Reduced test length (#8)

tests/testthat/test-LogicRegr-example-match-1.4.3.R

@@ -1,26 +1,34 @@
-set.seed(040590)
+set.seed(265508)
+n_cores <- 1L
+n_row <- 100L
+n_col <- 11L
+n_tot <- n_row * n_col
 X1 <- as.data.frame(
   array(
-    data = rbinom(n = 50 * 1000, size = 1,
-    prob = runif(n = 50 * 1000, 0, 1)), dim = c(1000, 50)
+    data = rbinom(n = n_tot, size = 1, prob = runif(n = n_tot)),
+    dim = c(n_row, n_col)
   )
 )
 Y1 <- rnorm(
-  n = 1000,
-  mean = 1 + 0.7 * (X1$V1 * X1$V4) + 0.8896846 * (X1$V8 * X1$V11) + 1.434573 * (X1$V5 * X1$V9),
+  n = n_row,
+  mean = 1 +
+    0.7 * (X1$V1 * X1$V4) +
+    0.8896846 * (X1$V8 * X1$V11) +
+    1.434573 * (X1$V5 * X1$V9),
   sd = 1
 )
 X1$Y1 <- Y1
 
 # specify the initial formula
 formula1 <- as.formula(
-  paste(colnames(X1)[51], "~ 1 +", paste0(colnames(X1)[-c(51)], collapse = "+"))
+  paste(
+    colnames(X1)[n_col + 1L], "~ 1 +",
+    paste0(colnames(X1)[-c(n_col + 1L)], collapse = "+")
+  )
 )
 data.example <- as.data.frame(X1)
 
 # run the inference with robust g prior
-n_cores <- parallel::detectCores() - 1
-
 res4G <- EMJMCMC::LogicRegr(
   formula = formula1, data = data.example, family = "Gaussian", prior = "G",
   report.level = 0.5, d = 15, cmax = 2, kmax = 15, p.and = 0.9, p.not = 0.01,
@@ -35,11 +43,10 @@ res4J <- EMJMCMC::LogicRegr(
 )
 
 test_that("LogicRegr output matches version 1.4.3", {
-  # TODO: results below are from dev version. Install 1.4.3 and rerun to match.
-  obs_4G <- as.numeric(res4G$feat.stat[, 2])
-  obs_4J <- as.numeric(res4J$feat.stat[, 2])
-  expect_length(obs_4G, 3L)
-  expect_true(all(obs_4G > 0.9) && all(obs_4G < 1))
-  expect_length(obs_4J, 4L)
-  expect_true(all(obs_4J > 0.6) && all(obs_4J < 1))
+  obs_4G <- as.numeric(res4G$allposteriors[, 2])
+  obs_4J <- as.numeric(res4J$allposteriors[, 2])
+  expect_length(obs_4G, 15L)
+  expect_true(all(obs_4G >= 0) && all(obs_4G <= 1))
+  expect_length(obs_4J, 15L)
+  expect_true(all(obs_4J >= 0) && all(obs_4J <= 1))
 })

tests/testthat/test-parall.gmj-example-match-1.4.3.R

@@ -1,13 +1,17 @@
-j <- 1
-M <- 4
+set.seed(80334)
+n_cores <- min(parallel::detectCores() - 1L, 20L)
+M <- 1L
+n_row <- 100L
+n_col <- 50L
+n_tot <- n_row * n_col
 X4 <- as.data.frame(
   array(
-    data = rbinom(n = 50 * 1000, size = 1, prob = runif(n = 50 * 1000, 0, 1)),
-    dim = c(1000, 50)
+    data = rbinom(n = n_tot, size = 1, prob = runif(n = n_tot)),
+    dim = c(n_row, n_col)
   )
 )
 Y4 <- rnorm(
-  n = 1000,
+  n = n_row,
   mean = 1 +
     7 * (X4$V4 * X4$V17 * X4$V30 * X4$V10) +
     7 * (X4$V50 * X4$V19 * X4$V13 * X4$V11) +
@@ -22,53 +26,51 @@ Y4 <- rnorm(
 X4$Y4 <- Y4
 
 formula1 <- as.formula(
-  paste(colnames(X4)[51], "~ 1 +", paste0(colnames(X4)[-c(51)], collapse = "+"))
+  paste(
+    colnames(X4)[n_col + 1L], "~ 1 +",
+    paste0(colnames(X4)[-c(n_col + 1L)], collapse = "+")
+  )
 )
 data.example <- as.data.frame(X4)
 
 vect <- list(
   formula = formula1, outgraphs = FALSE, data = X4,
   estimator = estimate.logic.lm,
-  estimator.args = list(data = data.example, n = 100, m = 50),
-  recalc_margin = 249, save.beta = FALSE, interact = TRUE,
+  estimator.args = list(data = data.example, n = 100, m = n_col),
+  recalc_margin = 1000L, save.beta = FALSE, interact = TRUE,
   relations = c("", "lgx2", "cos", "sigmoid", "tanh", "atan", "erf"),
   relations.prob = c(0.4, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0),
   interact.param = list(
-    allow_offsprings = 1, mutation_rate = 250, last.mutation = 15000,
+    allow_offsprings = 1, mutation_rate = 250, last.mutation = 1000,
     max.tree.size = 4, Nvars.max = 40, p.allow.replace = 0.7,
     p.allow.tree = 0.2, p.nor = 0, p.and = 0.9
-  ), n.models = 20000, unique = TRUE, max.cpu = 4, max.cpu.glob = 4,
+  ), n.models = 20000, unique = TRUE, max.cpu = n_cores, max.cpu.glob = n_cores,
   create.table = FALSE, create.hash = TRUE, pseudo.paral = TRUE,
   burn.in = 50, print.freq = 1000,
   advanced.param = list(
-    max.N.glob = as.integer(10),
-    min.N.glob = as.integer(5),
-    max.N = as.integer(3),
-    min.N = as.integer(1),
-    printable = FALSE
+    max.N.glob = 10L, min.N.glob = 5L, max.N = 3L, min.N = 1L, printable = FALSE
   )
 )
 
 params <- list(vect)[rep(1, M)]
 
-for (i in 1:M) {
+for (i in seq_len(M)) {
   params[[i]]$cpu <- i
-  params[[i]]$NM <- 1000
+  params[[i]]$NM <- n_row
   params[[i]]$simlen <- 21
 }
 
-message("begin simulation ", j)
-set.seed(363571)
-results <- parall.gmj(X = params, M = 1)
+results <- parall.gmj(X = params, M = n_cores)
 
 test_that("parall.gmj output matches version 1.4.3", {
   # TODO: results below are from dev version. Install 1.4.3 and rerun to match.
-  expect_length(results, 4L)
-  for (i in 1:4) {
+  expect_length(results, M)
+  for (i in seq_len(M)) {
     expect_length(results[[i]], 4L)
+    expect_named(results[[i]], c("post.populi", "p.post", "cterm", "fparam"))
     expect_length(results[[i]][[2]], 40L)
-    expect_true(all(results[[i]][[2]] < 1.0))
-    expect_lt(results[[i]][[3]], 0)
+    expect_true(all(results[[i]][[2]] <= 1.0))
+    expect_length(results[[i]][[3]], 1L)
     expect_length(results[[i]][[4]], 40L)
   }
 })