edit code

.ipynb_checkpoints/utils-checkpoint.py

@@ -59,6 +59,8 @@ def get_multiple_predictions(csv):
     # Vectorise text and get topic predictions 
     text_vectors = tfidf.transform(df['sequence_clean'])
     pred_results = pd.DataFrame(svc.predict(text_vectors), columns = labels)
+    pred_results['others'] = pred_results[labels].max(axis=1)
+    pred_results['others'] = pred_results['others'].apply(lambda x: 1 if x == 0 else 0)
 
     # Vectorise text and get sentiment predictions 
     text_vectors_sentiment = tfidf_sentiment.transform(df['sequence_clean'])
@@ -66,8 +68,6 @@ def get_multiple_predictions(csv):
     
     # Join back to original sequence
     final_results = df.join(pred_results).join(pred_results_sentiment)
-    final_results['others'] = final_results[labels].max(axis=1)
-    final_results['others'] = final_results['others'].apply(lambda x: 1 if x == 0 else 0)
     
     final_results.drop(columns=['sequence_clean'], inplace=True)
     

utils.py

@@ -59,6 +59,8 @@ def get_multiple_predictions(csv):
     # Vectorise text and get topic predictions 
     text_vectors = tfidf.transform(df['sequence_clean'])
     pred_results = pd.DataFrame(svc.predict(text_vectors), columns = labels)
+    pred_results['others'] = pred_results[labels].max(axis=1)
+    pred_results['others'] = pred_results['others'].apply(lambda x: 1 if x == 0 else 0)
 
     # Vectorise text and get sentiment predictions 
     text_vectors_sentiment = tfidf_sentiment.transform(df['sequence_clean'])
@@ -66,8 +68,6 @@ def get_multiple_predictions(csv):
     
     # Join back to original sequence
     final_results = df.join(pred_results).join(pred_results_sentiment)
-    final_results['others'] = final_results[labels].max(axis=1)
-    final_results['others'] = final_results['others'].apply(lambda x: 1 if x == 0 else 0)
     
     final_results.drop(columns=['sequence_clean'], inplace=True)