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* 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
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* Apply suggestions from code review
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* update predict_proba() docstring

* remove unnecessary np.array()

* Update python-package/lightgbm/dask.py
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* fix assertion

* fix test use of len()

* restore np.array() in tests

* use np.asarray() instead

* use toarray()

* remove empty functions in compat
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* 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
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* Update tests/python_package_test/test_dask.py

_train_part model.fit args to lines, pt2
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* Update python-package/lightgbm/dask.py

_train_part model.fit args to lines pt3
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* Update tests/python_package_test/test_dask.py

dask_model.fit args to lines
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* Update tests/python_package_test/test_dask.py
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* Update python-package/lightgbm/dask.py

use is instead of id()
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update tests/python_package_test/test_dask.py
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* Update tests/python_package_test/test_dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* 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
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* Update python-package/lightgbm/dask.py
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* 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
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* dask custom eval note to f-string pt 2
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* dask custom eval note to f-string pt 3
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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
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* 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
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* Update python-package/lightgbm/dask.py
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* Updated branch

* Updated file as per specifications

* Removed warning as per specification

* update other places

* Apply suggestions from code review
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* revert unnecessary change

* Apply suggestions from code review
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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
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* Correct spelling

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

There are a few grammar fixes too.
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* 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

* [dask] allow tight control over ports

* getting there, getting there

* fix params maybe

* fixing params

* remove unnecessary stuff

* fix tests

* fixes

* some minor changes

* fix flaky test

* linting

* more linting

* clarify parameter description

* add warning

* revert docs change

* Update python-package/lightgbm/dask.py

* Apply suggestions from code review
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* trying to fix stuff

* this is working

* update tests

* Apply suggestions from code review
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* indent
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* run isort in CI linting job

* workaround conda compatibility issues

* minor mypy type errors fixed

* fix some warnings from mypy

* fix 3 mypy warnings

* selectively ignored some mypy errors

* minor mypy type errors fixed

* minor mypy type errors fixed

* minor mypy type errors fixed

* added import

* Update python-package/lightgbm/callback.py

* Apply suggestions from code review

* Apply suggestions from code review
Co-authored-by: James Lamb <jaylamb20@gmail.com>
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* Update dask.py

* Update sklearn.py

* got fit() working

* add predict()

* predict_proba()

* remove custom objective docs

* Apply suggestions from code review
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* fix capitalization

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

* fix tests

* fix tests

* fix test comments

* simplify tests

* Apply suggestions from code review

* add support for pandas categorical columns

* remove commented code

* quotes

* syntax error

* fix shape for ranker test

* Apply suggestions from code review
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* Update tests/python_package_test/test_dask.py

* trying

* fix tests

* remove unnecessary debugging stuff

* skip accuracy checks on categorical

* use category columns as categorical features
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* Update test_dask.py

* Update dask.py

* Update .vsts-ci.yml

* Revert "Update .vsts-ci.yml"

This reverts commit 98422be5b5095f0585de333b5b5545356776ef88.

* Update dask.py

* Update dask.py

* starting on Dask client

* more docs stuff

* fix pickling

* just copy docstrings

* fit docs

* switch test order

* linting

* use client kwarg

* remove inner set_params()

* add type hints

* fix type hints

* remove commented code

* reorder

* fix tests, add client_ property

* Apply suggestions from code review
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* fix tests

* linting

* simplify
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* add type hints in dask module

* starting on asserts

* remove unused code

* add hints for dtypes

* replace accidentally-removed docstrings

* revert unrelated change

* Update python-package/lightgbm/dask.py

* empty commit

* fix hints on group

* capitalize array

* hide hints in signatures

* empty commit

* sphinx version

* Apply suggestions from code review
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* fix hint for MatrixLike

* Update python-package/lightgbm/dask.py
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* Apply suggestions from code review
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* update docstring

* empty commit
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* [dask] add tests on warnings, fix incorrect variable in log

* Update tests/python_package_test/test_dask.py
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* Update sklearn.py

* Update dask.py