241 lines
9.5 KiB
Python
241 lines
9.5 KiB
Python
# 预加载资源
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import logging
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import time
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from collections import defaultdict
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import os
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import json
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import numpy as np
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from app.core.config import DB_CONFIG, RECOMMEND_PATH_PREFIX
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logger = logging.getLogger()
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import pymysql
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from concurrent.futures import ThreadPoolExecutor
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HEAT_VECTOR_FILE = 'heat_vectors_data/heat_vectors.json' # 可动态加载或配置
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matrix_data = {
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"interaction_matrix": None,
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"feature_matrix": None,
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"user_index_interaction": None,
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"sketch_index_interaction": None,
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"user_index_feature": None,
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"sketch_index_feature": None,
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"iid_to_sketch": None,
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"category_to_iids": None,
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"cached_scores": {},
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"cached_valid_idxs": {},
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"category_sketch_idxs_inter": None,
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"category_sketch_idxs_feature": None,
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"user_inter_full": dict(),
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"user_feat_full": dict(),
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"brand_feature_matrix": None,
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"brand_index_map": None,
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"heat_data": {},
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}
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def load_resources():
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"""加载所有矩阵和映射关系，并触发预缓存"""
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try:
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start_time = time.time()
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# 清空缓存
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matrix_data["cached_scores"].clear()
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matrix_data["cached_valid_idxs"].clear()
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# 加载数据
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sketch_to_iid = np.load(f'{RECOMMEND_PATH_PREFIX}sketch_to_iid.npy', allow_pickle=True).item()
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matrix_data["iid_to_sketch"] = {v: k for k, v in sketch_to_iid.items()}
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matrix_data["interaction_matrix"] = np.load(f"{RECOMMEND_PATH_PREFIX}interaction_matrix.npy", allow_pickle=True)
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matrix_data["user_index_interaction"] = np.load(f"{RECOMMEND_PATH_PREFIX}user_index_interaction_matrix.npy", allow_pickle=True).item()
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matrix_data["sketch_index_interaction"] = np.load(f"{RECOMMEND_PATH_PREFIX}sketch_index_interaction_matrix.npy",
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allow_pickle=True).item()
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matrix_data["feature_matrix"] = np.load(f"{RECOMMEND_PATH_PREFIX}feature_matrix.npy", allow_pickle=True)
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brand_feature_path = f"{RECOMMEND_PATH_PREFIX}brand_feature_matrix.npy"
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if os.path.exists(brand_feature_path):
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matrix_data["brand_feature_matrix"] = np.load(brand_feature_path, allow_pickle=True)
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else:
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logger.warning("brand_feature_matrix 文件不存在，使用空数组")
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matrix_data["brand_feature_matrix"] = np.array([])
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# brand_index_map
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brand_index_path = f"{RECOMMEND_PATH_PREFIX}brand_index_map.npy"
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if os.path.exists(brand_index_path):
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matrix_data["brand_index_map"] = np.load(brand_index_path, allow_pickle=True).item()
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else:
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logger.warning("brand_index_map 文件不存在，使用空字典")
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matrix_data["brand_index_map"] = {}
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matrix_data["user_index_feature"] = np.load(f"{RECOMMEND_PATH_PREFIX}user_index_feature_matrix.npy", allow_pickle=True).item()
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matrix_data["sketch_index_feature"] = np.load(f"{RECOMMEND_PATH_PREFIX}sketch_index_feature_matrix.npy", allow_pickle=True).item()
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category_to_iid_map = np.load(f"{RECOMMEND_PATH_PREFIX}iid_to_category_interaction_matrix.npy", allow_pickle=True).item()
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matrix_data["category_to_iids"] = defaultdict(list)
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for iid, cat in category_to_iid_map.items():
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matrix_data["category_to_iids"][cat].append(iid)
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logger.info(f"资源加载完成，耗时: {time.time() - start_time:.2f}秒")
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# 触发预缓存
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precache_user_category()
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if os.path.exists(HEAT_VECTOR_FILE):
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with open(HEAT_VECTOR_FILE, 'r', encoding='utf-8') as f:
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heat_json = json.load(f)
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matrix_data["heat_data"] = heat_json.get("data", {})
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logger.info(f"热度向量数据加载完成，共加载 {len(matrix_data['heat_data'])} 个类别")
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else:
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matrix_data["heat_data"] = {}
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except Exception as e:
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logger.error(f"资源加载失败: {str(e)}")
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raise RuntimeError("初始化失败")
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def precache_user_category():
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"""优化后的用户分类预缓存（添加耗时统计）"""
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if not all([
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matrix_data["interaction_matrix"] is not None,
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matrix_data["feature_matrix"] is not None,
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matrix_data["user_index_interaction"] is not None
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]):
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logger.warning("资源未加载完成，跳过预缓存")
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return
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start_time = time.perf_counter()
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time_stats = {
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"get_all_user_categories": 0,
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"process_user_category": 0,
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"thread_execution": 0,
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"cache_update": 0,
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"total": 0,
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}
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# 统计用户类别获取时间
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t1 = time.perf_counter()
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user_categories = get_all_user_categories()
