pyanno4rt.optimization.components

Components module.

The module aims to provide methods and classes to handle dose-related and outcome model-based component functions for the optimization problem.

Overview

Classes
`ConventionalComponentClass`	Conventional component template class.
`MachineLearningComponentClass`	Machine learning component template class.
`RadiobiologyComponentClass`	Radiobiology component template class.
`DecisionTreeNTCP`	Decision tree NTCP component class.
`DecisionTreeTCP`	Decision tree TCP component class.
`DoseUniformity`	Dose uniformity component class.
`EquivalentUniformDose`	Equivalent uniform dose (EUD) component class.
`KNeighborsNTCP`	K-nearest neighbors NTCP component class.
`KNeighborsTCP`	K-nearest neighbors TCP component class.
`LogisticRegressionNTCP`	Logistic regression NTCP component class.
`LogisticRegressionTCP`	Logistic regression TCP component class.
`LQPoissonTCP`	Linear-quadratic Poisson TCP component class.
`LymanKutcherBurmanNTCP`	Lyman-Kutcher-Burman (LKB) NTCP component class.
`MaximumDVH`	Maximum dose-volume histogram (Maximum DVH) component class.
`MeanDose`	Mean dose component class.
`MinimumDVH`	Minimum dose-volume histogram (Minimum DVH) component class.
`NaiveBayesNTCP`	Naive Bayes NTCP component class.
`NaiveBayesTCP`	Naive Bayes TCP component class.
`NeuralNetworkNTCP`	Neural network NTCP component class.
`NeuralNetworkTCP`	Neural network TCP component class.
`RandomForestNTCP`	Random forest NTCP component class.
`RandomForestTCP`	Random forest TCP component class.
`SquaredDeviation`	Squared deviation component class.
`SquaredOverdosing`	Squared overdosing component class.
`SquaredUnderdosing`	Squared underdosing component class.
`SupportVectorMachineNTCP`	Support vector machine NTCP component class.
`SupportVectorMachineTCP`	Support vector machine TCP component class.

Attributes
`component_map`	-

Classes

class pyanno4rt.optimization.components.ConventionalComponentClass(name, parameter_name, parameter_category, parameter_value, embedding, weight, rank, bounds, link, identifier, display)[source]

Conventional component template class.

Parameters:

name (str) – Name of the component class.
parameter_name (tuple) – Name of the component parameters.
parameter_category (tuple) – Category of the component parameters.
parameter_value (tuple) – Value of the component parameters.
embedding ({'active', 'passive'}) – 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) – Weight of the component function.
rank (int) – Rank of the component in the lexicographic order.
bounds (None or list) – Constraint bounds for the component.
link (None or list) – Other segments used for joint evaluation.
identifier (None or str) – Additional string for naming the component.
display (bool) – Indicator for the display of the component.

name

See ‘Parameters’.

Type:: str

parameter_name

See ‘Parameters’.

Type:: tuple

parameter_category

See ‘Parameters’.

Type:: tuple

parameter_value

See ‘Parameters’.

Type:: list

embedding

See ‘Parameters’.

Type:: {‘active’, ‘passive’}

weight

See ‘Parameters’.

Type:: float

rank

See ‘Parameters’.

Type:: int

bounds

See ‘Parameters’.

Type:: list

link

See ‘Parameters’.

Type:: list

identifier

See ‘Parameters’.

Type:: None or str

display

See ‘Parameters’.

Type:: bool

adjusted_parameters

Indicator for the adjustment of the parameters due to fractionation.

Type:: bool

RETURNS_OUTCOME

Indicator for the outcome focus of the component.

Type:: bool

DEPENDS_ON_MODEL

Indicator for the model dependency of the component.

