Add sleep intraday features with RAPIDS provider
Meng Li
parent
f565ac8a11
commit
8377c12efb
15
Snakefile
15
Snakefile
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@ -257,11 +257,16 @@ for provider in config["FITBIT_SLEEP_SUMMARY"]["PROVIDERS"].keys():
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files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
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files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
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# for provider in config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"].keys():
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# if config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]:
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# files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_intraday_raw.csv", pid=config["PIDS"]))
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# files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_intraday_parsed.csv", pid=config["PIDS"]))
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# files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_intraday_parsed_with_datetime.csv", pid=config["PIDS"]))
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for provider in config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"].keys():
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if config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"][provider]["COMPUTE"]:
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files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_intraday_raw.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/raw/{pid}/fitbit_sleep_intraday_parsed.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled_with_datetime.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/interim/{pid}/fitbit_sleep_intraday_features/fitbit_sleep_intraday_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"][provider]["SRC_LANGUAGE"].lower(), provider_key=provider.lower()))
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files_to_compute.extend(expand("data/processed/features/{pid}/fitbit_sleep_intraday.csv", pid=config["PIDS"]))
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files_to_compute.extend(expand("data/processed/features/{pid}/all_sensor_features.csv", pid=config["PIDS"]))
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files_to_compute.append("data/processed/features/all_participants/all_sensor_features.csv")
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for provider in config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"].keys():
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if config["FITBIT_STEPS_SUMMARY"]["PROVIDERS"][provider]["COMPUTE"]:
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26
config.yaml
26
config.yaml
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@ -379,6 +379,32 @@ FITBIT_SLEEP_SUMMARY:
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SRC_FOLDER: "rapids" # inside src/features/fitbit_sleep_summary
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SRC_LANGUAGE: "python"
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# See https://www.rapids.science/latest/features/fitbit-sleep-intraday/
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FITBIT_SLEEP_INTRADAY:
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TABLE: fitbit_data
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INCLUDE_SLEEP_LATER_THAN: &include_sleep_later_than
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0 # a number ranged from 0 (midnight) to 1439 (23:59)
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REFERENCE_TIME: &reference_time
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MIDNIGHT # chosen from "MIDNIGHT" and "START_OF_THE_SEGMENT"
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PROVIDERS:
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RAPIDS:
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COMPUTE: False
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FEATURES:
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LEVELS_AND_TYPES_COMBINING_ALL: True
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LEVELS_AND_TYPES: [countepisode, sumduration, maxduration, minduration, avgduration, medianduration, stdduration]
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RATIOS_TYPE: [count, duration]
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RATIOS_SCOPE: [ACROSS_LEVELS, ACROSS_TYPES, WITHIN_LEVELS, WITHIN_TYPES]
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ROUTINE: [starttimefirstmainsleep, endtimelastmainsleep, starttimefirstnap, endtimelastnap]
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SLEEP_LEVELS:
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CLASSIC: [awake, restless, asleep]
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STAGES: [wake, deep, light, rem]
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UNIFIED: [awake, asleep]
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SLEEP_TYPES: [main, nap]
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INCLUDE_SLEEP_LATER_THAN: *include_sleep_later_than
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REFERENCE_TIME: *reference_time
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SRC_FOLDER: "rapids" # inside src/features/fitbit_sleep_intraday
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SRC_LANGUAGE: "python"
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# See https://www.rapids.science/latest/features/fitbit-steps-summary/
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FITBIT_STEPS_SUMMARY:
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TABLE: steps_summary
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@ -5,7 +5,7 @@ Sensor parameters description for `[FITBIT_SLEEP_INTRADAY]`:
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|Key | Description |
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|----------------|-----------------------------------------------------------------------------------------------------------------------------------
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|`[TABLE]`| Database table name or file path where the sleep intraday data is stored. The configuration keys in [Device Data Source Configuration](../../setup/configuration/#device-data-source-configuration) control whether this parameter is interpreted as table or file.
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|`[INCLUDE_EPISODES_LATER_THAN]`| All sleep episodes that started after this time will be included in the feature computation. It is a number ranging from 0 (midnight) to 1439 (23:59) which denotes the number of minutes after midnight. If a segment is longer than one day, this value is for every day.
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|`[INCLUDE_SLEEP_LATER_THAN]`| All resampled sleep rows (bin interval: one minute) that started after this time will be included in the feature computation. It is a number ranging from 0 (midnight) to 1439 (23:59) which denotes the number of minutes after midnight. If a segment is longer than one day, this value is for every day.
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|`[REFERENCE_TIME]`| The reference point from which the `[ROUTINE]` features are to be computed. Chosen from `MIDNIGHT` and `START_OF_THE_SEGMENT`, default is `MIDNIGHT`. If you have multiple time segments per day it might be more informative to set this flag to `START_OF_THE_SEGMENT`.
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The format of the column(s) containing the Fitbit sensor data can be `JSON` or `PLAIN_TEXT`. The data in `JSON` format is obtained directly from the Fitbit API. We support `PLAIN_TEXT` in case you already parsed your data and don't have access to your participants' Fitbit accounts anymore. If your data is in `JSON` format then summary and intraday data come packed together.
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@ -23,7 +23,14 @@ We provide examples of the input format that RAPIDS expects, note that both exam
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep": [{"awakeCount": 1, "awakeDuration": 3, "awakeningsCount": 8, "dateOfSleep": "2020-10-09", "duration": 19320000, "efficiency": 96, "endTime": "2020-10-09T05:57:30.000", "isMainSleep": true, "logId": 14161136803, "minuteData": [{"dateTime": "00:35:30", "value": "2"}, {"dateTime": "00:36:30", "value": "1"}, {"dateTime": "00:37:30", "value": "1"},...], "minutesAfterWakeup": 0, "minutesAsleep": 309, "minutesAwake": 13, "minutesToFallAsleep": 0, "restlessCount": 7, "restlessDuration": 10, "startTime": "2020-10-09T00:35:30.000", "timeInBed": 322}], "summary": {"totalMinutesAsleep": 309, "totalSleepRecords": 1, "totalTimeInBed": 322}}
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=== "PLAIN_TEXT"
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Will update this section later.
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All columns are mandatory, however, all except `local_date_time` and `duration` can be empty if you don't have that data. Just have in mind that some features might be inaccurate or empty as `type_episode_id`, `level`, `is_main_sleep`, and `type` are used for sleep episodes extraction. `type_episode_id` is based on where it is extracted: if it is extracted from the 1st "minutesData" block, the `type_episode_id` field will be 0. Similarly, the kth block will be k-1.
