tests/testthat/test_ossvm.R
In schiffner/locClass: Collection of Local Classification Methods
context("ossvm")
test_that("ossvm: misspecified arguments", {
data(iris)
# wrong variable names
expect_error(ossvm(formula = Species ~ V1, data = iris, wf = "gaussian", bw = 10))
# wrong class
expect_error(ossvm(formula = iris, data = iris, wf = "gaussian", bw = 10))
expect_error(ossvm(iris, data = iris, wf = "gaussian", bw = 10))
# target variable also in x
expect_error(ossvm(y = iris$Species, x = iris, wf = "gaussian", bw = 10))
expect_warning(ossvm(Species ~ Species + Petal.Width, data = iris, wf = "gaussian", bw = 10)) ## warning, Species on RHS removed
# missing x
expect_error(ossvm(y = iris$Species, wf = "gaussian", bw = 10))
})
# test_that("ossvm throws a warning if y variable is numeric", {
# data(iris)
# formula, data
# expect_that(ossvm(formula = as.numeric(Species) ~ ., data = iris, wf = "gaussian", bw = 10), gives_warning("'y' was coerced to a factor"))
# y, x
# expect_that(ossvm(y = iris[,1], x = iris[,-1], wf = "gaussian", bw = 10), gives_warning("'y' was coerced to a factor"))
# })
test_that("ossvm works if only one predictor variable is given", {
data(iris)
fit <- ossvm(Species ~ Petal.Width, data = iris, wf = "gaussian", bw = 5)
predict(fit)
})
test_that("ossvm: training data from only one class", {
data(iris)
expect_that(ossvm(Species ~ ., data = iris, bw = 2, subset = 1:50), throws_error("training data from only one class"))
expect_error(ossvm(Species ~ ., data = iris, bw = 2, subset = 1))
expect_that(ossvm(y = iris$Species, x = iris[,-5], bw = 2, subset = 1:50), throws_error("training data from only one class"))
expect_error(ossvm(y = iris$Species, x = iris[,-5], bw = 2, subset = 1))
})
test_that("ossvm: subsetting works", {
data(iris)
# formula, data
expect_that(fit1 <- ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = 1:80), gives_warning("group virginica is empty"))
expect_that(fit2 <- ossvm(Species ~ ., data = iris[1:80,], wf = "gaussian", bw = 2), gives_warning("group virginica is empty"))
expect_equal(fit1[-1],fit2[-1])
expect_equal(nrow(fit1$x), 80)
expect_equal(length(fit1$y), 80)
# x, y
expect_that(fit1 <- ossvm(y = iris$Species, x = iris[,-5], wf = "gaussian", bw = 2, subset = 1:80), gives_warning("group virginica is empty"))
expect_that(fit2 <- ossvm(y = iris$Species[1:80], x = iris[1:80,-5], wf = "gaussian", bw = 2), gives_warning("group virginica is empty"))
expect_equal(fit1[-1],fit2[-1])
expect_equal(nrow(fit1$x), 80)
expect_equal(length(fit1$y), 80)
# wrong specification of subset argument
expect_error(ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = iris[1:10,]))
expect_error(ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = FALSE)) #???
expect_error(ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = 0)) #???
expect_error(ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = -10:50))
})
test_that("ossvm: NA handling works correctly", {
### NA in x
data(iris)
irisna <- iris
irisna[1:10, c(1,3)] <- NA
## formula, data
# na.fail
expect_that(ossvm(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.fail), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- ossvm(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.omit), gives_warning("group virginica is empty"))
expect_that(fit2 <- ossvm(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 11:60), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(1:2,31)], fit2[-c(1:2,31)])
expect_equivalent(fit1[2], fit2[2])
## x, y
# na.fail
expect_that(ossvm(y = irisna$Species, x = irisna[,-5], wf = "gaussian", bw = 10, subset = 6:60, na.action = na.fail), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- ossvm(y = irisna$Species, x = irisna[,-5], wf = "gaussian", bw = 10, subset = 6:60, na.action = na.omit), gives_warning("group virginica is empty"))##
expect_that(fit2 <- ossvm(y = irisna$Species, x = irisna[,-5], wf = "gaussian", bw = 10, subset = 11:60), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(1:2,31)], fit2[-c(1:2,31)])
expect_equivalent(fit1[2], fit2[2])
### NA in y
irisna <- iris
irisna$Species[1:10] <- NA
## formula, data
# na.fail
expect_that(ossvm(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.fail), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- ossvm(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.omit), gives_warning("group virginica is empty"))
expect_that(fit2 <- ossvm(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 11:60), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(1:2,31)], fit2[-c(1:2,31)])
expect_equivalent(fit1[2], fit2[2])
## x, y
# na.fail
expect_that(ossvm(y = irisna$Species, x = irisna[,-5], wf = "gaussian", bw = 10, subset = 6:60, na.action = na.fail), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- ossvm(y = irisna$Species, x = irisna[,-5], wf = "gaussian", bw = 10, subset = 6:60, na.action = na.omit), gives_warning("group virginica is empty"))
expect_that(fit2 <- ossvm(y = irisna$Species, x = irisna[,-5], wf = "gaussian", bw = 10, subset = 11:60), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(1:2,31)], fit2[-c(1:2,31)])
expect_equivalent(fit1[2], fit2[2])
### NA in subset
subset <- 6:60
subset[1:5] <- NA
## formula, data
# na.fail
expect_that(ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = subset, na.action = na.fail), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = subset, na.action = na.omit), gives_warning("group virginica is empty"))
expect_that(fit2 <- ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = 11:60), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(1:2,31)], fit2[-c(1:2,31)])
expect_equivalent(fit1[2], fit2[2])
## x, y
# na.fail
expect_that(ossvm(y = iris$Species, x = iris[,-5], wf = "gaussian", bw = 10, subset = subset, na.action = na.fail), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- ossvm(y = iris$Species, x = iris[,-5], wf = "gaussian", bw = 10, subset = subset, na.action = na.omit), gives_warning("group virginica is empty"))
expect_that(fit2 <- ossvm(y = iris$Species, x = iris[,-5], wf = "gaussian", bw = 10, subset = 11:60), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(1:2,31)], fit2[-c(1:2,31)])
expect_equivalent(fit1[2], fit2[2])
})
test_that("ossvm: try all weight functions", {
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2, probability = TRUE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = gaussian(2), probability = TRUE)
fit3 <- ossvm(x = iris[,-5], y = iris$Species, wf = "gaussian", bw = 2, probability = TRUE)
fit4 <- ossvm(x = iris[,-5], y = iris$Species, wf = gaussian(2), probability = TRUE)
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit3[-c(1,4)], fit4[-c(1,4)])
expect_equal(fit2[-c(1,2,32)], fit4[-c(1,2)])
expect_equivalent(fit2[2], fit4[2])
set.seed(120)
pred1 <- predict(fit1, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred2 <- predict(fit2, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred3 <- predict(fit3, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred4 <- predict(fit4, probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", bw = 5, k = 30, probability = TRUE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = gaussian(bw = 5, k = 30), probability = TRUE)
fit3 <- ossvm(x = iris[,-5], y = iris$Species, wf = "gaussian", bw = 5, k = 30, probability = TRUE)
fit4 <- ossvm(x = iris[,-5], y = iris$Species, wf = gaussian(bw = 5, k = 30), probability = TRUE)
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit3[-c(1,4)], fit4[-c(1,4)])
expect_equal(fit2[-c(1,2,32)], fit4[-c(1,2)])
expect_equivalent(fit2[2], fit4[2])
set.seed(120)
pred1 <- predict(fit1, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred2 <- predict(fit2, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred3 <- predict(fit3, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred4 <- predict(fit4, probability = TRUE, decision.values = TRUE)
