* function name KDtree -> KDTree

* correct similar mistakes

* Add ref

* Fix pep8

…n#9038)

* don't set fit_params in __init__ in BaseSearchCV

* fix doctest

…kit-learn#9077)

…ng. (scikit-learn#9083)

* start work on separating instance-level tests

* minor refactoring / fixes to work without tags

* add clone into check_supervised_y_2d estimator check (which made other checks fail)

* remove duplicate check_estimator_unfitted assert

* add issue reference to whatsnew entry

* added some clones, minor fixes from vene's review

* rename estimator arg to estimator_org to make a visible distinction before and after cloning.

* more renaming for more explicit clones

* org -> orig

* allclose, fix orig stuff

* don't use set_testing_parameters in the checks!

* minor fixes for allclose

* fix some test, add more tests on classes

* added the test using pickles.

* move assert_almost_equal_dense_sparse to utils.testing, rename to assert_allclose_sparse_dense, test it

* make assert_allclose_dense_sparse more stringent

* more allclose fixes

* run test_check_estimator on all estimators

* rename set_testing_parameters to set_checking_parameters so nose doesn't think it's a tests (and I don't want to import stuff from nose as we want to remove it)

* fix in set_checking_parameters so that common tests pass

* more fixes to assert_allclose_dense_sparse

* rename alg to clusterer, don't scream even though I really want to

* ok this is not a pretty strict test that runs check_estimator with and without fitting on an instance. I also check if ``fit`` is called on the instance that is passed.

* simplify test as they didn't help at all

* it works!!! omfg

* run check_estimator clone test only on one of the configs, don't run locally by default

* Add `slow_test` decorator and documentation

* run test_check_estimator only on some estimators

* fix diags in test for older scipy

* fix pep8 and shorten

* use joblib.hash for inequality check because the pickle state machine is weird

…cikit-learn#8951)

Remove OutlierDetectionMixin, which was only used by by EllipticEnvelope

…_score

Remove the eleven average precision score

Add better tests.

* resurrect quantile scaler

* move the code in the pre-processing module

* first draft

* Add tests.

* Fix bug in QuantileNormalizer.

* Add quantile_normalizer.

* Implement pickling

* create a specific function for dense transform

* Create a fit function for the dense case

* Create a toy examples

* First draft with sparse matrices

* remove useless functions and non-negative sparse compatibility

* fix slice call

* Fix tests of QuantileNormalizer.

* Fix estimator compatibility

* List of functions became tuple of functions
* Check X consistency at transform and inverse transform time

* fix doc

* Add negative ValueError tests for QuantileNormalizer.

* Fix cosmetics

* Fix compatibility numpy <= 1.8

* Add n_features tests and correct ValueError.

* PEP8

* fix fill_value for early scipy compatibility

* simplify sampling

* Fix tests.

* removing last pring

* Change choice for permutation

* cosmetics

* fix remove remaining choice

* DOC

* Fix inconsistencies

* pep8

* Add checker for init parameters.

* hack bounds and make a test

* FIX/TST bounds are provided by the fitting and not X at transform

* PEP8

* FIX/TST axis should be <= 1

* PEP8

* ENH Add parameter ignore_implicit_zeros

* ENH match output distribution

* ENH clip the data to avoid infinity due to output PDF

* FIX ENH restraint to uniform and norm

* [MRG] ENH Add example comparing the distribution of all scaling preprocessor (#2)

* ENH Add example comparing the distribution of all scaling preprocessor

* Remove Jupyter notebook convert

* FIX/ENH Select feat before not after; Plot interquantile data range for all

* Add heatmap legend

* Remove comment maybe?