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|type_episode_id |local_date_time |duration |level |is_main_sleep |type |
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|---------------- |------------------- |--------- |---------- |-------------- |-------------- |
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|0 |2020-10-07 15:55:00 |60 |awake |0 |classic |
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|0 |2020-10-07 15:56:00 |60 |awake |0 |classic |
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|0 |2020-10-07 15:57:00 |60 |restless |0 |classic |
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??? example "Example of the structure of source data with Fitbit’s sleep API Version 1.2"
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@ -36,7 +43,14 @@ We provide examples of the input format that RAPIDS expects, note that both exam
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|a748ee1a-1d0b-4ae9-9074-279a2b6ba524 |{"sleep":[{"dateOfSleep":"2020-10-12","duration":28980000,"efficiency":93,"endTime":"2020-10-12T09:34:30.000","infoCode":0,"isMainSleep":true,"levels":{"data":[{"dateTime":"2020-10-12T01:31:00.000","level":"wake","seconds":600},{"dateTime":"2020-10-12T01:41:00.000","level":"light","seconds":60},{"dateTime":"2020-10-12T01:42:00.000","level":"deep","seconds":2340},...], "summary":{"deep":{"count":4,"minutes":63,"thirtyDayAvgMinutes":59},"light":{"count":27,"minutes":257,"thirtyDayAvgMinutes":364},"rem":{"count":5,"minutes":94,"thirtyDayAvgMinutes":58},"wake":{"count":24,"minutes":69,"thirtyDayAvgMinutes":95}}},"logId":26589710673,"minutesAfterWakeup":0,"minutesAsleep":415,"minutesAwake":68,"minutesToFallAsleep":0,"startTime":"2020-10-12T01:31:00.000","timeInBed":483,"type":"stages"}],"summary":{"stages":{"deep":63,"light":257,"rem":94,"wake":69},"totalMinutesAsleep":415,"totalSleepRecords":1,"totalTimeInBed":483}}
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=== "PLAIN_TEXT"
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Will update this section later.
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All columns are mandatory, however, all except `local_date_time` and `duration` can be empty if you don't have that data. Just have in mind that some features might be inaccurate or empty as `type_episode_id`, `level`, `is_main_sleep`, and `type` are used for sleep episodes extraction. `type_episode_id` is based on where it is extracted: if it is extracted from the 1st "data" and "shortData" block, the `type_episode_id` field will be 0. Similarly, the kth block will be k-1.
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|type_episode_id |local_date_time |duration |level |is_main_sleep |type |
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|---------------- |------------------- |--------- |---------- |-------------- |-------------- |
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|0 |2020-10-10 15:36:30 |60 |restless |1 |stages |
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|0 |2020-10-10 15:37:30 |660 |asleep |1 |stages |
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|0 |2020-10-10 15:48:30 |60 |restless |1 |stages |
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## RAPIDS provider
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@ -45,10 +59,10 @@ We provide examples of the input format that RAPIDS expects, note that both exam
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!!! info "File Sequence"
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```bash
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# [might update this section later]
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- data/raw/{pid}/fitbit_sleep_intraday_raw.csv
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- data/raw/{pid}/fitbit_sleep_intraday_parsed.csv
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- data/raw/{pid}/fitbit_sleep_intraday_parsed_with_datetime.csv
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- data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled.csv
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- data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled_with_datetime.csv
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- data/interim/{pid}/fitbit_sleep_intraday_features/fitbit_sleep_intraday_{language}_{provider_key}.csv
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- data/processed/features/{pid}/fitbit_sleep_intraday.csv
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```
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@ -67,13 +81,14 @@ Parameters description for `[FITBIT_SLEEP_INTRADAY][PROVIDERS][RAPIDS]`:
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Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS][LEVELS_AND_TYPES]`:
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|Feature |Units |Description |
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|-------------------------- |-------------- |-------------------------------------------------------------|
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|countepisode`[LEVEL][TYPE]` |episodes |Number of `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]`and `[TYPE]`can also be `all` when ``LEVELS_AND_TYPES_COMBINING_ALL`` is True, which groups all `CLASSIC`, `STAGE`, `UNIFIED` levels and both sleep types.
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|sumduration`[LEVEL][TYPE]` |minutes |Total duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]`and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which groups all `CLASSIC`, `STAGE`, `UNIFIED` levels and both sleep types.
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|maxduration`[LEVEL][TYPE]` |minutes | Longest duration of any `[LEVEL][TYPE]`sleep episode. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]`and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which groups all `CLASSIC`, `STAGE`, `UNIFIED` levels and both sleep types.
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|minduration`[LEVEL][TYPE]` |minutes | Shortest duration of any `[LEVEL][TYPE]`sleep episode. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]`and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which groups all `CLASSIC`, `STAGE`, `UNIFIED` levels and both sleep types.
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|avgduration`[LEVEL][TYPE]` |minutes | Average duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]`and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which groups all `CLASSIC`, `STAGE`, `UNIFIED` levels and both sleep types.
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|stdduration`[LEVEL][TYPE]` |minutes | Standard deviation duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]`and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which groups all `CLASSIC`, `STAGE`, `UNIFIED` levels and both sleep types.
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|------------------------------- |-------------- |-------------------------------------------------------------|
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|countepisode`[LEVEL][TYPE]` |episodes |Number of `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]`and `[TYPE]` can also be `all` when ``LEVELS_AND_TYPES_COMBINING_ALL`` is True, which ignores the levels and groups by sleep types.
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|sumduration`[LEVEL][TYPE]` |minutes |Total duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]` and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which ignores the levels and groups by sleep types.
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|maxduration`[LEVEL][TYPE]` |minutes | Longest duration of any `[LEVEL][TYPE]`sleep episode. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]` and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which ignores the levels and groups by sleep types.
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|minduration`[LEVEL][TYPE]` |minutes | Shortest duration of any `[LEVEL][TYPE]`sleep episode. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]` and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which ignores the levels and groups by sleep types.
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|avgduration`[LEVEL][TYPE]` |minutes | Average duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]` and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which ignores the levels and groups by sleep types.
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|medianduration`[LEVEL][TYPE]` |minutes | Median duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]` and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which ignores the levels and groups by sleep types.
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|stdduration`[LEVEL][TYPE]` |minutes | Standard deviation duration of all `[LEVEL][TYPE]`sleep episodes. `[LEVEL]`is one of `[SLEEP_LEVELS]` (e.g. awake-classic or rem-stages) and `[TYPE]` is one of `[SLEEP_TYPES]` (e.g. main). Both `[LEVEL]` and `[TYPE]`can also be `all` when `LEVELS_AND_TYPES_COMBINING_ALL` is True, which ignores the levels and groups by sleep types.
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Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS]` RATIOS `[ACROSS_LEVELS]`:
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@ -122,7 +137,7 @@ Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][RAPIDS][ROUTINE]`:
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!!! note "Assumptions/Observations"
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1. Deleting values from `[SLEEP_LEVELS]` or `[SLEEP_TYPES]` will only change the features you receive from `[LEVELS_AND_TYPES]`. For example if `STAGES` only contains `[rem, light]` you will not receive `countepisode[wake|deep][TYPE]` or sum, max, min, avg, or std `duration`. These values will not influence `RATIOS` or `ROUTINE` features.
