code snippet for Tuning Hyperparameters (part I): SuccessiveHalving

SHXGBEstimator_illustration

class SHXGBEstimator(SHBaseEstimator):
    def __init__(self,model):
        self.model = model
        self.env = {'best_score':-np.infty,'best_iteration':-1,'earlier_stop':False}
def update(self,Xtrain,ytrain,Xval,yval,scoring,n_iterations):
         dtrain = DMatrix(data=Xtrain,label=ytrain)
         for i in range(n_iterations-self.model.n_estimators):
             # note:
             # this is a get, but the internal booster in XGBClassifier is also updated
             # add unit test for controle if future updates
             self.model.get_booster().update(dtrain,iteration=self.model.n_estimators)
             self.model.n_estimators += 1