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Update Fitbit HR feature name

pull/103/head
Meng Li 2020-11-20 11:31:16 -05:00
parent d3241c79f1
commit 10384204a1
2 changed files with 46 additions and 46 deletions
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48
src/features/fitbit_heartrate_intraday/rapids/main.py
View File

@ -18,26 +18,26 @@ def statsFeatures(heartrate_data, features, features_type, heartrate_features):
else: else:
raise ValueError("features_type can only be one of ['hr', 'restinghr', 'caloriesoutofrange', 'caloriesfatburn', 'caloriescardio', 'caloriespeak'].") raise ValueError("features_type can only be one of ['hr', 'restinghr', 'caloriesoutofrange', 'caloriesfatburn', 'caloriescardio', 'caloriespeak'].")
if "sum" + features_type in features: if "intradaysum" + features_type in features:
heartrate_features["heartrate_rapids_sum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum() heartrate_features["heartrate_rapids_intradaysum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum()
if "max" + features_type in features: if "intradaymax" + features_type in features:
heartrate_features["heartrate_rapids_max" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() heartrate_features["heartrate_rapids_intradaymax" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max()
if "min" + features_type in features: if "intradaymin" + features_type in features:
heartrate_features["heartrate_rapids_min" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() heartrate_features["heartrate_rapids_intradaymin" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min()
if "avg" + features_type in features: if "intradayavg" + features_type in features:
heartrate_features["heartrate_rapids_avg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean() heartrate_features["heartrate_rapids_intradayavg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean()
if "median" + features_type in features: if "intradaymedian" + features_type in features:
heartrate_features["heartrate_rapids_median" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median() heartrate_features["heartrate_rapids_intradaymedian" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median()
if "mode" + features_type in features: if "intradaymode" + features_type in features:
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]) heartrate_features["heartrate_rapids_intradaymode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0])
if "std" + features_type in features: if "intradaystd" + features_type in features:
heartrate_features["heartrate_rapids_std" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std() heartrate_features["heartrate_rapids_intradaystd" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std()
if "diffmaxmode" + features_type in features: if "intradaydiffmaxmode" + features_type in features:
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]) heartrate_features["heartrate_rapids_intradaydiffmaxmode" + 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])
if "diffminmode" + features_type in features: if "intradaydiffminmode" + features_type in features:
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() heartrate_features["heartrate_rapids_intradaydiffminmode" + 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()
if "entropy" + features_type in features: if "intradayentropy" + features_type in features:
heartrate_features["heartrate_rapids_entropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy) heartrate_features["heartrate_rapids_intradayentropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy)
return heartrate_features return heartrate_features
@ -54,8 +54,8 @@ def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, day_seg
heartrate_intraday_features = statsFeatures(heartrate_intraday_data, features, "hr", heartrate_intraday_features) heartrate_intraday_features = statsFeatures(heartrate_intraday_data, features, "hr", heartrate_intraday_features)
# get number of minutes in each heart rate zone # get number of minutes in each heart rate zone
for feature_name in list(set(["minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"]) & set(features)): for feature_name in list(set(["intradayminutesonoutofrangezone", "intradayminutesonfatburnzone", "intradayminutesoncardiozone", "intradayminutesonpeakzone"]) & set(features)):
heartrate_zone = heartrate_intraday_data[heartrate_intraday_data["heartrate_zone"] == feature_name[9:-4]] heartrate_zone = heartrate_intraday_data[heartrate_intraday_data["heartrate_zone"] == feature_name[17:-4]]
heartrate_intraday_features["heartrate_rapids_" + feature_name] = heartrate_zone.groupby(["local_segment"])["device_id"].count() / num_rows_per_minute heartrate_intraday_features["heartrate_rapids_" + feature_name] = heartrate_zone.groupby(["local_segment"])["device_id"].count() / num_rows_per_minute
heartrate_intraday_features.fillna(value={"heartrate_rapids_" + feature_name: 0}, inplace=True) heartrate_intraday_features.fillna(value={"heartrate_rapids_" + feature_name: 0}, inplace=True)
heartrate_intraday_features.reset_index(inplace=True) heartrate_intraday_features.reset_index(inplace=True)
@ -67,9 +67,9 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg
heartrate_intraday_data = pd.read_csv(sensor_data_files["sensor_data"]) heartrate_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
requested_intraday_features = provider["FEATURES"] requested_intraday_features = ["intraday" + x for x in provider["FEATURES"]]
# name of the features this function can compute # name of the features this function can compute
base_intraday_features_names = ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "minutesonoutofrangezone", "minutesonfatburnzone", "minutesoncardiozone", "minutesonpeakzone"] base_intraday_features_names = ["intradaymaxhr", "intradayminhr", "intradayavghr", "intradaymedianhr", "intradaymodehr", "intradaystdhr", "intradaydiffmaxmodehr", "intradaydiffminmodehr", "intradayentropyhr", "intradayminutesonoutofrangezone", "intradayminutesonfatburnzone", "intradayminutesoncardiozone", "intradayminutesonpeakzone"]
# the subset of requested features this function can compute # the subset of requested features this function can compute
intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names)) intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))

