[MRG] Speed up plot_digits_linkage.py example #21598 #21678
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Thanks for the PR @yarkhinephyo!
I left a couple of comments in the code 🤓. Let me know what do you think.
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I think the main message of the example is quite clearly visible without the nudging data augmentation that also makes the code more complex for little benefit.
However the analysis could be improved to better reflect what we observe (both in main and in this branch). Let me suggest the following:
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 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.
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…t-learn#21678) * Reduce num of samples in plot-digit-linkage example * Remove unnecessary random_state * Remove nudge_images * Address PR comment, elaborate analysis
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Changing calls from matplotlib.pyplot.text to matplot.pyplot.scatter using Latex markers for digits speeds up the plotting process and cuts down the plotting runtime to 25% to 35% of the original time. |
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That's righ 8000 t @siavrez. I've just tested it and it runs 17 times faster. The original implementation runs slower because we used What do you think @ogrisel, @adrinjalali? |
…t-learn#21678) * Reduce num of samples in plot-digit-linkage example * Remove unnecessary random_state * Remove nudge_images * Address PR comment, elaborate analysis
…t-learn#21678) * Reduce num of samples in plot-digit-linkage example * Remove unnecessary random_state * Remove nudge_images * Address PR comment, elaborate analysis
…t-learn#21678) * Reduce num of samples in plot-digit-linkage example * Remove unnecessary random_state * Remove nudge_images * Address PR comment, elaborate analysis
Reference Issues/PRs
#21598
What does this implement/fix? Explain your changes.
Speeds up
../examples/cluster/plot_digits_linkage.pyfrom 32 sec to 20 sec by reducing the number of digits dataset samples from 1800 to 800.Additionally, increased the font size of the numbers and added a random state for
manifold.SpectralEmbedding.Before:
After:
Any other comments?
Nil