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1. Deleting values from `[SLEEP_LEVELS]` or `[SLEEP_TYPES]` will only change the features you receive from `[LEVELS_AND_TYPES]`. For example if `STAGES` only contains `[rem, light]` you will not receive `countepisode[wake|deep][TYPE]` or sum, max, min, avg, median, or std `duration`. These values will not influence `RATIOS` or `ROUTINE` features.
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2. Any `[LEVEL]` grouping is done within the elements of each class `CLASSIC`, `STAGES`, and `UNIFIED`. That is, we never combine `CLASSIC` or `STAGES` types to compute features when `LEVELS_AND_TYPES_COMBINING_ALL` is True or when computing `RATIOS`.
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@ -660,6 +660,40 @@ rule fitbit_sleep_summary_r_features:
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script:
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"../src/features/entry.R"
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rule resample_sleep_episodes:
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input:
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"data/raw/{pid}/fitbit_sleep_intraday_parsed.csv"
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output:
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"data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled.csv"
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script:
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"../src/features/utils/resample_episodes.R"
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rule fitbit_sleep_intraday_python_features:
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input:
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sensor_data = "data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled_with_datetime.csv",
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time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
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params:
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provider = lambda wildcards: config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()],
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provider_key = "{provider_key}",
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sensor_key = "fitbit_sleep_intraday"
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output:
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"data/interim/{pid}/fitbit_sleep_intraday_features/fitbit_sleep_intraday_python_{provider_key}.csv"
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script:
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"../src/features/entry.py"
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rule fitbit_sleep_intraday_r_features:
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input:
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sensor_data = "data/interim/{pid}/fitbit_sleep_intraday_episodes_resampled_with_datetime.csv",
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time_segments_labels = "data/interim/time_segments/{pid}_time_segments_labels.csv"
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params:
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provider = lambda wildcards: config["FITBIT_SLEEP_INTRADAY"]["PROVIDERS"][wildcards.provider_key.upper()],
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provider_key = "{provider_key}",
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sensor_key = "fitbit_sleep_intraday"
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output:
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"data/interim/{pid}/fitbit_sleep_intraday_features/fitbit_sleep_intraday_r_{provider_key}.csv"
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script:
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"../src/features/entry.R"
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rule merge_sensor_features_for_individual_participants:
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input:
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feature_files = input_merge_sensor_features_for_individual_participants
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@ -14,66 +14,53 @@ SLEEP_SUMMARY_COLUMNS_V1_2 = ("device_id", "efficiency",
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"timestamp")
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SLEEP_SUMMARY_COLUMNS_V1 = SLEEP_SUMMARY_COLUMNS_V1_2 + ("count_awake", "duration_awake", "count_awakenings", "count_restless", "duration_restless")
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SLEEP_INTRADAY_COLUMNS = ("device_id",
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SLEEP_INTRADAY_COLUMNS = (# Extract "type_episode_id" field based on summary data: start from 0
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"type_episode_id",
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"duration",
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# For "classic" type, original_level is one of {"awake", "restless", "asleep"}
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# For "stages" type, original_level is one of {"wake", "deep", "light", "rem"}
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"level",
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# For "classic" type, unified_level is one of {0, 1} where 0: awake {"awake" + "restless"}, 1: asleep {"asleep"}
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# For "stages" type, unified_level is one of {0, 1} where 0: awake {"wake"}, 1: asleep {"deep" + "light" + "rem"}
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"unified_level",
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# one of {0, 1} where 0: nap, 1: main sleep
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# One of {0, 1} where 0: nap, 1: main sleep
|
||||
"is_main_sleep",
|
||||
# one of {"classic", "stages"}
|
||||
# One of {"classic", "stages"}
|
||||
"type",
|
||||
"local_date_time",
|
||||
"timestamp")
|
||||
"start_timestamp",
|
||||
"end_timestamp")
|
||||
|
||||
def mergeLongAndShortData(data_summary):
|
||||
longData = pd.DataFrame(columns=['dateTime', 'level', 'seconds'])
|
||||
shortData = pd.DataFrame(columns=['dateTime','level', 'seconds'])
|
||||
|
||||
windowLength = 30
|
||||
def mergeLongAndShortData(data_intraday):
|
||||
long_data = pd.DataFrame(columns=["dateTime", "level"])
|
||||
short_data = pd.DataFrame(columns=["dateTime", "level"])
|
||||
|
||||
for data in data_summary['data']:
|
||||
origEntry = data
|
||||
window_length = 30
|
||||
|
||||
for data in data_intraday["data"]:
|
||||
counter = 0
|
||||
numberOfSplits = origEntry['seconds']//windowLength
|
||||
for times in range(numberOfSplits):
|
||||
newRow = {'dateTime':dateutil.parser.parse(origEntry['dateTime'])+timedelta(seconds=counter*windowLength),'level':origEntry['level'],'seconds':windowLength}
|
||||
longData = longData.append(newRow, ignore_index = True)
|
||||
for times in range(data["seconds"] // window_length):
|
||||
row = {"dateTime": dateutil.parser.parse(data["dateTime"])+timedelta(seconds=counter*window_length), "level": data["level"]}
|
||||
long_data = long_data.append(row, ignore_index = True)
|
||||
counter = counter + 1
|
||||
|
||||
for data in data_summary['shortData']:
|
||||
origEntry = data
|
||||
for data in data_intraday["shortData"]:
|
||||
counter = 0
|
||||
numberOfSplits = origEntry['seconds']//windowLength
|
||||
for times in range(numberOfSplits):
|
||||
