* add pprint for estimators
* strip color from length, add color option
* Minor cleaning, fixes, factoring and docs
* Added some basic tests
* Fixed line length issue
* fixed flake8 and added visual test for review
* Fixed test
* Fixed Python 2 issues (inspect.signature import)
* Trying to fix flake8 again
* Added special repr for functions
* Added some other visual tests
* Changed _format_function in to _format_callable
because callable() returns True also for class objects (which we want to
reprensent with their name as well anyway)
* Consistent output in Python 2 and 3
* WIP
* Now using the builtin pprint module
* pep8
* Added changed_only param
* Fixed printing when string would fit in less than line width
* Fixed printing of steps parameter
* Fixed changed_only param for short estimators
* fixed pep8
* Added some more description in docstring
* changed_only is now an option from set_config()
* Put _pprint.py into sklearn/utils, added tests
* Added doctest NORMALIZE_WHITESPACE where needed
* Fixed tests
* fix test-doc
* fixing test that passed before....
* Fixed tests
* Added test for changed_only and long lines
* typo
* Added authors names
* Added license file
* Added ellipsis based on number of elements in sequence + added increasinly aggressive repr strategies
* Updated whatsnew
* dont use increaingly aggressive strategy
* Fixed tests
* Removed LICENSE file and put license text in _pprint.py
* fixed test_base
* Sorted parameters dictionary for consistent output in 3.5
* Actually using OrderedDict...
* Addressed comments
* Added test for NaN changed parameter
* Update whatsnew
* Added example to set_config()
* Removed example
* Added example in gallery
* Spelling
* First draft on elasticnet penaly for LogisticRegression
* Some basic tests
* Doc update
* First draft for LogisticRegressionCV.
It seems to be working for binary classification and for multiclass when
multi_class='ovr'. I'm having a hard time figuring out the intricacies
of multi_class='multinomial'.
* Changed default to None for l1_ratio.
added warning message is user sets l1_ratio while penalty is not
elastic-net
* Some more doc
* Updated example to plot elastic net sparsity
* Fixed flake8
* Fixed test by not modifying attribute in fit
* Fixed doc issues
* WIP
* Partially fixed logistic_reg_CV for multinomial.
Also added some comments that are hopefully clear.
Still need to fix refit=False
* Fixed doc issue
* WIP
* Fixed test for refit=False in LogisticRegressionCV
* Fixed Python 2 numpy version issue
* minor doc updates
* Weird doc error...
* Added test to ensure that elastic net is at least as good as L1 or L2
once l1_ratio has been optimized with grid search
Also addressed minor reviews
* Fixed test
* addressed comments
* Added back ignore warning on tests
* Added a functional test
* Scale data in test... Now failing
* elastic-net --> elasticnet
* Updated doc for some attributes and checked their shape in tests
* Added l1_ratio dimension to coefs_paths and scores attr
* improve example + fix test
* FIX incorrect lagged_update in SAGA
* Add non-regression test for SAGA's bug
* FIX flake8 and warning
* Re fixed warning
* Updated some tests
* Addressed comments
* more comments and added dimension to LogisticRegressionCV.n_iter_ attribute
* Updated whatsnew for 0.21
* better doc shape looks
* Fixed whatnew entry after merges
* Added dot
* Addressed comments + standardized optional default param docstrings
* Addessed comments
* use swapaxes instead of unsupported moveaxis (hopefully fixes tests)
* Change default solver in LogisticRegression
* This is an API change, not a feature
* Decrease numerical precision in LogisticRegression doctest
* ENH add multi_class='auto' for LR, default from 0.22
* No warning when binary
* 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
Skeleton for a glossary of concepts and API elements.
This responds to at least three issues:
* Many aspects of scikit-learn API for users and developers are known
tacitly by core contributors (and the stack overflow crowd), but are
not written down in a consistent place.
* What is written is in an ad-hoc narrative style which may be useful
for introduction, but is difficult to refer to and to maintain.
* Parameters such as `n_jobs` and methods like `decision_function` are
described repeatedly in documentation giving sometimes more sometimes
less information. This glossary allows us to use "See :term:`the
glossary <n_jobs>`." so that parameter descriptions in the
API reference can remain brief (just as not every numpy operation
needs to describe broadcasting).
Guillaume Lemaitre
Hanmin Qin