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+from dao.redis_db import Redis
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+import pandas as pd
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+import numpy as np
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+from tqdm import tqdm
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+from scipy.sparse import csr_matrix
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+from joblib import Parallel, delayed
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+import joblib
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+
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+class ItemCF:
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+ def __init__(self, score_path, similatity_path):
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+ self._recommendations = {}
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+ self._score_df = pd.read_csv(score_path)
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+ self._similarity_df = pd.read_csv(similatity_path, index_col=0)
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+ self._similarity_matrix = csr_matrix(self._similarity_df.values)
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+ self._shop_index = {shop: idx for idx, shop in enumerate(self._similarity_df.index)}
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+ self._index_shop = {idx: shop for idx, shop in enumerate(self._similarity_df.index)}
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+
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+ def train(self, n=100, k=10, top_n=100, n_jobs=4):
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+ def process_product(product_code, scores):
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+ # 获取热度最高的n个商户
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+ top_n_shops = scores.nlargest(n, "SCORE")["BB_RETAIL_CUSTOMER_CODE"].values
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+ top_n_indices = [self._shop_index[shop] for shop in top_n_shops]
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+
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+ # 找到每个商户最相似的k个商户
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+ similar_shops = {}
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+ for shop_idx in top_n_indices:
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+ similarities = self._similarity_matrix[shop_idx].toarray().flatten()
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+ similar_indices = np.argpartition(similarities, -k-1)[-k-1:]
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+ similar_indices = similar_indices[similar_indices != shop_idx][:k]
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+ similar_shops[self._index_shop[shop_idx]] = [self._index_shop[idx] for idx in similar_indices]
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+
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+ # 生成候选商户列表
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+ candidate_shops = list(set([m for sublist in similar_shops.values() for m in sublist]))
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+ candidate_indices = [self._shop_index[shop] for shop in candidate_shops]
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+
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+ # 计算每个候选商户的兴趣得分
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+ interest_scores = {}
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+ for candidate_idx in candidate_indices:
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+ interest_score = 0
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+ for shop_idx in top_n_indices:
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+ if self._index_shop[candidate_idx] in similar_shops[self._index_shop[shop_idx]]:
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+ shop_score = scores[scores["BB_RETAIL_CUSTOMER_CODE"]==self._index_shop[shop_idx]]["SCORE"].values[0]
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+ interest_score += shop_score * self._similarity_matrix[shop_idx, candidate_idx]
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+ interest_scores[self._index_shop[candidate_idx]] = interest_score
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+
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+ # 将候选商户的兴趣得分转换为字典列表，并按照从大到小排列
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+ sorted_candidates = sorted([{shop_id: s} for shop_id, s in interest_scores.items()],
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+ key=lambda x: list(x.values())[0], reverse=True)[:top_n]
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+
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+ return product_code, sorted_candidates
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+
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+ # 并行处理每个品规
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+ results = Parallel(n_jobs=n_jobs)(delayed(process_product)(product_code, scores)
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+ for product_code, scores in tqdm(self._score_df.groupby("PRODUCT_CODE"), desc="正在计算候选得分..."))
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+
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+ # 存储结果
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+ self._recommendations = {product_code: sorted_candidates for product_code, sorted_candidates in results}
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+
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+ def to_redis_zset(self):
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+ """
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+ 将 self._recommendations 中的数据保存到 Redis 的 Sorted Set (ZSET) 中
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+ 存储格式为 fc:product_code，其中商户 ID 作为成员，得分作为分数
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+ """
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+ redis_db = Redis()
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+ for product_code, recommendations in self._recommendations.items():
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+ redis_key = f"fc:{product_code}"
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+ zset_data = {}
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+ for rec in recommendations:
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+ for shop_id, score in rec.items():
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+ try:
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+ zset_data[shop_id] = float(score)
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+ except ValueError as e:
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+ print(f"Error converting score to float for shop_id {shop_id}: {score}")
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+ raise e
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+
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+ redis_db.redis.zadd(redis_key, zset_data)
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+
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+ def inference(self, product_code):
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+ """
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+ 从 Redis 中读取推荐结果，并返回 {shop_id: score} 的列表
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+ """
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+ redis_db = Redis()
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+ redis_key = f"fc:{product_code}"
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+ recommendations = redis_db.redis.zrange(redis_key, 0, -1, withscores=True, desc=True)
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+
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+ # 将推荐结果转换为 {shop_id: score} 的字典列表
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+ result = [{shop_id: float(score)} for shop_id, score in recommendations]
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+
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+ return result
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+
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+if __name__ == "__main__":
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+ score_path = "./models/recall/itemCF/matrix/score.csv"
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+ similarity_path = "./models/recall/itemCF/matrix/similarity.csv"
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+ itemcf_model = ItemCF(score_path, similarity_path)
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+ # itemcf_model.train(n_jobs=4)
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+ # recommend_list = itemcf_model.inference(110102)
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+ # itemcf_model.to_redis_zset()
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+ # print(len(recommend_list))
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+ # print(recommend_list)
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+ joblib.dump(itemcf_model, "itemCF.model")
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+
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+ # model = joblib.load("./itemCF.model")
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+ # recommend_list = model.inference(110102)
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+ # print(len(recommend_list))
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+ # print(recommend_list)
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Sherlock1011