* print train scores when verbose > 3 in _fit_and_score
* train_scores computed if verbose > 3, and 3 decimals places only
* flake8 warnings solved
* print train score if return_train_score
test coverage increased
* added test for multimetric;
tests cleaned using pytest parametrize
* fixing failed tests for python2...
* revert changes in _scorer
* modified whats_new
* modified whats_new again
#### Reference Issues/PRs
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Example: Fixes#1234. See also #3456.
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This PR addresses issue #12466.
#### What does this implement/fix? Explain your changes.
This PR does the 3 following things:
- Rewrite the `cv` parameter description in `GridSearchCV`
- Link the new `CV splitter` description to an existing example
- Add an example with a custom iterable
Thanks for reviewing this!
Close#12466
* 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
In order to fix#11408, this swaps `joblib` and `_joblib`. It however, allows users to access joblib's `Memory` or `Parallel` functionality without accessing `sklearn.externals._joblib` by importing `Memory`, `Parallel`, etc. into `sklearn.utils`.
* fix cross_val_predict for binary classification in decision_function
* Add unit tests
* Add unit tests
* Add unit tests
* better fix
* fix conflict
* fix broken
* only calculate n_classes if one of 'decision_function', 'predict_proba', 'predict_log_proba'
* add test for SVC ovo in cross_val_predict
* flake8 fix
* fix case of ovo and imbalanced folds for binary classification
* change assert_raises to assert_raise_message for ovo case
* fix flake8 linetoo long
* add comments and clearer tests
* improve comments and error message for OvO
* fix .format error with L
* use assert_raises_regex for better error message
* raise error in decision_function special cases. change predict_log_proba missing classes to minimum numpy value
* fix broken tests due to special cases of decision_function
* add modified test for decision_function behavior that does not trigger edge cases
* fix typos
* fix typos
* escape regex .
* escape regex .
* address comments. one unaddressed comment
* simplify code
* flake
* wrong classes range
* address comments. adjust error message
* add warning
* change warning to runtimewarning
* add test for the warning
* Use assert_warns_message rather than assert_warns
Other minor fixes
* Note on class-absent replacement values
* Improve error message
* Fix Rouseeuw1984 broken link
* Change label vbgmm to bgmm
Previously modified with PR #6651
* Change tag name
Old refers to new tag added with PR #7388
* Remove prefix underscore to match tag
* Realign to fit 80 chars
* Link to metrics.rst.
pairwise metrics yet to be documented
* Remove tag as LSHForest is deprecated
* Remove all references to randomized_l1 and sphx_glr_auto_examples_linear_model_plot_sparse_recovery.py.
It is deprecated.
* Fix few Sphinx warnings
* Realign to 80 chars
* Changes based on PR review
* Remove unused ref in calibration
* Fix link ref in covariance.rst
* Fix linking issues
* Differentiate Rouseeuw1999 tag within file.
* Change all duplicate Rouseeuw1999 tags
* Remove numbers from tag Rousseeuw
* ENH cross_val_score now supports multiple metrics
* DOCFIX permutation_test_score
* ENH validate multiple metric scorers
* ENH Move validation of multimetric scoring param out
* ENH GridSearchCV and RandomizedSearchCV now support multiple metrics
* EXA Add an example demonstrating the multiple metric in GridSearchCV
* ENH Let check_multimetric_scoring tell if its multimetric or not
* FIX For single metric name of scorer should remain 'score'
* ENH validation_curve and learning_curve now support multiple metrics
* MNT move _aggregate_score_dicts helper into _validation.py
* TST More testing/ Fixing scores to the correct values
* EXA Add cross_val_score to multimetric example
* Rename to multiple_metric_evaluation.py
* MNT Remove scaffolding
* FIX doctest imports
* FIX wrap the scorer and unwrap the score when using _score() in rfe
* TST Cleanup the tests. Test for is_multimetric too
* TST Make sure it registers as single metric when scoring is of that type
* PEP8
* Don't use dict comprehension to make it work in python2.6
* ENH/FIX/TST grid_scores_ should not be available for multimetric evaluation
* FIX+TST delegated methods NA when multimetric is enabled...
