func (KNN *KNNClassifier) canUseOptimisations(what base.FixedDataGrid) bool {
// Check that the two have exactly the same layout
if !base.CheckStrictlyCompatible(what, KNN.TrainingData) {
return false
}
// Check that the two are DenseInstances
whatd, ok1 := what.(*base.DenseInstances)
_, ok2 := KNN.TrainingData.(*base.DenseInstances)
if !ok1 || !ok2 {
return false
}
// Check that no Class Attributes are mixed in with the data
classAttrs := whatd.AllClassAttributes()
normalAttrs := base.NonClassAttributes(whatd)
// Retrieve all the AGs
ags := whatd.AllAttributeGroups()
classAttrGroups := make([]base.AttributeGroup, 0)
for agName := range ags {
ag := ags[agName]
attrs := ag.Attributes()
matched := false
for _, a := range attrs {
for _, c := range classAttrs {
if a.Equals(c) {
matched = true
}
}
}
if matched {
classAttrGroups = append(classAttrGroups, ag)
}
}
for _, cag := range classAttrGroups {
attrs := cag.Attributes()
common := base.AttributeIntersect(normalAttrs, attrs)
if len(common) != 0 {
return false
}
}
// Check that all of the Attributes are numeric
for _, a := range normalAttrs {
if _, ok := a.(*base.FloatAttribute); !ok {
return false
}
}
// If that's fine, return true
return true
}
func TestBinaryFilter(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 Attributes to the filter
bFilt := NewBinaryConvertFilter()
bAttrs := base.NonClassAttributes(inst)
for _, a := range bAttrs {
bFilt.AddAttribute(a)
}
bFilt.Train()
// Construct a LazilyFilteredInstances to handle it
instF := base.NewLazilyFilteredInstances(inst, bFilt)
Convey("All the non-class Attributes should be binary...", func() {
// Check that all the Attributes are the right type
for _, a := range base.NonClassAttributes(instF) {
_, ok := a.(*base.BinaryAttribute)
So(ok, ShouldEqual, true)
}
})
// Check that all the class Attributes made it
Convey("All the class Attributes should have survived...", func() {
origClassAttrs := inst.AllClassAttributes()
newClassAttrs := instF.AllClassAttributes()
intersectClassAttrs := base.AttributeIntersect(origClassAttrs, newClassAttrs)
So(len(intersectClassAttrs), ShouldEqual, len(origClassAttrs))
})
// Check that the Attributes have the right names
Convey("Attribute names should be correct...", func() {
origNames := []string{"floatAttr", "shouldBe1Binary",
"shouldBe3Binary_stoicism", "shouldBe3Binary_heroism",
"shouldBe3Binary_romanticism", "arbitraryClass"}
origMap := make(map[string]bool)
for _, a := range origNames {
origMap[a] = false
}
for _, a := range instF.AllAttributes() {
name := a.GetName()
_, ok := origMap[name]
if !ok {
t.Error(fmt.Sprintf("Weird: %s", name))
}
origMap[name] = true
}
for a := range origMap {
So(origMap[a], ShouldEqual, true)
}
})
// Check that the Attributes have been discretised correctly
Convey("Discretisation should have worked", func() {
// Build Attribute map
attrMap := make(map[string]base.Attribute)
for _, a := range instF.AllAttributes() {
attrMap[a.GetName()] = a
}
// For each attribute
for name := range attrMap {
attr := attrMap[name]
// Retrieve AttributeSpec
as, err := instF.GetAttribute(attr)
So(err, ShouldEqual, nil)
if name == "floatAttr" {
So(instF.Get(as, 0), ShouldResemble, []byte{1})
