scikit-learn to ONNX Tutorial

The tutorial goes from a simple example which converts a pipeline to a more complex example involving operator not actually implemented in ONNX operators or ONNX ML Operators.

• The easy case
  • Train and deploy a scikit-learn pipeline
  • Benchmark ONNX conversion
  • What is the opset number?
  • One model, many possible conversions with options
  • Black list operators when converting
  • Choose appropriate output of a classifier
  • Issues when switching to float
  • Funny discrepancies
  • Intermediate results and investigation
  • Dataframe as an input
  • Transfer Learning with ONNX
  • Store arrays in one onnx graph
  • Modify the ONNX graph
• Using converter from other libraries
  • Convert a pipeline with a LightGBM classifier
  • Convert a pipeline with a XGBoost model
• A custom converter for a custom model
  • Implement a new converter
  • Two ways to implement a converter
  • Implement a new converter using other converters
  • A new converter with options
  • Change the number of outputs by adding a parser
  • Add a parser to handle dataframes
  • Converter for WOEEncoder from categorical_encoder
  • Converter for WOE
• Extend ONNX, extend runtime
  • Fast design with a python runtime
  • Fast runtime with onnxruntime
• Complex Scenarios
  • TfIdf and sparse matrices
  • Convert a pipeline with a LightGBM regressor

The tutorial was tested with following version:

<<<

import sys
import numpy
import scipy
import onnx
import onnxruntime
import lightgbm
import xgboost
import sklearn
import onnxconverter_common
import onnxmltools
import skl2onnx
import pyquickhelper
import mlprodict
import onnxcustom

print("python {}".format(sys.version_info))
mods = [numpy, scipy, sklearn, lightgbm, xgboost,
        onnx, onnxmltools, onnxruntime, onnxcustom,
        onnxconverter_common,
        skl2onnx, mlprodict, pyquickhelper]
mods = [(m.__name__, m.__version__) for m in mods]
mx = max(len(_[0]) for _ in mods) + 1
for name, vers in sorted(mods):
    print("{}{}{}".format(name, " " * (mx - len(name)), vers))

>>>

    python sys.version_info(major=3, minor=9, micro=1, releaselevel='final', serial=0)
    lightgbm             3.3.2
    mlprodict            0.8.1747
    numpy                1.22.3
    onnx                 1.11.0
    onnxconverter_common 1.10.0
    onnxcustom           0.4.293
    onnxmltools          1.10.0
    onnxruntime          1.11.993+cpu
    pyquickhelper        1.11.3711
    scipy                1.8.0
    skl2onnx             1.11
    sklearn              1.0.2
    xgboost              1.5.2

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The easy case