* some bug fixes.
* minor fixes to whatsnew
* typo in whatsnew
* add test for n_components = 1 transform in dict learning
* feature extraction doc fix
* fix broken test
* revert aggressive input validation changes
* in SelectFromModel, don't store threshold_ in transform. If we called "fit", use estimates from last "fit".
* move score from EllipticEnvelope to OutlierDetectionMixin
* revert changes to Tfidf documentation
* remove dummy input validation from whatsnew
* fix text feature tests
* rewrite from_model threshold again...
* remove stray condition
* fix self.estimator -> estimator, slightly more interesting test
* typo in comment
* Fix issues in SparseEncoder, add tests.
more explicit explanation of SparseEncoder change, add issue numbers to whatsnew
* minor fixes in whats_new.rst
* slightly more consistency with tuples for shapes
* not longer typo
* Extended explanation of using class_weight in RandomForestClassifier
* Extended explanation of using class_weight in DecisionTreeClassifier,ExtraTreesClassifier and compute_sample_weight()
* Rephrased description.
* Rephrased description (remove "indicator")
* PR to 7288
Use _BaseComposition as base
* Fix flakes problem
* Change ``pipeline``, add more tests and other changes
1. Use ``_BaseComposition`` in class ``Pipeline`` and ``FeatureUnion``
2. Add tests of soft voting ``transform`` when one estimator is set to None
3. Add estimator name validation in ``_BaseComposition`` and tests
4. Other requested changes.
* Remove the unused import warn
* Add more test and documentation
* resolve conflict with master
* Add testing cases and modify documentation
* Add to whats_new.rst
* Fix too many blank lines
* Change named_steps to Bunch object
* Update named_steps attribute documentation
* Add test for named steps bunch object
* Delete whitespace in test_pipeline
* Update test_pipeline.py
* Add comment for named_steps usage
* Move dataset/Bunch to utils
* Fix to PEP8 format
* Add __getattribute method to Bunch class, Fix pep8 bug
* Remove __getattribute__, update test_pipeline
* Update test with conflict and non-conflict named_steps
* Add reference to class Pipeline
* Fixed depth formula in iforest
* Added non-regression test for issue #8549
* reverted some whitespace changes
* Made changes to what's new and whitespace changes
* Update whats_new.rst
* Update whats_new.rst
* fixed faulty whitespace
* faulty whitespace fix and change to whats new
* added constants to iforest average_path_length and the according non regression test
* COSMIT
* Update whats_new.rst
* Corrected IsolationForest average path formula and added integer array equiv test
* changed line to under 80 char
* Update whats_new.rst
* Update whats_new.rst
* reran tests
* redefine np.euler_gamma
* added import statement for euler_gammma in iforest and test_iforest
* changed np.euler_gamma to euler_gamma
* fix small formatting issue
* fix small formatting issue
* modified average_path_length tests
* formatting fix + removed redundant tests
* fix import error
* retry remote server error
* retry remote server error
* retry remote server error
* re-added some iforest tests
* re-added some iforest tests
* ENH Accept keyword parameters to hyperparameter search fit methods
Deprecate ``fit_params`` as a constructor argument to the hyperparameter search classes and instead accept keyword parameters to the ``fit`` methods. This makes the ``fit`` methods of these functions conform to the Estimator API and allows the use of hyperparameter search functions in other CV utility functions such as ``cross_val_predict``.
* CR: Expanded tests, remove deprecated use in Ridge
* Make tests consistent in Python 2 and 3
ensure that estimators only add private attributes and attributes with
trailing _
in cases when existing estimators don't follow this new rule, we deprecate the
attributes and make them follow this rule
* 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
* addressed comments in the PR about parameters in check_array
* update the test case for the evaluation of estimators with pandas series
* bug fix, need to check for *not* None explicitly
* updated with isinstance check if the documentation says there is acceptance of floats
* ran pep8 linter on modified files
* moving the test case to estimators_check
* add a predict function into the testing pandas.Series class
* avoid running anything beyond the newly added meta checks
* check if pandas is installed before running the specific test
* changed the order of the try-catch to check for sample_weight param beforehand
* pass on import error rather than printing something to std out
* improve test case naming and pd.Series check in the bad estimator class
* address a pep8 linter error with unused import
* pep8 warning disabled for potential unused import
* throw a warning when SkipTest is raised
* add a SkipTestWarning
* updated the whats_new.rst with this issue
* rebase and fix a spacing issue
* 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
* Add test for __dict__ for estimator checks
check that "predict", "transform", "decision_function" or
"predict_proba" methods do not change the state of __dict__ of any
estimator
see #7297
* Add a test to test_check_estimator
that shows that check_estimator fails on an estimator that violates this
* Fixed bug where NMF's n_iter_ attribute was set by calls to transform
* Rewriting of cythonization in setup.py
By using Cython.Build.cythonize and switching between .c and .pyx files
as appropriate cython dependencies are correctly taken into account.
