GitLab

* Update test_dask.py

* Update test_dask.py

* centralize Python-package logging in one place

* continue

* fix test name

* removed unused import

* enhance test

* fix lint

* hotfix test

* workaround for GPU test

* remove custom logger from Dask-package

* replace one log func with flags by multiple funcs

* move make_ranking into utils

* do not cache

* TST Migrates test_plotting.py to pytest

* STY Fixes linting

* [dask] use keyword args for internal function calls

* add missing comma

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

* revert whitespace changes

* test style
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update test_dual.py

* Update .vsts-ci.yml

* Update .vsts-ci.yml

* Update test_dual.py

* Update .vsts-ci.yml

* Update .vsts-ci.yml

* Update .vsts-ci.yml

* adding pred_contrib support

* add tests

* linting

* remove raw_score

* add pred kwargs

* faster tests

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

* changes to tests

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

* ranker support wip

* fix ranker tests

* fix _make_ranking rnd gen bug, add sleep to help w stoch binding port failed exceptions

* add wait_for_workers to prevent Binding port exception

* another attempt to stabilize test_dask.py

* requested changes: docstrings, dask_ml, tuples for list_of_parts

* fix lint bug, add group param to test_ranker_local_predict

* decorator to skip tests with errors on fixture teardown

* remove gpu ranker tests, reduce make_ranking data complexity

* another attempt to
silence client, decorator does not silence fixture errors

* address requested changes on 1/20/20

* skip test_dask for all GPU tasks

* address changes requested on 1/21/21

* issubclass instead of __qualname__
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* parity in group docstr with sklearn
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* _make_ranking docstr cleanup
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* TST Migrates tset_engine.py to pytest

* ENH Apply suggestions

* ENH Uses temp path

* ENH Fixes typos

[dask] allow parameter aliases for local_listen_port, num_threads, tree_learner (fixes #3671) (#3789)

* [dask] allow parameter aliases for tree_learner and local_listen_port (fixes #3671)

* num_thread too

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

* empty commit
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* speed up tests

* [dask] reduce test times

* starting work

* fixed port-binding issue on localhost

* minor cleanup

* updates

* getting closer

* definitely working for LocalCluster

* it works, it works

* docs

* add tests

* removing testing-only files

* linting

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

* remove duplicated code

* remove unnecessary listen()
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* TST Migrates test_basic.py to use pytest

* STY Linting

* CI Force CI to run

* [R-package] allow access to params in Booster

* remove unnecessary whitespace

* fix test on resetting params

* remove pytest_cache

* Update R-package/tests/testthat/test_custom_objective.R

* small code and docs refactoring

* Update CMakeLists.txt

* Update .vsts-ci.yml

* Update test.sh

* continue

* continue

* revert stable sort for all-unique values

* Add Eigen library.

* Working for simple test.

* Apply changes to config params.

* Handle nan data.

* Update docs.

* Add test.

* Only load raw data if boosting=gbdt_linear

* Remove unneeded code.

* Minor updates.

* Update to work with sk-learn interface.

* Update to work with chunked datasets.

* Throw error if we try to create a Booster with an already-constructed dataset having incompatible parameters.

* Save raw data in binary dataset file.

* Update docs and fix parameter checking.

* Fix dataset loading.

* Add test for regularization.

* Fix bugs when saving and loading tree.

* Add test for load/save linear model.

* Remove unneeded code.

* Fix case where not enough leaf data for linear model.

* Simplify code.

* Speed up code.

* Speed up code.

* Simplify code.

* Speed up code.

* Fix bugs.

* Working version.

* Store feature data column-wise (not fully working yet).

* Fix bugs.

* Speed up.

* Speed up.

* Remove unneeded code.

* Small speedup.

* Speed up.

* Minor updates.

* Remove unneeded code.

* Fix bug.

* Fix bug.

* Speed up.

* Speed up.

* Simplify code.

* Remove unneeded code.

* Fix bug, add more tests.

* Fix bug and add test.

* Only store numerical features

* Fix bug and speed up using templates.

* Speed up prediction.

* Fix bug with regularisation

* Visual studio files.

* Working version

* Only check nans if necessary

* Store coeff matrix as an array.

* Align cache lines

* Align cache lines

* Preallocation coefficient calculation matrices

* Small speedups

* Small speedup

* Reverse cache alignment changes

* Change to dynamic schedule

* Update docs.

