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scikit-learn/sklearn/model_selection/__init__.py

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FEA Successive halving for faster parameter search (#13900) * More flexible grid search interface * added info dict parameter * Put back removed test * renamed info into more_results * Passed grroups as well since we need n_to use get_n_splits(X, y, groups) * port * pep8 * dabl -> sklearn * add _required_parameters * skipping check in rst file if pandas not installed * Update sklearn/model_selection/_search_successive_halving.py Co-Authored-By: Joel Nothman <joel.nothman@gmail.com> * renamed into GridHalvingSearchCV and RandomHalvingSearchCV * Addressed thomas' comments * repr * removed passing group as a parameter to evaluate_candidates * Joels comments * pep8 * reorganized user user guide * renaming * update user guide * remove groups support + pass fit_params * parameter renaming * pep8 * r_i -> resource_iter * fixed r_i issues * examples + removed use of word budget * Added inpute checking tests * added cv_resutlts_ user guide * minor title change * fixed doc layout * Addressed some comments * properly pass down fit_params * change default value of force_exhaust_resources and update doc * should fix doc * Used check_fit_params * Update section about min_resources and number of candidates * Clarified ratio section * Use ~ to refer to classes * fixed doc checks * Apply suggestions from code review Co-authored-by: Joel Nothman <joel.nothman@gmail.com> * Addressed easy comments from Joel * missed some * updated docstring of run_search * Used f strings instead of format * remove candidate duplication checks * fix example * Addressed easy comments * rotate ticks labels * Added discussion in the intro as suggested by Joel * Split examples into sections * minor changes * remove force_exhaust_budget and introduce min_resources=exhaust * some minor validation * Added a n_resources_ attribute * update examples * Addressed comments * passing CV instead of X,y * minor revert for handling fit_params * updated docs * fix len * whatsnew * Add test for sampling when all_list * minor change to top-k * Force CV splits to be consistent across calls * reorder parameters * reduced diff * added tests for top_k * put back doc for groups * not sure what went wrong * put import at its place * some comment * Addressed comments * Added tests for cv_results_ and base estimator inputs * pep8 * avoid monkeypatching * rename df * use Joel's suggestions for testing masks * Made it experimental * Should fix docs * whats new entry * Apply suggestions from code review Co-authored-by: Andreas Mueller <t3kcit@gmail.com> * Addressed comments to docs * Addressed comments in examples * minor doc update * minor renaming in UG * forgot some * some sad note about splitter statefulness :'( * Addressed comments * ratio -> factor Co-authored-by: Joel Nothman <joel.nothman@gmail.com> Co-authored-by: Andreas Mueller <t3kcit@gmail.com>
2020-09-09 23:12:35 +08:00
import typing
Main Commits - Major -------------------- * 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 * TST Add tests for the new model_selection members * ENH Wrap the old-style cv obj/iterables instead of using iter_cv * ENH Use scipy's binomial coefficient function comb for calucation of nCk * ENH Few enhancements to the split module * ENH Improve check_cv input validation and docstring * MAINT _get_test_folds(X, y, labels) --> _get_test_folds(labels) * TST if 1d arrays for X introduce any errors * ENH use 1d X arrays for all tests; * ENH X_10 --> X (global var) Minor ----- * ENH _PartitionIterator --> _BaseCrossValidator; * ENH CVIterator --> CVIterableWrapper * TST Import the old SKF locally * FIX/TST Clean up the split module's tests. * DOC Improve documentation of the cv parameter * COSMIT consistently hyphenate cross-validation/cross-validator * TST Calculate n_samples from X * COSMIT Use separate lines for each import. * COSMIT cross_validation_generator --> cross_validator 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 * FIX avoid memory cost when sampling from large parameter grids ENH Major to Minor incremental enhancements to the model_selection Squashed commit messages - (For reference) Major ----- * ENH p --> n_labels * FIX *ShuffleSplit: all float/invalid type errors at init and int error at split * FIX make PredefinedSplit accept test_folds in constructor; Cleanup docstrings * ENH+TST KFold: make rng to be generated at every split call for reproducibility * FIX/MAINT KFold: make shuffle a public attr * FIX Make CVIterableWrapper private. * FIX reuse len_cv instead of recalculating it * FIX Prevent adding *SearchCV estimators from the old grid_search module * re-FIX In all_estimators: the sorting to use only the 1st item (name) To avoid collision between the old and the new GridSearch classes. * FIX test_validate.py: Use 2D X (1D X is being detected as a single sample) * MAINT validate.py --> validation.py * MAINT make the submodules private * MAINT Support old cv/gs/lc until 0.19 * FIX/MAINT n_splits --> get_n_splits * FIX/TST test_logistic.py/test_ovr_multinomial_iris: pass predefined folds as an iterable * MAINT expose BaseCrossValidator * Update the model_selection module with changes from master - From #5161 - - MAINT remove redundant p variable - - Add check for sparse prediction in cross_val_predict - From #5201 - DOC improve random_state param doc - From #5190 - LabelKFold and test - From #4583 - LabelShuffleSplit and tests - From #5300 - shuffle the `labels` not the `indxs` in LabelKFold + tests - From #5378 - Make the GridSearchCV docs more accurate. - From #5458 - Remove shuffle from LabelKFold - From #5466(#4270) - Gaussian Process by Jan Metzen - From #4826 - Move custom error / warnings into sklearn.exception Minor ----- * ENH Make the KFold shuffling test stronger * FIX/DOC Use the higher level model_selection module as ref * DOC in check_cv "y : array-like, optional" * DOC a supervised learning problem --> supervised learning problems * DOC cross-validators --> cross-validation strategies * DOC Correct Olivier Grisel's name ;) * MINOR/FIX cv_indices --> kfold * FIX/DOC Align the 'See also' section of the new KFold, LeaveOneOut * TST/FIX imports on separate lines * FIX use __class__ instead of classmethod * TST/FIX import directly from model_selection * COSMIT Relocate the random_state documentation * COSMIT remove pass * MAINT Remove deprecation warnings from old tests * FIX correct import at test_split * FIX/MAINT Move P_sparse, X, y defns to top; rm unused W_sparse, X_sparse * FIX random state to avoid doctest failure * TST n_splits and split wrapping of _CVIterableWrapper * FIX/MAINT Use multilabel indicator matrix directly * TST/DOC clarify why we conflate classes 0 and 1 * DOC add comment that this was taken from BaseEstimator * FIX use of labels is not needed in stratified k fold * Fix cross_validation reference * Fix the labels param doc FIX/DOC/MAINT Addressing the review comments by Arnaud and Andy COSMIT Sort the members alphabetically COSMIT len_cv --> n_splits COSMIT Merge 2 if; FIX Use kwargs DOC Add my name to the authors :D DOC make labels parameter consistent FIX Remove hack for boolean indices; + COSMIT idx --> indices; DOC Add Returns COSMIT preds --> predictions DOC Add Returns and neatly arrange X, y, labels FIX idx(s)/ind(s)--> indice(s) COSMIT Merge if and else to elif COSMIT n --> n_samples COSMIT Use bincount only once COSMIT cls --> class_i / class_i (ith class indices) --> perm_indices_class_i FIX/ENH/TST Addressing the final reviews COSMIT c --> count FIX/TST make check_cv raise ValueError for string cv value TST nested cv (gs inside cross_val_score) works for diff cvs FIX/ENH Raise ValueError when labels is None for label based cvs; TST if labels is being passed correctly to the cv and that the ValueError is being propagated to the cross_val_score/predict and grid search FIX pass labels to cross_val_score FIX use make_classification DOC Add Returns; COSMIT Remove scaffolding TST add a test to check the _build_repr helper REVERT the old GS/RS should also be tested by the common tests. ENH Add a tuple of all/label based CVS FIX raise VE even at get_n_splits if labels is None FIX Fabian's comments PEP8
2015-06-05 03:45:10 +08:00
from ._split import BaseCrossValidator
Make BaseShuffleSplit public (#20056)
2021-06-12 21:23:58 +08:00
from ._split import BaseShuffleSplit
Main Commits - Major -------------------- * 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 * TST Add tests for the new model_selection members * ENH Wrap the old-style cv obj/iterables instead of using iter_cv * ENH Use scipy's binomial coefficient function comb for calucation of nCk * ENH Few enhancements to the split module * ENH Improve check_cv input validation and docstring * MAINT _get_test_folds(X, y, labels) --> _get_test_folds(labels) * TST if 1d arrays for X introduce any errors * ENH use 1d X arrays for all tests; * ENH X_10 --> X (global var) Minor ----- * ENH _PartitionIterator --> _BaseCrossValidator; * ENH CVIterator --> CVIterableWrapper * TST Import the old SKF locally * FIX/TST Clean up the split module's tests. * DOC Improve documentation of the cv parameter * COSMIT consistently hyphenate cross-validation/cross-validator * TST Calculate n_samples from X * COSMIT Use separate lines for each import. * COSMIT cross_validation_generator --> cross_validator 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 * FIX avoid memory cost when sampling from large parameter grids ENH Major to Minor incremental enhancements to the model_selection Squashed commit messages - (For reference) Major ----- * ENH p --> n_labels * FIX *ShuffleSplit: all float/invalid type errors at init and int error at split * FIX make PredefinedSplit accept test_folds in constructor; Cleanup docstrings * ENH+TST KFold: make rng to be generated at every split call for reproducibility * FIX/MAINT KFold: make shuffle a public attr * FIX Make CVIterableWrapper private. * FIX reuse len_cv instead of recalculating it * FIX Prevent adding *SearchCV estimators from the old grid_search module * re-FIX In all_estimators: the sorting to use only the 1st item (name) To avoid collision between the old and the new GridSearch classes. * FIX test_validate.py: Use 2D X (1D X is being detected as a single sample) * MAINT validate.py --> validation.py * MAINT make the submodules private * MAINT Support old cv/gs/lc until 0.19 * FIX/MAINT n_splits --> get_n_splits * FIX/TST test_logistic.py/test_ovr_multinomial_iris: pass predefined folds as an iterable * MAINT expose BaseCrossValidator * Update the model_selection module with changes from master - From #5161 - - MAINT remove redundant p variable - - Add check for sparse prediction in cross_val_predict - From #5201 - DOC improve random_state param doc - From #5190 - LabelKFold and test - From #4583 - LabelShuffleSplit and tests - From #5300 - shuffle the `labels` not the `indxs` in LabelKFold + tests - From #5378 - Make the GridSearchCV docs more accurate. - From #5458 - Remove shuffle from LabelKFold - From #5466(#4270) - Gaussian Process by Jan Metzen - From #4826 - Move custom error / warnings into sklearn.exception Minor ----- * ENH Make the KFold shuffling test stronger * FIX/DOC Use the higher level model_selection module as ref * DOC in check_cv "y : array-like, optional" * DOC a supervised learning problem --> supervised learning problems * DOC cross-validators --> cross-validation strategies * DOC Correct Olivier Grisel's name ;) * MINOR/FIX cv_indices --> kfold * FIX/DOC Align the 'See also' section of the new KFold, LeaveOneOut * TST/FIX imports on separate lines * FIX use __class__ instead of classmethod * TST/FIX import directly from model_selection * COSMIT Relocate the random_state documentation * COSMIT remove pass * MAINT Remove deprecation warnings from old tests * FIX correct import at test_split * FIX/MAINT Move P_sparse, X, y defns to top; rm unused W_sparse, X_sparse * FIX random state to avoid doctest failure * TST n_splits and split wrapping of _CVIterableWrapper * FIX/MAINT Use multilabel indicator matrix directly * TST/DOC clarify why we conflate classes 0 and 1 * DOC add comment that this was taken from BaseEstimator * FIX use of labels is not needed in stratified k fold * Fix cross_validation reference * Fix the labels param doc FIX/DOC/MAINT Addressing the review comments by Arnaud and Andy COSMIT Sort the members alphabetically COSMIT len_cv --> n_splits COSMIT Merge 2 if; FIX Use kwargs DOC Add my name to the authors :D DOC make labels parameter consistent FIX Remove hack for boolean indices; + COSMIT idx --> indices; DOC Add Returns COSMIT preds --> predictions DOC Add Returns and neatly arrange X, y, labels FIX idx(s)/ind(s)--> indice(s) COSMIT Merge if and else to elif COSMIT n --> n_samples COSMIT Use bincount only once COSMIT cls --> class_i / class_i (ith class indices) --> perm_indices_class_i FIX/ENH/TST Addressing the final reviews COSMIT c --> count FIX/TST make check_cv raise ValueError for string cv value TST nested cv (gs inside cross_val_score) works for diff cvs FIX/ENH Raise ValueError when labels is None for label based cvs; TST if labels is being passed correctly to the cv and that the ValueError is being propagated to the cross_val_score/predict and grid search FIX pass labels to cross_val_score FIX use make_classification DOC Add Returns; COSMIT Remove scaffolding TST add a test to check the _build_repr helper REVERT the old GS/RS should also be tested by the common tests. ENH Add a tuple of all/label based CVS FIX raise VE even at get_n_splits if labels is None FIX Fabian's comments PEP8
2015-06-05 03:45:10 +08:00
from ._split import KFold
[MRG+1] ENH/MNT Rename labels --> groups in CV tools (#6660)
2016-09-12 01:14:41 +08:00
from ._split import GroupKFold
