feat: add random walk sampling

gnn.go

@@ -8,25 +8,27 @@ import (
 )
 
 const (
-	Input  int     = 1433
-	Hidden int     = 50
-	Output int     = 7
-	Sample int     = 1000
-	Batch  int     = 10
-	RateWo float64 = 0.06
-	RateWi float64 = 0.08
-	RateB  float64 = 0.1
+	Input   int     = 1433
+	Hidden  int     = 64
+	Output  int     = 7
+	Sample  int     = 10000
+	Batch   int     = 5
+	Dropout float64 = 0.5
+	RateWo  float64 = 0.1
+	RateWi  float64 = 0.1
+	RateB   float64 = 0.1
 )
 
 type (
 	Parameter struct {
-		Wo, Wi, B Matrix
+		Wo, Wi, B, A Matrix
 	}
 
 	Layer struct {
 		d       int
 		f       func(Matrix) Matrix
 		p       Parameter
+		D       Vector
 		O, I, E Matrix
 	}
 )
@@ -42,10 +44,8 @@ func ReLU(A Matrix) Matrix {
 
 func Softmax(A Matrix) Matrix {
 	for i := 0; i < A.N(); i++ {
-		max, sum := 0., 0.
-		for j := 0; j < A.M(); j++ {
-			max = math.Max(max, A[i][j])
-		}
+		_, max := A[i].Max()
+		sum := 0.
 		for j := 0; j < A.M(); j++ {
 			A[i][j] = math.Exp(A[i][j] - max)
 			sum += A[i][j]
@@ -57,90 +57,95 @@ func Softmax(A Matrix) Matrix {
 	return A
 }
 
-func GetEmbedding(G Graph, u, k int, l []Layer) Vector {
+func GetEmbedding(G Graph, u, k int, l []Layer, train bool) Matrix {
 	if k == 0 {
-		return G.X[u]
+		l[k].E = MakeMatrix(1, l[k].d).Add(Matrix{G.X[u]})
+		if train && l[k].D != nil {
+			l[k].E.Dropout(l[k].D)
+		}
+		return l[k].E
 	}
 	l[k-1].O, l[k-1].I, l[k].E = MakeMatrix(1, l[k-1].d), MakeMatrix(1, l[k-1].d), MakeMatrix(1, l[k].d)
 	Do, Di := 0, 0
 	for v, w := range G.A[u] {
 		if w == 1 {
-			l[k-1].O.Add(Matrix{GetEmbedding(G, v, k-1, l)})
+			l[k-1].O.Add(GetEmbedding(G, v, k-1, l, train))
 			Do++
 		} else {
-			l[k-1].I.Add(Matrix{GetEmbedding(G, v, k-1, l)})
+			l[k-1].I.Add(GetEmbedding(G, v, k-1, l, train))
 			Di++
 		}
 	}
 	if Do > 0 {
-		l[k].E.Add(Multiply(l[k-1].O.Divide(float64(Do)), l[k].p.Wo))
+		l[k].E.Add(l[k-1].O.Divide(float64(Do)).Multiply(l[k].p.Wo))
 	}
 	if Di > 0 {
-		l[k].E.Add(Multiply(l[k-1].I.Divide(float64(Di)), l[k].p.Wi))
+		l[k].E.Add(l[k-1].I.Divide(float64(Di)).Multiply(l[k].p.Wi))
 	}
-	l[k].E.Add(Multiply(Matrix{GetEmbedding(G, u, k-1, l)}, l[k].p.B))
-	return l[k].f(l[k].E)[0]
+	l[k].E.Add(GetEmbedding(G, u, k-1, l, train).Multiply(l[k].p.B))
+	if train && l[k].D != nil {
+		l[k].E.Dropout(l[k].D)
+	}
+	return l[k].f(l[k].E)
 }
 
