func main() {
var tree base.Classifier
rand.Seed(time.Now().UTC().UnixNano())
// Load in the iris dataset
iris, err := base.ParseCSVToInstances("../datasets/iris_headers.csv", true)
if err != nil {
panic(err)
}
// Discretise the iris dataset with Chi-Merge
filt := filters.NewChiMergeFilter(iris, 0.99)
filt.AddAllNumericAttributes()
filt.Build()
filt.Run(iris)
// Create a 60-40 training-test split
insts := base.InstancesTrainTestSplit(iris, 0.60)
//
// First up, use ID3
//
tree = trees.NewID3DecisionTree(0.6)
// (Parameter controls train-prune split.)
// Train the ID3 tree
tree.Fit(insts[0])
// Generate predictions
predictions := tree.Predict(insts[1])
// Evaluate
fmt.Println("ID3 Performance")
cf := eval.GetConfusionMatrix(insts[1], predictions)
fmt.Println(eval.GetSummary(cf))
//
// Next up, Random Trees
//
// Consider two randomly-chosen attributes
tree = trees.NewRandomTree(2)
tree.Fit(insts[0])
predictions = tree.Predict(insts[1])
fmt.Println("RandomTree Performance")
cf = eval.GetConfusionMatrix(insts[1], predictions)
fmt.Println(eval.GetSummary(cf))
//
// Finally, Random Forests
//
tree = ensemble.NewRandomForest(100, 3)
tree.Fit(insts[0])
predictions = tree.Predict(insts[1])
fmt.Println("RandomForest Performance")
cf = eval.GetConfusionMatrix(insts[1], predictions)
fmt.Println(eval.GetSummary(cf))
}
func main() {
var tree base.Classifier
// Load in the iris dataset
iris, err := base.ParseCSVToInstances("../datasets/iris_headers.csv", true)
if err != nil {
panic(err)
}
for i := 1; i < 60; i += 2 {
// Demonstrate the effect of adding more trees to the forest
// and also how much better it is without discretisation.
rand.Seed(44111342)
tree = ensemble.NewRandomForest(i, 4)
cfs, err := evaluation.GenerateCrossFoldValidationConfusionMatrices(iris, tree, 5)
if err != nil {
panic(err)
}
mean, variance := evaluation.GetCrossValidatedMetric(cfs, evaluation.GetAccuracy)
stdev := math.Sqrt(variance)
fmt.Printf("%d\t%.2f\t(+/- %.2f)\n", i, mean, stdev*2)
}
}
func main() {
var tree base.Classifier
rand.Seed(44111342)
// Load in the iris dataset
iris, err := base.ParseCSVToInstances("../datasets/iris_headers.csv", true)
if err != nil {
panic(err)
}
// Discretise the iris dataset with Chi-Merge
filt := filters.NewChiMergeFilter(iris, 0.999)
for _, a := range base.NonClassFloatAttributes(iris) {
filt.AddAttribute(a)
}
filt.Train()
irisf := base.NewLazilyFilteredInstances(iris, filt)
// Create a 60-40 training-test split
trainData, testData := base.InstancesTrainTestSplit(irisf, 0.60)
//
// First up, use ID3
//
tree = trees.NewID3DecisionTree(0.6)
// (Parameter controls train-prune split.)
// Train the ID3 tree
err = tree.Fit(trainData)
if err != nil {
panic(err)
}
// Generate predictions
predictions, err := tree.Predict(testData)
if err != nil {
panic(err)
}
// Evaluate
fmt.Println("ID3 Performance (information gain)")
cf, err := evaluation.GetConfusionMatrix(testData, predictions)
if err != nil {
panic(fmt.Sprintf("Unable to get confusion matrix: %s", err.Error()))
}
fmt.Println(evaluation.GetSummary(cf))
tree = trees.NewID3DecisionTreeFromRule(0.6, new(trees.InformationGainRatioRuleGenerator))
// (Parameter controls train-prune split.)
// Train the ID3 tree
err = tree.Fit(trainData)
if err != nil {
panic(err)
}
// Generate predictions
predictions, err = tree.Predict(testData)
if err != nil {
panic(err)
}
// Evaluate
fmt.Println("ID3 Performance (information gain ratio)")
cf, err = evaluation.GetConfusionMatrix(testData, predictions)
if err != nil {
panic(fmt.Sprintf("Unable to get confusion matrix: %s", err.Error()))
}
fmt.Println(evaluation.GetSummary(cf))
tree = trees.NewID3DecisionTreeFromRule(0.6, new(trees.GiniCoefficientRuleGenerator))
// (Parameter controls train-prune split.)
// Train the ID3 tree
err = tree.Fit(trainData)
if err != nil {
panic(err)
}
// Generate predictions
predictions, err = tree.Predict(testData)
if err != nil {
panic(err)
}
// Evaluate
fmt.Println("ID3 Performance (gini index generator)")
cf, err = evaluation.GetConfusionMatrix(testData, predictions)
if err != nil {
panic(fmt.Sprintf("Unable to get confusion matrix: %s", err.Error()))
}
fmt.Println(evaluation.GetSummary(cf))
//
// Next up, Random Trees
//
// Consider two randomly-chosen attributes
tree = trees.NewRandomTree(2)
err = tree.Fit(testData)
if err != nil {
panic(err)
}
predictions, err = tree.Predict(testData)
if err != nil {
panic(err)
}
fmt.Println("RandomTree Performance")
cf, err = evaluation.GetConfusionMatrix(testData, predictions)
if err != nil {
panic(fmt.Sprintf("Unable to get confusion matrix: %s", err.Error()))
}
fmt.Println(evaluation.GetSummary(cf))
//
// Finally, Random Forests
//
tree = ensemble.NewRandomForest(70, 3)
err = tree.Fit(trainData)
if err != nil {
panic(err)
}
predictions, err = tree.Predict(testData)
if err != nil {
panic(err)
}
fmt.Println("RandomForest Performance")
cf, err = evaluation.GetConfusionMatrix(testData, predictions)
if err != nil {
panic(fmt.Sprintf("Unable to get confusion matrix: %s", err.Error()))
}
fmt.Println(evaluation.GetSummary(cf))
}
