refactor: create log_wandb method

lib/questions_eval/run_mimoracle.py

@@ -249,6 +249,19 @@ def merge_evaluate_df(df: pd.DataFrame, row: pd.Series, evaluation_chain) -> pd.
     return df_joined
 
 
+def log_wandb(df: pd.DataFrame) -> dict:
+    log_dict = {
+        f"{stat}/score/{score_type}": (
+            df[f"{score_type}"].agg(stat)
+            if stat == "sum"
+            else df[f"{score_type}"].agg("sum") / len(df)
+        )
+        for stat in ["sum", "mean"]
+        for score_type in ["consistency", "conformity", "coverage"]
+    }
+    return log_dict
+
+
 @hydra.main(config_path="./configs", config_name="run_mimoracle.yaml")
 def main(cfg: DictConfig):
     # Initialize WandB and log the models
@@ -283,15 +296,7 @@ def main(cfg: DictConfig):
     df_joined = merge_evaluate_df(df, df_questions, evaluation_chain)
 
     # Log results in wandb
-    log_dict = {
-        f"{stat}/score/{score_type}": (
-            df_joined[f"{score_type}"].agg(stat)
-            if stat == "sum"
-            else df_joined[f"{score_type}"].agg("sum") / len(df_joined)
-        )
-        for stat in ["sum", "mean"]
-        for score_type in ["consistency", "conformity", "coverage"]
-    }
+    log_dict = log_wandb(df_joined)
     for key, value in log_dict.items():
         wandb.run.summary[key] = value
     wandb.log({"dataset/evaluation_mimoracle_gpt4o_retest": wandb.Table(dataframe=df_joined)})