pyanno4rt.optimization.components._support_vector_machine_ntcp

Support vector machine NTCP component.

Overview

Classes
`SupportVectorMachineNTCP`	Support vector machine NTCP component class.

Classes

class pyanno4rt.optimization.components._support_vector_machine_ntcp.SupportVectorMachineNTCP(model_parameters, embedding='active', weight=1.0, rank=1, bounds=None, link=None, identifier=None, display=True)[source]

Bases: pyanno4rt.optimization.components.MachineLearningComponentClass

Support vector machine NTCP component class.

This class provides methods to compute the value and the gradient of the support vector machine NTCP component, as well as to add the support vector machine model.

Parameters:

model_parameters (dict) – Dictionary with the data handling & learning model parameters, see the class MachineLearningComponentClass.
embedding ({'active', 'passive'}, default='active') – Mode of embedding for the component. In ‘passive’ mode, the component value is computed and tracked, but not considered in the optimization problem, unlike in ‘active’ mode.
weight (int or float, default=1.0) – Weight of the component function.
rank (int, default=1) – Rank of the component in the lexicographic order.
bounds (None or list, default=None) – Constraint bounds for the component.
link (None or list, default=None) – Other segments used for joint evaluation.
identifier (None or str, default=None) – Additional string for naming the component.
display (bool, default=True) – Indicator for the display of the component.

data_model_handler

The object used to handle the dataset, the feature map generation and the feature (re-)calculation.

Type:: object of class DataModelHandler

model

The object used to preprocess, tune, train, inspect and evaluate the support vector machine model.

Type:: object of class SupportVectorMachineModel

parameter_value

Value of the primal/dual support vector machine model coefficients.

Type:: list

decision_function

Decision function for the fitted kernel type.

Type:: callable

decision_gradient

Decision gradient for the fitted kernel type.

Type:: callable

bounds

See ‘Parameters’. Transformed by the inverse Platt scaling function.

Type:: list

Overview

Methods
`add_model`()	Add the support vector machine model to the component.
`compute_value`(*args)	Compute the component value.
`compute_gradient`(*args)	Compute the component gradient.

Members

add_model()[source]: Add the support vector machine model to the component.

compute_value(*args)[source]

Compute the component value.

Parameters:: *args (tuple) – Keyworded parameters, where args[0] must be the dose vector(s) to evaluate and args[1] the corresponding segment(s).
Returns:: Value of the component function.
Return type:: float

compute_gradient(*args)[source]

Compute the component gradient.

Parameters:: *args (tuple) – Keyworded parameters, where args[0] must be the dose vector(s) to evaluate and args[1] the corresponding segment(s).
Returns:: Value of the component gradient.
Return type:: ndarray