def __init__(self, features, trees): """ Boosted ODT, which follows REP conventions though cannot be fiited. :param features: list of strings, features used. :param trees: list of tuples, each tuple represents a tree. Tuple is features (features, cuts, leaf_values) """ Classifier.__init__(self, features) self.trees = trees
def __init__(self, base_estimator, n_folds=2, random_state=None, train_features=None, parallel_profile=None, group_feature=None): self.group_feature = group_feature self.train_features = train_features self.estimators = [] self.parallel_profile = parallel_profile self.n_folds = n_folds self.base_estimator = base_estimator self._folds_indices = None self.random_state = random_state self._random_number = None # setting features directly Classifier.__init__(self, features=self._features())