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
  • Choose appropriate output of a classifier
  • Black list operators when converting
  • Issues when switching to float
  • Intermediate results and investigation
  • Store arrays in one onnx graph
  • Dataframe as an input
  • Modify the ONNX graph
• Using converters from other libraries
  • Convert a pipeline with a LightGBM classifier
  • Convert a pipeline with a LightGBM regressor
  • Convert a pipeline with a XGBoost model
  • Convert a pipeline with a CatBoost classifier
• A custom converter for a custom model
  • Implement a new converter
  • Two ways to implement a converter
  • Issues with FunctionTransformer
  • Implement a new converter using other converters
  • A new converter with options
  • Change the number of outputs by adding a parser
• Advanced scenarios
  • Tricky issue when converting CountVectorizer or TfidfVectorizer
  • TfIdf and sparse matrices
  • Converter for WOE
• Write converters for other libraries
  • Converter for pyod.models.iforest.IForest

The tutorial was tested with following version:

<<<

try: import catboost except Exception as e: print("Unable to import catboost due to", e) catboost = None import numpy import scipy import sklearn import lightgbm import onnx import onnxmltools import onnxruntime import xgboost import skl2onnx mods = [ numpy, scipy, sklearn, lightgbm, xgboost, catboost, onnx, onnxmltools, onnxruntime, skl2onnx, ] mods = [(m.__name__, m.__version__) for m in mods if m is not None] mx = max(len(_[0]) for _ in mods) + 1 for name, vers in sorted(mods): print("%s%s%s" % (name, " " * (mx - len(name)), vers)) 

>>>

 catboost 1.2.8 lightgbm 4.6.0 numpy 2.3.1 onnx 1.19.0 onnxmltools 1.14.0 onnxruntime 1.23.0 scipy 1.15.2 skl2onnx 1.19.1 sklearn 1.7.1 xgboost 3.0.2