Simplify plots: version OK with R6 classes

[talweg.git] / pkg / tests / testthat / test.computeFilaments.R

diff --git a/pkg/tests/testthat/test.computeFilaments.R b/pkg/tests/testthat/test.computeFilaments.R
index 9de6274..34e07f7 100644 (file)
--- a/pkg/tests/testthat/test.computeFilaments.R
+++ b/pkg/tests/testthat/test.computeFilaments.R
@@ -1,36 +1,80 @@
-#TODO: toy dataset, check that indices returned are correct + colors
+context("Check that computeFilaments behaves as expected")
 
-context("Check that getParamsDirs behaves as expected")
+#shorthand: map 1->1, 2->2, 3->3, 4->1, ..., 149->2, 150->3
+I = function(i)
+       (i-1) %% 3 + 1
 
-test_that("on input of sufficient size, beta is estimated accurately enough", {
-       n = 100000
-       d = 2
-       K = 2
-       Pr = c(0.5, 0.5)
-
-       betas_ref = array( c(1,0,0,1 , 1,-2,3,1), dim=c(2,2,2) )
-       for (i in 1:(dim(betas_ref)[3]))
+#MOCK data; NOTE: could be in inst/testdata as well
+getDataTest = function(n)
+{
+       data = Data$new()
+       x = seq(0,10,0.1)
+       L = length(x)
+       s1 = cos(x)
+       s2 = sin(x)
+       s3 = c( s1[1:(L%/%2)] , s2[(L%/%2+1):L] )
+       #sum((s1-s2)^2) == 97.59381
+       #sum((s1-s3)^2) == 57.03051
+       #sum((s2-s3)^2) == 40.5633
+       s = list( s1, s2, s3 )
+       series = list()
+       for (i in seq_len(n))
        {
-               beta_ref = betas_ref[,,i]
-               #all parameters are supposed to be of norm 1: thus, normalize beta_ref
-               norm2 = sqrt(colSums(beta_ref^2))
-               beta_ref = beta_ref / norm2[col(beta_ref)]
+               serie = s[[I(i)]] + rnorm(L,sd=0.01)
+               level = mean(serie)
+               serie = serie - level
+               # 10 series with NAs for index 2
+               if (I(i) == 2 && i >= 60 && i<= 90)
+                       serie[sample(seq_len(L),1)] = NA
+               data$append(c(), serie, level, c(), c()) #no need for more
+       }
+       data
+}
 
-               io = generateSampleIO(n, d, K, Pr, beta_ref)
-               beta = getParamsDirs(io$X, io$Y, K)
-               betas = .labelSwitchingAlign(
-                       array( c(beta_ref,beta), dim=c(d,K,2) ), compare_to="first", ls_mode="exact")
+test_that("output is as expected on simulated series",
+{
+       data = getDataTest(150)
 
-               #Some traces: 0 is not well estimated, but others are OK
-               cat("\n\nReference parameter matrix:\n")
-               print(beta_ref)
-               cat("Estimated parameter matrix:\n")
-               print(betas[,,2])
-               cat("Difference norm (Matrix norm ||.||_1, max. abs. sum on a column)\n")
-               diff_norm = norm(beta_ref - betas[,,2])
-               cat(diff_norm,"\n")
+       # index 143 : serie type 2
+       f = computeFilaments(data, 143, limit=60, plot=FALSE)
 
-               #NOTE: 0.5 is loose threshold, but values around 0.3 are expected...
-               expect_that( diff_norm, is_less_than(0.5) )
+       # Expected output: 50-3-10 series of type 2, then 23 series of type 3 (closest next)
+       expect_identical(length(f$neighb_indices), as.integer(60))
+       expect_identical(length(f$colors), as.integer(60))
+       expect_equal(f$index, 143)
+       expect_true(all(I(f$neighb_indices) >= 2))
+       for (i in 1:37)
+       {
+               expect_equal(I(f$neighb_indices[i]), 2)
+               expect_match(f$colors[i], f$colors[1])
+       }
+       for (i in 38:60)
+       {
+               expect_equal(I(f$neighb_indices[i]), 3)
+               expect_match(f$colors[i], f$colors[38])
+       }
+       expect_match(f$colors[1], "#1*")
+       expect_match(f$colors[38], "#E*")
+
+       # index 142 : serie type 1
+       f = computeFilaments(data, 142, limit=50, plot=FALSE)
+
+       # Expected output: 50-10-3 series of type 1, then 13 series of type 3 (closest next)
+       # NOTE: -10 because only past days with no-NAs tomorrow => exclude type 1 in [60,90[
+       expect_identical(length(f$neighb_indices), as.integer(50))
+       expect_identical(length(f$colors), as.integer(50))
+       expect_equal(f$index, 142)
+       expect_true(all(I(f$neighb_indices) != 2))
+       for (i in 1:37)
+       {
+               expect_equal(I(f$neighb_indices[i]), 1)
+               expect_match(f$colors[i], f$colors[1])
+       }
+       for (i in 38:50)
+       {
+               expect_equal(I(f$neighb_indices[i]), 3)
+               expect_match(f$colors[i], f$colors[38])
        }
+       expect_match(f$colors[1], "#1*")
+       expect_match(f$colors[38], "#E*")
 })