revdep/checks.noindex/mlr/new/mlr.Rcheck/mlr-Ex.R
In gbm-developers/gbm: Generalized Boosted Regression Models
pkgname <- "mlr"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
library('mlr')
base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("FailureModel")
### * FailureModel
flush(stderr()); flush(stdout())
### Name: FailureModel
### Title: Failure model.
### Aliases: FailureModel
### ** Examples
configureMlr(on.learner.error = "warn")
data = iris
data$newfeat = 1 # will make LDA crash
task = makeClassifTask(data = data, target = "Species")
m = train("classif.lda", task) # LDA crashed, but mlr catches this
print(m)
print(m$learner.model) # the error message
p = predict(m, task) # this will predict NAs
print(p)
print(performance(p))
configureMlr(on.learner.error = "stop")
cleanEx()
nameEx("Task")
### * Task
flush(stderr()); flush(stdout())
### Name: Task
### Title: Create a classification, regression, survival, cluster,
### cost-sensitive classification or multilabel task.
### Aliases: Task
### ** Examples
if (requireNamespace("mlbench")) {
library(mlbench)
data(BostonHousing)
data(Ionosphere)
makeClassifTask(data = iris, target = "Species")
makeRegrTask(data = BostonHousing, target = "medv")
# an example of a classification task with more than those standard arguments:
blocking = factor(c(rep(1, 51), rep(2, 300)))
makeClassifTask(id = "myIonosphere", data = Ionosphere, target = "Class",
positive = "good", blocking = blocking)
makeClusterTask(data = iris[, -5L])
}
cleanEx()
nameEx("benchmark")
### * benchmark
flush(stderr()); flush(stdout())
### Name: benchmark
### Title: Benchmark experiment for multiple learners and tasks.
### Aliases: benchmark
### ** Examples
lrns = list(makeLearner("classif.lda"), makeLearner("classif.rpart"))
tasks = list(iris.task, sonar.task)
rdesc = makeResampleDesc("CV", iters = 2L)
meas = list(acc, ber)
bmr = benchmark(lrns, tasks, rdesc, measures = meas)
rmat = convertBMRToRankMatrix(bmr)
print(rmat)
plotBMRSummary(bmr)
plotBMRBoxplots(bmr, ber, style = "violin")
plotBMRRanksAsBarChart(bmr, pos = "stack")
friedmanTestBMR(bmr)
friedmanPostHocTestBMR(bmr, p.value = 0.05)
cleanEx()
nameEx("calculateConfusionMatrix")
### * calculateConfusionMatrix
flush(stderr()); flush(stdout())
### Name: calculateConfusionMatrix
### Title: Confusion matrix.
### Aliases: calculateConfusionMatrix print.ConfusionMatrix
### ** Examples
# get confusion matrix after simple manual prediction
allinds = 1:150
train = sample(allinds, 75)
test = setdiff(allinds, train)
mod = train("classif.lda", iris.task, subset = train)
pred = predict(mod, iris.task, subset = test)
print(calculateConfusionMatrix(pred))
print(calculateConfusionMatrix(pred, sums = TRUE))
print(calculateConfusionMatrix(pred, relative = TRUE))
# now after cross-validation
r = crossval("classif.lda", iris.task, iters = 2L)
print(calculateConfusionMatrix(r$pred))
cleanEx()
nameEx("calculateROCMeasures")
### * calculateROCMeasures
flush(stderr()); flush(stdout())
### Name: calculateROCMeasures
### Title: Calculate receiver operator measures.
### Aliases: calculateROCMeasures print.ROCMeasures
### ** Examples
lrn = makeLearner("classif.rpart", predict.type = "prob")
fit = train(lrn, sonar.task)
pred = predict(fit, task = sonar.task)
calculateROCMeasures(pred)
cleanEx()
nameEx("capLargeValues")
### * capLargeValues
flush(stderr()); flush(stdout())
### Name: capLargeValues
### Title: Convert large/infinite numeric values in a data.frame or task.
### Aliases: capLargeValues
### ** Examples
capLargeValues(iris, threshold = 5, impute = 5)
cleanEx()
nameEx("convertBMRToRankMatrix")
### * convertBMRToRankMatrix
flush(stderr()); flush(stdout())
### Name: convertBMRToRankMatrix
### Title: Convert BenchmarkResult to a rank-matrix.
### Aliases: convertBMRToRankMatrix
### ** Examples
# see benchmark
cleanEx()
nameEx("convertMLBenchObjToTask")
### * convertMLBenchObjToTask
flush(stderr()); flush(stdout())
### Name: convertMLBenchObjToTask
### Title: Convert a machine learning benchmark / demo object from package
### mlbench to a task.
### Aliases: convertMLBenchObjToTask
### ** Examples
print(convertMLBenchObjToTask("Ionosphere"))
print(convertMLBenchObjToTask("mlbench.spirals", n = 100, sd = 0.1))
cleanEx()
nameEx("createSpatialResamplingPlots")
### * createSpatialResamplingPlots
flush(stderr()); flush(stdout())
### Name: createSpatialResamplingPlots
### Title: Create (spatial) resampling plot objects.
### Aliases: createSpatialResamplingPlots
### ** Examples
cleanEx()
nameEx("estimateRelativeOverfitting")
### * estimateRelativeOverfitting
flush(stderr()); flush(stdout())
### Name: estimateRelativeOverfitting
### Title: Estimate relative overfitting.
### Aliases: estimateRelativeOverfitting
### ** Examples
task = makeClassifTask(data = iris, target = "Species")
rdesc = makeResampleDesc("CV", iters = 2)
estimateRelativeOverfitting(rdesc, acc, task, makeLearner("classif.knn"))
estimateRelativeOverfitting(rdesc, acc, task, makeLearner("classif.lda"))
rpred = resample("classif.knn", task, rdesc)$pred
estimateRelativeOverfitting(rpred, acc, task)
cleanEx()
nameEx("extractFDAFeatures")
### * extractFDAFeatures
flush(stderr()); flush(stdout())
### Name: extractFDAFeatures
### Title: Extract features from functional data.
### Aliases: extractFDAFeatures
### ** Examples
df = data.frame(x = matrix(rnorm(24), ncol = 8), y = factor(c("a", "a", "b")))
fdf = makeFunctionalData(df, fd.features = list(x1 = 1:4, x2 = 5:8), exclude.cols = "y")
task = makeClassifTask(data = fdf, target = "y")
extracted = extractFDAFeatures(task,
feat.methods = list("x1" = extractFDAFourier(), "x2" = extractFDAWavelets(filter = "haar")))
print(extracted$task)
reextractFDAFeatures(task, extracted$desc)
cleanEx()
nameEx("filterFeatures")
### * filterFeatures
flush(stderr()); flush(stdout())
### Name: filterFeatures
### Title: Filter features by thresholding filter values.
### Aliases: filterFeatures
### ** Examples
# simple filter
filterFeatures(iris.task, method = "FSelectorRcpp_gain.ratio", abs = 2)
# ensemble filter
filterFeatures(iris.task, method = "E-min",
base.methods = c("FSelectorRcpp_gain.ratio",
"FSelectorRcpp_information.gain"), abs = 2)
cleanEx()
nameEx("friedmanPostHocTestBMR")
### * friedmanPostHocTestBMR
flush(stderr()); flush(stdout())
### Name: friedmanPostHocTestBMR
### Title: Perform a posthoc Friedman-Nemenyi test.
### Aliases: friedmanPostHocTestBMR
### ** Examples
# see benchmark
cleanEx()
nameEx("friedmanTestBMR")
### * friedmanTestBMR
flush(stderr()); flush(stdout())
### Name: friedmanTestBMR
### Title: Perform overall Friedman test for a BenchmarkResult.
### Aliases: friedmanTestBMR
### ** Examples
# see benchmark
cleanEx()
nameEx("generateFeatureImportanceData")
### * generateFeatureImportanceData
flush(stderr()); flush(stdout())
### Name: generateFeatureImportanceData
### Title: Generate feature importance.
### Aliases: generateFeatureImportanceData FeatureImportanceData
### ** Examples
lrn = makeLearner("classif.rpart", predict.type = "prob")
fit = train(lrn, iris.task)
imp = generateFeatureImportanceData(iris.task, "permutation.importance",
lrn, "Petal.Width", nmc = 10L, local = TRUE)
cleanEx()
nameEx("generateFilterValuesData")
### * generateFilterValuesData
flush(stderr()); flush(stdout())
### Name: generateFilterValuesData
### Title: Calculates feature filter values.
### Aliases: generateFilterValuesData FilterValues
### ** Examples
# two simple filter methods
fval = generateFilterValuesData(iris.task,
method = c("FSelectorRcpp_gain.ratio", "FSelectorRcpp_information.gain"))
# using ensemble method "E-mean"
fval = generateFilterValuesData(iris.task,
method = list("E-mean", c("FSelectorRcpp_gain.ratio",
"FSelectorRcpp_information.gain")))
cleanEx()
nameEx("generateHyperParsEffectData")
### * generateHyperParsEffectData
flush(stderr()); flush(stdout())
### Name: generateHyperParsEffectData
### Title: Generate hyperparameter effect data.
### Aliases: generateHyperParsEffectData
### ** Examples
## Not run:
##D # 3-fold cross validation
##D ps = makeParamSet(makeDiscreteParam("C", values = 2^(-4:4)))
##D ctrl = makeTuneControlGrid()
##D rdesc = makeResampleDesc("CV", iters = 3L)
##D res = tuneParams("classif.ksvm", task = pid.task, resampling = rdesc,
##D par.set = ps, control = ctrl)
##D data = generateHyperParsEffectData(res)
##D plt = plotHyperParsEffect(data, x = "C", y = "mmce.test.mean")
##D plt + ylab("Misclassification Error")
##D
##D # nested cross validation
##D ps = makeParamSet(makeDiscreteParam("C", values = 2^(-4:4)))
##D ctrl = makeTuneControlGrid()
##D rdesc = makeResampleDesc("CV", iters = 3L)
##D lrn = makeTuneWrapper("classif.ksvm", control = ctrl,
##D resampling = rdesc, par.set = ps)
##D res = resample(lrn, task = pid.task, resampling = cv2,
##D extract = getTuneResult)
##D data = generateHyperParsEffectData(res)
##D plotHyperParsEffect(data, x = "C", y = "mmce.test.mean", plot.type = "line")
## End(Not run)
cleanEx()
nameEx("generateLearningCurveData")
### * generateLearningCurveData
flush(stderr()); flush(stdout())
### Name: generateLearningCurveData
### Title: Generates a learning curve.
### Aliases: generateLearningCurveData LearningCurveData
### ** Examples
r = generateLearningCurveData(list("classif.rpart", "classif.knn"),
task = sonar.task, percs = seq(0.2, 1, by = 0.2),
measures = list(tp, fp, tn, fn),
resampling = makeResampleDesc(method = "Subsample", iters = 5),
show.info = FALSE)
plotLearningCurve(r)
cleanEx()
nameEx("generatePartialDependenceData")
### * generatePartialDependenceData
flush(stderr()); flush(stdout())
