* we do not transform y
* more
* added Deprecation Warning to transform() to remove Y parameter
* more
* ENH ensure FunctionTransformer's transform/inverse_transform doesn't permit y
* Undo changes to pls_. It will be done in a separate PR (see #9160)
* flake8
* Update whatsnew
* Fully undo PLS changes
* resurrect quantile scaler
* move the code in the pre-processing module
* first draft
* Add tests.
* Fix bug in QuantileNormalizer.
* Add quantile_normalizer.
* Implement pickling
* create a specific function for dense transform
* Create a fit function for the dense case
* Create a toy examples
* First draft with sparse matrices
* remove useless functions and non-negative sparse compatibility
* fix slice call
* Fix tests of QuantileNormalizer.
* Fix estimator compatibility
* List of functions became tuple of functions
* Check X consistency at transform and inverse transform time
* fix doc
* Add negative ValueError tests for QuantileNormalizer.
* Fix cosmetics
* Fix compatibility numpy <= 1.8
* Add n_features tests and correct ValueError.
* PEP8
* fix fill_value for early scipy compatibility
* simplify sampling
* Fix tests.
* removing last pring
* Change choice for permutation
* cosmetics
* fix remove remaining choice
* DOC
* Fix inconsistencies
* pep8
* Add checker for init parameters.
* hack bounds and make a test
* FIX/TST bounds are provided by the fitting and not X at transform
* PEP8
* FIX/TST axis should be <= 1
* PEP8
* ENH Add parameter ignore_implicit_zeros
* ENH match output distribution
* ENH clip the data to avoid infinity due to output PDF
* FIX ENH restraint to uniform and norm
* [MRG] ENH Add example comparing the distribution of all scaling preprocessor (#2)
* ENH Add example comparing the distribution of all scaling preprocessor
* Remove Jupyter notebook convert
* FIX/ENH Select feat before not after; Plot interquantile data range for all
* Add heatmap legend
* Remove comment maybe?
* Move doc from robust_scaling to plot_all_scaling; Need to update doc
* Update the doc
* Better aesthetics; Better spacing and plot colormap only at end
* Shameless author re-ordering ;P
* Use env python for she-bang
* TST Validity of output_pdf
* EXA Use OrderedDict; Make it easier to add more transformations
* FIX PEP8 and replace scipy.stats by str in example
* FIX remove useless import
* COSMET change variable names
* FIX change output_pdf occurence to output_distribution
* FIX partial fixies from comments
* COMIT change class name and code structure
* COSMIT change direction to inverse
* FIX factorize transform in _transform_col
* PEP8
* FIX change the magic 10
* FIX add interp1d to fixes
* FIX/TST allow negative entries when ignore_implicit_zeros is True
* FIX use np.interp instead of sp.interpolate.interp1d
* FIX/TST fix tests
* DOC start checking doc
* TST add test to check the behaviour of interp numpy
* TST/EHN Add the possibility to add noise to compute quantile
* FIX factorize quantile computation
* FIX fixes issues
* PEP8
* FIX/DOC correct doc
* TST/DOC improve doc and add random state
* EXA add examples to illustrate the use of smoothing_noise
* FIX/DOC fix some grammar
* DOC fix example
* DOC/EXA make plot titles more succint
* EXA improve explanation
* EXA improve the docstring
* DOC add a bit more documentation
* FIX advance review
* TST add subsampling test
* DOC/TST better example for the docstring
* DOC add ellipsis to docstring
* FIX address olivier comments
* FIX remove random_state in sparse.rand
* FIX spelling doc
* FIX cite example in user guide and docstring
* FIX olivier comments
* EHN improve the example comparing all the pre-processing methods
* FIX/DOC remove title
* FIX change the scaling of the figure
* FIX plotting layout
* FIX ratio w/h
* Reorder and reword the plot_all_scaling example
* Fix aspect ratio and better explanations in the plot_all_scaling.py example
* Fix broken link and remove useless sentence
* FIX fix couples of spelling
* FIX comments joel
* FIX/DOC address documentation comments
* FIX address comments joel
* FIX inline sparse and dense transform
* PEP8
* TST/DOC temporary skipping test
* FIX raise an error if n_quantiles > subsample
* FIX wording in smoothing_noise example
* EXA Denis comments
* FIX rephrasing
* FIX make smoothing_noise to be a boolearn and change doc
* FIX address comments
* FIX verbose the doc slightly more
* PEP8/DOC
* ENH: 2-ways interpolation to avoid smoothing_noise
Simplifies also the code, examples, and documentation
* remove stuff to be removed 0.19
* more changes
* remove classes from 0.19 whatsnew
* remove _LearntSelectorMixin
* remove ProjectedGradientNMF, load_lwf_*
* minor fixes
* remove more copy from logistic regression path
* remove lda, qda from __init__.__all__
* remove pg solver in nmf from tests etc
* remove class_weight="auto" from tests
* doctest change for decision_function_shape="ovr"
* remove transfrom from tree test, minor fixes to tree tests
* some fixes in the tests
* undo changes in functions which still allow 1d input...
* also allow 1d in scale
* more test fixes...
* last test fixes in forest and tree
* svm default value change doctest failures
* pep8
* remove more class_weight="auto" stuff
* minor cosmetics in docstrings deprecated / removed behavior.
* say that store_covariance has been moved to __init__ in discriminant_analysis
* BUG: MultiLabelBinarizer makes invalid CSR matrix
See https://github.com/scipy/scipy/issues/6719 for context.
The gist is that the `inverse` array may have a different dtype than `yt.indices`, which causes trouble down the line because, in those cases, `yt.indices` and `yt.indptr` have different dtypes.
Alternately, we could insert `yt.check_format(full_check=False)` after modifying the sparse matrix members.
* Fixing for old numpy
Older versions don't support kwargs for `astype`
* Adding tests
* line-wrapping
* adding comment to tests
[ci skip]
* added rationale comment
[ci skip]
* use less nose.tools import to simplify future transition to activly developing test suites/runners
* assert_equal -> assert_array_equal in test_feature_hasher_pairs_with_string_values
and one missed ImportError that should be replaced with AttributeError
* test for py2.6 compat with except AttributeError
* fix importing of SkipTest
* force using nose in python2.6 for now
* there was no assert_dict_equal in py2.6. but we can use assert_equal
although failed test will look a little bit ugly
* remove nose imports from doc/datasets
* fixes issue scikit-learn/scikit-learn#7194
* Added test
* Making `selected='all'` explicit on test
* Updated whats_new.rst
* Fixed typo on `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
ENH NonBLASDotWarning -> EfficiencyWarning; Improve error message
DOC Add exceptions module to modules/classes.rst
MAINT Move ConvergenceWarning, UndefinedMetricWarning et al into exceptions
MAINT Remove ChangedBehaviorWarning from base
DOC/FIX Improve DataConversionWarning's docstring
Charlie Newey