tests/testthat/test_BenchmarkF.R
In BIMSBbioinfo/reg2gene: Predicting the target genes for regulatory elements
# #######################################
# # -------- ConfusionMatrix & Benchmark tests
#
#
# Dataset description:
# examples created to cover following possibilities for benchmarking and
# filtering
# 0) anchor1(re2gene) overlaps anchor1 [1 ranges in benchmark dataset]
# chr1 [ 1, 4] --- chr1 [ 30, 31]
# 1) anchor1(re2gene) overlaps anchor1 [2 ranges in benchmark dataset]
# chr1 [200, 201] * | chr1:202-203
# 2) anchor1 overlaps anchor2 [2 ranges in benchmark dataset]
# chr1 [ 5, 9] * | chr1:31-32
# 3) anchor1(re2gene) overlaps anchor2 [1 range in benchmark dataset] but
# as well anchor1 from another GInteraction pair
# chr1 [ 1, 4] --- chr1 [ 5, 9]
# 4) no overlap with benchmark dataset but NOT filtered out
# chr1 [ 24, 29] --- chr1 [ 5, 10]
# 5) no overlap with benchmark dataset but filtered out
# chr1 [ 15, 20] --- chr1 [ 21, 25]
# 6) another chr example
# chr2 [ 1, 4] --- chr2 [ 5, 9]
# 7) >2 anchor1 overlaps - a1a2,a1a2,a1a1
# chr1 [100, 101] --- chr1 [102, 103]
# 8) input anchors overlap
# chr1 [ 30, 31] --- chr1 [ 31, 32]
library(reg2gene)
require(GenomicRanges)
require(InteractionSet)
require(stringr)
require(testthat)
reg2Gene <- GInteractions(GRReg1_toy,GRReg1_toy$reg)
benchData <- GInteractions(GRReg2_toy,GRReg2_toy$reg)
benchDataList <- list(benchData,reg2Gene)
# for CM data
reg2GeneBench <- reg2Gene
reg2GeneBench$PValue <- seq(0, 1,length.out = length(reg2Gene))
##################################################
# -----------------------------------------------#
# Benchmark testing()
# NOTE: did not test ignore.strands function:
test_that("test that benchmark/filter inputs correct GInteractions object
form", {
PredefinedMetaData <- c("anchor1.Bench1Exp","anchor1.Filter2Exp",
"anchor1.Bench2Exp","anchor1.Filter1Exp")
expect_is(reg2Gene,"GInteractions")
expect_is(benchData,"GInteractions")
expect_true(all(PredefinedMetaData%in%colnames(mcols(reg2Gene))),
"Predifined filter/bench results missing in reg2gene")
})
test_that("test that benchmark/filter inputs correct list of GIs
object form", {
expect_is(benchDataList,"list","List: not inputed correctly to benchmarkInteractions ")
expect_failure(expect_is(benchDataList,"GInteractions"))
expect_equal(length(benchDataList),2)
expect_equal(sapply(benchDataList, length),c(14,9))
})
test_that("test that benchmark outputs correct GIs object form", {
benchR <- benchmarkInteractions(interactions=reg2Gene,
benchInteractions = benchData,
binary=TRUE)
expect_is(benchR,"GInteractions")
expect_true("Bench"%in%names(mcols(benchR)),"Bench column not returned")
# test functionality:
# ------------------
# binary=T returns binary 1 and 0
expect_false(all(benchR$Bench==benchR$anchor1.Bench1Exp),"should be binary
benchmark")
expect_true(all(as.logical(benchR$Bench)==as.logical(
benchR$anchor1.Bench1Exp)),"counts to binary benchmark")
# binary=F returns not >1 as well
benchR <- benchmarkInteractions (reg2Gene,benchData,binary=F)
expect_true(all(benchR$Bench==benchR$anchor1.Bench1Exp),"shouldn't be
binary benchmark")
# Checking what happends when anchor1&anchor2 both overlap only one region
A1A1A2 <- benchmarkInteractions (reg2Gene[1],
benchData[1])
expect_equal(A1A1A2$Bench,0)
# Check filtering procedure
benchR <- benchmarkInteractions(reg2Gene,benchData,
binary=TRUE,
preFilter = TRUE )
expect_true(all(as.logical(benchR$Filter)==as.logical(
benchR$anchor1.Filter1Exp)),"filter fails")