Main Commits - Major -------------------- * 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 * TST Add tests for the new model_selection members * ENH Wrap the old-style cv obj/iterables instead of using iter_cv * ENH Use scipy's binomial coefficient function comb for calucation of nCk * ENH Few enhancements to the split module * ENH Improve check_cv input validation and docstring * MAINT _get_test_folds(X, y, labels) --> _get_test_folds(labels) * TST if 1d arrays for X introduce any errors * ENH use 1d X arrays for all tests; * ENH X_10 --> X (global var) Minor ----- * ENH _PartitionIterator --> _BaseCrossValidator; * ENH CVIterator --> CVIterableWrapper * TST Import the old SKF locally * FIX/TST Clean up the split module's tests. * DOC Improve documentation of the cv parameter * COSMIT consistently hyphenate cross-validation/cross-validator * TST Calculate n_samples from X * COSMIT Use separate lines for each import. * COSMIT cross_validation_generator --> cross_validator 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 * FIX avoid memory cost when sampling from large parameter grids ENH Major to Minor incremental enhancements to the model_selection Squashed commit messages - (For reference) Major ----- * ENH p --> n_labels * FIX *ShuffleSplit: all float/invalid type errors at init and int error at split * FIX make PredefinedSplit accept test_folds in constructor; Cleanup docstrings * ENH+TST KFold: make rng to be generated at every split call for reproducibility * FIX/MAINT KFold: make shuffle a public attr * FIX Make CVIterableWrapper private. * FIX reuse len_cv instead of recalculating it * FIX Prevent adding *SearchCV estimators from the old grid_search module * re-FIX In all_estimators: the sorting to use only the 1st item (name) To avoid collision between the old and the new GridSearch classes. * FIX test_validate.py: Use 2D X (1D X is being detected as a single sample) * MAINT validate.py --> validation.py * MAINT make the submodules private * MAINT Support old cv/gs/lc until 0.19 * FIX/MAINT n_splits --> get_n_splits * FIX/TST test_logistic.py/test_ovr_multinomial_iris: pass predefined folds as an iterable * MAINT expose BaseCrossValidator * Update the model_selection module with changes from master - From #5161 - - MAINT remove redundant p variable - - Add check for sparse prediction in cross_val_predict - From #5201 - DOC improve random_state param doc - From #5190 - LabelKFold and test - From #4583 - LabelShuffleSplit and tests - From #5300 - shuffle the `labels` not the `indxs` in LabelKFold + tests - From #5378 - Make the GridSearchCV docs more accurate. - From #5458 - Remove shuffle from LabelKFold - From #5466(#4270) - Gaussian Process by Jan Metzen - From #4826 - Move custom error / warnings into sklearn.exception Minor ----- * ENH Make the KFold shuffling test stronger * FIX/DOC Use the higher level model_selection module as ref * DOC in check_cv "y : array-like, optional" * DOC a supervised learning problem --> supervised learning problems * DOC cross-validators --> cross-validation strategies * DOC Correct Olivier Grisel's name ;) * MINOR/FIX cv_indices --> kfold * FIX/DOC Align the 'See also' section of the new KFold, LeaveOneOut * TST/FIX imports on separate lines * FIX use __class__ instead of classmethod * TST/FIX import directly from model_selection * COSMIT Relocate the random_state documentation * COSMIT remove pass * MAINT Remove deprecation warnings from old tests * FIX correct import at test_split * FIX/MAINT Move P_sparse, X, y defns to top; rm unused W_sparse, X_sparse * FIX random state to avoid doctest failure * TST n_splits and split wrapping of _CVIterableWrapper * FIX/MAINT Use multilabel indicator matrix directly * TST/DOC clarify why we conflate classes 0 and 1 * DOC add comment that this was taken from BaseEstimator * FIX use of labels is not needed in stratified k fold * Fix cross_validation reference * Fix the labels param doc FIX/DOC/MAINT Addressing the review comments by Arnaud and Andy COSMIT Sort the members alphabetically COSMIT len_cv --> n_splits COSMIT Merge 2 if; FIX Use kwargs DOC Add my name to the authors :D DOC make labels parameter consistent FIX Remove hack for boolean indices; + COSMIT idx --> indices; DOC Add Returns COSMIT preds --> predictions DOC Add Returns and neatly arrange X, y, labels FIX idx(s)/ind(s)--> indice(s) COSMIT Merge if and else to elif COSMIT n --> n_samples COSMIT Use bincount only once COSMIT cls --> class_i / class_i (ith class indices) --> perm_indices_class_i FIX/ENH/TST Addressing the final reviews COSMIT c --> count FIX/TST make check_cv raise ValueError for string cv value TST nested cv (gs inside cross_val_score) works for diff cvs FIX/ENH Raise ValueError when labels is None for label based cvs; TST if labels is being passed correctly to the cv and that the ValueError is being propagated to the cross_val_score/predict and grid search FIX pass labels to cross_val_score FIX use make_classification DOC Add Returns; COSMIT Remove scaffolding TST add a test to check the _build_repr helper REVERT the old GS/RS should also be tested by the common tests. ENH Add a tuple of all/label based CVS FIX raise VE even at get_n_splits if labels is None FIX Fabian's comments PEP8
2015-06-05 03:45:10 +08:00
from ._split import StratifiedKFold
Rename `TimeSeriesCV` to `TimeSeriesSplit` (#7245) * rename TimeSeriesCV to TimeSeriesSplit * Add TimeSeriesSplit * Add whats new
2016-08-26 10:34:33 +08:00
from ._split import TimeSeriesSplit
[MRG+1] ENH/MNT Rename labels --> groups in CV tools (#6660)
2016-09-12 01:14:41 +08:00
from ._split import LeaveOneGroupOut
Main Commits - Major -------------------- * 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 * TST Add tests for the new model_selection members * ENH Wrap the old-style cv obj/iterables instead of using iter_cv * ENH Use scipy's binomial coefficient function comb for calucation of nCk * ENH Few enhancements to the split module * ENH Improve check_cv input validation and docstring * MAINT _get_test_folds(X, y, labels) --> _get_test_folds(labels) * TST if 1d arrays for X introduce any errors * ENH use 1d X arrays for all tests; * ENH X_10 --> X (global var) Minor ----- * ENH _PartitionIterator --> _BaseCrossValidator; * ENH CVIterator --> CVIterableWrapper * TST Import the old SKF locally * FIX/TST Clean up the split module's tests. * DOC Improve documentation of the cv parameter * COSMIT consistently hyphenate cross-validation/cross-validator * TST Calculate n_samples from X * COSMIT Use separate lines for each import. * COSMIT cross_validation_generator --> cross_validator 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 * FIX avoid memory cost when sampling from large parameter grids ENH Major to Minor incremental enhancements to the model_selection Squashed commit messages - (For reference) Major ----- * ENH p --> n_labels * FIX *ShuffleSplit: all float/invalid type errors at init and int error at split * FIX make PredefinedSplit accept test_folds in constructor; Cleanup docstrings * ENH+TST KFold: make rng to be generated at every split call for reproducibility * FIX/MAINT KFold: make shuffle a public attr * FIX Make CVIterableWrapper private. * FIX reuse len_cv instead of recalculating it * FIX Prevent adding *SearchCV estimators from the old grid_search module * re-FIX In all_estimators: the sorting to use only the 1st item (name) To avoid collision between the old and the new GridSearch classes. * FIX test_validate.py: Use 2D X (1D X is being detected as a single sample) * MAINT validate.py --> validation.py * MAINT make the submodules private * MAINT Support old cv/gs/lc until 0.19 * FIX/MAINT n_splits --> get_n_splits * FIX/TST test_logistic.py/test_ovr_multinomial_iris: pass predefined folds as an iterable * MAINT expose BaseCrossValidator * Update the model_selection module with changes from master - From #5161 - - MAINT remove redundant p variable - - Add check for sparse prediction in cross_val_predict - From #5201 - DOC improve random_state param doc - From #5190 - LabelKFold and test - From #4583 - LabelShuffleSplit and tests - From #5300 - shuffle the `labels` not the `indxs` in LabelKFold + tests - From #5378 - Make the GridSearchCV docs more accurate. - From #5458 - Remove shuffle from LabelKFold - From #5466(#4270) - Gaussian Process by Jan Metzen - From #4826 - Move custom error / warnings into sklearn.exception Minor ----- * ENH Make the KFold shuffling test stronger * FIX/DOC Use the higher level model_selection module as ref * DOC in check_cv "y : array-like, optional" * DOC a supervised learning problem --> supervised learning problems * DOC cross-validators --> cross-validation strategies * DOC Correct Olivier Grisel's name ;) * MINOR/FIX cv_indices --> kfold * FIX/DOC Align the 'See also' section of the new KFold, LeaveOneOut * TST/FIX imports on separate lines * FIX use __class__ instead of classmethod * TST/FIX import directly from model_selection * COSMIT Relocate the random_state documentation * COSMIT remove pass * MAINT Remove deprecation warnings from old tests * FIX correct import at test_split * FIX/MAINT Move P_sparse, X, y defns to top; rm unused W_sparse, X_sparse * FIX random state to avoid doctest failure * TST n_splits and split wrapping of _CVIterableWrapper * FIX/MAINT Use multilabel indicator matrix directly * TST/DOC clarify why we conflate classes 0 and 1 * DOC add comment that this was taken from BaseEstimator * FIX use of labels is not needed in stratified k fold * Fix cross_validation reference * Fix the labels param doc FIX/DOC/MAINT Addressing the review comments by Arnaud and Andy COSMIT Sort the members alphabetically COSMIT len_cv --> n_splits COSMIT Merge 2 if; FIX Use kwargs DOC Add my name to the authors :D DOC make labels parameter consistent FIX Remove hack for boolean indices; + COSMIT idx --> indices; DOC Add Returns COSMIT preds --> predictions DOC Add Returns and neatly arrange X, y, labels FIX idx(s)/ind(s)--> indice(s) COSMIT Merge if and else to elif COSMIT n --> n_samples COSMIT Use bincount only once COSMIT cls --> class_i / class_i (ith class indices) --> perm_indices_class_i FIX/ENH/TST Addressing the final reviews COSMIT c --> count FIX/TST make check_cv raise ValueError for string cv value TST nested cv (gs inside cross_val_score) works for diff cvs FIX/ENH Raise ValueError when labels is None for label based cvs; TST if labels is being passed correctly to the cv and that the ValueError is being propagated to the cross_val_score/predict and grid search FIX pass labels to cross_val_score FIX use make_classification DOC Add Returns; COSMIT Remove scaffolding TST add a test to check the _build_repr helper REVERT the old GS/RS should also be tested by the common tests. ENH Add a tuple of all/label based CVS FIX raise VE even at get_n_splits if labels is None FIX Fabian's comments PEP8
2015-06-05 03:45:10 +08:00
from ._split import LeaveOneOut
[MRG+1] ENH/MNT Rename labels --> groups in CV tools (#6660)
2016-09-12 01:14:41 +08:00
from ._split import LeavePGroupsOut
Main Commits - Major -------------------- * 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 * TST Add tests for the new model_selection members * ENH Wrap the old-style cv obj/iterables instead of using iter_cv * ENH Use scipy's binomial coefficient function comb for calucation of nCk * ENH Few enhancements to the split module * ENH Improve check_cv input validation and docstring * MAINT _get_test_folds(X, y, labels) --> _get_test_folds(labels) * TST if 1d arrays for X introduce any errors * ENH use 1d X arrays for all tests; * ENH X_10 --> X (global var) Minor ----- * ENH _PartitionIterator --> _BaseCrossValidator; * ENH CVIterator --> CVIterableWrapper * TST Import the old SKF locally * FIX/TST Clean up the split module's tests. * DOC Improve documentation of the cv parameter * COSMIT consistently hyphenate cross-validation/cross-validator * TST Calculate n_samples from X * COSMIT Use separate lines for each import. * COSMIT cross_validation_generator --> cross_validator 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 * FIX avoid memory cost when sampling from large parameter grids ENH Major to Minor incremental enhancements to the model_selection Squashed commit messages - (For reference) Major ----- * ENH p --> n_labels * FIX *ShuffleSplit: all float/invalid type errors at init and int error at split * FIX make PredefinedSplit accept test_folds in constructor; Cleanup docstrings * ENH+TST KFold: make rng to be generated at every split call for reproducibility * FIX/MAINT KFold: make shuffle a public attr * FIX Make CVIterableWrapper private. * FIX reuse len_cv instead of recalculating it * FIX Prevent adding *SearchCV estimators from the old grid_search module * re-FIX In all_estimators: the sorting to use only the 1st item (name) To avoid collision between the old and the new GridSearch classes. * FIX test_validate.py: Use 2D X (1D X is being detected as a single sample) * MAINT validate.py --> validation.py * MAINT make the submodules private * MAINT Support old cv/gs/lc until 0.19 * FIX/MAINT n_splits --> get_n_splits * FIX/TST test_logistic.py/test_ovr_multinomial_iris: pass predefined folds as an iterable * MAINT expose BaseCrossValidator * Update the model_selection module with changes from master - From #5161 - - MAINT remove redundant p variable - - Add check for sparse prediction in cross_val_predict - From #5201 - DOC improve random_state param doc - From #5190 - LabelKFold and test - From #4583 - LabelShuffleSplit and tests - From #5300 - shuffle the `labels` not the `indxs` in LabelKFold + tests - From #5378 - Make the GridSearchCV docs more accurate. - From #5458 - Remove shuffle from LabelKFold - From #5466(#4270) - Gaussian Process by Jan Metzen - From #4826 - Move custom error / warnings into sklearn.exception Minor ----- * ENH Make the KFold shuffling test stronger * FIX/DOC Use the higher level model_selection module as ref * DOC in check_cv "y : array-like, optional" * DOC a supervised learning problem --> supervised learning problems * DOC cross-validators --> cross-validation strategies * DOC Correct Olivier Grisel's name ;) * MINOR/FIX cv_indices --> kfold * FIX/DOC Align the 'See also' section of the new KFold, LeaveOneOut * TST/FIX imports on separate lines * FIX use __class__ instead of classmethod * TST/FIX import directly from model_selection * COSMIT Relocate the random_state documentation * COSMIT remove pass * MAINT Remove deprecation warnings from old tests * FIX correct import at test_split * FIX/MAINT Move P_sparse, X, y defns to top; rm unused W_sparse, X_sparse * FIX random state to avoid doctest failure * TST n_splits and split wrapping of _CVIterableWrapper * FIX/MAINT Use multilabel indicator matrix directly * TST/DOC clarify why we conflate classes 0 and 1 * DOC add comment that this was taken from BaseEstimator * FIX use of labels is not needed in stratified k fold * Fix cross_validation reference * Fix the labels param doc FIX/DOC/MAINT Addressing the review comments by Arnaud and Andy COSMIT Sort the members alphabetically COSMIT len_cv --> n_splits COSMIT Merge 2 if; FIX Use kwargs DOC Add my name to the authors :D DOC make labels parameter consistent FIX Remove hack for boolean indices; + COSMIT idx --> indices; DOC Add Returns COSMIT preds --> predictions DOC Add Returns and neatly arrange X, y, labels FIX idx(s)/ind(s)--> indice(s) COSMIT Merge if and else to elif COSMIT n --> n_samples COSMIT Use bincount only once COSMIT cls --> class_i / class_i (ith class indices) --> perm_indices_class_i FIX/ENH/TST Addressing the final reviews COSMIT c --> count FIX/TST make check_cv raise ValueError for string cv value TST nested cv (gs inside cross_val_score) works for diff cvs FIX/ENH Raise ValueError when labels is None for label based cvs; TST if labels is being passed correctly to the cv and that the ValueError is being propagated to the cross_val_score/predict and grid search FIX pass labels to cross_val_score FIX use make_classification DOC Add Returns; COSMIT Remove scaffolding TST add a test to check the _build_repr helper REVERT the old GS/RS should also be tested by the common tests. ENH Add a tuple of all/label based CVS FIX raise VE even at get_n_splits if labels is None FIX Fabian's comments PEP8