TST Add general tests to GridSearchCV and RandomizedSearchCV
* ENH add option to disable delegation on multimetric scoring
* Remove old function from __all__
* flake8
* FIX revert disable_on_multimetric
* stash
* Fix incorrect rebase
* [ci skip]
* Make sure refit works as expected and remove irrelevant tests
* Allow passing standard scorers by name in multimetric scorers
* Fix example
* flake8
* Address reviews
* Fix indentation
* Ensure {'acc': 'accuracy'} and ['precision'] are valid inputs
* Test that for single metric, 'score' is a key
* Typos
* Fix incorrect rebase
* Compare multimetric grid search with multiple single metric searches
* Test X, y list and pandas input; Test multimetric for unsupervised grid search
* Fix tests; Unsupervised multimetric gs will not pass until #8117 is merged
* Make a plot of Precision vs ROC AUC for RandomForest varying the n_estimators
* Add example to grid_search.rst
* Use the classic tuning of C param in SVM instead of estimators in RF
* FIX Remove scoring arg in deafult scorer test
* flake8
* Search for min_samples_split in DTC; Also show f-score
* REVIEW Make check_multimetric_scoring private
* FIX Add more samples to see if 3% mismatch on 32 bit systems gets fixed
* REVIEW Plot best score; Shorten legends
* REVIEW/COSMIT multimetric --> multi-metric
* REVIEW Mark the best scores of P/R scores too
* Revert "FIX Add more samples to see if 3% mismatch on 32 bit systems gets fixed"
This reverts commit ba766d98353380a186fbc3dade211670ee72726d.
* ENH Use looping for iid testing
* FIX use param grid as scipy's stats dist in 0.12 do not accept seed
* ENH more looping less code; Use small non-noisy dataset
* FIX Use named arg after expanded args
* TST More testing of the refit parameter
* Test that in multimetric search refit to single metric, the delegated methods
work as expected.
* Test that setting probability=False works with multimetric too
* Test refit=False gives sensible error
* COSMIT multimetric --> multi-metric
* REV Correct example doc
* COSMIT
* REVIEW Make tests stronger; Fix bugs in _check_multimetric_scorer
* REVIEW refit param: Raise for empty strings
* TST Invalid refit params
* REVIEW Use <scorer_name> alone; recall --> Recall
* REV specify when we expect scorers to not be None
* FLAKE8
* REVERT multimetrics in learning_curve and validation_curve
* REVIEW Simpler coding style
* COSMIT
* COSMIT
* REV Compress example a bit. Move comment to top
* FIX fit_grid_point's previous API must be preserved
* Flake8
* TST Use loop; Compare with single-metric
* REVIEW Use dict-comprehension instead of helper
* REVIEW Remove redundant test
* Fix tests incorrect braces
* COSMIT
* REVIEW Use regexp
* REV Simplify aggregation of score dicts
* FIX precision and accuracy test
* FIX doctest and flake8
* TST the best_* attributes multimetric with single metric
* Address @jnothman's review
* Address more comments \o/
* DOCFIXES
* Fix use the validated fit_param from fit's arguments
* Revert alpha to a lower value as before
* Using def instead of lambda
* Address @jnothman's review batch 1: Fix tests / Doc fixes
* Remove superfluous tests
* Remove more superfluous testing
* TST/FIX loop over refit and check found n_clusters
* Cosmetic touches
* Use zip instead of manually listing the keys
* Fix inverse_transform
* FIX bug in fit_grid_point; Allow only single score
TST if fit_grid_point works as intended
* ENH Use only ROC-AUC and F1-score
* Fix typos and flake8; Address Andy's reviews
MNT Add a comment on why we do such a transpose + some fixes
* ENH Better error messages for incorrect multimetric scoring values +...
ENH Avoid exception traceback while using incorrect scoring string
* Dict keys must be of string type only
* 1. Better error message for invalid scoring 2...
