* 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
* Added override of fit_transform to LabelBinarizer
* Updated fit_transform to call base class method
* Changed fit_transform for code consistency
* Removed whitespace on blank lines
* Fixed line wrap issues for doc gen.
* Used line cont. for term defs
* Standardized bracket usage, fixed line cont. indent level
* Remove Python 2.6 support
Some details about some slightly orthogonal changes:
* Note about cheking safely for nan is likely not valid any more (commit
introducing it is c80ca91b)
* scipy.linalg.qr econ parameter removed since scipy 0.9 in favour of
mode='economic'
* Remove unnecessary libgfortran in conda create command
* Putative fix by setting the random seed
* Revert unintended change
* Reinstate previous logic for checking for NaNs
* Reinstate change in error message
Error messages from Python 2.7 assertRegexp does not contain the
function name, in contrast with Python 3 assertRegex
* BUG: MultiLabelBinarizer makes invalid CSR matrix
See https://github.com/scipy/scipy/issues/6719 for context.
The gist is that the `inverse` array may have a different dtype than `yt.indices`, which causes trouble down the line because, in those cases, `yt.indices` and `yt.indptr` have different dtypes.
Alternately, we could insert `yt.check_format(full_check=False)` after modifying the sparse matrix members.
* Fixing for old numpy
Older versions don't support kwargs for `astype`
* Adding tests
* line-wrapping
* adding comment to tests
[ci skip]
* added rationale comment
[ci skip]
* use less nose.tools import to simplify future transition to activly developing test suites/runners
* assert_equal -> assert_array_equal in test_feature_hasher_pairs_with_string_values
and one missed ImportError that should be replaced with AttributeError
* test for py2.6 compat with except AttributeError
* fix importing of SkipTest
* force using nose in python2.6 for now
* there was no assert_dict_equal in py2.6. but we can use assert_equal
although failed test will look a little bit ugly
* remove nose imports from doc/datasets
* refer users to the other encoders to do one hot encoding for labels.
* added to the 'see more' for labelbinarizer, multilabelbinarizer, and labelencoder' as well as an example to multilabel binarizer
* added note about y labels to the OneHotEncoder docstring
* removed example from MultiLabelBinarizer
* documentation should specify LabelBinarizer, not MultiLabelBinarizer in OHE
* fixes issue scikit-learn/scikit-learn#7194
* Added test
* Making `selected='all'` explicit on test
* Updated whats_new.rst
* Fixed typo on `whats_new.rst`
Replicate solution to 9a520779c2 except that `_pairwise` should always be `True` for `KernelCenterer` because it's supposed to receive a Gram matrix. This should make `KernelCenterer` usable in `Pipeline`s.
Happy to add tests, just tell me what should be covered.
Minghui Liu