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test_that("generatePartialDependenceData", {
m = c(4, 10)
# test regression with interactions, centering, and mixed factor features
fr = train("regr.rpart", regr.task)
dr = generatePartialDependenceData(fr, input = regr.task,
features = c("lstat", "chas"), interaction = TRUE, n = m)
nfeat = length(dr$features)
nfacet = length(unique(regr.df[["chas"]]))
n = getTaskSize(regr.task)
expect_equal(nrow(dr$data), m[1] * nfeat)
expect_true(all(dr$data$medv >= min(regr.df$medv) | dr$data$medv <= max(regr.df$medv)))
plotPartialDependence(dr, facet = "chas")
dir = tempdir()
path = file.path(dir, "test.svg")
suppressMessages(ggsave(path))
doc = XML::xmlParse(path)
expect_equal(length(XML::getNodeSet(doc, grey.rect.xpath, ns.svg)), nfacet)
expect_equal(length(XML::getNodeSet(doc, black.circle.xpath, ns.svg)), nfacet * m[1])
# check that if the input is a data.frame things work
dr.df = generatePartialDependenceData(fr, input = regr.df, features = "lstat")
# check that the interactions and centering work with ICE
dr = generatePartialDependenceData(fr, input = regr.task,
features = c("lstat", "chas"), interaction = TRUE, individual = TRUE, n = m)
expect_equal(nrow(dr$data), m[1] * nfeat * m[2])
plotPartialDependence(dr, facet = "chas", data = regr.df, p = 1)
suppressMessages(ggsave(path))
doc = XML::xmlParse(path)
expect_equal(length(XML::getNodeSet(doc, grey.rect.xpath, ns.svg)), nfacet)
# black.circle.xpath counts points which are omitted when individual = TRUE
expect_equal(length(XML::getNodeSet(doc, black.circle.xpath, ns.svg)), n + prod(m) * nfacet)
# check that multiple features w/o interaction work with a label outputting classifier with
# an appropriate aggregation function
fc = train("classif.rpart", multiclass.task)
dc = generatePartialDependenceData(fc, input = multiclass.task, features = c("Petal.Width", "Petal.Length"),
fun = function(x) table(x) / length(x), n = m)
nfeat = length(dc$features)
n = getTaskSize(multiclass.task)
plotPartialDependence(dc, data = multiclass.df)
suppressMessages(ggsave(path))
doc = XML::xmlParse(path)
# minus one because the of the legend
expect_equal(length(XML::getNodeSet(doc, grey.rect.xpath, ns.svg)), nfeat)
expect_equal(length(XML::getNodeSet(doc, red.circle.xpath, ns.svg)) - 1, nfeat * m[1])
expect_equal(length(XML::getNodeSet(doc, blue.circle.xpath, ns.svg)) - 1, nfeat * m[1])
expect_equal(length(XML::getNodeSet(doc, green.circle.xpath, ns.svg)) - 1, nfeat * m[1])
fcp = train(makeLearner("classif.svm", predict.type = "prob"), multiclass.task)
# test that with classif but predict.type = "response" we can use fun which
# return a vector
dcp = generatePartialDependenceData(fcp, input = multiclass.task,
features = "Petal.Width",
fun = function(x) quantile(x, c(.025, .5, .975)), n = m)
plotPartialDependence(dcp)
# check that probability outputting classifiers work w/ interactions
dcp = generatePartialDependenceData(fcp, input = multiclass.task, features = c("Petal.Width", "Petal.Length"),
interaction = TRUE, n = m)
nfacet = length(unique(dcp$data$Petal.Length))
ntarget = length(dcp$target)
plotPartialDependence(dcp, "tile")
# check that probability outputting classifiers work with ICE
dcp = generatePartialDependenceData(fcp, input = multiclass.task, features = c("Petal.Width", "Petal.Length"),
interaction = TRUE, individual = TRUE, n = m)
# check that survival tasks work with multiple features
fs = train("surv.rpart", surv.task)
ds = generatePartialDependenceData(fs, input = surv.task, features = c("x1", "x2"), n = m)
nfeat = length(ds$features)
n = getTaskSize(surv.task)
plotPartialDependence(ds, data = surv.df)
suppressMessages(ggsave(path))
doc = XML::xmlParse(path)
expect_equal(length(XML::getNodeSet(doc, grey.rect.xpath, ns.svg)), nfeat)
expect_equal(length(XML::getNodeSet(doc, black.circle.xpath, ns.svg)), m[1] * nfeat)
