BrandCultivation/inference.py

from database import RedisDatabaseHelper, MySqlDao
from models.item2vec import Item2VecModel
from models.rank.data.config import CustConfig, ProductConfig, ShopConfig, OrderConfig
from models.rank.data.utils import sample_data_clear
from models.rank.gbdt_lr_inference import GbdtLrModel
from utils.result_process import get_cust_list_from_history_order, split_relation_subtable, generate_report
import pandas as pd

redis = RedisDatabaseHelper().redis
dao = MySqlDao()
gbdtlr_model = GbdtLrModel("./models/rank/weights/00000000000000000000000011445301/gbdtlr_model.pkl")
item2vec = Item2VecModel("00000000000000000000000011445301")

def get_itemcf_recall(city_uuid, product_id):
    """协同召回"""
    key = f"fc:{city_uuid}:{product_id}"
    recall_list = redis.zrevrange(key, 0, -1, withscores=False)
    return recall_list

def get_hot_recall(city_uuid):
    """热度召回"""
    key = f"hot:{city_uuid}:sale_qty"
    recall_list = redis.zrevrange(key, 0, -1, withscores=False)
    return recall_list

def get_recall_cust(city_uuid, product_id, recall_count):
    """根据协同过滤和热度召回召回商户
    """
    itemcf_recall_list = get_itemcf_recall(city_uuid, product_id)
    hot_recall_list = get_hot_recall(city_uuid)
    
    result = list(dict.fromkeys(itemcf_recall_list))
    
    # 如果结果不足，从hot_recall中补齐
    if len(result) < recall_count:
        hot_recall_set = set(hot_recall_list) - set(result)
        additional_items = [item for item in hot_recall_list if item in hot_recall_set]
        needed = recall_count - len(result)
        result.extend(additional_items[:needed])
    return result[:recall_count]

def generate_recommend_sample(city_uuid, product_id):
    """生成预测数据集"""
    product_in_order = dao.get_product_from_order(city_uuid)["product_code"].unique().tolist()
    if product_id in product_in_order:
        recall_count = 1000
        cust_list = get_recall_cust(city_uuid, product_id, recall_count)
    else:
        cust_list = item2vec.get_recommend_cust_list(product_id)["cust_code"].to_list()
    
    
    # 获取卷烟的信息
    product_data = dao.get_product_by_id(city_uuid, product_id)[ProductConfig.FEATURE_COLUMNS]
    filter_dict = product_data.to_dict("records")[0]
    
    cust_data = dao.get_cust_by_ids(city_uuid, cust_list)[CustConfig.FEATURE_COLUMNS]
    shop_data = dao.get_shop_by_ids(city_uuid, cust_list)[ShopConfig.FEATURE_COLUMNS]
    
    product_data = sample_data_clear(product_data, ProductConfig)
    cust_data = sample_data_clear(cust_data, CustConfig)
    shop_data = sample_data_clear(shop_data, ShopConfig)
    
    cust_feats = shop_data.set_index("cust_code")
    cust_data = cust_data.join(cust_feats, on="BB_RETAIL_CUSTOMER_CODE", how="inner")
    
    feats_map = gbdtlr_model.generate_feats_map(product_data, cust_data)
    
    return feats_map, filter_dict, cust_list

def get_recommend_list_by_gbdt_lr(city_uuid, product_id):
    """根据gbdt-lr进行打分并获得推荐列表，适用于推荐历史订单中存在的卷烟"""
    feats_sample, _, cust_list = generate_recommend_sample(city_uuid, product_id)
    recommend_list = gbdtlr_model.get_recommend_list(feats_sample, cust_list)
    return recommend_list
    

def gbdt_lr_inference(city_uuid, product_id):
    pass

def generate_features_shap(city_uuid, product_id, delivery_count):
    feats_sample, filter_dict, cust_list = generate_recommend_sample(city_uuid, product_id)
    
