X-Git-Url: https://git.auder.net/?a=blobdiff_plain;ds=sidebyside;f=pkg%2Ftests%2Ftestthat%2Ftest.computeFilaments.R;h=ec39340af424847e263d91108eeb591c9d32e779;hb=eef545170c5a76b710184db6b695c15b20759177;hp=9de6274a3162840a9b8ffa444b8c47c07dcb2de8;hpb=1e20780ee1505fac6c7ed68d340892c497524561;p=talweg.git

diff --git a/pkg/tests/testthat/test.computeFilaments.R b/pkg/tests/testthat/test.computeFilaments.R
index 9de6274..ec39340 100644
--- a/pkg/tests/testthat/test.computeFilaments.R
+++ b/pkg/tests/testthat/test.computeFilaments.R
@@ -1,36 +1,87 @@
-#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$new()
+	x = seq(0,9.5,0.1)
+	L = length(x) #96 1/4h
+	s1 = cos(x)
+	s2 = sin(x)
+	s3 = c( s1[1:(L%/%2)] , s2[(L%/%2+1):L] )
+	#sum((s1-s2)^2) == 96
+	#sum((s1-s3)^2) == 58
+	#sum((s2-s3)^2) == 38
+	s = list(s1, s2, s3)
+	series = list()
+	for (i in seq_len(n))
 	{
-		beta_ref = betas_ref[,,i]
-		#all parameters are supposed to be of norm 1: thus, normalize beta_ref
-		norm2 = sqrt(colSums(beta_ref^2))
-		beta_ref = beta_ref / norm2[col(beta_ref)]
+		serie = s[[I(i)]] + rnorm(L,sd=0.01)
+		level = mean(serie)
+		serie = serie - level
+		# 10 series with NAs for index 2
+		if (I(i) == 2 && i >= 60 && i<= 90)
+			serie[sample(seq_len(L),1)] = NA
+		time = as.POSIXct(i*15*60, origin="2007-01-01", tz="GMT")
+		exo = runif(4)
+		exo_hat = runif(4)
+		data$append(time, serie, level, exo, exo_hat)
+	}
+	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
+	pred = computeForecast(data, 143, "Neighbors", "Zero",
+		horizon=length(data$getSerie(1)), simtype="endo", h_window=1)
+	f = computeFilaments(data, pred, 1, 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
+	pred = computeForecast(data, 142, "Neighbors", "Zero",
+		horizon=length(data$getSerie(1)), simtype="endo", h_window=1)
+	f = computeFilaments(data, pred, 1, limit=50, plot=FALSE)
+
+	# Expected output: 50-10-3 series of type 1, then 13 series of type 3 (closest next)
+	# NOTE: -10 because only past days with no-NAs tomorrow => exclude type 1 in [60,90[
+	expect_identical(length(f$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*")
 })