func (rdt *RandomDecisionTree) SingleTreeBuild(samples []*core.MapBasedSample) Tree {
tree := Tree{}
queue := list.New()
root := TreeNode{depth: 0, left: -1, right: -1, prediction: core.NewArrayVector(), samples: []int{}}
for i := 0; i < len(samples); i++ {
k := rand.Intn(len(samples))
root.AddSample(k)
root.prediction.AddValue(samples[k].Label, 1.0)
}
root.sample_count = len(root.samples)
root.prediction.Scale(1.0 / root.prediction.Sum())
queue.PushBack(&root)
tree.AddTreeNode(&root)
for {
nodes := DTGetElementFromQueue(queue, 10)
if len(nodes) == 0 {
break
}
for _, node := range nodes {
rdt.AppendNodeToTree(samples, node, queue, &tree)
}
}
return tree
}
func (dt *CART) SingleTreeBuild(samples []*core.MapBasedSample, feature_select_prob float64, bootstrap bool) Tree {
tree := Tree{}
queue := list.New()
root := TreeNode{depth: 0, left: -1, right: -1, prediction: core.NewArrayVector(), samples: []int{}}
if !bootstrap {
for i, sample := range samples {
root.AddSample(i)
root.prediction.AddValue(sample.Label, 1.0)
}
} else {
for i := 0; i < len(samples); i++ {
k := rand.Intn(len(samples))
root.AddSample(k)
root.prediction.AddValue(samples[k].Label, 1.0)
}
}
root.sample_count = len(root.samples)
root.prediction.Scale(1.0 / root.prediction.Sum())
queue.PushBack(&root)
tree.AddTreeNode(&root)
for {
nodes := DTGetElementFromQueue(queue, 10)
if len(nodes) == 0 {
break
}
for _, node := range nodes {
dt.AppendNodeToTree(samples, node, queue, &tree, feature_select_prob)
}
}
return tree
}
func (dt *RegressionTree) SingleTreeBuild(samples []*core.MapBasedSample, select_features map[int64]bool) Tree {
tree := Tree{}
queue := list.New()
root := TreeNode{depth: 0, left: -1, right: -1, prediction: core.NewArrayVector(), samples: []int{}}
total := 0.0
positive := 0.0
for i, sample := range samples {
root.AddSample(i)
total += 1.0
positive += sample.Prediction
}
root.sample_count = len(root.samples)
root.prediction.SetValue(0, positive/total)
queue.PushBack(&root)
tree.AddTreeNode(&root)
for {
nodes := dt.GetElementFromQueue(queue, 10)
if len(nodes) == 0 {
break
}
for _, node := range nodes {
dt.AppendNodeToTree(samples, node, queue, &tree, select_features)
}
}
return tree
}
func (dt *RegressionTree) AppendNodeToTree(samples []*core.MapBasedSample, node *TreeNode, queue *list.List, tree *Tree, select_features map[int64]bool) {
if node.depth >= dt.params.MaxDepth {
return
}
dt.FindBestSplit(samples, node, select_features)
if node.feature_split.Id < 0 {
return
}
left_node := TreeNode{depth: node.depth + 1, left: -1, right: -1, prediction: core.NewArrayVector(), sample_count: 0, samples: []int{}}
right_node := TreeNode{depth: node.depth + 1, left: -1, right: -1, prediction: core.NewArrayVector(), sample_count: 0, samples: []int{}}
left_positive := 0.0
left_total := 0.0
right_positive := 0.0
right_total := 0.0
for _, k := range node.samples {
if dt.GoLeft(samples[k], node.feature_split) {
left_node.samples = append(left_node.samples, k)
left_positive += samples[k].Prediction
left_total += 1.0
} else {
right_node.samples = append(right_node.samples, k)
right_positive += samples[k].Prediction
right_total += 1.0
}
}
node.samples = nil
if len(left_node.samples) > dt.params.MinLeafSize {
left_node.sample_count = len(left_node.samples)
left_node.prediction.SetValue(0, left_positive/left_total)
queue.PushBack(&left_node)
node.left = len(tree.nodes)
tree.AddTreeNode(&left_node)
}
if len(right_node.samples) > dt.params.MinLeafSize {
right_node.sample_count = len(right_node.samples)
right_node.prediction.SetValue(0, right_positive/right_total)
queue.PushBack(&right_node)
node.right = len(tree.nodes)
tree.AddTreeNode(&right_node)
