Handle object type from pandas in inplace_predict. dmlc/xgboost#6927
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1.4.1 Patch Release
This is a bug fix release.
Fix GPU implementation of AUC on some large datasets. (#6866)
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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)
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.
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)
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.
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)
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Bumps xgboost from 1.3.3 to 1.4.2.
Release notes
Sourced from xgboost's releases.
... (truncated)
Changelog
Sourced from xgboost's changelog.
... (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 notobject
. (#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)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)