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

import umap

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

mnist = fetch_openml("mnist_784", version=1)
X_train, X_test, y_train, y_test = train_test_split(,,, random_state=42

reducer = umap.UMAP(random_state=42)
embedding_train = reducer.fit_transform(X_train)
embedding_test = reducer.transform(X_test)

fig, ax = plt.subplots(1, 2, sharex=True, sharey=True, figsize=(12, 10))
    embedding_train[:, 0], embedding_train[:, 1], c=y_train, cmap="Spectral"  # , s=0.1
    embedding_test[:, 0], embedding_test[:, 1], c=y_test, cmap="Spectral"  # , s=0.1
plt.setp(ax[0], xticks=[], yticks=[])
plt.setp(ax[1], xticks=[], yticks=[])
plt.suptitle("MNIST data embedded into two dimensions by UMAP", fontsize=18)
ax[0].set_title("Training Set", fontsize=12)
ax[1].set_title("Test Set", fontsize=12)

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

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