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Closes#12798
#### What does this implement/fix? Explain your changes.
This PR deprecates the use of `logistic_regression_path` and makes it private.
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* First draft on elasticnet penaly for LogisticRegression
* Some basic tests
* Doc update
* First draft for LogisticRegressionCV.
It seems to be working for binary classification and for multiclass when
multi_class='ovr'. I'm having a hard time figuring out the intricacies
of multi_class='multinomial'.
* Changed default to None for l1_ratio.
added warning message is user sets l1_ratio while penalty is not
elastic-net
* Some more doc
* Updated example to plot elastic net sparsity
* Fixed flake8
* Fixed test by not modifying attribute in fit
* Fixed doc issues
* WIP
* Partially fixed logistic_reg_CV for multinomial.
Also added some comments that are hopefully clear.
Still need to fix refit=False
* Fixed doc issue
* WIP
* Fixed test for refit=False in LogisticRegressionCV
* Fixed Python 2 numpy version issue
* minor doc updates
* Weird doc error...
* Added test to ensure that elastic net is at least as good as L1 or L2
once l1_ratio has been optimized with grid search
Also addressed minor reviews
* Fixed test
* addressed comments
* Added back ignore warning on tests
* Added a functional test
* Scale data in test... Now failing
* elastic-net --> elasticnet
* Updated doc for some attributes and checked their shape in tests
* Added l1_ratio dimension to coefs_paths and scores attr
* improve example + fix test
* FIX incorrect lagged_update in SAGA
* Add non-regression test for SAGA's bug
* FIX flake8 and warning
* Re fixed warning
* Updated some tests
* Addressed comments
* more comments and added dimension to LogisticRegressionCV.n_iter_ attribute
* Updated whatsnew for 0.21
* better doc shape looks
* Fixed whatnew entry after merges
* Added dot
* Addressed comments + standardized optional default param docstrings
* Addessed comments
* use swapaxes instead of unsupported moveaxis (hopefully fixes tests)
* Created 'cross-validation estimator' in glossary and referenced it where
needed
* Addressed adrinjalali comments
* updated glossary entry
* updaed docstrings according to commets
* Change default solver in LogisticRegression
* This is an API change, not a feature
* Decrease numerical precision in LogisticRegression doctest
* ENH add multi_class='auto' for LR, default from 0.22
* No warning when binary
* add FutureWarning for methods with defaults=3
* add explicit cv values to fix assertion errors
* add tests for catching the FutureWarning
* Write current deprecation version
* Add deprecation in docstring
* change default cv value to None
* change cv from 3 to 5 in the examples
* upgrade doctests
* update doctest in tutorial
* update doctest in cross-validation doc
* fix tests
* add entry to whats new
* address Gael comments
* address Gael comments 2
* fix wrong indentation
* update doc
* add docstring deprecation warning in CV subclasses
* address Andy's comments
* fix PR number
* fix flake8
* add filterwarnings in tests
* fix doctests
* cv=None mendatory in Ridge
* fix warning related errors
* skip some doctests warnings
* make travis happy
* change from deprecated to versionchanged
* fix doctests and remove skipping
* address comments
In order to fix#11408, this swaps `joblib` and `_joblib`. It however, allows users to access joblib's `Memory` or `Parallel` functionality without accessing `sklearn.externals._joblib` by importing `Memory`, `Parallel`, etc. into `sklearn.utils`.
* improved docstring for the `solver` parameter of LogisticRegression
* further improve docstring on `n_jobs` and `solver`
* added warning when self.solver == 'liblinear' & self.n_jobs != -1
in LogisticRegression
* corrected typo: warning => warnings
* TST/DOC reverse doc and add test
* Add a test to ensure not changing the input's data type
Test that np.float32 input data is not cast to np.float64 when using LR + newton-cg
* [WIP] Force X to remain float32. (self.coef_ remains float64 even if X is not)
* [WIP] ensure self.coef_ same type as X
* keep the np.float32 when multi_class='multinomial'
* Avoid hardcoded type for multinomial
* pass flake8
* Ensure that the results in 32bits are the same as in 64
* Address Gael's comments for multi_class=='ovr'
* Add multi_class=='multinominal' to test
* Add support for multi_class=='multinominal'
* prefer float64 to float32
* Force X and y to have the same type
* Revert "Add support for multi_class=='multinominal'"
This reverts commit 4ac33e8c02.
* remvert more stuff
* clean up some commmented code
* allow class_weight to take advantage of float32
* Add a test where X.dtype is different of y.dtype
* Address @raghavrv comments
* address the rest of @raghavrv's comments
* Revert class_weight
* Avoid copying if dtype matches
* Address alex comment to the cast from inside _multinomial_loss_grad
* address alex comment
* add sparsity test
* Addressed Tom comment of checking that we keep the 64 aswell
Rüdiger Busche
Hanmin Qin