FIX remove lambdas from text preprocessing #14430
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rth
reviewed
Jul 22, 2019
Thanks for looking into it!
Could you please run benchmarks/bench_text_vectorizers.py before and after this PR and report results?
| return lambda doc: self._char_wb_ngrams( | ||
| preprocess(self.decode(doc))) | ||
| return partial(_analyze, ngrams=self._char_wb_ngrams, | ||
| preprocessor=preprocess, decoder=self.decode) |
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Side note: this is really a use-case for toolz.functoolz.compose a shame that we can't use it.
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Sure thing! BeforeAfter |
jnothman
reviewed
Jul 23, 2019
Ideally we'd have a non-regression test that checks that all build_* methods result in objects that can be pickled and restored.
rth
changed the title
rth
approved these changes
Jul 25, 2019
Thanks @deniederhut !
I'm not overly enthusiastic about the addition of _preprocess and _analyze function, but I don't see another way of fixing pickling.
Unless we ask people to use cloudpickle?
thomasjpfan
reviewed
Jul 25, 2019
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Please add an entry to the change log at |
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Hm... Circle is showing Does this need to be rebased? |
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Please resolve conflicts (hopefully that would also fix CI by merging master in). |
Lambda functions are non-serializable under the stdlib pickle module. This commit replaces the lambdas found in three text preprocessing functions with hidden functions for chaining a sequence of preprocessing steps that can be partialed where appropriate. Closes scikit-learn#12833
The function has been modified, so testing for identity is no longer appropriate.
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Yup! That did the trick for the CI |
rth
approved these changes
Aug 1, 2019
thomasjpfan
approved these changes
Aug 1, 2019
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Thank you @deniederhut! |
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Lambda functions are non-serializable under the stdlib pickle
module. This commit replaces the lambdas found in three text
preprocessing functions with hidden functions for chaining
a sequence of preprocessing steps that can be partialed where
appropriate.
Reference Issues/PRs
Closes #12833
What does this implement/fix? Explain your changes.
Instead of composing functions with lambdas, create chains
of preprocessing steps inside single functions that can
be decomposed with partialing.