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

config.yaml

@@ -26,7 +26,7 @@ TIME_SEGMENTS: &time_segments
   INCLUDE_PAST_PERIODIC_SEGMENTS: TRUE # Only relevant if TYPE=PERIODIC, see docs
   TAILORED_EVENTS: # Only relevant if TYPE=EVENT
     COMPUTE: True
-    SEGMENTING_METHOD: "stress_event" # 30_before, 90_before, stress_event
+    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]
 
@@ -104,9 +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"
+    CATALOGUE_FILE: "data/external/play_store_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
+    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 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
+    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/
@@ -114,24 +114,32 @@ 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"
+    CATALOGUE_FILE: "data/external/play_store_application_genre_catalogue.csv"
-    PACKAGE_NAMES_HASHED: True
+    # 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 or not to update CATALOGUE_FILE, if CATALOGUE_SOURCE is equal to GOOGLE all scraped genres will be saved to CATALOGUE_FILE
+    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 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
+    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"]
+        games: ["Puzzle", "Card", "Casual", "Board", "Strategy", "Trivia", "Word", "Adventure", "Role Playing", "Simulation", "Board, Brain Games", "Racing"]
-        entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"]
+        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"]
+      SINGLE_APPS: []
-        dating: ["com.tinder", "com.relance.happycouple", "com.kiwi.joyride"]
+      EXCLUDED_CATEGORIES: ["System", "STRAW"]
-      SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps
+      # Note: A special option here is "is_system_app".
-      EXCLUDED_CATEGORIES: []
+      # This excludes applications that have is_system_app = TRUE, which is a separate column in the table.
-      EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] # TODO list system apps?
+      # 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"]
@@ -745,6 +753,6 @@ PARAMS_FOR_ANALYSIS:
   TARGET:
     COMPUTE: True
     LABEL: appraisal_stressfulness_event_mean
-    ALL_LABELS: [appraisal_stressfulness_event_mean, appraisal_threat_mean, appraisal_challenge_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/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

environment.yml

@@ -1,165 +1,30 @@
 name: rapids
 channels:
   - conda-forge
-  - defaults
 dependencies:
-  - _libgcc_mutex=0.1
+    - auto-sklearn
-  - _openmp_mutex=4.5
+    - hmmlearn
-  - _py-xgboost-mutex=2.0
+    - imbalanced-learn
-  - appdirs=1.4.4
+    - jsonschema
-  - arrow=0.16.0
+    - lightgbm
-  - asn1crypto=1.4.0
+    - matplotlib
-  - astropy=4.2.1
+    - numpy
-  - attrs=20.3.0
+    - pandas
-  - binaryornot=0.4.4
+    - peakutils
-  - blas=1.0
+    - pip
-  - brotlipy=0.7.0
+    - plotly
-  - bzip2=1.0.8
+    - python-dateutil
-  - ca-certificates=2021.7.5
+    - pytz
-  - certifi=2021.5.30
+    - pywavelets
-  - cffi=1.14.4
+    - pyyaml
-  - chardet=3.0.4
+    - scikit-learn
-  - click=7.1.2
+    - scipy
-  - colorama=0.4.4
+    - seaborn
-  - cookiecutter=1.6.0
+    - setuptools
-  - cryptography=3.3.1
+    - bioconda::snakemake 
-  - datrie=0.8.2
+    - bioconda::snakemake-minimal
-  - docutils=0.16
+    - tqdm
-  - future=0.18.2
+    - xgboost
-  - gitdb=4.0.5
+    - pip:
-  - gitdb2=4.0.2
+        - biosppy
-  - gitpython=3.1.11
+        - cr_features>=0.2
-  - 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
-  - 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
-    - auto-sklearn==0.14.7
-    - bidict==0.22.0
-    - biosppy==0.8.0
-    - build==0.8.0
-    - cached-property==1.5.2
-    - cloudpickle==2.2.0
-    - configargparse==0.15.1
-    - configspace==0.4.21
-    - cr-features==0.2.1
-    - cycler==0.11.0
-    - cython==0.29.32
-    - dask==2022.2.0
-    - decorator==4.4.2
-    - distributed==2022.2.0
-    - distro==1.7.0
-    - emcee==3.1.2
-    - fonttools==4.33.2
-    - fsspec==2022.8.2
-    - h5py==3.6.0
-    - heapdict==1.0.1
-    - hmmlearn==0.2.7
-    - ipython-genutils==0.2.0
-    - jupyter-core==4.6.3
-    - kiwisolver==1.4.2
-    - liac-arff==2.5.0
-    - locket==1.0.0
-    - matplotlib==3.5.1
-    - msgpack==1.0.4
-    - nbformat==5.0.7
-    - opencv-python==4.5.5.64
-    - packaging==21.3
-    - partd==1.3.0
-    - peakutils==1.3.3
-    - pep517==0.13.0
-    - pillow==9.1.0
-    - pulp==2.4
-    - pynisher==0.6.4
-    - pyparsing==2.4.7
-    - pyrfr==0.8.3
-    - pyrsistent==0.15.5
-    - pywavelets==1.3.0
-    - ratelimiter==1.2.0.post0
-    - scikit-learn==0.24.2
-    - scipy==1.7.3
-    - seaborn==0.11.2
-    - shortuuid==1.0.8
-    - smac==1.2
-    - snakemake==5.30.2
-    - sortedcontainers==2.4.0
-    - tblib==1.7.0
-    - tomli==2.0.1
-    - toolz==0.12.0
-    - toposort==1.5
-    - tornado==6.2
-    - traitlets==4.3.3
-    - typing-extensions==4.2.0
-    - zict==2.2.0
-prefix: /opt/conda/envs/rapids

