[MRG] Model selection clean up old files - Reuse as much as possible from the new module(s) #2
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why is there a landscape bot here?! do you have some hooks on your repo? |
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ah this is a PR to your branch... |
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haha yes! wait I'll disable it :) |
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Main Commits ------------ * ENH Reogranize classes/fn from grid_search into search.py * ENH Reogranize classes/fn from cross_validation into split.py * ENH Reogranize cls/fn from cross_validation/learning_curve into validate.py * MAINT Merge _check_cv into check_cv inside the model_selection module * MAINT Update all the imports to point to the model_selection module * FIX use iter_cv to iterate throught the new style/old style cv objs Commits merged manually ----------------------- * FIX Document the random_state attribute in RandomSearchCV * MAINT Use check_cv instead of _check_cv * ENH refactor OVO decision function, use it in SVC for sklearn-like decision_function shape
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raghavrv
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Dec 7, 2016
…scikit-learn#7838) * initial commit for return_std * initial commit for return_std * adding tests, examples, ARD predict_std * adding tests, examples, ARD predict_std * a smidge more documentation * a smidge more documentation * Missed a few PEP8 issues * Changing predict_std to return_std #1 * Changing predict_std to return_std #2 * Changing predict_std to return_std #3 * Changing predict_std to return_std final * adding better plots via polynomial regression * trying to fix flake error * fix to ARD plotting issue * fixing some flakes * Two blank lines part 1 * Two blank lines part 2 * More newlines! * Even more newlines * adding info to the doc string for the two plot files * Rephrasing "polynomial" for Bayesian Ridge Regression * Updating "polynomia" for ARD * Adding more formal references * Another asked-for improvement to doc string. * Fixing flake8 errors * Cleaning up the tests a smidge. * A few more flakes * requested fixes from Andy * Mini bug fix * Final pep8 fix * pep8 fix round 2 * Fix beta_ to alpha_ in the comments
raghavrv
pushed a commit
that referenced
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Jun 10, 2017
* 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
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@jnothman @amueller @vene Please take a look!
This should clean up a lot.
I am not sure if I should clean up the tests.. There will be a lot of duplicated tests but I felt not removing them will ensure that we are able to import the duplicated modules from cross validation and that they work as before...