[SPARK-57957][ML][TESTS] Deflake GaussianMixtureSuite 'GMM support instance weighting' on macOS using well-posed data#57035
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…stance weighting' on macOS using well-posed data ### What changes were proposed in this pull request? Change the `GMM support instance weighting` test to fit `rDataset` with `k=2` instead of fitting `k=5` on `KMeansSuite.generateKMeansData(50, 3, 5)`. ### Why are the changes needed? The generated KMeans data is 5 clusters of identical points (zero within-cluster variance). Fitting k=5 Gaussians makes the covariances singular and the EM fit ill-posed, so the uniform-weighted and unweighted fits converge to different component-collapse patterns. On the macOS-26 runner (`build_maven_java21_macos26`) this deterministically fails the mixture-weight comparison (0.0197 vs 0.1047). Reducing the instance weight (as in SPARK-37317) and increasing maxIter were both verified on macOS-26 to NOT help (maxIter makes a component collapse further, 7.6e-11 vs 0.116). Running the same unweighted==uniform-weighted invariant on well-posed data (`rDataset`, real variance, k=2 - already used stably by another test in this suite) makes both fits converge to the same optimum. Verified green on a macOS-26 runner. ### Does this PR introduce _any_ user-facing change? No, test-only. ### How was this patch tested? Ran GaussianMixtureSuite on a macOS-26 GitHub Actions runner: 13 succeeded, 0 failed (previously the instance-weighting test failed deterministically on macOS-26). ### Was this patch authored or co-authored using generative AI tooling? Yes. This pull request and its description were written by Isaac.
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…tance weighting' on macOS using well-posed data ### What changes were proposed in this pull request? Change the `GMM support instance weighting` test to fit `rDataset` with `k=2` instead of fitting `k=5` on `KMeansSuite.generateKMeansData(50, 3, 5)`. ### Why are the changes needed? The generated KMeans data is 5 clusters of identical points (zero within-cluster variance). Fitting k=5 Gaussians makes the covariances singular and the EM fit ill-posed, so the uniform-weighted and unweighted fits converge to different component-collapse patterns. On the macOS-26 runner (`build_maven_java21_macos26`) this deterministically fails the mixture-weight comparison (0.0197 vs 0.1047). Reducing the instance weight (as in SPARK-37317, which reduced 100->90) and increasing maxIter were both verified on a macOS-26 runner to NOT help — increasing maxIter actually makes a component collapse further (7.6e-11 vs 0.116). Running the same `unweighted == uniform-weighted` invariant on well-posed data (`rDataset`, real variance, `k=2` — already used stably by another test in this suite) makes both fits converge to the same optimum. ### Does this PR introduce _any_ user-facing change? No, test-only. ### How was this patch tested? `GaussianMixtureSuite` on a macOS-26 GitHub Actions runner. - **Before (failing on `apache/spark` macOS-26, `build_maven_java21_macos26`):** https://github.com/apache/spark/actions/runs/28753698265/job/85259893661 — `GMM support instance weighting *** FAILED ***`, `Expected 0.01972564065075309 and 0.10476714410584831 to be within 0.001`. - **After (passing with this change, macOS-26 runner):** https://github.com/HyukjinKwon/spark-agent6/actions/runs/28773420633/job/85311928908 — `GaussianMixtureSuite: Tests: succeeded 13, failed 0`, `All tests passed.` Also passes on Linux. ### Was this patch authored or co-authored using generative AI tooling? Yes. This pull request and its description were written by Isaac. Closes #57035 from HyukjinKwon/ci-fix/agent5-gmm-macos-wellposed. Authored-by: Hyukjin Kwon <gurwls223@apache.org> Signed-off-by: Hyukjin Kwon <hyukjin.kwon@databricks.com> (cherry picked from commit 0a6bbee) Signed-off-by: Hyukjin Kwon <hyukjin.kwon@databricks.com>
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…tance weighting' on macOS using well-posed data ### What changes were proposed in this pull request? Change the `GMM support instance weighting` test to fit `rDataset` with `k=2` instead of fitting `k=5` on `KMeansSuite.generateKMeansData(50, 3, 5)`. ### Why are the changes needed? The generated KMeans data is 5 clusters of identical points (zero within-cluster variance). Fitting k=5 Gaussians makes the covariances singular and the EM fit ill-posed, so the uniform-weighted and unweighted fits converge to different component-collapse patterns. On the macOS-26 runner (`build_maven_java21_macos26`) this deterministically fails the mixture-weight comparison (0.0197 vs 0.1047). Reducing the instance weight (as in SPARK-37317, which reduced 100->90) and increasing maxIter were both verified on a macOS-26 runner to NOT help — increasing maxIter actually makes a component collapse further (7.6e-11 