如果我使用 GridsearchCV，如何在 Xgboost 中使用 model.evals_result()？

Question

我正在使用 xgboost 回归器，如果我使用 GridsearchCV，我有一个关于如何使用 model.evals_result() 的问题

我知道如果我不使用 Gridsearch 我可以使用下面的代码得到我想要的

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.33, random_state=1,shuffle=False)

evals_result = {}
eval_s = [(X_train, y_train), (X_test, y_test)]

gbm = xgb.XGBRegressor()
gbm.fit(X_train, y_train,eval_metric=["rmse"],eval_set=eval_s)

results = gbm.evals_result()

但是如果我在代码中使用 GridsearchCV （见下文），我将无法获得 evals_result() 。

有人提供线索吗？

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.33, random_state=1,shuffle=False)

gbm_param_grid = {'learning_rate': [.01, .1, .5, .9],
                          'n_estimators': [200, 300],
                          'subsample': [0.3, 0.5, 0.9]
                          }

fit_params = {"early_stopping_rounds": 100,
                      "eval_metric": "mae",
                      "eval_set": [(X_train, y_train), (X_test, y_test)]}

evals_result = {}
eval_s = [(X_train, y_train), (X_test, y_test)]

gbm = xgb.XGBRegressor()
tscv = TimeSeriesSplit(n_splits=2)
xgb_Gridcv = GridSearchCV(estimator=gbm, param_grid=gbm_param_grid, cv=tscv,refit = True, verbose=0)

xgb_Gridcv.fit(X_train, y_train,eval_metric=["rmse"],eval_set=eval_s)
        ypred = xgb_Gridcv.predict(X_test) 

现在当我运行时 results = gbm.evals_result() 出现此错误

Traceback (most recent call last):
  File "/Users/prasadkamath/.conda/envs/Pk/lib/python3.5/site-packages/IPython/core/interactiveshell.py", line 2961, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File "<ipython-input-11-95ef57081806>", line 1, in <module>
    results = gbm.evals_result()
  File "/Users/prasadkamath/.conda/envs/Pk/lib/python3.5/site-packages/xgboost/sklearn.py", line 401, in evals_result
    if self.evals_result_:
AttributeError: 'XGBRegressor' object has no attribute 'evals_result_'

Answer 1

一般来说，您可以evals_result直接访问字典，而不是访问模型的方法，例如 xgb_model.evals_result()。例如：

eval_s = [(X_train, y_train), (X_test, y_test)]
evals_result = {}
xgb_model = xgb.train(param, 
                      train_orig_data_dmat, 
                      num_boost_round=100,
                      evals=eval_s,
                      early_stopping_rounds=10,
                      evals_result=evals_result)
print(evals_result)

将分别打印出训练和测试的错误以及您定义的任何评估指标。这是另一个更详细的参考：https://github.com/dmlc/xgboost/blob/master/demo/guide-python/evals_result.py