newRow = {'dateTime':dateutil.parser.parse(origEntry['dateTime'])+timedelta(seconds=counter*windowLength),'level':origEntry['level'],'seconds':windowLength}
|
||||
shortData = shortData.append(newRow,ignore_index = True)
|
||||
for times in range(data["seconds"] // window_length):
|
||||
row = {"dateTime": dateutil.parser.parse(data["dateTime"])+timedelta(seconds=counter*window_length), "level": data["level"]}
|
||||
short_data = short_data.append(row, ignore_index = True)
|
||||
counter = counter + 1
|
||||
longData.set_index('dateTime',inplace=True)
|
||||
shortData.set_index('dateTime',inplace=True)
|
||||
longData['level'] = np.where(longData.index.isin(shortData.index) == True,'wake',longData['level'])
|
||||
long_data.set_index("dateTime",inplace=True)
|
||||
short_data.set_index("dateTime",inplace=True)
|
||||
long_data["level"] = np.where(long_data.index.isin(short_data.index) == True, "wake", long_data["level"])
|
||||
|
||||
longData.reset_index(inplace=True)
|
||||
long_data.reset_index(inplace=True)
|
||||
|
||||
return longData.values.tolist()
|
||||
|
||||
def classicData1min(data_summary):
|
||||
dataList = list()
|
||||
for data in data_summary['data']:
|
||||
origEntry = data
|
||||
counter = 0
|
||||
timeDuration = 60
|
||||
numberOfSplits = origEntry['seconds']//timeDuration
|
||||
for times in range(numberOfSplits):
|
||||
newRow = {'dateTime':dateutil.parser.parse(origEntry['dateTime'])+timedelta(seconds=counter*timeDuration),'level':origEntry['level'],'seconds':timeDuration}
|
||||
dataList.append(newRow)
|
||||
counter = counter + 1
|
||||
return dataList
|
||||
return long_data.values.tolist()
|
||||
|
||||
# Parse one record for sleep API version 1
|
||||
def parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type):
|
||||
def parseOneRecordForV1(record, device_id, type_episode_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type):
|
||||
|
||||
sleep_record_type = "classic"
|
||||
|
||||
|
|
@ -110,16 +97,16 @@ def parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, rec
|
|||
d_original_level = SLEEP_CODE2LEVEL[int(data["value"])-1]
|
||||
|
||||
|
||||
row_intraday = (device_id,
|
||||
row_intraday = (type_episode_id, 60,
|
||||
d_original_level, -1, d_is_main_sleep, sleep_record_type,
|
||||
d_datetime, 0)
|
||||
d_datetime, 0, 0)
|
||||
|
||||
records_intraday.append(row_intraday)
|
||||
|
||||
return records_summary, records_intraday
|
||||
|
||||
# Parse one record for sleep API version 1.2
|
||||
def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type):
|
||||
def parseOneRecordForV12(record, device_id, type_episode_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type):
|
||||
|
||||
sleep_record_type = record['type']
|
||||
|
||||
|
|
@ -138,52 +125,24 @@ def parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, re
|
|||
|
||||
# Intraday data
|
||||
if fitbit_data_type == "intraday":
|
||||
if sleep_record_type == 'classic':
|
||||
start_date = d_start_datetime.date()
|
||||
end_date = d_end_datetime.date()
|
||||
is_before_midnight = True
|
||||
curr_date = start_date
|
||||
data_summary = record['levels']
|
||||
dataSplitted = classicData1min(data_summary) ##Calling the function to split the data in regular 60 seconds interval
|
||||
for data in dataSplitted:
|
||||
# For overnight episodes, use end_date once we are over midnight
|
||||
d_time = data["dateTime"].time()
|
||||
if is_before_midnight and d_time.hour == 0:
|
||||
curr_date = end_date
|
||||
d_datetime = datetime.combine(curr_date, d_time)
|
||||
if sleep_record_type == "classic":
|
||||
for data in record["levels"]["data"]:
|
||||
d_datetime = dateutil.parser.parse(data["dateTime"])
|
||||
|
||||
d_original_level = data["level"]
|
||||
|
||||
row_intraday = (device_id,
|
||||
d_original_level, -1, d_is_main_sleep, sleep_record_type,
|
||||
d_datetime, 0)
|
||||
row_intraday = (type_episode_id, data["seconds"],
|
||||
data["level"], -1, d_is_main_sleep, sleep_record_type,
|
||||
d_datetime, 0, 0)
|
||||
records_intraday.append(row_intraday)
|
||||
else:
|
||||
# For sleep type "stages"
|
||||
start_date = d_start_datetime.date()
|
||||
end_date = d_end_datetime.date()
|
||||
is_before_midnight = True
|
||||
curr_date = start_date
|
||||
data_summary = record['levels']
|
||||
dataList = mergeLongAndShortData(data_summary)
|
||||
for data in dataList:
|
||||
|
||||
d_time = data[0].time()
|
||||
if is_before_midnight and d_time.hour == 0:
|
||||
curr_date = end_date
|
||||
d_datetime = datetime.combine(curr_date, d_time)
|
||||
|
||||
d_original_level = data[1]
|
||||
|
||||
row_intraday = (device_id,
|
||||
d_original_level, -1, d_is_main_sleep, sleep_record_type,
|
||||
d_datetime, 0)
|
||||
for data in mergeLongAndShortData(record["levels"]):
|
||||
row_intraday = (type_episode_id, 30,
|
||||
data[1], -1, d_is_main_sleep, sleep_record_type,
|
||||
data[0], 0, 0)
|
||||
|
||||
records_intraday.append(row_intraday)
|
||||
|
||||
return records_summary, records_intraday
|
||||
|
||||
|
||||
|
||||
def parseSleepData(sleep_data, fitbit_data_type):
|
||||
SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1_2
|
||||
|
|
@ -194,6 +153,7 @@ def parseSleepData(sleep_data, fitbit_data_type):
|
|||
return pd.DataFrame(columns=SLEEP_INTRADAY_COLUMNS)
|
||||
device_id = sleep_data["device_id"].iloc[0]
|
||||
records_summary, records_intraday = [], []
|
||||
type_episode_id = 0
|
||||
# Parse JSON into individual records
|
||||
for multi_record in sleep_data.fitbit_data:
|
||||
for record in json.loads(multi_record)["sleep"]:
|
||||
|
|
@ -203,11 +163,13 @@ def parseSleepData(sleep_data, fitbit_data_type):
|
|||
# For sleep API version 1
|
||||
if "awakeCount" in record:
|
||||
SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1
|
||||
records_summary, records_intraday = parseOneRecordForV1(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type)
|
||||
records_summary, records_intraday = parseOneRecordForV1(record, device_id, type_episode_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type)
|
||||
# For sleep API version 1.2
|
||||
else:
|
||||
SLEEP_SUMMARY_COLUMNS = SLEEP_SUMMARY_COLUMNS_V1_2
|
||||
records_summary, records_intraday = parseOneRecordForV12(record, device_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type)
|
||||
records_summary, records_intraday = parseOneRecordForV12(record, device_id, type_episode_id, d_is_main_sleep, records_summary, records_intraday, fitbit_data_type)
|
||||
|
||||
type_episode_id = type_episode_id + 1
|
||||
|
||||
if fitbit_data_type == "summary":
|
||||
parsed_data = pd.DataFrame(data=records_summary, columns=SLEEP_SUMMARY_COLUMNS)
|
||||
|
|
@ -216,6 +178,19 @@ def parseSleepData(sleep_data, fitbit_data_type):
|
|||
|
||||
return parsed_data
|
||||
|
||||
def mergeSleepEpisodes(sleep_data, cols_for_groupby):
|
||||
sleep_episodes = pd.DataFrame(columns=["type_episode_id", "level_episode_id", "level", "unified_level", "is_main_sleep", "type", "start_timestamp", "end_timestamp"])
|
||||
if not sleep_data.empty:
|
||||
sleep_data = sleep_data.groupby(by=cols_for_groupby)
|
||||
sleep_episodes = sleep_data[["start_timestamp"]].first()
|
||||
sleep_episodes["end_timestamp"] = sleep_data["end_timestamp"].last()
|
||||
|
||||
sleep_episodes.reset_index(inplace=True, drop=False)
|
||||
|
||||
return sleep_episodes
|
||||
|
||||
|
||||
|
||||
timezone = snakemake.params["timezone"]
|
||||
column_format = snakemake.params["column_format"]
|
||||
fitbit_data_type = snakemake.params["fitbit_data_type"]
|
||||
|
|
@ -237,6 +212,9 @@ elif column_format == "PLAIN_TEXT":
|
|||
else:
|
||||
raise ValueError("column_format can only be one of ['JSON', 'PLAIN_TEXT'].")
