I strongly agree".
|
||||
|
||||
# Parameters
|
||||
# ----------
|
||||
# df_esm_preprocessed: pd.DataFrame
|
||||
# A preprocessed dataframe of esm data.
|
||||
|
||||
# Returns
|
||||
# -------
|
||||
# df_esm_clean: pd.DataFrame
|
||||
# A subset of the original dataframe.
|
||||
|
||||
# """
|
||||
# df_esm_clean = df_esm_preprocessed[
|
||||
# df_esm_preprocessed["esm_status"] == ESM_STATUS_ANSWERED
|
||||
# ]
|
||||
# df_esm_clean = df_esm_clean[
|
||||
# ~df_esm_clean["esm_user_answer"].isin(
|
||||
# [ANSWER_DAY_FINISHED, ANSWER_DAY_OFF, ANSWER_SET_EVENING]
|
||||
# )
|
||||
# ]
|
||||
# df_esm_clean["esm_user_answer_numeric"] = np.nan
|
||||
# esm_type_numeric = [
|
||||
# ESM.ESM_TYPE.get("radio"),
|
||||
# ESM.ESM_TYPE.get("scale"),
|
||||
# ESM.ESM_TYPE.get("number"),
|
||||
# ]
|
||||
# df_esm_clean.loc[
|
||||
# df_esm_clean["esm_type"].isin(esm_type_numeric)
|
||||
# ] = df_esm_clean.loc[df_esm_clean["esm_type"].isin(esm_type_numeric)].assign(
|
||||
# esm_user_answer_numeric=lambda x: x.esm_user_answer.str.slice(stop=1).astype(
|
||||
# int
|
||||
# )
|
||||
# )
|
||||
# return df_esm_clean
|
||||
|
|
@ -42,8 +42,7 @@ def straw_features(sensor_data_files, time_segment, provider, filter_data_by_seg
|
|||
requested_features = provider["FEATURES"]
|
||||
# name of the features this function can compute
|
||||
requested_scales = provider["SCALES"]
|
||||
base_features_names = ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support",
|
||||
"appraisal_stressfulness_period", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]
|
||||
base_features_names = ["PANAS_positive_affect", "PANAS_negative_affect", "JCQ_job_demand", "JCQ_job_control", "JCQ_supervisor_support", "JCQ_coworker_support"]
|
||||
#TODO Check valid questionnaire and feature names.
|
||||
# the subset of requested features this function can compute
|
||||
features_to_compute = list(set(requested_features) & set(base_features_names))
|
||||
|
|
@ -53,6 +52,7 @@ def straw_features(sensor_data_files, time_segment, provider, filter_data_by_seg
|
|||
|
||||
if not esm_data.empty:
|
||||
esm_features = pd.DataFrame()
|
||||
|
||||
for scale in requested_scales:
|
||||
questionnaire_id = QUESTIONNAIRE_IDS[scale]
|
||||
mask = esm_data["questionnaire_id"] == questionnaire_id
|
||||
|
|
@ -60,7 +60,4 @@ def straw_features(sensor_data_files, time_segment, provider, filter_data_by_seg
|
|||
#TODO Create the column esm_user_score in esm_clean. Currently, this is only done when reversing.
|
||||
|
||||
esm_features = esm_features.reset_index()
|
||||
if 'index' in esm_features: # In calse of empty esm_features df
|
||||
esm_features.rename(columns={'index': 'local_segment'}, inplace=True)
|
||||
|
||||
return esm_features
|
||||
|
|
|
|||
|
|
@ -1,260 +0,0 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
import datetime
|
||||
|
||||
import math, sys, yaml
|
||||
|
||||
from esm_preprocess import clean_up_esm
|
||||
from esm import classify_sessions_by_completion_time, preprocess_esm
|
||||
|
||||
input_data_files = dict(snakemake.input)
|
||||
|
||||
def format_timestamp(x):
|
||||
"""This method formates inputed timestamp into format "HH MM SS". Including spaces. If there is no hours or minutes present
|
||||
that part is ignored, e.g., "MM SS" or just "SS".
|
||||
|
||||
Args:
|
||||
x (int): unix timestamp in seconds
|
||||
|
||||
Returns:
|
||||
str: formatted timestamp using "HH MM SS" sintax
|
||||
"""
|
||||
tstring=""
|
||||
space = False
|
||||
if x//3600 > 0:
|
||||
tstring += f"{x//3600}H"
|
||||
space = True
|
||||
if x % 3600 // 60 > 0:
|
||||
tstring += f" {x % 3600 // 60}M" if "H" in tstring else f"{x % 3600 // 60}M"
|
||||
if x % 60 > 0:
|
||||
tstring += f" {x % 60}S" if "M" in tstring or "H" in tstring else f"{x % 60}S"
|
||||
|
||||
return tstring
|
||||
|
||||
|
||||
def extract_ers(esm_df):
|
||||
"""This method has two major functionalities:
|
||||
(1) It prepares STRAW event-related segments file with the use of esm file. The execution protocol is depended on
|
||||
the segmenting method specified in the config.yaml file.
|
||||
(2) It prepares and writes csv with targets and corresponding time segments labels. This is later used
|
||||
in the overall cleaning script (straw).
|
||||
|
||||
Details about each segmenting method are listed below by each corresponding condition. Refer to the RAPIDS documentation for the
|
||||
ERS file format: https://www.rapids.science/1.9/setup/configuration/#time-segments -> event segments
|
||||
|
||||
Args:
|
||||
esm_df (DataFrame): read esm file that is dependend on the current participant.
|
||||
|
||||
Returns:
|
||||
extracted_ers (DataFrame): dataframe with all necessary information to write event-related segments file
|
||||
in the correct format.
|
||||
"""
|
||||
|
||||
pd.set_option("display.max_rows", 100)
|
||||
pd.set_option("display.max_columns", None)
|
||||
|
||||
with open('config.yaml', 'r') as stream:
|
||||
config = yaml.load(stream, Loader=yaml.FullLoader)
|
||||
|
||||
pd.DataFrame(columns=["label"]).to_csv(snakemake.output[1]) # Create an empty stress_events_targets file
|
||||
|
||||
esm_preprocessed = clean_up_esm(preprocess_esm(esm_df))
|
||||
|
||||
# Take only ema_completed sessions responses
|
||||
classified = classify_sessions_by_completion_time(esm_preprocessed)
|
||||
esm_filtered_sessions = classified[classified["session_response"] == 'ema_completed'].reset_index()[['device_id', 'esm_session']]
|
||||
esm_df = esm_preprocessed.loc[(esm_preprocessed['device_id'].isin(esm_filtered_sessions['device_id'])) & (esm_preprocessed['esm_session'].isin(esm_filtered_sessions['esm_session']))]
|
||||
|
||||
segmenting_method = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["SEGMENTING_METHOD"]
|
||||
|
||||
if segmenting_method in ["30_before", "90_before"]: # takes 30-minute peroid before the questionnaire + the duration of the questionnaire
|
||||
""" '30-minutes and 90-minutes before' have the same fundamental logic with couple of deviations that will be explained below.
|
||||
Both take x-minute period before the questionnaire that is summed with the questionnaire duration.
|
||||
All questionnaire durations over 15 minutes are excluded from the querying.