vs 0.116). Running the same `unweighted == uniform-weighted` invariant on well-posed data (`rDataset`, real variance, `k=2` — already used stably by another test in this suite) makes both fits converge to the same optimum. ### Does this PR introduce _any_ user-facing change? No, test-only. ### How was this patch tested? `GaussianMixtureSuite` on a macOS-26 GitHub Actions runner. - **Before (failing on `apache/spark` macOS-26, `build_maven_java21_macos26`):** https://github.com/apache/spark/actions/runs/28753698265/job/85259893661 — `GMM support instance weighting *** FAILED ***`, `Expected 0.01972564065075309 and 0.10476714410584831 to be within 0.001`. - **After (passing with this change, macOS-26 runner):** https://github.com/HyukjinKwon/spark-agent6/actions/runs/28773420633/job/85311928908 — `GaussianMixtureSuite: Tests: succeeded 13, failed 0`, `All tests passed.` Also passes on Linux. ### Was this patch authored or co-authored using generative AI tooling? Yes. This pull request and its description were written by Isaac. Closes #57035 from HyukjinKwon/ci-fix/agent5-gmm-macos-wellposed. Authored-by: Hyukjin Kwon <gurwls223@apache.org> Signed-off-by: Hyukjin Kwon <hyukjin.kwon@databricks.com> (cherry picked from commit 0a6bbee) Signed-off-by: Hyukjin Kwon <hyukjin.kwon@databricks.com>
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…tance weighting' on macOS using well-posed data ### What changes were proposed in this pull request? Change the `GMM support instance weighting` test to fit `rDataset` with `k=2` instead of fitting `k=5` on `KMeansSuite.generateKMeansData(50, 3, 5)`. ### Why are the changes needed? The generated KMeans data is 5 clusters of identical points (zero within-cluster variance). Fitting k=5 Gaussians makes the covariances singular and the EM fit ill-posed, so the uniform-weighted and unweighted fits converge to different component-collapse patterns. On the macOS-26 runner (`build_maven_java21_macos26`) this deterministically fails the mixture-weight comparison (0.0197 vs 0.1047). Reducing the instance weight (as in SPARK-37317, which reduced 100->90) and increasing maxIter were both verified on a macOS-26 runner to NOT help — increasing maxIter actually makes a component collapse further (7.6e-11 vs 0.116). Running the same `unweighted == uniform-weighted` invariant on well-posed data (`rDataset`, real variance, `k=2` — already used stably by another test in this suite) makes both fits converge to the same optimum. ### Does this PR introduce _any_ user-facing change? No, test-only. ### How was this patch tested? `GaussianMixtureSuite` on a macOS-26 GitHub Actions runner. - **Before (failing on `apache/spark` macOS-26, `build_maven_java21_macos26`):** https://github.com/apache/spark/actions/runs/28753698265/job/85259893661 — `GMM support instance weighting *** FAILED ***`, `Expected 0.01972564065075309 and 0.10476714410584831 to be within 0.001`. - **After (passing with this change, macOS-26 runner):** https://github.com/HyukjinKwon/spark-agent6/actions/runs/28773420633/job/85311928908 — `GaussianMixtureSuite: Tests: succeeded 13, failed 0`, `All tests passed.` Also passes on Linux. ### Was this patch authored or co-authored using generative AI tooling? Yes. This pull request and its description were written by Isaac. Closes #57035 from HyukjinKwon/ci-fix/agent5-gmm-macos-wellposed. Authored-by: Hyukjin Kwon <gurwls223@apache.org> Signed-off-by: Hyukjin Kwon <hyukjin.kwon@databricks.com> (cherry picked from commit 0a6bbee) Signed-off-by: Hyukjin Kwon <hyukjin.kwon@databricks.com>
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…tance weighting' on macOS using well-posed data ### What changes were proposed in this pull request? Change the `GMM support instance weighting` test to fit `rDataset` with `k=2` instead of fitting `k=5` on `KMeansSuite.generateKMeansData(50, 3, 5)`. ### Why are the changes needed? The generated KMeans data is 5 clusters of identical points (zero within-cluster variance). Fitting k=5 Gaussians makes the covariances singular and the EM fit ill-posed, so the uniform-weighted and unweighted fits converge to different component-collapse patterns. On the macOS-26 runner (`build_maven_java21_macos26`) this deterministically fails the mixture-weight comparison (0.0197 vs 0.1047). Reducing the instance weight (as in SPARK-37317, which reduced 100->90) and increasing maxIter were both verified on a macOS-26 runner to NOT help — increasing maxIter actually makes a component collapse further (7.6e-11 vs 0.116). Running the same `unweighted == uniform-weighted` invariant on well-posed data (`rDataset`, real variance, `k=2` — already used stably by another test in this suite) makes both fits converge to the same optimum. ### Does this PR introduce _any_ user-facing change? No, test-only. ### How was this patch tested? `GaussianMixtureSuite` on a macOS-26 GitHub Actions runner. - **Before (failing on `apache/spark` macOS-26, `build_maven_java21_macos26`):** https://github.com/apache/spark/actions/runs/28753698265/job/85259893661 — `GMM support instance weighting *** FAILED ***`, `Expected 0.01972564065075309 and 0.10476714410584831 to be within 0.001`. - **After (passing with this change, macOS-26 runner):** https://github.com/HyukjinKwon/spark-agent6/actions/runs/28773420633/job/85311928908 — `GaussianMixtureSuite: Tests: succeeded 13, failed 0`, `All tests passed.