* we do not transform y
* more
* added Deprecation Warning to transform() to remove Y parameter
* more
* ENH ensure FunctionTransformer's transform/inverse_transform doesn't permit y
* Undo changes to pls_. It will be done in a separate PR (see #9160)
* flake8
* Update whatsnew
* Fully undo PLS changes
* 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
* 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
* Remove Python 2.6 support
Some details about some slightly orthogonal changes:
* Note about cheking safely for nan is likely not valid any more (commit
introducing it is c80ca91b)
* scipy.linalg.qr econ parameter removed since scipy 0.9 in favour of
mode='economic'
* Remove unnecessary libgfortran in conda create command
* Putative fix by setting the random seed
* Revert unintended change
* Reinstate previous logic for checking for NaNs
* Reinstate change in error message
Error messages from Python 2.7 assertRegexp does not contain the
function name, in contrast with Python 3 assertRegex
* refer users to the other encoders to do one hot encoding for labels.
* added to the 'see more' for labelbinarizer, multilabelbinarizer, and labelencoder' as well as an example to multilabel binarizer
* added note about y labels to the OneHotEncoder docstring
* removed example from MultiLabelBinarizer
* documentation should specify LabelBinarizer, not MultiLabelBinarizer in OHE
* fixes issue scikit-learn/scikit-learn#7194
* Added test
* Making `selected='all'` explicit on test
* Updated whats_new.rst
* Fixed typo on `whats_new.rst`
Replicate solution to 9a520779c2 except that `_pairwise` should always be `True` for `KernelCenterer` because it's supposed to receive a Gram matrix. This should make `KernelCenterer` usable in `Pipeline`s.
Happy to add tests, just tell me what should be covered.
Passing 1D arrays to check_array, without setting `ensure_2d` to false now
raises a deprecation warning before reshaping it. This will later throw an
error.
All Scaler classes also throw warnings when 1D arrays are passed.
All unit tests/doctests are modified to ensure that no 1D arrays are passed,
except in explicit 1D array tests where the warnings have been silenced.
Additional tests are also included which check for different 1D array cases.
2D array tests with one samples and one features are also added and where
they failed, `check_array` call has been modified to give a more useful error
message
ENH improve check_array to warn on dtype conversions
ENH make check_array accept several dtypes
ENH change validation with improved check_array
ENH change astype to avoid copy if possible
ENH remove warn_if_not_float
Polynomial features are computed by iterating over all combinations
of features. For each combination of features, the product of the
columns indexed by the combination is computed.
The fit method is now a no-op, and the transform method works with any
number of features (regardless of what fit was called with).
np.bincount raises an Exception with empty input arrays for numpy versions < 1.6.2
Add utils.fixes.bincount to tackle this issue and use it instead of np.bincount
DOC max_iterations -> max_iter. Make it consistent with kmeans
MAINT Replace the deprecated dx parameter with d in the docstrings
MAINT Deprecation warning for max_iterations parameter.
OneHotEncoder fails with a error that is not helpful if a missing
categorical feature is present during transformation.
Add handle_unknown with an "error" and an "ignore" option.
(Venkat) Raghav, Rajagopalan