import json from collections import defaultdict,OrderedDict, Counter from tqdm import tqdm import random import matplotlib.pyplot as plt def analyze_and_visualize_sources(final_dataset, output_image_path="source_distribution_selected_data.png", output_json_path="source_counts.json"): """ 统计 final_dataset 中 source 的分布，并绘制饼图和保存为 JSON 文件。 :param final_dataset: 输入的数据列表，每个元素是一个字典 :param output_image_path: 饼图保存路径（默认为 "source_distribution.png"） :param output_json_path: JSON 文件保存路径（默认为 "source_counts.json"） """ # 提取所有包含 source 的项 sources = [item["source"] for item in final_dataset if "source" in item] # 统计 source 的分布 source_counts = Counter(sources) # 将统计结果保存为 JSON 文件 with open(output_json_path, "w", encoding="utf-8") as f: json.dump(source_counts, f, ensure_ascii=False, indent=4) print(f"Source 分布已保存到 {output_json_path}") # 绘制饼图 labels = list(source_counts.keys()) counts = list(source_counts.values()) plt.figure(figsize=(8, 8)) plt.pie(counts, labels=labels, autopct='%1.1f%%', startangle=140) plt.title("Source Distribution") plt.axis('equal') # 确保饼图为正圆 # 保存饼图为图片文件 plt.savefig(output_image_path) plt.close() print(f"Source 分布饼图已保存到 {output_image_path}") # 读取原始数据 with open('/share/project/sunshuang/deep_search/data_for_rl/musique_tagged/musique_tagged_domain_keypoints_keywords_count.json') as f: data = json.load(f) for idx, item in enumerate(data): # 增加Question key if "question" in item: item["Question"] = item["question"] # step 1 预处理 Domain 分类 # 统计原始 domain 分布 print("step 1: preprocss domain data") domain_counter = defaultdict(int) for item in data: domain = item["domain_keypoints"]["domain"] domain_counter[domain] += 1 # 确定有效 domain（>=100 条） # valid_domains = {d for d, cnt in domain_counter.items() if cnt >= 100} # other_domains = [d for d, cnt in domain_counter.items() if cnt < 100] valid_domains = {d for d, cnt in domain_counter.items() if cnt >= 500} other_domains = [d for d, cnt in domain_counter.items() if cnt < 500] print(f"valid_domains: {len(valid_domains)}") print(f"other_domains: {len(other_domains)}") # 重构数据集（合并 small domains 到 other） processed_data = [] for item in data: original_domain = item["domain_keypoints"]["domain"] if original_domain in valid_domains: new_domain = original_domain else: new_domain = "other" new_item = { **item, "domain_keypoints": { **item["domain_keypoints"], "domain": new_domain } } processed_data.append(new_item) # step 2 分配样本配额 # 计算新 domain 分布 print("step 2: calculate domain distribution") new_domain_counts = defaultdict(int) for item in processed_data: domain = item["domain_keypoints"]["domain"] new_domain_counts[domain] += 1 # 确定最终 domain 列表 final_domains = list(valid_domains) if new_domain_counts.get("other", 0) > 0: final_domains.append("other") print(f"new_domain_counts: {len(new_domain_counts)}") print(f"final_domains: {len(final_domains)}") # 分配 2000 条样本配额 total_samples = 4000 num_domains = len(final_domains) base_quota = total_samples // num_domains remainder = total_samples % num_domains print(f"base_quota: {base_quota}") print(f"remainder: {remainder}") domain_quotas = {} # cnttt = 0 for idx, domain in enumerate(final_domains): domain_quotas[domain] = base_quota + (1 if idx < remainder else 0) # cnttt += domain_quotas[domain] # print(f"cnttt: {cnttt}") # step 3 统计 Keypoints 分布 print("step 3 calculating keypoints distribution") domain_keypoint_dist = defaultdict(lambda: defaultdict(int)) for item in