junos
/
rapids
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity

Refactor fitbit sleep features

pull/95/head
Meng Li 2020-06-12 18:44:05 -04:00
parent dcc7ca14e3
commit e133b8b530
4 changed files with 82 additions and 60 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

3
config.yaml
View File

@ -131,7 +131,8 @@ STEP:
SLEEP:
DAY_SEGMENTS: *day_segments
SLEEP_TYPES: ["main", "nap", "all"]
DAILY_FEATURES_FROM_SUMMARY_DATA: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"]
# Only daily features are extracted from summary data
SUMMARY_FEATURES: ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"]
WIFI:
DAY_SEGMENTS: *day_segments

4
rules/features.snakefile
View File

@ -218,8 +218,8 @@ rule fitbit_sleep_features:
sleep_intraday_data = "data/raw/{pid}/fitbit_sleep_intraday_with_datetime.csv"
params:
day_segment = "{day_segment}",
sleep_types = config["SLEEP"]["SLEEP_TYPES"],
daily_features_from_summary_data = config["SLEEP"]["DAILY_FEATURES_FROM_SUMMARY_DATA"]
summary_features = config["SLEEP"]["SUMMARY_FEATURES"],
sleep_types = config["SLEEP"]["SLEEP_TYPES"]
output:
"data/processed/{pid}/fitbit_sleep_{day_segment}.csv"
script:

70
src/features/fitbit_sleep/fitbit_sleep_base.py 100644
View File

@ -0,0 +1,70 @@
import pandas as pd
import itertools
def dailyFeaturesFromSummaryData(sleep_daily_features, sleep_summary_data, summary_features, sleep_type):
if sleep_type == "main":
sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 1]
elif sleep_type == "nap":
sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 0]
elif sleep_type == "all":
pass
else:
raise ValueError("sleep_type can only be one of ['main', 'nap', 'all'].")
features_sum = sleep_summary_data[["minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed", "local_end_date"]].groupby(["local_end_date"]).sum()
features_sum.index.rename("local_date", inplace=True)
if "sumdurationafterwakeup" in summary_features:
sleep_daily_features["sleep_daily_sumdurationafterwakeup" + sleep_type] = features_sum["minutes_after_wakeup"]
if "sumdurationasleep" in summary_features:
sleep_daily_features["sleep_daily_sumdurationasleep" + sleep_type] = features_sum["minutes_asleep"]
if "sumdurationawake" in summary_features:
sleep_daily_features["sleep_daily_sumdurationawake" + sleep_type] = features_sum["minutes_awake"]
if "sumdurationtofallasleep" in summary_features:
sleep_daily_features["sleep_daily_sumdurationtofallasleep" + sleep_type] = features_sum["minutes_to_fall_asleep"]
if "sumdurationinbed" in summary_features:
sleep_daily_features["sleep_daily_sumdurationinbed" + sleep_type] = features_sum["minutes_in_bed"]
features_avg = sleep_summary_data[["efficiency", "local_end_date"]].groupby(["local_end_date"]).mean()
features_avg.index.rename("local_date", inplace=True)
if "avgefficiency" in summary_features:
sleep_daily_features["sleep_daily_avgefficiency" + sleep_type] = features_avg["efficiency"]
features_count = sleep_summary_data[["local_start_date_time", "local_end_date"]].groupby(["local_end_date"]).count()
features_count.index.rename("local_date", inplace=True)
if "countepisode" in summary_features:
sleep_daily_features["sleep_daily_countepisode" + sleep_type] = features_count["local_start_date_time"]
return sleep_daily_features
def base_fitbit_sleep_features(sleep_summary_data, day_segment, requested_summary_features, requested_sleep_type):
if not day_segment == "daily":
return pd.DataFrame(columns=["local_date"])
else:
# name of the features this function can compute
base_summary_features_names = ["sumdurationafterwakeup", "sumdurationasleep", "sumdurationawake", "sumdurationtofallasleep", "sumdurationinbed", "avgefficiency", "countepisode"]
base_sleep_type = ["main", "nap", "all"]
# the subset of requested features this function can compute
summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features_names))
sleep_type_to_compute = list(set(requested_sleep_type) & set(base_sleep_type))
# full names
features_fullnames_to_compute = ["".join(feature) for feature in itertools.product(summary_features_to_compute, sleep_type_to_compute)]
colnames_can_be_zero = ["sleep_daily_" + x for x in [col for col in features_fullnames_to_compute if "avgefficiency" not in col]]
if sleep_summary_data.empty:
sleep_summary_features = pd.DataFrame(columns=["local_date"] + ["sleep_daily_" + x for x in features_fullnames_to_compute])
else:
sleep_summary_features = pd.DataFrame(columns=["sleep_daily_" + x for x in features_fullnames_to_compute])
for sleep_type in sleep_type_to_compute:
sleep_summary_features = dailyFeaturesFromSummaryData(sleep_summary_features, sleep_summary_data, summary_features_to_compute, sleep_type)
sleep_summary_features[colnames_can_be_zero] = sleep_summary_features[colnames_can_be_zero].fillna(0)
sleep_summary_features = sleep_summary_features.reset_index()
return sleep_summary_features

