getWorkflow: Obtain the workflow object corresponding to an ID

In performanceEstimation: An Infra-Structure for Performance Estimation of Predictive Models

Description

This function can be used to obtain the Workflow object corresponding to an ID used in a performance estimation experiment. This allows you for instance to check the full details of the workflow corresponding to that ID (e.g. the function implementing the workflow, the parameters and their values, etc.)

Usage

getWorkflow(var, obj)

Arguments

var	The string with the workflow ID
obj	A ComparisonResults object with the data from a performance estimation experiment

Value

A Workflow object

Author(s)

Luis Torgo [e-mail]

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

Workflow, runWorkflow, performanceEstimation

Examples

## Not run: 
## Estimating MSE for 3 variants of both
## regression trees and SVMs, on  two data sets, using one repetition
## of 10-fold CV
library(e1071)
library(DMwR)
data(swiss)
data(mtcars)

## running the estimation experiment
res <- performanceEstimation(
  c(PredTask(Infant.Mortality ~ .,swiss),PredTask(mpg ~ ., mtcars)),
  c(workflowVariants(learner="svm",
                     learner.pars=list(cost=c(1,10),gamma=c(0.01,0.5))),
    workflowVariants(learner="rpartXse",
                     learner.pars=list(se=c(0,0.5,1)))
  ),
  EstimationTask("mse",method=CV(nReps=2,nFolds=5))
  )

## Get the workflow corresponding to the ID svm.v2
getWorkflow("svm.v2",res)

## End(Not run)

Example output

Loading required package: lattice
Loading required package: grid


##### PERFORMANCE ESTIMATION USING  CROSS VALIDATION  #####

** PREDICTIVE TASK :: swiss.Infant.Mortality

++ MODEL/WORKFLOW :: svm.v1 
Task for estimating  mse  using
 2 x 5 - Fold Cross Validation
	 Run with seed =  1234 
Iteration :**********


++ MODEL/WORKFLOW :: svm.v2 
Task for estimating  mse  using
 2 x 5 - Fold Cross Validation
	 Run with seed =  1234 
Iteration :**********


++ MODEL/WORKFLOW :: svm.v3 
Task for estimating  mse  using
 2 x 5 - Fold Cross Validation
	 Run with seed =  1234 
Iteration :**********


++ MODEL/WORKFLOW :: svm.v4 
Task for estimating  mse  using
 2 x 5 - Fold Cross Validation
	 Run with seed =  1234 
Iteration :**********


++ MODEL/WORKFLOW :: rpartXse.v1 
Task for estimating  mse  using
 2 x 5 - Fold Cross Validation
	 Run with seed =  1234 
Iteration :**********


++ MODEL/WORKFLOW :: rpartXse.v2 
Task for estimating  mse  using
 2 x 5 - Fold Cross Validation
	 Run with seed =  1234 
Iteration :**********


++ MODEL/WORKFLOW :: rpartXse.v3 
Task for estimating  mse  using
 2 x 5 - Fold Cross Validation
	 Run with seed =  1234 
Iteration :**********


** PREDICTIVE TASK :: mtcars.mpg

++ MODEL/WORKFLOW :: svm.v1 
Task for estimating  mse  using
 2 x 5 - Fold Cross Validation
	 Run with seed =  1234 
Iteration :**********


++ MODEL/WORKFLOW :: svm.v2 
Task for estimating  mse  using
 2 x 5 - Fold Cross Validation
	 Run with seed =  1234 
Iteration :**********


++ MODEL/WORKFLOW :: svm.v3 
Task for estimating  mse  using
 2 x 5 - Fold Cross Validation
	 Run with seed =  1234 
Iteration :**********


++ MODEL/WORKFLOW :: svm.v4 
Task for estimating  mse  using
 2 x 5 - Fold Cross Validation
	 Run with seed =  1234 
Iteration :**********


++ MODEL/WORKFLOW :: rpartXse.v1 
Task for estimating  mse  using
 2 x 5 - Fold Cross Validation
	 Run with seed =  1234 
Iteration :**********


++ MODEL/WORKFLOW :: rpartXse.v2 
Task for estimating  mse  using
 2 x 5 - Fold Cross Validation
	 Run with seed =  1234 
Iteration :**********


++ MODEL/WORKFLOW :: rpartXse.v3 
Task for estimating  mse  using
 2 x 5 - Fold Cross Validation
	 Run with seed =  1234 
Iteration :**********
Workflow Object:
	Workflow ID       ::  svm.v2 
	Workflow Function ::  standardWF
	     Parameter values:
		 learner.pars  -> cost=10 gamma=0.01 
		 learner  -> svm 

performanceEstimation documentation built on May 2, 2019, 6:01 a.m.