Golang base.NewLazilyFilteredInstances函数代码示例

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 TestChiMergeFilter(t *testing.T) {
	Convey("Chi-Merge Filter", t, func() {
		// See http://sci2s.ugr.es/keel/pdf/algorithm/congreso/1992-Kerber-ChimErge-AAAI92.pdf
		//   Randy Kerber, ChiMerge: Discretisation of Numeric Attributes, 1992
		instances, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
		So(err, ShouldBeNil)

		Convey("Create and train the filter", func() {
			filter := NewChiMergeFilter(instances, 0.90)
			filter.AddAttribute(instances.AllAttributes()[0])
			filter.AddAttribute(instances.AllAttributes()[1])
			filter.Train()

			Convey("Filter the dataset", func() {
				filteredInstances := base.NewLazilyFilteredInstances(instances, filter)

				classAttributes := filteredInstances.AllClassAttributes()

				Convey("There should only be one class attribute", func() {
					So(len(classAttributes), ShouldEqual, 1)
				})

				expectedClassAttribute := "Species"

				Convey(fmt.Sprintf("The class attribute should be %s", expectedClassAttribute), func() {
					So(classAttributes[0].GetName(), ShouldEqual, expectedClassAttribute)
				})
			})
		})
	})
}

func main() {

	var tree base.Classifier

	rand.Seed(44111342)

	// Load in the iris dataset
	iris, err := base.ParseCSVToInstances("/home/kralli/go/src/github.com/sjwhitworth/golearn/examples/datasets/iris_headers.csv", true)
	if err != nil {
		panic(err)
	}

	// Discretise the iris dataset with Chi-Merge
	filt := filters.NewChiMergeFilter(iris, 0.999)
	for _, a := range base.NonClassFloatAttributes(iris) {
		filt.AddAttribute(a)
	}
	filt.Train()
	irisf := base.NewLazilyFilteredInstances(iris, filt)

	// Create a 60-40 training-test split
	//testData
	trainData, _ := base.InstancesTrainTestSplit(iris, 0.60)

	findBestSplit(trainData)

	//fmt.Println(trainData)
	//fmt.Println(testData)

	fmt.Println(tree)
	fmt.Println(irisf)
}

// Predict issues predictions. Each class-specific classifier is expected
// to output a value between 0 (indicating that a given instance is not
// a given class) and 1 (indicating that the given instance is definitely
// that class). For each instance, the class with the highest value is chosen.
// The result is undefined if several underlying models output the same value.
func (m *OneVsAllModel) Predict(what base.FixedDataGrid) (base.FixedDataGrid, error) {
	ret := base.GeneratePredictionVector(what)
	vecs := make([]base.FixedDataGrid, m.maxClassVal+1)
	specs := make([]base.AttributeSpec, m.maxClassVal+1)
	for i := uint64(0); i <= m.maxClassVal; i++ {
		f := m.filters[i]
		c := base.NewLazilyFilteredInstances(what, f)
		p, err := m.classifiers[i].Predict(c)
		if err != nil {
			return nil, err
		}
		vecs[i] = p
		specs[i] = base.ResolveAttributes(p, p.AllClassAttributes())[0]
	}
	_, rows := ret.Size()
	spec := base.ResolveAttributes(ret, ret.AllClassAttributes())[0]
	for i := 0; i < rows; i++ {
		class := uint64(0)
		best := 0.0
		for j := uint64(0); j <= m.maxClassVal; j++ {
			val := base.UnpackBytesToFloat(vecs[j].Get(specs[j], i))
			if val > best {
				class = j
				best = val
			}
		}
		ret.Set(spec, i, base.PackU64ToBytes(class))
	}
	return ret, nil
}

func TestBinaryFilterClassPreservation(t *testing.T) {
	Convey("Given a contrived dataset...", t, func() {
		// Read the contrived dataset
		inst, err := base.ParseCSVToInstances("./binary_test.csv", true)
		So(err, ShouldEqual, nil)

		// Add all Attributes to the filter
		bFilt := NewBinaryConvertFilter()
		bAttrs := inst.AllAttributes()
		for _, a := range bAttrs {
			bFilt.AddAttribute(a)
		}
		bFilt.Train()

		// Construct a LazilyFilteredInstances to handle it
		instF := base.NewLazilyFilteredInstances(inst, bFilt)

		Convey("All the expected class Attributes should be present if discretised...", func() {
			attrMap := make(map[string]bool)
			attrMap["arbitraryClass_hi"] = false
			attrMap["arbitraryClass_there"] = false
			attrMap["arbitraryClass_world"] = false

			for _, a := range instF.AllClassAttributes() {
				attrMap[a.GetName()] = true
			}

