Parsnip compatible tabnet model

Usage

tabnet(
  mode = "unknown",
  cat_emb_dim = NULL,
  decision_width = NULL,
  attention_width = NULL,
  num_steps = NULL,
  mask_type = NULL,
  mask_topk = NULL,
  num_independent = NULL,
  num_shared = NULL,
  num_independent_decoder = NULL,
  num_shared_decoder = NULL,
  penalty = NULL,
  feature_reusage = NULL,
  momentum = NULL,
  epochs = NULL,
  batch_size = NULL,
  virtual_batch_size = NULL,
  learn_rate = NULL,
  optimizer = NULL,
  loss = NULL,
  clip_value = NULL,
  drop_last = NULL,
  lr_scheduler = NULL,
  rate_decay = NULL,
  rate_step_size = NULL,
  checkpoint_epochs = NULL,
  verbose = NULL,
  importance_sample_size = NULL,
  early_stopping_monitor = NULL,
  early_stopping_tolerance = NULL,
  early_stopping_patience = NULL,
  skip_importance = NULL,
  tabnet_model = NULL,
  from_epoch = NULL
)

Arguments

mode: A single character string for the type of model. Possible values for this model are "unknown", "regression", or "classification".
cat_emb_dim: Size of the embedding of categorical features. If int, all categorical features will have same embedding size, if list of int, every corresponding feature will have specific embedding size.
decision_width: (int) Width of the decision prediction layer. Bigger values gives more capacity to the model with the risk of overfitting. Values typically range from 8 to 64.
attention_width: (int) Width of the attention embedding for each mask. According to the paper n_d = n_a is usually a good choice. (default=8)
num_steps: (int) Number of steps in the architecture (usually between 3 and 10)
mask_type: (character) Final layer of feature selector in the attentive_transformer block, either "sparsemax", "entmax" or "entmax15".Defaults to "sparsemax".
mask_topk: (int) mask sparsity top-k for sparsemax15 and entmax15. See entmax15() for detail.
num_independent: Number of independent Gated Linear Units layers at each step of the encoder. Usual values range from 1 to 5.
num_shared: Number of shared Gated Linear Units at each step of the encoder. Usual values at each step of the decoder. range from 1 to 5
num_independent_decoder: For pretraining, number of independent Gated Linear Units layers Usual values range from 1 to 5.
num_shared_decoder: For pretraining, number of shared Gated Linear Units at each step of the decoder. Usual values range from 1 to 5.
penalty: This is the extra sparsity loss coefficient as proposed in the original paper. The bigger this coefficient is, the sparser your model will be in terms of feature selection. Depending on the difficulty of your problem, reducing this value could help (default 1e-3).
feature_reusage: (num) This is the coefficient for feature reusage in the masks. A value close to 1 will make mask selection least correlated between layers. Values range from 1 to 2.
momentum: Momentum for batch normalization, typically ranges from 0.01 to 0.4 (default=0.02)
epochs: (int) Number of training epochs.
batch_size: (int) Number of examples per batch, large batch sizes are recommended. (default: 1024^2)
virtual_batch_size: (int) Size of the mini batches used for "Ghost Batch Normalization" (default=256^2)
learn_rate: initial learning rate for the optimizer.
optimizer: the optimization method. currently only "adam" is supported, you can also pass any torch optimizer function.
loss: (character or function) Loss function for training (default to mse for regression and cross entropy for classification)
clip_value: If a num is given this will clip the gradient at clip_value. Pass NULL to not clip.
drop_last: (logical) Whether to drop last batch if not complete during training
lr_scheduler: if NULL, no learning rate decay is used. If "step" decays the learning rate by lr_decay every step_size epochs. If "reduce_on_plateau" decays the learning rate by lr_decay when no improvement after step_size epochs. It can also be a torch::lr_scheduler function that only takes the optimizer as parameter. The step method is called once per epoch.
rate_decay: multiplies the initial learning rate by rate_decay every rate_step_size epochs. Unused if lr_scheduler is a torch::lr_scheduler or NULL.
rate_step_size: the learning rate scheduler step size. Unused if lr_scheduler is a torch::lr_scheduler or NULL.
checkpoint_epochs: checkpoint model weights and architecture every checkpoint_epochs. (default is 10). This may cause large memory usage. Use 0 to disable checkpoints.
verbose: (logical) Whether to print progress and loss values during training.
importance_sample_size: sample of the dataset to compute importance metrics. If the dataset is larger than 1e5 obs we will use a sample of size 1e5 and display a warning.
early_stopping_monitor: Metric to monitor for early_stopping. One of "valid_loss", "train_loss" or "auto" (defaults to "auto").
early_stopping_tolerance: Minimum relative improvement to reset the patience counter. 0.01 for 1% tolerance (default 0)
early_stopping_patience: Number of epochs without improving until stopping training. (default=5)
skip_importance: if feature importance calculation should be skipped (default: FALSE)
tabnet_model: A previously fitted tabnet_model object to continue the fitting on. if NULL (the default) a brand new model is initialized.
from_epoch: When a tabnet_model is provided, restore the network weights from a specific epoch. Default is last available checkpoint for restored model, or last epoch for in-memory model.

Value

A TabNet parsnip instance. It can be used to fit tabnet models using parsnip machinery.

Threading

TabNet uses torch as its backend for computation and torch uses all available threads by default.

You can control the number of threads used by torch with:

torch::torch_set_num_threads(1)
torch::torch_set_num_interop_threads(1)

Examples