pyanno4rt.optimization.components._random_forest_tcp

Random forest TCP component.

Overview

Classes
`RandomForestTCP`	Random forest TCP component class.

Classes

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

Bases: pyanno4rt.optimization.components.MachineLearningComponentClass

Random forest TCP component class.

This class provides methods to compute the value and the gradient of the random forest TCP component, as well as to add the random forest 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 random forest model.

Type:: object of class RandomForestModel

parameter_value

Value of the random forest model parameters.

Type:: list

bounds

See ‘Parameters’.

Type:: list

Overview

Methods
`add_model`()	Add the random forest model to the component.
`compute_value`(*args)	Compute the component value.
`compute_gradient`(*args)	Compute the component gradient.

Members

add_model()[source]: Add the random forest 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