Auto Class — Auto • mlr3automl

This class is the base class for all autos.

Value

Object of class R6::R6Class and Auto.

Public fields

id: (character(1)).
properties: (character()).
task_types: (character()).
packages: (character()).
devices: (character()).
n_cpu: (integer(1))
Number of CPUs a single training of the learner uses.
n_gpu: (integer(1))
Number of GPUs a single training of the learner uses.

Methods

`Auto$new()`

Creates a new instance of this R6 class.

Usage

Auto$new(
  id,
  properties = character(0),
  task_types = character(0),
  packages = character(0),
  devices = character(0),
  n_cpu = 1L,
  n_gpu = 0L
)

Arguments

id: (character(1)).
properties: (character()).
task_types: (character()).
packages: (character()).
devices: (character()).
n_cpu: (integer(1)).
n_gpu: (integer(1)).

`Auto$check()`

Check if the auto is compatible with the task.

Usage

Auto$check(task, memory_limit = Inf, large_data_set = FALSE, devices)

Arguments

task: (mlr3::Task).
memory_limit: (integer(1)).
large_data_set: (logical(1)).
devices: (character())
Devices to use. Allowed values are "cpu" and "cuda". Default is "cpu".

`Auto$graph()`

Create the graph for the auto.

Usage

Auto$graph(task, measure, n_threads, timeout, devices)

Arguments

task: (mlr3::Task).
measure: (mlr3::Measure).
n_threads: (integer(1)).
timeout: (integer(1)).
devices: (character())
Devices to use. Allowed values are "cpu" and "cuda". Default is "cpu".

`Auto$early_stopping_rounds()`

Estimate the number of early stopping rounds (the patience) for a learner. budget is the maximum number of training rounds (boosting iterations or epochs) the learner may use. The patience is capped well below the budget, otherwise early stopping and validation-based internal tuning can never trigger and the learner always trains for the full budget.

Usage

Auto$early_stopping_rounds(task, budget = Inf)

Arguments

task: (mlr3::Task).
budget: (integer(1))
Maximum number of training rounds (boosting iterations or epochs) the learner may use.

`Auto$estimate_memory()`

Estimate the memory for the auto. The estimate is the host memory in MB, so learners that allocate on the gpu return -Inf.

Usage

Auto$estimate_memory(task, devices = "cpu")

Arguments

task: (mlr3::Task).
devices: (character())
Devices to use. Allowed values are "cpu" and "cuda". Default is "cpu".

`Auto$finalize_model()`

Prepare the graph learner for the final model fit. Called after tuning to undo tuning-only setup (e.g., timeout callbacks).

Usage

Auto$finalize_model(graph_learner)

Arguments

graph_learner: (mlr3pipelines::GraphLearner).

`Auto$design_default()`

Default hyperparameters for the learner.

Usage

Auto$design_default(task)

Arguments

task: (mlr3::Task).

`Auto$design_set()`

Get the initial hyperparameter set for the learner.

Usage

Auto$design_set(task, measure, size)

Arguments

task: (mlr3::Task).
measure: (mlr3::Measure).
size: (integer(1)).

`Auto$search_space()`

Get the search space for the learner.

Usage

Auto$search_space(task)

Arguments

task: (mlr3::Task).

`Auto$clone()`

The objects of this class are cloneable with this method.

Usage

Auto$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.