The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
In the following, we explain the counterfactuals
workflow for both a classification and a regression task using concrete
use cases.
library("counterfactuals")
library("iml")
library("rpart")The Predictor class of the iml package
provides the necessary flexibility to cover classification and
regression models fitted with diverse R packages. In the introduction
vignette, we saw models fitted with the mlr3 and
randomForest packages. In the following, we show extensions
to - an classification tree fitted with the caret package,
the mlr (a predecesor of mlr3) and
tidymodels. For each model we generate counterfactuals for
the 100th row of the plasma dataset of the gamlss.data
package using the WhatIf method.
data(plasma, package = "gamlss.data")
x_interest = plasma[100L,]library("caret")
treecaret = caret::train(retplasma ~ ., data = plasma[-100L,], method = "rpart",
tuneGrid = data.frame(cp = 0.01))
predcaret = Predictor$new(model = treecaret, data = plasma[-100L,], y = "retplasma")
predcaret$predict(x_interest)
#> .prediction
#> 1 342.9231
nicecaret = NICERegr$new(predcaret, optimization = "proximity",
margin_correct = 0.5, return_multiple = FALSE)
nicecaret$find_counterfactuals(x_interest, desired_outcome = c(500, Inf))
#> 1 Counterfactual(s)
#>
#> Desired outcome range: [500, Inf]
#>
#> Head:
#> age sex smokstat bmi vituse calories fat fiber alcohol cholesterol betadiet retdiet betaplasma
#> <int> <fctr> <fctr> <num> <fctr> <num> <num> <num> <num> <num> <int> <int> <int>
#> 1: 46 1 3 35.25969 3 2667.5 131.6 10.1 0 550.5 1210 1291 218library("tidymodels")
treetm = decision_tree(mode = "regression", engine = "rpart") %>%
fit(retplasma ~ ., data = plasma[-100L,])
predtm = Predictor$new(model = treetm, data = plasma[-100L,], y = "retplasma")
predtm$predict(x_interest)
#> .pred
#> 1 342.9231
nicetm = NICERegr$new(predtm, optimization = "proximity",
margin_correct = 0.5, return_multiple = FALSE)
nicetm$find_counterfactuals(x_interest, desired_outcome = c(500, Inf))
#> 1 Counterfactual(s)
#>
#> Desired outcome range: [500, Inf]
#>
#> Head:
#> age sex smokstat bmi vituse calories fat fiber alcohol cholesterol betadiet retdiet betaplasma
#> <int> <fctr> <fctr> <num> <fctr> <num> <num> <num> <num> <num> <int> <int> <int>
#> 1: 46 1 3 35.25969 3 2667.5 131.6 10.1 0 550.5 1210 1291 218library("mlr")
task = mlr::makeRegrTask(data = plasma[-100L,], target = "retplasma")
mod = mlr::makeLearner("regr.rpart")
treemlr = mlr::train(mod, task)
predmlr = Predictor$new(model = treemlr, data = plasma[-100L,], y = "retplasma")
predmlr$predict(x_interest)
#> .prediction
#> 1 342.9231
nicemlr = NICERegr$new(predmlr, optimization = "proximity",
margin_correct = 0.5, return_multiple = FALSE)
nicemlr$find_counterfactuals(x_interest, desired_outcome = c(500, Inf))
#> 1 Counterfactual(s)
#>
#> Desired outcome range: [500, Inf]
#>
#> Head:
#> age sex smokstat bmi vituse calories fat fiber alcohol cholesterol betadiet retdiet betaplasma
#> <int> <fctr> <fctr> <num> <fctr> <num> <num> <num> <num> <num> <int> <int> <int>
#> 1: 46 1 3 35.25969 3 2667.5 131.6 10.1 0 550.5 1210 1291 218treerpart = rpart(retplasma ~ ., data = plasma[-100L,])
predrpart = Predictor$new(model = treerpart, data = plasma[-100L,], y = "retplasma")
predrpart$predict(x_interest)
#> pred
#> 1 342.9231
nicerpart = NICERegr$new(predrpart, optimization = "proximity",
margin_correct = 0.5, return_multiple = FALSE)
nicerpart$find_counterfactuals(x_interest, desired_outcome = c(500, Inf))
#> 1 Counterfactual(s)
#>
#> Desired outcome range: [500, Inf]
#>
#> Head:
#> age sex smokstat bmi vituse calories fat fiber alcohol cholesterol betadiet retdiet betaplasma
#> <int> <fctr> <fctr> <num> <fctr> <num> <num> <num> <num> <num> <int> <int> <int>
#> 1: 46 1 3 35.25969 3 2667.5 131.6 10.1 0 550.5 1210 1291 218These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.