| | import pandas as pd |
| | import random |
| | import copy |
| | from tqdm import tqdm |
| | import time |
| | import json |
| | from openai import OpenAI |
| | import os |
| | import dotenv |
| | import tempfile |
| | import numpy as np |
| | import pytrec_eval |
| | from typing import List, Dict, Any, Optional |
| | from dataclasses import dataclass |
| | from concurrent.futures import ThreadPoolExecutor |
| |
|
| | dotenv.load_dotenv() |
| |
|
| | @dataclass |
| | class RankingResult: |
| | query: str |
| | correct_passage: str |
| | ranking: str |
| | correct_idx: int |
| | passages: List[str] |
| | ranks: List[int] |
| |
|
| | class Evaluator: |
| | @staticmethod |
| | def clean_ranking_response(response: str) -> List[int]: |
| | return [int(num) for num in ''.join(c if c.isdigit() else ' ' for c in response).split()] |
| | |
| | @staticmethod |
| | def write_trec_files(results: List[RankingResult]) -> tuple[str, str]: |
| | run_file = tempfile.NamedTemporaryFile(delete=False).name |
| | qrels_file = tempfile.NamedTemporaryFile(delete=False).name |
| | |
| | with open(run_file, 'w') as f_run, open(qrels_file, 'w') as f_qrel: |
| | for i, result in enumerate(results): |
| | qid = str(i) |
| | correct_docid = f"passage_{result.correct_idx}" |
| | f_qrel.write(f"{qid} 0 {correct_docid} 1\n") |
| | seen_ranks = set() |
| | adjusted_ranks = [] |
| | |
| | for rank in result.ranks: |
| | while rank in seen_ranks: |
| | rank += 1 |
| | seen_ranks.add(rank) |
| | adjusted_ranks.append(rank) |
| | |
| | for rank_position, passage_num in enumerate(adjusted_ranks, 1): |
| | docid = f"passage_{passage_num+1}" |
| | score = 1.0/rank_position |
| | f_run.write(f"{qid} Q0 {docid} {rank_position} {score:.4f} run\n") |
| | |
| | return qrels_file, run_file |
| | |
| | @staticmethod |
| | def calculate_metrics(qrels_file: str, run_file: str) -> Dict[str, float]: |
| | with open(qrels_file) as f_qrel, open(run_file) as f_run: |
| | qrel = pytrec_eval.parse_qrel(f_qrel) |
| | run = pytrec_eval.parse_run(f_run) |
| | |
| | evaluator = pytrec_eval.RelevanceEvaluator( |
| | qrel, |
| | {'ndcg_cut.1', 'ndcg_cut.5', 'ndcg_cut.10', 'recip_rank', 'recall.5'} |
| | ) |
| | scores = evaluator.evaluate(run) |
| | |
| | metrics = { |
| | 'NDCG@1': 0.0, |
| | 'NDCG@5': 0.0, |
| | 'NDCG@10': 0.0, |
| | 'MRR': 0.0, |
| | 'Recall@5': 0.0 |
| | } |
| | |
| | for query_scores in scores.values(): |
| | metrics['NDCG@1'] += query_scores['ndcg_cut_1'] |
| | metrics['NDCG@5'] += query_scores['ndcg_cut_5'] |
| | metrics['NDCG@10'] += query_scores['ndcg_cut_10'] |
| | metrics['MRR'] += query_scores['recip_rank'] |
| | metrics['Recall@5'] += query_scores['recall_5'] |
| | |
| | num_queries = len(scores) |
| | return {k: round(v / num_queries, 4) for k, v in metrics.items()} |
| | |
| |
|
| | def load_results(filename: str) -> List[RankingResult]: |
| | with open(filename, 'r', encoding='utf-8') as f: |
| | results_data = json.load(f) |
| | |
| | results = [] |
| | for data in results_data: |
| | result = RankingResult( |
| | query=data['query'], |
| | correct_passage=data['correct_passage'], |
| | ranking=data['ranking'], |
| | correct_idx=data['correct_idx'], |
| | passages=data['passages'], |
| | ranks=data['ranks'] |
| | ) |
| | results.append(result) |
| | |
| | return results |
| |
|
| | def main(): |
| | loaded_results = load_results('./your-output.json') |
| | qrels_file, run_file = Evaluator.write_trec_files(loaded_results) |
| | |
| | print("\nQRELS file contents:") |
| | with open(qrels_file, 'r') as f: |
| | print(f.read()) |
| | |
| | print("\nRun file contents:") |
| | with open(run_file, 'r') as f: |
| | print(f.read()) |
| | |
| | metrics = Evaluator.calculate_metrics(qrels_file, run_file) |
| | |
| | print("\nEvaluation Results:") |
| | for metric, score in metrics.items(): |
| | print(f"{metric}: {score:.4f}") |
| | |
| | os.unlink(qrels_file) |
| | os.unlink(run_file) |
| | results_df = pd.DataFrame([vars(r) for r in loaded_results]) |
| | results_df.to_csv('ranking-results.csv', index=False) |
| | |
| | main() |
| |
|
