DOC Accelerate plot_johnson_lindenstrauss_bound.py example#21795
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glemaitre merged 6 commits intoscikit-learn:mainfrom May 4, 2022
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DOC Accelerate plot_johnson_lindenstrauss_bound.py example#21795glemaitre merged 6 commits intoscikit-learn:mainfrom
glemaitre merged 6 commits intoscikit-learn:mainfrom
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What speed-up do you observe locally? Weirdly enough, in the Anyways the text of of the example would need to be adjusted if we change the number of components but I am not sure it's worth it. Maybe you could try to reduce the number of document from 500 to 300 instead. The number of pairwise distances should be decreased from 250,000 to 90,000 which should yield approximately a 3x speed up on this example (assuming that the data fetching step is negligible...). |
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On And on my branch: I don't know why the new runtime is 50s while on my computer the computation time of the third example (10000 to 6000 components) is reduced from 19s to 12s. If we reduce the number of document from 500 to 300 and keep 10k components, we have: And the outputs are: 
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Somehow running this is slower (33s) than what we have already in |
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…arn#21795) Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
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Co-authored-by: Adrin Jalali <adrin.jalali@gmail.com> Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
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Reference Issues/PRs
References #21598
What does this implement/fix? Explain your changes.
Speed up
../examples/miscellaneous/plot_johnson_lindenstrauss_bound.pyby reducing the number of samples (n_components_range) from 10000 to 6000. The execution time is fast withn_components=300andn_components=1000, but slow (more than 10 seconds) withn_components=10000.Output before the changes:
And after:
Any other comments?
The other figures are exactly the same, no changes.