pyanno4rt.optimization.components._k_nearest_neighbors_ntcp

K-nearest neighbors NTCP component.

Overview

Classes
`KNeighborsNTCP`	K-nearest neighbors NTCP component class.

Classes

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

Bases: pyanno4rt.optimization.components.MachineLearningComponentClass

K-nearest neighbors NTCP component class.

This class provides methods to compute the value and the gradient of the k-nearest neighbors NTCP component, as well as to add the k-nearest neighbors 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 k-nearest neighbors model.

Type:: object of class KNeighborsModel

parameter_value

Value of the k-nearest neighbors model parameters.

Type:: list

bounds

See ‘Parameters’.

Type:: list

Overview

Methods
`add_model`()	Add the k-nearest neighbors model to the component.
`compute_value`(*args)	Compute the component value.
`compute_gradient`(*args)	Compute the component gradient.

Members

add_model()[source]: Add the k-nearest neighbors 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