### Name: generatePartialDependenceData
### Title: Generate partial dependence.
### Aliases: generatePartialDependenceData PartialDependenceData
### ** Examples
lrn = makeLearner("regr.svm")
fit = train(lrn, bh.task)
pd = generatePartialDependenceData(fit, bh.task, "lstat")
plotPartialDependence(pd, data = getTaskData(bh.task))
lrn = makeLearner("classif.rpart", predict.type = "prob")
fit = train(lrn, iris.task)
pd = generatePartialDependenceData(fit, iris.task, "Petal.Width")
plotPartialDependence(pd, data = getTaskData(iris.task))
cleanEx()
nameEx("getCaretParamSet")
### * getCaretParamSet
flush(stderr()); flush(stdout())
### Name: getCaretParamSet
### Title: Get tuning parameters from a learner of the caret R-package.
### Aliases: getCaretParamSet
### ** Examples
if (requireNamespace("caret") && requireNamespace("mlbench")) {
library(caret)
classifTask = makeClassifTask(data = iris, target = "Species")
# (1) classification (random forest) with discretized parameters
getCaretParamSet("rf", length = 9L, task = classifTask, discretize = TRUE)
# (2) regression (gradient boosting machine) without discretized parameters
library(mlbench)
data(BostonHousing)
regrTask = makeRegrTask(data = BostonHousing, target = "medv")
getCaretParamSet("gbm", length = 9L, task = regrTask, discretize = FALSE)
}
cleanEx()
nameEx("getHyperPars")
### * getHyperPars
flush(stderr()); flush(stdout())
### Name: getHyperPars
### Title: Get current parameter settings for a learner.
### Aliases: getHyperPars
### ** Examples
getHyperPars(makeLearner("classif.ranger"))
## set learner hyperparameter `mtry` manually
getHyperPars(makeLearner("classif.ranger", mtry = 100))
cleanEx()
nameEx("getMultilabelBinaryPerformances")
### * getMultilabelBinaryPerformances
flush(stderr()); flush(stdout())
### Name: getMultilabelBinaryPerformances
### Title: Retrieve binary classification measures for multilabel
### classification predictions.
### Aliases: getMultilabelBinaryPerformances
### ** Examples
# see makeMultilabelBinaryRelevanceWrapper
cleanEx()
nameEx("getNestedTuneResultsOptPathDf")
### * getNestedTuneResultsOptPathDf
flush(stderr()); flush(stdout())
### Name: getNestedTuneResultsOptPathDf
### Title: Get the 'opt.path's from each tuning step from the outer
### resampling.
### Aliases: getNestedTuneResultsOptPathDf
### ** Examples
# see example of makeTuneWrapper
cleanEx()
nameEx("getNestedTuneResultsX")
### * getNestedTuneResultsX
flush(stderr()); flush(stdout())
### Name: getNestedTuneResultsX
### Title: Get the tuned hyperparameter settings from a nested tuning.
### Aliases: getNestedTuneResultsX
### ** Examples
# see example of makeTuneWrapper
cleanEx()
nameEx("getOOBPreds")
### * getOOBPreds
flush(stderr()); flush(stdout())
### Name: getOOBPreds
### Title: Extracts out-of-bag predictions from trained models.
### Aliases: getOOBPreds
### ** Examples
training.set = sample(1:150, 50)
lrn = makeLearner("classif.ranger", predict.type = "prob", predict.threshold = 0.6)
mod = train(lrn, sonar.task, subset = training.set)
oob = getOOBPreds(mod, sonar.task)
oob
performance(oob, measures = list(auc, mmce))
cleanEx()
nameEx("getPredictionProbabilities")
### * getPredictionProbabilities
flush(stderr()); flush(stdout())
### Name: getPredictionProbabilities
### Title: Get probabilities for some classes.
### Aliases: getPredictionProbabilities
### ** Examples
task = makeClassifTask(data = iris, target = "Species")
lrn = makeLearner("classif.lda", predict.type = "prob")
mod = train(lrn, task)
# predict probabilities
pred = predict(mod, newdata = iris)
# Get probabilities for all classes
head(getPredictionProbabilities(pred))
# Get probabilities for a subset of classes
head(getPredictionProbabilities(pred, c("setosa", "virginica")))
cleanEx()
nameEx("getResamplingIndices")
### * getResamplingIndices
flush(stderr()); flush(stdout())
### Name: getResamplingIndices
### Title: Get the resampling indices from a tuning or feature selection
### wrapper..
### Aliases: getResamplingIndices
### ** Examples
task = makeClassifTask(data = iris, target = "Species")
lrn = makeLearner("classif.rpart")
# stupid mini grid
ps = makeParamSet(
makeDiscreteParam("cp", values = c(0.05, 0.1)),
makeDiscreteParam("minsplit", values = c(10, 20))
)
ctrl = makeTuneControlGrid()
inner = makeResampleDesc("Holdout")
outer = makeResampleDesc("CV", iters = 2)
lrn = makeTuneWrapper(lrn, resampling = inner, par.set = ps, control = ctrl)
# nested resampling for evaluation
# we also extract tuned hyper pars in each iteration and by that the resampling indices
r = resample(lrn, task, outer, extract = getTuneResult)
# get tuning indices
getResamplingIndices(r, inner = TRUE)
cleanEx()
nameEx("getTaskData")
### * getTaskData
flush(stderr()); flush(stdout())
### Name: getTaskData
### Title: Extract data in task.
### Aliases: getTaskData
### ** Examples
library("mlbench")
data(BreastCancer)
df = BreastCancer
df$Id = NULL
task = makeClassifTask(id = "BreastCancer", data = df, target = "Class", positive = "malignant")
head(getTaskData)
head(getTaskData(task, features = c("Cell.size", "Cell.shape"), recode.target = "-1+1"))
head(getTaskData(task, subset = 1:100, recode.target = "01"))
cleanEx()
nameEx("getTaskTargets")
### * getTaskTargets
flush(stderr()); flush(stdout())
### Name: getTaskTargets
### Title: Get target data of task.
### Aliases: getTaskTargets
### ** Examples
task = makeClassifTask(data = iris, target = "Species")
getTaskTargets(task)
cleanEx()
nameEx("impute")
### * impute
flush(stderr()); flush(stdout())
### Name: impute
### Title: Impute and re-impute data
### Aliases: impute
### ** Examples
df = data.frame(x = c(1, 1, NA), y = factor(c("a", "a", "b")), z = 1:3)
imputed = impute(df, target = character(0), cols = list(x = 99, y = imputeMode()))
print(imputed$data)
reimpute(data.frame(x = NA_real_), imputed$desc)
cleanEx()
nameEx("joinClassLevels")
### * joinClassLevels
flush(stderr()); flush(stdout())
### Name: joinClassLevels
### Title: Join some class existing levels to new, larger class levels for
### classification problems.
### Aliases: joinClassLevels
### ** Examples
joinClassLevels(iris.task, new.levels = list(foo = c("setosa", "virginica")))
cleanEx()
nameEx("listLearners")
### * listLearners
flush(stderr()); flush(stdout())
### Name: listLearners
### Title: Find matching learning algorithms.
### Aliases: listLearners listLearners.default listLearners.character
### listLearners.Task
### ** Examples
## Not run:
##D listLearners("classif", properties = c("multiclass", "prob"))
##D data = iris
##D task = makeClassifTask(data = data, target = "Species")
##D listLearners(task)
## End(Not run)
cleanEx()
nameEx("makeAggregation")
### * makeAggregation
flush(stderr()); flush(stdout())
### Name: makeAggregation
### Title: Specify your own aggregation of measures.
### Aliases: makeAggregation
### ** Examples
# computes the interquartile range on all performance values
test.iqr = makeAggregation(
id = "test.iqr", name = "Test set interquartile range",
properties = "req.test",
fun = function(task, perf.test, perf.train, measure, group, pred) IQR(perf.test)
)
cleanEx()
nameEx("makeClassificationViaRegressionWrapper")
### * makeClassificationViaRegressionWrapper
flush(stderr()); flush(stdout())
### Name: makeClassificationViaRegressionWrapper
### Title: Classification via regression wrapper.
### Aliases: makeClassificationViaRegressionWrapper
### ** Examples
lrn = makeLearner("regr.rpart")
lrn = makeClassificationViaRegressionWrapper(lrn)
mod = train(lrn, sonar.task, subset = 1:140)
predictions = predict(mod, newdata = getTaskData(sonar.task)[141:208, 1:60])
cleanEx()
nameEx("makeFeatSelWrapper")
### * makeFeatSelWrapper
flush(stderr()); flush(stdout())
### Name: makeFeatSelWrapper
### Title: Fuse learner with feature selection.
### Aliases: makeFeatSelWrapper
### ** Examples
# nested resampling with feature selection (with a nonsense algorithm for selection)
outer = makeResampleDesc("CV", iters = 2L)
inner = makeResampleDesc("Holdout")
ctrl = makeFeatSelControlRandom(maxit = 1)
lrn = makeFeatSelWrapper("classif.ksvm", resampling = inner, control = ctrl)
# we also extract the selected features for all iteration here
r = resample(lrn, iris.task, outer, extract = getFeatSelResult)
cleanEx()
nameEx("makeFilterWrapper")
### * makeFilterWrapper
flush(stderr()); flush(stdout())
### Name: makeFilterWrapper
### Title: Fuse learner with a feature filter method.
### Aliases: makeFilterWrapper
### ** Examples
cleanEx()
nameEx("makeFunctionalData")
### * makeFunctionalData
flush(stderr()); flush(stdout())
### Name: makeFunctionalData
### Title: Create a data.frame containing functional features from a normal
### data.frame.
### Aliases: makeFunctionalData
### ** Examples
# data.frame where columns 1:6 and 8:10 belong to a functional feature
d1 = data.frame(matrix(rnorm(100), nrow = 10), "target" = seq_len(10))
# Transform to functional data
d2 = makeFunctionalData(d1, fd.features = list("fd1" = 1:6, "fd2" = 8:10))
# Create a regression task
makeRegrTask(data = d2, target = "target")
cleanEx()
nameEx("makeLearner")
### * makeLearner
flush(stderr()); flush(stdout())
### Name: makeLearner
### Title: Create learner object.
### Aliases: makeLearner Learner
### ** Examples
makeLearner("classif.rpart")
makeLearner("classif.lda", predict.type = "prob")
lrn = makeLearner("classif.lda", method = "t", nu = 10)
getHyperPars(lrn)
cleanEx()
nameEx("makeLearners")
### * makeLearners
flush(stderr()); flush(stdout())
### Name: makeLearners
### Title: Create multiple learners at once.
### Aliases: makeLearners
### ** Examples
makeLearners(c("rpart", "lda"), type = "classif", predict.type = "prob")
cleanEx()
nameEx("makeMeasure")
### * makeMeasure
flush(stderr()); flush(stdout())
