Refactor screen features
Meng Li
parent
6562754777
commit
2d0ef98f26
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@ -0,0 +1,63 @@
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import pandas as pd
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import itertools
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from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
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def getEpisodeDurationFeatures(screen_data, day_segment, episode, features, phone_sensed_bins, bin_size, 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[["time_diff"]].groupby(["local_start_date"]).count().rename(columns = {"time_diff": "screen_" + day_segment + "_countepisode" + episode})], axis = 1)
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if "episodepersensedminutes" in features:
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for date, row in screen_data_episode[["time_diff"]].groupby(["local_start_date"]).count().iterrows():
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sensed_minutes = phone_sensed_bins.loc[date, :].sum() * bin_size
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episode_per_sensedminutes = row["time_diff"] / (1 if sensed_minutes == 0 else sensed_minutes)
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duration_helper.loc[date, "screen_" + day_segment + "_episodepersensedminutes" + episode] = episode_per_sensedminutes
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if "sumduration" in features:
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duration_helper = pd.concat([duration_helper, screen_data_episode[["time_diff"]].groupby(["local_start_date"]).sum().rename(columns = {"time_diff": "screen_" + day_segment + "_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[["time_diff"]].groupby(["local_start_date"]).max().rename(columns = {"time_diff": "screen_" + day_segment + "_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[["time_diff"]].groupby(["local_start_date"]).min().rename(columns = {"time_diff": "screen_" + day_segment + "_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[["time_diff"]].groupby(["local_start_date"]).mean().rename(columns = {"time_diff":"screen_" + day_segment + "_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[["time_diff"]].groupby(["local_start_date"]).std().rename(columns = {"time_diff":"screen_" + day_segment + "_stdduration" + episode})], axis = 1)
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if "firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) in features:
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duration_helper = pd.concat([duration_helper, pd.DataFrame(screen_data_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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def base_screen_features(screen_data, phone_sensed_bins, day_segment, params):
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reference_hour_first_use = params["reference_hour_first_use"]
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bin_size = params["bin_size"]
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requested_features_deltas = params["requested_features_deltas"]
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requested_episode_types = params["requested_episode_types"]
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# name of the features this function can compute
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base_features_deltas = ["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_deltas_to_compute = list(set(requested_features_deltas) & set(base_features_deltas))
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episode_type_to_compute = list(set(requested_episode_types) & set(base_episode_type))
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features_deltas_to_compute = ["firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) if feature_name == "firstuseafter" else feature_name for feature_name in features_deltas_to_compute]
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features_to_compute = ["".join(feature) for feature in itertools.product(features_deltas_to_compute, episode_type_to_compute)]
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screen_features = pd.DataFrame(columns=["local_date"]+["screen_" + day_segment + "_" + x for x in features_to_compute])
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if not screen_data.empty:
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# preprocess day_segment and episodes
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screen_data = splitOvernightEpisodes(screen_data, [], ["episode"])
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if (not screen_data.empty) and (day_segment != "daily"):
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screen_data = splitMultiSegmentEpisodes(screen_data, day_segment, [])
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screen_data.set_index(["local_start_date"],inplace=True)
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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, day_segment, episode, features_deltas_to_compute, phone_sensed_bins, bin_size, reference_hour_first_use)], axis=1)
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if not screen_features.empty:
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screen_features = screen_features.rename_axis("local_date").reset_index()
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return screen_features
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@ -1,63 +1,24 @@
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import pandas as pd
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import numpy as np
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import datetime
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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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from screen.screen_base import base_screen_features
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def getEpisodeDurationFeatures(screen_deltas, episode, features, 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 features:
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duration_helper = pd.concat([duration_helper, screen_deltas_episode[["time_diff"]].groupby(["local_start_date"]).count().rename(columns = {"time_diff": "screen_" + day_segment + "_countepisode" + episode})], axis = 1)
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if "episodepersensedminutes" in features:
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for date, row in screen_deltas_episode[["time_diff"]].groupby(["local_start_date"]).count().iterrows():
