func TestPredict(t *testing.T) {
a := NewAveragePerceptron(10, 1.2, 0.5, 0.3)
if a == nil {
t.Errorf("Unable to create average perceptron")
}
absPath, _ := filepath.Abs("../examples/datasets/house-votes-84.csv")
rawData, err := base.ParseCSVToInstances(absPath, true)
if err != nil {
t.Fail()
}
trainData, testData := base.InstancesTrainTestSplit(rawData, 0.5)
a.Fit(trainData)
if a.trained == false {
t.Errorf("Perceptron was not trained")
}
predictions := a.Predict(testData)
cf, err := evaluation.GetConfusionMatrix(testData, predictions)
if err != nil {
t.Errorf("Couldn't get confusion matrix: %s", err)
t.Fail()
}
fmt.Println(evaluation.GetSummary(cf))
fmt.Println(trainData)
fmt.Println(testData)
if evaluation.GetAccuracy(cf) < 0.65 {
t.Errorf("Perceptron not trained correctly")
}
}
func BenchmarkKNNWithNoOpts(b *testing.B) {
// Load
train, test := readMnist()
cls := NewKnnClassifier("euclidean", 1)
cls.AllowOptimisations = false
cls.Fit(train)
predictions := cls.Predict(test)
c, err := evaluation.GetConfusionMatrix(test, predictions)
if err != nil {
panic(err)
}
fmt.Println(evaluation.GetSummary(c))
fmt.Println(evaluation.GetAccuracy(c))
}
func TestMultiSVMUnweighted(t *testing.T) {
Convey("Loading data...", t, func() {
inst, err := base.ParseCSVToInstances("../examples/datasets/articles.csv", false)
So(err, ShouldBeNil)
X, Y := base.InstancesTrainTestSplit(inst, 0.4)
m := NewMultiLinearSVC("l1", "l2", true, 1.0, 1e-4, nil)
m.Fit(X)
Convey("Predictions should work...", func() {
predictions, err := m.Predict(Y)
cf, err := evaluation.GetConfusionMatrix(Y, predictions)
So(err, ShouldEqual, nil)
So(evaluation.GetAccuracy(cf), ShouldBeGreaterThan, 0.70)
})
})
}
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)
})
})
})
}
func TestMultiSVMWeighted(t *testing.T) {
Convey("Loading data...", t, func() {
weights := make(map[string]float64)
weights["Finance"] = 0.1739
weights["Tech"] = 0.0750
weights["Politics"] = 0.4928
inst, err := base.ParseCSVToInstances("../examples/datasets/articles.csv", false)
So(err, ShouldBeNil)
X, Y := base.InstancesTrainTestSplit(inst, 0.4)
m := NewMultiLinearSVC("l1", "l2", true, 0.62, 1e-4, weights)
m.Fit(X)
Convey("Predictions should work...", func() {
predictions, err := m.Predict(Y)
cf, err := evaluation.GetConfusionMatrix(Y, predictions)
So(err, ShouldEqual, nil)
So(evaluation.GetAccuracy(cf), ShouldBeGreaterThan, 0.70)
})
})
}
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 TestOneVsAllModel(t *testing.T) {
classifierFunc := func(c string) base.Classifier {
m, err := linear_models.NewLinearSVC("l1", "l2", true, 1.0, 1e-4)
if err != nil {
panic(err)
}
return m
}
Convey("Given data", t, func() {
inst, err := base.ParseCSVToInstances("../examples/datasets/iris_headers.csv", true)
So(err, ShouldBeNil)
X, Y := base.InstancesTrainTestSplit(inst, 0.4)
m := NewOneVsAllModel(classifierFunc)
m.Fit(X)
Convey("The maximum class index should be 2", func() {
So(m.maxClassVal, ShouldEqual, 2)
})
Convey("There should be three of everything...", func() {
So(len(m.filters), ShouldEqual, 3)
So(len(m.classifiers), ShouldEqual, 3)
})
Convey("Predictions should work...", func() {
predictions, err := m.Predict(Y)
So(err, ShouldEqual, nil)
cf, err := evaluation.GetConfusionMatrix(Y, predictions)
So(err, ShouldEqual, nil)
fmt.Println(evaluation.GetAccuracy(cf))
fmt.Println(evaluation.GetSummary(cf))
})
})
}
func verifyTreeClassification(trainData, testData base.FixedDataGrid) {
rand.Seed(44414515)
Convey("Using InferID3Tree to create the tree and do the fitting", func() {
Convey("Using a RandomTreeRule", func() {
randomTreeRuleGenerator := new(RandomTreeRuleGenerator)
randomTreeRuleGenerator.Attributes = 2
root := InferID3Tree(trainData, randomTreeRuleGenerator)
Convey("Predicting with the tree", func() {
predictions, err := root.Predict(testData)
So(err, ShouldBeNil)
confusionMatrix, err := evaluation.GetConfusionMatrix(testData, predictions)
So(err, ShouldBeNil)
Convey("Predictions should be somewhat accurate", func() {
So(evaluation.GetAccuracy(confusionMatrix), ShouldBeGreaterThan, 0.5)
})
})
})
Convey("Using a InformationGainRule", func() {
informationGainRuleGenerator := new(InformationGainRuleGenerator)
root := InferID3Tree(trainData, informationGainRuleGenerator)
Convey("Predicting with the tree", func() {
predictions, err := root.Predict(testData)
So(err, ShouldBeNil)
confusionMatrix, err := evaluation.GetConfusionMatrix(testData, predictions)
So(err, ShouldBeNil)
Convey("Predictions should be somewhat accurate", func() {
So(evaluation.GetAccuracy(confusionMatrix), ShouldBeGreaterThan, 0.5)
})
})
