MLLearnerBase: R6 Class to construct learners
In mlexperiments: Machine Learning Experiments
MLLearnerBase R Documentation
R6 Class to construct learners
Description
The MLLearnerBase class is used to construct a learner object that can be
used with the experiment classes from the mlexperiments package. It is
thought to serve as a class to inherit from when creating new learners.
Details
The learner class exposes 4 methods that can be defined:
-
$fit A wrapper around the private function fun_fit, which needs to
be defined for every learner. The return value of this function is the
fitted model.
-
$predict A wrapper around the private function fun_predict,
which needs to be defined for every learner. The function must accept the
three arguments model, newdata, and ncores and is a wrapper around
the respective learner's predict-function. In order to allow the passing of
further arguments, the ellipsis (...) can be used. The function should
return the prediction results.
-
$cross_validation A wrapper around the private function
fun_optim_cv, which needs to be defined when hyperparameters should be
optimized with a grid search (required for use with
MLTuneParameters, and MLNestedCV).
-
$bayesian_scoring_function A wrapper around the private function
fun_bayesian_scoring_function, which needs to be defined when
hyperparameters should be optimized with a Bayesian process (required for
use with MLTuneParameters, and
MLNestedCV).
For further details please refer to the package's vignette.
Public fields
cluster_exportA character vector defining the (internal)
functions that need to be exported to the parallelization cluster.
This is only required when performing a Bayesian hyperparameter
optimization. See also parallel::clusterExport().
metric_optimization_higher_betterA logical. Defines the direction
of the optimization metric used throughout the hyperparameter
optimization. This field is set automatically during the initialization
of the MLLearnerBase object. Its purpose is to make it accessible by
the evaluation functions from MLTuneParameters.
environmentThe environment in which to search for the functions
of the learner (default: -1L).
seedSeed for reproducible results.
Methods
Public methods
-
-
-
-
-
-
Method new()
Create a new MLLearnerBase object.
Usage
MLLearnerBase$new(metric_optimization_higher_better)
Arguments
metric_optimization_higher_betterA logical. Defines the direction
of the optimization metric used throughout the hyperparameter
optimization.
Returns
A new MLLearnerBase R6 object.
Examples
MLLearnerBase$new(metric_optimization_higher_better = FALSE)
Method cross_validation()
Perform a cross-validation with an MLLearnerBase.
Usage
MLLearnerBase$cross_validation(...)
Arguments
...Arguments to be passed to the learner's cross-validation
function.
Details
A wrapper around the private function fun_optim_cv, which needs to be
defined when hyperparameters should be optimized with a grid search
(required for use with MLTuneParameters, and
MLNestedCV.
However, the function should be never executed directly but by the
respective experiment wrappers MLTuneParameters, and
MLNestedCV.
For further details please refer to the package's vignette.
Returns
The fitted model.
Examples
learner <- MLLearnerBase$new(metric_optimization_higher_better = FALSE)
\dontrun{
# This example cannot be run without further adaptions.
# The method `$cross_validation()` needs to be overwritten when
# inheriting from this class.
learner$cross_validation()
}
Method fit()
Fit a MLLearnerBase object.
Usage
MLLearnerBase$fit(...)
Arguments
...Arguments to be passed to the learner's fitting function.
Details
A wrapper around the private function fun_fit, which needs to be
defined for every learner. The return value of this function is the
fitted model.
However, the function should be never executed directly but by the
respective experiment wrappers MLTuneParameters,
MLCrossValidation, and
MLNestedCV.
For further details please refer to the package's vignette.
Returns
The fitted model.
Examples
learner <- MLLearnerBase$new(metric_optimization_higher_better = FALSE)
\dontrun{
# This example cannot be run without further adaptions.
# The method `$fit()` needs to be overwritten when
# inheriting from this class.
learner$fit()
}
Method predict()
Make predictions from a fitted MLLearnerBase object.
Usage
MLLearnerBase$predict(model, newdata, ncores = -1L, ...)
