revdep/checks.noindex/pmml/old/pmml.Rcheck/tests/testthat/test_pmml.ARIMA.R

In gbm-developers/gbm: Generalized Boosted Regression Models

library(forecast)
data("WWWusage")
data("AirPassengers")
data("JohnsonJohnson")

text_to_num_list <- function(xml_element) {
  # Convert string that contains a list of numbers to a list of numbers
  aa <- xmlToList(xml_element)
  cc <- strsplit(aa, split = " ", fixed = TRUE)[[1]]
  dd <- as.numeric(cc)
}

expect_equal_ttnl <- function(xml_element, current) {
  expect_equal(text_to_num_list(xml_element), current, tolerance = 1e-2)
}

expect_equal_num <- function(target, current) {
  expect_equal(as.numeric(target), current, tolerance = 1e-2)
}


# teardown({detach("package:forecast", unload=TRUE)})

fit_0 <- Arima(WWWusage, order = c(3, 1, 1))

test_that("error when object is not ARIMA", {
  expect_error(pmml.ARIMA("foo"), "Not a legitimate ARIMA object")
})

test_that("error when ts_type is not in c('arima', 'state_space')", {
  expect_error(pmml(fit_0, ts_type = "foo"), 'ts_type must be one of "arima" or "statespace".')
})

test_that("Error when transforms is not NULL", {
  expect_error(pmml(fit_0, transforms = "foo"), "Transforms are not supported for ARIMA forecast models.")
})

test_that(".check_cpi_levels erros correctly", {
  # Expect no error when cpi_levels is between 0 and 1
  expect_error(pmml(fit_0, cpi_levels = c(0.3, 0.49, 0.9)), NA)

  # Expect no error with mixed percent and fraction
  expect_error(pmml(fit_0, cpi_levels = c(86, 0.49, 0.9)), NA)

  expect_error(pmml(fit_0, cpi_levels = NULL), "Length of cpi_levels must be greater than 0.")

  expect_error(pmml(fit_0, cpi_levels = c("a", "3")), "cpi_levels must be numeric.")

  expect_error(pmml(fit_0, cpi_levels = c(-3, 101)), "cpi_levels out of range.")
})


test_that("DataDictionary node contains expected elements", {
  fit_2 <- auto.arima(WWWusage)
  p_fit_2 <- pmml(fit_2)
  expect_equal(toString(p_fit_2[[2]]), "<DataDictionary numberOfFields=\"2\">\n <DataField name=\"ts_value\" optype=\"continuous\" dataType=\"double\"/>\n <DataField name=\"h\" optype=\"continuous\" dataType=\"double\"/>\n</DataDictionary>")
})

test_that("MiningSchema node contains expected elements", {
  fit_3 <- auto.arima(WWWusage)
  p_fit_3 <- pmml(fit_3)
  expect_equal(toString(p_fit_3[[3]][[1]]), "<MiningSchema>\n <MiningField name=\"ts_value\" usageType=\"predicted\" invalidValueTreatment=\"returnInvalid\"/>\n <MiningField name=\"h\" usageType=\"supplementary\" invalidValueTreatment=\"returnInvalid\"/>\n</MiningSchema>")
})


test_that("Output node contains expected elements", {
  fit_4 <- auto.arima(WWWusage)
  p_fit_4 <- pmml(fit_4)
  expect_equal(
    toString(p_fit_4[[3]][[2]][[1]]),
    "<OutputField name=\"Predicted_ts_value\" optype=\"continuous\" dataType=\"double\" feature=\"predictedValue\"/>"
  )
})

test_that("NonseasonalComponent node contains required elements 1", {
  s <- ts(data = c(11357.92, 10605.95, 16998.57, 6563.75, 6607.69, 9839.0))
  fit_5 <- Arima(s, order = c(3, 1, 1))
  p_fit_5 <- pmml(fit_5)

  # NonseasonalComponent attributes
  expect_equal(xmlGetAttr(p_fit_5[[3]][[4]][[1]], name = "p"), 3)
  expect_equal(xmlGetAttr(p_fit_5[[3]][[4]][[1]], name = "d"), 1)
  expect_equal(xmlGetAttr(p_fit_5[[3]][[4]][[1]], name = "q"), 1)

  # AR component
  expect_equal_ttnl(
    p_fit_5[[3]][[4]][[1]][[1]][[1]][[1]],
    c(-0.19693368896618, 0.0882676656284808, 0.9429079310464)
  )


