catboost 回归/CatBoostRegressor 的销售预测错误

编程问答 2022-05-25

问题描述

d = {'customer':['A','B','C','A'],'season':[1,2,3,4],'cat1': ['BAGS','TSHIRT','DRESS','BELT'],'cat2': ['high','low','high','medium'],'sale': [10,20,15,50]}
df = pd.DataFrame(data=d)
df

第 5 季的预期输出

d = {'customer':['A','season': [5,5,5],'sale': [?,?,?]}
df = pd.DataFrame(data=d)
df

我试过了

df=df.groupby(['customer','season','cat1','cat2'])['Sales'].sum().sort_values(ascending=False).reset_index()
from sklearn.model_selection import train_test_split
X=df[['customer','cat2']]
y=df[['Sales']]

X.season=X.season.astype(float)
X_train,X_test,y_train,y_test = train_test_split(X,y,train_size = 0.90,random_state =42)
X_train,X_val,y_val = train_test_split(X_train,train_size = 0.85,random_state =42)
categorical_features_indices = np.where(X.dtypes != np.float)[0]
import catboost
from catboost import MetricVisualizer,Pool,CatBoostRegressor,cv
train_pool = Pool(data=X_train,label=y_train,cat_features=categorical_features_indices)
val_pool = Pool(data=X_val,label=y_val,cat_features=categorical_features_indices)
test_pool = Pool(data=X_test,label=y_test,cat_features=categorical_features_indices)


params = {
   'iterations':900,'loss_function': 'RMSE','learning_rate': 0.0109,#1 0.102,'depth': 6,'l2_leaf_reg': 6,'border_count': 7,'thread_count': 7,'bagging_temperature': 2,'random_strength': 2.23,'colsample_bylevel': 0.85,'custom_metric': ['MAPE','R2'],'eval_metric': 'R2','random_seed': 41,'max_ctr_complexity': 2,'logging_level': 'Silent','use_best_model':False # Takes
}


reg_model = CatBoostRegressor(**params)
reg_model.fit(train_pool,eval_set=val_pool,plot=True,verbose=100)



X['season']=5
X['Predict_sales']=reg_model.predict(X)

上面的代码没有报错。

我的问题是：如果输入 5、6、7、8，我的预测值不会改变，但是季节是一个连续值。我做错了什么，我如何预测第 6、7、8 季等等。

解决方法

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