2015-06-05 03:45:10 +08:00
from ._split import LeavePOut
[MRG+1] Repeated K-Fold and Repeated Stratified K-Fold (#8120) * 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
2017-03-05 04:39:04 +08:00
from ._split import RepeatedKFold
from ._split import RepeatedStratifiedKFold
Main Commits - Major -------------------- * 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 * TST Add tests for the new model_selection members * ENH Wrap the old-style cv obj/iterables instead of using iter_cv * ENH Use scipy's binomial coefficient function comb for calucation of nCk * ENH Few enhancements to the split module * ENH Improve check_cv input validation and docstring * MAINT _get_test_folds(X, y, labels) --> _get_test_folds(labels) * TST if 1d arrays for X introduce any errors * ENH use 1d X arrays for all tests; * ENH X_10 --> X (global var) Minor ----- * ENH _PartitionIterator --> _BaseCrossValidator; * ENH CVIterator --> CVIterableWrapper * TST Import the old SKF locally * FIX/TST Clean up the split module's tests. * DOC Improve documentation of the cv parameter * COSMIT consistently hyphenate cross-validation/cross-validator * TST Calculate n_samples from X * COSMIT Use separate lines for each import. * COSMIT cross_validation_generator --> cross_validator 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 * FIX avoid memory cost when sampling from large parameter grids ENH Major to Minor incremental enhancements to the model_selection Squashed commit messages - (For reference) Major ----- * ENH p --> n_labels * FIX *ShuffleSplit: all float/invalid type errors at init and int error at split * FIX make PredefinedSplit accept test_folds in constructor; Cleanup docstrings * ENH+TST KFold: make rng to be generated at every split call for reproducibility * FIX/MAINT KFold: make shuffle a public attr * FIX Make CVIterableWrapper private. * FIX reuse len_cv instead of recalculating it * FIX Prevent adding *SearchCV estimators from the old grid_search module * re-FIX In all_estimators: the sorting to use only the 1st item (name) To avoid collision between the old and the new GridSearch classes. * FIX test_validate.py: Use 2D X (1D X is being detected as a single sample) * MAINT validate.py --> validation.py * MAINT make the submodules private * MAINT Support old cv/gs/lc until 0.19 * FIX/MAINT n_splits --> get_n_splits * FIX/TST test_logistic.py/test_ovr_multinomial_iris: pass predefined folds as an iterable * MAINT expose BaseCrossValidator * Update the model_selection module with changes from master - From #5161 - - MAINT remove redundant p variable - - Add check for sparse prediction in cross_val_predict - From #5201 - DOC improve random_state param doc - From #5190 - LabelKFold and test - From #4583 - LabelShuffleSplit and tests - From #5300 - shuffle the `labels` not the `indxs` in LabelKFold + tests - From #5378 - Make the GridSearchCV docs more accurate. - From #5458 - Remove shuffle from LabelKFold - From #5466(#4270) - Gaussian Process by Jan Metzen - From #4826 - Move custom error / warnings into sklearn.exception Minor ----- * ENH Make the KFold shuffling test stronger * FIX/DOC Use the higher level model_selection module as ref * DOC in check_cv "y : array-like, optional" * DOC a supervised learning problem --> supervised learning problems * DOC cross-validators --> cross-validation strategies * DOC Correct Olivier Grisel's name ;) * MINOR/FIX cv_indices --> kfold * FIX/DOC Align the 'See also' section of the new KFold, LeaveOneOut * TST/FIX imports on separate lines * FIX use __class__ instead of classmethod * TST/FIX import directly from model_selection * COSMIT Relocate the random_state documentation * COSMIT remove pass * MAINT Remove deprecation warnings from old tests * FIX correct import at test_split * FIX/MAINT Move P_sparse, X, y defns to top; rm unused W_sparse, X_sparse * FIX random state to avoid doctest failure * TST n_splits and split wrapping of _CVIterableWrapper * FIX/MAINT Use multilabel indicator matrix directly * TST/DOC clarify why we conflate classes 0 and 1 * DOC add comment that this was taken from BaseEstimator * FIX use of labels is not needed in stratified k fold * Fix cross_validation reference * Fix the labels param doc FIX/DOC/MAINT Addressing the review comments by Arnaud and Andy COSMIT Sort the members alphabetically COSMIT len_cv --> n_splits COSMIT Merge 2 if; FIX Use kwargs DOC Add my name to the authors :D DOC make labels parameter consistent FIX Remove hack for boolean indices; + COSMIT idx --> indices; DOC Add Returns COSMIT preds --> predictions DOC Add Returns and neatly arrange X, y, labels FIX idx(s)/ind(s)--> indice(s) COSMIT Merge if and else to elif COSMIT n --> n_samples COSMIT Use bincount only once COSMIT cls --> class_i / class_i (ith class indices) --> perm_indices_class_i FIX/ENH/TST Addressing the final reviews COSMIT c --> count FIX/TST make check_cv raise ValueError for string cv value TST nested cv (gs inside cross_val_score) works for diff cvs FIX/ENH Raise ValueError when labels is None for label based cvs; TST if labels is being passed correctly to the cv and that the ValueError is being propagated to the cross_val_score/predict and grid search FIX pass labels to cross_val_score FIX use make_classification DOC Add Returns; COSMIT Remove scaffolding TST add a test to check the _build_repr helper REVERT the old GS/RS should also be tested by the common tests. ENH Add a tuple of all/label based CVS FIX raise VE even at get_n_splits if labels is None FIX Fabian's comments PEP8
2015-06-05 03:45:10 +08:00
from ._split import ShuffleSplit
[MRG+1] ENH/MNT Rename labels --> groups in CV tools (#6660)
2016-09-12 01:14:41 +08:00
from ._split import GroupShuffleSplit
Main Commits - Major -------------------- * 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 * TST Add tests for the new model_selection members * ENH Wrap the old-style cv obj/iterables instead of using iter_cv * ENH Use scipy's binomial coefficient function comb for calucation of nCk * ENH Few enhancements to the split module * ENH Improve check_cv input validation and docstring * MAINT _get_test_folds(X, y, labels) --> _get_test_folds(labels) * TST if 1d arrays for X introduce any errors * ENH use 1d X arrays for all tests; * ENH X_10 --> X (global var) Minor ----- * ENH _PartitionIterator --> _BaseCrossValidator; * ENH CVIterator --> CVIterableWrapper * TST Import the old SKF locally * FIX/TST Clean up the split module's tests. * DOC Improve documentation of the cv parameter * COSMIT consistently hyphenate cross-validation/cross-validator * TST Calculate n_samples from X * COSMIT Use separate lines for each import. * COSMIT cross_validation_generator --> cross_validator 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 * FIX avoid memory cost when sampling from large parameter grids ENH Major to Minor incremental enhancements to the model_selection Squashed commit messages - (For reference) Major ----- * ENH p --> n_labels * FIX *ShuffleSplit: all float/invalid type errors at init and int error at split * FIX make PredefinedSplit accept test_folds in constructor; Cleanup docstrings * ENH+TST KFold: make rng to be generated at every split call for reproducibility * FIX/MAINT KFold: make shuffle a public attr * FIX Make CVIterableWrapper private. * FIX reuse len_cv instead of recalculating it * FIX Prevent adding *SearchCV estimators from the old grid_search module * re-FIX In all_estimators: the sorting to use only the 1st item (name) To avoid collision between the old and the new GridSearch classes. * FIX test_validate.py: Use 2D X (1D X is being detected as a single sample) * MAINT validate.py --> validation.py * MAINT make the submodules private * MAINT Support old cv/gs/lc until 0.19 * FIX/MAINT n_splits --> get_n_splits * FIX/TST test_logistic.py/test_ovr_multinomial_iris: pass predefined folds as an iterable * MAINT expose BaseCrossValidator * Update the model_selection module with changes from master - From #5161 - - MAINT remove redundant p variable - - Add check for sparse prediction in cross_val_predict - From #5201 - DOC improve random_state param doc - From #5190 - LabelKFold and test - From #4583 - LabelShuffleSplit and tests - From #5300 - shuffle the `labels` not the `indxs` in LabelKFold + tests - From #5378 - Make the GridSearchCV docs more accurate. - From #5458 - Remove shuffle from LabelKFold - From #5466(#4270) - Gaussian Process by Jan Metzen - From #4826 - Move custom error / warnings into sklearn.exception Minor ----- * ENH Make the KFold shuffling test stronger * FIX/DOC Use the higher level model_selection module as ref * DOC in check_cv "y : array-like, optional" * DOC a supervised learning problem --> supervised learning problems * DOC cross-validators --> cross-validation strategies * DOC Correct Olivier Grisel's name ;) * MINOR/FIX cv_indices --> kfold * FIX/DOC Align the 'See also' section of the new KFold, LeaveOneOut * TST/FIX imports on separate lines * FIX use __class__ instead of classmethod * TST/FIX import directly from model_selection * COSMIT Relocate the random_state documentation * COSMIT remove pass * MAINT Remove deprecation warnings from old tests * FIX correct import at test_split * FIX/MAINT Move P_sparse, X, y defns to top; rm unused W_sparse, X_sparse * FIX random state to avoid doctest failure * TST n_splits and split wrapping of _CVIterableWrapper * FIX/MAINT Use multilabel indicator matrix directly * TST/DOC clarify why we conflate classes 0 and 1 * DOC add comment that this was taken from BaseEstimator * FIX use of labels is not needed in stratified k fold * Fix cross_validation reference * Fix the labels param doc FIX/DOC/MAINT Addressing the review comments by Arnaud and Andy COSMIT Sort the members alphabetically COSMIT len_cv --> n_splits COSMIT Merge 2 if; FIX Use kwargs DOC Add my name to the authors :D DOC make labels parameter consistent FIX Remove hack for boolean indices; + COSMIT idx --> indices; DOC Add Returns COSMIT preds --> predictions DOC Add Returns and neatly arrange X, y, labels FIX idx(s)/ind(s)--> indice(s) COSMIT Merge if and else to elif COSMIT n --> n_samples COSMIT Use bincount only once COSMIT cls --> class_i / class_i (ith class indices) --> perm_indices_class_i FIX/ENH/TST Addressing the final reviews COSMIT c --> count FIX/TST make check_cv raise ValueError for string cv value TST nested cv (gs inside cross_val_score) works for diff cvs FIX/ENH Raise ValueError when labels is None for label based cvs; TST if labels is being passed correctly to the cv and that the ValueError is being propagated to the cross_val_score/predict and grid search FIX pass labels to cross_val_score FIX use make_classification DOC Add Returns; COSMIT Remove scaffolding TST add a test to check the _build_repr helper REVERT the old GS/RS should also be tested by the common tests. ENH Add a tuple of all/label based CVS FIX raise VE even at get_n_splits if labels is None FIX Fabian's comments PEP8
2015-06-05 03:45:10 +08:00
from ._split import StratifiedShuffleSplit
Stratified Group KFold implementation (#18649) * Initial implementation * Forgot to add to second __add__ list * Update split method parameter doc * Added example; changed default test_size to 0.1; added to author list * StratifiedGroupKFold impl and other improvements * Add class to __all__ spec * Remove random_state when no shuffle * Tighter formatting * Update the implementation of StratifiedGroupKFold * Add StratifiedGroupKFold to __init__ * Add y checks to StartifiedGroupKFold * Raise error if n_splits > max num samples in class * Warn if n_splits > mn num samples in class * Add SGKfold to general repr test * Add SGKFold to 2d_y test case * Add SGKfold to value erros test case Parameters are the same as for StratifiedKFold to ensure similar behavior given n_groups == n_samples * Add SGKFold to StratifiedKFold test cases The idea is to ensure similar behavior when groups are trivial (n_groups == n_samples) * Add SGKFold to reproducibility test case * Add SGKFold to GroupKFold test case * Add SGKFold to nested cv test case * Add SGKFold to random_state with shuffle=False test case * Add SGKFold to constant splits test case * Fix repr test case * Fix formatting issues * Add samples to a fold with least num samples Required to produce balanced size folds when the distribution of y is more or less the same * Remove GroupShuffleSplit impl * Add notes to StratifiedGroupKFold * Fix doctest * Added stratified group kfold tests * Better variable naming * Add section to documentation * Remove leftover StratifiedGroupShuffleSplit import * Add changelist and reference to original kernel * Better naming for least populated class check * Better expression for number of labels * Remove use of Counter We already have this data in output of np.unique * Add tests for homogeneous groups * Add StratifiedGroupKFold test against GroupKFold * Add changes to changelist in docstring * Add StratifiedGroupKFold to classes.rst * Fix description of StratifiedGroupKFold * Move license notice out of docstring * Disambiguate labels to classes in doc * Add changelog entry * Fix changelog author entry * Fix StratifiedGroupKFold docstring * Better variable names * Remove defaultdict in favor of numpy indexing * Extracted best_fold search into a separate method * Make use of numpy broadcasting instead of for loop * Encode groups and use arrays instead of dicts * Use numpy sort instead of python * Clarify shuffling behavior of StratifiedGroupKF in docs * Switch name from label_idx to class_idx * Remove accidentally leftover comment * Fix np.sort keyword to support numpy < 1.15 * Fix typo in docstring * Add StratifiedGroupKFold to visualization doc * Add visualization for uneven group as an example * Fix image numbers to match updated example * Add author * Add SGKF visualization to docs * Add comments for groups in stratified CV tests Co-authored-by: Leandro Hermida <hermidal@cs.umd.edu> Co-authored-by: marrodion <rodion_martynov@epam.com>
