(feature_matrix=None, feature_matrix_shape=None, test_ratio=0.3, missing_rate=0.5,
                   split_count=10, all_features=True, saving_path='.')
return __split_data_matrix(data_matrix=feature_matrix, matrix_shape=feature_matrix_shape, test_ratio=test_ratio,
                               initial_label_rate=1-missing_rate, split_count=split_count,
                               all_class=all_features, partially_labeled=True, saving_path=saving_path)
Split given feature matrix in feature querying setting.
    Giving one of feature_matrix or feature_matrix_shape to split the data.

    The matrix will be split randomly in _split_count times, the testing set
    is the set of instances with complete feature vectors. The training set
    has missing feature with the rate of missing_rate.

    Parameters
    ----------
    feature_matrix: array-like, optional
        Feature matrix, shape [n_samples, n_labels].

    feature_matrix_shape: tuple, optional (default=None)
        The shape of data_matrix, should be a tuple with 2 elements.
        The first one is the number of instances, and the other is the
        number of feature.

    test_ratio: float, optional (default=0.3)
        Ratio of test set.

    missing_rate: float, optional (default=0.5)
        Ratio of missing value.

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

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

    saving_path: str, optional (default='.')
        Giving None to disable saving.

    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 given feature matrix in feature querying setting.
    Giving one of feature_matrix or feature_matrix_shape to split the data.
Split given feature matrix in feature querying setting . Giving one of feature_matrix or feature_matrix_shape to split the data .
def split_features(feature_matrix=None, feature_matrix_shape=None, test_ratio=0.3, missing_rate=0.5,
                   split_count=10, all_features=True, saving_path='.'):
    """
    Split given feature matrix in feature querying setting.
    Giving one of feature_matrix or feature_matrix_shape to split the data.

    The matrix will be split randomly in _split_count times, the testing set
    is the set of instances with complete feature vectors. The training set
    has missing feature with the rate of missing_rate.

    Parameters
    ----------
    feature_matrix: array-like, optional
        Feature matrix, shape [n_samples, n_labels].

    feature_matrix_shape: tuple, optional (default=None)
        The shape of data_matrix, should be a tuple with 2 elements.
        The first one is the number of instances, and the other is the
        number of feature.

    test_ratio: float, optional (default=0.3)
        Ratio of test set.

    missing_rate: float, optional (default=0.5)
        Ratio of missing value.

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

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

    saving_path: str, optional (default='.')
        Giving None to disable saving.

    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]

    """
    return __split_data_matrix(data_matrix=feature_matrix, matrix_shape=feature_matrix_shape, test_ratio=test_ratio,
                               initial_label_rate=1-missing_rate, split_count=split_count,
                               all_class=all_features, partially_labeled=True, saving_path=saving_path)
def split_features ( feature_matrix = None , feature_matrix_shape = None , test_ratio = 0.3 , missing_rate = 0.5 , split_count = 10 , all_features = True , saving_path = '.' ) : return __split_data_matrix ( data_matrix = feature_matrix , matrix_shape = feature_matrix_shape , test_ratio = test_ratio , initial_label_rate = 1 - missing_rate , split_count = split_count , all_class = all_features , partially_labeled = True , saving_path = saving_path )