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Add unlocksperminute feature and remove countdiff for screen sensor

replace/7ff83f73c0a0844ca45c09889569266d8b614ddb
Meng Li 2019-12-04 16:04:37 -05:00
parent 975dc80c4b
commit 4390979143
3 changed files with 22 additions and 21 deletions
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2
config.yaml
View File

@ -64,6 +64,6 @@ BATTERY:
SCREEN:
DAY_SEGMENTS: *day_segments
METRICS_EVENT: ["counton", "countunlock"]
METRICS_EVENTS: ["counton", "countunlock", "unlocksperminute"]
METRICS_DELTAS: ["sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
EPISODES: ["unlock"]

6
rules/features.snakefile
View File

@ -94,14 +94,14 @@ rule battery_metrics:
rule screen_metrics:
input:
screen_events = "data/raw/{pid}/screen_with_datetime.csv",
screen_deltas = "data/processed/{pid}/screen_deltas.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_event = config["SCREEN"]["METRICS_EVENT"],
metrics_events = config["SCREEN"]["METRICS_EVENTS"],
metrics_deltas = config["SCREEN"]["METRICS_DELTAS"],
episodes = config["SCREEN"]["EPISODES"]
output:
"data/processed/{pid}/screen_{day_segment}.csv"
script:
"../src/features/screen_metrics.py"

35
src/features/screen_metrics.py
View File

@ -22,7 +22,7 @@ def getEpisodeDurationFeatures(screen_deltas, episode, metrics):
duration_helper = duration_helper.fillna(0)
return duration_helper
def getEventFeatures(screen_data, metrics_event):
def getEventFeatures(screen_data, metrics_events, phone_sensed_bins):
# 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"})
@ -33,34 +33,35 @@ def getEventFeatures(screen_data, metrics_event):
count_helper = pd.concat([count_on, count_off, count_lock, count_unlock], axis = 1)
count_helper = count_helper.fillna(0).astype(np.int64)
# count on-off; unlock-lock
count_helper["diff_count_on_off"] = count_helper["count_on"] - count_helper["count_off"]
count_helper["diff_count_unlock_lock"] = count_helper["count_unlock"] - count_helper["count_lock"]
# get unlocks per minute
for date, row in count_helper.iterrows():
sensed_minutes = phone_sensed_bins.loc[date, :].sum() * 5
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_event:
event_features["screen_" + day_segment + "_counton"] = count_helper[["count_on", "count_off"]].max(axis=1)
if "countunlock" in metrics_event:
event_features["screen_" + day_segment + "_countunlock"] = count_helper[["count_lock", "count_unlock"]].max(axis=1)
############################################################################################
# check missing values
event_features["screen_" + day_segment + "_diffcountonoff"] = count_helper["diff_count_on_off"]
event_features["screen_" + day_segment + "_diffcountunlocklock"] = count_helper["diff_count_unlock_lock"]
############################################################################################
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_event = snakemake.params["metrics_event"]
metrics_events = snakemake.params["metrics_events"]
metrics_deltas = snakemake.params["metrics_deltas"]
episodes = snakemake.params["episodes"]
if screen_data.empty:
metrics_deltas_name = ["".join(metric) for metric in itertools.product(metrics_deltas,episodes)]
screen_features = pd.DataFrame(columns=["local_date"]+["screen_" + day_segment + "_" + x for x in metrics_event + metrics_deltas_name])
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)
@ -73,7 +74,7 @@ else:
screen_deltas.set_index(["local_start_date"],inplace=True)
# extract features for events and episodes
event_features = getEventFeatures(screen_data, metrics_event)
event_features = getEventFeatures(screen_data, metrics_events, phone_sensed_bins)
if screen_deltas.empty:
metrics_deltas_name = ["".join(metric) for metric in itertools.product(metrics_deltas,episodes)]