|
||||
"""
|
||||
# Extract time-relevant information
|
||||
extracted_ers = esm_df.groupby(["device_id", "esm_session"])['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index() # questionnaire length
|
||||
extracted_ers["label"] = f"straw_event_{segmenting_method}_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3)
|
||||
extracted_ers[['event_timestamp', 'device_id']] = esm_df.groupby(["device_id", "esm_session"])['timestamp'].min().reset_index()[['timestamp', 'device_id']]
|
||||
extracted_ers = extracted_ers[extracted_ers["timestamp"] <= 15 * 60].reset_index(drop=True) # ensure that the longest duration of the questionnaire anwsering is 15 min
|
||||
extracted_ers["shift_direction"] = -1
|
||||
|
||||
if segmenting_method == "30_before":
|
||||
"""The method 30-minutes before simply takes 30 minutes before the questionnaire and sums it with the questionnaire duration.
|
||||
The timestamps are formatted with the help of format_timestamp() method.
|
||||
"""
|
||||
time_before_questionnaire = 30 * 60 # in seconds (30 minutes)
|
||||
|
||||
extracted_ers["length"] = (extracted_ers["timestamp"] + time_before_questionnaire).apply(lambda x: format_timestamp(x))
|
||||
extracted_ers["shift"] = time_before_questionnaire
|
||||
extracted_ers["shift"] = extracted_ers["shift"].apply(lambda x: format_timestamp(x))
|
||||
|
||||
elif segmenting_method == "90_before":
|
||||
"""The method 90-minutes before has an important condition. If the time between the current and the previous questionnaire is
|
||||
longer then 90 minutes it takes 90 minutes, otherwise it takes the original time difference between the questionnaires.
|
||||
"""
|
||||
time_before_questionnaire = 90 * 60 # in seconds (90 minutes)
|
||||
|
||||
extracted_ers[['end_event_timestamp', 'device_id']] = esm_df.groupby(["device_id", "esm_session"])['timestamp'].max().reset_index()[['timestamp', 'device_id']]
|
||||
|
||||
extracted_ers['diffs'] = extracted_ers['event_timestamp'].astype('int64') - extracted_ers['end_event_timestamp'].shift(1, fill_value=0).astype('int64')
|
||||
extracted_ers.loc[extracted_ers['diffs'] > time_before_questionnaire * 1000, 'diffs'] = time_before_questionnaire * 1000
|
||||
|
||||
extracted_ers["diffs"] = (extracted_ers["diffs"] / 1000).apply(lambda x: math.ceil(x))
|
||||
|
||||
extracted_ers["length"] = (extracted_ers["timestamp"] + extracted_ers["diffs"]).apply(lambda x: format_timestamp(x))
|
||||
extracted_ers["shift"] = extracted_ers["diffs"].apply(lambda x: format_timestamp(x))
|
||||
|
||||
elif segmenting_method == "stress_event":
|
||||
"""
|
||||
TODO: update documentation for this condition
|
||||
This is a special case of the method as it consists of two important parts:
|
||||
(1) Generating of the ERS file (same as the methods above) and
|
||||
(2) Generating targets file alongside with the correct time segment labels.
|
||||
|
||||
This extracts event-related segments, depended on the event time and duration specified by the participant in the next
|
||||
questionnaire. Additionally, 5 minutes before the specified start time of this event is taken to take into a account the
|
||||
possiblity of the participant not remembering the start time percisely => this parameter can be manipulated with the variable
|
||||
"time_before_event" which is defined below.
|
||||
|
||||
In case if the participant marked that no stressful event happened, the default of 30 minutes before the event is choosen.
|
||||
In this case, se_threat and se_challenge are NaN.
|
||||
|
||||
By default, this method also excludes all events that are longer then 2.5 hours so that the segments are easily comparable.
|
||||
"""
|
||||
|
||||
ioi = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["INTERVAL_OF_INTEREST"] * 60 # interval of interest in seconds
|
||||
ioi_error_tolerance = config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["IOI_ERROR_TOLERANCE"] * 60 # interval of interest error tolerance in seconds
|
||||
|
||||
# Get and join required data
|
||||
extracted_ers = esm_df.groupby(["device_id", "esm_session"])['timestamp'].apply(lambda x: math.ceil((x.max() - x.min()) / 1000)).reset_index().rename(columns={'timestamp': 'session_length'}) # questionnaire length
|
||||
extracted_ers = extracted_ers[extracted_ers["session_length"] <= 15 * 60].reset_index(drop=True) # ensure that the longest duration of the questionnaire answering is 15 min
|
||||
session_start_timestamp = esm_df.groupby(['device_id', 'esm_session'])['timestamp'].min().to_frame().rename(columns={'timestamp': 'session_start_timestamp'}) # questionnaire start timestamp
|
||||
session_end_timestamp = esm_df.groupby(['device_id', 'esm_session'])['timestamp'].max().to_frame().rename(columns={'timestamp': 'session_end_timestamp'}) # questionnaire end timestamp
|
||||
|
||||
# Users' answers for the stressfulness event (se) start times and durations
|
||||
se_time = esm_df[esm_df.questionnaire_id == 90.].set_index(['device_id', 'esm_session'])['esm_user_answer'].to_frame().rename(columns={'esm_user_answer': 'se_time'})
|
||||
se_duration = esm_df[esm_df.questionnaire_id == 91.].set_index(['device_id', 'esm_session'])['esm_user_answer'].to_frame().rename(columns={'esm_user_answer': 'se_duration'})
|
||||
|
||||
# Make se_durations to the appropriate lengths
|
||||
|
||||
# Extracted 3 targets that will be transfered in the csv file to the cleaning script.
|
||||
se_stressfulness_event_tg = esm_df[esm_df.questionnaire_id == 87.].set_index(['device_id', 'esm_session'])['esm_user_answer_numeric'].to_frame().rename(columns={'esm_user_answer_numeric': 'appraisal_stressfulness_event'})
|
||||
se_threat_tg = esm_df[esm_df.questionnaire_id == 88.].groupby(["device_id", "esm_session"]).mean(numeric_only=True)['esm_user_answer_numeric'].to_frame().rename(columns={'esm_user_answer_numeric': 'appraisal_threat'})
|
||||
se_challenge_tg = esm_df[esm_df.questionnaire_id == 89.].groupby(["device_id", "esm_session"]).mean(numeric_only=True)['esm_user_answer_numeric'].to_frame().rename(columns={'esm_user_answer_numeric': 'appraisal_challenge'})
|
||||
|
||||
# All relevant features are joined by inner join to remove standalone columns (e.g., stressfulness event target has larger count)
|
||||
extracted_ers = extracted_ers.join(session_start_timestamp, on=['device_id', 'esm_session'], how='inner') \
|
||||
.join(session_end_timestamp, on=['device_id', 'esm_session'], how='inner') \
|
||||
.join(se_stressfulness_event_tg, on=['device_id', 'esm_session'], how='inner') \
|
||||
.join(se_time, on=['device_id', 'esm_session'], how='left') \
|
||||
.join(se_duration, on=['device_id', 'esm_session'], how='left') \
|
||||
.join(se_threat_tg, on=['device_id', 'esm_session'], how='left') \
|
||||
.join(se_challenge_tg, on=['device_id', 'esm_session'], how='left')
|
||||
|
||||
# Filter-out the sessions that are not useful. Because of the ambiguity this excludes:
|
||||
# (1) straw event times that are marked as "0 - I don't remember"
|
||||
extracted_ers = extracted_ers[~extracted_ers.se_time.astype(str).str.startswith("0 - ")]
|
||||
extracted_ers.reset_index(drop=True, inplace=True)
|
||||
|
||||
extracted_ers.loc[extracted_ers.se_duration.astype(str).str.startswith("0 - "), 'se_duration'] = 0
|
||||
|
||||
# Add default duration in case if participant answered that no stressful event occured
|
||||
extracted_ers["se_duration"] = extracted_ers["se_duration"].fillna(int((ioi + 2*ioi_error_tolerance) * 1000))