# pred1 <- predict(fit1, newdata = iris[sample(1:150),], probability = TRUE, decision.values = TRUE)
# pred2 <- predict(fit2, newdata = iris[1:10,], probability = TRUE, decision.values = TRUE)
# pred3 <- predict(fit3, newdata = iris[1,-5], probability = TRUE, decision.values = TRUE)
# pred4 <- predict(fit4, newdata = iris[1,-5], probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "epanechnikov", bw = 5, k = 30, probability = TRUE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = epanechnikov(bw = 5, k = 30), probability = TRUE)
fit3 <- ossvm(x = iris[,-5], y = iris$Species, wf = "epanechnikov", bw = 5, k = 30, probability = TRUE)
fit4 <- ossvm(x = iris[,-5], y = iris$Species, wf = epanechnikov(5, 30), probability = TRUE)
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit3[-c(1,4)], fit4[-c(1,4)])
expect_equal(fit2[-c(1,2,32)], fit4[-c(1,2)])
expect_equivalent(fit2[2], fit4[2])
set.seed(120)
pred1 <- predict(fit1, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred2 <- predict(fit2, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred3 <- predict(fit3, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred4 <- predict(fit4, probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, k = 30, probability = TRUE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = rectangular(bw = 5, k = 30), probability = TRUE)
fit3 <- ossvm(x = iris[,-5], y = iris$Species, wf = "rectangular", bw = 5, k = 30, probability = TRUE)
fit4 <- ossvm(x = iris[,-5], y = iris$Species, wf = rectangular(5, 30), probability = TRUE)
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit3[-c(1,4)], fit4[-c(1,4)])
expect_equal(fit2[-c(1,2,32)], fit4[-c(1,2)])
expect_equivalent(fit2[2], fit4[2])
set.seed(120)
pred1 <- predict(fit1, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred2 <- predict(fit2, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred3 <- predict(fit3, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred4 <- predict(fit4, probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "triangular", bw = 5, k = 30, probability = TRUE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = triangular(5, k = 30), probability = TRUE)
fit3 <- ossvm(x = iris[,-5], y = iris$Species, wf = "triangular", bw = 5, k = 30, probability = TRUE)
fit4 <- ossvm(x = iris[,-5], y = iris$Species, wf = triangular(5, 30), probability = TRUE)
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit3[-c(1,4)], fit4[-c(1,4)])
expect_equal(fit2[-c(1,2,32)], fit4[-c(1,2)])
expect_equivalent(fit2[2], fit4[2])
set.seed(120)
pred1 <- predict(fit1, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred2 <- predict(fit2, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred3 <- predict(fit3, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred4 <- predict(fit4, probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "biweight", bw = 5, probability = TRUE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = biweight(5), probability = TRUE)
fit3 <- ossvm(x = iris[,-5], y = iris$Species, wf = "biweight", bw = 5, probability = TRUE)
fit4 <- ossvm(x = iris[,-5], y = iris$Species, wf = biweight(5), probability = TRUE)
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit3[-c(1,4)], fit4[-c(1,4)])
expect_equal(fit2[-c(1,2,32)], fit4[-c(1,2)])
expect_equivalent(fit2[2], fit4[2])
set.seed(120)
pred1 <- predict(fit1, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred2 <- predict(fit2, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred3 <- predict(fit3, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred4 <- predict(fit4, probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "optcosine", bw = 5, k = 30, probability = TRUE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = optcosine(5, k = 30), probability = TRUE)
fit3 <- ossvm(x = iris[,-5], y = iris$Species, wf = "optcosine", bw = 5, k = 30, probability = TRUE)
fit4 <- ossvm(x = iris[,-5], y = iris$Species, wf = optcosine(5, 30), probability = TRUE)
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit3[-c(1,4)], fit4[-c(1,4)])
expect_equal(fit2[-c(1,2,32)], fit4[-c(1,2)])
expect_equivalent(fit2[2], fit4[2])
set.seed(120)
pred1 <- predict(fit1, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred2 <- predict(fit2, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred3 <- predict(fit3, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred4 <- predict(fit4, probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "cosine", bw = 5, k = 30, probability = TRUE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = cosine(5, k = 30), probability = TRUE)
fit3 <- ossvm(x = iris[,-5], y = iris$Species, wf = "cosine", bw = 5, k = 30, probability = TRUE)
fit4 <- ossvm(x = iris[,-5], y = iris$Species, wf = cosine(5, 30), probability = TRUE)
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit3[-c(1,4)], fit4[-c(1,4)])
expect_equal(fit2[-c(1,2,32)], fit4[-c(1,2)])
expect_equivalent(fit2[2], fit4[2])
set.seed(120)
pred1 <- predict(fit1, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred2 <- predict(fit2, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred3 <- predict(fit3, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred4 <- predict(fit4, probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
})
test_that("ossvm: local solution with rectangular window function and large bw and global solution coincide", {
data(iris)
library(e1071)
## newdata missing
fit1 <- wsvm(formula = Species ~ ., data = iris)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = rectangular(20))
fit3 <- svm(Species ~ ., data = iris)
pred1 <- predict(fit1)
pred2 <- predict(fit2)
pred3 <- predict(fit3)
expect_equal(pred1, pred2)
expect_equal(pred1, pred3)
## newdata given
fit1 <- wsvm(formula = Species ~ ., data = iris, probability = TRUE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = rectangular(8), probability = TRUE)
fit3 <- svm(Species ~ ., data = iris, probability = TRUE)
pred1 <- predict(fit1, newdata = iris, probability = TRUE, decision.values = TRUE)
pred2 <- predict(fit2, newdata = iris, probability = TRUE, decision.values = TRUE)
pred3 <- predict(fit3, newdata = iris, probability = TRUE, decision.values = TRUE)
# pred1 <- predict(fit1, newdata = iris[1,], probability = TRUE, decision.values = TRUE)
# pred2 <- predict(fit2, newdata = iris[1,], probability = TRUE, decision.values = TRUE)
# pred3 <- predict(fit3, newdata = iris[1,], probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2)
expect_equal(pred1, pred3)
expect_equal(pred2, pred3)
})
test_that("ossvm: labels vector set correctly",{
## all classes, correct order
data(iris)
iris[,1:4] <- scale(iris[,1:4])
k <- 100
n <- 90
x <- as.matrix(iris[,-5])
dist <- sqrt(colSums((t(x) - x[n,])^2))
w <- rectangular(k = k)(dist)
fit1 <- wsvm(Species ~ ., data = iris, case.weights = w/sum(w) * 150, probability = TRUE, scale = FALSE)
set.seed(120)
pred1 <- predict(fit1, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
fit2 <- ossvm(Species ~ ., data = iris, probability = TRUE, wf = "rectangular", k = k, scale = FALSE)
set.seed(120)
pred2 <- predict(fit2, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2) # not exactly equal
## all classes, but different order 1
data(iris)
iris[,1:4] <- scale(iris[,1:4])
perm <- 150:1 ## 3, 2, 1
iris <- iris[perm,]
k = 100
n <- 90
x <- as.matrix(iris[,-5])
dist <- sqrt(colSums((t(x) - x[n,])^2))
w <- rectangular(k = k)(dist)
fit1 <- wsvm(Species ~ ., data = iris, case.weights = w/sum(w) * 150, probability = TRUE, scale = FALSE)
set.seed(120)
pred1 <- predict(fit1, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