Type:: bool

Overview

Methods
`get_parameter_value`()	Get the value of the parameters.
`set_parameter_value`(*args)	Set the value of the parameters.
`get_weight_value`()	Get the value of the weight.
`set_weight_value`(*args)	Set the value of the weight.
`compute_value`(*args)	abc Compute the component value.
`compute_gradient`(*args)	abc Compute the component gradient.

Members

get_parameter_value()[source]

Get the value of the parameters.

Returns:: Value of the parameters.
Return type:: list

set_parameter_value(*args)[source]

Set the value of the parameters.

Parameters:: *args (tuple) – Keyworded parameters. args[0] should give the value to be set.

get_weight_value()[source]

Get the value of the weight.

Returns:: Value of the weight.
Return type:: float

set_weight_value(*args)[source]

Set the value of the weight.

Parameters:: *args (tuple) – Keyworded parameters. args[0] should give the value to be set.

abstract compute_value(*args)[source]: Compute the component value.

abstract compute_gradient(*args)[source]: Compute the component gradient.

class pyanno4rt.optimization.components.MachineLearningComponentClass(name, parameter_name, parameter_category, model_parameters, embedding, weight, rank, bounds, link, identifier, display)[source]

Machine learning component template class.

Parameters:

name (str) – Name of the component class.
parameter_name (tuple) – Name of the component parameters.
parameter_category (tuple) – Category of the component parameters.
model_parameters (dict) –
Dictionary with the data handling & learning model parameters:
- model_labelstr
  Label for the machine learning model.
- model_folder_pathNone or str, default=None
  Path to a folder for loading an external machine learning model.
- data_pathstr
  Path to the data set used for fitting the machine learning model.
- feature_filterdict, default={‘features’: [], ‘filter_mode’: ‘remove’}
  Dictionary with a list of feature names and a value from {‘retain’, ‘remove’} as an indicator for retaining/removing the features prior to model fitting.
- label_namestr
  Name of the label variable.
- label_boundslist, default=[1, 1]
  Bounds for the label values to binarize into positive (value lies inside the bounds) and negative class (value lies outside the bounds).
- time_variable_nameNone or str, default=None
  Name of the time-after-radiotherapy variable (unit should be days).
- label_viewpoint{‘early’, ‘late’, ‘long-term’, ‘longitudinal’, ‘profile’}, default=’longitudinal’
  Time of observation for the presence of tumor control and/or normal tissue complication events. The options can be described as follows:
  - ’early’ : event between 0 and 6 months after treatment
  - ’late’ : event between 6 and 15 months after treatment
  - ’long-term’ : event between 15 and 24 months after treatment
  - ’longitudinal’ : no period, time after treatment as covariate
  - ’profile’ : TCP/NTCP profiling over time, multi-label scenario with one label per month (up to 24 labels in total).
- fuzzy_matchingbool, default=True
  Indicator for the use of fuzzy string matching to generate the feature map (if False, exact string matching is applied).
- preprocessing_stepslist, default=[‘Identity’]
  Sequence of labels associated with preprocessing algorithms to preprocess the input features.
  