` Also passes on Linux. ### Was this patch authored or co-authored using generative AI tooling? Yes. This pull request and its description were written by Isaac. Closes #57035 from HyukjinKwon/ci-fix/agent5-gmm-macos-wellposed. Authored-by: Hyukjin Kwon <gurwls223@apache.org> Signed-off-by: Hyukjin Kwon <hyukjin.kwon@databricks.com> (cherry picked from commit 0a6bbee) Signed-off-by: Hyukjin Kwon <hyukjin.kwon@databricks.com>
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…tance weighting' on macOS using well-posed data ### What changes were proposed in this pull request? Change the `GMM support instance weighting` test to fit `rDataset` with `k=2` instead of fitting `k=5` on `KMeansSuite.generateKMeansData(50, 3, 5)`. ### Why are the changes needed? The generated KMeans data is 5 clusters of identical points (zero within-cluster variance). Fitting k=5 Gaussians makes the covariances singular and the EM fit ill-posed, so the uniform-weighted and unweighted fits converge to different component-collapse patterns. On the macOS-26 runner (`build_maven_java21_macos26`) this deterministically fails the mixture-weight comparison (0.0197 vs 0.1047). Reducing the instance weight (as in SPARK-37317, which reduced 100->90) and increasing maxIter were both verified on a macOS-26 runner to NOT help — increasing maxIter actually makes a component collapse further (7.6e-11 vs 0.116). Running the same `unweighted == uniform-weighted` invariant on well-posed data (`rDataset`, real variance, `k=2` — already used stably by another test in this suite) makes both fits converge to the same optimum. ### Does this PR introduce _any_ user-facing change? No, test-only. ### How was this patch tested? `GaussianMixtureSuite` on a macOS-26 GitHub Actions runner. - **Before (failing on `apache/spark` macOS-26, `build_maven_java21_macos26`):** https://github.com/apache/spark/actions/runs/28753698265/job/85259893661 — `GMM support instance weighting *** FAILED ***`, `Expected 0.01972564065075309 and 0.10476714410584831 to be within 0.001`. - **After (passing with this change, macOS-26 runner):** https://github.com/HyukjinKwon/spark-agent6/actions/runs/28773420633/job/85311928908 — `GaussianMixtureSuite: Tests: succeeded 13, failed 0`, `All tests passed.` Also passes on Linux. ### Was this patch authored or co-authored using generative AI tooling? Yes. This pull request and its description were written by Isaac. Closes #57035 from HyukjinKwon/ci-fix/agent5-gmm-macos-wellposed. Authored-by: Hyukjin Kwon <gurwls223@apache.org> Signed-off-by: Hyukjin Kwon <hyukjin.kwon@databricks.com> (cherry picked from commit 0a6bbee) Signed-off-by: Hyukjin Kwon <hyukjin.kwon@databricks.com>
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What changes were proposed in this pull request?
Change the
GMM support instance weightingtest to fitrDatasetwithk=2instead of fittingk=5onKMeansSuite.generateKMeansData(50, 3, 5).Why are the changes needed?
The generated KMeans data is 5 clusters of identical points (zero within-cluster variance). Fitting k=5 Gaussians makes the covariances singular and the EM fit ill-posed, so the uniform-weighted and unweighted fits converge to different component-collapse patterns. On the macOS-26 runner (
build_maven_java21_macos26) this deterministically fails the mixture-weight comparison (0.0197 vs 0.1047).Reducing the instance weight (as in SPARK-37317, which reduced 100->90) and increasing maxIter were both verified on a macOS-26 runner to NOT help — increasing maxIter actually makes a component collapse further (7.6e-11 vs 0.116). Running the same
unweighted == uniform-weightedinvariant on well-posed data (rDataset, real variance,k=2— already used stably by another test in this suite) makes both fits converge to the same optimum.Does this PR introduce any user-facing change?
No, test-only.
How was this patch tested?
GaussianMixtureSuiteon a macOS-26 GitHub Actions runner.apache/sparkmacOS-26,build_maven_java21_macos26): https://github.com/apache/spark/actions/runs/28753698265/job/85259893661 —GMM support instance weighting *** FAILED ***,Expected 0.01972564065075309 and 0.10476714410584831 to be within 0.001.GaussianMixtureSuite: Tests: succeeded 13, failed 0,All tests passed.Also passes on Linux.
Was this patch authored or co-authored using generative AI tooling?
Yes.
This pull request and its description were written by Isaac.