* Refactor so that linear tree learner is not a separate class.

* Add refit capability.

* Speed up

* Small speedups.

* Speed up add prediction to score.

* Fix bug

* Fix bug and speed up.

* Speed up dataload.

* Speed up dataload

* Use vectors instead of pointers

* Fix bug

* Add OMP exception handling.

* Change return type of LGBM_BoosterGetLinear to bool

* Change return type of LGBM_BoosterGetLinear back to int, only parameter type needed to change

* Remove unused internal_parent_ property of tree

* Remove unused parameter to CreateTreeLearner

* Remove reference to LinearTreeLearner

* Minor style issues

* Remove unneeded check

* Reverse temporary testing change

* Fix Visual Studio project files

* Restore LightGBM.vcxproj.filters

* Speed up

* Speed up

* Simplify code

* Update docs

* Simplify code

* Initialise storage space for max num threads

* Move Eigen to include directory and delete unused files

* Remove old files.

* Fix so it compiles with mingw

* Fix gpu tree learner

* Change AddPredictionToScore back to const

* Fix python lint error

* Fix C++ lint errors

* Change eigen to a submodule

* Update comment

* Add the eigen folder

* Try to fix build issues with eigen

* Remove eigen files

* Add eigen as submodule

* Fix include paths

* Exclude eigen files from Python linter

* Ignore eigen folders for pydocstyle

* Fix C++ linting errors

* Fix docs

* Fix docs

* Exclude eigen directories from doxygen

* Update manifest to include eigen

* Update build_r to include eigen files

* Fix compiler warnings

* Store raw feature data as float

* Use float for calculating linear coefficients

* Remove eigen directory from GLOB

* Don't compile linear model code when building R package

* Fix doxygen issue

* Fix lint issue

* Fix lint issue

* Remove uneeded code

* Restore delected lines

* Restore delected lines

* Change return type of has_raw to bool

* Update docs

* Rename some variables and functions for readability

* Make tree_learner parameter const in AddScore

* Fix style issues

* Pass vectors as const reference when setting tree properties

* Make temporary storage of serial_tree_learner mutable so we can make the object's methods const

* Remove get_raw_size, use num_numeric_features instead

* Fix typo

* Make contains_nan_ and any_nan_ properties immutable again

* Remove data_has_nan_ property of tree

* Remove temporary test code

* Make linear_tree a dataset param

* Fix lint error

* Make LinearTreeLearner a separate class

* Fix lint errors

* Fix lint error

* Add linear_tree_learner.o

* Simulate omp_get_max_threads if openmp is not available

* Update PushOneData to also store raw data.

* Cast size to int

* Fix bug in ReshapeRaw

* Speed up code with multithreading

* Use OMP_NUM_THREADS

* Speed up with multithreading

* Update to use ArrayToString

* Fix tests

* Fix test

* Fix bug introduced in merge

* Minor updates

* Update docs

* migrated implementation from dask/dask-lightgbm

* relaxed tests

* tests skipped in case that MPI is used

* fixed python 2.7 import + tests disabled on windows

* python < 3.6 is not supported in tests

* tests enabled only for linux

* tests disabled for mpi interface

* dask version pinned to >= 2.0

* added @jameslamb as code owner

* added missing pandas dependency

* code refactoring, removed code duplication - lightgbm.dask.LGBMClassifier.fit is the same as lightgbm.dask.LGBMRegressor.fit

* fixed refactoring

* code deduplication - fit method moved into mixin class

* fixed CODEOWNERS

* removed unnecessary import

* skip the module execution on python < 3.6 and on platform different than linux.

* removed skip for python < 3.6

* review comments

* removed noqa, renamed API classes, renamed local variables

[docs][ci] added docs about GPU support out of the box for Windows wheels and small refactoring for dual test (#3660)

* added docs about GPU support out of the box for Windows and small refactoring for dual test

* test

* Revert "test"

This reverts commit 45188103c2c9b3773bc62d97fbe865a2e5dd9120.

* fix docs

* fix docs

* hotfix config

* Apply suggestions from code review
Co-authored-by: TP Boudreau <tpboudreau@gmail.com>
Co-authored-by: TP Boudreau <tpboudreau@gmail.com>

* Add specialized OpenCL/Python package build path

* Refer to upstream OpenCL repository

* Reset build job count in setup.py

* Run test for dual CPU/GPU code in CI

* TEMPORARY: refer to OpenCL fork to ensure Linux CI builds succeed

* Try switching to OpenCL 2.0

* Add --opencl-python-pacakge option to wheel

* Fix some typos.