  The following preprocessing steps are currently available:
  - ’Identity’ Identity
  - ’StandardScaler’ StandardScaler
  - ’Whitening’ Whitening
- architecture{‘input-convex’, ‘standard’}, default=’input-convex’
  Type of architecture for the neural network model.
- max_hidden_layersint, default=2
  Maximum number of hidden layers for the neural network model.
- tune_spacedict, default={}
  Search space for the Bayesian hyperparameter optimization.
- tune_evaluationsint, default=50
  Number of evaluation steps (trials) for the Bayesian hyperparameter optimization.
- tune_score{‘AUC’, ‘Brier score’, ‘Logloss’}, default=’Logloss’
  Scoring function for the evaluation of the hyperparameter set candidates.
- tune_splitsint, default=5
  Number of splits for the stratified cross-validation within each hyperparameter optimization step.
- inspect_modelbool, default=False
  Indicator for the inspection of the machine learning model.
- evaluate_modelbool, default=False
  Indicator for the evaluation of the machine learning model.
- oof_splitsint, default=5
  Number of splits for the stratified cross-validation within the out-of-folds evaluation step.
- write_featuresbool, default=True
  Indicator for writing the iteratively calculated feature vectors into a feature history.
- display_optionsdict, default={‘graphs’: [‘AUC-ROC’, ‘AUC-PR’, ‘F1’], ‘kpis’: [‘Logloss’, ‘Brier score’, ‘Subset accuracy’, ‘Cohen Kappa’, ‘Hamming loss’, ‘Jaccard score’, ‘Precision’, ‘Recall’, ‘F1 score’, ‘MCC’, ‘AUC’]}
  Dictionary with the graph and KPI display options.
embedding ({'active', 'passive'}) – 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) – Weight of the component function.
rank (int, default=1) – Rank of the component in the lexicographic order.
bounds (None or list) – Constraint bounds for the component.
link (None or list) – Other segments used for joint evaluation.
identifier (None or str) – Additional string for naming the component.
display (bool) – Indicator for the display of the component.

name

See ‘Parameters’.

Type:: str

parameter_name

See ‘Parameters’.

Type:: tuple

parameter_category

See ‘Parameters’.

Type:: tuple

parameter_value

Value of the component parameters.

Type:: list

embedding

See ‘Parameters’.

Type:: {‘active’, ‘passive’}

weight

See ‘Parameters’.

Type:: float

rank

See ‘Parameters’.

Type:: int

bounds

See ‘Parameters’.

Type:: list

link

See ‘Parameters’.

Type:: list

identifier

See ‘Parameters’.

Type:: None or str

display

See ‘Parameters’.

Type:: bool

model_parameters

See ‘Parameters’.

Type:: dict

data_model_handler

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

Type:: None

model

Initial variable for the object used to preprocess, tune, train, inspect and evaluate the machine learning model.

Type:: None

adjusted_parameters

Indicator for the adjustment of the parameters due to fractionation.

Type:: bool

RETURNS_OUTCOME

Indicator for the outcome focus of the component.

Type:: bool

DEPENDS_ON_MODEL

Indicator for the model dependency of the component.

Type:: bool

Overview

Methods
`get_parameter_value`()	Get the value of the parameters.
`set_parameter_value`(*args)	Set the value of the parameters.
`get_weight_value`()	Get the value of the weight.
`set_weight_value`(*args)	Set the value of the weight.
`add_model`()	abc Add the machine learning model to the component.
`compute_value`(*args)	abc Compute the component value.
`compute_gradient`(*args)	abc Compute the component gradient.

Members

get_parameter_value()[source]

Get the value of the parameters.

Returns:: Value of the parameters.
Return type:: list

set_parameter_value(*args)[source]

Set the value of the parameters.

Parameters:: *args (tuple) – Keyworded parameters. args[0] should give the value to be set.

get_weight_value()[source]

Get the value of the weight.

Returns:: Value of the weight.
Return type:: float

set_weight_value(*args)[source]

Set the value of the weight.

Parameters:: *args (tuple) – Keyworded parameters. args[0] should give the value to be set.

abstract add_model()[source]: Add the machine learning model to the component.

abstract compute_value(*args)[source]: Compute the component value.

abstract compute_gradient(*args)[source]: Compute the component gradient.

class pyanno4rt.optimization.components.RadiobiologyComponentClass(name, parameter_name, parameter_category, parameter_value, embedding, weight, rank, bounds, link, identifier, display)[source]

Radiobiology component template class.

Parameters:

name (str) – Name of the component class.
parameter_name (tuple) – Name of the component parameters.
parameter_category (tuple) – Category of the component parameters.
parameter_value (tuple) – Value of the component parameters.
embedding ({'active', 'passive'}) – 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) – Weight of the component function.
rank (int, default=1) – Rank of the component in the lexicographic order.
bounds (None or list) – Constraint bounds for the component.
link (None or list) – Other segments used for joint evaluation.
identifier (None or str) – Additional string for naming the component.
display (bool) – Indicator for the display of the component.

name

See ‘Parameters’.

