Examples for RandomForest, ID3 and Random trees

ensemble/randomforest.go

@@ -4,6 +4,7 @@ import (
 	base "github.com/sjwhitworth/golearn/base"
 	meta "github.com/sjwhitworth/golearn/meta"
 	trees "github.com/sjwhitworth/golearn/trees"
+	"fmt"
 )
 
 // RandomForest classifies instances using an ensemble
@@ -18,8 +19,8 @@ type RandomForest struct {
 // NewRandomForests generates and return a new random forests
 // forestSize controls the number of trees that get built
 // features controls the number of features used to build each tree
-func NewRandomForest(forestSize int, features int) RandomForest {
-	ret := RandomForest{
+func NewRandomForest(forestSize int, features int) *RandomForest {
+	ret := &RandomForest{
 		base.BaseClassifier{},
 		forestSize,
 		features,
@@ -43,3 +44,7 @@ func (f *RandomForest) Fit(on *base.Instances) {
 func (f *RandomForest) Predict(with *base.Instances) *base.Instances {
 	return f.Model.Predict(with)
 }
+
+func (f *RandomForest) String() string {
+	return fmt.Sprintf("RandomForest(ForestSize: %d, Features:%d, %s\n)", f.ForestSize, f.Features, f.Model)
+}

examples/trees/trees.go

@@ -0,0 +1,75 @@
+// Demonstrates decision tree classification
+
+package main
+
+import (
+	"fmt"
+	base "github.com/sjwhitworth/golearn/base"
+	eval "github.com/sjwhitworth/golearn/evaluation"
+	filters "github.com/sjwhitworth/golearn/filters"
+	ensemble "github.com/sjwhitworth/golearn/ensemble"
+	trees "github.com/sjwhitworth/golearn/trees"
+	"math/rand"
+	"time"
+)
+
+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))
+}

trees/tree_test.go

@@ -121,7 +121,7 @@ func TestInformationGain(testEnv *testing.T) {
 func TestID3Inference(testEnv *testing.T) {
 
 	// Import the "PlayTennis" dataset
-	inst, err := base.ParseCSVToInstances("./tennis.csv", true)
+	inst, err := base.ParseCSVToInstances("../examples/datasets/tennis.csv", true)
 	if err != nil {
 		panic(err)
 	}
@@ -198,7 +198,7 @@ func TestID3Classification(testEnv *testing.T) {
 func TestID3(testEnv *testing.T) {
 
 	// Import the "PlayTennis" dataset
-	inst, err := base.ParseCSVToInstances("./tennis.csv", true)
+	inst, err := base.ParseCSVToInstances("../examples/datasets/tennis.csv", true)
 	if err != nil {
 		panic(err)
 	}