* Fixed typo in an example
* Removed un-needed lines from example
* Added tests on the validity of parameters
* Fixed error msg for train_size issue
* Added tests to check the validation of test and train sizes
* Switched to parameterized test
* Fixed typo in error msg
* Swithced to pytest.raises
* Swithced to pytest.raises also when checking msg
* Improved the validity tests and their unit testing
#### Reference Issues/PRs
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This PR addresses issue #12466.
#### What does this implement/fix? Explain your changes.
This PR does the 3 following things:
- Rewrite the `cv` parameter description in `GridSearchCV`
- Link the new `CV splitter` description to an existing example
- Add an example with a custom iterable
Thanks for reviewing this!
Close#12466
* 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
Closes https://github.com/scikit-learn/scikit-learn/issues/11121
This PR removes the deprecation warning about ABC being moved from `collections` to `collections.abc` when importing scikit-learn in Python 3.7.
In the end, I put `collections.abc.{Sequence, Iterable, Mapping, Sized}` in the namespace of `sklearn.utils.fixes`. This was the simplest way I could find, and while it has the drawback of obfuscating the real module name, other approached appeared more problematic and a similar approach is currently used e.g. for `utils.fixes.signature` which is an alias for `inspect.signature`.
We can't just patch six with https://github.com/benjaminp/six/pull/241, because sklearn uses six from 5 years ago, which would need updating and I'm not sure if it could have side effects (e.g. for pickling backward compatibility etc).
**Edit**: This adds a test checking that generally no warnings are raised when importing scikit-learn top-level modules.
* Use ' '.join(row) for multi-label targets in StratifiedShuffleSplit because str(row) uses an ellipsis when len(row) > 1000
* Add a new test for multilabel problems with more than a thousand labels
* rm dupes
* add check_supervised_y_no_nan in classifier checks: this implies changes for Ridge classifiers
* fix docstrings/comments
* FIX check fitting 1d X array raises error and FIX check fitting 2d array with only 1 feature either works or returns informative message
* modify check_fit2d_1sample in common tests so that it checks fitting either works or returns an informative message
* rm SpectralClustering case for the moment
* uniformize error messages for 1 sample case and fix SpectralClustering with ensure_min_samples=2
* add unit test for mean_shift when n_samples * quantile < 1
* FIX travis with ensure_min_samples=2 in _PLS
* try fix for failing tSNE test
* typos
* take @agramfort's review into account
* sc to fix string in gaussian_process
* add the class that is present to preserve information of previous message in gpc.py
* Adding parameter information to docstring.
* Removing trailing whitespace from lines.
* Adding details of parameter to formal Parameters section.
* Shortened lines to meet requirements.
* DOC Add NOTE that unless random_state is set, split will not be identical
* TST use np.testing.assert_equal for nested lists/arrays
* TST Make sure cv param can be a generator
* DOC rank_ becomes a link when rendered
* Use test_...
* Remove blank line; Add if shuffle is True
* Fix tests
* Explicitly test for GeneratorType
* TST Add the else clause
* TST Add comment on usage of np.testing.assert_array_equal
* TYPO
* MNT Remove if ;
* Address Joel's comments
* merge the identical points in doc
* DOC address Andy's comments
* Move comment to before the check for generator type
* regression test and fix for 2d stratified shuffle split
* strengthen non-overlap sss tests
* clarify test and comment
* remove iter from tests, use str instead of hash
* add shuffle paramater to train_test_split
* fix syntax error
* fix variable name
* fix formatting in doctest output
* fix doctest output
* refactor shuffle paramater into ShuffleSplit and StratifiedShuffleSplit
* include shuffle option in tests
* rollback refactor
* revert to simpler version of unshuffled split
* fix flake8 errors
* revert changes to ShuffleSplit
* revert BaseShuffleSplit
* more reversions
* fix indentation
* remove shuffle parameter from CVclass
* add text to NotImplementedError
* change indexing to use numpy.arange rather than range
* specify precondition for stratify to be None in docstring
* remove needless argument checking
* add parameter checking as in LeavePGroupsOut
* add examples with dummy inputs
* add unittest for a get_n_splits method in LeaveOneGroupOut and LeavePGroupsOut classes
* X and y can be ommited in a get_n_splits function.