|
||||
|
||||
# Prepare data to fit the data structure in the CSV file ...
|
||||
# Add the event time as the end of the questionnaire if no stress event occured
|
||||
extracted_ers['se_time'] = extracted_ers['se_time'].fillna(extracted_ers['session_start_timestamp'])
|
||||
# Type could be an int (timestamp [ms]) which stays the same, and datetime str which is converted to timestamp in miliseconds
|
||||
extracted_ers['event_timestamp'] = extracted_ers['se_time'].apply(lambda x: x if isinstance(x, int) else pd.to_datetime(x).timestamp() * 1000).astype('int64')
|
||||
extracted_ers['shift_direction'] = -1
|
||||
|
||||
""">>>>> begin section (could be optimized) <<<<<"""
|
||||
|
||||
# Checks whether the duration is marked with "1 - It's still ongoing" which means that the end of the current questionnaire
|
||||
# is taken as end time of the segment. Else the user input duration is taken.
|
||||
extracted_ers['se_duration'] = \
|
||||
np.where(
|
||||
extracted_ers['se_duration'].astype(str).str.startswith("1 - "),
|
||||
extracted_ers['session_end_timestamp'] - extracted_ers['event_timestamp'],
|
||||
extracted_ers['se_duration']
|
||||
)
|
||||
|
||||
# This converts the rows of timestamps in miliseconds and the rows with datetime... to timestamp in seconds.
|
||||
extracted_ers['se_duration'] = \
|
||||
extracted_ers['se_duration'].apply(lambda x: math.ceil(x / 1000) if isinstance(x, int) else (pd.to_datetime(x).hour * 60 + pd.to_datetime(x).minute) * 60)
|
||||
|
||||
# Check explicitley whether min duration is at least 0. This will eliminate rows that would be investigated after the end of the questionnaire.
|
||||
extracted_ers = extracted_ers[extracted_ers['session_end_timestamp'] - extracted_ers['event_timestamp'] >= 0]
|
||||
# Double check whether min se_duration is at least 0. Filter-out the rest. Negative values are considered invalid.
|
||||
extracted_ers = extracted_ers[extracted_ers["se_duration"] >= 0].reset_index(drop=True)
|
||||
|
||||
""">>>>> end section <<<<<"""
|
||||
|
||||
# Simply override all durations to be of an equal amount
|
||||
extracted_ers['se_duration'] = ioi + 2*ioi_error_tolerance
|
||||
|
||||
# If target is 0 then shift by the total stress event duration, otherwise shift it by ioi_tolerance
|
||||
extracted_ers['shift'] = \
|
||||
np.where(
|
||||
extracted_ers['appraisal_stressfulness_event'] == 0,
|
||||
extracted_ers['se_duration'],
|
||||
ioi_error_tolerance
|
||||
)
|
||||
|
||||
extracted_ers['shift'] = extracted_ers['shift'].apply(lambda x: format_timestamp(int(x)))
|
||||
extracted_ers['length'] = extracted_ers['se_duration'].apply(lambda x: format_timestamp(int(x)))
|
||||
|
||||
# Drop event_timestamp duplicates in case in the user is referencing the same event over multiple questionnaires
|
||||
extracted_ers.drop_duplicates(subset=["event_timestamp"], keep='first', inplace=True)
|
||||
extracted_ers.reset_index(drop=True, inplace=True)
|
||||
|
||||
extracted_ers["label"] = f"straw_event_{segmenting_method}_" + snakemake.params["pid"] + "_" + extracted_ers.index.astype(str).str.zfill(3)
|
||||
|
||||
# Write the csv of extracted ERS labels with targets related to stressfulness event
|
||||
extracted_ers[["label", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"]].to_csv(snakemake.output[1], index=False)
|
||||
|
||||
else:
|
||||
raise Exception("Please select correct target method for the event-related segments.")
|
||||
extracted_ers = pd.DataFrame(columns=["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"])
|
||||
|
||||
return extracted_ers[["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"]]
|
||||
|
||||
|
||||
"""
|
||||
Here the code is executed - this .py file is used both for extraction of the STRAW time_segments file for the individual
|
||||
participant, and also for merging all participant's files into one combined file which is later used for the time segments
|
||||
to all sensors assignment.
|
||||
|
||||
There are two files involved (see rules extract_event_information_from_esm and merge_event_related_segments_files in preprocessing.smk)
|
||||
(1) ERS file which contains all the information about the time segment timings and
|
||||
(2) targets file which has corresponding target value for the segment label which is later used to merge with other features in the cleaning script.
|
||||
For more information, see the comment in the method above.
