context("classif_svm")
# 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)
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)
set.seed(getOption("mlr.debug.seed"))
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.list)
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)
})
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