Create Autoencoder PipeOp
create_autoencoder_pipeop.RdCreates an mlr3pipelines PipeOp that trains a torch autoencoder on the training data and replaces features with latent representations.
Usage
create_autoencoder_pipeop(latent_dim = 32L, hidden_layers = c(128L, 64L),
activation = "relu", dropout = 0.1, epochs = 100L, batch_size = 32L,
lr = 0.001, weight_decay = 0, loss = "mse", validation_split = 0.1,
early_stopping = TRUE, patience = 10L, min_delta = 0, device = "cpu",
seed = NULL, pretrained = NULL, freeze_encoder = FALSE, concat = FALSE,
prefix = "ae_")Arguments
- latent_dim
Integer, size of latent representation.
Integer vector of hidden layer sizes.
- activation
Activation function.
- dropout
Dropout rate between 0 and 1.
- epochs
Number of training epochs.
- batch_size
Batch size.
- lr
Learning rate.
- weight_decay
L2 weight decay.
- loss
Loss function: "mse" or "mae".
- validation_split
Fraction of data for validation.
- early_stopping
Logical, enable early stopping.
- patience
Early stopping patience.
- min_delta
Minimum improvement to reset patience.
- device
"cpu" or "cuda".
- seed
Optional random seed.
- pretrained
Optional path to a saved autoencoder state.
- freeze_encoder
Logical, freeze encoder weights during training.
- concat
Logical, if TRUE returns original + latent features.
- prefix
Prefix for latent feature names.