|
||||
|
||||
# Drop duplicates
|
||||
parsed_data.drop_duplicates(inplace=True)
|
||||
|
||||
if parsed_data.shape[0] > 0 and fitbit_data_type == "summary":
|
||||
if sleep_episode_timestamp != "start" and sleep_episode_timestamp != "end":
|
||||
raise ValueError("SLEEP_EPISODE_TIMESTAMP can only be one of ['start', 'end'].")
|
||||
|
|
@ -245,6 +223,10 @@ if parsed_data.shape[0] > 0 and fitbit_data_type == "summary":
|
|||
|
||||
if not pd.isnull(local_start_date) and not pd.isnull(local_end_date):
|
||||
parsed_data = parsed_data.loc[(parsed_data[datetime_column] >= local_start_date) & (parsed_data[datetime_column] < local_end_date)]
|
||||
|
||||
# Sort by "local_start_date_time" column
|
||||
parsed_data.sort_values(by="local_start_date_time", ascending=True, inplace=True)
|
||||
|
||||
parsed_data["timestamp"] = parsed_data[datetime_column].dt.tz_localize(timezone, ambiguous=False, nonexistent="NaT").dropna().astype(np.int64) // 10**6
|
||||
parsed_data.dropna(subset=['timestamp'], inplace=True)
|
||||
parsed_data.drop(["local_start_date_time", "local_end_date_time"], axis = 1, inplace=True)
|
||||
|
|
@ -252,8 +234,18 @@ if parsed_data.shape[0] > 0 and fitbit_data_type == "summary":
|
|||
if parsed_data.shape[0] > 0 and fitbit_data_type == "intraday":
|
||||
if not pd.isnull(local_start_date) and not pd.isnull(local_end_date):
|
||||
parsed_data = parsed_data.loc[(parsed_data["local_date_time"] >= local_start_date) & (parsed_data["local_date_time"] < local_end_date)]
|
||||
parsed_data["timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone, ambiguous=False, nonexistent="NaT").dropna().astype(np.int64) // 10**6
|
||||
parsed_data.dropna(subset=['timestamp'], inplace=True)
|
||||
parsed_data["unified_level"] = np.where(parsed_data["level"].isin(["awake", "wake", "restless"]), 0, 1)
|
||||
|
||||
# Sort by "local_date_time" column
|
||||
parsed_data.sort_values(by="local_date_time", ascending=True, inplace=True)
|
||||
|
||||
parsed_data["start_timestamp"] = parsed_data["local_date_time"].dt.tz_localize(timezone, ambiguous=False, nonexistent="NaT").dropna().astype(np.int64) // 10**6
|
||||
parsed_data.dropna(subset=['start_timestamp'], inplace=True)
|
||||
parsed_data["end_timestamp"] = parsed_data["start_timestamp"] + ((parsed_data["duration"] - 1) * 1000) + 999
|
||||
parsed_data["unified_level"] = np.where(parsed_data["level"].isin(["awake", "restless", "wake"]), 0, 1)
|
||||
|
||||
# Put consecutive rows with the same "level" field together and merge episodes
|
||||
parsed_data.insert(2, "level_episode_id", (parsed_data[["type_episode_id", "level"]] != parsed_data[["type_episode_id", "level"]].shift()).any(axis=1).cumsum())
|
||||
parsed_data = mergeSleepEpisodes(parsed_data, ["type_episode_id", "level_episode_id", "level", "unified_level", "is_main_sleep", "type"])
|
||||
|
||||
|
||||
parsed_data.to_csv(snakemake.output[0], index=False)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,265 @@
|
|||
import pandas as pd
|
||||
from datetime import datetime
|
||||
import itertools
|
||||
|
||||
def featuresFullNames(intraday_features_to_compute, sleep_levels_to_compute, sleep_types_to_compute, consider_all):
|
||||
|
||||
features_fullname = ["local_segment"]
|
||||
|
||||
sleep_level_with_group = []
|
||||
for sleep_level_group in sleep_levels_to_compute:
|
||||
for sleep_level in sleep_levels_to_compute[sleep_level_group]:
|
||||
sleep_level_with_group.append(sleep_level + sleep_level_group.lower())
|
||||
|
||||
if consider_all:
|
||||
features_fullname.extend([x[0] + x[1] + x[2] for x in itertools.product(intraday_features_to_compute["LEVELS_AND_TYPES"], sleep_level_with_group + ["all"], sleep_types_to_compute + ["all"])])
|
||||
else:
|
||||
features_fullname.extend([x[0] + x[1] + x[2] for x in itertools.product(intraday_features_to_compute["LEVELS_AND_TYPES"], sleep_level_with_group, sleep_types_to_compute)])
|
||||
if "ACROSS_LEVELS" in intraday_features_to_compute["RATIOS_SCOPE"]:
|
||||
features_fullname.extend(["ratio" + x[0] + x[1] for x in itertools.product(intraday_features_to_compute["RATIOS_TYPE"], sleep_level_with_group)])
|
||||
if "ACROSS_TYPES" in intraday_features_to_compute["RATIOS_SCOPE"] and "main" in sleep_types_to_compute:
|
||||
features_fullname.extend(["ratio" + x + "main" for x in intraday_features_to_compute["RATIOS_TYPE"]])
|
||||
if "WITHIN_LEVELS" in intraday_features_to_compute["RATIOS_SCOPE"]:
|
||||
features_fullname.extend(["ratio" + x[0] + x[1] + "within" + x[2] for x in itertools.product(intraday_features_to_compute["RATIOS_TYPE"], sleep_types_to_compute, sleep_level_with_group)])
|
||||
if "WITHIN_TYPES" in intraday_features_to_compute["RATIOS_SCOPE"]:
|
||||
features_fullname.extend(["ratio" + x[0] + x[1] + "within" + x[2] for x in itertools.product(intraday_features_to_compute["RATIOS_TYPE"], sleep_level_with_group, sleep_types_to_compute)])
|
||||
features_fullname.extend(intraday_features_to_compute["ROUTINE"])
|
||||
return features_fullname
|
||||
|
||||
def mergeSleepEpisodes(sleep_data, cols_for_groupby):
|
||||
|
||||
sleep_episodes = pd.DataFrame(columns=["local_segment", "duration", "start_timestamp", "end_timestamp", "local_start_date_time", "local_end_date_time", "start_minutes"])
|
||||
|
||||
if cols_for_groupby and (not sleep_data.empty):
|
||||
sleep_data = sleep_data.groupby(by=cols_for_groupby)
|
||||
sleep_episodes = sleep_data[["duration"]].sum()
|
||||
sleep_episodes["start_timestamp"] = sleep_data["start_timestamp"].first()
|
||||
sleep_episodes["end_timestamp"] = sleep_data["end_timestamp"].last()
|
||||
sleep_episodes["local_start_date_time"] = sleep_data["local_start_date_time"].first()
