(self, round=None)
Get split of one fold experiment.

        Parameters:
        -----------
        round: int
            The number of fold. 0 <= round < split_count

        Returns
        -------
        train_idx: list
            index of training set, shape like [n_split_count, n_training_indexes]

        test_idx: list
            index of testing set, shape like [n_split_count, n_testing_indexes]

        label_idx: list
            index of labeling set, shape like [n_split_count, n_labeling_indexes]

        unlabel_idx: list
            index of unlabeling set, shape like [n_split_count, n_unlabeling_indexes]
Get split of one fold experiment.
Get split of one fold experiment .
def get_split(self, round=None):
        """Get split of one fold experiment.

        Parameters:
        -----------
        round: int
            The number of fold. 0 <= round < split_count

        Returns
        -------
        train_idx: list
            index of training set, shape like [n_split_count, n_training_indexes]

        test_idx: list
            index of testing set, shape like [n_split_count, n_testing_indexes]

        label_idx: list
            index of labeling set, shape like [n_split_count, n_labeling_indexes]

        unlabel_idx: list
            index of unlabeling set, shape like [n_split_count, n_unlabeling_indexes]
        """
        if not self._split:
            raise Exception("The split setting is unknown, use split_AL() first.")
        if round is not None:
            assert (0 <= round < self.split_count)
            if self.query_type == 'Features':
                return copy.copy(self.train_idx[round]), copy.copy(self.test_idx[round]), FeatureIndexCollection(
                    self.label_idx[round], self._X.shape[1]), FeatureIndexCollection(self.unlabel_idx[round],
                                                                                     self._X.shape[1])
            else:
                if self._target_type == 'multilabel':
                    return copy.copy(self.train_idx[round]), copy.copy(self.test_idx[round]), MultiLabelIndexCollection(
                        self.label_idx[round], self._label_num), MultiLabelIndexCollection(self.unlabel_idx[round],
                                                                                           self._label_num)
                else:
                    return copy.copy(self.train_idx[round]), copy.copy(self.test_idx[round]), IndexCollection(
                        self.label_idx[round]), IndexCollection(self.unlabel_idx[round])
        else:
            return copy.deepcopy(self.train_idx), copy.deepcopy(self.test_idx), \
                   copy.deepcopy(self.label_idx), copy.deepcopy(self.unlabel_idx)
def get_split ( self , round = None ) : if not self . _split : raise Exception ( "The split setting is unknown, use split_AL() first." ) if round is not None : assert ( 0 <= round < self . split_count ) if self . query_type == 'Features' : return copy . copy ( self . train_idx [ round ] ) , copy . copy ( self . test_idx [ round ] ) , FeatureIndexCollection ( self . label_idx [ round ] , self . _X . shape [ 1 ] ) , FeatureIndexCollection ( self . unlabel_idx [ round ] , self . _X . shape [ 1 ] ) else : if self . _target_type == 'multilabel' : return copy . copy ( self . train_idx [ round ] ) , copy . copy ( self . test_idx [ round ] ) , MultiLabelIndexCollection ( self . label_idx [ round ] , self . _label_num ) , MultiLabelIndexCollection ( self . unlabel_idx [ round ] , self . _label_num ) else : return copy . copy ( self . train_idx [ round ] ) , copy . copy ( self . test_idx [ round ] ) , IndexCollection ( self . label_idx [ round ] ) , IndexCollection ( self . unlabel_idx [ round ] ) else : return copy . deepcopy ( self . train_idx ) , copy . deepcopy ( self . test_idx ) , copy . deepcopy ( self . label_idx ) , copy . deepcopy ( self . unlabel_idx )