Change own to custom categories name

pull/149/head
JulioV 2021-06-22 16:56:09 -04:00 committed by Weiyu
parent e74c745f86
commit 065a926a87
4 changed files with 10 additions and 10 deletions

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@ -113,7 +113,7 @@ PHONE_APPLICATIONS_FOREGROUND:
COMPUTE: False COMPUTE: False
INCLUDE_EPISODE_FEATURES: False INCLUDE_EPISODE_FEATURES: False
SINGLE_CATEGORIES: ["all", "email"] SINGLE_CATEGORIES: ["all", "email"]
OWN_CATEGORIES: CUSTOM_CATEGORIES:
social_media: ['com.google.android.youtube','com.snapchat.android','com.instagram.android','com.zhiliaoapp.musically','com.facebook.katana'] social_media: ['com.google.android.youtube','com.snapchat.android','com.instagram.android','com.zhiliaoapp.musically','com.facebook.katana']
dating: ['com.tinder','com.relance.happycouple','com.kiwi.joyride'] dating: ['com.tinder','com.relance.happycouple','com.kiwi.joyride']
MULTIPLE_CATEGORIES: MULTIPLE_CATEGORIES:

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@ -36,7 +36,7 @@ Parameters description for `[PHONE_APPLICATIONS_FOREGROUND][PROVIDERS][RAPIDS]`:
|`[INCLUDE_EPISODE_FEATURES]`| Set to `True` to extract application episode features as well from the `RAPIDS` provider| |`[INCLUDE_EPISODE_FEATURES]`| Set to `True` to extract application episode features as well from the `RAPIDS` provider|
|`[FEATURES]` | Features to be computed, see table below |`[FEATURES]` | Features to be computed, see table below
|`[SINGLE_CATEGORIES]` | An array of app categories to be *included* in the feature extraction computation. The special keyword `all` represents a category with all the apps from each participant. By default we use the category catalogue pointed by `[APPLICATION_CATEGORIES][CATALOGUE_FILE]` (see the Sensor parameters description table above) |`[SINGLE_CATEGORIES]` | An array of app categories to be *included* in the feature extraction computation. The special keyword `all` represents a category with all the apps from each participant. By default we use the category catalogue pointed by `[APPLICATION_CATEGORIES][CATALOGUE_FILE]` (see the Sensor parameters description table above)
|`[OWN_CATEGORIES]` | An array of collections representing your own app categories. They key of each element is the name of the in-house category and the value is an array of the package names (apps) included in that category. |`[CUSTOM_CATEGORIES]` | An array of collections representing your own app categories. They key of each element is the name of the in-house category and the value is an array of the package names (apps) included in that category.
|`[MULTIPLE_CATEGORIES]` | An array of collections representing meta-categories (a group of categories). They key of each element is the name of the `meta-category` and the value is an array of member app categories. By default we use the category catalogue pointed by `[APPLICATION_CATEGORIES][CATALOGUE_FILE]` (see the Sensor parameters description table above) |`[MULTIPLE_CATEGORIES]` | An array of collections representing meta-categories (a group of categories). They key of each element is the name of the `meta-category` and the value is an array of member app categories. By default we use the category catalogue pointed by `[APPLICATION_CATEGORIES][CATALOGUE_FILE]` (see the Sensor parameters description table above)
|`[SINGLE_APPS]` | An array of apps to be *included* in the feature extraction computation. Use their package name (e.g. `com.google.android.youtube`) or the reserved keyword `top1global` (the most used app by a participant over the whole monitoring study) |`[SINGLE_APPS]` | An array of apps to be *included* in the feature extraction computation. Use their package name (e.g. `com.google.android.youtube`) or the reserved keyword `top1global` (the most used app by a participant over the whole monitoring study)
|`[EXCLUDED_CATEGORIES]` | An array of app categories to be *excluded* from the feature extraction computation. By default we use the category catalogue pointed by `[APPLICATION_CATEGORIES][CATALOGUE_FILE]` (see the Sensor parameters description table above) |`[EXCLUDED_CATEGORIES]` | An array of app categories to be *excluded* from the feature extraction computation. By default we use the category catalogue pointed by `[APPLICATION_CATEGORIES][CATALOGUE_FILE]` (see the Sensor parameters description table above)