  # MA component - MACoefficients
  expect_equal_ttnl(p_fit_5[[3]][[4]][[1]][[2]][[1]][[1]][[1]], 0.999467612244043)


  # MA component - Residuals
  expect_equal_ttnl(p_fit_5[[3]][[4]][[1]][[2]][[2]][[1]][[1]], -846.776313143145)
})

test_that("non-seasonal ARIMA node contains correct attributes", {
  s <- ts(data = c(11357.92, 10605.95, 16998.57, 6563.75, 6607.69, 9839.0))
  fit_6 <- Arima(s, order = c(0, 0, 1))
  p_fit_6 <- pmml(fit_6)

  expect_equal_num(xmlGetAttr(p_fit_6[[3]][[4]], name = "RMSE"), sqrt(fit_6$sigma2))
  expect_equal(xmlGetAttr(p_fit_6[[3]][[4]], name = "transformation"), "none")
  expect_equal_num(xmlGetAttr(p_fit_6[[3]][[4]], name = "constantTerm"), 10327.6226360507)
  expect_equal(xmlGetAttr(p_fit_6[[3]][[4]], name = "predictionMethod"), "conditionalLeastSquares")
})


test_that("seasonal ARIMA model contains correct elements 1", {
  fit_7 <- Arima(JohnsonJohnson, order = c(0, 0, 2), seasonal = c(0, 0, 1))
  p_fit_7 <- pmml(fit_7, exact_least_squares = FALSE)

  expect_equal(xmlGetAttr(p_fit_7[[3]][[4]][[1]], name = "p"), 0)
  expect_equal(xmlGetAttr(p_fit_7[[3]][[4]][[1]], name = "d"), 0)
  expect_equal(xmlGetAttr(p_fit_7[[3]][[4]][[1]], name = "q"), 2)

  expect_equal(xmlGetAttr(p_fit_7[[3]][[4]][[2]], name = "P"), 0)
  expect_equal(xmlGetAttr(p_fit_7[[3]][[4]][[2]], name = "D"), 0)
  expect_equal(xmlGetAttr(p_fit_7[[3]][[4]][[2]], name = "Q"), 1)
  expect_equal(xmlGetAttr(p_fit_7[[3]][[4]][[2]], name = "period"), 4)

  expect_equal_ttnl(p_fit_7[[3]][[4]][[2]][[1]][[1]][[1]][[1]], 0.999999926590528)

  # Seasonal residuals array should have 6 elements.
  expect_equal_ttnl(
    p_fit_7[[3]][[4]][[2]][[1]][[2]][[1]][[1]],
    c(
      0.840294788225463, 1.79540974711022, 3.43600813764863,
      0.595257922098859, 1.560371580367, 1.33444221515274
    )
  )
})

test_that("seasonal ARIMA model contains correct elements 2", {
  fit_8 <- Arima(AirPassengers, order = c(1, 1, 1), seasonal = c(1, 1, 1))
  p_fit_8 <- pmml(fit_8, exact_least_squares = FALSE)


  expect_equal(xmlGetAttr(p_fit_8[[3]][[4]][[1]], name = "p"), 1)
  expect_equal(xmlGetAttr(p_fit_8[[3]][[4]][[1]], name = "d"), 1)
  expect_equal(xmlGetAttr(p_fit_8[[3]][[4]][[1]], name = "q"), 1)

  expect_equal(xmlGetAttr(p_fit_8[[3]][[4]][[2]], name = "P"), 1)
  expect_equal(xmlGetAttr(p_fit_8[[3]][[4]][[2]], name = "D"), 1)
  expect_equal(xmlGetAttr(p_fit_8[[3]][[4]][[2]], name = "Q"), 1)
  expect_equal(xmlGetAttr(p_fit_8[[3]][[4]][[2]], name = "period"), 12)

  expect_equal_ttnl(p_fit_8[[3]][[4]][[2]][[1]][[1]][[1]], -0.926970851026725)
  # Seasonal residuals array should have 13 elements.
  expect_equal_ttnl(
    p_fit_8[[3]][[4]][[2]][[2]][[2]][[1]][[1]],
    c(
      16.6406514721613, -2.15049966299974, -8.78471974960152, -34.7165557860653,
      42.1922537998818, 2.72016048914321, 5.57048733936438, 14.8646481189408,
      -23.3767800799817, -7.00132283870703, 5.56068703039644, -24.4368048030507,
      -7.86142188676456
    )
  )
})


