Update metric.py

metric.py

@@ -1,5 +1,10 @@
+import os
 from openai import OpenAI
-client = OpenAI(api_key = '')
+if "OPENAI" in os.environ:
+    pass
+else:
+    print('Doesn\'t find OPENAI')
+client = OpenAI(api_key = os.environ['OPENAI'])
 
 import pandas as pd
 from huggingface_hub import hf_hub_download
@@ -26,9 +31,47 @@ def compute(params):
     )
     submission_df = pd.read_csv(submission_file)
 
+    public_ids = solution_df[solution_df.split == "public"][params.submission_id_col].values
+    private_ids = solution_df[solution_df.split == "private"][params.submission_id_col].values
+
+    public_solution_df = solution_df[solution_df[params.submission_id_col].isin(public_ids)]
+    public_submission_df = submission_df[submission_df[params.submission_id_col].isin(public_ids)]
+
+    private_solution_df = solution_df[solution_df[params.submission_id_col].isin(private_ids)]
+    private_submission_df = submission_df[submission_df[params.submission_id_col].isin(private_ids)]
+
+    public_solution_df = public_solution_df.sort_values(params.submission_id_col).reset_index(drop=True)
+    public_submission_df = public_submission_df.sort_values(params.submission_id_col).reset_index(drop=True)
+
+    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])
+
+
+
+    ## LLM Scoring Evaluation
+    
+
+    def _metric(outputs, targets):
+        # input example: public_solution_df[target_cols], public_submission_df[target_cols]
+        
+        score = 0.5
+        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:
@@ -44,8 +87,10 @@ def compute(params):
     except:
         print("Error w/ api")
 
+    
+
     private_score = public_score
-        
+    
     metric_dict = {"public_score": {"metric1": public_score},
                    "private_score": {"metric1": private_score}
                    }