			So(attrMap["arbitraryClass_hi"], ShouldEqual, true)
			So(attrMap["arbitraryClass_there"], ShouldEqual, true)
			So(attrMap["arbitraryClass_world"], ShouldEqual, true)
		})
	})
}

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 TestRandomTreeClassificationAfterDiscretisation(t *testing.T) {
	Convey("Predictions on filtered data with a Random Tree", t, func() {
		instances, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
		So(err, ShouldBeNil)

		trainData, testData := base.InstancesTrainTestSplit(instances, 0.6)

		filter := filters.NewChiMergeFilter(instances, 0.9)
		for _, a := range base.NonClassFloatAttributes(instances) {
			filter.AddAttribute(a)
		}
		filter.Train()
		filteredTrainData := base.NewLazilyFilteredInstances(trainData, filter)
		filteredTestData := base.NewLazilyFilteredInstances(testData, filter)
		verifyTreeClassification(filteredTrainData, filteredTestData)
	})
}

func (m *MultiLayerNet) convertToFloatInsts(X base.FixedDataGrid) base.FixedDataGrid {

	// Make sure everything's a FloatAttribute
	fFilt := filters.NewFloatConvertFilter()
	for _, a := range X.AllAttributes() {
		fFilt.AddAttribute(a)
	}
	fFilt.Train()
	insts := base.NewLazilyFilteredInstances(X, fFilt)
	return insts
}

func convertToBinary(src base.FixedDataGrid) base.FixedDataGrid {
	// Convert to binary
	b := filters.NewBinaryConvertFilter()
	attrs := base.NonClassAttributes(src)
	for _, a := range attrs {
		b.AddAttribute(a)
	}
	b.Train()
	ret := base.NewLazilyFilteredInstances(src, b)
	return ret
}

func TestBaggedModelRandomForest(t *testing.T) {
	Convey("Given data", t, func() {
		inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
		So(err, ShouldBeNil)

		Convey("Splitting the data into training and test data", func() {
			trainData, testData := base.InstancesTrainTestSplit(inst, 0.6)

			Convey("Filtering the split datasets", func() {
				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)

				Convey("Fitting and Predicting with a Bagged Model of 10 Random Trees", func() {
					rf := new(BaggedModel)
					for i := 0; i < 10; i++ {
						rf.AddModel(trees.NewRandomTree(2))
					}

					rf.Fit(trainDataf)
					predictions := rf.Predict(testDataf)

					confusionMat, err := evaluation.GetConfusionMatrix(testDataf, predictions)
					So(err, ShouldBeNil)

					Convey("Predictions are somewhat accurate", func() {
						So(evaluation.GetAccuracy(confusionMat), ShouldBeGreaterThan, 0.5)
					})
				})
			})
		})
	})
}

func TestBinning(t *testing.T) {
	Convey("Given some data and a reference", t, func() {
		// Read the data
		inst1, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
		if err != nil {
			panic(err)
		}

		inst2, err := base.ParseCSVToInstances("../examples/datasets/iris_binned.csv", true)
		if err != nil {
			panic(err)
		}
		//
		// Construct the binning filter
		binAttr := inst1.AllAttributes()[0]
		filt := NewBinningFilter(inst1, 10)
		filt.AddAttribute(binAttr)
		filt.Train()
		inst1f := base.NewLazilyFilteredInstances(inst1, filt)

		// Retrieve the categorical version of the original Attribute
		var cAttr base.Attribute
		for _, a := range inst1f.AllAttributes() {
			if a.GetName() == binAttr.GetName() {
				cAttr = a
			}
		}

		cAttrSpec, err := inst1f.GetAttribute(cAttr)
		So(err, ShouldEqual, nil)
		binAttrSpec, err := inst2.GetAttribute(binAttr)
		So(err, ShouldEqual, nil)

		//
		// Create the LazilyFilteredInstances
		// and check the values
		Convey("Discretized version should match reference", func() {
			_, rows := inst1.Size()
			for i := 0; i < rows; i++ {
				val1 := inst1f.Get(cAttrSpec, i)
				val2 := inst2.Get(binAttrSpec, i)
				val1s := cAttr.GetStringFromSysVal(val1)
				val2s := binAttr.GetStringFromSysVal(val2)
				So(val1s, ShouldEqual, val2s)
			}
		})
	})
}

func TestRandomForest(t *testing.T) {
	Convey("Given a valid CSV file", t, func() {
		inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
		So(err, ShouldBeNil)