fit2 <- ossvm(Species ~ ., data = iris, probability = TRUE, wf = "rectangular", k = k, scale = FALSE)
set.seed(120)
pred2 <- predict(fit2, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
expect_equivalent(pred1, pred2)
expect_equal(attr(pred1, "probabilities"), attr(pred2, "probabilities")[,3:1, drop = FALSE]) # not exactly equal
expect_equal(as.vector(attr(pred1, "decision.values")), -as.vector(attr(pred2, "decision.values")[,3:1]))
## all classes, but different order 2
data(iris)
iris[,1:4] <- scale(iris[,1:4])
perm <- c(150:101,1:100) ## 3, 1, 2
iris <- iris[perm,]
k = 100
n <- 90
x <- as.matrix(iris[,-5])
dist <- sqrt(colSums((t(x) - x[n,])^2))
w <- rectangular(k = k)(dist)
fit1 <- wsvm(Species ~ ., data = iris, case.weights = w/sum(w) * 150, probability = TRUE, scale = FALSE)
set.seed(120)
pred1 <- predict(fit1, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
fit2 <- ossvm(Species ~ ., data = iris, probability = TRUE, wf = "rectangular", k = k, scale = FALSE)
set.seed(120)
pred2 <- predict(fit2, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
expect_equivalent(pred1, pred2)
expect_equal(attr(pred1, "probabilities"), attr(pred2, "probabilities")[,c(3,1,2), drop = FALSE])
expect_equivalent(c(1,-1,-1)*attr(pred1, "decision.values")[c(3,1,2)], as.vector(attr(pred2, "decision.values")))
## 2 classes, correct order
data(iris)
iris[,1:4] <- scale(iris[,1:4])
k <- 50
n <- 90
x <- as.matrix(iris[,-5])
dist <- sqrt(colSums((t(x) - x[n,])^2))
w <- rectangular(k = k)(dist)
fit1 <- wsvm(Species ~ ., data = iris, case.weights = w/sum(w) * 150, probability = TRUE, scale = FALSE)
set.seed(120)
pred1 <- predict(fit1, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
fit2 <- ossvm(Species ~ ., data = iris, probability = TRUE, wf = "rectangular", k = k, scale = FALSE)
set.seed(120)
pred2 <- predict(fit2, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
expect_equivalent(pred1, pred2)
expect_equal(attr(pred1, "probabilities"), attr(pred2, "probabilities")[,colnames(attr(pred1, "probabilities")), drop = FALSE])
expect_equal(as.numeric(attr(pred1, "decision.values")), attr(pred2, "decision.values")[,!is.na(attr(pred2, "decision.values"))])
## 2 classes, but different order 1
data(iris)
iris[,1:4] <- scale(iris[,1:4])
perm <- 150:1
iris <- iris[perm,]
k <- 40
n <- 99
x <- as.matrix(iris[,-5])
dist <- sqrt(colSums((t(x) - x[n,])^2))
w <- rectangular(k = k)(dist)
fit1 <- wsvm(Species ~ ., data = iris, case.weights = w/sum(w) * 150, probability = TRUE, scale = FALSE)
set.seed(120)
pred1 <- predict(fit1, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
fit2 <- ossvm(Species ~ ., data = iris, probability = TRUE, wf = "rectangular", k = k, scale = FALSE)
set.seed(120)
pred2 <- predict(fit2, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
expect_equivalent(pred1, pred2)
expect_equal(attr(pred1, "probabilities"), attr(pred2, "probabilities")[,colnames(attr(pred1, "probabilities")), drop = FALSE])
expect_equal(as.numeric(attr(pred1, "decision.values")), -attr(pred2, "decision.values")[!is.na(attr(pred2, "decision.values"))])
## 2 classes, but different order 2
data(iris)
iris[,1:4] <- scale(iris[,1:4])
perm <- c(150:101,1:100)
iris <- iris[perm,]
k <- 50
n <- 90
x <- as.matrix(iris[,-5])
dist <- sqrt(colSums((t(x) - x[n,])^2))
w <- rectangular(k = k)(dist)
fit1 <- wsvm(Species ~ ., data = iris, case.weights = w/sum(w) * 150, probability = TRUE, scale = FALSE)
set.seed(120)
pred1 <- predict(fit1, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
fit2 <- ossvm(Species ~ ., data = iris, probability = TRUE, wf = "rectangular", k = k, scale = FALSE)
set.seed(120)
pred2 <- predict(fit2, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
expect_equivalent(pred1, pred2)
expect_equal(attr(pred1, "probabilities"), attr(pred2, "probabilities")[,colnames(attr(pred1, "probabilities")), drop = FALSE])
expect_equal(as.numeric(attr(pred1, "decision.values")), attr(pred2, "decision.values")[!is.na(attr(pred2, "decision.values"))])
})
test_that("ossvm: arguments related to weighting misspecified", {
# bw, k not required
expect_that(fit1 <- ossvm(Species ~ ., data = iris, wf = gaussian(0.5), k = 30, bw = 0.5), gives_warning(c("argument 'k' is ignored", "argument 'bw' is ignored")))
fit2 <- ossvm(Species ~ ., data = iris, wf = gaussian(0.5))
expect_equal(fit1[-1], fit2[-1])
expect_that(fit1 <- ossvm(Species ~ ., data = iris, wf = gaussian(0.5), bw = 0.5), gives_warning("argument 'bw' is ignored"))
fit2 <- ossvm(Species ~ ., data = iris, wf = gaussian(0.5))
expect_equal(fit1[-1], fit2[-1])
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_equal(fit1$bw, 0.5)
expect_equal(fit1$adaptive, FALSE)
expect_that(fit1 <- ossvm(Species ~ ., data = iris, wf = function(x) exp(-x), bw = 0.5, k = 30), gives_warning(c("argument 'k' is ignored", "argument 'bw' is ignored")))
expect_that(fit2 <- ossvm(Species ~ ., data = iris, wf = function(x) exp(-x), k = 30), gives_warning("argument 'k' is ignored"))
expect_equal(fit1[-1], fit2[-1])
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_equal(fit1$bw, NULL)
expect_equal(fit1$adaptive, NULL)
expect_that(fit1 <- ossvm(Species ~ ., data = iris, wf = function(x) exp(-x), bw = 0.5), gives_warning("argument 'bw' is ignored"))
fit2 <- ossvm(Species ~ ., data = iris, wf = function(x) exp(-x))
expect_equal(fit1[-1], fit2[-1])
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_equal(fit1$bw, NULL)
expect_equal(fit1$adaptive, NULL)
# missing quotes
fit <- ossvm(formula = Species ~ ., data = iris, wf = gaussian) ## error because length(weights) and nrow(x) are different
expect_error(predict(fit))
# bw, k missing
expect_that(ossvm(formula = Species ~ ., data = iris, wf = gaussian()), throws_error("either 'bw' or 'k' have to be specified"))
expect_that(ossvm(formula = Species ~ ., data = iris, wf = gaussian(), k = 10), throws_error("either 'bw' or 'k' have to be specified"))
expect_that(ossvm(Species ~ ., data = iris), throws_error("either 'bw' or 'k' have to be specified"))
# bw < 0
expect_that(ossvm(formula = Species ~ ., data = iris, wf = "gaussian", bw = -5), throws_error("'bw' must be positive"))
expect_that(ossvm(formula = Species ~ ., data = iris, wf = "cosine", k = 10, bw = -50), throws_error("'bw' must be positive"))
# bw vector
expect_that(ossvm(formula = Species ~., data = iris, wf = "gaussian", bw = rep(1, nrow(iris))), gives_warning("only first element of 'bw' used"))
# k < 0
expect_that(ossvm(formula = Species ~ ., data = iris, wf = "gaussian", k =-7, bw = 50), throws_error("'k' must be positive"))
# k too small
#fit <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", k = 5, bw = 0.005)
#expect_equal(length(is.na(predict(fit)$class)), 150)
# k too large
expect_that(ossvm(formula = Species ~ ., data = iris, k = 250, wf = "gaussian", bw = 50), throws_error("'k' is larger than 'n'"))
# k vector
expect_that(ossvm(formula = Species ~., data = iris, wf = "gaussian", k = rep(50, nrow(iris))), gives_warning("only first element of 'k' used"))
})
test_that("ossvm: weighting schemes work", {
## wf with finite support
# fixed bw
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = rectangular(bw = 5))
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit1$bw, 5)
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_true(!fit1$adaptive)
# adaptive bw, only knn
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "rectangular", k = 50)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = rectangular(k = 50))
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit1$k, 50)
expect_equal(fit1$bw, NULL)
expect_true(fit1$nn.only)
expect_true(fit1$adaptive)
# fixed bw, only knn
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, k = 50)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = rectangular(bw = 5, k = 50))
