Bring back requested fields in config.yaml.

Update coding files based on 7e565c34db98265afcda922a337493781fdd8ed5 in supermodule.

.gitignore

@@ -100,6 +100,9 @@ data/external/*
 !/data/external/wiki_tz.csv
 !/data/external/main_study_usernames.csv
 !/data/external/timezone.csv
+!/data/external/play_store_application_genre_catalogue.csv
+!/data/external/play_store_categories_count.csv
+
 
 data/raw/*
 !/data/raw/.gitkeep

README.md

@@ -16,7 +16,7 @@ By [MoSHI](https://www.moshi.pitt.edu/), [University of Pittsburgh](https://www.
 
 For RAPIDS installation refer to to the [documentation](https://www.rapids.science/1.8/setup/installation/)
 
-## For the installation of the Docker version
+### For the installation of the Docker version
 
 1. Follow the [instructions](https://www.rapids.science/1.8/setup/installation/) to setup RAPIDS via Docker (from scratch).
 
@@ -46,7 +46,7 @@ Type R to go to the interactive R session and then:
     ```
 
 6. Install cr-features module 
-From: https://repo.ijs.si/matjazbostic/calculatingfeatures.git -> branch modifications_for_rapids. 
+From: https://repo.ijs.si/matjazbostic/calculatingfeatures.git -> branch master. 
 Then follow the "cr-features module" section below.  
 
 7. Install all required packages from environment.yml, prune also deletes conda packages not present in environment file.
@@ -62,7 +62,7 @@ Then follow the "cr-features module" section below.
     conda env export --no-builds | sed 's/^.*libgfortran.*$/  - libgfortran/' | sed 's/^.*mkl=.*$/  - mkl/' >  environment.yml
     ```
 
-## cr-features module 
+### cr-features module 
 
 This RAPIDS extension uses cr-features library accessible [here](https://repo.ijs.si/matjazbostic/calculatingfeatures).
 
@@ -78,4 +78,124 @@ To use cr-features library:
     e.g. pip install ./calculatingfeatures if the folder is copied to main parent directory
     cr-features package has to be built and installed everytime to get the newest version. 
     Or an the newest version of the docker image must be used.   
-    ```
+    ```
+
+## Updating RAPIDS
+
+To update RAPIDS, first pull and merge [origin]( https://github.com/carissalow/rapids), such as with:
+
+```commandline
+git fetch --progress "origin" refs/heads/master
+git merge --no-ff origin/master
+```
+
+Next, update the conda and R virtual environment.
+
+```bash
+R -e 'renv::restore(repos = c(CRAN = "https://packagemanager.rstudio.com/all/__linux__/focal/latest"))'
+```
+
+## Custom configuration
+### Credentials
+
+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.
+It should contain:
+
+```yaml
+PSQL_STRAW:
+  database: staw
+  host: 212.235.208.113
+  password: password
+  port: 5432
+  user: staw_db
+```
+
+where`password` needs to be specified as well.
+
+## Possible installation issues
+### Missing dependencies for RPostgres
+
+To install `RPostgres` R package (used to connect to the PostgreSQL database), an error might occur:
+
+```text
+------------------------- ANTICONF ERROR ---------------------------
+Configuration failed because libpq was not found. Try installing:
+   * deb: libpq-dev (Debian, Ubuntu, etc)
+   * rpm: postgresql-devel (Fedora, EPEL)
+   * rpm: postgreql8-devel, psstgresql92-devel, postgresql93-devel, or postgresql94-devel (Amazon Linux)
+   * csw: postgresql_dev (Solaris)
+   * brew: libpq (OSX)
+If libpq is already installed, check that either:
+  (i)  'pkg-config' is in your PATH AND PKG_CONFIG_PATH contains a libpq.pc file; or
+  (ii) 'pg_config' is in your PATH.
+If neither can detect , you can set INCLUDE_DIR
+and LIB_DIR manually via:
+  R CMD INSTALL --configure-vars='INCLUDE_DIR=... LIB_DIR=...'
+--------------------------[ ERROR MESSAGE ]----------------------------
+  <stdin>:1:10: fatal error: libpq-fe.h: No such file or directory
+compilation terminated.
+```
+
+The library requires `libpq` for compiling from source, so install accordingly.
+
+### Timezone environment variable for tidyverse (relevant for WSL2)
+
+One of the R packages, `tidyverse` might need access to the `TZ` environment variable during the installation.
+On Ubuntu 20.04 on WSL2 this triggers the following error:
+
+```text
+> install.packages('tidyverse')
+
+ERROR: configuration failed for package ‘xml2’
+System has not been booted with systemd as init system (PID 1). Can't operate.
+Failed to create bus connection: Host is down
+Warning in system("timedatectl", intern = TRUE) :
+  running command 'timedatectl' had status 1
+Error in loadNamespace(j <- i[[1L]], c(lib.loc, .libPaths()), versionCheck = vI[[j]]) :
+  namespace ‘xml2’ 1.3.1 is already loaded, but >= 1.3.2 is required
+Calls: <Anonymous> ... namespaceImportFrom -> asNamespace -> loadNamespace
+Execution halted
+ERROR: lazy loading failed for package ‘tidyverse’
+```
+
+This happens because WSL2 does not use the `timedatectl` service, which provides this variable.
+
+```bash
+~$ timedatectl
+System has not been booted with systemd as init system (PID 1). Can't operate.
+Failed to create bus connection: Host is down
+```
+
+and later 
+
+```bash 
+Warning message:
+In system("timedatectl", intern = TRUE) :
+  running command 'timedatectl' had status 1
+Execution halted
+```
+
+This can be amended by setting the environment variable manually before attempting to install `tidyverse`:
+
+```bash
+export TZ='Europe/Ljubljana'
+```
+
+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`.
+
+## Possible runtime issues
+### Unix end of line characters
+
+Upon running rapids, an error might occur:
+
+```bash
+/usr/bin/env: ‘python3\r’: No such file or directory
+```
+
+This is due to Windows style end of line characters. 
+To amend this, I added a `.gitattributes` files to force `git` to checkout `rapids` using Unix EOL characters.
+If this still fails, `dos2unix` can be used to change them.
+
+### System has not been booted with systemd as init system (PID 1)
+
+See [the installation issue above](#Timezone-environment-variable-for-tidyverse-(relevant-for-WSL2)).

Snakefile

@@ -33,12 +33,6 @@ for provider in config["PHONE_DATA_YIELD"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_data_yield.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_DATA_YIELD"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_data_yield.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys():
     if config["PHONE_MESSAGES"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -48,12 +42,6 @@ for provider in config["PHONE_MESSAGES"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_messages.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_MESSAGES"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_messages.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_CALLS"]["PROVIDERS"].keys():
     if config["PHONE_CALLS"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -68,12 +56,6 @@ for provider in config["PHONE_CALLS"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_calls.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_CALLS"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_calls.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_BLUETOOTH"]["PROVIDERS"].keys():
     if config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -83,12 +65,6 @@ for provider in config["PHONE_BLUETOOTH"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_bluetooth.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_BLUETOOTH"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_bluetooth.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"].keys():
     if config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -101,12 +77,6 @@ for provider in config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_activity_recognition.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_ACTIVITY_RECOGNITION"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_activity_recognition.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_BATTERY"]["PROVIDERS"].keys():
     if config["PHONE_BATTERY"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -118,12 +88,6 @@ for provider in config["PHONE_BATTERY"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_battery.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_BATTERY"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_battery.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_SCREEN"]["PROVIDERS"].keys():
     if config["PHONE_SCREEN"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -140,12 +104,6 @@ for provider in config["PHONE_SCREEN"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_screen.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_SCREEN"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_screen.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_LIGHT"]["PROVIDERS"].keys():
     if config["PHONE_LIGHT"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -155,12 +113,6 @@ for provider in config["PHONE_LIGHT"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_light.csv", pid=config["PIDS"],))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_LIGHT"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_light.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_ACCELEROMETER"]["PROVIDERS"].keys():
     if config["PHONE_ACCELEROMETER"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -184,12 +136,6 @@ for provider in config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_applications_foreground.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_applications_foreground.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_WIFI_VISIBLE"]["PROVIDERS"].keys():
     if config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -199,12 +145,6 @@ for provider in config["PHONE_WIFI_VISIBLE"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_wifi_visible.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_WIFI_VISIBLE"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_wifi_visible.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["PHONE_WIFI_CONNECTED"]["PROVIDERS"].keys():
     if config["PHONE_WIFI_CONNECTED"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -233,12 +173,15 @@ for provider in config["PHONE_ESM"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_esm.csv", pid=config["PIDS"]))
         # files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv",pid=config["PIDS"]))
         # files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_ESM"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_esm.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
+
+for provider in config["PHONE_SPEECH"]["PROVIDERS"].keys():
+    if config["PHONE_SPEECH"]["PROVIDERS"][provider]["COMPUTE"]:
+        files_to_compute.extend(expand("data/raw/{pid}/phone_speech_raw.csv",pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/raw/{pid}/phone_speech_with_datetime.csv",pid=config["PIDS"]))
+        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()))
+        files_to_compute.extend(expand("data/processed/features/{pid}/phone_speech.csv", pid=config["PIDS"]))
+        files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
+        files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
 
 # We can delete these if's as soon as we add feature PROVIDERS to any of these sensors
 if isinstance(config["PHONE_APPLICATIONS_CRASHES"]["PROVIDERS"], dict):
@@ -304,12 +247,6 @@ for provider in config["PHONE_LOCATIONS"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/phone_locations.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["LIST"] and config["STANDARDIZATION"]["PROVIDERS"]["OTHER"]["COMPUTE"] \
-            and config["PHONE_LOCATIONS"]["PROVIDERS"][provider]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_phone_locations.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["FITBIT_CALORIES_INTRADAY"]["PROVIDERS"].keys():
     if config["FITBIT_CALORIES_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -400,13 +337,6 @@ for provider in config["EMPATICA_ACCELEROMETER"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/empatica_accelerometer.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"] \
-            and config["EMPATICA_ACCELEROMETER"]["PROVIDERS"][provider]["WINDOWS"]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/interim/{pid}/empatica_accelerometer_features/z_empatica_accelerometer_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower()))
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_empatica_accelerometer.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
      
 for provider in config["EMPATICA_HEARTRATE"]["PROVIDERS"].keys():
     if config["EMPATICA_HEARTRATE"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -426,13 +356,6 @@ for provider in config["EMPATICA_TEMPERATURE"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/empatica_temperature.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"] \
-            and config["EMPATICA_TEMPERATURE"]["PROVIDERS"][provider]["WINDOWS"]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/interim/{pid}/empatica_temperature_features/z_empatica_temperature_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower()))
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_empatica_temperature.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"].keys():
     if config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -442,13 +365,6 @@ for provider in config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/empatica_electrodermal_activity.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"] \
-            and config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"][provider]["WINDOWS"]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/interim/{pid}/empatica_electrodermal_activity_features/z_empatica_electrodermal_activity_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower()))
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_empatica_electrodermal_activity.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"].keys():
     if config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -458,13 +374,6 @@ for provider in config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/empatica_blood_volume_pulse.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"] \
-            and config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"][provider]["WINDOWS"]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/interim/{pid}/empatica_blood_volume_pulse_features/z_empatica_blood_volume_pulse_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower()))
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_empatica_blood_volume_pulse.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
 
