autoemulate.emulators.ensemble#
- class Ensemble(emulators=None, jitter=0.0001, device=None)[source]#
Bases:
GaussianEmulatorEnsemble emulator that aggregates multiple Emulator instances to provide UQ.
Ensemble emulator that aggregates multiple Emulator instances and returns a GaussianLike representing the ensemble posterior. Note that an Emulator instance may also be an Ensemble itself.
- class EnsembleMLP(x, y, standardize_x=True, standardize_y=True, activation_cls=<class 'torch.nn.modules.activation.ReLU'>, loss_fn_cls=<class 'torch.nn.modules.loss.MSELoss'>, epochs=100, batch_size=16, layer_dims=None, weight_init='default', scale=1.0, bias_init='default', dropout_prob=None, lr=0.01, params_size=1, random_seed=None, device=None, scheduler_cls=None, scheduler_params=None, n_emulators=4)[source]#
Bases:
EnsembleEnsemble of MLP emulators.
This class is an ensemble of MLP emulators, each initialized with the same input and output data.
- class DropoutEnsemble(model, standardize_x=True, standardize_y=True, n_samples=20, jitter=0.0001, device=None)[source]#
Bases:
GaussianEmulator,TorchDeviceMixinMonte-Carlo Dropout ensemble.
DropoutEnsemble does a number of forward passes with dropout on, and computes mean and epistemic covariance across them.
- class EnsembleMLPDropout(x, y, standardize_x=True, standardize_y=True, activation_cls=<class 'torch.nn.modules.activation.ReLU'>, loss_fn_cls=<class 'torch.nn.modules.loss.MSELoss'>, epochs=100, batch_size=16, layer_dims=None, weight_init='default', scale=1.0, bias_init='default', dropout_prob=0.2, lr=0.01, params_size=1, random_seed=None, device=None, scheduler_cls=None, scheduler_params=None)[source]#
Bases:
DropoutEnsembleEnsemble of MLP emulators with dropout.