2020-12-15 02:30:34 +01:00
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import pandas as pd
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2021-02-12 02:56:27 +01:00
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from scipy.stats import entropy
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def statsFeatures(temperature_data, features, temperature_features):
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col_name = "temperature"
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if "sumtemp" in features:
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temperature_features["sumtemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].sum()
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if "maxtemp" in features:
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temperature_features["maxtemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].max()
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if "mintemp" in features:
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temperature_features["mintemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].min()
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if "avgtemp" in features:
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temperature_features["avgtemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].mean()
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if "mediantemp" in features:
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temperature_features["mediantemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].median()
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if "modetemp" in features:
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temperature_features["modetemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].agg(lambda x: pd.Series.mode(x)[0])
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if "stdtemp" in features:
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temperature_features["stdtemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].std()
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if "diffmaxmodetemp" in features:
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temperature_features["diffmaxmodetemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].max() - \
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temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].agg(lambda x: pd.Series.mode(x)[0])
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if "diffminmodetemp" in features:
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temperature_features["diffminmodetemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].agg(lambda x: pd.Series.mode(x)[0]) - \
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temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].min()
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if "entropytemp" in features:
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temperature_features["entropytemp"] = temperature_data[["local_segment", col_name]].groupby(["local_segment"])[
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col_name].agg(entropy)
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return temperature_features
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def extractTempFeaturesFromIntradayData(temperature_intraday_data, features, time_segment, filter_data_by_segment):
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temperature_intraday_features = pd.DataFrame(columns=["local_segment"] + features)
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if not temperature_intraday_data.empty:
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temperature_intraday_data = filter_data_by_segment(temperature_intraday_data, time_segment)
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if not temperature_intraday_data.empty:
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temperature_intraday_features = pd.DataFrame()
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# get stats of temperature
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temperature_intraday_features = statsFeatures(temperature_intraday_data, features, temperature_intraday_features)
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2021-02-16 01:00:04 +01:00
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temperature_intraday_features.reset_index(inplace=True)
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2021-02-12 02:56:27 +01:00
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return temperature_intraday_features
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2020-12-15 02:30:34 +01:00
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def dbdp_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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2021-02-12 02:56:27 +01:00
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temperature_intraday_data = pd.read_csv(sensor_data_files["sensor_data"])
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2020-12-15 02:30:34 +01:00
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2021-02-12 02:56:27 +01:00
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requested_intraday_features = provider["FEATURES"]
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2020-12-15 02:30:34 +01:00
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# name of the features this function can compute
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2021-02-12 02:56:27 +01:00
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base_intraday_features_names = ["maxtemp", "mintemp", "avgtemp", "mediantemp", "modetemp", "stdtemp", "diffmaxmodetemp",
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"diffminmodetemp", "entropytemp"]
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2020-12-15 02:30:34 +01:00
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# the subset of requested features this function can compute
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2021-02-12 02:56:27 +01:00
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intraday_features_to_compute = list(set(requested_intraday_features) & set(base_intraday_features_names))
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2020-12-15 02:30:34 +01:00
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2021-02-12 02:56:27 +01:00
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# extract features from intraday data
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temperature_intraday_features = extractTempFeaturesFromIntradayData(temperature_intraday_data,
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intraday_features_to_compute, time_segment,
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filter_data_by_segment)
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2020-12-15 02:30:34 +01:00
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2021-02-12 02:56:27 +01:00
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return temperature_intraday_features
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