From 1ff516cd48fd2155a9abbe0b1b1cd939b0ae6484 Mon Sep 17 00:00:00 2001 From: Rishabh Srivastava Date: Mon, 14 Aug 2023 10:18:01 +0000 Subject: [PATCH] renamed fiels for openai runner --- eval/openai_runner.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/eval/openai_runner.py b/eval/openai_runner.py index ce24220..01ead56 100644 --- a/eval/openai_runner.py +++ b/eval/openai_runner.py @@ -14,8 +14,8 @@ def run_openai_eval(args): question_query_df["generated_query"] = "" question_query_df["reason"] = "" question_query_df["error_msg"] = "" + question_query_df["exact_match"] = 0 question_query_df["correct"] = 0 - question_query_df["subset"] = 0 question_query_df["error_query_gen"] = 0 question_query_df["error_db_exec"] = 0 question_query_df["timeout"] = 0 @@ -84,7 +84,7 @@ def run_openai_eval(args): db_name = row["db_name"] question = row["question"] query_category = row["query_category"] - correct = subset = 0 + exact_match = correct = 0 generated_result = expected_result = None db_creds = { "host": "localhost", @@ -103,23 +103,23 @@ def run_openai_eval(args): query_gen, db_name, db_creds, args.timeout_exec ) generated_result = generated_result.rename(columns=str.lower) - correct = subset = int( + exact_match = correct = int( compare_df( expected_result, generated_result, query_category, question ) ) - if not correct: - subset = subset_df( + if not exact_match: + correct = subset_df( df_sub=expected_result, df_super=generated_result, query_category=query_category, question=question, verbose=args.verbose, ) + row["exact_match"] = int(exact_match) row["correct"] = int(correct) - row["subset"] = int(subset) row["error_msg"] = "" - if subset: + if correct: total_correct += 1 except QueryCanceledError as e: row["timeout"] = 1 @@ -136,8 +136,8 @@ def run_openai_eval(args): output_df.to_csv(args.output_file, index=False, float_format="%.2f") # get average accuracy - avg_acc = output_df["correct"].sum() / len(output_df) + avg_acc = output_df["exact_match"].sum() / len(output_df) print(f"Average accuracy: {avg_acc:.2f}") # get average subset or correct accuracy - avg_subset = output_df["subset"].sum() / len(output_df) + avg_subset = output_df["correct"].sum() / len(output_df) print(f"Average subset accuracy: {avg_subset:.2f}")