2021-03-20 18:57:42 +08:00
from ._split import StratifiedGroupKFold
Main Commits - Major -------------------- * 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 * TST Add tests for the new model_selection members * ENH Wrap the old-style cv obj/iterables instead of using iter_cv * ENH Use scipy's binomial coefficient function comb for calucation of nCk * ENH Few enhancements to the split module * ENH Improve check_cv input validation and docstring * MAINT _get_test_folds(X, y, labels) --> _get_test_folds(labels) * TST if 1d arrays for X introduce any errors * ENH use 1d X arrays for all tests; * ENH X_10 --> X (global var) Minor ----- * ENH _PartitionIterator --> _BaseCrossValidator; * ENH CVIterator --> CVIterableWrapper * TST Import the old SKF locally * FIX/TST Clean up the split module's tests. * DOC Improve documentation of the cv parameter * COSMIT consistently hyphenate cross-validation/cross-validator * TST Calculate n_samples from X * COSMIT Use separate lines for each import. * COSMIT cross_validation_generator --> cross_validator 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 * FIX avoid memory cost when sampling from large parameter grids ENH Major to Minor incremental enhancements to the model_selection Squashed commit messages - (For reference) Major ----- * ENH p --> n_labels * FIX *ShuffleSplit: all float/invalid type errors at init and int error at split * FIX make PredefinedSplit accept test_folds in constructor; Cleanup docstrings * ENH+TST KFold: make rng to be generated at every split call for reproducibility * FIX/MAINT KFold: make shuffle a public attr * FIX Make CVIterableWrapper private. * FIX reuse len_cv instead of recalculating it * FIX Prevent adding *SearchCV estimators from the old grid_search module * re-FIX In all_estimators: the sorting to use only the 1st item (name) To avoid collision between the old and the new GridSearch classes. * FIX test_validate.py: Use 2D X (1D X is being detected as a single sample) * MAINT validate.py --> validation.py * MAINT make the submodules private * MAINT Support old cv/gs/lc until 0.19 * FIX/MAINT n_splits --> get_n_splits * FIX/TST test_logistic.py/test_ovr_multinomial_iris: pass predefined folds as an iterable * MAINT expose BaseCrossValidator * Update the model_selection module with changes from master - From #5161 - - MAINT remove redundant p variable - - Add check for sparse prediction in cross_val_predict - From #5201 - DOC improve random_state param doc - From #5190 - LabelKFold and test - From #4583 - LabelShuffleSplit and tests - From #5300 - shuffle the `labels` not the `indxs` in LabelKFold + tests - From #5378 - Make the GridSearchCV docs more accurate. - From #5458 - Remove shuffle from LabelKFold - From #5466(#4270) - Gaussian Process by Jan Metzen - From #4826 - Move custom error / warnings into sklearn.exception Minor ----- * ENH Make the KFold shuffling test stronger * FIX/DOC Use the higher level model_selection module as ref * DOC in check_cv "y : array-like, optional" * DOC a supervised learning problem --> supervised learning problems * DOC cross-validators --> cross-validation strategies * DOC Correct Olivier Grisel's name ;) * MINOR/FIX cv_indices --> kfold * FIX/DOC Align the 'See also' section of the new KFold, LeaveOneOut * TST/FIX imports on separate lines * FIX use __class__ instead of classmethod * TST/FIX import directly from model_selection * COSMIT Relocate the random_state documentation * COSMIT remove pass * MAINT Remove deprecation warnings from old tests * FIX correct import at test_split * FIX/MAINT Move P_sparse, X, y defns to top; rm unused W_sparse, X_sparse * FIX random state to avoid doctest failure * TST n_splits and split wrapping of _CVIterableWrapper * FIX/MAINT Use multilabel indicator matrix directly * TST/DOC clarify why we conflate classes 0 and 1 * DOC add comment that this was taken from BaseEstimator * FIX use of labels is not needed in stratified k fold * Fix cross_validation reference * Fix the labels param doc FIX/DOC/MAINT Addressing the review comments by Arnaud and Andy COSMIT Sort the members alphabetically COSMIT len_cv --> n_splits COSMIT Merge 2 if; FIX Use kwargs DOC Add my name to the authors :D DOC make labels parameter consistent FIX Remove hack for boolean indices; + COSMIT idx --> indices; DOC Add Returns COSMIT preds --> predictions DOC Add Returns and neatly arrange X, y, labels FIX idx(s)/ind(s)--> indice(s) COSMIT Merge if and else to elif COSMIT n --> n_samples COSMIT Use bincount only once COSMIT cls --> class_i / class_i (ith class indices) --> perm_indices_class_i FIX/ENH/TST Addressing the final reviews COSMIT c --> count FIX/TST make check_cv raise ValueError for string cv value TST nested cv (gs inside cross_val_score) works for diff cvs FIX/ENH Raise ValueError when labels is None for label based cvs; TST if labels is being passed correctly to the cv and that the ValueError is being propagated to the cross_val_score/predict and grid search FIX pass labels to cross_val_score FIX use make_classification DOC Add Returns; COSMIT Remove scaffolding TST add a test to check the _build_repr helper REVERT the old GS/RS should also be tested by the common tests. ENH Add a tuple of all/label based CVS FIX raise VE even at get_n_splits if labels is None FIX Fabian's comments PEP8
2015-06-05 03:45:10 +08:00
from ._split import PredefinedSplit
from ._split import train_test_split
from ._split import check_cv
from ._validation import cross_val_score
from ._validation import cross_val_predict
[MRG + 2] ENH Allow `cross_val_score`, `GridSearchCV` et al. to evaluate on multiple metrics (#7388) * ENH cross_val_score now supports multiple metrics * DOCFIX permutation_test_score * ENH validate multiple metric scorers * ENH Move validation of multimetric scoring param out * ENH GridSearchCV and RandomizedSearchCV now support multiple metrics * EXA Add an example demonstrating the multiple metric in GridSearchCV * ENH Let check_multimetric_scoring tell if its multimetric or not * FIX For single metric name of scorer should remain 'score' * ENH validation_curve and learning_curve now support multiple metrics * MNT move _aggregate_score_dicts helper into _validation.py * TST More testing/ Fixing scores to the correct values * EXA Add cross_val_score to multimetric example * Rename to multiple_metric_evaluation.py * MNT Remove scaffolding * FIX doctest imports * FIX wrap the scorer and unwrap the score when using _score() in rfe * TST Cleanup the tests. Test for is_multimetric too * TST Make sure it registers as single metric when scoring is of that type * PEP8 * Don't use dict comprehension to make it work in python2.6 * ENH/FIX/TST grid_scores_ should not be available for multimetric evaluation * FIX+TST delegated methods NA when multimetric is enabled... TST Add general tests to GridSearchCV and RandomizedSearchCV * ENH add option to disable delegation on multimetric scoring * Remove old function from __all__ * flake8 * FIX revert disable_on_multimetric * stash * Fix incorrect rebase * [ci skip] * Make sure refit works as expected and remove irrelevant tests * Allow passing standard scorers by name in multimetric scorers * Fix example * flake8 * Address reviews * Fix indentation * Ensure {'acc': 'accuracy'} and ['precision'] are valid inputs * Test that for single metric, 'score' is a key * Typos * Fix incorrect rebase * Compare multimetric grid search with multiple single metric searches * Test X, y list and pandas input; Test multimetric for unsupervised grid search * Fix tests; Unsupervised multimetric gs will not pass until #8117 is merged * Make a plot of Precision vs ROC AUC for RandomForest varying the n_estimators * Add example to grid_search.rst * Use the classic tuning of C param in SVM instead of estimators in RF * FIX Remove scoring arg in deafult scorer test * flake8 * Search for min_samples_split in DTC; Also show f-score * REVIEW Make check_multimetric_scoring private * FIX Add more samples to see if 3% mismatch on 32 bit systems gets fixed * REVIEW Plot best score; Shorten legends * REVIEW/COSMIT multimetric --> multi-metric * REVIEW Mark the best scores of P/R scores too * Revert "FIX Add more samples to see if 3% mismatch on 32 bit systems gets fixed" This reverts commit ba766d98353380a186fbc3dade211670ee72726d. * ENH Use looping for iid testing * FIX use param grid as scipy's stats dist in 0.12 do not accept seed * ENH more looping less code; Use small non-noisy dataset * FIX Use named arg after expanded args * TST More testing of the refit parameter * Test that in multimetric search refit to single metric, the delegated methods work as expected. * Test that setting probability=False works with multimetric too * Test refit=False gives sensible error * COSMIT multimetric --> multi-metric * REV Correct example doc * COSMIT * REVIEW Make tests stronger; Fix bugs in _check_multimetric_scorer * REVIEW refit param: Raise for empty strings * TST Invalid refit params * REVIEW Use <scorer_name> alone; recall --> Recall * REV specify when we expect scorers to not be None * FLAKE8 * REVERT multimetrics in learning_curve and validation_curve * REVIEW Simpler coding style * COSMIT * COSMIT * REV Compress example a bit. Move comment to top * FIX fit_grid_point's previous API must be preserved * Flake8 * TST Use loop; Compare with single-metric * REVIEW Use dict-comprehension instead of helper * REVIEW Remove redundant test * Fix tests incorrect braces * COSMIT * REVIEW Use regexp * REV Simplify aggregation of score dicts * FIX precision and accuracy test * FIX doctest and flake8 * TST the best_* attributes multimetric with single metric * Address @jnothman's review * Address more comments \o/ * DOCFIXES * Fix use the validated fit_param from fit's arguments * Revert alpha to a lower value as before * Using def instead of lambda * Address @jnothman's review batch 1: Fix tests / Doc fixes * Remove superfluous tests * Remove more superfluous testing * TST/FIX loop over refit and check found n_clusters * Cosmetic touches * Use zip instead of manually listing the keys * Fix inverse_transform * FIX bug in fit_grid_point; Allow only single score TST if fit_grid_point works as intended * ENH Use only ROC-AUC and F1-score * Fix typos and flake8; Address Andy's reviews MNT Add a comment on why we do such a transpose + some fixes * ENH Better error messages for incorrect multimetric scoring values +... ENH Avoid exception traceback while using incorrect scoring string * Dict keys must be of string type only * 1. Better error message for invalid scoring 2... Internal functions return single score for single metric scoring * Fix test failures and shuffle tests * Avoid wrapping scorer as dict in learning_curve * Remove doc example as asked for * Some leftover ones * Don't wrap scorer in validation_curve either * Add a doc example and skip it as dict order fails doctest * Import zip from six for python2.7 compat * Make cross_val_score return a cv_results-like dict * Add relevant sections to userguide * Flake8 fixes * Add whatsnew and fix broken links * Use AUC and accuracy instead of f1 * Fix failing doctests cross_validation.rst * DOC add the wrapper example for metrics that return multiple return values * Address andy's comments * Be less weird * Address more of andy's comments * Make a separate cross_validate function to return dict and a cross_val_score * Update the docs to reflect the new cross_validate function * Add cross_validate to toc-tree * Add more tests on type of cross_validate return and time limits * FIX failing doctests * FIX ensure keys are not plural * DOC fix * Address some pending comments * Remove the comment as it is irrelevant now * Remove excess blank line * Fix flake8 inconsistencies * Allow fit_times to be 0 to conform with windows precision * DOC specify how refit param is to be set in multiple metric case * TST ensure cross_validate works for string single metrics + address @jnothman's reviews * Doc fixes * Remove the shape and transform parameter of _aggregate_score_dicts * Address Joel's doc comments * Fix broken doctest * Fix the spurious file * Address Andy's comments * MNT Remove erroneous entry * Address Andy's comments * FIX broken links * Update whats_new.rst missing newline
2017-07-07 23:12:31 +08:00
from ._validation import cross_validate
