deep_search/data_syn/data_keypoints_stats.py

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

# 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 key_points 并统计每个关键词的出现次数
key_points_counter = Counter()

for entry in data:
    domain_keypoints = entry.get('domain_keypoints', {})
    key_points = domain_keypoints.get('key_points', [])
    key_points_counter.update(key_points)



key_points_counter = OrderedDict(sorted(key_points_counter.items(), key=lambda item: item[1], reverse=True))
# 打印统计结果
# print("Key Points Count:")
# for key, count in key_points_counter.items():
#     print(f"{key}: {count}")

# 将统计结果保存为 JSON 文件
key_points_count_file = 'key_points_count.json'
with open(key_points_count_file, 'w') as f:
    json.dump(dict(key_points_counter), f, indent=4)

print(f"Key points count saved to '{key_points_count_file}'")

# 绘制饼图
labels = [f'{key} ({count})' for key, count in key_points_counter.items()]
sizes = list(key_points_counter.values())

plt.figure(figsize=(12, 7))
plt.pie(
    sizes,
    labels=labels,
    autopct='%1.1f%%',
    startangle=140
)
plt.title('Key Points Distribution')
plt.tight_layout()
plt.savefig('key_points_distribution_pie.png', bbox_inches='tight')
plt.close()

print("Pie chart saved as 'key_points_distribution_pie.png'")