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sociality-
...
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junos | 63f5a526fc | |
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junos | a36da99ccb | |
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junos | d678be0641 | |
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junos | cb006ed0cf | |
junos | 9ca58ed204 | |
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junos | f8088172e9 | |
junos | 801fbe1c10 |
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@ -100,6 +100,9 @@ data/external/*
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|||
!/data/external/wiki_tz.csv
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||||
!/data/external/main_study_usernames.csv
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!/data/external/timezone.csv
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!/data/external/play_store_application_genre_catalogue.csv
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!/data/external/play_store_categories_count.csv
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||||
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||||
data/raw/*
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||||
!/data/raw/.gitkeep
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||||
|
|
42
config.yaml
42
config.yaml
|
@ -26,7 +26,7 @@ TIME_SEGMENTS: &time_segments
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INCLUDE_PAST_PERIODIC_SEGMENTS: TRUE # Only relevant if TYPE=PERIODIC, see docs
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TAILORED_EVENTS: # Only relevant if TYPE=EVENT
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COMPUTE: True
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||||
SEGMENTING_METHOD: "stress_event" # 30_before, 90_before, stress_event
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SEGMENTING_METHOD: "30_before" # 30_before, 90_before, stress_event
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INTERVAL_OF_INTEREST: 10 # duration of event of interest [minutes]
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||||
IOI_ERROR_TOLERANCE: 5 # interval of interest erorr tolerance (before and after IOI) [minutes]
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||||
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||||
|
@ -104,9 +104,9 @@ PHONE_APPLICATIONS_CRASHES:
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CONTAINER: applications_crashes
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APPLICATION_CATEGORIES:
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CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store)
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CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv"
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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
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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
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CATALOGUE_FILE: "data/external/play_store_application_genre_catalogue.csv"
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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
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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
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PROVIDERS: # None implemented yet but this sensor can be used in PHONE_DATA_YIELD
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# See https://www.rapids.science/latest/features/phone-applications-foreground/
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|
@ -114,24 +114,32 @@ PHONE_APPLICATIONS_FOREGROUND:
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CONTAINER: applications
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APPLICATION_CATEGORIES:
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CATALOGUE_SOURCE: FILE # FILE (genres are read from CATALOGUE_FILE) or GOOGLE (genres are scrapped from the Play Store)
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CATALOGUE_FILE: "data/external/stachl_application_genre_catalogue.csv"
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||||
PACKAGE_NAMES_HASHED: True
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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
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||||
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
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CATALOGUE_FILE: "data/external/play_store_application_genre_catalogue.csv"
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# Refer to data/external/play_store_categories_count.csv for a list of categories (genres) and their frequency.
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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
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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
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||||
PROVIDERS:
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||||
RAPIDS:
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COMPUTE: True
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INCLUDE_EPISODE_FEATURES: True
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SINGLE_CATEGORIES: ["all", "email"]
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SINGLE_CATEGORIES: ["Productivity", "Tools", "Communication", "Education", "Social"]
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MULTIPLE_CATEGORIES:
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social: ["socialnetworks", "socialmediatools"]
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entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"]
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games: ["Puzzle", "Card", "Casual", "Board", "Strategy", "Trivia", "Word", "Adventure", "Role Playing", "Simulation", "Board, Brain Games", "Racing"]
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social: ["Communication", "Social", "Dating"]
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productivity: ["Tools", "Productivity", "Finance", "Education", "News & Magazines", "Business", "Books & Reference"]
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health: ["Health & Fitness", "Lifestyle", "Food & Drink", "Sports", "Medical", "Parenting"]
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entertainment: ["Shopping", "Music & Audio", "Entertainment", "Travel & Local", "Photography", "Video Players & Editors", "Personalization", "House & Home", "Art & Design", "Auto & Vehicles", "Entertainment,Music & Video",
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"Puzzle", "Card", "Casual", "Board", "Strategy", "Trivia", "Word", "Adventure", "Role Playing", "Simulation", "Board, Brain Games", "Racing" # Add all games.
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||||
]
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maps_weather: ["Maps & Navigation", "Weather"]
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CUSTOM_CATEGORIES:
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social_media: ["com.google.android.youtube", "com.snapchat.android", "com.instagram.android", "com.zhiliaoapp.musically", "com.facebook.katana"]
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||||
dating: ["com.tinder", "com.relance.happycouple", "com.kiwi.joyride"]
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||||
SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps
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||||
EXCLUDED_CATEGORIES: []
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||||
EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"] # TODO list system apps?
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||||
SINGLE_APPS: []
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||||
EXCLUDED_CATEGORIES: ["System", "STRAW"]
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# Note: A special option here is "is_system_app".
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# This excludes applications that have is_system_app = TRUE, which is a separate column in the table.
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||||
# However, all of these applications have been assigned System category.
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# I will therefore filter by that category, which is a superset and is more complete. JL
|
||||
EXCLUDED_APPS: []
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||||
FEATURES:
|
||||
APP_EVENTS: ["countevent", "timeoffirstuse", "timeoflastuse", "frequencyentropy"]
|
||||
APP_EPISODES: ["countepisode", "minduration", "maxduration", "meanduration", "sumduration"]
|
||||
|
@ -745,6 +753,6 @@ PARAMS_FOR_ANALYSIS:
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|||
TARGET:
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||||
COMPUTE: True
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||||
LABEL: appraisal_stressfulness_event_mean
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||||
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]
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||||
# PANAS_positive_affect_mean, PANAS_negative_affect_mean, JCQ_job_demand_mean, JCQ_job_control_mean, JCQ_supervisor_support_mean,
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||||
# JCQ_coworker_support_mean, appraisal_stressfulness_period_mean, appraisal_stressfulness_event_mean, appraisal_threat_mean, appraisal_challenge_mean
|
||||
|
|
File diff suppressed because it is too large
Load Diff
|
@ -0,0 +1,45 @@
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|||
genre,n
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||||
System,261
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||||
Tools,96
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||||
Productivity,71
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||||
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
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||||
Social,16
|
||||
Weather,16
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||||
Food & Drink,14
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||||
Sports,14
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||||
Other,13
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||||
Photography,13
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||||
Puzzle,13
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||||
Video Players & Editors,12
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||||
Card,9
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||||
Casual,9
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||||
Personalization,8
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||||
Medical,7
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||||
Board,5
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||||
Strategy,4
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||||
House & Home,3
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||||
Trivia,3
|
||||
Word,3
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||||
Adventure,2
|
||||
Art & Design,2
|
||||
Auto & Vehicles,2
|
||||
Dating,2
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||||
Role Playing,2
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||||
STRAW,2
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||||
Simulation,2
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||||
"Board,Brain Games",1
|
||||
"Entertainment,Music & Video",1
|
||||
Parenting,1
|
||||
Racing,1
|
|
187
environment.yml
187
environment.yml
|
@ -1,165 +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
|
||||
- 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
|
||||
- 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
|
||||
|
|
338
renv.lock
338
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",
|
||||
|
|
|
@ -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"
|
||||
|
||||
|
|
|
@ -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)){
|
||||
|
|
|
@ -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']
|
||||
|
|
|
@ -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)})
|
||||
|
|
|
@ -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)})
|
||||
|
|
|
@ -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"])]:
|
||||
|
|
|
@ -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)
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||||
return features
|
||||
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|
@ -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_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_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') \
|
||||
|
|
|
@ -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
|
||||
|
|
Loading…
Reference in New Issue