Merge branch 'master' of https://github.com/carissalow/rapids
JulioV
commit
12ceb87090
|
|
@ -88,9 +88,9 @@ BATTERY:
|
|||
|
||||
SCREEN:
|
||||
DAY_SEGMENTS: *day_segments
|
||||
METRICS_EVENTS: ["counton", "countunlock", "unlocksperminute"]
|
||||
METRICS_DELTAS: ["sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
|
||||
EPISODES: ["unlock"]
|
||||
REFERENCE_HOUR_FIRST_USE: 0
|
||||
METRICS_DELTAS: ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"]
|
||||
EPISODE_TYPES: ["unlock"]
|
||||
|
||||
LIGHT:
|
||||
DAY_SEGMENTS: *day_segments
|
||||
|
|
|
|||
|
|
@ -98,14 +98,13 @@ rule battery_metrics:
|
|||
|
||||
rule screen_metrics:
|
||||
input:
|
||||
screen_events = "data/raw/{pid}/screen_with_datetime.csv",
|
||||
screen_deltas = "data/processed/{pid}/screen_deltas.csv",
|
||||
phone_sensed_bins = "data/interim/{pid}/phone_sensed_bins.csv"
|
||||
params:
|
||||
day_segment = "{day_segment}",
|
||||
metrics_events = config["SCREEN"]["METRICS_EVENTS"],
|
||||
reference_hour_first_use = config["SCREEN"]["REFERENCE_HOUR_FIRST_USE"],
|
||||
metrics_deltas = config["SCREEN"]["METRICS_DELTAS"],
|
||||
episodes = config["SCREEN"]["EPISODES"],
|
||||
episode_types = config["SCREEN"]["EPISODE_TYPES"],
|
||||
bin_size = config["PHONE_VALID_SENSED_DAYS"]["BIN_SIZE"]
|
||||
output:
|
||||
"data/processed/{pid}/screen_{day_segment}.csv"
|
||||
|
|
|
|||
|
|
@ -5,9 +5,16 @@ import itertools
|
|||
from datetime import datetime, timedelta, time
|
||||
from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
|
||||
|
||||
def getEpisodeDurationFeatures(screen_deltas, episode, metrics):
|
||||
def getEpisodeDurationFeatures(screen_deltas, episode, metrics, phone_sensed_bins, bin_size, reference_hour_first_use):
|
||||
screen_deltas_episode = screen_deltas[screen_deltas["episode"] == episode]
|
||||
duration_helper = pd.DataFrame()
|
||||
if "countepisode" in metrics:
|
||||
duration_helper = pd.concat([duration_helper, screen_deltas_episode.groupby(["local_start_date"]).count()[["time_diff"]].rename(columns = {"time_diff": "screen_" + day_segment + "_countepisode" + episode})], axis = 1)
|
||||
if "episodepersensedminutes" in metrics:
|
||||
for date, row in screen_deltas_episode.groupby(["local_start_date"]).count()[["time_diff"]].iterrows():
|
||||
sensed_minutes = phone_sensed_bins.loc[date, :].sum() * bin_size
|
||||
episode_per_sensedminutes = row["time_diff"] / (1 if sensed_minutes == 0 else sensed_minutes)
|
||||
duration_helper.loc[date, "screen_" + day_segment + "_episodepersensedminutes" + episode] = episode_per_sensedminutes
|
||||
if "sumduration" in metrics:
|
||||
duration_helper = pd.concat([duration_helper, screen_deltas_episode.groupby(["local_start_date"]).sum()[["time_diff"]].rename(columns = {"time_diff": "screen_" + day_segment + "_sumduration" + episode})], axis = 1)
|
||||
if "maxduration" in metrics:
|
||||
|
|
@ -18,76 +25,38 @@ def getEpisodeDurationFeatures(screen_deltas, episode, metrics):
|
|||
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)
|
||||
if "stdduration" in metrics:
|
||||
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)
|
||||
|
||||
duration_helper = duration_helper.fillna(0)
|
||||
if "firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) in metrics:
|
||||
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)
|
||||
return duration_helper
|
||||
|
||||
def getEventFeatures(screen_data, metrics_events, phone_sensed_bins, bin_size):
|
||||
if screen_data.empty:
|
||||
return pd.DataFrame(columns=["screen_" + day_segment + "_" + x for x in metrics_events])
|
||||
# get count_helper
|
||||
screen_status = screen_data.groupby(["local_date", "screen_status"]).count()[["timestamp"]].reset_index()
|
||||
count_on = screen_status[screen_status["screen_status"] == 0].set_index("local_date")[["timestamp"]].rename(columns = {"timestamp": "count_on"})
|
||||
count_off = screen_status[screen_status["screen_status"] == 1].set_index("local_date")[["timestamp"]].rename(columns = {"timestamp": "count_off"})
|
||||
count_lock = screen_status[screen_status["screen_status"] == 2].set_index("local_date")[["timestamp"]].rename(columns = {"timestamp": "count_lock"})
|
||||
count_unlock = screen_status[screen_status["screen_status"] == 3].set_index("local_date")[["timestamp"]].rename(columns = {"timestamp": "count_unlock"})
|
||||
|
||||
