* 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
* FIX enforce deterministic behaviour in BaseBagging
* DOC add entry in whats new for model changes
* add comments
* revert space
* revert space right place
* Typo
* FIX change estimators_samples_
* TST check size training set and hash during bootstrapping
* DOC/TST API entry in doc and style correction
* DOC move entry in the right section
* DOC add missing author and issue
* DOC add details in whats new entry
* FIX add hash from joblib in utils
* iter
* 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
* fixed OOB_Score bug for bagging slassifiers.
See: https://github.com/scikit-learn/scikit-learn/issues/8933
* Added white space
* more white space fixing
* Adding test for oob_score validity
* removing pandas, replacing with numpy matrices
* fixing white space
* more white space fixing
* white space ...
* fixed labels to allow for strings
* white space
* simplifying test
* white space
* reformatting test
* white space
* pressed enter at end of file
* removing line at end of file
* FIX adaboost estimators not randomising correctly
(fixes#7408)
FIX ensure nested random_state is set in ensembles
* DOC add what's new
* Only affect *__random_state, not *_random_state for now
* TST More informative assertions for ensemble tests
* More specific testing of different random_states
* Remove heavy memory footprint for OOB scoring from bagging.
- Remove `estimators_samples` attribute from `BaseBagging`
- Add method `_get_estimators_samples` to `BaseBagging` that
returns a generator producing sample indices on demand.
- Slight refactor of `_parallel_build_estimators()` to
isolate and group lines accessing random state.
* Replaced `BaseBagging.estimators_samples_` attribute with property.
- Accessing `BaseBagging.estimators_samples_` now triggers call to
`BaseBagging._get_estimators_samples` as well as deprecation
warning for release 0.20.
- This should make the fix for the OOB memory issue fully backwards
compatible.
* Remove print statement.
* Added test, made new method more general.
- Added test to `test_bagging.py` to ensure that indices generated
on the fly are identical to indices generated at fit time.
- Refactored `_get_estimators_samples()` to `_get_estimators_indices()`,
now both feature and samples indices are returned.
- Refactored `estimators_samples_()` to deal with above.
* Rename index generating functions and arguments.
* `estimators_samples_()` returns list instead of generator
* Removed `estimators_samples_` deprection warning.
* Actually removed `estimators_samples_` deprecation warning.
* New mask generation function, new bagging test, new BaseBagging attributes.
- Added new private function in bagging.py that converts indices to
a boolean mask.
- Added new bagging test to make sure identical OOB scores are generated
when the same estimator if fit with fixed random state and identical
training data.
- Added new private attributes `BaseBagging._max_features` and
`BaseBagging._max_samples` to store validated input values.
* Streamlined code, improved `estimators_samples_()` documentation.
- Removed `max_samples` argument from `_parallel_build_estimators()`,
this value is now accessed via `ensemble._max_samples`.
- Removed validation of `ensemble.max_features` and `max_samples`,
instead use `ensemble._max_features` and `ensemble._max_samples`
which are assumed to be already validated.
- Removed unnecessary `samples` variable from `_parallel_build_estimators()`.
- Changed the way `random_state` is generated in `_parallel_build_estimators()`
and `BaseBagging._get_estimators_data_draws()` to direct numpy method to
reflect that seeds created in `BaseBagging._fit()` are trustworthy.
- Due to above removed generation of new seed for each estimator in
`_parallel_build_estimators()` and `BaseBagging._get_estimators_data_draws()`.
- Added documentation to `BaseBagging.estimators_samples_()` property
indicating the reason it's generated dynamically and the associated performance
penalty.
- Returned `BaggingClassifier._set_oob_score()` and `BaggingRegressor._set_oob_score()`
to directly accessing `self.estimators_samples_`.
* Streamlined code, improved `estimators_samples_()` documentation.
- Removed `max_samples` argument from `_parallel_build_estimators()`,
this value is now accessed via `ensemble._max_samples`.
- Removed validation of `ensemble.max_features` and `max_samples`,
instead use `ensemble._max_features` and `ensemble._max_samples`
which are assumed to be already validated.
- Removed unnecessary `samples` variable from `_parallel_build_estimators()`.
- Changed the way `random_state` is generated in `_parallel_build_estimators()`
and `BaseBagging._get_estimators_data_draws()` to direct numpy method to
reflect that seeds created in `BaseBagging._fit()` are trustworthy.
- Due to above removed generation of new seed for each estimator in
`_parallel_build_estimators()` and `BaseBagging._get_estimators_data_draws()`.
- Added documentation to `BaseBagging.estimators_samples_()` property
indicating the reason it's generated dynamically and the associated performance
penalty.
- Returned `BaggingClassifier._set_oob_score()` and `BaggingRegressor._set_oob_score()`
to directly accessing `self.estimators_samples_`.
* PEP8, remove optional arguments, add public function to utils.
- Update indentation and line length to conform to PEP8.
- Update `estimators_samples_()` docstring to conform to PEP8 and
PEP257.
- Remove optional arguments to `BaseBagging._get_estimators_data_draws()`,
rename to `BaseBagging._get_estimators_indices()`.
- Remove optional arguments from `_generate_mask_from_indices()`,
move to `utils.metaestimators`, rename to `indices_to_mask()`.
* Add indices_to_mask to __all__ in utils.metaestimators
* Move utils function, new test for `estimators_samples` correctness.
- Moved `indices_to_mask` from `utils.metaestimators` to `utils.validation`
- Added new test to `ensemble.test_bagging` to make sure refitting
an individual estimator from the bagging ensemble using the corresponding
samples identified in `BaseBagging.estimators_samples` returns the same model.
* Adjust/remove tests, move `indices_to_mask` to `utils.__init__`.
- Remove test_bagging.test_consistent_index_sampling
- Update formatting checks in test_bagging.test_estimators_samples
to make sure each mask is numpy boolean array
- Move indices_to_mask from utils.validation to utils.__init__
* update class docstring for `estimators_samples_`
- Updated class docstring of `estimators_samples_` attribute for `BaggingClassifier` and `BaggingRegressor` to indicate that samples are identified with boolean masks.
* PEP8, made `max_samples` arg optional in _fit()
- Fixed PEP8 issues.
- Gave `max_samples` argument of `BaseBagging._fit()` a default
value of `None` as indicated in the docstring. Added code to
check for and process `None` value.
- Added a few more comments to `_fit()`.
* 2 new `max_samples` consistency tests, update whats_new.rst
* Minor change
* Update whats_new.rst
--------------------
* 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
Removed the need for printing attributes to raise
exceptions in the tests
Removed unnecessary setting of attributes in the
initialisation.
Moved check for warm_start and oob_score
Test that oob_score is a good estimate of generalization
error in case of warm_start.
Test that oob_score is computed only on most recent
training samples with newly added classifiers
BaggingClassifier and BaggingRegressor now support warm_starts. Added
basic tests and documentation of the new functionality. Heavily
inspired by work on warm_start for Random forests.
Rüdiger Busche
Hanmin Qin