78 lines
4.2 KiB
Python
78 lines
4.2 KiB
Python
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_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 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 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 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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apps_data = pd.read_csv(snakemake.input[0], parse_dates=["local_date_time", "local_date"], encoding="ISO-8859-1")
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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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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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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(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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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_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_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_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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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_features(filtered_data, col_name, features, 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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