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",
+      "Repository": "CRAN",
-      "Hash": "66458e906b2112a8b1639964efd77d7c"
+      "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",
+      "Repository": "CRAN",
-      "Hash": "5581a5ddc8fe2ac5e0d092ec2de4c4ae"
+      "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",
+      "Repository": "CRAN",
-      "Hash": "b7d7f1e926dfcd57c74ce93f5c048e80"
+      "Hash": "9b2191ede20fa29828139b9900922e51"
     },
     "caret": {
       "Package": "caret",
@@ -263,10 +270,10 @@
     },
     "cli": {
       "Package": "cli",
-      "Version": "2.2.0",
+      "Version": "3.6.1",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "3ef298932294b775fa0a3eeaa3a645b0"
+      "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",
+      "Repository": "CRAN",
-      "Hash": "ba66e5a750d39067d888aa7af797fed2"
+      "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",
+      "Repository": "CRAN",
-      "Hash": "d0d76c11ec807eb3f000eba4e3eb0f68"
+      "Hash": "eb5742d256a0d9306d85ea68756d8187"
     },
     "dtplyr": {
       "Package": "dtplyr",
-      "Version": "1.1.0",
+      "Version": "1.3.1",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "1e14e4c5b2814de5225312394bc316da"
+      "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",
+      "Repository": "CRAN",
-      "Hash": "1cb4279e697650f0bd78cd3601ee7576"
+      "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",
+      "Repository": "CRAN",
-      "Hash": "4ded8b439797f7b1693bd3d238d0106b"
+      "Hash": "d494daf77c4aa7f084dbbe6ca5dcaca7"
     },
     "ggraph": {
       "Package": "ggraph",
@@ -557,16 +578,30 @@
     },
     "glue": {
       "Package": "glue",
-      "Version": "1.4.2",
+      "Version": "1.6.2",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "6efd734b14c6471cfe443345f3e35e29"
+      "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",
+      "Repository": "CRAN",
-      "Hash": "221d0ad75dfa03ebf17b1a4cc5c31dfc"
+      "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",
+      "Repository": "CRAN",
-      "Hash": "a525aba14184fec243f9eaec62fbed43"
+      "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",
+      "Repository": "CRAN",
-      "Hash": "98138e0994d41508c7a6b84a0600cfcb"
+      "Hash": "a4269a09a9b865579b2635c77e572374"
     },
     "knitr": {
       "Package": "knitr",
@@ -760,10 +802,10 @@
     },
     "lifecycle": {
       "Package": "lifecycle",
-      "Version": "1.0.0",
+      "Version": "1.0.3",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "3471fb65971f1a7b2d4ae7848cf2db8d"
+      "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",
+      "Repository": "CRAN",
-      "Hash": "5b5b02f621d39a499def7923a5aee746"
+      "Hash": "e25f18436e3efd42c7c590a1c4c15390"
     },
     "magrittr": {
       "Package": "magrittr",
-      "Version": "2.0.1",
+      "Version": "2.0.3",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "41287f1ac7d28a92f0a286ed507928d3"
+      "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",
+      "Repository": "CRAN",
-      "Hash": "9fd59716311ee82cba83dc2826fc5577"
+      "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",
+      "Repository": "CRAN",
-      "Hash": "3b3dd89b2ee115a8b54e93a34cd546b4"
+      "Hash": "15da5a8412f317beeee6175fbc76f4bb"
     },
     "pkgbuild": {
       "Package": "pkgbuild",
@@ -977,10 +1026,10 @@
     },
     "processx": {
       "Package": "processx",
-      "Version": "3.4.5",
+      "Version": "3.8.0",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "22aab6098cb14edd0a5973a8438b569b"
+      "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",
+      "Repository": "CRAN",
-      "Hash": "97def703420c8ab10d8f0e6c72101e02"
+      "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",
+      "Repository": "CRAN",
