Refactor screen features

src/features/screen/screen_base.py

@@ -0,0 +1,63 @@
+import pandas as pd
+import itertools
+from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
+
+def getEpisodeDurationFeatures(screen_data, day_segment, episode, features, phone_sensed_bins, bin_size, reference_hour_first_use):
+    screen_data_episode = screen_data[screen_data["episode"] == episode]
+    duration_helper = pd.DataFrame()
+    if "countepisode" in features:
+        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)
+    if "episodepersensedminutes" in features:
+        for date, row in screen_data_episode[["time_diff"]].groupby(["local_start_date"]).count().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 features:
+        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)
+    if "maxduration" in features:
+        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)        
+    if "minduration" in features:
+        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)
+    if "avgduration" in features:
+        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)
+    if "stdduration" in features:
+        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)
+    if "firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) in features:
+        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)
+    return duration_helper
+
+
+def base_screen_features(screen_data, phone_sensed_bins, day_segment, params):
+
+    reference_hour_first_use = params["reference_hour_first_use"]
+    bin_size = params["bin_size"]
+    requested_features_deltas = params["requested_features_deltas"]
+    requested_episode_types = params["requested_episode_types"]
+
+    # name of the features this function can compute
+    base_features_deltas = ["countepisode", "episodepersensedminutes", "sumduration", "maxduration", "minduration", "avgduration", "stdduration", "firstuseafter"]
+    base_episode_type = ["unlock"]
+    # the subset of requested features this function can compute
+    features_deltas_to_compute = list(set(requested_features_deltas) & set(base_features_deltas))
+    episode_type_to_compute = list(set(requested_episode_types) & set(base_episode_type))
+    
+    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]
+    features_to_compute = ["".join(feature) for feature in itertools.product(features_deltas_to_compute, episode_type_to_compute)]
+
+    screen_features = pd.DataFrame(columns=["local_date"]+["screen_" + day_segment + "_" + x for x in features_to_compute])
+    if not screen_data.empty:
+        # preprocess day_segment and episodes
+        screen_data = splitOvernightEpisodes(screen_data, [], ["episode"])
+        if (not screen_data.empty) and (day_segment != "daily"):
+            screen_data = splitMultiSegmentEpisodes(screen_data, day_segment, [])
+        screen_data.set_index(["local_start_date"],inplace=True)
+
+        if not screen_data.empty:
+            screen_features = pd.DataFrame()
+            for episode in episode_type_to_compute:
+                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)
+
+        if not screen_features.empty:
+            screen_features = screen_features.rename_axis("local_date").reset_index()
+
+    return screen_features

src/features/screen_features.py

@@ -1,63 +1,24 @@
 import pandas as pd
-import numpy as np
-import datetime
 import itertools
-from datetime import datetime, timedelta, time
+from screen.screen_base import base_screen_features
-from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
 
-def getEpisodeDurationFeatures(screen_deltas, episode, features, phone_sensed_bins, bin_size, reference_hour_first_use):
+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"])
-    screen_deltas_episode = screen_deltas[screen_deltas["episode"] == episode]
-    duration_helper = pd.DataFrame()
-    if "countepisode" in features:
-        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)
-    if "episodepersensedminutes" in features:
-        for date, row in screen_deltas_episode[["time_diff"]].groupby(["local_start_date"]).count().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 features:
-        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)
-    if "maxduration" in features:
-        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)        
-    if "minduration" in features:
-        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)
-    if "avgduration" in features:
-        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)
-    if "stdduration" in features:
-        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)
-    if "firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) in features:
-        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
-
-
-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"]
-reference_hour_first_use = snakemake.params["reference_hour_first_use"]
+screen_features = pd.DataFrame(columns=["local_date"])
-features_deltas = snakemake.params["features_deltas"]
-episode_types = snakemake.params["episode_types"]
-bin_size = snakemake.params["bin_size"]
 
-features_deltas = ["firstuseafter" + "{0:0=2d}".format(reference_hour_first_use) if feature_name == "firstuseafter" else feature_name for feature_name in features_deltas]
+params = {}
+params["reference_hour_first_use"] = snakemake.params["reference_hour_first_use"]
+params["bin_size"] = snakemake.params["bin_size"]
+params["requested_features_deltas"] = snakemake.params["features_deltas"]
+params["requested_episode_types"] = snakemake.params["episode_types"]
 
-features_deltas_name = ["".join(feature) for feature in itertools.product(features_deltas, episode_types)]
+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"]]
+requested_features = ["".join(feature) for feature in itertools.product(requested_features_deltas, params["requested_episode_types"])]
 
-screen_features = pd.DataFrame(columns=["local_date"]+["screen_" + day_segment + "_" + x for x in features_deltas_name])
+screen_features = screen_features.merge(base_screen_features(screen_data, phone_sensed_bins, day_segment, params), on="local_date", how="outer")
-if not screen_deltas.empty:
-    # preprocess day_segment and episodes
-    screen_deltas = splitOvernightEpisodes(screen_deltas, [], ["episode"])
-    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)
 
-    if not screen_deltas.empty:
+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"
-        screen_features = pd.DataFrame()
-        for episode in episode_types:
-            screen_features = pd.concat([screen_features, getEpisodeDurationFeatures(screen_deltas, episode, features_deltas, phone_sensed_bins, bin_size, reference_hour_first_use)], axis=1)
 
-    if not screen_features.empty:
+screen_features.to_csv(snakemake.output[0], index=False)
-        screen_features = screen_features.rename_axis("local_date").reset_index()
-
-screen_features.to_csv(snakemake.output[0], index=False)