module onnx_conv.scorers.register
#
Short summary#
module mlprodict.onnx_conv.scorers.register
Registers new converters.
Classes#
class |
truncated documentation |
---|---|
Wraps a scoring function into a transformer. Function @see fn register_scorers must be called to register the converter … |
Functions#
function |
truncated documentation |
---|---|
Selects the appropriate converter for a @see cl CustomScorerTransform. |
|
This function updates the inputs and the outputs for a @see cl CustomScorerTransform. |
|
Computes the output shapes for a @see cl CustomScorerTransform. |
|
Does nothing. |
|
Registers operators for @see cl CustomScorerTransform. |
Properties#
property |
truncated documentation |
---|---|
|
HTML representation of estimator. This is redundant with the logic of _repr_mimebundle_. The latter should … |
Methods#
method |
truncated documentation |
---|---|
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.