| # from googlesearch import search | |
| # from typing import List | |
| # def google_search(query: str, num_results: int = 10) -> List[str]: | |
| # """ | |
| # Execute a Google search and return a list of URLs. | |
| # Args: | |
| # query (str): The search query to submit to Google. | |
| # num_results (int, optional): The number of search results to return. Default is 10. | |
| # Returns: | |
| # List[str]: A list of URLs matching the search query. | |
| # """ | |
| # links = list(search(query, num_results=10)) | |
| # return links | |
| # res = google_search("when does season 14 of grey's anatomy come out -site:en.wikipedia.org") | |
| # print(res) | |
| # def run_evaluation_for_eval(filtered_data, input_list, output_list, dataset_name, output_dir, total_time, split, apply_backoff=False): | |
| # if dataset_name != 'eval': | |
| # raise ValueError(f"Unsupported dataset: {dataset_name}") | |
| # else: | |
| # # Existing evaluation for other datasets | |
| # avg_em, avg_acc, avg_f1, avg_math = [], [], [], [] | |
| # num_valid_answer = 0 | |
| # # If the dataset is eval, track metrics per source | |
| # source_metrics = {} | |
| # for item, input_prompt, result in zip(filtered_data, input_list, output_list): | |
| # if type(result) == str: | |
| # item['Output'] = result | |
| # else: | |
| # item['Output'] = result.outputs[0].text | |
| # if dataset_name in ['gpqa', 'medmcqa']: | |
| # labeled_answer = item["Correct Choice"] | |
| # # labeled_choice_answer = item["Correct Answer"] | |
| # mode = 'choose' | |
| # elif dataset_name in ['math500', 'aime', 'amc']: | |
| # labeled_answer = item["answer"] | |
| # mode = 'gen' | |
| # elif dataset_name in ['eval', 'chinese_simpleqa', 'simpleqa', 'nq', 'triviaqa', 'hotpotqa', 'musique', 'bamboogle', '2wiki']: | |
| # labeled_answer = item["answer"] | |
| # mode = 'qa' | |
| # elif dataset_name in ['pubhealth']: | |
| # labeled_answer = item["answer"] | |
| # mode = 'choose' | |
| # else: | |
| # raise ValueError(f"Unknown dataset_name: {dataset_name}") | |
| # metric, pred_answer = evaluate_predictions(output=item['Output'], labeled_answer=labeled_answer, mode=mode) | |
| # item['Pred_Answer'] = pred_answer | |
| # item['Metrics'] = metric | |
| # item['Question'] = input_prompt | |
| # # Determine the validity of the predicted answer | |
| # my_method_valid = (pred_answer != '' and not (mode == 'choose' and dataset_name == 'gpqa' and len(pred_answer) > 1)) | |
| # avg_em.append(metric['em']) | |
| # avg_acc.append(metric['acc']) | |
| # avg_f1.append(metric['f1']) | |
| # avg_math.append(metric['math_equal']) | |
| # if my_method_valid: | |
| # num_valid_answer += 1 | |
| # # If the dataset is GPQA, attempt to track metrics per source | |
| # if dataset_name == 'eval': | |
| # source = item.get("source", "Unknown") | |
| # if source not in source_metrics: | |
| # source_metrics[source] = {'em': [], 'acc': [], 'f1': [], 'math_equal': [], 'num_valid_answer': 0, 'total_num': 0} | |
| # source_metrics[source]['total_num'] += 1 | |
| # source_metrics[source]['em'].append(metric['em']) | |
| # source_metrics[source]['acc'].append(metric['acc']) | |
| # source_metrics[source]['f1'].append(metric['f1']) | |
| # source_metrics[source]['math_equal'].append(metric['math_equal']) | |
| # if my_method_valid: | |
| # source_metrics[source]['num_valid_answer'] += 1 | |
| # t = time.localtime() | |
| # result_json_name = f'{split}.{t.tm_mon}.{t.tm_mday},{t.tm_hour}:{t.tm_min}.json' | |
| # metrics_json_name = f'{split}.{t.tm_mon}.{t.tm_mday},{t.tm_hour}:{t.tm_min}.metrics.json' | |
| # # Compute overall metrics | |
| # overall_results = { | |
| # 'em': np.mean(avg_em) if len(avg_em) > 0 else 0.0, | |
| # 'acc': np.mean(avg_acc) if len(avg_acc) > 0 else 0.0, | |
| # 'f1': np.mean(avg_f1) if len(avg_f1) > 0 else 0.0, | |
| # 'math_equal': np.mean(avg_math) if len(avg_em) > 0 else 0.0, | |
| # 'num_valid_answer': f'{num_valid_answer} of {len(input_list)}', | |
| # 'query_latency': f'{(total_time / len(input_list) * 1000):.0f} ms', | |
| # } | |
| # # If the dataset is eval, output average metrics per source | |
| # source_avg_metrics = {} | |
| # if dataset_name == 'eval': | |
| # for dm, m in source_metrics.items(): | |
| # source_avg_metrics[dm] = { | |
| # 'em': np.mean(m['em']) if len(m['em']) > 0 else 0, | |
| # 'acc': np.mean(m['acc']) if len(m['acc']) > 0 else 0, | |
| # 'f1': np.mean(m['f1']) if len(m['f1']) > 0 else 0, | |
| # 'math_equal': np.mean(m['math_equal']) if len(m['math_equal']) > 0 else 0, | |
| # 'num_valid_answer': f'{m["num_valid_answer"]} of {m["total_num"]}' | |
| # } | |
| # # 保存总体和分source的指标 | |
| # final_metrics = {'overall': overall_results} | |
| # if dataset_name == 'eval': | |
| # final_metrics['per_source'] = source_avg_metrics | |
| # t = time.localtime() | |
| # result_json_name = f'{split}.{t.tm_mon}.{t.tm_mday},{t.tm_hour}:{t.tm_min}.json' | |
| # metrics_json_name = f'{split}.{t.tm_mon}.{t.tm_mday},{t.tm_hour}:{t.tm_min}.metrics.json' | |
| # if apply_backoff: | |
| # result_json_name = output_dir | |
| # metrics_json_name = output_dir.replace('.json', '.metrics.backoff.json') | |
| # # Save prediction results and metrics | |
| # with open(os.path.join(output_dir, result_json_name), mode='w', encoding='utf-8') as json_file: | |
| # json.dump(filtered_data, json_file, indent=4, ensure_ascii=False) | |
| # with open(os.path.join(output_dir, metrics_json_name), mode='w', encoding='utf-8') as json_file: | |
| # json.dump(final_metrics, json_file, indent=4, ensure_ascii=False) | |
| import json | |
| with open('/share/project/sunshuang/deep_search/search_o1/cache_simpleqa_exurls_qwq/url_cache.json', 'r') as f: | |
| data = json.load(f) | |
| print(f"data len: {len(data)}") | |
| print(f"data[0]: {data['https://www.transparency.org/en/cpi/2024']}") |