performanceEstimation (version 1.1.0)

mergeEstimationRes: Merging several ComparisonResults class objects

Description

This function can be used to join several objects of class ComparisonResults into a single object. The merge is carried out assuming that there is something in common between the objects (e.g. all use the same workflows on different tasks), and that the user specifies which property should be used for the merging process.

Usage

mergeEstimationRes(..., by = "tasks")

Arguments

...

The ComparisonResults class object names separated by commas

by

The dimension of the ComparisonResults class objects that should be used for the merge. All objects should have the same values on the remaining dimensions of an estimation experiment. For instance, if you merge by "tasks" (the default) it means that the objects being merged should include estimation results on the same set of workflows on the same set of metrics, using the same estimation method and settings. The only thing that changes between the objects in this example is the set of tasks. Possible values of this argument are: "tasks", "workflows" and "metrics".

Value

The result of this function is a ComparisonResults object.

Details

The objects of class ComparisonResults (type "class?ComparisonResults" for details) contain several information on the results of an estimation expriment for several workflows on several predictive tasks. Sometimes, when you are trying too many workflows on too many tasks, it is convinient to run these variants on different calls to the function performanceEstimation. After all calls are completed we frequently want to have all results on a single object. This is the objective of the current function: allow you to merge these different ComparisonResults objects into a single one. For being mergeable the objects need to have things in common otherwise it makes no sense to merge them. For instance, we could split our very large experiment by calling performanceEstimation with different tasks, although the rest (the workflows and the estimation task) stays the same. See the Examples section for some illustrations.

References

Torgo, L. (2014) An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in R. arXiv:1412.0436 [cs.MS] http://arxiv.org/abs/1412.0436

See Also

performanceEstimation, ComparisonResults, subset

Examples

## Not run: 
# ## Run some experiments with the swiss data and two different
# ## prediction models
# data(swiss)
# 
# exp1 <- performanceEstimation(
#   PredTask(Infant.Mortality ~ .,swiss),
#   workflowVariants(learner="svm",
#                    learner.pars=list(cost=c(1,10),gamma=c(0.01,0.5))),
#   EstimationTask("mse")
#   )
# 
# exp2 <- performanceEstimation(
#   PredTask(Infant.Mortality ~ .,swiss),
#   Workflow(learner="lm"),
#   EstimationTask("mse")
#   )
# 
# 
# ## joining the two experiments by workflows
# all <- mergeEstimationRes(exp1,exp2,by="workflows")
# topPerformers(all) # check the best results
# 
# ## now an example by adding new metrics
# exp3 <- performanceEstimation(
#   PredTask(Infant.Mortality ~ .,swiss),
#   Workflow(learner="lm"),
#   EstimationTask(metrics=c("mae","totTime"))
#   )
# 
# allLM <- mergeEstimationRes(exp2,exp3,by="metrics")
# topPerformers(allLM) 
# 
# ## End(Not run)