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Cluster Learner

Source: R/LearnerClust.R
LearnerClust.Rd

This Learner specializes mlr3::Learner for cluster problems:

  • task_type is set to "clust".

  • Creates mlr3::Predictions of class PredictionClust.

  • Possible values for predict_types are:

    • "partition": Integer indicating the cluster membership.

    • "prob": Probability for belonging to each cluster.

Predefined learners can be found in the mlr3misc::Dictionary mlr3::mlr_learners.

Super class

mlr3::Learner -> LearnerClust

Public fields

assignments

(NULL | vector())
Cluster assignments from learned model.

save_assignments

(logical())
Should assignments for 'train' data be saved in the learner? Default is TRUE.

Methods

Public methods

Inherited methods

Method new()

Creates a new instance of this R6 class.

Usage

LearnerClust$new(
  id,
  param_set = ps(),
  predict_types = "partition",
  feature_types = character(),
  properties = character(),
  packages = character(),
  label = NA_character_,
  man = NA_character_
)

Arguments

id

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

param_set

(paradox::ParamSet)
Set of hyperparameters.

predict_types

(character())
Supported predict types. Must be a subset of mlr_reflections$learner_predict_types.

feature_types

(character())
Feature types the learner operates on. Must be a subset of mlr_reflections$task_feature_types.

properties

(character())
Set of properties of the mlr3::Learner. Must be a subset of mlr_reflections$learner_properties. The following properties are currently standardized and understood by learners in mlr3:

  • "missings": The learner can handle missing values in the data.

  • "weights": The learner supports observation weights.

  • "importance": The learner supports extraction of importance scores, i.e. comes with an $importance() extractor function (see section on optional extractors in mlr3::Learner).

  • "selected_features": The learner supports extraction of the set of selected features, i.e. comes with a $selected_features() extractor function (see section on optional extractors in mlr3::Learner).

  • "oob_error": The learner supports extraction of estimated out of bag error, i.e. comes with a oob_error() extractor function (see section on optional extractors in mlr3::Learner).

packages

(character())
Set of required packages. A warning is signaled by the constructor if at least one of the packages is not installed, but loaded (not attached) later on-demand via requireNamespace().

label

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

man

(character(1))
String in the format [pkg]::[topic] pointing to a manual page for this object. The referenced help package can be opened via method $help().

Method reset()

Reset assignments field before calling parent's reset().

Usage

LearnerClust$reset()

Method clone()

The objects of this class are cloneable with this method.

Usage

LearnerClust$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3)
library(mlr3cluster)
ids = mlr_learners$keys("^clust")
ids
#>  [1] "clust.MBatchKMeans" "clust.SimpleKMeans" "clust.agnes"       
#>  [4] "clust.ap"           "clust.bico"         "clust.birch"       
#>  [7] "clust.cmeans"       "clust.cobweb"       "clust.dbscan"      
#> [10] "clust.dbscan_fpc"   "clust.diana"        "clust.em"          
#> [13] "clust.fanny"        "clust.featureless"  "clust.ff"          
#> [16] "clust.hclust"       "clust.hdbscan"      "clust.kkmeans"     
#> [19] "clust.kmeans"       "clust.mclust"       "clust.meanshift"   
#> [22] "clust.optics"       "clust.pam"          "clust.xmeans"      

# get a specific learner from mlr_learners:
learner = lrn("clust.kmeans")
print(learner)
#> <LearnerClustKMeans:clust.kmeans>: K-Means
#> * Model: -
#> * Parameters: centers=2
#> * Packages: mlr3, mlr3cluster, stats, clue
#> * Predict Types:  [partition]
#> * Feature Types: logical, integer, numeric
#> * Properties: complete, exclusive, partitional