mirror of
https://github.com/sjwhitworth/golearn.git
synced 2025-04-30 13:48:57 +08:00
43 lines
1.1 KiB
Go
43 lines
1.1 KiB
Go
package meta
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import (
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"fmt"
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base "github.com/sjwhitworth/golearn/base"
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eval "github.com/sjwhitworth/golearn/evaluation"
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filters "github.com/sjwhitworth/golearn/filters"
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trees "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 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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panic(err)
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}
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rand.Seed(time.Now().UnixNano())
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insts := base.InstancesTrainTestSplit(inst, 0.6)
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filt := filters.NewBinningFilter(insts[0], 10)
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filt.AddAllNumericAttributes()
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filt.Build()
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filt.Run(insts[1])
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filt.Run(insts[0])
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rf := new(BaggedModel)
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rf.RandomFeatures = 2
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rf.SelectedFeatures = make(map[int][]base.Attribute)
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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(insts[0])
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fmt.Println(rf)
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predictions := rf.Predict(insts[1])
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fmt.Println(predictions)
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confusionMat := eval.GetConfusionMatrix(insts[1], predictions)
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fmt.Println(confusionMat)
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fmt.Println(eval.GetMacroPrecision(confusionMat))
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fmt.Println(eval.GetMacroRecall(confusionMat))
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fmt.Println(eval.GetSummary(confusionMat))
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}
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