Arguments
modelA fitted model of the learner (as returned by
MLLearnerBase$fit()).
newdataThe new data for which predictions should be made using
the model.
ncoresAn integer to specify the number of cores used for
parallelization (default: -1L).
...Further arguments to be passed to the learner's predict
function.
Details
A wrapper around the private function fun_predict, which needs to be
defined for every learner. The function must accept the three arguments
model, newdata, and ncores and is a wrapper around the respective
learner's predict-function. In order to allow the passing of further
arguments, the ellipsis (...) can be used. The function should
return the prediction results.
However, the function should be never executed directly but by the
respective experiment wrappers MLTuneParameters,
MLCrossValidation, and
MLNestedCV.
For further details please refer to the package's vignette.
Returns
The predictions for newdata.
Examples
learner <- MLLearnerBase$new(metric_optimization_higher_better = FALSE)
\dontrun{
# This example cannot be run without further adaptions.
# The method `$predict()` needs to be overwritten when
# inheriting from this class.
learner$fit()
learner$predict()
}
Method bayesian_scoring_function()
Perform a Bayesian hyperparameter optimization with an MLLearnerBase.
Usage
MLLearnerBase$bayesian_scoring_function(...)
Arguments
...Arguments to be passed to the learner's Bayesian scoring
function.
Details
A wrapper around the private function fun_bayesian_scoring_function,
which needs to be defined when hyperparameters should be optimized with
a Bayesian process (required for use with
MLTuneParameters, and MLNestedCV.
However, the function should be never executed directly but by the
respective experiment wrappers MLTuneParameters, and
MLNestedCV.
For further details please refer to the package's vignette.
Returns
The results of the Bayesian scoring.
Examples
learner <- MLLearnerBase$new(metric_optimization_higher_better = FALSE)
\dontrun{
# This example cannot be run without further adaptions.
# The method `$bayesian_scoring_function()` needs to be overwritten when
# inheriting from this class.
learner$bayesian_scoring_function()
}
Method clone()
The objects of this class are cloneable with this method.
Usage
MLLearnerBase$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
MLTuneParameters,
MLCrossValidation, and
MLNestedCV
MLTuneParameters,
MLCrossValidation, and
MLNestedCV
MLTuneParameters,
MLCrossValidation, and
MLNestedCV
ParBayesianOptimization::bayesOpt(),
MLTuneParameters, and MLNestedCV
Examples
MLLearnerBase$new(metric_optimization_higher_better = FALSE)
## ------------------------------------------------
## Method `MLLearnerBase$new`
## ------------------------------------------------
MLLearnerBase$new(metric_optimization_higher_better = FALSE)
## ------------------------------------------------
## Method `MLLearnerBase$cross_validation`
## ------------------------------------------------
learner <- MLLearnerBase$new(metric_optimization_higher_better = FALSE)
## Not run:
# This example cannot be run without further adaptions.
# The method `$cross_validation()` needs to be overwritten when
# inheriting from this class.
learner$cross_validation()
## End(Not run)
## ------------------------------------------------
## Method `MLLearnerBase$fit`
## ------------------------------------------------
learner <- MLLearnerBase$new(metric_optimization_higher_better = FALSE)
## Not run:
# This example cannot be run without further adaptions.
# The method `$fit()` needs to be overwritten when
# inheriting from this class.
learner$fit()
## End(Not run)
## ------------------------------------------------
## Method `MLLearnerBase$predict`
## ------------------------------------------------
learner <- MLLearnerBase$new(metric_optimization_higher_better = FALSE)
## Not run:
# This example cannot be run without further adaptions.
# The method `$predict()` needs to be overwritten when
# inheriting from this class.
learner$fit()
learner$predict()
## End(Not run)
## ------------------------------------------------
## Method `MLLearnerBase$bayesian_scoring_function`
## ------------------------------------------------
learner <- MLLearnerBase$new(metric_optimization_higher_better = FALSE)
## Not run:
# This example cannot be run without further adaptions.
# The method `$bayesian_scoring_function()` needs to be overwritten when
# inheriting from this class.
learner$bayesian_scoring_function()
## End(Not run)
mlexperiments documentation built on April 12, 2025, 1:40 a.m.