|
||||
"""
|
||||
if snakemake.params["stage"] == "extract":
|
||||
esm_df = pd.read_csv(input_data_files['esm_raw_input'])
|
||||
|
||||
extracted_ers = extract_ers(esm_df)
|
||||
|
||||
extracted_ers.to_csv(snakemake.output[0], index=False)
|
||||
|
||||
elif snakemake.params["stage"] == "merge":
|
||||
|
||||
input_data_files = dict(snakemake.input)
|
||||
straw_events = pd.DataFrame(columns=["label", "event_timestamp", "length", "shift", "shift_direction", "device_id"])
|
||||
stress_events_targets = pd.DataFrame(columns=["label", "appraisal_stressfulness_event", "appraisal_threat", "appraisal_challenge"])
|
||||
|
||||
for input_file in input_data_files["ers_files"]:
|
||||
ers_df = pd.read_csv(input_file)
|
||||
straw_events = pd.concat([straw_events, ers_df], axis=0, ignore_index=True)
|
||||
|
||||
straw_events.to_csv(snakemake.output[0], index=False)
|
||||
|
||||
for input_file in input_data_files["se_files"]:
|
||||
se_df = pd.read_csv(input_file)
|
||||
stress_events_targets = pd.concat([stress_events_targets, se_df], axis=0, ignore_index=True)
|
||||
|
||||
stress_events_targets.to_csv(snakemake.output[1], index=False)
|
||||
|
||||
|
||||
|
||||
|
|
@ -29,7 +29,7 @@ def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_se
|
|||
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().fillna(0)
|
||||
light_features["stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std()
|
||||
|
||||
light_features = light_features.reset_index()
|
||||
|
||||
|
|
|
|||
|
|
@ -26,9 +26,7 @@ barnett_daily_features <- function(snakemake){
|
|||
location <- location %>%
|
||||
mutate(is_daily = str_detect(assigned_segments, paste0(".*#", datetime_start_regex, ",", datetime_end_regex, ".*")))
|
||||
|
||||
does_not_span = nrow(segment_labels) == 0 || nrow(location) == 0 || all(location$is_daily == FALSE) || (max(location$timestamp) - min(location$timestamp) < 86400000)
|
||||
|
||||
if(is.na(does_not_span) || does_not_span){
|
||||
if(nrow(segment_labels) == 0 || nrow(location) == 0 || all(location$is_daily == FALSE) || (max(location$timestamp) - min(location$timestamp) < 86400000)){
|
||||
warning("Barnett's location features cannot be computed for data or time segments that do not span one or more entire days (00:00:00 to 23:59:59). Values below point to the problem:",
|
||||
"\nLocation data rows within a daily time segment: ", nrow(filter(location, is_daily)),
|
||||
"\nLocation data time span in days: ", round((max(location$timestamp) - min(location$timestamp)) / 86400000, 2)
|
||||
|
|
|
|||
|
|
@ -115,7 +115,7 @@ cluster_on = provider["CLUSTER_ON"]
|
|||
strategy = provider["INFER_HOME_LOCATION_STRATEGY"]
|
||||
days_threshold = provider["MINIMUM_DAYS_TO_DETECT_HOME_CHANGES"]
|
||||
|
||||
if not location_data.timestamp.is_monotonic_increasing:
|
||||
if not location_data.timestamp.is_monotonic:
|
||||
location_data.sort_values(by=["timestamp"], inplace=True)
|
||||
|
||||
location_data["duration_in_seconds"] = -1 * location_data.timestamp.diff(-1) / 1000
|
||||
|
|
|
|||
|
|
@ -37,8 +37,7 @@ def variance_and_logvariance_features(location_data, location_features):
|
|||
location_data["longitude_for_wvar"] = (location_data["double_longitude"] - location_data["longitude_wavg"]) ** 2 * location_data["duration"] * 60
|
||||
|
||||
location_features["locationvariance"] = ((location_data_grouped["latitude_for_wvar"].sum() + location_data_grouped["longitude_for_wvar"].sum()) / (location_data_grouped["duration"].sum() * 60 - 1)).fillna(0)
|
||||
|
||||
location_features["loglocationvariance"] = np.log10(location_features["locationvariance"]).replace(-np.inf, -1000000)
|
||||
location_features["loglocationvariance"] = np.log10(location_features["locationvariance"]).replace(-np.inf, np.nan)
|
||||
|
||||
return location_features
|
||||
|
||||
|
|
|
|||
|
|
@ -65,15 +65,6 @@ rapids_features <- function(sensor_data_files, time_segment, provider){
|
|||
features <- message_features_of_type(messages_of_type, message_type, time_segment, requested_features)
|
||||
messages_features <- merge(messages_features, features, all=TRUE)
|
||||
}
|
||||
# Fill seleted columns with a high number
|
||||
time_cols <- select(messages_features, contains("timefirstmessages") | contains("timelastmessages")) %>%
|
||||
colnames(.)
|
||||
|
||||
messages_features <- messages_features %>%
|
||||
mutate_at(., time_cols, ~replace(., is.na(.), 1500))
|
||||
|
||||
# Fill NA values with 0
|
||||
messages_features <- messages_features %>% mutate_all(~replace(., is.na(.), 0))
|
||||
|
||||
messages_features <- messages_features %>% mutate_at(vars(contains("countmostfrequentcontact") | contains("distinctcontacts") | contains("count")), list( ~ replace_na(., 0)))
|
||||
return(messages_features)
|
||||
}
|
||||
|
|
@ -15,7 +15,7 @@ def getEpisodeDurationFeatures(screen_data, time_segment, episode, features, ref
|
|||
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().fillna(0).rename(columns = {"duration":"stdduration" + episode})], axis = 1)
|
||||
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
|
||||
|
|
|
|||
|
|
@ -1,30 +0,0 @@
|
|||
import pandas as pd
|
||||
|
||||
|
||||
def straw_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
|
||||
speech_data = pd.read_csv(sensor_data_files["sensor_data"])
|
||||
requested_features = provider["FEATURES"]
|
||||
# name of the features this function can compute+
|
||||
base_features_names = ["meanspeech", "stdspeech", "nlargest", "nsmallest", "medianspeech"]
|
||||
features_to_compute = list(set(requested_features) & set(base_features_names))
|
||||
speech_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
|
||||
|
||||
if not speech_data.empty:
|
||||
speech_data = filter_data_by_segment(speech_data, time_segment)
|
||||
|
||||
if not speech_data.empty:
|
||||
speech_features = pd.DataFrame()
|
||||
if "meanspeech" in features_to_compute:
|
||||
speech_features["meanspeech"] = speech_data.groupby(["local_segment"])['speech_proportion'].mean()
|
||||
if "stdspeech" in features_to_compute:
|
||||
speech_features["stdspeech"] = speech_data.groupby(["local_segment"])['speech_proportion'].std()
|
||||
if "nlargest" in features_to_compute:
|
||||
speech_features["nlargest"] = speech_data.groupby(["local_segment"])['speech_proportion'].apply(lambda x: x.nlargest(5).mean())
|
||||
if "nsmallest" in features_to_compute:
|
||||
speech_features["nsmallest"] = speech_data.groupby(["local_segment"])['speech_proportion'].apply(lambda x: x.nsmallest(5).mean())
|
||||
if "medianspeech" in features_to_compute:
|
||||
speech_features["medianspeech"] = speech_data.groupby(["local_segment"])['speech_proportion'].median()
|
||||
|
||||
speech_features = speech_features.reset_index()
|
||||
|
||||
return speech_features
|
||||
|
|
@ -9,26 +9,21 @@ compute_wifi_feature <- function(data, feature, time_segment){
|
|||
"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 %>%
|
||||
filter(bssid != "") %>%
|
||||
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)
|
||||
|
||||
data <- data %>% filter_data_by_segment(time_segment)
|
||||
|
||||
return(data %>%
|
||||
filter(bssid == mostuniquedevice) %>%
|
||||
group_by(local_segment) %>%
|
||||
summarise(!!feature := n())
|
||||
)
|
||||
|
||||
summarise(!!feature := n()) %>%
|
||||
replace(is.na(.), 0))
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -48,6 +43,6 @@ rapids_features <- function(sensor_data_files, time_segment, provider){
|
|||
feature <- compute_wifi_feature(wifi_data, feature_name, time_segment)
|
||||
features <- merge(features, feature, by="local_segment", all = TRUE)
|
||||
}
|
||||