# issue 1180 test
pd = generatePartialDependenceData(fr, input = regr.task,
features = c("lstat", "chas"), n = m)
plotPartialDependence(pd)
# check that bounds work for regression
db = generatePartialDependenceData(fr, input = regr.task, features = c("lstat", "chas"),
interaction = TRUE,
fun = function(x) quantile(x, c(.25, .5, .75)), n = m)
nfacet = length(unique(regr.df[["chas"]]))
n = getTaskSize(regr.task)
expect_equal(colnames(db$data), c("medv", "Function", "lstat", "chas"))
plotPartialDependence(db, facet = "chas", data = regr.df)
suppressMessages(ggsave(path))
doc = XML::xmlParse(path)
expect_equal(length(XML::getNodeSet(doc, grey.rect.xpath, ns.svg)), nfacet * 3)
expect_equal(length(XML::getNodeSet(doc, black.circle.xpath, ns.svg)), nfacet * 3 * m[1] + n * 3)
# check derivative and factor feature failure
expect_error(generatePartialDependenceData(fr, input = regr.task, features = c("lstat", "chas"),
derivative = TRUE))
# check interaction + derivative failure
expect_error(generatePartialDependenceData(fr, input = regr.task, features = c("lstat", "chas"),
interaction = TRUE, derivative = TRUE))
fcpb = train(makeLearner("classif.rpart", predict.type = "prob"), binaryclass.task)
bc = generatePartialDependenceData(fcpb, input = binaryclass.task, features = c("V11", "V12"),
individual = TRUE, n = m)
## tests for binary classification plotting, discovered whilst trying to merge
## pr #142
plotPartialDependence(bc)
bc = generatePartialDependenceData(fcpb, input = binaryclass.task, features = c("V11", "V12"), n = m)
plotPartialDependence(bc)
# check that derivative estimation works for ICE and pd for classification and regression
fr = train(makeLearner("regr.ksvm"), regr.task)
pfr = generatePartialDependenceData(fr, input = regr.df, features = c("lstat", "crim"),
derivative = TRUE, individual = FALSE, n = m)
pfri = generatePartialDependenceData(fr, input = regr.df,
features = c("lstat", "crim"),
derivative = TRUE, individual = TRUE, n = m)
fc = train(makeLearner("classif.ksvm", predict.type = "prob"), multiclass.task)
pfc = generatePartialDependenceData(fc, input = multiclass.df,
features = c("Petal.Width", "Petal.Length"),
derivative = TRUE, n = m)
fs = train(makeLearner("surv.coxph"), surv.task)
pfs = generatePartialDependenceData(fs, input = surv.df,
features = c("x1", "x2"),
derivative = TRUE, n = m)
# check that se estimation works
fse = train(makeLearner("regr.lm", predict.type = "se"), regr.task)
pfse = generatePartialDependenceData(fse, input = regr.task, features = c("lstat", "crim"),
bounds = c(-2, 2), n = m)
plotPartialDependence(pfse)
# check that tile + contour plots work for two and three features with regression and survival
expect_error(plotPartialDependence(ds, geom = "tile")) # interaction == FALSE
tfr = generatePartialDependenceData(fr, regr.df, features = c("lstat", "crim", "chas"),
interaction = TRUE, n = m)
plotPartialDependence(tfr, geom = "tile", facet = "chas", data = regr.df)
tfs = generatePartialDependenceData(fs, surv.df, c("x1", "x2"), interaction = TRUE)
plotPartialDependence(tfs, geom = "tile", data = surv.df)
# facetting works with plotPartialDependence:
q = plotPartialDependence(dr, facet = "chas", data = regr.df,
facet.wrap.nrow = 2L)
testFacetting(q, 2L)
q = plotPartialDependence(dr, facet = "chas", facet.wrap.ncol = 2L,
data = regr.df)
testFacetting(q, ncol = 2L)
# bug found in pr #1206
pd = generatePartialDependenceData(fcp, multiclass.task, "Petal.Width",
individual = TRUE, n = m)
# issue 63 in the tutorial
pd = generatePartialDependenceData(fcp, multiclass.task, "Petal.Width",
individual = TRUE, derivative = TRUE, n = m)
# test that would have caught a bug that occurs when the jacobian is estimated
pd.der.classif = generatePartialDependenceData(fcp, multiclass.task, "Petal.Width",
derivative = TRUE, n = m)
})
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