-// A += B * C / d
-func StartCalc(wg *sync.WaitGroup, A, B, C Matrix, d float64) {
+// A += B * C
+func StartCalc(wg *sync.WaitGroup, A, B, C Matrix) {
 	wg.Add(1)
 	go func() {
-		A.Add(Multiply(B.Transpose(), C).Divide(d))
+		A.Add(B.Transpose().Multiply(C))
 		wg.Done()
 	}()
 }
 
-// A += B
-func StartRefine(wg *sync.WaitGroup, A, B Matrix) {
+// A += B / c
+func StartRefine(wg *sync.WaitGroup, A, B Matrix, c float64) {
 	wg.Add(1)
 	go func() {
-		A.Add(B)
+		A.Add(B.Divide(c))
 		wg.Done()
 	}()
 }
 
 func Train(G Graph) []Layer {
-	p1 := Parameter{MakeRandomMatrix(Input, Hidden), MakeRandomMatrix(Input, Hidden), MakeRandomMatrix(Input, Hidden)}
-	p2 := Parameter{MakeRandomMatrix(Hidden, Output), MakeRandomMatrix(Hidden, Output), MakeRandomMatrix(Hidden, Output)}
+	p1 := Parameter{MakeRandomMatrix(Input, Hidden), MakeRandomMatrix(Input, Hidden), MakeRandomMatrix(Input, Hidden), MakeRandomMatrix(Input*2, 1)}
+	p2 := Parameter{MakeRandomMatrix(Hidden, Output), MakeRandomMatrix(Hidden, Output), MakeRandomMatrix(Hidden, Output), MakeRandomMatrix(Hidden*2, 1)}
 	l := []Layer{{d: Input}, {d: Hidden, f: ReLU, p: p1}, {d: Output, f: Softmax, p: p2}}
 	for i := 0; i < Sample; i++ {
-		if i%10 == 0 {
+		if i%100 == 0 {
 			fmt.Println("sampling", i)
 		}
 		var wg sync.WaitGroup
+		l[0].D, l[1].D = MakeDropoutVector(Input), MakeDropoutVector(Hidden)
 		DWo2, DWi2, DB2 := MakeMatrix(Hidden, Output), MakeMatrix(Hidden, Output), MakeMatrix(Hidden, Output)
 		DWo1, DWi1, DB1 := MakeMatrix(Input, Hidden), MakeMatrix(Input, Hidden), MakeMatrix(Input, Hidden)
-		for u, j := nodeId[rand.Intn(len(nodeId))], 0; j < Batch; j++ {
-			GetEmbedding(G, u, 2, l)
-			delta := MakeMatrix(1, Output)
-			delta[0][G.L[u]] = 1
-			delta.Sub(l[2].E)
-			StartCalc(&wg, DWo2, l[1].O, delta, float64(Batch)/RateWo)
-			StartCalc(&wg, DWi2, l[1].I, delta, float64(Batch)/RateWi)
-			StartCalc(&wg, DB2, l[1].E, delta, float64(Batch)/RateB)
-			delta = Multiply(delta, l[2].p.B.Transpose())
+		for j := 0; j < Batch; j++ {
+			u := nodeId[rand.Intn(len(nodeId))]
+			GetEmbedding(G, u, 2, l, true)
+			// GCN
+			delta := MakeMatrix(1, Output).Sub(l[2].E)
+			delta[0][G.L[u]] += 1
+			StartCalc(&wg, DWo2, l[1].O, delta)
+			StartCalc(&wg, DWi2, l[1].I, delta)
+			StartCalc(&wg, DB2, l[1].E, delta)
+			delta = delta.Multiply(l[2].p.B.Transpose())
 			for k := 0; k < Hidden; k++ {
 				if l[1].E[0][k] == 0 {
 					delta[0][k] = 0
 				}
 			}
-			StartCalc(&wg, DWo1, l[0].O, delta, float64(Batch)/RateWo)
-			StartCalc(&wg, DWi1, l[0].I, delta, float64(Batch)/RateWi)
-			StartCalc(&wg, DB1, Matrix{G.X[u]}, delta, float64(Batch)/RateB)
-			neighbor := make([]int, 0)
-			for v := range G.A[u] {
-				neighbor = append(neighbor, v)
-			}
-			u = neighbor[rand.Intn(len(neighbor))]
+			StartCalc(&wg, DWo1, l[0].O, delta)
+			StartCalc(&wg, DWi1, l[0].I, delta)
+			StartCalc(&wg, DB1, l[0].E, delta)
+			// GAT
 			wg.Wait()
 		}
-		StartRefine(&wg, l[2].p.Wo, DWo2)
-		StartRefine(&wg, l[2].p.Wi, DWi2)
-		StartRefine(&wg, l[2].p.B, DB2)
-		StartRefine(&wg, l[1].p.Wo, DWo1)
-		StartRefine(&wg, l[1].p.Wi, DWi1)
-		StartRefine(&wg, l[1].p.B, DB1)
+		StartRefine(&wg, l[2].p.Wo, DWo2, float64(Batch)/RateWo)
+		StartRefine(&wg, l[2].p.Wi, DWi2, float64(Batch)/RateWi)
+		StartRefine(&wg, l[2].p.B, DB2, float64(Batch)/RateB)
+		StartRefine(&wg, l[1].p.Wo, DWo1, float64(Batch)/RateWo)
+		StartRefine(&wg, l[1].p.Wi, DWi1, float64(Batch)/RateWi)
+		StartRefine(&wg, l[1].p.B, DB1, float64(Batch)/RateB)
 		wg.Wait()
 	}
 	return l

