tests/testthat/test_base_wsvm.R
In schiffner/locClass: Collection of Local Classification Methods
context("wsvm")
test_that("wsvm: misspecified arguments", {
data(iris)
# wrong variable names
expect_error(wsvm(formula = Species ~ V1, data = iris))
# wrong class
expect_error(wsvm(formula = iris, data = iris))
# target variable also in x
expect_error(wsvm(y = iris$Species, x = iris)) ## funktioniert, sollte aber nicht
# missing x
expect_error(wsvm(y = iris$Species))
})
test_that("wsvm works if only one predictor variable is given", {
data(iris)
fit <- wsvm(Species ~ Petal.Width, data = iris, subset = 6:60)
expect_equal(ncol(fit$SV), 1)
})
test_that("wsvm: training data from only one class", {
data(iris)
## y factor
expect_that(wsvm(Species ~ ., data = iris, subset = 1:50), throws_error("need training data from at least two classes"))
#expect_that(wsvm(Species ~ ., data = iris, subset = 1), throws_error("training data from only one class"))
expect_that(wsvm(y = iris$Species, x = iris[,-5], subset = 1:50), throws_error("need training data from at least two classes"))
## y integer & type = C-classification
irisint <- iris
irisint$Species <- as.numeric(irisint$Species)
expect_that(wsvm(Species ~ ., data = irisint, subset = 1:50, type = "C-classification"), throws_error("need training data from at least two classes"))
#expect_that(wsvm(Species ~ ., data = irisint, subset = 1, type = "C-classification"), throws_error("training data from only one class"))
expect_that(wsvm(y = irisint$Species, x = irisint[,-5], subset = 1:50, type = "C-classification"), throws_error("need training data from at least two classes"))
## y integer & type = nu-classification
expect_that(wsvm(Species ~ ., data = irisint, subset = 1:50, type = "nu-classification"), throws_error("need training data from at least two classes"))
#expect_that(wsvm(Species ~ ., data = irisint, subset = 1, type = "nu-classification"), throws_error("training data from only one class"))
expect_that(wsvm(y = irisint$Species, x = irisint[,-5], subset = 1:50, type = "C-classification"), throws_error("need training data from at least two classes"))
})
test_that("wsvm.default and wsvm.formula yield the same results", {
data(iris)
x <- iris[,-5]
y <- iris$Species
# neither subset nor case.weights
fit1 <- wsvm(formula = Species ~ ., data = iris)
fit2 <- wsvm(x = x, y = y)
expect_equal(fit1[-c(1,32)], fit2[-1])
# subset
fit1 <- wsvm(Species ~ ., data = iris, subset = seq(1,150,3))
fit2 <- wsvm(x = x, y = y, subset = seq(1,150,3))
expect_equal(fit1[-c(1,32)], fit2[-1])
# case.weights
fit1 <- wsvm(formula = Species ~ ., data = iris, case.weights = 1:150)
fit2 <- wsvm(x = x, y = y, case.weights = 1:150)
expect_equal(fit1[-c(1,32)], fit2[-1])
# subset & case.weights
fit1 <- wsvm(Species ~ ., data = iris, subset = seq(1,150,3), case.weights = 1:150)
fit2 <- wsvm(x = x, y = y, subset = seq(1,150,3), case.weights = 1:150)
expect_equal(fit1[-c(1,32)], fit2[-1])
})
test_that("wsvm: weighting works correctly", {
data(iris)
## check if weighted solution with all weights = 1 equals unweighted solution
fit1 <- wsvm(Species ~ ., data = iris)
fit2 <- wsvm(Species ~ ., data = iris, weights = rep(1,150))
expect_equal(fit1[-1],fit2[-1])
## returned weights
a <- rep(1,150)
names(a) <- 1:150
expect_equal(fit1$case.weights, a)
expect_equal(fit2$case.weights, a)
## weights and subsetting
# formula, data
fit <- wsvm(Species ~ ., data = iris, subset = 11:60)
a <- rep(1,50)
names(a) <- 11:60
expect_equal(fit$case.weights, a)
# formula, data, weights
fit <- wsvm(Species ~ ., data = iris, case.weights = rep(1:3, 50), subset = 11:60)
b <- rep(1:3,50)[11:60]
names(b) <- 11:60
expect_equal(fit$case.weights, b)
# x, grouping
fit <- wsvm(x = iris[,-5], y = iris$Species, subset = 11:60)
expect_equal(fit$case.weights, a)
# x, grouping, weights
fit <- wsvm(x = iris[,-5], y = iris$Species, case.weights = rep(1:3, 50), subset = 11:60)
expect_equal(fit$case.weights, b)
## wrong specification of weights argument
# weights in a matrix
weight <- matrix(seq(1:150),nrow=50)
expect_error(wsvm(Species ~ ., data = iris, case.weights = weight))
