rapids/src/features/empatica_heartrate/dbdp/main.py

77 lines
4.1 KiB
Python
Raw Normal View History

2020-12-15 02:30:34 +01:00
import pandas as pd
from scipy.stats import entropy
2020-12-15 02:30:34 +01:00
def statsFeatures(heartrate_data, features, heartrate_features):
col_name = "heartrate"
if "sumhr" in features:
heartrate_features["sumhr"] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].sum()
if "maxhr" in features:
heartrate_features["maxhr"] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].max()
if "minhr" in features:
heartrate_features["minhr"] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].min()
if "avghr" in features:
heartrate_features["avghr"] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].mean()
if "medianhr" in features:
heartrate_features["medianhr"] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].median()
if "modehr" in features:
heartrate_features["modehr"] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].agg(lambda x: pd.Series.mode(x)[0])
if "stdhr" in features:
heartrate_features["stdhr"] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].std()
if "diffmaxmodehr" in features:
heartrate_features["diffmaxmodehr"] = 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 "diffminmodehr" in features:
heartrate_features["diffminmodehr"] = 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 "entropyhr" in features:
heartrate_features["entropyhr"] = heartrate_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].agg(entropy)
return heartrate_features
2020-12-15 02:30:34 +01:00
def extractHRFeaturesFromIntradayData(heartrate_intraday_data, features, time_segment, filter_data_by_segment):
heartrate_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
if not heartrate_intraday_data.empty:
heartrate_intraday_data = filter_data_by_segment(heartrate_intraday_data, time_segment)
if not heartrate_intraday_data.empty:
heartrate_intraday_features = pd.DataFrame()
# get stats of heartrate
heartrate_intraday_features = statsFeatures(heartrate_intraday_data, features, heartrate_intraday_features)
heartrate_intraday_features.reset_index(inplace=True)
return heartrate_intraday_features
2020-12-15 02:30:34 +01:00
def dbdp_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
heartrate_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
requested_intraday_features = provider["FEATURES"]
# name of the features this function can compute
base_intraday_features_names = ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr",
"diffminmodehr", "entropyhr"]
# the subset of requested features this function can compute
intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
# extract features from intraday data
heartrate_intraday_features = extractHRFeaturesFromIntradayData(heartrate_intraday_data,
intraday_features_to_compute, time_segment,
filter_data_by_segment)
return heartrate_intraday_features