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@ -68,11 +68,11 @@ def process_app_features(data, requested_features, time_segment, provider, filte
for mcategory_name, mcategory_content in provider["MULTIPLE_CATEGORIES"].items(): for mcategory_name, mcategory_content in provider["MULTIPLE_CATEGORIES"].items():
if len(mcategory_content) > 0 and mcategory_name not in excluded_categories: if len(mcategory_content) > 0 and mcategory_name not in excluded_categories:
multiple_categories[mcategory_name] = mcategory_content multiple_categories[mcategory_name] = mcategory_content
own_categories = {} custom_categories = {}
if isinstance(provider["OWN_CATEGORIES"], dict): if isinstance(provider["CUSTOM_CATEGORIES"], dict):
for owncategory_name, owncategory_content in provider["OWN_CATEGORIES"].items(): for owncategory_name, owncategory_content in provider["CUSTOM_CATEGORIES"].items():
if len(owncategory_content) > 0 and owncategory_name not in excluded_categories: if len(owncategory_content) > 0 and owncategory_name not in excluded_categories:
own_categories[owncategory_name] = owncategory_content custom_categories[owncategory_name] = owncategory_content
single_apps = provider["SINGLE_APPS"] single_apps = provider["SINGLE_APPS"]
single_categories = list(set(single_categories) - set(excluded_categories)) single_categories = list(set(single_categories) - set(excluded_categories))
single_apps = list(set(single_apps) - set(excluded_apps)) single_apps = list(set(single_apps) - set(excluded_apps))
@ -84,7 +84,7 @@ def process_app_features(data, requested_features, time_segment, provider, filte
# exclude apps in the excluded_apps list # exclude apps in the excluded_apps list
data = data[~data["package_name"].isin(excluded_apps)] data = data[~data["package_name"].isin(excluded_apps)]
features = pd.DataFrame(columns=["local_segment"] + ["".join(feature) for feature in itertools.product(requested_features, single_categories + list(own_categories.keys()) + list(multiple_categories.keys()) + single_apps)]) features = pd.DataFrame(columns=["local_segment"] + ["".join(feature) for feature in itertools.product(requested_features, single_categories + list(custom_categories.keys()) + list(multiple_categories.keys()) + single_apps)])
if not data.empty: if not data.empty:
# deep copy the data for the top1global computation # deep copy the data for the top1global computation
data_global = data.copy() data_global = data.copy()
@ -102,7 +102,7 @@ def process_app_features(data, requested_features, time_segment, provider, filte
filtered_data = data[data["genre"].isin([sc])] filtered_data = data[data["genre"].isin([sc])]
features = compute_features(filtered_data, sc, requested_features, features, time_segment) features = compute_features(filtered_data, sc, requested_features, features, time_segment)
# own categories # own categories
for owncategory_name, owncategory_content in own_categories.items(): for owncategory_name, owncategory_content in custom_categories.items():
filtered_data = data[data["package_name"].isin(owncategory_content)] filtered_data = data[data["package_name"].isin(owncategory_content)]
features = compute_features(filtered_data, owncategory_name, requested_features, features, time_segment) features = compute_features(filtered_data, owncategory_name, requested_features, features, time_segment)
# multiple categories # multiple categories

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@ -346,7 +346,7 @@ properties:
uniqueItems: True uniqueItems: True
items: items:
type: string type: string
OWN_CATEGORIES: CUSTOM_CATEGORIES:
type: ["null", object] type: ["null", object]
additionalProperties: additionalProperties:
type: array type: array