Merge features into a common df.

But first, group communication by the grouping_variable.

exploration/ex_ml_pipeline.py

@@ -159,11 +159,15 @@ from machine_learning import pipeline
 # %%
 with open("../machine_learning/config/minimal_features.yaml", "r") as file:
     sensor_features_params = yaml.safe_load(file)
+print(sensor_features_params)
 
 # %%
 sensor_features = pipeline.SensorFeatures(**sensor_features_params)
 sensor_features.data_types
 
+# %%
+sensor_features.data_types = ["proximity", "communication"]
+
 # %%
 sensor_features.get_sensor_data("proximity")
 
@@ -179,6 +183,12 @@ sensor_features.calculate_features()
 # %%
 sensor_features.get_features("proximity", "all")
 
+# %%
+sensor_features.get_features("communication", "all")
+
+# %%
+sensor_features.get_features("all", "all")
+
 # %%
 with open("../machine_learning/config/minimal_labels.yaml", "r") as file:
     labels_params = yaml.safe_load(file)

machine_learning/config/minimal_features.yaml

@@ -1,4 +1,4 @@
-grouping_variable: date_lj
+grouping_variable: [date_lj]
 features:
   proximity:
     all

machine_learning/pipeline.py

@@ -12,7 +12,7 @@ from machine_learning import QUESTIONNAIRE_IDS, QUESTIONNAIRE_IDS_RENAME
 class SensorFeatures:
     def __init__(
         self,
-        grouping_variable: str,
+        grouping_variable: list,
         features: dict,
         participants_usernames: Collection = None,
     ):
@@ -26,6 +26,8 @@ class SensorFeatures:
             )
         self.participants_usernames = participants_usernames
 
+        self.df_features_all = pd.DataFrame()
+
         self.df_proximity = pd.DataFrame()
         self.df_proximity_counts = pd.DataFrame()
 
@@ -58,13 +60,21 @@ class SensorFeatures:
     def calculate_features(self):
         if "proximity" in self.data_types:
             self.df_proximity_counts = proximity.count_proximity(
-                self.df_proximity, ["participant_id", self.grouping_variable]
+                self.df_proximity, self.grouping_variable
             )
+            self.df_features_all = safe_outer_merge_on_index(
+                self.df_features_all, self.df_proximity_counts
+            )
+
         if "communication" in self.data_types:
             self.df_calls_sms = communication.calls_sms_features(
-                df_calls=self.df_calls, df_sms=self.df_sms
+                df_calls=self.df_calls,
+                df_sms=self.df_sms,
+                group_by=self.grouping_variable,
+            )
+            self.df_features_all = safe_outer_merge_on_index(
+                self.df_features_all, self.df_calls_sms
             )
-            # TODO Think about joining dataframes.
 
     def get_features(self, data_type, feature_names) -> pd.DataFrame:
         if data_type == "proximity":
@@ -75,6 +85,8 @@ class SensorFeatures:
             if feature_names == "all":
                 feature_names = communication.FEATURES_CALLS_SMS_ALL
             return self.df_calls_sms[feature_names]
+        elif data_type == "all":
+            return self.df_features_all
         else:
             raise KeyError("This data type has not been implemented.")
 
@@ -124,6 +136,22 @@ class Labels:
             raise KeyError("This questionnaire has not been implemented as a label.")
 
 
+def safe_outer_merge_on_index(left, right):
+    if left.empty:
+        return right
+    elif right.empty:
+        return left
+    else:
+        return pd.merge(
+            left,
+            right,
+            how="outer",
+            left_index=True,
+            right_index=True,
+            validate="one_to_one",
+        )
+
+
 class MachineLearningPipeline:
     def __init__(
         self,