# weights < 0
expect_error(wsvm(Species ~ ., data = iris, case.weights = rep(-5, 150)))
# weights true/false
expect_error(wsvm(Species ~ ., data = iris, case.weights = TRUE))
})
test_that("wsvm: subsetting works", {
data(iris)
# formula, data
fit1 <- wsvm(Species ~ ., data = iris, subset = 1:80)
fit2 <- wsvm(Species ~ ., data = iris[1:80,])
expect_equal(fit1[-1], fit2[-1])
# formula, data, weights
fit1 <- wsvm(Species ~ ., data = iris, case.weights = rep(1:3, each = 50), subset = 1:80)
fit2 <- wsvm(Species ~ ., data = iris[1:80,], case.weights = rep(1:3, each = 50)[1:80])
expect_equal(fit1[-1],fit2[-1])
expect_equal(length(fit1$case.weights), 80)
# x, grouping
fit1 <- wsvm(y = iris$Species, x = iris[,-5], subset = 1:80)
fit2 <- wsvm(y = iris$Species[1:80], x = iris[1:80,-5])
expect_equal(fit1[-1], fit2[-1])
# x, grouping, weights
fit1 <- wsvm(y = iris$Species, x = iris[,-5], case.weights = rep(1:3, each = 50), subset = 1:80)
fit2 <- wsvm(y = iris$Species[1:80], x = iris[1:80,-5], case.weights = rep(1:3, each = 50)[1:80])
expect_equal(fit1[-1], fit2[-1])
expect_equal(length(fit1$case.weights), 80)
# wrong specification of subset argument
expect_error(wsvm(Species ~ ., data = iris, subset = iris[1:10,]))
expect_error(wsvm(Species ~ ., data = iris, subset = FALSE))
expect_error(wsvm(Species ~ ., data = iris, subset = 0))
expect_error(wsvm(Species ~ ., data = iris, subset = -10:50))
})
test_that("wsvm: NA handling works correctly", {
### NA in x
data(iris)
irisna <- iris
irisna[1:10, c(1,3)] <- NA
## formula, data
# na.fail
expect_error(wsvm(Species ~ ., data = irisna, subset = 6:60, na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(Species ~ ., data = irisna, subset = 6:60, na.action = na.omit)
fit2 <- wsvm(Species ~ ., data = irisna, subset = 11:60)
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
a <- rep(1, 50)
names(a) <- 11:60
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], a)
expect_equal(fit2$case.weights, a)
## formula, data, weights
# na.fail
expect_error(wsvm(Species ~ ., data = irisna, subset = 6:60, case.weights = rep(1:3, 50), na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(Species ~ ., data = irisna, subset = 6:60, case.weights = rep(1:3, 50), na.action = na.omit)
fit2 <- wsvm(Species ~ ., data = irisna, subset = 11:60, case.weights = rep(1:3, 50))
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
b <- rep(1:3,50)[11:60]
names(b) <- 11:60
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], b)
expect_equal(fit2$case.weights, b)
## x, grouping
# na.fail
expect_error(wsvm(y = irisna$Species, x = irisna[,-5], subset = 6:60, na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(y = irisna$Species, x = irisna[,-5], subset = 6:60, na.action = na.omit)
fit2 <- wsvm(y = irisna$Species, x = irisna[,-5], subset = 11:60)
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], a)
expect_equal(fit2$case.weights, a)
## x, grouping, weights
# na.fail
expect_error(wsvm(y = irisna$Species, x = irisna[,-5], subset = 6:60, case.weights = rep(1:3, 50), na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(y = irisna$Species, x = irisna[,-5], subset = 6:60, case.weights = rep(1:3, 50), na.action = na.omit)
fit2 <- wsvm(y = irisna$Species, x = irisna[,-5], subset = 11:60, case.weights = rep(1:3, 50))
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], b)
expect_equal(fit2$case.weights, b)
### NA in grouping
irisna <- iris
irisna$Species[1:10] <- NA
## formula, data
# na.fail
expect_error(wsvm(Species ~ ., data = irisna, subset = 6:60, na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(Species ~ ., data = irisna, subset = 6:60, na.action = na.omit)
fit2 <- wsvm(Species ~ ., data = irisna, subset = 11:60)
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], a)
expect_equal(fit2$case.weights, a)
## formula, data, weights
# na.fail
expect_error(wsvm(Species ~ ., data = irisna, subset = 6:60, case.weights = rep(1:3, 50), na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(Species ~ ., data = irisna, subset = 6:60, case.weights = rep(1:3, 50), na.action = na.omit)
fit2 <- wsvm(Species ~ ., data = irisna, subset = 11:60, case.weights = rep(1:3, 50))
