def make_random_MLdataset(max_num_classes = 20,
                          min_class_size = 20,
                          max_class_size = 50,
                          max_dim = 100,
                          stratified = True):
    "Generates a random MLDataset for use in testing."

    smallest = min(min_class_size, max_class_size)
    max_class_size = max(min_class_size, max_class_size)
    largest = max(50, max_class_size)
    largest = max(smallest+3,largest)

    if max_num_classes != 2:
        num_classes = np.random.randint(2, max_num_classes, 1)
    else:
        num_classes = 2

    if type(num_classes) == np.ndarray:
        num_classes = num_classes[0]
    if not stratified:
        class_sizes = np.random.random_integers(smallest, largest, num_classes)
    else:
        class_sizes = np.repeat(np.random.randint(smallest, largest), num_classes)

    num_features = np.random.randint(min(3, max_dim), max(3, max_dim), 1)[0]
    # feat_names = [ str(x) for x in range(num_features)]

    class_ids = list()
    labels = list()
    for cl in range(num_classes):
        class_ids.append('class-{}'.format(cl))
        labels.append(int(cl))

    ds = MLDataset()
    for cc, class_ in enumerate(class_ids):
        subids = [ 's{}-c{}'.format(ix,cc) for ix in range(class_sizes[cc]) ]
        for sid in subids:
            ds.add_sample(sid, feat_generator(num_features), int(cc), class_)

    return ds
Generates a random MLDataset for use in testing.