Update HR main.py
Meng Li
parent
daa88b8d25
commit
93055dacee
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@ -266,7 +266,7 @@ FITBIT_HEARTRATE:
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RAPIDS:
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COMPUTE: False
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FEATURES:
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SUMMARY: ["restinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["caloriesoutofrange", "caloriesfatburn", "caloriescardio", "caloriespeak"]
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SUMMARY: ["maxrestinghr", "minrestinghr", "avgrestinghr", "medianrestinghr", "moderestinghr", "stdrestinghr", "diffmaxmoderestinghr", "diffminmoderestinghr", "entropyrestinghr"] # calories features' accuracy depend on the accuracy of the participants fitbit profile (e.g. height, weight) use these with care: ["sumcaloriesoutofrange", "maxcaloriesoutofrange", "mincaloriesoutofrange", "avgcaloriesoutofrange", "mediancaloriesoutofrange", "stdcaloriesoutofrange", "entropycaloriesoutofrange", "sumcaloriesfatburn", "maxcaloriesfatburn", "mincaloriesfatburn", "avgcaloriesfatburn", "mediancaloriesfatburn", "stdcaloriesfatburn", "entropycaloriesfatburn", "sumcaloriescardio", "maxcaloriescardio", "mincaloriescardio", "avgcaloriescardio", "mediancaloriescardio", "stdcaloriescardio", "entropycaloriescardio", "sumcaloriespeak", "maxcaloriespeak", "mincaloriespeak", "avgcaloriespeak", "mediancaloriespeak", "stdcaloriespeak", "entropycaloriespeak"]
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INTRADAY: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]
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SRC_FOLDER: "rapids" # inside src/features/fitbit_heartrate
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SRC_LANGUAGE: "python"
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@ -1,20 +1,59 @@
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import pandas as pd
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from scipy.stats import entropy
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def statsFeatures(heartrate_data, features, features_type, heartrate_features):
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if features_type == "hr":
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col_name = "heartrate"
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elif features_type == "restinghr":
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col_name = "heartrate_daily_restinghr"
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elif features_type == "caloriesoutofrange":
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col_name = "heartrate_daily_caloriesoutofrange"
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elif features_type == "caloriesfatburn":
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col_name = "heartrate_daily_caloriesfatburn"
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elif features_type == "caloriescardio":
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col_name = "heartrate_daily_caloriescardio"
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elif features_type == "caloriespeak":
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col_name = "heartrate_daily_caloriespeak"
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else:
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raise ValueError("features_type can only be one of ['hr', 'restinghr', 'caloriesoutofrange', 'caloriesfatburn', 'caloriescardio', 'caloriespeak'].")
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if "sum" + features_type in features:
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heartrate_features["heartrate_rapids_sum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum()
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if "max" + features_type in features:
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heartrate_features["heartrate_rapids_max" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max()
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if "min" + features_type in features:
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heartrate_features["heartrate_rapids_min" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min()
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if "avg" + features_type in features:
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heartrate_features["heartrate_rapids_avg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean()
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if "median" + features_type in features:
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heartrate_features["heartrate_rapids_median" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median()
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if "mode" + features_type in features:
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heartrate_features["heartrate_rapids_mode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0])
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if "std" + features_type in features:
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heartrate_features["heartrate_rapids_std" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std()
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if "diffmaxmode" + features_type in features:
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heartrate_features["heartrate_rapids_diffmaxmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0])
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if "diffminmode" + features_type in features:
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heartrate_features["heartrate_rapids_diffminmode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min()
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if "entropy" + features_type in features:
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heartrate_features["heartrate_rapids_entropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy)
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return heartrate_features
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def extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features):
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heartrate_summary_data.set_index("local_segment", inplace=True)
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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_rapids_restinghr"] = heartrate_summary_data["heartrate_daily_restinghr"]
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# get stats of resting heartrate
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heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "restinghr", heartrate_summary_features)
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# get stats of calories features
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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_rapids_caloriesoutofrange"] = heartrate_summary_data["heartrate_daily_caloriesoutofrange"]
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if "caloriesfatburn" in summary_features:
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heartrate_summary_features["heartrate_rapids_caloriesfatburn"] = heartrate_summary_data["heartrate_daily_caloriesfatburn"]
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if "caloriescardio" in summary_features:
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heartrate_summary_features["heartrate_rapids_caloriescardio"] = heartrate_summary_data["heartrate_daily_caloriescardio"]
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if "caloriespeak" in summary_features:
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heartrate_summary_features["heartrate_rapids_caloriespeak"] = heartrate_summary_data["heartrate_daily_caloriespeak"]
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heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "caloriesoutofrange", heartrate_summary_features)
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heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "caloriesfatburn", heartrate_summary_features)
