89 lines
6.4 KiB
Python
89 lines
6.4 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: None if len(pd.Series.mode(x)) == 0 else 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: None if len(pd.Series.mode(x)) == 0 else 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: None if len(pd.Series.mode(x)) == 0 else 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 extractHRFeaturesFromSummaryData(heartrate_summary_data, summary_features):
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heartrate_summary_features = pd.DataFrame()
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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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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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def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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heartrate_summary_data = pd.read_csv(sensor_data_files["sensor_data"])
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requested_summary_features = provider["FEATURES"]
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# name of the features this function can compute
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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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# 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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# extract features from summary data
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heartrate_summary_features = pd.DataFrame(columns=["local_segment"] + 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, time_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(time_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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return heartrate_summary_features
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