* Better documentation where if_delegate_has_method is inapplicable
Fixes#19923
Introduces an elegant alternative for if_delegate_has_method, a decorator called available_if, which uses a callable to determine if an object should show a particular attribute.
If the callable also accepted the attribute name being decorated as a parameter, this could be used to implement if_delegate_has_method.
We could also replace if_delegate_has_method in all cases, since the code might be more readable as @available_if(lambda self, method: hasattr(self.base_estimator, method))
Possible alternative naming of available_if: hasattr_if.
TODO: add tests of the new decorator
* Fix use of super()
* Fixes to silly errors...
* add noqa
* Fix docstring issues
* remove debugging statements
* Use available_if instead of if_delegate_has_method in multioutput
* Public to private
* Rm unused import
* Black
* Use a sentinel instead of for-else with early return
* update reference docs
* TST
* DOC Add change log
* Update doc/whats_new/v1.0.rst
* Make test make sense
* Black
* Define available_if function in class
* Another way to express multioutput delegations
* Initial implementation
* Forgot to add to second __add__ list
* Update split method parameter doc
* Added example; changed default test_size to 0.1; added to author list
* StratifiedGroupKFold impl and other improvements
* Add class to __all__ spec
* Remove random_state when no shuffle
* Tighter formatting
* Update the implementation of StratifiedGroupKFold
* Add StratifiedGroupKFold to __init__
* Add y checks to StartifiedGroupKFold
* Raise error if n_splits > max num samples in class
* Warn if n_splits > mn num samples in class
* Add SGKfold to general repr test
* Add SGKFold to 2d_y test case
* Add SGKfold to value erros test case
Parameters are the same as for StratifiedKFold
to ensure similar behavior given n_groups == n_samples
* Add SGKFold to StratifiedKFold test cases
The idea is to ensure similar behavior when groups are trivial
(n_groups == n_samples)
* Add SGKFold to reproducibility test case
* Add SGKFold to GroupKFold test case
* Add SGKFold to nested cv test case
* Add SGKFold to random_state with shuffle=False test case
* Add SGKFold to constant splits test case
* Fix repr test case
* Fix formatting issues
* Add samples to a fold with least num samples
Required to produce balanced size folds when the distribution of y is
more or less the same
* Remove GroupShuffleSplit impl
* Add notes to StratifiedGroupKFold
* Fix doctest
* Added stratified group kfold tests
* Better variable naming
* Add section to documentation
* Remove leftover StratifiedGroupShuffleSplit import
* Add changelist and reference to original kernel
* Better naming for least populated class check
* Better expression for number of labels
* Remove use of Counter
We already have this data in output of np.unique
* Add tests for homogeneous groups
* Add StratifiedGroupKFold test against GroupKFold
* Add changes to changelist in docstring
* Add StratifiedGroupKFold to classes.rst
* Fix description of StratifiedGroupKFold
* Move license notice out of docstring
* Disambiguate labels to classes in doc
* Add changelog entry
* Fix changelog author entry
* Fix StratifiedGroupKFold docstring
* Better variable names
* Remove defaultdict in favor of numpy indexing
* Extracted best_fold search into a separate method
* Make use of numpy broadcasting instead of for loop
* Encode groups and use arrays instead of dicts
* Use numpy sort instead of python
* Clarify shuffling behavior of StratifiedGroupKF in docs
* Switch name from label_idx to class_idx
* Remove accidentally leftover comment
* Fix np.sort keyword to support numpy < 1.15
* Fix typo in docstring
* Add StratifiedGroupKFold to visualization doc
* Add visualization for uneven group as an example
* Fix image numbers to match updated example
* Add author
* Add SGKF visualization to docs
* Add comments for groups in stratified CV tests
Co-authored-by: Leandro Hermida <hermidal@cs.umd.edu>
Co-authored-by: marrodion <rodion_martynov@epam.com>
Co-authored-by: Nicolas Hug <contact@nicolas-hug.com>
Co-authored-by: Jeremiah Johnson <jwjohnson314@gmail.com>
Co-authored-by: Roman Yurchak <rth.yurchak@gmail.com>
* More flexible grid search interface
* added info dict parameter
* Put back removed test
* renamed info into more_results
* Passed grroups as well since we need n_to use get_n_splits(X, y, groups)
* port
* pep8
* dabl -> sklearn
* add _required_parameters
* skipping check in rst file if pandas not installed
* Update sklearn/model_selection/_search_successive_halving.py
Co-Authored-By: Joel Nothman <joel.nothman@gmail.com>
* renamed into GridHalvingSearchCV and RandomHalvingSearchCV
* Addressed thomas' comments
