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
}
func (gdpe *gradientDescentParameterEstimator) Train(ds dataset.Dataset) error {
if !ds.AllFeaturesFloats() {
return gdeErrors.NewNonFloatFeaturesError()
}
if !ds.AllTargetsFloats() {
return gdeErrors.NewNonFloatTargetError()
}
if ds.NumTargets() != 1 {
return gdeErrors.NewInvalidNumberOfTargetsError(ds.NumTargets())
}
if ds.NumFeatures() == 0 {
return gdeErrors.NewNoFeaturesError()
}
gdpe.trainingSet = ds
return nil
}
"github.com/amitkgupta/goodlearn/data/columntype"
"github.com/amitkgupta/goodlearn/data/dataset"
"github.com/amitkgupta/goodlearn/data/slice"
"github.com/amitkgupta/goodlearn/evaluation/crossvalidation"
. "github.com/onsi/ginkgo"
. "github.com/onsi/gomega"
)
var _ = Describe("CrossValidation", func() {
Describe("SplitDataset", func() {
var (
originalSet dataset.Dataset
trainingRatio float64
trainingSet dataset.Dataset
testSet dataset.Dataset
err error
)
JustBeforeEach(func() {
trainingSet, testSet, err = crossvalidation.SplitDataset(
originalSet,
trainingRatio,
rand.NewSource(5330), // SEED
)
})
BeforeEach(func() {
columnTypes, columnTypesError := columntype.StringsToColumnTypes([]string{"0"})
Ω(columnTypesError).ShouldNot(HaveOccurred())
package dataset_test
import (
"github.com/amitkgupta/goodlearn/data/columntype"
"github.com/amitkgupta/goodlearn/data/dataset"
"github.com/amitkgupta/goodlearn/data/row"
. "github.com/onsi/ginkgo"
. "github.com/onsi/gomega"
)
var _ = Describe("Dataset", func() {
var ds dataset.Dataset
Describe("AllFeaturesFloats", func() {
Context("When all features are floats", func() {
BeforeEach(func() {
columnTypes, err := columntype.StringsToColumnTypes([]string{"1.0", "1.0"})
Ω(err).ShouldNot(HaveOccurred())
ds = dataset.NewDataset([]int{0, 1}, []int{}, columnTypes)
})
It("Returns true", func() {
Ω(ds.AllFeaturesFloats()).Should(BeTrue())
})
})
Context("When not all features are floats", func() {
BeforeEach(func() {
columnTypes, err := columntype.StringsToColumnTypes([]string{"x", "1.0"})
func (regressor *linearRegressor) Train(trainingData dataset.Dataset) error {
if !trainingData.AllFeaturesFloats() {
return linearerrors.NewNonFloatFeaturesError()
}
if !trainingData.AllTargetsFloats() {
return linearerrors.NewNonFloatTargetsError()
}
if trainingData.NumTargets() != 1 {
return linearerrors.NewInvalidNumberOfTargetsError(trainingData.NumTargets())
}
if trainingData.NumFeatures() == 0 {
return linearerrors.NewNoFeaturesError()
}
estimator, err := gradientdescentestimator.NewGradientDescentParameterEstimator(
defaultLearningRate,
defaultPrecision,
defaultMaxIterations,
gradientdescentestimator.LinearModelLeastSquaresLossGradient,
)
if err != nil {
return linearerrors.NewEstimatorConstructionError(err)
}
err = estimator.Train(trainingData)
if err != nil {
return linearerrors.NewEstimatorTrainingError(err)
}
coefficients, err := estimator.Estimate(defaultInitialCoefficientEstimate(trainingData.NumFeatures()))
if err != nil {
return linearerrors.NewEstimatorEstimationError(err)
}
regressor.coefficients = coefficients
return nil
}
It("Returns an error", func() {
_, err := knn.NewKNNClassifier(-3)
Ω(err).Should(HaveOccurred())
})
})
Context("When given a positive value for k", func() {
It("Returns an error", func() {
_, err := knn.NewKNNClassifier(5)
Ω(err).ShouldNot(HaveOccurred())
})
})
})
Describe("Train", func() {
var trainingData dataset.Dataset
BeforeEach(func() {
kNNClassifier, _ = knn.NewKNNClassifier(1)
})
Context("When the dataset is empty", func() {
BeforeEach(func() {
columnTypes, err := columntype.StringsToColumnTypes([]string{"hi", "0", "0"})
Ω(err).ShouldNot(HaveOccurred())
trainingData = dataset.NewDataset([]int{1, 2}, []int{0}, columnTypes)
})
It("Returns an error", func() {
err := kNNClassifier.Train(trainingData)
Context("Given positive learning rate, precisions, and max iterations", func() {
It("Does not return an error", func() {
_, err := gradientdescentestimator.NewGradientDescentParameterEstimator(
0.3,
0.3,
100,
lossGradient,
)
Ω(err).ShouldNot(HaveOccurred())
})
})
})
Describe("Train", func() {
var trainingSet dataset.Dataset
var estimator parameterestimator.ParameterEstimator
BeforeEach(func() {
var err error
estimator, err = gradientdescentestimator.NewGradientDescentParameterEstimator(
0.3,
0.3,
100,
lossGradient,
)
Ω(err).ShouldNot(HaveOccurred())
})
Context("Given a dataset with non-float features", func() {
BeforeEach(func() {
"github.com/amitkgupta/goodlearn/data/dataset"
"github.com/amitkgupta/goodlearn/data/row"
"github.com/amitkgupta/goodlearn/data/slice"
"github.com/amitkgupta/goodlearn/errors/regressor/linearerrors"
"github.com/amitkgupta/goodlearn/regressor"
"github.com/amitkgupta/goodlearn/regressor/linear"
. "github.com/onsi/ginkgo"
. "github.com/onsi/gomega"
)
var _ = Describe("LinearRegressor", func() {
var linearRegressor regressor.Regressor
Describe("Train", func() {
var trainingData dataset.Dataset
BeforeEach(func() {
linearRegressor = linear.NewLinearRegressor()
})
Context("When the dataset's features are not all floats", func() {
BeforeEach(func() {
columnTypes, err := columntype.StringsToColumnTypes([]string{"3.3", "bye", "0"})
Ω(err).ShouldNot(HaveOccurred())
trainingData = dataset.NewDataset([]int{1, 2}, []int{0}, columnTypes)
})
It("Returns an error", func() {
err := linearRegressor.Train(trainingData)