Type:: str

parameter_name

See ‘Parameters’.

Type:: tuple

parameter_category

See ‘Parameters’.

Type:: tuple

parameter_value

See ‘Parameters’.

Type:: list

embedding

See ‘Parameters’.

Type:: {‘active’, ‘passive’}

weight

See ‘Parameters’.

Type:: float

rank

See ‘Parameters’.

Type:: int

bounds

See ‘Parameters’.

Type:: list

link

See ‘Parameters’.

Type:: list

identifier

See ‘Parameters’.

Type:: None or str

display

See ‘Parameters’.

Type:: bool

adjusted_parameters

Indicator for the adjustment of the parameters due to fractionation.

Type:: bool

RETURNS_OUTCOME

Indicator for the outcome focus of the component.

Type:: bool

DEPENDS_ON_MODEL

Indicator for the model dependency of the component.

Type:: bool

Overview

Methods
`get_parameter_value`()	Get the value of the parameters.
`set_parameter_value`(*args)	Set the value of the parameters.
`get_weight_value`()	Get the value of the weight.
`set_weight_value`(*args)	Set the value of the weight.
`compute_value`(*args)	abc Compute the component value.
`compute_gradient`(*args)	abc Compute the component gradient.

Members

get_parameter_value()[source]

Get the value of the parameters.

Returns:: Value of the parameters.
Return type:: list

set_parameter_value(*args)[source]

Set the value of the parameters.

Parameters:: *args (tuple) – Keyworded parameters. args[0] should give the value to be set.

get_weight_value()[source]

Get the value of the weight.

Returns:: Value of the weight.
Return type:: float

set_weight_value(*args)[source]

Set the value of the weight.

Parameters:: *args (tuple) – Keyworded parameters. args[0] should give the value to be set.

abstract compute_value(*args)[source]: Compute the component value.

abstract compute_gradient(*args)[source]: Compute the component gradient.

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

Bases: pyanno4rt.optimization.components.MachineLearningComponentClass

Decision tree NTCP component class.

This class provides methods to compute the value and the gradient of the decision tree NTCP component, as well as to add the decision tree 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 decision tree model.

Type:: object of class DecisionTreeModel

parameter_value

Value of the decision tree model parameters.

Type:: list

bounds

See ‘Parameters’.

Type:: list

Overview

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

Members

add_model()[source]: Add the decision tree 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

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

Bases: pyanno4rt.optimization.components.MachineLearningComponentClass

Decision tree TCP component class.

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

Type:: object of class DecisionTreeModel

parameter_value

Value of the decision tree model parameters.

Type:: list

bounds

See ‘Parameters’.

Type:: list

Overview

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

Members

add_model()[source]: Add the decision tree 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

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

Bases: pyanno4rt.optimization.components.ConventionalComponentClass

Dose uniformity component class.

This class provides methods to compute the value and the gradient of the dose uniformity component.

Parameters:

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.

parameter_value

Value of the component parameters.

Type:: list

Overview

Methods
`compute_value`(*args)	Return the component value from the jitted ‘compute’ function.
`compute_gradient`(*args)	Return the component gradient from the jitted ‘differentiate’ function.

Members

compute_value(*args)[source]

Return the component value from the jitted ‘compute’ function.

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

compute_gradient(*args)[source]

Return the component gradient from the jitted ‘differentiate’ function.

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

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

Bases: pyanno4rt.optimization.components.ConventionalComponentClass

Equivalent uniform dose (EUD) component class.

This class provides methods to compute the value and the gradient of the EUD component.

Parameters:

target_eud (int or float) – Target value for the EUD.
volume_parameter (int or float) – Dose-volume effect parameter.
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.

parameter_value

Value of the component parameters.

