[MRG + 1] MNT Platform independent hash collision tests in FeatureHasher#9710
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amueller
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| @ignore_warnings(category=DeprecationWarning) | ||
| def test_hash_collisions(): |
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You could be really sure and do X = [list("Thequickbrownfoxjumped")]
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LGTM. (I think you meant .5 ** 8 = 0.004) |
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@amueller Thanks for the review. Increased the vocabulary size as you suggested.
Yes thanks, I keep making typos in every other comment, apparently. |
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have you tried finding a docker to reproduce somehow?
…On 8 Sep 2017 10:47 pm, "Roman Yurchak" ***@***.***> wrote:
This PR aims to address the current failures of test_hasher_alternate_sign
on non amd64 platforms #9393 (comment)
<#9393 (comment)>
that is likely due to the fact the current test rely on Murmurhash3 results
to yield a particular hash value (that produces a collision) while it is
actually platform dependent #9393 (comment)
<#9393 (comment)>
. Since the original issue couldn't be reproduced, there is no guarantee
that this would fix it (hopefully it would), but in any case, it would make
the test_hasher_alternate_sign more robust ...
*Note:* these tests here rely on the fact that when hashing 8 strings
with alternate_sign=True, some of them will get a negative sign and some
a positive one (it's a 50%/50% probability). However, there is still a
(0.5)**2 = .004 probability that on a given platform all the signs will be
positive (in which case these tests will fail) but hopefully, that's
unlikely enough...
cc @jnothman <https://github.com/jnothman>
------------------------------
You can view, comment on, or merge this pull request online at:
#9710
Commit Summary
- More robust hash collision tests in the FeatureHasher
File Changes
- *M* sklearn/feature_extraction/tests/test_feature_hasher.py
<https://github.com/scikit-learn/scikit-learn/pull/9710/files#diff-0>
(37)
Patch Links:
- https://github.com/scikit-learn/scikit-learn/pull/9710.patch
- https://github.com/scikit-learn/scikit-learn/pull/9710.diff
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remove outdated comment fix also for FeatureUnion [MRG+2] Limiting n_components by both n_features and n_samples instead of just n_features (Recreated PR) (scikit-learn#8742) [MRG+1] Remove hard dependency on nose (scikit-learn#9670) MAINT Stop vendoring sphinx-gallery (scikit-learn#9403) CI upgrade travis to run on new numpy release (scikit-learn#9096) CI Make it possible to run doctests in .rst files with pytest (scikit-learn#9697) * doc/datasets/conftest.py to implement the equivalent of nose fixtures * add conftest.py in root folder to ensure that sklearn local folder is used rather than the package in site-packages * test doc with pytest in Travis * move custom_data_home definition from nose fixture to .rst file [MRG+1] avoid integer overflow by using floats for matthews_corrcoef (scikit-learn#9693) * Fix bug#9622: avoid integer overflow by using floats for matthews_corrcoef * matthews_corrcoef: cosmetic change requested by jnothman * Add test_matthews_corrcoef_overflow for Bug#9622 * test_matthews_corrcoef_overflow: clean-up and make deterministic * matthews_corrcoef: pass dtype=np.float64 to sum & trace instead of using astype * test_matthews_corrcoef_overflow: add simple deterministic tests TST Platform independent hash collision tests in FeatureHasher (scikit-learn#9710) TST More informative error message in test_preserve_trustworthiness_approximately (scikit-learn#9738) add some rudimentary tests for meta-estimators fix extra whitespace in error message add missing if_delegate_has_method in pipeline don't test tuple pipeline for now only copy list if not list already? doesn't seem to help?
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This PR aims to address the current failures of
test_hasher_alternate_signon non amd64 platforms #9393 (comment) that is likely due to the fact the current test rely on Murmurhash3 results to yield a particular hash value (that produces a collision) while it is actually platform dependent #9393 (comment) . Since the original issue couldn't be reproduced, there is no guarantee that this would fix it (hopefully it would), but in any case, it would make thetest_hasher_alternate_signmore robust ...Note: these tests here rely on the fact that when hashing 8 strings with
alternate_sign=True, some of them will get a negative sign and some a positive one (it's a 50%/50% probability). However, there is still a (0.5)**2 = .004 probability that on a given platform all the signs will be positive (in which case these tests will fail) but hopefully, that's unlikely enough...cc @jnothman