* Move doc from robust_scaling to plot_all_scaling; Need to update doc

* Update the doc

* Better aesthetics; Better spacing and plot colormap only at end

* Shameless author re-ordering ;P

* Use env python for she-bang

* TST Validity of output_pdf

* EXA Use OrderedDict; Make it easier to add more transformations

* FIX PEP8 and replace scipy.stats by str in example

* FIX remove useless import

* COSMET change variable names

* FIX change output_pdf occurence to output_distribution

* FIX partial fixies from comments

* COMIT change class name and code structure

* COSMIT change direction to inverse

* FIX factorize transform in _transform_col

* PEP8

* FIX change the magic 10

* FIX add interp1d to fixes

* FIX/TST allow negative entries when ignore_implicit_zeros is True

* FIX use np.interp instead of sp.interpolate.interp1d

* FIX/TST fix tests

* DOC start checking doc

* TST add test to check the behaviour of interp numpy

* TST/EHN Add the possibility to add noise to compute quantile

* FIX factorize quantile computation

* FIX fixes issues

* PEP8

* FIX/DOC correct doc

* TST/DOC improve doc and add random state

* EXA add examples to illustrate the use of smoothing_noise

* FIX/DOC fix some grammar

* DOC fix example

* DOC/EXA make plot titles more succint

* EXA improve explanation

* EXA improve the docstring

* DOC add a bit more documentation

* FIX advance review

* TST add subsampling test

* DOC/TST better example for the docstring

* DOC add ellipsis to docstring

* FIX address olivier comments

* FIX remove random_state in sparse.rand

* FIX spelling doc

* FIX cite example in user guide and docstring

* FIX olivier comments

* EHN improve the example comparing all the pre-processing methods

* FIX/DOC remove title

* FIX change the scaling of the figure

* FIX plotting layout

* FIX ratio w/h

* Reorder and reword the plot_all_scaling example

* Fix aspect ratio and better explanations in the plot_all_scaling.py example

* Fix broken link and remove useless sentence

* FIX fix couples of spelling

* FIX comments joel

* FIX/DOC address documentation comments

* FIX address comments joel

* FIX inline sparse and dense transform

* PEP8

* TST/DOC temporary skipping test

* FIX raise an error if n_quantiles > subsample

* FIX wording in smoothing_noise example

* EXA Denis comments

* FIX rephrasing

* FIX make smoothing_noise to be a boolearn and change doc

* FIX address comments

* FIX verbose the doc slightly more

* PEP8/DOC

* ENH: 2-ways interpolation to avoid smoothing_noise

Simplifies also the code, examples, and documentation

…#9089)

* DOC add hyperlink to example

* Remove useless change

* DOC fix hyperlink

* DOC fix links

* Add deprecation message and test.

* Adding performance warning and ignore_warnings in test

* Add deprecation to whatsnew and remove LSHForest references from docs.

Removing benchmark for lsh

…ll hierarchy (scikit-learn#9004)

* FIX : remove n_nonzero_coefs from attr of LassoLarsCV + clean up call to Lars._fit

* cleanup

* fix deprecation warning + clarify warning

* add test

* pep8

* adddress comments

* fix OVR classifier edgecase bugs

* add regression tests for OVO and OVR decision function shapes

… classifier (scikit-learn#9063)

* FIX/TST revert scikit-learn#5802 and raise error for faulty classifier

* FIX check_estimator take care of the rest

* correcting information criterion calculation in least_angle.py

The information criterion calculation is not compatible with the original paper 
Zou, Hui, Trevor Hastie, and Robert Tibshirani. "On the “degrees of freedom” of the lasso." The Annals of Statistics 35.5 (2007): 2173-2192.
APA

* FIX : fix AIC/BIC computation in LassoLarsIC

* update what's new

* fix test

* fix test

* address comments

* DOC comments and docstring on criterion computation

with sphinx 1.6

to fix pdf doc generation

Long commit messages can trigger a pager which is not what you want
when running flake8_diff.sh in a terminal.

now that the sprint is over.

…ressor, added 2 tests (scikit-learn#8931)

* regression test and fix for 2d stratified shuffle split

* strengthen non-overlap sss tests

* clarify test and comment

* remove iter from tests, use str instead of hash

… 0.21 (scikit-learn#7459)

…cikit-learn#9033)

…scikit-learn#9068)

…cikit-learn#9119)

Also invert the solvers check to highlight the fact that new solver should support both 32 bit and 64 bit float by default from now on.