filter2 <- benchmarkInteractions(reg2Gene,reg2Gene,
binary=TRUE,
preFilter = TRUE )
expect_true(all(as.logical(filter2$Filter)==as.logical(
benchR$anchor1.Filter2Exp)),"filter fails")
# FORCE BY NAME
reg2Gene$name <- reg2Gene$anchor1.name
benchData$name <- benchData$anchor1.name
ForcingName <- benchmarkInteractions(reg2Gene,
benchData,
binary=FALSE,
forceByName = T)
expect_equal(ForcingName$Bench,c(1,2,0,2,1,0,3,1,2))
})
test_that("test that benchmark outputs correct GIs object for list of GIs",{
benchRList <- benchmarkInteractions (reg2Gene,benchDataList,binary=TRUE)
# OUTPUT OK:
expect_is(benchRList,"GInteractions")
#if bench input list, then bench column N should be equal to the length of
# the list
expect_equal(length(mcols(reg2Gene))+length(benchDataList),
length(mcols(benchRList)))
# names of the list added ok
names(benchDataList) <- c("B1","B2")
benchRList <- benchmarkInteractions (reg2Gene,benchDataList,binary=TRUE)
expect_true(all(c("B1","B2")%in%colnames(mcols(benchRList))),
"list names adjusted correctly in benchmark f()")
# test functionality:
# ------------------
expect_equal(benchRList$B1,c(1,1,0,0,1,1,1,1,1))
expect_equal(benchRList$B2,rep(1,9))
# testing binary argument
benchRList <- benchmarkInteractions (reg2Gene,benchDataList,binary=FALSE)
expect_equal(benchRList$B1,c(2,1,0,0,1,2,3,2,2))
})
#######################################
# -------- ConfusionMatrixReg2Gene
#
# context("confusionMatrix function runs correctly")
test_that("Input tests for confusionMatrix", {
expect_is(reg2GeneBench,"GInteractions")
# given programmed examples, ppv should be returned as 1
expect_equal(confusionMatrix(reg2GeneBench,
thresholdID="PValue",
benchCol = "anchor1.Bench1Exp"),1)
expect_error(confusionMatrix(reg2GeneBench))
})
# TEST benchCol: DONE
test_that("benchCol for confusionMatrix is correctly used", {
# works if benchCol defined + PPV
expect_is(confusionMatrix(interactionsBench=reg2GeneBench,
thresholdID="PValue",
benchCol = "anchor1.Bench1Exp"),"numeric")
# works if benchCol defined + PPV + filter column added
expect_is(confusionMatrix(reg2GeneBench,
thresholdID="PValue",
benchCol = "anchor1.Bench1Exp",
prefilterCol="anchor1.Filter1Exp"),"numeric")
# works if benchCol defined + ConfusionMatrix statistics
expect_is(confusionMatrix(reg2GeneBench,
thresholdID="PValue",
benchCol = "anchor1.Bench1Exp",
statistics = "ConfusionMatrix"),"list")
# NOT working if benchCol NOT defined & Bench column does NOT exist
expect_error(confusionMatrix(reg2GeneBench,
thresholdID="PValue",
statistics = "ConfusionMatrix"))
# works if benchCol NOT defined, but Benchmark column exists
reg2GeneBench$Bench <- reg2GeneBench$anchor1.Bench1Exp
expect_is(confusionMatrix(reg2GeneBench,
thresholdID="PValue",
statistics = "ConfusionMatrix"),"list")
})
# TEST thresholdID argument: DONE
test_that("thresholdID for confusionMatrix is correctly used", {
# if thresholdID not defined - expect error
expect_success(expect_error(confusionMatrix(reg2GeneBench,
benchCol = "anchor1.Bench1Exp")))
# if thresholdID wrongly defined - expect error
expect_success(expect_error(confusionMatrix(reg2GeneBench,
thresholdID="PVal",
benchCol = "anchor1.Bench1Exp")))
# all fine
expect_is(confusionMatrix(reg2GeneBench,
thresholdID="PValue",
benchCol = "anchor1.Bench1Exp"),"numeric")
})
# TEST prefilterCol argument: DONE
test_that("prefilterCol for confusionMatrix is correctly used", {
# function works well with both statistics:ConfusionMatrix and filter on
expect_failure(expect_error(confusionMatrix(reg2GeneBench,
