如何解决如何在 XGBRegressor 的 MultiOutputRegressor 上使用验证集?
我正在使用以下 MultIoUtputRegressor:
from xgboost import XGBRegressor
from sklearn.multIoUtput import MultIoUtputRegressor
#Define the estimator
estimator = XGBRegressor(
objective = 'reg:squarederror'
)
# Define the model
my_model = MultIoUtputRegressor(estimator = estimator,n_jobs = -1).fit(X_train,y_train)
我想使用验证集来评估我的 XGBRegressor 的性能,但是我相信 MultIoUtputRegressor
不支持将 eval_set
传递给 fit 函数。
在这种情况下我如何使用验证集?是否有任何变通方法可以调整 XGBRegressor 以获得多个输出?
解决方法
您可以尝试像这样编辑 fit
对象的 MultiOutputRegressor
方法:
from sklearn.utils.validation import _check_fit_params
from sklearn.base import is_classifier
from sklearn.utils.fixes import delayed
from joblib import Parallel
from sklearn.multioutput import _fit_estimator
class MyMultiOutputRegressor(MultiOutputRegressor):
def fit(self,X,y,sample_weight=None,**fit_params):
""" Fit the model to data.
Fit a separate model for each output variable.
Parameters
----------
X : {array-like,sparse matrix} of shape (n_samples,n_features)
Data.
y : {array-like,n_outputs)
Multi-output targets. An indicator matrix turns on multilabel
estimation.
sample_weight : array-like of shape (n_samples,),default=None
Sample weights. If None,then samples are equally weighted.
Only supported if the underlying regressor supports sample
weights.
**fit_params : dict of string -> object
Parameters passed to the ``estimator.fit`` method of each step.
.. versionadded:: 0.23
Returns
-------
self : object
"""
if not hasattr(self.estimator,"fit"):
raise ValueError("The base estimator should implement"
" a fit method")
X,y = self._validate_data(X,force_all_finite=False,multi_output=True,accept_sparse=True)
if is_classifier(self):
check_classification_targets(y)
if y.ndim == 1:
raise ValueError("y must have at least two dimensions for "
"multi-output regression but has only one.")
if (sample_weight is not None and
not has_fit_parameter(self.estimator,'sample_weight')):
raise ValueError("Underlying estimator does not support"
" sample weights.")
fit_params_validated = _check_fit_params(X,fit_params)
[(X_test,Y_test)] = fit_params_validated.pop('eval_set')
self.estimators_ = Parallel(n_jobs=self.n_jobs)(
delayed(_fit_estimator)(
self.estimator,y[:,i],sample_weight,**fit_params_validated,eval_set=[(X_test,Y_test[:,i])])
for i in range(y.shape[1]))
return self
然后将 eval_set
传递给 fit
方法:
fit_params = dict(
eval_set=[(X_test,Y_test)],early_stopping_rounds=10
)
model.fit(X_train,Y_train,**fit_params)
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