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  1. 30 Sep, 2020 2 commits
  3. 11 Sep, 2020 1 commit
  5. 06 Sep, 2020 1 commit
  7. 11 Aug, 2020 1 commit
  9. 06 Aug, 2020 1 commit
  11. 15 Jul, 2020 1 commit
  13. 28 Jun, 2020 1 commit
    • Ilya Matiach's avatar
      adding sparse support to TreeSHAP in lightgbm (#3000) · 9f367d11
      Ilya Matiach authored 
      * adding sparse support to TreeSHAP in lightgbm

* updating based on comments

* updated based on comments, used fromiter instead of frombuffer

* updated based on comments

* fixed limits import order

* fix sparse feature contribs to work with more than int32 max rows

* really fixed int64 max error and build warnings

* added sparse test with >int32 max rows

* fixed python side reshape check on sparse data

* updated based on latest comments

* fixed comments

* added CSC INT32_MAX validation to test, fixed comments
      9f367d11
  15. 23 Jun, 2020 1 commit
    • Belinda Trotta's avatar
      Interaction constraints (#3126) · bca2da97
      Belinda Trotta authored 
      * Add interaction constraints functionality.

* Minor fixes.

* Minor fixes.

* Change lambda to function.

* Fix gpu bug, remove extra blank lines.

* Fix gpu bug.

* Fix style issues.

* Try to fix segfault on MACOS.

* Fix bug.

* Fix bug.

* Fix bugs.

* Change parameter format for R.

* Fix R style issues.

* Change string formatting code.

* Change docs to say R package not supported.

* Remove R functionality, moving to separate PR.

* Keep track of branch features in tree object.

* Only track branch features when feature interactions are enabled.

* Fix lint error.

* Update docs and simplify tests.
      bca2da97
  17. 11 Jun, 2020 1 commit
  19. 20 May, 2020 1 commit
  21. 05 May, 2020 1 commit
  23. 10 Apr, 2020 1 commit
  25. 20 Mar, 2020 1 commit
    • Alberto Ferreira's avatar
      Fix SWIG methods that return char** (#2850) · 91185c3a
      Alberto Ferreira authored 
      

* [swig] Fix SWIG methods that return char** with StringArray.

+ [new] Add StringArray class to manage and manipulate arrays of fixed-length strings:

  This class is now used to wrap any char** parameters, manage memory and
  manipulate the strings.

  Such class is defined at swig/StringArray.hpp and wrapped in StringArray.i.

+ [API+fix] Wrap LGBM_BoosterGetFeatureNames it resulted in segfault before:

  Added wrapper LGBM_BoosterGetFeatureNamesSWIG(BoosterHandle) that
  only receives the booster handle and figures how much memory to allocate
  for strings and returns a StringArray which can be easily converted to String[].

+ [API+safety] For consistency, LGBM_BoosterGetEvalNamesSWIG was wrapped as well:

  * Refactor to detect any kind of errors and removed all the parameters
    besides the BoosterHandle (much simpler API to use in Java).
  * No assumptions are made about the required string space necessary (128 before).
  * The amount of required string memory is computed internally

+ [safety] No possibility of undefined behaviour

  The two methods wrapped above now compute the necessary string storage space
  prior to allocation, as the low-level C API calls would crash the process
  irreversibly if they write more memory than which is passed to them.

* Changes to C API and wrappers support char**

To support the latest SWIG changes that enable proper char**
return support that is safe, the C API was changed.

The respecive wrappers in R and Python were changed too.

* Cleanup indentation in new lightgbm_R.cpp code

* Adress review code-style comments.

* Update swig/StringArray.hpp
Co-Authored-By: default avatarNikita Titov <nekit94-08@mail.ru>

* Update python-package/lightgbm/basic.py
Co-Authored-By: default avatarNikita Titov <nekit94-08@mail.ru>

* Update src/lightgbm_R.cpp
Co-Authored-By: default avatarNikita Titov <nekit94-08@mail.ru>
Co-authored-by: default avataralberto.ferreira <alberto.ferreira@feedzai.com>
Co-authored-by: default avatarNikita Titov <nekit94-08@mail.ru>
      91185c3a
  27. 16 Mar, 2020 2 commits
  29. 06 Mar, 2020 1 commit
  31. 20 Feb, 2020 1 commit
    • Joan Fontanals's avatar
      Add capability to get possible max and min values for a model (#2737) · 18e7de4f
      Joan Fontanals authored 
      

* Add capability to get possible max and min values for a model

* Change implementation to have return value in tree.cpp, change naming to upper and lower bound, move implementation to gdbt.cpp

* Update include/LightGBM/c_api.h
Co-Authored-By: default avatarNikita Titov <nekit94-08@mail.ru>

* Change iteration to avoid potential overflow, add bindings to R and Python and a basic test

