Extract screen features

Snakefile

@@ -26,6 +26,9 @@ rule all:
         expand("data/processed/{pid}/battery_{day_segment}.csv",
                             pid = config["PIDS"],
                             day_segment = config["BATTERY"]["DAY_SEGMENTS"]),
+        expand("data/processed/{pid}/screen_{day_segment}.csv",
+                            pid = config["PIDS"],
+                            day_segment = config["SCREEN"]["DAY_SEGMENTS"]),
         # Reports
         expand("reports/figures/{pid}/{sensor}_heatmap_rows.html", pid=config["PIDS"], sensor=config["SENSORS"]),
         expand("reports/figures/{pid}/compliance_heatmap.html", pid=config["PIDS"], sensor=config["SENSORS"]),

config.yaml

@@ -61,4 +61,9 @@ GOOGLE_ACTIVITY_RECOGNITION:
 BATTERY:
   DAY_SEGMENTS: *day_segments
   METRICS: ["countdischarge", "sumdurationdischarge", "countcharge", "sumdurationcharge", "avgconsumptionrate", "maxconsumptionrate"]
-  
+
+SCREEN:
+  DAY_SEGMENTS: *day_segments
+  METRICS_EVENT: ["counton", "countunlock"]
+  METRICS_EPISODE: ["sumduration", "maxduration", "minduration", "avgduration", "stdduration"]
+  EPISODES: ["unlock"]

rules/features.snakefile

@@ -73,3 +73,18 @@ rule battery_metrics:
         "data/processed/{pid}/battery_{day_segment}.csv"
     script:
         "../src/features/battery_metrics.py"
+
+rule screen_metrics:
+    input:
+        screen_events = "data/raw/{pid}/screen_with_datetime.csv",
+        screen_deltas = "data/processed/{pid}/screen_deltas.csv"
+    params:
+        day_segment = "{day_segment}",
+        metrics_event = config["SCREEN"]["METRICS_EVENT"],
+        metrics_episode = config["SCREEN"]["METRICS_EPISODE"],
+        episodes = config["SCREEN"]["EPISODES"]
+    output:
+        "data/processed/{pid}/screen_{day_segment}.csv"
+    script:
+        "../src/features/screen_metrics.py"
+

src/features/screen_metrics.py

@@ -0,0 +1,83 @@
+import pandas as pd
+import numpy as np
+import datetime
+from datetime import datetime, timedelta, time
+from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
+
+def getEpisodeDurationFeatures(screen_deltas, episode, metrics):
+    screen_deltas_episode = screen_deltas[screen_deltas["episode"] == episode]
+    duration_helper = pd.DataFrame()
+    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:
+        duration_helper = pd.concat([duration_helper, screen_deltas_episode.groupby(["local_start_date"]).max()[["time_diff"]].rename(columns = {"time_diff": "screen_" + day_segment + "_maxduration" + episode})], axis = 1)        
+    if "minduration" in metrics:
+        duration_helper = pd.concat([duration_helper, screen_deltas_episode.groupby(["local_start_date"]).min()[["time_diff"]].rename(columns = {"time_diff": "screen_" + day_segment + "_minduration" + episode})], axis = 1)
+    if "avgduration" in 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)
+    return duration_helper
+
+def getEventFeatures(screen_data, metrics_event):
+    # 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)
+
+    # 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"]
+    
+    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"]
+    ############################################################################################
+
+    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"])
+day_segment = snakemake.params["day_segment"]
+metrics_event = snakemake.params["metrics_event"]
+metrics_episode = snakemake.params["metrics_episode"]
+episodes = snakemake.params["episodes"]
+
+if screen_data.empty:
+    metrics_episode_name = ["".join(metric) for metric in itertools.product(metrics_episode,episodes)]
+    screen_features = pd.DataFrame(columns=["local_date"]+["screen_" + day_segment + "_" + x for x in metrics_event + metrics_episode_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)
+
+    # 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]
+        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_event)
+    duration_features = pd.DataFrame()
+    for episode in episodes:
+        duration_features = pd.concat([duration_features, getEpisodeDurationFeatures(screen_deltas, episode, metrics_episode)], axis=1)
+
+    screen_features = pd.concat([event_features, duration_features], axis = 1).fillna(0)
+    screen_features.reset_index(inplace=True)
+
+screen_features.to_csv(snakemake.output[0], index=False)