|
||||
sleep_episodes["local_end_date_time"] = sleep_data["local_end_date_time"].last()
|
||||
sleep_episodes["start_minutes"] = sleep_data["start_minutes"].first()
|
||||
|
||||
sleep_episodes.reset_index(inplace=True, drop=False)
|
||||
|
||||
return sleep_episodes
|
||||
|
||||
def statsFeatures(sleep_episodes, features, episode_type):
|
||||
|
||||
episode_features = pd.DataFrame(columns=[feature + episode_type for feature in features])
|
||||
if sleep_episodes.empty:
|
||||
return episode_features
|
||||
|
||||
if "countepisode" in features:
|
||||
episode_features["countepisode" + episode_type] = sleep_episodes[["local_segment", "duration"]].groupby(["local_segment"])["duration"].count()
|
||||
if "sumduration" in features:
|
||||
episode_features["sumduration" + episode_type] = sleep_episodes[["local_segment", "duration"]].groupby(["local_segment"])["duration"].sum()
|
||||
if "maxduration" in features:
|
||||
episode_features["maxduration" + episode_type] = sleep_episodes[["local_segment", "duration"]].groupby(["local_segment"])["duration"].max()
|
||||
if "minduration" in features:
|
||||
episode_features["minduration" + episode_type] = sleep_episodes[["local_segment", "duration"]].groupby(["local_segment"])["duration"].min()
|
||||
if "avgduration" in features:
|
||||
episode_features["avgduration" + episode_type] = sleep_episodes[["local_segment", "duration"]].groupby(["local_segment"])["duration"].mean()
|
||||
if "medianduration" in features:
|
||||
episode_features["medianduration" + episode_type] = sleep_episodes[["local_segment", "duration"]].groupby(["local_segment"])["duration"].median()
|
||||
if "stdduration" in features:
|
||||
episode_features["stdduration" + episode_type] = sleep_episodes[["local_segment", "duration"]].groupby(["local_segment"])["duration"].std()
|
||||
|
||||
return episode_features
|
||||
|
||||
def allStatsFeatures(sleep_data, base_sleep_levels, base_sleep_types, features, sleep_intraday_features):
|
||||
|
||||
# For CLASSIC
|
||||
for sleep_level, sleep_type in itertools.product(base_sleep_levels["CLASSIC"] + ["all"], base_sleep_types + ["all"]):
|
||||
sleep_episodes_classic = sleep_data[sleep_data["is_main_sleep"] == (1 if sleep_type == "main" else 0)] if sleep_type != "all" else sleep_data
|
||||
sleep_episodes_classic = sleep_episodes_classic[sleep_episodes_classic["level"] == sleep_level] if sleep_level != "all" else sleep_episodes_classic
|
||||
sleep_intraday_features = pd.concat([sleep_intraday_features, statsFeatures(sleep_episodes_classic, features, sleep_level + "classic" + sleep_type)], axis=1)
|
||||
|
||||
# For STAGES
|
||||
for sleep_level, sleep_type in itertools.product(base_sleep_levels["STAGES"] + ["all"], base_sleep_types + ["all"]):
|
||||
sleep_episodes_stages = sleep_data[sleep_data["is_main_sleep"] == (1 if sleep_type == "main" else 0)] if sleep_type != "all" else sleep_data
|
||||
sleep_episodes_stages = sleep_episodes_stages[sleep_episodes_stages["level"] == sleep_level] if sleep_level != "all" else sleep_episodes_stages
|
||||
sleep_intraday_features = pd.concat([sleep_intraday_features, statsFeatures(sleep_episodes_stages, features, sleep_level + "stages" + sleep_type)], axis=1)
|
||||
|
||||
# For UNIFIED
|
||||
for sleep_level, sleep_type in itertools.product(base_sleep_levels["UNIFIED"] + ["all"], base_sleep_types + ["all"]):
|
||||
sleep_episodes_unified = sleep_data[sleep_data["is_main_sleep"] == (1 if sleep_type == "main" else 0)] if sleep_type != "all" else sleep_data
|
||||
sleep_episodes_unified = sleep_episodes_unified[sleep_episodes_unified["unified_level"] == (0 if sleep_level == "awake" else 1)] if sleep_level != "all" else sleep_episodes_unified
|
||||
sleep_episodes_unified = mergeSleepEpisodes(sleep_episodes_unified, ["local_segment", "unified_level_episode_id"])
|
||||
sleep_intraday_features = pd.concat([sleep_intraday_features, statsFeatures(sleep_episodes_unified, features, sleep_level + "unified" + sleep_type)], axis=1)
|
||||
|
||||
# Ignore the levels (e.g. countepisode[all][main])
|
||||
for sleep_type in base_sleep_types + ["all"]:
|
||||
sleep_episodes_none = sleep_data[sleep_data["is_main_sleep"] == (1 if sleep_type == "main" else 0)] if sleep_type != "all" else sleep_data
|
||||
sleep_episodes_none = mergeSleepEpisodes(sleep_episodes_none, ["local_segment", "type_episode_id"])
|
||||
sleep_intraday_features = pd.concat([sleep_intraday_features, statsFeatures(sleep_episodes_none, features, "all" + sleep_type)], axis=1)
|
||||
|
||||
return sleep_intraday_features
|
||||
|
||||
|
||||
# Since all the stats features have been computed no matter they are requested or not,
|
||||
# we can pick the related features to calculate the RATIOS features directly.
|
||||
# Take ACROSS_LEVELS RATIOS features as an example:
|
||||
# ratiocount[remstages] = countepisode[remstages][all] / countepisode[all][all]
|
||||
def ratiosFeatures(sleep_intraday_features, ratios_types, ratios_scopes, sleep_levels, sleep_types):
|
||||
|
||||
# Put sleep_level_group and sleep_level together.
|
||||
# For example:
|
||||
# input (sleep_levels): {"CLASSIC": ["awake", "restless", "asleep"], "UNIFIED": ["awake", "asleep"]}
|
||||
# output (sleep_level_with_group): [("classic", "awake"), ("classic", "restless"), ("classic", "asleep"), ("unified", "awake"), ("unified", "asleep")]
|
||||
sleep_level_with_group = []
|
||||
for sleep_level_group in sleep_levels:
|
||||
for sleep_level in sleep_levels[sleep_level_group]:
|
||||
sleep_level_with_group.append((sleep_level_group.lower(), sleep_level))
|
||||
|
||||
# ACROSS LEVELS
|
||||
if "ACROSS_LEVELS" in ratios_scopes:
