Update metric.py

metric.py

@@ -46,53 +46,54 @@ def compute(params):
     private_solution_df = private_solution_df.sort_values(params.submission_id_col).reset_index(drop=True)
     private_submission_df = private_submission_df.sort_values(params.submission_id_col).reset_index(drop=True)
 
-    # METRICS Calculation Evaluation
-    # _metric = SOME METRIC FUNCTION
-    def _metric(outputs, targets):
-        # input example: public_solution_df[target_cols], public_submission_df[target_cols]
-        
-        score = 0.5
-        return score
-
-    target_cols = [col for col in solution_df.columns if col not in [params.submission_id_col, "split"]]
-    public_score = _metric(public_solution_df[target_cols], public_submission_df[target_cols])
-    private_score = _metric(private_solution_df[target_cols], private_submission_df[target_cols])
-
 
+    print('public_solution_df', public_solution_df)
+    print('private_solution_df', private_solution_df)
+    
+    # # METRICS Calculation Evaluation
+    # # _metric = SOME METRIC FUNCTION
+    # def _metric(outputs, targets):
+    #     # input example: public_solution_df[target_cols], public_submission_df[target_cols]
+        
+    #     score = 0.5
+    #     return score
 
-    ## LLM Scoring Evaluation
     
 
+    ## LLM Scoring Evaluation
     def _metric(outputs, targets):
-        # input example: public_solution_df[target_cols], public_submission_df[target_cols]
-        
-        score = 0.5
+        # inputs: public_solution_df[target_cols], public_submission_df[target_cols]
+        # output: score
+        for row, output in enumerate(outputs):
+            answer = output['pred']
+            label = str(targets.iloc[row]['pred'])
+            
+            prompt=f"Give me a score from 1 to 10 (higher is better) judging how similar these two captions are. Caption one: {answer}. Caption two: {label}\nScore:"
+            
+            try:
+                response = client.completions.create(
+                    engine="gpt-3.5-turbo-instruct",
+                    prompt=prompt,
+                    temperature=0,
+                    max_tokens=1,
+                )
+                
+                score = int(response.choices[0].text.strip())
+                
+            except:
+                print("Error: API Calling")
+                return
+
         return score
-        
-    submitted_answer = str(submission_df.iloc[0]['pred'])
-    gt = str(solution_df.iloc[0]['pred'])
-    
-    prompt=f"Give me a score from 1 to 10 (higher is better) judging how similar these two captions are. Caption one: {submitted_answer}. Caption two: {gt}\nScore:"
-    
-    try:
-        response = client.completions.create(
-            engine="gpt-3.5-turbo-instruct",
-            prompt=prompt,
-            temperature=0,
-            max_tokens=1,
-        )
-        
-        public_score = int(response.choices[0].text.strip())
-        
-    except:
-        print("Error w/ api")
 
-    
+    target_cols = [col for col in solution_df.columns if col not in [params.submission_id_col, "split"]]
+    public_score = _metric(public_solution_df[target_cols], public_submission_df[target_cols])
+    private_score = _metric(private_solution_df[target_cols], private_submission_df[target_cols])
 
-    private_score = public_score
+    metric_name = "example_metric"
     
-    metric_dict = {"public_score": {"metric1": public_score},
-                   "private_score": {"metric1": private_score}
+    metric_dict = {"public_score": {metric_name: public_score},
+                   "private_score": {metric_name: private_score}
                    }
 
     return metric_dict