test_that("seasonal ARIMA model contains correct elements 3", {
  fit_9 <- Arima(AirPassengers, order = c(1, 2, 3), seasonal = c(1, 2, 1))
  p_fit_9 <- pmml(fit_9, exact_least_squares = FALSE)

  expect_equal_num(xmlGetAttr(p_fit_9[[3]][[4]], name = "constantTerm"), 0)

  expect_equal(xmlGetAttr(p_fit_9[[3]][[4]][[1]], name = "p"), 1)
  expect_equal(xmlGetAttr(p_fit_9[[3]][[4]][[1]], name = "d"), 2)
  expect_equal(xmlGetAttr(p_fit_9[[3]][[4]][[1]], name = "q"), 3)

  expect_equal(xmlGetAttr(p_fit_9[[3]][[4]][[2]], name = "P"), 1)
  expect_equal(xmlGetAttr(p_fit_9[[3]][[4]][[2]], name = "D"), 2)
  expect_equal(xmlGetAttr(p_fit_9[[3]][[4]][[2]], name = "Q"), 1)
  expect_equal(xmlGetAttr(p_fit_9[[3]][[4]][[2]], name = "period"), 12)

  # NonseasonalComponent coefficients
  expect_equal_ttnl(p_fit_9[[3]][[4]][[1]][[1]][[1]][[1]], -0.918811953411307)
  expect_equal_ttnl(
    p_fit_9[[3]][[4]][[1]][[2]][[1]][[1]][[1]],
    c(-0.488398025152718, -0.981238492527093, 0.47123365713457)
  )
  expect_equal_ttnl(
    p_fit_9[[3]][[4]][[1]][[2]][[2]][[1]][[1]],
    c(10.6024485251517, -16.6110095847448, -3.45077032800267)
  )

  # SeasonalComponent coefficients
  expect_equal_ttnl(p_fit_9[[3]][[4]][[2]][[1]][[1]][[1]], -0.33060697133757)
  expect_equal_ttnl(p_fit_9[[3]][[4]][[2]][[2]][[1]][[1]][[1]], -0.964840603841212)
  # Seasonal residuals array should have 15 elements.
  expect_equal_ttnl(
    p_fit_9[[3]][[4]][[2]][[2]][[2]][[1]][[1]],
    c(
      -0.231414430272416, 7.85449813502021, 14.0341170808424,
      0.488050253167314, -3.47775177064476, -34.7531345572846,
      36.9803974323111, -0.721770286617552, 3.21345471099022, 5.83445945385706,
      -21.7707201166725, -1.21862197305704, 10.6024485251517, -16.6110095847448,
      -3.45077032800267
    )
  )
})

test_that("Seasonal ARIMA with 0,0,0 non-seasonal component contains NonseasonalComponent with zero values", {
  fit_10 <- Arima(AirPassengers, order = c(0, 0, 0), seasonal = c(1, 2, 1))
  p_fit_10 <- pmml(fit_10, exact_least_squares = FALSE)
  expect_equal(toString(p_fit_10[[3]][[4]][[1]]), "<NonseasonalComponent p=\"0\" d=\"0\" q=\"0\"/>")
})

test_that("ARIMA with both intercept and drift terms throws error", {
  # drift and intercept
  fit_11 <- Arima(AirPassengers, order = c(1, 0, 1), include.drift = TRUE)
  expect_error(pmml(fit_11), "ARIMA models with a drift term are not supported.")

  fit_12 <- Arima(AirPassengers, order = c(2, 0, 2), include.drift = TRUE)
  expect_error(pmml(fit_12), "ARIMA models with a drift term are not supported.")

  # drift term only
  fit_12a <- Arima(AirPassengers, order = c(2, 1, 2), include.drift = TRUE)
  expect_error(pmml(fit_12a), "ARIMA models with a drift term are not supported.")
})

test_that("Error if exact_least_squares is not logical", {
  fit_13 <- auto.arima(WWWusage)
  expect_error(pmml(fit_13, exact_least_squares = "foo"),
    "exact_least_squares must be logical (TRUE/FALSE).",
    fixed = TRUE
  )
})

test_that("exact_least_squares has no effect if model is non-seasonal", {
  fit_14 <- Arima(AirPassengers, order = c(2, 2, 1))
  p_fit_14 <- pmml(fit_14, exact_least_squares = TRUE)
  expect_equal(xmlGetAttr(p_fit_14[[3]][[4]], name = "predictionMethod"), "conditionalLeastSquares")
})