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit1$bw, 5)
expect_equal(fit1$k, 50)
expect_true(fit1$nn.only)
expect_true(!fit1$adaptive)
# nn.only not needed
expect_that(ossvm(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, nn.only = TRUE), gives_warning("argument 'nn.only' is ignored"))
# nn.only has to be TRUE if bw and k are both given
expect_that(ossvm(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, k = 50, nn.only = FALSE), throws_error("if 'bw' and 'k' are given argument 'nn.only' must be TRUE"))
## wf with infinite support
# fixed bw
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", bw = 0.5)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = gaussian(bw = 0.5))
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit1$bw, 0.5)
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_true(!fit1$adaptive)
# adaptive bw, only knn
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", k = 50)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = gaussian(k = 50))
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit1$bw, NULL)
expect_equal(fit1$k, 50)
expect_equal(fit1$nn.only, TRUE)
expect_true(fit1$adaptive)
# adaptive bw, all obs
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", k = 50, nn.only = FALSE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = gaussian(k = 50, nn.only = FALSE))
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit1$bw, NULL)
expect_equal(fit1$k, 50)
expect_equal(fit1$nn.only, FALSE)
expect_true(fit1$adaptive)
# fixed bw, only knn
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", bw = 1, k = 50)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = gaussian(bw = 1, k = 50))
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit1$bw, 1)
expect_equal(fit1$k, 50)
expect_equal(fit1$nn.only, TRUE)
expect_true(!fit1$adaptive)
# nn.only has to be TRUE if bw and k are both given
expect_that(ossvm(formula = Species ~ ., data = iris, wf = "gaussian", bw = 1, k = 50, nn.only = FALSE), throws_error("if 'bw' and 'k' are given argument 'nn.only' must be TRUE"))
})
#=================================================================================================================
context("predict.ossvm")
test_that("predict.ossvm works correctly with formula and data.frame interface and with missing newdata", {
data(iris)
ran <- sample(1:150,100)
## formula, data
fit <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran, probability = TRUE)
pred <- predict(fit)
pred2 <- predict(fit, newdata = iris[ran,]) #####
expect_equal(names(pred), rownames(iris)[ran])
## formula, data, newdata
pred <- predict(fit, newdata = iris[-ran,], probability = TRUE, decision.values = TRUE)
expect_equal(names(pred), rownames(iris)[-ran])
expect_equal(rownames(attr(pred, "probabilities")), rownames(iris)[-ran])
expect_equal(rownames(attr(pred, "decision.values")), rownames(iris)[-ran])
## y, x
fit <- ossvm(x = iris[ran,-5], y = iris$Species[ran], wf = "gaussian", bw = 2, probability = TRUE)
pred <- predict(fit)
expect_equal(names(pred), rownames(iris)[ran])
## y, x, newdata
pred <- predict(fit, newdata = iris[-ran,-5], probability = TRUE, decision.values = TRUE)
expect_equal(names(pred), rownames(iris)[-ran])
expect_equal(rownames(attr(pred, "probabilities")), rownames(iris)[-ran])
expect_equal(rownames(attr(pred, "decision.values")), rownames(iris)[-ran])
})
test_that("predict.ossvm: retrieving training data works", {
data(iris)
## no subset
# formula, data
fit <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris)
expect_equal(pred1, pred2)
# y, x
fit <- ossvm(x = iris[,-5], y = iris$Species, wf = "gaussian", bw = 2)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris[,-5])
expect_equal(pred1, pred2)
## subset
ran <- sample(1:150,100)
# formula, data
fit <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris[ran,])
expect_equal(pred1, pred2)
# y, x
fit <- ossvm(x = iris[ran,-5], y = iris$Species[ran], wf = "gaussian", bw = 2)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris[ran,-5])
expect_equal(pred1, pred2)
})
test_that("predict.ossvm works with missing classes in the training data", {
data(iris)
ran <- sample(1:150,100)
expect_that(fit <- ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = 1:100, probability = TRUE), gives_warning("group virginica is empty"))
expect_equal(length(fit$y), 100)
expect_equal(nrow(fit$x), 100)
expect_equal(fit$nclass, 2)
pred <- predict(fit, newdata = iris[-ran,], probability = TRUE, decision.values = TRUE)
expect_equal(nlevels(pred), 3)
expect_equal(ncol(attr(pred, "probabilities")), 2)
expect_equal(ncol(attr(pred, "decision.values")), 1)
})
test_that("predict.ossvm works with one single predictor variable", {
data(iris)
ran <- sample(1:150,100)
fit <- ossvm(Species ~ Petal.Width, data = iris, wf = "gaussian", bw = 2, subset = ran, probability = TRUE)
expect_equal(ncol(fit$x), 1)
predict(fit, newdata = iris[-ran,], probability = TRUE, decision.values = TRUE)
})
test_that("predict.ossvm works with one single test observation", {
data(iris)
ran <- sample(1:150,100)
fit <- ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran, probability = TRUE)
pred <- predict(fit, newdata = iris[5,])
expect_equal(length(pred), 1)
a <- factor("setosa", levels = c("setosa", "versicolor", "virginica"))
names(a) = "5"
expect_equal(pred, a)
pred <- predict(fit, newdata = iris[5,], probability = TRUE, decision.values = TRUE)
expect_equal(length(pred), 1)
expect_equal(dim(attr(pred, "probabilities")), c(1, 3))
expect_equal(dim(attr(pred, "decision.values")), c(1, 3))
pred <- predict(fit, newdata = iris[58,])
expect_equal(length(pred), 1)
a <- factor("versicolor", levels = c("setosa", "versicolor", "virginica"))
names(a) = "58"
expect_equal(pred, a)
pred <- predict(fit, newdata = iris[58,], probability = TRUE, decision.values = TRUE)
expect_equal(length(pred), 1)
expect_equal(dim(attr(pred, "probabilities")), c(1, 3))
expect_equal(dim(attr(pred, "decision.values")), c(1, 3))
})
test_that("predict.ossvm works with one single predictor variable and one single test observation", {
data(iris)
ran <- sample(1:150,100)
fit <- ossvm(Species ~ Petal.Width, data = iris, wf = "gaussian", bw = 2, subset = ran, probability = TRUE)
expect_equal(ncol(fit$x), 1)
pred <- predict(fit, newdata = iris[5,], probability = TRUE, decision.values = TRUE)
expect_equal(length(pred), 1)
expect_equal(dim(attr(pred, "probabilities")), c(1, 3))
expect_equal(dim(attr(pred, "decision.values")), c(1, 3))
})
test_that("predict.ossvm: NA handling in newdata works", {
data(iris)
ran <- sample(1:150,100)
irisna <- iris
irisna[1:17,c(1,3)] <- NA
fit <- ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 50, subset = ran, probability = TRUE)
## na.omit
pred <- predict(fit, newdata = irisna, na.action = na.omit, probability = TRUE, decision.values = TRUE)
expect_equal(length(pred), 133)
expect_equal(names(pred), as.character(18:150))
expect_equal(nrow(attr(pred, "probabilities")), 133)
expect_equal(rownames(attr(pred, "probabilities")), as.character(18:150))
expect_equal(nrow(attr(pred, "decision.values")), 133)
expect_equal(rownames(attr(pred, "decision.values")), as.character(18:150))
## na.fail
expect_that(predict(fit, newdata = irisna, na.action = na.fail, probability = TRUE, decision.values = TRUE), throws_error("missing values in object"))
})
test_that("predict.ossvm: misspecified arguments", {
data(iris)
ran <- sample(1:150,100)
fit <- ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran)
# errors in newdata
expect_error(predict(fit, newdata = TRUE))
expect_error(predict(fit, newdata = -50:50))
})
schiffner/locClass documentation built on May 29, 2019, 3:39 p.m.
