Add applications_foreground features

Snakefile

@@ -41,6 +41,9 @@ rule all:
         expand("data/processed/{pid}/accelerometer_{day_segment}.csv",
                             pid = config["PIDS"],
                             day_segment = config["ACCELEROMETER"]["DAY_SEGMENTS"]),
+        expand("data/processed/{pid}/applications_foreground_{day_segment}.csv",
+                            pid = config["PIDS"],
+                            day_segment = config["APPLICATIONS_FOREGROUND"]["DAY_SEGMENTS"]),
         expand("data/raw/{pid}/fitbit_{fitbit_sensor}_with_datetime.csv",
                             pid=config["PIDS"],
                             fitbit_sensor=config["FITBIT_SENSORS"]),

config.yaml

@@ -91,6 +91,17 @@ ACCELEROMETER:
   DAY_SEGMENTS: *day_segments
   METRICS: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude", "ratioexertionalactivityepisodes", "sumexertionalactivityepisodes", "longestexertionalactivityepisode", "longestnonexertionalactivityepisode", "countexertionalactivityepisodes", "countnonexertionalactivityepisodes"]
 
+APPLICATIONS_FOREGROUND:
+  DAY_SEGMENTS: *day_segments
+  SINGLE_CATEGORIES: ["all", "video"]
+  MULTIPLE_CATEGORIES:
+    social: ["socialnetworks", "socialmediatools"]
+    entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"]
+  SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps
+  EXCLUDED_CATEGORIES: ["system_apps", "video"]
+  EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"]
+  METRICS: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"]
+
 HEARTRATE:
   DAY_SEGMENTS: *day_segments
   METRICS: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "lengthoutofrange", "lengthfatburn", "lengthcardio", "lengthpeak"]

rules/features.snakefile

@@ -131,6 +131,22 @@ rule accelerometer_metrics:
     script:
         "../src/features/accelerometer_metrics.py"
 
+rule applications_foreground_metrics:
+    input:
+        "data/interim/{pid}/applications_foreground_with_datetime_with_genre.csv",
+    params:
+        day_segment = "{day_segment}",
+        single_categories = config["APPLICATIONS_FOREGROUND"]["SINGLE_CATEGORIES"],
+        multiple_categories = config["APPLICATIONS_FOREGROUND"]["MULTIPLE_CATEGORIES"],
+        single_apps = config["APPLICATIONS_FOREGROUND"]["SINGLE_APPS"],
+        excluded_categories = config["APPLICATIONS_FOREGROUND"]["EXCLUDED_CATEGORIES"],
+        excluded_apps = config["APPLICATIONS_FOREGROUND"]["EXCLUDED_APPS"],
+        metrics = config["APPLICATIONS_FOREGROUND"]["METRICS"],
+    output:
+        "data/processed/{pid}/applications_foreground_{day_segment}.csv"
+    script:
+        "../src/features/applications_foreground_metrics.py"
+
 rule fitbit_heartrate_metrics:
     input:
         "data/raw/{pid}/fitbit_heartrate_with_datetime.csv",

src/features/applications_foreground_metrics.py

@@ -0,0 +1,76 @@
+import pandas as pd
+import numpy as np
+import itertools
+from scipy.stats import entropy
+
+
+def compute_metrics(filtered_data, apps_type, metrics, apps_features):
+    if "count" in metrics:
+        apps_features["apps_" + day_segment + "_count" + apps_type] = filtered_data.groupby(["local_date"]).count()["timestamp"]
+    if "timeoffirstuse" in metrics:
+        time_first_event = filtered_data.sort_values(by="timestamp", ascending=True).drop_duplicates(subset="local_date", keep="first").set_index("local_date")
+        apps_features["apps_" + day_segment + "_timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"]
+    if "timeoflastuse" in metrics:
+        time_last_event = filtered_data.sort_values(by="timestamp", ascending=False).drop_duplicates(subset="local_date", keep="first").set_index("local_date")
+        apps_features["apps_" + day_segment + "_timeoflastuse" + apps_type] = time_last_event["local_hour"] * 60 + time_last_event["local_minute"]
+    if "frequencyentropy" in metrics:
+        apps_with_count = filtered_data.groupby(["local_date","application_name"]).count().sort_values(by="timestamp", ascending=False).reset_index()
+        apps_features["apps_" + day_segment + "_frequencyentropy" + apps_type] = apps_with_count.groupby("local_date")["timestamp"].agg(entropy)
+    return apps_features
+
+
+apps_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"])
+day_segment = snakemake.params["day_segment"]
+single_categories = snakemake.params["single_categories"]
+multiple_categories_with_genres = snakemake.params["multiple_categories"]
+single_apps = snakemake.params["single_apps"]
+excluded_categories = snakemake.params["excluded_categories"]
+excluded_apps = snakemake.params["excluded_apps"]
+metrics = snakemake.params["metrics"]
+
+single_categories = list(set(single_categories) - set(excluded_categories))
+multiple_categories = list(multiple_categories_with_genres.keys() - set(excluded_categories))
+apps = list(set(single_apps) - set(excluded_apps))
+
+# exclude categories in the excluded_categories list
+if "system_apps" in excluded_categories:
+    apps_data = apps_data[apps_data["is_system_app"] == 0]
+apps_data = apps_data[~apps_data["genre"].isin(excluded_categories)]
+# exclude apps in the excluded_apps list
+apps_data = apps_data[~apps_data["application_name"].isin(excluded_apps)]
+
+# deep copy the apps_data for the top1global computation
+apps_data_global = apps_data.copy()
+
+apps_features = pd.DataFrame(columns=["local_date"] + ["apps_" + day_segment + "_" + x for x in ["".join(metric) for metric in itertools.product(metrics, single_categories + multiple_categories + apps)]])
+if not apps_data.empty:
+    apps_features = pd.DataFrame()
+    if day_segment != "daily":
+        apps_data =apps_data[apps_data["local_day_segment"] == day_segment]
+    
+    # single category
+    for sc in single_categories:
+        if sc == "all":
+            apps_features = compute_metrics(apps_data, "all", metrics, apps_features)
+        else:
+            filtered_data = apps_data[apps_data["genre"].isin([sc])]
+            apps_features = compute_metrics(filtered_data, sc, metrics, apps_features)
+    # multiple category
+    for mc in multiple_categories:
+        filtered_data = apps_data[apps_data["genre"].isin(multiple_categories_with_genres[mc])]
+        apps_features = compute_metrics(filtered_data, mc, metrics, apps_features)
+    # single apps
+    for app in apps:
+        col_name = app
+        if app == "top1global":
+            # get the most used app
+            apps_with_count = apps_data_global.groupby(["local_date","package_name"]).count().sort_values(by="timestamp", ascending=False).reset_index()
+            app = apps_with_count.iloc[0]["package_name"]
+            col_name = "top1global"
+        
+        filtered_data = apps_data[apps_data["package_name"].isin([app])]
+        apps_features = compute_metrics(filtered_data, col_name, metrics, apps_features)
+
+    apps_features = apps_features.reset_index()
+
+apps_features.to_csv(snakemake.output[0], index=False)