65
src/features/fitbit_sleep_features.py
View File

@ -1,67 +1,18 @@
import pandas as pd
from fitbit_sleep.fitbit_sleep_base import base_fitbit_sleep_features
import itertools
def dailyFeaturesFromSummaryData(sleep_summary_data, sleep_type):
if sleep_type == "main":
sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 1]
elif sleep_type == "nap":
sleep_summary_data = sleep_summary_data[sleep_summary_data["is_main_sleep"] == 0]
elif sleep_type == "all":
pass
else:
raise ValueError("sleep_type can only be one of ['main', 'nap', 'all'].")
features_sum = sleep_summary_data[["minutes_after_wakeup", "minutes_asleep", "minutes_awake", "minutes_to_fall_asleep", "minutes_in_bed", "local_end_date"]].groupby(["local_end_date"]).sum()
features_sum.index.rename("local_date", inplace=True)
if "sumdurationafterwakeup" in daily_features_from_summary_data:
sleep_daily_features["sleep_daily_sumdurationafterwakeup" + sleep_type] = features_sum["minutes_after_wakeup"]
if "sumdurationasleep" in daily_features_from_summary_data:
sleep_daily_features["sleep_daily_sumdurationasleep" + sleep_type] = features_sum["minutes_asleep"]
if "sumdurationawake" in daily_features_from_summary_data:
sleep_daily_features["sleep_daily_sumdurationawake" + sleep_type] = features_sum["minutes_awake"]
if "sumdurationtofallasleep" in daily_features_from_summary_data:
sleep_daily_features["sleep_daily_sumdurationtofallasleep" + sleep_type] = features_sum["minutes_to_fall_asleep"]
if "sumdurationinbed" in daily_features_from_summary_data:
sleep_daily_features["sleep_daily_sumdurationinbed" + sleep_type] = features_sum["minutes_in_bed"]
features_avg = sleep_summary_data[["efficiency", "local_end_date"]].groupby(["local_end_date"]).mean()
features_avg.index.rename("local_date", inplace=True)
if "avgefficiency" in daily_features_from_summary_data:
sleep_daily_features["sleep_daily_avgefficiency" + sleep_type] = features_avg["efficiency"]
features_count = sleep_summary_data[["local_start_date_time", "local_end_date"]].groupby(["local_end_date"]).count()
features_count.index.rename("local_date", inplace=True)
if "countepisode" in daily_features_from_summary_data:
sleep_daily_features["sleep_daily_count" + sleep_type] = features_count["local_start_date_time"]
return sleep_daily_features
sleep_summary_data = pd.read_csv(snakemake.input["sleep_summary_data"])
sleep_types = snakemake.params["sleep_types"]
daily_features_from_summary_data = snakemake.params["daily_features_from_summary_data"]
requested_summary_features = snakemake.params["summary_features"]
requested_sleep_type = snakemake.params["sleep_types"]
day_segment = snakemake.params["day_segment"]
sleep_features = pd.DataFrame(columns=["local_date"])
daily_features_can_be_zero = list(set(daily_features_from_summary_data) - set(["avgefficiency"]))
colnames_can_be_zero = ["sleep_daily_" + x for x in ["".join(feature) for feature in itertools.product(daily_features_can_be_zero, sleep_types)]]
sleep_features = sleep_features.merge(base_fitbit_sleep_features(sleep_summary_data, day_segment, requested_summary_features, requested_sleep_type), on="local_date", how="outer")
colnames = ["sleep_daily_" + x for x in ["".join(feature) for feature in itertools.product(daily_features_from_summary_data, sleep_types)]]
requested_features = ["".join(feature) for feature in itertools.product(requested_summary_features, requested_sleep_type)] if day_segment == "daily" else []
if sleep_summary_data.empty:
sleep_daily_features = pd.DataFrame(columns=["local_date"] + colnames)
else:
sleep_daily_features = pd.DataFrame(columns=colnames)
for sleep_type in sleep_types:
sleep_daily_features = dailyFeaturesFromSummaryData(sleep_summary_data, sleep_type)
assert len(requested_features) + 1 == sleep_features.shape[1], "The number of features in the output dataframe (=" + str(sleep_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your fitbit sleep feature extraction functions"
sleep_daily_features[colnames_can_be_zero] = sleep_daily_features[colnames_can_be_zero].fillna(0)
sleep_features.to_csv(snakemake.output[0], index=False)
if day_segment == "daily":
sleep_daily_features.to_csv(snakemake.output[0])
else:
pd.DataFrame().to_csv(snakemake.output[0])