 for provider in config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"].keys():
     if config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][provider]["COMPUTE"]:
@@ -474,13 +383,6 @@ for provider in config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"].keys():
         files_to_compute.extend(expand("data/processed/features/{pid}/empatica_inter_beat_interval.csv", pid=config["PIDS"]))
         files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
         files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
-        if provider in config["STANDARDIZATION"]["PROVIDERS"] and config["STANDARDIZATION"]["PROVIDERS"][provider]["COMPUTE"] \
-            and config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"][provider]["WINDOWS"]["STANDARDIZE_FEATURES"]:
-            files_to_compute.extend(expand("data/interim/{pid}/empatica_inter_beat_interval_features/z_empatica_inter_beat_interval_{language}_{provider_key}_windows.csv", pid=config["PIDS"], language=get_script_language(config["STANDARDIZATION"]["PROVIDERS"][provider]["SRC_SCRIPT"]), provider_key=provider.lower()))
-            files_to_compute.extend(expand("data/processed/features/{pid}/z_empatica_inter_beat_interval.csv", pid=config["PIDS"]))
-            if config["STANDARDIZATION"]["MERGE_ALL"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"]))
-                files_to_compute.append("data/processed/features/all_participants/z_all_sensor_features.csv")
      
 if isinstance(config["EMPATICA_TAGS"]["PROVIDERS"], dict):
     for provider in config["EMPATICA_TAGS"]["PROVIDERS"].keys():
@@ -517,24 +419,16 @@ for provider in config["ALL_CLEANING_INDIVIDUAL"]["PROVIDERS"].keys():
     if config["ALL_CLEANING_INDIVIDUAL"]["PROVIDERS"][provider]["COMPUTE"]:
         if provider == "STRAW":
             files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features_cleaned_" + provider.lower() + "_py.csv", pid=config["PIDS"]))
-            if config["ALL_CLEANING_INDIVIDUAL"]["CLEAN_STANDARDIZED"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features_cleaned_" + provider.lower() + "_py.csv", pid=config["PIDS"]))
         else:
             files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features_cleaned_" + provider.lower() + "_R.csv", pid=config["PIDS"]))
-            if config["ALL_CLEANING_INDIVIDUAL"]["CLEAN_STANDARDIZED"]:
-                files_to_compute.extend(expand("data/processed/features/{pid}/z_all_sensor_features_cleaned_" + provider.lower() + "_R.csv", pid=config["PIDS"]))
 
 for provider in config["ALL_CLEANING_OVERALL"]["PROVIDERS"].keys():
     if config["ALL_CLEANING_OVERALL"]["PROVIDERS"][provider]["COMPUTE"]:
         if provider == "STRAW":
-            files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +"_py.csv"))
-            if config["ALL_CLEANING_OVERALL"]["CLEAN_STANDARDIZED"]:
-                files_to_compute.extend(expand("data/processed/features/all_participants/z_all_sensor_features_cleaned_" + provider.lower() +"_py.csv"))
+            for target in config["PARAMS_FOR_ANALYSIS"]["TARGET"]["ALL_LABELS"]:
+                files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +"_py_(" + target + ").csv"))
         else:
-            files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +"_R.csv"))
-            if config["ALL_CLEANING_OVERALL"]["CLEAN_STANDARDIZED"]:
-                files_to_compute.extend(expand("data/processed/features/all_participants/z_all_sensor_features_cleaned_" + provider.lower() +"_R.csv"))
-        
+            files_to_compute.extend(expand("data/processed/features/all_participants/all_sensor_features_cleaned_" + provider.lower() +"_R.csv"))     
 
 # Baseline features
 if config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["COMPUTE"]:
@@ -545,12 +439,9 @@ if config["PARAMS_FOR_ANALYSIS"]["BASELINE"]["COMPUTE"]:
 
 # Targets (labels)
 if config["PARAMS_FOR_ANALYSIS"]["TARGET"]["COMPUTE"]:
-    # files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/input.csv", pid=config["PIDS"]))
-    # files_to_compute.extend(expand("data/processed/models/population_model/input.csv"))
-    files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/z_input.csv", pid=config["PIDS"]))
-    files_to_compute.extend(expand("data/processed/models/population_model/z_input.csv"))
-
-#files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/output_{cv_method}/baselines.csv", pid=config["PIDS"], cv_method=config["PARAMS_FOR_ANALYSIS"]["CV_METHODS"]))
+    files_to_compute.extend(expand("data/processed/models/individual_model/{pid}/input.csv", pid=config["PIDS"]))
+    for target in config["PARAMS_FOR_ANALYSIS"]["TARGET"]["ALL_LABELS"]:
+        files_to_compute.extend(expand("data/processed/models/population_model/input_" + target + ".csv"))
 
 rule all:
     input:

automl_test.py

@@ -0,0 +1,57 @@
+from pprint import pprint
+import sklearn.metrics
+import autosklearn.regression
+
+import datetime
+import importlib
+import os
+import sys
+
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+import seaborn as sns
+import yaml
+
+from sklearn import linear_model, svm, kernel_ridge, gaussian_process
+from sklearn.model_selection import LeaveOneGroupOut, cross_val_score, train_test_split
+from sklearn.metrics import mean_squared_error, r2_score
+from sklearn.impute import SimpleImputer
+
+model_input = pd.read_csv("data/processed/models/population_model/input_PANAS_negative_affect_mean.csv") # Standardizirani podatki
+
+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"]
+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()
+mode_categorical_features = categorical_features.mode().iloc[0]
+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()

config.yaml

@@ -21,9 +21,14 @@ CREATE_PARTICIPANT_FILES:
 
 # See https://www.rapids.science/latest/setup/configuration/#time-segments
 TIME_SEGMENTS: &time_segments
-  TYPE: PERIODIC # FREQUENCY, PERIODIC, EVENT
-  FILE: "data/external/timesegments_daily.csv"
+  TYPE: EVENT # FREQUENCY, PERIODIC, EVENT
+  FILE: "data/external/straw_events.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: 
@@ -70,7 +75,6 @@ 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
@@ -93,7 +97,6 @@ PHONE_ACTIVITY_RECOGNITION:
         STATIONARY: ["still", "tilting"]
         MOBILE: ["on_foot", "walking", "running", "on_bicycle"]
         VEHICLE: ["in_vehicle"]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_activity_recognition/rapids/main.py
 
 # See https://www.rapids.science/latest/features/phone-applications-crashes/
@@ -101,9 +104,9 @@ 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/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
+    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
   PROVIDERS: # None implemented yet but this sensor can be used in PHONE_DATA_YIELD
 
 # See https://www.rapids.science/latest/features/phone-applications-foreground/
@@ -111,30 +114,37 @@ 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/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
+    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
   PROVIDERS:
     RAPIDS:
       COMPUTE: True
       INCLUDE_EPISODE_FEATURES: True
-      SINGLE_CATEGORIES: ["all", "email"]
+      SINGLE_CATEGORIES: ["Productivity", "Tools", "Communication", "Education", "Social"]
       MULTIPLE_CATEGORIES:
-        social: ["socialnetworks", "socialmediatools"]
-        entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"]
+        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"]
       CUSTOM_CATEGORIES:
-        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?
+      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: []
       FEATURES: 
         APP_EVENTS: ["countevent", "timeoffirstuse", "timeoflastuse", "frequencyentropy"]
         APP_EPISODES: ["countepisode", "minduration", "maxduration", "meanduration", "sumduration"]
       IGNORE_EPISODES_SHORTER_THAN: 0 # in minutes, set to 0 to disable
       IGNORE_EPISODES_LONGER_THAN: 300 # in minutes, set to 0 to disable
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_applications_foreground/rapids/main.py
 
 # See https://www.rapids.science/latest/features/phone-applications-notifications/
@@ -155,7 +165,6 @@ PHONE_BATTERY:
     RAPIDS:
       COMPUTE: True
       FEATURES: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_battery/rapids/main.py
 
 # See https://www.rapids.science/latest/features/phone-bluetooth/
@@ -163,9 +172,8 @@ PHONE_BLUETOOTH:
   CONTAINER: bluetooth
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_bluetooth/rapids/main.R
 
     DORYAB:
@@ -183,7 +191,6 @@ PHONE_BLUETOOTH:
             DEVICES: ["countscans", "uniquedevices", "meanscans", "stdscans"]
             SCANS_MOST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
             SCANS_LEAST_FREQUENT_DEVICE: ["withinsegments", "acrosssegments", "acrossdataset"]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_bluetooth/doryab/main.py
 
 # See https://www.rapids.science/latest/features/phone-calls/
@@ -198,7 +205,6 @@ PHONE_CALLS:
         missed:  [count, distinctcontacts, timefirstcall, timelastcall, countmostfrequentcontact]
         incoming: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact]
         outgoing: [count, distinctcontacts, meanduration, sumduration, minduration, maxduration, stdduration, modeduration, entropyduration, timefirstcall, timelastcall, countmostfrequentcontact]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_calls/rapids/main.R
 
 # See https://www.rapids.science/latest/features/phone-conversation/
@@ -238,7 +244,6 @@ PHONE_DATA_YIELD:
       COMPUTE: True
       FEATURES: [ratiovalidyieldedminutes, ratiovalidyieldedhours]
       MINUTE_RATIO_THRESHOLD_FOR_VALID_YIELDED_HOURS: 0.5 # 0 to 1, minimum percentage of valid minutes in an hour to be considered valid.
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_data_yield/rapids/main.R
 
 PHONE_ESM:
@@ -246,9 +251,9 @@ 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"]
+      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"]
       FEATURES: [mean]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_esm/straw/main.py
 
 # See https://www.rapids.science/latest/features/phone-keyboard/
@@ -267,7 +272,6 @@ PHONE_LIGHT:
     RAPIDS:
       COMPUTE: True
       FEATURES: ["count", "maxlux", "minlux", "avglux", "medianlux", "stdlux"]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_light/rapids/main.py
 
 # See https://www.rapids.science/latest/features/phone-locations/
@@ -292,7 +296,6 @@ PHONE_LOCATIONS:
       MINIMUM_DAYS_TO_DETECT_HOME_CHANGES: 3
       CLUSTERING_ALGORITHM: DBSCAN # DBSCAN, OPTICS
       RADIUS_FOR_HOME: 100
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_locations/doryab/main.py
 
