* Add _RepeatedSplits and RepeatedKFold class
* Add RepeatedStratifiedKFold and doc for repeated cvs
* Change default value of n_repeats
* Change input parameters of repeated cv constructor to n_splits, n_repeats, random_state
* Generate random states in split function rather than store it beforehand
* Doc changes, inheriting RepeatedKFold, RepeatedStratifiedKFold from _RepeatedSplits and other review changes
* Remove blank line, put testcases for deterministic split in loop and add StopIteration check in testcase
* Using rng directly as random_state param to create cv instance and added a check for cvargs
* Fix pep8 warnings
* Changing default values for n_splits and n_repeats and add entry in changelog
* Adding name to the feature
* Missing space
* 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
* ENH Implement mean squared log error in sklearn.metrics.regression
* TST Add tests for mean squared log error.
* DOC Write user guide and docstring about mean squared log error.
* ENH Add neg_mean_squared_log_error in metrics.scorer
* DOC framework for keeping API refs for deprecated classes/funcs
* DOC tagging deprecated for 0.20
* suggestion for LDA/QDA deprecation
* simplify deprecation message for GaussianProcess
* simplify deprecation messages
* fixup avoid import QuadraticDiscriminantAnalysis in qda.QDA (and similar for LDA).
* fixup test alias lda.LDA is instance of LinearDiscriminantAnalysis
* LOF algorithm
add tests and example
fix DepreciationWarning by reshape(1,-1) one-sample data
LOF with inheritance
lof and lof2 return same score
fix bugs
fix bugs
optimized and cosmit
rm lof2
cosmit
rm MixinLOF + fit_predict
fix travis - optimize pairwise_distance like in KNeighborsMixin.kneighbors
add comparison example + doc
LOF -> LocalOutlierFactor
cosmit
change LOF API:
-fit(X).predict() and fit(X).decision_function() do prediction on X without
considering samples as their own neighbors (ie without considering X as a
new dataset as does fit(X).predict(X))
-rm fit_predict() method
-add a contamination parameter st predict returns a binary value like other
anomaly detection algos
cosmit
doc + debug example
correction doc
pass on doc + examples
pep8 + fix warnings
first attempt at fixing API issues
minor changes
takes into account tguillemot advice
-remove pairwise_distance calculation as to heavy in memory
-add benchmarks
cosmit
minor changes + deals with duplicates
fix depreciation warnings
* factorize the two for loops
* take into account @albertthomas88 review and cosmit
* fix doc
* alex review + rebase
* make predict private add outlier_factor_ attribute and update tests
* make fit_predict take y argument
* fix benchmarks file
* update examples
* make decision_function public (rm X=None default)
* fix travis
* take into account tguillemot review + remove useless k_distance function
* fix broken links :meth:`kneighbors`
* cosmit
* whatsnew
* amueller review + remove _local_outlier_factor method
* add n_neighbors_ parameter the effective nb neighbors we use
* make decision_function private and negative_outlier_factor attribute
* 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.
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
Add gradient calculation in _huber_loss_and_gradient
Add tests to check the correctness of the loss and gradient
Fix for old scipy
Add parameter sigma for robust linear regression
Add gradient formula to robust _huber_loss_and_gradient
Add fit_intercept option and fix tests
Add docs to HuberRegressor and the helper functions
Add example demonstrating ridge_regression vs huber_regression
Add sample_weight implementation
Add scaling invariant huber test
Remove exp and add bounds to fmin_l_bfgs_b
Add sparse data support
Add more tests and refactoring of code
Add narrative docs
review huber regressor
Minor additions to docs and tests
Minor fixes that deals with dealing with NaN values in targets
and old verions of SciPy and NumPy
Add HuberRegressor to robust estimator
Refactored computation of gradient and make docs render properly
Temp
Remove float64 dtype conversion
trivial optimizations and add a note about R
Remove sample_weights special_casing
address @amueller comments
example + benchmark
explanation
make some private functions + fix public API
IForest using BaseForest base class for trees
debug + plot_iforest
classic anomaly detection datasets and benchmark
small modif
BaseBagging inheritance
shuffle dataset before benchmarking
BaseBagging inheritance
remove class label 4 from shuttle dataset
pep8 + rm shuttle.csv bench_IsolationForest.png + doc decision_function
add tests
remove comments
fetching kddcup99 and shuttle datasets
fetching kddcup99 and shuttle datasets
pep8
fetching kddcup99 and shuttle datasets
pep8
new files iforest.py and test_iforest.py
sc
alternative to pandas (but very slow)
in kddcup99.py
faster parser
sc
pep8 + cleanup + simplification
example outlier detection
clean and correct
idem
random_state added
percent10=True in benchmark
mc
remove shuttle + minor changes
sc
undo modif on forest.py and recompile cython on _tree.c
fix travis
cosmit
change bagging to fix travis
Revert "change bagging to fix travis"
This reverts commit 30ea500eb818c7a2c6ea5c3d63e75c6935aa3a35.
add max_samples_ in BaseBagging.fit to fix travis
mc
API : don't add fit param but use a private _fit + update tests + examples to avoid warning
adapt to the new structure of _tree.pyx
cosmit
add performance test for iforest
add _tree.c _utils.c _criterion.c
TST : pass on tests
remove test
relax roc-auc to fix AppVeyor
add test on toy samples
Handle depth averaging at python level
plot example: rm html add png
load_kddcup99 -> fetch_kddcup99 + doc
Take into account arjoly comments
sh -> shuffle
add decision_path code from #5487 to bench
Take into account arjoly comments
Revert "add decision_path code from #5487 to bench"
This reverts commit 46ad44ab487f4fd2728d927cbe09000330e8663e.
fix bug with max_samples != int
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
The LDA accronym for Linear Discriminant Analysis is ambiguous
because of the newly introduced Latent Dirichlet Allocation model.
We therefore deprecate the sklearn.lda.LDA and sklearn.lda.QDA
in favor of explicit names.
Changed SubjectIndependentKFold to DisjointGroupKFold
cosmetic changes test (fix seed correctly, use assert_equal for
meaningful error messages)
Changed name to DisjointLabelKFold
Added example of use
FIX: whitespace related doctest failure
FIX: Python 2.6 requires the field numbers in print
FIX: change docstring to comment in test function
DOC: moved docstring from function to class
FIX: added call to parent class
FIX: error in calling the parent
DOC: fixed doctest
FIX: doctest
Cosmetic changes (minor refactoring)
Optimised code (use np.bincount)
Cosmetic: use samples instead of weight for clarity
Minor fix: removed shuffle parameter
Cosmetic
Use mergesort instead of quicksort for reproducibility.
Changed variable name 'y' to 'label'.
Added test for degenerate case where n_folds > n_labels.
Documented the requirement n_labels > n_folds.
DOC: improved description + added see also sections.
Fixed dtype of temporary arrays.
Improved test: check that one label is not in both test and training.
Added documentation for DisjoinLabelKFold.
Hanmin Qin
Neeraj Gangwar