* 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
* Support of the collections.Sequence type has been added to the _check_param_grid method from model_selection.
* test_grid_search_when_param_grid_includes_range test was refactored (parts that are not nessesary have been removed).
* test_grid_search_bad_param_grid now checks that value is not string. This is important since string is a Sequence.
* _check_param_grid now checks is the type is not string together with the check for other types.
* whats_new.rst has been updated to include information about bug fix for bug #7322.
* Description of the fix for the bug #7322 has been updated.
* Fix for indented in model_selection._search.py.
This allows advanced users who are using standard Python package
management tools to install scikit-learn[alldeps], which includes the
appropriate numpy and scipy dependencies.
* Add the new BayesianGaussianMixture class.
Add the test file for the BayesianGaussianMixture.
* Add the use of the cholesky decomposition of the precision matrix.
* Fix some bugs.
* Modification of GaussianMixture class.
The purpose here is to prepare the integration of BayesianGaussianMixture.
* Fix comments.
* Modification of the Docstring.
* Add license and author.
* Fix pb typo of eq 10.64 and 10.62.
* Correct VBGMM bugs.
* Fix full version.
* Fix the precision normalisation pb.
* Fix all cov_type algo for BayesianGaussianMixture.
* Optimisation of spherical and diag computation.
* Code simplification.
* Check the Gaussian Mixture tests are ok.
* Add test.
* Add new tests for BayesianGaussianMixture and GaussianMixture.
* Add the bayesian_gaussian_example and the doc.
* Fix comments.
* Fix review comments and add license and author.
* Fix test compare covar type.
* Fix reviews.
* Fix tests.
* Fix review comments.
* Correct reviews.
* Fix travis pb.
* Fix circleci pb.
* Fix review comments.
* Fix typo.
* Fix comments.
Add reg_covar and what's new.
* Fix comments.
* Fix comments.
* [ci skip] Correct legend.
* Add flake8 linting on the diff
with respect to common ancestor of branch and scikit-learn/scikit-learn
remote.
Add a flake8-diff target in the Makefile and mention it in the doc.
* Only install flake8 when needed
Pipeline and FeatureUnion steps may now be set with set_params, and transformers may be replaced with None to effectively remove them.
Also test and improve ducktyping of Pipeline methods
* parallelized VotingClassifier
* rename list to list_ to avoid problems
* Added new tests for sample_weight, multithreading and multiprocessing
* assert_equal -> assert_array_equal
* Fixed sample_weight existence check and test_sample_weight
* Code is clearer now
* Tests refactoring, 'backend' parameter removed
* Tests indentation fix
* reverted parallel predict and predict_proba to single threaded version
* what's new section added
* minor fixes
* check for sample_weight support in underlying estimators added
* newline at the end of test
fix a typo in whatsnew
refactor conditional and move dtype check before np.clip
general cleanup of log_loss
remove dtype checks
edit non-regression test and wordings
fix non-regression test
misc doc fixes / clarifications + final touches
fix naming of y_score2 variable
specify log loss is only valid for 2 labels or more
fixes as per existing pull request #6714
fixed log_loss bug
enhance log_loss labels option feature
log_loss
changed test log_loss case
u
add ValueError in log_loss
fixes as per existing pull request #6714
fixed error message when y_pred and y_test labels don't match
fixed error message when y_pred and y_test labels don't match
corrected doc/whats_new.rst for syntax and with correct formatting of credits
additional formatting fixes for doc/whats_new.rst
fixed versionadded comment
removed superfluous line
removed superflous line
ElasticNet and Lasso no longer implicitly convert float32 dtype input to float64 internally.
* Make helper functions in cd use fused types
* Import cblas float functions
* Make enet_coordinate_descent support fused types
* Make dense case work
* Refactor format
* Remove redundant change
* Add cblas files
* Avoid redundant code
* Remove redundant c files and import
* Recover unnecessary change
* Update comment
* Make coef_ type consistent
* Test float32 input
* Add user warning when fitting float32 data with small alpha
* Fix bug
* Change variable to floating type
* Make cd sparse support fused types
* Make CD support fused types when data is sparse
* Add referenced src files
* Avoid duplicated code
* Avoid type casting
* Fix indentation in test
* Avoid type casting in sparse implementation
* Fix indentation
* Fix duplicated intialization code
* Follow PEP8
* Raise tmp precision to double
* Add 64 bit computer check
* Fix test
* Add constraint
* PEP 8
* Make saxpy have the same structure as daxpy
Hopefully this fixes the problems outlined in PR #6913
* Remove wrong hardware test
* Remove dsdot
* Remove redundant asarray
* Add test for fit_intercept
* Make _preprocess_data support other dtypes
* Add concrete value
* Workaround
* Fix error msg
* Move declarartion
* Remove redundant comment
* Add tests
* Test normalize
* Delete warning
* Fix comment
* Add error msg
* Add error msg
* Add what's new
* Fix error msg
* Remove heavy memory footprint for OOB scoring from bagging.
- Remove `estimators_samples` attribute from `BaseBagging`
- Add method `_get_estimators_samples` to `BaseBagging` that
returns a generator producing sample indices on demand.
- Slight refactor of `_parallel_build_estimators()` to
isolate and group lines accessing random state.
