* 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
Fixes#8316
* Don't use test classes to group tests
* only use formatting for parts of the string that change
* Flake 8 column limit
* Make the modification of the estimator more explicit in the tests
* As suggested in code review, prefer formatting over two literals
* Also assert, that __setstate__ overwriting works in mixin
* Remove cache property
* Use assertion functions from sklearn.utils.testing
* remove the protocol argument in tests
* Rename attributes to better convey their purpose
* Revert change of module in TreeNoVersion
* Adhere to column-limit
* changelog entry
* Fix commit message
* 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
* BF: issue 5522 (cloning objects with pandas.Dataframe attributes)
* super conservative fix, pending GP and VBGMM fixes.
* TST improved test for df param
* add deprecation warning, add whatsnew entry
* fixed place of deprecation warning, added test for deprecation warning.
* pep8
* fix whitespace error
Depreciation of the GMM class.
Modification of the GaussianMixture class.
Some functions from the original GSoC code have been removed, renamed or simplified.
Some new functions have been introduced (as the 'check_parameters' function).
Some parameters names have been changed :
- covars_ -> covariances_ : to be coherent with sklearn/covariances
Addition of the parameter 'warm_start' allowing to fit data by using the previous computation.
The old examples have been modified to replace the deprecated GMM class by the new GaussianMixture class.
Every exemple use the eigenvectors norm to solve the scale ellipse problem (Issues 6548).
Correction of all commentaries from the PR
- Rename MixtureBase -> BaseMixture
- Remove n_features_
- Fix some problems
- Add some tests
Correction of the bic/aic test.
Fix the test_check_means and test_check_covariances.
Remove all references to the deprecated GMM class.
Remove initialized_.
Add and correct docstring.
Correct the order of random_state.
Fix small typo.
Some fix in prevision of the integration of the new BayesianGaussianMixture class.
Modification in preparation of the integration of the BayesianGaussianMixture class.
Add 'best_n_iter' attribute.
Fix some bugs and tests.
Change the parameter order in the documentation.
Change best_n_iter_ name to n_iter_.
Fix of the warm_start problem.
Fix the divergence error message.
Correction of the random state init in the test file.
Fix the testing problems.
Update and add comments into the monotonic test.
in other modules where warnings are deprecated.
Prefix warnings imported from sklearn.exceptions instead of
suffixing to prevent showing both the suffixed warning
and the deprecated warning during tab completion.
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
* Removed dead and useless code in the utils submodule;
* decoupled sklearn.base from the bicluster module (no more deep import);
* rewrote tests to test the API, not the implementation;
* added input validation to the get_submatrix method (but it still needs
documentation, because I've no idea what to pass for data);
* got rid of the subdirectory when it turned out only one file was left.
Parameter introspection requires introspecting the keyword arguments of the
constructor of Python classes deriving from BaseEstimator.
This can be problematic for classes that do not override the default
constructor. Introspecting the implicit constructor used to raise a TypeError
but this is no longer the case in Python 3.4+. Hence with use a explicit check
for the implicit constructor prior to using inspection.
Finn O'Shea