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test_that("Impute data frame", {
data = data.frame(f = letters[c(1, 1, 1, 1, 2)], x = rep(1., 5),
y = c(1, 2, 3, 3, 4), z = NA, stringsAsFactors = TRUE)
target = "z"
data[6, ] = NA
# median
imputed = impute(data, target = target, cols = list(x = imputeMedian(),
y = imputeMedian()))$data
expect_equal(imputed$x[6], 1)
expect_equal(imputed$y[6], 3)
# mode
imputed = impute(data, target = target, cols = list(f = imputeMode(),
x = imputeMode(), y = imputeMode()))$data
expect_equal(as.character(imputed$f[6]), "a")
expect_equal(imputed$x[6], 1)
expect_equal(imputed$y[6], 3)
# min / max
imputed = impute(data, target = target, cols = list(x = imputeMin(2),
y = imputeMax(0)))$data
expect_equal(imputed$x[6], 1)
expect_equal(imputed$y[6], max(data$y, na.rm = TRUE))
imputed = impute(data, target = target, classes = list(numeric = imputeMax()))$data
expect_equal(imputed$x[6], 1)
expect_equal(imputed$y[6], max(data$y, na.rm = TRUE) + diff(range(data$y, na.rm = TRUE)))
# normal
imputed = impute(data, target = target, cols = list(x = imputeNormal(),
y = imputeNormal()))$data
expect_equal(imputed$x[6], 1)
# hist / table
imputed = impute(data, target = target, cols = list(f = imputeHist(),
x = imputeHist(), y = imputeHist(breaks = 1, use.mids = FALSE)))$data
expect_true(imputed$f[6] %in% c("a", "b"))
expect_equal(imputed$x[6], 0.5)
expect_equal(imputed$x[6], 0.5)
expect_true(imputed$y[6] >= 0 && imputed$y[6] <= 5)
# learner
data2 = data.frame(V1 = 1:10, V2 = 1:10, V3 = 1:10,
col = factor(rep(1:2, c(3, 7))), z = 1:10)
data2$V2[9:10] = NA
data2$V3[1:2] = NA
data2$col[8:10] = NA
# we impute col case 1: feature used for imputation (V1) does not have
# missings used to check functionality with a learner that does not have
# property "missings" (see #1035)
lrn = makeLearner("classif.lda")
expect_false(hasLearnerProperties(lrn, "missings"))
imputed = impute(data2, target = target, cols = list(col = imputeLearner(lrn, features = "V1")))$data
expect_true(all(imputed$col[8:10] == "2"))
# case 2: feature used for imputation (V2) has missings only in rows where col
# has missings in this case the imputation task does not have property
# "missings", but a learner with property "missings" is required for
# imputation
expect_error(impute(data2, target = target, cols = list(col = imputeLearner("classif.lda", features = "V2"))), "used for imputation has/have missing values, but learner")
lrn = makeLearner("classif.naiveBayes")
expect_true(hasLearnerProperties(lrn, "missings"))
imputed = impute(data2, target = target, cols = list(col = imputeLearner(lrn, features = "V2")))$data
expect_true(all(imputed$col[8:10] == "2"))
# case 3: feature used for imputation (V3) has missings only in rows where col
# does not have missings in this case the imputation task has property
# "missings"
expect_error(impute(data2, target = target, cols = list(col = imputeLearner("classif.lda", features = "V3"))), "used for imputation has/have missing values, but learner")
imputed = impute(data2, target = target, cols = list(col = imputeLearner("classif.naiveBayes", features = "V3")))$data
expect_true(all(imputed$col[8:10] == "2"))
# we had an issue here (see #26) where e.g. imputation for integer/numeric
# features via a classif learner showed inconsistent behavior and resulted in
# weird error messages
data2$col2 = as.integer(data2$col)
# case 1: impute an integer (data2$col2) with a classif learner (integers are
# coerced to factors by checkTaskData) using learner classif.lvq1 because it
# doesn't work with integer targets (see #26)
set.seed(getOption("mlr.debug.seed"))
imputed = impute(data2, cols = list(col2 = imputeLearner("classif.lvq1",
features = "V1")))$data
expect_true(all(imputed$col2[8:10] == "2"))
# case 2: impute a numeric (data$x) with a classif learner
expect_error(impute(data, target = target,
cols = list(x = imputeLearner("classif.naiveBayes"))),
"Assertion on 'x' failed")
# case 3: impute a factor (data$f) with a regr learner
expect_error(impute(data, target = target,
cols = list(f = imputeLearner("regr.rpart"))),
"Assertion on 'f' failed")
# constant replacements
imputed = impute(data, target = target, cols = list(f = "xxx", x = 999, y = 1000))$data
expect_equal(as.character(imputed$f[6]), "xxx")
expect_equal(imputed$x[6], 999)
expect_equal(imputed$y[6], 1000)
# some reimputations
x = impute(data, target = target, cols = list(f = "xxx", x = imputeMode(),
y = imputeMax(2)), impute.new.levels = TRUE)
imputed = reimpute(data, x$desc)
expect_equal(x$data, imputed)
expect_error(reimpute(data.frame(f = factor("newlvl"), x = NA), x$desc), "column types have changed")
imputed = reimpute(data.frame(f = factor("newlvl"), x = NA_real_), x$desc)
expect_equal(as.character(imputed$f), "xxx")
expect_equal(imputed$x, 1)
