calls_sms_features() now returns all communication features.

features/communication.py

@@ -14,7 +14,7 @@ FEATURES_CALLS = (
     + ["duration_total_" + call_types.get(1), "duration_total_" + call_types.get(2)]
     + ["duration_max_" + call_types.get(1), "duration_max_" + call_types.get(2)]
     + ["no_" + call_types.get(1) + "_ratio", "no_" + call_types.get(2) + "_ratio"]
-    + ["no_contacts"]
+    + ["no_contacts_calls"]
 )
 
 # FEATURES_CALLS =
@@ -29,7 +29,7 @@ FEATURES_SMS = (
     ["no_sms_all"]
     + ["no_" + sms_type for sms_type in sms_types.values()]
     + ["no_" + sms_types.get(1) + "_ratio", "no_" + sms_types.get(2) + "_ratio"]
-    + ["no_contacts"]
+    + ["no_contacts_sms"]
 )
 # FEATURES_SMS =
 # ["no_sms_all",
@@ -156,6 +156,7 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
         * the number of communication contacts by type. 
     """
     if "call_type" in comm_df:
+        data_type = "calls"
         comm_counts = (
             comm_df.value_counts(subset=["participant_id", "call_type"])
             .unstack()
@@ -197,6 +198,7 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
         # If there were no missed calls, this exception is raised.
         # But we are dropping the column anyway, so no need to deal with the exception.
     elif "message_type" in comm_df:
+        data_type = "sms"
         comm_counts = (
             comm_df.value_counts(subset=["participant_id", "message_type"])
             .unstack()
@@ -216,7 +218,7 @@ def count_comms(comm_df: pd.DataFrame) -> pd.DataFrame:
         enumerate_contacts(comm_df)
         .groupby(["participant_id"])
         .nunique()["contact_id"]
-        .rename("no_contacts")
+        .rename("no_contacts_" + data_type)
     )
     # Number of communication contacts
     comm_features = comm_features.join(comm_contacts_counts)
@@ -282,7 +284,7 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
     df_calls_sms: pd.DataFrame
         The list of features relating calls and sms data for every participant.
         These are:
-        * proportion_calls:
+        * proportion_calls_all:
             proportion of calls in total number of communications
         * proportion_calls_incoming:
             proportion of incoming calls in total number of incoming/received communications
@@ -295,9 +297,7 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
     """
     count_calls = count_comms(df_calls)
     count_sms = count_comms(df_sms)
-    count_joined = count_calls.merge(
+    count_joined = count_calls.join(count_sms).assign(
-        count_sms, on="participant_id", suffixes=("_calls", "_sms")
-    ).assign(  # Merge calls and sms features
         proportion_calls_all=(
             lambda x: x.no_calls_all / (x.no_calls_all + x.no_sms_all)
         ),
@@ -314,7 +314,5 @@ def calls_sms_features(df_calls: pd.DataFrame, df_sms: pd.DataFrame) -> pd.DataF
             lambda x: x.no_contacts_calls / (x.no_contacts_calls + x.no_contacts_sms)
         )
         # Calculate new features and create additional columns
-    )[
+    )
-        FEATURES_CONTACT
-    ]  # Filter out only the relevant features
     return count_joined

test/test_communication.py

@@ -86,8 +86,8 @@ class CallsFeatures(unittest.TestCase):
 
     def test_calls_sms_features(self):
         self.features_call_sms = calls_sms_features(self.calls, self.sms)
-        print(self.features_call_sms)
         self.assertIsInstance(self.features_call_sms, pd.DataFrame)
         self.assertCountEqual(
-            self.features_call_sms.columns.to_list(), FEATURES_CONTACT
+            self.features_call_sms.columns.to_list(),
+            FEATURES_CALLS + FEATURES_SMS + FEATURES_CONTACT,
         )