* Remove intermediate cmake target

* Restrict OpenCL headers to documented API version

* Refresh the environment after installation

* Try testing with two processes

* Use command line definition to activate integrated build

* Try newer OpenCL runtime

* If we hit OpenCL code path that's good enough

* Make tests run correctly

* Correct directory

* Flag reference to unofficial repo with FIXME

* TEMPORARY: update private repo tag for dependency

* Further post-merge fixes.

* Fix attribute name.

* Reformat with black.

* Rewrite config.h before library compilation for Windows wheel

* Run dual cpu/gpu test once only for Windows bdist CI

* Avoid C API tests when building gpu library for wheel

* TEMPORARY -- add clinfo tool to vm for debugging

* Increase compilation logging

* Rename OpenCL registry parameter after driver installation

* Install legacy OpenCL runtime

* ...

* Remove config.h change, clinfo installation, latest opencl runtime reference

* Add explicit script exit

* revert to 18.1 intel driver

* Run test_dual.py in two different modes

* Revert "Run test_dual.py in two different modes"

This reverts commit cbfec02659816f0cb321e1184994a9c2f37d2a77.

* Remove unused expected failure test

* Install OpenCL in separate powershell script

* fix setup.py imports

* activate conda env after session update

* separate update functions; exit opencl installation on error

* nothing; start pipeline

* download from original source

* specify tls version for installer download

* eliminate variable from command

* download opencl installer in parts

* remove duplicate setup.py command class entry

* download smaller parts

* remove redundant bdist wheel class

* Import chocolatey modules and use RefreshEnv

* insert pipeline cache for opencl installer

* rerun to ensure pipeline cache hit

* add comments, test another pipeline cache hit

* remove cache task; download full installer file from new location

* try another installer download timing

* reinsert pipeline cache

* retain cache task; download full file on cache miss

* test cache hit again

* remove todo
Co-authored-by: TP Boudreau <tpboudreau@gmail.com>

* Update setup.py

* Update .appveyor.yml

* Update .travis.yml

* Update .vsts-ci.yml

* Update __init__.py

* Update test.sh

* Update test_windows.ps1

* Update advanced_example.py

* Update requirements_base.txt

* Update conf.py

* Update conf.py

* Update test_engine.py

* Update utils.py

* Update dockerfile-r

* Update README.md

* Update dockerfile.gpu

* Update test_consistency.py

* Update basic.py

* Update compat.py

* Update engine.py

* Update sklearn.py

* Update sklearn.py

* Update callback.py

* Update setup.py

* Update __init__.py

* Update plotting.py

* Update sklearn.py

* Update engine.py

* Update compat.py

* Update callback.py

* Update basic.py

* Update compat.py

* Update basic.py

* Update basic.py

* Update compat.py

* Update compat.py

* Update plotting.py

* Update engine.py

* Update basic.py

* Update sklearn.py

* Update compat.py

* Update engine.py

* Update engine.py

* Update callback.py

* Update basic.py

* Update basic.py

* Update basic.py

* Update basic.py

* Update basic.py

* Update sklearn.py

* Update sklearn.py

* Update plotting.py

* Update sklearn.py

* Update compat.py

* Update compat.py

* Update engine.py

* Update plotting.py

* Update sklearn.py

* Update basic.py

* Update basic.py

* Update basic.py

* Update basic.py

* Update compat.py

* Update compat.py

* Update compat.py

* Update engine.py

* Update basic.py

* Update compat.py

* Update basic.py

* Update basic.py

* Update basic.py

* Update compat.py

* Update compat.py

* Update basic.py

* Update basic.py

* Update .vsts-ci.yml

* Update .vsts-ci.yml

* Update conf.py

* Revert "Update dockerfile-r"

This reverts commit 4ff6ffc7e3eeda24cc6a59a3bb0c973f02d9d71c.

* TST make sklearn integration test compatible with 0.24

* remove useless import

* remove outdated comment

* order import

* use parametrize_with_checks

* change the reason

* skip constructible if != 0.23

* make tests behave the same across sklearn version

* linter

* address suggestions

* [ci] [python] reduce unnecessary data loading in tests

* add profiling files to gitignore

* just use cache()

* default on cache size

* patch lru_cache on Python 2.7

* linting

* reduce duplicated code

* missing warnings

* fix imports

* fix lru_cache backport

* missing kwargs

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

* reduce duplicated code

* cache in test_plotting
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Add support to optimize for NDCG at a given truncation level

In order to correctly optimize for NDCG@_k_, one should exclude pairs containing both documents beyond the top-_k_ (as they don't affect NDCG@_k_ when swapped).