features <- features %>% mutate_all(~replace(., is.na(.), 0))
|
||||
|
||||
return(features)
|
||||
}
|
||||
|
|
|
|||
|
|
@ -1,17 +0,0 @@
|
|||
source("renv/activate.R")
|
||||
|
||||
library(tidyr)
|
||||
library(purrr)
|
||||
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_segment = "character", local_segment_label = "character", local_segment_start_datetime="character", local_segment_end_datetime="character"))),
|
||||
pid = str_match(filename, ".*/(.*)/z_all_sensor_features.csv")[,2]) %>%
|
||||
unnest(cols = c(file_contents)) %>%
|
||||
select(-filename)
|
||||
|
||||
write.csv(features_of_all_participants, snakemake@output[[1]], row.names = FALSE)
|
||||
|
|
@ -88,13 +88,11 @@ def chunk_episodes(sensor_episodes):
|
|||
|
||||
return merged_sensor_episodes
|
||||
|
||||
def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_files, time_segments_file, calc_windows=False):
|
||||
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"])
|
||||
sensor_fo_features = pd.DataFrame(columns=["local_segment"])
|
||||
sensor_so_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()))
|
||||
|
|
@ -108,20 +106,7 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file
|
|||
time_segments_labels["label"] = [""]
|
||||
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, calc_windows=calc_windows)
|
||||
|
||||
# In case of calc_window = True
|
||||
if isinstance(features, tuple):
|
||||
if not "local_segment" in features[0].columns or not "local_segment" in features[1].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 (" + provider["SRC_SCRIPT"] + ")\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[0].columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features[0].columns]
|
||||
features[1].columns = ["{}{}".format("" if col.startswith("local_segment") else (sensor_key + "_"+ provider_key + "_"), col) for col in features[1].columns]
|
||||
if not features[0].empty:
|
||||
sensor_fo_features = pd.concat([sensor_fo_features, features[0]], axis=0, sort=False)
|
||||
if not features[1].empty:
|
||||
sensor_so_features = pd.concat([sensor_so_features, features[1]], axis=0, sort=False)
|
||||
else:
|
||||
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 (" + provider["SRC_SCRIPT"] + ")\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]
|
||||
|
|
@ -129,27 +114,6 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file
|
|||
else:
|
||||
for feature in provider["FEATURES"]:
|
||||
sensor_features[feature] = None
|
||||
|
||||
if calc_windows:
|
||||
segment_colums = pd.DataFrame()
|
||||
sensor_fo_features['local_segment'] = sensor_fo_features['local_segment'].str.replace(r'_RR\d+SS', '')
|
||||
split_segemnt_columns = sensor_fo_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_fo_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]])
|
||||
|
||||
segment_colums = pd.DataFrame()
|
||||
sensor_so_features['local_segment'] = sensor_so_features['local_segment'].str.replace(r'_RR\d+SS', '')
|
||||
split_segemnt_columns = sensor_so_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_so_features.insert(1 + i, segment_colums.columns[i], segment_colums[segment_colums.columns[i]])
|
||||
|
||||
return sensor_fo_features, sensor_so_features
|
||||
|
||||
else:
|
||||
segment_colums = pd.DataFrame()
|
||||
sensor_features['local_segment'] = sensor_features['local_segment'].str.replace(r'_RR\d+SS', '')
|
||||
split_segemnt_columns = sensor_features["local_segment"].str.split(pat="(.*)#(.*),(.*)", expand=True)
|
||||
|
|
@ -160,16 +124,12 @@ def fetch_provider_features(provider, provider_key, sensor_key, sensor_data_file
|
|||
|
||||
return sensor_features
|
||||
|
||||
def run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files, target=False):
|
||||
def run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files):
|
||||
from importlib import import_module, util
|
||||
print("{} Processing {} {}".format(rapids_log_tag, sensor_key, provider_key))
|
||||
|
||||
cleaning_module = import_path(provider["SRC_SCRIPT"])
|
||||
cleaning_function = getattr(cleaning_module, provider_key.lower() + "_cleaning")
|
||||
|
||||
if target:
|
||||
sensor_features = cleaning_function(sensor_data_files, provider, target)
|
||||
else:
|
||||
sensor_features = cleaning_function(sensor_data_files, provider)
|
||||
|
||||
return sensor_features
|
||||
|
|
@ -1,19 +0,0 @@
|
|||
import pandas as pd
|
||||
import sys
|
||||
import warnings
|
||||
|
||||
def retain_target_column(df_input: pd.DataFrame, target_variable_name: str):
|
||||
column_names = df_input.columns
|
||||
esm_names_index = column_names.str.startswith("phone_esm_straw")
|
||||
# Find all columns coming from phone_esm, since these are not features for our purposes and we will drop them.
|
||||
esm_names = column_names[esm_names_index]
|
||||
target_variable_index = esm_names.str.contains(target_variable_name)
|
||||
if all(~target_variable_index):
|
||||
warnings.warn(f"The requested target (, {target_variable_name} ,)cannot be found in the dataset. Please check the names of phone_esm_ columns in cleaned python file")
|
||||
return None
|
||||
|
||||
sensor_features_plus_target = df_input.drop(esm_names, axis=1)
|
||||
sensor_features_plus_target["target"] = df_input[esm_names[target_variable_index]]
|
||||
# We will only keep one column related to phone_esm and that will be our target variable.
|
||||
# Add it back to the very and of the data frame and rename it to target.
|
||||
return sensor_features_plus_target
|
||||
|
|
@ -1,24 +0,0 @@
|
|||
import pandas as pd
|
||||
|
||||
from helper import retain_target_column
|
||||
|
||||
sensor_features = pd.read_csv(snakemake.input["cleaned_sensor_features"])
|
||||
|
||||
all_baseline_features = pd.DataFrame()
|
||||
for baseline_features_path in snakemake.input["demographic_features"]:
|
||||
pid = baseline_features_path.split("/")[3]
|
||||
baseline_features = pd.read_csv(baseline_features_path)
|
||||
baseline_features = baseline_features.assign(pid=pid)
|
||||
all_baseline_features = pd.concat([all_baseline_features, baseline_features], axis=0)
|
||||
|
||||
# merge sensor features and baseline features
|
||||
if not sensor_features.empty:
|
||||
features = sensor_features.merge(all_baseline_features, on="pid", how="left")
|
||||
|
||||
target_variable_name = snakemake.params["target_variable"]
|
||||
model_input = retain_target_column(features, target_variable_name)
|
||||
|
||||
model_input.to_csv(snakemake.output[0], index=False)
|
||||
|
||||
else:
|
||||
sensor_features.to_csv(snakemake.output[0], index=False)
|
||||
|
|
@ -1,13 +0,0 @@
|
|||
import pandas as pd
|
||||
|
||||
from helper import retain_target_column
|
||||
|
||||
cleaned_sensor_features = pd.read_csv(snakemake.input["cleaned_sensor_features"])
|
||||
target_variable_name = snakemake.params["target_variable"]
|
||||
|
||||
model_input = retain_target_column(cleaned_sensor_features, target_variable_name)
|
||||
|
||||
if model_input is None:
|
||||
pd.DataFrame().to_csv(snakemake.output[0])
|
||||
else:
|
||||
model_input.to_csv(snakemake.output[0], index=False)
|
||||
|
|
@ -24,12 +24,12 @@ def colors2colorscale(colors):
|
|||
def getDataForPlot(phone_data_yield_per_segment):
|
||||
# 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")
|
||||
# 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_segment_start_datetimes", "minutes_after_segment_start"])[["sensor"]].max().reset_index()
|
||||
|
||||
# impute missing rows with 0
|
||||
columns_for_full_index = phone_data_yield_per_segment[["local_segment_start_datetimes", "length"]].drop_duplicates(keep="first")
|
||||
|
|
|
|||
Binary file not shown.