|
||||
# Get the cross product of ratios_types and sleep_level_with_group.
|
||||
# For example:
|
||||
# input: ratios_types is ["count", "duration"], sleep_level_with_group is [("classic", "awake"), ("classic", "restless"), ("unified", "asleep")]
|
||||
# output:
|
||||
# 1) ratios_type: "count", sleep_levels_combined: ("classic", "awake")
|
||||
# 2) ratios_type: "count", sleep_levels_combined: ("classic", "restless")
|
||||
# 3) ratios_type: "count", sleep_levels_combined: ("unified", "asleep")
|
||||
# 4) ratios_type: "duration", sleep_levels_combined: ("classic", "awake")
|
||||
# 5) ratios_type: "duration", sleep_levels_combined: ("classic", "restless")
|
||||
# 6) ratios_type: "duration", sleep_levels_combined: ("unified", "asleep")
|
||||
for ratios_type, sleep_levels_combined in itertools.product(ratios_types, sleep_level_with_group):
|
||||
sleep_level_group, sleep_level = sleep_levels_combined[0], sleep_levels_combined[1]
|
||||
agg_func = "countepisode" if ratios_type == "count" else "sumduration"
|
||||
across_levels = (sleep_intraday_features[agg_func + sleep_level + sleep_level_group + "all"] / sleep_intraday_features[agg_func + "all" + sleep_level_group + "all"]).to_frame().rename(columns={0: "ratio" + ratios_type + sleep_level + sleep_level_group})
|
||||
sleep_intraday_features = pd.concat([sleep_intraday_features, across_levels], axis=1)
|
||||
|
||||
# ACROSS TYPES
|
||||
if "ACROSS_TYPES" in ratios_scopes:
|
||||
for ratios_type in ratios_types:
|
||||
agg_func = "countepisode" if ratios_type == "count" else "sumduration"
|
||||
across_types = (sleep_intraday_features[agg_func + "allmain"] / sleep_intraday_features[agg_func + "allall"]).to_frame().rename(columns={0: "ratio" + ratios_type + "main"})
|
||||
sleep_intraday_features = pd.concat([sleep_intraday_features, across_types], axis=1)
|
||||
|
||||
# Get the cross product of ratios_types, sleep_level_with_group, and sleep_types.
|
||||
# For example:
|
||||
# input:
|
||||
# ratios_types is ["count", "duration"]
|
||||
# sleep_level_with_group is [("classic", "awake"), ("unified", "asleep")]
|
||||
# sleep_types is ["main", "nap"]
|
||||
# output:
|
||||
# 1) ratios_type: "count", sleep_levels_combined: ("classic", "awake"), sleep_type: "main"
|
||||
# 2) ratios_type: "count", sleep_levels_combined: ("classic", "awake"), sleep_type: "nap"
|
||||
# 3) ratios_type: "count", sleep_levels_combined: ("unified", "asleep"), sleep_type: "main"
|
||||
# 4) ratios_type: "count", sleep_levels_combined: ("unified", "asleep"), sleep_type: "nap"
|
||||
# 5) ratios_type: "duration", sleep_levels_combined: ("classic", "awake"), sleep_type: "main"
|
||||
# 6) ratios_type: "duration", sleep_levels_combined: ("classic", "awake"), sleep_type: "nap"
|
||||
# 7) ratios_type: "duration", sleep_levels_combined: ("unified", "asleep"), sleep_type: "main"
|
||||
# 8) ratios_type: "duration", sleep_levels_combined: ("unified", "asleep"), sleep_type: "nap"
|
||||
for ratios_type, sleep_levels_combined, sleep_type in itertools.product(ratios_types, sleep_level_with_group, sleep_types):
|
||||
sleep_level_group, sleep_level = sleep_levels_combined[0], sleep_levels_combined[1]
|
||||
agg_func = "countepisode" if ratios_type == "count" else "sumduration"
|
||||
|
||||
# WITHIN LEVELS
|
||||
if "WITHIN_LEVELS" in ratios_scopes:
|
||||
within_levels = (sleep_intraday_features[agg_func + sleep_level + sleep_level_group + sleep_type] / sleep_intraday_features[agg_func + sleep_level + sleep_level_group + "all"]).to_frame().rename(columns={0: "ratio" + ratios_type + sleep_type + "within" + sleep_level + sleep_level_group})
|
||||
sleep_intraday_features = pd.concat([sleep_intraday_features, within_levels], axis=1)
|
||||
|
||||
# WITHIN TYPES
|
||||
if "WITHIN_TYPES" in ratios_scopes:
|
||||
within_types = (sleep_intraday_features[agg_func + sleep_level + sleep_level_group + sleep_type] / sleep_intraday_features[agg_func + "all" + sleep_level_group + sleep_type]).to_frame().rename(columns={0: "ratio" + ratios_type + sleep_level + sleep_level_group + "within" + sleep_type})
|
||||
sleep_intraday_features = pd.concat([sleep_intraday_features, within_types], axis=1)
|
||||
|
||||
return sleep_intraday_features
|
||||
|
||||
|
||||
def singleSleepTypeRoutineFeatures(sleep_intraday_data, routine, reference_time, sleep_type, sleep_intraday_features):
|
||||
|
||||
sleep_intraday_data = sleep_intraday_data[sleep_intraday_data["is_main_sleep"] == (1 if sleep_type == "mainsleep" else 0)]
|
||||
if "starttimefirst" + sleep_type in routine:
|
||||
grouped_first = sleep_intraday_data.groupby(["local_segment"]).first()
|
||||
if reference_time == "MIDNIGHT":
|
||||
sleep_intraday_features["starttimefirst" + sleep_type] = grouped_first["start_minutes"]
|
||||
elif reference_time == "START_OF_THE_SEGMENT":
|
||||
sleep_intraday_features["starttimefirst" + sleep_type] = (grouped_first["start_timestamp"] - grouped_first["segment_start_timestamp"]) / (60 * 1000)
|
||||
else:
|
||||
raise ValueError("Please check FITBIT_SLEEP_INTRADAY section of config.yaml: REFERENCE_TIME can only be MIDNIGHT or START_OF_THE_SEGMENT.")
|
||||
|
||||
if "endtimelast" + sleep_type in routine:
|
||||
grouped_last = sleep_intraday_data.groupby(["local_segment"]).last()
|
||||
if reference_time == "MIDNIGHT":
|
||||
sleep_intraday_features["endtimelast" + sleep_type] = grouped_last["local_end_date_time"].apply(lambda x: x.hour * 60 + x.minute + x.second / 60)
|
||||
elif reference_time == "START_OF_THE_SEGMENT":
|
||||
sleep_intraday_features["endtimelast" + sleep_type] = (grouped_last["end_timestamp"] - grouped_last["segment_start_timestamp"]) / (60 * 1000)
|
||||
else:
|
||||
raise ValueError("Please check FITBIT_SLEEP_INTRADAY section of config.yaml: REFERENCE_TIME can only be MIDNIGHT or START_OF_THE_SEGMENT.")