44
src/features/fitbit_heartrate_summary/rapids/main.py
View File

@ -18,26 +18,26 @@ def statsFeatures(heartrate_data, features, features_type, heartrate_features):
else: else:
raise ValueError("features_type can only be one of ['hr', 'restinghr', 'caloriesoutofrange', 'caloriesfatburn', 'caloriescardio', 'caloriespeak'].") raise ValueError("features_type can only be one of ['hr', 'restinghr', 'caloriesoutofrange', 'caloriesfatburn', 'caloriescardio', 'caloriespeak'].")
if "sum" + features_type in features: if "summarysum" + features_type in features:
heartrate_features["heartrate_rapids_sum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum() heartrate_features["heartrate_rapids_summarysum" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].sum()
if "max" + features_type in features: if "summarymax" + features_type in features:
heartrate_features["heartrate_rapids_max" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max() heartrate_features["heartrate_rapids_summarymax" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].max()
if "min" + features_type in features: if "summarymin" + features_type in features:
heartrate_features["heartrate_rapids_min" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min() heartrate_features["heartrate_rapids_summarymin" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].min()
if "avg" + features_type in features: if "summaryavg" + features_type in features:
heartrate_features["heartrate_rapids_avg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean() heartrate_features["heartrate_rapids_summaryavg" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].mean()
if "median" + features_type in features: if "summarymedian" + features_type in features:
heartrate_features["heartrate_rapids_median" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median() heartrate_features["heartrate_rapids_summarymedian" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].median()
if "mode" + features_type in features: if "summarymode" + features_type in features:
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]) heartrate_features["heartrate_rapids_summarymode" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(lambda x: pd.Series.mode(x)[0])
if "std" + features_type in features: if "summarystd" + features_type in features:
heartrate_features["heartrate_rapids_std" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std() heartrate_features["heartrate_rapids_summarystd" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].std()
if "diffmaxmode" + features_type in features: if "summarydiffmaxmode" + features_type in features:
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]) heartrate_features["heartrate_rapids_summarydiffmaxmode" + 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])
if "diffminmode" + features_type in features: if "summarydiffminmode" + features_type in features:
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() heartrate_features["heartrate_rapids_summarydiffminmode" + 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()
if "entropy" + features_type in features: if "summaryentropy" + features_type in features:
heartrate_features["heartrate_rapids_entropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy) heartrate_features["heartrate_rapids_summaryentropy" + features_type] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[col_name].agg(entropy)
return heartrate_features return heartrate_features
@ -63,9 +63,9 @@ def rapids_features(sensor_data_files, day_segment, provider, filter_data_by_seg
heartrate_summary_data = pd.read_csv(sensor_data_files["sensor_data"]) heartrate_summary_data = pd.read_csv(sensor_data_files["sensor_data"])
requested_summary_features = provider["FEATURES"] requested_summary_features = ["summary" + x for x in provider["FEATURES"]]
# name of the features this function can compute # name of the features this function can compute
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"] base_summary_features_names = ["summarymaxrestinghr", "summaryminrestinghr", "summaryavgrestinghr", "summarymedianrestinghr", "summarymoderestinghr", "summarystdrestinghr", "summarydiffmaxmoderestinghr", "summarydiffminmoderestinghr", "summaryentropyrestinghr", "summarysumcaloriesoutofrange", "summarymaxcaloriesoutofrange", "summarymincaloriesoutofrange", "summaryavgcaloriesoutofrange", "summarymediancaloriesoutofrange", "summarystdcaloriesoutofrange", "summaryentropycaloriesoutofrange", "summarysumcaloriesfatburn", "summarymaxcaloriesfatburn", "summarymincaloriesfatburn", "summaryavgcaloriesfatburn", "summarymediancaloriesfatburn", "summarystdcaloriesfatburn", "summaryentropycaloriesfatburn", "summarysumcaloriescardio", "summarymaxcaloriescardio", "summarymincaloriescardio", "summaryavgcaloriescardio", "summarymediancaloriescardio", "summarystdcaloriescardio", "summaryentropycaloriescardio", "summarysumcaloriespeak", "summarymaxcaloriespeak", "summarymincaloriespeak", "summaryavgcaloriespeak", "summarymediancaloriespeak", "summarystdcaloriespeak", "summaryentropycaloriespeak"]
# the subset of requested features this function can compute # the subset of requested features this function can compute
summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features_names)) summary_features_to_compute = list(set(requested_summary_features) & set(base_summary_features_names))