汽车交易情况分析及价格预测4_模型融合
一、加权融合
def Weighted_method(test_pre1,test_pre2,test_pre3,w=[1/3,1/3,1/3]): Weighted_result = w[0]*pd.Series(test_pre1)+w[1]*pd.Series(test_pre2)+w[2]*pd.Series(test_pre3) return Weighted_result # Init the Weight w = [0.3,0.4,0.3] # 测试验证集准确度 val_pre = Weighted_method(val_lgb,val_xgb,val_gbdt,w) MAE_Weighted = mean_absolute_error(y_val,val_pre) print('MAE of Weighted of val:',MAE_Weighted) # 预测数据部分 subA = Weighted_method(subA_lgb,subA_xgb,subA_gbdt,w) print('Sta inf:') Sta_inf(subA) # 生成提交文件 sub = pd.DataFrame() sub['SaleID'] = X_test.index sub['price'] = subA sub.to_csv('./sub_Weighted.csv',index=False)
# 与简单的LR(线性回归)进行对比 val_lr_pred = model_lr.predict(x_val) MAE_lr = mean_absolute_error(y_val,val_lr_pred) print('MAE of lr:',MAE_lr)
Starking融合
第一层
train_lgb_pred = model_lgb.predict(x_train) train_xgb_pred = model_xgb.predict(x_train) train_gbdt_pred = model_gbdt.predict(x_train) Strak_X_train = pd.DataFrame() Strak_X_train['Method_1'] = train_lgb_pred Strak_X_train['Method_2'] = train_xgb_pred Strak_X_train['Method_3'] = train_gbdt_pred Strak_X_val = pd.DataFrame() Strak_X_val['Method_1'] = val_lgb Strak_X_val['Method_2'] = val_xgb Strak_X_val['Method_3'] = val_gbdt Strak_X_test = pd.DataFrame() Strak_X_test['Method_1'] = subA_lgb Strak_X_test['Method_2'] = subA_xgb Strak_X_test['Method_3'] = subA_gbdt Strak_X_test.head()
第二层
model_lr_Stacking = build_model_lr(Strak_X_train,y_train) ## 训练集 train_pre_Stacking = model_lr_Stacking.predict(Strak_X_train) print('MAE of Stacking-LR:',mean_absolute_error(y_train,train_pre_Stacking)) ## 验证集 val_pre_Stacking = model_lr_Stacking.predict(Strak_X_val) print('MAE of Stacking-LR:',mean_absolute_error(y_val,val_pre_Stacking)) ## 预测集 print('Predict Stacking-LR...') subA_Stacking = model_lr_Stacking.predict(Strak_X_test)
subA_Stacking[subA_Stacking<10]=10 ## 去除过小的预测值 sub = pd.DataFrame() sub['SaleID'] = X_test.index sub['price'] = subA_Stacking sub.to_csv('./sub_Stacking.csv',index=False) print('Sta inf:') Sta_inf(subA_Stacking)