Added test for SampleSplitSuperLearner

tests/testthat/test-SampleSplitSuperLearner.R

@@ -0,0 +1,37 @@
+########################
+# Simulate data
+set.seed(23432)
+# Create small training set for quick tests.
+n <- 80
+p <- 6
+X <- data.frame(matrix(rnorm(n * p), nrow = n, ncol = p))
+colnames(X) <- paste0("X", 1:p)
+gen_y = function(x) x[, 1] + sqrt(abs(x[, 2] * x[, 3])) + x[, 2] - x[, 3] + rnorm(nrow(x))
+Y <- gen_y(X)
+
+########################
+# Run the SampleSplitSuperLearner
+SL.library = c("SL.randomForest", "SL.glmnet", "SL.glm", "SL.mean")
+
+# default split: 80% in training, 20% in validation
+defaultSplit = formals(SampleSplitSuperLearner)$split
+sssl1 = SampleSplitSuperLearner(Y = Y, X = X, SL.library = SL.library)
+expect_equal(length(sssl1$validRows), (1 - defaultSplit) * n)
+expect_false(any(sssl1$errorsInLibrary))
+expect_false(any(sssl1$errorsInCVLibrary))
+
+# split by specifying row numbers in validation set
+validRows = seq(from = round(n/5), to = n, by = round(n/10))
+sssl2 = SampleSplitSuperLearner(Y = Y, X = X, SL.library = SL.library,
+								split = validRows)
+expect_equal(sssl2$validRows, validRows)
+expect_false(any(sssl2$errorsInLibrary))
+expect_false(any(sssl2$errorsInCVLibrary))
+
+# split with only one observation in validation set
+validRows = 1
+sssl3 = SampleSplitSuperLearner(Y = Y, X = X, SL.library = SL.library,
+								split = validRows)
+expect_equal(sssl3$validRows, validRows)
+expect_false(any(sssl3$errorsInLibrary))
+expect_false(any(sssl3$errorsInCVLibrary))