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sklearn-onnx 1.14.0 documentation
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sklearn-onnx 1.14.0 documentation
  • Introduction
  • Tutorial
    • 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
      • Transfer Learning with ONNX
    • 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
      • Implement a new converter using other converters
      • A new converter with options
      • Change the number of outputs by adding a parser
    • Extend ONNX, extend runtime
      • Fast design with a python runtime
      • Fast runtime with onnxruntime
    • 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
  • API Summary
  • Gallery of examples
    • Metadata
    • Draw a pipeline
    • ONNX Runtime Backend for ONNX
    • Logging, verbose
    • Probabilities or raw scores
    • Train, convert and predict a model
    • Investigate a pipeline
    • Compare CDist with scipy
    • Convert a pipeline with a LightGbm model
    • Probabilities as a vector or as a ZipMap
    • Convert a model with a reduced list of operators
    • Benchmark a pipeline
    • Convert a pipeline with a XGBoost model
    • Custom Operator for NMF Decomposition
    • Discrepencies with StandardScaler
    • Discrepencies with GaussianProcessorRegressor: use of double
    • Errors with onnxruntime
    • Play with ONNX operators
    • Different ways to convert a model
    • TfIdfVectorizer with ONNX
    • Convert a pipeline with ColumnTransformer
    • Walk through intermediate outputs
    • When a custom model is neither a classifier nor a regressor (alternative)
    • When a custom model is neither a classifier nor a regressor
    • Write your own converter for your own model
  • Convert a pipeline
  • Converters with options
  • Supported scikit-learn Models
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Gallery of examples#

Metadata

Metadata

Metadata
Draw a pipeline

Draw a pipeline

Draw a pipeline
ONNX Runtime Backend for ONNX

ONNX Runtime Backend for ONNX

ONNX Runtime Backend for ONNX
Logging, verbose

Logging, verbose

Logging, verbose
Probabilities or raw scores

Probabilities or raw scores

Probabilities or raw scores
Train, convert and predict a model

Train, convert and predict a model

Train, convert and predict a model
Investigate a pipeline

Investigate a pipeline

Investigate a pipeline
Compare CDist with scipy

Compare CDist with scipy

Compare CDist with scipy
Convert a pipeline with a LightGbm model

Convert a pipeline with a LightGbm model

Convert a pipeline with a LightGbm model
Probabilities as a vector or as a ZipMap

Probabilities as a vector or as a ZipMap

Probabilities as a vector or as a ZipMap
Convert a model with a reduced list of operators

Convert a model with a reduced list of operators

Convert a model with a reduced list of operators
Benchmark a pipeline

Benchmark a pipeline

Benchmark a pipeline
Convert a pipeline with a XGBoost model

Convert a pipeline with a XGBoost model

Convert a pipeline with a XGBoost model
Custom Operator for NMF Decomposition

Custom Operator for NMF Decomposition

Custom Operator for NMF Decomposition
Discrepencies with StandardScaler

Discrepencies with StandardScaler

Discrepencies with StandardScaler
Discrepencies with GaussianProcessorRegressor: use of double

Discrepencies with GaussianProcessorRegressor: use of double

Discrepencies with GaussianProcessorRegressor: use of double
Errors with onnxruntime

Errors with onnxruntime

Errors with onnxruntime
Play with ONNX operators

Play with ONNX operators

Play with ONNX operators
Different ways to convert a model

Different ways to convert a model

Different ways to convert a model
TfIdfVectorizer with ONNX

TfIdfVectorizer with ONNX

TfIdfVectorizer with ONNX
Convert a pipeline with ColumnTransformer

Convert a pipeline with ColumnTransformer

Convert a pipeline with ColumnTransformer
Walk through intermediate outputs

Walk through intermediate outputs

Walk through intermediate outputs
When a custom model is neither a classifier nor a regressor (alternative)

When a custom model is neither a classifier nor a regressor (alternative)

When a custom model is neither a classifier nor a regressor (alternative)
When a custom model is neither a classifier nor a regressor

When a custom model is neither a classifier nor a regressor

When a custom model is neither a classifier nor a regressor
Write your own converter for your own model

Write your own converter for your own model

Write your own converter for your own model

Download all examples in Python source code: auto_examples_python.zip

Download all examples in Jupyter notebooks: auto_examples_jupyter.zip

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