* fix error tuple passed to order
* fix linting
* Update sklearn/tests/test_multioutput.py
Co-authored-by: Roman Yurchak <rth.yurchak@gmail.com>
* updated test and whatsnew
* doc fix
* Update doc/whats_new/v0.24.rst
Co-authored-by: Roman Yurchak <rth.yurchak@gmail.com>
* Update sklearn/tests/test_multioutput.py
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
* Update sklearn/tests/test_multioutput.py
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
* code review fix
* code review fix
* added test
* code review fix
* Update sklearn/multioutput.py
Co-authored-by: Joel Nothman <joel.nothman@gmail.com>
Co-authored-by: Roman Yurchak <rth.yurchak@gmail.com>
Co-authored-by: Thomas J. Fan <thomasjpfan@gmail.com>
Co-authored-by: Joel Nothman <joel.nothman@gmail.com>
* normalize_components in sparsePCA
* changed default strategy of Dummy to prior and removed outputs_2d ttribute
* removed usage of None to drop estimator in ensemble and behaviour param of IsolationForest
* remove support for drop=None in Voting
* removed some warning decorators
* remove feature_extraction.extract_patches
* removed VectorizerMixin and copy parameter from TFIDFVectorizer
* kernel.set_params now raises attributeerror
* removed fig from plot_partial_dependence
* removed iid parameter of search estimators
* removed brier_scorer
* raise error in split when shuffle is False and random_state is not None
* removed MultiOutputEstimator
* removed base classes of NaiveBayes
* removed drop from pipeline
* removed utils in random_projection
* removed presort and classes_ in trees
* flake8
* fixed some tests
* flake
* fixed docstring
* fixed other one
* some left
* mmmm
* Added ChangedBehaviorWarning in sgd models
if tol is None while max_iter is set
* Changed to FutureWarning and clarified None meaning
* Ignored warningin tests
* Ignore warnings in tests, round 2
* 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
In order to fix#11408, this swaps `joblib` and `_joblib`. It however, allows users to access joblib's `Memory` or `Parallel` functionality without accessing `sklearn.externals._joblib` by importing `Memory`, `Parallel`, etc. into `sklearn.utils`.
* 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
* Return list instead of 3d array for MultiOutputClassifier.predict_proba
* Update flake8, docstring, variable name
- Changed `rs` to `rng` to follow convention.
- Made sure changes were flake8 approved
- Add `\` to continue docstring for `predict_proba` return value.
* Sub random.choice for np.random.choice
`np.random.choice` isn’t available in Numpy 1.6, so opt for the Python
version instead.
* Make test labels deterministic
* Remove hanging chad...
* Add bug fix and API change to whats new
Register OneVsRestRegressor as meta estimator
Rename to a more sensible name
Parallel predict and sparse support
Started MultiOutput documentation
Move code to new file multioutput.py
Continuing the move to new multioutput module
Added sample weight support
Better test for sample weights and actually support weights
Added a new test using weighted vs repeated samples to
test sample weight support. Uncovered that weights
were not actually passed on to underlying estimator.
Comment on multiprocess overheads
Move parallel_helper to utils.fixes
This helper works around a python2 limitation on pickling
instance methods
Example of multi-output regression with gradient boosting
Switch to uniform weighted score and updated example
The example now uses a RF with and without the MultiOutput
meta estimator
Added note for removing `score` method
Addressing comments on MultiOutputRegressor
MultiOutputregressor better test for weighted samples
Fix ups
Use explicit keyword argument for passing sample weights and
fix random_state on train-test split in the example
Guillaume Lemaitre
Hanmin Qin