Change own to custom categories name

config.yaml

@@ -113,7 +113,7 @@ PHONE_APPLICATIONS_FOREGROUND:
       COMPUTE: False
       INCLUDE_EPISODE_FEATURES: False
       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']
         dating: ['com.tinder','com.relance.happycouple','com.kiwi.joyride']
       MULTIPLE_CATEGORIES:

docs/features/phone-applications-foreground.md

@@ -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|
 |`[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)
-|`[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)
 |`[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)

src/features/phone_applications_foreground/rapids/main.py

@@ -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():
             if len(mcategory_content) > 0 and mcategory_name not in excluded_categories:
                 multiple_categories[mcategory_name] = mcategory_content
-    own_categories = {}
-    if isinstance(provider["OWN_CATEGORIES"], dict):
-        for owncategory_name, owncategory_content in provider["OWN_CATEGORIES"].items():
+    custom_categories = {}
+    if isinstance(provider["CUSTOM_CATEGORIES"], dict):
+        for owncategory_name, owncategory_content in provider["CUSTOM_CATEGORIES"].items():
             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_categories = list(set(single_categories) - set(excluded_categories))
     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
     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:
         # deep copy the data for the top1global computation
         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])]
                     features = compute_features(filtered_data, sc, requested_features, features, time_segment)
             # 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)]
                 features = compute_features(filtered_data, owncategory_name, requested_features, features, time_segment)
             # multiple categories
@@ -142,5 +142,5 @@ def rapids_features(sensor_data_files, time_segment, provider, filter_data_by_se
         episodes_features = process_app_features(episode_data, requested_episodes_features, time_segment, provider, filter_data_by_segment)
         
         features = pd.merge(episodes_features, features, how='outer', on='local_segment')
-        
+
     return features

tools/config.schema.yaml

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