### Name: makeMeasure
### Title: Construct performance measure.
### Aliases: makeMeasure Measure
### ** Examples
f = function(task, model, pred, extra.args) {
sum((pred$data$response - pred$data$truth)^2)
}
makeMeasure(id = "my.sse", minimize = TRUE,
properties = c("regr", "response"), fun = f)
cleanEx()
nameEx("makeModelMultiplexer")
### * makeModelMultiplexer
flush(stderr()); flush(stdout())
### Name: makeModelMultiplexer
### Title: Create model multiplexer for model selection to tune over
### multiple possible models.
### Aliases: makeModelMultiplexer ModelMultiplexer
### ** Examples
set.seed(123)
cleanEx()
nameEx("makeModelMultiplexerParamSet")
### * makeModelMultiplexerParamSet
flush(stderr()); flush(stdout())
### Name: makeModelMultiplexerParamSet
### Title: Creates a parameter set for model multiplexer tuning.
### Aliases: makeModelMultiplexerParamSet
### ** Examples
# See makeModelMultiplexer
cleanEx()
nameEx("makeMultilabelBinaryRelevanceWrapper")
### * makeMultilabelBinaryRelevanceWrapper
flush(stderr()); flush(stdout())
### Name: makeMultilabelBinaryRelevanceWrapper
### Title: Use binary relevance method to create a multilabel learner.
### Aliases: makeMultilabelBinaryRelevanceWrapper
### ** Examples
d = getTaskData(yeast.task)
# drop some labels so example runs faster
d = d[seq(1, nrow(d), by = 20), c(1:2, 15:17)]
task = makeMultilabelTask(data = d, target = c("label1", "label2"))
lrn = makeLearner("classif.rpart")
lrn = makeMultilabelBinaryRelevanceWrapper(lrn)
lrn = setPredictType(lrn, "prob")
# train, predict and evaluate
mod = train(lrn, task)
pred = predict(mod, task)
performance(pred, measure = list(multilabel.hamloss, multilabel.subset01, multilabel.f1))
# the next call basically has the same structure for any multilabel meta wrapper
getMultilabelBinaryPerformances(pred, measures = list(mmce, auc))
# above works also with predictions from resample!
cleanEx()
nameEx("makeMultilabelClassifierChainsWrapper")
### * makeMultilabelClassifierChainsWrapper
flush(stderr()); flush(stdout())
### Name: makeMultilabelClassifierChainsWrapper
### Title: Use classifier chains method (CC) to create a multilabel
### learner.
### Aliases: makeMultilabelClassifierChainsWrapper
### ** Examples
d = getTaskData(yeast.task)
# drop some labels so example runs faster
d = d[seq(1, nrow(d), by = 20), c(1:2, 15:17)]
task = makeMultilabelTask(data = d, target = c("label1", "label2"))
lrn = makeLearner("classif.rpart")
lrn = makeMultilabelBinaryRelevanceWrapper(lrn)
lrn = setPredictType(lrn, "prob")
# train, predict and evaluate
mod = train(lrn, task)
pred = predict(mod, task)
performance(pred, measure = list(multilabel.hamloss, multilabel.subset01, multilabel.f1))
# the next call basically has the same structure for any multilabel meta wrapper
getMultilabelBinaryPerformances(pred, measures = list(mmce, auc))
# above works also with predictions from resample!
cleanEx()
nameEx("makeMultilabelDBRWrapper")
### * makeMultilabelDBRWrapper
flush(stderr()); flush(stdout())
### Name: makeMultilabelDBRWrapper
### Title: Use dependent binary relevance method (DBR) to create a
### multilabel learner.
### Aliases: makeMultilabelDBRWrapper
### ** Examples
d = getTaskData(yeast.task)
# drop some labels so example runs faster
d = d[seq(1, nrow(d), by = 20), c(1:2, 15:17)]
task = makeMultilabelTask(data = d, target = c("label1", "label2"))
lrn = makeLearner("classif.rpart")
lrn = makeMultilabelBinaryRelevanceWrapper(lrn)
lrn = setPredictType(lrn, "prob")
# train, predict and evaluate
mod = train(lrn, task)
pred = predict(mod, task)
performance(pred, measure = list(multilabel.hamloss, multilabel.subset01, multilabel.f1))
# the next call basically has the same structure for any multilabel meta wrapper
getMultilabelBinaryPerformances(pred, measures = list(mmce, auc))
# above works also with predictions from resample!
cleanEx()
nameEx("makeMultilabelNestedStackingWrapper")
### * makeMultilabelNestedStackingWrapper
flush(stderr()); flush(stdout())
### Name: makeMultilabelNestedStackingWrapper
### Title: Use nested stacking method to create a multilabel learner.
### Aliases: makeMultilabelNestedStackingWrapper
### ** Examples
d = getTaskData(yeast.task)
# drop some labels so example runs faster
d = d[seq(1, nrow(d), by = 20), c(1:2, 15:17)]
task = makeMultilabelTask(data = d, target = c("label1", "label2"))
lrn = makeLearner("classif.rpart")
lrn = makeMultilabelBinaryRelevanceWrapper(lrn)
lrn = setPredictType(lrn, "prob")
# train, predict and evaluate
mod = train(lrn, task)
pred = predict(mod, task)
performance(pred, measure = list(multilabel.hamloss, multilabel.subset01, multilabel.f1))
# the next call basically has the same structure for any multilabel meta wrapper
getMultilabelBinaryPerformances(pred, measures = list(mmce, auc))
# above works also with predictions from resample!
cleanEx()
nameEx("makeMultilabelStackingWrapper")
### * makeMultilabelStackingWrapper
flush(stderr()); flush(stdout())
### Name: makeMultilabelStackingWrapper
### Title: Use stacking method (stacked generalization) to create a
### multilabel learner.
### Aliases: makeMultilabelStackingWrapper
### ** Examples
d = getTaskData(yeast.task)
# drop some labels so example runs faster
d = d[seq(1, nrow(d), by = 20), c(1:2, 15:17)]
task = makeMultilabelTask(data = d, target = c("label1", "label2"))
lrn = makeLearner("classif.rpart")
lrn = makeMultilabelBinaryRelevanceWrapper(lrn)
lrn = setPredictType(lrn, "prob")
# train, predict and evaluate
mod = train(lrn, task)
pred = predict(mod, task)
performance(pred, measure = list(multilabel.hamloss, multilabel.subset01, multilabel.f1))
# the next call basically has the same structure for any multilabel meta wrapper
getMultilabelBinaryPerformances(pred, measures = list(mmce, auc))
# above works also with predictions from resample!
cleanEx()
nameEx("makeResampleDesc")
### * makeResampleDesc
flush(stderr()); flush(stdout())
### Name: makeResampleDesc
### Title: Create a description object for a resampling strategy.
### Aliases: makeResampleDesc ResampleDesc hout cv2 cv3 cv5 cv10
### ** Examples
# Bootstraping
makeResampleDesc("Bootstrap", iters = 10)
makeResampleDesc("Bootstrap", iters = 10, predict = "both")
# Subsampling
makeResampleDesc("Subsample", iters = 10, split = 3 / 4)
makeResampleDesc("Subsample", iters = 10)
# Holdout a.k.a. test sample estimation
makeResampleDesc("Holdout")
cleanEx()
nameEx("makeResampleInstance")
### * makeResampleInstance
flush(stderr()); flush(stdout())
### Name: makeResampleInstance
### Title: Instantiates a resampling strategy object.
### Aliases: makeResampleInstance ResampleInstance
### ** Examples
rdesc = makeResampleDesc("Bootstrap", iters = 10)
rin = makeResampleInstance(rdesc, task = iris.task)
rdesc = makeResampleDesc("CV", iters = 50)
rin = makeResampleInstance(rdesc, size = nrow(iris))
rin = makeResampleInstance("CV", iters = 10, task = iris.task)
cleanEx()
nameEx("makeStackedLearner")
### * makeStackedLearner
flush(stderr()); flush(stdout())
### Name: makeStackedLearner
### Title: Create a stacked learner object.
### Aliases: makeStackedLearner
### ** Examples
# Classification
data(iris)
tsk = makeClassifTask(data = iris, target = "Species")
base = c("classif.rpart", "classif.lda", "classif.svm")
lrns = lapply(base, makeLearner)
lrns = lapply(lrns, setPredictType, "prob")
m = makeStackedLearner(base.learners = lrns,
predict.type = "prob", method = "hill.climb")
tmp = train(m, tsk)
res = predict(tmp, tsk)
# Regression
data(BostonHousing, package = "mlbench")
tsk = makeRegrTask(data = BostonHousing, target = "medv")
base = c("regr.rpart", "regr.svm")
lrns = lapply(base, makeLearner)
m = makeStackedLearner(base.learners = lrns,
predict.type = "response", method = "compress")
tmp = train(m, tsk)
res = predict(tmp, tsk)
cleanEx()
nameEx("makeTuneWrapper")
### * makeTuneWrapper
flush(stderr()); flush(stdout())
### Name: makeTuneWrapper
### Title: Fuse learner with tuning.
### Aliases: makeTuneWrapper
### ** Examples
cleanEx()
nameEx("makeWeightedClassesWrapper")
### * makeWeightedClassesWrapper
flush(stderr()); flush(stdout())
### Name: makeWeightedClassesWrapper
### Title: Wraps a classifier for weighted fitting where each class
### receives a weight.
### Aliases: makeWeightedClassesWrapper
### ** Examples
cleanEx()
nameEx("performance")
### * performance
flush(stderr()); flush(stdout())
### Name: performance
### Title: Measure performance of prediction.
### Aliases: performance
### ** Examples
training.set = seq(1, nrow(iris), by = 2)
test.set = seq(2, nrow(iris), by = 2)
task = makeClassifTask(data = iris, target = "Species")
lrn = makeLearner("classif.lda")
mod = train(lrn, task, subset = training.set)
pred = predict(mod, newdata = iris[test.set, ])
performance(pred, measures = mmce)
# Compute multiple performance measures at once
ms = list("mmce" = mmce, "acc" = acc, "timetrain" = timetrain)
performance(pred, measures = ms, task, mod)
cleanEx()
nameEx("plotBMRBoxplots")
### * plotBMRBoxplots
flush(stderr()); flush(stdout())
### Name: plotBMRBoxplots
### Title: Create box or violin plots for a BenchmarkResult.
### Aliases: plotBMRBoxplots
### ** Examples
# see benchmark
cleanEx()
nameEx("plotBMRRanksAsBarChart")
### * plotBMRRanksAsBarChart
flush(stderr()); flush(stdout())
### Name: plotBMRRanksAsBarChart
### Title: Create a bar chart for ranks in a BenchmarkResult.
### Aliases: plotBMRRanksAsBarChart
### ** Examples
# see benchmark
cleanEx()
nameEx("plotBMRSummary")
### * plotBMRSummary
flush(stderr()); flush(stdout())
