43 lines
2.3 KiB
Python
43 lines
2.3 KiB
Python
import pandas as pd
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def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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ar_episodes = pd.read_csv(sensor_data_files["sensor_episodes"])
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activity_classes = provider["ACTIVITY_CLASSES"]
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# name of the features this function can compute
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base_features_names = ["count","mostcommonactivity","countuniqueactivities","durationstationary","durationmobile","durationvehicle"]
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# the subset of requested features this function can compute
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requested_features = provider["FEATURES"]
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features_to_compute = list(set(requested_features) & set(base_features_names))
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ar_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
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if not ar_episodes.empty:
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ar_episodes = filter_data_by_segment(ar_episodes, time_segment)
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if not ar_episodes.empty:
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ar_features = pd.DataFrame()
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if "count" in features_to_compute:
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ar_features["count"] = ar_episodes.groupby(["local_segment"]).count()["episode_id"]
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if "mostcommonactivity" in features_to_compute:
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ar_features["mostcommonactivity"] = ar_episodes.groupby(["local_segment"])["activity_type"].agg(lambda x: pd.Series.mode(x)[0])
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if "countuniqueactivities" in features_to_compute:
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ar_features["countuniqueactivities"] = ar_episodes.groupby(["local_segment"])["activity_type"].nunique()
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# duration features
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for column, activity_labels in activity_classes.items():
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if "duration" + column.lower() in features_to_compute:
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filtered_data = ar_episodes[ar_episodes["activity_name"].isin(pd.Series(activity_labels))]
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if not filtered_data.empty:
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ar_features["duration" + column.lower()] = ar_episodes[ar_episodes["activity_name"].isin(pd.Series(activity_labels))].groupby(["local_segment"])["duration"].sum()
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else:
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ar_features["duration" + column.lower()] = 0
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ar_features.index.names = ["local_segment"]
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ar_features = ar_features.reset_index()
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ar_features.fillna(value={"count": 0, "countuniqueactivities": 0, "durationstationary": 0, "durationmobile": 0, "durationvehicle": 0}, inplace=True)
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return ar_features
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