77 lines
6.3 KiB
Python
77 lines
6.3 KiB
Python
import pandas as pd
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from scipy.stats import entropy
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def extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features):
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heartrate_summary_features = pd.DataFrame()
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if "restinghr" in summary_features:
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heartrate_summary_features["heartrate_daily_restinghr"] = heartrate_summary_data["heartrate_daily_restinghr"]
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# calories features might be inaccurate: they depend on users' fitbit profile (weight, height, etc.)
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if "caloriesoutofrange" in summary_features:
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heartrate_summary_features["heartrate_daily_caloriesoutofrange"] = heartrate_summary_data["heartrate_daily_caloriesoutofrange"]
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if "caloriesfatburn" in summary_features:
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heartrate_summary_features["heartrate_daily_caloriesfatburn"] = heartrate_summary_data["heartrate_daily_caloriesfatburn"]
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if "caloriescardio" in summary_features:
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heartrate_summary_features["heartrate_daily_caloriescardio"] = heartrate_summary_data["heartrate_daily_caloriescardio"]
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if "caloriespeak" in summary_features:
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heartrate_summary_features["heartrate_daily_caloriespeak"] = heartrate_summary_data["heartrate_daily_caloriespeak"]
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heartrate_summary_features.reset_index(inplace=True)
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return heartrate_summary_features
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def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, day_segment):
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heartrate_intraday_features = pd.DataFrame(columns=["local_date"] + ["heartrate_" + day_segment + "_" + x for x in features])
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if not heartrate_intraday_data.empty:
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device_id = heartrate_intraday_data["device_id"][0]
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num_rows_per_minute = heartrate_intraday_data.groupby(["local_date", "local_hour", "local_minute"]).count().mean()["device_id"]
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if day_segment != "daily":
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heartrate_intraday_data = heartrate_intraday_data[heartrate_intraday_data["local_day_segment"] == day_segment]
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if not heartrate_intraday_data.empty:
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heartrate_intraday_features = pd.DataFrame()
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# get stats of heartrate
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if "maxhr" in features:
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heartrate_intraday_features["heartrate_" + day_segment + "_maxhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].max()
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if "minhr" in features:
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heartrate_intraday_features["heartrate_" + day_segment + "_minhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].min()
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if "avghr" in features:
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heartrate_intraday_features["heartrate_" + day_segment + "_avghr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].mean()
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if "medianhr" in features:
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heartrate_intraday_features["heartrate_" + day_segment + "_medianhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].median()
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if "modehr" in features:
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heartrate_intraday_features["heartrate_" + day_segment + "_modehr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0])
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if "stdhr" in features:
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heartrate_intraday_features["heartrate_" + day_segment + "_stdhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].std()
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if "diffmaxmodehr" in features:
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heartrate_intraday_features["heartrate_" + day_segment + "_diffmaxmodehr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].max() - heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0])
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if "diffminmodehr" in features:
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heartrate_intraday_features["heartrate_" + day_segment + "_diffminmodehr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].min()
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if "entropyhr" in features:
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heartrate_intraday_features["heartrate_" + day_segment + "_entropyhr"] = heartrate_intraday_data[["local_date", "heartrate"]].groupby(["local_date"])["heartrate"].agg(entropy)
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# get number of minutes in each heart rate zone
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for feature_name in list(set(["minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]) & set(features)):
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heartrate_zone = heartrate_intraday_data[heartrate_intraday_data["heartrate_zone"] == feature_name[9:-4]]
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heartrate_intraday_features["heartrate_" + day_segment + "_" + feature_name] = heartrate_zone.groupby(["local_date"])["device_id"].count() / num_rows_per_minute
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heartrate_intraday_features.fillna(value={"heartrate_" + day_segment + "_" + feature_name: 0}, inplace=True)
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heartrate_intraday_features.reset_index(inplace=True)
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return heartrate_intraday_features
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def base_fitbit_heartrate_features(heartrate_summary_data, heartrate_intraday_data, day_segment, requested_summary_features, requested_intraday_features):
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# name of the features this function can compute
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base_summary_features_names = ["restinghr", "caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"]
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base_intraday_features_names = ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]
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# the subset of requested features this function can compute
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summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features_names))
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intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
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heartrate_intraday_features = extractHRFeaturesFromIntradayData(heartrate_intraday_data, intraday_features_to_compute, day_segment)
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if not heartrate_summary_data.empty and day_segment == "daily" and summary_features_to_compute != []:
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heartrate_summary_features = extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features_to_compute)
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heartrate_features = heartrate_intraday_features.merge(heartrate_summary_features, on=["local_date"], how="outer")
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else:
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heartrate_features = heartrate_intraday_features
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return heartrate_features
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