|
||||
|
||||
return sleep_intraday_features
|
||||
|
||||
def routineFeatures(sleep_intraday_data, routine, reference_time, sleep_type, sleep_intraday_features):
|
||||
|
||||
if "starttimefirstmainsleep" in routine or "endtimelastmainsleep" in routine:
|
||||
sleep_intraday_features = singleSleepTypeRoutineFeatures(sleep_intraday_data, routine, reference_time, "mainsleep", sleep_intraday_features)
|
||||
|
||||
if "starttimefirstnap" in routine or "endtimelastnap" in routine:
|
||||
sleep_intraday_features = singleSleepTypeRoutineFeatures(sleep_intraday_data, routine, reference_time, "nap", sleep_intraday_features)
|
||||
|
||||
return sleep_intraday_features
|
||||
|
||||
|
||||
def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
|
||||
|
||||
sleep_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
|
||||
|
||||
consider_all = provider["FEATURES"]["LEVELS_AND_TYPES_COMBINING_ALL"]
|
||||
include_sleep_later_than = provider["INCLUDE_SLEEP_LATER_THAN"]
|
||||
reference_time = provider["REFERENCE_TIME"]
|
||||
|
||||
requested_intraday_features = provider["FEATURES"]
|
||||
requested_sleep_levels = provider["SLEEP_LEVELS"]
|
||||
requested_sleep_types = provider["SLEEP_TYPES"]
|
||||
|
||||
# Name of the features this function can compute
|
||||
base_intraday_features = {"LEVELS_AND_TYPES": ["countepisode", "sumduration", "maxduration", "minduration", "avgduration", "medianduration", "stdduration"],
|
||||
"RATIOS_TYPE": ["count", "duration"],
|
||||
"RATIOS_SCOPE": ["ACROSS_LEVELS", "ACROSS_TYPES", "WITHIN_LEVELS", "WITHIN_TYPES"],
|
||||
"ROUTINE": ["starttimefirstmainsleep", "endtimelastmainsleep", "starttimefirstnap", "endtimelastnap"]}
|
||||
base_sleep_levels = {"CLASSIC": ["awake", "restless", "asleep"],
|
||||
"STAGES": ["wake", "deep", "light", "rem"],
|
||||
"UNIFIED": ["awake", "asleep"]}
|
||||
base_sleep_types = ["main", "nap"]
|
||||
|
||||
# The subset of requested features this function can compute
|
||||
intraday_features_to_compute = {key: list(set(requested_intraday_features[key]) & set(base_intraday_features[key])) for key in requested_intraday_features if key in base_intraday_features}
|
||||
sleep_levels_to_compute = {key: list(set(requested_sleep_levels[key]) & set(base_sleep_levels[key])) for key in requested_sleep_levels if key in base_sleep_levels}
|
||||
sleep_types_to_compute = list(set(requested_sleep_types) & set(base_sleep_types))
|
||||
|
||||
# Full names
|
||||
features_fullnames = featuresFullNames(intraday_features_to_compute, sleep_levels_to_compute, sleep_types_to_compute, consider_all)
|
||||
sleep_intraday_features = pd.DataFrame(columns=features_fullnames)
|
||||
|
||||
# Include sleep later than
|
||||
start_minutes = sleep_intraday_data.groupby("start_timestamp").first()["local_time"].apply(lambda x: int(x.split(":")[0]) * 60 + int(x.split(":")[1]) + int(x.split(":")[2]) / 60).to_frame().rename(columns={"local_time": "start_minutes"}).reset_index()
|
||||
sleep_intraday_data = sleep_intraday_data.merge(start_minutes, on="start_timestamp", how="left")
|
||||
sleep_intraday_data = sleep_intraday_data[sleep_intraday_data["start_minutes"] >= include_sleep_later_than]
|
||||
|
||||
sleep_intraday_data = filter_data_by_segment(sleep_intraday_data, time_segment)
|
||||
|
||||
# While level_episode_id is based on levels provided by Fitbit (classic & stages), unified_level_episode_id is based on unified_level.
|
||||
sleep_intraday_data.insert(3, "unified_level_episode_id", (sleep_intraday_data[["type_episode_id", "unified_level"]] != sleep_intraday_data[["type_episode_id", "unified_level"]].shift()).any(axis=1).cumsum())
|
||||
|
||||
if not sleep_intraday_data.empty:
|
||||
|
||||
sleep_intraday_features = pd.DataFrame()
|
||||
|
||||
# ALL LEVELS AND TYPES: compute all stats features no matter they are requested or not
|
||||
sleep_intraday_features = allStatsFeatures(sleep_intraday_data, base_sleep_levels, base_sleep_types, base_intraday_features["LEVELS_AND_TYPES"], sleep_intraday_features)
|
||||
|
||||
# RATIOS: only compute requested features
|
||||
sleep_intraday_features = ratiosFeatures(sleep_intraday_features, intraday_features_to_compute["RATIOS_TYPE"], intraday_features_to_compute["RATIOS_SCOPE"], sleep_levels_to_compute, sleep_types_to_compute)
|
||||
|
||||
# ROUTINE: only compute requested features
|
||||
sleep_intraday_features = routineFeatures(sleep_intraday_data, intraday_features_to_compute["ROUTINE"], reference_time, sleep_types_to_compute, sleep_intraday_features)
|
||||
|
||||
# Reset index and discard features which are not requested by user
|
||||
sleep_intraday_features.index.name = "local_segment"
|
||||
sleep_intraday_features.reset_index(inplace=True)
|
||||
sleep_intraday_features = sleep_intraday_features[features_fullnames]
|
||||
|
||||
|
||||
return sleep_intraday_features
|
||||
|
|
@ -55,7 +55,7 @@ def chunk_episodes(sensor_episodes):
|
|||
sensor_episodes["duration"] = (sensor_episodes["chunked_end_timestamp"] - sensor_episodes["chunked_start_timestamp"]) / (1000 * 60)
|
||||
|
||||
# Merge episodes
|
||||
cols_for_groupby = [col for col in sensor_episodes.columns if col not in ["timestamps_segment", "timestamp", "assigned_segments", "start_datetime", "end_datetime", "start_timestamp", "end_timestamp", "duration", "segment_start_timestamp", "segment_end_timestamp", "chunked_start_timestamp", "chunked_end_timestamp"]]
|
||||
cols_for_groupby = [col for col in sensor_episodes.columns if col not in ["timestamps_segment", "timestamp", "assigned_segments", "start_datetime", "end_datetime", "start_timestamp", "end_timestamp", "duration", "chunked_start_timestamp", "chunked_end_timestamp"]]
|
||||
|
||||
sensor_episodes_grouped = sensor_episodes.groupby(by=cols_for_groupby)
|
||||
merged_sensor_episodes = sensor_episodes_grouped[["duration"]].sum()
|
||||
|
|
|
|||
Loading…
Reference in New Issue