module `onnx_conv.scorers.register`#

Short summary#

module mlprodict.onnx_conv.scorers.register

Registers new converters.

Classes#

class	truncated documentation
`CustomScorerTransform`	Wraps a scoring function into a transformer. Function @see fn register_scorers must be called to register the converter …

Functions#

function	truncated documentation
`custom_scorer_transform_converter`	Selects the appropriate converter for a @see cl CustomScorerTransform.
`custom_scorer_transform_parser`	This function updates the inputs and the outputs for a @see cl CustomScorerTransform.
`custom_scorer_transform_shape_calculator`	Computes the output shapes for a @see cl CustomScorerTransform.
`empty_shape_calculator`	Does nothing.
`register_scorers`	Registers operators for @see cl CustomScorerTransform.

Properties#

property	truncated documentation
`_repr_html_`	HTML representation of estimator. This is redundant with the logic of _repr_mimebundle_. The latter should …

Methods#

method	truncated documentation
`__init__`
`__repr__`

Documentation#

@file @brief Registers new converters.

class mlprodict.onnx_conv.scorers.register.CustomScorerTransform(name, fct, kwargs)#

Bases: BaseEstimator, TransformerMixin

Wraps a scoring function into a transformer. Function @see fn register_scorers must be called to register the converter associated to this transform. It takes two inputs, expected values and predicted values and returns a score for each observation.

@param name function name @param fct python function @param kwargs parameters function

__init__(name, fct, kwargs)#: @param name function name @param fct python function @param kwargs parameters function

__repr__()#: Return repr(self).

_sklearn_auto_wrap_output_keys = {'transform'}#

mlprodict.onnx_conv.scorers.register.custom_scorer_transform_converter(scope, operator, container)#: Selects the appropriate converter for a @see cl CustomScorerTransform.

mlprodict.onnx_conv.scorers.register.custom_scorer_transform_parser(scope, model, inputs, custom_parsers=None)#

This function updates the inputs and the outputs for a @see cl CustomScorerTransform.

Parameters:

scope – Scope object
model – A scikit-learn object (e.g., OneHotEncoder or LogisticRegression)
inputs – A list of variables
custom_parsers – parsers determines which outputs is expected for which particular task, default parsers are defined for classifiers, regressors, pipeline but they can be rewritten, custom_parsers is a dictionary { type: fct_parser(scope, model, inputs, custom_parsers=None) }

Returns:

A list of output variables which will be passed to next stage

mlprodict.onnx_conv.scorers.register.custom_scorer_transform_shape_calculator(operator)#: Computes the output shapes for a @see cl CustomScorerTransform.

mlprodict.onnx_conv.scorers.register.empty_shape_calculator(operator)#: Does nothing.

mlprodict.onnx_conv.scorers.register.register_scorers()#: Registers operators for @see cl CustomScorerTransform.

module onnx_conv.scorers.register#