benchCol = "anchor1.Bench1Exp",
prefilterCol = "anchor1.Filter1Exp",
thresholdID = "PValue",
statistics="ConfusionMatrix")))
# function works well with both statistics:ConfusionMatrix and filter off
expect_failure(expect_error(confusionMatrix(reg2GeneBench,
benchCol = "anchor1.Bench1Exp",
thresholdID = "PValue",
statistics="ConfusionMatrix")))
# function should fail if thresholdID is missing
expect_error(confusionMatrix(reg2GeneBench,benchCol = "anchor1.Bench1Exp",
statistics="ConfusionMatrix"))
# function works well with statistics:PPV
expect_failure(expect_error(confusionMatrix(reg2GeneBench,
benchCol = "anchor1.Bench1Exp",
thresholdID = "PValue",
statistics="PPV")))
# function works when prefilterCol defined
expect_is(confusionMatrix(reg2GeneBench,
benchCol = "anchor1.Bench1Exp",
prefilterCol = "anchor1.Filter1Exp",
thresholdID="PValue"),"numeric")
# function works when prefilterCol NOT defined
expect_is(confusionMatrix(reg2GeneBench,
benchCol = "anchor1.Bench1Exp",
thresholdID="PValue"),"numeric")
######################################
#---------functionality given prefilterCol
# functionality when prefilterCol==NULL (not defined)
ConfusionMatrix.F <- confusionMatrix(reg2GeneBench,
benchCol = "anchor1.Bench1Exp",
prefilterCol = "anchor1.Filter1Exp",
thresholdID="PValue",
statistics="ConfusionMatrix")
# functionality when prefilterCol!=NULL
ConfusionMatrix.NF <- confusionMatrix(reg2GeneBench,
benchCol = "anchor1.Bench1Exp",
thresholdID="PValue",
statistics="ConfusionMatrix")
# filtering gives different N of TN given toy example
expect_true(ConfusionMatrix.F$TN!=ConfusionMatrix.NF$TN)
})
# thresholdValue and statistics is calculated ok: DONE
test_that("thresholdValue and statistics for confusionMatrix is correct", {
# function works when no thresholdValue defined - 0.05 default
expect_is(confusionMatrix(reg2GeneBench,
benchCol = "anchor1.Bench1Exp",
prefilterCol = "anchor1.Filter1Exp",
thresholdID="PValue"),"numeric")
#########################################
# Correct functionality:
ConfusionMatrix.F <- confusionMatrix(reg2GeneBench,
thresholdID = "PValue",
thresholdValue = 0.05,
benchCol = "anchor1.Bench1Exp",
prefilterCol = "anchor1.Filter1Exp",
statistics = "ConfusionMatrix")
ConfusionMatrix.NF <- confusionMatrix(reg2GeneBench,
thresholdID = "PValue",
thresholdValue = 0.05,
benchCol = "anchor1.Bench1Exp",
statistics = "ConfusionMatrix")
# filtering produces different results in confusion matrix
expect_true(any(unlist(ConfusionMatrix.F)!=unlist(ConfusionMatrix.NF)))
# different thresholdValue produces different results in confusion matrix
ConfusionMatrix.FT <- confusionMatrix(reg2GeneBench,
thresholdID = "PValue",
thresholdValue = 0.5,
benchCol = "anchor1.Bench1Exp",
prefilterCol = "anchor1.Filter1Exp",
statistics = "ConfusionMatrix")
expect_true(any(unlist(ConfusionMatrix.F)!=unlist(ConfusionMatrix.FT)))
#############################
# functionality is calculated correctly:
# TESTING TP,FP,TN,FN:
# benchmarking is correct
ConfusionMatrix.F$NPV <- round(ConfusionMatrix.F$NPV,3)
expect_true(all(unlist(ConfusionMatrix.F)==c(1,0,1,6,1,0.143,0.25,0.25)))
ConfusionMatrix.NF$Accuracy <- round(ConfusionMatrix.NF$Accuracy,3)
expect_true(all(unlist(ConfusionMatrix.NF)==c(1,0,2,6,1,0.25,0.333,0.25)))
ConfusionMatrix.FT$F1 <- round(ConfusionMatrix.FT$F1,3)
expect_true(all(unlist(ConfusionMatrix.FT)==c(3,1,0,4,.75,0,0.375,0.545)))
})
BIMSBbioinfo/reg2gene documentation built on May 3, 2019, 6:42 p.m.
