tests/testthat/test_pipeline_over_linear_test_data.R

In antononcube/ERTMon-R: Event Records Transformations Monad

context("Pipeline over linear test data")
library(ERTMon)

testData <-
  ERTMonSimpleTestData( numberOfEntities = 10, numberOfVariables = 3, 
                        timeInterval = 900, numberOfTimeCells = 42, randomStartTimesQ = TRUE, 
                        variableFunction = "Linear", 
                        exportDirectoryName = NULL )


testData$ComputationSpecification$Aggregation.function <- "Count"
testData$ComputationSpecification$Aggregation.interval.length <- 3*900
testData$ComputationSpecification <- unique(testData$ComputationSpecification)

testData$ComputationSpecification <-
  testData$ComputationSpecification %>% 
  dplyr::group_by( Variable ) %>% 
  dplyr::filter( dplyr::row_number() == dplyr::n() ) %>% 
  dplyr::ungroup()

ertmon0 <-
  ERTMonUnit() %>%
  ERTMonSetEventRecords( testData$EventRecords ) %>%
  ERTMonSetEntityAttributes( testData$EntityAttributes ) %>%
  ERTMonSetComputationSpecification( testData$ComputationSpecification ) %>% 
  ERTMonProcessEventRecords( alignmentSpec = "MinTime" )

fmat0 <- as.matrix( ertmon0 %>% ERTMonTakeFeatureMatrix )

dfTCI0 <- ertmon0 %>% ERTMonTakeTimeCellsInterpretation
dfTCI0 <- dfTCI0[ grep("^Var", dfTCI0$MatrixName), ]

## Positive time are expected because of alignmentSpec = "MinTime".
test_that("Positive time grid cells interpretation times", {
  expect_equal( mean( dfTCI0$StartTime >= 0 ), 1 )
  expect_equal( mean( dfTCI0$EndTime >= 0 ), 1 )
  expect_equal( mean( dfTCI0$EndTime - dfTCI0$StartTime > 0 ), 1 )
})

## The aggregation function is "Count" and the aggregation time interval is 3 times
## larger than the time interval used in the simple test data creation.
## Hence we expect all 3's for the variable features. (With prefixex "Var".)
test_that("Counts of three for each time cell", {
  expect_equal( mean( as.numeric(fmat0[, grep( "^Var", colnames(fmat0), value = T)]) == 3 ), 1 )
})

colVec1 <- purrr::map_chr( strsplit( grep( "Var.1", colnames(fmat0), value = T, fixed = T), "\\."), function(x) x[[length(x)]])
colVec2 <- purrr::map_chr( strsplit( grep( "Var.2", colnames(fmat0), value = T, fixed = T), "\\."), function(x) x[[length(x)]])
colVec3 <- purrr::map_chr( strsplit( grep( "Var.3", colnames(fmat0), value = T, fixed = T), "\\."), function(x) x[[length(x)]])


test_that("The feature matrix has properly ordered columns", {
  expect_equal( mean( as.numeric(colVec1) == sort(as.numeric(colVec1)) ), 1 )
  expect_equal( mean( as.numeric(colVec2) == sort(as.numeric(colVec2)) ), 1 )
  expect_equal( mean( as.numeric(colVec3) == sort(as.numeric(colVec3)) ), 1 )
  
})