* Use cythonize once on the root config rather than in each subpackage
* Fix for Windows
* Remove caching from Travis
Cython dependencies are taken care of by Cython.Build.cythonize and
based on file timestamps, so .C and .so files will always be rebuild
from scratch on each build in Travis.
* Specify .pyx in setup.files for cython generated extensions
More natural this way. Tweak the extensions to generate from .c and .cpp
files for a release.
* COSMIT Remove commented out code
* Check cython version is greater than 0.23
* COSMIT better names for functions
* flake8 fix (imported module not at top of file)
* Install cython 0.23 for Python 2.6
now that cython >= 0.23 requirement is enforced in setup.py
* Use module constant for minimum required cython version
* Fix Travis install.sh
No easy way to put comments inside multi-line command
* Norm inconsistency between RFE and SelectFromModel (was _LearntSelectorMixin) #2121
* safe_pwr utility
* Norm fix
* Removed safe_pwr
* 1D arrays support for norm fix
* Test case for 2d coef in SelectFromModel
* Fix numpy version requirement for norm fix
* Implement fixes suggested by @jnothman
* Add numpy version requiring the fix.
* Test get_params invariance in common estimator tests
Remove test_get_params_invariance() from `test_common.py` and add
test call to _yield_all_tests() in `estimator_checks.py` to make
sure that get_params(deep=False) of a given Estimator returns a
subset of get_params(deep=True).
Compared to test_get_params_invariance(), it is NOT tested anymore
whether the given Estimator has an attribute get_params since
class BaseEstimator in `base.py` defines such an attribute
for each Estimator.
Partially addresses issue #7533
Also related to issue #4465
* Move test_transformer_n_iter() to estimator_checks.py
Remove the test test_transformer_n_iter() from tests/test_common.py
and perform the test logic in utils/estimator_checks.py instead.
Specifically, the method _yield_transformer_checks() now yields
check_transformer_n_iter() as part of the set of tests for
transformers.
test_transformer_n_iter() tests that that transformers with an
attribute max_iter, return the attribute of n_iter at least 1.
Partially addresses latter part of issue #7533
* Move test_non_transformer_estimators_n_iter() to estimator_checks.py
Remove the test_non_transformer_estimators_n_iter() from
tests/test_common.py; perform the test logic in
utils/estimator_checks.py instead.
Specifically, the method _yield_non_meta_checks() now yields
check_non_transformer_estimators_n_iter().
test_transformer_n_iter() tests that that estimators that are not
transformers with an attribute max_iter, return the attribute n_iter
of at least 1.
NOTE: The current implementation makes said test run for more
estimators than before this commit.
For some of these estimators, the test fails. This needs to be addressed
(see FIXME in line 111-115 of utils/estimator_checks.py for a potential
place to start).
Partially addresses latter part of issue #7533
* Fix check_non_transformer_estimators_n_iter calls
test_transformer_n_iter() test is now only run for
estimators where the test is applicable.
Partially addresses latter part of issue #7533
* Run check_non_transformer_estimators_n_iter on multi-class estimators
To do this, use helper method multioutput_estimator_convert_y_2d.
Also remove multi_output parameter from
check_non_transformer_estimators_n_iter since this parameter is not
used anywhere and corresponding cases should be handled by said
helper method.
Also, some pep8 line length fixes.
* Fix documentation for n_iter tests
There was some confusion between attributes and parameters.
Also rename n_iter to n_iter_
* FIX unstable cumsum in utils.random
* equal_nan = true for isclose
since numpy < 1.9 sum is as unstable as cumsum, fallback to np.cumsum
* added axis parameter to stable_cumsum
* FIX unstable sumsum in ensemble.weight_boosting and utils.stats
* FIX axis problem in stable_cumsum
* FIX unstable cumsum in mixture.gmm and mixture.dpgmm
* FIX unstable cumsum in cluster.k_means_, decomposition.pca, and manifold.locally_linear
* FIX unstable sumsum in dataset.samples_generator
* added docstring for parameter axis of stable_cumsum
* added comment for why fall back to np.cumsum when np version < 1.9
* remove unneeded stable_cumsum
* added stable_cumsum's axis testing
* FIX numpy docstring for make_sparse_spd_matrix
* change stable_cumsum from error to warning
* FIX Subclass a new MaskedArray which allows pickling even when dype=object
* TST unpickling too
* FIX Use MaskedArray from utils.fixes rather than from numpy
* FIX imports
* Don't assign a variable
* FIX np --> numpy
* Use tostring instead of tobytes for old numpy
* COSMIT pickle-able --> picklable
* use #noqa comment to turn off flake8
* TST/ENH Check if the pickled est's predict matches with the original one's
* 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
Taehoon Lee