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], b)
expect_equal(fit2$case.weights, b)
## x, grouping
# na.fail
expect_error(wsvm(y = irisna$Species, x = irisna[,-5], subset = 6:60, na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(y = irisna$Species, x = irisna[,-5], subset = 6:60, na.action = na.omit)
fit2 <- wsvm(y = irisna$Species, x = irisna[,-5], subset = 11:60)
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], a)
expect_equal(fit2$case.weights, a)
## x, grouping, weights
# na.fail
expect_error(wsvm(y = irisna$Species, x = irisna[,-5], subset = 6:60, case.weights = rep(1:3, 50), na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(y = irisna$Species, x = irisna[,-5], subset = 6:60, case.weights = rep(1:3, 50), na.action = na.omit)
fit2 <- wsvm(y = irisna$Species, x = irisna[,-5], subset = 11:60, case.weights = rep(1:3, 50))
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], b)
expect_equal(fit2$case.weights, b)
### NA in weights
weights <- rep(1:3,50)
weights[1:10] <- NA
## formula, data, weights
# na.fail
expect_error(wsvm(Species ~ ., data = iris, subset = 6:60, case.weights = weights, na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(Species ~ ., data = iris, subset = 6:60, case.weights = weights, na.action = na.omit)
fit2 <- wsvm(Species ~ ., data = iris, subset = 11:60, case.weights = weights)
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], b)
expect_equal(fit2$case.weights, b)
## x, grouping, weights
# na.fail
expect_error(wsvm(y = iris$Species, x = iris[,-5], subset = 6:60, case.weights = weights, na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(y = iris$Species, x = iris[,-5], subset = 6:60, case.weights = weights, na.action = na.omit)
fit2 <- wsvm(y = iris$Species, x = iris[,-5], subset = 11:60, case.weights = weights)
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], b)
expect_equal(fit2$case.weights, b)
### NA in subset
subset <- 6:60
subset[1:5] <- NA
## formula, data
# na.fail
expect_error(wsvm(Species ~ ., data = iris, subset = subset, na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(Species ~ ., data = iris, subset = subset, na.action = na.omit)
fit2 <- wsvm(Species ~ ., data = iris, subset = 11:60)
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], a)
expect_equal(fit2$case.weights, a)
## formula, data, weights
# na.fail
expect_error(wsvm(Species ~ ., data = iris, subset = subset, case.weights = rep(1:3, 50), na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(Species ~ ., data = iris, subset = subset, case.weights = rep(1:3, 50), na.action = na.omit)
fit2 <- wsvm(Species ~ ., data = iris, subset = 11:60, case.weights = rep(1:3, 50))
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], b)
expect_equal(fit2$case.weights, b)
## x, grouping
# na.fail
expect_error(wsvm(y = iris$Species, x = iris[,-5], subset = subset, na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(y = iris$Species, x = iris[,-5], subset = subset, na.action = na.omit)
fit2 <- wsvm(y = iris$Species, x = iris[,-5], subset = 11:60)
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], a)
expect_equal(fit2$case.weights, a)
## x, grouping, weights
# na.fail
expect_error(wsvm(y = iris$Species, x = iris[,-5], subset = subset, case.weights = rep(1:3, 50), na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(y = iris$Species, x = iris[,-5], subset = subset, case.weights = rep(1:3, 50), na.action = na.omit)
fit2 <- wsvm(y = iris$Species, x = iris[,-5], subset = 11:60, case.weights = rep(1:3, 50))
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], b)
expect_equal(fit2$case.weights, b)
})
#=================================================================================================================
context("predict.wsvm")
test_that("predict.wsvm works correctly with formula and data.frame interface and with missing newdata", {
data(iris)
ran <- sample(1:150,100)
## formula, data
fit <- wsvm(formula = Species ~ ., data = iris, subset = ran, probability = TRUE)
pred <- predict(fit, probability = TRUE, decision.values = TRUE) # keine probs/decision
expect_equal(names(pred), rownames(iris)[ran])