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sensed_minutes = phone_sensed_bins.loc[date, :].sum() * bin_size
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episode_per_sensedminutes = row["time_diff"] / (1 if sensed_minutes == 0 else sensed_minutes)
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duration_helper.loc[date, "screen_" + day_segment + "_episodepersensedminutes" + episode] = episode_per_sensedminutes
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if "sumduration" in features:
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duration_helper = pd.concat([duration_helper, screen_deltas_episode[["time_diff"]].groupby(["local_start_date"]).sum().rename(columns = {"time_diff": "screen_" + day_segment + "_sumduration" + episode})], axis = 1)
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if "maxduration" in features:
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duration_helper = pd.concat([duration_helper, screen_deltas_episode[["time_diff"]].groupby(["local_start_date"]).max().rename(columns = {"time_diff": "screen_" + day_segment + "_maxduration" + episode})], axis = 1)
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if "minduration" in features:
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duration_helper = pd.concat([duration_helper, screen_deltas_episode[["time_diff"]].groupby(["local_start_date"]).min().rename(columns = {"time_diff": "screen_" + day_segment + "_minduration" + episode})], axis = 1)
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if "avgduration" in features:
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duration_helper = pd.concat([duration_helper, screen_deltas_episode[["time_diff"]].groupby(["local_start_date"]).mean().rename(columns = {"time_diff":"screen_" + day_segment + "_avgduration" + episode})], axis = 1)
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if "stdduration" in features:
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duration_helper = pd.concat([duration_helper, screen_deltas_episode[["time_diff"]].groupby(["local_start_date"]).std().rename(columns = {"time_diff":"screen_" + day_segment + "_stdduration" + episode})], axis = 1)
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if "firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) in features:
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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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screen_deltas = pd.read_csv(snakemake.input["screen_deltas"], parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"])
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screen_data = pd.read_csv(snakemake.input["screen_deltas"], parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"])
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phone_sensed_bins = pd.read_csv(snakemake.input["phone_sensed_bins"], parse_dates=["local_date"], index_col="local_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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features_deltas = snakemake.params["features_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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screen_features = pd.DataFrame(columns=["local_date"])
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features_deltas = ["firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) if feature_name == "firstuseafter" else feature_name for feature_name in features_deltas]
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params = {}
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params["reference_hour_first_use"] = snakemake.params["reference_hour_first_use"]
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params["bin_size"] = snakemake.params["bin_size"]
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params["requested_features_deltas"] = snakemake.params["features_deltas"]
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params["requested_episode_types"] = snakemake.params["episode_types"]
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features_deltas_name = ["".join(feature) for feature in itertools.product(features_deltas, episode_types)]
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requested_features_deltas = ["firstuseafter" + "{0:0=2d}".format(params["reference_hour_first_use"]) if feature_name == "firstuseafter" else feature_name for feature_name in params["requested_features_deltas"]]
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requested_features = ["".join(feature) for feature in itertools.product(requested_features_deltas, params["requested_episode_types"])]
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screen_features = pd.DataFrame(columns=["local_date"]+["screen_" + day_segment + "_" + x for x in features_deltas_name])
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if not screen_deltas.empty:
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# preprocess day_segment and episodes
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screen_deltas = splitOvernightEpisodes(screen_deltas, [], ["episode"])
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if (not screen_deltas.empty) and (day_segment != "daily"):
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screen_deltas = splitMultiSegmentEpisodes(screen_deltas, day_segment, [])
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screen_deltas.set_index(["local_start_date"],inplace=True)
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screen_features = screen_features.merge(base_screen_features(screen_data, phone_sensed_bins, day_segment, params), on="local_date", how="outer")
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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, features_deltas, phone_sensed_bins, bin_size, reference_hour_first_use)], axis=1)
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assert len(requested_features) + 1 == screen_features.shape[1], "The number of features in the output dataframe (=" + str(screen_features.shape[1]) + ") does not match the expected value (=" + str(len(requested_features)) + " + 1). Verify your screen feature extraction functions"
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if not screen_features.empty:
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screen_features = screen_features.rename_axis("local_date").reset_index()
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screen_features.to_csv(snakemake.output[0], index=False)
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screen_features.to_csv(snakemake.output[0], index=False)
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