     BARNETT:
@@ -300,7 +303,6 @@ PHONE_LOCATIONS:
       FEATURES: ["hometime","disttravelled","rog","maxdiam","maxhomedist","siglocsvisited","avgflightlen","stdflightlen","avgflightdur","stdflightdur","probpause","siglocentropy","circdnrtn","wkenddayrtn"]
       IF_MULTIPLE_TIMEZONES: USE_MOST_COMMON
       MINUTES_DATA_USED: False # Use this for quality control purposes, how many minutes of data (location coordinates gruped by minute) were used to compute features
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_locations/barnett/main.R
 
 # See https://www.rapids.science/latest/features/phone-log/
@@ -320,7 +322,6 @@ PHONE_MESSAGES:
       FEATURES: 
         received: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
         sent: [count, distinctcontacts, timefirstmessage, timelastmessage, countmostfrequentcontact]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_messages/rapids/main.R
 
 # See https://www.rapids.science/latest/features/phone-screen/
@@ -334,9 +335,17 @@ PHONE_SCREEN:
       IGNORE_EPISODES_LONGER_THAN: 360 # in minutes, set to 0 to disable
       FEATURES: ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"] # "episodepersensedminutes" needs to be added later
       EPISODE_TYPES: ["unlock"]
-      STANDARDIZE_FEATURES: True
       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
@@ -353,7 +362,6 @@ PHONE_WIFI_VISIBLE:
     RAPIDS:
       COMPUTE: True
       FEATURES: ["countscans", "uniquedevices", "countscansmostuniquedevice"]
-      STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/phone_wifi_visible/rapids/main.R
 
 
@@ -455,7 +463,6 @@ 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]
@@ -528,7 +535,6 @@ EMPATICA_ACCELEROMETER:
         COMPUTE: True
         WINDOW_LENGTH: 15 # specify window length in seconds
         SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows']
-        STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/empatica_accelerometer/cr/main.py
 
 
@@ -557,7 +563,6 @@ EMPATICA_TEMPERATURE:
         COMPUTE: True
         WINDOW_LENGTH: 300 # specify window length in seconds
         SECOND_ORDER_FEATURES: ['mean', 'median', 'sd', 'nlargest', 'nsmallest', 'count_windows']
-        STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/empatica_temperature/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-electrodermal-activity/
@@ -579,7 +584,6 @@ EMPATICA_ELECTRODERMAL_ACTIVITY:
         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]
-        STANDARDIZE_FEATURES: True
         IMPUTE_NANS: True
       SRC_SCRIPT: src/features/empatica_electrodermal_activity/cr/main.py
 
@@ -599,7 +603,6 @@ EMPATICA_BLOOD_VOLUME_PULSE:
         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']
-        STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/empatica_blood_volume_pulse/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-inter-beat-interval/
@@ -619,7 +622,6 @@ EMPATICA_INTER_BEAT_INTERVAL:
         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']
-        STANDARDIZE_FEATURES: True
       SRC_SCRIPT: src/features/empatica_inter_beat_interval/cr/main.py
 
 # See https://www.rapids.science/latest/features/empatica-tags/
@@ -667,10 +669,9 @@ HEATMAP_FEATURE_CORRELATION_MATRIX:
 ########################################################################################################################
 
 ALL_CLEANING_INDIVIDUAL:
-  CLEAN_STANDARDIZED: True
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       IMPUTE_SELECTED_EVENT_FEATURES:
         COMPUTE: False
         MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
@@ -684,28 +685,25 @@ ALL_CLEANING_INDIVIDUAL:
         MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
         CORR_THRESHOLD: 0.95
       SRC_SCRIPT: src/features/all_cleaning_individual/rapids/main.R
-    STRAW: # currently the same as RAPIDS provider with a change in selecting the imputation type
+    STRAW:
       COMPUTE: True
-      IMPUTE_PHONE_SELECTED_EVENT_FEATURES:
-        COMPUTE: False
-        TYPE: median # options: zero, mean, median or k-nearest
-        MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
-      COLS_NAN_THRESHOLD: 1 # set to 1 to disable
+      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
-      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
       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:
-  CLEAN_STANDARDIZED: True
   PROVIDERS:
     RAPIDS:
-      COMPUTE: True
+      COMPUTE: False
       IMPUTE_SELECTED_EVENT_FEATURES:
         COMPUTE: False
         MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
@@ -719,40 +717,23 @@ ALL_CLEANING_OVERALL:
         MIN_OVERLAP_FOR_CORR_THRESHOLD: 0.5
         CORR_THRESHOLD: 0.95
       SRC_SCRIPT: src/features/all_cleaning_overall/rapids/main.R
-    STRAW: # currently the same as RAPIDS provider with a change in selecting the imputation type
+    STRAW:
       COMPUTE: True
-      IMPUTE_PHONE_SELECTED_EVENT_FEATURES:
-        COMPUTE: False
-        TYPE: median # options: zero, mean, median or k-nearest
-        MIN_DATA_YIELDED_MINUTES_TO_IMPUTE: 0.33
-      COLS_NAN_THRESHOLD: 1 # set to 1 to disable
+      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
-      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
       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
 
 
-########################################################################################################################
-#                                                 Z-score standardization                                              #
-########################################################################################################################
-
-STANDARDIZATION: # Standardization for both providers is executed if only one of two providers is marked COMPUTE: TRUE
-  MERGE_ALL: True # Creates the joint standardized file for each participant and all participants. Similar to merge_sensor_features_for_all_participants rule  
-  PROVIDERS:
-    CR:
-      COMPUTE: True
-      SRC_SCRIPT: src/features/standardization/main.py
-    OTHER:
-      COMPUTE: True
-      LIST: [RAPIDS, DORYAB, BARNETT, STRAW]
-      SRC_SCRIPT: src/features/standardization/main.py
-
-
 ########################################################################################################################
 #                                                      Baseline                                                        #
 ########################################################################################################################
@@ -771,4 +752,7 @@ PARAMS_FOR_ANALYSIS:
 
   TARGET:
     COMPUTE: True
-    LABEL: PANAS_negative_affect_mean
+    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

data/external/participant_files/p01.yaml

@@ -1,11 +1,11 @@
 PHONE:
-  DEVICE_IDS: [a748ee1a-1d0b-4ae9-9074-279a2b6ba524] # the participant's AWARE device id
-  PLATFORMS: [android] # or ios
-  LABEL: MyTestP01 # any string
-  START_DATE: 2020-01-01 # this can also be empty
-  END_DATE: 2021-01-01 # this can also be empty
+  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: [empatica1]
-  LABEL: test01
-  START_DATE: 
-  END_DATE: 
+  DEVICE_IDS: [uploader_53573]
+  LABEL: uploader_53573
+  START_DATE: 2021-05-21 09:21:24
+  END_DATE: 2021-07-12 17:32:07

data/external/play_store_categories_count.csv

@@ -0,0 +1,45 @@
+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

data/external/timesegments_daily.csv

@@ -1,2 +1,3 @@
 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

donotmakechanges.py

@@ -0,0 +1,39 @@
+"""
+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 ...
+"""
+# !

environment.yml

@@ -1,140 +1,30 @@
 name: rapids
 channels:
   - conda-forge
-  - defaults
 dependencies:
-  - _libgcc_mutex=0.1
-  - _openmp_mutex=4.5
-  - _py-xgboost-mutex=2.0
-  - appdirs=1.4.4
-  - arrow=0.16.0
-  - asn1crypto=1.4.0
-  - astropy=4.2.1
-  - attrs=20.3.0
-  - binaryornot=0.4.4
-  - blas=1.0
-  - brotlipy=0.7.0
-  - bzip2=1.0.8
-  - ca-certificates=2021.7.5
-  - certifi=2021.5.30
-  - cffi=1.14.4
-  - chardet=3.0.4
-  - click=7.1.2
-  - colorama=0.4.4
-  - cookiecutter=1.6.0
-  - cryptography=3.3.1
-  - datrie=0.8.2
-  - docutils=0.16
-  - future=0.18.2
-  - gitdb=4.0.5
-  - gitdb2=4.0.2
-  - gitpython=3.1.11
-  - idna=2.10
-  - imbalanced-learn=0.6.2
-  - importlib-metadata=2.0.0
-  - importlib_metadata=2.0.0
-  - intel-openmp=2019.4
-  - jinja2=2.11.2
-  - jinja2-time=0.2.0
-  - joblib=1.0.0
-  - jsonschema=3.2.0
-  - ld_impl_linux-64=2.36.1
-  - libblas=3.8.0
-  - libcblas=3.8.0
-  - libcxx=10.0.0
-  - libcxxabi=10.0.0
-  - libedit=3.1.20191231
-  - libffi=3.3
-  - libgcc-ng=11.2.0
-  - libgfortran
-  - libgfortran
-  - libgfortran
-  - liblapack=3.8.0
-  - libopenblas=0.3.10
-  - libstdcxx-ng=11.2.0
-  - libxgboost=0.90
-  - libzlib=1.2.11
-  - lightgbm=3.1.1
-  - llvm-openmp=10.0.0
-  - markupsafe=1.1.1
-  - mkl
-  - mkl-service=2.3.0
-  - mkl_fft=1.2.0
-  - mkl_random=1.1.1
-  - more-itertools=8.6.0
-  - ncurses=6.2
-  - numpy=1.19.2
-  - numpy-base=1.19.2
-  - openblas=0.3.4
-  - openssl=1.1.1k
-  - pandas=1.1.5
-  - pbr=5.5.1
-  - pip=20.3.3
-  - plotly=4.14.1
-  - poyo=0.5.0
-  - psutil=5.7.2
-  - py-xgboost=0.90
-  - pycparser=2.20
-  - pyerfa=1.7.1.1
-  - pyopenssl=20.0.1
-  - pysocks=1.7.1
-  - python=3.7.9
-  - python-dateutil=2.8.1
-  - python_abi=3.7
-  - pytz=2020.4
-  - pyyaml=5.3.1
-  - readline=8.0
-  - requests=2.25.0
-  - retrying=1.3.3
-  - scikit-learn=0.23.2
-  - scipy=1.5.2
-  - setuptools=51.0.0
-  - six=1.15.0
-  - smmap=3.0.4
-  - smmap2=3.0.1
-  - sqlite=3.33.0
-  - threadpoolctl=2.1.0
-  - tk=8.6.10
-  - tqdm=4.62.0
-  - urllib3=1.25.11
-  - wheel=0.36.2
-  - whichcraft=0.6.1
-  - wrapt=1.12.1
-  - xgboost=0.90
-  - xz=5.2.5
-  - yaml=0.2.5
-  - zipp=3.4.0
-  - zlib=1.2.11
-  - pip:
-    - amply==0.1.4
-    - bidict==0.22.0
-    - biosppy==0.8.0
-    - cached-property==1.5.2
-    - configargparse==0.15.1
-    - cr-features==0.2.1
-    - cycler==0.11.0
-    - decorator==4.4.2
-    - fonttools==4.33.2
-    - h5py==3.6.0
-    - hmmlearn==0.2.7
-    - ipython-genutils==0.2.0
-    - jupyter-core==4.6.3
-    - kiwisolver==1.4.2
-    - matplotlib==3.5.1
-    - nbformat==5.0.7
-    - opencv-python==4.5.5.64
-    - packaging==21.3
-    - peakutils==1.3.3
-    - pillow==9.1.0
-    - pulp==2.4
-    - pyparsing==2.4.7
-    - pyrsistent==0.15.5
-    - pywavelets==1.3.0
-    - ratelimiter==1.2.0.post0
-    - seaborn==0.11.2
-    - shortuuid==1.0.8
-    - snakemake==5.30.2
-    - toposort==1.5
-    - traitlets==4.3.3
-    - typing-extensions==4.2.0
-prefix: /opt/conda/envs/rapids
+    - 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
+    - pip:
+        - biosppy
+        - cr_features>=0.2