* Adjust test values

* Consider const correctness and multithreading protection

* Update test values

* Update test values

* Add test to check that model is exactly the same in all platforms

* Try to parse the model to get the expected values

* Try to parse the model to get the expected values

* Fix implementation, num_leaves can be lower than the leaf_value_ size

* Do not check for num_leaves to be smaller than actual size and get back to test with hardcoded value

* Change test order

* Add gpu_use_dp option in test

* Remove helper test method

* Update src/c_api.cpp
Co-Authored-By: default avatarNikita Titov <nekit94-08@mail.ru>

* Update src/io/tree.cpp
Co-Authored-By: default avatarNikita Titov <nekit94-08@mail.ru>

* Update src/io/tree.cpp
Co-Authored-By: default avatarNikita Titov <nekit94-08@mail.ru>

* Update tests/python_package_test/test_basic.py
Co-Authored-By: default avatarNikita Titov <nekit94-08@mail.ru>

* Remoove imports
Co-authored-by: default avatarNikita Titov <nekit94-08@mail.ru>
      18e7de4f
  33. 19 Feb, 2020 1 commit
    • Guolin Ke's avatar
      [python] [R-package] refine the parameters for Dataset (#2594) · 9f79e840
      Guolin Ke authored 
      

* reset

* fix a bug

* fix test

* Update c_api.h

* support to no filter features by min_data

* add warning in reset config

* refine warnings for override dataset's parameter

* some cleans

* clean code

* clean code

* refine C API function doxygen comments

* refined new param description

* refined doxygen comments for R API function

* removed stuff related to int8

* break long line in warning message

* removed tests which results cannot be validated anymore

* added test for warnings about unchangeable params

* write parameter from dataset to booster

* consider free_raw_data.

* fix params

* fix bug

* implementing R

* fix typo

* filter params in R

* fix R

* not min_data

* refined tests

* fixed linting

* refine

* pilint

* add docstring

* fix docstring

* R lint

* updated description for C API function

* use param aliases in Python

* fixed typo

* fixed typo

* added more params to test

* removed debug print

* fix dataset construct place

* fix merge bug

* Update feature_histogram.hpp

* add is_sparse back

* remove unused parameters

* fix lint

* add data random seed

* update

* [R-package] centrallized Dataset parameter aliases and added tests on Dataset parameter updating (#2767)
Co-authored-by: default avatarNikita Titov <nekit94-08@mail.ru>
Co-authored-by: default avatarJames Lamb <jaylamb20@gmail.com>
      9f79e840
  35. 03 Feb, 2020 1 commit
  37. 14 Jan, 2020 2 commits
  39. 10 Jan, 2020 1 commit
    • Patrick Ford's avatar
      [python] Output model to a pandas DataFrame (#2592) · 301402c8
      Patrick Ford authored 
      * trees_to_df method and unit test added. PEP 8 fixes for integration.

* Co-Authored-By: Nikita Titov <nekit94-08@mail.ru>

Post-review changes

* changes from second round of reviews from striker

* third round of review. formatting and added 2 more tests

* replaced pandas dot attribute accessor with string attribute accessor

* dealt with single tree edge case and minor refactor of tests

* slight refactor for checking if tree is a single node
      301402c8
  41. 29 Dec, 2019 1 commit
  43. 08 Dec, 2019 1 commit
  45. 27 Oct, 2019 1 commit
  47. 22 Oct, 2019 1 commit
  49. 21 Oct, 2019 1 commit
  51. 26 Sep, 2019 3 commits
  53. 15 Sep, 2019 1 commit
    • kenmatsu4's avatar
      [python] Bug fix for first_metric_only on earlystopping. (#2209) · 84754399
      kenmatsu4 authored 
      * Bug fix for first_metric_only if the first metric is train metric.

* Update bug fix for feval issue.

* Disable feval for first_metric_only.

* Additional test items.

* Fix wrong assertEqual settings & formating.

* Change dataset of test.

* Fix random seed for test.

* Modiry assumed test result due to different sklearn verion between CI and local.

* Remove f-string

* Applying variable  assumed test result for test.

* Fix flake8 error.

* Modifying  in accordance with review comments.

* Modifying for pylint.

* simplified tests

* Deleting error criteria `if eval_metric is None`.

* Delete test items of classification.

* Simplifying if condition.

* Applying first_metric_only for sklearn wrapper.

* Modifying test_sklearn for comforming to python 2.x

* Fix flake8 error.

* Additional fix for sklearn and add tests.

* Bug fix and add test cases.

* some refactor

* fixed lint

* fixed lint

* Fix duplicated metrics scores to pass the test.

* Fix the case first_metric_only not in params.

* Converting metrics aliases.

* Add comment.

* Modify comment for pylint.

* Modify comment for pydocstyle.

