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.

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()

Total running time of the script: ( 0 minutes 0.000 seconds)

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