main.go

@@ -2,12 +2,14 @@ package main
 
 import (
 	"fmt"
+	"math/rand"
 	"os"
 )
 
 const (
-	Node = 2708
-	Edge = 5429
+	Node    int     = 2708
+	Edge    int     = 5429
+	Labeled float64 = 0.05
 )
 
 var (
@@ -36,7 +38,6 @@ func main() {
 		}
 		var u int
 		fmt.Fscan(file, &u)
-		nodeId = append(nodeId, u)
 		V := MakeVector(Input)
 		for j := 0; j < Input; j++ {
 			fmt.Fscan(file, &V[j])
@@ -45,7 +46,12 @@ func main() {
 		fmt.Fscan(file, &label)
 		for j := 0; j < Output; j++ {
 			if labelName[j] == label {
-				G.AddNode(u, V, j)
+				if rand.Float64() < Labeled {
+					G.AddNode(u, V, j)
+					nodeId = append(nodeId, u)
+				} else {
+					G.AddNode(u, V, j+Output)
+				}
 				break
 			}
 		}
@@ -58,23 +64,22 @@ func main() {
 	for i := 0; i < Edge; i++ {
 		var u, v int
 		fmt.Fscan(file, &u, &v)
-		G.AddEdge(u, v)
+		G.AddEdge(v, u)
 	}
 	file.Close()
 	l := Train(G)
-	cnt := 0
+	cnt1, cnt2 := 0, 0
 	for u := range G.X {
-		GetEmbedding(G, u, 2, l)
-		id, max := 0, 0.
-		for i := 0; i < Output; i++ {
-			if l[2].E[0][i] > max {
-				id, max = i, l[2].E[0][i]
-			}
-		}
+		GetEmbedding(G, u, 2, l, false)
+		id, _ := l[2].E[0].Max()
 		fmt.Println(u, id)
 		if G.L[u] == id {
-			cnt++
+			cnt1++
+		}
+		if G.L[u] == id+Output {
+			cnt2++
 		}
 	}
-	fmt.Println(cnt, "/", Node)
+	fmt.Println(cnt1, "/", len(nodeId), ",", cnt2, "/", Node-len(nodeId))
+	fmt.Println(100.*cnt1/len(nodeId), ",", 100.*cnt2/(Node-len(nodeId)))
 }

