Fill communication features with appropriate values.

machine_learning/pipeline.py

@@ -75,6 +75,11 @@ class SensorFeatures:
             self.df_features_all = safe_outer_merge_on_index(
                 self.df_features_all, self.df_calls_sms
             )
+            self.df_features_all.fillna(
+                value=communication.FILL_NA_CALLS_SMS_ALL,
+                inplace=True,
+                downcast="infer",
+            )
 
     def get_features(self, data_type, feature_names) -> pd.DataFrame:
         if data_type == "proximity":