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[MRG] add seeds when n_jobs=1 and use seed as random_state #9288

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Merged
merged 5 commits into from
Aug 16, 2019
Merged

[MRG] add seeds when n_jobs=1 and use seed as random_state #9288

merged 5 commits into from
Aug 16, 2019

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bryanyang0528
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@bryanyang0528 bryanyang0528 commented Jul 6, 2017 •
edited by amueller
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Reference Issue

Fixes #9287, Fixes #9784

What does this implement/fix? Explain your changes.

no matter how many n_jobs, the random_state in kmeans_single should be the same.
So I added seeds = random_state.randint(np.iinfo(np.int32).max, size=n_init if n_jobs=1
and use seed instead original random_state in the for loop.

Any other comments?

I didn't revise test cases for this change yet. I'll update them if you think this change is good.

@jnothman
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jnothman commented Jul 6, 2017

This seems reasonable except insofar as KMeans with a fixed random state might have been returning the same model for a long time. I'm not sure it's worth breaking users' clusterings,

lesteve
lesteve reviewed Jul 6, 2017
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sklearn/cluster/k_means_.py Outdated
@@ -338,12 +338,13 @@ def k_means(X, n_clusters, init='k-means++', precompute_distances='auto',
if n_jobs == 1:
# For a single thread, less memory is needed if we just store one set
# of the best results (as opposed to one set per run per thread).
for it in range(n_init):
seeds = random_state.randint(np.iinfo(np.int32).max, size=n_init)
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You can move the seeds assignment outside of the if clause since it used both for n_job == 1 and n_jobs != 1.

@bryanyang0528
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@jnothman Even though creating seeds for n_jobs =1, seeds will be the same with the fixed random_state which might return the same model. But the model might be not the same as the model generated by the current method.

@lesteve lesteve mentioned this pull request Sep 20, 2017
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@amueller amueller mentioned this pull request Aug 5, 2019
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amueller commented Aug 5, 2019

some duplication with #9785

@adrinjalali
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This looks good to me. It can take the test from the other PR and I'd say it's almost good to go.

@jnothman you still worried about backward compatibility here?

@amueller
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amueller commented Aug 6, 2019

I think it's a good fix.

@adrinjalali
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@bryanyang0528 would you have time to address the comments, and rebase on the latest master here?

@bryanyang0528
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@adrinjalali no problem. Thanks!

@jnothman
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Any chance you can add a test?

@bryanyang0528
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@jnothman no problem.

@adrinjalali
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@bryanyang0528 tests failing :)

@bryanyang0528 bryanyang0528 reopened this Aug 12, 2019
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bryanyang0528 commented Aug 12, 2019 •
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@adrinjalali I'm not sure why tests failed only on py35_conda_openblas and pylatest_conda_mkl_pandas. And No module named 'sklearn.__check_build._check_build' happened in circleci:doc. Are there any suggestions or hints for figuring out the issues?

p.s. I notice that recent PRs in sklearn are failed in these tests steps either.

@thomasjpfan
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Merge with master should fix the issue.

@adrinjalali
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@bryanyang0528 please avoid force pushing. The errors are not related to you, you can ignore the ones which fail to create the environment.

@adrinjalali
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or merge master as @thomasjpfan suggests.

@bryanyang0528
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bryanyang0528 commented Aug 12, 2019 •
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@adrinjalali @thomasjpfan Thank you for suggestions.

@bryanyang0528
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@adrinjalali Thank you for help, all tests passed. What should I do for next step?

adrinjalali
adrinjalali approved these changes Aug 13, 2019
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This LGTM, ping @jnothman since I know he had some reservations about this solution. To me this is a fix, and therefore I wouldn't mind the change.

@amueller amueller changed the title [WIP] add seeds when n_jobs=1 and use seed as random_state [MRG] add seeds when n_jobs=1 and use seed as random_state Aug 13, 2019
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amueller approved these changes Aug 13, 2019
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happy to wait for @jnothman but looks good to me.

NicolasHug
NicolasHug approved these changes Aug 13, 2019
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LGTM as a bug-fix, thanks @bryanyang0528

@jnothman
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I'm happy with the fix.

@jnothman
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I'm happy with the fix.
Please add an entry to the change log at doc/whats_new/v0.22.rst. Like the other entries there, please reference this pull request with :pr: and credit yourself (and other contributors if applicable) with :user:

Please also note the change at the top of that file under Changed Models

bryanyang0528 and others added 2 commits August 16, 2019 00:16
@amueller
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thanks!

@bryanyang0528
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Thanks!

@amueller amueller merged commit e8f2708 into scikit-learn:master Aug 16, 2019
@jeromedockes jeromedockes mentioned this pull request Oct 14, 2019
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ariapoy referenced this pull request in ntucllab/libact Aug 2, 2021
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Upgrade to sklearn==0.22.
0b61fa4
@ariapoy ariapoy mentioned this pull request Aug 4, 2021
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@amueller amueller amueller approved these changes

@NicolasHug NicolasHug NicolasHug approved these changes

@adrinjalali adrinjalali adrinjalali approved these changes

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Successfully merging this pull request may close these issues.

KMeans gives slightly different result for n_jobs=1 vs. n_jobs > 1 Inconsistence results of Kmeans between n_job = 1 and n_jobs > 1
7 participants
@bryanyang0528 @jnothman @amueller @adrinjalali @thomasjpfan @NicolasHug @lesteve