mirror of
https://github.com/sjwhitworth/golearn.git
synced 2025-04-28 13:48:56 +08:00
93 lines
2.3 KiB
Go
93 lines
2.3 KiB
Go
package meta
|
|
|
|
import (
|
|
"github.com/sjwhitworth/golearn/base"
|
|
eval "github.com/sjwhitworth/golearn/evaluation"
|
|
"github.com/sjwhitworth/golearn/filters"
|
|
"github.com/sjwhitworth/golearn/trees"
|
|
"math/rand"
|
|
"testing"
|
|
"time"
|
|
)
|
|
|
|
func BenchmarkBaggingRandomForestFit(t *testing.B) {
|
|
inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
|
|
if err != nil {
|
|
t.Fatal("Unable to parse CSV to instances: %s", err.Error())
|
|
}
|
|
|
|
rand.Seed(time.Now().UnixNano())
|
|
filt := filters.NewChiMergeFilter(inst, 0.90)
|
|
for _, a := range base.NonClassFloatAttributes(inst) {
|
|
filt.AddAttribute(a)
|
|
}
|
|
filt.Train()
|
|
instf := base.NewLazilyFilteredInstances(inst, filt)
|
|
|
|
rf := new(BaggedModel)
|
|
for i := 0; i < 10; i++ {
|
|
rf.AddModel(trees.NewRandomTree(2))
|
|
}
|
|
|
|
t.ResetTimer()
|
|
for i := 0; i < 20; i++ {
|
|
rf.Fit(instf)
|
|
}
|
|
}
|
|
|
|
func BenchmarkBaggingRandomForestPredict(t *testing.B) {
|
|
inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
|
|
if err != nil {
|
|
t.Fatal("Unable to parse CSV to instances: %s", err.Error())
|
|
}
|
|
|
|
rand.Seed(time.Now().UnixNano())
|
|
filt := filters.NewChiMergeFilter(inst, 0.90)
|
|
for _, a := range base.NonClassFloatAttributes(inst) {
|
|
filt.AddAttribute(a)
|
|
}
|
|
filt.Train()
|
|
instf := base.NewLazilyFilteredInstances(inst, filt)
|
|
|
|
rf := new(BaggedModel)
|
|
for i := 0; i < 10; i++ {
|
|
rf.AddModel(trees.NewRandomTree(2))
|
|
}
|
|
|
|
rf.Fit(instf)
|
|
t.ResetTimer()
|
|
for i := 0; i < 20; i++ {
|
|
rf.Predict(instf)
|
|
}
|
|
}
|
|
|
|
func TestRandomForest1(t *testing.T) {
|
|
inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
|
|
if err != nil {
|
|
t.Fatal("Unable to parse CSV to instances: %s", err.Error())
|
|
}
|
|
trainData, testData := base.InstancesTrainTestSplit(inst, 0.6)
|
|
|
|
rand.Seed(time.Now().UnixNano())
|
|
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)
|
|
predictions := rf.Predict(testDataf)
|
|
confusionMat, err := eval.GetConfusionMatrix(testDataf, predictions)
|
|
if err != nil {
|
|
t.Fatalf("Unable to get confusion matrix: %s", err.Error())
|
|
}
|
|
_ = eval.GetSummary(confusionMat)
|
|
}
|