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add tests for kfold xval
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tests/testthat/test-kfold-xval.R

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# test kfold_xval class
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test_that('kfold xval',{
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set.seed('57475')
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# dataset
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D=iris_dataset()
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# iterator
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I = kfold_xval(folds=5,method='venetian')*(mean_centre()+PLSDA())
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# metric
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B=balanced_accuracy()
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# run
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I=run(I,D,B)
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# calculate metric
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expect_equal(I$metric$mean,0.23)
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# iterator
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I = kfold_xval(folds=5,method='blocks')*(mean_centre()+PLSDA())
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# metric
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B=balanced_accuracy()
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# run
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I=run(I,D,B)
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# calculate metric
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expect_equal(I$metric$mean,0.305)
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# iterator
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set.seed(57475)
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I = kfold_xval(folds=5,method='random')*(mean_centre()+PLSDA())
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# metric
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B=balanced_accuracy()
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# run
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I=run(I,D,B)
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# calculate metric
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expect_equal(I$metric$mean,0.2)
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})
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test_that('kfold xval metric plot',{
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set.seed('57475')
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# dataset
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D=iris_dataset()
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# iterator
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I = kfold_xval(folds=5,method='venetian')*(mean_centre()+PLSDA())
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# metric
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B=balanced_accuracy()
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# run
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I=run(I,D,B)
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# chart
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C = kfoldxcv_metric()
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gg=chart.plot(C,I)
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expect_true(is(gg,'ggplot'))
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})
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test_that('kfold xval grid plot',{
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set.seed('57475')
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# dataset
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D=iris_dataset()
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# iterator
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I = kfold_xval(folds=5,method='venetian')*(mean_centre()+PLSDA())
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# metric
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B=balanced_accuracy()
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# run
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I=run(I,D,B)
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# chart
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C = kfoldxcv_grid()
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gg=chart.plot(C,I)
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expect_true(is(gg[[1]],'ggplot'))
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})

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