Xgboost Auto

Xgboost auto.

Value

Object of class R6::R6Class and AutoXgboost.

Super class

Auto -> AutoXgboost

Methods

Public methods

• AutoXgboost$new()

• AutoXgboost$graph()

• AutoXgboost$finalize_model()

• AutoXgboost$estimate_memory()

• AutoXgboost$internal_measure()

• AutoXgboost$clone()

Inherited methods

• Auto$check()
• Auto$design_default()
• Auto$design_set()
• Auto$early_stopping_rounds()
• Auto$search_space()

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AutoXgboost$new()

Creates a new instance of this R6 class.

Usage

AutoXgboost$new(id = "xgboost")

Arguments

id

(character(1))
Identifier for the new instance.

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AutoXgboost$graph()

Create the graph for the auto.

Usage

AutoXgboost$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".

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AutoXgboost$finalize_model()

Prepare the graph learner for the final model fit.

Usage

AutoXgboost$finalize_model(graph_learner)

Arguments

graph_learner

(mlr3pipelines::GraphLearner).

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AutoXgboost$estimate_memory()

Estimate the memory for the auto.

Usage

AutoXgboost$estimate_memory(task)

Arguments

task

(mlr3::Task).

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AutoXgboost$internal_measure()

Get the internal measure for the auto.

Usage

AutoXgboost$internal_measure(measure, task)

Arguments

measure

(mlr3::Measure).

task

(mlr3::Task).

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AutoXgboost$clone()

The objects of this class are cloneable with this method.

Usage

AutoXgboost$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

auto("xgboost")
#> <AutoXgboost>
#>   Inherits from: <Auto>
#>   Public:
#>     check: function (task, memory_limit = Inf, large_data_set = FALSE, devices) 
#>     clone: function (deep = FALSE) 
#>     design_default: function (task) 
#>     design_set: function (task, measure, size) 
#>     devices: cpu cuda
#>     early_stopping_rounds: function (task, budget = Inf) 
#>     estimate_memory: function (task) 
#>     finalize_model: function (graph_learner) 
#>     graph: function (task, measure, n_threads, timeout, devices) 
#>     id: xgboost
#>     initialize: function (id = "xgboost") 
#>     internal_measure: function (measure, task) 
#>     packages: mlr3 mlr3learners xgboost
#>     properties: internal_tuning large_data_sets
#>     search_space: function (task) 
#>     task_types: classif regr
#>   Private:
#>     .default_values: list
#>     .search_space: ParamSet, R6