Nothing
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### Spatial Prediction Model Object Oriented Framework ###
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####RecursiveForecastModel Test Functions
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#######################################TODO#####################################
test_that('test functions load',{
source('help-ForecastModel.R')
source('help-MatrixData.R')
})
is_same_RecursiveForecastModel_as = function(rhs){
return(function(lhs){
TRUE
})
}
test_RecursiveForecastModel = function(model,modelName,modelEquivalence,data,dataName,dataEquivalence){
boolean_fit_works = FALSE
boolean_predict_works = FALSE
boolean_forecast_works=FALSE
expect_true("RecursiveForecastModel" %in% class(model))
firstModel = model$clone(TRUE)
firstData = data$clone(TRUE)
results = test_ForecastModel(model,modelName,modelEquivalence,data,dataName,dataEquivalence)
expect_that(firstModel,modelEquivalence(model))
expect_that(firstData,dataEquivalence(data))
boolean_fit_works = results[1]
boolean_predict_works = results[2]
boolean_forecast_works = results[3]
test_that(paste(modelName,'fit','respects','predCols','for data',dataName),{
firstModel = model$clone(TRUE)
firstData = data$clone(TRUE)
tempModel = model$clone(TRUE)
tempData = data$clone(TRUE)
if(!boolean_fit_works){
skip("Could not fit the data")
}
expect_that({
tempModel$fit(tempData)
tempModel$predCols
},equals(model$predCols))
expect_that(firstModel,modelEquivalence(model))
expect_that(firstData,dataEquivalence(data))
})
test_that(paste(modelName,'fit','respects','data','for data',dataName),{
firstModel = model$clone(TRUE)
firstData = data$clone(TRUE)
tempModel = model$clone(TRUE)
tempData = data$clone(TRUE)
if(!boolean_fit_works){
skip("Could not fit the data")
}
expect_that({
tempModel$fit(tempData)
tempModel$data
},dataEquivalence(data))
expect_that(firstModel,modelEquivalence(model))
expect_that(firstData,dataEquivalence(data))
})
test_that(paste(modelName,'predict','respects','predCols','for data',dataName),{
firstModel = model$clone(TRUE)
firstData = data$clone(TRUE)
tempModel = model$clone(TRUE)
tempData = data$clone(TRUE)
if(!boolean_predict_works){
skip("Could not fit the data")
}
expect_that({
tempModel$fit(tempData)
#Take a subset here
tempModel$predict(tempData)
tempModel$predCols
},equals(model$predCols))
expect_that(firstModel,modelEquivalence(model))
expect_that(firstData,dataEquivalence(data))
})
test_that(paste(modelName,'predict','respects','data','for data',dataName),{
firstModel = model$clone(TRUE)
firstData = data$clone(TRUE)
tempModel = model$clone(TRUE)
tempData = data$clone(TRUE)
if(!boolean_predict_works){
skip("Could not fit the data")
}
expect_that({
tempModel$fit(tempData)
tempModel$predict(tempData)
tempModel$data
},dataEquivalence(data))
expect_that(firstModel,modelEquivalence(model))
expect_that(firstData,dataEquivalence(data))
})
return(c(boolean_fit_works,boolean_predict_works))
}
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