62 lines
3.3 KiB
Python
62 lines
3.3 KiB
Python
import pandas as pd
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import numpy as np
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import scipy.stats as stats
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from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
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def base_ar_features(ar_data, ar_deltas, day_segment, requested_features):
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# name of the features this function can compute
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base_features_names = ["count","mostcommonactivity","countuniqueactivities","activitychangecount","sumstationary","summobile","sumvehicle"]
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# the subset of requested features this function can compute
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features_to_compute = list(set(requested_features) & set(base_features_names))
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if ar_data.empty:
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ar_features = pd.DataFrame(columns = ["local_date"] + ["ar_" + day_segment + "_" + x for x in features_to_compute])
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else:
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ar_features = pd.DataFrame()
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ar_deltas = splitOvernightEpisodes(ar_deltas, [],["activity"])
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if day_segment != "daily":
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ar_deltas = splitMultiSegmentEpisodes(ar_deltas, day_segment, [])
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ar_data.local_date_time = pd.to_datetime(ar_data.local_date_time)
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resampledData = ar_data.set_index(ar_data.local_date_time)
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resampledData.drop(columns=["local_date_time"], inplace=True)
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if(day_segment!="daily"):
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resampledData = resampledData.loc[resampledData["local_day_segment"] == day_segment]
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if resampledData.empty:
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ar_features = pd.DataFrame(columns = ["ar_" + day_segment + "_" + x for x in features_to_compute])
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else:
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#Finding the count of samples of the day
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if "count" in features_to_compute:
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ar_features["ar_" + day_segment + "_count"] = resampledData["activity_type"].resample("D").count()
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#Finding most common activity of the day
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if "mostcommonactivity" in features_to_compute:
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ar_features["ar_" + day_segment + "_mostcommonactivity"] = resampledData["activity_type"].resample("D").apply(lambda x: stats.mode(x)[0] if len(stats.mode(x)[0]) != 0 else None)
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#finding different number of activities during a day
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if "countuniqueactivities" in features_to_compute:
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ar_features["ar_" + day_segment + "_countuniqueactivities"] = resampledData["activity_type"].resample("D").nunique()
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#finding Number of times activity changed
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if "activitychangecount" in features_to_compute:
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resampledData["activity_type_shift"] = resampledData["activity_type"].shift().fillna(resampledData["activity_type"].head(1))
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resampledData["different_activity"] = np.where(resampledData["activity_type"]!=resampledData["activity_type_shift"],1,0)
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ar_features["ar_" + day_segment + "_activitychangecount"] = resampledData["different_activity"].resample("D").sum()
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deltas_features = {"sumstationary":["still","tilting"],
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"summobile":["on_foot","walking","running","on_bicycle"],
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"sumvehicle":["in_vehicle"]}
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for column, activity_labels in deltas_features.items():
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if column in features_to_compute:
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ar_features["ar_" + day_segment + "_" + column] = ar_deltas[ar_deltas["activity"].isin(pd.Series(activity_labels))].groupby(["local_start_date"])["time_diff"].sum()
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ar_features.index.names = ["local_date"]
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ar_features = ar_features.reset_index()
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return ar_features
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