		Convey("When Chi-Merge filtering the data", func() {
			filt := filters.NewChiMergeFilter(inst, 0.90)
			for _, a := range base.NonClassFloatAttributes(inst) {
				filt.AddAttribute(a)
			}
			filt.Train()
			instf := base.NewLazilyFilteredInstances(inst, filt)

			Convey("Splitting the data into test and training sets", func() {
				trainData, testData := base.InstancesTrainTestSplit(instf, 0.60)

				Convey("Fitting and predicting with a Random Forest", func() {
					rf := NewRandomForest(10, 3)
					err = rf.Fit(trainData)
					So(err, ShouldBeNil)

					predictions, err := rf.Predict(testData)
					So(err, ShouldBeNil)

					confusionMat, err := evaluation.GetConfusionMatrix(testData, predictions)
					So(err, ShouldBeNil)

					Convey("Predictions should be somewhat accurate", func() {
						So(evaluation.GetAccuracy(confusionMat), ShouldBeGreaterThan, 0.35)
					})
				})
			})
		})

		Convey("Fitting with a Random Forest with too many features compared to the data", func() {
			rf := NewRandomForest(10, len(base.NonClassAttributes(inst))+1)
			err = rf.Fit(inst)

			Convey("Should return an error", func() {
				So(err, ShouldNotBeNil)
			})
		})
	})
}

// Fit creates n filtered datasets (where n is the number of values
// a CategoricalAttribute can take) and uses them to train the
// underlying classifiers.
func (m *OneVsAllModel) Fit(using base.FixedDataGrid) {
	var classAttr *base.CategoricalAttribute
	// Do some validation
	classAttrs := using.AllClassAttributes()
	for _, a := range classAttrs {
		if c, ok := a.(*base.CategoricalAttribute); !ok {
			panic("Unsupported ClassAttribute type")
		} else {
			classAttr = c
		}
	}
	attrs := m.generateAttributes(using)

	// Find the highest stored value
	val := uint64(0)
	classVals := classAttr.GetValues()
	for _, s := range classVals {
		cur := base.UnpackBytesToU64(classAttr.GetSysValFromString(s))
		if cur > val {
			val = cur
		}
	}
	if val == 0 {
		panic("Must have more than one class!")
	}
	m.maxClassVal = val

	// Create individual filtered instances for training
	filters := make([]*oneVsAllFilter, val+1)
	classifiers := make([]base.Classifier, val+1)
	for i := uint64(0); i <= val; i++ {
		f := &oneVsAllFilter{
			attrs,
			classAttr,
			i,
		}
		filters[i] = f
		classifiers[i] = m.NewClassifierFunction(classVals[int(i)])
		classifiers[i].Fit(base.NewLazilyFilteredInstances(using, f))
	}

	m.filters = filters
	m.classifiers = classifiers
}

func TestChiMerge4(testEnv *testing.T) {
	// See http://sci2s.ugr.es/keel/pdf/algorithm/congreso/1992-Kerber-ChimErge-AAAI92.pdf
	//   Randy Kerber, ChiMerge: Discretisation of Numeric Attributes, 1992
	inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
	if err != nil {
		panic(err)
	}

	filt := NewChiMergeFilter(inst, 0.90)
	filt.AddAttribute(inst.AllAttributes()[0])
	filt.AddAttribute(inst.AllAttributes()[1])
	filt.Train()
	instf := base.NewLazilyFilteredInstances(inst, filt)
	fmt.Println(instf)
	fmt.Println(instf.String())
	clsAttrs := instf.AllClassAttributes()
	if len(clsAttrs) != 1 {
		panic(fmt.Sprintf("%d != %d", len(clsAttrs), 1))
	}
	if clsAttrs[0].GetName() != "Species" {
		panic("Class Attribute wrong!")
	}
}

func TestRandomForest1(testEnv *testing.T) {
	inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
	if err != nil {
		panic(err)
	}

	filt := filters.NewChiMergeFilter(inst, 0.90)
	for _, a := range base.NonClassFloatAttributes(inst) {
		filt.AddAttribute(a)
	}
	filt.Train()
	instf := base.NewLazilyFilteredInstances(inst, filt)

	trainData, testData := base.InstancesTrainTestSplit(instf, 0.60)

	rf := NewRandomForest(10, 3)
	rf.Fit(trainData)
	predictions := rf.Predict(testData)
	fmt.Println(predictions)
	confusionMat := eval.GetConfusionMatrix(testData, predictions)
	fmt.Println(confusionMat)
	fmt.Println(eval.GetSummary(confusionMat))
}