### Name: plotBMRSummary
### Title: Plot a benchmark summary.
### Aliases: plotBMRSummary
### ** Examples
# see benchmark
cleanEx()
nameEx("plotCalibration")
### * plotCalibration
flush(stderr()); flush(stdout())
### Name: plotCalibration
### Title: Plot calibration data using ggplot2.
### Aliases: plotCalibration
### ** Examples
## Not run:
##D lrns = list(makeLearner("classif.rpart", predict.type = "prob"),
##D makeLearner("classif.nnet", predict.type = "prob"))
##D fit = lapply(lrns, train, task = iris.task)
##D pred = lapply(fit, predict, task = iris.task)
##D names(pred) = c("rpart", "nnet")
##D out = generateCalibrationData(pred, groups = 3)
##D plotCalibration(out)
##D
##D fit = lapply(lrns, train, task = sonar.task)
##D pred = lapply(fit, predict, task = sonar.task)
##D names(pred) = c("rpart", "lda")
##D out = generateCalibrationData(pred)
##D plotCalibration(out)
## End(Not run)
cleanEx()
nameEx("plotCritDifferences")
### * plotCritDifferences
flush(stderr()); flush(stdout())
### Name: plotCritDifferences
### Title: Plot critical differences for a selected measure.
### Aliases: plotCritDifferences
### ** Examples
# see benchmark
cleanEx()
nameEx("plotFilterValues")
### * plotFilterValues
flush(stderr()); flush(stdout())
### Name: plotFilterValues
### Title: Plot filter values using ggplot2.
### Aliases: plotFilterValues
### ** Examples
fv = generateFilterValuesData(iris.task, method = "variance")
plotFilterValues(fv)
cleanEx()
nameEx("plotHyperParsEffect")
### * plotHyperParsEffect
flush(stderr()); flush(stdout())
### Name: plotHyperParsEffect
### Title: Plot the hyperparameter effects data
### Aliases: plotHyperParsEffect
### ** Examples
# see generateHyperParsEffectData
cleanEx()
nameEx("plotROCCurves")
### * plotROCCurves
flush(stderr()); flush(stdout())
### Name: plotROCCurves
### Title: Plots a ROC curve using ggplot2.
### Aliases: plotROCCurves
### ** Examples
cleanEx()
nameEx("plotThreshVsPerf")
### * plotThreshVsPerf
flush(stderr()); flush(stdout())
### Name: plotThreshVsPerf
### Title: Plot threshold vs. performance(s) for 2-class classification
### using ggplot2.
### Aliases: plotThreshVsPerf
### ** Examples
lrn = makeLearner("classif.rpart", predict.type = "prob")
mod = train(lrn, sonar.task)
pred = predict(mod, sonar.task)
pvs = generateThreshVsPerfData(pred, list(acc, setAggregation(acc, train.mean)))
plotThreshVsPerf(pvs)
cleanEx()
nameEx("plotTuneMultiCritResult")
### * plotTuneMultiCritResult
flush(stderr()); flush(stdout())
### Name: plotTuneMultiCritResult
### Title: Plots multi-criteria results after tuning using ggplot2.
### Aliases: plotTuneMultiCritResult
### ** Examples
# see tuneParamsMultiCrit
cleanEx()
nameEx("predict.WrappedModel")
### * predict.WrappedModel
flush(stderr()); flush(stdout())
### Name: predict.WrappedModel
### Title: Predict new data.
### Aliases: predict.WrappedModel
### ** Examples
# train and predict
train.set = seq(1, 150, 2)
test.set = seq(2, 150, 2)
model = train("classif.lda", iris.task, subset = train.set)
p = predict(model, newdata = iris, subset = test.set)
print(p)
predict(model, task = iris.task, subset = test.set)
# predict now probabiliies instead of class labels
lrn = makeLearner("classif.lda", predict.type = "prob")
model = train(lrn, iris.task, subset = train.set)
p = predict(model, task = iris.task, subset = test.set)
print(p)
getPredictionProbabilities(p)
cleanEx()
nameEx("resample")
### * resample
flush(stderr()); flush(stdout())
### Name: resample
### Title: Fit models according to a resampling strategy.
### Aliases: resample crossval repcv holdout subsample bootstrapOOB
### bootstrapB632 bootstrapB632plus growingcv fixedcv
### ** Examples
task = makeClassifTask(data = iris, target = "Species")
rdesc = makeResampleDesc("CV", iters = 2)
r = resample(makeLearner("classif.qda"), task, rdesc)
print(r$aggr)
print(r$measures.test)
print(r$pred)
# include the training set performance as well
rdesc = makeResampleDesc("CV", iters = 2, predict = "both")
r = resample(makeLearner("classif.qda"), task, rdesc,
measures = list(mmce, setAggregation(mmce, train.mean)))
print(r$aggr)
cleanEx()
nameEx("selectFeatures")
### * selectFeatures
flush(stderr()); flush(stdout())
### Name: selectFeatures
### Title: Feature selection by wrapper approach.
### Aliases: selectFeatures
### ** Examples
cleanEx()
nameEx("setHyperPars")
### * setHyperPars
flush(stderr()); flush(stdout())
### Name: setHyperPars
### Title: Set the hyperparameters of a learner object.
### Aliases: setHyperPars
### ** Examples
cl1 = makeLearner("classif.ksvm", sigma = 1)
cl2 = setHyperPars(cl1, sigma = 10, par.vals = list(C = 2))
print(cl1)
# note the now set and altered hyperparameters:
print(cl2)
cleanEx()
nameEx("setThreshold")
### * setThreshold
flush(stderr()); flush(stdout())
### Name: setThreshold
### Title: Set threshold of prediction object.
### Aliases: setThreshold
### ** Examples
# create task and train learner (LDA)
task = makeClassifTask(data = iris, target = "Species")
lrn = makeLearner("classif.lda", predict.type = "prob")
mod = train(lrn, task)
# predict probabilities and compute performance
pred = predict(mod, newdata = iris)
performance(pred, measures = mmce)
head(as.data.frame(pred))
# adjust threshold and predict probabilities again
threshold = c(setosa = 0.4, versicolor = 0.3, virginica = 0.3)
pred = setThreshold(pred, threshold = threshold)
performance(pred, measures = mmce)
head(as.data.frame(pred))
cleanEx()
nameEx("subsetTask")
### * subsetTask
flush(stderr()); flush(stdout())
### Name: subsetTask
### Title: Subset data in task.
### Aliases: subsetTask
### ** Examples
task = makeClassifTask(data = iris, target = "Species")
subsetTask(task, subset = 1:100)
cleanEx()
nameEx("summarizeColumns")
### * summarizeColumns
flush(stderr()); flush(stdout())
### Name: summarizeColumns
### Title: Summarize columns of data.frame or task.
### Aliases: summarizeColumns
### ** Examples
summarizeColumns(iris)
cleanEx()
nameEx("summarizeLevels")
### * summarizeLevels
flush(stderr()); flush(stdout())
### Name: summarizeLevels
### Title: Summarizes factors of a data.frame by tabling them.
### Aliases: summarizeLevels
### ** Examples
summarizeLevels(iris)
cleanEx()
nameEx("train")
### * train
flush(stderr()); flush(stdout())
### Name: train
### Title: Train a learning algorithm.
### Aliases: train
### ** Examples
training.set = sample(seq_len(nrow(iris)), nrow(iris) / 2)
## use linear discriminant analysis to classify iris data
task = makeClassifTask(data = iris, target = "Species")
learner = makeLearner("classif.lda", method = "mle")
mod = train(learner, task, subset = training.set)
print(mod)
## use random forest to classify iris data
task = makeClassifTask(data = iris, target = "Species")
learner = makeLearner("classif.rpart", minsplit = 7, predict.type = "prob")
mod = train(learner, task, subset = training.set)
print(mod)
cleanEx()
nameEx("tuneParams")
### * tuneParams
flush(stderr()); flush(stdout())
### Name: tuneParams
### Title: Hyperparameter tuning.
### Aliases: tuneParams
### ** Examples
set.seed(123)
# a grid search for an SVM (with a tiny number of points...)
# note how easily we can optimize on a log-scale
ps = makeParamSet(
makeNumericParam("C", lower = -12, upper = 12, trafo = function(x) 2^x),
makeNumericParam("sigma", lower = -12, upper = 12, trafo = function(x) 2^x)
)
ctrl = makeTuneControlGrid(resolution = 2L)
rdesc = makeResampleDesc("CV", iters = 2L)
res = tuneParams("classif.ksvm", iris.task, rdesc, par.set = ps, control = ctrl)
print(res)
# access data for all evaluated points
df = as.data.frame(res$opt.path)
df1 = as.data.frame(res$opt.path, trafo = TRUE)
print(head(df[, -ncol(df)]))
print(head(df1[, -ncol(df)]))
# access data for all evaluated points - alternative
df2 = generateHyperParsEffectData(res)
df3 = generateHyperParsEffectData(res, trafo = TRUE)
print(head(df2$data[, -ncol(df2$data)]))
print(head(df3$data[, -ncol(df3$data)]))
## Not run:
##D # we optimize the SVM over 3 kernels simultanously
##D # note how we use dependent params (requires = ...) and iterated F-racing here
##D ps = makeParamSet(
##D makeNumericParam("C", lower = -12, upper = 12, trafo = function(x) 2^x),
##D makeDiscreteParam("kernel", values = c("vanilladot", "polydot", "rbfdot")),
##D makeNumericParam("sigma", lower = -12, upper = 12, trafo = function(x) 2^x,
##D requires = quote(kernel == "rbfdot")),
##D makeIntegerParam("degree", lower = 2L, upper = 5L,
##D requires = quote(kernel == "polydot"))
##D )
##D print(ps)
##D ctrl = makeTuneControlIrace(maxExperiments = 5, nbIterations = 1, minNbSurvival = 1)
##D rdesc = makeResampleDesc("Holdout")
##D res = tuneParams("classif.ksvm", iris.task, rdesc, par.set = ps, control = ctrl)
##D print(res)
##D df = as.data.frame(res$opt.path)
##D print(head(df[, -ncol(df)]))
##D
##D # include the training set performance as well
##D rdesc = makeResampleDesc("Holdout", predict = "both")
##D res = tuneParams("classif.ksvm", iris.task, rdesc, par.set = ps,
##D control = ctrl, measures = list(mmce, setAggregation(mmce, train.mean)))
##D print(res)
##D df2 = as.data.frame(res$opt.path)
##D print(head(df2[, -ncol(df2)]))
## End(Not run)
cleanEx()
nameEx("tuneParamsMultiCrit")
### * tuneParamsMultiCrit
flush(stderr()); flush(stdout())
### Name: tuneParamsMultiCrit
### Title: Hyperparameter tuning for multiple measures at once.
### Aliases: tuneParamsMultiCrit
### ** Examples
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
gbm-developers/gbm documentation built on Feb. 16, 2024, 6:13 p.m.