## formula, data, newdata
fit <- wsvm(formula = Species ~ ., data = iris, subset = ran, probability = TRUE)
predict(fit, newdata = iris[-ran,], probability = TRUE, decision.values = TRUE)
## grouping, x
fit <- wsvm(x = iris[,-5], y = iris$Species, subset = ran, probability = TRUE)
pred <- predict(fit, probability = TRUE, decision.values = TRUE) ## keine probs/decision
expect_equal(names(pred), rownames(iris)[ran])
## grouping, x, newdata
fit <- wsvm(x = iris[,-5], y = iris$Species, subset = ran, probability = TRUE)
predict(fit, newdata = iris[-ran,-5], probability = TRUE, decision.values = TRUE)
})
test_that("predict.wsvm: retrieving training data works", {
data(iris)
## no subset
# formula, data
fit <- wsvm(formula = Species ~ ., data = iris)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris)
expect_equal(pred1, pred2)
# y, x
fit <- wsvm(x = iris[,-5], y = iris$Species)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris[,-5])
expect_equal(pred1, pred2)
## subset
ran <- sample(1:150,100)
# formula, data
fit <- wsvm(formula = Species ~ ., data = iris, subset = ran)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris[ran,])
expect_equal(pred1, pred2)
# y, x
fit <- wsvm(x = iris[ran,-5], y = iris$Species[ran])
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris[ran,-5])
expect_equal(pred1, pred2)
})
test_that("predict.wsvm works with missing classes in the training data", {
data(iris)
ran <- sample(1:150,100)
fit <- wsvm(Species ~ ., data = iris, subset = 1:100) ## no warning
pred <- predict(fit, newdata = iris[-ran,])
expect_equal(nlevels(pred), 3)
fit <- wsvm(Species ~ ., data = iris, subset = 1:100, probability = TRUE) ## no warning
pred <- predict(fit, newdata = iris[-ran,], probability = TRUE)
expect_equal(nlevels(pred), 3)
posterior <- attr(pred, "probabilities")
expect_equal(ncol(posterior), 2)
# a <- rep(0,50)
# names(a) <- rownames(posterior)
# expect_equal(posterior[,3], a)
})
test_that("predict.wsvm works with one single predictor variable", {
data(iris)
ran <- sample(1:150,100)
fit <- wsvm(Species ~ Petal.Width, data = iris, subset = ran)
#expect_equal(ncol(fit$means), 1)
#expect_equal(dim(fit$cov), rep(1, 2))
predict(fit, newdata = iris[-ran,])
})
test_that("predict.wsvm works with one single test observation", {
data(iris)
ran <- sample(1:150,100)
fit <- wsvm(Species ~ Petal.Width, data = iris, subset = ran, probability = TRUE)
pred <- predict(fit, newdata = iris[5,], probability = TRUE)
posterior <- attr(pred, "probabilities")
expect_equal(length(pred), 1)
expect_equal(dim(posterior), c(1, 3))
a <- factor("setosa", levels = c("setosa", "versicolor", "virginica"))
names(a) <- 5
attr(a, "probabilities") <- posterior
expect_equal(pred, a) ##???
pred <- predict(fit, newdata = iris[58,], probability = TRUE)
posterior <- attr(pred, "probabilities")
expect_equal(length(pred), 1)
expect_equal(dim(posterior), c(1, 3))
a <- factor("versicolor", levels = c("setosa", "versicolor", "virginica"))
names(a) <- 58
attr(a, "probabilities") <- posterior
expect_equal(pred, a)
})
test_that("predict.wsvm works with one single predictor variable and one single test observation", {
data(iris)
ran <- sample(1:150,100)
fit <- wsvm(Species ~ Petal.Width, data = iris, subset = ran, probability = TRUE)
expect_equal(ncol(fit$SV), 1)
pred <- predict(fit, newdata = iris[5,], probability = TRUE)
expect_equal(length(pred), 1)
posterior <- attr(pred, "probabilities")
expect_equal(dim(posterior), c(1, 3))
})
test_that("predict.wsvm: NA handling in newdata works", {
data(iris)
ran <- sample(1:150,100)
irisna <- iris
irisna[1:17,c(1,3)] <- NA
fit <- wsvm(Species ~ ., data = iris, subset = ran)
pred <- predict(fit, newdata = irisna) ## default: na.omit
expect_equal(length(pred), 133)
expect_error(pred <- predict(fit, newdata = irisna, na.action = na.fail))
})
test_that("predict.wsvm: misspecified arguments", {
data(iris)
ran <- sample(1:150,100)
fit <- wsvm(Species ~ Petal.Width, data = iris, subset = ran)
# errors in newdata
expect_error(predict(fit, newdata = TRUE))
expect_error(predict(fit, newdata = -50:50))
# errors in prior
})
schiffner/locClass documentation built on May 29, 2019, 3:39 p.m.