So(instF.Get(as, 1), ShouldResemble, []byte{1})
So(instF.Get(as, 2), ShouldResemble, []byte{0})
} else if name == "shouldBe1Binary" {
So(instF.Get(as, 0), ShouldResemble, []byte{0})
So(instF.Get(as, 1), ShouldResemble, []byte{1})
So(instF.Get(as, 2), ShouldResemble, []byte{1})
} else if name == "shouldBe3Binary_stoicism" {
So(instF.Get(as, 0), ShouldResemble, []byte{1})
So(instF.Get(as, 1), ShouldResemble, []byte{0})
So(instF.Get(as, 2), ShouldResemble, []byte{0})
} else if name == "shouldBe3Binary_heroism" {
So(instF.Get(as, 0), ShouldResemble, []byte{0})
So(instF.Get(as, 1), ShouldResemble, []byte{1})
So(instF.Get(as, 2), ShouldResemble, []byte{0})
} else if name == "shouldBe3Binary_romanticism" {
So(instF.Get(as, 0), ShouldResemble, []byte{0})
So(instF.Get(as, 1), ShouldResemble, []byte{0})
So(instF.Get(as, 2), ShouldResemble, []byte{1})
} else if name == "arbitraryClass" {
} else {
t.Error("Shouldn't have %s", name)
}
}
})
})
}
func TestFloatFilter(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 Attributes to the filter
bFilt := NewFloatConvertFilter()
bAttrs := base.NonClassAttributes(inst)
for _, a := range bAttrs {
bFilt.AddAttribute(a)
}
bFilt.Train()
// Construct a LazilyFilteredInstances to handle it
instF := base.NewLazilyFilteredInstances(inst, bFilt)
Convey("All the non-class Attributes should be floats...", func() {
// Check that all the Attributes are the right type
for _, a := range base.NonClassAttributes(instF) {
_, ok := a.(*base.FloatAttribute)
So(ok, ShouldEqual, true)
}
})
// Check that all the class Attributes made it
Convey("All the class Attributes should have survived...", func() {
origClassAttrs := inst.AllClassAttributes()
newClassAttrs := instF.AllClassAttributes()
intersectClassAttrs := base.AttributeIntersect(origClassAttrs, newClassAttrs)
So(len(intersectClassAttrs), ShouldEqual, len(origClassAttrs))
})
// Check that the Attributes have the right names
Convey("Attribute names should be correct...", func() {
origNames := []string{"floatAttr", "shouldBe1Binary",
"shouldBe3Binary_stoicism", "shouldBe3Binary_heroism",
"shouldBe3Binary_romanticism", "arbitraryClass"}
origMap := make(map[string]bool)
for _, a := range origNames {
origMap[a] = false
}
for _, a := range instF.AllAttributes() {
name := a.GetName()
_, ok := origMap[name]
So(ok, ShouldBeTrue)
origMap[name] = true
}
for a := range origMap {
So(origMap[a], ShouldEqual, true)
}
})
Convey("All Attributes should be the correct type...", func() {
for _, a := range instF.AllAttributes() {
if a.GetName() == "arbitraryClass" {
_, ok := a.(*base.CategoricalAttribute)
So(ok, ShouldEqual, true)
} else {
_, ok := a.(*base.FloatAttribute)
So(ok, ShouldEqual, true)
}
}
})
// Check that the Attributes have been discretised correctly
Convey("FloatConversion should have worked", func() {
// Build Attribute map
attrMap := make(map[string]base.Attribute)
for _, a := range instF.AllAttributes() {
attrMap[a.GetName()] = a
}
// For each attribute
for name := range attrMap {
So(name, ShouldBeIn, []string{
"floatAttr",
"shouldBe1Binary",
"shouldBe3Binary_stoicism",
"shouldBe3Binary_heroism",
"shouldBe3Binary_romanticism",