R Package Documentation
Browse R Packages
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
pkgname <- "mlr"
source(file.path(R.home("share"), "R", "examples-header.R"))
options(warn = 1)
library('mlr')
base::assign(".oldSearch", base::search(), pos = 'CheckExEnv')
base::assign(".old_wd", base::getwd(), pos = 'CheckExEnv')
cleanEx()
nameEx("FailureModel")
### * FailureModel
flush(stderr()); flush(stdout())
### Name: FailureModel
### Title: Failure model.
### Aliases: FailureModel
### ** Examples
configureMlr(on.learner.error = "warn")
data = iris
data$newfeat = 1 # will make LDA crash
task = makeClassifTask(data = data, target = "Species")
m = train("classif.lda", task) # LDA crashed, but mlr catches this
print(m)
print(m$learner.model) # the error message
p = predict(m, task) # this will predict NAs
print(p)
print(performance(p))
configureMlr(on.learner.error = "stop")
cleanEx()
nameEx("Task")
### * Task
flush(stderr()); flush(stdout())
### Name: Task
### Title: Create a classification, regression, survival, cluster,
### cost-sensitive classification or multilabel task.
### Aliases: Task
### ** Examples
if (requireNamespace("mlbench")) {
library(mlbench)
data(BostonHousing)
data(Ionosphere)
makeClassifTask(data = iris, target = "Species")
makeRegrTask(data = BostonHousing, target = "medv")
# an example of a classification task with more than those standard arguments:
blocking = factor(c(rep(1, 51), rep(2, 300)))
makeClassifTask(id = "myIonosphere", data = Ionosphere, target = "Class",
positive = "good", blocking = blocking)
makeClusterTask(data = iris[, -5L])
}
cleanEx()
nameEx("benchmark")
### * benchmark
flush(stderr()); flush(stdout())
### Name: benchmark
### Title: Benchmark experiment for multiple learners and tasks.
### Aliases: benchmark
### ** Examples
lrns = list(makeLearner("classif.lda"), makeLearner("classif.rpart"))
tasks = list(iris.task, sonar.task)
rdesc = makeResampleDesc("CV", iters = 2L)
meas = list(acc, ber)
bmr = benchmark(lrns, tasks, rdesc, measures = meas)
rmat = convertBMRToRankMatrix(bmr)
print(rmat)
plotBMRSummary(bmr)
plotBMRBoxplots(bmr, ber, style = "violin")
plotBMRRanksAsBarChart(bmr, pos = "stack")
friedmanTestBMR(bmr)
friedmanPostHocTestBMR(bmr, p.value = 0.05)
cleanEx()
nameEx("calculateConfusionMatrix")
### * calculateConfusionMatrix
flush(stderr()); flush(stdout())
### Name: calculateConfusionMatrix
### Title: Confusion matrix.
### Aliases: calculateConfusionMatrix print.ConfusionMatrix
### ** Examples
# get confusion matrix after simple manual prediction
allinds = 1:150
train = sample(allinds, 75)
test = setdiff(allinds, train)
mod = train("classif.lda", iris.task, subset = train)
pred = predict(mod, iris.task, subset = test)
print(calculateConfusionMatrix(pred))
print(calculateConfusionMatrix(pred, sums = TRUE))
print(calculateConfusionMatrix(pred, relative = TRUE))
# now after cross-validation
r = crossval("classif.lda", iris.task, iters = 2L)
print(calculateConfusionMatrix(r$pred))
cleanEx()
nameEx("calculateROCMeasures")
### * calculateROCMeasures
flush(stderr()); flush(stdout())
### Name: calculateROCMeasures
### Title: Calculate receiver operator measures.
### Aliases: calculateROCMeasures print.ROCMeasures
### ** Examples
lrn = makeLearner("classif.rpart", predict.type = "prob")
fit = train(lrn, sonar.task)
pred = predict(fit, task = sonar.task)
calculateROCMeasures(pred)
cleanEx()
nameEx("capLargeValues")
### * capLargeValues
flush(stderr()); flush(stdout())
### Name: capLargeValues
### Title: Convert large/infinite numeric values in a data.frame or task.
### Aliases: capLargeValues
### ** Examples
capLargeValues(iris, threshold = 5, impute = 5)
cleanEx()
nameEx("convertBMRToRankMatrix")
### * convertBMRToRankMatrix
flush(stderr()); flush(stdout())
### Name: convertBMRToRankMatrix
### Title: Convert BenchmarkResult to a rank-matrix.
### Aliases: convertBMRToRankMatrix
### ** Examples
# see benchmark
cleanEx()
nameEx("convertMLBenchObjToTask")
### * convertMLBenchObjToTask
flush(stderr()); flush(stdout())
### Name: convertMLBenchObjToTask
### Title: Convert a machine learning benchmark / demo object from package
### mlbench to a task.
### Aliases: convertMLBenchObjToTask
### ** Examples
print(convertMLBenchObjToTask("Ionosphere"))
print(convertMLBenchObjToTask("mlbench.spirals", n = 100, sd = 0.1))
cleanEx()
nameEx("createSpatialResamplingPlots")
### * createSpatialResamplingPlots
flush(stderr()); flush(stdout())
### Name: createSpatialResamplingPlots
### Title: Create (spatial) resampling plot objects.
### Aliases: createSpatialResamplingPlots
### ** Examples
cleanEx()
nameEx("estimateRelativeOverfitting")
### * estimateRelativeOverfitting
flush(stderr()); flush(stdout())
### Name: estimateRelativeOverfitting
### Title: Estimate relative overfitting.
### Aliases: estimateRelativeOverfitting
### ** Examples
task = makeClassifTask(data = iris, target = "Species")
rdesc = makeResampleDesc("CV", iters = 2)
estimateRelativeOverfitting(rdesc, acc, task, makeLearner("classif.knn"))
estimateRelativeOverfitting(rdesc, acc, task, makeLearner("classif.lda"))
rpred = resample("classif.knn", task, rdesc)$pred
estimateRelativeOverfitting(rpred, acc, task)
cleanEx()
nameEx("extractFDAFeatures")
### * extractFDAFeatures
flush(stderr()); flush(stdout())
### Name: extractFDAFeatures
### Title: Extract features from functional data.
### Aliases: extractFDAFeatures
### ** Examples
df = data.frame(x = matrix(rnorm(24), ncol = 8), y = factor(c("a", "a", "b")))
fdf = makeFunctionalData(df, fd.features = list(x1 = 1:4, x2 = 5:8), exclude.cols = "y")
task = makeClassifTask(data = fdf, target = "y")
extracted = extractFDAFeatures(task,
feat.methods = list("x1" = extractFDAFourier(), "x2" = extractFDAWavelets(filter = "haar")))
print(extracted$task)
reextractFDAFeatures(task, extracted$desc)
cleanEx()
nameEx("filterFeatures")
### * filterFeatures
flush(stderr()); flush(stdout())
### Name: filterFeatures
### Title: Filter features by thresholding filter values.
### Aliases: filterFeatures
### ** Examples
# simple filter
filterFeatures(iris.task, method = "FSelectorRcpp_gain.ratio", abs = 2)
# ensemble filter
filterFeatures(iris.task, method = "E-min",
base.methods = c("FSelectorRcpp_gain.ratio",
"FSelectorRcpp_information.gain"), abs = 2)
cleanEx()
nameEx("friedmanPostHocTestBMR")
### * friedmanPostHocTestBMR
flush(stderr()); flush(stdout())
### Name: friedmanPostHocTestBMR
### Title: Perform a posthoc Friedman-Nemenyi test.
### Aliases: friedmanPostHocTestBMR
### ** Examples
# see benchmark
cleanEx()
nameEx("friedmanTestBMR")
### * friedmanTestBMR
flush(stderr()); flush(stdout())
### Name: friedmanTestBMR
### Title: Perform overall Friedman test for a BenchmarkResult.
### Aliases: friedmanTestBMR
### ** Examples
# see benchmark
cleanEx()
nameEx("generateFeatureImportanceData")
### * generateFeatureImportanceData
flush(stderr()); flush(stdout())
### Name: generateFeatureImportanceData
### Title: Generate feature importance.
### Aliases: generateFeatureImportanceData FeatureImportanceData
### ** Examples
lrn = makeLearner("classif.rpart", predict.type = "prob")
fit = train(lrn, iris.task)
imp = generateFeatureImportanceData(iris.task, "permutation.importance",
lrn, "Petal.Width", nmc = 10L, local = TRUE)
cleanEx()
nameEx("generateFilterValuesData")
### * generateFilterValuesData
flush(stderr()); flush(stdout())
### Name: generateFilterValuesData
### Title: Calculates feature filter values.
### Aliases: generateFilterValuesData FilterValues
### ** Examples
# two simple filter methods
fval = generateFilterValuesData(iris.task,
method = c("FSelectorRcpp_gain.ratio", "FSelectorRcpp_information.gain"))
# using ensemble method "E-mean"
fval = generateFilterValuesData(iris.task,
method = list("E-mean", c("FSelectorRcpp_gain.ratio",
"FSelectorRcpp_information.gain")))
cleanEx()
nameEx("generateHyperParsEffectData")
### * generateHyperParsEffectData
flush(stderr()); flush(stdout())
### Name: generateHyperParsEffectData
### Title: Generate hyperparameter effect data.
### Aliases: generateHyperParsEffectData
### ** Examples
## Not run:
##D # 3-fold cross validation
##D ps = makeParamSet(makeDiscreteParam("C", values = 2^(-4:4)))
##D ctrl = makeTuneControlGrid()
##D rdesc = makeResampleDesc("CV", iters = 3L)
##D res = tuneParams("classif.ksvm", task = pid.task, resampling = rdesc,
##D par.set = ps, control = ctrl)
##D data = generateHyperParsEffectData(res)
##D plt = plotHyperParsEffect(data, x = "C", y = "mmce.test.mean")
##D plt + ylab("Misclassification Error")
##D
##D # nested cross validation
##D ps = makeParamSet(makeDiscreteParam("C", values = 2^(-4:4)))
##D ctrl = makeTuneControlGrid()
##D rdesc = makeResampleDesc("CV", iters = 3L)
##D lrn = makeTuneWrapper("classif.ksvm", control = ctrl,
##D resampling = rdesc, par.set = ps)
##D res = resample(lrn, task = pid.task, resampling = cv2,
##D extract = getTuneResult)
##D data = generateHyperParsEffectData(res)
##D plotHyperParsEffect(data, x = "C", y = "mmce.test.mean", plot.type = "line")
## End(Not run)
cleanEx()
nameEx("generateLearningCurveData")
### * generateLearningCurveData
flush(stderr()); flush(stdout())
### Name: generateLearningCurveData
### Title: Generates a learning curve.
### Aliases: generateLearningCurveData LearningCurveData
### ** Examples
r = generateLearningCurveData(list("classif.rpart", "classif.knn"),
task = sonar.task, percs = seq(0.2, 1, by = 0.2),
measures = list(tp, fp, tn, fn),
resampling = makeResampleDesc(method = "Subsample", iters = 5),
show.info = FALSE)
plotLearningCurve(r)
cleanEx()
nameEx("generatePartialDependenceData")
### * generatePartialDependenceData
flush(stderr()); flush(stdout())