|
Before Width: | Height: | Size: 12 KiB |
|
|
@ -1,39 +0,0 @@
|
|||
import pandas as pd
|
||||
import seaborn as sns
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
|
||||
participant = "p01"
|
||||
all_sensors = ["eda", "ibi", "temp", "acc"]
|
||||
|
||||
for sensor in all_sensors:
|
||||
|
||||
if sensor == "eda":
|
||||
path = f"/rapids/data/interim/{participant}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv"
|
||||
elif sensor == "bvp":
|
||||
path = f"/rapids/data/interim/{participant}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_cr_windows.csv"
|
||||
elif sensor == "ibi":
|
||||
path = f"/rapids/data/interim/{participant}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_cr_windows.csv"
|
||||
elif sensor == "acc":
|
||||
path = f"/rapids/data/interim/{participant}/empatica_accelerometer_features/empatica_accelerometer_python_cr_windows.csv"
|
||||
elif sensor == "temp":
|
||||
path = f"/rapids/data/interim/{participant}/empatica_temperature_features/empatica_temperature_python_cr_windows.csv"
|
||||
else:
|
||||
path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv" # all features all participants
|
||||
|
||||
|
||||
df = pd.read_csv(path)
|
||||
print(df)
|
||||
is_NaN = df.isnull()
|
||||
row_has_NaN = is_NaN.any(axis=1)
|
||||
rows_with_NaN = df[row_has_NaN]
|
||||
|
||||
print("All rows:", len(df.index))
|
||||
print("\nCount NaN vals:", rows_with_NaN.size)
|
||||
print("\nDf mean:")
|
||||
print(df.mean())
|
||||
|
||||
sns.heatmap(df.isna(), cbar=False)
|
||||
plt.savefig(f'{sensor}_{participant}_windows_NaN.png', bbox_inches='tight')
|
||||
|
||||
|
||||
|
|
@ -1,285 +0,0 @@
|
|||
import pandas as pd
|
||||
import seaborn as sns
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
|
||||
path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv"
|
||||
df = pd.read_csv(path)
|
||||
|
||||
# activity_recognition
|
||||
|
||||
cols = [col for col in df.columns if "activity_recognition" in col]
|
||||
df_x = df[cols]
|
||||
|
||||
print(len(cols))
|
||||
print(df_x)
|
||||
|
||||
df_x = df_x.dropna(axis=0, how="all")
|
||||
sns.heatmap(df_x.isna(), xticklabels=1)
|
||||
plt.savefig(f'activity_recognition_values', bbox_inches='tight')
|
||||
|
||||
df_q = pd.DataFrame()
|
||||
for col in df_x:
|
||||
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
|
||||
|
||||
sns.heatmap(df_q, cbar=False, xticklabels=1)
|
||||
plt.savefig(f'cut_activity_recognition_values', bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
# applications_foreground
|
||||
|
||||
cols = [col for col in df.columns if "applications_foreground" in col]
|
||||
df_x = df[cols]
|
||||
|
||||
print(len(cols))
|
||||
print(df_x)
|
||||
|
||||
df_x = df_x.dropna(axis=0, how="all")
|
||||
sns.heatmap(df_x.isna(), xticklabels=1)
|
||||
plt.savefig(f'applications_foreground_values', bbox_inches='tight')
|
||||
|
||||
df_q = pd.DataFrame()
|
||||
for col in df_x:
|
||||
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
|
||||
|
||||
sns.heatmap(df_q, cbar=False, xticklabels=1)
|
||||
plt.savefig(f'cut_applications_foreground_values', bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
# battery
|
||||
|
||||
cols = [col for col in df.columns if "phone_battery" in col]
|
||||
df_x = df[cols]
|
||||
|
||||
print(len(cols))
|
||||
print(df_x)
|
||||
|
||||
df_x = df_x.dropna(axis=0, how="all")
|
||||
sns.heatmap(df_x.isna(), xticklabels=1)
|
||||
plt.savefig(f'phone_battery_values', bbox_inches='tight')
|
||||
|
||||
df_q = pd.DataFrame()
|
||||
for col in df_x:
|
||||
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
|
||||
|
||||
sns.heatmap(df_q, cbar=False, xticklabels=1)
|
||||
plt.savefig(f'cut_phone_battery_values', bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
# bluetooth_doryab
|
||||
|
||||
cols = [col for col in df.columns if "bluetooth_doryab" in col]
|
||||
df_x = df[cols]
|
||||
|
||||
print(len(cols))
|
||||
print(df_x)
|
||||
|
||||
df_x = df_x.dropna(axis=0, how="all")
|
||||
sns.heatmap(df_x.isna(), xticklabels=1)
|
||||
plt.savefig(f'bluetooth_doryab_values', bbox_inches='tight')
|
||||
|
||||
df_q = pd.DataFrame()
|
||||
for col in df_x:
|
||||
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
|
||||
|
||||
sns.heatmap(df_q, cbar=False, xticklabels=1)
|
||||
plt.savefig(f'cut_bluetooth_doryab_values', bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
# bluetooth_rapids
|
||||
|
||||
cols = [col for col in df.columns if "bluetooth_rapids" in col]
|
||||
df_x = df[cols]
|
||||
|
||||
print(len(cols))
|
||||
print(df_x)
|
||||
|
||||
df_x = df_x.dropna(axis=0, how="all")
|
||||
sns.heatmap(df_x.isna(), xticklabels=1)
|
||||
plt.savefig(f'bluetooth_rapids_values', bbox_inches='tight')
|
||||
|
||||
df_q = pd.DataFrame()
|
||||
for col in df_x:
|
||||
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
|
||||
|
||||
sns.heatmap(df_q, cbar=False, xticklabels=1)
|
||||
plt.savefig(f'cut_bluetooth_rapids_values', bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
# calls
|
||||
|
||||
cols = [col for col in df.columns if "phone_calls" in col]
|
||||
df_x = df[cols]
|
||||
|
||||
print(len(cols))
|
||||
print(df_x)
|
||||
|
||||
df_x = df_x.dropna(axis=0, how="all")
|
||||
sns.heatmap(df_x.isna(), xticklabels=1)
|
||||
plt.savefig(f'phone_calls_values', bbox_inches='tight')
|
||||
|
||||
df_q = pd.DataFrame()
|
||||
for col in df_x:
|
||||
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
|
||||
|
||||
sns.heatmap(df_q, cbar=False, xticklabels=1)
|
||||
plt.savefig(f'cut_phone_calls_values', bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
# data_yield
|
||||
|
||||
cols = [col for col in df.columns if "data_yield" in col]
|
||||
df_x = df[cols]
|
||||
|
||||
print(len(cols))
|
||||
print(df_x)
|
||||
|
||||
df_x = df_x.dropna(axis=0, how="all")
|
||||
sns.heatmap(df_x.isna(), xticklabels=1)
|
||||
plt.savefig(f'data_yield_values', bbox_inches='tight')
|
||||
|
||||
df_q = pd.DataFrame()
|
||||
for col in df_x:
|
||||
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
|
||||
|
||||
sns.heatmap(df_q, cbar=False, xticklabels=1)
