Add applications_foreground features
parent
770764ec8a
commit
0840235280
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@ -41,6 +41,9 @@ rule all:
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expand("data/processed/{pid}/accelerometer_{day_segment}.csv",
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pid = config["PIDS"],
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day_segment = config["ACCELEROMETER"]["DAY_SEGMENTS"]),
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expand("data/processed/{pid}/applications_foreground_{day_segment}.csv",
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pid = config["PIDS"],
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day_segment = config["APPLICATIONS_FOREGROUND"]["DAY_SEGMENTS"]),
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expand("data/raw/{pid}/fitbit_{fitbit_sensor}_with_datetime.csv",
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pid=config["PIDS"],
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fitbit_sensor=config["FITBIT_SENSORS"]),
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11
config.yaml
11
config.yaml
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@ -91,6 +91,17 @@ ACCELEROMETER:
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DAY_SEGMENTS: *day_segments
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METRICS: ["maxmagnitude", "minmagnitude", "avgmagnitude", "medianmagnitude", "stdmagnitude", "ratioexertionalactivityepisodes", "sumexertionalactivityepisodes", "longestexertionalactivityepisode", "longestnonexertionalactivityepisode", "countexertionalactivityepisodes", "countnonexertionalactivityepisodes"]
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APPLICATIONS_FOREGROUND:
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DAY_SEGMENTS: *day_segments
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SINGLE_CATEGORIES: ["all", "video"]
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MULTIPLE_CATEGORIES:
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social: ["socialnetworks", "socialmediatools"]
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entertainment: ["entertainment", "gamingknowledge", "gamingcasual", "gamingadventure", "gamingstrategy", "gamingtoolscommunity", "gamingroleplaying", "gamingaction", "gaminglogic", "gamingsports", "gamingsimulation"]
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SINGLE_APPS: ["top1global", "com.facebook.moments", "com.google.android.youtube", "com.twitter.android"] # There's no entropy for single apps
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EXCLUDED_CATEGORIES: ["system_apps", "video"]
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EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"]
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METRICS: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"]
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HEARTRATE:
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DAY_SEGMENTS: *day_segments
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METRICS: ["maxhr", "minhr", "avghr", "medianhr", "modehr", "stdhr", "diffmaxmodehr", "diffminmodehr", "entropyhr", "lengthoutofrange", "lengthfatburn", "lengthcardio", "lengthpeak"]
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@ -131,6 +131,22 @@ rule accelerometer_metrics:
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script:
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"../src/features/accelerometer_metrics.py"
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rule applications_foreground_metrics:
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input:
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"data/interim/{pid}/applications_foreground_with_datetime_with_genre.csv",
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params:
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day_segment = "{day_segment}",
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single_categories = config["APPLICATIONS_FOREGROUND"]["SINGLE_CATEGORIES"],
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multiple_categories = config["APPLICATIONS_FOREGROUND"]["MULTIPLE_CATEGORIES"],
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single_apps = config["APPLICATIONS_FOREGROUND"]["SINGLE_APPS"],
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excluded_categories = config["APPLICATIONS_FOREGROUND"]["EXCLUDED_CATEGORIES"],
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excluded_apps = config["APPLICATIONS_FOREGROUND"]["EXCLUDED_APPS"],
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metrics = config["APPLICATIONS_FOREGROUND"]["METRICS"],
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output:
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"data/processed/{pid}/applications_foreground_{day_segment}.csv"
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script:
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"../src/features/applications_foreground_metrics.py"
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rule fitbit_heartrate_metrics:
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input:
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"data/raw/{pid}/fitbit_heartrate_with_datetime.csv",
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@ -0,0 +1,76 @@
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import pandas as pd
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import numpy as np
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import itertools
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from scipy.stats import entropy
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def compute_metrics(filtered_data, apps_type, metrics, apps_features):
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if "count" in metrics:
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apps_features["apps_" + day_segment + "_count" + apps_type] = filtered_data.groupby(["local_date"]).count()["timestamp"]
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if "timeoffirstuse" in metrics:
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time_first_event = filtered_data.sort_values(by="timestamp", ascending=True).drop_duplicates(subset="local_date", keep="first").set_index("local_date")
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apps_features["apps_" + day_segment + "_timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"]
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if "timeoflastuse" in metrics:
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time_last_event = filtered_data.sort_values(by="timestamp", ascending=False).drop_duplicates(subset="local_date", keep="first").set_index("local_date")
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apps_features["apps_" + day_segment + "_timeoflastuse" + apps_type] = time_last_event["local_hour"] * 60 + time_last_event["local_minute"]
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if "frequencyentropy" in metrics:
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apps_with_count = filtered_data.groupby(["local_date","application_name"]).count().sort_values(by="timestamp", ascending=False).reset_index()
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apps_features["apps_" + day_segment + "_frequencyentropy" + apps_type] = apps_with_count.groupby("local_date")["timestamp"].agg(entropy)
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return apps_features
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apps_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"])
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day_segment = snakemake.params["day_segment"]
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single_categories = snakemake.params["single_categories"]
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multiple_categories_with_genres = snakemake.params["multiple_categories"]
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single_apps = snakemake.params["single_apps"]
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excluded_categories = snakemake.params["excluded_categories"]
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excluded_apps = snakemake.params["excluded_apps"]
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metrics = snakemake.params["metrics"]
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single_categories = list(set(single_categories) - set(excluded_categories))
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multiple_categories = list(multiple_categories_with_genres.keys() - set(excluded_categories))
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apps = list(set(single_apps) - set(excluded_apps))
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# exclude categories in the excluded_categories list
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if "system_apps" in excluded_categories:
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apps_data = apps_data[apps_data["is_system_app"] == 0]
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apps_data = apps_data[~apps_data["genre"].isin(excluded_categories)]
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# exclude apps in the excluded_apps list
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apps_data = apps_data[~apps_data["application_name"].isin(excluded_apps)]
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# deep copy the apps_data for the top1global computation
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apps_data_global = apps_data.copy()
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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)]])
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if not apps_data.empty:
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apps_features = pd.DataFrame()
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if day_segment != "daily":
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apps_data =apps_data[apps_data["local_day_segment"] == day_segment]
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# single category
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for sc in single_categories:
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if sc == "all":
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apps_features = compute_metrics(apps_data, "all", metrics, apps_features)
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else:
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filtered_data = apps_data[apps_data["genre"].isin([sc])]
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apps_features = compute_metrics(filtered_data, sc, metrics, apps_features)
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# multiple category
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for mc in multiple_categories:
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filtered_data = apps_data[apps_data["genre"].isin(multiple_categories_with_genres[mc])]
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apps_features = compute_metrics(filtered_data, mc, metrics, apps_features)
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# single apps
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for app in apps:
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col_name = app
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if app == "top1global":
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# get the most used app
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apps_with_count = apps_data_global.groupby(["local_date","package_name"]).count().sort_values(by="timestamp", ascending=False).reset_index()
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app = apps_with_count.iloc[0]["package_name"]
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col_name = "top1global"
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filtered_data = apps_data[apps_data["package_name"].isin([app])]
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apps_features = compute_metrics(filtered_data, col_name, metrics, apps_features)
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apps_features = apps_features.reset_index()
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apps_features.to_csv(snakemake.output[0], index=False)
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