| 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() |
|
|
