/*
splitTreeByGain calculate the gain in all dataset, and split into two node:
left and right.
Return node with the split information.
*/
func (runtime *Runtime) splitTreeByGain(D tabula.ClasetInterface) (
node *binary.BTNode,
e error,
) {
node = &binary.BTNode{}
D.RecountMajorMinor()
// if dataset is empty return node labeled with majority classes in
// dataset.
nrow := D.GetNRow()
if nrow <= 0 {
if DEBUG >= 2 {
fmt.Printf("[cart] empty dataset (%s) : %v\n",
D.MajorityClass(), D)
}
node.Value = NodeValue{
IsLeaf: true,
Class: D.MajorityClass(),
Size: 0,
}
return node, nil
}
// if all dataset is in the same class, return node as leaf with class
// is set to that class.
single, name := D.IsInSingleClass()
if single {
if DEBUG >= 2 {
fmt.Printf("[cart] in single class (%s): %v\n", name,
D.GetColumns())
}
node.Value = NodeValue{
IsLeaf: true,
Class: name,
Size: nrow,
}
return node, nil
}
if DEBUG >= 2 {
fmt.Println("[cart] D:", D)
}
// calculate the Gini gain for each attribute.
gains := runtime.computeGain(D)
// get attribute with maximum Gini gain.
MaxGainIdx := gini.FindMaxGain(&gains)
MaxGain := gains[MaxGainIdx]
// if maxgain value is 0, use majority class as node and terminate
// the process
if MaxGain.GetMaxGainValue() == 0 {
if DEBUG >= 2 {
fmt.Println("[cart] max gain 0 with target",
D.GetClassAsStrings(),
" and majority class is ", D.MajorityClass())
}
node.Value = NodeValue{
IsLeaf: true,
Class: D.MajorityClass(),
Size: 0,
}
return node, nil
}
// using the sorted index in MaxGain, sort all field in dataset
tabula.SortColumnsByIndex(D, MaxGain.SortedIndex)
if DEBUG >= 2 {
fmt.Println("[cart] maxgain:", MaxGain)
}
// Now that we have attribute with max gain in MaxGainIdx, and their
// gain dan partition value in Gains[MaxGainIdx] and
// GetMaxPartValue(), we split the dataset based on type of max-gain
// attribute.
// If its continuous, split the attribute using numeric value.
// If its discrete, split the attribute using subset (partition) of
// nominal values.
var splitV interface{}
if MaxGain.IsContinu {
splitV = MaxGain.GetMaxPartGainValue()
} else {
attrPartV := MaxGain.GetMaxPartGainValue()
attrSubV := attrPartV.(tekstus.ListStrings)
splitV = attrSubV[0].Normalize()
}
if DEBUG >= 2 {
fmt.Println("[cart] maxgainindex:", MaxGainIdx)
fmt.Println("[cart] split v:", splitV)
}
node.Value = NodeValue{
SplitAttrName: D.GetColumn(MaxGainIdx).GetName(),
IsLeaf: false,
IsContinu: MaxGain.IsContinu,
Size: nrow,
SplitAttrIdx: MaxGainIdx,
SplitV: splitV,
}
dsL, dsR, e := tabula.SplitRowsByValue(D, MaxGainIdx, splitV)
if e != nil {
return node, e
}
splitL := dsL.(tabula.ClasetInterface)
splitR := dsR.(tabula.ClasetInterface)
// Set the flag to parent in attribute referenced by
// MaxGainIdx, so it will not computed again in the next round.
cols := splitL.GetColumns()
for x := range *cols {
if x == MaxGainIdx {
(*cols)[x].Flag = ColFlagParent
} else {
(*cols)[x].Flag = 0
}
}
cols = splitR.GetColumns()
for x := range *cols {
if x == MaxGainIdx {
(*cols)[x].Flag = ColFlagParent
} else {
(*cols)[x].Flag = 0
}
}
nodeLeft, e := runtime.splitTreeByGain(splitL)
if e != nil {
return node, e
}
nodeRight, e := runtime.splitTreeByGain(splitR)
if e != nil {
return node, e
}
node.SetLeft(nodeLeft)
node.SetRight(nodeRight)
return node, nil
}
//
// createForest will create and return a forest and run the training `samples`
// on it.
//
// Algorithm,
// (1) Initialize forest.
// (2) For 0 to maximum number of tree in forest,
// (2.1) grow one tree until success.
// (2.2) If tree tp-rate and tn-rate greater than threshold, stop growing.
// (3) Calculate weight.
// (4) TODO: Move true-negative from samples. The collection of true-negative
// will be used again to test the model and after test and the sample with FP
// will be moved to training samples again.
// (5) Refill samples with false-positive.
//
func (crf *Runtime) createForest(samples tabula.ClasetInterface) (
forest *rf.Runtime, e error,
) {
var cm *classifier.CM
var stat *classifier.Stat
fmt.Println(tag, "Forest samples:", samples)
// (1)
forest = &rf.Runtime{
Runtime: classifier.Runtime{
RunOOB: true,
},
NTree: crf.NTree,
NRandomFeature: crf.NRandomFeature,
}
e = forest.Initialize(samples)
if e != nil {
return nil, e
}
// (2)
for t := 0; t < crf.NTree; t++ {
if DEBUG >= 2 {
fmt.Println(tag, "Tree #", t)
}
// (2.1)
for {
cm, stat, e = forest.GrowTree(samples)
if e == nil {
break
}
}
// (2.2)
if stat.TPRate > crf.TPRate &&
stat.TNRate > crf.TNRate {
break
}
}
e = forest.Finalize()
if e != nil {
return nil, e
}
// (3)
crf.computeWeight(stat)
if DEBUG >= 1 {
fmt.Println(tag, "Weight:", stat.FMeasure)
}
// (4)
crf.deleteTrueNegative(samples, cm)
// (5)
crf.runTPSet(samples)
samples.RecountMajorMinor()
return forest, nil
}