Improved error handling

Square2rect

Sparse PCA

Robertlayton kmeans transform2

Multilabel

Update random forest example to demo n_jobs=2

Hmmc

… estimator

…13243)

* Remove unused code

* Squash all the PR 9040 commits

initial PR commit

seq_dataset.pyx generated from template

seq_dataset.pyx generated from template #2

rename variables

fused types consistency test for seq_dataset

a

sklearn/utils/tests/test_seq_dataset.py

new if statement

add doc

sklearn/utils/seq_dataset.pyx.tp

minor changes

minor changes

typo fix

check numeric accuracy only up 5th decimal

Address oliver's request for changing test name

add test for make_dataset and rename a variable in test_seq_dataset

* FIX tests

* TST more numerically stable test_sgd.test_tol_parameter

* Added benchmarks to compare SAGA 32b and 64b

* Fixing gael's comments

* fix

* solve some issues

* PEP8

* Address lesteve comments

* fix merging

* avoid using assert_equal

* use all_close

* use explicit ArrayDataset64 and CSRDataset64

* fix: remove unused import

* Use parametrized to cover ArrayDaset-CSRDataset-32-64 matrix

* for consistency use 32 first then 64 + add 64 suffix to variables

* it would be cool if this worked !!!

* more verbose version

* revert SGD changes as much as possible.

* Add solvers back to bench_saga

* make 64 explicit in the naming

* remove checking native python type + add comparison between 32 64

* Add whatsnew with everyone with commits

* simplify a bit the testing

* simplify the parametrize

* update whatsnew

* fix pep8

* initial commit

* used random class

* fixed failing testcases, reverted __init__.py

* fixed failing testcases #2
- passed rng as parameter to ParameterSampler class
- changed seed from 0 to 42 (as original)

* fixed failing testcases #2
- passed rng as parameter to SparseRandomProjection class

* fixed failing testcases scikit-learn#4
- passed rng as parameter to GaussianRandomProjection class

* fixed failing test case because of flake 8

…13243)

* Remove unused code

* Squash all the PR 9040 commits

initial PR commit

seq_dataset.pyx generated from template

seq_dataset.pyx generated from template scikit-learn#2

rename variables

fused types consistency test for seq_dataset

a

sklearn/utils/tests/test_seq_dataset.py

new if statement

add doc

sklearn/utils/seq_dataset.pyx.tp

minor changes

minor changes

typo fix

check numeric accuracy only up 5th decimal

Address oliver's request for changing test name

add test for make_dataset and rename a variable in test_seq_dataset

* FIX tests

* TST more numerically stable test_sgd.test_tol_parameter

* Added benchmarks to compare SAGA 32b and 64b

* Fixing gael's comments

* fix

* solve some issues

* PEP8

* Address lesteve comments

* fix merging

* avoid using assert_equal

* use all_close

* use explicit ArrayDataset64 and CSRDataset64

* fix: remove unused import

* Use parametrized to cover ArrayDaset-CSRDataset-32-64 matrix

* for consistency use 32 first then 64 + add 64 suffix to variables

* it would be cool if this worked !!!

* more verbose version

* revert SGD changes as much as possible.

* Add solvers back to bench_saga

* make 64 explicit in the naming

* remove checking native python type + add comparison between 32 64

* Add whatsnew with everyone with commits

* simplify a bit the testing

* simplify the parametrize

* update whatsnew

* fix pep8

* initial commit

* used random class

* fixed failing testcases, reverted __init__.py

* fixed failing testcases scikit-learn#2
- passed rng as parameter to ParameterSampler class
- changed seed from 0 to 42 (as original)

* fixed failing testcases scikit-learn#2
- passed rng as parameter to SparseRandomProjection class

* fixed failing testcases scikit-learn#4
- passed rng as parameter to GaussianRandomProjection class

* fixed failing test case because of flake 8

Tversions

Varimax PR: address review comments

…#10591)

* Initial add DET curve to classification metrics

* Add DET to exports

* Fix DET-curve doctest errors

- Sample snippet in  model_evaluation documentation was outdated.

