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"""
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()
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