xgboostbase.py 文件源码

def __init__(self, a_clf=None, a_grid_search=False):
        """Class constructor.

        Args:
          a_clf (classifier or None):
            classifier to use or None for default
          a_grid_search (bool): use grid search for estimating
            hyper-parameters

        """
        classifier = a_clf
        self._gs = a_grid_search
        if a_clf is None:
            classifier = XGBClassifier(max_depth=MAX_DEPTH,
                                       n_estimators=NTREES,
                                       learning_rate=ALPHA,
                                       objective="multi:softprob")
            self._clf = classifier
        # latest version of XGBoost cannot deal with non-sparse feature vectors
        self._model = Pipeline([("vect", DictVectorizer()),
                                ("clf", classifier)])