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context("wsvm")
test_that("wsvm: misspecified arguments", {
data(iris)
# wrong variable names
expect_error(wsvm(formula = Species ~ V1, data = iris))
# wrong class
expect_error(wsvm(formula = iris, data = iris))
# target variable also in x
expect_error(wsvm(y = iris$Species, x = iris)) ## funktioniert, sollte aber nicht
# missing x
expect_error(wsvm(y = iris$Species))
})
test_that("wsvm works if only one predictor variable is given", {
data(iris)
fit <- wsvm(Species ~ Petal.Width, data = iris, subset = 6:60)
expect_equal(ncol(fit$SV), 1)
})
test_that("wsvm: training data from only one class", {
data(iris)
## y factor
expect_that(wsvm(Species ~ ., data = iris, subset = 1:50), throws_error("need training data from at least two classes"))
#expect_that(wsvm(Species ~ ., data = iris, subset = 1), throws_error("training data from only one class"))
expect_that(wsvm(y = iris$Species, x = iris[,-5], subset = 1:50), throws_error("need training data from at least two classes"))
## y integer & type = C-classification
irisint <- iris
irisint$Species <- as.numeric(irisint$Species)
expect_that(wsvm(Species ~ ., data = irisint, subset = 1:50, type = "C-classification"), throws_error("need training data from at least two classes"))
#expect_that(wsvm(Species ~ ., data = irisint, subset = 1, type = "C-classification"), throws_error("training data from only one class"))
expect_that(wsvm(y = irisint$Species, x = irisint[,-5], subset = 1:50, type = "C-classification"), throws_error("need training data from at least two classes"))
## y integer & type = nu-classification
expect_that(wsvm(Species ~ ., data = irisint, subset = 1:50, type = "nu-classification"), throws_error("need training data from at least two classes"))
#expect_that(wsvm(Species ~ ., data = irisint, subset = 1, type = "nu-classification"), throws_error("training data from only one class"))
expect_that(wsvm(y = irisint$Species, x = irisint[,-5], subset = 1:50, type = "C-classification"), throws_error("need training data from at least two classes"))
})
test_that("wsvm.default and wsvm.formula yield the same results", {
data(iris)
x <- iris[,-5]
y <- iris$Species
# neither subset nor case.weights
fit1 <- wsvm(formula = Species ~ ., data = iris)
fit2 <- wsvm(x = x, y = y)
expect_equal(fit1[-c(1,32)], fit2[-1])
# subset
fit1 <- wsvm(Species ~ ., data = iris, subset = seq(1,150,3))
fit2 <- wsvm(x = x, y = y, subset = seq(1,150,3))
expect_equal(fit1[-c(1,32)], fit2[-1])
# case.weights
fit1 <- wsvm(formula = Species ~ ., data = iris, case.weights = 1:150)
fit2 <- wsvm(x = x, y = y, case.weights = 1:150)
expect_equal(fit1[-c(1,32)], fit2[-1])
# subset & case.weights
fit1 <- wsvm(Species ~ ., data = iris, subset = seq(1,150,3), case.weights = 1:150)
fit2 <- wsvm(x = x, y = y, subset = seq(1,150,3), case.weights = 1:150)
expect_equal(fit1[-c(1,32)], fit2[-1])
})
test_that("wsvm: weighting works correctly", {
data(iris)
## check if weighted solution with all weights = 1 equals unweighted solution
fit1 <- wsvm(Species ~ ., data = iris)
fit2 <- wsvm(Species ~ ., data = iris, weights = rep(1,150))
expect_equal(fit1[-1],fit2[-1])
## returned weights
a <- rep(1,150)
names(a) <- 1:150
expect_equal(fit1$case.weights, a)
expect_equal(fit2$case.weights, a)
## weights and subsetting
# formula, data
fit <- wsvm(Species ~ ., data = iris, subset = 11:60)
a <- rep(1,50)
names(a) <- 11:60
expect_equal(fit$case.weights, a)
# formula, data, weights
fit <- wsvm(Species ~ ., data = iris, case.weights = rep(1:3, 50), subset = 11:60)
b <- rep(1:3,50)[11:60]
names(b) <- 11:60
expect_equal(fit$case.weights, b)
# x, grouping
fit <- wsvm(x = iris[,-5], y = iris$Species, subset = 11:60)
expect_equal(fit$case.weights, a)
# x, grouping, weights
fit <- wsvm(x = iris[,-5], y = iris$Species, case.weights = rep(1:3, 50), subset = 11:60)
expect_equal(fit$case.weights, b)
## wrong specification of weights argument
# weights in a matrix
weight <- matrix(seq(1:150),nrow=50)
expect_error(wsvm(Species ~ ., data = iris, case.weights = weight))
# weights < 0
expect_error(wsvm(Species ~ ., data = iris, case.weights = rep(-5, 150)))
