GitLab

[python-package] Use scikit-learn interpretation of negative `n_jobs` and change default to number of cores (#5105)

* use joblib formula for negative n_jobs

* correction for n_jobs calculation

* use more robust cpu_count from joblib

* change default n_jobs to number of cores

* fix detection of num_threads under parameters

* better handling of n_jobs at prediction time

* fix incorrect usage of list.pop

* correct pop/remove yet again

* Update python-package/lightgbm/sklearn.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update tests/python_package_test/test_sklearn.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update tests/python_package_test/test_sklearn.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* add comments clarifying negative n_jobs

* fix CI (code taken from PR comment)

* change default to n_jobs=None in dask interface

* corrections for handling of n_jobs

* linter

* corrections for predict-time n_jobs

* linter

* add more comments about n_jobs values

* linter

* more corrections

* linter

* linter

* linter

* Update python-package/lightgbm/compat.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/sklearn.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/sklearn.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/sklearn.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/sklearn.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* workaround for passing test about outputs with multiple threads

* Update tests/python_package_test/test_sklearn.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update tests/python_package_test/test_sklearn.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* docs: weight parameter non-negative

* docs: weights non negative only for train data

* docs: weights should be non negative for validation data

* typo in html render

* docs: brief weights non-negative description

* add test for custom objective with regressor

* add test for custom binary classification objective with classifier

* isort

* got tests working for multiclass

* update docs

* train deeper model for classifier

* Apply suggestions from code review
Co-authored-by: José Morales <jmoralz92@gmail.com>

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* update multiclass tests

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* fix multiclass probabilities

* linting
Co-authored-by: José Morales <jmoralz92@gmail.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* [python-package][dask] handle failures parsing work host names

* add tests

* revert local testing changes

* [python-package] fix mypy error about missing type hint in dask.py

* list of lists

* use different variable

* use append()

* Update test_plotting.py

* Update dask.py

* Update sklearn.py

* Update test_sklearn.py

* Update basic.py

* Update engine.py

* Update test_engine.py

* Update basic.py

* Update basic.py

* Update engine.py

* [python] add type hints for custom objective and metric functions in scikit-learn interface

* update type hints

* remote unnecessary input

* Update python-package/lightgbm/sklearn.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* remove type hint on objective being callable
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* [docs] [dask] Add return information to Dask fit() docs (fixes #4402)

* put return before note

[python-package] Support 2d collections as input for `init_score` in multiclass classification task (#4150)

* initial implementation of init_score for multiclass classification

* check for 1d or 2d collection in init_score

* remove dataset import

* initial comments

* update dask test and docstrings

* update docstrings

* move logic to set_field. reshape back on get_field

* add type hints and update docstrings for dask. fix Dataset.set_field

* revert wrong docstrings and type hints

* add extra comma for consistency

* prefix private functions with underscore

add type hints to new functions

make commas consistent in dask and basic

* add missing spaces after type hint

* remove shape condition for dataframe in is_2d_collection
Co-authored-by: Nikita Titov <nekit94-12@hotmail.com>

* deprecate `silent` and standalone `verbose` args. Prefer global `verbose` param

* simplify code

* Rephrase warning messages

* Reffer to string type as `str` and and commas in `list of ...` types

* update `libpath.py` too

* find all needed ports in each worker at once

* lint

* better naming

* use _HostWorkers in test

* call predict on one row of data to determine output shape

* make DaskLGBMRanker predict method equal to the others

* remove extra drop_axis

[dask] Make output of feature contribution predictions for sparse matrices match those from sklearn estimators (fixes #3881) (#4378)

* test_classifier working

* adding tests

* docs

* tests

* revert unnecessary changes in tests

* test output type

* linting

* linting

* use from_delayed() instead

* docstring pycodestyle is happy with

* isort

* put pytest skips back

* respect sparse return type

* fix doc

* remove unnecessary dask_array_concatenate()

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* update predict_proba() docstring

* remove unnecessary np.array()

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* fix assertion

* fix test use of len()

* restore np.array() in tests

* use np.asarray() instead

* use toarray()

* remove empty functions in compat
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update test_sklearn.py

* Update test_basic.py

* Update dask.py

* Update basic.py

* Update basic.py

* Update basic.py

* Update basic.py

* Update callback.py

* es WiP, need to add eval_sample_weight and eval_group

* add weight, group to dask es. WiP.

* dask es reorg

* Update python-package/lightgbm/dask.py

_train_part model.fit args to lines
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update tests/python_package_test/test_dask.py

_train_part model.fit args to lines, pt2
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update python-package/lightgbm/dask.py

_train_part model.fit args to lines pt3
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update tests/python_package_test/test_dask.py

dask_model.fit args to lines
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update tests/python_package_test/test_dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update python-package/lightgbm/dask.py

use is instead of id()
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update python-package/lightgbm/dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update python-package/lightgbm/dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update python-package/lightgbm/dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update tests/python_package_test/test_dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update tests/python_package_test/test_dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update python-package/lightgbm/dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update python-package/lightgbm/dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update python-package/lightgbm/dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* applying changes to eval_set PR WiP

* dask support for eval_names, eval_metric, eval_stopping_rounds

* add evals_result checks and other eval_set attribute-related test checks. need to merge master - WiP