* fix error messages
* update examples
* fix test for an error message
* Revert "fix test for an error message"
This reverts commit 68b984207c.
* fix test for an error message
* fix error messages
* remove tailing white spaces
* add periods to messages
* test for ValueError’s of get_n_splits methods of LeaveOneOut / LeavePOut classes
* fix documents:
* parameter name: group -> groups
* modfy white space
* Add _RepeatedSplits and RepeatedKFold class
* Add RepeatedStratifiedKFold and doc for repeated cvs
* Change default value of n_repeats
* Change input parameters of repeated cv constructor to n_splits, n_repeats, random_state
* Generate random states in split function rather than store it beforehand
* Doc changes, inheriting RepeatedKFold, RepeatedStratifiedKFold from _RepeatedSplits and other review changes
* Remove blank line, put testcases for deterministic split in loop and add StopIteration check in testcase
* Using rng directly as random_state param to create cv instance and added a check for cvargs
* Fix pep8 warnings
* Changing default values for n_splits and n_repeats and add entry in changelog
* Adding name to the feature
* Missing space
* DOC Add whatnew for the 3 mod-sel bugfixes
* Move to 0.18.1; Change name universally to Raghav RV
* Raghav R V --> Raghav RV
* Edit mailmap to include my other email
* FIX raise an error message when n_groups > actual number of groups (#7681)
This change addresses issue #7681:
- Raise ValueError when n_groups > actual number of unique groups in LeaveOneGroupOut and LeavePGroupsOut.
- Add unit test.
* Make requested changes
- Check error message with `assert_raise_message`
- Pass parameters to `assert_raise_message` instead of defining functions
* Update condition and exception message
--------------------
* ENH Reogranize classes/fn from grid_search into search.py
* ENH Reogranize classes/fn from cross_validation into split.py
* ENH Reogranize cls/fn from cross_validation/learning_curve into validate.py
* MAINT Merge _check_cv into check_cv inside the model_selection module
* MAINT Update all the imports to point to the model_selection module
* FIX use iter_cv to iterate throught the new style/old style cv objs
* TST Add tests for the new model_selection members
* ENH Wrap the old-style cv obj/iterables instead of using iter_cv
* ENH Use scipy's binomial coefficient function comb for calucation of nCk
* ENH Few enhancements to the split module
* ENH Improve check_cv input validation and docstring
* MAINT _get_test_folds(X, y, labels) --> _get_test_folds(labels)
* TST if 1d arrays for X introduce any errors
* ENH use 1d X arrays for all tests;
* ENH X_10 --> X (global var)
Minor
-----
* ENH _PartitionIterator --> _BaseCrossValidator;
* ENH CVIterator --> CVIterableWrapper
* TST Import the old SKF locally
* FIX/TST Clean up the split module's tests.
* DOC Improve documentation of the cv parameter
* COSMIT consistently hyphenate cross-validation/cross-validator
* TST Calculate n_samples from X
* COSMIT Use separate lines for each import.
* COSMIT cross_validation_generator --> cross_validator
Commits merged manually
-----------------------
* FIX Document the random_state attribute in RandomSearchCV
* MAINT Use check_cv instead of _check_cv
* ENH refactor OVO decision function, use it in SVC for sklearn-like
decision_function shape
* FIX avoid memory cost when sampling from large parameter grids
ENH Major to Minor incremental enhancements to the model_selection
Squashed commit messages - (For reference)
Major
-----
* ENH p --> n_labels
* FIX *ShuffleSplit: all float/invalid type errors at init and int error at split
* FIX make PredefinedSplit accept test_folds in constructor; Cleanup docstrings
* ENH+TST KFold: make rng to be generated at every split call for reproducibility
* FIX/MAINT KFold: make shuffle a public attr
* FIX Make CVIterableWrapper private.
