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[MRG+1] BUG: fix svd_solver validation in PCA.fit #8496
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This is what alerted me to the bug: import numpy as np
from sklearn.decomposition import PCA
X = np.random.rand(100, 3)
PCA(svd_solver='bad_argument').fit_transform(X)
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Codecov Report
@@ Coverage Diff @@
## master #8496 +/- ##
==========================================
+ Coverage 95.47% 95.48% +<.01%
==========================================
Files 342 342
Lines 60907 60912 +5
==========================================
+ Hits 58154 58159 +5
Misses 2753 2753
Continue to review full report at Codecov.
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Hmm... I guess I need a regression test here. |
OK, good to go I think 😄 |
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LGTM. +1 for merge
* BUG: fix svd_solver validation in PCA.fit * TST: add test of pca svd_solver
* BUG: fix svd_solver validation in PCA.fit * TST: add test of pca svd_solver
* BUG: fix svd_solver validation in PCA.fit * TST: add test of pca svd_solver
* BUG: fix svd_solver validation in PCA.fit * TST: add test of pca svd_solver
* BUG: fix svd_solver validation in PCA.fit * TST: add test of pca svd_solver
* BUG: fix svd_solver validation in PCA.fit * TST: add test of pca svd_solver
Minor issue:
svd_solver
argument was not being validated. This should fix it.