module onnxrt.ops_cpu.op_linear_regressor#

Inheritance diagram of mlprodict.onnxrt.ops_cpu.op_linear_regressor

Short summary#

module mlprodict.onnxrt.ops_cpu.op_linear_regressor

Runtime operator.

source on GitHub

Classes#

class

truncated documentation

LinearRegressor

LinearRegressor (ai.onnx.ml) ============================ Generalized linear regression evaluation. If targets is set …

Properties#

property

truncated documentation

args_default

Returns the list of arguments as well as the list of parameters with the default values (close to the signature). …

args_default_modified

Returns the list of modified parameters.

args_mandatory

Returns the list of optional arguments.

args_optional

Returns the list of optional arguments.

atts_value

Returns all parameters in a dictionary.

Methods#

method

truncated documentation

__init__

_run

Documentation#

Runtime operator.

source on GitHub

class mlprodict.onnxrt.ops_cpu.op_linear_regressor.LinearRegressor(ai.onnx.ml)#

Bases: mlprodict.onnxrt.ops_cpu._op.OpRunUnaryNum

Generalized linear regression evaluation.

If targets is set to 1 (default) then univariate regression is performed.

If targets is set to M then M sets of coefficients must be passed in as a sequence and M results will be output for each input n in N.

The coefficients array is of length n, and the coefficients for each target are contiguous. Intercepts are optional but if provided must match the number of targets.

Attributes

  • coefficients: Weights of the model(s). default value cannot be automatically retrieved (FLOATS)

  • intercepts: Weights of the intercepts, if used. default value cannot be automatically retrieved (FLOATS)

  • post_transform: Indicates the transform to apply to the regression output vector. One of ‘NONE,’ ‘SOFTMAX,’ ‘LOGISTIC,’ ‘SOFTMAX_ZERO,’ or ‘PROBIT’ Default value is nameposttransformsNONEtypeSTRING (STRING)

  • targets: The total number of regression targets, 1 if not defined. Default value is nametargetsi1typeINT (INT)

Inputs

  • X (heterogeneous)T: Data to be regressed.

Outputs

  • Y (heterogeneous)tensor(float): Regression outputs (one per target, per example).

Type Constraints

  • T tensor(float), tensor(double), tensor(int64), tensor(int32): The input must be a tensor of a numeric type.

Version

Onnx name: LinearRegressor

This version of the operator has been available since version 1 of domain ai.onnx.ml.

Runtime implementation: LinearRegressor

Parameters

  • onnx_node – onnx node

  • desc – internal representation

  • expected_attributes – expected attributes for this node

  • options – runtime options

source on GitHub

__init__(onnx_node, desc=None, **options)#
Parameters

  • onnx_node – onnx node

  • desc – internal representation

  • expected_attributes – expected attributes for this node

  • options – runtime options

source on GitHub

_run(x)#

Should be overwritten.

source on GitHub

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