autoemulate.emulators.neural_net_sk

autoemulate.emulators.neural_net_sk#

class NeuralNetSk(hidden_layer_sizes=(100, 100), activation='relu', solver='adam', alpha=0.0001, learning_rate='constant', learning_rate_init=0.001, max_iter=200, tol=0.0001, random_state=None)[source]#

Bases: BaseEstimator, RegressorMixin

Multi-layer perceptron Emulator.

Wraps MLPRegressor from scikit-learn.

fit(X, y)[source]#

Fits the emulator to the data.

Parameters:

  • X ({array-like, sparse matrix}, shape (n_samples, n_features)) – The training input samples.

  • y (array-like, shape (n_samples,) or (n_samples, n_outputs)) – The target values (class labels in classification, real numbers in regression).

Returns:

self – Returns self.

Return type:

object

predict(X)[source]#

Predicts the output of the simulator for a given input.

Parameters:

X ({array-like, sparse matrix}, shape (n_samples, n_features)) – The training input samples.

Returns:

y – Model predictions.

Return type:

ndarray, shape (n_samples,)

get_grid_params(search_type='random')[source]#

Returns the grid parameters of the emulator.