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| MLLearnerBase | R Documentation |
R6 Class to construct learners
Description
The MLLearnerBase class is used to construct a learner object that can be
used with the experiment classes from the mlexperiments package. It is
thought to serve as a class to inherit from when creating new learners.
Details
The learner class exposes 4 methods that can be defined:
-
$fitA wrapper around the private functionfun_fit, which needs to be defined for every learner. The return value of this function is the fitted model. -
$predictA wrapper around the private functionfun_predict, which needs to be defined for every learner. The function must accept the three argumentsmodel,newdata, andncoresand is a wrapper around the respective learner's predict-function. In order to allow the passing of further arguments, the ellipsis (...) can be used. The function should return the prediction results. -
$cross_validationA wrapper around the private functionfun_optim_cv, which needs to be defined when hyperparameters should be optimized with a grid search (required for use with MLTuneParameters, and MLNestedCV). -
$bayesian_scoring_functionA wrapper around the private functionfun_bayesian_scoring_function, which needs to be defined when hyperparameters should be optimized with a Bayesian process (required for use with MLTuneParameters, and MLNestedCV).
For further details please refer to the package's vignette.
Public fields
cluster_exportA character vector defining the (internal) functions that need to be exported to the parallelization cluster. This is only required when performing a Bayesian hyperparameter optimization. See also
parallel::clusterExport().metric_optimization_higher_betterA logical. Defines the direction of the optimization metric used throughout the hyperparameter optimization. This field is set automatically during the initialization of the
MLLearnerBaseobject. Its purpose is to make it accessible by the evaluation functions from MLTuneParameters.environmentThe environment in which to search for the functions of the learner (default:
-1L).seedSeed for reproducible results.
Methods
Public methods
Method new()
Create a new MLLearnerBase object.
Usage
MLLearnerBase$new(metric_optimization_higher_better)
Arguments
metric_optimization_higher_betterA logical. Defines the direction of the optimization metric used throughout the hyperparameter optimization.
Returns
A new MLLearnerBase R6 object.
Examples
MLLearnerBase$new(metric_optimization_higher_better = FALSE)
Method cross_validation()
Perform a cross-validation with an MLLearnerBase.
Usage
MLLearnerBase$cross_validation(...)
Arguments
...Arguments to be passed to the learner's cross-validation function.
Details
A wrapper around the private function fun_optim_cv, which needs to be
defined when hyperparameters should be optimized with a grid search
(required for use with MLTuneParameters, and
MLNestedCV.
However, the function should be never executed directly but by the
respective experiment wrappers MLTuneParameters, and
MLNestedCV.
For further details please refer to the package's vignette.
Returns
The fitted model.
Examples
learner <- MLLearnerBase$new(metric_optimization_higher_better = FALSE)
\dontrun{
# This example cannot be run without further adaptions.
# The method `$cross_validation()` needs to be overwritten when
# inheriting from this class.
learner$cross_validation()
}
Method fit()
Fit a MLLearnerBase object.
Usage
MLLearnerBase$fit(...)
Arguments
...Arguments to be passed to the learner's fitting function.
Details
A wrapper around the private function fun_fit, which needs to be
defined for every learner. The return value of this function is the
fitted model.
However, the function should be never executed directly but by the
respective experiment wrappers MLTuneParameters,
MLCrossValidation, and
MLNestedCV.
For further details please refer to the package's vignette.
Returns
The fitted model.
Examples
learner <- MLLearnerBase$new(metric_optimization_higher_better = FALSE)
\dontrun{
# This example cannot be run without further adaptions.
# The method `$fit()` needs to be overwritten when
# inheriting from this class.
learner$fit()
}
Method predict()
Make predictions from a fitted MLLearnerBase object.
Usage
MLLearnerBase$predict(model, newdata, ncores = -1L, ...)
