(self, test_ratio=0.3, initial_label_rate=0.05,
                 split_count=10, all_class=True)
return self._train_idx, self._test_idx, self._label_idx, self._unlabel_idx
split dataset for active learning experiment.

        Parameters
        ----------
        test_ratio: float, optional (default=0.3)
            ratio of test set

        initial_label_rate: float, optional (default=0.05)
            ratio of initial label set or the existed features (missing rate = 1-initial_label_rate)
            e.g. initial_labelset*(1-test_ratio)*n_samples

        split_count: int, optional (default=10)
            random split data _split_count times

        all_class: bool, optional (default=True)
            whether each split will contain at least one instance for each class.
            If False, a totally random split will be performed.

        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]
split dataset for active learning experiment.
split dataset for active learning experiment .
def split_AL(self, test_ratio=0.3, initial_label_rate=0.05,
                 split_count=10, all_class=True):
        """split dataset for active learning experiment.

        Parameters
        ----------
        test_ratio: float, optional (default=0.3)
            ratio of test set

        initial_label_rate: float, optional (default=0.05)
            ratio of initial label set or the existed features (missing rate = 1-initial_label_rate)
            e.g. initial_labelset*(1-test_ratio)*n_samples

        split_count: int, optional (default=10)
            random split data _split_count times

        all_class: bool, optional (default=True)
            whether each split will contain at least one instance for each class.
            If False, a totally random split will be performed.

        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]

        """
        self._split_count = split_count
        self._split = True
        self._train_idx, self._test_idx, self._label_idx, self._unlabel_idx = split(
            X=self._X,
            y=self._y,
            test_ratio=test_ratio,
            initial_label_rate=initial_label_rate,
            split_count=split_count,
            all_class=all_class)
        return self._train_idx, self._test_idx, self._label_idx, self._unlabel_idx
def split_AL ( self , test_ratio = 0.3 , initial_label_rate = 0.05 , split_count = 10 , all_class = True ) : self . _split_count = split_count self . _split = True self . _train_idx , self . _test_idx , self . _label_idx , self . _unlabel_idx = split ( X = self . _X , y = self . _y , test_ratio = test_ratio , initial_label_rate = initial_label_rate , split_count = split_count , all_class = all_class ) return self . _train_idx , self . _test_idx , self . _label_idx , self . _unlabel_idx