Type:: list

Overview

Methods
`compute_value`(*args)	Return the component value from the jitted ‘compute’ function.
`compute_gradient`(*args)	Return the component gradient from the jitted ‘differentiate’ function.

Members

compute_value(*args)[source]

Return the component value from the jitted ‘compute’ function.

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

compute_gradient(*args)[source]

Return the component gradient from the jitted ‘differentiate’ function.

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

class pyanno4rt.optimization.components.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

class pyanno4rt.optimization.components.KNeighborsTCP(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 TCP component class.

This class provides methods to compute the value and the gradient of the k-nearest neighbors TCP 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

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

Bases: pyanno4rt.optimization.components.MachineLearningComponentClass

Logistic regression NTCP component class.

This class provides methods to compute the value and the gradient of the logistic regression NTCP component, as well as to add the logistic regression 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 logistic regression model.

Type:: object of class LogisticRegressionModel

parameter_value

Value of the logistic regression model coefficients.

Type:: list

intercept_value

Value of the logistic regression model intercept.

Type:: None or list

bounds

See ‘Parameters’. Transformed by the inverse sigmoid function.

Type:: list

Overview

Methods
`get_intercept_value`()	Get the value of the intercept.
`set_intercept_value`(*args)	Set the value of the intercept.
`add_model`()	Add the logistic regression model to the component.
`compute_value`(*args)	Compute the component value.
`compute_gradient`(*args)	Compute the component gradient.

Members

get_intercept_value()[source]

Get the value of the intercept.

Returns:: Value of the intercept.
Return type:: list

set_intercept_value(*args)[source]

Set the value of the intercept.

Parameters:: *args (tuple) – Keyworded parameters. args[0] should give the value to be set.

add_model()[source]: Add the logistic regression 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

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

Bases: pyanno4rt.optimization.components.MachineLearningComponentClass

Logistic regression TCP component class.

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

Type:: object of class LogisticRegressionModel

parameter_value

Value of the logistic regression model coefficients.

Type:: list

intercept_value

Value of the logistic regression model intercept.

Type:: None or list

bounds

See ‘Parameters’. Transformed by the inverse sigmoid function.

Type:: list

Overview

Methods
`get_intercept_value`()	Get the value of the intercept.
`set_intercept_value`(*args)	Set the value of the intercept.
`add_model`()	Add the logistic regression model to the component.
`compute_value`(*args)	Compute the component value.
`compute_gradient`(*args)	Compute the component gradient.

Members

get_intercept_value()[source]

Get the value of the intercept.

Returns:: Value of the intercept.
Return type:: list

set_intercept_value(*args)[source]

Set the value of the intercept.

Parameters:: *args (tuple) – Keyworded parameters. args[0] should give the value to be set.

add_model()[source]: Add the logistic regression 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

class pyanno4rt.optimization.components.LQPoissonTCP(alpha=None, beta=None, volume_parameter=None, embedding='active', weight=1.0, rank=1, bounds=None, link=None, identifier=None, display=True)[source]

Bases: pyanno4rt.optimization.components.RadiobiologyComponentClass

Linear-quadratic Poisson TCP component class.

This class provides methods to compute the value and the gradient of the linear-quadratic Poisson TCP component.

Parameters:

alpha (int or float) – Alpha coefficient for the tumor volume (in the LQ model).
beta (int or float) – Beta coefficient for the tumor volume (in the LQ model).
volume_parameter (int or float) – Dose-volume effect parameter.
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.

parameter_value

Value of the component parameters.

Type:: list

Overview

Methods
`compute_value`(*args)	Return the component value from the jitted ‘compute’ function.
`compute_gradient`(*args)	Return the component gradient from the jitted ‘differentiate’ function.

Members

compute_value(*args)[source]

Return the component value from the jitted ‘compute’ function.

