[tune](deps): Bump xgboost from 1.3.3 to 1.4.2 in /python/requirements/tune

[dependabot]

Bumps xgboost from 1.3.3 to 1.4.2.

Release notes

Sourced from xgboost's releases.

1.4.2 Patch Release

This is a patch release for Python package with following fixes:

• Handle the latest version of cupy.ndarray in inplace_predict. dmlc/xgboost#6933
• Ensure output array from predict_leaf is (n_samples, ) when there's only 1 tree. 1.4.0 outputs (n_samples, 1). dmlc/xgboost#6889
• Fix empty dataset handling with multi-class AUC. dmlc/xgboost#6947
• Handle object type from pandas in inplace_predict. dmlc/xgboost#6927

You can verify the downloaded source code xgboost.tar.gz by running this on your unix shell:

echo "3ffd4a90cd03efde596e51cadf7f344c8b6c91aefd06cc92db349cd47056c05a *xgboost.tar.gz" | shasum -a 256 --check

1.4.1 Patch Release

This is a bug fix release.

• Fix GPU implementation of AUC on some large datasets. (#6866)

You can verify the downloaded source code xgboost.tar.gz by running this on your unix shell:

echo "f3a37e5ddac10786e46423db874b29af413eed49fd9baed85035bbfee6fc6635 *xgboost.tar.gz" | shasum -a 256 --check

Release 1.4.0 stable

Introduction of pre-built binary package for R, with GPU support

Starting with release 1.4.0, users now have the option of installing {xgboost} without having to build it from the source. This is particularly advantageous for users who want to take advantage of the GPU algorithm (gpu_hist), as previously they'd have to build {xgboost} from the source using CMake and NVCC. Now installing {xgboost} with GPU support is as easy as: R CMD INSTALL ./xgboost_r_gpu_linux.tar.gz. (#6827)

See the instructions at https://xgboost.readthedocs.io/en/latest/build.html

Improvements on prediction functions

XGBoost has many prediction types including shap value computation and inplace prediction. In 1.4 we overhauled the underlying prediction functions for C API and Python API with an unified interface. (#6777, #6693, #6653, #6662, #6648, #6668, #6804)

• Starting with 1.4, sklearn interface prediction will use inplace predict by default when input data is supported.
• Users can use inplace predict with dart booster and enable GPU acceleration just like gbtree.
• Also all prediction functions with tree models are now thread-safe. Inplace predict is improved with base_margin support.
• A new set of C predict functions are exposed in the public interface.
• A user-visible change is a newly added parameter called strict_shape. See https://xgboost.readthedocs.io/en/latest/prediction.html for more details.

Improvement on Dask interface

• Starting with 1.4, the Dask interface is considered to be feature-complete, which means

... (truncated)

Changelog

Sourced from xgboost's changelog.

v1.4.2 (2021.05.13)

This is a patch release for Python package with following fixes:

• Handle the latest version of cupy.ndarray in inplace_predict. (#6933)
• Ensure output array from predict_leaf is (n_samples, ) when there's only 1 tree. 1.4.0 outputs (n_samples, 1). (#6889)
• Fix empty dataset handling with multi-class AUC. (#6947)
• Handle object type from pandas in inplace_predict. (#6927)

v1.4.1 (2021.04.20)

This is a bug fix release.

• Fix GPU implementation of AUC on some large datasets. (#6866)

v1.4.0 (2021.04.12)

Introduction of pre-built binary package for R, with GPU support

Starting with release 1.4.0, users now have the option of installing {xgboost} without having to build it from the source. This is particularly advantageous for users who want to take advantage of the GPU algorithm (gpu_hist), as previously they'd have to build {xgboost} from the source using CMake and NVCC. Now installing {xgboost} with GPU support is as easy as: R CMD INSTALL ./xgboost_r_gpu_linux.tar.gz. (#6827)

See the instructions at https://xgboost.readthedocs.io/en/latest/build.html

Improvements on prediction functions

XGBoost has many prediction types including shap value computation and inplace prediction. In 1.4 we overhauled the underlying prediction functions for C API and Python API with an unified interface. (#6777, #6693, #6653, #6662, #6648, #6668, #6804)

• Starting with 1.4, sklearn interface prediction will use inplace predict by default when input data is supported.
• Users can use inplace predict with dart booster and enable GPU acceleration just like gbtree.
• Also all prediction functions with tree models are now thread-safe. Inplace predict is improved with base_margin support.
• A new set of C predict functions are exposed in the public interface.
• A user-visible change is a newly added parameter called strict_shape. See https://xgboost.readthedocs.io/en/latest/prediction.html for more details.

Improvement on Dask interface

• Starting with 1.4, the Dask interface is considered to be feature-complete, which means all of the models found in the single node Python interface are now supported in Dask, including but not limited to ranking and random forest. Also, the prediction function is significantly faster and supports shap value computation.
  • Most of the parameters found in single node sklearn interface are supported by Dask interface. (#6471, #6591)
  • Implements learning to rank. On the Dask interface, we use the newly added support of query ID to enable group structure. (#6576)
  • The Dask interface has Python type hints support. (#6519)

... (truncated)

Commits

• 522b897 Make 1.4.2 patch release. (#6962)
• 8147d78 [back port] Fix multiclass auc with empty dataset. (#6947) (#6960)
• 651c4ac [CI] Fix CI/CD pipeline broken by latest auditwheel (4.0.0) (#6951) (#6952)
• 1fb75d2 Make it easier to release GPU/CPU code artifacts to Maven Central (#6907)
• 6609211 [backport] Enforce input data is not object. (#6927) (#6938)
• b78ad1e [backport] Ensure predict leaf output 1-dim vector where there's only 1 tree....
• 96f8843 [backport] CI fixes (#6933)
• a6d1fbf Fix warning on Windows. (#6883)
• a347ef7 Release 1.4.1 (#6874)
• d05c47d [back port] Copy output data for argsort. (#6866) (#6870)
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[dependabot]

Superseded by #71.