Arguments
modelA fitted model of the learner (as returned by
MLLearnerBase$fit()).newdataThe new data for which predictions should be made using the
model.ncoresAn integer to specify the number of cores used for parallelization (default:
-1L)....Further arguments to be passed to the learner's predict function.
Details
A wrapper around the private function fun_predict, which needs to be
defined for every learner. The function must accept the three arguments
model, newdata, and ncores and is a wrapper around the respective
learner's predict-function. In order to allow the passing of further
arguments, the ellipsis (...) can be used. The function should
return the prediction results.
However, the function should be never executed directly but by the
respective experiment wrappers MLTuneParameters,
MLCrossValidation, and
MLNestedCV.
For further details please refer to the package's vignette.
Returns
The predictions for newdata.
Examples
learner <- MLLearnerBase$new(metric_optimization_higher_better = FALSE)
\dontrun{
# This example cannot be run without further adaptions.
# The method `$predict()` needs to be overwritten when
# inheriting from this class.
learner$fit()
learner$predict()
}
Method bayesian_scoring_function()
Perform a Bayesian hyperparameter optimization with an MLLearnerBase.
Usage
MLLearnerBase$bayesian_scoring_function(...)
Arguments
...Arguments to be passed to the learner's Bayesian scoring function.
Details
A wrapper around the private function fun_bayesian_scoring_function,
which needs to be defined when hyperparameters should be optimized with
a Bayesian process (required for use with
MLTuneParameters, and MLNestedCV.
However, the function should be never executed directly but by the
respective experiment wrappers MLTuneParameters, and
MLNestedCV.
For further details please refer to the package's vignette.
Returns
The results of the Bayesian scoring.
Examples
learner <- MLLearnerBase$new(metric_optimization_higher_better = FALSE)
\dontrun{
# This example cannot be run without further adaptions.
# The method `$bayesian_scoring_function()` needs to be overwritten when
# inheriting from this class.
learner$bayesian_scoring_function()
}
Method clone()
The objects of this class are cloneable with this method.
Usage
MLLearnerBase$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
See Also
MLTuneParameters, MLCrossValidation, and MLNestedCV
MLTuneParameters, MLCrossValidation, and MLNestedCV
MLTuneParameters, MLCrossValidation, and MLNestedCV
ParBayesianOptimization::bayesOpt(),
MLTuneParameters, and MLNestedCV
Examples
MLLearnerBase$new(metric_optimization_higher_better = FALSE)
## ------------------------------------------------
## Method `MLLearnerBase$new`
## ------------------------------------------------
MLLearnerBase$new(metric_optimization_higher_better = FALSE)
## ------------------------------------------------
## Method `MLLearnerBase$cross_validation`
## ------------------------------------------------
learner <- MLLearnerBase$new(metric_optimization_higher_better = FALSE)
## Not run:
# This example cannot be run without further adaptions.
# The method `$cross_validation()` needs to be overwritten when
# inheriting from this class.
learner$cross_validation()
## End(Not run)
## ------------------------------------------------
## Method `MLLearnerBase$fit`
## ------------------------------------------------
learner <- MLLearnerBase$new(metric_optimization_higher_better = FALSE)
## Not run:
# This example cannot be run without further adaptions.
# The method `$fit()` needs to be overwritten when
# inheriting from this class.
learner$fit()
## End(Not run)
## ------------------------------------------------
## Method `MLLearnerBase$predict`
## ------------------------------------------------
learner <- MLLearnerBase$new(metric_optimization_higher_better = FALSE)
## Not run:
# This example cannot be run without further adaptions.
# The method `$predict()` needs to be overwritten when
# inheriting from this class.
learner$fit()
learner$predict()
## End(Not run)
## ------------------------------------------------
## Method `MLLearnerBase$bayesian_scoring_function`
## ------------------------------------------------
learner <- MLLearnerBase$new(metric_optimization_higher_better = FALSE)
## Not run:
# This example cannot be run without further adaptions.
# The method `$bayesian_scoring_function()` needs to be overwritten when
# inheriting from this class.
learner$bayesian_scoring_function()
## End(Not run)
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