func SplitDataset(ds dataset.Dataset, trainingRatio float64, source rand.Source) (dataset.Dataset, dataset.Dataset, error) {
if trainingRatio < 0 || trainingRatio > 1 {
return nil, nil, fmt.Errorf("Unable to split dataset with invalid ratio %.2f", trainingRatio)
}
numRows := ds.NumRows()
if numRows == 0 {
return nil, nil, errors.New("Cannot split empty dataset")
}
r := rand.New(source)
perm := r.Perm(numRows)
trainingRowMap := make([]int, 0, numRows)
testRowMap := make([]int, 0, numRows)
for _, rowIndex := range perm {
if r.Float64() < trainingRatio {
trainingRowMap = append(trainingRowMap, rowIndex)
} else {
testRowMap = append(testRowMap, rowIndex)
}
}
return dataset.NewSubset(ds, trainingRowMap), dataset.NewSubset(ds, testRowMap), nil
}
func (classifier *kNNClassifier) Train(trainingData dataset.Dataset) error {
if !trainingData.AllFeaturesFloats() {
return knnerrors.NewNonFloatFeaturesTrainingSetError()
}
if trainingData.NumRows() == 0 {
return knnerrors.NewEmptyTrainingDatasetError()
}
classifier.trainingData = trainingData
return nil
}
Context("when the dataset has rows and the ratio is valid", func() {
var expectedTrainingTargets, expectedTestTargets []slice.Slice
BeforeEach(func() {
for i := 0; i < 20; i++ {
originalSet.AddRowFromStrings([]string{strconv.Itoa(i)})
}
expectedTrainingTargets = makeSingleFloatTargets(16, 13, 1, 2, 19, 6, 5, 14, 10, 11, 4, 15, 8, 3, 9)
expectedTestTargets = makeSingleFloatTargets(12, 7, 17, 0, 18)
})
It("splits the dataset into two according to the given ratio", func() {
Ω(err).ShouldNot(HaveOccurred())
Ω(trainingSet.NumRows()).Should(Equal(len(expectedTrainingTargets)))
for i := 0; i < trainingSet.NumRows(); i++ {
trainingRow, rowErr := trainingSet.Row(i)
Ω(rowErr).ShouldNot(HaveOccurred())
Ω(trainingRow.Target().Equals(expectedTrainingTargets[i])).Should(BeTrue())
}
Ω(testSet.NumRows()).Should(Equal(len(expectedTestTargets)))
for i := 0; i < testSet.NumRows(); i++ {
testRow, rowErr := testSet.Row(i)
Ω(rowErr).ShouldNot(HaveOccurred())
Ω(testRow.Target().Equals(expectedTestTargets[i])).Should(BeTrue())
}
})
})
})
Ω(ds.NumFeatures()).Should(Equal(2))
Ω(ds.NumTargets()).Should(Equal(3))
})
})
Describe("Adding, Counting, and Getting rows", func() {
BeforeEach(func() {
columnTypes, err := columntype.StringsToColumnTypes([]string{"1.0", "x", "x", "1.0", "x"})
Ω(err).ShouldNot(HaveOccurred())
ds = dataset.NewDataset([]int{1, 3}, []int{0, 2, 4}, columnTypes)
})
Context("When the dataset is empty", func() {
It("Has 0 rows", func() {
Ω(ds.NumRows()).To(BeZero())
})
Context("When getting a row", func() {
It("Returns an error", func() {
_, err := ds.Row(0)
Ω(err).Should(HaveOccurred())
_, err = ds.Row(-1)
Ω(err).Should(HaveOccurred())
_, err = ds.Row(1)
Ω(err).Should(HaveOccurred())
})
})