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context("ossvm")
test_that("ossvm: misspecified arguments", {
data(iris)
# wrong variable names
expect_error(ossvm(formula = Species ~ V1, data = iris, wf = "gaussian", bw = 10))
# wrong class
expect_error(ossvm(formula = iris, data = iris, wf = "gaussian", bw = 10))
expect_error(ossvm(iris, data = iris, wf = "gaussian", bw = 10))
# target variable also in x
expect_error(ossvm(y = iris$Species, x = iris, wf = "gaussian", bw = 10))
expect_warning(ossvm(Species ~ Species + Petal.Width, data = iris, wf = "gaussian", bw = 10)) ## warning, Species on RHS removed
# missing x
expect_error(ossvm(y = iris$Species, wf = "gaussian", bw = 10))
})
# test_that("ossvm throws a warning if y variable is numeric", {
# data(iris)
# formula, data
# expect_that(ossvm(formula = as.numeric(Species) ~ ., data = iris, wf = "gaussian", bw = 10), gives_warning("'y' was coerced to a factor"))
# y, x
# expect_that(ossvm(y = iris[,1], x = iris[,-1], wf = "gaussian", bw = 10), gives_warning("'y' was coerced to a factor"))
# })
test_that("ossvm works if only one predictor variable is given", {
data(iris)
fit <- ossvm(Species ~ Petal.Width, data = iris, wf = "gaussian", bw = 5)
predict(fit)
})
test_that("ossvm: training data from only one class", {
data(iris)
expect_that(ossvm(Species ~ ., data = iris, bw = 2, subset = 1:50), throws_error("training data from only one class"))
expect_error(ossvm(Species ~ ., data = iris, bw = 2, subset = 1))
expect_that(ossvm(y = iris$Species, x = iris[,-5], bw = 2, subset = 1:50), throws_error("training data from only one class"))
expect_error(ossvm(y = iris$Species, x = iris[,-5], bw = 2, subset = 1))
})
test_that("ossvm: subsetting works", {
data(iris)
# formula, data
expect_that(fit1 <- ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = 1:80), gives_warning("group virginica is empty"))
expect_that(fit2 <- ossvm(Species ~ ., data = iris[1:80,], wf = "gaussian", bw = 2), gives_warning("group virginica is empty"))
expect_equal(fit1[-1],fit2[-1])
expect_equal(nrow(fit1$x), 80)
expect_equal(length(fit1$y), 80)
# x, y
expect_that(fit1 <- ossvm(y = iris$Species, x = iris[,-5], wf = "gaussian", bw = 2, subset = 1:80), gives_warning("group virginica is empty"))
expect_that(fit2 <- ossvm(y = iris$Species[1:80], x = iris[1:80,-5], wf = "gaussian", bw = 2), gives_warning("group virginica is empty"))
expect_equal(fit1[-1],fit2[-1])
expect_equal(nrow(fit1$x), 80)
expect_equal(length(fit1$y), 80)
# wrong specification of subset argument
expect_error(ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = iris[1:10,]))
expect_error(ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = FALSE)) #???
expect_error(ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = 0)) #???
expect_error(ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = -10:50))
})
test_that("ossvm: NA handling works correctly", {
### NA in x
data(iris)
irisna <- iris
irisna[1:10, c(1,3)] <- NA
## formula, data
# na.fail
expect_that(ossvm(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.fail), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- ossvm(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.omit), gives_warning("group virginica is empty"))
expect_that(fit2 <- ossvm(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 11:60), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(1:2,31)], fit2[-c(1:2,31)])
expect_equivalent(fit1[2], fit2[2])
## x, y
# na.fail
expect_that(ossvm(y = irisna$Species, x = irisna[,-5], wf = "gaussian", bw = 10, subset = 6:60, na.action = na.fail), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- ossvm(y = irisna$Species, x = irisna[,-5], wf = "gaussian", bw = 10, subset = 6:60, na.action = na.omit), gives_warning("group virginica is empty"))##
expect_that(fit2 <- ossvm(y = irisna$Species, x = irisna[,-5], wf = "gaussian", bw = 10, subset = 11:60), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(1:2,31)], fit2[-c(1:2,31)])
expect_equivalent(fit1[2], fit2[2])
### NA in y
irisna <- iris
irisna$Species[1:10] <- NA
## formula, data
# na.fail
expect_that(ossvm(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.fail), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- ossvm(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 6:60, na.action = na.omit), gives_warning("group virginica is empty"))
expect_that(fit2 <- ossvm(Species ~ ., data = irisna, wf = "gaussian", bw = 10, subset = 11:60), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(1:2,31)], fit2[-c(1:2,31)])
expect_equivalent(fit1[2], fit2[2])
## x, y
# na.fail
expect_that(ossvm(y = irisna$Species, x = irisna[,-5], wf = "gaussian", bw = 10, subset = 6:60, na.action = na.fail), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- ossvm(y = irisna$Species, x = irisna[,-5], wf = "gaussian", bw = 10, subset = 6:60, na.action = na.omit), gives_warning("group virginica is empty"))
expect_that(fit2 <- ossvm(y = irisna$Species, x = irisna[,-5], wf = "gaussian", bw = 10, subset = 11:60), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(1:2,31)], fit2[-c(1:2,31)])
expect_equivalent(fit1[2], fit2[2])
### NA in subset
subset <- 6:60
subset[1:5] <- NA
## formula, data
# na.fail
expect_that(ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = subset, na.action = na.fail), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = subset, na.action = na.omit), gives_warning("group virginica is empty"))
expect_that(fit2 <- ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = 11:60), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(1:2,31)], fit2[-c(1:2,31)])
expect_equivalent(fit1[2], fit2[2])
## x, y
# na.fail
expect_that(ossvm(y = iris$Species, x = iris[,-5], wf = "gaussian", bw = 10, subset = subset, na.action = na.fail), throws_error("missing values in object"))
# check if na.omit works correctly
expect_that(fit1 <- ossvm(y = iris$Species, x = iris[,-5], wf = "gaussian", bw = 10, subset = subset, na.action = na.omit), gives_warning("group virginica is empty"))
expect_that(fit2 <- ossvm(y = iris$Species, x = iris[,-5], wf = "gaussian", bw = 10, subset = 11:60), gives_warning("group virginica is empty"))
expect_equal(fit1[-c(1:2,31)], fit2[-c(1:2,31)])
expect_equivalent(fit1[2], fit2[2])
})
test_that("ossvm: try all weight functions", {
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2, probability = TRUE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = gaussian(2), probability = TRUE)
fit3 <- ossvm(x = iris[,-5], y = iris$Species, wf = "gaussian", bw = 2, probability = TRUE)
fit4 <- ossvm(x = iris[,-5], y = iris$Species, wf = gaussian(2), probability = TRUE)
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit3[-c(1,4)], fit4[-c(1,4)])
expect_equal(fit2[-c(1,2,32)], fit4[-c(1,2)])
expect_equivalent(fit2[2], fit4[2])
set.seed(120)
pred1 <- predict(fit1, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred2 <- predict(fit2, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred3 <- predict(fit3, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred4 <- predict(fit4, probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", bw = 5, k = 30, probability = TRUE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = gaussian(bw = 5, k = 30), probability = TRUE)
fit3 <- ossvm(x = iris[,-5], y = iris$Species, wf = "gaussian", bw = 5, k = 30, probability = TRUE)
fit4 <- ossvm(x = iris[,-5], y = iris$Species, wf = gaussian(bw = 5, k = 30), probability = TRUE)
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit3[-c(1,4)], fit4[-c(1,4)])
expect_equal(fit2[-c(1,2,32)], fit4[-c(1,2)])
expect_equivalent(fit2[2], fit4[2])
set.seed(120)
pred1 <- predict(fit1, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred2 <- predict(fit2, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred3 <- predict(fit3, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred4 <- predict(fit4, probability = TRUE, decision.values = TRUE)