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# #######################################
# # -------- ConfusionMatrix & Benchmark tests
#
#
# Dataset description:
# examples created to cover following possibilities for benchmarking and
# filtering
# 0) anchor1(re2gene) overlaps anchor1 [1 ranges in benchmark dataset]
# chr1 [ 1, 4] --- chr1 [ 30, 31]
# 1) anchor1(re2gene) overlaps anchor1 [2 ranges in benchmark dataset]
# chr1 [200, 201] * | chr1:202-203
# 2) anchor1 overlaps anchor2 [2 ranges in benchmark dataset]
# chr1 [ 5, 9] * | chr1:31-32
# 3) anchor1(re2gene) overlaps anchor2 [1 range in benchmark dataset] but
# as well anchor1 from another GInteraction pair
# chr1 [ 1, 4] --- chr1 [ 5, 9]
# 4) no overlap with benchmark dataset but NOT filtered out
# chr1 [ 24, 29] --- chr1 [ 5, 10]
# 5) no overlap with benchmark dataset but filtered out
# chr1 [ 15, 20] --- chr1 [ 21, 25]
# 6) another chr example
# chr2 [ 1, 4] --- chr2 [ 5, 9]
# 7) >2 anchor1 overlaps - a1a2,a1a2,a1a1
# chr1 [100, 101] --- chr1 [102, 103]
# 8) input anchors overlap
# chr1 [ 30, 31] --- chr1 [ 31, 32]
library(reg2gene)
require(GenomicRanges)
require(InteractionSet)
require(stringr)
require(testthat)
reg2Gene <- GInteractions(GRReg1_toy,GRReg1_toy$reg)
benchData <- GInteractions(GRReg2_toy,GRReg2_toy$reg)
benchDataList <- list(benchData,reg2Gene)
# for CM data
reg2GeneBench <- reg2Gene
reg2GeneBench$PValue <- seq(0, 1,length.out = length(reg2Gene))
##################################################
# -----------------------------------------------#
# Benchmark testing()
# NOTE: did not test ignore.strands function:
test_that("test that benchmark/filter inputs correct GInteractions object
form", {
PredefinedMetaData <- c("anchor1.Bench1Exp","anchor1.Filter2Exp",
"anchor1.Bench2Exp","anchor1.Filter1Exp")
expect_is(reg2Gene,"GInteractions")
expect_is(benchData,"GInteractions")
expect_true(all(PredefinedMetaData%in%colnames(mcols(reg2Gene))),
"Predifined filter/bench results missing in reg2gene")
})
test_that("test that benchmark/filter inputs correct list of GIs
object form", {
expect_is(benchDataList,"list","List: not inputed correctly to benchmarkInteractions ")
expect_failure(expect_is(benchDataList,"GInteractions"))
expect_equal(length(benchDataList),2)
expect_equal(sapply(benchDataList, length),c(14,9))
})
test_that("test that benchmark outputs correct GIs object form", {
benchR <- benchmarkInteractions(interactions=reg2Gene,
benchInteractions = benchData,
binary=TRUE)
expect_is(benchR,"GInteractions")
expect_true("Bench"%in%names(mcols(benchR)),"Bench column not returned")
# test functionality:
# ------------------
# binary=T returns binary 1 and 0
expect_false(all(benchR$Bench==benchR$anchor1.Bench1Exp),"should be binary
benchmark")
expect_true(all(as.logical(benchR$Bench)==as.logical(
benchR$anchor1.Bench1Exp)),"counts to binary benchmark")
# binary=F returns not >1 as well
benchR <- benchmarkInteractions (reg2Gene,benchData,binary=F)
expect_true(all(benchR$Bench==benchR$anchor1.Bench1Exp),"shouldn't be
binary benchmark")
# Checking what happends when anchor1&anchor2 both overlap only one region
A1A1A2 <- benchmarkInteractions (reg2Gene[1],
benchData[1])
expect_equal(A1A1A2$Bench,0)
# Check filtering procedure
benchR <- benchmarkInteractions(reg2Gene,benchData,
binary=TRUE,
preFilter = TRUE )
expect_true(all(as.logical(benchR$Filter)==as.logical(
benchR$anchor1.Filter1Exp)),"filter fails")
filter2 <- benchmarkInteractions(reg2Gene,reg2Gene,