test_that("exact_least_squares=TRUE results in exactLeastSquares for seasonal model", {
  fit_15 <- Arima(AirPassengers, order = c(2, 2, 1), seasonal = c(1, 1, 1))
  p_fit_15 <- pmml(fit_15, exact_least_squares = TRUE)
  expect_equal(xmlGetAttr(p_fit_15[[3]][[4]], name = "predictionMethod"), "exactLeastSquares")
})

test_that("default arg for exact_least_squares results in exactLeastSquares for seasonal model", {
  fit_15b <- Arima(AirPassengers, order = c(2, 2, 1), seasonal = c(1, 1, 1))
  p_fit_15b <- pmml(fit_15b)
  expect_equal(xmlGetAttr(p_fit_15b[[3]][[4]], name = "predictionMethod"), "exactLeastSquares")
})

test_that("exact_least_squares=FALSE results in conditionalLeastSquares for seasonal model", {
  fit_15c <- Arima(AirPassengers, order = c(2, 2, 1), seasonal = c(1, 1, 1))
  p_fit_15c <- pmml(fit_15c, exact_least_squares = FALSE)
  expect_equal(xmlGetAttr(p_fit_15c[[3]][[4]], name = "predictionMethod"), "conditionalLeastSquares")
})

test_that("RMSE attribute equals sqrt(sigma2) from R object", {
  fit_16 <- Arima(WWWusage, order = c(2, 1, 3))
  p_fit_16 <- pmml(fit_16)
  expect_equal_num(xmlGetAttr(p_fit_16[[3]][[4]], name = "RMSE"), sqrt(fit_16$sigma2))
})

test_that("seasonal models do not include CPI in Output", {
  fit_17 <- Arima(AirPassengers, order = c(2, 2, 2), seasonal = c(1, 1, 1))
  p_fit_17 <- pmml(fit_17, exact_least_squares = FALSE)
  expect_equal(toString(p_fit_17[[3]][[2]]), "<Output>\n <OutputField name=\"Predicted_ts_value\" optype=\"continuous\" dataType=\"double\" feature=\"predictedValue\"/>\n</Output>")
})


test_that("non-seasonal models include CPI in Output", {
  fit_18 <- Arima(AirPassengers, order = c(2, 2, 2))
  p_fit_18 <- pmml(fit_18)
  expect_equal(toString(p_fit_18[[3]][[2]]), "<Output>\n <OutputField name=\"Predicted_ts_value\" optype=\"continuous\" dataType=\"double\" feature=\"predictedValue\"/>\n <OutputField name=\"cpi_80_lower\" optype=\"continuous\" dataType=\"double\" feature=\"confidenceIntervalLower\" value=\"80\"/>\n <OutputField name=\"cpi_80_upper\" optype=\"continuous\" dataType=\"double\" feature=\"confidenceIntervalUpper\" value=\"80\"/>\n <OutputField name=\"cpi_95_lower\" optype=\"continuous\" dataType=\"double\" feature=\"confidenceIntervalLower\" value=\"95\"/>\n <OutputField name=\"cpi_95_upper\" optype=\"continuous\" dataType=\"double\" feature=\"confidenceIntervalUpper\" value=\"95\"/>\n</Output>")
})

test_that("FinalOmega is 0", {
  fit_20 <- Arima(AirPassengers, order = c(1, 1, 0), seasonal = c(0, 1, 1))
  p_fit_20 <- pmml(fit_20, exact_least_squares = TRUE)
  expect_equal(toString(p_fit_20[[3]][[4]][[3]][[1]][[1]]), "<FinalOmega>\n <Matrix kind=\"symmetric\" nbRows=\"1\" nbCols=\"1\">\n  <Array type=\"real\" n=\"1\">0</Array>\n </Matrix>\n</FinalOmega>")
})

test_that("seasonal models with ELS contain correct matrices", {
  skip_on_cran() # string comparison on CRAN results in failure due to mismatch in some numbers, 8 digits after decimal
  skip_on_ci()
  fit_21 <- Arima(AirPassengers, order = c(1, 2, 0), seasonal = c(0, 1, 1))
  p_fit_21 <- pmml(fit_21, exact_least_squares = TRUE)