renv.lock

@@ -1,6 +1,6 @@
 {
   "R": {
-    "Version": "4.1.2",
+    "Version": "4.2.3",
     "Repositories": [
       {
         "Name": "CRAN",
@@ -46,10 +46,10 @@
     },
     "Hmisc": {
       "Package": "Hmisc",
-      "Version": "4.4-2",
+      "Version": "5.0-1",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "66458e906b2112a8b1639964efd77d7c"
+      "Repository": "CRAN",
+      "Hash": "bf9fe82c010a468fb32f913ff56d65e1"
     },
     "KernSmooth": {
       "Package": "KernSmooth",
@@ -104,7 +104,7 @@
       "Package": "RPostgres",
       "Version": "1.4.4",
       "Source": "Repository",
-      "Repository": "CRAN",
+      "Repository": "RSPM",
       "Hash": "c593ecb8dbca9faf3906431be610ca28"
     },
     "Rcpp": {
@@ -181,7 +181,7 @@
       "Package": "base64enc",
       "Version": "0.1-3",
       "Source": "Repository",
-      "Repository": "CRAN",
+      "Repository": "RSPM",
       "Hash": "543776ae6848fde2f48ff3816d0628bc"
     },
     "bit": {
@@ -221,17 +221,24 @@
     },
     "broom": {
       "Package": "broom",
-      "Version": "0.7.3",
+      "Version": "1.0.4",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "5581a5ddc8fe2ac5e0d092ec2de4c4ae"
+      "Repository": "CRAN",
+      "Hash": "f62b2504021369a2449c54bbda362d30"
+    },
+    "cachem": {
+      "Package": "cachem",
+      "Version": "1.0.7",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "cda74447c42f529de601fe4d4050daef"
     },
     "callr": {
       "Package": "callr",
-      "Version": "3.5.1",
+      "Version": "3.7.3",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "b7d7f1e926dfcd57c74ce93f5c048e80"
+      "Repository": "CRAN",
+      "Hash": "9b2191ede20fa29828139b9900922e51"
     },
     "caret": {
       "Package": "caret",
@@ -263,10 +270,10 @@
     },
     "cli": {
       "Package": "cli",
-      "Version": "2.2.0",
+      "Version": "3.6.1",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "3ef298932294b775fa0a3eeaa3a645b0"
+      "Repository": "CRAN",
+      "Hash": "89e6d8219950eac806ae0c489052048a"
     },
     "clipr": {
       "Package": "clipr",
@@ -286,7 +293,7 @@
       "Package": "codetools",
       "Version": "0.2-18",
       "Source": "Repository",
-      "Repository": "CRAN",
+      "Repository": "RSPM",
       "Hash": "019388fc48e48b3da0d3a76ff94608a8"
     },
     "colorspace": {
@@ -303,6 +310,13 @@
       "Repository": "RSPM",
       "Hash": "0f22be39ec1d141fd03683c06f3a6e67"
     },
+    "conflicted": {
+      "Package": "conflicted",
+      "Version": "1.2.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "bb097fccb22d156624fd07cd2894ddb6"
+    },
     "corpcor": {
       "Package": "corpcor",
       "Version": "1.6.9",
@@ -319,10 +333,10 @@
     },
     "cpp11": {
       "Package": "cpp11",
-      "Version": "0.2.4",
+      "Version": "0.4.3",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "ba66e5a750d39067d888aa7af797fed2"
+      "Repository": "CRAN",
+      "Hash": "ed588261931ee3be2c700d22e94a29ab"
     },
     "crayon": {
       "Package": "crayon",
@@ -354,10 +368,10 @@
     },
     "dbplyr": {
       "Package": "dbplyr",
-      "Version": "2.1.1",
+      "Version": "2.3.2",
       "Source": "Repository",
       "Repository": "CRAN",
-      "Hash": "1f37fa4ab2f5f7eded42f78b9a887182"
+      "Hash": "d24305b92db333726aed162a2c23a147"
     },
     "desc": {
       "Package": "desc",
@@ -382,17 +396,17 @@
     },
     "dplyr": {
       "Package": "dplyr",
-      "Version": "1.0.5",
+      "Version": "1.1.1",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "d0d76c11ec807eb3f000eba4e3eb0f68"
+      "Repository": "CRAN",
+      "Hash": "eb5742d256a0d9306d85ea68756d8187"
     },
     "dtplyr": {
       "Package": "dtplyr",
-      "Version": "1.1.0",
+      "Version": "1.3.1",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "1e14e4c5b2814de5225312394bc316da"
+      "Repository": "CRAN",
+      "Hash": "54ed3ea01b11e81a86544faaecfef8e2"
     },
     "e1071": {
       "Package": "e1071",
@@ -419,7 +433,7 @@
       "Package": "evaluate",
       "Version": "0.14",
       "Source": "Repository",
-      "Repository": "CRAN",
+      "Repository": "RSPM",
       "Hash": "ec8ca05cffcc70569eaaad8469d2a3a7"
     },
     "fansi": {
@@ -452,10 +466,10 @@
     },
     "forcats": {
       "Package": "forcats",
-      "Version": "0.5.0",
+      "Version": "1.0.0",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "1cb4279e697650f0bd78cd3601ee7576"
+      "Repository": "CRAN",
+      "Hash": "1a0a9a3d5083d0d573c4214576f1e690"
     },
     "foreach": {
       "Package": "foreach",
@@ -492,6 +506,13 @@
       "Repository": "RSPM",
       "Hash": "f568ce73d3d59582b0f7babd0eb33d07"
     },
+    "gargle": {
+      "Package": "gargle",
+      "Version": "1.3.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "bb3208dcdfeb2e68bf33c87601b3cbe3"
+    },
     "gclus": {
       "Package": "gclus",
       "Version": "1.3.2",
@@ -515,10 +536,10 @@
     },
     "ggplot2": {
       "Package": "ggplot2",
-      "Version": "3.3.2",
+      "Version": "3.4.1",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "4ded8b439797f7b1693bd3d238d0106b"
+      "Repository": "CRAN",
+      "Hash": "d494daf77c4aa7f084dbbe6ca5dcaca7"
     },
     "ggraph": {
       "Package": "ggraph",
@@ -557,16 +578,30 @@
     },
     "glue": {
       "Package": "glue",
-      "Version": "1.4.2",
+      "Version": "1.6.2",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "6efd734b14c6471cfe443345f3e35e29"
+      "Repository": "CRAN",
+      "Hash": "4f2596dfb05dac67b9dc558e5c6fba2e"
+    },
+    "googledrive": {
+      "Package": "googledrive",
+      "Version": "2.1.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "e88ba642951bc8d1898ba0d12581850b"
+    },
+    "googlesheets4": {
+      "Package": "googlesheets4",
+      "Version": "1.1.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "fd7b97bd862a14297b0bb7ed28a3dada"
     },
     "gower": {
       "Package": "gower",
       "Version": "0.2.2",
       "Source": "Repository",
-      "Repository": "CRAN",
+      "Repository": "RSPM",
       "Hash": "be6a2b3529928bd803d1c437d1d43152"
     },
     "graphlayouts": {
@@ -599,10 +634,10 @@
     },
     "haven": {
       "Package": "haven",
-      "Version": "2.3.1",
+      "Version": "2.5.2",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "221d0ad75dfa03ebf17b1a4cc5c31dfc"
+      "Repository": "CRAN",
+      "Hash": "8b331e659e67d757db0fcc28e689c501"
     },
     "highr": {
       "Package": "highr",
@@ -613,10 +648,10 @@
     },
     "hms": {
       "Package": "hms",
-      "Version": "1.1.1",
+      "Version": "1.1.3",
       "Source": "Repository",
       "Repository": "CRAN",
-      "Hash": "5b8a2dd0fdbe2ab4f6081e6c7be6dfca"
+      "Hash": "b59377caa7ed00fa41808342002138f9"
     },
     "htmlTable": {
       "Package": "htmlTable",
@@ -648,10 +683,10 @@
     },
     "httr": {
       "Package": "httr",
-      "Version": "1.4.2",
+      "Version": "1.4.5",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "a525aba14184fec243f9eaec62fbed43"
+      "Repository": "CRAN",
+      "Hash": "f6844033201269bec3ca0097bc6c97b3"
     },
     "huge": {
       "Package": "huge",
@@ -660,6 +695,13 @@
       "Repository": "RSPM",
       "Hash": "a4cde4dd1d2551edb99a3273a4ad34ea"
     },
+    "ids": {
+      "Package": "ids",
+      "Version": "1.0.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "99df65cfef20e525ed38c3d2577f7190"
+    },
     "igraph": {
       "Package": "igraph",
       "Version": "1.2.6",
@@ -704,10 +746,10 @@
     },
     "jsonlite": {
       "Package": "jsonlite",
-      "Version": "1.7.2",
+      "Version": "1.8.4",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "98138e0994d41508c7a6b84a0600cfcb"
+      "Repository": "CRAN",
+      "Hash": "a4269a09a9b865579b2635c77e572374"
     },
     "knitr": {
       "Package": "knitr",
@@ -760,10 +802,10 @@
     },
     "lifecycle": {
       "Package": "lifecycle",
-      "Version": "1.0.0",
+      "Version": "1.0.3",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "3471fb65971f1a7b2d4ae7848cf2db8d"
+      "Repository": "CRAN",
+      "Hash": "001cecbeac1cff9301bdc3775ee46a86"
     },
     "listenv": {
       "Package": "listenv",
@@ -774,17 +816,17 @@
     },
     "lubridate": {
       "Package": "lubridate",
-      "Version": "1.7.9.2",
+      "Version": "1.9.2",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "5b5b02f621d39a499def7923a5aee746"
+      "Repository": "CRAN",
+      "Hash": "e25f18436e3efd42c7c590a1c4c15390"
     },
     "magrittr": {
       "Package": "magrittr",
-      "Version": "2.0.1",
+      "Version": "2.0.3",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "41287f1ac7d28a92f0a286ed507928d3"
+      "Repository": "CRAN",
+      "Hash": "7ce2733a9826b3aeb1775d56fd305472"
     },
     "markdown": {
       "Package": "markdown",
@@ -800,6 +842,13 @@
       "Repository": "RSPM",
       "Hash": "67101e7448dfd9add4ac418623060262"
     },
+    "memoise": {
+      "Package": "memoise",
+      "Version": "2.0.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "e2817ccf4a065c5d9d7f2cfbe7c1d78c"
+    },
     "mgcv": {
       "Package": "mgcv",
       "Version": "1.8-33",
@@ -830,10 +879,10 @@
     },
     "modelr": {
       "Package": "modelr",
-      "Version": "0.1.8",
+      "Version": "0.1.11",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "9fd59716311ee82cba83dc2826fc5577"
+      "Repository": "CRAN",
+      "Hash": "4f50122dc256b1b6996a4703fecea821"
     },
     "munsell": {
       "Package": "munsell",
@@ -888,7 +937,7 @@
       "Package": "parallelly",
       "Version": "1.29.0",
       "Source": "Repository",
