feat（新功能）: sketch 推荐算法

fix（修复bug）:
docs（文档变更）:
refactor（重构）:
test(增加测试):

app/service/recommend/service.py

@@ -0,0 +1,172 @@
+# 预加载资源
+import logging
+import time
+from collections import defaultdict
+
+import numpy as np
+
+from app.core.config import DB_CONFIG, RECOMMEND_PATH_PREFIX
+
+logger = logging.getLogger()
+import pymysql
+
+matrix_data = {
+    "interaction_matrix": None,
+    "feature_matrix": None,
+    "user_index_interaction": None,
+    "sketch_index_interaction": None,
+    "user_index_feature": None,
+    "sketch_index_feature": None,
+    "iid_to_sketch": None,
+    "category_to_iids": None,
+    "cached_scores": {},
+    "cached_valid_idxs": {},
+    "category_sketch_idxs_inter": None,
+    "category_sketch_idxs_feature": None,
+    "user_inter_full": dict(),
+    "user_feat_full": dict(),
+}
+
+
+def load_resources():
+    """加载所有矩阵和映射关系，并触发预缓存"""
+    try:
+        start_time = time.time()
+
+        # 清空缓存
+        matrix_data["cached_scores"].clear()
+        matrix_data["cached_valid_idxs"].clear()
+
+        # 加载数据
+        sketch_to_iid = np.load(f'{RECOMMEND_PATH_PREFIX}sketch_to_iid.npy', allow_pickle=True).item()
+        matrix_data["iid_to_sketch"] = {v: k for k, v in sketch_to_iid.items()}
+
+        matrix_data["interaction_matrix"] = np.load(f"{RECOMMEND_PATH_PREFIX}interaction_matrix.npy", allow_pickle=True)
+        matrix_data["user_index_interaction"] = np.load(f"{RECOMMEND_PATH_PREFIX}user_index_interaction_matrix.npy", allow_pickle=True).item()
+        matrix_data["sketch_index_interaction"] = np.load(f"{RECOMMEND_PATH_PREFIX}sketch_index_interaction_matrix.npy",
+                                                          allow_pickle=True).item()
+
+        matrix_data["feature_matrix"] = np.load(f"{RECOMMEND_PATH_PREFIX}feature_matrix.npy", allow_pickle=True)
+        matrix_data["user_index_feature"] = np.load(f"{RECOMMEND_PATH_PREFIX}user_index_feature_matrix.npy", allow_pickle=True).item()
+        matrix_data["sketch_index_feature"] = np.load(f"{RECOMMEND_PATH_PREFIX}sketch_index_feature_matrix.npy", allow_pickle=True).item()
+
+        category_to_iid_map = np.load(f"{RECOMMEND_PATH_PREFIX}iid_to_category_interaction_matrix.npy", allow_pickle=True).item()
+        matrix_data["category_to_iids"] = defaultdict(list)
+        for iid, cat in category_to_iid_map.items():
+            matrix_data["category_to_iids"][cat].append(iid)
+
+        logger.info(f"资源加载完成，耗时: {time.time() - start_time:.2f}秒")
+
+        # 触发预缓存
+        precache_user_category()
+
+    except Exception as e:
+        logger.error(f"资源加载失败: {str(e)}")
+        raise RuntimeError("初始化失败")
+
+
+def precache_user_category():
+    """预缓存用户-分类组合数据"""
+    if not all([
+        matrix_data["interaction_matrix"] is not None,
+        matrix_data["feature_matrix"] is not None,
+        matrix_data["user_index_interaction"] is not None
+    ]):
+        logger.warning("资源未加载完成，跳过预缓存")
+        return
+
+    start_time = time.time()
+    user_categories = get_all_user_categories()
+
+    precached_count = 0
+    for user_id, categories in user_categories.items():
+        for category in categories:
+            cache_key = (user_id, category)
+            if cache_key in matrix_data["cached_scores"]:
+                continue
+
+            try:
+                # 获取用户索引
+                user_idx_inter = matrix_data["user_index_interaction"].get(user_id)
+                user_idx_feature = matrix_data["user_index_feature"].get(user_id)
+
+                # 获取类别对应的iid列表
+                category_iids = matrix_data["category_to_iids"].get(category, [])
+
+                # 过滤有效草图索引
+                valid_sketch_idxs_inter = [
+                    idx for iid, idx in matrix_data["sketch_index_interaction"].items()
+                    if iid in category_iids
+                ]
+
+                # 处理交互分数
+                if user_idx_inter is not None and valid_sketch_idxs_inter:
+                    raw_inter_scores = matrix_data["interaction_matrix"][user_idx_inter, valid_sketch_idxs_inter]
+                    processed_inter = raw_inter_scores * 0.7
+                else:
+                    processed_inter = np.array([])
+
+                # 处理特征分数
+                valid_sketch_idxs_feature = [
+                    idx for iid, idx in matrix_data["sketch_index_feature"].items()
+                    if iid in category_iids
+                ]
+
+                if user_idx_feature is not None and valid_sketch_idxs_feature:
+                    raw_feat_scores = matrix_data["feature_matrix"][user_idx_feature, valid_sketch_idxs_feature]
+                    raw_feat_scores = (raw_feat_scores - np.min(raw_feat_scores)) / (
+                            np.max(raw_feat_scores) - np.min(raw_feat_scores) + 1e-8)
+                    processed_feat = raw_feat_scores * 0.3
+                else:
+                    processed_feat = np.array([])
+
+                # 缓存结果
+                if len(processed_inter) == len(processed_feat):
+                    matrix_data["cached_scores"][cache_key] = (processed_inter, processed_feat)
+                    matrix_data["cached_valid_idxs"][cache_key] = valid_sketch_idxs_inter
+                    precached_count += 1
+
+            except Exception as e:
+                logger.error(f"预缓存失败 (user={user_id}, category={category}): {str(e)}")
+
+    logger.info(f"预缓存完成，共缓存 {precached_count} 个组合，耗时: {time.time() - start_time:.2f}秒")
+
+
+def get_all_user_categories():
+    """获取所有用户及其对应的分类"""
+    conn = None
+    try:
+        conn = pymysql.connect(**DB_CONFIG)
+        cursor = conn.cursor()
+
+        query = """
+            SELECT DISTINCT account_id, path 
+            FROM user_preference_log_prediction
+        """
+        cursor.execute(query)
+        results = cursor.fetchall()
+
+        user_categories = defaultdict(set)
+        for account_id, path in results:
+            category = get_category_from_path(path)
+            user_categories[account_id].add(category)
+
+        return dict(user_categories)
+
+    except Exception as e:
+        logger.error(f"数据库查询失败: {str(e)}")
+        return {}
+    finally:
+        if conn:
+            conn.close()
+
+
+def get_category_from_path(path: str) -> str:
+    """从路径解析类别"""
+    try:
+        parts = path.split('/')
+        if len(parts) >= 4:
+            return f"{parts[2]}_{parts[3]}"
+        return "unknown"
+    except:
+        return "unknown"