GenerateResult.py 文件源码

def predict_sku():
    print('????.')
    xgb_model = xgb.Booster({'nthread':-1})
    xgb_model.load_model('./model/xgb_sku.model')
    Online = pd.read_csv("./feat/online_sku_feat.csv")
    Online_drop_cols = ['user_id', 'sku_id', 'cate', 'brand']
    donline = xgb.DMatrix(Online.drop(Online_drop_cols, axis=1))

    ##??
    xgb_proba = xgb_model.predict(donline)
    sku_proba = Online[['user_id', 'sku_id']]
    sku_proba.loc[:,'sku_proba'] = xgb_proba

    ##???????????????
    sku_proba = sku_proba.groupby(['user_id'], as_index=False).apply(lambda t: t[t.sku_proba == t.sku_proba.max()]).reset_index()[['user_id', 'sku_id', 'sku_proba']]
    ##??????????
    user_proba = pd.read_csv("./online_user_proba.csv")

    ##???????????
    sku_proba.sort_values(by="sku_proba", ascending=False, inplace=True)
    user_proba.sort_values(by="proba", ascending=False, inplace=True)

    ##???? ? ???? ???500??
    Top_user = user_proba.iloc[:500]
    Top_sku = sku_proba.iloc[:500][['user_id', 'sku_id']]
    Top_user = sku_proba[sku_proba.user_id.isin(Top_user.user_id)]
    Top_user = Top_user.groupby(['user_id'], as_index=False).apply(lambda t: t[t.sku_proba == t.sku_proba.max()]).reset_index()[['user_id', 'sku_id']]

    pred = pd.concat([Top_sku, Top_user])
    pred = pred.drop_duplicates()
    pred = pred[pred.user_id.duplicated()==False]

    pred.astype(int).to_csv("online_submit.csv", index=False)
    print('????.')