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fix: ruff

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This commit is contained in:
tcmofashi
2025-06-03 16:09:17 +08:00
parent 897d97c154
commit 103ab0ff8e
14 changed files with 414 additions and 358 deletions
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180
scripts/analyze_group_similarity.py
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@@ -1,37 +1,38 @@
import json
import os
from pathlib import Path
import numpy as np
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import matplotlib.pyplot as plt
import seaborn as sns
import networkx as nx
import matplotlib as mpl
import sqlite3
# 设置中文字体
plt.rcParams['font.sans-serif'] = ['Microsoft YaHei'] # 使用微软雅黑
plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
plt.rcParams['font.family'] = 'sans-serif'
plt.rcParams["font.sans-serif"] = ["Microsoft YaHei"] # 使用微软雅黑
plt.rcParams["axes.unicode_minus"] = False # 用来正常显示负号
plt.rcParams["font.family"] = "sans-serif"
# 获取脚本所在目录
SCRIPT_DIR = Path(__file__).parent
def get_group_name(stream_id):
"""从数据库中获取群组名称"""
conn = sqlite3.connect('data/maibot.db')
conn = sqlite3.connect("data/maibot.db")
cursor = conn.cursor()
cursor.execute('''
cursor.execute(
"""
SELECT group_name, user_nickname, platform
FROM chat_streams
WHERE stream_id = ?
''', (stream_id,))
""",
(stream_id,),
)
result = cursor.fetchone()
conn.close()
if result:
group_name, user_nickname, platform = result
if group_name:
@@ -42,139 +43,148 @@ def get_group_name(stream_id):
return f"{platform}-{stream_id[:8]}"
return stream_id
def load_group_data(group_dir):
"""加载单个群组的数据"""
json_path = Path(group_dir) / "expressions.json"
if not json_path.exists():
return [], [], []
with open(json_path, 'r', encoding='utf-8') as f:
with open(json_path, "r", encoding="utf-8") as f:
data = json.load(f)
situations = []
styles = []
combined = []
for item in data:
count = item['count']
situations.extend([item['situation']] * count)
styles.extend([item['style']] * count)
count = item["count"]
situations.extend([item["situation"]] * count)
styles.extend([item["style"]] * count)
combined.extend([f"{item['situation']} {item['style']}"] * count)
return situations, styles, combined
def analyze_group_similarity():
# 获取所有群组目录
base_dir = Path("data/expression/learnt_style")
group_dirs = [d for d in base_dir.iterdir() if d.is_dir()]
group_ids = [d.name for d in group_dirs]
# 获取群组名称
group_names = [get_group_name(group_id) for group_id in group_ids]
# 加载所有群组的数据
group_situations = []
group_styles = []
group_combined = []
for d in group_dirs:
situations, styles, combined = load_group_data(d)
group_situations.append(' '.join(situations))
group_styles.append(' '.join(styles))
group_combined.append(' '.join(combined))
group_situations.append(" ".join(situations))
group_styles.append(" ".join(styles))
group_combined.append(" ".join(combined))
# 创建TF-IDF向量化器
vectorizer = TfidfVectorizer()
# 计算三种相似度矩阵
situation_matrix = cosine_similarity(vectorizer.fit_transform(group_situations))
style_matrix = cosine_similarity(vectorizer.fit_transform(group_styles))
combined_matrix = cosine_similarity(vectorizer.fit_transform(group_combined))
# 对相似度矩阵进行对数变换
log_situation_matrix = np.log1p(situation_matrix)
log_style_matrix = np.log1p(style_matrix)
