test_that("diagram_distance detects incorrect parameters correctly",{ D = data.frame(dimension = c(0),birth = c(0),death = c(1)) expect_error(diagram_distance(D1 = NULL,D2 = D,dim = 1),"TDA/TDAstats") expect_error(diagram_distance(D1 = D,D2 = NULL,dim = 1),"TDA/TDAstats") expect_error(diagram_distance(D1 = D,D2 = D,dim = "2"),"numeric") expect_error(diagram_distance(D1 = D,D2 = D,dim = 1,p = "2"),"numeric") expect_error(diagram_distance(D1 = D,D2 = D,dim = 1,distance = "Wasserstein"),"distance must") expect_error(diagram_distance(D1 = D,D2 = D,dim = 1,distance = "fisher",sigma = NA),"sigma must") expect_error(diagram_distance(D1 = D,D2 = D,dim = 1,distance = "fisher",sigma = 1,rho = 0),"positive") expect_error(diagram_distance(D1 = D,D2 = D,dim = 1,distance = "fisher",sigma = 1,rho = NA),"NA") }) # test_that("diagram_distance can accept inputs from either TDA/TDAstats homology output or diagram_to_df function, with or without cycle location",{ # # skip_if_not_installed("TDA") # skip_if_not_installed("TDAstats") # D1 = TDA::ripsDiag(data.frame(x = runif(50,0,1),y = runif(50,0,1)),maxscale = 1,maxdimension = 1) # D2 = TDA::alphaComplexDiag(data.frame(x = runif(50,0,1),y = runif(50,0,1)),maxdimension = 1) # D3 = TDA::ripsDiag(data.frame(x = runif(50,0,1),y = runif(50,0,1)),maxscale = 1,maxdimension = 1,library = "dionysus",location = T) # D4 = TDAstats::calculate_homology(data.frame(x = runif(50,0,1),y = runif(50,0,1)),threshold = 1) # D5 = TDAstats::calculate_homology(data.frame(x = runif(50,0,1),y = runif(50,0,1)),threshold = 10,dim = 1) # expect_gte(diagram_distance(D1 = D1,D2 = D2,dim = 1),0) # expect_gte(diagram_distance(D1 = diagram_to_df(D1),D2 = D2,dim = 1),0) # expect_gte(diagram_distance(D1 = D1,D2 = diagram_to_df(D2),dim = 1),0) # expect_gte(diagram_distance(D1 = D3,D2 = diagram_to_df(D2),dim = 1),0) # expect_gte(diagram_distance(D1 = D1,D2 = diagram_to_df(D3),dim = 1),0) # expect_gte(diagram_distance(D1 = D1,D2 = D4,dim = 1),0) # expect_error(diagram_distance(D1 = D1,D2 = D2,dim = 0),"Inf") # # }) test_that("diagram_distance is computing correctly",{ D1 = data.frame(dimension = 0,birth = 2,death = 3) D2 = data.frame(dimension = 0,birth = c(2,5),death = c(3.1,6)) expect_identical(diagram_distance(D1,D2,dim = 0,distance = "wasserstein",p = 2),sqrt(0.1^2+0.5^2)) expect_identical(diagram_distance(D2,D1,dim = 0,distance = "wasserstein",p = 2),sqrt(0.1^2+0.5^2)) expect_identical(diagram_distance(D1,D2,dim = 0,distance = "wasserstein",p = 3),(0.1^3+0.5^3)^(1/3)) expect_equal(diagram_distance(D1 = D1,D2 = D2,distance = "fisher",dim = 0,sigma = 1),diagram_distance(D1 = D2,D2 = D1,distance = "fisher",dim = 0,sigma = 1)) expect_identical(diagram_distance(D1 = D1,D2 = D2,p = Inf,distance = "wasserstein",dim = 0),0.5) expect_identical(diagram_distance(D1 = D2,D2 = D1,p = Inf,distance = "wasserstein",dim = 0),0.5) expect_identical(diagram_distance(D1 = D1,D2 = D1,p = Inf,distance = "wasserstein",dim = 0),0) expect_identical(diagram_distance(D1 = D1,D2 = D1,p = 2,distance = "wasserstein",dim = 0),0) expect_identical(diagram_distance(D1 = D1,D2 = D1,distance = "fisher",sigma = 1,dim = 0),0) expect_identical(diagram_distance(D1 = D1,D2 = D2,dim = 1),0) D1$dimension = 1 expect_identical(diagram_distance(D1 = D1,D2 = D2,dim = 1,p = 2,distance = "wasserstein"),sqrt(0.5^2)) expect_identical(diagram_distance(D1 = D1,D2 = D2,dim = 1,p = Inf,distance = "wasserstein"),0.5) expect_identical(diagram_distance(D1 = D2,D2 = D1,dim = 1,p = 2,distance = "wasserstein"),sqrt(0.5^2)) expect_identical(diagram_distance(D1 = D2,D2 = D1,dim = 1,p = Inf,distance = "wasserstein"),0.5) # this example was picked the TDA function wasserstein disagrees with the actual minimum values # for p = 2,3, but diagram_distance gets the correct answer D1 = data.frame(dimension = c(0,0),birth = c(0,0),death = c(0.9640122,1.3467424)) D2 = data.frame(dimension = c(0,0),birth = c(0,0),death = c(1.233867,1.398447)) phom1 = D1 phom2 = D2 D1_subset <- D1[,2:3] D2_subset <- D2[,2:3] diag1 <- D1_subset[0,] diag2 <- D2_subset[0,] D1_subset <- D1_subset[which(D1_subset[,1] != D1_subset[,2]),] D2_subset <- D2_subset[which(D2_subset[,1] != D2_subset[,2]),] if(nrow(D1_subset) > 0) { for(i in 1:nrow(D1_subset)) { proj_diag <- mean(as.numeric(D1_subset[i,])) diag1 <- rbind(diag1,data.frame(birth = proj_diag,death = proj_diag)) } } if(nrow(D2_subset) > 0) { for(i in 1:nrow(D2_subset)) { proj_diag <- mean(as.numeric(D2_subset[i,])) diag2 <- rbind(diag2,data.frame(birth = proj_diag,death = proj_diag)) } } D1_subset <- rbind(D1_subset,diag2) D2_subset <- rbind(D2_subset,diag1) dist_mat_bottleneck <- as.matrix(rdist::cdist(D1_subset,D2_subset,metric = "maximum")) dist_mat_2 <- dist_mat_bottleneck^2 dist_mat_3 <- dist_mat_bottleneck^3 min_bottleneck = Inf min_wass_2 = Inf min_wass_3 = Inf perms = matrix(data = c(1,2,3,4,1,2,4,3,1,3,2,4,1,3,4,2,1,4,2,3,1,4,3,2,2,1,3,4,2,1,4,3,2,3,1,4,2,3,4,1,2,4,1,3,2,4,3,1,3,1,2,4,3,1,4,2,3,2,1,4,3,2,4,1,3,4,1,2,3,4,2,1,4,1,2,3,4,1,3,2,4,2,1,3,4,2,3,1,4,3,1,2,4,3,2,1),nrow = 24,ncol = 4,byrow = T) class(perms) <- c("matrix","array") for(i in 1:nrow(perms)) { for(j in 1:nrow(perms)) { if(i != j) { temp = cbind(data.frame(x = perms[i,]),data.frame(y = perms[j,])) temp = as.matrix(temp[which(temp[,1] <= (nrow(D1_subset) - nrow(diag2)) | temp[,2] <= (nrow(D2_subset) - nrow(diag1))),]) if(max(dist_mat_bottleneck[temp]) < min_bottleneck) { min_bottleneck <- max(dist_mat_bottleneck[temp]) } if(sqrt(sum(dist_mat_2[temp])) < min_wass_2) { min_wass_2 <- sqrt(sum(dist_mat_2[temp])) } if((sum(dist_mat_3[temp]))^(1/3) < min_wass_3) { min_wass_3 <- (sum(dist_mat_3[temp]))^(1/3) } } } } expect_equal(diagram_distance(phom1,phom2,p = 2),min_wass_2) expect_equal(diagram_distance(phom1,phom2,p = 3),min_wass_3) expect_equal(diagram_distance(phom1,phom2,p = Inf),min_bottleneck) # expect_equal(diagram_distance(phom1,phom2,distance = "fisher",sigma = 1),diagram_distance(phom1,phom2,distance = "fisher",sigma = 1,rho = 0.0001),tolerance = 0.0001) }) test_that("distance_matrix detects incorrect parameters correctly",{ D1 <- data.frame(dimension = 0,birth = 2,death = 3) D2 <- data.frame(dimension = 0,birth = 2,death = 3.1) D3 <- data.frame(dimension = 0,birth = c(2,5),death = c(3.1,6)) expect_error(distance_matrix(diagrams = list(D1,D2,D3),num_workers = 0),"num_workers") expect_error(distance_matrix(diagrams = list(D1,D2,D3),num_workers = "2"),"num_workers") expect_error(distance_matrix(diagrams = list(D1,D2,D3),num_workers = 1.1),"whole") }) test_that("distance_matrix is computing correctly",{ D1 <- data.frame(dimension = 0,birth = 2,death = 3) D2 <- data.frame(dimension = 0,birth = 2,death = 3.1) D3 <- data.frame(dimension = 0,birth = c(2,5),death = c(3.1,6)) m1 <- matrix(data = c(0,diagram_distance(D1,D2,dim = 0,p = 2,distance = "wasserstein"),diagram_distance(D1,D2,dim = 0,p = 2,distance = "wasserstein"),0),byrow = T,nrow = 2,ncol = 2) m2 <- matrix(data = c(0,diagram_distance(D1,D2,dim = 0,p = 3,distance = "wasserstein"),diagram_distance(D1,D3,dim = 0,p = 3,distance = "wasserstein"),diagram_distance(D1,D2,dim = 0,p = 3,distance = "wasserstein"),0,diagram_distance(D2,D3,dim = 0,p = 3,distance = "wasserstein"),diagram_distance(D1,D3,dim = 0,p = 3,distance = "wasserstein"),diagram_distance(D3,D2,dim = 0,p = 3,distance = "wasserstein"),0),byrow = T,nrow = 3,ncol = 3) m3 <- matrix(data = c(0,diagram_distance(D1,D3,dim = 0,distance = "fisher",sigma = 1),diagram_distance(D1,D2,dim = 0,distance = "fisher",sigma = 1),diagram_distance(D3,D2,dim = 0,distance = "fisher",sigma = 1)),byrow = T,nrow = 2,ncol = 2) expect_identical(distance_matrix(diagrams = list(D1,D2),dim = 0,distance = "wasserstein",p = 2,num_workers = 2),m1) expect_equal(distance_matrix(diagrams = list(D1,D2,D3),dim = 0,distance = "wasserstein",p = 3,num_workers = 2),m2) expect_equal(distance_matrix(diagrams = list(D1,D2),other_diagrams = list(D1,D3),dim = 0,distance = "fisher",sigma = 1,num_workers = 2),m3) # expect_equal(distance_matrix(diagrams = list(D1,D2),other_diagrams = list(D1,D3),dim = 0,distance = "fisher",sigma = 1,num_workers = 2,rho = 0.00001),m3,tolerance = 0.001) })