mirror of
https://github.com/sjwhitworth/golearn.git
synced 2025-04-26 13:49:14 +08:00
90 lines
2.3 KiB
Go
90 lines
2.3 KiB
Go
package meta
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import (
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"github.com/sjwhitworth/golearn/base"
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eval "github.com/sjwhitworth/golearn/evaluation"
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"github.com/sjwhitworth/golearn/filters"
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"github.com/sjwhitworth/golearn/trees"
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"math/rand"
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"testing"
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"time"
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)
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func BenchmarkBaggingRandomForestFit(testEnv *testing.B) {
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inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
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if err != nil {
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testEnv.Fatal("Unable to parse CSV to instances: %s", err.Error())
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}
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rand.Seed(time.Now().UnixNano())
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filt := filters.NewChiMergeFilter(inst, 0.90)
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for _, a := range base.NonClassFloatAttributes(inst) {
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filt.AddAttribute(a)
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}
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filt.Train()
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instf := base.NewLazilyFilteredInstances(inst, filt)
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rf := new(BaggedModel)
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for i := 0; i < 10; i++ {
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rf.AddModel(trees.NewRandomTree(2))
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}
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testEnv.ResetTimer()
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for i := 0; i < 20; i++ {
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rf.Fit(instf)
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}
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}
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func BenchmarkBaggingRandomForestPredict(testEnv *testing.B) {
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inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
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if err != nil {
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testEnv.Fatal("Unable to parse CSV to instances: %s", err.Error())
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}
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rand.Seed(time.Now().UnixNano())
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filt := filters.NewChiMergeFilter(inst, 0.90)
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for _, a := range base.NonClassFloatAttributes(inst) {
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filt.AddAttribute(a)
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}
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filt.Train()
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instf := base.NewLazilyFilteredInstances(inst, filt)
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rf := new(BaggedModel)
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for i := 0; i < 10; i++ {
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rf.AddModel(trees.NewRandomTree(2))
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}
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rf.Fit(instf)
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testEnv.ResetTimer()
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for i := 0; i < 20; i++ {
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rf.Predict(instf)
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}
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}
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func TestRandomForest1(testEnv *testing.T) {
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inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
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if err != nil {
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testEnv.Fatal("Unable to parse CSV to instances: %s", err.Error())
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}
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trainData, testData := base.InstancesTrainTestSplit(inst, 0.6)
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rand.Seed(time.Now().UnixNano())
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filt := filters.NewChiMergeFilter(inst, 0.90)
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for _, a := range base.NonClassFloatAttributes(inst) {
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filt.AddAttribute(a)
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}
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filt.Train()
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trainDataf := base.NewLazilyFilteredInstances(trainData, filt)
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testDataf := base.NewLazilyFilteredInstances(testData, filt)
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rf := new(BaggedModel)
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for i := 0; i < 10; i++ {
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rf.AddModel(trees.NewRandomTree(2))
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}
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rf.Fit(trainDataf)
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predictions := rf.Predict(testDataf)
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confusionMat := eval.GetConfusionMatrix(testDataf, predictions)
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_ = eval.GetSummary(confusionMat)
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}
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