-      "Repository": "CRAN",
+      "Repository": "RSPM",
       "Hash": "b5f399c9ce96977e22ef32c20b6cfe87"
     },
     "pbapply": {
@@ -907,10 +956,10 @@
     },
     "pillar": {
       "Package": "pillar",
-      "Version": "1.4.7",
+      "Version": "1.9.0",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "3b3dd89b2ee115a8b54e93a34cd546b4"
+      "Repository": "CRAN",
+      "Hash": "15da5a8412f317beeee6175fbc76f4bb"
     },
     "pkgbuild": {
       "Package": "pkgbuild",
@@ -977,10 +1026,10 @@
     },
     "processx": {
       "Package": "processx",
-      "Version": "3.4.5",
+      "Version": "3.8.0",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "22aab6098cb14edd0a5973a8438b569b"
+      "Repository": "CRAN",
+      "Hash": "a33ee2d9bf07564efb888ad98410da84"
     },
     "prodlim": {
       "Package": "prodlim",
@@ -1000,7 +1049,7 @@
       "Package": "progressr",
       "Version": "0.9.0",
       "Source": "Repository",
-      "Repository": "CRAN",
+      "Repository": "RSPM",
       "Hash": "ca0d80ecc29903f7579edbabd91f4199"
     },
     "promises": {
@@ -1033,10 +1082,10 @@
     },
     "purrr": {
       "Package": "purrr",
-      "Version": "0.3.4",
+      "Version": "1.0.1",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "97def703420c8ab10d8f0e6c72101e02"
+      "Repository": "CRAN",
+      "Hash": "d71c815267c640f17ddbf7f16144b4bb"
     },
     "qap": {
       "Package": "qap",
@@ -1052,6 +1101,13 @@
       "Repository": "RSPM",
       "Hash": "d35964686307333a7121eb41c7dcd4e0"
     },
+    "ragg": {
+      "Package": "ragg",
+      "Version": "1.2.5",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "690bc058ea2b1b8a407d3cfe3dce3ef9"
+    },
     "rappdirs": {
       "Package": "rappdirs",
       "Version": "0.3.3",
@@ -1061,17 +1117,17 @@
     },
     "readr": {
       "Package": "readr",
-      "Version": "1.4.0",
+      "Version": "2.1.4",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "2639976851f71f330264a9c9c3d43a61"
+      "Repository": "CRAN",
+      "Hash": "b5047343b3825f37ad9d3b5d89aa1078"
     },
     "readxl": {
       "Package": "readxl",
-      "Version": "1.3.1",
+      "Version": "1.4.2",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "63537c483c2dbec8d9e3183b3735254a"
+      "Repository": "CRAN",
+      "Hash": "2e6020b1399d95f947ed867045e9ca17"
     },
     "recipes": {
       "Package": "recipes",
@@ -1110,10 +1166,10 @@
     },
     "reprex": {
       "Package": "reprex",
-      "Version": "0.3.0",
+      "Version": "2.0.2",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "b06bfb3504cc8a4579fd5567646f745b"
+      "Repository": "CRAN",
+      "Hash": "d66fe009d4c20b7ab1927eb405db9ee2"
     },
     "reshape2": {
       "Package": "reshape2",
@@ -1138,10 +1194,10 @@
     },
     "rlang": {
       "Package": "rlang",
-      "Version": "0.4.10",
+      "Version": "1.1.0",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "599df23c40a4fce9c7b4764f28c37857"
+      "Repository": "CRAN",
+      "Hash": "dc079ccd156cde8647360f473c1fa718"
     },
     "rmarkdown": {
       "Package": "rmarkdown",
@@ -1173,24 +1229,24 @@
     },
     "rstudioapi": {
       "Package": "rstudioapi",
-      "Version": "0.13",
+      "Version": "0.14",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "06c85365a03fdaf699966cc1d3cf53ea"
+      "Repository": "CRAN",
+      "Hash": "690bd2acc42a9166ce34845884459320"
     },
     "rvest": {
       "Package": "rvest",
-      "Version": "0.3.6",
+      "Version": "1.0.3",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "a9795ccb2d608330e841998b67156764"
+      "Repository": "CRAN",
+      "Hash": "a4a5ac819a467808c60e36e92ddf195e"
     },
     "scales": {
       "Package": "scales",
-      "Version": "1.1.1",
+      "Version": "1.2.1",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "6f76f71042411426ec8df6c54f34e6dd"
+      "Repository": "CRAN",
+      "Hash": "906cb23d2f1c5680b8ce439b44c6fa63"
     },
     "selectr": {
       "Package": "selectr",
@@ -1236,17 +1292,17 @@
     },
     "stringi": {
       "Package": "stringi",
-      "Version": "1.5.3",
+      "Version": "1.7.12",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "a063ebea753c92910a4cca7b18bc1f05"
+      "Repository": "CRAN",
+      "Hash": "ca8bd84263c77310739d2cf64d84d7c9"
     },
     "stringr": {
       "Package": "stringr",
-      "Version": "1.4.0",
+      "Version": "1.5.0",
       "Source": "Repository",
       "Repository": "CRAN",
-      "Hash": "0759e6b6c0957edb1311028a49a35e76"
+      "Hash": "671a4d384ae9d32fc47a14e98bfa3dc8"
     },
     "survival": {
       "Package": "survival",
@@ -1262,6 +1318,13 @@
       "Repository": "RSPM",
       "Hash": "b227d13e29222b4574486cfcbde077fa"
     },
+    "systemfonts": {
+      "Package": "systemfonts",
+      "Version": "1.0.4",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "90b28393209827327de889f49935140a"
+    },
     "testthat": {
       "Package": "testthat",
       "Version": "3.0.1",
@@ -1269,12 +1332,19 @@
       "Repository": "RSPM",
       "Hash": "17826764cb92d8b5aae6619896e5a161"
     },
+    "textshaping": {
+      "Package": "textshaping",
+      "Version": "0.3.6",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "1ab6223d3670fac7143202cb6a2d43d5"
+    },
     "tibble": {
       "Package": "tibble",
-      "Version": "3.0.4",
+      "Version": "3.2.1",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "71dffd8544691c520dd8e41ed2d7e070"
+      "Repository": "CRAN",
+      "Hash": "a84e2cc86d07289b3b6f5069df7a004c"
     },
     "tidygraph": {
       "Package": "tidygraph",
@@ -1285,24 +1355,24 @@
     },
     "tidyr": {
       "Package": "tidyr",
-      "Version": "1.1.2",
+      "Version": "1.3.0",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "c40b2d5824d829190f4b825f4496dfae"
+      "Repository": "CRAN",
+      "Hash": "e47debdc7ce599b070c8e78e8ac0cfcf"
     },
     "tidyselect": {
       "Package": "tidyselect",
-      "Version": "1.1.0",
+      "Version": "1.2.0",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "6ea435c354e8448819627cf686f66e0a"
+      "Repository": "CRAN",
+      "Hash": "79540e5fcd9e0435af547d885f184fd5"
     },
     "tidyverse": {
       "Package": "tidyverse",
-      "Version": "1.3.0",
+      "Version": "2.0.0",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "bd51be662f359fa99021f3d51e911490"
+      "Repository": "CRAN",
+      "Hash": "c328568cd14ea89a83bd4ca7f54ae07e"
     },
     "timeDate": {
       "Package": "timeDate",
@@ -1311,6 +1381,13 @@
       "Repository": "RSPM",
       "Hash": "fde4fc571f5f61978652c229d4713845"
     },
+    "timechange": {
+      "Package": "timechange",
+      "Version": "0.2.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "8548b44f79a35ba1791308b61e6012d7"
+    },
     "tinytex": {
       "Package": "tinytex",
       "Version": "0.28",
@@ -1332,6 +1409,13 @@
       "Repository": "RSPM",
       "Hash": "fc77eb5297507cccfa3349a606061030"
     },
+    "tzdb": {
+      "Package": "tzdb",
+      "Version": "0.3.0",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "b2e1cbce7c903eaf23ec05c58e59fb5e"
+    },
     "utf8": {
       "Package": "utf8",
       "Version": "1.1.4",
@@ -1339,12 +1423,19 @@
       "Repository": "RSPM",
       "Hash": "4a5081acfb7b81a572e4384a7aaf2af1"
     },
-    "vctrs": {
-      "Package": "vctrs",
-      "Version": "0.3.8",
+    "uuid": {
+      "Package": "uuid",
+      "Version": "1.1-0",
       "Source": "Repository",
       "Repository": "CRAN",
-      "Hash": "ecf749a1b39ea72bd9b51b76292261f1"
+      "Hash": "f1cb46c157d080b729159d407be83496"
+    },
+    "vctrs": {
+      "Package": "vctrs",
+      "Version": "0.6.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "06eceb3a5d716fd0654cc23ca3d71a99"
     },
     "viridis": {
       "Package": "viridis",
@@ -1360,6 +1451,13 @@
       "Repository": "RSPM",
       "Hash": "ce4f6271baa94776db692f1cb2055bee"
     },
+    "vroom": {
+      "Package": "vroom",
+      "Version": "1.6.1",
+      "Source": "Repository",
+      "Repository": "CRAN",
+      "Hash": "7015a74373b83ffaef64023f4a0f5033"
+    },
     "waldo": {
       "Package": "waldo",
       "Version": "0.2.3",
@@ -1376,10 +1474,10 @@
     },
     "withr": {
       "Package": "withr",
-      "Version": "2.3.0",
+      "Version": "2.5.0",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "7307d79f58d1885b38c4f4f1a8cb19dd"
+      "Repository": "CRAN",
+      "Hash": "c0e49a9760983e81e55cdd9be92e7182"
     },
     "xfun": {
       "Package": "xfun",
@@ -1390,10 +1488,10 @@
     },
     "xml2": {
       "Package": "xml2",
-      "Version": "1.3.2",
+      "Version": "1.3.3",
       "Source": "Repository",
-      "Repository": "RSPM",
-      "Hash": "d4d71a75dd3ea9eb5fa28cc21f9585e2"
+      "Repository": "CRAN",
+      "Hash": "40682ed6a969ea5abfd351eb67833adc"
     },
     "xtable": {
       "Package": "xtable",

rules/common.smk

@@ -40,15 +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_empaticas_standardized_features_files(wildcards):
-    feature_files = []
-    if "empatica" in wildcards.sensor_key:
-        for provider_key, provider in config[(wildcards.sensor_key).upper()]["PROVIDERS"].items():
-            if provider["COMPUTE"] and provider.get("WINDOWS", False) and provider["WINDOWS"]["COMPUTE"]:
-                if "empatica" in wildcards.sensor_key:
-                    feature_files.extend(expand("data/interim/{{pid}}/{sensor_key}_features/z_{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():
@@ -82,18 +73,6 @@ def input_merge_sensor_features_for_individual_participants(wildcards):
                     break
     return feature_files
 
