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fix：更新群表达方式可视化脚本，更新数据库迁移脚本

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This commit is contained in:
SengokuCola
2025-06-03 11:46:28 +08:00
parent 70d5516dcc
commit 94c7072cc9
3 changed files with 100 additions and 75 deletions
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140
scripts/analyze_group_similarity.py
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@@ -42,21 +42,26 @@ def get_group_name(stream_id):
return f"{platform}-{stream_id[:8]}"
return stream_id
def load_group_expressions(group_dir):
"""加载单个群组的表达方式数据"""
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 []
return [], [], []
with open(json_path, 'r', encoding='utf-8') as f:
data = json.load(f)
# 将所有表达方式合并成一个文本
all_expressions = []
for item in data:
all_expressions.extend([item['style']] * item['count'])
situations = []
styles = []
combined = []
return ' '.join(all_expressions)
for item in data:
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
@@ -67,70 +72,109 @@ def analyze_group_similarity():
# 获取群组名称
group_names = [get_group_name(group_id) for group_id in group_ids]
# 加载所有群组的表达方式
group_texts = [load_group_expressions(d) for d in group_dirs]
# 加载所有群组的数据
group_situations = []
group_styles = []
group_combined = []
# 使用TF-IDF向量化文本
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))
# 创建TF-IDF向量化器
vectorizer = TfidfVectorizer()
tfidf_matrix = vectorizer.fit_transform(group_texts)
# 计算余弦相似度
similarity_matrix = cosine_similarity(tfidf_matrix)
# 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_similarity_matrix = np.log1p(similarity_matrix)
log_situation_matrix = np.log1p(situation_matrix)
log_style_matrix = np.log1p(style_matrix)
log_combined_matrix = np.log1p(combined_matrix)
# 创建热力图
plt.figure(figsize=(15, 12))
sns.heatmap(log_similarity_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)) # 调整最大值以匹配对数变换
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')
# 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')
plt.tight_layout()
plt.savefig(SCRIPT_DIR / 'group_similarity_heatmap.png', dpi=300, bbox_inches='tight')
plt.savefig(SCRIPT_DIR / 'group_similarity_heatmaps.png', dpi=300, bbox_inches='tight')
plt.close()
# 创建网络图
G = nx.Graph()
# 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')
# 添加节点
for group_id, group_name in zip(group_ids, group_names):
G.add_node(group_id, label=group_name)
# 添加边（使用对数变换后的相似度）
for i in range(len(group_ids)):
for j in range(i+1, len(group_ids)):
if log_similarity_matrix[i][j] > np.log1p(0.05): # 调整阈值
G.add_edge(group_ids[i], group_ids[j],
weight=log_similarity_matrix[i][j])
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')
# 绘制网络图
plt.figure(figsize=(20, 20))
pos = nx.spring_layout(G, k=1, iterations=50)
# 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])
# 绘制节点
nx.draw_networkx_nodes(G, pos, node_size=20000, node_color='lightblue', alpha=0.8)
# 找出共同的场景
common_situations = set(situations1) & set(situations2)
if common_situations:
f.write('\n共同场景:\n')
for situation in common_situations:
f.write(f'- {situation}\n')
# 绘制边
edges = G.edges()
weights = [G[u][v]['weight'] * 40 for u, v in edges] # 增加线条粗细系数
nx.draw_networkx_edges(G, pos, width=weights, alpha=0.6, edge_color='gray')
# 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')
for style in common_styles:
f.write(f'- {style}\n')
# 添加标签
labels = {node: G.nodes[node]['label'] for node in G.nodes()}
nx.draw_networkx_labels(G, pos, labels, font_size=20, font_weight='bold')
plt.title('群组表达方式相似度网络图\n(连线粗细表示对数变换后的相似度)')
plt.axis('off')
plt.tight_layout()
plt.savefig(SCRIPT_DIR / 'group_similarity_network.png', dpi=300, bbox_inches='tight')
plt.close()
f.write('\n' + '-' * 50 + '\n\n')
if __name__ == "__main__":
analyze_group_similarity()

19
scripts/mongodb_to_sqlite.py
View File

@@ -182,25 +182,6 @@ class MongoToSQLiteMigrator:
enable_validation=False, # 禁用数据验证
unique_fields=["stream_id"],
),
# LLM使用记录迁移配置
MigrationConfig(
mongo_collection="llm_usage",
target_model=LLMUsage,
field_mapping={
"model_name": "model_name",
"user_id": "user_id",
"request_type": "request_type",
"endpoint": "endpoint",
"prompt_tokens": "prompt_tokens",
"completion_tokens": "completion_tokens",
"total_tokens": "total_tokens",
"cost": "cost",
"status": "status",
"timestamp": "timestamp",
},
enable_validation=True, # 禁用数据验证"
unique_fields=["user_id", "prompt_tokens", "completion_tokens", "total_tokens", "cost"], # 组合唯一性
),
# 消息迁移配置
MigrationConfig(
mongo_collection="messages",

2
src/plugins/test_plugin/actions/mute_action.py
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@@ -22,7 +22,7 @@ class MuteAction(PluginAction):
"当有人发了擦边，或者色情内容时使用",
"当有人要求禁言自己时使用",
]
default = True # 默认动作，是否手动添加到使用集
default = False # 默认动作，是否手动添加到使用集
associated_types = ["command", "text"]
# associated_types = ["text"]