### Name: generatePartialDependenceData
### Title: Generate partial dependence.
### Aliases: generatePartialDependenceData PartialDependenceData
### ** Examples
lrn = makeLearner("regr.svm")
fit = train(lrn, bh.task)
pd = generatePartialDependenceData(fit, bh.task, "lstat")
plotPartialDependence(pd, data = getTaskData(bh.task))
lrn = makeLearner("classif.rpart", predict.type = "prob")
fit = train(lrn, iris.task)
pd = generatePartialDependenceData(fit, iris.task, "Petal.Width")
plotPartialDependence(pd, data = getTaskData(iris.task))
cleanEx()
nameEx("getCaretParamSet")
### * getCaretParamSet
flush(stderr()); flush(stdout())
### Name: getCaretParamSet
### Title: Get tuning parameters from a learner of the caret R-package.
### Aliases: getCaretParamSet
### ** Examples
if (requireNamespace("caret") && requireNamespace("mlbench")) {
library(caret)
classifTask = makeClassifTask(data = iris, target = "Species")
# (1) classification (random forest) with discretized parameters
getCaretParamSet("rf", length = 9L, task = classifTask, discretize = TRUE)
# (2) regression (gradient boosting machine) without discretized parameters
library(mlbench)
data(BostonHousing)
regrTask = makeRegrTask(data = BostonHousing, target = "medv")
getCaretParamSet("gbm", length = 9L, task = regrTask, discretize = FALSE)
}
cleanEx()
nameEx("getHyperPars")
### * getHyperPars
flush(stderr()); flush(stdout())
### Name: getHyperPars
### Title: Get current parameter settings for a learner.
### Aliases: getHyperPars
### ** Examples
getHyperPars(makeLearner("classif.ranger"))
## set learner hyperparameter `mtry` manually
getHyperPars(makeLearner("classif.ranger", mtry = 100))
cleanEx()
nameEx("getMultilabelBinaryPerformances")
### * getMultilabelBinaryPerformances
flush(stderr()); flush(stdout())
### Name: getMultilabelBinaryPerformances
### Title: Retrieve binary classification measures for multilabel
### classification predictions.
### Aliases: getMultilabelBinaryPerformances
### ** Examples
# see makeMultilabelBinaryRelevanceWrapper
cleanEx()
nameEx("getNestedTuneResultsOptPathDf")
### * getNestedTuneResultsOptPathDf
flush(stderr()); flush(stdout())
### Name: getNestedTuneResultsOptPathDf
### Title: Get the 'opt.path's from each tuning step from the outer
### resampling.
### Aliases: getNestedTuneResultsOptPathDf
### ** Examples
# see example of makeTuneWrapper
cleanEx()
nameEx("getNestedTuneResultsX")
### * getNestedTuneResultsX
flush(stderr()); flush(stdout())
### Name: getNestedTuneResultsX
### Title: Get the tuned hyperparameter settings from a nested tuning.
### Aliases: getNestedTuneResultsX
### ** Examples
# see example of makeTuneWrapper
cleanEx()
nameEx("getOOBPreds")
### * getOOBPreds
flush(stderr()); flush(stdout())
### Name: getOOBPreds
### Title: Extracts out-of-bag predictions from trained models.
### Aliases: getOOBPreds
### ** Examples
training.set = sample(1:150, 50)
lrn = makeLearner("classif.ranger", predict.type = "prob", predict.threshold = 0.6)
mod = train(lrn, sonar.task, subset = training.set)
oob = getOOBPreds(mod, sonar.task)
oob
performance(oob, measures = list(auc, mmce))
cleanEx()
nameEx("getPredictionProbabilities")
### * getPredictionProbabilities
flush(stderr()); flush(stdout())
### Name: getPredictionProbabilities
### Title: Get probabilities for some classes.
### Aliases: getPredictionProbabilities
### ** Examples
task = makeClassifTask(data = iris, target = "Species")
lrn = makeLearner("classif.lda", predict.type = "prob")
mod = train(lrn, task)
# predict probabilities
pred = predict(mod, newdata = iris)
# Get probabilities for all classes
head(getPredictionProbabilities(pred))
# Get probabilities for a subset of classes
head(getPredictionProbabilities(pred, c("setosa", "virginica")))
cleanEx()
nameEx("getResamplingIndices")
### * getResamplingIndices
flush(stderr()); flush(stdout())
### Name: getResamplingIndices
### Title: Get the resampling indices from a tuning or feature selection
### wrapper..
### Aliases: getResamplingIndices
### ** Examples
task = makeClassifTask(data = iris, target = "Species")
lrn = makeLearner("classif.rpart")
# stupid mini grid
ps = makeParamSet(
makeDiscreteParam("cp", values = c(0.05, 0.1)),
makeDiscreteParam("minsplit", values = c(10, 20))
)
ctrl = makeTuneControlGrid()
inner = makeResampleDesc("Holdout")
outer = makeResampleDesc("CV", iters = 2)
lrn = makeTuneWrapper(lrn, resampling = inner, par.set = ps, control = ctrl)
# nested resampling for evaluation
# we also extract tuned hyper pars in each iteration and by that the resampling indices
r = resample(lrn, task, outer, extract = getTuneResult)
# get tuning indices
getResamplingIndices(r, inner = TRUE)
cleanEx()
nameEx("getTaskData")
### * getTaskData
flush(stderr()); flush(stdout())
### Name: getTaskData
### Title: Extract data in task.
### Aliases: getTaskData
### ** Examples
library("mlbench")
data(BreastCancer)
df = BreastCancer
df$Id = NULL
task = makeClassifTask(id = "BreastCancer", data = df, target = "Class", positive = "malignant")
head(getTaskData)
head(getTaskData(task, features = c("Cell.size", "Cell.shape"), recode.target = "-1+1"))
head(getTaskData(task, subset = 1:100, recode.target = "01"))
cleanEx()
nameEx("getTaskTargets")
### * getTaskTargets
flush(stderr()); flush(stdout())
### Name: getTaskTargets
### Title: Get target data of task.
### Aliases: getTaskTargets
### ** Examples
task = makeClassifTask(data = iris, target = "Species")
getTaskTargets(task)
cleanEx()
nameEx("impute")
### * impute
flush(stderr()); flush(stdout())
### Name: impute
### Title: Impute and re-impute data
### Aliases: impute
### ** Examples
df = data.frame(x = c(1, 1, NA), y = factor(c("a", "a", "b")), z = 1:3)
imputed = impute(df, target = character(0), cols = list(x = 99, y = imputeMode()))
print(imputed$data)
reimpute(data.frame(x = NA_real_), imputed$desc)
cleanEx()
nameEx("joinClassLevels")
### * joinClassLevels
flush(stderr()); flush(stdout())
### Name: joinClassLevels
### Title: Join some class existing levels to new, larger class levels for
### classification problems.
### Aliases: joinClassLevels
### ** Examples
joinClassLevels(iris.task, new.levels = list(foo = c("setosa", "virginica")))
cleanEx()
nameEx("listLearners")
### * listLearners
flush(stderr()); flush(stdout())
### Name: listLearners
### Title: Find matching learning algorithms.
### Aliases: listLearners listLearners.default listLearners.character
### listLearners.Task
### ** Examples
## Not run:
##D listLearners("classif", properties = c("multiclass", "prob"))
##D data = iris
##D task = makeClassifTask(data = data, target = "Species")
##D listLearners(task)
## End(Not run)
cleanEx()
nameEx("makeAggregation")
### * makeAggregation
flush(stderr()); flush(stdout())
### Name: makeAggregation
### Title: Specify your own aggregation of measures.
### Aliases: makeAggregation
### ** Examples
# computes the interquartile range on all performance values
test.iqr = makeAggregation(
id = "test.iqr", name = "Test set interquartile range",
properties = "req.test",
fun = function(task, perf.test, perf.train, measure, group, pred) IQR(perf.test)
)
cleanEx()
nameEx("makeClassificationViaRegressionWrapper")
### * makeClassificationViaRegressionWrapper
flush(stderr()); flush(stdout())
### Name: makeClassificationViaRegressionWrapper
### Title: Classification via regression wrapper.
### Aliases: makeClassificationViaRegressionWrapper
### ** Examples
lrn = makeLearner("regr.rpart")
lrn = makeClassificationViaRegressionWrapper(lrn)
mod = train(lrn, sonar.task, subset = 1:140)
predictions = predict(mod, newdata = getTaskData(sonar.task)[141:208, 1:60])
cleanEx()
nameEx("makeFeatSelWrapper")
### * makeFeatSelWrapper
flush(stderr()); flush(stdout())
### Name: makeFeatSelWrapper
### Title: Fuse learner with feature selection.
### Aliases: makeFeatSelWrapper
### ** Examples
# nested resampling with feature selection (with a nonsense algorithm for selection)
outer = makeResampleDesc("CV", iters = 2L)
inner = makeResampleDesc("Holdout")
ctrl = makeFeatSelControlRandom(maxit = 1)
lrn = makeFeatSelWrapper("classif.ksvm", resampling = inner, control = ctrl)
# we also extract the selected features for all iteration here
r = resample(lrn, iris.task, outer, extract = getFeatSelResult)
cleanEx()
nameEx("makeFilterWrapper")
### * makeFilterWrapper
flush(stderr()); flush(stdout())
### Name: makeFilterWrapper
### Title: Fuse learner with a feature filter method.
### Aliases: makeFilterWrapper
### ** Examples
cleanEx()
nameEx("makeFunctionalData")
### * makeFunctionalData
flush(stderr()); flush(stdout())
### Name: makeFunctionalData
### Title: Create a data.frame containing functional features from a normal
### data.frame.
### Aliases: makeFunctionalData
### ** Examples
# data.frame where columns 1:6 and 8:10 belong to a functional feature
d1 = data.frame(matrix(rnorm(100), nrow = 10), "target" = seq_len(10))
# Transform to functional data
d2 = makeFunctionalData(d1, fd.features = list("fd1" = 1:6, "fd2" = 8:10))
# Create a regression task
makeRegrTask(data = d2, target = "target")
cleanEx()
nameEx("makeLearner")
### * makeLearner
flush(stderr()); flush(stdout())
### Name: makeLearner
### Title: Create learner object.
### Aliases: makeLearner Learner
### ** Examples
makeLearner("classif.rpart")
makeLearner("classif.lda", predict.type = "prob")
lrn = makeLearner("classif.lda", method = "t", nu = 10)
getHyperPars(lrn)
cleanEx()
nameEx("makeLearners")
### * makeLearners
flush(stderr()); flush(stdout())
### Name: makeLearners
### Title: Create multiple learners at once.
### Aliases: makeLearners
### ** Examples
makeLearners(c("rpart", "lda"), type = "classif", predict.type = "prob")
cleanEx()
nameEx("makeMeasure")
### * makeMeasure
flush(stderr()); flush(stdout())