}
}
func (dt *CART) AppendNodeToTree(samples []*core.MapBasedSample, node *TreeNode, queue *list.List, tree *Tree, feature_select_prob float64) {
if node.depth >= dt.params.MaxDepth {
return
}
if dt.continuous_features {
dt.FindBestSplitOfContinusousFeature(samples, node, feature_select_prob)
} else {
dt.FindBestSplitOfBinaryFeature(samples, node, feature_select_prob)
}
if node.feature_split.Id < 0 {
return
}
left_node := TreeNode{depth: node.depth + 1, left: -1, right: -1, prediction: nil, sample_count: 0, samples: []int{}}
right_node := TreeNode{depth: node.depth + 1, left: -1, right: -1, prediction: nil, sample_count: 0, samples: []int{}}
left_node.prediction = core.NewArrayVector()
right_node.prediction = core.NewArrayVector()
for _, k := range node.samples {
if DTGoLeft(samples[k], node.feature_split) {
left_node.samples = append(left_node.samples, k)
left_node.prediction.AddValue(samples[k].Label, 1.0)
} else {
right_node.samples = append(right_node.samples, k)
right_node.prediction.AddValue(samples[k].Label, 1.0)
}
}
node.samples = nil
if len(left_node.samples) > dt.params.MinLeafSize {
left_node.sample_count = len(left_node.samples)
left_node.prediction.Scale(1.0 / left_node.prediction.Sum())
queue.PushBack(&left_node)
node.left = len(tree.nodes)
tree.AddTreeNode(&left_node)
}
if len(right_node.samples) > dt.params.MinLeafSize {
right_node.sample_count = len(right_node.samples)
right_node.prediction.Scale(1.0 / right_node.prediction.Sum())
queue.PushBack(&right_node)
node.right = len(tree.nodes)
tree.AddTreeNode(&right_node)
}
}
func (rdt *RandomDecisionTree) PredictMultiClass(sample *core.Sample) *core.ArrayVector {
msample := sample.ToMapBasedSample()
predictions := core.NewArrayVector()
total := 0.0
for _, tree := range rdt.trees {
node, _ := PredictBySingleTree(tree, msample)
predictions.AddVector(node.prediction, 1.0)
total += 1.0
}
predictions.Scale(1.0 / total)
return predictions
}
func (rdt *RandomDecisionTree) AppendNodeToTree(samples []*core.MapBasedSample, node *TreeNode, queue *list.List, tree *Tree) {
node.prediction = core.NewArrayVector()
for _, k := range node.samples {
node.prediction.AddValue(samples[k].Label, 1.0)
}
node.prediction.Scale(1.0 / node.prediction.Sum())
random_sample := samples[node.samples[rand.Intn(len(node.samples))]]
split := core.Feature{Id: -1, Value: -1.0}
for fid, fvalue := range random_sample.Features {
if split.Id < 0 || rand.Intn(len(random_sample.Features)) == 0 {
split.Id = fid
split.Value = fvalue
}
}
if split.Id < 0 || node.depth > rdt.params.MaxDepth {
return
}
node.feature_split = split
left_node := TreeNode{depth: node.depth + 1, left: -1, right: -1, prediction: nil, sample_count: 0, samples: []int{}}
right_node := TreeNode{depth: node.depth + 1, left: -1, right: -1, prediction: nil, sample_count: 0, samples: []int{}}
for _, k := range node.samples {
if DTGoLeft(samples[k], node.feature_split) {
left_node.samples = append(left_node.samples, k)
} else {
right_node.samples = append(right_node.samples, k)
}
}
node.samples = nil
if len(left_node.samples) == 0 || len(right_node.samples) == 0 {
return
}
if len(left_node.samples) > rdt.params.MinLeafSize {
queue.PushBack(&left_node)
node.left = len(tree.nodes)
tree.AddTreeNode(&left_node)
}
if len(right_node.samples) > rdt.params.MinLeafSize {
queue.PushBack(&right_node)
node.right = len(tree.nodes)
tree.AddTreeNode(&right_node)
}
}
func (dt *CART) FindBestSplitOfBinaryFeature(samples []*core.MapBasedSample, node *TreeNode, feature_select_prob float64) {
feature_right_dis := make(map[int64]*core.ArrayVector)
total_dis := core.NewArrayVector()
for i, k := range node.samples {