matrix.go

@@ -60,16 +60,7 @@ func (A Matrix) Sub(B Matrix) Matrix {
 	return A
 }
 
-func (A Matrix) Divide(b float64) Matrix {
-	for i := 0; i < A.N(); i++ {
-		for j := 0; j < A.M(); j++ {
-			A[i][j] /= b
-		}
-	}
-	return A
-}
-
-func Multiply(A, B Matrix) Matrix {
+func (A Matrix) Multiply(B Matrix) Matrix {
 	if A.M() != B.N() {
 		panic("Incompatible Dimensions")
 	}
@@ -87,3 +78,27 @@ func Multiply(A, B Matrix) Matrix {
 	}
 	return C
 }
+
+func (A Matrix) Divide(b float64) Matrix {
+	if b == 1 {
+		return A
+	}
+	for i := 0; i < A.N(); i++ {
+		for j := 0; j < A.M(); j++ {
+			A[i][j] /= b
+		}
+	}
+	return A
+}
+
+func (A Matrix) Dropout(B Vector) Matrix {
+	if A.M() != B.N() {
+		panic("Incompatible Dimensions")
+	}
+	for i := 0; i < A.N(); i++ {
+		for j := 0; j < A.M(); j++ {
+			A[i][j] *= B[j]
+		}
+	}
+	return A
+}

test.out

@@ -26,4 +26,2813 @@ reading node 2400
 reading node 2500
 reading node 2600
 reading node 2700
-2288 / 2708
+sampling 0
+sampling 100
+sampling 200
+sampling 300
+sampling 400
+sampling 500
+sampling 600
+sampling 700
+sampling 800
+sampling 900
+sampling 1000
+sampling 1100
+sampling 1200
+sampling 1300
+sampling 1400
+sampling 1500
+sampling 1600
+sampling 1700
+sampling 1800
+sampling 1900
+sampling 2000
+sampling 2100
+sampling 2200
+sampling 2300
+sampling 2400
+sampling 2500
+sampling 2600
+sampling 2700
+sampling 2800
+sampling 2900
+sampling 3000
+sampling 3100
+sampling 3200
+sampling 3300
+sampling 3400
+sampling 3500
+sampling 3600
+sampling 3700
+sampling 3800
+sampling 3900
+sampling 4000
+sampling 4100
+sampling 4200
+sampling 4300
+sampling 4400
+sampling 4500
+sampling 4600
+sampling 4700
+sampling 4800
+sampling 4900
+sampling 5000
+sampling 5100
+sampling 5200
+sampling 5300
+sampling 5400
+sampling 5500
+sampling 5600
+sampling 5700
+sampling 5800
+sampling 5900
+sampling 6000
+sampling 6100
+sampling 6200
+sampling 6300
+sampling 6400
+sampling 6500
+sampling 6600
+sampling 6700
+sampling 6800
+sampling 6900
+sampling 7000
+sampling 7100
+sampling 7200
+sampling 7300
+sampling 7400
+sampling 7500
+sampling 7600
+sampling 7700
+sampling 7800
+sampling 7900
+sampling 8000
+sampling 8100
+sampling 8200
+sampling 8300
+sampling 8400
+sampling 8500
+sampling 8600
+sampling 8700
+sampling 8800
+sampling 8900
+sampling 9000
+sampling 9100
+sampling 9200
+sampling 9300
+sampling 9400
+sampling 9500
+sampling 9600
+sampling 9700
+sampling 9800
+sampling 9900
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+124 / 124 , 1587 / 2584
+100 , 61

vector.go

@@ -18,6 +18,26 @@ func MakeRandomVector(n int) Vector {
 	return V
 }
 
+func MakeDropoutVector(n int) Vector {
+	V := make(Vector, n)
+	for i := 0; i < n; i++ {
+		if rand.Float64() >= Dropout {
+			V[i] = 1 / (1 - Dropout)
+		}
+	}
+	return V
+}
+
 func (V Vector) N() int {
 	return len(V)
 }
+
+func (V Vector) Max() (int, float64) {
+	id, max := 0, V[0]
+	for i := 1; i < V.N(); i++ {
+		if V[i] > max {
+			id, max = i, V[i]
+		}
+	}
+	return id, max
+}