# weights true/false
expect_error(wsvm(Species ~ ., data = iris, case.weights = TRUE))
})
test_that("wsvm: subsetting works", {
data(iris)
# formula, data
fit1 <- wsvm(Species ~ ., data = iris, subset = 1:80)
fit2 <- wsvm(Species ~ ., data = iris[1:80,])
expect_equal(fit1[-1], fit2[-1])
# formula, data, weights
fit1 <- wsvm(Species ~ ., data = iris, case.weights = rep(1:3, each = 50), subset = 1:80)
fit2 <- wsvm(Species ~ ., data = iris[1:80,], case.weights = rep(1:3, each = 50)[1:80])
expect_equal(fit1[-1],fit2[-1])
expect_equal(length(fit1$case.weights), 80)
# x, grouping
fit1 <- wsvm(y = iris$Species, x = iris[,-5], subset = 1:80)
fit2 <- wsvm(y = iris$Species[1:80], x = iris[1:80,-5])
expect_equal(fit1[-1], fit2[-1])
# x, grouping, weights
fit1 <- wsvm(y = iris$Species, x = iris[,-5], case.weights = rep(1:3, each = 50), subset = 1:80)
fit2 <- wsvm(y = iris$Species[1:80], x = iris[1:80,-5], case.weights = rep(1:3, each = 50)[1:80])
expect_equal(fit1[-1], fit2[-1])
expect_equal(length(fit1$case.weights), 80)
# wrong specification of subset argument
expect_error(wsvm(Species ~ ., data = iris, subset = iris[1:10,]))
expect_error(wsvm(Species ~ ., data = iris, subset = FALSE))
expect_error(wsvm(Species ~ ., data = iris, subset = 0))
expect_error(wsvm(Species ~ ., data = iris, subset = -10:50))
})
test_that("wsvm: NA handling works correctly", {
### NA in x
data(iris)
irisna <- iris
irisna[1:10, c(1,3)] <- NA
## formula, data
# na.fail
expect_error(wsvm(Species ~ ., data = irisna, subset = 6:60, na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(Species ~ ., data = irisna, subset = 6:60, na.action = na.omit)
fit2 <- wsvm(Species ~ ., data = irisna, subset = 11:60)
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
a <- rep(1, 50)
names(a) <- 11:60
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], a)
expect_equal(fit2$case.weights, a)
## formula, data, weights
# na.fail
expect_error(wsvm(Species ~ ., data = irisna, subset = 6:60, case.weights = rep(1:3, 50), na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(Species ~ ., data = irisna, subset = 6:60, case.weights = rep(1:3, 50), na.action = na.omit)
fit2 <- wsvm(Species ~ ., data = irisna, subset = 11:60, case.weights = rep(1:3, 50))
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
b <- rep(1:3,50)[11:60]
names(b) <- 11:60
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], b)
expect_equal(fit2$case.weights, b)
## x, grouping
# na.fail
expect_error(wsvm(y = irisna$Species, x = irisna[,-5], subset = 6:60, na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(y = irisna$Species, x = irisna[,-5], subset = 6:60, na.action = na.omit)
fit2 <- wsvm(y = irisna$Species, x = irisna[,-5], subset = 11:60)
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], a)
expect_equal(fit2$case.weights, a)
## x, grouping, weights
# na.fail
expect_error(wsvm(y = irisna$Species, x = irisna[,-5], subset = 6:60, case.weights = rep(1:3, 50), na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(y = irisna$Species, x = irisna[,-5], subset = 6:60, case.weights = rep(1:3, 50), na.action = na.omit)
fit2 <- wsvm(y = irisna$Species, x = irisna[,-5], subset = 11:60, case.weights = rep(1:3, 50))
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], b)
expect_equal(fit2$case.weights, b)
### NA in grouping
irisna <- iris
irisna$Species[1:10] <- NA
## formula, data
# na.fail
expect_error(wsvm(Species ~ ., data = irisna, subset = 6:60, na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(Species ~ ., data = irisna, subset = 6:60, na.action = na.omit)
fit2 <- wsvm(Species ~ ., data = irisna, subset = 11:60)
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], a)
expect_equal(fit2$case.weights, a)
## formula, data, weights
# na.fail
expect_error(wsvm(Species ~ ., data = irisna, subset = 6:60, case.weights = rep(1:3, 50), na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(Species ~ ., data = irisna, subset = 6:60, case.weights = rep(1:3, 50), na.action = na.omit)
fit2 <- wsvm(Species ~ ., data = irisna, subset = 11:60, case.weights = rep(1:3, 50))
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], b)