tqdm(processed_data, desc="Calculating keypoints distribution"): domain = item["domain_keypoints"]["domain"] keypoints = item["domain_keypoints"]["key_points"] for kp in keypoints: domain_keypoint_dist[domain][kp] += 1 # domain内部按照key points降序排列 for domain, keypoint_counts in domain_keypoint_dist.items(): sorted_keypoints = sorted(keypoint_counts.items(), key=lambda x: x[1], reverse=True) domain_keypoint_dist[domain] = OrderedDict(sorted_keypoints) # 保存分布文件 with open("domain_keypoints_distribution_without_remove_dup.json", "w") as f: json.dump(domain_keypoint_dist, f, indent=4,ensure_ascii=False) # step 4 分层抽样与去重 print("step 4: select data") final_dataset = [] question_set = set() real_selected_cnt = 0 for domain in tqdm(final_domains, desc="Processing domains"): # 获取当前 domain 的所有数据 domain_data = [item for item in processed_data if item["domain_keypoints"]["domain"] == domain] print(f"-------------- process {domain}") print(f"data: {len(domain_data)}") # 计算 keypoint 配额 keypoints = list(domain_keypoint_dist[domain].keys()) # random.shuffle(keypoints) # 随机打乱顺序 print(f"key points {len(keypoints)}") print(f"domain_quotas: {domain_quotas[domain]}") # kp_base = domain_quotas[domain] // len(keypoints) # print(f"kp_base: {kp_base}") # kp_remainder = domain_quotas[domain] % len(keypoints) # print(f"kp_remainder: {kp_remainder}") # 因为domain_quotas远远小于keypoint的数目，改为将所有的数据按照total降序排序，然后从上往下选，重复上面的操作，直到选完 sorted_domain_data = sorted(domain_data, key=lambda x: x["keywords_count"]["total"], reverse=True) selected_cnt = 0 cycle_cnt = 0 selected_index = [] # 已选择的index while selected_cnt < domain_quotas[domain]: # 当前选择的数目小于domain_quotas[domain] cycle_cnt += 1 # print(f"cycle_cnt: {cycle_cnt}") selected_keypoints = set() # 每次循环都清零 for idx, item in enumerate(sorted_domain_data): # 遍历所有的数据 if selected_cnt >= domain_quotas[domain]: break if idx not in selected_index: # 当前数据未被选择 dup_keypoints = False for key_point in item["domain_keypoints"]["key_points"]: # 确保数据的key point不包含在已经选择的key point里 if key_point in selected_keypoints: dup_keypoints = True break if dup_keypoints: # 重复key points continue if item["Question"] not in question_set: final_dataset.append(item) question_set.add(item["Question"]) selected_keypoints.update(item["domain_keypoints"]["key_points"]) selected_index.append(idx) selected_cnt += 1 real_selected_cnt += selected_cnt print(f"cycle_cnt: {cycle_cnt}") print(f"real_selected_cnt: {real_selected_cnt}") # # 按 keypoint 分层抽样 # selected = [] # for kp_idx, kp in enumerate(keypoints): # # 分配配额 # kp_quota = kp_base + (1 if kp_idx < kp_remainder else 0) # # print(f"kp_quota: {kp_quota}") # # 获取包含该 keypoint 的问题 # kp_items = [item for item in domain_data if kp in item["domain_keypoints"]["key_points"]] # # 按 total 降序排序并选择 # sorted_items = sorted(kp_items, key=lambda x: x["keywords_count"]["total"], reverse=True) # selected.extend(sorted_items[:kp_quota]) # # 去重并保留顺序，这一步导致选不够 # dup_cnt = 0 # for item in selected: # q = item["Question"] # if q not in question_set: # final_dataset.append(item) # question_set.add(q) # else: # dup_cnt += 1 # print(f"dup_cnt: {dup_cnt}") # 精确控制最终数量 # final_dataset = sorted(final_dataset, key=lambda x: x["idx"]) print(f"final_dataset len: {len(final_dataset)}") # final_dataset = final_dataset analyze_and_visualize_sources(final_dataset) # step 5 保存结果 with open("final_selected_dataset.json", "w") as f: json.dump(final_dataset, f, indent=4, ensure_ascii=False)