binary=TRUE,
preFilter = TRUE )
expect_true(all(as.logical(filter2$Filter)==as.logical(
benchR$anchor1.Filter2Exp)),"filter fails")
# FORCE BY NAME
reg2Gene$name <- reg2Gene$anchor1.name
benchData$name <- benchData$anchor1.name
ForcingName <- benchmarkInteractions(reg2Gene,
benchData,
binary=FALSE,
forceByName = T)
expect_equal(ForcingName$Bench,c(1,2,0,2,1,0,3,1,2))
})
test_that("test that benchmark outputs correct GIs object for list of GIs",{
benchRList <- benchmarkInteractions (reg2Gene,benchDataList,binary=TRUE)
# OUTPUT OK:
expect_is(benchRList,"GInteractions")
#if bench input list, then bench column N should be equal to the length of
# the list
expect_equal(length(mcols(reg2Gene))+length(benchDataList),
length(mcols(benchRList)))
# names of the list added ok
names(benchDataList) <- c("B1","B2")
benchRList <- benchmarkInteractions (reg2Gene,benchDataList,binary=TRUE)
expect_true(all(c("B1","B2")%in%colnames(mcols(benchRList))),
"list names adjusted correctly in benchmark f()")
# test functionality:
# ------------------
expect_equal(benchRList$B1,c(1,1,0,0,1,1,1,1,1))
expect_equal(benchRList$B2,rep(1,9))
# testing binary argument
benchRList <- benchmarkInteractions (reg2Gene,benchDataList,binary=FALSE)
expect_equal(benchRList$B1,c(2,1,0,0,1,2,3,2,2))
})
#######################################
# -------- ConfusionMatrixReg2Gene
#
# context("confusionMatrix function runs correctly")
test_that("Input tests for confusionMatrix", {
expect_is(reg2GeneBench,"GInteractions")
# given programmed examples, ppv should be returned as 1
expect_equal(confusionMatrix(reg2GeneBench,
thresholdID="PValue",
benchCol = "anchor1.Bench1Exp"),1)
expect_error(confusionMatrix(reg2GeneBench))
})
# TEST benchCol: DONE
test_that("benchCol for confusionMatrix is correctly used", {
# works if benchCol defined + PPV
expect_is(confusionMatrix(interactionsBench=reg2GeneBench,
thresholdID="PValue",
benchCol = "anchor1.Bench1Exp"),"numeric")
# works if benchCol defined + PPV + filter column added
expect_is(confusionMatrix(reg2GeneBench,
thresholdID="PValue",
benchCol = "anchor1.Bench1Exp",
prefilterCol="anchor1.Filter1Exp"),"numeric")
# works if benchCol defined + ConfusionMatrix statistics
expect_is(confusionMatrix(reg2GeneBench,
thresholdID="PValue",
benchCol = "anchor1.Bench1Exp",
statistics = "ConfusionMatrix"),"list")
# NOT working if benchCol NOT defined & Bench column does NOT exist
expect_error(confusionMatrix(reg2GeneBench,
thresholdID="PValue",
statistics = "ConfusionMatrix"))
# works if benchCol NOT defined, but Benchmark column exists
reg2GeneBench$Bench <- reg2GeneBench$anchor1.Bench1Exp
expect_is(confusionMatrix(reg2GeneBench,
thresholdID="PValue",
statistics = "ConfusionMatrix"),"list")
})
# TEST thresholdID argument: DONE
test_that("thresholdID for confusionMatrix is correctly used", {
# if thresholdID not defined - expect error
expect_success(expect_error(confusionMatrix(reg2GeneBench,
benchCol = "anchor1.Bench1Exp")))
# if thresholdID wrongly defined - expect error
expect_success(expect_error(confusionMatrix(reg2GeneBench,
thresholdID="PVal",
benchCol = "anchor1.Bench1Exp")))
# all fine
expect_is(confusionMatrix(reg2GeneBench,
thresholdID="PValue",
benchCol = "anchor1.Bench1Exp"),"numeric")
})
# TEST prefilterCol argument: DONE
test_that("prefilterCol for confusionMatrix is correctly used", {
# function works well with both statistics:ConfusionMatrix and filter on
expect_failure(expect_error(confusionMatrix(reg2GeneBench,
