Add firstuseafterTIME feature for screen sensor
parent
85f4ec2ab6
commit
c8b7275084
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@ -87,7 +87,8 @@ BATTERY:
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SCREEN:
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DAY_SEGMENTS: *day_segments
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METRICS_DELTAS: ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
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REFERENCE_HOUR_FIRST_USE: 0
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METRICS_DELTAS: ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"]
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EPISODE_TYPES: ["unlock"]
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LIGHT:
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@ -101,6 +101,7 @@ rule screen_metrics:
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phone_sensed_bins = "data/interim/{pid}/phone_sensed_bins.csv"
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params:
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day_segment = "{day_segment}",
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reference_hour_first_use = config["SCREEN"]["REFERENCE_HOUR_FIRST_USE"],
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metrics_deltas = config["SCREEN"]["METRICS_DELTAS"],
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episode_types = config["SCREEN"]["EPISODE_TYPES"],
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bin_size = config["PHONE_VALID_SENSED_DAYS"]["BIN_SIZE"]
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@ -5,7 +5,7 @@ import itertools
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from datetime import datetime, timedelta, time
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from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
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def getEpisodeDurationFeatures(screen_deltas, episode, metrics, phone_sensed_bins, bin_size):
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def getEpisodeDurationFeatures(screen_deltas, episode, metrics, phone_sensed_bins, bin_size, reference_hour_first_use):
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screen_deltas_episode = screen_deltas[screen_deltas["episode"] == episode]
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duration_helper = pd.DataFrame()
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if "countepisode" in metrics:
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@ -25,8 +25,8 @@ def getEpisodeDurationFeatures(screen_deltas, episode, metrics, phone_sensed_bin
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duration_helper = pd.concat([duration_helper, screen_deltas_episode.groupby(["local_start_date"]).mean()[["time_diff"]].rename(columns = {"time_diff":"screen_" + day_segment + "_avgduration" + episode})], axis = 1)
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if "stdduration" in metrics:
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duration_helper = pd.concat([duration_helper, screen_deltas_episode.groupby(["local_start_date"]).std()[["time_diff"]].rename(columns = {"time_diff":"screen_" + day_segment + "_stdduration" + episode})], axis = 1)
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duration_helper = duration_helper.fillna(0)
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if "firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) in metrics:
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duration_helper = pd.concat([duration_helper, pd.DataFrame(screen_deltas_episode.groupby(["local_start_date"]).first()[["local_start_date_time"]].local_start_date_time.apply(lambda x: (x.to_pydatetime().hour - reference_hour_first_use) * 3600 + x.to_pydatetime().minute * 60 + x.to_pydatetime().second)).rename(columns = {"local_start_date_time":"screen_" + day_segment + "_firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) + episode})], axis = 1)
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return duration_helper
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@ -35,10 +35,13 @@ phone_sensed_bins = pd.read_csv(snakemake.input["phone_sensed_bins"], parse_date
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phone_sensed_bins[phone_sensed_bins > 0] = 1
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day_segment = snakemake.params["day_segment"]
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reference_hour_first_use = snakemake.params["reference_hour_first_use"]
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metrics_deltas = snakemake.params["metrics_deltas"]
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episode_types = snakemake.params["episode_types"]
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bin_size = snakemake.params["bin_size"]
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metrics_deltas = ["firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) if feature_name == "firstuseafter" else feature_name for feature_name in metrics_deltas]
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metrics_deltas_name = ["".join(metric) for metric in itertools.product(metrics_deltas, episode_types)]
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screen_features = pd.DataFrame(columns=["local_date"]+["screen_" + day_segment + "_" + x for x in metrics_deltas_name])
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@ -52,7 +55,7 @@ if not screen_deltas.empty:
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if not screen_deltas.empty:
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screen_features = pd.DataFrame()
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for episode in episode_types:
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screen_features = pd.concat([screen_features, getEpisodeDurationFeatures(screen_deltas, episode, metrics_deltas, phone_sensed_bins, bin_size)], axis=1)
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screen_features = pd.concat([screen_features, getEpisodeDurationFeatures(screen_deltas, episode, metrics_deltas, phone_sensed_bins, bin_size, reference_hour_first_use)], axis=1)
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screen_features = screen_features.rename_axis("local_date").reset_index()
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