* Clarify wording in DET-curve computation

- Align to the wording of ranking module to make it consistent.
- Add correct describtion of input and outputs.
- Update and fix non-existent links

* Beautify DET curve documentation source

- Limit line length to 80 characters.

* Expand DET curve documentation

- Add an example plot to show difference between ROC and DET curves.
- Expand Usage Note section with background information and properties
of DET curves.

* Update DET-curve documentation

- Fix typos and some grammar improvements.
- Use named references to avoid potential conflicts with other sections.
- Remove unneeded references and improved existing ones by using e.g.
using versioned links.

* Select relevant DET points using slice object

* Remove some dubiety from DET curve doc-string

* Add DET curve contributors

* Add tests for DET curves

* Streamline DET test by using parametrization

* Increase verbosity of DET curve error handling

- Explicitly sanity check input before computing a DET curve.
- Add test for perfect scores.
- Adapt indentation style to match the test module.

* Add reference for DET curves in invariance test

* Add automated invariance checks for DET curves

* Resolve merge artifacts

* Make doctest happy

* Fix whitespaces for doctest

* Revert unintended whitespace changes

* Revert unintended white space changes #2

* Fix typos and grammar

* Fix white space in doc

* Streamline test code

* Remove rebase artifacts

* Fix PR link in doc

* Fix test_ranking

* Fix rebase errors

* Fix import

* Bring back newlines

- Swallowed by copy/paste

* Remove uncited ref link

* Remove matplotlib deprecation warning

* Bring back hidden reference

* Add motivation to DET example

* Fix lint

* Add citation

* Use modern matplotlib API

Co-authored-by: Jeremy Karnowski <jeremy.karnowski@gmail.com>
Co-authored-by: Julien Cornebise <julien@cornebise.com>
Co-authored-by: Daniel Mohns <daniel.mohns@zenguard.org>

…scikit-learn#10591)

* Initial add DET curve to classification metrics

* Add DET to exports

* Fix DET-curve doctest errors

- Sample snippet in  model_evaluation documentation was outdated.

* Clarify wording in DET-curve computation

- Align to the wording of ranking module to make it consistent.
- Add correct describtion of input and outputs.
- Update and fix non-existent links

* Beautify DET curve documentation source

- Limit line length to 80 characters.

* Expand DET curve documentation

- Add an example plot to show difference between ROC and DET curves.
- Expand Usage Note section with background information and properties
of DET curves.

* Update DET-curve documentation

- Fix typos and some grammar improvements.
- Use named references to avoid potential conflicts with other sections.
- Remove unneeded references and improved existing ones by using e.g.
using versioned links.

* Select relevant DET points using slice object

* Remove some dubiety from DET curve doc-string

* Add DET curve contributors

* Add tests for DET curves

* Streamline DET test by using parametrization

* Increase verbosity of DET curve error handling

- Explicitly sanity check input before computing a DET curve.
- Add test for perfect scores.
- Adapt indentation style to match the test module.

* Add reference for DET curves in invariance test

* Add automated invariance checks for DET curves

* Resolve merge artifacts

* Make doctest happy

* Fix whitespaces for doctest

* Revert unintended whitespace changes

* Revert unintended white space changes scikit-learn#2

* Fix typos and grammar

* Fix white space in doc

* Streamline test code

* Remove rebase artifacts

* Fix PR link in doc

* Fix test_ranking

* Fix rebase errors

* Fix import

* Bring back newlines

- Swallowed by copy/paste

* Remove uncited ref link

* Remove matplotlib deprecation warning

* Bring back hidden reference

* Add motivation to DET example

* Fix lint

* Add citation

* Use modern matplotlib API

Co-authored-by: Jeremy Karnowski <jeremy.karnowski@gmail.com>
Co-authored-by: Julien Cornebise <julien@cornebise.com>
Co-authored-by: Daniel Mohns <daniel.mohns@zenguard.org>

read.me 수정

Merge pull request scikit-learn#2 from dataignitelab/master

Feature/agent

Merging changes from the main repository

…cython-oop

ParallelReduction class hierarchy (POC for scikit-learn#20254)

pr feedback

deprecation warning for negative ndcg

Co-authored-by: Guillaume Lemaitre <g.lemaitre58@gmail.com>

Better CSS for doc links