bug fix

src/team_comm_tools/feature_builder.py

@@ -560,7 +560,7 @@ def preprocess_chat_data(self) -> None:
 
         # create the appropriate grouping variables and assert the columns are present
         self.chat_data = preprocess_conversation_columns(self.chat_data, self.column_names, self.grouping_keys, self.cumulative_grouping, self.within_task)
-        # assert_key_columns_present(self.chat_data, self.column_names)
+        assert_key_columns_present(self.chat_data, self.column_names)
         self.chat_data = remove_unhashable_cols(self.chat_data, self.column_names)
 
         # save original column with no preprocessing

src/team_comm_tools/utils/preprocess.py

@@ -37,15 +37,6 @@ def preprocess_conversation_columns(df: pd.DataFrame, column_names: dict, groupi
     else: # case 2: grouping multiple keys, or case 3 but not 3 layers
         df['conversation_num'] = df.groupby(grouping_keys).ngroup()
         df = df[df.columns.tolist()[-1:] + df.columns.tolist()[0:-1]] # make the new column first
-    # assert key columns are present
-    for role, col in column_names.items():
-        if role == 'timestamp_col':
-            continue # skip timestamp column
-        if col not in df.columns:
-            raise KeyError(f"Missing required columns in DataFrame: '{col}' (expected for {role})\n Columns available: {df.columns}")
-        else:
-            print(f"Confirmed that data has {role} column: {col}!")
-            df[col] = df[col].fillna('')
 
     return df