|
||||
plt.savefig(f'cut_data_yield_values', bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
# esm
|
||||
|
||||
cols = [col for col in df.columns if "phone_esm" in col]
|
||||
df_x = df[cols]
|
||||
|
||||
print(len(cols))
|
||||
print(df_x)
|
||||
|
||||
df_x = df_x.dropna(axis=0, how="all")
|
||||
sns.heatmap(df_x.isna(), xticklabels=1)
|
||||
plt.savefig(f'phone_esm_values', bbox_inches='tight')
|
||||
|
||||
df_q = pd.DataFrame()
|
||||
for col in df_x:
|
||||
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
|
||||
|
||||
sns.heatmap(df_q, cbar=False, xticklabels=1)
|
||||
plt.savefig(f'cut_phone_esm_values', bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
# light
|
||||
|
||||
cols = [col for col in df.columns if "phone_light" in col]
|
||||
df_x = df[cols]
|
||||
|
||||
print(len(cols))
|
||||
print(df_x)
|
||||
|
||||
df_x = df_x.dropna(axis=0, how="all")
|
||||
sns.heatmap(df_x.isna(), xticklabels=1)
|
||||
plt.savefig(f'phone_light_values', bbox_inches='tight')
|
||||
|
||||
df_q = pd.DataFrame()
|
||||
for col in df_x:
|
||||
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
|
||||
|
||||
sns.heatmap(df_q, cbar=False, xticklabels=1)
|
||||
plt.savefig(f'cut_phone_light_values', bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
# locations_doryab
|
||||
|
||||
cols = [col for col in df.columns if "locations_doryab" in col]
|
||||
df_x = df[cols]
|
||||
|
||||
print(len(cols))
|
||||
print(df_x)
|
||||
|
||||
df_x = df_x.dropna(axis=0, how="all")
|
||||
sns.heatmap(df_x.isna(), xticklabels=1)
|
||||
plt.savefig(f'locations_doryab_values', bbox_inches='tight')
|
||||
|
||||
df_q = pd.DataFrame()
|
||||
for col in df_x:
|
||||
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
|
||||
|
||||
sns.heatmap(df_q, cbar=False, xticklabels=1)
|
||||
plt.savefig(f'cut_locations_doryab_values', bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
# locations_barnett
|
||||
|
||||
# Not working
|
||||
|
||||
# messages
|
||||
|
||||
cols = [col for col in df.columns if "phone_messages" in col]
|
||||
df_x = df[cols]
|
||||
|
||||
print(len(cols))
|
||||
print(df_x)
|
||||
|
||||
df_x = df_x.dropna(axis=0, how="all")
|
||||
sns.heatmap(df_x.isna(), xticklabels=1)
|
||||
plt.savefig(f'phone_messages_values', bbox_inches='tight')
|
||||
|
||||
df_q = pd.DataFrame()
|
||||
for col in df_x:
|
||||
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
|
||||
|
||||
sns.heatmap(df_q, cbar=False, xticklabels=1)
|
||||
plt.savefig(f'cut_phone_messages_values', bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
# screen
|
||||
|
||||
cols = [col for col in df.columns if "phone_screen" in col]
|
||||
df_x = df[cols]
|
||||
|
||||
print(len(cols))
|
||||
print(df_x)
|
||||
|
||||
df_x = df_x.dropna(axis=0, how="all")
|
||||
sns.heatmap(df_x.isna(), xticklabels=1)
|
||||
plt.savefig(f'phone_screen_values', bbox_inches='tight')
|
||||
|
||||
df_q = pd.DataFrame()
|
||||
for col in df_x:
|
||||
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
|
||||
|
||||
sns.heatmap(df_q, cbar=False, xticklabels=1)
|
||||
plt.savefig(f'cut_phone_screen_values', bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
# wifi_visible
|
||||
|
||||
cols = [col for col in df.columns if "wifi_visible" in col]
|
||||
df_x = df[cols]
|
||||
|
||||
print(len(cols))
|
||||
print(df_x)
|
||||
|
||||
df_x = df_x.dropna(axis=0, how="all")
|
||||
sns.heatmap(df_x.isna(), xticklabels=1)
|
||||
plt.savefig(f'wifi_visible_values', bbox_inches='tight')
|
||||
|
||||
df_q = pd.DataFrame()
|
||||
for col in df_x:
|
||||
df_q[col] = pd.to_numeric(pd.cut(df_x[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
|
||||
|
||||
sns.heatmap(df_q, cbar=False, xticklabels=1)
|
||||
plt.savefig(f'cut_wifi_visible_values', bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
# All features
|
||||
|
||||
print(len(df))
|
||||
print(df)
|
||||
|
||||
# df = df.dropna(axis=0, how="all")
|
||||
# df = df.dropna(axis=1, how="all")
|
||||
sns.heatmap(df.isna())
|
||||
plt.savefig(f'all_features', bbox_inches='tight')
|
||||
|
||||
print(df.columns[df.isna().all()].tolist())
|
||||
print("All NaNs:", df.isna().sum().sum())
|
||||
print("Df shape NaNs:", df.shape)
|
||||
|
|
@ -1,23 +0,0 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
import seaborn as sns
|
||||
import matplotlib.pyplot as plt
|
||||
import os, sys
|
||||
|
||||
participant = "p032"
|
||||
|
||||
folder = f"/rapids/data/processed/features/{participant}/"
|
||||
for filename in os.listdir(folder):
|
||||
if filename.startswith("phone_"):
|
||||
df = pd.read_csv(f"{folder}{filename}")
|
||||
plt.figure()
|
||||
sns.heatmap(df[[col for col in df if col.startswith('phone_')]], cbar=True)
|
||||
plt.savefig(f'{participant}_{filename}.png', bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
plt.figure()
|
||||
sns.heatmap(df[[col for col in df if col.startswith('phone_')]].isna(), cbar=True)
|
||||
plt.savefig(f'is_na_{participant}_{filename}.png', bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
|
||||
|
|
@ -1,70 +0,0 @@
|
|||
import pandas as pd
|
||||
import numpy as np
|
||||
import matplotlib.pyplot as plt
|
||||
from sklearn.preprocessing import StandardScaler
|
||||
import sys
|
||||
|
||||
sys.path.append('/rapids/')
|
||||
from src.features import cr_features_helper_methods as crhm
|
||||
|
||||
pd.set_option("display.max_columns", None)
|
||||
features_win = pd.read_csv("data/interim/p031/empatica_temperature_features/empatica_temperature_python_cr_windows.csv", usecols=[0, 1, 2, 3, 4, 5])
|
||||
|
||||
# First standardization method
|
||||
excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime', "empatica_temperature_cr_level_1"]
|
||||
z1_windows = features_win.copy()
|
||||
z1_windows.loc[:, ~z1_windows.columns.isin(excluded_columns)] = StandardScaler().fit_transform(z1_windows.loc[:, ~z1_windows.columns.isin(excluded_columns)])
|
||||
z1 = crhm.extract_second_order_features(z1_windows, ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows'], prefix="empatica_temperature_cr_")
|
||||
z1 = z1.iloc[:,4:]
|