-def input_merge_standardized_sensor_features_for_individual_participants(wildcards):
-    feature_files = []
-    for config_key in config.keys():
-        if config_key.startswith(("PHONE", "FITBIT", "EMPATICA")) 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"] and ("STANDARDIZE_FEATURES" in provider.keys() and provider["STANDARDIZE_FEATURES"] or 
-                "WINDOWS" in provider.keys() and "STANDARDIZE_FEATURES" in provider["WINDOWS"].keys() and provider["WINDOWS"]["STANDARDIZE_FEATURES"]):
-                    feature_files.append("data/processed/features/{pid}/z_" + config_key.lower() + ".csv")
-                    break
-    
-    return feature_files
-
 def get_phone_sensor_names():
     phone_sensor_names = []
     for config_key in config.keys():

rules/features.smk

@@ -345,6 +345,19 @@ rule esm_features:
     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"
+    script:
+        "../src/features/entry.py"
+
 rule phone_keyboard_python_features:
     input:
         sensor_data = "data/raw/{pid}/phone_keyboard_with_datetime.csv",
@@ -796,20 +809,6 @@ rule empatica_accelerometer_python_features:
     script:
         "../src/features/entry.py"
 
-rule empatica_accelerometer_python_features_standardization:
-    input:
-        windows_features_data = "data/interim/{pid}/empatica_accelerometer_features/empatica_accelerometer_python_{provider_key}_windows.csv"
-    params:
-        provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"],
-        provider_key = "{provider_key}",
-        sensor_key = "empatica_accelerometer",
-        provider_main = config["EMPATICA_ACCELEROMETER"]["PROVIDERS"]["CR"]
-    output:
-        "data/interim/{pid}/empatica_accelerometer_features/z_empatica_accelerometer_python_{provider_key}.csv",
-        "data/interim/{pid}/empatica_accelerometer_features/z_empatica_accelerometer_python_{provider_key}_windows.csv"
-    script:
-        "../src/features/standardization/main.py"
-
 rule empatica_accelerometer_r_features:
     input:
         sensor_data = "data/raw/{pid}/empatica_accelerometer_with_datetime.csv",
@@ -864,20 +863,6 @@ rule empatica_temperature_python_features:
     script:
         "../src/features/entry.py"
 
-rule empatica_temperature_python_features_standardization:
-    input:
-        windows_features_data = "data/interim/{pid}/empatica_temperature_features/empatica_temperature_python_{provider_key}_windows.csv"
-    params:
-        provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"],
-        provider_key = "{provider_key}",
-        sensor_key = "empatica_temperature",
-        provider_main = config["EMPATICA_TEMPERATURE"]["PROVIDERS"]["CR"]
-    output:
-        "data/interim/{pid}/empatica_temperature_features/z_empatica_temperature_python_{provider_key}.csv",
-        "data/interim/{pid}/empatica_temperature_features/z_empatica_temperature_python_{provider_key}_windows.csv"
-    script:
-        "../src/features/standardization/main.py"
-
 rule empatica_temperature_r_features:
     input:
         sensor_data = "data/raw/{pid}/empatica_temperature_with_datetime.csv",
@@ -905,20 +890,6 @@ rule empatica_electrodermal_activity_python_features:
     script:
         "../src/features/entry.py"
 
-rule empatica_electrodermal_activity_python_features_standardization:
-    input:
-        windows_features_data = "data/interim/{pid}/empatica_electrodermal_activity_features/empatica_electrodermal_activity_python_{provider_key}_windows.csv"
-    params:
-        provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"],
-        provider_key = "{provider_key}",
-        sensor_key = "empatica_electrodermal_activity",
-        provider_main = config["EMPATICA_ELECTRODERMAL_ACTIVITY"]["PROVIDERS"]["CR"]
-    output:
-        "data/interim/{pid}/empatica_electrodermal_activity_features/z_empatica_electrodermal_activity_python_{provider_key}.csv",
-        "data/interim/{pid}/empatica_electrodermal_activity_features/z_empatica_electrodermal_activity_python_{provider_key}_windows.csv"
-    script:
-        "../src/features/standardization/main.py"
-
 rule empatica_electrodermal_activity_r_features:
     input:
         sensor_data = "data/raw/{pid}/empatica_electrodermal_activity_with_datetime.csv",
@@ -946,20 +917,6 @@ rule empatica_blood_volume_pulse_python_features:
     script:
         "../src/features/entry.py"
 
-rule empatica_blood_volume_pulse_python_cr_features_standardization:
-    input:
-        windows_features_data = "data/interim/{pid}/empatica_blood_volume_pulse_features/empatica_blood_volume_pulse_python_{provider_key}_windows.csv"
-    params:
-        provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"],
-        provider_key = "{provider_key}",
-        sensor_key = "empatica_blood_volume_pulse",
-        provider_main = config["EMPATICA_BLOOD_VOLUME_PULSE"]["PROVIDERS"]["CR"]
-    output:
-        "data/interim/{pid}/empatica_blood_volume_pulse_features/z_empatica_blood_volume_pulse_python_{provider_key}.csv",
-        "data/interim/{pid}/empatica_blood_volume_pulse_features/z_empatica_blood_volume_pulse_python_{provider_key}_windows.csv"
-    script:
-        "../src/features/standardization/main.py"
-
 rule empatica_blood_volume_pulse_r_features:
     input:
         sensor_data = "data/raw/{pid}/empatica_blood_volume_pulse_with_datetime.csv",
@@ -987,20 +944,6 @@ rule empatica_inter_beat_interval_python_features:
     script:
         "../src/features/entry.py"
 
-rule empatica_inter_beat_interval_python_features_standardization:
-    input:
-        windows_features_data = "data/interim/{pid}/empatica_inter_beat_interval_features/empatica_inter_beat_interval_python_{provider_key}_windows.csv"
-    params:
-        provider = config["STANDARDIZATION"]["PROVIDERS"]["CR"],
-        provider_key = "{provider_key}",
-        sensor_key = "empatica_inter_beat_interval",
-        provider_main = config["EMPATICA_INTER_BEAT_INTERVAL"]["PROVIDERS"]["CR"]
-    output:
-        "data/interim/{pid}/empatica_inter_beat_interval_features/z_empatica_inter_beat_interval_python_{provider_key}.csv",
-        "data/interim/{pid}/empatica_inter_beat_interval_features/z_empatica_inter_beat_interval_python_{provider_key}_windows.csv"
-    script:
-        "../src/features/standardization/main.py"
-
 rule empatica_inter_beat_interval_r_features:
     input:
         sensor_data = "data/raw/{pid}/empatica_inter_beat_interval_with_datetime.csv",
@@ -1048,38 +991,6 @@ rule merge_sensor_features_for_individual_participants:
     script:
         "../src/features/utils/merge_sensor_features_for_individual_participants.R"
 
-rule join_standardized_features_from_empatica:
-    input:
-        sensor_features = find_empaticas_standardized_features_files
-    wildcard_constraints:
-        sensor_key = '(empatica).*'
-    output:
-        "data/processed/features/{pid}/z_{sensor_key}.csv"
-    script:
-        "../src/features/utils/join_features_from_providers.R"
-
-rule standardize_features_from_providers_no_empatica:
-    input:
-        sensor_features = find_joint_non_empatica_sensor_files
-    wildcard_constraints:
-        sensor_key = '(phone|fitbit).*'
-    params:
-        provider = config["STANDARDIZATION"]["PROVIDERS"]["OTHER"],
-        provider_key = "OTHER",
-        sensor_key = "{sensor_key}"
-    output:
-        "data/processed/features/{pid}/z_{sensor_key}.csv"
-    script:
-        "../src/features/standardization/main.py"
-
-rule merge_standardized_sensor_features_for_individual_participants:
-    input:
-        feature_files = input_merge_standardized_sensor_features_for_individual_participants
-    output:
-        "data/processed/features/{pid}/z_all_sensor_features.csv"
-    script:
-        "../src/features/utils/merge_sensor_features_for_individual_participants.R"
-
 rule merge_sensor_features_for_all_participants:
     input:
         feature_files = expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"])
@@ -1088,14 +999,6 @@ rule merge_sensor_features_for_all_participants:
     script:
         "../src/features/utils/merge_sensor_features_for_all_participants.R"
 
-rule merge_standardized_sensor_features_for_all_participants:
-    input:
-        feature_files = expand("data/processed/features/{pid}/z_all_sensor_features.csv", pid=config["PIDS"])
-    output:
-        "data/processed/features/all_participants/z_all_sensor_features.csv"
-    script:
-        "../src/features/utils/merge_standardized_sensor_features_for_all_participants.R"
-
 rule clean_sensor_features_for_individual_participants:
     input:
         sensor_data = rules.merge_sensor_features_for_individual_participants.output
@@ -1107,7 +1010,7 @@ rule clean_sensor_features_for_individual_participants:
         script_extension = "{script_extension}",
         sensor_key = "all_cleaning_individual" 
     output:
-        "data/processed/features/{pid}/all_sensor_features_cleaned_{provider_key}_{script_extension}.csv" # bo predstavljalo probleme za naprej (kako iskati datoteke + standardizacija itd.)
+        "data/processed/features/{pid}/all_sensor_features_cleaned_{provider_key}_{script_extension}.csv" 
     script:
         "../src/features/entry.{params.script_extension}"
 
