search

kjappelbaum / oximachinerunner

An easy API for using oximachine.
MIT License
7 stars 5 forks source link

chore(deps): update xgboost requirement from ~=1.3.3 to ~=1.4.2 #63

Closed dependabot[bot] closed 3 years ago

dependabot[bot] commented 3 years ago

Updates the requirements on xgboost to permit the latest version.

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
Changelog

Sourced from xgboost's changelog.

XGBoost Change Log

This file records the changes in xgboost library in reverse chronological order.

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)
    • All models can be safely pickled. (#6651)
    • Random forest estimators are now supported. (#6602)
    • Shap value computation is now supported. (#6575, #6645, #6614)
    • Evaluation result is printed on the scheduler process. (#6609)

... (truncated)

Commits


Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Dependabot commands and options
You can trigger Dependabot actions by commenting on this PR: - `@dependabot rebase` will rebase this PR - `@dependabot recreate` will recreate this PR, overwriting any edits that have been made to it - `@dependabot merge` will merge this PR after your CI passes on it - `@dependabot squash and merge` will squash and merge this PR after your CI passes on it - `@dependabot cancel merge` will cancel a previously requested merge and block automerging - `@dependabot reopen` will reopen this PR if it is closed - `@dependabot close` will close this PR and stop Dependabot recreating it. You can achieve the same result by closing it manually - `@dependabot ignore this major version` will close this PR and stop Dependabot creating any more for this major version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this minor version` will close this PR and stop Dependabot creating any more for this minor version (unless you reopen the PR or upgrade to it yourself) - `@dependabot ignore this dependency` will close this PR and stop Dependabot creating any more for this dependency (unless you reopen the PR or upgrade to it yourself)