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Staging area for ongoing enhancements to Ray focused on improving integration with AWS and other Amazon technologies.
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[tune](deps): Bump xgboost from 1.3.3 to 1.5.2 in /python/requirements/ml #91

Closed dependabot[bot] closed 2 years ago

dependabot[bot] commented 2 years ago

Bumps xgboost from 1.3.3 to 1.5.2.

Release notes

Sourced from xgboost's releases.

1.5.2 Patch Release

This is a patch release for compatibility with latest dependencies and bug fixes.

  • [dask] Fix asyncio with latest dask and distributed.
  • [R] Fix single sample SHAP prediction.
  • [Python] Update python classifier to indicate support for latest Python versions.
  • [Python] Fix with latest mypy and pylint.
  • Fix indexing type for bitfield, which may affect missing value and categorical data.
  • Fix num_boosted_rounds for linear model.
  • Fix early stopping with linear model.

1.5.1 Patch Release

This is a patch release for compatibility with the latest dependencies and bug fixes. Also, all GPU-compatible binaries are built with CUDA 11.0.

  • [Python] Handle missing values in dataframe with category dtype. (#7331)

  • [R] Fix R CRAN failures about prediction and some compiler warnings.

  • [JVM packages] Fix compatibility with latest Spark (#7438, #7376)

  • Support building with CTK11.5. (#7379)

  • Check user input for iteration in inplace predict.

  • Handle OMP_THREAD_LIMIT environment variable.

  • [doc] Fix broken links. (#7341)

Artifacts

You can verify the downloaded packages by running this on your Unix shell:

echo "<hash> <artifact>" | shasum -a 256 --check

3a6cc7526c0dff1186f01b53dcbac5c58f12781988400e2d340dda61ef8d14ca  xgboost_r_gpu_linux_afb9dfd4210e8b8db8fe03380f83b404b1721443.tar.gz
6f74deb62776f1e2fd030e1fa08b93ba95b32ac69cc4096b4bcec3821dd0a480  xgboost_r_gpu_win64_afb9dfd4210e8b8db8fe03380f83b404b1721443.tar.gz
565dea0320ed4b6f807dbb92a8a57e86ec16db50eff9a3f405c651d1f53a259d  xgboost.tar.gz

Release 1.5.0 stable

This release comes with many exciting new features and optimizations, along with some bug fixes. We will describe the experimental categorical data support and the external memory interface independently. Package-specific new features will be listed in respective sections.

Development on categorical data support

In version 1.3, XGBoost introduced an experimental feature for handling categorical data natively, without one-hot encoding. XGBoost can fit categorical splits in decision trees. (Currently, the generated splits will be of form x \in {v}, where the input is compared to a single category value. A future version of XGBoost will generate splits that compare the input against a list of multiple category values.)

Most of the other features, including prediction, SHAP value computation, feature importance, and model plotting were revised to natively handle categorical splits. Also,

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Changelog

Sourced from xgboost's changelog.

XGBoost Change Log

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

v1.5.0 (2021 Oct 11)

This release comes with many exciting new features and optimizations, along with some bug fixes. We will describe the experimental categorical data support and the external memory interface independently. Package-specific new features will be listed in respective sections.

Development on categorical data support

In version 1.3, XGBoost introduced an experimental feature for handling categorical data natively, without one-hot encoding. XGBoost can fit categorical splits in decision trees. (Currently, the generated splits will be of form x \in {v}, where the input is compared to a single category value. A future version of XGBoost will generate splits that compare the input against a list of multiple category values.)

Most of the other features, including prediction, SHAP value computation, feature importance, and model plotting were revised to natively handle categorical splits. Also, all Python interfaces including native interface with and without quantized DMatrix, scikit-learn interface, and Dask interface now accept categorical data with a wide range of data structures support including numpy/cupy array and cuDF/pandas/modin dataframe. In practice, the following are required for enabling categorical data support during training:

  • Use Python package.
  • Use gpu_hist to train the model.
  • Use JSON model file format for saving the model.

Once the model is trained, it can be used with most of the features that are available on the Python package. For a quick introduction, see https://xgboost.readthedocs.io/en/latest/tutorials/categorical.html

Related PRs: (#7011, #7001, #7042, #7041, #7047, #7043, #7036, #7054, #7053, #7065, #7213, #7228, #7220, #7221, #7231, #7306)

  • Next steps

    • Revise the CPU training algorithm to handle categorical data natively and generate categorical splits
    • Extend the CPU and GPU algorithms to generate categorical splits of form x \in S where the input is compared with multiple category values. split. (#7081)

External memory

This release features a brand-new interface and implementation for external memory (also known as out-of-core training). (#6901, #7064, #7088, #7089, #7087, #7092, #7070, #7216). The new implementation leverages the data iterator interface, which is currently used to create DeviceQuantileDMatrix. For a quick introduction, see https://xgboost.readthedocs.io/en/latest/tutorials/external_memory.html#data-iterator . During the development of this new interface, lz4 compression is removed. (#7076).

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Commits


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dependabot[bot] commented 2 years ago

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dependabot[bot] commented 2 years ago

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