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

compute_gradient(*args)[source]

Return the component gradient from the jitted ‘differentiate’ function.

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

class pyanno4rt.optimization.components.LymanKutcherBurmanNTCP(tolerance_dose_50=None, slope_parameter=None, volume_parameter=None, embedding='active', weight=1.0, rank=1, bounds=None, link=None, identifier=None, display=True)[source]

Bases: pyanno4rt.optimization.components.RadiobiologyComponentClass

Lyman-Kutcher-Burman (LKB) NTCP component class.

This class provides methods to compute the value and the gradient of the LKB NTCP component.

Parameters:

tolerance_dose_50 (int or float) – Tolerance value for the dose at 50% tumor control.
slope_parameter (int or float) – Slope parameter.
volume_parameter (int or float) – Dose-volume effect parameter.
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.

parameter_value

Value of the component parameters.

Type:: list

Overview

Methods
`compute_value`(*args)	Return the component value from the jitted ‘compute’ function.
`compute_gradient`(*args)	Return the component gradient from the jitted ‘differentiate’ function.

Members

compute_value(*args)[source]

Return the component value from the jitted ‘compute’ function.

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

compute_gradient(*args)[source]

Return the component gradient from the jitted ‘differentiate’ function.

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

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

Bases: pyanno4rt.optimization.components.ConventionalComponentClass

Maximum dose-volume histogram (Maximum DVH) component class.

This class provides methods to compute the value and the gradient of the maximum DVH component.

Parameters:

target_dose (int or float) – Target value for the dose.
quantile_volume (int or float) – Volume level at which to evaluate the dose quantile.
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.

parameter_value

Value of the component parameters.

Type:: list

Overview

Methods
`compute_value`(*args)	Return the component value from the jitted ‘compute’ function.
`compute_gradient`(*args)	Return the component gradient from the jitted ‘differentiate’ function.

Members

compute_value(*args)[source]

Return the component value from the jitted ‘compute’ function.

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

compute_gradient(*args)[source]

Return the component gradient from the jitted ‘differentiate’ function.

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

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

Bases: pyanno4rt.optimization.components.ConventionalComponentClass

Mean dose component class.

This class provides methods to compute the value and the gradient of the mean dose component.

Parameters:

target_dose (int or float) – Target value for the dose.
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.

parameter_value

Value of the component parameters.

Type:: list

Overview

Methods
`compute_value`(*args)	Return the component value from the jitted ‘compute’ function.
`compute_gradient`(*args)	Return the component gradient from the jitted ‘differentiate’ function.

Members

compute_value(*args)[source]

Return the component value from the jitted ‘compute’ function.

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

compute_gradient(*args)[source]

Return the component gradient from the jitted ‘differentiate’ function.

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

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

Bases: pyanno4rt.optimization.components.ConventionalComponentClass

Minimum dose-volume histogram (Minimum DVH) component class.

This class provides methods to compute the value and the gradient of the minimum DVH component.

Parameters:

target_dose (int or float) – Target value for the dose.
quantile_volume (int or float) – Volume level at which to evaluate the dose quantile.
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.

parameter_value

Value of the component parameters.

Type:: list

Overview

Methods
`compute_value`(*args)	Return the component value from the jitted ‘compute’ function.
`compute_gradient`(*args)	Return the component gradient from the jitted ‘differentiate’ function.

Members

compute_value(*args)[source]

Return the component value from the jitted ‘compute’ function.

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

compute_gradient(*args)[source]

Return the component gradient from the jitted ‘differentiate’ function.

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

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

Bases: pyanno4rt.optimization.components.MachineLearningComponentClass

Naive Bayes NTCP component class.

This class provides methods to compute the value and the gradient of the naive Bayes NTCP component, as well as to add the naive Bayes 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 naive Bayes model.

Type:: object of class NaiveBayesModel

parameter_value

Value of the naive Bayes model parameters.

Type:: list

bounds

See ‘Parameters’.

