meta: merge from v2-instances

meta/bagging.go

@@ -21,23 +21,18 @@ type BaggedModel struct {
 
 // generateTrainingAttrs selects RandomFeatures number of base.Attributes from
 // the provided base.Instances.
-func (b *BaggedModel) generateTrainingAttrs(model int, from *base.Instances) []base.Attribute {
+func (b *BaggedModel) generateTrainingAttrs(model int, from base.FixedDataGrid) []base.Attribute {
 	ret := make([]base.Attribute, 0)
+	attrs := base.NonClassAttributes(from)
 	if b.RandomFeatures == 0 {
-		for j := 0; j < from.Cols; j++ {
-			attr := from.GetAttr(j)
-			ret = append(ret, attr)
-		}
+		ret = attrs
 	} else {
 		for {
 			if len(ret) >= b.RandomFeatures {
 				break
 			}
-			attrIndex := rand.Intn(from.Cols)
-			if attrIndex == from.ClassIndex {
-				continue
-			}
-			attr := from.GetAttr(attrIndex)
+			attrIndex := rand.Intn(len(attrs))
+			attr := attrs[attrIndex]
 			matched := false
 			for _, a := range ret {
 				if a.Equals(attr) {
@@ -50,7 +45,9 @@ func (b *BaggedModel) generateTrainingAttrs(model int, from *base.Instances) []b
 			}
 		}
 	}
-	ret = append(ret, from.GetClassAttr())
+	for _, a := range from.AllClassAttributes() {
+		ret = append(ret, a)
+	}
 	b.lock.Lock()
 	b.selectedAttributes[model] = ret
 	b.lock.Unlock()
@@ -60,18 +57,19 @@ func (b *BaggedModel) generateTrainingAttrs(model int, from *base.Instances) []b
 // generatePredictionInstances returns a modified version of the
 // requested base.Instances with only the base.Attributes selected
 // for training the model.
-func (b *BaggedModel) generatePredictionInstances(model int, from *base.Instances) *base.Instances {
+func (b *BaggedModel) generatePredictionInstances(model int, from base.FixedDataGrid) base.FixedDataGrid {
 	selected := b.selectedAttributes[model]
-	return from.SelectAttributes(selected)
+	return base.NewInstancesViewFromAttrs(from, selected)
 }
 
 // generateTrainingInstances generates RandomFeatures number of
 // attributes and returns a modified version of base.Instances
 // for training the model
-func (b *BaggedModel) generateTrainingInstances(model int, from *base.Instances) *base.Instances {
-	insts := from.SampleWithReplacement(from.Rows)
+func (b *BaggedModel) generateTrainingInstances(model int, from base.FixedDataGrid) base.FixedDataGrid {
+	_, rows := from.Size()
+	insts := base.SampleWithReplacement(from, rows)
 	selected := b.generateTrainingAttrs(model, from)
-	return insts.SelectAttributes(selected)
+	return base.NewInstancesViewFromAttrs(insts, selected)
 }
 
 // AddModel adds a base.Classifier to the current model
@@ -81,12 +79,12 @@ func (b *BaggedModel) AddModel(m base.Classifier) {
 
 // Fit generates and trains each model on a randomised subset of
 // Instances.
-func (b *BaggedModel) Fit(from *base.Instances) {
+func (b *BaggedModel) Fit(from base.FixedDataGrid) {
 	var wait sync.WaitGroup
 	b.selectedAttributes = make(map[int][]base.Attribute)
 	for i, m := range b.Models {
 		wait.Add(1)
-		go func(c base.Classifier, f *base.Instances, model int) {
+		go func(c base.Classifier, f base.FixedDataGrid, model int) {
 			l := b.generateTrainingInstances(model, f)
 			c.Fit(l)
 			wait.Done()
@@ -100,10 +98,10 @@ func (b *BaggedModel) Fit(from *base.Instances) {
 //
 // IMPORTANT: in the event of a tie, the first class which
 // achieved the tie value is output.
-func (b *BaggedModel) Predict(from *base.Instances) *base.Instances {
+func (b *BaggedModel) Predict(from base.FixedDataGrid) base.FixedDataGrid {
 	n := runtime.NumCPU()
 	// Channel to receive the results as they come in
-	votes := make(chan *base.Instances, n)
+	votes := make(chan base.DataGrid, n)
 	// Count the votes for each class
 	voting := make(map[int](map[string]int))
 