Main Commits - Major -------------------- * 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 * TST Add tests for the new model_selection members * ENH Wrap the old-style cv obj/iterables instead of using iter_cv * ENH Use scipy's binomial coefficient function comb for calucation of nCk * ENH Few enhancements to the split module * ENH Improve check_cv input validation and docstring * MAINT _get_test_folds(X, y, labels) --> _get_test_folds(labels) * TST if 1d arrays for X introduce any errors * ENH use 1d X arrays for all tests; * ENH X_10 --> X (global var) Minor ----- * ENH _PartitionIterator --> _BaseCrossValidator; * ENH CVIterator --> CVIterableWrapper * TST Import the old SKF locally * FIX/TST Clean up the split module's tests. * DOC Improve documentation of the cv parameter * COSMIT consistently hyphenate cross-validation/cross-validator * TST Calculate n_samples from X * COSMIT Use separate lines for each import. * COSMIT cross_validation_generator --> cross_validator 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 * FIX avoid memory cost when sampling from large parameter grids ENH Major to Minor incremental enhancements to the model_selection Squashed commit messages - (For reference) Major ----- * ENH p --> n_labels * FIX *ShuffleSplit: all float/invalid type errors at init and int error at split * FIX make PredefinedSplit accept test_folds in constructor; Cleanup docstrings * ENH+TST KFold: make rng to be generated at every split call for reproducibility * FIX/MAINT KFold: make shuffle a public attr * FIX Make CVIterableWrapper private. * FIX reuse len_cv instead of recalculating it * FIX Prevent adding *SearchCV estimators from the old grid_search module * re-FIX In all_estimators: the sorting to use only the 1st item (name) To avoid collision between the old and the new GridSearch classes. * FIX test_validate.py: Use 2D X (1D X is being detected as a single sample) * MAINT validate.py --> validation.py * MAINT make the submodules private * MAINT Support old cv/gs/lc until 0.19 * FIX/MAINT n_splits --> get_n_splits * FIX/TST test_logistic.py/test_ovr_multinomial_iris: pass predefined folds as an iterable * MAINT expose BaseCrossValidator * Update the model_selection module with changes from master - From #5161 - - MAINT remove redundant p variable - - Add check for sparse prediction in cross_val_predict - From #5201 - DOC improve random_state param doc - From #5190 - LabelKFold and test - From #4583 - LabelShuffleSplit and tests - From #5300 - shuffle the `labels` not the `indxs` in LabelKFold + tests - From #5378 - Make the GridSearchCV docs more accurate. - From #5458 - Remove shuffle from LabelKFold - From #5466(#4270) - Gaussian Process by Jan Metzen - From #4826 - Move custom error / warnings into sklearn.exception Minor ----- * ENH Make the KFold shuffling test stronger * FIX/DOC Use the higher level model_selection module as ref * DOC in check_cv "y : array-like, optional" * DOC a supervised learning problem --> supervised learning problems * DOC cross-validators --> cross-validation strategies * DOC Correct Olivier Grisel's name ;) * MINOR/FIX cv_indices --> kfold * FIX/DOC Align the 'See also' section of the new KFold, LeaveOneOut * TST/FIX imports on separate lines * FIX use __class__ instead of classmethod * TST/FIX import directly from model_selection * COSMIT Relocate the random_state documentation * COSMIT remove pass * MAINT Remove deprecation warnings from old tests * FIX correct import at test_split * FIX/MAINT Move P_sparse, X, y defns to top; rm unused W_sparse, X_sparse * FIX random state to avoid doctest failure * TST n_splits and split wrapping of _CVIterableWrapper * FIX/MAINT Use multilabel indicator matrix directly * TST/DOC clarify why we conflate classes 0 and 1 * DOC add comment that this was taken from BaseEstimator * FIX use of labels is not needed in stratified k fold * Fix cross_validation reference * Fix the labels param doc FIX/DOC/MAINT Addressing the review comments by Arnaud and Andy COSMIT Sort the members alphabetically COSMIT len_cv --> n_splits COSMIT Merge 2 if; FIX Use kwargs DOC Add my name to the authors :D DOC make labels parameter consistent FIX Remove hack for boolean indices; + COSMIT idx --> indices; DOC Add Returns COSMIT preds --> predictions DOC Add Returns and neatly arrange X, y, labels FIX idx(s)/ind(s)--> indice(s) COSMIT Merge if and else to elif COSMIT n --> n_samples COSMIT Use bincount only once COSMIT cls --> class_i / class_i (ith class indices) --> perm_indices_class_i FIX/ENH/TST Addressing the final reviews COSMIT c --> count FIX/TST make check_cv raise ValueError for string cv value TST nested cv (gs inside cross_val_score) works for diff cvs FIX/ENH Raise ValueError when labels is None for label based cvs; TST if labels is being passed correctly to the cv and that the ValueError is being propagated to the cross_val_score/predict and grid search FIX pass labels to cross_val_score FIX use make_classification DOC Add Returns; COSMIT Remove scaffolding TST add a test to check the _build_repr helper REVERT the old GS/RS should also be tested by the common tests. ENH Add a tuple of all/label based CVS FIX raise VE even at get_n_splits if labels is None FIX Fabian's comments PEP8
2015-06-05 03:45:10 +08:00
from ._validation import learning_curve
from ._validation import permutation_test_score
from ._validation import validation_curve
from ._search import GridSearchCV
from ._search import RandomizedSearchCV
from ._search import ParameterGrid
from ._search import ParameterSampler
FEA Successive halving for faster parameter search (#13900) * More flexible grid search interface * added info dict parameter * Put back removed test * renamed info into more_results * Passed grroups as well since we need n_to use get_n_splits(X, y, groups) * port * pep8 * dabl -> sklearn * add _required_parameters * skipping check in rst file if pandas not installed * Update sklearn/model_selection/_search_successive_halving.py Co-Authored-By: Joel Nothman <joel.nothman@gmail.com> * renamed into GridHalvingSearchCV and RandomHalvingSearchCV * Addressed thomas' comments * repr * removed passing group as a parameter to evaluate_candidates * Joels comments * pep8 * reorganized user user guide * renaming * update user guide * remove groups support + pass fit_params * parameter renaming * pep8 * r_i -> resource_iter * fixed r_i issues * examples + removed use of word budget * Added inpute checking tests * added cv_resutlts_ user guide * minor title change * fixed doc layout * Addressed some comments * properly pass down fit_params * change default value of force_exhaust_resources and update doc * should fix doc * Used check_fit_params * Update section about min_resources and number of candidates * Clarified ratio section * Use ~ to refer to classes * fixed doc checks * Apply suggestions from code review Co-authored-by: Joel Nothman <joel.nothman@gmail.com> * Addressed easy comments from Joel * missed some * updated docstring of run_search * Used f strings instead of format * remove candidate duplication checks * fix example * Addressed easy comments * rotate ticks labels * Added discussion in the intro as suggested by Joel * Split examples into sections * minor changes * remove force_exhaust_budget and introduce min_resources=exhaust * some minor validation * Added a n_resources_ attribute * update examples * Addressed comments * passing CV instead of X,y * minor revert for handling fit_params * updated docs * fix len * whatsnew * Add test for sampling when all_list * minor change to top-k * Force CV splits to be consistent across calls * reorder parameters * reduced diff * added tests for top_k * put back doc for groups * not sure what went wrong * put import at its place * some comment * Addressed comments * Added tests for cv_results_ and base estimator inputs * pep8 * avoid monkeypatching * rename df * use Joel's suggestions for testing masks * Made it experimental * Should fix docs * whats new entry * Apply suggestions from code review Co-authored-by: Andreas Mueller <t3kcit@gmail.com> * Addressed comments to docs * Addressed comments in examples * minor doc update * minor renaming in UG * forgot some * some sad note about splitter statefulness :'( * Addressed comments * ratio -> factor Co-authored-by: Joel Nothman <joel.nothman@gmail.com> Co-authored-by: Andreas Mueller <t3kcit@gmail.com>
2020-09-09 23:12:35 +08:00
if typing.TYPE_CHECKING:
# Avoid errors in type checkers (e.g. mypy) for experimental estimators.
# TODO: remove this check once the estimator is no longer experimental.
from ._search_successive_halving import ( # noqa
HalvingGridSearchCV,
HalvingRandomSearchCV,
)
__all__ = [
"BaseCrossValidator",
Make BaseShuffleSplit public (#20056)
2021-06-12 21:23:58 +08:00
"BaseShuffleSplit",
Main Commits - Major -------------------- * 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 * TST Add tests for the new model_selection members * ENH Wrap the old-style cv obj/iterables instead of using iter_cv * ENH Use scipy's binomial coefficient function comb for calucation of nCk * ENH Few enhancements to the split module * ENH Improve check_cv input validation and docstring * MAINT _get_test_folds(X, y, labels) --> _get_test_folds(labels) * TST if 1d arrays for X introduce any errors * ENH use 1d X arrays for all tests; * ENH X_10 --> X (global var) Minor ----- * ENH _PartitionIterator --> _BaseCrossValidator; * ENH CVIterator --> CVIterableWrapper * TST Import the old SKF locally * FIX/TST Clean up the split module's tests. * DOC Improve documentation of the cv parameter * COSMIT consistently hyphenate cross-validation/cross-validator * TST Calculate n_samples from X * COSMIT Use separate lines for each import. * COSMIT cross_validation_generator --> cross_validator 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 * FIX avoid memory cost when sampling from large parameter grids ENH Major to Minor incremental enhancements to the model_selection Squashed commit messages - (For reference) Major ----- * ENH p --> n_labels * FIX *ShuffleSplit: all float/invalid type errors at init and int error at split * FIX make PredefinedSplit accept test_folds in constructor; Cleanup docstrings * ENH+TST KFold: make rng to be generated at every split call for reproducibility * FIX/MAINT KFold: make shuffle a public attr * FIX Make CVIterableWrapper private. * FIX reuse len_cv instead of recalculating it * FIX Prevent adding *SearchCV estimators from the old grid_search module * re-FIX In all_estimators: the sorting to use only the 1st item (name) To avoid collision between the old and the new GridSearch classes. * FIX test_validate.py: Use 2D X (1D X is being detected as a single sample) * MAINT validate.py --> validation.py * MAINT make the submodules private * MAINT Support old cv/gs/lc until 0.19 * FIX/MAINT n_splits --> get_n_splits * FIX/TST test_logistic.py/test_ovr_multinomial_iris: pass predefined folds as an iterable * MAINT expose BaseCrossValidator * Update the model_selection module with changes from master - From #5161 - - MAINT remove redundant p variable - - Add check for sparse prediction in cross_val_predict - From #5201 - DOC improve random_state param doc - From #5190 - LabelKFold and test - From #4583 - LabelShuffleSplit and tests - From #5300 - shuffle the `labels` not the `indxs` in LabelKFold + tests - From #5378 - Make the GridSearchCV docs more accurate. - From #5458 - Remove shuffle from LabelKFold - From #5466(#4270) - Gaussian Process by Jan Metzen - From #4826 - Move custom error / warnings into sklearn.exception Minor ----- * ENH Make the KFold shuffling test stronger * FIX/DOC Use the higher level model_selection module as ref * DOC in check_cv "y : array-like, optional" * DOC a supervised learning problem --> supervised learning problems * DOC cross-validators --> cross-validation strategies * DOC Correct Olivier Grisel's name ;) * MINOR/FIX cv_indices --> kfold * FIX/DOC Align the 'See also' section of the new KFold, LeaveOneOut * TST/FIX imports on separate lines * FIX use __class__ instead of classmethod * TST/FIX import directly from model_selection * COSMIT Relocate the random_state documentation * COSMIT remove pass * MAINT Remove deprecation warnings from old tests * FIX correct import at test_split * FIX/MAINT Move P_sparse, X, y defns to top; rm unused W_sparse, X_sparse * FIX random state to avoid doctest failure * TST n_splits and split wrapping of _CVIterableWrapper * FIX/MAINT Use multilabel indicator matrix directly * TST/DOC clarify why we conflate classes 0 and 1 * DOC add comment that this was taken from BaseEstimator * FIX use of labels is not needed in stratified k fold * Fix cross_validation reference * Fix the labels param doc FIX/DOC/MAINT Addressing the review comments by Arnaud and Andy COSMIT Sort the members alphabetically COSMIT len_cv --> n_splits COSMIT Merge 2 if; FIX Use kwargs DOC Add my name to the authors :D DOC make labels parameter consistent FIX Remove hack for boolean indices; + COSMIT idx --> indices; DOC Add Returns COSMIT preds --> predictions DOC Add Returns and neatly arrange X, y, labels FIX idx(s)/ind(s)--> indice(s) COSMIT Merge if and else to elif COSMIT n --> n_samples COSMIT Use bincount only once COSMIT cls --> class_i / class_i (ith class indices) --> perm_indices_class_i FIX/ENH/TST Addressing the final reviews COSMIT c --> count FIX/TST make check_cv raise ValueError for string cv value TST nested cv (gs inside cross_val_score) works for diff cvs FIX/ENH Raise ValueError when labels is None for label based cvs; TST if labels is being passed correctly to the cv and that the ValueError is being propagated to the cross_val_score/predict and grid search FIX pass labels to cross_val_score FIX use make_classification DOC Add Returns; COSMIT Remove scaffolding TST add a test to check the _build_repr helper REVERT the old GS/RS should also be tested by the common tests. ENH Add a tuple of all/label based CVS FIX raise VE even at get_n_splits if labels is None FIX Fabian's comments PEP8
2015-06-05 03:45:10 +08:00
"GridSearchCV",
Rename `TimeSeriesCV` to `TimeSeriesSplit` (#7245) * rename TimeSeriesCV to TimeSeriesSplit * Add TimeSeriesSplit * Add whats new
2016-08-26 10:34:33 +08:00
"TimeSeriesSplit",
