--------------------
* 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.
seales