# pred1 <- predict(fit1, newdata = iris[sample(1:150),], probability = TRUE, decision.values = TRUE)
# pred2 <- predict(fit2, newdata = iris[1:10,], probability = TRUE, decision.values = TRUE)
# pred3 <- predict(fit3, newdata = iris[1,-5], probability = TRUE, decision.values = TRUE)
# pred4 <- predict(fit4, newdata = iris[1,-5], probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "epanechnikov", bw = 5, k = 30, probability = TRUE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = epanechnikov(bw = 5, k = 30), probability = TRUE)
fit3 <- ossvm(x = iris[,-5], y = iris$Species, wf = "epanechnikov", bw = 5, k = 30, probability = TRUE)
fit4 <- ossvm(x = iris[,-5], y = iris$Species, wf = epanechnikov(5, 30), probability = TRUE)
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit3[-c(1,4)], fit4[-c(1,4)])
expect_equal(fit2[-c(1,2,32)], fit4[-c(1,2)])
expect_equivalent(fit2[2], fit4[2])
set.seed(120)
pred1 <- predict(fit1, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred2 <- predict(fit2, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred3 <- predict(fit3, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred4 <- predict(fit4, probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, k = 30, probability = TRUE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = rectangular(bw = 5, k = 30), probability = TRUE)
fit3 <- ossvm(x = iris[,-5], y = iris$Species, wf = "rectangular", bw = 5, k = 30, probability = TRUE)
fit4 <- ossvm(x = iris[,-5], y = iris$Species, wf = rectangular(5, 30), probability = TRUE)
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit3[-c(1,4)], fit4[-c(1,4)])
expect_equal(fit2[-c(1,2,32)], fit4[-c(1,2)])
expect_equivalent(fit2[2], fit4[2])
set.seed(120)
pred1 <- predict(fit1, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred2 <- predict(fit2, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred3 <- predict(fit3, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred4 <- predict(fit4, probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "triangular", bw = 5, k = 30, probability = TRUE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = triangular(5, k = 30), probability = TRUE)
fit3 <- ossvm(x = iris[,-5], y = iris$Species, wf = "triangular", bw = 5, k = 30, probability = TRUE)
fit4 <- ossvm(x = iris[,-5], y = iris$Species, wf = triangular(5, 30), probability = TRUE)
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit3[-c(1,4)], fit4[-c(1,4)])
expect_equal(fit2[-c(1,2,32)], fit4[-c(1,2)])
expect_equivalent(fit2[2], fit4[2])
set.seed(120)
pred1 <- predict(fit1, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred2 <- predict(fit2, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred3 <- predict(fit3, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred4 <- predict(fit4, probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "biweight", bw = 5, probability = TRUE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = biweight(5), probability = TRUE)
fit3 <- ossvm(x = iris[,-5], y = iris$Species, wf = "biweight", bw = 5, probability = TRUE)
fit4 <- ossvm(x = iris[,-5], y = iris$Species, wf = biweight(5), probability = TRUE)
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit3[-c(1,4)], fit4[-c(1,4)])
expect_equal(fit2[-c(1,2,32)], fit4[-c(1,2)])
expect_equivalent(fit2[2], fit4[2])
set.seed(120)
pred1 <- predict(fit1, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred2 <- predict(fit2, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred3 <- predict(fit3, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred4 <- predict(fit4, probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "optcosine", bw = 5, k = 30, probability = TRUE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = optcosine(5, k = 30), probability = TRUE)
fit3 <- ossvm(x = iris[,-5], y = iris$Species, wf = "optcosine", bw = 5, k = 30, probability = TRUE)
fit4 <- ossvm(x = iris[,-5], y = iris$Species, wf = optcosine(5, 30), probability = TRUE)
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit3[-c(1,4)], fit4[-c(1,4)])
expect_equal(fit2[-c(1,2,32)], fit4[-c(1,2)])
expect_equivalent(fit2[2], fit4[2])
set.seed(120)
pred1 <- predict(fit1, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred2 <- predict(fit2, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred3 <- predict(fit3, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred4 <- predict(fit4, probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "cosine", bw = 5, k = 30, probability = TRUE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = cosine(5, k = 30), probability = TRUE)
fit3 <- ossvm(x = iris[,-5], y = iris$Species, wf = "cosine", bw = 5, k = 30, probability = TRUE)
fit4 <- ossvm(x = iris[,-5], y = iris$Species, wf = cosine(5, 30), probability = TRUE)
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit3[-c(1,4)], fit4[-c(1,4)])
expect_equal(fit2[-c(1,2,32)], fit4[-c(1,2)])
expect_equivalent(fit2[2], fit4[2])
set.seed(120)
pred1 <- predict(fit1, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred2 <- predict(fit2, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred3 <- predict(fit3, probability = TRUE, decision.values = TRUE)
set.seed(120)
pred4 <- predict(fit4, probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2)
expect_equal(pred3, pred4)
expect_equal(pred2, pred4)
})
test_that("ossvm: local solution with rectangular window function and large bw and global solution coincide", {
data(iris)
library(e1071)
## newdata missing
fit1 <- wsvm(formula = Species ~ ., data = iris)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = rectangular(20))
fit3 <- svm(Species ~ ., data = iris)
pred1 <- predict(fit1)
pred2 <- predict(fit2)
pred3 <- predict(fit3)
expect_equal(pred1, pred2)
expect_equal(pred1, pred3)
## newdata given
fit1 <- wsvm(formula = Species ~ ., data = iris, probability = TRUE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = rectangular(8), probability = TRUE)
fit3 <- svm(Species ~ ., data = iris, probability = TRUE)
pred1 <- predict(fit1, newdata = iris, probability = TRUE, decision.values = TRUE)
pred2 <- predict(fit2, newdata = iris, probability = TRUE, decision.values = TRUE)
pred3 <- predict(fit3, newdata = iris, probability = TRUE, decision.values = TRUE)
# pred1 <- predict(fit1, newdata = iris[1,], probability = TRUE, decision.values = TRUE)
# pred2 <- predict(fit2, newdata = iris[1,], probability = TRUE, decision.values = TRUE)
# pred3 <- predict(fit3, newdata = iris[1,], probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2)
expect_equal(pred1, pred3)
expect_equal(pred2, pred3)
})
test_that("ossvm: labels vector set correctly",{
## all classes, correct order
data(iris)
iris[,1:4] <- scale(iris[,1:4])
k <- 100
n <- 90
x <- as.matrix(iris[,-5])
dist <- sqrt(colSums((t(x) - x[n,])^2))
w <- rectangular(k = k)(dist)
fit1 <- wsvm(Species ~ ., data = iris, case.weights = w/sum(w) * 150, probability = TRUE, scale = FALSE)
set.seed(120)
pred1 <- predict(fit1, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
fit2 <- ossvm(Species ~ ., data = iris, probability = TRUE, wf = "rectangular", k = k, scale = FALSE)
set.seed(120)
pred2 <- predict(fit2, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
expect_equal(pred1, pred2) # not exactly equal
## all classes, but different order 1
data(iris)
iris[,1:4] <- scale(iris[,1:4])
perm <- 150:1 ## 3, 2, 1
iris <- iris[perm,]
k = 100
n <- 90
x <- as.matrix(iris[,-5])
dist <- sqrt(colSums((t(x) - x[n,])^2))
w <- rectangular(k = k)(dist)
fit1 <- wsvm(Species ~ ., data = iris, case.weights = w/sum(w) * 150, probability = TRUE, scale = FALSE)
set.seed(120)
pred1 <- predict(fit1, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
