module `onnxrt.ops_cpu.op_linear_regressor`#

Short summary#

module mlprodict.onnxrt.ops_cpu.op_linear_regressor

Runtime operator.

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: 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

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

_run(x, attributes=None, verbose=0, fLOG=None)#

Should be overwritten.