* FIX test_common: assert_greater_equal(iter_, 1)
The previous check was too strict. It is fine for an estimator to
decide that is has converged in one pass over the data.
* STYLE PEP8
* FIX test_reconstruct_patches_perfect_color too strict
* STYLE flake8 fixes
Register OneVsRestRegressor as meta estimator
Rename to a more sensible name
Parallel predict and sparse support
Started MultiOutput documentation
Move code to new file multioutput.py
Continuing the move to new multioutput module
Added sample weight support
Better test for sample weights and actually support weights
Added a new test using weighted vs repeated samples to
test sample weight support. Uncovered that weights
were not actually passed on to underlying estimator.
Comment on multiprocess overheads
Move parallel_helper to utils.fixes
This helper works around a python2 limitation on pickling
instance methods
Example of multi-output regression with gradient boosting
Switch to uniform weighted score and updated example
The example now uses a RF with and without the MultiOutput
meta estimator
Added note for removing `score` method
Addressing comments on MultiOutputRegressor
MultiOutputregressor better test for weighted samples
Fix ups
Use explicit keyword argument for passing sample weights and
fix random_state on train-test split in the example
Add gradient calculation in _huber_loss_and_gradient
Add tests to check the correctness of the loss and gradient
Fix for old scipy
Add parameter sigma for robust linear regression
Add gradient formula to robust _huber_loss_and_gradient
Add fit_intercept option and fix tests
Add docs to HuberRegressor and the helper functions
Add example demonstrating ridge_regression vs huber_regression
Add sample_weight implementation
Add scaling invariant huber test
Remove exp and add bounds to fmin_l_bfgs_b
Add sparse data support
Add more tests and refactoring of code
Add narrative docs
review huber regressor
Minor additions to docs and tests
Minor fixes that deals with dealing with NaN values in targets
and old verions of SciPy and NumPy
Add HuberRegressor to robust estimator
Refactored computation of gradient and make docs render properly
Temp
Remove float64 dtype conversion
trivial optimizations and add a note about R
Remove sample_weights special_casing
address @amueller comments
in other modules where warnings are deprecated.
Prefix warnings imported from sklearn.exceptions instead of
suffixing to prevent showing both the suffixed warning
and the deprecated warning during tab completion.
--------------------
* 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
`v0 = random_state.rand(M.shape[0])` leads to an initial residual vector in ARPACK which is all positive. However, this is not the absolute or squared residual, but a true difference. Thus, it is better to initialize with `v0=random_state.uniform(-1, 1, M.shape[0])` to have an equally distributed sign. This is the way that ARPACK initializes the residuals.
The effect of the previous initialization is that eigsh frequently does not converge to the correct eigenvalues, e.g. negative eigenvalues for s.p.d. matrix, which leads to an incorrect null-space.
- initialized all occurences of sklearn.utils.arpack.eigsh the same way it would be initialzed by ARPACK
- regression test to test behavior of new initialization
Olivier Grisel