Internal functions return single score for single metric scoring
* Fix test failures and shuffle tests
* Avoid wrapping scorer as dict in learning_curve
* Remove doc example as asked for
* Some leftover ones
* Don't wrap scorer in validation_curve either
* Add a doc example and skip it as dict order fails doctest
* Import zip from six for python2.7 compat
* Make cross_val_score return a cv_results-like dict
* Add relevant sections to userguide
* Flake8 fixes
* Add whatsnew and fix broken links
* Use AUC and accuracy instead of f1
* Fix failing doctests cross_validation.rst
* DOC add the wrapper example for metrics that return multiple return values
* Address andy's comments
* Be less weird
* Address more of andy's comments
* Make a separate cross_validate function to return dict and a cross_val_score
* Update the docs to reflect the new cross_validate function
* Add cross_validate to toc-tree
* Add more tests on type of cross_validate return and time limits
* FIX failing doctests
* FIX ensure keys are not plural
* DOC fix
* Address some pending comments
* Remove the comment as it is irrelevant now
* Remove excess blank line
* Fix flake8 inconsistencies
* Allow fit_times to be 0 to conform with windows precision
* DOC specify how refit param is to be set in multiple metric case
* TST ensure cross_validate works for string single metrics + address @jnothman's reviews
* Doc fixes
* Remove the shape and transform parameter of _aggregate_score_dicts
* Address Joel's doc comments
* Fix broken doctest
* Fix the spurious file
* Address Andy's comments
* MNT Remove erroneous entry
* Address Andy's comments
* FIX broken links
* Update whats_new.rst
missing newline
Squashed commits:
[94fd9f4] split data using _safe_split in _permutaion_test_scorer
[522053b] adding test case test_permutation_test_score_pandas() to check if permutation_test_score plays nice with pandas dataframe/series
[21b23ce] running test_permutation_test_score_pandas on iris data to prevent warnings.
[15a48bf] adding safe_indexing to _shuffle function
[9ea5c9e] adding test case test_permutation_test_score_pandas() to check if permutation_test_score plays nice with pandas dataframe/series
[3cf5e8f] split data using _safe_split in _permutaion_test_scorer to fix error when using Pandas DataFrame/Series
* Chooses randomly the indices for different training sizes
* Bring back deleted line
* Rewrote the description of 'shuffle' attribute
* use random.sample instead of np.random.choice
* replace tabs with spaces
* merge to master
* Added shuffle in model-selection's learning_curve method
* Added shuffle for incremental learning + addressed Joel's comment
* Shorten long lines
* Add 2 blank spaces between test cases
* Addressed Joel's review comments
* Added 2 blank lines between methods
* Added non regression test for learning_curve with shuffle
* Fixed indentions
* Fixed space issues
* Modified test cases + small code improvements
* Fix some style issues
* Addressed Joel's comments - removed _shuffle_train_indices, more test cases and added new entry under 0.19/enhancements
* Added some modifications in whats_new.rst
* Resolved issue #6894 and #6895:
Now *SearchCV.results_ includes both timing and training scores.
wrote new test (sklearn/model_selection/test_search.py)
and new doctest (sklearn/model_selection/_search.py)
added a few more lines in the docstring of GridSearchCV and RandomizedSearchCV.
Revised code according to suggestions.
Add a few more lines to test_grid_search_results():
1. check test_rank_score always >= 1
2. check all regular scores (test/train_mean/std_score) and timing >= 0
3. check all regular scores <= 1
Note that timing can be greater than 1 in general, and std of regular scores
always <= 1 because the scores are bounded between 0 and 1.
* ENH/FIX timing and training score.
* ENH separate fit / score times
* Make score_time=0 if errored; Ignore warnings in test
* Cleanup docstrings
* ENH Use helper to store the results
* Move fit time computation to else of try...except...else
* DOC readable sample scores
* COSMIT Add a commnent on why time test is >= 0 instead of > 0
(Windows time.time precision is not accurate enought to be non-zero
for trivial fits)
* Convey that times are in seconds
Roman Yurchak
Hanmin Qin