    if product_id in dao.get_product_from_order(city_uuid)["product_code"].unique().tolist():
        # 如果推荐商品为新卷烟，走iterm2vec
        recommend_data = gbdtlr_model.get_recommend_list(feats_sample, cust_list)
    else:
        recommend_data = item2vec.get_recommend_cust_list(product_id).to_dict("records")
    result = gbdtlr_model.generate_shap_interance(feats_sample)
    generate_report(city_uuid, result, filter_dict, recommend_data, delivery_count, "./data")

def eval(city_uuid, product_code):
    """推荐效果验证"""
    eval_report = get_cust_list_from_history_order(city_uuid, product_code)
    eval_report.to_csv("./data/效果验证表.csv", index=False)
    
def generate_similarity_product(product_code):
    product_similarity_map = item2vec.generate_product_similarity_map(product_code)
    product_similarity_map = product_similarity_map[["product_name", "similarity", "brand_name", "factory_name", "is_low_tar", "is_medium", "is_tiny", "is_coarse", "is_exploding_beads", "is_abnormity", "is_cig", "is_chuangxin", "direct_retail_price", "tbc_total_length", "product_style"]]
    product_similarity_map = product_similarity_map.rename(
        columns={
            "product_name": "卷烟名称",
            "similarity": "相似度",
            "factory_name": "生产厂商",
            "brand_name": "品牌名称",
            "is_low_tar":                "低焦油卷烟",
            "is_medium":                 "中支烟",
            "is_tiny":                   "细支烟",
            "is_coarse":                 "粗支烟",
            "is_exploding_beads":        "爆珠烟",
            "is_abnormity":              "异形包装",
            "is_cig":                    "雪茄烟",
            "is_chuangxin":              "创新品类",
            "direct_retail_price":       "卷烟建议零售价",
            "tbc_total_length":          "烟支总长度",
            "product_style":             "包装类型",
        }
    )
    product_similarity_map.to_excel("./data/相似卷烟表.xlsx", index=False)

def generate_delivery_strategy():
    
    pass

def run():
    pass

if __name__ == '__main__':
    generate_features_shap("00000000000000000000000011445301", "350139", delivery_count=5000)
    generate_similarity_product("350139")
    eval("00000000000000000000000011445301", "350355")
    
    # recommend_list = get_recommend_list_by_gbdt_lr("00000000000000000000000011445301", "350139")
    # recommend_list = pd.DataFrame(recommend_list)
    # recommend_list.to_csv("./data/recommend_list.csv", index=False, encoding="utf-8-sig")
    
    # 拿龙军数据
    # data = dao.get_order_by_cust("00000000000000000000000011445301", "445323105795")
    # data = data.groupby(["cust_code", "product_code", "product_name"], as_index=False)["sale_qty"].sum()
    # data.to_csv("./data/cust.csv", index=False)
    
    # city_uuid = "00000000000000000000000011445301"
    # order_data = dao.get_order_by_cust("00000000000000000000000011445301", "445323105795")
    # order_data["sale_qty"] = order_data["sale_qty"].fillna(0)
    # order_data = order_data.infer_objects(copy=False)
    # order_data = order_data.groupby(["cust_code", "product_code", "product_name"], as_index=False)["sale_qty"].sum()
    
    # cust_data = dao.load_cust_data(city_uuid)[CustConfig.FEATURE_COLUMNS]
    # sample_data_clear(cust_data, CustConfig)
    # shop_data = dao.load_shopping_data(city_uuid)[ShopConfig.FEATURE_COLUMNS]
    # sample_data_clear(shop_data, ShopConfig)
    # cust_ids = shop_data.set_index("cust_code")
    # cust_data = cust_data.join(cust_ids, on="BB_RETAIL_CUSTOMER_CODE", how="inner")
    
    # product_data = dao.load_product_data(city_uuid)[ProductConfig.FEATURE_COLUMNS]
    # sample_data_clear(product_data, ProductConfig)
    
    # order_data = order_data.merge(product_data, on="product_code", how="inner")
    # order_data = order_data.merge(cust_data, left_on='cust_code', right_on='BB_RETAIL_CUSTOMER_CODE', how="inner")
    
    # result = gbdtlr_model.inference_from_sample(order_data)
    # result.to_csv("./data/junlong.csv", index=False)