tabnet | R Documentation |
Parsnip compatible tabnet model
tabnet(
mode = "unknown",
cat_emb_dim = NULL,
decision_width = NULL,
attention_width = NULL,
num_steps = NULL,
mask_type = 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,
lr_decay = NULL,
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
)
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 |
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 |
(float) 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 1.0 to 2.0. |
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 float is given this will clip the gradient at
clip_value. Pass |
drop_last |
(logical) Whether to drop last batch if not complete during training |
lr_scheduler |
if |
lr_decay |
multiplies the initial learning rate by |
step_size |
the learning rate scheduler step size. Unused if
|
checkpoint_epochs |
checkpoint model weights and architecture every
|
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: |
tabnet_model |
A previously fitted TabNet model object to continue the fitting on.
if |
from_epoch |
When a |
A TabNet parsnip
instance. It can be used to fit tabnet models using
parsnip
machinery.
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)
tabnet_fit
library(parsnip)
data("ames", package = "modeldata")
model <- tabnet() %>%
set_mode("regression") %>%
set_engine("torch")
model %>%
fit(Sale_Price ~ ., data = ames)
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