Main Commits - Major -------------------- * 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 * TST Add tests for the new model_selection members * ENH Wrap the old-style cv obj/iterables instead of using iter_cv * ENH Use scipy's binomial coefficient function comb for calucation of nCk * ENH Few enhancements to the split module * ENH Improve check_cv input validation and docstring * MAINT _get_test_folds(X, y, labels) --> _get_test_folds(labels) * TST if 1d arrays for X introduce any errors * ENH use 1d X arrays for all tests; * ENH X_10 --> X (global var) Minor ----- * ENH _PartitionIterator --> _BaseCrossValidator; * ENH CVIterator --> CVIterableWrapper * TST Import the old SKF locally * FIX/TST Clean up the split module's tests. * DOC Improve documentation of the cv parameter * COSMIT consistently hyphenate cross-validation/cross-validator * TST Calculate n_samples from X * COSMIT Use separate lines for each import. * COSMIT cross_validation_generator --> cross_validator 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 * FIX avoid memory cost when sampling from large parameter grids ENH Major to Minor incremental enhancements to the model_selection Squashed commit messages - (For reference) Major ----- * ENH p --> n_labels * FIX *ShuffleSplit: all float/invalid type errors at init and int error at split * FIX make PredefinedSplit accept test_folds in constructor; Cleanup docstrings * ENH+TST KFold: make rng to be generated at every split call for reproducibility * FIX/MAINT KFold: make shuffle a public attr * FIX Make CVIterableWrapper private. * FIX reuse len_cv instead of recalculating it * FIX Prevent adding *SearchCV estimators from the old grid_search module * re-FIX In all_estimators: the sorting to use only the 1st item (name) To avoid collision between the old and the new GridSearch classes. * FIX test_validate.py: Use 2D X (1D X is being detected as a single sample) * MAINT validate.py --> validation.py * MAINT make the submodules private * MAINT Support old cv/gs/lc until 0.19 * FIX/MAINT n_splits --> get_n_splits * FIX/TST test_logistic.py/test_ovr_multinomial_iris: pass predefined folds as an iterable * MAINT expose BaseCrossValidator * Update the model_selection module with changes from master - From #5161 - - MAINT remove redundant p variable - - Add check for sparse prediction in cross_val_predict - From #5201 - DOC improve random_state param doc - From #5190 - LabelKFold and test - From #4583 - LabelShuffleSplit and tests - From #5300 - shuffle the `labels` not the `indxs` in LabelKFold + tests - From #5378 - Make the GridSearchCV docs more accurate. - From #5458 - Remove shuffle from LabelKFold - From #5466(#4270) - Gaussian Process by Jan Metzen - From #4826 - Move custom error / warnings into sklearn.exception Minor ----- * ENH Make the KFold shuffling test stronger * FIX/DOC Use the higher level model_selection module as ref * DOC in check_cv "y : array-like, optional" * DOC a supervised learning problem --> supervised learning problems * DOC cross-validators --> cross-validation strategies * DOC Correct Olivier Grisel's name ;) * MINOR/FIX cv_indices --> kfold * FIX/DOC Align the 'See also' section of the new KFold, LeaveOneOut * TST/FIX imports on separate lines * FIX use __class__ instead of classmethod * TST/FIX import directly from model_selection * COSMIT Relocate the random_state documentation * COSMIT remove pass * MAINT Remove deprecation warnings from old tests * FIX correct import at test_split * FIX/MAINT Move P_sparse, X, y defns to top; rm unused W_sparse, X_sparse * FIX random state to avoid doctest failure * TST n_splits and split wrapping of _CVIterableWrapper * FIX/MAINT Use multilabel indicator matrix directly * TST/DOC clarify why we conflate classes 0 and 1 * DOC add comment that this was taken from BaseEstimator * FIX use of labels is not needed in stratified k fold * Fix cross_validation reference * Fix the labels param doc FIX/DOC/MAINT Addressing the review comments by Arnaud and Andy COSMIT Sort the members alphabetically COSMIT len_cv --> n_splits COSMIT Merge 2 if; FIX Use kwargs DOC Add my name to the authors :D DOC make labels parameter consistent FIX Remove hack for boolean indices; + COSMIT idx --> indices; DOC Add Returns COSMIT preds --> predictions DOC Add Returns and neatly arrange X, y, labels FIX idx(s)/ind(s)--> indice(s) COSMIT Merge if and else to elif COSMIT n --> n_samples COSMIT Use bincount only once COSMIT cls --> class_i / class_i (ith class indices) --> perm_indices_class_i FIX/ENH/TST Addressing the final reviews COSMIT c --> count FIX/TST make check_cv raise ValueError for string cv value TST nested cv (gs inside cross_val_score) works for diff cvs FIX/ENH Raise ValueError when labels is None for label based cvs; TST if labels is being passed correctly to the cv and that the ValueError is being propagated to the cross_val_score/predict and grid search FIX pass labels to cross_val_score FIX use make_classification DOC Add Returns; COSMIT Remove scaffolding TST add a test to check the _build_repr helper REVERT the old GS/RS should also be tested by the common tests. ENH Add a tuple of all/label based CVS FIX raise VE even at get_n_splits if labels is None FIX Fabian's comments PEP8
2015-06-05 03:45:10 +08:00
"KFold",
[MRG+1] ENH/MNT Rename labels --> groups in CV tools (#6660)
2016-09-12 01:14:41 +08:00
"GroupKFold",
"GroupShuffleSplit",
"LeaveOneGroupOut",
Main Commits - Major -------------------- * 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 * TST Add tests for the new model_selection members * ENH Wrap the old-style cv obj/iterables instead of using iter_cv * ENH Use scipy's binomial coefficient function comb for calucation of nCk * ENH Few enhancements to the split module * ENH Improve check_cv input validation and docstring * MAINT _get_test_folds(X, y, labels) --> _get_test_folds(labels) * TST if 1d arrays for X introduce any errors * ENH use 1d X arrays for all tests; * ENH X_10 --> X (global var) Minor ----- * ENH _PartitionIterator --> _BaseCrossValidator; * ENH CVIterator --> CVIterableWrapper * TST Import the old SKF locally * FIX/TST Clean up the split module's tests. * DOC Improve documentation of the cv parameter * COSMIT consistently hyphenate cross-validation/cross-validator * TST Calculate n_samples from X * COSMIT Use separate lines for each import. * COSMIT cross_validation_generator --> cross_validator 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 * FIX avoid memory cost when sampling from large parameter grids ENH Major to Minor incremental enhancements to the model_selection Squashed commit messages - (For reference) Major ----- * ENH p --> n_labels * FIX *ShuffleSplit: all float/invalid type errors at init and int error at split * FIX make PredefinedSplit accept test_folds in constructor; Cleanup docstrings * ENH+TST KFold: make rng to be generated at every split call for reproducibility * FIX/MAINT KFold: make shuffle a public attr * FIX Make CVIterableWrapper private. * FIX reuse len_cv instead of recalculating it * FIX Prevent adding *SearchCV estimators from the old grid_search module * re-FIX In all_estimators: the sorting to use only the 1st item (name) To avoid collision between the old and the new GridSearch classes. * FIX test_validate.py: Use 2D X (1D X is being detected as a single sample) * MAINT validate.py --> validation.py * MAINT make the submodules private * MAINT Support old cv/gs/lc until 0.19 * FIX/MAINT n_splits --> get_n_splits * FIX/TST test_logistic.py/test_ovr_multinomial_iris: pass predefined folds as an iterable * MAINT expose BaseCrossValidator * Update the model_selection module with changes from master - From #5161 - - MAINT remove redundant p variable - - Add check for sparse prediction in cross_val_predict - From #5201 - DOC improve random_state param doc - From #5190 - LabelKFold and test - From #4583 - LabelShuffleSplit and tests - From #5300 - shuffle the `labels` not the `indxs` in LabelKFold + tests - From #5378 - Make the GridSearchCV docs more accurate. - From #5458 - Remove shuffle from LabelKFold - From #5466(#4270) - Gaussian Process by Jan Metzen - From #4826 - Move custom error / warnings into sklearn.exception Minor ----- * ENH Make the KFold shuffling test stronger * FIX/DOC Use the higher level model_selection module as ref * DOC in check_cv "y : array-like, optional" * DOC a supervised learning problem --> supervised learning problems * DOC cross-validators --> cross-validation strategies * DOC Correct Olivier Grisel's name ;) * MINOR/FIX cv_indices --> kfold * FIX/DOC Align the 'See also' section of the new KFold, LeaveOneOut * TST/FIX imports on separate lines * FIX use __class__ instead of classmethod * TST/FIX import directly from model_selection * COSMIT Relocate the random_state documentation * COSMIT remove pass * MAINT Remove deprecation warnings from old tests * FIX correct import at test_split * FIX/MAINT Move P_sparse, X, y defns to top; rm unused W_sparse, X_sparse * FIX random state to avoid doctest failure * TST n_splits and split wrapping of _CVIterableWrapper * FIX/MAINT Use multilabel indicator matrix directly * TST/DOC clarify why we conflate classes 0 and 1 * DOC add comment that this was taken from BaseEstimator * FIX use of labels is not needed in stratified k fold * Fix cross_validation reference * Fix the labels param doc FIX/DOC/MAINT Addressing the review comments by Arnaud and Andy COSMIT Sort the members alphabetically COSMIT len_cv --> n_splits COSMIT Merge 2 if; FIX Use kwargs DOC Add my name to the authors :D DOC make labels parameter consistent FIX Remove hack for boolean indices; + COSMIT idx --> indices; DOC Add Returns COSMIT preds --> predictions DOC Add Returns and neatly arrange X, y, labels FIX idx(s)/ind(s)--> indice(s) COSMIT Merge if and else to elif COSMIT n --> n_samples COSMIT Use bincount only once COSMIT cls --> class_i / class_i (ith class indices) --> perm_indices_class_i FIX/ENH/TST Addressing the final reviews COSMIT c --> count FIX/TST make check_cv raise ValueError for string cv value TST nested cv (gs inside cross_val_score) works for diff cvs FIX/ENH Raise ValueError when labels is None for label based cvs; TST if labels is being passed correctly to the cv and that the ValueError is being propagated to the cross_val_score/predict and grid search FIX pass labels to cross_val_score FIX use make_classification DOC Add Returns; COSMIT Remove scaffolding TST add a test to check the _build_repr helper REVERT the old GS/RS should also be tested by the common tests. ENH Add a tuple of all/label based CVS FIX raise VE even at get_n_splits if labels is None FIX Fabian's comments PEP8
2015-06-05 03:45:10 +08:00
"LeaveOneOut",
[MRG+1] ENH/MNT Rename labels --> groups in CV tools (#6660)
2016-09-12 01:14:41 +08:00
"LeavePGroupsOut",
Main Commits - Major -------------------- * 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 * TST Add tests for the new model_selection members * ENH Wrap the old-style cv obj/iterables instead of using iter_cv * ENH Use scipy's binomial coefficient function comb for calucation of nCk * ENH Few enhancements to the split module * ENH Improve check_cv input validation and docstring * MAINT _get_test_folds(X, y, labels) --> _get_test_folds(labels) * TST if 1d arrays for X introduce any errors * ENH use 1d X arrays for all tests; * ENH X_10 --> X (global var) Minor ----- * ENH _PartitionIterator --> _BaseCrossValidator; * ENH CVIterator --> CVIterableWrapper * TST Import the old SKF locally * FIX/TST Clean up the split module's tests. * DOC Improve documentation of the cv parameter * COSMIT consistently hyphenate cross-validation/cross-validator * TST Calculate n_samples from X * COSMIT Use separate lines for each import. * COSMIT cross_validation_generator --> cross_validator 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 * FIX avoid memory cost when sampling from large parameter grids ENH Major to Minor incremental enhancements to the model_selection Squashed commit messages - (For reference) Major ----- * ENH p --> n_labels * FIX *ShuffleSplit: all float/invalid type errors at init and int error at split * FIX make PredefinedSplit accept test_folds in constructor; Cleanup docstrings * ENH+TST KFold: make rng to be generated at every split call for reproducibility * FIX/MAINT KFold: make shuffle a public attr * FIX Make CVIterableWrapper private. * FIX reuse len_cv instead of recalculating it * FIX Prevent adding *SearchCV estimators from the old grid_search module * re-FIX In all_estimators: the sorting to use only the 1st item (name) To avoid collision between the old and the new GridSearch classes. * FIX test_validate.py: Use 2D X (1D X is being detected as a single sample) * MAINT validate.py --> validation.py * MAINT make the submodules private * MAINT Support old cv/gs/lc until 0.19 * FIX/MAINT n_splits --> get_n_splits * FIX/TST test_logistic.py/test_ovr_multinomial_iris: pass predefined folds as an iterable * MAINT expose BaseCrossValidator * Update the model_selection module with changes from master - From #5161 - - MAINT remove redundant p variable - - Add check for sparse prediction in cross_val_predict - From #5201 - DOC improve random_state param doc - From #5190 - LabelKFold and test - From #4583 - LabelShuffleSplit and tests - From #5300 - shuffle the `labels` not the `indxs` in LabelKFold + tests - From #5378 - Make the GridSearchCV docs more accurate. - From #5458 - Remove shuffle from LabelKFold - From #5466(#4270) - Gaussian Process by Jan Metzen - From #4826 - Move custom error / warnings into sklearn.exception Minor ----- * ENH Make the KFold shuffling test stronger * FIX/DOC Use the higher level model_selection module as ref * DOC in check_cv "y : array-like, optional" * DOC a supervised learning problem --> supervised learning problems * DOC cross-validators --> cross-validation strategies * DOC Correct Olivier Grisel's name ;) * MINOR/FIX cv_indices --> kfold * FIX/DOC Align the 'See also' section of the new KFold, LeaveOneOut * TST/FIX imports on separate lines * FIX use __class__ instead of classmethod * TST/FIX import directly from model_selection * COSMIT Relocate the random_state documentation * COSMIT remove pass * MAINT Remove deprecation warnings from old tests * FIX correct import at test_split * FIX/MAINT Move P_sparse, X, y defns to top; rm unused W_sparse, X_sparse * FIX random state to avoid doctest failure * TST n_splits and split wrapping of _CVIterableWrapper * FIX/MAINT Use multilabel indicator matrix directly * TST/DOC clarify why we conflate classes 0 and 1 * DOC add comment that this was taken from BaseEstimator * FIX use of labels is not needed in stratified k fold * Fix cross_validation reference * Fix the labels param doc FIX/DOC/MAINT Addressing the review comments by Arnaud and Andy COSMIT Sort the members alphabetically COSMIT len_cv --> n_splits COSMIT Merge 2 if; FIX Use kwargs DOC Add my name to the authors :D DOC make labels parameter consistent FIX Remove hack for boolean indices; + COSMIT idx --> indices; DOC Add Returns COSMIT preds --> predictions DOC Add Returns and neatly arrange X, y, labels FIX idx(s)/ind(s)--> indice(s) COSMIT Merge if and else to elif COSMIT n --> n_samples COSMIT Use bincount only once COSMIT cls --> class_i / class_i (ith class indices) --> perm_indices_class_i FIX/ENH/TST Addressing the final reviews COSMIT c --> count FIX/TST make check_cv raise ValueError for string cv value TST nested cv (gs inside cross_val_score) works for diff cvs FIX/ENH Raise ValueError when labels is None for label based cvs; TST if labels is being passed correctly to the cv and that the ValueError is being propagated to the cross_val_score/predict and grid search FIX pass labels to cross_val_score FIX use make_classification DOC Add Returns; COSMIT Remove scaffolding TST add a test to check the _build_repr helper REVERT the old GS/RS should also be tested by the common tests. ENH Add a tuple of all/label based CVS FIX raise VE even at get_n_splits if labels is None FIX Fabian's comments PEP8
2015-06-05 03:45:10 +08:00