fit2 <- ossvm(Species ~ ., data = iris, probability = TRUE, wf = "rectangular", k = k, scale = FALSE)
set.seed(120)
pred2 <- predict(fit2, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
expect_equivalent(pred1, pred2)
expect_equal(attr(pred1, "probabilities"), attr(pred2, "probabilities")[,3:1, drop = FALSE]) # not exactly equal
expect_equal(as.vector(attr(pred1, "decision.values")), -as.vector(attr(pred2, "decision.values")[,3:1]))
## all classes, but different order 2
data(iris)
iris[,1:4] <- scale(iris[,1:4])
perm <- c(150:101,1:100) ## 3, 1, 2
iris <- iris[perm,]
k = 100
n <- 90
x <- as.matrix(iris[,-5])
dist <- sqrt(colSums((t(x) - x[n,])^2))
w <- rectangular(k = k)(dist)
fit1 <- wsvm(Species ~ ., data = iris, case.weights = w/sum(w) * 150, probability = TRUE, scale = FALSE)
set.seed(120)
pred1 <- predict(fit1, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
fit2 <- ossvm(Species ~ ., data = iris, probability = TRUE, wf = "rectangular", k = k, scale = FALSE)
set.seed(120)
pred2 <- predict(fit2, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
expect_equivalent(pred1, pred2)
expect_equal(attr(pred1, "probabilities"), attr(pred2, "probabilities")[,c(3,1,2), drop = FALSE])
expect_equivalent(c(1,-1,-1)*attr(pred1, "decision.values")[c(3,1,2)], as.vector(attr(pred2, "decision.values")))
## 2 classes, correct order
data(iris)
iris[,1:4] <- scale(iris[,1:4])
k <- 50
n <- 90
x <- as.matrix(iris[,-5])
dist <- sqrt(colSums((t(x) - x[n,])^2))
w <- rectangular(k = k)(dist)
fit1 <- wsvm(Species ~ ., data = iris, case.weights = w/sum(w) * 150, probability = TRUE, scale = FALSE)
set.seed(120)
pred1 <- predict(fit1, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
fit2 <- ossvm(Species ~ ., data = iris, probability = TRUE, wf = "rectangular", k = k, scale = FALSE)
set.seed(120)
pred2 <- predict(fit2, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
expect_equivalent(pred1, pred2)
expect_equal(attr(pred1, "probabilities"), attr(pred2, "probabilities")[,colnames(attr(pred1, "probabilities")), drop = FALSE])
expect_equal(as.numeric(attr(pred1, "decision.values")), attr(pred2, "decision.values")[,!is.na(attr(pred2, "decision.values"))])
## 2 classes, but different order 1
data(iris)
iris[,1:4] <- scale(iris[,1:4])
perm <- 150:1
iris <- iris[perm,]
k <- 40
n <- 99
x <- as.matrix(iris[,-5])
dist <- sqrt(colSums((t(x) - x[n,])^2))
w <- rectangular(k = k)(dist)
fit1 <- wsvm(Species ~ ., data = iris, case.weights = w/sum(w) * 150, probability = TRUE, scale = FALSE)
set.seed(120)
pred1 <- predict(fit1, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
fit2 <- ossvm(Species ~ ., data = iris, probability = TRUE, wf = "rectangular", k = k, scale = FALSE)
set.seed(120)
pred2 <- predict(fit2, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
expect_equivalent(pred1, pred2)
expect_equal(attr(pred1, "probabilities"), attr(pred2, "probabilities")[,colnames(attr(pred1, "probabilities")), drop = FALSE])
expect_equal(as.numeric(attr(pred1, "decision.values")), -attr(pred2, "decision.values")[!is.na(attr(pred2, "decision.values"))])
## 2 classes, but different order 2
data(iris)
iris[,1:4] <- scale(iris[,1:4])
perm <- c(150:101,1:100)
iris <- iris[perm,]
k <- 50
n <- 90
x <- as.matrix(iris[,-5])
dist <- sqrt(colSums((t(x) - x[n,])^2))
w <- rectangular(k = k)(dist)
fit1 <- wsvm(Species ~ ., data = iris, case.weights = w/sum(w) * 150, probability = TRUE, scale = FALSE)
set.seed(120)
pred1 <- predict(fit1, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
fit2 <- ossvm(Species ~ ., data = iris, probability = TRUE, wf = "rectangular", k = k, scale = FALSE)
set.seed(120)
pred2 <- predict(fit2, newdata = iris[n,], probability = TRUE, decision.values = TRUE)
expect_equivalent(pred1, pred2)
expect_equal(attr(pred1, "probabilities"), attr(pred2, "probabilities")[,colnames(attr(pred1, "probabilities")), drop = FALSE])
expect_equal(as.numeric(attr(pred1, "decision.values")), attr(pred2, "decision.values")[!is.na(attr(pred2, "decision.values"))])
})
test_that("ossvm: arguments related to weighting misspecified", {
# bw, k not required
expect_that(fit1 <- ossvm(Species ~ ., data = iris, wf = gaussian(0.5), k = 30, bw = 0.5), gives_warning(c("argument 'k' is ignored", "argument 'bw' is ignored")))
fit2 <- ossvm(Species ~ ., data = iris, wf = gaussian(0.5))
expect_equal(fit1[-1], fit2[-1])
expect_that(fit1 <- ossvm(Species ~ ., data = iris, wf = gaussian(0.5), bw = 0.5), gives_warning("argument 'bw' is ignored"))
fit2 <- ossvm(Species ~ ., data = iris, wf = gaussian(0.5))
expect_equal(fit1[-1], fit2[-1])
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_equal(fit1$bw, 0.5)
expect_equal(fit1$adaptive, FALSE)
expect_that(fit1 <- ossvm(Species ~ ., data = iris, wf = function(x) exp(-x), bw = 0.5, k = 30), gives_warning(c("argument 'k' is ignored", "argument 'bw' is ignored")))
expect_that(fit2 <- ossvm(Species ~ ., data = iris, wf = function(x) exp(-x), k = 30), gives_warning("argument 'k' is ignored"))
expect_equal(fit1[-1], fit2[-1])
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_equal(fit1$bw, NULL)
expect_equal(fit1$adaptive, NULL)
expect_that(fit1 <- ossvm(Species ~ ., data = iris, wf = function(x) exp(-x), bw = 0.5), gives_warning("argument 'bw' is ignored"))
fit2 <- ossvm(Species ~ ., data = iris, wf = function(x) exp(-x))
expect_equal(fit1[-1], fit2[-1])
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_equal(fit1$bw, NULL)
expect_equal(fit1$adaptive, NULL)
# missing quotes
fit <- ossvm(formula = Species ~ ., data = iris, wf = gaussian) ## error because length(weights) and nrow(x) are different
expect_error(predict(fit))
# bw, k missing
expect_that(ossvm(formula = Species ~ ., data = iris, wf = gaussian()), throws_error("either 'bw' or 'k' have to be specified"))
expect_that(ossvm(formula = Species ~ ., data = iris, wf = gaussian(), k = 10), throws_error("either 'bw' or 'k' have to be specified"))
expect_that(ossvm(Species ~ ., data = iris), throws_error("either 'bw' or 'k' have to be specified"))
# bw < 0
expect_that(ossvm(formula = Species ~ ., data = iris, wf = "gaussian", bw = -5), throws_error("'bw' must be positive"))
expect_that(ossvm(formula = Species ~ ., data = iris, wf = "cosine", k = 10, bw = -50), throws_error("'bw' must be positive"))
# bw vector
expect_that(ossvm(formula = Species ~., data = iris, wf = "gaussian", bw = rep(1, nrow(iris))), gives_warning("only first element of 'bw' used"))
# k < 0
expect_that(ossvm(formula = Species ~ ., data = iris, wf = "gaussian", k =-7, bw = 50), throws_error("'k' must be positive"))
# k too small
#fit <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", k = 5, bw = 0.005)
#expect_equal(length(is.na(predict(fit)$class)), 150)
# k too large
expect_that(ossvm(formula = Species ~ ., data = iris, k = 250, wf = "gaussian", bw = 50), throws_error("'k' is larger than 'n'"))
# k vector
expect_that(ossvm(formula = Species ~., data = iris, wf = "gaussian", k = rep(50, nrow(iris))), gives_warning("only first element of 'k' used"))
})
test_that("ossvm: weighting schemes work", {
## wf with finite support
# fixed bw
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = rectangular(bw = 5))
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit1$bw, 5)
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_true(!fit1$adaptive)
# adaptive bw, only knn
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "rectangular", k = 50)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = rectangular(k = 50))
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit1$k, 50)
expect_equal(fit1$bw, NULL)
expect_true(fit1$nn.only)
expect_true(fit1$adaptive)
# fixed bw, only knn
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, k = 50)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = rectangular(bw = 5, k = 50))
