* 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
* 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
* 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
The description of LassoLarsCV compared the number of samples with the number of observations, but it was meant to compare the number of samples to the number of features (or dimensions) of the data. I changed "observations" to "features" in the following sentence:
> However, :class:`LassoLarsCV` has the advantage of exploring more relevant values of `alpha` parameter, and
if the number of samples is very small compared to the number of observations, it is often faster than :class:`LassoCV`.
* Add supervised cluster metrics to metrics.scorers
* Add all the supervised cluster metrics to the tests
* Add test for fowlkes_mallows_score in unsupervised grid search
* COSMIT: Clarify comment on CLUSTER_SCORERS
* Fix doctest
* DOC: complete list of online learners
I have purposely left out the RBM, because it is a model that has fallen
out of fashion and should probably not be used.
* DOC: Add MaxAbsScaler to online learners
* 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 adding a warning on the relation between C and alpha
* DOC removing extra character
* DOC: changes to the relation described
* DOC fixing typo
* DOC fixing typo
* DOC fixing link to Ridge
* DOC link enhancement
* DOC fixing line length
* 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
Remi Rampin
Hanmin Qin