* Example plots render poorly in dev
* flake8 + bias_variance
* title padding
* misc ensemble variance plotting
don't use rcParams to set size of a single figure,
put legend outside of plot
* semisupervised plotting fixes
use explicit kwargs in subplots_adjust, change hspace, don't change aspect ratio of imshow.
* Simplifying margin plotting in SVM examples (#8501)
* updated to use contour levels on decision function
* separating unbalanced class now uses a red line to show the change in the decision boundary when the classes are weighted
* corrected the target variable from Y to y
* DOC Updates to SVM examples
* Fixing flake8 issues
* Altered make_blobs to move clusters to corners and be more compact
* Reverted changes converting Y to y
* Fixes for flake8 errors
* 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
Use a sparse matrix representation of the neighbors.
Re-factored the QuadTree implementation to avoid insertion errors.
Various fixes in the gradient descent schedule to get the Barnes Hut and exact solvers to behave more robustly and consistently.
* 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
* Files for my dev environment with Docker
* Fixing label clamping (alpha=0 for hard clamping)
* Deprecating alpha, fixing its value to zero
* Correct way to deprecate alpha for LabelPropagation
The previous way was breaking the test
sklearn.tests.test_common.test_all_estimators
* Detailed info for LabelSpreading's alpha parameter
Based on the original paper.
* Minor changes in the deprecation message
* Improving "deprecated" doc string and raising DeprecationWarning
* Using a local "alpha" in "fit" to deprecate LabelPropagation's alpha
This solution isn't great, but it sets the correct value for alpha
without violating the restrictions imposed by the tests.
* Removal of my development files
* Using sphinx's "deprecated" tag (jnothman's suggestion)
* Deprecation warning: stating that the alpha's value will be ignored
* Use __init__ with alpha=None
* Update what's new
* Try fix RuntimeWarning in test_alpha_deprecation
* DOC Indent deprecation details
* DOC wording
* Update docs
* Change to the one true implementation.
* Add sanity-checked impl. of Label{Propagation,Spreading}
* Raise ValueError if alpha is invalid in LabelSpreading.
* Add a normalizing step before clamping to LabelPropagation.
* Fix flake8 errors.
* Remove duplicate imports.
* DOC Update What's New.
* Specify alpha's value in the error.
* Tidy up tests.
Add a test and add references, where needed.
* Add comment to non-regression test.
* Fix documentation.
* Move check for alpha into fit from __init__.
* Fix corner case of LabelSpreading with alpha=None.
* alpha -> self.variant
* Make Whats_new more explicit.
* Simplify impl. of Label{Propagation,Spreading}.
* variant -> _variant.
Hristo
Hanmin Qin