Nothing
context("eventClassification")
test_that("wrongInputs lead to errors",
{
x <- stationBData[1000:2000,-1]
m <- buildEDModel(x)
newDataReal <- stationBData[2001:2020,-1]
expect_error(predict(m,newDataReal[,-1]),regexp = "Predictions dimensions do not match newData")
})
test_that("prediction works correct",
{
x <- stationBData[1000:2000,-1]
m <- buildEDModel(x)
newDataReal <- stationBData[2001:2020,-1]
p <- predict(m)
expect_equal(nrow(p),10)
expect_equal(ncol(p),ncol(stationBData)-1)
expect_equal(anyNA(p),FALSE)
expect_equal(is.numeric(p),TRUE)
p <- predict(m,newDataReal)$lastPredictedEvents
expect_equal(nrow(p),nrow(newDataReal))
expect_equal(ncol(p),ncol(stationBData)-1+1)
expect_equal(typeof(p$Event),"logical")
m1 <- buildEDModel(x,postProcessorControl = list(nStandardDeviationseventThreshold = 0.1))
m2 <- buildEDModel(x,postProcessorControl = list(nStandardDeviationseventThreshold = 50))
p1 <- predict(m1,newDataReal)$lastPredictedEvents
p2 <- predict(m2,newDataReal)$lastPredictedEvents
sum(p1$Event)
expect_true(sum(p1$Event)>sum(p2$Event))
expect_true(!any(p2$Event))
#Check that normalization is really deactivated if setting says so
x <- stationBData[1000:2000,-1]
m <- buildEDModel(x, dataPreparationControl = list(useNormalization = F))
newDataReal <- stationBData[2001:2020,-1]
p <- predict(m)
expect_true(any(p > 50))
})
test_that("print works",
{
## Build some ed object
ed <- detectEvents(stationBData[1000:2000,-1],nIterationsRefit = 50,
verbosityLevel = 0,ignoreVarianceWarning = TRUE,
buildModelAlgo = "ForecastArima")
printRes <- capture.output(print(ed))
expect_equal(printRes[1], "Event Detection Object with 11 ARIMA submodels")
ed$modelList <- ed$modelList[1]
printRes <- capture.output(print(ed))
expect_equal(printRes[1], "Event Detection Object with 1 ARIMA submodel")
ed$modelList <- NULL
printRes <- capture.output(print(ed))
expect_equal(printRes[1], "Event Detection Object with no fitted models")
})
test_that("neural network prediction works",
{
x <- stationBData[100:300,-1]
m <- buildEDModel(x, dataPrepators = "ImputeTSMean", buildModelAlgo = "NeuralNetwork")
excludedVariable <- length(m$excludedVariables)
newDataReal <- stationBData[301:320,-1]
p <- predict.NeuralNetwork(m,newDataReal)
expect_equal(nrow(p$predictions),20)
expect_equal(ncol(p$predictions),(ncol(stationBData)-1-excludedVariable))
expect_equal(anyNA(p),FALSE)
p <- predict.NeuralNetwork(m,newDataReal)$lastPredictedEvents
expect_equal(nrow(p),nrow(newDataReal))
expect_equal(ncol(p),(ncol(stationBData)-1+1))
expect_equal(typeof(p$Event),"logical")
m1 <- buildEDModel(x,buildModelAlgo = "NeuralNetwork",postProcessorControl = list(nStandardDeviationseventThreshold = 0.01),buildNeuralNetModelControl = list(algorithm="rprop+",hidden= 2))
m2 <- buildEDModel(x,buildModelAlgo = "NeuralNetwork",postProcessorControl = list(nStandardDeviationseventThreshold = 50),buildNeuralNetModelControl = list(algorithm="rprop+",hidden= 2))
p1 <- predict.NeuralNetwork(m1,newDataReal)$lastPredictedEvents
p2 <- predict.NeuralNetwork(m2,newDataReal)$lastPredictedEvents
sum(p1$Event)
expect_true(sum(p1$Event)>sum(p2$Event))
expect_true(!any(p2$Event))
})
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