Type:: list

Overview

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

Members

add_model()[source]: Add the naive Bayes 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

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

Bases: pyanno4rt.optimization.components.MachineLearningComponentClass

Naive Bayes TCP component class.

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

Type:: object of class NaiveBayesModel

parameter_value

Value of the naive Bayes model parameters.

Type:: list

bounds

See ‘Parameters’.

Type:: list

Overview

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

Members

add_model()[source]: Add the naive Bayes 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

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

Bases: pyanno4rt.optimization.components.MachineLearningComponentClass

Neural network NTCP component class.

This class provides methods to compute the value and the gradient of the neural network NTCP component, as well as to add the neural network 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 neural network model.

Type:: object of class NeuralNetworkModel

parameter_value

Value of the neural network model parameters.

Type:: list

bounds

See ‘Parameters’. Transformed by the inverse sigmoid function.

Type:: list

Overview

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

Members

add_model()[source]: Add the neural network 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

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

Bases: pyanno4rt.optimization.components.MachineLearningComponentClass

Neural network TCP component class.

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

Type:: object of class NeuralNetworkModel

parameter_value

Value of the neural network model parameters.

Type:: list

bounds

See ‘Parameters’. Transformed by the inverse sigmoid function.

Type:: list

Overview

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

Members

add_model()[source]: Add the neural network 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

class pyanno4rt.optimization.components.RandomForestNTCP(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 NTCP component class.

This class provides methods to compute the value and the gradient of the random forest NTCP 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

class pyanno4rt.optimization.components.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

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

Bases: pyanno4rt.optimization.components.ConventionalComponentClass

Squared deviation component class.

This class provides methods to compute the value and the gradient of the squared deviation component.

Parameters:

target_dose (int or float) – Target value for the dose.
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.

parameter_value

Value of the component parameters.

Type:: list

Overview

Methods
`compute_value`(*args)	Return the component value from the jitted ‘compute’ function.
`compute_gradient`(*args)	Return the component gradient from the jitted ‘differentiate’ function.

Members

compute_value(*args)[source]

Return the component value from the jitted ‘compute’ function.

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

compute_gradient(*args)[source]

Return the component gradient from the jitted ‘differentiate’ function.

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

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

Bases: pyanno4rt.optimization.components.ConventionalComponentClass

Squared overdosing component class.

This class provides methods to compute the value and the gradient of the squared overdosing component.

Parameters:

maximum_dose (int or float) – Maximum value for the dose.
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.

parameter_value

Value of the component parameters.

Type:: list

Overview

Methods
`compute_value`(*args)	Return the component value from the jitted ‘compute’ function.
`compute_gradient`(*args)	Return the component gradient from the jitted ‘differentiate’ function.

Members

compute_value(*args)[source]

Return the component value from the jitted ‘compute’ function.

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

compute_gradient(*args)[source]

Return the component gradient from the jitted ‘differentiate’ function.

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

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

Bases: pyanno4rt.optimization.components.ConventionalComponentClass

Squared underdosing component class.

This class provides methods to compute the value and the gradient of the squared underdosing component.

Parameters:

minimum_dose (int or float) – Minimum value for the dose.
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.

parameter_value

Value of the component parameters.

Type:: list

Overview

Methods
`compute_value`(*args)	Return the component value from the jitted ‘compute’ function.
`compute_gradient`(*args)	Return the component gradient from the jitted ‘differentiate’ function.

Members

compute_value(*args)[source]

Return the component value from the jitted ‘compute’ function.

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

compute_gradient(*args)[source]

Return the component gradient from the jitted ‘differentiate’ function.

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

class pyanno4rt.optimization.components.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

class pyanno4rt.optimization.components.SupportVectorMachineTCP(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 TCP component class.

This class provides methods to compute the value and the gradient of the support vector machine TCP 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

Attributes

pyanno4rt.optimization.components.component_map