randomforest

GoDoc: https://godoc.org/github.com/malaschitz/randomForest

This fork add Saving/Loading functions see the section Saving/Loading click on this link

Test:

go test ./... -cover -coverpkg=.  

randomForest

Random Forest implementation in golang.

Simple Random Forest

	xData := [][]float64{}
	yData := []int{}
	for i := 0; i < 1000; i++ {
		x := []float64{rand.Float64(), rand.Float64(), rand.Float64(), rand.Float64()}
		y := int(x[0] + x[1] + x[2] + x[3])
		xData = append(xData, x)
		yData = append(yData, y)
	}
	forest := randomForest.Forest{}		
	forest.Data = randomforest.ForestData{X: xData, Class: yData}
	forest.Train(1000)
	//test
	fmt.Println("Vote", forest.Vote([]float64{0.1, 0.1, 0.1, 0.1})) 
	fmt.Println("Vote", forest.Vote([]float64{0.9, 0.9, 0.9, 0.9}))

Extremely Randomized Trees

	forest.TrainX(1000)	

Deep Forest

Deep forest inspired by https://arxiv.org/abs/1705.07366

    dForest := forest.BuildDeepForest()
    dForest.Train(20, 100, 1000) //20 small forest with 100 trees help to build deep forest with 1000 trees

Continuos Random Forest

Continuos Random Forest for data where are still new and new data (forex, wheather, user logs, ...). New data create a new trees and oldest trees are removed.

forest := randomForest.Forest{}
data := []float64{rand.Float64(), rand.Float64()}
res := 1; //result
forest.AddDataRow(data, res, 1000, 10, 2000) 
// AddDataRow : add new row, trim oldest row if there is more than 1000 rows, calculate a new 10 trees, but remove oldest trees if there is more than 2000 trees.

Boruta Algorithm for feature selection

Boruta algorithm was developed as package for language R. It is one of most effective feature selection algorithm. There is paper in Journal of Statistical Software.

Boruta algorithm use random forest for selection important features.

	xData := ... //data
	yData := ... //labels
	selectedFeatures := randomforest.BorutaDefault(xData, yData)
	// or randomforest.BorutaDefault(xData, yData, 100, 20, 0.05, true, true)

In /examples is example with MNIST database. On picture are selected features (495 from 784) from images.

Saving/Loading

Saving

Will Save the forest structure into binary file

File name format:

forest-UUID[:8]+sha256.bin

	if fileName, err := forest.Save("saved/"); err != nil {
		t.Error(err)
		return
	}

Loading

Will load forest structure from binary file :

	if forest, errForest = Load("saved/forestTest.bin"); errForest != nil {
		return
	}
	
	fmt.Println("Vote", forest.Vote([]float64{0.9, 0.9, 0.9, 0.9}))