81 lines
5.3 KiB
Python
81 lines
5.3 KiB
Python
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["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["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["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["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["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["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["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["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["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["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 extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, time_segment, filter_data_by_segment):
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heartrate_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
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if not heartrate_intraday_data.empty:
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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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heartrate_intraday_data = filter_data_by_segment(heartrate_intraday_data, time_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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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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heartrate_zone = heartrate_intraday_data[heartrate_intraday_data["heartrate_zone"] == feature_name[9:-4]]
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heartrate_intraday_features[feature_name] = heartrate_zone.groupby(["local_segment"])["device_id"].count() / num_rows_per_minute
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heartrate_intraday_features.fillna(value={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 rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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heartrate_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
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requested_intraday_features = provider["FEATURES"]
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# name of the features this function can compute
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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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intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
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# extract features from intraday data
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heartrate_intraday_features = extractHRFeaturesFromIntradayData(heartrate_intraday_data, intraday_features_to_compute, time_segment, filter_data_by_segment)
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return heartrate_intraday_features
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