||||
# print(z1)
|
||||
|
||||
# Second standardization method
|
||||
so_features_reg = crhm.extract_second_order_features(features_win, ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows'], prefix="empatica_temperature_cr_")
|
||||
so_features_reg = so_features_reg.iloc[:,4:]
|
||||
z2 = pd.DataFrame(StandardScaler().fit_transform(so_features_reg), columns=so_features_reg.columns)
|
||||
# print(z2)
|
||||
|
||||
# Standardization of the first standardization method values
|
||||
z1_z = pd.DataFrame(StandardScaler().fit_transform(z1), columns=z1.columns)
|
||||
# print(z1_z)
|
||||
|
||||
# For SD
|
||||
fig, axs = plt.subplots(3, figsize=(8, 10))
|
||||
axs[0].plot(z1['empatica_temperature_cr_squareSumOfComponent_X_SO_sd'])
|
||||
axs[0].set_title("Z1 - standardizirana okna, nato ekstrahiranje značilk SO")
|
||||
|
||||
axs[1].plot(z2['empatica_temperature_cr_squareSumOfComponent_X_SO_sd'])
|
||||
axs[1].set_title("Z2 - ekstrahirane značilke SO 'normalnih' vrednosti, nato standardizacija")
|
||||
|
||||
axs[2].plot(z1_z['empatica_temperature_cr_squareSumOfComponent_X_SO_sd'])
|
||||
axs[2].set_title("Standardiziran Z1")
|
||||
|
||||
fig.suptitle('Z-Score methods for temperature_squareSumOfComponent_SO_sd')
|
||||
plt.savefig('z_score_comparison_temperature_squareSumOfComponent_X_SO_sd', bbox_inches='tight')
|
||||
|
||||
showcase = pd.DataFrame()
|
||||
showcase['Z1__SD'] = z1['empatica_temperature_cr_squareSumOfComponent_X_SO_sd']
|
||||
showcase['Z2__SD'] = z2['empatica_temperature_cr_squareSumOfComponent_X_SO_sd']
|
||||
showcase['Z1__SD_STANDARDIZED'] = z1_z['empatica_temperature_cr_squareSumOfComponent_X_SO_sd']
|
||||
print(showcase)
|
||||
|
||||
# For
|
||||
fig, axs = plt.subplots(3, figsize=(8, 10))
|
||||
axs[0].plot(z1['empatica_temperature_cr_squareSumOfComponent_X_SO_nlargest'])
|
||||
axs[0].set_title("Z1 - standardizirana okna, nato ekstrahiranje značilk SO")
|
||||
|
||||
axs[1].plot(z2['empatica_temperature_cr_squareSumOfComponent_X_SO_nlargest'])
|
||||
axs[1].set_title("Z2")
|
||||
|
||||
axs[2].plot(z1_z['empatica_temperature_cr_squareSumOfComponent_X_SO_nlargest'])
|
||||
axs[2].set_title("Standardized Z1")
|
||||
|
||||
fig.suptitle('Z-Score methods for temperature_squareSumOfComponent_SO_nlargest')
|
||||
plt.savefig('z_score_comparison_temperature_squareSumOfComponent_X_SO_nlargest', bbox_inches='tight')
|
||||
|
||||
showcase2 = pd.DataFrame()
|
||||
showcase2['Z1__nlargest'] = z1['empatica_temperature_cr_squareSumOfComponent_X_SO_nlargest']
|
||||
showcase2['Z2__nlargest'] = z2['empatica_temperature_cr_squareSumOfComponent_X_SO_nlargest']
|
||||
showcase2['Z1__nlargest_STANDARDIZED'] = z1_z['empatica_temperature_cr_squareSumOfComponent_X_SO_nlargest']
|
||||
print(showcase2)
|
||||
|
||||
|
|
@ -1,38 +0,0 @@
|
|||
import numpy as np
|
||||
import pandas as pd
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
import sys
|
||||
|
||||
df = pd.read_csv(f"/rapids/data/raw/p03/empatica_accelerometer_raw.csv")
|
||||
|
||||
|
||||
df['date'] = pd.to_datetime(df['timestamp'],unit='ms')
|
||||
df.set_index('date', inplace=True)
|
||||
print(df)
|
||||
df = df['double_values_0'].resample("31ms").mean()
|
||||
print(df)
|
||||
|
||||
st='2021-05-21 12:28:27'
|
||||
en='2021-05-21 12:59:12'
|
||||
|
||||
df = df.loc[(df.index > st) & (df.index < en)]
|
||||
plt.plot(df)
|
||||
|
||||
plt.savefig(f'NaN.png')
|
||||
sys.exit()
|
||||
|
||||
|
||||
plt.plot(df)
|
||||
|
||||
esm = pd.read_csv(f"/rapids/data/raw/p03/phone_esm_raw.csv")
|
||||
|
||||
esm['date'] = pd.to_datetime(esm['timestamp'],unit='ms')
|
||||
esm = esm[esm['date']]
|
||||
esm.set_index('date', inplace=True)
|
||||
print(esm)
|
||||
|
||||
esm = esm['esm_session'].resample("2900ms").mean()
|
||||
|
||||
plt.plot(esm)
|
||||
plt.savefig(f'NaN.png')
|
||||
|
|
@ -1,48 +0,0 @@
|
|||
import pandas as pd
|
||||
import seaborn as sns
|
||||
import matplotlib.pyplot as plt
|
||||
from itertools import compress
|
||||
|
||||
|
||||
participant = "p031"
|
||||
sensor = "eda"
|
||||
|
||||
if sensor == "eda":
|
||||
path = f"/rapids/data/interim/{participant}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_cr_windows.csv"
|
||||
elif sensor == "bvp":
|
||||
path = f"/rapids/data/interim/{participant}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_cr_windows.csv"
|
||||
elif sensor == "ibi":
|
||||
path = f"/rapids/data/interim/{participant}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_cr_windows.csv"
|
||||
elif sensor == "acc":
|
||||
path = f"/rapids/data/interim/{participant}/empatica_accelerometer_features/empatica_accelerometer_python_cr_windows.csv"
|
||||
elif sensor == "temp":
|
||||
path = f"/rapids/data/interim/{participant}/empatica_temperature_features/empatica_temperature_python_cr_windows.csv"
|
||||
else:
|
||||
path = "/rapids/data/processed/features/all_participants/all_sensor_features.csv" # all features all participants"
|
||||
|
||||
|
||||
df = pd.read_csv(path)
|
||||
df_num_peaks_zero = df[df["empatica_electrodermal_activity_cr_numPeaks"] == 0]
|
||||
columns_num_peaks_zero = df_num_peaks_zero.columns[df_num_peaks_zero.isna().any()].tolist()
|
||||
|
||||
df_num_peaks_non_zero = df[df["empatica_electrodermal_activity_cr_numPeaks"] != 0]
|
||||
df_num_peaks_non_zero = df_num_peaks_non_zero[columns_num_peaks_zero]
|
||||
|
||||
pd.set_option('display.max_columns', None)
|
||||
|
||||
df_q = pd.DataFrame()
|
||||
for col in df_num_peaks_non_zero:
|
||||
df_q[col] = pd.to_numeric(pd.cut(df_num_peaks_non_zero[col], bins=[-1,0,0.000000000001,1000], labels=[-1,0,1], right=False))
|
||||
|
||||
sns.heatmap(df_q)
|
||||
plt.savefig(f'eda_{participant}_window_non_zero_peak_other_vals.png', bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
# Filter columns that do not contain 0
|
||||
non_zero_cols = list(compress(columns_num_peaks_zero, df_num_peaks_non_zero.all().tolist()))
|
||||
zero_cols = list(set(columns_num_peaks_zero) - set(non_zero_cols))
|
||||
|
||||
print(non_zero_cols, "\n")
|
||||
print(zero_cols)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue