tabnet (version 0.4.0)

tabnet_fit: Tabnet model

Description

Fits the TabNet: Attentive Interpretable Tabular Learning model

Usage

tabnet_fit(x, ...)
# S3 method for default
tabnet_fit(x, ...)
# S3 method for data.frame
tabnet_fit(
  x,
  y,
  tabnet_model = NULL,
  config = tabnet_config(),
  ...,
  from_epoch = NULL
)
# S3 method for formula
tabnet_fit(
  formula,
  data,
  tabnet_model = NULL,
  config = tabnet_config(),
  ...,
  from_epoch = NULL
)
# S3 method for recipe
tabnet_fit(
  x,
  data,
  tabnet_model = NULL,
  config = tabnet_config(),
  ...,
  from_epoch = NULL
)

Value

A TabNet model object. It can be used for serialization, predictions, or further fitting.

Arguments

x

Depending on the context:

• A data frame of predictors.

• A matrix of predictors.

• A recipe specifying a set of preprocessing steps created from recipes::recipe().

The predictor data should be standardized (e.g. centered or scaled). The model treats categorical predictors internally thus, you don't need to make any treatment.

...

Model hyperparameters. Any hyperparameters set here will update those set by the config argument. See tabnet_config() for a list of all possible hyperparameters.

y

When x is a data frame or matrix, y is the outcome specified as:

• A data frame with 1 or many numeric column (regression) or 1 or many categorical columns (classification) .

• A matrix with 1 column.

• A vector, either numeric or categorical.

tabnet_model

A previously fitted TabNet model object to continue the fitting on. if NULL (the default) a brand new model is initialized.

config

A set of hyperparameters created using the tabnet_config function. If no argument is supplied, this will use the default values in tabnet_config().

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.

formula

A formula specifying the outcome terms on the left-hand side, and the predictor terms on the right-hand side.

data

When a recipe or formula is used, data is specified as:

• A data frame containing both the predictors and the outcome.

Fitting a pre-trained model

When providing a parent tabnet_model parameter, the model fitting resumes from that model weights at the following epoch:

• last fitted epoch for a model already in torch context

• Last model checkpoint epoch for a model loaded from file

• the epoch related to a checkpoint matching or preceding the from_epoch value if provided The model fitting metrics append on top of the parent metrics in the returned TabNet model.

Multi-outcome

TabNet allows multi-outcome prediction, the outcomes must all be numeric or all be categorical, and the data frame method shall be used for x and y.

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

if (torch::torch_is_installed()) {

# regression using formula specification
data("ames", package = "modeldata")
fit <- tabnet_fit(Sale_Price ~ ., data = ames, epochs = 1)

# classification using data-frame specification
data("attrition", package = "modeldata")
attrition_x <- attrition[,-which(names(attrition) == "Attrition")]
fit <- tabnet_fit(attrition_x, attrition$Attrition, epochs = 1)
}