  # FinalStateVector
  expect_equal_ttnl(p_fit_21[[3]][[4]][[3]][[1]][[2]][[1]][[1]], c(fit_21$model$T %*% fit_21$model$a))

  # TransitionMatrix
  expect_equal(toString(p_fit_21[[3]][[4]][[3]][[1]][[3]][[1]]), "<Matrix nbRows=\"27\" nbCols=\"27\">\n <Array type=\"real\">-0.650958812319475 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">1 0 0 0 0 0 0 0 0 0 0 0 0 2 -1 0 0 0 0 0 0 0 0 0 1 -2 1</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0</Array>\n <Array type=\"real\">0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0</Array>\n</Matrix>")

  # MeasurementMatrix
  expect_equal(toString(p_fit_21[[3]][[4]][[3]][[1]][[4]][[1]]), "<Matrix nbRows=\"1\" nbCols=\"27\">\n <Array type=\"real\">1 0 0 0 0 0 0 0 0 0 0 0 0 2 -1 0 0 0 0 0 0 0 0 0 1 -2 1</Array>\n</Matrix>")
})


test_that("Output dataType changes according to ts_type", {
  fit_23 <- Arima(AirPassengers, order = c(1, 2, 0))
  p_fit_23 <- pmml(fit_23, ts_type = "arima")
  p_fit_23_b <- pmml(fit_23, ts_type = "statespace")

  expect_equal(toString(p_fit_23[[3]][[2]]), "<Output>\n <OutputField name=\"Predicted_ts_value\" optype=\"continuous\" dataType=\"double\" feature=\"predictedValue\"/>\n <OutputField name=\"cpi_80_lower\" optype=\"continuous\" dataType=\"double\" feature=\"confidenceIntervalLower\" value=\"80\"/>\n <OutputField name=\"cpi_80_upper\" optype=\"continuous\" dataType=\"double\" feature=\"confidenceIntervalUpper\" value=\"80\"/>\n <OutputField name=\"cpi_95_lower\" optype=\"continuous\" dataType=\"double\" feature=\"confidenceIntervalLower\" value=\"95\"/>\n <OutputField name=\"cpi_95_upper\" optype=\"continuous\" dataType=\"double\" feature=\"confidenceIntervalUpper\" value=\"95\"/>\n</Output>")
  expect_equal(toString(p_fit_23_b[[3]][[2]]), "<Output>\n <OutputField name=\"Predicted_ts_value\" optype=\"continuous\" dataType=\"string\" feature=\"predictedValue\">\n  <Extension extender=\"ADAPA\" name=\"dataType\" value=\"json\"/>\n </OutputField>\n <OutputField name=\"cpi_80_lower\" optype=\"continuous\" dataType=\"string\" feature=\"confidenceIntervalLower\" value=\"80\">\n  <Extension extender=\"ADAPA\" name=\"dataType\" value=\"json\"/>\n </OutputField>\n <OutputField name=\"cpi_80_upper\" optype=\"continuous\" dataType=\"string\" feature=\"confidenceIntervalUpper\" value=\"80\">\n  <Extension extender=\"ADAPA\" name=\"dataType\" value=\"json\"/>\n </OutputField>\n <OutputField name=\"cpi_95_lower\" optype=\"continuous\" dataType=\"string\" feature=\"confidenceIntervalLower\" value=\"95\">\n  <Extension extender=\"ADAPA\" name=\"dataType\" value=\"json\"/>\n </OutputField>\n <OutputField name=\"cpi_95_upper\" optype=\"continuous\" dataType=\"string\" feature=\"confidenceIntervalUpper\" value=\"95\">\n  <Extension extender=\"ADAPA\" name=\"dataType\" value=\"json\"/>\n </OutputField>\n</Output>")
})



## Tests for StateSpaceModel

test_that("bestFit TimeSeriesModel node matches ts_type", {
  fit_22 <- Arima(WWWusage, c(1, 1, 1))
  p_fit_22 <- pmml(fit_22, ts_type = "statespace")
  expect_equal(xmlGetAttr(p_fit_22[[3]], name = "bestFit"), "StateSpaceModel")

  p_fit_22_a <- pmml(fit_22, ts_type = "arima")
  expect_equal(xmlGetAttr(p_fit_22_a[[3]], name = "bestFit"), "ARIMA")

  # test that the default is "arima"
  p_fit_22_b <- pmml(fit_22)
  expect_equal(xmlGetAttr(p_fit_22_b[[3]], name = "bestFit"), "ARIMA")
})