* repr
* removed passing group as a parameter to evaluate_candidates
* Joels comments
* pep8
* reorganized user user guide
* renaming
* update user guide
* remove groups support + pass fit_params
* parameter renaming
* pep8
* r_i -> resource_iter
* fixed r_i issues
* examples + removed use of word budget
* Added inpute checking tests
* added cv_resutlts_ user guide
* minor title change
* fixed doc layout
* Addressed some comments
* properly pass down fit_params
* change default value of force_exhaust_resources and update doc
* should fix doc
* Used check_fit_params
* Update section about min_resources and number of candidates
* Clarified ratio section
* Use ~ to refer to classes
* fixed doc checks
* Apply suggestions from code review
Co-authored-by: Joel Nothman <joel.nothman@gmail.com>
* Addressed easy comments from Joel
* missed some
* updated docstring of run_search
* Used f strings instead of format
* remove candidate duplication checks
* fix example
* Addressed easy comments
* rotate ticks labels
* Added discussion in the intro as suggested by Joel
* Split examples into sections
* minor changes
* remove force_exhaust_budget and introduce min_resources=exhaust
* some minor validation
* Added a n_resources_ attribute
* update examples
* Addressed comments
* passing CV instead of X,y
* minor revert for handling fit_params
* updated docs
* fix len
* whatsnew
* Add test for sampling when all_list
* minor change to top-k
* Force CV splits to be consistent across calls
* reorder parameters
* reduced diff
* added tests for top_k
* put back doc for groups
* not sure what went wrong
* put import at its place
* some comment
* Addressed comments
* Added tests for cv_results_ and base estimator inputs
* pep8
* avoid monkeypatching
* rename df
* use Joel's suggestions for testing masks
* Made it experimental
* Should fix docs
* whats new entry
* Apply suggestions from code review
Co-authored-by: Andreas Mueller <t3kcit@gmail.com>
* Addressed comments to docs
* Addressed comments in examples
* minor doc update
* minor renaming in UG
* forgot some
* some sad note about splitter statefulness :'(
* Addressed comments
* ratio -> factor
Co-authored-by: Joel Nothman <joel.nothman@gmail.com>
Co-authored-by: Andreas Mueller <t3kcit@gmail.com>
Co-authored-by: rasbt <mail@sebastianraschka.com>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>
* Add Tensor Sketch algorithm
* Add user guide entry
* Add example
* Add benchmark
Co-authored-by: Christian Lorentzen <lorentzen.ch@googlemail.com>
Co-authored-by: Tom Dupré la Tour <tom.dupre-la-tour@m4x.org>
Co-authored-by: Roman Yurchak <rth.yurchak@gmail.com>
* Initial add DET curve to classification metrics
* Add DET to exports
* Fix DET-curve doctest errors
- Sample snippet in model_evaluation documentation was outdated.
* Clarify wording in DET-curve computation
- Align to the wording of ranking module to make it consistent.
- Add correct describtion of input and outputs.
- Update and fix non-existent links
* Beautify DET curve documentation source
- Limit line length to 80 characters.
* Expand DET curve documentation
- Add an example plot to show difference between ROC and DET curves.
- Expand Usage Note section with background information and properties
of DET curves.
* Update DET-curve documentation
- Fix typos and some grammar improvements.
- Use named references to avoid potential conflicts with other sections.
- Remove unneeded references and improved existing ones by using e.g.
using versioned links.
* Select relevant DET points using slice object
* Remove some dubiety from DET curve doc-string
* Add DET curve contributors
* Add tests for DET curves
* Streamline DET test by using parametrization
* Increase verbosity of DET curve error handling
- Explicitly sanity check input before computing a DET curve.
- Add test for perfect scores.
- Adapt indentation style to match the test module.
* Add reference for DET curves in invariance test
* Add automated invariance checks for DET curves
* Resolve merge artifacts
* Make doctest happy
* Fix whitespaces for doctest
* Revert unintended whitespace changes
* Revert unintended white space changes #2
* Fix typos and grammar
* Fix white space in doc
* Streamline test code
* Remove rebase artifacts
* Fix PR link in doc
* Fix test_ranking
* Fix rebase errors
* Fix import
* Bring back newlines
- Swallowed by copy/paste
* Remove uncited ref link
* Remove matplotlib deprecation warning
* Bring back hidden reference
* Add motivation to DET example
* Fix lint
* Add citation
* Use modern matplotlib API
Co-authored-by: Jeremy Karnowski <jeremy.karnowski@gmail.com>
Co-authored-by: Julien Cornebise <julien@cornebise.com>
Co-authored-by: Daniel Mohns <daniel.mohns@zenguard.org>
Thomas J. Fan