* FIX reuse len_cv instead of recalculating it
* FIX Prevent adding *SearchCV estimators from the old grid_search module
* re-FIX In all_estimators: the sorting to use only the 1st item (name)
To avoid collision between the old and the new GridSearch classes.
* FIX test_validate.py: Use 2D X (1D X is being detected as a single sample)
* MAINT validate.py --> validation.py
* MAINT make the submodules private
* MAINT Support old cv/gs/lc until 0.19
* FIX/MAINT n_splits --> get_n_splits
* FIX/TST test_logistic.py/test_ovr_multinomial_iris:
pass predefined folds as an iterable
* MAINT expose BaseCrossValidator
* Update the model_selection module with changes from master
- From #5161
- - MAINT remove redundant p variable
- - Add check for sparse prediction in cross_val_predict
- From #5201 - DOC improve random_state param doc
- From #5190 - LabelKFold and test
- From #4583 - LabelShuffleSplit and tests
- From #5300 - shuffle the `labels` not the `indxs` in LabelKFold + tests
- From #5378 - Make the GridSearchCV docs more accurate.
- From #5458 - Remove shuffle from LabelKFold
- From #5466(#4270) - Gaussian Process by Jan Metzen
- From #4826 - Move custom error / warnings into sklearn.exception
Minor
-----
* ENH Make the KFold shuffling test stronger
* FIX/DOC Use the higher level model_selection module as ref
* DOC in check_cv "y : array-like, optional"
* DOC a supervised learning problem --> supervised learning problems
* DOC cross-validators --> cross-validation strategies
* DOC Correct Olivier Grisel's name ;)
* MINOR/FIX cv_indices --> kfold
* FIX/DOC Align the 'See also' section of the new KFold, LeaveOneOut
* TST/FIX imports on separate lines
* FIX use __class__ instead of classmethod
* TST/FIX import directly from model_selection
* COSMIT Relocate the random_state documentation
* COSMIT remove pass
* MAINT Remove deprecation warnings from old tests
* FIX correct import at test_split
* FIX/MAINT Move P_sparse, X, y defns to top; rm unused W_sparse, X_sparse
* FIX random state to avoid doctest failure
* TST n_splits and split wrapping of _CVIterableWrapper
* FIX/MAINT Use multilabel indicator matrix directly
* TST/DOC clarify why we conflate classes 0 and 1
* DOC add comment that this was taken from BaseEstimator
* FIX use of labels is not needed in stratified k fold
* Fix cross_validation reference
* Fix the labels param doc
FIX/DOC/MAINT Addressing the review comments by Arnaud and Andy
COSMIT Sort the members alphabetically
COSMIT len_cv --> n_splits
COSMIT Merge 2 if; FIX Use kwargs
DOC Add my name to the authors :D
DOC make labels parameter consistent
FIX Remove hack for boolean indices; + COSMIT idx --> indices; DOC Add Returns
COSMIT preds --> predictions
DOC Add Returns and neatly arrange X, y, labels
FIX idx(s)/ind(s)--> indice(s)
COSMIT Merge if and else to elif
COSMIT n --> n_samples
COSMIT Use bincount only once
COSMIT cls --> class_i / class_i (ith class indices) -->
perm_indices_class_i
FIX/ENH/TST Addressing the final reviews
COSMIT c --> count
FIX/TST make check_cv raise ValueError for string cv value
TST nested cv (gs inside cross_val_score) works for diff cvs
FIX/ENH Raise ValueError when labels is None for label based cvs;
TST if labels is being passed correctly to the cv and that the
ValueError is being propagated to the cross_val_score/predict and grid
search
FIX pass labels to cross_val_score
FIX use make_classification
DOC Add Returns; COSMIT Remove scaffolding
TST add a test to check the _build_repr helper
REVERT the old GS/RS should also be tested by the common tests.
ENH Add a tuple of all/label based CVS
FIX raise VE even at get_n_splits if labels is None
FIX Fabian's comments
PEP8
Roman Yurchak
Hanmin Qin