"arbitraryClass",
})
attr := attrMap[name]
as, err := instF.GetAttribute(attr)
So(err, ShouldEqual, nil)
if name == "floatAttr" {
So(instF.Get(as, 0), ShouldResemble, base.PackFloatToBytes(1.0))
So(instF.Get(as, 1), ShouldResemble, base.PackFloatToBytes(1.0))
So(instF.Get(as, 2), ShouldResemble, base.PackFloatToBytes(0.0))
} else if name == "shouldBe1Binary" {
So(instF.Get(as, 0), ShouldResemble, base.PackFloatToBytes(0.0))
So(instF.Get(as, 1), ShouldResemble, base.PackFloatToBytes(1.0))
So(instF.Get(as, 2), ShouldResemble, base.PackFloatToBytes(1.0))
} else if name == "shouldBe3Binary_stoicism" {
So(instF.Get(as, 0), ShouldResemble, base.PackFloatToBytes(1.0))
So(instF.Get(as, 1), ShouldResemble, base.PackFloatToBytes(0.0))
So(instF.Get(as, 2), ShouldResemble, base.PackFloatToBytes(0.0))
} else if name == "shouldBe3Binary_heroism" {
So(instF.Get(as, 0), ShouldResemble, base.PackFloatToBytes(0.0))
So(instF.Get(as, 1), ShouldResemble, base.PackFloatToBytes(1.0))
So(instF.Get(as, 2), ShouldResemble, base.PackFloatToBytes(0.0))
} else if name == "shouldBe3Binary_romanticism" {
So(instF.Get(as, 0), ShouldResemble, base.PackFloatToBytes(0.0))
So(instF.Get(as, 1), ShouldResemble, base.PackFloatToBytes(0.0))
So(instF.Get(as, 2), ShouldResemble, base.PackFloatToBytes(1.0))
} else if name == "arbitraryClass" {
}
}
})
})
}
func (KNN *KNNClassifier) optimisedEuclideanPredict(d *base.DenseInstances) base.FixedDataGrid {
// Create return vector
ret := base.GeneratePredictionVector(d)
// Type-assert training data
tr := KNN.TrainingData.(*base.DenseInstances)
// Enumeration of AttributeGroups
agPos := make(map[string]int)
agTrain := tr.AllAttributeGroups()
agPred := d.AllAttributeGroups()
classAttrs := tr.AllClassAttributes()
counter := 0
for ag := range agTrain {
// Detect whether the AttributeGroup has any classes in it
attrs := agTrain[ag].Attributes()
//matched := false
if len(base.AttributeIntersect(classAttrs, attrs)) == 0 {
agPos[ag] = counter
}
counter++
}
// Pointers to the start of each prediction row
rowPointers := make([]*C.double, len(agPred))
trainPointers := make([]*C.double, len(agPred))
rowSizes := make([]int, len(agPred))
for ag := range agPred {
if ap, ok := agPos[ag]; ok {
rowPointers[ap] = (*C.double)(unsafe.Pointer(&(agPred[ag].Storage()[0])))
trainPointers[ap] = (*C.double)(unsafe.Pointer(&(agTrain[ag].Storage()[0])))
rowSizes[ap] = agPred[ag].RowSizeInBytes() / 8
}
}
_, predRows := d.Size()
_, trainRows := tr.Size()
// Crete the distance vector
distanceVec := distanceRecs(make([]_Ctype_struct_dist, trainRows))
// Additional datastructures
voteVec := make([]int, KNN.NearestNeighbours)
maxMap := make(map[string]int)
for row := 0; row < predRows; row++ {
for i := 0; i < trainRows; i++ {
distanceVec[i].dist = 0
}
for ag := range agPred {
if ap, ok := agPos[ag]; ok {
C.euclidean_distance(
&(distanceVec[0]),
C.int(trainRows),
C.int(len(agPred[ag].Attributes())),
C.int(row),
trainPointers[ap],
rowPointers[ap],
)
}
}
sort.Sort(distanceVec)
votes := distanceVec[:KNN.NearestNeighbours]
for i, v := range votes {
voteVec[i] = int(v.p)
}
maxClass := KNN.vote(maxMap, voteVec)
base.SetClass(ret, row, maxClass)
}
return ret
}
