添加 kmeans (存档)

cucyuqing/cmd/kmeans.py

@@ -0,0 +1,70 @@
+import numpy
+import asyncio
+import json
+from sklearn.cluster import KMeans
+
+from cucyuqing.pg import pool, get_cur
+
+
+async def main():
+    # 从 PG 数据库获取数据
+    await pool.open()
+    async with get_cur() as cur:
+        await cur.execute(
+            """
+            SELECT id, title, content, embedding
+            FROM risk_news
+            WHERE NOT embedding_updated_at IS NULL
+                AND time > now() - interval '14 day'
+            ORDER BY time desc
+            LIMIT 1000
+        ;"""
+        )
+        rows = await cur.fetchall()
+        docs = [
+            {
+                "id": row[0],
+                "title": row[1],
+                "content": row[2],
+            }
+            for row in rows
+        ]
+        embeddings = [numpy.array(json.loads(row[3])) for row in rows]
+
+    # 设置聚类的数量
+    num_clusters = 50
+
+    # 初始化KMeans模型
+    kmeans = KMeans(n_clusters=num_clusters, random_state=42)
+
+    # 进行聚类
+    kmeans.fit(embeddings)
+
+    # 获取每个样本的聚类标签
+    labels: list[int] = kmeans.labels_  # type: ignore
+
+    # 计算每个样本到其聚类中心的距离
+    distances = kmeans.transform(embeddings)
+    # 找到每个聚类中距离中心最近的文档
+    closest_docs = {}
+    for i, label in enumerate(labels):
+        distance_to_center = distances[i][label]
+        if label not in closest_docs or distance_to_center < closest_docs[label][0]:
+            closest_docs[label] = (distance_to_center, docs[i])
+
+    # 输出每个聚类中距离中心最近的文档
+    for label, (distance, doc) in closest_docs.items():
+        print(f"聚类 {label} 最近的文档: {doc['title']} 距离: {distance}")
+
+    sorted_samples: list[tuple[int, int]] = sorted(enumerate(labels), key=lambda x: x[1])
+
+    # 随机选择一个聚类
+    random_cluster = numpy.random.choice(num_clusters)
+    for i, label in sorted_samples:
+        if label == random_cluster:
+            print(f"聚类 {label} 文档: {docs[i]['title']}")
+
+
+
+if __name__ == "__main__":
+    asyncio.run(main())

cucyuqing/cmd/risk-analyze.py

@@ -10,19 +10,16 @@ from cucyuqing.utils import print
 from cucyuqing.config import OPENAI_RISK_LLM_API_KEY, OPENAI_RISK_LLM_BASE_URL
 from cucyuqing.pg import pool, get_cur
 from cucyuqing.mysql import mysql
+from cucyuqing.dbscan import run_dbscan
 
 
 async def main():
     await pool.open()
-    print(await batch_risk_analyze(["你是老师", "我是初音未来"]))
+    dbscan_result = await run_dbscan()
-
+    docs = [cluster[0] for cluster in dbscan_result.clusters]
-async def get_docs() -> list[dict]:
+    analyze_rusult = await batch_risk_analyze([doc.title for doc in docs])
-    # [TODO]
+    for result, doc in zip(analyze_rusult, docs):
-    raise NotImplemented
+        print(f"风险: {result} 标题: {doc.title}")
-    await mysql.execute("""
-    """)
-    return []
-
 
 
 async def batch_risk_analyze(

cucyuqing/dbscan.py

@@ -0,0 +1,94 @@
+from typing_extensions import Doc
+import numpy
+import asyncio
+import json
+from dataclasses import dataclass
+from sklearn.cluster import DBSCAN
+from sklearn.metrics import pairwise_distances
+
+from cucyuqing.pg import pool, get_cur
+
+@dataclass
+class Document:
+    id: int
+    title: str
+    content: str
+    similarity: float = 0.0
+
+@dataclass
+class DBScanResult:
+    noise: list[Document]
+    clusters: list[list[Document]]
+
+from sklearn.metrics.pairwise import cosine_similarity
+
+async def run_dbscan() -> DBScanResult:
+    # 从 PG 数据库获取数据
+    async with get_cur() as cur:
+        await cur.execute(
+            """
+            SELECT id, title, content, embedding
+            FROM risk_news
+            WHERE NOT embedding_updated_at IS NULL
+                AND time > now() - interval '14 day'
+            ORDER BY time desc
+            LIMIT 10000
+            ;"""
+        )
+        rows = await cur.fetchall()
+        docs: list[Document] = [
+            Document(row[0], row[1], row[2])
+            for row in rows
+        ]
+        embeddings = [numpy.array(json.loads(row[3])) for row in rows]
+
+    # 计算余弦距离矩阵
+    cosine_distances = pairwise_distances(embeddings, metric='cosine')
+
+    # 初始化DBSCAN模型
+    dbscan = DBSCAN(eps=0.25, min_samples=2, metric='precomputed')  # Adjust eps as needed
+
+    # 进行聚类
+    dbscan.fit(cosine_distances)
+
+    # 获取每个样本的聚类标签
+    labels: list[int] = dbscan.labels_  # type: ignore
+
+    # 输出每个聚类中的文档
+    ret: DBScanResult = DBScanResult(noise=[], clusters=[])
+    unique_labels = set(labels)
+    for label in unique_labels:
+        class_member_mask = (labels == label)
+        cluster_docs = [docs[i] for i in range(len(labels)) if class_member_mask[i]]  # type: ignore
+        cluster_embeddings = [embeddings[i] for i in range(len(labels)) if class_member_mask[i]] # type: ignore
+
+        if label == -1:
+            # -1 is the label for noise points
+            ret.noise = cluster_docs
+        else:
+            # 计算质心
+            centroid = numpy.mean(cluster_embeddings, axis=0).reshape(1, -1)
+            # 计算相似度
+            similarities = cosine_similarity(centroid, cluster_embeddings).flatten()
+            # 根据相似度排序
+            sorted_indices = numpy.argsort(similarities)[::-1]
+            sorted_cluster_docs = []
+            for i in sorted_indices:
+                doc = cluster_docs[i]
+                doc.similarity = similarities[i]
+                sorted_cluster_docs.append(doc)
+            ret.clusters.append(sorted_cluster_docs)
+    
+    return ret
+
+async def main():
+    await pool.open()
+    result = await run_dbscan()
+    print(f"噪声文档: {len(result.noise)}")
+    for i, cluster in enumerate(result.clusters):
+        print('----------------')
+        for doc in cluster:
+            print(f"聚类 {i} 文档: {doc.title} 相似度: {doc.similarity}")
+
+if __name__ == '__main__':
+    asyncio.run(main())

requirements.txt

@@ -8,3 +8,4 @@ databases[aiomysql]
 openai
 tokenizers
 tqdm
+scikit-learn