@@ -111,21 +109,20 @@ func (b *BaggedModel) Predict(from *base.Instances) *base.Instances {
 	var votingwait sync.WaitGroup
 	votingwait.Add(1)
 	go func() {
-		for {
+		for { // Need to resolve the voting problem
 			incoming, ok := <-votes
 			if ok {
-				// Step through each prediction
-				for j := 0; j < incoming.Rows; j++ {
+				cSpecs := base.ResolveAllAttributes(incoming, incoming.AllClassAttributes())
+				incoming.MapOverRows(cSpecs, func(row [][]byte, predRow int) (bool, error) {
 					// Check if we've seen this class before...
-					if _, ok := voting[j]; !ok {
+					if _, ok := voting[predRow]; !ok {
 						// If we haven't, create an entry
-						voting[j] = make(map[string]int)
+						voting[predRow] = make(map[string]int)
 						// Continue on the current row
-						j--
-						continue
-					}
-					voting[j][incoming.GetClass(j)]++
 					}
+					voting[predRow][base.GetClass(incoming, predRow)]++
+					return true, nil
+				})
 			} else {
 				votingwait.Done()
 				break
@@ -162,7 +159,7 @@ func (b *BaggedModel) Predict(from *base.Instances) *base.Instances {
 	votingwait.Wait()  // All the votes are in
 
 	// Generate the overall consensus
-	ret := from.GeneratePredictionVector()
+	ret := base.GeneratePredictionVector(from)
 	for i := range voting {
 		maxClass := ""
 		maxCount := 0
@@ -174,7 +171,7 @@ func (b *BaggedModel) Predict(from *base.Instances) *base.Instances {
 				maxCount = votes
 			}
 		}
-		ret.SetAttrStr(i, 0, maxClass)
+		base.SetClass(ret, i, maxClass)
 	}
 	return ret
 }

meta/bagging_test.go

@@ -19,16 +19,18 @@ func BenchmarkBaggingRandomForestFit(testEnv *testing.B) {
 
 	rand.Seed(time.Now().UnixNano())
 	filt := filters.NewChiMergeFilter(inst, 0.90)
-	filt.AddAllNumericAttributes()
-	filt.Build()
-	filt.Run(inst)
+	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))
 	}
 	testEnv.ResetTimer()
 	for i := 0; i < 20; i++ {
-		rf.Fit(inst)
+		rf.Fit(instf)
 	}
 }
 
@@ -40,17 +42,19 @@ func BenchmarkBaggingRandomForestPredict(testEnv *testing.B) {
 
 	rand.Seed(time.Now().UnixNano())
 	filt := filters.NewChiMergeFilter(inst, 0.90)
-	filt.AddAllNumericAttributes()
-	filt.Build()
-	filt.Run(inst)
+	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(inst)
+	rf.Fit(instf)
 	testEnv.ResetTimer()
 	for i := 0; i < 20; i++ {
-		rf.Predict(inst)
+		rf.Predict(instf)
 	}
 }
 
@@ -63,19 +67,21 @@ func TestRandomForest1(testEnv *testing.T) {
 	rand.Seed(time.Now().UnixNano())
 	trainData, testData := base.InstancesTrainTestSplit(inst, 0.6)
 	filt := filters.NewChiMergeFilter(inst, 0.90)
-	filt.AddAllNumericAttributes()
-	filt.Build()
-	filt.Run(testData)
-	filt.Run(trainData)
+	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(trainData)
+	rf.Fit(trainDataf)
 	fmt.Println(rf)
-	predictions := rf.Predict(testData)
+	predictions := rf.Predict(testDataf)
 	fmt.Println(predictions)
-	confusionMat := eval.GetConfusionMatrix(testData, predictions)
+	confusionMat := eval.GetConfusionMatrix(testDataf, predictions)
 	fmt.Println(confusionMat)
 	fmt.Println(eval.GetMacroPrecision(confusionMat))
 	fmt.Println(eval.GetMacroRecall(confusionMat))

meta/meta.go

@@ -0,0 +1,13 @@
+/*
+
+	Meta contains base.Classifier implementations which
+		combine the outputs of others defined elsewhere.
+
+	Bagging:
+		Bootstraps samples of the original training set
+		with a number of selected attributes, and uses
+		that to train an ensemble of models. Predictions
+		are generated via majority voting.
+*/
+
+package meta