if i > 10 && rand.Float64() > dt.params.SamplingRatio {
continue
}
total_dis.AddValue(samples[k].Label, 1.0)
for fid, _ := range samples[k].Features {
if dt.RandByFeatureId(fid) > feature_select_prob {
continue
}
_, ok := feature_right_dis[fid]
if !ok {
feature_right_dis[fid] = core.NewArrayVector()
}
feature_right_dis[fid].AddValue(samples[k].Label, 1.0)
}
}
min_gini := 1.0
node.feature_split = core.Feature{Id: -1, Value: 0}
for fid, right_dis := range feature_right_dis {
left_dis := total_dis.Copy()
left_dis.AddVector(right_dis, -1.0)
gini := core.Gini(left_dis, right_dis)
if min_gini > gini {
min_gini = gini
node.feature_split.Id = fid
node.feature_split.Value = 1.0
}
}
if min_gini > dt.params.GiniThreshold {
node.feature_split.Id = -1
node.feature_split.Value = 0.0
}
}
func (algo *NeuralNetwork) PredictMultiClass(sample *core.Sample) *core.ArrayVector {
y := core.NewVector()
z := core.NewArrayVector()
for i := int64(0); i < algo.Params.Hidden; i++ {
sum := float64(0)
for _, f := range sample.Features {
sum += f.Value * algo.Model.L1.Data[i].GetValue(f.Id)
}
y.Data[i] = util.Sigmoid(sum)
}
y.Data[algo.Params.Hidden] = 1
for i := 0; i <= int(algo.MaxLabel); i++ {
sum := float64(0)
for j := int64(0); j <= algo.Params.Hidden; j++ {
sum += y.GetValue(j) * algo.Model.L2.GetValue(j, int64(i))
}
z.SetValue(i, sum)
}
z = z.SoftMaxNorm()
return z
}
func (c *KNN) PredictMultiClass(sample *core.Sample) *core.ArrayVector {
x := sample.GetFeatureVector()
predictions := []*eval.LabelPrediction{}
for i, s := range c.sv {
predictions = append(predictions, &(eval.LabelPrediction{Label: c.labels[i], Prediction: c.Kernel(s, x)}))
}
compare := func(p1, p2 *eval.LabelPrediction) bool {
return p1.Prediction > p2.Prediction
}
eval.By(compare).Sort(predictions)
ret := core.NewArrayVector()
for i, pred := range predictions {
if i > c.k {
break
}
ret.AddValue(pred.Label, 1.0)
}
return ret
}
func (t *Tree) FromString(buf string) {
lines := strings.Split(buf, "\n")
size, _ := strconv.Atoi(lines[0])
t.nodes = make([]*TreeNode, size+1, size+1)
for _, line := range lines[1:] {
if len(line) == 0 {
break
}
tks := strings.Split(line, "\t")
node := TreeNode{}
i, _ := strconv.Atoi(tks[0])
node.left, _ = strconv.Atoi(tks[1])
node.right, _ = strconv.Atoi(tks[2])
node.depth, _ = strconv.Atoi(tks[3])
node.prediction = core.NewArrayVector()
node.prediction.FromString(tks[4])
node.sample_count, _ = strconv.Atoi(tks[5])
node.feature_split = core.Feature{}
node.feature_split.Id, _ = strconv.ParseInt(tks[6], 10, 64)
node.feature_split.Value, _ = strconv.ParseFloat(tks[7], 64)
t.nodes[i] = &node
}
}
func (dt *CART) FindBestSplitOfContinusousFeature(samples []*core.MapBasedSample, node *TreeNode, feature_select_prob float64) {
feature_weight_labels := make(map[int64]*core.FeatureLabelDistribution)
total_dis := core.NewArrayVector()
for i, k := range node.samples {
if i > 10 && rand.Float64() > dt.params.SamplingRatio {
continue
}
total_dis.AddValue(samples[k].Label, 1.0)
for fid, fvalue := range samples[k].Features {
if dt.RandByFeatureId(fid) > feature_select_prob {
continue
}
_, ok := feature_weight_labels[fid]
if !ok {
feature_weight_labels[fid] = core.NewFeatureLabelDistribution()
}
feature_weight_labels[fid].AddWeightLabel(fvalue, samples[k].Label)
}
}
min_gini := 1.0
node.feature_split = core.Feature{Id: -1, Value: 0}
for fid, distribution := range feature_weight_labels {
sort.Sort(distribution)
split, gini := distribution.BestSplitByGini(total_dis)
if min_gini > gini {
min_gini = gini
node.feature_split.Id = fid
node.feature_split.Value = split
}
}
if min_gini > dt.params.GiniThreshold {
node.feature_split.Id = -1
node.feature_split.Value = 0.0
}
}