ForeTiS.model.mlp

Module Contents

Classes

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

class ForeTiS.model.mlp.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 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 (Linear (+ ActivationFunction) (+ BatchNorm) + Dropout)

• Linear output layer

• Dropout layer

Number of units in the first 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