adding more refactor tests

tests/assets/refactor.ranked_list.adjusted.txt

@@ -0,0 +1 @@
+99,62,60,37,54,4,16,22,92,78,7,69,77,5,82,90,50,26,10,38,32,18,52,89,95,11,27,45,24,57,21,93,76,72,39,43,20,88,30,40,87,67,29,79,1,15,12,100,73,98,75,70,65,86,44,48,9,31,3,14,61,66,46,97,91,80,33,68,84,25,51,36,13,59,2,74,94,96,23,28,71,81,55,49,83,42,35,58,63,64,85,53,6,19,34,17,8,56,47,41

tests/assets/refactor.ranked_list.txt

@@ -1 +1 @@
-99,62,60,37,54,4,16,22,92,78,7,69,77,5,82,90,50,26,10,38,32,18,52,89,95,11,27,45,24,57,21,93,76,72,39,43,20,88,30,40,87,67,29,79,1,15,12,100,73,98,75,70,65,86,44,48,9,31,3,14,61,66,46,97,91,80,33,68,84,25,51,36,13,59,2,74,94,96,23,28,71,81,55,49,83,42,35,58,63,64,85,53,6,19,34,17,8,56,47,41
+62,37,99,54,16,60,4,69,77,78,92,10,90,45,22,5,95,18,93,38,26,21,50,7,24,32,52,43,88,57,76,79,12,40,82,44,39,89,75,86,100,87,98,72,70,27,11,29,15,31,48,30,73,61,1,46,9,13,66,97,91,36,80,51,65,84,20,28,96,2,74,67,3,68,59,94,14,23,33,49,81,71,25,58,85,55,42,63,83,34,64,53,35,17,19,56,6,8,41,47

tests/testthat/test_refactor.R

@@ -2,30 +2,45 @@ library("TCA")
 
 context("Test ReFACTor")
 
-# test_that("Compare the result of refactor with those of the matlab version of refactor", {
-#
-#   skip_on_cran()
-#
-#   basedir <- "../assets/"
-#
-#   # load data and the matlab results
-#   X <- as.matrix(read.table(paste(basedir,"X.txt",sep=""), header = FALSE, sep=","))
-#   rownames(X) <- 1:nrow(X)
-#   C <- as.matrix(read.table(paste(basedir,"C2.txt",sep=""), header = FALSE, sep=","))
-#   t <- 50
-#   k <- 3
-#   matlab.ref_comp <- as.matrix(read.table(paste(basedir,"refactor.R_est.txt",sep=""), header = FALSE, sep=","))
-#   matlab.ranked_list <- as.matrix(read.table(paste(basedir,"refactor.ranked_list.txt",sep=""), header = FALSE, sep=","))
-#
-#   # run refactor
-#   ref <- refactor(t(X), k, sparsity = t, C = C, sd_threshold = 0, num_comp = ncol(matlab.ref_comp), debug = TRUE, log_file = NULL)
-#
-#   # evaluating the feature ranking
-#   expect_equal(sum(as.numeric(substring(ref[["ranked_list"]], 2)) == matlab.ranked_list), ncol(X))
-#
-#   # evaluate correlation of the refactor components
-#   for (h in 1:2){
-#     expect_equal(abs(cor(matlab.ref_comp[,h],ref[["scores"]][,h])) > 0.99, TRUE)
-#   }
-#
-# })
+test_that("Compare the result of refactor with those of the matlab version of refactor", {
+
+  skip_on_cran()
+
+  basedir <- "../assets/"
+
+  # load data and the matlab results
+  X <- as.matrix(read.table(paste(basedir,"X.txt",sep=""), header = FALSE, sep=","))
+  rownames(X) <- 1:nrow(X)
+  C <- as.matrix(read.table(paste(basedir,"C2.txt",sep=""), header = FALSE, sep=","))
+  # add intercept
+  C <- cbind(matrix(1,nrow(X),1),C)
+  t <- 50
+  k <- 3
+  matlab.ref_comp <- as.matrix(read.table(paste(basedir,"refactor.R_est.txt",sep=""), header = FALSE, sep=","))
+  matlab.ranked_list <- as.matrix(read.table(paste(basedir,"refactor.ranked_list.txt",sep=""), header = FALSE, sep=","))
+  matlab.ref_comp.adj <- as.matrix(read.table(paste(basedir,"refactor.R_est.adjusted.txt",sep=""), header = FALSE, sep=","))
+  matlab.ref_comp.adj.C.remove <- as.matrix(read.table(paste(basedir,"refactor.R_est.adjusted.C.remove.txt",sep=""), header = FALSE, sep=","))
+  matlab.ranked_list.adj <- as.matrix(read.table(paste(basedir,"refactor.ranked_list.adjusted.txt",sep=""), header = FALSE, sep=","))
+
+  # run refactor
+  ref1 <- refactor(t(X), k, sparsity = t, C = NULL, sd_threshold = 0, num_comp = ncol(matlab.ref_comp), debug = TRUE, log_file = NULL)
+  ref2 <- refactor(t(X), k, sparsity = t, C = C, C.remove = FALSE, sd_threshold = 0, num_comp = ncol(matlab.ref_comp), debug = TRUE, log_file = NULL)
+  ref3 <- refactor(t(X), k, sparsity = t, C = C, C.remove = TRUE, sd_threshold = 0, num_comp = ncol(matlab.ref_comp), debug = TRUE, log_file = NULL)
+
+  # evaluating the feature ranking
+  expect_equal(sum(as.numeric(substring(ref1[["ranked_list"]], 2)) == matlab.ranked_list), ncol(X))
+  expect_equal(sum(as.numeric(substring(ref2[["ranked_list"]], 2)) == matlab.ranked_list.adj), ncol(X))
+  expect_equal(sum(as.numeric(substring(ref3[["ranked_list"]], 2)) == matlab.ranked_list.adj), ncol(X))
+
+  # evaluate correlation of the refactor components
+  for (h in 1:ncol(matlab.ref_comp)){
+    expect_equal(abs(cor(matlab.ref_comp[,h],ref1[["scores"]][,h])) > 0.99, TRUE)
+
+    expect_equal(abs(cor(matlab.ref_comp.adj.C.remove[,h],ref3[["scores"]][,h])) > 0.99, TRUE)
+  }
+  # the version with C.remove=TRUE has more differences compared with the matlab version - evaluate the the first two components only.
+  for (h in 1:2){
+    expect_equal(abs(cor(matlab.ref_comp.adj[,h],ref2[["scores"]][,h])) > 0.99, TRUE)
+  }
+
+})