Two ways to implement a converter#

There are two ways to write a converter. The first one is less verbose and easier to understand (see The other is very verbose (see for an example).

The first way is used in Implement a new converter. This one demonstrates the second way which is usually the one used in other converter library. It is more verbose.

Custom model#

It basically copies what is in example :ref:`l-plot-custom-converter.

from skl2onnx.common.data_types import guess_proto_type
from onnxconverter_common.onnx_ops import apply_sub
from onnxruntime import InferenceSession
from skl2onnx import update_registered_converter
from skl2onnx import to_onnx
import numpy
from sklearn.base import TransformerMixin, BaseEstimator
from sklearn.datasets import load_iris

class DecorrelateTransformer(TransformerMixin, BaseEstimator):
    Decorrelates correlated gaussian features.

    :param alpha: avoids non inversible matrices
        by adding *alpha* identity matrix


    * `self.mean_`: average
    * `self.coef_`: square root of the coveriance matrix

    def __init__(self, alpha=0.0):
        self.alpha = alpha

    def fit(self, X, y=None, sample_weights=None):
        if sample_weights is not None:
            raise NotImplementedError("sample_weights != None is not implemented.")
        self.mean_ = numpy.mean(X, axis=0, keepdims=True)
        X = X - self.mean_
        V = X.T @ X / X.shape[0]
        if self.alpha != 0:
            V += numpy.identity(V.shape[0]) * self.alpha
        L, P = numpy.linalg.eig(V)
        Linv = L ** (-0.5)
        diag = numpy.diag(Linv)
        root = P @ diag @ P.transpose()
        self.coef_ = root
        return self

    def transform(self, X):
        return (X - self.mean_) @ self.coef_

data = load_iris()
X =

dec = DecorrelateTransformer()
pred = dec.transform(X[:5])
[[ 0.0167562   0.52111756 -1.24946737 -0.56194325]
 [-0.0727878  -0.80853732 -1.43841018 -0.37441392]
 [-0.69971891 -0.09950908 -1.2138161  -0.3499275 ]
 [-1.13063404 -0.13540568 -0.79087008 -0.73938966]
 [-0.35790036  0.91900236 -1.04034399 -0.6509266 ]]

Conversion into ONNX#

The shape calculator does not change.

def decorrelate_transformer_shape_calculator(operator):
    op = operator.raw_operator
    input_type = operator.inputs[0].type.__class__
    # The shape may be unknown. *get_first_dimension*
    # returns the appropriate value, None in most cases
    # meaning the transformer can process any batch of observations.
    input_dim = operator.inputs[0].get_first_dimension()
    output_type = input_type([input_dim, op.coef_.shape[1]])
    operator.outputs[0].type = output_type

The converter is different.

def decorrelate_transformer_converter(scope, operator, container):
    op = operator.raw_operator
    out = operator.outputs

    # We retrieve the unique input.
    X = operator.inputs[0]

    # In most case, computation happen in floats.
    # But it might be with double. ONNX is very strict
    # about types, every constant should have the same
    # type as the input.
    proto_dtype = guess_proto_type(X.type)

    mean_name = scope.get_unique_variable_name("mean")
        mean_name, proto_dtype, op.mean_.shape, list(op.mean_.ravel())

    coef_name = scope.get_unique_variable_name("coef")
        coef_name, proto_dtype, op.coef_.shape, list(op.coef_.ravel())

    op_name = scope.get_unique_operator_name("sub")
    sub_name = scope.get_unique_variable_name("sub")
    # This function is defined in package onnxconverter_common.
    # Most common operators can be added to the graph with
    # these functions. It handles the case when specifications
    # changed accross opsets (a parameter becomes an input
    # for example).
        scope, [X.full_name, mean_name], sub_name, container, operator_name=op_name

    op_name = scope.get_unique_operator_name("matmul")
    container.add_node("MatMul", [sub_name, coef_name], out[0].full_name, name=op_name)

We need to let skl2onnx know about the new converter.


onx = to_onnx(dec, X.astype(numpy.float32))

sess = InferenceSession(onx.SerializeToString(), providers=["CPUExecutionProvider"])

exp = dec.transform(X.astype(numpy.float32))
got =, {"X": X.astype(numpy.float32)})[0]

def diff(p1, p2):
    p1 = p1.ravel()
    p2 = p2.ravel()
    d = numpy.abs(p2 - p1)
    return d.max(), (d / numpy.abs(p1)).max()

print(diff(exp, got))
(6.04657619085458e-07, 0.0002951417065406967)

Let’s check it works as well with double.

onx = to_onnx(dec, X.astype(numpy.float64))

sess = InferenceSession(onx.SerializeToString(), providers=["CPUExecutionProvider"])

exp = dec.transform(X.astype(numpy.float64))
got =, {"X": X.astype(numpy.float64)})[0]
print(diff(exp, got))
(0.0, 0.0)

The differences are smaller with double as expected.

Total running time of the script: (0 minutes 0.073 seconds)

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