MAINT More test runtime optimizations #14136
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rth
commented
Jun 21, 2019
| @@ -605,7 +603,7 @@ def test_kl_divergence_not_nan(method): | |||
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| X = random_state.randn(50, 2) | |||
| tsne = TSNE(n_components=2, perplexity=2, learning_rate=100.0, | |||
| random_state=0, method=method, verbose=0, n_iter=1003) | |||
| random_state=0, method=method, verbose=0, n_iter=503) | |||
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We just want to be sure that tsne.kl_divergence_ is computed when n_iter % n_iter_check != 0 cf comment in the code above.
| @@ -678,7 +678,7 @@ def test_matthews_corrcoef_multiclass(): | |||
| assert_almost_equal(mcc, 0.) | |||
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| @pytest.mark.parametrize('n_points', [100, 10000, 1000000]) | |||
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The last case was taking 6s on my laptop, which is too much for a test, and doesn't seem to add much compared to the other two.
| for iid in (True, False): | ||
| for refit in (True, False): | ||
| random_searches = [] | ||
| for scoring in (('accuracy', 'recall'), 'accuracy', 'recall'): | ||
| # If True, for multi-metric pass refit='accuracy' | ||
| if refit: | ||
| probability = True |
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predict_proba is only used below when refit=True
| @@ -812,20 +812,21 @@ def split(self, X, y=None, groups=None): | |||
| 'not match total number of classes (3). ' | |||
| 'Results may not be appropriate for your use case.') | |||
| assert_warns_message(RuntimeWarning, warning_message, | |||
| cross_val_predict, LogisticRegression(), | |||
| cross_val_predict, | |||
| LogisticRegression(solver="liblinear"), | |||
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liblinear converges faster than lbfgs here
adrinjalali
approved these changes
Jun 22, 2019
koenvandevelde
pushed a commit
to koenvandevelde/scikit-learn
that referenced
this pull request
Jul 12, 2019
* Feature extraction / feature selection * Metrics, manifold, impute, GP optimization * Optimize mixture * Optimize model_selection * Fix tests * Lint
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Optimizes the test runtime of a few more modules.
For the changed modules, this PR reduces the runtime by 40% (or 30s),