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.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_mnist_example.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_mnist_example.py:


UMAP on the MNIST Digits dataset
--------------------------------

A simple example demonstrating how to use UMAP on a larger
dataset such as MNIST. We first pull the MNIST dataset and
then use UMAP to reduce it to only 2-dimensions for
easy visualisation.

Note that UMAP manages to both group the individual digit
classes, but also to retain the overall global structure
among the different digit classes -- keeping 1 far from
0, and grouping triplets of 3,5,8 and 4,7,9 which can
blend into one another in some cases.

.. GENERATED FROM PYTHON SOURCE LINES 16-36

.. code-block:: Python


    import umap
    from sklearn.datasets import fetch_openml
    import matplotlib.pyplot as plt
    import seaborn as sns

    sns.set(context="paper", style="white")

    mnist = fetch_openml("mnist_784", version=1)

    reducer = umap.UMAP(random_state=42)
    embedding = reducer.fit_transform(mnist.data)

    fig, ax = plt.subplots(figsize=(12, 10))
    color = mnist.target.astype(int)
    plt.scatter(embedding[:, 0], embedding[:, 1], c=color, cmap="Spectral", s=0.1)
    plt.setp(ax, xticks=[], yticks=[])
    plt.title("MNIST data embedded into two dimensions by UMAP", fontsize=18)

    plt.show()


.. _sphx_glr_download_auto_examples_plot_mnist_example.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

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    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_mnist_example.py <plot_mnist_example.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_mnist_example.zip <plot_mnist_example.zip>`


.. only:: html

 .. rst-class:: sphx-glr-signature

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