onnxmltools

What's Changed

• Add support to convert xgboost models with categorical features by @lukaswenzl in https://github.com/onnx/onnxmltools/pull/734
• fix issues with black by @xadupre in https://github.com/onnx/onnxmltools/pull/738
• Support xgboost 3, including for multiclass problems by @AsherWright in https://github.com/onnx/onnxmltools/pull/736
• fixes style issues by @xadupre in https://github.com/onnx/onnxmltools/pull/739
• Bump github/codeql-action from 2 to 4 by @dependabot[bot] in https://github.com/onnx/onnxmltools/pull/748
• Bump actions/cache from 4 to 5 by @dependabot[bot] in https://github.com/onnx/onnxmltools/pull/746
• Bump actions/checkout from 2 to 6 by @dependabot[bot] in https://github.com/onnx/onnxmltools/pull/747
• add worklow dispatch; Update ci.yml by @andife in https://github.com/onnx/onnxmltools/pull/749
• Bump actions/upload-artifact from 4 to 6 by @dependabot[bot] in https://github.com/onnx/onnxmltools/pull/744
• Bump actions/setup-python from 4 to 6 by @dependabot[bot] in https://github.com/onnx/onnxmltools/pull/745
• migrate to pyproject.toml; fix dependency failures by @andife in https://github.com/onnx/onnxmltools/pull/750

New Contributors

• @lukaswenzl made their first contribution in https://github.com/onnx/onnxmltools/pull/734
• @AsherWright made their first contribution in https://github.com/onnx/onnxmltools/pull/736
• @andife made their first contribution in https://github.com/onnx/onnxmltools/pull/749

Full Changelog: https://github.com/onnx/onnxmltools/compare/1.14.0...v1.15.0rc1

• Add tweedie objective to LightGBM options #722
• Support for "huber" objective in the LGBM Booster #705
• Remove import of split_complex_to_pairs and unused functions #714
• Removes dependency on onnxconveter-common #718

• Handle issue with binary classifier setting output to [N,1] vs [N,2], #681
• Fix multi regression with xgboost, #679, fixes issues No module named 'onnxconverter_common', onnx converted : xgboostRegressor multioutput model predicts 1 dimension instead of original 210 dimensions.

• Fix early stopping for XGBClassifier and xgboost > 2, #597
• Fix discrepancies with XGBRegressor and xgboost > 2, #670
• Support count:poisson for XGBRegressor, #666
• Supports XGBRFClassifier and XGBRFRegressor, #665
• ONNX_DFS_PATH to be set in the spark config, #653 (by @Ironwood-Cyber)
• Sparkml converter: support type StringType and StringType(), #639
• Add check for base_score in _get_attributes function #637, #626 (by @tolleybot)
• Support for lightgbm >= 4.0, #634

• #608 fix: Spark Imputer conversion with multiple input cols
• #607 fix: getTensorTypeFromSpark fails for Spark 3.3.0+
• #606 Add onnxruntime==1.14.0 to CI
• #605 Replace real images by dummy ones
• #602 Update CI with latext onnxruntime, xgboost
• #606 convert_lightgbm: Add shape to FloatTensor probabilities

• feat: add support for SparkML CountVectorizer conversion #560
• docs: update sparkml doc; cleanups. #559
• fix: 'SparkSession' object has no attribute 'util' #557
• feat: add support for SparkML KMeansModel conversion #556
• fix: SparkML StandardScaler conversion fails when withStd or withMean is set to true #555
• fix: Converter for SparkML VectorAssembler does not support vector inputs correctly #554
• fix: ONNX conversion for Spark OneHotEncoder model #552

• Fix conversion of XGBoost model after being restored #520
• Fix test case condition for onnx=1.11.0 #527
• Update CI for ORT 1.11.0 #539
• Adjust author and email #539

• Replace #507 + fix bug with XGBoost converter when base_score is None #510
• Use assertRegex instead of assertRegexpMatches for Python 3.11 compatibility. #508
• Support for opset 15 and update version to 1.10.0 #505
• add support for quantile objective for LGBM models #503
• Support parameter shape_override and other options for convert_tensorflow #497
• Implement option split to reduce discrepancies for lightgbm regressors #496

• Add requirements.txt to MANIFEST.in #493

LightGBM

• Improves lightgbm conversion speed #491
• Fix discovering classifier objective #480
• Fix missing type in lgbm regressor #488
• Support gamma objective in LGBMRegressor #484
• Allow to add custom post transform functions that are not supported by the ONNX spec yet #463
• Enable option zipmap for LGBM converter #452

XGBoost

• Use all tree when best_ntree_limit is not specified #459
• Fix discrepencies when xgboost trees are empty #447

Keras

• Switch to tf2onnx for tensorflow>=2.0 instead of keras2onnx #492

The major update for this release

1. Supports ONNX 1.7
2. Work with the new xgboost version
3. Remove Python 2.x support

Details: Add the flake8 to be the default code formatter (#401) Fixes #396, xgboost converter for xgboost >= 1.0.2 (#397) Support onnx 1.7 in CI build (#398) fixed the xgboost version (#395) fix ceiling-mode defaults for pool operators (AvgPool, MaxPool) (#388) Update documentation, add examples (#385) Remove support of python 2.7 (#383) upgrade to 1.7 (#384) Fix for onnx 1.7 release (#381) Ping h2o version==3.28.0.3 (#377) Fix xgboost converter (#373) xgboost not supporting 1.0 version. (#372)

Known issues: onnxmltools tf2onnx wrapper can only work with tf2onnx <= 1.5.6.

Major updates:

1. Moving onnxconverter-common package from onnxmltools repo
2. Fix CI/nightly build
3. Fix ImageScaler bias for opset 10
4. Fix lightgbm.Booster
5. Fixed XGboost classifier converter output labels
6. Set default_batch_size to 'None'

onnxmltools version 1.5.0 is now available! This version features ONNX Opset 10 support and code coverage.

How do I use the latest onnxmltools package?

pip install onnxmltools --upgrade
python -c "import onnxmltools"

This package includes converters for LightGBM, CoreML, Spark ML, LibSVM, XGBoost, and wrappers for conversion from scikit-learn and Keras.

Highlights since the last release

• Updating onnxmltools package version and requirements to 1.5.0 (#315)
• Opset 10 Updates
  • [Opset 10] Updates for thresholded relu (#308)
  • [Opset 10] Deprecate Upsample, create Resize op (#303)
  • [Opset 10] Pooling operator updates: AveragePool, MaxPool (#296)
  • Added apply_slice function to enable multiple versions of Slice (#291)
• Include code coverage / Improve CI Builds
  • Run code coverage on linux CI (#301)
  • Add support for Py3.7, onnx 1.5, onnxruntime 0.4 (#293)
• Fixing input to CoreML multiply for LeakyReLU (#297)
• Documentation update: Spark ML readme files (#289)

with some new converters, xgboost, libsvm, and pyspark. refactor onnxmltools structure by splitting keras and sklearn converters out.