@@ -1118,37 +1021,9 @@ rule clean_sensor_features_for_all_participants:
         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"
+        sensor_key = "all_cleaning_overall",
+        target = "{target}"
     output:
-        "data/processed/features/all_participants/all_sensor_features_cleaned_{provider_key}_{script_extension}.csv"
+        "data/processed/features/all_participants/all_sensor_features_cleaned_{provider_key}_{script_extension}_({target}).csv"
     script:
         "../src/features/entry.{params.script_extension}"
-
-rule clean_standardized_sensor_features_for_individual_participants:
-    input:
-        sensor_data = rules.merge_standardized_sensor_features_for_individual_participants.output
-    wildcard_constraints:
-        pid = "("+"|".join(config["PIDS"])+")"
-    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}/z_all_sensor_features_cleaned_{provider_key}_{script_extension}.csv"
-    script:
-        "../src/features/entry.{params.script_extension}"
-
-rule clean_standardized_sensor_features_for_all_participants:
-    input:
-        sensor_data = rules.merge_standardized_sensor_features_for_all_participants.output
-    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"
-    output:
-        "data/processed/features/all_participants/z_all_sensor_features_cleaned_{provider_key}_{script_extension}.csv"
-    script:
-        "../src/features/entry.{params.script_extension}"
-

rules/models.smk

@@ -30,43 +30,23 @@ rule baseline_features:
 
 rule select_target:
     input:
-        cleaned_sensor_features = "data/processed/features/{pid}/z_all_sensor_features_cleaned_straw_py.csv"
+        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}/z_input.csv"
+        "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/z_all_sensor_features_cleaned_straw_py.csv",
+        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=config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
+        target_variable="{target}"
     output:
-        "data/processed/models/population_model/z_input.csv"
+        "data/processed/models/population_model/input_{target}.csv"
     script:
         "../src/models/merge_features_and_targets_for_population_model.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.csv",
-#         demographic_features = expand("data/processed/features/{pid}/baseline_features.csv", pid=config["PIDS"]),
-#     params:
-#         target_variable=config["PARAMS_FOR_ANALYSIS"]["TARGET"]["LABEL"]
-#     output:
-#         "data/processed/models/population_model/input.csv"
-#     script:
-#         "../src/models/merge_features_and_targets_for_population_model.py"
 

rules/preprocessing.smk

@@ -247,5 +247,33 @@ 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"

src/data/application_categories.R

@@ -29,23 +29,16 @@ 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")
-    }
+    apps_with_genre <- left_join(apps, genre_catalogue, by = "package_name")
   }
 
   if(catalogue_source == "GOOGLE" || (catalogue_source == "FILE" && scrape_missing_genres)){

src/data/datetime/assign_to_time_segment.R

@@ -5,13 +5,16 @@ 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
 }

src/data/streams/aware_postgresql/format.yaml

@@ -349,3 +349,24 @@ 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
+
+
+      
+

src/data/streams/empatica_zip/container.py

@@ -136,8 +136,9 @@ def patch_ibi_with_bvp(ibi_data, bvp_data):
     # Begin with the cr-features part
     try:
         ibi_data, ibi_start_timestamp = empatica2d_to_array(ibi_data_file)
-    except IndexError as e:
+    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']

src/data/streams/rapids_columns.yaml

@@ -118,6 +118,11 @@ PHONE_SCREEN:
   - DEVICE_ID
   - SCREEN_STATUS
 
+PHONE_SPEECH:
+  - TIMESTAMP
+  - DEVICE_ID
+  - SPEECH_PROPORTION
+
 PHONE_WIFI_CONNECTED:
   - TIMESTAMP
   - DEVICE_ID

src/features/all_cleaning_individual/straw/main.py

@@ -1,88 +1,180 @@
 import pandas as pd
 import numpy as np
-import math, sys
+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])
-
-    # TODO: reorder the cleaning steps so it makes sense for the analysis
-    # TODO: add conditions that differentiates cleaning steps for standardized and nonstandardized features, for this
-    # the snakemake rules will also have to come with additional parameter (in rules/features.smk)
-
-    # Impute selected features event
-    impute_phone_features = provider["IMPUTE_PHONE_SELECTED_EVENT_FEATURES"]
-    if impute_phone_features["COMPUTE"]:
-        if not 'phone_data_yield_rapids_ratiovalidyieldedminutes' in features.columns:
-            raise KeyError("RAPIDS provider needs to impute the selected event features based on phone_data_yield_rapids_ratiovalidyieldedminutes column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyieldedminutes' in [FEATURES].")
-        
-        # TODO: if the type of the imputation will vary for different groups of features make conditional imputations here 
-        phone_cols = [col for col in features if \
-            col.startswith('phone_applications_foreground_rapids_') or
-            col.startswith('phone_battery_rapids_') or
-            col.startswith('phone_calls_rapids_') or
-            col.startswith('phone_keyboard_rapids_') or
-            col.startswith('phone_messages_rapids_') or
-            col.startswith('phone_screen_rapids_') or
-            col.startswith('phone_wifi_')]
-
-        mask = features['phone_data_yield_rapids_ratiovalidyieldedminutes'] > impute_phone_features['MIN_DATA_YIELDED_MINUTES_TO_IMPUTE']
-        features.loc[mask, phone_cols] = impute(features[mask][phone_cols], method=impute_phone_features["TYPE"].lower())
-
-    # Drop rows with the value of data_yield_column less than data_yield_ratio_threshold
-    data_yield_unit = provider["DATA_YIELD_FEATURE"].split("_")[3].lower()
-    data_yield_column = "phone_data_yield_rapids_ratiovalidyielded" + data_yield_unit
-
-    if not data_yield_column in features.columns:
-        raise KeyError(f"RAPIDS provider needs to impute the selected event features 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 provider["DATA_YIELD_RATIO_THRESHOLD"]:
-        features = features[features[data_yield_column] >= provider["DATA_YIELD_RATIO_THRESHOLD"]]
-
-    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm')] # For later preservation of esm_cols
-
-    # Remove cols if threshold of NaN values is passed
-    features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]    
     
-    # Remove cols where variance is 0
-    if provider["COLS_VAR_THRESHOLD"]:
-        features.drop(features.std()[features.std() == 0].index.values, axis=1, inplace=True)
+    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]
-    
-    # Drop highly correlated features - To-Do še en thershold var, ki je v config + kako se tretirajo NaNs?
+
+    # (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"]:
+    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]]
 
-        cor_matrix = valid_features.corr(method='spearman').abs()
-        upper_tri = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
-        to_drop = [column for column in upper_tri.columns if any(upper_tri[column] > drop_corr_features["CORR_THRESHOLD"])]
+        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)
 
-    # Remove rows if threshold of NaN values is passed
-    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)
+    # 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 impute(df, method='zero'):
-    
-    def k_nearest(df): # TODO: if needed, implement k-nearest imputation / interpolation
-        pass
 
-    return { # rest of the columns should be imputed with the selected method
+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()),
-        'k-nearest': k_nearest(df) 
+        '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")

src/features/all_cleaning_overall/straw/main.py

@@ -1,88 +1,275 @@
 import pandas as pd
 import numpy as np
-import math, sys
+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):
 
-def straw_cleaning(sensor_data_files, provider):
-    
     features = pd.read_csv(sensor_data_files["sensor_data"][0])
 
-    # TODO: reorder the cleaning steps so it makes sense for the analysis
-    # TODO: add conditions that differentiates cleaning steps for standardized and nonstandardized features, for this
-    # the snakemake rules will also have to come with additional parameter (in rules/features.smk)
+    with open('config.yaml', 'r') as stream:
+        config = yaml.load(stream, Loader=yaml.FullLoader)
 
-    # Impute selected features event
-    impute_phone_features = provider["IMPUTE_PHONE_SELECTED_EVENT_FEATURES"]
-    if impute_phone_features["COMPUTE"]:
-        if not 'phone_data_yield_rapids_ratiovalidyieldedminutes' in features.columns:
-            raise KeyError("RAPIDS provider needs to impute the selected event features based on phone_data_yield_rapids_ratiovalidyieldedminutes column, please set config[PHONE_DATA_YIELD][PROVIDERS][RAPIDS][COMPUTE] to True and include 'ratiovalidyieldedminutes' in [FEATURES].")
-        
-        # TODO: if the type of the imputation will vary for different groups of features make conditional imputations here 
-        phone_cols = [col for col in features if \
-            col.startswith('phone_applications_foreground_rapids_') or
-            col.startswith('phone_battery_rapids_') or
-            col.startswith('phone_calls_rapids_') or
-            col.startswith('phone_keyboard_rapids_') or
-            col.startswith('phone_messages_rapids_') or
-            col.startswith('phone_screen_rapids_') or
-            col.startswith('phone_wifi_')]
+    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')] # Get target (esm) columns
 
-        mask = features['phone_data_yield_rapids_ratiovalidyieldedminutes'] > impute_phone_features['MIN_DATA_YIELDED_MINUTES_TO_IMPUTE']
-        features.loc[mask, phone_cols] = impute(features[mask][phone_cols], method=impute_phone_features["TYPE"].lower())
+    excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
 
-    # Drop rows with the value of data_yield_column less than data_yield_ratio_threshold
-    data_yield_unit = provider["DATA_YIELD_FEATURE"].split("_")[3].lower()
-    data_yield_column = "phone_data_yield_rapids_ratiovalidyielded" + data_yield_unit
+    graph_bf_af(features, "1target_rows_before")
 
-    if not data_yield_column in features.columns:
-        raise KeyError(f"RAPIDS provider needs to impute the selected event features 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 provider["DATA_YIELD_RATIO_THRESHOLD"]:
-        features = features[features[data_yield_column] >= provider["DATA_YIELD_RATIO_THRESHOLD"]]
-
-    esm_cols = features.loc[:, features.columns.str.startswith('phone_esm')] # For later preservation of esm_cols
-
-    # Remove cols if threshold of NaN values is passed
-    features = features.loc[:, features.isna().sum() < provider["COLS_NAN_THRESHOLD"] * features.shape[0]]    
+    # (1.0) OVERRIDE STRESSFULNESS EVENT TARGETS IF ERS SEGMENTING_METHOD IS "STRESS_EVENT"
+    if config["TIME_SEGMENTS"]["TAILORED_EVENTS"]["SEGMENTING_METHOD"] == "stress_event": 
     
-    # Remove cols where variance is 0
+        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()[features.std() == 0].index.values, axis=1, inplace=True)
-    
+        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]
     
-    # Drop highly correlated features - To-Do še en thershold var, ki je v config + kako se tretirajo NaNs?
+    # (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"]:
+    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]]
 
-        cor_matrix = valid_features.corr(method='spearman').abs()
-        upper_tri = cor_matrix.where(np.triu(np.ones(cor_matrix.shape), k=1).astype(np.bool))
-        to_drop = [column for column in upper_tri.columns if any(upper_tri[column] > drop_corr_features["CORR_THRESHOLD"])]
+        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)
 
-    # Remove rows if threshold of NaN values is passed
-    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)
+    # 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 impute(df, method='zero'):
-    
-    def k_nearest(df): # TODO: if needed, implement k-nearest imputation / interpolation
-        pass
 