expect_equal(fit2$case.weights, b)
## x, grouping
# na.fail
expect_error(wsvm(y = irisna$Species, x = irisna[,-5], subset = 6:60, na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(y = irisna$Species, x = irisna[,-5], subset = 6:60, na.action = na.omit)
fit2 <- wsvm(y = irisna$Species, x = irisna[,-5], subset = 11:60)
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], a)
expect_equal(fit2$case.weights, a)
## x, grouping, weights
# na.fail
expect_error(wsvm(y = irisna$Species, x = irisna[,-5], subset = 6:60, case.weights = rep(1:3, 50), na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(y = irisna$Species, x = irisna[,-5], subset = 6:60, case.weights = rep(1:3, 50), na.action = na.omit)
fit2 <- wsvm(y = irisna$Species, x = irisna[,-5], subset = 11:60, case.weights = rep(1:3, 50))
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], b)
expect_equal(fit2$case.weights, b)
### NA in weights
weights <- rep(1:3,50)
weights[1:10] <- NA
## formula, data, weights
# na.fail
expect_error(wsvm(Species ~ ., data = iris, subset = 6:60, case.weights = weights, na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(Species ~ ., data = iris, subset = 6:60, case.weights = weights, na.action = na.omit)
fit2 <- wsvm(Species ~ ., data = iris, subset = 11:60, case.weights = weights)
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], b)
expect_equal(fit2$case.weights, b)
## x, grouping, weights
# na.fail
expect_error(wsvm(y = iris$Species, x = iris[,-5], subset = 6:60, case.weights = weights, na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(y = iris$Species, x = iris[,-5], subset = 6:60, case.weights = weights, na.action = na.omit)
fit2 <- wsvm(y = iris$Species, x = iris[,-5], subset = 11:60, case.weights = weights)
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], b)
expect_equal(fit2$case.weights, b)
### NA in subset
subset <- 6:60
subset[1:5] <- NA
## formula, data
# na.fail
expect_error(wsvm(Species ~ ., data = iris, subset = subset, na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(Species ~ ., data = iris, subset = subset, na.action = na.omit)
fit2 <- wsvm(Species ~ ., data = iris, subset = 11:60)
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], a)
expect_equal(fit2$case.weights, a)
## formula, data, weights
# na.fail
expect_error(wsvm(Species ~ ., data = iris, subset = subset, case.weights = rep(1:3, 50), na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(Species ~ ., data = iris, subset = subset, case.weights = rep(1:3, 50), na.action = na.omit)
fit2 <- wsvm(Species ~ ., data = iris, subset = 11:60, case.weights = rep(1:3, 50))
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], b)
expect_equal(fit2$case.weights, b)
## x, grouping
# na.fail
expect_error(wsvm(y = iris$Species, x = iris[,-5], subset = subset, na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(y = iris$Species, x = iris[,-5], subset = subset, na.action = na.omit)
fit2 <- wsvm(y = iris$Species, x = iris[,-5], subset = 11:60)
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], a)
expect_equal(fit2$case.weights, a)
## x, grouping, weights
# na.fail
expect_error(wsvm(y = iris$Species, x = iris[,-5], subset = subset, case.weights = rep(1:3, 50), na.action = na.fail))
# check if na.omit works correctly
fit1 <- wsvm(y = iris$Species, x = iris[,-5], subset = subset, case.weights = rep(1:3, 50), na.action = na.omit)
fit2 <- wsvm(y = iris$Species, x = iris[,-5], subset = 11:60, case.weights = rep(1:3, 50))
expect_equal(fit1[-c(1,28,31)], fit2[-c(1,28,31)])
expect_equal(fit1$case.weights[1:length(fit1$case.weights)], b)
expect_equal(fit2$case.weights, b)
})
#=================================================================================================================
context("predict.wsvm")
test_that("predict.wsvm works correctly with formula and data.frame interface and with missing newdata", {
data(iris)
ran <- sample(1:150,100)
## formula, data
fit <- wsvm(formula = Species ~ ., data = iris, subset = ran, probability = TRUE)
pred <- predict(fit, probability = TRUE, decision.values = TRUE) # keine probs/decision
expect_equal(names(pred), rownames(iris)[ran])
