Onnxmltools

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](https://github.com/onnx/sklearn-onnx) and [Keras](https://github.com/onnx/keras-onnx).

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.