tests/testthat/test-dist-sorensen.R

In philentropy: Similarity and Distance Quantification Between Probability Functions

context("Test implementation of sorensen distance ...")


P <- 1:10 / sum(1:10)
Q <- 20:29 / sum(20:29)
V <- -10:10
W <- -20:0

# function to test distance matrix functionality
# for different distance measures
test_dist_matrix <- function(x, FUN) {
        dist.fun <- match.fun(FUN)
        res.dist.matrix <- matrix(NA_real_, nrow(x), nrow(x))
        
        for (i in 1:nrow(x)) {
                for (j in 1:nrow(x)) {
                        res.dist.matrix[i, j] <- dist.fun(x[i, ], x[j, ])
                }
        }
        return(res.dist.matrix[lower.tri(res.dist.matrix, diag = FALSE)])
}

test_sorensen_dist <- function(P, Q) {
        if (sum((P) + (Q) > 0)) {
                return(sum(abs((P) - (Q))) / sum((P) + (Q)))
        } else {
                return(NAN)
        }
}

test_that("distance(method = 'sorensen') computes the correct distance value.",
          {
                  
                  
                  expect_equal(as.vector(philentropy::distance(rbind(P, Q), method = "sorensen")),
                               test_sorensen_dist(P, Q))
                  
                  # test correct computation of distance matrix
                  distMat <-
                          rbind(rep(0.2, 5), rep(0.1, 5), c(5, 1, 7, 9, 5))
                  dist.vals <-
                          distance(distMat, method = "sorensen")
                  
                  expect_equal(dist.vals[lower.tri(dist.vals, diag = FALSE)],
                               test_dist_matrix(distMat, FUN = test_sorensen_dist))
                  
          })
 


test_that("Correct sorensen distance is computed when vectors contain 0 values ...", {
        P1 <- c(1,0)
        P2 <- c(0.5, 0.5)
        Q1 <- c(0.5,0.5)
        Q2 <- c(1,0)
        
        distMat <-
                rbind(P1,Q1,P2,Q2)
        dist.vals <-
                distance(distMat, method = "sorensen")
        
        expect_equal(dist.vals[lower.tri(dist.vals, diag = FALSE)],
                     test_dist_matrix(distMat, FUN = test_sorensen_dist))
        
})