## formula, data, newdata
fit <- wsvm(formula = Species ~ ., data = iris, subset = ran, probability = TRUE)
predict(fit, newdata = iris[-ran,], probability = TRUE, decision.values = TRUE)
## grouping, x
fit <- wsvm(x = iris[,-5], y = iris$Species, subset = ran, probability = TRUE)
pred <- predict(fit, probability = TRUE, decision.values = TRUE) ## keine probs/decision
expect_equal(names(pred), rownames(iris)[ran])
## grouping, x, newdata
fit <- wsvm(x = iris[,-5], y = iris$Species, subset = ran, probability = TRUE)
predict(fit, newdata = iris[-ran,-5], probability = TRUE, decision.values = TRUE)
})
test_that("predict.wsvm: retrieving training data works", {
data(iris)
## no subset
# formula, data
fit <- wsvm(formula = Species ~ ., data = iris)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris)
expect_equal(pred1, pred2)
# y, x
fit <- wsvm(x = iris[,-5], y = iris$Species)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris[,-5])
expect_equal(pred1, pred2)
## subset
ran <- sample(1:150,100)
# formula, data
fit <- wsvm(formula = Species ~ ., data = iris, subset = ran)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris[ran,])
expect_equal(pred1, pred2)
# y, x
fit <- wsvm(x = iris[ran,-5], y = iris$Species[ran])
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris[ran,-5])
expect_equal(pred1, pred2)
})
test_that("predict.wsvm works with missing classes in the training data", {
data(iris)
ran <- sample(1:150,100)
fit <- wsvm(Species ~ ., data = iris, subset = 1:100) ## no warning
pred <- predict(fit, newdata = iris[-ran,])
expect_equal(nlevels(pred), 3)
fit <- wsvm(Species ~ ., data = iris, subset = 1:100, probability = TRUE) ## no warning
pred <- predict(fit, newdata = iris[-ran,], probability = TRUE)
expect_equal(nlevels(pred), 3)
posterior <- attr(pred, "probabilities")
expect_equal(ncol(posterior), 2)
# a <- rep(0,50)
# names(a) <- rownames(posterior)
# expect_equal(posterior[,3], a)
})
test_that("predict.wsvm works with one single predictor variable", {
data(iris)
ran <- sample(1:150,100)
fit <- wsvm(Species ~ Petal.Width, data = iris, subset = ran)
#expect_equal(ncol(fit$means), 1)
#expect_equal(dim(fit$cov), rep(1, 2))
predict(fit, newdata = iris[-ran,])
})
test_that("predict.wsvm works with one single test observation", {
data(iris)
ran <- sample(1:150,100)
fit <- wsvm(Species ~ Petal.Width, data = iris, subset = ran, probability = TRUE)
pred <- predict(fit, newdata = iris[5,], probability = TRUE)
posterior <- attr(pred, "probabilities")
expect_equal(length(pred), 1)
expect_equal(dim(posterior), c(1, 3))
a <- factor("setosa", levels = c("setosa", "versicolor", "virginica"))
names(a) <- 5
attr(a, "probabilities") <- posterior
expect_equal(pred, a) ##???
pred <- predict(fit, newdata = iris[58,], probability = TRUE)
posterior <- attr(pred, "probabilities")
expect_equal(length(pred), 1)
expect_equal(dim(posterior), c(1, 3))
a <- factor("versicolor", levels = c("setosa", "versicolor", "virginica"))
names(a) <- 58
attr(a, "probabilities") <- posterior
expect_equal(pred, a)
})
test_that("predict.wsvm works with one single predictor variable and one single test observation", {
data(iris)
ran <- sample(1:150,100)
fit <- wsvm(Species ~ Petal.Width, data = iris, subset = ran, probability = TRUE)
expect_equal(ncol(fit$SV), 1)
pred <- predict(fit, newdata = iris[5,], probability = TRUE)
expect_equal(length(pred), 1)
posterior <- attr(pred, "probabilities")
expect_equal(dim(posterior), c(1, 3))
})
test_that("predict.wsvm: NA handling in newdata works", {
data(iris)
ran <- sample(1:150,100)
irisna <- iris
irisna[1:17,c(1,3)] <- NA
fit <- wsvm(Species ~ ., data = iris, subset = ran)
pred <- predict(fit, newdata = irisna) ## default: na.omit
expect_equal(length(pred), 133)
expect_error(pred <- predict(fit, newdata = irisna, na.action = na.fail))
})
test_that("predict.wsvm: misspecified arguments", {
data(iris)
ran <- sample(1:150,100)
fit <- wsvm(Species ~ Petal.Width, data = iris, subset = ran)
# errors in newdata
expect_error(predict(fit, newdata = TRUE))
expect_error(predict(fit, newdata = -50:50))
# errors in prior
})
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