deep_search/data_syn/data_stats.py

# # import json
# # import matplotlib.pyplot as plt
# # from collections import Counter

# # # 读取数据
# # file_path = '/share/project/sunshuang/deep_search/data_syn/data/mixed_data/splits/tagged_domain_keypoints/merged_tagged_domain_keypoints_keywords_count_hop.json'

# # with open(file_path, 'r') as f:
# #     data = json.load(f)

# # # 提取所需字段
# # domains = []
# # totals = []
# # special_totals = []
# # hops = []

# # for entry in data:
# #     domain_info = entry.get('domain_keypoints', {})
# #     # print(domain_info)
# #     # print(f"idx: {entry['idx']}")
# #     keywords_info = entry.get('keywords_count', {})
    
# #     domains.append(domain_info.get('domain', 'Unknown'))
# #     totals.append(keywords_info.get('total', 0))
# #     special_totals.append(keywords_info.get('special_total', 0))
# #     hops.append(entry.get('hop', 0))

# # # 统计domain分布并绘制饼图
# # domain_counts = Counter(domains)
# # domain_labels = [f'{dom} ({cnt})' for dom, cnt in domain_counts.items()]
# # domain_sizes = list(domain_counts.values())

# # plt.figure(figsize=(12, 7))
# # plt.pie(domain_sizes, labels=domain_labels, autopct='%1.1f%%', startangle=140)
# # plt.title('Domain Distribution (Count in Parentheses)')
# # plt.savefig('domain_distribution_pie.png')
# # plt.close()

# # # 辅助函数：绘制带数值标签的直方图
# # def plot_histogram(data, title, xlabel, output_filename):
# #     counts = Counter(data)
# #     sorted_items = sorted(counts.items())
# #     labels, values = zip(*sorted_items)
    
# #     plt.figure(figsize=(10, 6))
# #     bars = plt.bar(labels, values)
# #     plt.title(title)
# #     plt.xlabel(xlabel)
# #     plt.ylabel('Count')
    
# #     for bar in bars:
# #         height = bar.get_height()
# #         plt.text(bar.get_x() + bar.get_width()/2., height, str(height),
# #                  ha='center', va='bottom')
    
# #     plt.savefig(output_filename)
# #     plt.close()

# # # 绘制各个直方图
# # plot_histogram(totals, 'Total Distribution', 'Total Value', 'total_histogram.png')
# # plot_histogram(special_totals, 'Special Total Distribution', 'Special Total Value', 'special_total_histogram.png')
# # plot_histogram(hops, 'Hop Distribution', 'Hop Value', 'hop_histogram.png')

# import json
# import matplotlib.pyplot as plt
# from collections import Counter, OrderedDict

# # 读取数据
# file_path = '/share/project/sunshuang/deep_search/data_syn/data/mixed_data/splits/tagged_domain_keypoints/merged_tagged_domain_keypoints_keywords_count_hop.json'

# with open(file_path, 'r') as f:
#     data = json.load(f)

# # 提取所需字段
# domains = []
# totals = []
# special_totals = []
# hops = []

# for entry in data:
#     domain_info = entry.get('domain_keypoints', {})
#     keywords_info = entry.get('keywords_count', {})
    
#     domains.append(domain_info.get('domain', 'Unknown'))
#     totals.append(keywords_info.get('total', 0))
#     special_totals.append(keywords_info.get('special_total', 0))
#     hops.append(entry.get('hop', 0))

# # 统计domain分布
# domain_counts = Counter(domains)

# # 按照 value 降序排序
# domain_counts = OrderedDict(sorted(domain_counts.items(), key=lambda item: item[1], reverse=True))

# # 保存所有domain及其计数到JSON文件
# domain_count_file = 'domain_counts.json'
# with open(domain_count_file, 'w') as f:
#     json.dump(domain_counts, f, indent=4)

# # 合并数目少于10的domain为Other
# threshold = 100
# filtered_domains = {}
# other_count = 0

# for domain, count in domain_counts.items():
#     if count < threshold:
#         other_count += count
#     else:
#         filtered_domains[domain] = count

# if other_count > 0:
#     filtered_domains['Other'] = other_count

# # 绘制饼图
# domain_labels = [f'{dom} ({cnt})' for dom, cnt in filtered_domains.items()]
# domain_sizes = list(filtered_domains.values())