* fix lint errors in test_dask.py

* drop group_shape from _lgbmmodel_doc_fit.format for non-rankers, add support for eval_at for dask ranker

* add eval_at to test_dask eval_set ranker tests

* add back group_shape to lgbmmmodel docs, tighten tests

* drop random eval weights from early stopping, probably causing training to terminate too early

* add eval data templates to sklearn fit docs, add eval data docs to dask

* add n_features to _create_data, eval_set tests stop w/ desirable tree counts

* import alphabetically

* add back get_worker for eval_set error handling

* test_dask argmin typo

* push forgotten eval_names bugfix

* eval_stopping_rounds -> early_stopping_rounds, fix failing non-es test

* change default eval_at to tuple 1-5

* re-drop get_worker

* drop early stopping support from eval_set commits, move eval_set worker check prior to client.submit

* add eval_class_weight and eval_init_score to lightgbm/dask, WiP

* clean up eval_set tests, allow user to specify fewer eval_names, clswghts than eval_sets

* remove redundant backslash

* lint fixes

* fix eval_at, eval_metric duplication, let eval_at be Iterable not just Tuple

* use all data_outputs for test_eval_set tests

* undo newlines from first pr

* add custom_eval_metric test, correct issue with eval_at and metric names

* move _constant_metric outside of test

* dataset reference names instead of __strings__

* add padding to eval_set parts makes each part has same len(eval_set)

* eval set code clean up

* revert n_evals to be max len eval_set across all parts on worker

* pylint errors in _DatasetNames

* more pylint fixes

* pylinting...

* add by pytest.mark, mistakenly deleted during merge conflict resolution

* address code review comments

* add _pad_eval_names to handle nondeterministic evals_result_ valid set names

* change not evaluated evals_result_ test criteria

* address fit eval docs issues, switch _DatasetNames to Enum

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/dask.py
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* update eval_metrics, eval_at dask fit docstr to match sklearn, make tests reflect that l2 (rmse), logloss in evals_result_ by default

* address eval_set dict keys naming in docstr and training eval_set naming issue

* in test_dask check for obj-default metric names in eval_results, remove check for training key

* lint fixes for _pad_eval_names

* remove unnecessary breaklinen in _pad_eval_names docstr

* use Enum.member syntax not Enum.member.name

* remove str from supported eval_at types

* add whitespace and remove DaskDataframes mention from eval_ param docstrs in _train

* remove "of shape = [n_samples]" from group_shape docs

* add eval_at base_doc in DaskLGBMRanker.fit

* remove excess paren from eval_names docs in _train

* make requested changes to test_dask.py

* remove Optional() wrapper on eval_at

* add _lgbmmodel_doc_custom_eval_note to dask.py fit.__doc__

* fix ordering of .sklearn imports to attempt lint fix

* dask custom eval note to f-string pt1
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* dask custom eval note to f-string pt 2
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* dask custom eval note to f-string pt 3
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: James Lamb <jaylamb20@gmail.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* [dask] pass predict() kwargs through when input is a Dask Array

* add tests

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* add prediction early stopping params
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* added f-string to dask.py

* Update python-package/lightgbm/dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update python-package/lightgbm/dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Updated branch

* Updated file as per specifications

* Removed warning as per specification

* update other places

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* revert unnecessary change

* Apply suggestions from code review
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: James Lamb <jaylamb20@gmail.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Correct spelling

Most changes were in comments, and there were a few changes to literals for log output.

There were no changes to variable names, function names, IDs, or functionality.

* Clarify a phrase in a comment
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Clarify a phrase in a comment
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Clarify a phrase in a comment
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Correct spelling

Most are code comments, but one case is a literal in a logging message.

There are a few grammar fixes too.
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* fix typo in dask _train as mentioned in 4101

* Update python-package/lightgbm/dask.py
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* include voting_parallel tree_learner in test_regressor, test_classifier and test_ranker

* remove test for warnings and test for error when using feature_parallel

* use real names for tree_learner intest and include test for aliases. use the error message in the test for error in feature parallel

* split all tests with rf in test_classifier

* remove task parametrization for tree_learner aliases test. smaller input data from feature_parallel error

* define task for tree_learner aliases

* [dask] make random port search more resilient to random collisions

* linting

* more reliable ports check

* address review comments

* add error message

* [dask] run one training task on each worker

* add comment on pure

* missing ticks

* empty commit

* include test for prediction with raw_score

* close client

* initial comments

* update data creation and include ranking task

* linting

* update _create_data

* compare unique raw_predictions with values in leaves_df

* [dask] raise more informative error for duplicates in 'machines'

* uncomment

* avoid test failure

* Revert "avoid test failure"

This reverts commit 9442bdf00f193a19a923dc0deb46b7822cb6f601.

* include support for init_score

* use dataframe from init_score and test difference with and without init_score in local model

* revert refactoring

* initial docs. test between distributed models with and without init_score

* remove ranker from tests

* test value for root node and change docs

* comma

* re-include parametrize

* fix incorrect merge

* use single init_score and the booster_ attribute

* use np.float64 instead of float

* use socket.bind with port 0 and client.run to find random open ports

* include test for found ports

* find random open ports as default

* parametrize local_listen_port. type hint to _find_random_open_port. fid open ports only on workers with data.

* make indentation consistent and pass list of workers to client.run

* remove socket import

* change random port implementation

* fix test