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Update socialjetlag feature of sleep intraday: replace bedtime with midpoint sleep

feature/plugin_sentimental
Meng Li 2021-03-04 15:49:25 -05:00
parent 8992a9c9e2
commit aac87311e8
3 changed files with 5 additions and 5 deletions
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2
config.yaml
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@ -381,7 +381,7 @@ FITBIT_SLEEP_SUMMARY:
# See https://www.rapids.science/latest/features/fitbit-sleep-intraday/
FITBIT_SLEEP_INTRADAY:
TABLE: fitbit_data
TABLE: sleep_intraday
PROVIDERS:
RAPIDS:
COMPUTE: False

2
docs/features/fitbit-sleep-intraday.md
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@ -184,7 +184,7 @@ Features description for `[FITBIT_STEPS_INTRADAY][PROVIDERS][PRICE]`:
|stdstarttimeofepisodemain`[DAY_TYPE]` |minutes | Standard deviation of start time of the first `main` sleep episode of each day in a time segment. You can include daily start times from episodes detected on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|stdendtimeofepisodemain`[DAY_TYPE]` |minutes | Standard deviation of end time of the last `main` sleep episode of each day in a time segment. You can include daily end times from episodes detected on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|stdmidpointofepisodemain`[DAY_TYPE]` |minutes | Standard deviation of mid time between the start of the first `main` sleep episode and the end of the last `main` sleep episode of each day in a time segment. You can include episodes detected on weekend days, week days or both depending on the value of the `DAY_TYPE` flag.
|socialjetlag |minutes | Difference in minutes between the avgstarttimeofepisodemain (bed time) of weekends and weekdays.
|socialjetlag |minutes | Difference in minutes between the avgmidpointofepisodemain (bed time) of weekends and weekdays.
|meanssdstarttimeofepisodemain |minutes squared | Same as `avgstarttimeofepisodemain[DAY_TYPE]` but the average is computed over the squared differences of each pair of consecutive start times.
|meanssdendtimeofepisodemain |minutes squared | Same as `avgendtimeofepisodemain[DAY_TYPE]` but the average is computed over the squared differences of each pair of consecutive end times.
|meanssdmidpointofepisodemain |minutes squared | Same as `avgmidpointofepisodemain[DAY_TYPE]` but the average is computed over the squared differences of each pair of consecutive mid times.

6
src/features/fitbit_sleep_intraday/price/main.py
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@ -113,12 +113,12 @@ def statsOfDailyFeatures(daily_features, day_type, sleep_levels, intraday_featur
return sleep_intraday_features
def socialJetLagFeature(daily_features, sleep_intraday_features):
def socialJetLagFeature(daily_features, sleep_intraday_features):
daily_features_weekend = daily_features[daily_features["is_weekend"] == 1]
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features_weekend[["local_segment","starttimeofepisodemain"]].groupby("local_segment")["starttimeofepisodemain"].mean().to_frame().rename(columns={"starttimeofepisodemain": "helper_weekend"})], axis=1)
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features_weekend[["local_segment","midpointofepisodemain"]].groupby("local_segment")["midpointofepisodemain"].mean().to_frame().rename(columns={"midpointofepisodemain": "helper_weekend"})], axis=1)
daily_features_weekday = daily_features[daily_features["is_weekend"] == 0]
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features_weekday[["local_segment","starttimeofepisodemain"]].groupby("local_segment")["starttimeofepisodemain"].mean().to_frame().rename(columns={"starttimeofepisodemain": "helper_weekday"})], axis=1)
sleep_intraday_features = pd.concat([sleep_intraday_features, daily_features_weekday[["local_segment","midpointofepisodemain"]].groupby("local_segment")["midpointofepisodemain"].mean().to_frame().rename(columns={"midpointofepisodemain": "helper_weekday"})], axis=1)
sleep_intraday_features["socialjetlag"] = sleep_intraday_features["helper_weekend"] - sleep_intraday_features["helper_weekday"]