# plt.figure(figsize=(12, 7))
# plt.pie(domain_sizes, labels=domain_labels, autopct='%1.1f%%', startangle=140)
# plt.title('Domain Distribution (Count in Parentheses)')
# plt.savefig('domain_distribution_pie.png')
# plt.close()

# # 辅助函数：绘制带数值标签的直方图
# def plot_histogram(data, title, xlabel, output_filename):
#     counts = Counter(data)
#     sorted_items = sorted(counts.items())
#     labels, values = zip(*sorted_items)
    
#     plt.figure(figsize=(10, 6))
#     bars = plt.bar(labels, values)
#     plt.title(title)
#     plt.xlabel(xlabel)
#     plt.ylabel('Count')
    
#     for bar in bars:
#         height = bar.get_height()
#         plt.text(bar.get_x() + bar.get_width()/2., height, str(height),
#                  ha='center', va='bottom')
    
#     plt.savefig(output_filename)
#     plt.close()

# # 绘制各个直方图
# plot_histogram(totals, 'Total Distribution', 'Total Value', 'total_histogram.png')
# plot_histogram(special_totals, 'Special Total Distribution', 'Special Total Value', 'special_total_histogram.png')
# plot_histogram(hops, 'Hop Distribution', 'Hop Value', 'hop_histogram.png')



import json
import matplotlib.pyplot as plt
from collections import Counter, OrderedDict

# 读取数据
file_path = '/share/project/sunshuang/deep_search/data_for_rl/musique_tagged/final_selected_dataset.json'

with open(file_path, 'r') as f:
    data = json.load(f)

# 提取所需字段
domains = []
totals = []
special_totals = []
hops = []

for entry in data:
    domain_info = entry.get('domain_keypoints', {})
    keywords_info = entry.get('keywords_count', {})
    
    domains.append(domain_info.get('domain', 'Unknown'))
    totals.append(keywords_info.get('total', 0))
    special_totals.append(keywords_info.get('special_total', 0))
    hops.append(entry.get('hop', 0))

# 统计domain分布
domain_counts = Counter(domains)

# 按照 value 降序排序
domain_counts = OrderedDict(sorted(domain_counts.items(), key=lambda item: item[1], reverse=True))

# 保存所有domain及其计数到JSON文件
domain_count_file = 'domain_counts.json'
with open(domain_count_file, 'w') as f:
    json.dump(domain_counts, f, indent=4)

# 合并数目少于100的domain为Other
threshold = 1
filtered_domains = {}
other_count = 0

for domain, count in domain_counts.items():
    if count < threshold:
        other_count += count
    else:
        filtered_domains[domain] = count

if other_count > 0:
    filtered_domains['Other'] = other_count

# 绘制饼图
domain_labels = [f'{dom}' for dom, cnt in filtered_domains.items()]  # 只显示domain名称
domain_sizes = list(filtered_domains.values())

plt.figure(figsize=(12, 7))
plt.pie(
    domain_sizes,
    labels=None,  # 不直接在饼图上显示标签
    autopct='%1.1f%%',
    startangle=140
)

# 添加图例
plt.legend(
    labels=[f'{dom} ({cnt})' for dom, cnt in filtered_domains.items()],
    loc='upper right',
    bbox_to_anchor=(1.2, 1),  # 图例位置调整到右侧
    fontsize=10
)

plt.title('Domain Distribution (Count in Parentheses)')
plt.tight_layout()  # 优化布局
plt.savefig('domain_distribution_pie.png', bbox_inches='tight')  # 避免裁剪
plt.close()

# 辅助函数：绘制带数值标签的直方图
def plot_histogram(data, title, xlabel, output_filename):
    counts = Counter(data)
    sorted_items = sorted(counts.items())
    labels, values = zip(*sorted_items)
    
    plt.figure(figsize=(10, 6))
    bars = plt.bar(labels, values)
    plt.title(title)
    plt.xlabel(xlabel)
    plt.ylabel('Count')
    
    for bar in bars:
        height = bar.get_height()
        plt.text(bar.get_x() + bar.get_width()/2., height, str(height),
                 ha='center', va='bottom')
    
    plt.savefig(output_filename)
    plt.close()

# 绘制各个直方图
plot_histogram(totals, 'Total Distribution', 'Total Value', 'total_histogram.png')
plot_histogram(special_totals, 'Special Total Distribution', 'Special Total Value', 'special_total_histogram.png')
plot_histogram(hops, 'Hop Distribution', 'Hop Value', 'hop_histogram.png')