-      "Hash": "2639976851f71f330264a9c9c3d43a61"
+      "Hash": "b5047343b3825f37ad9d3b5d89aa1078"
     },
     "readxl": {
       "Package": "readxl",
-      "Version": "1.3.1",
+      "Version": "1.4.2",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "63537c483c2dbec8d9e3183b3735254a"
+      "Hash": "2e6020b1399d95f947ed867045e9ca17"
     },
     "recipes": {
       "Package": "recipes",
@@ -1110,10 +1166,10 @@
     },
     "reprex": {
       "Package": "reprex",
-      "Version": "0.3.0",
+      "Version": "2.0.2",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "b06bfb3504cc8a4579fd5567646f745b"
+      "Hash": "d66fe009d4c20b7ab1927eb405db9ee2"
     },
     "reshape2": {
       "Package": "reshape2",
@@ -1138,10 +1194,10 @@
     },
     "rlang": {
       "Package": "rlang",
-      "Version": "0.4.10",
+      "Version": "1.1.0",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "599df23c40a4fce9c7b4764f28c37857"
+      "Hash": "dc079ccd156cde8647360f473c1fa718"
     },
     "rmarkdown": {
       "Package": "rmarkdown",
@@ -1173,24 +1229,24 @@
     },
     "rstudioapi": {
       "Package": "rstudioapi",
-      "Version": "0.13",
+      "Version": "0.14",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "06c85365a03fdaf699966cc1d3cf53ea"
+      "Hash": "690bd2acc42a9166ce34845884459320"
     },
     "rvest": {
       "Package": "rvest",
-      "Version": "0.3.6",
+      "Version": "1.0.3",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "a9795ccb2d608330e841998b67156764"
+      "Hash": "a4a5ac819a467808c60e36e92ddf195e"
     },
     "scales": {
       "Package": "scales",
-      "Version": "1.1.1",
+      "Version": "1.2.1",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "6f76f71042411426ec8df6c54f34e6dd"
+      "Hash": "906cb23d2f1c5680b8ce439b44c6fa63"
     },
     "selectr": {
       "Package": "selectr",
@@ -1236,17 +1292,17 @@
     },
     "stringi": {
       "Package": "stringi",
-      "Version": "1.5.3",
+      "Version": "1.7.12",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "a063ebea753c92910a4cca7b18bc1f05"
+      "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",
+      "Repository": "CRAN",
-      "Hash": "71dffd8544691c520dd8e41ed2d7e070"
+      "Hash": "a84e2cc86d07289b3b6f5069df7a004c"
     },
     "tidygraph": {
       "Package": "tidygraph",
@@ -1285,24 +1355,24 @@
     },
     "tidyr": {
       "Package": "tidyr",
-      "Version": "1.1.2",
+      "Version": "1.3.0",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "c40b2d5824d829190f4b825f4496dfae"
+      "Hash": "e47debdc7ce599b070c8e78e8ac0cfcf"
     },
     "tidyselect": {
       "Package": "tidyselect",
-      "Version": "1.1.0",
+      "Version": "1.2.0",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "6ea435c354e8448819627cf686f66e0a"
+      "Hash": "79540e5fcd9e0435af547d885f184fd5"
     },
     "tidyverse": {
       "Package": "tidyverse",
-      "Version": "1.3.0",
+      "Version": "2.0.0",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "bd51be662f359fa99021f3d51e911490"
+      "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": {
+    "uuid": {
-      "Package": "vctrs",
+      "Package": "uuid",
-      "Version": "0.3.8",
+      "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",
+      "Repository": "CRAN",
-      "Hash": "7307d79f58d1885b38c4f4f1a8cb19dd"
+      "Hash": "c0e49a9760983e81e55cdd9be92e7182"
     },
     "xfun": {
       "Package": "xfun",
@@ -1390,10 +1488,10 @@
     },
     "xml2": {
       "Package": "xml2",
-      "Version": "1.3.2",
+      "Version": "1.3.3",
       "Source": "Repository",
-      "Repository": "RSPM",
+      "Repository": "CRAN",
-      "Hash": "d4d71a75dd3ea9eb5fa28cc21f9585e2"
+      "Hash": "40682ed6a969ea5abfd351eb67833adc"
     },
     "xtable": {
       "Package": "xtable",

