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Migrate app foreground to new file structure

pull/103/head
JulioV 2020-09-01 15:25:35 -04:00
parent 681a77f23c
commit 8d87f6e497
8 changed files with 159 additions and 101 deletions
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12
Snakefile
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@ -95,11 +95,13 @@ if config["ACCELEROMETER"]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["ACCELEROMETER"]["DB_TABLE"]))
files_to_compute.extend(expand("data/processed/{pid}/accelerometer_{day_segment}.csv", pid = config["PIDS"], day_segment = config["ACCELEROMETER"]["DAY_SEGMENTS"]))
if config["APPLICATIONS_FOREGROUND"]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]))
files_to_compute.extend(expand("data/interim/{pid}/{sensor}_with_datetime_with_genre.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]))
files_to_compute.extend(expand("data/processed/{pid}/applications_foreground_{day_segment}.csv", pid = config["PIDS"], day_segment = config["APPLICATIONS_FOREGROUND"]["DAY_SEGMENTS"]))
for provider in config["APPLICATIONS_FOREGROUND"]["PROVIDERS"].keys():
if config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["COMPUTE"]:
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_raw.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]))
files_to_compute.extend(expand("data/raw/{pid}/{sensor}_with_datetime_with_genre.csv", pid=config["PIDS"], sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]))
files_to_compute.extend(expand("data/interim/{pid}/{sensor_key}_features/{sensor_key}_{language}_{provider_key}.csv", pid=config["PIDS"], language=config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][provider]["SRC_LANGUAGE"], provider_key=provider, sensor_key="APPLICATIONS_FOREGROUND".lower()))
files_to_compute.extend(expand("data/processed/features/{pid}/{sensor_key}.csv", pid=config["PIDS"], sensor_key="APPLICATIONS_FOREGROUND".lower()))
for provider in config["WIFI"]["PROVIDERS"].keys():
if config["WIFI"]["PROVIDERS"][provider]["COMPUTE"]:

23
config.yaml
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@ -156,17 +156,20 @@ ACCELEROMETER:
VALID_SENSED_MINUTES: False
APPLICATIONS_FOREGROUND:
COMPUTE: False
DB_TABLE: applications_foreground
DAY_SEGMENTS: *day_segments
SINGLE_CATEGORIES: ["all", "email"]
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"]
EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"]
FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"]
PROVIDERS:
RAPIDS:
COMPUTE: TRUE
SINGLE_CATEGORIES: ["all", "email"]
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: []
EXCLUDED_APPS: ["com.fitbit.FitbitMobile", "com.aware.plugin.upmc.cancer"]
FEATURES: ["count", "timeoffirstuse", "timeoflastuse", "frequencyentropy"]
SRC_FOLDER: "rapids" # inside src/features/applications_foreground
SRC_LANGUAGE: "python"
HEARTRATE:
COMPUTE: False

30
rules/features.smk
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@ -224,21 +224,29 @@ rule accelerometer_features:
script:
"../src/features/accelerometer_features.py"
rule applications_foreground_features:
rule applications_foreground_r_features:
input:
expand("data/interim/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"])
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]),
day_segments_labels = "data/interim/day_segments_labels.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"],
features = config["APPLICATIONS_FOREGROUND"]["FEATURES"],
provider = lambda wildcards: config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
output:
"data/processed/{pid}/applications_foreground_{day_segment}.csv"
"data/interim/{pid}/applications_foreground_features/applications_foreground_r_{provider_key}.csv"
script:
"../src/features/applications_foreground_features.py"
"../src/features/applications_foreground/applications_foreground_entry.R"
rule applications_foreground_python_features:
input:
sensor_data = expand("data/raw/{{pid}}/{sensor}_with_datetime_with_genre.csv", sensor=config["APPLICATIONS_FOREGROUND"]["DB_TABLE"]),
day_segments_labels = "data/interim/day_segments_labels.csv"
params:
provider = lambda wildcards: config["APPLICATIONS_FOREGROUND"]["PROVIDERS"][wildcards.provider_key],
provider_key = "{provider_key}"
output:
"data/interim/{pid}/applications_foreground_features/applications_foreground_python_{provider_key}.csv"
script:
"../src/features/applications_foreground/applications_foreground_entry.py"
rule wifi_r_features:
input:

2
rules/preprocessing.smk
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@ -134,7 +134,7 @@ rule application_genres:
update_catalogue_file = config["APPLICATION_GENRES"]["UPDATE_CATALOGUE_FILE"],
scrape_missing_genres = config["APPLICATION_GENRES"]["SCRAPE_MISSING_GENRES"]
output:
"data/interim/{pid}/{sensor}_with_datetime_with_genre.csv"
"data/raw/{pid}/{sensor}_with_datetime_with_genre.csv"
script:
"../src/data/application_genres.R"