-    return { # rest of the columns should be imputed with the selected method
+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()),
-        'k-nearest': k_nearest(df) 
+        '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")

src/features/cr_features_helper_methods.py

@@ -15,13 +15,13 @@ def extract_second_order_features(intraday_features, so_features_names, prefix="
         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().add_suffix("_SO_mean")], axis=1)
+            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().add_suffix("_SO_median")], axis=1)
+            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().add_suffix("_SO_sd")], axis=1)
+            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"])]:

src/features/empatica_accelerometer/cr/main.py

@@ -43,7 +43,11 @@ def extract_acc_features_from_intraday_data(acc_intraday_data, features, window_
 
 
 def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
-    acc_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
+    
+    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"]
     

src/features/empatica_data_yield.py

@@ -0,0 +1,32 @@
+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

src/features/empatica_electrodermal_activity/cr/main.py

@@ -44,7 +44,11 @@ def extract_eda_features_from_intraday_data(eda_intraday_data, features, window_
 
 
 def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
-    eda_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
+
+    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"]
     

src/features/empatica_inter_beat_interval/cr/main.py

@@ -50,7 +50,11 @@ def extract_ibi_features_from_intraday_data(ibi_intraday_data, features, window_
 
 
 def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
-    ibi_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
+
+    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"]
     

src/features/empatica_temperature/cr/main.py

@@ -37,7 +37,10 @@ def extract_temp_features_from_intraday_data(temperature_intraday_data, features
 
 
 def cr_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
-    temperature_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
+    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"]
 

src/features/entry.py

@@ -13,7 +13,10 @@ calc_windows = True if (provider.get("WINDOWS", False) and provider["WINDOWS"].g
 
 if sensor_key == "all_cleaning_individual" or sensor_key == "all_cleaning_overall":
     # Data cleaning
-    sensor_features = run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files)
+    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"]

src/features/phone_activity_recognition/rapids/main.py

@@ -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}, inplace=True)
+    ar_features.fillna(value={"count": 0, "countuniqueactivities": 0, "durationstationary": 0, "durationmobile": 0, "durationvehicle": 0, "mostcommonactivity": 4}, inplace=True)
 
     return ar_features

src/features/phone_applications_foreground/rapids/main.py

@@ -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] = np.nan
+            apps_features["timeoffirstuse" + apps_type] = 1500 # np.nan
         else:
             apps_features["timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"]
     if "timeoflastuse" in requested_features:
         time_last_event = filtered_data.sort_values(by="timestamp", ascending=False).drop_duplicates(subset="local_segment", keep="first").set_index("local_segment")
         if time_last_event.empty:
-            apps_features["timeoflastuse" + apps_type] = np.nan
+            apps_features["timeoflastuse" + apps_type] = 1500 # np.nan
         else:
             apps_features["timeoflastuse" + apps_type] = time_last_event["local_hour"] * 60 + time_last_event["local_minute"]
     if "frequencyentropy" in requested_features:
         apps_with_count = filtered_data.groupby(["local_segment","application_name"]).count().sort_values(by="timestamp", ascending=False).reset_index()
         if (len(apps_with_count.index) < 2 ):
-            apps_features["frequencyentropy" + apps_type] = np.nan
+            apps_features["frequencyentropy" + apps_type] = 0 # np.nan
         else:    
             apps_features["frequencyentropy" + apps_type] = apps_with_count.groupby("local_segment")["timestamp"].agg(entropy)
     if "countevent" in requested_features:
@@ -43,6 +43,7 @@ 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):

src/features/phone_bluetooth/doryab/main.py

@@ -14,8 +14,8 @@ def deviceFeatures(devices, ownership, common_devices, features_to_compute, feat
         features = features.join(device_value_counts.groupby("local_segment")["bt_address"].nunique().to_frame("uniquedevices" + ownership), how="outer")
     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), 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")
     # 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")

src/features/phone_calls/rapids/main.R

@@ -88,6 +88,16 @@ 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)
     }
-    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)))
+
+    # 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))
+
     return(call_features)
 }

src/features/phone_esm/straw/esm.py

@@ -0,0 +1,274 @@
+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

src/features/phone_esm/straw/main.py

@@ -42,7 +42,8 @@ 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"]
+    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"]
     #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))
@@ -52,7 +53,6 @@ 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
@@ -60,4 +60,7 @@ 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

src/features/phone_esm/straw/process_user_event_related_segments.py

@@ -0,0 +1,260 @@
+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)
+
+
+

src/features/phone_light/rapids/main.py

@@ -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()
+                light_features["stdlux"] = light_data.groupby(["local_segment"])["double_light_lux"].std().fillna(0)
             
             light_features = light_features.reset_index()
 

src/features/phone_locations/doryab/add_doryab_extra_columns.py

@@ -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:
+if not location_data.timestamp.is_monotonic_increasing:
     location_data.sort_values(by=["timestamp"], inplace=True)
 
 location_data["duration_in_seconds"] = -1 * location_data.timestamp.diff(-1) / 1000

src/features/phone_locations/doryab/main.py

@@ -37,7 +37,8 @@ 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, np.nan)
+    
+    location_features["loglocationvariance"] = np.log10(location_features["locationvariance"]).replace(-np.inf, -1000000)
 
     return location_features
 

src/features/phone_messages/rapids/main.R

@@ -65,6 +65,15 @@ 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)
     }
-    messages_features <- messages_features %>% mutate_at(vars(contains("countmostfrequentcontact") | contains("distinctcontacts") | contains("count")), list( ~ replace_na(., 0)))
+    # 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))
+    
     return(messages_features)
 }

src/features/phone_screen/rapids/main.py

@@ -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().rename(columns = {"duration":"stdduration" + episode})], axis = 1)
+        duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].std().fillna(0).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

src/features/phone_speech/straw/main.py

@@ -0,0 +1,30 @@
+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

src/features/phone_wifi_visible/rapids/main.R

@@ -9,21 +9,26 @@ 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 %>% 
+    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()) %>%
-             replace(is.na(.), 0))
+             summarise(!!feature := n())
+    )
+
   }
 }
 
@@ -43,6 +48,6 @@ 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)
 }

src/features/standardization/main.py

@@ -1,50 +0,0 @@
-import pandas as pd
-import numpy as np
-from sklearn.preprocessing import StandardScaler
-
-import sys
-
-sensor_data_files = dict(snakemake.input)
-
-provider = snakemake.params["provider"]
-provider_key = snakemake.params["provider_key"]
-sensor_key = snakemake.params["sensor_key"]
-
-pd.set_option('display.max_columns', None)
-
-if provider_key == "cr":
-    sys.path.append('/rapids/src/features/')
-    from cr_features_helper_methods import extract_second_order_features
-
-    provider_main = snakemake.params["provider_main"]
-    prefix = sensor_key + "_" + provider_key + "_"
-
-    windows_features_data = pd.read_csv(sensor_data_files["windows_features_data"])
-    excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime', prefix + "level_1"]
-    
-    if windows_features_data.empty:
-        windows_features_data.to_csv(snakemake.output[1], index=False)
-        windows_features_data.to_csv(snakemake.output[0], index=False)
-    else:
-        windows_features_data.loc[:, ~windows_features_data.columns.isin(excluded_columns)] = StandardScaler().fit_transform(windows_features_data.loc[:, ~windows_features_data.columns.isin(excluded_columns)])
-
-        windows_features_data.to_csv(snakemake.output[1], index=False)
-
-        if provider_main["WINDOWS"]["COMPUTE"] and "SECOND_ORDER_FEATURES" in provider_main["WINDOWS"]:
-            so_features_names = provider_main["WINDOWS"]["SECOND_ORDER_FEATURES"]
-            windows_so_features_data = extract_second_order_features(windows_features_data, so_features_names, prefix)
-            windows_so_features_data.to_csv(snakemake.output[0], index=False)
-        else:
-            pd.DataFrame().to_csv(snakemake.output[0], index=False)
-
-else: 
-    for sensor_features in sensor_data_files["sensor_features"]:
-        if "/" + sensor_key + ".csv" in sensor_features:
-            sensor_data = pd.read_csv(sensor_features)
-            excluded_columns = ['local_segment', 'local_segment_label', 'local_segment_start_datetime', 'local_segment_end_datetime']
-
-            if not sensor_data.empty:
-                sensor_data.loc[:, ~sensor_data.columns.isin(excluded_columns)] = StandardScaler().fit_transform(sensor_data.loc[:, ~sensor_data.columns.isin(excluded_columns)])
-            
-            sensor_data.to_csv(snakemake.output[0], index=False)
-            break

src/features/utils/utils.py

@@ -160,12 +160,16 @@ 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):
+def run_provider_cleaning_script(provider, provider_key, sensor_key, sensor_data_files, target=False):
     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")
-    sensor_features = cleaning_function(sensor_data_files, provider)
+
+    if target:
+        sensor_features = cleaning_function(sensor_data_files, provider, target)
+    else:
+        sensor_features = cleaning_function(sensor_data_files, provider)
 
     return sensor_features

src/models/helper.py

@@ -1,5 +1,6 @@
 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
@@ -8,9 +9,9 @@ def retain_target_column(df_input: pd.DataFrame, target_variable_name: str):
     esm_names = column_names[esm_names_index]
     target_variable_index = esm_names.str.contains(target_variable_name)
     if all(~target_variable_index):
-        raise ValueError("The requested target (", target_variable_name,
-                         ")cannot be found in the dataset.",
-                         "Please check the names of phone_esm_ columns in z_all_sensor_features_cleaned_straw_py.csv")
+        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.

src/models/merge_features_and_targets_for_population_model.py

@@ -12,9 +12,13 @@ for baseline_features_path in snakemake.input["demographic_features"]:
     all_baseline_features = pd.concat([all_baseline_features, baseline_features], axis=0)
 
 # merge sensor features and baseline features
-features = sensor_features.merge(all_baseline_features, on="pid", how="left")
+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)
+    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)
+    model_input.to_csv(snakemake.output[0], index=False)
+    
+else:
+    sensor_features.to_csv(snakemake.output[0], index=False)

src/models/select_targets.py

@@ -6,6 +6,8 @@ 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)
-model_input.dropna(axis ="index", how="any", subset=["target"], inplace=True)
 
-model_input.to_csv(snakemake.output[0], index=False)
+if model_input is None:
+    pd.DataFrame().to_csv(snakemake.output[0])
+else:
+    model_input.to_csv(snakemake.output[0], index=False)

tests/scripts/missing_vals.py

@@ -3,8 +3,8 @@ import seaborn as sns
 import matplotlib.pyplot as plt
 
 
-participant = "p031"
-all_sensors = ["eda", "bvp", "ibi", "temp", "acc"]
+participant = "p01"
+all_sensors = ["eda", "ibi", "temp", "acc"]
 
 for sensor in all_sensors:
 

tests/scripts/phone_feats.py

@@ -0,0 +1,285 @@
+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)

tests/scripts/standardization_methods_test.py

@@ -0,0 +1,70 @@
+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)
+

tests/scripts/test_acc.py

@@ -0,0 +1,38 @@
+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')