Filtered out rows with dictionary in user_label_df

The idea is to check the data type using isinstance() and then apply this check on the entire data frame as a whole instead of doing it iteratively on each row which is much slower.

These rows are then filtered out of the original dataframe leaving behind only the non-dict rows.

emission/analysis/modelling/trip_model/greedy_similarity_binning.py

@@ -295,6 +295,9 @@ def _generate_predictions(self):
             # compute unique label sets and their probabilities in one cluster
             # 'p' refers to probability
             group_cols = user_label_df.columns.tolist()
+            # Filtering out rows from the user_label_df if they are dictionary objects which come from the survey inputs provided by the users instead of multilabels
+            if 'trip_user_input' in group_cols:
+                user_label_df = user_label_df.loc[user_label_df['trip_user_input'].apply(lambda x: not isinstance(x, dict))]
             unique_labels = user_label_df.groupby(group_cols).size().reset_index(name='uniqcount')
             unique_labels['p'] = unique_labels.uniqcount / sum_trips
             labels_columns = user_label_df.columns.to_list()