### Name: makeMeasure
### Title: Construct performance measure.
### Aliases: makeMeasure Measure
### ** Examples
f = function(task, model, pred, extra.args) {
sum((pred$data$response - pred$data$truth)^2)
}
makeMeasure(id = "my.sse", minimize = TRUE,
properties = c("regr", "response"), fun = f)
cleanEx()
nameEx("makeModelMultiplexer")
### * makeModelMultiplexer
flush(stderr()); flush(stdout())
### Name: makeModelMultiplexer
### Title: Create model multiplexer for model selection to tune over
### multiple possible models.
### Aliases: makeModelMultiplexer ModelMultiplexer
### ** Examples
set.seed(123)
cleanEx()
nameEx("makeModelMultiplexerParamSet")
### * makeModelMultiplexerParamSet
flush(stderr()); flush(stdout())
### Name: makeModelMultiplexerParamSet
### Title: Creates a parameter set for model multiplexer tuning.
### Aliases: makeModelMultiplexerParamSet
### ** Examples
# See makeModelMultiplexer
cleanEx()
nameEx("makeMultilabelBinaryRelevanceWrapper")
### * makeMultilabelBinaryRelevanceWrapper
flush(stderr()); flush(stdout())
### Name: makeMultilabelBinaryRelevanceWrapper
### Title: Use binary relevance method to create a multilabel learner.
### Aliases: makeMultilabelBinaryRelevanceWrapper
### ** Examples
d = getTaskData(yeast.task)
# drop some labels so example runs faster
d = d[seq(1, nrow(d), by = 20), c(1:2, 15:17)]
task = makeMultilabelTask(data = d, target = c("label1", "label2"))
lrn = makeLearner("classif.rpart")
lrn = makeMultilabelBinaryRelevanceWrapper(lrn)
lrn = setPredictType(lrn, "prob")
# train, predict and evaluate
mod = train(lrn, task)
pred = predict(mod, task)
performance(pred, measure = list(multilabel.hamloss, multilabel.subset01, multilabel.f1))
# the next call basically has the same structure for any multilabel meta wrapper
getMultilabelBinaryPerformances(pred, measures = list(mmce, auc))
# above works also with predictions from resample!
cleanEx()
nameEx("makeMultilabelClassifierChainsWrapper")
### * makeMultilabelClassifierChainsWrapper
flush(stderr()); flush(stdout())
### Name: makeMultilabelClassifierChainsWrapper
### Title: Use classifier chains method (CC) to create a multilabel
### learner.
### Aliases: makeMultilabelClassifierChainsWrapper
### ** Examples
d = getTaskData(yeast.task)
# drop some labels so example runs faster
d = d[seq(1, nrow(d), by = 20), c(1:2, 15:17)]
task = makeMultilabelTask(data = d, target = c("label1", "label2"))
lrn = makeLearner("classif.rpart")
lrn = makeMultilabelBinaryRelevanceWrapper(lrn)
lrn = setPredictType(lrn, "prob")
# train, predict and evaluate
mod = train(lrn, task)
pred = predict(mod, task)
performance(pred, measure = list(multilabel.hamloss, multilabel.subset01, multilabel.f1))
# the next call basically has the same structure for any multilabel meta wrapper
getMultilabelBinaryPerformances(pred, measures = list(mmce, auc))
# above works also with predictions from resample!
cleanEx()
nameEx("makeMultilabelDBRWrapper")
### * makeMultilabelDBRWrapper
flush(stderr()); flush(stdout())
### Name: makeMultilabelDBRWrapper
### Title: Use dependent binary relevance method (DBR) to create a
### multilabel learner.
### Aliases: makeMultilabelDBRWrapper
### ** Examples
d = getTaskData(yeast.task)
# drop some labels so example runs faster
d = d[seq(1, nrow(d), by = 20), c(1:2, 15:17)]
task = makeMultilabelTask(data = d, target = c("label1", "label2"))
lrn = makeLearner("classif.rpart")
lrn = makeMultilabelBinaryRelevanceWrapper(lrn)
lrn = setPredictType(lrn, "prob")
# train, predict and evaluate
mod = train(lrn, task)
pred = predict(mod, task)
performance(pred, measure = list(multilabel.hamloss, multilabel.subset01, multilabel.f1))
# the next call basically has the same structure for any multilabel meta wrapper
getMultilabelBinaryPerformances(pred, measures = list(mmce, auc))
# above works also with predictions from resample!
cleanEx()
nameEx("makeMultilabelNestedStackingWrapper")
### * makeMultilabelNestedStackingWrapper
flush(stderr()); flush(stdout())
### Name: makeMultilabelNestedStackingWrapper
### Title: Use nested stacking method to create a multilabel learner.
### Aliases: makeMultilabelNestedStackingWrapper
### ** Examples
d = getTaskData(yeast.task)
# drop some labels so example runs faster
d = d[seq(1, nrow(d), by = 20), c(1:2, 15:17)]
task = makeMultilabelTask(data = d, target = c("label1", "label2"))
lrn = makeLearner("classif.rpart")
lrn = makeMultilabelBinaryRelevanceWrapper(lrn)
lrn = setPredictType(lrn, "prob")
# train, predict and evaluate
mod = train(lrn, task)
pred = predict(mod, task)
performance(pred, measure = list(multilabel.hamloss, multilabel.subset01, multilabel.f1))
# the next call basically has the same structure for any multilabel meta wrapper
getMultilabelBinaryPerformances(pred, measures = list(mmce, auc))
# above works also with predictions from resample!
cleanEx()
nameEx("makeMultilabelStackingWrapper")
### * makeMultilabelStackingWrapper
flush(stderr()); flush(stdout())
### Name: makeMultilabelStackingWrapper
### Title: Use stacking method (stacked generalization) to create a
### multilabel learner.
### Aliases: makeMultilabelStackingWrapper
### ** Examples
d = getTaskData(yeast.task)
# drop some labels so example runs faster
d = d[seq(1, nrow(d), by = 20), c(1:2, 15:17)]
task = makeMultilabelTask(data = d, target = c("label1", "label2"))
lrn = makeLearner("classif.rpart")
lrn = makeMultilabelBinaryRelevanceWrapper(lrn)
lrn = setPredictType(lrn, "prob")
# train, predict and evaluate
mod = train(lrn, task)
pred = predict(mod, task)
performance(pred, measure = list(multilabel.hamloss, multilabel.subset01, multilabel.f1))
# the next call basically has the same structure for any multilabel meta wrapper
getMultilabelBinaryPerformances(pred, measures = list(mmce, auc))
# above works also with predictions from resample!
cleanEx()
nameEx("makeResampleDesc")
### * makeResampleDesc
flush(stderr()); flush(stdout())
### Name: makeResampleDesc
### Title: Create a description object for a resampling strategy.
### Aliases: makeResampleDesc ResampleDesc hout cv2 cv3 cv5 cv10
### ** Examples
# Bootstraping
makeResampleDesc("Bootstrap", iters = 10)
makeResampleDesc("Bootstrap", iters = 10, predict = "both")
# Subsampling
makeResampleDesc("Subsample", iters = 10, split = 3 / 4)
makeResampleDesc("Subsample", iters = 10)
# Holdout a.k.a. test sample estimation
makeResampleDesc("Holdout")
cleanEx()
nameEx("makeResampleInstance")
### * makeResampleInstance
flush(stderr()); flush(stdout())
### Name: makeResampleInstance
### Title: Instantiates a resampling strategy object.
### Aliases: makeResampleInstance ResampleInstance
### ** Examples
rdesc = makeResampleDesc("Bootstrap", iters = 10)
rin = makeResampleInstance(rdesc, task = iris.task)
rdesc = makeResampleDesc("CV", iters = 50)
rin = makeResampleInstance(rdesc, size = nrow(iris))
rin = makeResampleInstance("CV", iters = 10, task = iris.task)
cleanEx()
nameEx("makeStackedLearner")
### * makeStackedLearner
flush(stderr()); flush(stdout())
### Name: makeStackedLearner
### Title: Create a stacked learner object.
### Aliases: makeStackedLearner
### ** Examples
# Classification
data(iris)
tsk = makeClassifTask(data = iris, target = "Species")
base = c("classif.rpart", "classif.lda", "classif.svm")
lrns = lapply(base, makeLearner)
lrns = lapply(lrns, setPredictType, "prob")
m = makeStackedLearner(base.learners = lrns,
predict.type = "prob", method = "hill.climb")
tmp = train(m, tsk)
res = predict(tmp, tsk)
# Regression
data(BostonHousing, package = "mlbench")
tsk = makeRegrTask(data = BostonHousing, target = "medv")
base = c("regr.rpart", "regr.svm")
lrns = lapply(base, makeLearner)
m = makeStackedLearner(base.learners = lrns,
predict.type = "response", method = "compress")
tmp = train(m, tsk)
res = predict(tmp, tsk)
cleanEx()
nameEx("makeTuneWrapper")
### * makeTuneWrapper
flush(stderr()); flush(stdout())
### Name: makeTuneWrapper
### Title: Fuse learner with tuning.
### Aliases: makeTuneWrapper
### ** Examples
cleanEx()
nameEx("makeWeightedClassesWrapper")
### * makeWeightedClassesWrapper
flush(stderr()); flush(stdout())
### Name: makeWeightedClassesWrapper
### Title: Wraps a classifier for weighted fitting where each class
### receives a weight.
### Aliases: makeWeightedClassesWrapper
### ** Examples
cleanEx()
nameEx("performance")
### * performance
flush(stderr()); flush(stdout())
### Name: performance
### Title: Measure performance of prediction.
### Aliases: performance
### ** Examples
training.set = seq(1, nrow(iris), by = 2)
test.set = seq(2, nrow(iris), by = 2)
task = makeClassifTask(data = iris, target = "Species")
lrn = makeLearner("classif.lda")
mod = train(lrn, task, subset = training.set)
pred = predict(mod, newdata = iris[test.set, ])
performance(pred, measures = mmce)
# Compute multiple performance measures at once
ms = list("mmce" = mmce, "acc" = acc, "timetrain" = timetrain)
performance(pred, measures = ms, task, mod)
cleanEx()
nameEx("plotBMRBoxplots")
### * plotBMRBoxplots
flush(stderr()); flush(stdout())
### Name: plotBMRBoxplots
### Title: Create box or violin plots for a BenchmarkResult.
### Aliases: plotBMRBoxplots
### ** Examples
# see benchmark
cleanEx()
nameEx("plotBMRRanksAsBarChart")
### * plotBMRRanksAsBarChart
flush(stderr()); flush(stdout())
### Name: plotBMRRanksAsBarChart
### Title: Create a bar chart for ranks in a BenchmarkResult.
### Aliases: plotBMRRanksAsBarChart
### ** Examples
# see benchmark
cleanEx()
nameEx("plotBMRSummary")
### * plotBMRSummary
flush(stderr()); flush(stdout())