"LeavePOut",
[MRG+1] Repeated K-Fold and Repeated Stratified K-Fold (#8120) * 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
2017-03-05 04:39:04 +08:00
"RepeatedKFold",
"RepeatedStratifiedKFold",
Main Commits - Major -------------------- * 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 * TST Add tests for the new model_selection members * ENH Wrap the old-style cv obj/iterables instead of using iter_cv * ENH Use scipy's binomial coefficient function comb for calucation of nCk * ENH Few enhancements to the split module * ENH Improve check_cv input validation and docstring * MAINT _get_test_folds(X, y, labels) --> _get_test_folds(labels) * TST if 1d arrays for X introduce any errors * ENH use 1d X arrays for all tests; * ENH X_10 --> X (global var) Minor ----- * ENH _PartitionIterator --> _BaseCrossValidator; * ENH CVIterator --> CVIterableWrapper * TST Import the old SKF locally * FIX/TST Clean up the split module's tests. * DOC Improve documentation of the cv parameter * COSMIT consistently hyphenate cross-validation/cross-validator * TST Calculate n_samples from X * COSMIT Use separate lines for each import. * COSMIT cross_validation_generator --> cross_validator 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 * FIX avoid memory cost when sampling from large parameter grids ENH Major to Minor incremental enhancements to the model_selection Squashed commit messages - (For reference) Major ----- * ENH p --> n_labels * FIX *ShuffleSplit: all float/invalid type errors at init and int error at split * FIX make PredefinedSplit accept test_folds in constructor; Cleanup docstrings * ENH+TST KFold: make rng to be generated at every split call for reproducibility * FIX/MAINT KFold: make shuffle a public attr * FIX Make CVIterableWrapper private. * FIX reuse len_cv instead of recalculating it * FIX Prevent adding *SearchCV estimators from the old grid_search module * re-FIX In all_estimators: the sorting to use only the 1st item (name) To avoid collision between the old and the new GridSearch classes. * FIX test_validate.py: Use 2D X (1D X is being detected as a single sample) * MAINT validate.py --> validation.py * MAINT make the submodules private * MAINT Support old cv/gs/lc until 0.19 * FIX/MAINT n_splits --> get_n_splits * FIX/TST test_logistic.py/test_ovr_multinomial_iris: pass predefined folds as an iterable * MAINT expose BaseCrossValidator * Update the model_selection module with changes from master - From #5161 - - MAINT remove redundant p variable - - Add check for sparse prediction in cross_val_predict - From #5201 - DOC improve random_state param doc - From #5190 - LabelKFold and test - From #4583 - LabelShuffleSplit and tests - From #5300 - shuffle the `labels` not the `indxs` in LabelKFold + tests - From #5378 - Make the GridSearchCV docs more accurate. - From #5458 - Remove shuffle from LabelKFold - From #5466(#4270) - Gaussian Process by Jan Metzen - From #4826 - Move custom error / warnings into sklearn.exception Minor ----- * ENH Make the KFold shuffling test stronger * FIX/DOC Use the higher level model_selection module as ref * DOC in check_cv "y : array-like, optional" * DOC a supervised learning problem --> supervised learning problems * DOC cross-validators --> cross-validation strategies * DOC Correct Olivier Grisel's name ;) * MINOR/FIX cv_indices --> kfold * FIX/DOC Align the 'See also' section of the new KFold, LeaveOneOut * TST/FIX imports on separate lines * FIX use __class__ instead of classmethod * TST/FIX import directly from model_selection * COSMIT Relocate the random_state documentation * COSMIT remove pass * MAINT Remove deprecation warnings from old tests * FIX correct import at test_split * FIX/MAINT Move P_sparse, X, y defns to top; rm unused W_sparse, X_sparse * FIX random state to avoid doctest failure * TST n_splits and split wrapping of _CVIterableWrapper * FIX/MAINT Use multilabel indicator matrix directly * TST/DOC clarify why we conflate classes 0 and 1 * DOC add comment that this was taken from BaseEstimator * FIX use of labels is not needed in stratified k fold * Fix cross_validation reference * Fix the labels param doc FIX/DOC/MAINT Addressing the review comments by Arnaud and Andy COSMIT Sort the members alphabetically COSMIT len_cv --> n_splits COSMIT Merge 2 if; FIX Use kwargs DOC Add my name to the authors :D DOC make labels parameter consistent FIX Remove hack for boolean indices; + COSMIT idx --> indices; DOC Add Returns COSMIT preds --> predictions DOC Add Returns and neatly arrange X, y, labels FIX idx(s)/ind(s)--> indice(s) COSMIT Merge if and else to elif COSMIT n --> n_samples COSMIT Use bincount only once COSMIT cls --> class_i / class_i (ith class indices) --> perm_indices_class_i FIX/ENH/TST Addressing the final reviews COSMIT c --> count FIX/TST make check_cv raise ValueError for string cv value TST nested cv (gs inside cross_val_score) works for diff cvs FIX/ENH Raise ValueError when labels is None for label based cvs; TST if labels is being passed correctly to the cv and that the ValueError is being propagated to the cross_val_score/predict and grid search FIX pass labels to cross_val_score FIX use make_classification DOC Add Returns; COSMIT Remove scaffolding TST add a test to check the _build_repr helper REVERT the old GS/RS should also be tested by the common tests. ENH Add a tuple of all/label based CVS FIX raise VE even at get_n_splits if labels is None FIX Fabian's comments PEP8
2015-06-05 03:45:10 +08:00
"ParameterGrid",
"ParameterSampler",
"PredefinedSplit",
"RandomizedSearchCV",
"ShuffleSplit",
"StratifiedKFold",
Stratified Group KFold implementation (#18649) * Initial implementation * Forgot to add to second __add__ list * Update split method parameter doc * Added example; changed default test_size to 0.1; added to author list * StratifiedGroupKFold impl and other improvements * Add class to __all__ spec * Remove random_state when no shuffle * Tighter formatting * Update the implementation of StratifiedGroupKFold * Add StratifiedGroupKFold to __init__ * Add y checks to StartifiedGroupKFold * Raise error if n_splits > max num samples in class * Warn if n_splits > mn num samples in class * Add SGKfold to general repr test * Add SGKFold to 2d_y test case * Add SGKfold to value erros test case Parameters are the same as for StratifiedKFold to ensure similar behavior given n_groups == n_samples * Add SGKFold to StratifiedKFold test cases The idea is to ensure similar behavior when groups are trivial (n_groups == n_samples) * Add SGKFold to reproducibility test case * Add SGKFold to GroupKFold test case * Add SGKFold to nested cv test case * Add SGKFold to random_state with shuffle=False test case * Add SGKFold to constant splits test case * Fix repr test case * Fix formatting issues * Add samples to a fold with least num samples Required to produce balanced size folds when the distribution of y is more or less the same * Remove GroupShuffleSplit impl * Add notes to StratifiedGroupKFold * Fix doctest * Added stratified group kfold tests * Better variable naming * Add section to documentation * Remove leftover StratifiedGroupShuffleSplit import * Add changelist and reference to original kernel * Better naming for least populated class check * Better expression for number of labels * Remove use of Counter We already have this data in output of np.unique * Add tests for homogeneous groups * Add StratifiedGroupKFold test against GroupKFold * Add changes to changelist in docstring * Add StratifiedGroupKFold to classes.rst * Fix description of StratifiedGroupKFold * Move license notice out of docstring * Disambiguate labels to classes in doc * Add changelog entry * Fix changelog author entry * Fix StratifiedGroupKFold docstring * Better variable names * Remove defaultdict in favor of numpy indexing * Extracted best_fold search into a separate method * Make use of numpy broadcasting instead of for loop * Encode groups and use arrays instead of dicts * Use numpy sort instead of python * Clarify shuffling behavior of StratifiedGroupKF in docs * Switch name from label_idx to class_idx * Remove accidentally leftover comment * Fix np.sort keyword to support numpy < 1.15 * Fix typo in docstring * Add StratifiedGroupKFold to visualization doc * Add visualization for uneven group as an example * Fix image numbers to match updated example * Add author * Add SGKF visualization to docs * Add comments for groups in stratified CV tests Co-authored-by: Leandro Hermida <hermidal@cs.umd.edu> Co-authored-by: marrodion <rodion_martynov@epam.com>
2021-03-20 18:57:42 +08:00
"StratifiedGroupKFold",
Main Commits - Major -------------------- * 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 * TST Add tests for the new model_selection members * ENH Wrap the old-style cv obj/iterables instead of using iter_cv * ENH Use scipy's binomial coefficient function comb for calucation of nCk * ENH Few enhancements to the split module * ENH Improve check_cv input validation and docstring * MAINT _get_test_folds(X, y, labels) --> _get_test_folds(labels) * TST if 1d arrays for X introduce any errors * ENH use 1d X arrays for all tests; * ENH X_10 --> X (global var) Minor ----- * ENH _PartitionIterator --> _BaseCrossValidator; * ENH CVIterator --> CVIterableWrapper * TST Import the old SKF locally * FIX/TST Clean up the split module's tests. * DOC Improve documentation of the cv parameter * COSMIT consistently hyphenate cross-validation/cross-validator * TST Calculate n_samples from X * COSMIT Use separate lines for each import. * COSMIT cross_validation_generator --> cross_validator 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 * FIX avoid memory cost when sampling from large parameter grids ENH Major to Minor incremental enhancements to the model_selection Squashed commit messages - (For reference) Major ----- * ENH p --> n_labels * FIX *ShuffleSplit: all float/invalid type errors at init and int error at split * FIX make PredefinedSplit accept test_folds in constructor; Cleanup docstrings * ENH+TST KFold: make rng to be generated at every split call for reproducibility * FIX/MAINT KFold: make shuffle a public attr * FIX Make CVIterableWrapper private. * FIX reuse len_cv instead of recalculating it * FIX Prevent adding *SearchCV estimators from the old grid_search module * re-FIX In all_estimators: the sorting to use only the 1st item (name) To avoid collision between the old and the new GridSearch classes. * FIX test_validate.py: Use 2D X (1D X is being detected as a single sample) * MAINT validate.py --> validation.py * MAINT make the submodules private * MAINT Support old cv/gs/lc until 0.19 * FIX/MAINT n_splits --> get_n_splits * FIX/TST test_logistic.py/test_ovr_multinomial_iris: pass predefined folds as an iterable * MAINT expose BaseCrossValidator * Update the model_selection module with changes from master - From #5161 - - MAINT remove redundant p variable - - Add check for sparse prediction in cross_val_predict - From #5201 - DOC improve random_state param doc - From #5190 - LabelKFold and test - From #4583 - LabelShuffleSplit and tests - From #5300 - shuffle the `labels` not the `indxs` in LabelKFold + tests - From #5378 - Make the GridSearchCV docs more accurate. - From #5458 - Remove shuffle from LabelKFold - From #5466(#4270) - Gaussian Process by Jan Metzen - From #4826 - Move custom error / warnings into sklearn.exception Minor ----- * ENH Make the KFold shuffling test stronger * FIX/DOC Use the higher level model_selection module as ref * DOC in check_cv "y : array-like, optional" * DOC a supervised learning problem --> supervised learning problems * DOC cross-validators --> cross-validation strategies * DOC Correct Olivier Grisel's name ;) * MINOR/FIX cv_indices --> kfold * FIX/DOC Align the 'See also' section of the new KFold, LeaveOneOut * TST/FIX imports on separate lines * FIX use __class__ instead of classmethod * TST/FIX import directly from model_selection * COSMIT Relocate the random_state documentation * COSMIT remove pass * MAINT Remove deprecation warnings from old tests * FIX correct import at test_split * FIX/MAINT Move P_sparse, X, y defns to top; rm unused W_sparse, X_sparse * FIX random state to avoid doctest failure * TST n_splits and split wrapping of _CVIterableWrapper * FIX/MAINT Use multilabel indicator matrix directly * TST/DOC clarify why we conflate classes 0 and 1 * DOC add comment that this was taken from BaseEstimator * FIX use of labels is not needed in stratified k fold * Fix cross_validation reference * Fix the labels param doc FIX/DOC/MAINT Addressing the review comments by Arnaud and Andy COSMIT Sort the members alphabetically COSMIT len_cv --> n_splits COSMIT Merge 2 if; FIX Use kwargs DOC Add my name to the authors :D DOC make labels parameter consistent FIX Remove hack for boolean indices; + COSMIT idx --> indices; DOC Add Returns COSMIT preds --> predictions DOC Add Returns and neatly arrange X, y, labels FIX idx(s)/ind(s)--> indice(s) COSMIT Merge if and else to elif COSMIT n --> n_samples COSMIT Use bincount only once COSMIT cls --> class_i / class_i (ith class indices) --> perm_indices_class_i FIX/ENH/TST Addressing the final reviews COSMIT c --> count FIX/TST make check_cv raise ValueError for string cv value TST nested cv (gs inside cross_val_score) works for diff cvs FIX/ENH Raise ValueError when labels is None for label based cvs; TST if labels is being passed correctly to the cv and that the ValueError is being propagated to the cross_val_score/predict and grid search FIX pass labels to cross_val_score FIX use make_classification DOC Add Returns; COSMIT Remove scaffolding TST add a test to check the _build_repr helper REVERT the old GS/RS should also be tested by the common tests. ENH Add a tuple of all/label based CVS FIX raise VE even at get_n_splits if labels is None FIX Fabian's comments PEP8
2015-06-05 03:45:10 +08:00
"StratifiedShuffleSplit",
"check_cv",
"cross_val_predict",
"cross_val_score",
