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Merge pull request #55 from ocbe-uio/issue-53
Fixed issue #53
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,120 @@ | ||
| # Install and load the package, set the seed | ||
| set.seed(1235) | ||
|
|
||
| # Generate the data | ||
| N <- 100 | ||
| p <- 50 | ||
| nz <- 4 | ||
| K <- nz | ||
| X <- matrix(rnorm(n = N * p), nrow = N, ncol = p) | ||
| mx <- colMeans(X) | ||
| sx <- sqrt(apply(X, 2, var)) | ||
| X <- scale(X, mx, sx) | ||
| X <- matrix(as.numeric(X), N, p) | ||
| Z <- matrix(rnorm(N * nz), N, nz) | ||
| mz <- colMeans(Z) | ||
| sz <- sqrt(apply(Z, 2, var)) | ||
| Z <- scale(Z, mz, sz) | ||
| beta_1 <- rep(x = 0, times = p) | ||
| beta_2 <- rep(x = 0, times = p) | ||
| beta_3 <- rep(x = 0, times = p) | ||
| beta_4 <- rep(x = 0, times = p) | ||
| beta_5 <- rep(x = 0, times = p) | ||
| beta_6 <- rep(x = 0, times = p) | ||
|
|
||
| beta_1[1:5] <- c(2, 2, 2, 2, 2) | ||
| beta_2[1:5] <- c(2, 2, 2, 2, 2) | ||
| beta_3[6:10] <- c(2, 2, 2, -2, -2) | ||
| beta_4[6:10] <- c(2, 2, 2, -2, -2) | ||
| beta_5[11:15] <- c(-2, -2, -2, -2, -2) | ||
| beta_6[11:15] <- c(-2, -2, -2, -2, -2) | ||
|
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||
| Beta <- cbind(beta_1, beta_2, beta_3, beta_4, beta_5, beta_6) | ||
|
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| colnames(Beta) <- 1:6 | ||
|
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||
| theta <- array(0, c(p, K, 6)) | ||
| theta[1, 1, 1] <- 2 | ||
| theta[3, 2, 1] <- 2 | ||
| theta[4, 3, 1] <- -2 | ||
| theta[5, 4, 1] <- -2 | ||
| theta[1, 1, 2] <- 2 | ||
| theta[3, 2, 2] <- 2 | ||
| theta[4, 3, 2] <- -2 | ||
| theta[5, 4, 2] <- -2 | ||
| theta[6, 1, 3] <- 2 | ||
| theta[8, 2, 3] <- 2 | ||
| theta[9, 3, 3] <- -2 | ||
| theta[10, 4, 3] <- -2 | ||
| theta[6, 1, 4] <- 2 | ||
| theta[8, 2, 4] <- 2 | ||
| theta[9, 3, 4] <- -2 | ||
| theta[10, 4, 4] <- -2 | ||
| theta[11, 1, 5] <- 2 | ||
| theta[13, 2, 5] <- 2 | ||
| theta[14, 3, 5] <- -2 | ||
| theta[15, 4, 5] <- -2 | ||
| theta[11, 1, 6] <- 2 | ||
| theta[13, 2, 6] <- 2 | ||
| theta[14, 3, 6] <- -2 | ||
| theta[15, 4, 6] <- -2 | ||
|
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||
| pliable <- matrix(0, N, 6) | ||
| for (e in 1:6) { | ||
| pliable[, e] <- compute_pliable(X, Z, theta[, , e]) | ||
| } | ||
| esd <- diag(6) | ||
| e <- MASS::mvrnorm(N, mu = rep(0, 6), Sigma = esd) | ||
| y_train <- X %*% Beta + pliable + e | ||
| y <- y_train | ||
| colnames(y) <- paste0("y", seq_len(ncol(y))) | ||
| TT <- tree_parms(y) | ||
|
|
||
| gg1 <- matrix(0, 2, 2) | ||
| gg1[1, ] <- c(0.02, 0.02) | ||
| gg1[2, ] <- c(0.2, 0.2) | ||
| nlambda <- 2 | ||
| e.abs <- 1E-4 | ||
| e.rel <- 1E-2 | ||
| alpha <- 0.5 | ||
| tol <- 1E-3 | ||
| # Fitting models | ||
| set.seed(1235) | ||
| fit_C <- MADMMplasso( | ||
| X, Z, y, | ||
| alpha = alpha, my_lambda = NULL, | ||
| lambda_min = 0.001, max_it = 5000, e.abs = e.abs, e.rel = e.rel, maxgrid = nlambda, | ||
| nlambda = nlambda, rho = 5, tree = TT, my_print = FALSE, alph = 1, | ||
| pal = FALSE, gg = gg1, tol = tol, legacy = FALSE, cl = 2L | ||
| ) | ||
|
|
||
| set.seed(1235) | ||
| fit_R <- MADMMplasso( | ||
| X, Z, y, | ||
| alpha = alpha, my_lambda = NULL, | ||
| lambda_min = 0.001, max_it = 5000, e.abs = e.abs, e.rel = e.rel, maxgrid = nlambda, | ||
| nlambda = nlambda, rho = 5, tree = TT, my_print = FALSE, alph = 1, | ||
| pal = FALSE, gg = gg1, tol = tol, legacy = TRUE, cl = 2L | ||
| ) | ||
|
|
||
| test_that("C++ and R versions basically output the same thing", { | ||
| expect_named(fit_C$beta, names(fit_R$beta)) | ||
| tl <- 1e1 | ||
| expect_equal(fit_C$beta0[[1]], as.matrix(fit_R$beta0[[1]]), tolerance = tl) | ||
| expect_equal(as.vector(fit_C$beta[[1]]), as.vector(fit_R$beta[[1]]), tolerance = tl) | ||
| expect_equal(as.vector(fit_C$BETA_hat[[1]]), as.vector(fit_R$BETA_hat[[1]]), tolerance = tl) | ||
| expect_equal(fit_C$theta0[[1]], fit_R$theta0[[1]], tolerance = tl) | ||
| for (i in 1:6) { | ||
| expect_equal( | ||
| as.vector(fit_C$theta[[1]][, , i]), | ||
| as.vector(fit_R$theta[[1]][, , i]), | ||
| tolerance = tl | ||
| ) | ||
| } | ||
| expect_equal(fit_C$path, fit_R$path, tolerance = tl) | ||
| expect_identical(fit_C$Lambdas, fit_R$Lambdas) | ||
| expect_equal(fit_C$non_zero, as.matrix(fit_R$non_zero), tolerance = tl) | ||
| expect_equal(fit_C$LOSS, as.matrix(fit_R$LOSS), tolerance = tl) | ||
| expect_equal(fit_C$Y_HAT[[1]], fit_R$Y_HAT[[1]], tolerance = tl) | ||
| expect_identical(fit_C$gg, fit_R$gg) | ||
| }) |