### Name: plotBMRSummary
### Title: Plot a benchmark summary.
### Aliases: plotBMRSummary
### ** Examples
# see benchmark
cleanEx()
nameEx("plotCalibration")
### * plotCalibration
flush(stderr()); flush(stdout())
### Name: plotCalibration
### Title: Plot calibration data using ggplot2.
### Aliases: plotCalibration
### ** Examples
## Not run:
##D lrns = list(makeLearner("classif.rpart", predict.type = "prob"),
##D makeLearner("classif.nnet", predict.type = "prob"))
##D fit = lapply(lrns, train, task = iris.task)
##D pred = lapply(fit, predict, task = iris.task)
##D names(pred) = c("rpart", "nnet")
##D out = generateCalibrationData(pred, groups = 3)
##D plotCalibration(out)
##D
##D fit = lapply(lrns, train, task = sonar.task)
##D pred = lapply(fit, predict, task = sonar.task)
##D names(pred) = c("rpart", "lda")
##D out = generateCalibrationData(pred)
##D plotCalibration(out)
## End(Not run)
cleanEx()
nameEx("plotCritDifferences")
### * plotCritDifferences
flush(stderr()); flush(stdout())
### Name: plotCritDifferences
### Title: Plot critical differences for a selected measure.
### Aliases: plotCritDifferences
### ** Examples
# see benchmark
cleanEx()
nameEx("plotFilterValues")
### * plotFilterValues
flush(stderr()); flush(stdout())
### Name: plotFilterValues
### Title: Plot filter values using ggplot2.
### Aliases: plotFilterValues
### ** Examples
fv = generateFilterValuesData(iris.task, method = "variance")
plotFilterValues(fv)
cleanEx()
nameEx("plotHyperParsEffect")
### * plotHyperParsEffect
flush(stderr()); flush(stdout())
### Name: plotHyperParsEffect
### Title: Plot the hyperparameter effects data
### Aliases: plotHyperParsEffect
### ** Examples
# see generateHyperParsEffectData
cleanEx()
nameEx("plotROCCurves")
### * plotROCCurves
flush(stderr()); flush(stdout())
### Name: plotROCCurves
### Title: Plots a ROC curve using ggplot2.
### Aliases: plotROCCurves
### ** Examples
cleanEx()
nameEx("plotThreshVsPerf")
### * plotThreshVsPerf
flush(stderr()); flush(stdout())
### Name: plotThreshVsPerf
### Title: Plot threshold vs. performance(s) for 2-class classification
### using ggplot2.
### Aliases: plotThreshVsPerf
### ** Examples
lrn = makeLearner("classif.rpart", predict.type = "prob")
mod = train(lrn, sonar.task)
pred = predict(mod, sonar.task)
pvs = generateThreshVsPerfData(pred, list(acc, setAggregation(acc, train.mean)))
plotThreshVsPerf(pvs)
cleanEx()
nameEx("plotTuneMultiCritResult")
### * plotTuneMultiCritResult
flush(stderr()); flush(stdout())
### Name: plotTuneMultiCritResult
### Title: Plots multi-criteria results after tuning using ggplot2.
### Aliases: plotTuneMultiCritResult
### ** Examples
# see tuneParamsMultiCrit
cleanEx()
nameEx("predict.WrappedModel")
### * predict.WrappedModel
flush(stderr()); flush(stdout())
### Name: predict.WrappedModel
### Title: Predict new data.
### Aliases: predict.WrappedModel
### ** Examples
# train and predict
train.set = seq(1, 150, 2)
test.set = seq(2, 150, 2)
model = train("classif.lda", iris.task, subset = train.set)
p = predict(model, newdata = iris, subset = test.set)
print(p)
predict(model, task = iris.task, subset = test.set)
# predict now probabiliies instead of class labels
lrn = makeLearner("classif.lda", predict.type = "prob")
model = train(lrn, iris.task, subset = train.set)
p = predict(model, task = iris.task, subset = test.set)
print(p)
getPredictionProbabilities(p)
cleanEx()
nameEx("resample")
### * resample
flush(stderr()); flush(stdout())
### Name: resample
### Title: Fit models according to a resampling strategy.
### Aliases: resample crossval repcv holdout subsample bootstrapOOB
### bootstrapB632 bootstrapB632plus growingcv fixedcv
### ** Examples
task = makeClassifTask(data = iris, target = "Species")
rdesc = makeResampleDesc("CV", iters = 2)
r = resample(makeLearner("classif.qda"), task, rdesc)
print(r$aggr)
print(r$measures.test)
print(r$pred)
# include the training set performance as well
rdesc = makeResampleDesc("CV", iters = 2, predict = "both")
r = resample(makeLearner("classif.qda"), task, rdesc,
measures = list(mmce, setAggregation(mmce, train.mean)))
print(r$aggr)
cleanEx()
nameEx("selectFeatures")
### * selectFeatures
flush(stderr()); flush(stdout())
### Name: selectFeatures
### Title: Feature selection by wrapper approach.
### Aliases: selectFeatures
### ** Examples
cleanEx()
nameEx("setHyperPars")
### * setHyperPars
flush(stderr()); flush(stdout())
### Name: setHyperPars
### Title: Set the hyperparameters of a learner object.
### Aliases: setHyperPars
### ** Examples
cl1 = makeLearner("classif.ksvm", sigma = 1)
cl2 = setHyperPars(cl1, sigma = 10, par.vals = list(C = 2))
print(cl1)
# note the now set and altered hyperparameters:
print(cl2)
cleanEx()
nameEx("setThreshold")
### * setThreshold
flush(stderr()); flush(stdout())
### Name: setThreshold
### Title: Set threshold of prediction object.
### Aliases: setThreshold
### ** Examples
# create task and train learner (LDA)
task = makeClassifTask(data = iris, target = "Species")
lrn = makeLearner("classif.lda", predict.type = "prob")
mod = train(lrn, task)
# predict probabilities and compute performance
pred = predict(mod, newdata = iris)
performance(pred, measures = mmce)
head(as.data.frame(pred))
# adjust threshold and predict probabilities again
threshold = c(setosa = 0.4, versicolor = 0.3, virginica = 0.3)
pred = setThreshold(pred, threshold = threshold)
performance(pred, measures = mmce)
head(as.data.frame(pred))
cleanEx()
nameEx("subsetTask")
### * subsetTask
flush(stderr()); flush(stdout())
### Name: subsetTask
### Title: Subset data in task.
### Aliases: subsetTask
### ** Examples
task = makeClassifTask(data = iris, target = "Species")
subsetTask(task, subset = 1:100)
cleanEx()
nameEx("summarizeColumns")
### * summarizeColumns
flush(stderr()); flush(stdout())
### Name: summarizeColumns
### Title: Summarize columns of data.frame or task.
### Aliases: summarizeColumns
### ** Examples
summarizeColumns(iris)
cleanEx()
nameEx("summarizeLevels")
### * summarizeLevels
flush(stderr()); flush(stdout())
### Name: summarizeLevels
### Title: Summarizes factors of a data.frame by tabling them.
### Aliases: summarizeLevels
### ** Examples
summarizeLevels(iris)
cleanEx()
nameEx("train")
### * train
flush(stderr()); flush(stdout())
### Name: train
### Title: Train a learning algorithm.
### Aliases: train
### ** Examples
training.set = sample(seq_len(nrow(iris)), nrow(iris) / 2)
## use linear discriminant analysis to classify iris data
task = makeClassifTask(data = iris, target = "Species")
learner = makeLearner("classif.lda", method = "mle")
mod = train(learner, task, subset = training.set)
print(mod)
## use random forest to classify iris data
task = makeClassifTask(data = iris, target = "Species")
learner = makeLearner("classif.rpart", minsplit = 7, predict.type = "prob")
mod = train(learner, task, subset = training.set)
print(mod)
cleanEx()
nameEx("tuneParams")
### * tuneParams
flush(stderr()); flush(stdout())
### Name: tuneParams
### Title: Hyperparameter tuning.
### Aliases: tuneParams
### ** Examples
set.seed(123)
# a grid search for an SVM (with a tiny number of points...)
# note how easily we can optimize on a log-scale
ps = makeParamSet(
makeNumericParam("C", lower = -12, upper = 12, trafo = function(x) 2^x),
makeNumericParam("sigma", lower = -12, upper = 12, trafo = function(x) 2^x)
)
ctrl = makeTuneControlGrid(resolution = 2L)
rdesc = makeResampleDesc("CV", iters = 2L)
res = tuneParams("classif.ksvm", iris.task, rdesc, par.set = ps, control = ctrl)
print(res)
# access data for all evaluated points
df = as.data.frame(res$opt.path)
df1 = as.data.frame(res$opt.path, trafo = TRUE)
print(head(df[, -ncol(df)]))
print(head(df1[, -ncol(df)]))
# access data for all evaluated points - alternative
df2 = generateHyperParsEffectData(res)
df3 = generateHyperParsEffectData(res, trafo = TRUE)
print(head(df2$data[, -ncol(df2$data)]))
print(head(df3$data[, -ncol(df3$data)]))
## Not run:
##D # we optimize the SVM over 3 kernels simultanously
##D # note how we use dependent params (requires = ...) and iterated F-racing here
##D ps = makeParamSet(
##D makeNumericParam("C", lower = -12, upper = 12, trafo = function(x) 2^x),
##D makeDiscreteParam("kernel", values = c("vanilladot", "polydot", "rbfdot")),
##D makeNumericParam("sigma", lower = -12, upper = 12, trafo = function(x) 2^x,
##D requires = quote(kernel == "rbfdot")),
##D makeIntegerParam("degree", lower = 2L, upper = 5L,
##D requires = quote(kernel == "polydot"))
##D )
##D print(ps)
##D ctrl = makeTuneControlIrace(maxExperiments = 5, nbIterations = 1, minNbSurvival = 1)
##D rdesc = makeResampleDesc("Holdout")
##D res = tuneParams("classif.ksvm", iris.task, rdesc, par.set = ps, control = ctrl)
##D print(res)
##D df = as.data.frame(res$opt.path)
##D print(head(df[, -ncol(df)]))
##D
##D # include the training set performance as well
##D rdesc = makeResampleDesc("Holdout", predict = "both")
##D res = tuneParams("classif.ksvm", iris.task, rdesc, par.set = ps,
##D control = ctrl, measures = list(mmce, setAggregation(mmce, train.mean)))
##D print(res)
##D df2 = as.data.frame(res$opt.path)
##D print(head(df2[, -ncol(df2)]))
## End(Not run)
cleanEx()
nameEx("tuneParamsMultiCrit")
### * tuneParamsMultiCrit
flush(stderr()); flush(stdout())
### Name: tuneParamsMultiCrit
### Title: Hyperparameter tuning for multiple measures at once.
### Aliases: tuneParamsMultiCrit
### ** Examples
### * <FOOTER>
###
cleanEx()
options(digits = 7L)
base::cat("Time elapsed: ", proc.time() - base::get("ptime", pos = 'CheckExEnv'),"\n")
grDevices::dev.off()
###
### Local variables: ***
### mode: outline-minor ***
### outline-regexp: "\\(> \\)?### [*]+" ***
### End: ***
quit('no')
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