rules/preprocessing.smk

@@ -247,6 +247,8 @@ 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"
 

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_name")
-      apps_with_genre <- left_join(apps, genre_catalogue, by = "package_hash")
-    } else {
-      apps_with_genre <- left_join(apps, genre_catalogue, by = "package_name")
-    }
   }
 
   if(catalogue_source == "GOOGLE" || (catalogue_source == "FILE" && scrape_missing_genres)){

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/features/all_cleaning_individual/straw/main.py

@@ -120,7 +120,7 @@ def straw_cleaning(sensor_data_files, provider):
     esm_cols = features.loc[:, features.columns.str.startswith('phone_esm_straw')]
 
     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)
 
     fe5 = features.copy()
 
@@ -134,7 +134,7 @@ def straw_cleaning(sensor_data_files, provider):
         valid_features = features[numerical_cols].loc[:, features[numerical_cols].isna().sum() < drop_corr_features['MIN_OVERLAP_FOR_CORR_THRESHOLD'] * features[numerical_cols].shape[0]]
 
         corr_matrix = valid_features.corr().abs()
-        upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))
+        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)
@@ -150,12 +150,14 @@ def straw_cleaning(sensor_data_files, provider):
 
     return features
 
+
+def k_nearest(df):
+    pd.set_option('display.max_columns', None)
+    imputer = KNNImputer(n_neighbors=3)
+    return pd.DataFrame(imputer.fit_transform(df), columns=df.columns)
+
+
 def impute(df, method='zero'):
-    
-    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)
 
     return {
         'zero': df.fillna(0),
@@ -165,6 +167,7 @@ def impute(df, method='zero'):
         '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)})

src/features/all_cleaning_overall/straw/main.py

@@ -146,7 +146,7 @@ def straw_cleaning(sensor_data_files, provider, target):
     # (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")
 
@@ -200,7 +200,7 @@ def straw_cleaning(sensor_data_files, provider, target):
         valid_features = features[numerical_cols].loc[:, features[numerical_cols].isna().sum() < drop_corr_features['MIN_OVERLAP_FOR_CORR_THRESHOLD'] * features[numerical_cols].shape[0]]
 
         corr_matrix = valid_features.corr().abs()
-        upper = corr_matrix.where(np.triu(np.ones(corr_matrix.shape), k=1).astype(np.bool))
+        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")
@@ -245,11 +245,13 @@ def straw_cleaning(sensor_data_files, provider, target):
 
     return features
 
+
+def k_nearest(df):
+    imputer = KNNImputer(n_neighbors=3)
+    return pd.DataFrame(imputer.fit_transform(df), columns=df.columns)
+
+
 def impute(df, method='zero'):
-    
-    def k_nearest(df):
-        imputer = KNNImputer(n_neighbors=3)
-        return pd.DataFrame(imputer.fit_transform(df), columns=df.columns)
 
     return {
         'zero': df.fillna(0),
@@ -259,6 +261,7 @@ def impute(df, method='zero'):
         '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)})

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().fillna(0).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_data_yield.py

@@ -26,7 +26,7 @@ def calculate_empatica_data_yield(features): # TODO
     # 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).fillna(0)
+    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/phone_esm/straw/process_user_event_related_segments.py

@@ -140,8 +140,8 @@ def extract_ers(esm_df):
 
         # 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()['esm_user_answer_numeric'].to_frame().rename(columns={'esm_user_answer_numeric': 'appraisal_threat'})
+        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()['esm_user_answer_numeric'].to_frame().rename(columns={'esm_user_answer_numeric': 'appraisal_challenge'})
+        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') \

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