test_that("a solution is found in the two-tensor case and Y", { I = 10 J = 5 K = 3 df = array(rnorm(I*J*K), c(I,J,K)) datasets = list(df, df) Y = matrix(rnorm(I), nrow=I, ncol=1) modes = list(c(1,2,3), c(1,4,5)) Z = setupCMTFdata(datasets, modes) expect_no_error(acmtfr_opt(Z, Y, 1, max_iter=2)) }) test_that("a solution is found when running LBFGS", { I = 10 J = 5 K = 3 df = array(rnorm(I*J*K), c(I,J,K)) datasets = list(df, df) Y = matrix(rnorm(I), nrow=I, ncol=1) modes = list(c(1,2,3), c(1,4,5)) Z = setupCMTFdata(datasets, modes) expect_no_error(acmtfr_opt(Z, Y, 1, max_iter=2, method="L-BFGS")) }) test_that("the objective is very high if an incorrect solution is found", { set.seed(123) I = 10 J = 5 K = 3 L = 8 M = 3 A = array(rnorm(I*2), c(I, 2)) B = array(rnorm(J*2), c(J, 2)) C = array(rnorm(K*2), c(K, 2)) D = array(rnorm(L*2), c(L, 2)) E = array(rnorm(M*2), c(M, 2)) df1 = reinflateTensor(A, B, C) df2 = reinflateTensor(A, D, E) datasets = list(df1, df2) modes = list(c(1,2,3), c(1,4,5)) Z = setupCMTFdata(datasets, modes) Y = matrix(rnorm(I), nrow=I, ncol=1) result = acmtfr_opt(Z, Y, 2, initialization="random", max_iter = 2) expect_gt(result$f, 0) }) test_that("allOutput=TRUE gives a list of expected length", { set.seed(123) I = 10 J = 5 K = 3 L = 8 M = 3 A = array(rnorm(I*2), c(I, 2)) B = array(rnorm(J*2), c(J, 2)) C = array(rnorm(K*2), c(K, 2)) D = array(rnorm(L*2), c(L, 2)) E = array(rnorm(M*2), c(M, 2)) df1 = reinflateTensor(A, B, C) df2 = reinflateTensor(A, D, E) datasets = list(df1, df2) modes = list(c(1,2,3), c(1,4,5)) Z = setupCMTFdata(datasets, modes) Y = matrix(rnorm(I), nrow=I, ncol=1) results = acmtfr_opt(Z, Y, 2, initialization="random", nstart=2, max_iter=2, allOutput=TRUE) expect_equal(length(results), 2) }) test_that("the sum of all loss terms is equal to f", { set.seed(123) I = 10 J = 5 K = 3 L = 8 M = 3 A = array(rnorm(I*2), c(I, 2)) B = array(rnorm(J*2), c(J, 2)) C = array(rnorm(K*2), c(K, 2)) D = array(rnorm(L*2), c(L, 2)) E = array(rnorm(M*2), c(M, 2)) df1 = reinflateTensor(A, B, C) df2 = reinflateTensor(A, D, E) datasets = list(df1, df2) modes = list(c(1,2,3), c(1,4,5)) Z = setupCMTFdata(datasets, modes) Y = matrix(rnorm(I), nrow=I, ncol=1) model = acmtfr_opt(Z, Y, 2, initialization="random", nstart=1, max_iter=2) f = sum(model$f_per_block) + model$f_y + sum(model$f_norms) + sum(model$f_lambda) expect_equal(model$f, f) }) test_that("running in parallel works", { skip_on_cran() set.seed(123) I = 10 J = 5 K = 3 L = 8 M = 3 A = array(rnorm(I*2), c(I, 2)) B = array(rnorm(J*2), c(J, 2)) C = array(rnorm(K*2), c(K, 2)) D = array(rnorm(L*2), c(L, 2)) E = array(rnorm(M*2), c(M, 2)) df1 = reinflateTensor(A, B, C) df2 = reinflateTensor(A, D, E) datasets = list(df1, df2) modes = list(c(1,2,3), c(1,4,5)) Z = setupCMTFdata(datasets, modes) Y = matrix(rnorm(I), nrow=I, ncol=1) expect_no_error(acmtfr_opt(Z,Y,2,initialization="random", nstart=2, max_iter=2, numCores=2)) }) test_that("different settings of pi yield a different fit", { set.seed(123) I = 10 J = 5 K = 3 L = 8 M = 3 A = array(rnorm(I*2), c(I, 2)) B = array(rnorm(J*2), c(J, 2)) C = array(rnorm(K*2), c(K, 2)) D = array(rnorm(L*2), c(L, 2)) E = array(rnorm(M*2), c(M, 2)) df1 = reinflateTensor(A, B, C) df2 = reinflateTensor(A, D, E) datasets = list(df1, df2) modes = list(c(1,2,3), c(1,4,5)) Z = setupCMTFdata(datasets, modes) Y = matrix(rnorm(I), nrow=I, ncol=1) model1 = acmtfr_opt(Z,Y,2,initialization="nvec",pi=0.1, nstart=1, max_iter=10) model2 = acmtfr_opt(Z,Y,2,initialization="nvec",pi=0.9, nstart=1, max_iter=10) expect_true(model1$f != model2$f) }) test_that("pi=1 gives post-hoc regression coefficients", { set.seed(123) I = 10 J = 5 K = 3 L = 8 M = 3 A = array(rnorm(I*2), c(I, 2)) B = array(rnorm(J*2), c(J, 2)) C = array(rnorm(K*2), c(K, 2)) D = array(rnorm(L*2), c(L, 2)) E = array(rnorm(M*2), c(M, 2)) df1 = reinflateTensor(A, B, C) df2 = reinflateTensor(A, D, E) datasets = list(df1, df2) modes = list(c(1,2,3), c(1,4,5)) Z = setupCMTFdata(datasets, modes) Y = matrix(rnorm(I), nrow=I, ncol=1) model = acmtfr_opt(Z,Y,2,initialization="nvec",pi=1, nstart=1, max_iter=2) expect_equal(dim(model$rho), c(2,1)) }) test_that("pi=0 throws no errors", { set.seed(123) I = 10 J = 5 K = 3 L = 8 M = 3 A = array(rnorm(I*2), c(I, 2)) B = array(rnorm(J*2), c(J, 2)) C = array(rnorm(K*2), c(K, 2)) D = array(rnorm(L*2), c(L, 2)) E = array(rnorm(M*2), c(M, 2)) df1 = reinflateTensor(A, B, C) df2 = reinflateTensor(A, D, E) datasets = list(df1, df2) modes = list(c(1,2,3), c(1,4,5)) Z = setupCMTFdata(datasets, modes) Y = matrix(A[,1]) expect_no_error(acmtfr_opt(Z,Y,2,initialization="random",pi=0, nstart=1, max_iter=2)) }) test_that("computing too many components is handled gracefully in the solve step", { set.seed(123) I = 21 J = 5 K = 3 L = 8 M = 3 A = array(rnorm(I*2), c(I, 2)) B = array(rnorm(J*2), c(J, 2)) C = array(rnorm(K*2), c(K, 2)) D = array(rnorm(L*2), c(L, 2)) E = array(rnorm(M*2), c(M, 2)) df1 = reinflateTensor(A, B, C) df2 = reinflateTensor(A, D, E) datasets = list(df1, df2) modes = list(c(1,2,3), c(1,4,5)) Z = setupCMTFdata(datasets, modes) Y = matrix(A[,1]) expect_no_error(acmtfr_opt(Z,Y,10,initialization="random",pi=0.95, nstart=1, max_iter=2)) })