mirror of
https://github.com/sjwhitworth/golearn.git
synced 2025-04-26 13:49:14 +08:00
93 lines
2.6 KiB
Go
93 lines
2.6 KiB
Go
package trees
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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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"math"
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"testing"
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)
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func TestRandomTree(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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filt := filters.NewBinningFilter(inst, 10)
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filt.AddAllNumericAttributes()
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filt.Build()
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filt.Run(inst)
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fmt.Println(inst)
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r := new(RandomTreeRuleGenerator)
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r.Attributes = 2
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root := InferDecisionTree(inst, r)
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fmt.Println(root)
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}
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func TestRandomTreeClassification(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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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[0])
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filt.Run(insts[1])
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fmt.Println(inst)
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r := new(RandomTreeRuleGenerator)
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r.Attributes = 2
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root := InferDecisionTree(insts[0], r)
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fmt.Println(root)
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predictions := root.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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func TestRandomTreeClassification2(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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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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fmt.Println(insts[1])
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filt.Run(insts[1])
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filt.Run(insts[0])
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root := NewRandomTree(2)
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root.Fit(insts[0])
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fmt.Println(root)
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predictions := root.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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func TestInformationGain(testEnv *testing.T) {
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outlook := make(map[string]map[string]int)
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outlook["sunny"] = make(map[string]int)
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outlook["overcast"] = make(map[string]int)
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outlook["rain"] = make(map[string]int)
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outlook["sunny"]["play"] = 2
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outlook["sunny"]["noplay"] = 3
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outlook["overcast"]["play"] = 4
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outlook["rain"]["play"] = 3
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outlook["rain"]["noplay"] = 2
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entropy := getSplitEntropy(outlook)
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if math.Abs(entropy-0.694) > 0.001 {
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testEnv.Error(entropy)
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}
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}
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