Tabnet model — tabnet

Fits the TabNet: Attentive Interpretable Tabular Learning model

tabnet_fit(x, ...)

# Default S3 method
tabnet_fit(x, ...)

# S3 method for class 'data.frame'
tabnet_fit(
  x,
  y,
  tabnet_model = NULL,
  config = tabnet_config(),
  ...,
  from_epoch = NULL,
  weights = NULL
)

# S3 method for class 'formula'
tabnet_fit(
  formula,
  data,
  tabnet_model = NULL,
  config = tabnet_config(),
  ...,
  from_epoch = NULL,
  weights = NULL
)

# S3 method for class 'recipe'
tabnet_fit(
  x,
  data,
  tabnet_model = NULL,
  config = tabnet_config(),
  ...,
  from_epoch = NULL,
  weights = NULL
)

# S3 method for class 'Node'
tabnet_fit(
  x,
  tabnet_model = NULL,
  config = tabnet_config(),
  ...,
  from_epoch = NULL
)

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().
A Node where tree will be used as hierarchical outcome, and attributes will be used as predictors.

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. The model treats missing values 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.

weights

Unused. Placeholder for hardhat::importance_weight() variables.

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.

Value

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

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, which is usually named multi-label classification or multi-output regression when outcomes are numerical. Multi-outcome currently expect outcomes to be either all numeric or all categorical.

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


data("ames", package = "modeldata")
data("attrition", package = "modeldata")
ids <- sample(nrow(attrition), 256)

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

## Single-outcome classification using data-frame specification
attrition_x <- attrition[,-which(names(attrition) == "Attrition")]
fit <- tabnet_fit(attrition_x, attrition$Attrition, epochs = 1, verbose = TRUE)
#> [Epoch 001] Loss: 0.995773

## Multi-outcome regression on `Sale_Price` and `Pool_Area` in `ames` dataset using formula,
ames_fit <- tabnet_fit(Sale_Price + Pool_Area ~ ., data = ames[ids,], epochs = 2, valid_split = 0.2)

## Multi-label classification on `Attrition` and `JobSatisfaction` in
## `attrition` dataset using recipe
library(recipes)
#> 
#> Attaching package: ‘recipes’
#> The following object is masked from ‘package:stats’:
#> 
#>     step
rec <- recipe(Attrition + JobSatisfaction ~ ., data = attrition[ids,]) %>%
  step_normalize(all_numeric(), -all_outcomes())

attrition_fit <- tabnet_fit(rec, data = attrition[ids,], epochs = 2, valid_split = 0.2)

## Hierarchical classification on  `acme`
data(acme, package = "data.tree")

acme_fit <- tabnet_fit(acme, epochs = 2, verbose = TRUE)
#> [Epoch 001] Loss: 2.754614
#> [Epoch 002] Loss: 1.907745

# Note: Dataset number of rows and model number of epochs should be increased
# for publication-level results.