mlr_learners_graph {mlr3pipelines}

Encapsulate a Graph as a Learner

Description

A Learner that encapsulates a Graph to be used in mlr3 resampling and benchmarks.

The Graph must return a single Prediction on its ⁠$predict()⁠ call. The result of the ⁠$train()⁠ call is discarded, only the internal state changes during training are used.

The predict_type of a GraphLearner can be obtained or set via it's predict_type active binding. Setting a new predict type will try to set the predict_type in all relevant PipeOp / Learner encapsulated within the Graph. Similarly, the predict_type of a Graph will always be the smallest denominator in the Graph.

A GraphLearner is always constructed in an untrained state. When the graph argument has a non-NULL ⁠$state⁠, it is ignored.

Format

R6Class object inheriting from mlr3::Learner.

Construction

GraphLearner$new(graph, id = NULL, param_vals = list(), task_type = NULL, predict_type = NULL)

• graph :: Graph | PipeOp
Graph to wrap. Can be a PipeOp, which is automatically converted to a Graph. This argument is usually cloned, unless clone_graph is FALSE; to access the Graph inside GraphLearner by-reference, use ⁠$graph⁠.
  

• id :: character(1) Identifier of the resulting Learner.

• param_vals :: named list
  List of hyperparameter settings, overwriting the hyperparameter settings . Default list().

• task_type :: character(1)
  What task_type the GraphLearner should have; usually automatically inferred for Graphs that are simple enough.

• predict_type :: character(1)
  What predict_type the GraphLearner should have; usually automatically inferred for Graphs that are simple enough.

• clone_graph :: logical(1)
  Whether to clone graph upon construction. Unintentionally changing graph by reference can lead to unexpected behaviour, so TRUE (default) is recommended. In particular, note that the ⁠$state⁠ of ⁠$graph⁠ is set to NULL by reference on construction of GraphLearner, during ⁠$train()⁠, and during ⁠$predict()⁠ when clone_graph is FALSE.

Fields

Fields inherited from PipeOp, as well as:

• graph :: Graph
Graph that is being wrapped. This field contains the prototype of the Graph that is being trained, but does not contain the model. Use graph_model to access the trained Graph after ⁠$train()⁠. Read-only.

• graph_model :: Learner
Graph that is being wrapped. This Graph contains a trained state after ⁠$train()⁠. Read-only.

• internal_tuned_values :: named list() or NULL
  The internal tuned parameter values collected from all PipeOps. NULL is returned if the learner is not trained or none of the wrapped learners supports internal tuning.

• internal_valid_scores :: named list() or NULL
  The internal validation scores as retrieved from the PipeOps. The names are prefixed with the respective IDs of the PipeOps. NULL is returned if the learner is not trained or none of the wrapped learners supports internal validation.

• validate :: numeric(1), "predefined", "test" or NULL
  How to construct the validation data. This also has to be configured for the individual PipeOps such as PipeOpLearner, see set_validate.GraphLearner. For more details on the possible values, see mlr3::Learner.

• marshaled :: logical(1)
  Whether the learner is marshaled.

Methods

• marshal(...)
  (any) -> self
  Marshal the model.

• unmarshal(...)
  (any) -> self
  Unmarshal the model.

Internals

as_graph() is called on the graph argument, so it can technically also be a list of things, which is automatically converted to a Graph via gunion(); however, this will usually not result in a valid Graph that can work as a Learner. graph can furthermore be a Learner, which is then automatically wrapped in a Graph, which is then again wrapped in a GraphLearner object; this usually only adds overhead and is not recommended.

See Also

Other Learners: mlr_learners_avg

Examples

library("mlr3")

graph = po("pca") %>>% lrn("classif.rpart")

lr = GraphLearner$new(graph)
lr = as_learner(graph)  # equivalent

lr$train(tsk("iris"))

lr$graph$state  # untrained version!
# The following is therefore NULL:
lr$graph$pipeops$classif.rpart$learner_model$model

# To access the trained model from the PipeOpLearner's Learner, use:
lr$graph_model$pipeops$classif.rpart$learner_model$model

# Feature importance (of principal components):
lr$graph_model$pipeops$classif.rpart$learner_model$importance()