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# we cannot do a prob test, as set.seed sems not to work on e1071 svm for the prob parameters!
# requirePackagesOrSkip("e1071", default.method = "load")
# set.seed(1)
# m1=svm(Species~., data=iris, probability=T)
# set.seed(1)
# m2=svm(Species~., data=iris, probability=T)
# all.equal(m1, m2)
# UPD 12/2019: The above issue seems to be solved. However, we have no param
# "probability" in "classif.svm"?
test_that("classif_svm", {
requirePackagesOrSkip("e1071", default.method = "load")
parset.list = list(
list(),
list(gamma = 20),
list(kernel = "sigmoid", gamma = 10),
list(kernel = "polynomial", degree = 3, coef0 = 2, gamma = 1.5)
)
old.predicts.list = list()
old.probs.list = list()
for (i in seq_along(parset.list)) {
parset = parset.list[[i]]
pars = list(formula = multiclass.formula, data = multiclass.train)
pars = c(pars, parset)
m1 = do.call(e1071::svm, pars)
pars$probability = TRUE
m2 = do.call(e1071::svm, pars)
old.predicts.list[[i]] = predict(m1, newdata = multiclass.test)
old.probs.list[[i]] = predict(m2, newdata = multiclass.test,
probability = TRUE)
}
testSimpleParsets("classif.svm", multiclass.df, multiclass.target,
multiclass.train.inds, old.predicts.list, parset.list)
# testProbParsets("classif.svm", multiclass.df, multiclass.target,
# multiclass.train.inds, old.probs.list, parset.list2)
tt = function(formula, data, subset = 1:150, ...) {
e1071::svm(formula, data = data[subset, ], kernel = "polynomial",
degree = 3, coef0 = 2, gamma = 1.5)
}
testCV("classif.svm", multiclass.df, multiclass.target, tune.train = tt,
parset = list(kernel = "polynomial", degree = 3, coef0 = 2, gamma = 1.5))
lrn = makeLearner("classif.svm", scale = FALSE)
model = train(lrn, multiclass.task)
preds = predict(model, multiclass.task)
expect_lt(performance(preds), 0.3)
lrn = makeLearner("classif.svm", scale = TRUE)
model = train(lrn, multiclass.task)
preds = predict(model, multiclass.task)
expect_lt(performance(preds), 0.3)
})
test_that("classif_svm with many features", {
xt = cbind(as.data.frame(matrix(rnorm(4e4), ncol = 2e4)),
x = as.factor(c("a", "b")))
xt.task = makeClassifTask("xt", xt, "x")
# the given task has many features, the formula interface fails
expect_silent(train("classif.svm", xt.task))
})
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