benchCol = "anchor1.Bench1Exp",
prefilterCol = "anchor1.Filter1Exp",
thresholdID = "PValue",
statistics="ConfusionMatrix")))
# function works well with both statistics:ConfusionMatrix and filter off
expect_failure(expect_error(confusionMatrix(reg2GeneBench,
benchCol = "anchor1.Bench1Exp",
thresholdID = "PValue",
statistics="ConfusionMatrix")))
# function should fail if thresholdID is missing
expect_error(confusionMatrix(reg2GeneBench,benchCol = "anchor1.Bench1Exp",
statistics="ConfusionMatrix"))
# function works well with statistics:PPV
expect_failure(expect_error(confusionMatrix(reg2GeneBench,
benchCol = "anchor1.Bench1Exp",
thresholdID = "PValue",
statistics="PPV")))
# function works when prefilterCol defined
expect_is(confusionMatrix(reg2GeneBench,
benchCol = "anchor1.Bench1Exp",
prefilterCol = "anchor1.Filter1Exp",
thresholdID="PValue"),"numeric")
# function works when prefilterCol NOT defined
expect_is(confusionMatrix(reg2GeneBench,
benchCol = "anchor1.Bench1Exp",
thresholdID="PValue"),"numeric")
######################################
#---------functionality given prefilterCol
# functionality when prefilterCol==NULL (not defined)
ConfusionMatrix.F <- confusionMatrix(reg2GeneBench,
benchCol = "anchor1.Bench1Exp",
prefilterCol = "anchor1.Filter1Exp",
thresholdID="PValue",
statistics="ConfusionMatrix")
# functionality when prefilterCol!=NULL
ConfusionMatrix.NF <- confusionMatrix(reg2GeneBench,
benchCol = "anchor1.Bench1Exp",
thresholdID="PValue",
statistics="ConfusionMatrix")
# filtering gives different N of TN given toy example
expect_true(ConfusionMatrix.F$TN!=ConfusionMatrix.NF$TN)
})
# thresholdValue and statistics is calculated ok: DONE
test_that("thresholdValue and statistics for confusionMatrix is correct", {
# function works when no thresholdValue defined - 0.05 default
expect_is(confusionMatrix(reg2GeneBench,
benchCol = "anchor1.Bench1Exp",
prefilterCol = "anchor1.Filter1Exp",
thresholdID="PValue"),"numeric")
#########################################
# Correct functionality:
ConfusionMatrix.F <- confusionMatrix(reg2GeneBench,
thresholdID = "PValue",
thresholdValue = 0.05,
benchCol = "anchor1.Bench1Exp",
prefilterCol = "anchor1.Filter1Exp",
statistics = "ConfusionMatrix")
ConfusionMatrix.NF <- confusionMatrix(reg2GeneBench,
thresholdID = "PValue",
thresholdValue = 0.05,
benchCol = "anchor1.Bench1Exp",
statistics = "ConfusionMatrix")
# filtering produces different results in confusion matrix
expect_true(any(unlist(ConfusionMatrix.F)!=unlist(ConfusionMatrix.NF)))
# different thresholdValue produces different results in confusion matrix
ConfusionMatrix.FT <- confusionMatrix(reg2GeneBench,
thresholdID = "PValue",
thresholdValue = 0.5,
benchCol = "anchor1.Bench1Exp",
prefilterCol = "anchor1.Filter1Exp",
statistics = "ConfusionMatrix")
expect_true(any(unlist(ConfusionMatrix.F)!=unlist(ConfusionMatrix.FT)))
#############################
# functionality is calculated correctly:
# TESTING TP,FP,TN,FN:
# benchmarking is correct
ConfusionMatrix.F$NPV <- round(ConfusionMatrix.F$NPV,3)
expect_true(all(unlist(ConfusionMatrix.F)==c(1,0,1,6,1,0.143,0.25,0.25)))
ConfusionMatrix.NF$Accuracy <- round(ConfusionMatrix.NF$Accuracy,3)
expect_true(all(unlist(ConfusionMatrix.NF)==c(1,0,2,6,1,0.25,0.333,0.25)))
ConfusionMatrix.FT$F1 <- round(ConfusionMatrix.FT$F1,3)
expect_true(all(unlist(ConfusionMatrix.FT)==c(3,1,0,4,.75,0,0.375,0.545)))
})
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