* Update rank_objective.hpp

* Apply suggestions from code review
Co-authored-by: Guolin Ke <guolin.ke@outlook.com>

* Update rank_objective.hpp

remove the additional branching: get high_rank and low_rank by one "if".

* Update config.h

add description to lambdarank_truncation_level parameter

* Update Parameters.rst

* Update test_sklearn.py

update expected NDCG value for a test, as it was affected by the underlying change in the algorithm

* Update test_sklearn.py

update NDCG@3 reference value

* fix R learning-to-rank tests

* Update rank_objective.hpp

* Update include/LightGBM/config.h
Co-authored-by: Guolin Ke <guolin.ke@outlook.com>

* Update Parameters.rst
Co-authored-by: Guolin Ke <guolin.ke@outlook.com>
Co-authored-by: James Lamb <jaylamb20@gmail.com>

* fix subset bug

* typo

* add fixme tag

* bin mapper

* fix test

* fix add_features_from

* Update dataset.cpp

* fix merge bug

* added Python merge code

* added test for add_features

* Update dataset.cpp

* Update src/io/dataset.cpp

* continue implementing

* warn users about categorical features
Co-authored-by: StrikerRUS <nekit94-12@hotmail.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Implement average precision score

* Fix lint errors

* Change name to average_precision

* Add to R-package list of metrics

* Empty commit to trigger CI jobs

* Change name to average_precision

* No need to pass the tree to all fuctions related to monotone constraints because the pointer is shared.

* Fix OppositeChildShouldBeUpdated numerical split optimisation.

* No need to use constraints when computing the output of the root.

* Refactor existing constraints.

* Add advanced constraints method.

* Update tests.

* Add override.

* linting.

* Add override.

* Simplify condition in LeftRightContainsRelevantInformation.

* Add virtual destructor to FeatureConstraint.

* Remove redundant blank line.

* linting of else.

* Indentation.

* Lint else.

* Replaced non-const reference by pointers.

* Forgotten reference.

* Leverage USE_MC for efficiency.

* Make constraints const again in feature_histogram.hpp.

* Update docs.

* Add "advanced" to the monotone constraints options.

* Update monotone constraints restrictions.

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Remove superfluous parenthesis.

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Fix loop iterator.
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Remove std namespace qualifier.

* Fix unsigned_int size_t comparison.

* Set num_features as int for consistency with the rest of the codebase.

* Make sure constraints exist before recomputing them.

* Initialize previous constraints in UpdateConstraints.

* Update monotone constraints restrictions.

* Refactor UpdateConstraints loop.

* Update src/io/config.cpp
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Delete white spaces.
Co-authored-by: Charles Auguste <charles.auguste@sig.com>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Update auc_mu metric to use data weights if provided

* Calculate class sizes and total weights in Init so we only do it once

* Fix lint error

* Empty commit to trigger CI jobs

* Refactors sklearn API to allow a list of evaluation metrics in the parameter eval_metric of the class (and subclasses of) LGBMModel. Also adds unit tests for this functionality

* Simplify expression to check whether the user passed one or multiple metrics to eval_metric parameter

* Simplify new tests by using custom metrics already defined in the test file

* Update docstring to reflect the fact that the parameter "feval" from the "train" and "cv" functions can also receive a list of callables

* Remove oxford comma from docstrings

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

* Use named-parameters to make sure code is compatible with future versions of scikit-learn

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

* Remove throwaway return value to make code more succinct
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

* Move statement to group together the code related to feval

* Avoid modifying original args as it causes errors in scikit-learn tools

For details see: https://github.com/microsoft/LightGBM/pull/2619



* Consolidate multiple eval-metrics unit-tests into one test
Co-authored-by: German I Ramirez-Espinoza <gire@home>
Co-authored-by: Nikita Titov <nekit94-08@mail.ru>

simplify start_iteration param for predict in Python and some code cleanup for start_iteration (#3288)

* simplify start_iteration param for predict in Python and some code cleanup for start_iteration

* revert docs changes about the prediction result shape