74
src/features/applications_foreground/applications_foreground_base.py
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@ -1,74 +0,0 @@
import pandas as pd
import itertools
from scipy.stats import entropy
def compute_features(filtered_data, apps_type, requested_features, apps_features, day_segment):
# There is the rare occasion that filtered_data is empty (found in testing)
if "timeoffirstuse" in requested_features:
time_first_event = filtered_data.sort_values(by="timestamp", ascending=True).drop_duplicates(subset="local_date", keep="first").set_index("local_date")
if time_first_event.empty:
apps_features["apps_" + day_segment + "_timeoffirstuse" + apps_type] = 'NA'
else:
apps_features["apps_" + day_segment + "_timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"]
if "timeoflastuse" in requested_features:
time_last_event = filtered_data.sort_values(by="timestamp", ascending=False).drop_duplicates(subset="local_date", keep="first").set_index("local_date")
if time_last_event.empty:
apps_features["apps_" + day_segment + "_timeoflastuse" + apps_type] = 'NA'
else:
apps_features["apps_" + day_segment + "_timeoflastuse" + apps_type] = time_last_event["local_hour"] * 60 + time_last_event["local_minute"]
if "frequencyentropy" in requested_features:
apps_with_count = filtered_data.groupby(["local_date","application_name"]).count().sort_values(by="timestamp", ascending=False).reset_index()
if (len(apps_with_count.index) < 2 ):
apps_features["apps_" + day_segment + "_frequencyentropy" + apps_type] = 'NA'
else:
apps_features["apps_" + day_segment + "_frequencyentropy" + apps_type] = apps_with_count.groupby("local_date")["timestamp"].agg(entropy)
if "count" in requested_features:
apps_features["apps_" + day_segment + "_count" + apps_type] = filtered_data.groupby(["local_date"]).count()["timestamp"]
apps_features.fillna(value={"apps_" + day_segment + "_count" + apps_type: 0}, inplace=True)
return apps_features
def base_applications_foreground_features(apps_data, day_segment, requested_features, params):
multiple_categories_with_genres = params["multiple_categories_with_genres"]
single_categories = params["single_categories"]
multiple_categories = params["multiple_categories"]
apps = params["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(feature) for feature in itertools.product(requested_features, single_categories + multiple_categories + apps)]])
if not apps_data.empty:
if day_segment != "daily":
apps_data =apps_data[apps_data["local_day_segment"] == day_segment]
if not apps_data.empty:
apps_features = pd.DataFrame()
# single category
single_categories.sort()
for sc in single_categories:
if sc == "all":
apps_features = compute_features(apps_data, "all", requested_features, apps_features, day_segment)
else:
filtered_data = apps_data[apps_data["genre"].isin([sc])]
apps_features = compute_features(filtered_data, sc, requested_features, apps_features, day_segment)
# multiple category
for mc in multiple_categories:
filtered_data = apps_data[apps_data["genre"].isin(multiple_categories_with_genres[mc])]
apps_features = compute_features(filtered_data, mc, requested_features, apps_features, day_segment)
# 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_features(filtered_data, col_name, requested_features, apps_features, day_segment)
apps_features = apps_features.reset_index()
return apps_features

13
src/features/applications_foreground/applications_foreground_entry.R 100644
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@ -0,0 +1,13 @@
source("renv/activate.R")
source("src/features/utils/utils.R")
library("dplyr")
library("tidyr")
sensor_data_file <- snakemake@input[["sensor_data"]]
day_segments_file <- snakemake@input[["day_segments_labels"]]
provider <- snakemake@params["provider"][["provider"]]
provider_key <- snakemake@params["provider_key"]
sensor_features <- fetch_provider_features(provider, provider_key, "applications_foreground", sensor_data_file, day_segments_file)
write.csv(sensor_features, snakemake@output[[1]], row.names = FALSE)