})
Convey("Using a GiniCoefficientRuleGenerator", func() {
gRuleGen := new(GiniCoefficientRuleGenerator)
root := InferID3Tree(trainData, gRuleGen)
Convey("Predicting with the tree", func() {
predictions, err := root.Predict(testData)
So(err, ShouldBeNil)
confusionMatrix, err := evaluation.GetConfusionMatrix(testData, predictions)
So(err, ShouldBeNil)
Convey("Predictions should be somewhat accurate", func() {
So(evaluation.GetAccuracy(confusionMatrix), ShouldBeGreaterThan, 0.5)
})
})
})
Convey("Using a InformationGainRatioRuleGenerator", func() {
gRuleGen := new(InformationGainRatioRuleGenerator)
root := InferID3Tree(trainData, gRuleGen)
Convey("Predicting with the tree", func() {
predictions, err := root.Predict(testData)
So(err, ShouldBeNil)
confusionMatrix, err := evaluation.GetConfusionMatrix(testData, predictions)
So(err, ShouldBeNil)
Convey("Predictions should be somewhat accurate", func() {
So(evaluation.GetAccuracy(confusionMatrix), ShouldBeGreaterThan, 0.5)
})
})
})
})
Convey("Using NewRandomTree to create the tree", func() {
root := NewRandomTree(2)
Convey("Fitting with the tree", func() {
err := root.Fit(trainData)
So(err, ShouldBeNil)
Convey("Predicting with the tree, *without* pruning first", func() {
predictions, err := root.Predict(testData)
So(err, ShouldBeNil)
confusionMatrix, err := evaluation.GetConfusionMatrix(testData, predictions)
So(err, ShouldBeNil)
Convey("Predictions should be somewhat accurate", func() {
So(evaluation.GetAccuracy(confusionMatrix), ShouldBeGreaterThan, 0.5)
})
})
Convey("Predicting with the tree, pruning first", func() {
root.Prune(testData)
predictions, err := root.Predict(testData)
So(err, ShouldBeNil)
confusionMatrix, err := evaluation.GetConfusionMatrix(testData, predictions)
So(err, ShouldBeNil)
Convey("Predictions should be somewhat accurate", func() {
So(evaluation.GetAccuracy(confusionMatrix), ShouldBeGreaterThan, 0.4)
})
})
})
})
}
// computeAccuracy is a helper method for Prune()
func computeAccuracy(predictions base.FixedDataGrid, from base.FixedDataGrid) float64 {
cf, _ := evaluation.GetConfusionMatrix(from, predictions)
return evaluation.GetAccuracy(cf)
}
// computeAccuracy is a helper method for Prune()
func computeAccuracy(predictions *base.Instances, from *base.Instances) float64 {
cf := eval.GetConfusionMatrix(from, predictions)
return eval.GetAccuracy(cf)
}
func TestRandomTreeClassification(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)
Convey("Using InferID3Tree to create the tree and do the fitting", func() {
Convey("Using a RandomTreeRule", func() {
randomTreeRuleGenerator := new(RandomTreeRuleGenerator)
randomTreeRuleGenerator.Attributes = 2
root := InferID3Tree(filteredTrainData, randomTreeRuleGenerator)
Convey("Predicting with the tree", func() {
predictions, err := root.Predict(filteredTestData)
So(err, ShouldBeNil)
confusionMatrix, err := evaluation.GetConfusionMatrix(filteredTestData, predictions)
So(err, ShouldBeNil)
Convey("Predictions should be somewhat accurate", func() {
So(evaluation.GetAccuracy(confusionMatrix), ShouldBeGreaterThan, 0.5)
})
})
})
Convey("Using a InformationGainRule", func() {
informationGainRuleGenerator := new(InformationGainRuleGenerator)
root := InferID3Tree(filteredTrainData, informationGainRuleGenerator)
Convey("Predicting with the tree", func() {
predictions, err := root.Predict(filteredTestData)
So(err, ShouldBeNil)
confusionMatrix, err := evaluation.GetConfusionMatrix(filteredTestData, predictions)
So(err, ShouldBeNil)
Convey("Predictions should be somewhat accurate", func() {
So(evaluation.GetAccuracy(confusionMatrix), ShouldBeGreaterThan, 0.5)
})
})
})
})
Convey("Using NewRandomTree to create the tree", func() {
root := NewRandomTree(2)
Convey("Fitting with the tree", func() {
err = root.Fit(filteredTrainData)
So(err, ShouldBeNil)
Convey("Predicting with the tree, *without* pruning first", func() {
predictions, err := root.Predict(filteredTestData)
So(err, ShouldBeNil)
confusionMatrix, err := evaluation.GetConfusionMatrix(filteredTestData, predictions)
So(err, ShouldBeNil)
Convey("Predictions should be somewhat accurate", func() {
So(evaluation.GetAccuracy(confusionMatrix), ShouldBeGreaterThan, 0.5)
})
})
Convey("Predicting with the tree, pruning first", func() {
root.Prune(filteredTestData)
predictions, err := root.Predict(filteredTestData)
So(err, ShouldBeNil)
confusionMatrix, err := evaluation.GetConfusionMatrix(filteredTestData, predictions)
So(err, ShouldBeNil)
Convey("Predictions should be somewhat accurate", func() {
So(evaluation.GetAccuracy(confusionMatrix), ShouldBeGreaterThan, 0.4)
})
})
})
})
})
}