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heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "caloriescardio", heartrate_summary_features)
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heartrate_summary_features = statsFeatures(heartrate_summary_data, summary_features, "caloriespeak", heartrate_summary_features)
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heartrate_summary_features.reset_index(inplace=True)
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return heartrate_summary_features
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@ -29,24 +68,7 @@ def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, day_seg
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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_rapids_maxhr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].max()
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if "minhr" in features:
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heartrate_intraday_features["heartrate_rapids_minhr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].min()
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if "avghr" in features:
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heartrate_intraday_features["heartrate_rapids_avghr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].mean()
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if "medianhr" in features:
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heartrate_intraday_features["heartrate_rapids_medianhr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].median()
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if "modehr" in features:
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heartrate_intraday_features["heartrate_rapids_modehr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["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_rapids_stdhr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].std()
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if "diffmaxmodehr" in features:
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heartrate_intraday_features["heartrate_rapids_diffmaxmodehr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].max() - heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["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_rapids_diffminmodehr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].agg(lambda x: pd.Series.mode(x)[0]) - heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].min()
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if "entropyhr" in features:
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heartrate_intraday_features["heartrate_rapids_entropyhr"] = heartrate_intraday_data[["local_segment", "heartrate"]].groupby(["local_segment"])["heartrate"].agg(entropy)
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heartrate_intraday_features = statsFeatures(heartrate_intraday_data, features, "hr", heartrate_intraday_features)
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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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@ -66,29 +88,32 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg
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requested_summary_features = provider["FEATURES"]["SUMMARY"]
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requested_intraday_features = provider["FEATURES"]["INTRADAY"]
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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_summary_features_names = ["maxrestinghr", "minrestinghr", "avgrestinghr", "medianrestinghr", "moderestinghr", "stdrestinghr", "diffmaxmoderestinghr", "diffminmoderestinghr", "entropyrestinghr", "sumcaloriesoutofrange", "maxcaloriesoutofrange", "mincaloriesoutofrange", "avgcaloriesoutofrange", "mediancaloriesoutofrange", "stdcaloriesoutofrange", "entropycaloriesoutofrange", "sumcaloriesfatburn", "maxcaloriesfatburn", "mincaloriesfatburn", "avgcaloriesfatburn", "mediancaloriesfatburn", "stdcaloriesfatburn", "entropycaloriesfatburn", "sumcaloriescardio", "maxcaloriescardio", "mincaloriescardio", "avgcaloriescardio", "mediancaloriescardio", "stdcaloriescardio", "entropycaloriescardio", "sumcaloriespeak", "maxcaloriespeak", "mincaloriespeak", "avgcaloriespeak", "mediancaloriespeak", "stdcaloriespeak", "entropycaloriespeak"]
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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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# extract features from summary data
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heartrate_summary_features = pd.DataFrame(columns=["local_segment"] + ["heartrate_rapids_" + x for x in summary_features_to_compute])
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if not heartrate_summary_data.empty:
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heartrate_summary_data = filter_data_by_segment(heartrate_summary_data, day_segment)
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if not heartrate_summary_data.empty:
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# only keep the segments start at 00:00:00 and end at 23:59:59
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datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00"
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datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59"
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segment_regex = "{}#{},{}".format(day_segment, datetime_start_regex, datetime_end_regex)
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heartrate_summary_data = heartrate_summary_data[heartrate_summary_data["local_segment"].str.match(segment_regex)]
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if not heartrate_summary_data.empty:
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heartrate_summary_features = extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features_to_compute)
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# extract features from intraday data
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heartrate_intraday_features = extractHRFeaturesFromIntradayData(heartrate_intraday_data, intraday_features_to_compute, day_segment, filter_data_by_segment)
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if not heartrate_summary_data.empty and day_segment == "daily" and summary_features_to_compute != []:
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# filter by segment and skipping any non-daily segment
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heartrate_summary_data = filter_data_by_segment(heartrate_summary_data, "daily")
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datetime_start_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 00:00:00"
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datetime_end_regex = "[0-9]{4}[\\-|\\/][0-9]{2}[\\-|\\/][0-9]{2} 23:59:59"
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segment_regex = "daily#{},{}".format(datetime_start_regex, datetime_end_regex)
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heartrate_summary_data = heartrate_summary_data[heartrate_summary_data["local_segment"].str.match(segment_regex)]
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# extract daily features from summary data
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heartrate_summary_features = extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features_to_compute)
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# merge summary features and intraday features
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heartrate_features = heartrate_intraday_features.merge(heartrate_summary_features, on=["local_segment"], how="outer")
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else:
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heartrate_features = heartrate_intraday_features
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# merge summary features and intraday features
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heartrate_features = heartrate_intraday_features.merge(heartrate_summary_features, on=["local_segment"], how="outer")
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return heartrate_features
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