fetch_ordered_mnist

kooplearn.datasets.fetch_ordered_mnist(*, num_digits: int = 10, data_home: str | PathLike | None = None, cache: bool = True, n_retries: int = 3, delay: float = 1.0)[source]

Fetch the MNIST dataset and return an ordered subset interleaving samples from each digit class.

This function wraps sklearn.datasets.fetch_openml() for the MNIST dataset (OpenML ID 554) and reorders the samples so that digits 0 through num_digits - 1 are interleaved in the output. This is useful for generating class-balanced or periodic sequences for Koopman operator regression experiments.

The MNIST dataset contains 70,000 grayscale handwritten digits (60,000 for training and 10,000 for testing) of size 28×28.

Parameters:

  • num_digits (int, default 10) – Number of digit classes to include, from 1 to 10. For example, num_digits=3 returns only digits 0, 1, and 2.

  • data_home (str or path-like, optional) – Specify an alternative download and cache folder for the dataset. By default, scikit-learn stores data in ~/scikit_learn_data.

  • cache (bool, default True) – Whether to cache the downloaded dataset.

  • n_retries (int, default 3) – Number of times to retry downloading if network errors occur.

  • delay (float, default 1.0) – Number of seconds between retries during download.

Returns:

  • images (ndarray of shape (n_samples, 28, 28)) – Array of grayscale MNIST images, with values scaled to the [0, 1] range (float64).

  • targets (ndarray of shape (n_samples,)) – Corresponding digit labels (integers in [0, num_digits - 1]).

Notes

The dataset is reordered so that classes are interleaved in the returned arrays. For example, with num_digits=3, the ordering will be:

[0, 1, 2, 0, 1, 2, 0, 1, 2, ...]

Examples

>>> from kooplearn.datasets import fetch_ordered_mnist
>>> images, targets = fetch_ordered_mnist(num_digits=3)
>>> images.shape
(20709, 28, 28)
>>> np.unique(targets)
array([0, 1, 2])