8000 [MRG] Speed up plot_digits_linkage.py example #21598 by yarkhinephyo · Pull Request #21678 · scikit-learn/scikit-learn · GitHub
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[MRG] Speed up plot_digits_linkage.py example #21598 #21678

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Merged
merged 6 commits into from
Nov 19, 2021
Merged

[MRG] Speed up plot_digits_linkage.py example #21598 #21678

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cef8131
Reduce num of samples in plot-digit-linkage example
yarkhinephyo Nov 15, 2021
62feab0
Remove unnecessary random_state
yarkhinephyo Nov 16, 2021
0db7fd2
Merge branch 'main' into speed-up-plot-digit-linkage
yarkhinephyo Nov 17, 2021
79a776a
Remove nudge_images
yarkhinephyo Nov 18, 2021
35b77b7
Address PR comment, elaborate analysis
yarkhinephyo Nov 19, 2021
4e810cf
Merge branch 'main' into speed-up-plot-digit-linkage
yarkhinephyo Nov 19, 2021
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31 changes: 11 additions & 20 deletions examples/cluster/plot_digits_linkage.py
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Expand Up @@ -12,10 +12,18 @@

What this example shows us is the behavior "rich getting richer" of
agglomerative clustering that tends to create uneven cluster sizes.

This behavior is pronounced for the average linkage strategy,
that ends up with a couple of singleton clusters, while in the case
of single linkage we get a single central cluster with all other clusters
being drawn from noise points around the fringes.
that ends up with a couple of clusters with few datapoints.

The case of single linkage is even more pathologic with a very
large cluster covering most digits, an intermediate size (clean)
cluster with most zero digits and all other clusters being drawn
from noise points around the fringes.

The other linkage strategies lead to more evenly distributed
clusters that are therefore likely to be less sensible to a
random resampling of the dataset.

"""

Expand All @@ -25,7 +33,6 @@
from time import time

import numpy as np
from scipy import ndimage
from matplotlib import pyplot as plt

from sklearn import manifold, datasets
Expand All @@ -36,22 +43,6 @@
np.random.seed(0)


def nudge_images(X, y):
# Having a larger dataset shows more clearly the behavior of the
# methods, but we multiply the size of the dataset only by 2, as the
# cost of the hierarchical clustering methods are strongly
# super-linear in n_samples
shift = lambda x: ndimage.shift(
x.reshape((8, 8)), 0.3 * np.random.normal(size=2), mode="constant"
).ravel()
X = np.concatenate([X, np.apply_along_axis(shift, 1, X)])
Y = np.concatenate([y, y], axis=0)
return X, Y


X, y = nudge_images(X, y)


# ----------------------------------------------------------------------
# Visualize the clustering
def plot_clustering(X_red, labels, title=None):
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