* Replaced `BaseBagging.estimators_samples_` attribute with property.
- Accessing `BaseBagging.estimators_samples_` now triggers call to
`BaseBagging._get_estimators_samples` as well as deprecation
warning for release 0.20.
- This should make the fix for the OOB memory issue fully backwards
compatible.
* Remove print statement.
* Added test, made new method more general.
- Added test to `test_bagging.py` to ensure that indices generated
on the fly are identical to indices generated at fit time.
- Refactored `_get_estimators_samples()` to `_get_estimators_indices()`,
now both feature and samples indices are returned.
- Refactored `estimators_samples_()` to deal with above.
* Rename index generating functions and arguments.
* `estimators_samples_()` returns list instead of generator
* Removed `estimators_samples_` deprection warning.
* Actually removed `estimators_samples_` deprecation warning.
* New mask generation function, new bagging test, new BaseBagging attributes.
- Added new private function in bagging.py that converts indices to
a boolean mask.
- Added new bagging test to make sure identical OOB scores are generated
when the same estimator if fit with fixed random state and identical
training data.
- Added new private attributes `BaseBagging._max_features` and
`BaseBagging._max_samples` to store validated input values.
* Streamlined code, improved `estimators_samples_()` documentation.
- Removed `max_samples` argument from `_parallel_build_estimators()`,
this value is now accessed via `ensemble._max_samples`.
- Removed validation of `ensemble.max_features` and `max_samples`,
instead use `ensemble._max_features` and `ensemble._max_samples`
which are assumed to be already validated.
- Removed unnecessary `samples` variable from `_parallel_build_estimators()`.
- Changed the way `random_state` is generated in `_parallel_build_estimators()`
and `BaseBagging._get_estimators_data_draws()` to direct numpy method to
reflect that seeds created in `BaseBagging._fit()` are trustworthy.
- Due to above removed generation of new seed for each estimator in
`_parallel_build_estimators()` and `BaseBagging._get_estimators_data_draws()`.
- Added documentation to `BaseBagging.estimators_samples_()` property
indicating the reason it's generated dynamically and the associated performance
penalty.
- Returned `BaggingClassifier._set_oob_score()` and `BaggingRegressor._set_oob_score()`
to directly accessing `self.estimators_samples_`.
* Streamlined code, improved `estimators_samples_()` documentation.
- Removed `max_samples` argument from `_parallel_build_estimators()`,
this value is now accessed via `ensemble._max_samples`.
- Removed validation of `ensemble.max_features` and `max_samples`,
instead use `ensemble._max_features` and `ensemble._max_samples`
which are assumed to be already validated.
- Removed unnecessary `samples` variable from `_parallel_build_estimators()`.
- Changed the way `random_state` is generated in `_parallel_build_estimators()`
and `BaseBagging._get_estimators_data_draws()` to direct numpy method to
reflect that seeds created in `BaseBagging._fit()` are trustworthy.
- Due to above removed generation of new seed for each estimator in
`_parallel_build_estimators()` and `BaseBagging._get_estimators_data_draws()`.
- Added documentation to `BaseBagging.estimators_samples_()` property
indicating the reason it's generated dynamically and the associated performance
penalty.
- Returned `BaggingClassifier._set_oob_score()` and `BaggingRegressor._set_oob_score()`
to directly accessing `self.estimators_samples_`.
* PEP8, remove optional arguments, add public function to utils.
- Update indentation and line length to conform to PEP8.
- Update `estimators_samples_()` docstring to conform to PEP8 and
PEP257.
- Remove optional arguments to `BaseBagging._get_estimators_data_draws()`,
rename to `BaseBagging._get_estimators_indices()`.
- Remove optional arguments from `_generate_mask_from_indices()`,
move to `utils.metaestimators`, rename to `indices_to_mask()`.
* Add indices_to_mask to __all__ in utils.metaestimators
* Move utils function, new test for `estimators_samples` correctness.
- Moved `indices_to_mask` from `utils.metaestimators` to `utils.validation`
- Added new test to `ensemble.test_bagging` to make sure refitting
an individual estimator from the bagging ensemble using the corresponding
samples identified in `BaseBagging.estimators_samples` returns the same model.
* Adjust/remove tests, move `indices_to_mask` to `utils.__init__`.
- Remove test_bagging.test_consistent_index_sampling
- Update formatting checks in test_bagging.test_estimators_samples
to make sure each mask is numpy boolean array
- Move indices_to_mask from utils.validation to utils.__init__
* update class docstring for `estimators_samples_`
- Updated class docstring of `estimators_samples_` attribute for `BaggingClassifier` and `BaggingRegressor` to indicate that samples are identified with boolean masks.
* PEP8, made `max_samples` arg optional in _fit()
- Fixed PEP8 issues.
- Gave `max_samples` argument of `BaseBagging._fit()` a default
value of `None` as indicated in the docstring. Added code to
check for and process `None` value.
- Added a few more comments to `_fit()`.
* 2 new `max_samples` consistency tests, update whats_new.rst
* Minor change
* Update whats_new.rst
Changes "the scikit" to "scikit-learn". While both might be technically accurate, explicitly calling the package "scikit-learn" helps with naming consistency.
Scott Andrews