Refactor applications_foreground features: replace "metrics" with "features"
Co-authored-by: Meng Li <AnnieLM1996@gmail.com>pull/95/head
Mingze Cao
parent
d1c38016de
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f46e8c0666
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@ -109,7 +109,7 @@ APPLICATIONS_FOREGROUND:
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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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FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"]
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HEARTRATE:
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DAY_SEGMENTS: *day_segments
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@ -451,7 +451,7 @@ See `Applications Foreground Config Code`_
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.. - Genre categorization of Applications Foreground dataset: ``expand("data/interim/{pid}/applications_foreground_with_datetime_with_genre.csv", pid=config["PIDS"]),``
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- Extract Applications Foreground Metrics:
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- Extract Applications Foreground Features:
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| ``expand("data/processed/{pid}/applications_foreground_{day_segment}.csv",``
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| ``pid=config["PIDS"],``
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@ -471,9 +471,9 @@ See `Applications Foreground Config Code`_
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- **Script:** ``../src/data/application_genres.R`` - See the application_genres.R_ script
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- **Rule:** ``rules/features.snakefile/applications_foreground_metrics`` - See the applications_foreground_metrics_ rule.
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- **Rule:** ``rules/features.snakefile/applications_foreground_features`` - See the applications_foreground_features_ rule.
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- **Script:** ``src/features/applications_foreground_metrics.py`` - See the applications_foreground_metrics.py_ script.
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- **Script:** ``src/features/applications_foreground_features.py`` - See the applications_foreground_features.py_ script.
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.. _applications-foreground-parameters:
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@ -488,14 +488,14 @@ multiple_categories Categories of apps that will be included for the data c
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single_apps Any Android app can be included in the list of apps used to collect data by adding the package name to this list. (E.g. Youtube)
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excluded_categories Categories of apps that will be excluded for the data collection. The available categories can be defined in the ``APPLICATION_GENRES`` in the ``config`` file. See :ref:`Assumtions and Observations <applications-foreground-observations>`.
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excluded_apps Any Android app can be excluded from the list of apps used to collect data by adding the package name to this list.
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metrics The different measures that can be retrieved from the dataset. See :ref:`Available Applications Foreground Metrics <applications-foreground-available-metrics>` Table below
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features The different measures that can be retrieved from the dataset. See :ref:`Available Applications Foreground Features <applications-foreground-available-features>` Table below
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==================== ===================
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.. _applications-foreground-available-metrics:
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.. _applications-foreground-available-features:
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**Available Applications Foreground Metrics**
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**Available Applications Foreground Features**
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The following table shows a list of the available metrics for the Applications Foreground dataset
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The following table shows a list of the available features for the Applications Foreground dataset
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================== ========= =============
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Name Units Description
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@ -1162,8 +1162,8 @@ stddurationactivebout minutes Std duration active bout: The standard
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.. _`Application Genres Config`: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L54
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.. _application_genres: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/preprocessing.snakefile#L81
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.. _application_genres.R: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/src/data/application_genres.R
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.. _applications_foreground_metrics: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L135
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.. _applications_foreground_metrics.py: https://github.com/carissalow/rapids/blob/master/src/features/accelerometer_metrics.py
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.. _applications_foreground_features: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L135
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.. _applications_foreground_features.py: https://github.com/carissalow/rapids/blob/master/src/features/accelerometer_features.py
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.. _`Battery Config Code`: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/config.yaml#L84
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.. _battery_deltas: https://github.com/carissalow/rapids/blob/765bb462636d5029a05f54d4c558487e3786b90b/rules/features.snakefile#L25
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.. _battery_deltas.R: https://github.com/carissalow/rapids/blob/master/src/features/battery_deltas.R
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@ -133,7 +133,7 @@ rule accelerometer_features:
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script:
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"../src/features/accelerometer_features.py"
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rule applications_foreground_metrics:
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rule applications_foreground_features:
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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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@ -143,11 +143,11 @@ rule applications_foreground_metrics:
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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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features = config["APPLICATIONS_FOREGROUND"]["FEATURES"],
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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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"../src/features/applications_foreground_features.py"
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rule fitbit_heartrate_metrics:
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input:
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@ -4,17 +4,17 @@ 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 "timeoffirstuse" in metrics:
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def compute_features(filtered_data, apps_type, requested_features, apps_features):
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if "timeoffirstuse" in requested_features:
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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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if "timeoflastuse" in requested_features:
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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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if "frequencyentropy" in requested_features:
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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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if "count" in metrics:
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if "count" in requested_features:
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apps_features["apps_" + day_segment + "_count" + apps_type] = filtered_data.groupby(["local_date"]).count()["timestamp"]
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apps_features.fillna(value={"apps_" + day_segment + "_count" + apps_type: 0}, inplace=True)
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return apps_features
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@ -27,7 +27,7 @@ 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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features = snakemake.params["features"]
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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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@ -43,7 +43,7 @@ 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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apps_features = pd.DataFrame(columns=["local_date"] + ["apps_" + day_segment + "_" + x for x in ["".join(feature) for feature in itertools.product(features, 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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@ -52,14 +52,14 @@ if not apps_data.empty:
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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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apps_features = compute_features(apps_data, "all", features, 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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apps_features = compute_features(filtered_data, sc, features, 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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apps_features = compute_features(filtered_data, mc, features, 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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@ -70,7 +70,7 @@ if not apps_data.empty:
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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 = compute_features(filtered_data, col_name, features, apps_features)
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apps_features = apps_features.reset_index()
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