70 lines
4.9 KiB
Python
70 lines
4.9 KiB
Python
import pandas as pd
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import itertools
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def getEpisodeDurationFeatures(screen_data, time_segment, episode, features, reference_hour_first_use):
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screen_data_episode = screen_data[screen_data["episode"] == episode]
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duration_helper = pd.DataFrame()
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if "countepisode" in features:
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duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].count().rename(columns = {"duration": "countepisode" + episode})], axis = 1)
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if "sumduration" in features:
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duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].sum().rename(columns = {"duration": "sumduration" + episode})], axis = 1)
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if "maxduration" in features:
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duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].max().rename(columns = {"duration": "maxduration" + episode})], axis = 1)
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if "minduration" in features:
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duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].min().rename(columns = {"duration": "minduration" + episode})], axis = 1)
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if "avgduration" in features:
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duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].mean().rename(columns = {"duration":"avgduration" + episode})], axis = 1)
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if "stdduration" in features:
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duration_helper = pd.concat([duration_helper, screen_data_episode.groupby(["local_segment"])[["duration"]].std().rename(columns = {"duration":"stdduration" + episode})], axis = 1)
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if "firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) in features:
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screen_data_episode_after_hour = screen_data_episode.copy()
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screen_data_episode_after_hour["hour"] = pd.to_datetime(screen_data_episode["local_start_date_time"]).dt.hour
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screen_data_episode_after_hour = screen_data_episode_after_hour[screen_data_episode_after_hour["hour"] >= reference_hour_first_use]
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duration_helper = pd.concat([duration_helper, pd.DataFrame(screen_data_episode_after_hour.groupby(["local_segment"])[["local_start_date_time"]].min().local_start_date_time.apply(lambda x: (x.to_pydatetime().hour - reference_hour_first_use) * 60 + x.to_pydatetime().minute + (x.to_pydatetime().second / 60))).rename(columns = {"local_start_date_time":"firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) + episode})], axis = 1)
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return duration_helper
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def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_segment, *args, **kwargs):
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screen_data = pd.read_csv(sensor_data_files["sensor_episodes"])
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reference_hour_first_use = provider["REFERENCE_HOUR_FIRST_USE"]
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requested_features_episodes = provider["FEATURES"]
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requested_episode_types = provider["EPISODE_TYPES"]
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ignore_episodes_shorter_than = provider["IGNORE_EPISODES_SHORTER_THAN"]
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ignore_episodes_longer_than = provider["IGNORE_EPISODES_LONGER_THAN"]
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# name of the features this function can compute
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base_features_episodes = ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"]
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base_episode_type = ["unlock"]
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# the subset of requested features this function can compute
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features_episodes_to_compute = list(set(requested_features_episodes) & set(base_features_episodes))
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episode_type_to_compute = list(set(requested_episode_types) & set(base_episode_type))
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features_episodes_to_compute = ["firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) if feature_name == "firstuseafter" else feature_name for feature_name in features_episodes_to_compute]
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features_to_compute = ["".join(feature) for feature in itertools.product(features_episodes_to_compute, episode_type_to_compute)]
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screen_features = pd.DataFrame(columns=["local_segment"] + features_to_compute)
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if not screen_data.empty:
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screen_data = filter_data_by_segment(screen_data, time_segment)
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if not screen_data.empty:
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if ignore_episodes_shorter_than > 0:
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screen_data = screen_data.query('@ignore_episodes_shorter_than <= duration')
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if ignore_episodes_longer_than > 0:
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screen_data = screen_data.query('duration <= @ignore_episodes_longer_than')
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if not screen_data.empty:
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screen_features = pd.DataFrame()
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for episode in episode_type_to_compute:
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screen_features = pd.concat([screen_features, getEpisodeDurationFeatures(screen_data, time_segment, episode, features_episodes_to_compute, reference_hour_first_use)], axis=1)
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if not screen_features.empty:
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screen_features.fillna(value={feature_name: 0 for feature_name in screen_features.columns if not feature_name.startswith(("stdduration", "firstuseafter"))}, inplace=True)
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screen_features = screen_features.reset_index()
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return screen_features
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