.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_fashion-mnist_example.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_fashion-mnist_example.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_plot_fashion-mnist_example.py:


UMAP on the Fashion MNIST Digits dataset using Datashader
---------------------------------------------------------

This is a simple example of using UMAP on the Fashion-MNIST
dataset. The goal of this example is largely to demonstrate
the use of datashader as an effective tool for visualising
UMAP results. In particular datashader allows visualisation
of very large datasets where overplotting can be a serious
problem. It supports coloring by categorical variables
(as shown in this example), or by continuous variables,
or by density (as is common in datashader examples).

.. GENERATED FROM PYTHON SOURCE LINES 14-75

.. code-block:: Python


    import umap
    import numpy as np
    import pandas as pd
    import requests
    import os
    import datashader as ds
    import datashader.utils as utils
    import datashader.transfer_functions as tf
    import matplotlib.pyplot as plt
    import seaborn as sns

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

    if not os.path.isfile("fashion-mnist.csv"):
        csv_data = requests.get("https://www.openml.org/data/get_csv/18238735/phpnBqZGZ")
        with open("fashion-mnist.csv", "w") as f:
            f.write(csv_data.text)
    source_df = pd.read_csv("fashion-mnist.csv")

    data = source_df.iloc[:, :784].values.astype(np.float32)
    target = source_df["class"].values

    pal = [
        "#9e0142",
        "#d8434e",
        "#f67a49",
        "#fdbf6f",
        "#feeda1",
        "#f1f9a9",
        "#bfe5a0",
        "#74c7a5",
        "#378ebb",
        "#5e4fa2",
    ]
    color_key = {str(d): c for d, c in enumerate(pal)}

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

    df = pd.DataFrame(embedding, columns=("x", "y"))
    df["class"] = pd.Series([str(x) for x in target], dtype="category")

    cvs = ds.Canvas(plot_width=400, plot_height=400)
    agg = cvs.points(df, "x", "y", ds.count_cat("class"))
    img = tf.shade(agg, color_key=color_key, how="eq_hist")

    utils.export_image(img, filename="fashion-mnist", background="black")

    image = plt.imread("fashion-mnist.png")
    fig, ax = plt.subplots(figsize=(6, 6))
    plt.imshow(image)
    plt.setp(ax, xticks=[], yticks=[])
    plt.title(
        "Fashion MNIST data embedded\n"
        "into two dimensions by UMAP\n"
        "visualised with Datashader",
        fontsize=12,
    )

    plt.show()


.. _sphx_glr_download_auto_examples_plot_fashion-mnist_example.py:

.. only:: html

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

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_fashion-mnist_example.ipynb <plot_fashion-mnist_example.ipynb>`

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

      :download:`Download Python source code: plot_fashion-mnist_example.py <plot_fashion-mnist_example.py>`

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

      :download:`Download zipped: plot_fashion-mnist_example.zip <plot_fashion-mnist_example.zip>`


.. only:: html

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

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