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit1$bw, 5)
expect_equal(fit1$k, 50)
expect_true(fit1$nn.only)
expect_true(!fit1$adaptive)
# nn.only not needed
expect_that(ossvm(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, nn.only = TRUE), gives_warning("argument 'nn.only' is ignored"))
# nn.only has to be TRUE if bw and k are both given
expect_that(ossvm(formula = Species ~ ., data = iris, wf = "rectangular", bw = 5, k = 50, nn.only = FALSE), throws_error("if 'bw' and 'k' are given argument 'nn.only' must be TRUE"))
## wf with infinite support
# fixed bw
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", bw = 0.5)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = gaussian(bw = 0.5))
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit1$bw, 0.5)
expect_equal(fit1$k, NULL)
expect_equal(fit1$nn.only, NULL)
expect_true(!fit1$adaptive)
# adaptive bw, only knn
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", k = 50)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = gaussian(k = 50))
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit1$bw, NULL)
expect_equal(fit1$k, 50)
expect_equal(fit1$nn.only, TRUE)
expect_true(fit1$adaptive)
# adaptive bw, all obs
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", k = 50, nn.only = FALSE)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = gaussian(k = 50, nn.only = FALSE))
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit1$bw, NULL)
expect_equal(fit1$k, 50)
expect_equal(fit1$nn.only, FALSE)
expect_true(fit1$adaptive)
# fixed bw, only knn
fit1 <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", bw = 1, k = 50)
fit2 <- ossvm(formula = Species ~ ., data = iris, wf = gaussian(bw = 1, k = 50))
expect_equal(fit1[-c(1,4)], fit2[-c(1,4)])
expect_equal(fit1$bw, 1)
expect_equal(fit1$k, 50)
expect_equal(fit1$nn.only, TRUE)
expect_true(!fit1$adaptive)
# nn.only has to be TRUE if bw and k are both given
expect_that(ossvm(formula = Species ~ ., data = iris, wf = "gaussian", bw = 1, k = 50, nn.only = FALSE), throws_error("if 'bw' and 'k' are given argument 'nn.only' must be TRUE"))
})
#=================================================================================================================
context("predict.ossvm")
test_that("predict.ossvm works correctly with formula and data.frame interface and with missing newdata", {
data(iris)
ran <- sample(1:150,100)
## formula, data
fit <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran, probability = TRUE)
pred <- predict(fit)
pred2 <- predict(fit, newdata = iris[ran,]) #####
expect_equal(names(pred), rownames(iris)[ran])
## formula, data, newdata
pred <- predict(fit, newdata = iris[-ran,], probability = TRUE, decision.values = TRUE)
expect_equal(names(pred), rownames(iris)[-ran])
expect_equal(rownames(attr(pred, "probabilities")), rownames(iris)[-ran])
expect_equal(rownames(attr(pred, "decision.values")), rownames(iris)[-ran])
## y, x
fit <- ossvm(x = iris[ran,-5], y = iris$Species[ran], wf = "gaussian", bw = 2, probability = TRUE)
pred <- predict(fit)
expect_equal(names(pred), rownames(iris)[ran])
## y, x, newdata
pred <- predict(fit, newdata = iris[-ran,-5], probability = TRUE, decision.values = TRUE)
expect_equal(names(pred), rownames(iris)[-ran])
expect_equal(rownames(attr(pred, "probabilities")), rownames(iris)[-ran])
expect_equal(rownames(attr(pred, "decision.values")), rownames(iris)[-ran])
})
test_that("predict.ossvm: retrieving training data works", {
data(iris)
## no subset
# formula, data
fit <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris)
expect_equal(pred1, pred2)
# y, x
fit <- ossvm(x = iris[,-5], y = iris$Species, wf = "gaussian", bw = 2)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris[,-5])
expect_equal(pred1, pred2)
## subset
ran <- sample(1:150,100)
# formula, data
fit <- ossvm(formula = Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris[ran,])
expect_equal(pred1, pred2)
# y, x
fit <- ossvm(x = iris[ran,-5], y = iris$Species[ran], wf = "gaussian", bw = 2)
pred1 <- predict(fit)
pred2 <- predict(fit, newdata = iris[ran,-5])
expect_equal(pred1, pred2)
})
test_that("predict.ossvm works with missing classes in the training data", {
data(iris)
ran <- sample(1:150,100)
expect_that(fit <- ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 10, subset = 1:100, probability = TRUE), gives_warning("group virginica is empty"))
expect_equal(length(fit$y), 100)
expect_equal(nrow(fit$x), 100)
expect_equal(fit$nclass, 2)
pred <- predict(fit, newdata = iris[-ran,], probability = TRUE, decision.values = TRUE)
expect_equal(nlevels(pred), 3)
expect_equal(ncol(attr(pred, "probabilities")), 2)
expect_equal(ncol(attr(pred, "decision.values")), 1)
})
test_that("predict.ossvm works with one single predictor variable", {
data(iris)
ran <- sample(1:150,100)
fit <- ossvm(Species ~ Petal.Width, data = iris, wf = "gaussian", bw = 2, subset = ran, probability = TRUE)
expect_equal(ncol(fit$x), 1)
predict(fit, newdata = iris[-ran,], probability = TRUE, decision.values = TRUE)
})
test_that("predict.ossvm works with one single test observation", {
data(iris)
ran <- sample(1:150,100)
fit <- ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran, probability = TRUE)
pred <- predict(fit, newdata = iris[5,])
expect_equal(length(pred), 1)
a <- factor("setosa", levels = c("setosa", "versicolor", "virginica"))
names(a) = "5"
expect_equal(pred, a)
pred <- predict(fit, newdata = iris[5,], probability = TRUE, decision.values = TRUE)
expect_equal(length(pred), 1)
expect_equal(dim(attr(pred, "probabilities")), c(1, 3))
expect_equal(dim(attr(pred, "decision.values")), c(1, 3))
pred <- predict(fit, newdata = iris[58,])
expect_equal(length(pred), 1)
a <- factor("versicolor", levels = c("setosa", "versicolor", "virginica"))
names(a) = "58"
expect_equal(pred, a)
pred <- predict(fit, newdata = iris[58,], probability = TRUE, decision.values = TRUE)
expect_equal(length(pred), 1)
expect_equal(dim(attr(pred, "probabilities")), c(1, 3))
expect_equal(dim(attr(pred, "decision.values")), c(1, 3))
})
test_that("predict.ossvm works with one single predictor variable and one single test observation", {
data(iris)
ran <- sample(1:150,100)
fit <- ossvm(Species ~ Petal.Width, data = iris, wf = "gaussian", bw = 2, subset = ran, probability = TRUE)
expect_equal(ncol(fit$x), 1)
pred <- predict(fit, newdata = iris[5,], probability = TRUE, decision.values = TRUE)
expect_equal(length(pred), 1)
expect_equal(dim(attr(pred, "probabilities")), c(1, 3))
expect_equal(dim(attr(pred, "decision.values")), c(1, 3))
})
test_that("predict.ossvm: NA handling in newdata works", {
data(iris)
ran <- sample(1:150,100)
irisna <- iris
irisna[1:17,c(1,3)] <- NA
fit <- ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 50, subset = ran, probability = TRUE)
## na.omit
pred <- predict(fit, newdata = irisna, na.action = na.omit, probability = TRUE, decision.values = TRUE)
expect_equal(length(pred), 133)
expect_equal(names(pred), as.character(18:150))
expect_equal(nrow(attr(pred, "probabilities")), 133)
expect_equal(rownames(attr(pred, "probabilities")), as.character(18:150))
expect_equal(nrow(attr(pred, "decision.values")), 133)
expect_equal(rownames(attr(pred, "decision.values")), as.character(18:150))
## na.fail
expect_that(predict(fit, newdata = irisna, na.action = na.fail, probability = TRUE, decision.values = TRUE), throws_error("missing values in object"))
})
test_that("predict.ossvm: misspecified arguments", {
data(iris)
ran <- sample(1:150,100)
fit <- ossvm(Species ~ ., data = iris, wf = "gaussian", bw = 2, subset = ran)
# errors in newdata
expect_error(predict(fit, newdata = TRUE))
expect_error(predict(fit, newdata = -50:50))
})
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