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

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import pandas as pd
from scipy.stats import entropy
def statsFeatures(temperature_data, features, temperature_features):
col_name = "temperature"
if "sumtemp" in features:
temperature_features["sumtemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].sum()
if "maxtemp" in features:
temperature_features["maxtemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].max()
if "mintemp" in features:
temperature_features["mintemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].min()
if "avgtemp" in features:
temperature_features["avgtemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].mean()
if "mediantemp" in features:
temperature_features["mediantemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].median()
if "modetemp" in features:
temperature_features["modetemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].agg(lambda x: pd.Series.mode(x)[0])
if "stdtemp" in features:
temperature_features["stdtemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].std()
if "diffmaxmodetemp" in features:
temperature_features["diffmaxmodetemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].max() - \
temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].agg(lambda x: pd.Series.mode(x)[0])
if "diffminmodetemp" in features:
temperature_features["diffminmodetemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].agg(lambda x: pd.Series.mode(x)[0]) - \
temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].min()
if "entropytemp" in features:
temperature_features["entropytemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
col_name].agg(entropy)
return temperature_features
def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment):
temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
if not temperature_intraday_data.empty:
temperature_intraday_data = filter_data_by_segment(temperature_intraday_data, time_segment)
if not temperature_intraday_data.empty:
temperature_intraday_features = pd.DataFrame()
# get stats of temperature
temperature_intraday_features = statsFeatures(temperature_intraday_data, features, temperature_intraday_features)
temperature_intraday_features.reset_index(inplace=True)
return temperature_intraday_features
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def dbdp_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
temperature_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
base_intraday_features_names = ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp",
"diffminmodetemp", "entropytemp"]
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# the subset of requested features this function can compute
intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
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# extract features from intraday data
temperature_intraday_features = extractTempFeaturesFromIntradayData(temperature_intraday_data,
intraday_features_to_compute, time_segment,
filter_data_by_segment)
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return temperature_intraday_features