desdeo_emo.surrogatemodels.EvoDN2

Module Contents

Classes

EvoDN2

Helper class that provides a standard way to create an ABC using

Functions

negative_r2_score(y_true, y_pred)

desdeo_emo.surrogatemodels.EvoDN2.negative_r2_score(y_true, y_pred)

class desdeo_emo.surrogatemodels.EvoDN2.EvoDN2(num_subnets: int = 4, num_subsets: int = 4, max_layers: int = 4, max_nodes: int = 4, p_omit: float = 0.2, w_low: float = -5.0, w_high: float = 5.0, subsets: list = None, activation_function: str = 'sigmoid', loss_function: str = 'mse', training_algorithm: desdeo_emo.EAs.BaseEA.BaseEA = PPGA, pop_size: int = 500, model_selection_criterion: str = 'min_error', verbose: int = 0)

Bases: desdeo_problem.surrogatemodels.SurrogateModels.BaseRegressor

Helper class that provides a standard way to create an ABC using inheritance.

fit(X: numpy.ndarray, y: numpy.ndarray)

_model_performance(individuals: numpy.ndarray = None, X: numpy.ndarray = None, y_true: numpy.ndarray = None)

_feed_forward(subnets, X)

_calculate_linear(previous_layer_output)

Calculate the final layer using LLSQ or

Parameters:

non_linear_layer (np.ndarray) – Output of the activation function

Returns:

• linear_layer (np.ndarray) – The optimized weight matrix of the upper part of the network

• predicted_values (np.ndarray) – The prediction of the model

activate(x)

predict(X)

select()

_create_individuals()