* Using split test set for two eval_set.

* added test case for metric aliases and length checks

* minor style fixes

* fixed rmse name and alias position

* Fix the case metric=[]

* Fix using env.model._train_data_name

* Fix wrong test condition.

* Move initial process to _init() func.

* Modify test setting for test_sklearn & training data matching on callback.py

* test_sklearn.py
-> A test case for training is wrong, so fixed.

* callback.py
-> A condition of if statement for detecting test dataset is wrong, so fixed.

* Support composite name metrics.

* Remove metric check process & reduce redundant test cases.

For #2273 fixed not only the order of metrics in cpp, removing metric check process at callback.py

* Revised according to the matters pointed out on a review.

* increased code readability

* Fix the issue of order of validation set.

* Changing to OrderdDict from default dict for score result.

* added missed check in cv function for first_metric_only and feval co-occurrence

* keep order only for metrics but not for datasets in best_score

* move OrderedDict initialization to init phase

* fixed minor printing issues

* move first metric detection to init phase and split can be performed without checks

* split only once during callback

* removed excess code

* fixed typo in variable name and squashed ifs

* use setdefault

* hotfix

* fixed failing test

* refined tests

* refined sklearn test

* Making "feval" effective on early stopping.

* allow feval and first_metric_only for cv

* removed unused code

* added tests for feval

* fixed printing

* add note about whitespaces in feval name

* Modifying final iteration process in case valid set is  training data.
      84754399
  55. 09 Sep, 2019 1 commit
  57. 08 Sep, 2019 1 commit
    • CharlesAuguste's avatar
      [python] Improved python tree plots (#2304) · f52be9be
      CharlesAuguste authored 
      * Some basic changes to the plot of the trees to make them readable.

* Squeezed the information in the nodes.

* Added colouring when a dictionnary mapping the features to the constraints is passed.

* Fix spaces.

* Added data percentage as an option in the nodes.

* Squeezed the information in the leaves.

* Important information is now in bold.

* Added a legend for the color of monotone splits.

* Changed "split_gain" to "gain" and "internal_value" to "value".

* Sqeezed leaves a bit more.

* Changed description in the legend.

* Revert "Sqeezed leaves a bit more."

This reverts commit dd8bf14a3ba604b0dfae3b7bb1c64b6784d15e03.

* Increased the readability for the gain.

* Tidied up the legend.

* Added the data percentage in the leaves.

* Added the monotone constraints to the dumped model.

* Monotone constraints are now specified automatically when plotting trees.

* Raise an exception instead of the bug that was here before.

* Removed operators on the branches for a clearer design.

* Small cleaning of the code.

* Setting a monotone constraint on a categorical feature now returns an exception instead of doing nothing.

* Fix bug when monotone constraints are empty.

* Fix another bug when monotone constraints are empty.

* Variable name change.

* Added is / isn't on every edge of the trees.

* Fix test "tree_create_digraph".

* Add new test for plotting trees with monotone constraints.

* Typo.

* Update documentation of categorical features.

* Typo.

* Information in nodes more explicit.

* Used regular strings instead of raw strings.

* Small refactoring.

* Some cleaning.

* Added future statement.

* Changed output for consistency.

* Updated documentation.

* Added comments for colors.

* Changed text on edges for more clarity.

* Small refactoring.

* Modified text in leaves for consistency with nodes.

* Updated default values and documentaton for consistency.

* Replaced CHECK with Log::Fatal for user-friendliness.

* Updated tests.

* Typo.

* Simplify imports.

* Swapped count and weight to improve readibility of the leaves in the plotted trees.

* Thresholds in bold.

* Made information in nodes written in a specific order.

* Added information to clarify legend.

* Code cleaning.
      f52be9be
  59. 07 Sep, 2019 2 commits
  61. 13 Aug, 2019 1 commit
  63. 07 Aug, 2019 1 commit
    • Madiyar's avatar
      [python] Deep copy params in _update_params of DataSet (#2310) · 5cff4e8e
      Madiyar authored 
      Otherwise, it would print `basic.py:762: UserWarning: categorical_feature in param dict is overridden.`. Because when updating the params for a validation test, the updated params for the train test was used which contains `'categorical_column'`.
      5cff4e8e
  65. 31 Jul, 2019 1 commit
  67. 12 Jul, 2019 1 commit
    • Guolin Ke's avatar
      fix init_model with subset (#2252) · 7360cff9
      Guolin Ke authored 
      * fix init_model with subset

* Update basic.py

* added test

* fix predictor naming issue

* Update basic.py

* fix bug

* fix pylint

* fix comments

* Update basic.py

* Update basic.py

* updated test

* fixed bug

* fixed lint

* fix warning

* add get_data before initial prediction

* refine the warning in get_data

* refine warning

* Update basic.py
      7360cff9