Delete the leftover class.

Add more prints.

machine_learning/pipeline.py

@@ -40,6 +40,7 @@ class SensorFeatures:
         print("SensorFeatures initialized.")
 
     def set_sensor_data(self):
+        print("Querying database ...")
         if "proximity" in self.data_types:
             self.df_proximity = proximity.get_proximity_data(
                 self.participants_usernames
@@ -65,6 +66,7 @@ class SensorFeatures:
             raise KeyError("This data type has not been implemented.")
 
     def calculate_features(self):
+        print("Calculating features ...")
         if "proximity" in self.data_types:
             self.df_proximity_counts = proximity.count_proximity(
                 self.df_proximity, self.grouping_variable
@@ -137,6 +139,7 @@ class Labels:
         print("Labels initialized.")
 
     def set_labels(self):
+        print("Querying database ...")
         self.df_esm = esm.get_esm_data(self.participants_usernames)
         print("Got ESM data from the DB.")
         self.df_esm_preprocessed = esm.preprocess_esm(self.df_esm)
@@ -162,6 +165,7 @@ class Labels:
             raise KeyError("This questionnaire has not been implemented as a label.")
 
     def aggregate_labels(self):
+        print("Aggregating labels ...")
         self.df_esm_means = (
             self.df_esm_clean.groupby(
                 ["participant_id", "questionnaire_id"] + self.grouping_variable
@@ -207,6 +211,7 @@ class ModelValidation:
         print("Validation method set.")
 
     def cross_validate(self):
+        print("Running cross validation ...")
         if self.model is None:
             raise TypeError(
                 "Please, specify a machine learning model first, by setting the .model attribute. "