Refactor google_activity_recognition feature: replace "metrics" with "features"

Co-authored-by: Meng Li <AnnieLM1996@gmail.com>

config.yaml

@@ -80,7 +80,7 @@ BLUETOOTH:
 
 GOOGLE_ACTIVITY_RECOGNITION:
   DAY_SEGMENTS: *day_segments
-  METRICS: ['count','mostcommonactivity','countuniqueactivities','activitychangecount','sumstationary','summobile','sumvehicle']
+  FEATURES: ['count','mostcommonactivity','countuniqueactivities','activitychangecount','sumstationary','summobile','sumvehicle']
 
 BATTERY:
   DAY_SEGMENTS: *day_segments

docs/features/extracted.rst

@@ -624,7 +624,7 @@ See `Google Activity Recognition Config Code`_
     
 ..  - Extract the deltas in Google Activity Recognition dataset: ``expand("data/processed/{pid}/plugin_google_activity_recognition_deltas.csv", pid=config["PIDS"]),``
     
-- Extract Sensor Metrics:
+- Extract Sensor Features:
 
     | ``expand("data/processed/{pid}/google_activity_recognition_{segment}.csv",pid=config["PIDS"],``
     |                ``segment = config["GOOGLE_ACTIVITY_RECOGNITION"]["DAY_SEGMENTS"]),``
@@ -643,7 +643,7 @@ See `Google Activity Recognition Config Code`_
 
     - **Script:** ``src/features/google_activity_recognition_deltas.R`` - See the google_activity_recognition_deltas.R_ script.
 
-- **Rule:** ``rules/features.snakefile/activity_metrics`` - See the activity_metrics_ rule.
+- **Rule:** ``rules/features.snakefile/activity_features`` - See the activity_features_ rule.
 
     - **Script:** ``ssrc/features/google_activity_recognition.py`` - See the google_activity_recognition.py_ script.
     
@@ -655,14 +655,14 @@ See `Google Activity Recognition Config Code`_
 Name	        Description
 ============    ===================
 day_segment     The particular ``day_segments`` that will be analyzed. The available options are ``daily``, ``morning``, ``afternoon``, ``evening``, ``night``
-metrics         The different measures that can be retrieved from the Google Activity Recognition dataset. See :ref:`Available Google Activity Recognition Metrics <google-activity-recognition-available-metrics>` Table below
+features         The different measures that can be retrieved from the Google Activity Recognition dataset. See :ref:`Available Google Activity Recognition Features <google-activity-recognition-available-features>` Table below
 ============    ===================
 
-.. _google-activity-recognition-available-metrics:
+.. _google-activity-recognition-available-features:
 
-**Available Google Activity Recognition Metrics**
+**Available Google Activity Recognition Features**
 
-The following table shows a list of the available metrics for the Google Activity Recognition dataset. 
+The following table shows a list of the available features for the Google Activity Recognition dataset. 
 
 ======================   ============    =============
 Name                     Units           Description

rules/features.snakefile

@@ -73,13 +73,13 @@ rule bluetooth_features:
     script:
         "../src/features/bluetooth_features.R"
         
-rule activity_metrics:
+rule activity_features:
     input:
         gar_events = "data/raw/{pid}/plugin_google_activity_recognition_with_datetime.csv",
         gar_deltas = "data/processed/{pid}/plugin_google_activity_recognition_deltas.csv"
     params:
         segment = "{day_segment}",
-        metrics = config["GOOGLE_ACTIVITY_RECOGNITION"]["METRICS"]
+        features = config["GOOGLE_ACTIVITY_RECOGNITION"]["FEATURES"]
     output:
         "data/processed/{pid}/google_activity_recognition_{day_segment}.csv"
     script:

src/features/google_activity_recognition.py

@@ -4,12 +4,12 @@ import scipy.stats as stats
 from features_utils import splitOvernightEpisodes, splitMultiSegmentEpisodes
 
 day_segment = snakemake.params["segment"]
-metrics = snakemake.params["metrics"]
+features = snakemake.params["features"]
 
 #Read csv into a pandas dataframe
 data = pd.read_csv(snakemake.input['gar_events'],parse_dates=['local_date_time'])
 ar_deltas = pd.read_csv(snakemake.input['gar_deltas'],parse_dates=["local_start_date_time", "local_end_date_time", "local_start_date", "local_end_date"])
-columns = list("ar_" + str(day_segment) + "_" + column for column in metrics)
+columns = list("ar_" + str(day_segment) + "_" + column for column in features)
 
 if data.empty:
     finalDataset = pd.DataFrame(columns = columns)
@@ -31,30 +31,30 @@ else:
         finalDataset = pd.DataFrame(columns = columns)
     else:
         #Finding the count of samples of the day
-        if("count" in metrics):
+        if("count" in features):
             finalDataset["ar_" + str(day_segment) + "_count"] = resampledData['activity_type'].resample('D').count()
 
         #Finding most common activity of the day
-        if("mostcommonactivity" in metrics):
+        if("mostcommonactivity" in features):
             finalDataset["ar_" + str(day_segment) + "_mostcommonactivity"] = resampledData['activity_type'].resample('D').apply(lambda x: stats.mode(x)[0] if len(stats.mode(x)[0]) != 0 else None)
 
         #finding different number of activities during a day
-        if("countuniqueactivities" in metrics):
+        if("countuniqueactivities" in features):
             finalDataset["ar_" + str(day_segment) + "_countuniqueactivities"] = resampledData['activity_type'].resample('D').nunique()
         
         #finding Number of times activity changed
-        if("activitychangecount" in metrics):
+        if("activitychangecount" in features):
             resampledData['activity_type_shift'] = resampledData['activity_type'].shift().fillna(resampledData['activity_type'].head(1))
             resampledData['different_activity'] = np.where(resampledData['activity_type']!=resampledData['activity_type_shift'],1,0)
             finalDataset["ar_" + str(day_segment) + "_activitychangecount"] = resampledData['different_activity'].resample('D').sum()
 
 
-        deltas_metrics = {'sumstationary':['still','tilting'], 
+        deltas_features = {'sumstationary':['still','tilting'], 
                         'summobile':['on_foot','running','on_bicycle'],
                         'sumvehicle':['in_vehicle']}
         
-        for column, activity_labels in deltas_metrics.items():
-            if column in metrics:
+        for column, activity_labels in deltas_features.items():
+            if column in features:
                 finalDataset["ar_" + str(day_segment) + "_"+str(column)] = (ar_deltas[ar_deltas['activity'].isin(pd.Series(activity_labels))]  
                         .groupby(['local_start_date'])['time_diff']  
                         .agg({"ar_" + str(day_segment) + "_" + str(column) :'sum'}))