Don't use global np.random.seed in tests #13356
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…into Don't_use_global_np.random.seed_in_tests
rth
reviewed
Mar 1, 2019
- passed rng as parameter to ParameterSampler class - changed seed from 0 to 42 (as original)
- passed rng as parameter to SparseRandomProjection class
- passed rng as parameter to GaussianRandomProjection class
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@rth - all tests have passed now |
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thx @praths007 |
xhluca
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Apr 28, 2019
* initial commit * used random class * fixed failing testcases, reverted __init__.py * fixed failing testcases #2 - passed rng as parameter to ParameterSampler class - changed seed from 0 to 42 (as original) * fixed failing testcases #2 - passed rng as parameter to SparseRandomProjection class * fixed failing testcases scikit-learn#4 - passed rng as parameter to GaussianRandomProjection class * fixed failing test case because of flake 8
xhluca
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Apr 28, 2019
This reverts commit 12c080d.
xhluca
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Apr 28, 2019
This reverts commit 12c080d.
koenvandevelde
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Jul 12, 2019
* initial commit * used random class * fixed failing testcases, reverted __init__.py * fixed failing testcases scikit-learn#2 - passed rng as parameter to ParameterSampler class - changed seed from 0 to 42 (as original) * fixed failing testcases scikit-learn#2 - passed rng as parameter to SparseRandomProjection class * fixed failing testcases scikit-learn#4 - passed rng as parameter to GaussianRandomProjection class * fixed failing test case because of flake 8
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Reference Issues/PRs
Don't use global np.random.seed in tests #13345
What does this implement/fix? Explain your changes.
Replaced instances in tests and doctests where seed was set globally using
np.random.seedwithrng = np.random.RandomStateso that the class variable
rngcan be used to generate random numbers instead of the global value of seed set using np.random.seed.Any other comments?
Issue #13345 can be checked for more details