doctest

    >>> X = np.random.rand(n_samples, n_features)

    >>> y = np.random.rand(n_samples, n_tasks)

    >>> w = np.ones_like(y)

    >>> dataset = DiskDataset.from_numpy(np.ones((100,n_tasks)), np.ones((100,n_tasks)), verbose=False)

    >>> dataset = DiskDataset.from_numpy(np.ones((100,n_tasks)), np.ones((100,n_tasks)))

    >>> splitter = SingletaskStratifiedSplitter(task_number=5, verbose=False)

    >>> train_dataset, test_dataset = splitter.train_test_split(dataset)