[MRG + 2] ENH Allow `cross_val_score`, `GridSearchCV` et al. to evaluate on multiple metrics (#7388) * ENH cross_val_score now supports multiple metrics * DOCFIX permutation_test_score * ENH validate multiple metric scorers * ENH Move validation of multimetric scoring param out * ENH GridSearchCV and RandomizedSearchCV now support multiple metrics * EXA Add an example demonstrating the multiple metric in GridSearchCV * ENH Let check_multimetric_scoring tell if its multimetric or not * FIX For single metric name of scorer should remain 'score' * ENH validation_curve and learning_curve now support multiple metrics * MNT move _aggregate_score_dicts helper into _validation.py * TST More testing/ Fixing scores to the correct values * EXA Add cross_val_score to multimetric example * Rename to multiple_metric_evaluation.py * MNT Remove scaffolding * FIX doctest imports * FIX wrap the scorer and unwrap the score when using _score() in rfe * TST Cleanup the tests. Test for is_multimetric too * TST Make sure it registers as single metric when scoring is of that type * PEP8 * Don't use dict comprehension to make it work in python2.6 * ENH/FIX/TST grid_scores_ should not be available for multimetric evaluation * FIX+TST delegated methods NA when multimetric is enabled... TST Add general tests to GridSearchCV and RandomizedSearchCV * ENH add option to disable delegation on multimetric scoring * Remove old function from __all__ * flake8 * FIX revert disable_on_multimetric * stash * Fix incorrect rebase * [ci skip] * Make sure refit works as expected and remove irrelevant tests * Allow passing standard scorers by name in multimetric scorers * Fix example * flake8 * Address reviews * Fix indentation * Ensure {'acc': 'accuracy'} and ['precision'] are valid inputs * Test that for single metric, 'score' is a key * Typos * Fix incorrect rebase * Compare multimetric grid search with multiple single metric searches * Test X, y list and pandas input; Test multimetric for unsupervised grid search * Fix tests; Unsupervised multimetric gs will not pass until #8117 is merged * Make a plot of Precision vs ROC AUC for RandomForest varying the n_estimators * Add example to grid_search.rst * Use the classic tuning of C param in SVM instead of estimators in RF * FIX Remove scoring arg in deafult scorer test * flake8 * Search for min_samples_split in DTC; Also show f-score * REVIEW Make check_multimetric_scoring private * FIX Add more samples to see if 3% mismatch on 32 bit systems gets fixed * REVIEW Plot best score; Shorten legends * REVIEW/COSMIT multimetric --> multi-metric * REVIEW Mark the best scores of P/R scores too * Revert "FIX Add more samples to see if 3% mismatch on 32 bit systems gets fixed" This reverts commit ba766d98353380a186fbc3dade211670ee72726d. * ENH Use looping for iid testing * FIX use param grid as scipy's stats dist in 0.12 do not accept seed * ENH more looping less code; Use small non-noisy dataset * FIX Use named arg after expanded args * TST More testing of the refit parameter * Test that in multimetric search refit to single metric, the delegated methods work as expected. * Test that setting probability=False works with multimetric too * Test refit=False gives sensible error * COSMIT multimetric --> multi-metric * REV Correct example doc * COSMIT * REVIEW Make tests stronger; Fix bugs in _check_multimetric_scorer * REVIEW refit param: Raise for empty strings * TST Invalid refit params * REVIEW Use <scorer_name> alone; recall --> Recall * REV specify when we expect scorers to not be None * FLAKE8 * REVERT multimetrics in learning_curve and validation_curve * REVIEW Simpler coding style * COSMIT * COSMIT * REV Compress example a bit. Move comment to top * FIX fit_grid_point's previous API must be preserved * Flake8 * TST Use loop; Compare with single-metric * REVIEW Use dict-comprehension instead of helper * REVIEW Remove redundant test * Fix tests incorrect braces * COSMIT * REVIEW Use regexp * REV Simplify aggregation of score dicts * FIX precision and accuracy test * FIX doctest and flake8 * TST the best_* attributes multimetric with single metric * Address @jnothman's review * Address more comments \o/ * DOCFIXES * Fix use the validated fit_param from fit's arguments * Revert alpha to a lower value as before * Using def instead of lambda * Address @jnothman's review batch 1: Fix tests / Doc fixes * Remove superfluous tests * Remove more superfluous testing * TST/FIX loop over refit and check found n_clusters * Cosmetic touches * Use zip instead of manually listing the keys * Fix inverse_transform * FIX bug in fit_grid_point; Allow only single score TST if fit_grid_point works as intended * ENH Use only ROC-AUC and F1-score * Fix typos and flake8; Address Andy's reviews MNT Add a comment on why we do such a transpose + some fixes * ENH Better error messages for incorrect multimetric scoring values +... ENH Avoid exception traceback while using incorrect scoring string * Dict keys must be of string type only * 1. Better error message for invalid scoring 2... Internal functions return single score for single metric scoring * Fix test failures and shuffle tests * Avoid wrapping scorer as dict in learning_curve * Remove doc example as asked for * Some leftover ones * Don't wrap scorer in validation_curve either * Add a doc example and skip it as dict order fails doctest * Import zip from six for python2.7 compat * Make cross_val_score return a cv_results-like dict * Add relevant sections to userguide * Flake8 fixes * Add whatsnew and fix broken links * Use AUC and accuracy instead of f1 * Fix failing doctests cross_validation.rst * DOC add the wrapper example for metrics that return multiple return values * Address andy's comments * Be less weird * Address more of andy's comments * Make a separate cross_validate function to return dict and a cross_val_score * Update the docs to reflect the new cross_validate function * Add cross_validate to toc-tree * Add more tests on type of cross_validate return and time limits * FIX failing doctests * FIX ensure keys are not plural * DOC fix * Address some pending comments * Remove the comment as it is irrelevant now * Remove excess blank line * Fix flake8 inconsistencies * Allow fit_times to be 0 to conform with windows precision * DOC specify how refit param is to be set in multiple metric case * TST ensure cross_validate works for string single metrics + address @jnothman's reviews * Doc fixes * Remove the shape and transform parameter of _aggregate_score_dicts * Address Joel's doc comments * Fix broken doctest * Fix the spurious file * Address Andy's comments * MNT Remove erroneous entry * Address Andy's comments * FIX broken links * Update whats_new.rst missing newline
2017-07-07 23:12:31 +08:00
"cross_validate",
Main Commits - Major -------------------- * 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 * TST Add tests for the new model_selection members * ENH Wrap the old-style cv obj/iterables instead of using iter_cv * ENH Use scipy's binomial coefficient function comb for calucation of nCk * ENH Few enhancements to the split module * ENH Improve check_cv input validation and docstring * MAINT _get_test_folds(X, y, labels) --> _get_test_folds(labels) * TST if 1d arrays for X introduce any errors * ENH use 1d X arrays for all tests; * ENH X_10 --> X (global var) Minor ----- * ENH _PartitionIterator --> _BaseCrossValidator; * ENH CVIterator --> CVIterableWrapper * TST Import the old SKF locally * FIX/TST Clean up the split module's tests. * DOC Improve documentation of the cv parameter * COSMIT consistently hyphenate cross-validation/cross-validator * TST Calculate n_samples from X * COSMIT Use separate lines for each import. * COSMIT cross_validation_generator --> cross_validator 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 * FIX avoid memory cost when sampling from large parameter grids ENH Major to Minor incremental enhancements to the model_selection Squashed commit messages - (For reference) Major ----- * ENH p --> n_labels * FIX *ShuffleSplit: all float/invalid type errors at init and int error at split * FIX make PredefinedSplit accept test_folds in constructor; Cleanup docstrings * ENH+TST KFold: make rng to be generated at every split call for reproducibility * FIX/MAINT KFold: make shuffle a public attr * FIX Make CVIterableWrapper private. * FIX reuse len_cv instead of recalculating it * FIX Prevent adding *SearchCV estimators from the old grid_search module * re-FIX In all_estimators: the sorting to use only the 1st item (name) To avoid collision between the old and the new GridSearch classes. * FIX test_validate.py: Use 2D X (1D X is being detected as a single sample) * MAINT validate.py --> validation.py * MAINT make the submodules private * MAINT Support old cv/gs/lc until 0.19 * FIX/MAINT n_splits --> get_n_splits * FIX/TST test_logistic.py/test_ovr_multinomial_iris: pass predefined folds as an iterable * MAINT expose BaseCrossValidator * Update the model_selection module with changes from master - From #5161 - - MAINT remove redundant p variable - - Add check for sparse prediction in cross_val_predict - From #5201 - DOC improve random_state param doc - From #5190 - LabelKFold and test - From #4583 - LabelShuffleSplit and tests - From #5300 - shuffle the `labels` not the `indxs` in LabelKFold + tests - From #5378 - Make the GridSearchCV docs more accurate. - From #5458 - Remove shuffle from LabelKFold - From #5466(#4270) - Gaussian Process by Jan Metzen - From #4826 - Move custom error / warnings into sklearn.exception Minor ----- * ENH Make the KFold shuffling test stronger * FIX/DOC Use the higher level model_selection module as ref * DOC in check_cv "y : array-like, optional" * DOC a supervised learning problem --> supervised learning problems * DOC cross-validators --> cross-validation strategies * DOC Correct Olivier Grisel's name ;) * MINOR/FIX cv_indices --> kfold * FIX/DOC Align the 'See also' section of the new KFold, LeaveOneOut * TST/FIX imports on separate lines * FIX use __class__ instead of classmethod * TST/FIX import directly from model_selection * COSMIT Relocate the random_state documentation * COSMIT remove pass * MAINT Remove deprecation warnings from old tests * FIX correct import at test_split * FIX/MAINT Move P_sparse, X, y defns to top; rm unused W_sparse, X_sparse * FIX random state to avoid doctest failure * TST n_splits and split wrapping of _CVIterableWrapper * FIX/MAINT Use multilabel indicator matrix directly * TST/DOC clarify why we conflate classes 0 and 1 * DOC add comment that this was taken from BaseEstimator * FIX use of labels is not needed in stratified k fold * Fix cross_validation reference * Fix the labels param doc FIX/DOC/MAINT Addressing the review comments by Arnaud and Andy COSMIT Sort the members alphabetically COSMIT len_cv --> n_splits COSMIT Merge 2 if; FIX Use kwargs DOC Add my name to the authors :D DOC make labels parameter consistent FIX Remove hack for boolean indices; + COSMIT idx --> indices; DOC Add Returns COSMIT preds --> predictions DOC Add Returns and neatly arrange X, y, labels FIX idx(s)/ind(s)--> indice(s) COSMIT Merge if and else to elif COSMIT n --> n_samples COSMIT Use bincount only once COSMIT cls --> class_i / class_i (ith class indices) --> perm_indices_class_i FIX/ENH/TST Addressing the final reviews COSMIT c --> count FIX/TST make check_cv raise ValueError for string cv value TST nested cv (gs inside cross_val_score) works for diff cvs FIX/ENH Raise ValueError when labels is None for label based cvs; TST if labels is being passed correctly to the cv and that the ValueError is being propagated to the cross_val_score/predict and grid search FIX pass labels to cross_val_score FIX use make_classification DOC Add Returns; COSMIT Remove scaffolding TST add a test to check the _build_repr helper REVERT the old GS/RS should also be tested by the common tests. ENH Add a tuple of all/label based CVS FIX raise VE even at get_n_splits if labels is None FIX Fabian's comments PEP8
2015-06-05 03:45:10 +08:00
"learning_curve",
"permutation_test_score",
"train_test_split",
FEA Successive halving for faster parameter search (#13900) * More flexible grid search interface * added info dict parameter * Put back removed test * renamed info into more_results * Passed grroups as well since we need n_to use get_n_splits(X, y, groups) * port * pep8 * dabl -> sklearn * add _required_parameters * skipping check in rst file if pandas not installed * Update sklearn/model_selection/_search_successive_halving.py Co-Authored-By: Joel Nothman <joel.nothman@gmail.com> * renamed into GridHalvingSearchCV and RandomHalvingSearchCV * Addressed thomas' comments * repr * removed passing group as a parameter to evaluate_candidates * Joels comments * pep8 * reorganized user user guide * renaming * update user guide * remove groups support + pass fit_params * parameter renaming * pep8 * r_i -> resource_iter * fixed r_i issues * examples + removed use of word budget * Added inpute checking tests * added cv_resutlts_ user guide * minor title change * fixed doc layout * Addressed some comments * properly pass down fit_params * change default value of force_exhaust_resources and update doc * should fix doc * Used check_fit_params * Update section about min_resources and number of candidates * Clarified ratio section * Use ~ to refer to classes * fixed doc checks * Apply suggestions from code review Co-authored-by: Joel Nothman <joel.nothman@gmail.com> * Addressed easy comments from Joel * missed some * updated docstring of run_search * Used f strings instead of format * remove candidate duplication checks * fix example * Addressed easy comments * rotate ticks labels * Added discussion in the intro as suggested by Joel * Split examples into sections * minor changes * remove force_exhaust_budget and introduce min_resources=exhaust * some minor validation * Added a n_resources_ attribute * update examples * Addressed comments * passing CV instead of X,y * minor revert for handling fit_params * updated docs * fix len * whatsnew * Add test for sampling when all_list * minor change to top-k * Force CV splits to be consistent across calls * reorder parameters * reduced diff * added tests for top_k * put back doc for groups * not sure what went wrong * put import at its place * some comment * Addressed comments * Added tests for cv_results_ and base estimator inputs * pep8 * avoid monkeypatching * rename df * use Joel's suggestions for testing masks * Made it experimental * Should fix docs * whats new entry * Apply suggestions from code review Co-authored-by: Andreas Mueller <t3kcit@gmail.com> * Addressed comments to docs * Addressed comments in examples * minor doc update * minor renaming in UG * forgot some * some sad note about splitter statefulness :'( * Addressed comments * ratio -> factor Co-authored-by: Joel Nothman <joel.nothman@gmail.com> Co-authored-by: Andreas Mueller <t3kcit@gmail.com>
2020-09-09 23:12:35 +08:00
"validation_curve",
]