log_combined_matrix = np.log1p(combined_matrix)
# 创建一个大图，包含三个子图
plt.figure(figsize=(45, 12))
# 场景相似度热力图
plt.subplot(1, 3, 1)
sns.heatmap(log_situation_matrix,
xticklabels=group_names,
yticklabels=group_names,
cmap='YlOrRd',
annot=True,
fmt='.2f',
vmin=0,
vmax=np.log1p(0.2))
plt.title('群组场景相似度热力图 (对数变换)')
plt.xticks(rotation=45, ha='right')
sns.heatmap(
log_situation_matrix,
xticklabels=group_names,
yticklabels=group_names,
cmap="YlOrRd",
annot=True,
fmt=".2f",
vmin=0,
vmax=np.log1p(0.2),
)
plt.title("群组场景相似度热力图 (对数变换)")
plt.xticks(rotation=45, ha="right")
# 表达方式相似度热力图
plt.subplot(1, 3, 2)
sns.heatmap(log_style_matrix,
xticklabels=group_names,
yticklabels=group_names,
cmap='YlOrRd',
annot=True,
fmt='.2f',
vmin=0,
vmax=np.log1p(0.2))
plt.title('群组表达方式相似度热力图 (对数变换)')
plt.xticks(rotation=45, ha='right')
sns.heatmap(
log_style_matrix,
xticklabels=group_names,
yticklabels=group_names,
cmap="YlOrRd",
annot=True,
fmt=".2f",
vmin=0,
vmax=np.log1p(0.2),
)
plt.title("群组表达方式相似度热力图 (对数变换)")
plt.xticks(rotation=45, ha="right")
# 组合相似度热力图
plt.subplot(1, 3, 3)
sns.heatmap(log_combined_matrix,
xticklabels=group_names,
yticklabels=group_names,
cmap='YlOrRd',
annot=True,
fmt='.2f',
vmin=0,
vmax=np.log1p(0.2))
plt.title('群组场景+表达方式相似度热力图 (对数变换)')
plt.xticks(rotation=45, ha='right')
sns.heatmap(
log_combined_matrix,
xticklabels=group_names,
yticklabels=group_names,
cmap="YlOrRd",
annot=True,
fmt=".2f",
vmin=0,
vmax=np.log1p(0.2),
)
plt.title("群组场景+表达方式相似度热力图 (对数变换)")
plt.xticks(rotation=45, ha="right")
plt.tight_layout()
plt.savefig(SCRIPT_DIR / 'group_similarity_heatmaps.png', dpi=300, bbox_inches='tight')
plt.savefig(SCRIPT_DIR / "group_similarity_heatmaps.png", dpi=300, bbox_inches="tight")
plt.close()
# 保存匹配详情到文本文件
with open(SCRIPT_DIR / 'group_similarity_details.txt', 'w', encoding='utf-8') as f:
f.write('群组相似度详情\n')
f.write('=' * 50 + '\n\n')
with open(SCRIPT_DIR / "group_similarity_details.txt", "w", encoding="utf-8") as f:
f.write("群组相似度详情\n")
f.write("=" * 50 + "\n\n")
for i in range(len(group_ids)):
for j in range(i+1, len(group_ids)):
for j in range(i + 1, len(group_ids)):
if log_combined_matrix[i][j] > np.log1p(0.05):
f.write(f'群组1: {group_names[i]}\n')
f.write(f'群组2: {group_names[j]}\n')
f.write(f'场景相似度: {situation_matrix[i][j]:.4f}\n')
f.write(f'表达方式相似度: {style_matrix[i][j]:.4f}\n')
f.write(f'组合相似度: {combined_matrix[i][j]:.4f}\n')
f.write(f"群组1: {group_names[i]}\n")
f.write(f"群组2: {group_names[j]}\n")
f.write(f"场景相似度: {situation_matrix[i][j]:.4f}\n")
f.write(f"表达方式相似度: {style_matrix[i][j]:.4f}\n")
f.write(f"组合相似度: {combined_matrix[i][j]:.4f}\n")
# 获取两个群组的数据
situations1, styles1, _ = load_group_data(group_dirs[i])
situations2, styles2, _ = load_group_data(group_dirs[j])
# 找出共同的场景
common_situations = set(situations1) & set(situations2)
if common_situations:
f.write('\n共同场景:\n')
f.write("\n共同场景:\n")
for situation in common_situations:
f.write(f'- {situation}\n')
f.write(f"- {situation}\n")
# 找出共同的表达方式
common_styles = set(styles1) & set(styles2)
if common_styles:
f.write('\n共同表达方式:\n')
f.write("\n共同表达方式:\n")
for style in common_styles:
f.write(f'- {style}\n')
f.write('\n' + '-' * 50 + '\n\n')
f.write(f"- {style}\n")
f.write("\n" + "-" * 50 + "\n\n")
if __name__ == "__main__":
analyze_group_similarity()