# FIXME: y was never in input data? therefore next test fails?
# expect_equal(imputed$y, 8)
x = impute(data, target = target, cols = list(f = "xxx"), impute.new.levels = FALSE)
imputed = reimpute(data.frame(f = factor("newlvl"), x = NA_real_), x$desc)
expect_true(is.na(imputed$f))
expect_true("xxx" %in% levels(imputed$f))
# dummies
x = impute(data, target = target, dummy.cols = "x")
expect_equal(x$data[["x.dummy"]], as.factor(c(rep(FALSE, 5), TRUE)))
expect_equal(reimpute(data, x$desc), x$data)
x = impute(data, target = target, dummy.cols = "x", dummy.type = "numeric")
expect_equal(x$data[["x.dummy"]], as.numeric(c(rep(FALSE, 5), TRUE)))
expect_equal(reimpute(data, x$desc), x$data)
x = impute(data, target = target, dummy.classes = "numeric")$data
expect_true(setequal(names(x), c(names(data), "x.dummy", "y.dummy")))
x = impute(data, target = target, dummy.classes = "numeric", dummy.cols = "z")$data
expect_true(setequal(names(x), c(names(data), "x.dummy", "y.dummy", "z.dummy")))
x = impute(data, target = character(0), classes = list(factor = imputeMode(), numeric = imputeMedian(),
integer = imputeMedian(), logical = imputeConstant(1)))
expect_true(all(!is.na(x$data)))
data2 = data[1:5, ]
x = impute(data2, target = target, dummy.classes = c("numeric", "logical", "factor"), force.dummies = TRUE)
expect_true(setequal(x$desc$dummies, c("f", "x", "y")))
x = impute(data2, target = target, dummy.classes = c("numeric", "logical", "factor"), force.dummies = FALSE)
expect_true(setequal(x$desc$dummies, character(0)))
})
test_that("Impute and reimpute task", {
data = data.frame(f = letters[c(1, 1, 1, 1, 2)], x = rep(1., 5), y = c(1, 2, 3, 3, 4),
stringsAsFactors = TRUE)
data[6L, ] = NA
classif.tar = factor(c(rep(c("a", "b"), 3L)))
regr.tar = rep(c(.1, .2), 3L)
# additional data-frame to check reimpute
data2 = data.frame(f = letters[c(2, 1, 1, 1, 1)], x = rep(2., 5), y = c(2, 4, 2, 3, 3),
stringsAsFactors = TRUE)
data2[6L, ] = NA
data2$z = classif.tar
# test classif task
data$z = classif.tar
data2$z = classif.tar
classif.tsk = makeClassifTask(data = data, target = "z")
imputed = impute(classif.tsk,
cols = list(f = imputeConstant("c"), x = imputeMean(), y = imputeMode()))
imputed.tsk.data = getTaskData(imputed$task)
imputed.data = impute(data, target = "z",
cols = list(f = imputeConstant("c"), x = imputeMean(), y = imputeMode()))$data
test.tsk = makeClassifTask(data = data2, target = "z")
imputed.desc = imputed$desc
imputed.test.tsk = reimpute(test.tsk, imputed.desc)
test.imputed = reimpute(data2, imputed.desc)
expect_identical(imputed.data, imputed.tsk.data)
expect_equal(class(classif.tsk), class(imputed$task))
expect_identical(test.imputed, imputed.test.tsk$env$data)
expect_equal(class(test.tsk), class(imputed.test.tsk))
})
test_that("ImputeWrapper", {
d = iris[seq(1, 150, 3), ]
d[1, 1] = NA_real_
task = makeClassifTask(data = d, target = "Species")
lrn = makeImputeWrapper("classif.rpart", classes = list(numeric = imputeMedian()))
m = train(lrn, task)
p = predict(m, task)
expect_true(!any(is.na(p$data$response)))
mm = getLearnerModel(m, more.unwrap = TRUE)
expect_output(print(mm), "root")
expect_s3_class(mm, "rpart")
mm = getLearnerModel(m, more.unwrap = FALSE)
expect_output(print(mm), "Model")
expect_s3_class(mm, "WrappedModel")
expect_match(lrn$id, "[.]imputed$")
})
test_that("Impute works on non missing data", { # we had issues here: 848,893
data = data.frame(a = c(1, 1, 2), b = 1:3)
impute.methods = list(
imputeConstant(0),
imputeMedian(),
imputeMean(),
imputeMode(),
imputeMin(),
imputeMax(),
imputeUniform(),
imputeNormal(),
imputeHist(),
imputeLearner(learner = makeLearner("regr.fnn"))
)
for (impute.method in impute.methods) {
imputed = impute(data, cols = list(a = impute.method))$data
expect_equal(data, imputed)
}
# test it in resampling
dat = data.frame(y = rnorm(10), a = c(NA, rnorm(9)), b = rnorm(10))
task = makeRegrTask(data = dat, target = "y")
implrn = imputeLearner(makeLearner("regr.rpart"))
lrn = makeImputeWrapper(makeLearner("regr.lm"), cols = list(a = implrn))
holdout(lrn, task)
})
test_that("Logicals are casted to factors instead of character (#1522)", {
x = data.frame(a = c(TRUE, FALSE, NA))
y = impute(x, cols = list(a = imputeConstant("__miss__")))
res = factor(c("TRUE", "FALSE", "__miss__"), levels = c("FALSE", "TRUE", "__miss__"))
expect_equal(y$data$a, res)
})
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