count_helper = pd.concat([count_on, count_off, count_lock, count_unlock], axis = 1)
|
||||
count_helper = count_helper.fillna(0).astype(np.int64)
|
||||
|
||||
# get unlocks per minute
|
||||
for date, row in count_helper.iterrows():
|
||||
sensed_minutes = phone_sensed_bins.loc[date, :].sum() * bin_size
|
||||
unlocks_per_minute = min(row["count_lock"], row["count_unlock"]) / (1 if sensed_minutes == 0 else sensed_minutes)
|
||||
count_helper.loc[date, "unlocks_per_minute"] = unlocks_per_minute
|
||||
|
||||
event_features = pd.DataFrame()
|
||||
if "counton" in metrics_events:
|
||||
event_features["screen_" + day_segment + "_counton"] = count_helper[["count_on", "count_off"]].min(axis=1)
|
||||
if "countunlock" in metrics_events:
|
||||
event_features["screen_" + day_segment + "_countunlock"] = count_helper[["count_lock", "count_unlock"]].min(axis=1)
|
||||
if "unlocksperminute" in metrics_events:
|
||||
event_features["screen_" + day_segment + "_unlocksperminute"] = count_helper["unlocks_per_minute"]
|
||||
|
||||
return event_features
|
||||
|
||||
screen_data = pd.read_csv(snakemake.input["screen_events"], parse_dates=["local_date_time", "local_date"])
|
||||
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"])
|
||||
phone_sensed_bins = pd.read_csv(snakemake.input["phone_sensed_bins"], parse_dates=["local_date"], index_col="local_date")
|
||||
phone_sensed_bins[phone_sensed_bins > 0] = 1
|
||||
|
||||
day_segment = snakemake.params["day_segment"]
|
||||
metrics_events = snakemake.params["metrics_events"]
|
||||
reference_hour_first_use = snakemake.params["reference_hour_first_use"]
|
||||
metrics_deltas = snakemake.params["metrics_deltas"]
|
||||
episodes = snakemake.params["episodes"]
|
||||
episode_types = snakemake.params["episode_types"]
|
||||
bin_size = snakemake.params["bin_size"]
|
||||
|
||||
metrics_deltas_name = ["".join(metric) for metric in itertools.product(metrics_deltas, episodes)]
|
||||
metrics_deltas = ["firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) if feature_name == "firstuseafter" else feature_name for feature_name in metrics_deltas]
|
||||
|
||||
if screen_data.empty:
|
||||
screen_features = pd.DataFrame(columns=["local_date"]+["screen_" + day_segment + "_" + x for x in metrics_events + metrics_deltas_name])
|
||||
else:
|
||||
# drop consecutive duplicates of screen_status keeping the last one
|
||||
screen_data = screen_data.loc[(screen_data[["screen_status"]].shift(-1) != screen_data[["screen_status"]]).any(axis=1)].reset_index(drop=True)
|
||||
metrics_deltas_name = ["".join(metric) for metric in itertools.product(metrics_deltas, episode_types)]
|
||||
|
||||
screen_features = pd.DataFrame(columns=["local_date"]+["screen_" + day_segment + "_" + x for x in metrics_deltas_name])
|
||||
if not screen_deltas.empty:
|
||||
# preprocess day_segment and episodes
|
||||
screen_deltas = splitOvernightEpisodes(screen_deltas, [], ["episode"])
|
||||
if day_segment != "daily":
|
||||
screen_data = screen_data[screen_data["local_day_segment"] == day_segment]
|
||||
if (not screen_deltas.empty) and (day_segment != "daily"):
|
||||
screen_deltas = splitMultiSegmentEpisodes(screen_deltas, day_segment, [])
|
||||
screen_deltas.set_index(["local_start_date"],inplace=True)
|
||||
|
||||
# extract features for events and episodes
|
||||
event_features = getEventFeatures(screen_data, metrics_events, phone_sensed_bins, bin_size)
|
||||
if not screen_deltas.empty:
|
||||
screen_features = pd.DataFrame()
|
||||
for episode in episode_types:
|
||||
screen_features = pd.concat([screen_features, getEpisodeDurationFeatures(screen_deltas, episode, metrics_deltas, phone_sensed_bins, bin_size, reference_hour_first_use)], axis=1)
|
||||
|
||||
if screen_deltas.empty:
|
||||
duration_features = pd.DataFrame(columns=["screen_" + day_segment + "_" + x for x in metrics_deltas_name])
|
||||
else:
|
||||
duration_features = pd.DataFrame()
|
||||
for episode in episodes:
|
||||
duration_features = pd.concat([duration_features, getEpisodeDurationFeatures(screen_deltas, episode, metrics_deltas)], axis=1)
|
||||
|
||||
screen_features = pd.concat([event_features, duration_features], axis = 1).fillna(0)
|
||||
screen_features = screen_features.rename_axis("local_date").reset_index()
|
||||
|
||||
screen_features.to_csv(snakemake.output[0], index=False)
|
||||
Loading…
Reference in New Issue