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time_stats["get_all_user_categories"] = time.perf_counter() - t1
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precached_count = 0
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def process_user_category(user_id, categories):
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"""单用户类别缓存计算（统计耗时）"""
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local_cache = {}
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local_valid_idxs = {}
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t_start = time.perf_counter()
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for category in categories:
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cache_key = (user_id, category)
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if cache_key in matrix_data["cached_scores"]:
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continue
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try:
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user_idx_inter = matrix_data["user_index_interaction"].get(user_id)
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user_idx_feature = matrix_data["user_index_feature"].get(user_id)
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# 统计获取类别 IID 耗时
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t_iid = time.perf_counter()
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category_iids = matrix_data["category_to_iids"].get(category, [])
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valid_sketch_idxs_inter = [matrix_data["sketch_index_interaction"][iid]
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for iid in category_iids if iid in matrix_data["sketch_index_interaction"]]
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valid_sketch_idxs_feature = [matrix_data["sketch_index_feature"][iid]
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for iid in category_iids if iid in matrix_data["sketch_index_feature"]]
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time_stats["process_user_category"] += time.perf_counter() - t_iid
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# 统计矩阵计算耗时
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t_matrix = time.perf_counter()
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processed_inter = np.zeros(len(valid_sketch_idxs_inter))
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if user_idx_inter is not None and valid_sketch_idxs_inter:
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raw_inter_scores = matrix_data["interaction_matrix"][user_idx_inter, valid_sketch_idxs_inter]
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processed_inter = raw_inter_scores * 0.7
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processed_feat = np.zeros(len(valid_sketch_idxs_feature))
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if user_idx_feature is not None and valid_sketch_idxs_feature:
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raw_feat_scores = matrix_data["feature_matrix"][user_idx_feature, valid_sketch_idxs_feature]
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raw_feat_scores = (raw_feat_scores - np.min(raw_feat_scores)) / (
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np.max(raw_feat_scores) - np.min(raw_feat_scores) + 1e-8)
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processed_feat = raw_feat_scores * 0.3
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time_stats["process_user_category"] += time.perf_counter() - t_matrix
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if len(processed_inter) == len(processed_feat):
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local_cache[cache_key] = (processed_inter, processed_feat)
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local_valid_idxs[cache_key] = valid_sketch_idxs_inter
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except Exception as e:
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logger.error(f"预缓存失败 (user={user_id}, category={category}): {str(e)}")
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return local_cache, local_valid_idxs
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# 统计线程执行时间
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t2 = time.perf_counter()
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with ThreadPoolExecutor(max_workers=8) as executor:
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futures = {executor.submit(process_user_category, user_id, categories): user_id for user_id, categories in user_categories.items()}
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for future in futures:
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try:
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t_cache = time.perf_counter()
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cache_part, valid_idxs_part = future.result()
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matrix_data["cached_scores"].update(cache_part)
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matrix_data["cached_valid_idxs"].update(valid_idxs_part)
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time_stats["cache_update"] += time.perf_counter() - t_cache
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precached_count += len(cache_part)
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except Exception as e:
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logger.error(f"线程执行错误: {str(e)}")
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time_stats["thread_execution"] = time.perf_counter() - t2
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time_stats["total"] = time.perf_counter() - start_time
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# 输出统计信息
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logger.info(f"""
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预缓存完成，共缓存 {precached_count} 组数据，耗时统计如下：
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- 获取用户类别数据: {time_stats["get_all_user_categories"]:.2f}s
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- 计算用户类别缓存: {time_stats["process_user_category"]:.2f}s
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- 线程任务执行: {time_stats["thread_execution"]:.2f}s
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- 更新缓存数据: {time_stats["cache_update"]:.2f}s
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- 总耗时: {time_stats["total"]:.2f}s
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""")
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def get_all_user_categories():
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"""获取所有用户及其对应的分类"""
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conn = None
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try:
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conn = pymysql.connect(**DB_CONFIG)
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cursor = conn.cursor()
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query = """
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SELECT DISTINCT account_id, path
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FROM user_preference_log_prediction
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"""
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cursor.execute(query)
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results = cursor.fetchall()
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user_categories = defaultdict(set)
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for account_id, path in results:
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category = get_category_from_path(path)
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user_categories[account_id].add(category)
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return dict(user_categories)
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except Exception as e:
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logger.error(f"数据库查询失败: {str(e)}")
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return {}
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finally:
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if conn:
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conn.close()
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def get_category_from_path(path: str) -> str:
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"""从路径解析类别"""
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try:
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parts = path.split('/')
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if len(parts) >= 4:
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return f"{parts[2]}_{parts[3]}"
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return "unknown"
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except:
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return "unknown"
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