func TestRandomForest1(testEnv *testing.T) { inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true) if err != nil { panic(err) } rand.Seed(time.Now().UnixNano()) insts := base.InstancesTrainTestSplit(inst, 0.6) filt := filters.NewChiMergeFilter(inst, 0.90) filt.AddAllNumericAttributes() filt.Build() filt.Run(insts[1]) filt.Run(insts[0]) rf := new(BaggedModel) for i := 0; i < 10; i++ { rf.AddModel(trees.NewRandomTree(2)) } rf.Fit(insts[0]) fmt.Println(rf) predictions := rf.Predict(insts[1]) fmt.Println(predictions) confusionMat := eval.GetConfusionMatrix(insts[1], predictions) fmt.Println(confusionMat) fmt.Println(eval.GetMacroPrecision(confusionMat)) fmt.Println(eval.GetMacroRecall(confusionMat)) fmt.Println(eval.GetSummary(confusionMat)) }
func TestPruning(testEnv *testing.T) { inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true) if err != nil { panic(err) } trainData, testData := base.InstancesTrainTestSplit(inst, 0.6) filt := filters.NewChiMergeFilter(inst, 0.90) filt.AddAllNumericAttributes() filt.Build() fmt.Println(testData) filt.Run(testData) filt.Run(trainData) root := NewRandomTree(2) fittrainData, fittestData := base.InstancesTrainTestSplit(trainData, 0.6) root.Fit(fittrainData) root.Prune(fittestData) fmt.Println(root) predictions := root.Predict(testData) fmt.Println(predictions) confusionMat := eval.GetConfusionMatrix(testData, predictions) fmt.Println(confusionMat) fmt.Println(eval.GetMacroPrecision(confusionMat)) fmt.Println(eval.GetMacroRecall(confusionMat)) fmt.Println(eval.GetSummary(confusionMat)) }
func TestRandomForest1(testEnv *testing.T) { inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true) if err != nil { panic(err) } rand.Seed(time.Now().UnixNano()) trainData, testData := base.InstancesTrainTestSplit(inst, 0.6) 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) fmt.Println(rf) predictions := rf.Predict(testDataf) fmt.Println(predictions) confusionMat := eval.GetConfusionMatrix(testDataf, predictions) fmt.Println(confusionMat) fmt.Println(eval.GetMacroPrecision(confusionMat)) fmt.Println(eval.GetMacroRecall(confusionMat)) fmt.Println(eval.GetSummary(confusionMat)) }
func TestID3Classification(testEnv *testing.T) { inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true) if err != nil { panic(err) } filt := filters.NewBinningFilter(inst, 10) filt.AddAllNumericAttributes() filt.Build() filt.Run(inst) fmt.Println(inst) trainData, testData := base.InstancesTrainTestSplit(inst, 0.70) // Build the decision tree rule := new(InformationGainRuleGenerator) root := InferID3Tree(trainData, rule) fmt.Println(root) predictions := root.Predict(testData) fmt.Println(predictions) confusionMat := eval.GetConfusionMatrix(testData, predictions) fmt.Println(confusionMat) fmt.Println(eval.GetMacroPrecision(confusionMat)) fmt.Println(eval.GetMacroRecall(confusionMat)) fmt.Println(eval.GetSummary(confusionMat)) }
func TestRandomTreeClassification2(testEnv *testing.T) { inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true) if err != nil { panic(err) } insts := base.InstancesTrainTestSplit(inst, 0.4) filt := filters.NewChiMergeFilter(inst, 0.90) filt.AddAllNumericAttributes() filt.Build() fmt.Println(insts[1]) filt.Run(insts[1]) filt.Run(insts[0]) root := NewRandomTree(2) root.Fit(insts[0]) fmt.Println(root) predictions := root.Predict(insts[1]) fmt.Println(predictions) confusionMat := eval.GetConfusionMatrix(insts[1], predictions) fmt.Println(confusionMat) fmt.Println(eval.GetMacroPrecision(confusionMat)) fmt.Println(eval.GetMacroRecall(confusionMat)) fmt.Println(eval.GetSummary(confusionMat)) }
func TestRandomTreeClassification(testEnv *testing.T) { inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true) if err != nil { panic(err) } trainData, testData := base.InstancesTrainTestSplit(inst, 0.6) filt := filters.NewChiMergeFilter(inst, 0.90) filt.AddAllNumericAttributes() filt.Build() filt.Run(trainData) filt.Run(testData) fmt.Println(inst) r := new(RandomTreeRuleGenerator) r.Attributes = 2 root := InferID3Tree(trainData, r) fmt.Println(root) predictions := root.Predict(testData) fmt.Println(predictions) confusionMat := eval.GetConfusionMatrix(testData, predictions) fmt.Println(confusionMat) fmt.Println(eval.GetMacroPrecision(confusionMat)) fmt.Println(eval.GetMacroRecall(confusionMat)) fmt.Println(eval.GetSummary(confusionMat)) }