18
src/features/applications_foreground/applications_foreground_entry.py 100644
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@ -0,0 +1,18 @@
import pandas as pd
from importlib import import_module, util
from pathlib import Path
# import fetch_provider_features from src/features/utils/utils.py
spec = util.spec_from_file_location("util", str(Path(snakemake.scriptdir).parent / "utils" / "utils.py"))
mod = util.module_from_spec(spec)
spec.loader.exec_module(mod)
fetch_provider_features = getattr(mod, "fetch_provider_features")
sensor_data_file = snakemake.input["sensor_data"][0]
day_segments_file = snakemake.input["day_segments_labels"]
provider = snakemake.params["provider"]
provider_key = snakemake.params["provider_key"]
sensor_features = fetch_provider_features(provider, provider_key, "applications_foreground", sensor_data_file, day_segments_file)
sensor_features.to_csv(snakemake.output[0], index=False)

88
src/features/applications_foreground/rapids/main.py 100644
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@ -0,0 +1,88 @@
import pandas as pd
import numpy as np
import itertools
from scipy.stats import entropy
def compute_features(filtered_data, apps_type, requested_features, apps_features, day_segment):
# There is the rare occasion that filtered_data is empty (found in testing)
if "timeoffirstuse" in requested_features:
time_first_event = filtered_data.sort_values(by="timestamp", ascending=True).drop_duplicates(subset="local_segment", keep="first").set_index("local_segment")
if time_first_event.empty:
apps_features["apps_rapids" + "_timeoffirstuse" + apps_type] = np.nan
else:
apps_features["apps_rapids" + "_timeoffirstuse" + apps_type] = time_first_event["local_hour"] * 60 + time_first_event["local_minute"]
if "timeoflastuse" in requested_features:
time_last_event = filtered_data.sort_values(by="timestamp", ascending=False).drop_duplicates(subset="local_segment", keep="first").set_index("local_segment")
if time_last_event.empty:
apps_features["apps_rapids" + "_timeoflastuse" + apps_type] = np.nan
else:
apps_features["apps_rapids" + "_timeoflastuse" + apps_type] = time_last_event["local_hour"] * 60 + time_last_event["local_minute"]
if "frequencyentropy" in requested_features:
apps_with_count = filtered_data.groupby(["local_segment","application_name"]).count().sort_values(by="timestamp", ascending=False).reset_index()
if (len(apps_with_count.index) < 2 ):
apps_features["apps_rapids" + "_frequencyentropy" + apps_type] = np.nan
else:
apps_features["apps_rapids" + "_frequencyentropy" + apps_type] = apps_with_count.groupby("local_segment")["timestamp"].agg(entropy)
if "count" in requested_features:
apps_features["apps_rapids" + "_count" + apps_type] = filtered_data.groupby(["local_segment"]).count()["timestamp"]
apps_features.fillna(value={"apps_rapids" + "_count" + apps_type: 0}, inplace=True)
return apps_features
def rapids_features(apps_data, day_segment, provider, filter_data_by_segment, *args, **kwargs):
requested_features = provider["FEATURES"]
excluded_categories = provider["EXCLUDED_CATEGORIES"]
excluded_apps = provider["EXCLUDED_APPS"]
multiple_categories_with_genres = provider["MULTIPLE_CATEGORIES"]
single_categories = provider["SINGLE_CATEGORIES"]
multiple_categories = provider["MULTIPLE_CATEGORIES"]
single_apps = provider["SINGLE_APPS"]
single_categories = list(set(single_categories) - set(excluded_categories))
multiple_categories = list(multiple_categories_with_genres.keys() - set(excluded_categories))
single_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["package_name"].isin(excluded_apps)]
apps_features = pd.DataFrame(columns=["local_segment"] + ["apps_rapids_" + "_" + x for x in ["".join(feature) for feature in itertools.product(requested_features, single_categories + multiple_categories + single_apps)]])
if not apps_data.empty:
apps_data = filter_data_by_segment(apps_data, day_segment)
# deep copy the apps_data for the top1global computation
apps_data_global = apps_data.copy()
if not apps_data.empty:
apps_features = pd.DataFrame()
# single category
single_categories.sort()
for sc in single_categories:
if sc == "all":
apps_features = compute_features(apps_data, "all", requested_features, apps_features, day_segment)
else:
filtered_data = apps_data[apps_data["genre"].isin([sc])]
apps_features = compute_features(filtered_data, sc, requested_features, apps_features, day_segment)
# multiple category
for mc in multiple_categories:
filtered_data = apps_data[apps_data["genre"].isin(multiple_categories_with_genres[mc])]
apps_features = compute_features(filtered_data, mc, requested_features, apps_features, day_segment)
# single apps
for app in single_apps:
col_name = app
if app == "top1global":
# get the most used app
apps_with_count = apps_data_global.groupby(["local_segment","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_features(filtered_data, col_name, requested_features, apps_features, day_segment)
apps_features = apps_features.reset_index()
return apps_features