func TestRandomForest1(testEnv *testing.T) {
inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
if err != nil {
panic(err)
}
rand.Seed(time.Now().UnixNano())
insts := base.InstancesTrainTestSplit(inst, 0.6)
filt := filters.NewChiMergeFilter(inst, 0.90)
filt.AddAllNumericAttributes()
filt.Build()
filt.Run(insts[1])
filt.Run(insts[0])
rf := new(BaggedModel)
for i := 0; i < 10; i++ {
rf.AddModel(trees.NewRandomTree(2))
}
rf.Fit(insts[0])
fmt.Println(rf)
predictions := rf.Predict(insts[1])
fmt.Println(predictions)
confusionMat := eval.GetConfusionMatrix(insts[1], predictions)
fmt.Println(confusionMat)
fmt.Println(eval.GetMacroPrecision(confusionMat))
fmt.Println(eval.GetMacroRecall(confusionMat))
fmt.Println(eval.GetSummary(confusionMat))
}
func TestPruning(testEnv *testing.T) {
inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
if err != nil {
panic(err)
}
trainData, testData := base.InstancesTrainTestSplit(inst, 0.6)
filt := filters.NewChiMergeFilter(inst, 0.90)
filt.AddAllNumericAttributes()
filt.Build()
fmt.Println(testData)
filt.Run(testData)
filt.Run(trainData)
root := NewRandomTree(2)
fittrainData, fittestData := base.InstancesTrainTestSplit(trainData, 0.6)
root.Fit(fittrainData)
root.Prune(fittestData)
fmt.Println(root)
predictions := root.Predict(testData)
fmt.Println(predictions)
confusionMat := eval.GetConfusionMatrix(testData, predictions)
fmt.Println(confusionMat)
fmt.Println(eval.GetMacroPrecision(confusionMat))
fmt.Println(eval.GetMacroRecall(confusionMat))
fmt.Println(eval.GetSummary(confusionMat))
}
func TestRandomForest1(testEnv *testing.T) {
inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
if err != nil {
panic(err)
}
rand.Seed(time.Now().UnixNano())
trainData, testData := base.InstancesTrainTestSplit(inst, 0.6)
filt := filters.NewChiMergeFilter(inst, 0.90)
for _, a := range base.NonClassFloatAttributes(inst) {
filt.AddAttribute(a)
}
filt.Train()
trainDataf := base.NewLazilyFilteredInstances(trainData, filt)
testDataf := base.NewLazilyFilteredInstances(testData, filt)
rf := new(BaggedModel)
for i := 0; i < 10; i++ {
rf.AddModel(trees.NewRandomTree(2))
}
rf.Fit(trainDataf)
fmt.Println(rf)
predictions := rf.Predict(testDataf)
fmt.Println(predictions)
confusionMat := eval.GetConfusionMatrix(testDataf, predictions)
fmt.Println(confusionMat)
fmt.Println(eval.GetMacroPrecision(confusionMat))
fmt.Println(eval.GetMacroRecall(confusionMat))
fmt.Println(eval.GetSummary(confusionMat))
}
func TestID3Classification(testEnv *testing.T) {
inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
if err != nil {
panic(err)
}
filt := filters.NewBinningFilter(inst, 10)
filt.AddAllNumericAttributes()
filt.Build()
filt.Run(inst)
fmt.Println(inst)
trainData, testData := base.InstancesTrainTestSplit(inst, 0.70)
// Build the decision tree
rule := new(InformationGainRuleGenerator)
root := InferID3Tree(trainData, rule)
fmt.Println(root)
predictions := root.Predict(testData)
fmt.Println(predictions)
confusionMat := eval.GetConfusionMatrix(testData, predictions)
fmt.Println(confusionMat)
fmt.Println(eval.GetMacroPrecision(confusionMat))
fmt.Println(eval.GetMacroRecall(confusionMat))
fmt.Println(eval.GetSummary(confusionMat))
}
func TestRandomTreeClassification2(testEnv *testing.T) {
inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
if err != nil {
panic(err)
}
insts := base.InstancesTrainTestSplit(inst, 0.4)
filt := filters.NewChiMergeFilter(inst, 0.90)
filt.AddAllNumericAttributes()
filt.Build()
fmt.Println(insts[1])
filt.Run(insts[1])
filt.Run(insts[0])
root := NewRandomTree(2)
root.Fit(insts[0])
fmt.Println(root)
predictions := root.Predict(insts[1])
fmt.Println(predictions)
confusionMat := eval.GetConfusionMatrix(insts[1], predictions)
fmt.Println(confusionMat)
fmt.Println(eval.GetMacroPrecision(confusionMat))
fmt.Println(eval.GetMacroRecall(confusionMat))
fmt.Println(eval.GetSummary(confusionMat))
}
func TestRandomTreeClassification(testEnv *testing.T) {
inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
if err != nil {
panic(err)
}
trainData, testData := base.InstancesTrainTestSplit(inst, 0.6)
filt := filters.NewChiMergeFilter(inst, 0.90)
filt.AddAllNumericAttributes()
filt.Build()
filt.Run(trainData)
filt.Run(testData)
fmt.Println(inst)
r := new(RandomTreeRuleGenerator)
r.Attributes = 2
root := InferID3Tree(trainData, r)
fmt.Println(root)
predictions := root.Predict(testData)
fmt.Println(predictions)
confusionMat := eval.GetConfusionMatrix(testData, predictions)
fmt.Println(confusionMat)
fmt.Println(eval.GetMacroPrecision(confusionMat))
fmt.Println(eval.GetMacroRecall(confusionMat))
fmt.Println(eval.GetSummary(confusionMat))
}