ForeTiS.model._additionalmodels.mlpbayes_intel

Module Contents

Classes

Mlp

Implementation of a class for a bayesian feedforward Multilayer Perceptron (MLP).
class ForeTiS.model._additionalmodels.mlpbayes_intel.Mlp(optuna_trial, datasets, featureset_name, optimize_featureset, pca_transform=None, current_model_name=None, batch_size=None, n_epochs=None, target_column=None)

Bases: ForeTiS.model._torch_model.TorchModel

Implementation of a class for a bayesian feedforward Multilayer Perceptron (MLP).

See BaseModel and TorchModel for more information on the attributes.

Parameters:

  • optuna_trial (optuna.trial.Trial)

  • datasets (list)

  • featureset_name (str)

  • optimize_featureset (bool)

  • pca_transform (bool)

  • current_model_name (str)

  • batch_size (int)

  • n_epochs (int)

  • target_column (str)

define_model()

Definition of an MLP network.

Architecture:

  • N_LAYERS of (bayesian Linear (+ ActivationFunction) (+ BatchNorm) + Dropout)

  • Bayesian Linear output layer

  • Dropout layer

Number of units in the first bayesian linear layer and percentage decrease after each may be fixed or optimized.

Return type:

torch.nn.Sequential

define_hyperparams_to_tune()

See BaseModel for more information on the format.

See TorchModel for more information on hyperparameters common for all torch models.

Return type:

dict

predict(X_in)

Implementation of a prediction based on input features for PyTorch models. See BaseModel for more information

Parameters:

X_in (pandas.DataFrame)

Return type:

numpy.array