Featureless Torch Learner

Featureless torch learner. Output is a constant weight that is learned during training. For classification, this should (asymptoptically) result in a majority class prediction when using the standard cross-entropy loss. For regression, this should result in the median for L1 loss and in the mean for L2 loss.

Dictionary

This Learner can be instantiated using the sugar function lrn():

lrn("classif.torch_featureless", ...)
lrn("regr.torch_featureless", ...)

Properties

• Supported task types: 'classif', 'regr'

• Predict Types:

  • classif: 'response', 'prob'

  • regr: 'response'

• Feature Types: “logical”, “integer”, “numeric”, “character”, “factor”, “ordered”, “POSIXct”, “lazy_tensor”

• Required Packages: mlr3, mlr3torch, torch

Parameters

Only those from LearnerTorch.

See also

Other Learner: mlr_learners.mlp, mlr_learners.tab_resnet, mlr_learners_torch, mlr_learners_torch_image, mlr_learners_torch_model

Super classes

mlr3::Learner -> mlr3torch::LearnerTorch -> LearnerTorchFeatureless

Methods

Public methods

• LearnerTorchFeatureless$new()

• LearnerTorchFeatureless$clone()

Inherited methods

• mlr3::Learner$base_learner()
• mlr3::Learner$encapsulate()
• mlr3::Learner$help()
• mlr3::Learner$predict()
• mlr3::Learner$predict_newdata()
• mlr3::Learner$reset()
• mlr3::Learner$train()
• mlr3torch::LearnerTorch$dataset()
• mlr3torch::LearnerTorch$format()
• mlr3torch::LearnerTorch$marshal()
• mlr3torch::LearnerTorch$print()
• mlr3torch::LearnerTorch$unmarshal()

---

Method new()

Creates a new instance of this R6 class.

Usage

LearnerTorchFeatureless$new(
  task_type,
  optimizer = NULL,
  loss = NULL,
  callbacks = list()
)

Arguments

task_type

(character(1))
The task type, either "classif" or "regr".

optimizer

(TorchOptimizer)
The optimizer to use for training. Per default, adam is used.

loss

(TorchLoss)
The loss used to train the network. Per default, mse is used for regression and cross_entropy for classification.

callbacks

(list() of TorchCallbacks)
The callbacks. Must have unique ids.

---

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerTorchFeatureless$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# Define the Learner and set parameter values
learner = lrn("classif.torch_featureless")
learner$param_set$set_values(
  epochs = 1, batch_size = 16, device = "cpu"
  
)

# Define a Task
task = tsk("iris")

# Create train and test set
ids = partition(task)

# Train the learner on the training ids
learner$train(task, row_ids = ids$train)

# Make predictions for the test rows
predictions = learner$predict(task, row_ids = ids$test)

# Score the predictions
predictions$score()
#> classif.ce 
#>       0.64