add L1 distance and fix some bug(althought not convergent)

tranE.py

 from random import uniform, sample
 from numpy import *
+from copy import deepcopy
 
 class TransE:
-    def __init__(self, entityList, relationList, tripleList, margin = 1, learingRate = 0.01, dim = 10):
-        self.margin=margin
+    def __init__(self, entityList, relationList, tripleList, margin = 1, learingRate = 0.00001, dim = 10, L1 = True):
+        self.margin = margin
         self.learingRate = learingRate
         self.dim = dim#向量维度
-        self.entityList = entityList#一开始，entityList是entity的list；初始化后，变为字典，key是entity，values是其向量。
+        self.entityList = entityList#一开始，entityList是entity的list；初始化后，变为字典，key是entity，values是其向量（使用narray）。
         self.relationList = relationList#理由同上
         self.tripleList = tripleList#理由同上
         self.loss = 0
+        self.L1 = L1
 
     def initialize(self):
         '''
@@ -43,21 +45,21 @@ class TransE:
     def transE(self, cI = 20):
         print("训练开始")
         for cycleIndex in range(cI):
-            if cycleIndex%10000==0:
-                print("第%d次循环"%cycleIndex)
-                print(self.loss)
-                self.loss = 0
-                self.writeRelationVector("c:\\relationVector.txt")
-                self.writeEntilyVector("c:\\entityVector.txt")
-            Sbatch = self.getSample()
+            Sbatch = self.getSample(150)
             Tbatch = []#元组对（原三元组，打碎的三元组）的列表 ：{((h,r,t),(h',r,t'))}
             for sbatch in Sbatch:
                 tripletWithCorruptedTriplet = (sbatch, self.getCorruptedTriplet(sbatch))
                 if(tripletWithCorruptedTriplet not in Tbatch):
                     Tbatch.append(tripletWithCorruptedTriplet)
             self.update(Tbatch)
+            if cycleIndex % 100 == 0:
+                print("第%d次循环"%cycleIndex)
+                print(self.loss)
+                self.writeRelationVector("c:\\relationVector.txt")
+                self.writeEntilyVector("c:\\entityVector.txt")
+                self.loss = 0
 
-    def getSample(self, size = 500):
+    def getSample(self, size):
         return sample(self.tripleList, size)
 
     def getCorruptedTriplet(self, triplet):
@@ -72,59 +74,84 @@ class TransE:
                 entityTemp = sample(self.entityList.keys(), 1)[0]
                 if entityTemp != triplet[0]:
                     break
-            corruptedTriplet = (entityTemp,triplet[1], triplet[2])
+            corruptedTriplet = (entityTemp, triplet[1], triplet[2])
         else:#大于等于0，打坏三元组的第二项
             while True:
                 entityTemp = sample(self.entityList.keys(), 1)[0]
                 if entityTemp != triplet[1]:
                     break
-            corruptedTriplet = (triplet[0],entityTemp, triplet[2])
+            corruptedTriplet = (triplet[0], entityTemp, triplet[2])
         return corruptedTriplet
 
     def update(self, Tbatch):
-        i = 0
-        while i < len(Tbatch):
-            tripletWithCorruptedTriplet = Tbatch[i]
-            headEntityVector = array(self.entityList[tripletWithCorruptedTriplet[0][0]])#tripletWithCorruptedTriplet是原三元组和打碎的三元组的元组tuple
-            tailEntityVector = array(self.entityList[tripletWithCorruptedTriplet[0][1]])
-            relationVector = array(self.relationList[tripletWithCorruptedTriplet[0][2]])
-            headEntityVectorWithCorruptedTriplet = array(self.entityList[tripletWithCorruptedTriplet[1][0]])
-            tailEntityVectorWithCorruptedTriplet = array(self.entityList[tripletWithCorruptedTriplet[1][1]])
-
-            distTriplet = distance(headEntityVector,tailEntityVector , relationVector)
-            distCorruptedTriplet = distance(headEntityVectorWithCorruptedTriplet,tailEntityVectorWithCorruptedTriplet ,  relationVector)
+        copyEntityList = deepcopy(self.entityList)
+        copyRelationList = deepcopy(self.relationList)
+        
+        for tripletWithCorruptedTriplet in Tbatch:
+            headEntityVector = copyEntityList[tripletWithCorruptedTriplet[0][0]]#tripletWithCorruptedTriplet是原三元组和打碎的三元组的元组tuple
+            tailEntityVector = copyEntityList[tripletWithCorruptedTriplet[0][1]]
+            relationVector = copyRelationList[tripletWithCorruptedTriplet[0][2]]
+            headEntityVectorWithCorruptedTriplet = copyEntityList[tripletWithCorruptedTriplet[1][0]]
+            tailEntityVectorWithCorruptedTriplet = copyEntityList[tripletWithCorruptedTriplet[1][1]]
+            
+            headEntityVectorBeforeBatch = self.entityList[tripletWithCorruptedTriplet[0][0]]#tripletWithCorruptedTriplet是原三元组和打碎的三元组的元组tuple
+            tailEntityVectorBeforeBatch = self.entityList[tripletWithCorruptedTriplet[0][1]]
+            relationVectorBeforeBatch = self.relationList[tripletWithCorruptedTriplet[0][2]]
+            headEntityVectorWithCorruptedTripletBeforeBatch = self.entityList[tripletWithCorruptedTriplet[1][0]]
+            tailEntityVectorWithCorruptedTripletBeforeBatch = self.entityList[tripletWithCorruptedTriplet[1][1]]
+            
+            if self.L1:
+                distTriplet = distanceL1(headEntityVectorBeforeBatch, tailEntityVectorBeforeBatch, relationVectorBeforeBatch)
+                distCorruptedTriplet = distanceL1(headEntityVectorWithCorruptedTripletBeforeBatch, tailEntityVectorWithCorruptedTripletBeforeBatch ,  relationVectorBeforeBatch)
+            else:
+                distTriplet = distanceL2(headEntityVectorBeforeBatch, tailEntityVectorBeforeBatch, relationVectorBeforeBatch)
+                distCorruptedTriplet = distanceL2(headEntityVectorWithCorruptedTripletBeforeBatch, tailEntityVectorWithCorruptedTripletBeforeBatch ,  relationVectorBeforeBatch)
             eg = self.margin + distTriplet - distCorruptedTriplet
             if eg > 0: #[function]+ 是一个取正值的函数
                 self.loss += eg
-                tempPositive = 2 * self.learingRate * (tailEntityVector - headEntityVector - relationVector)
-                tempNegtative = 2 * self.learingRate * (tailEntityVectorWithCorruptedTriplet - headEntityVectorWithCorruptedTriplet - relationVector)
-
-                temp1 = headEntityVector + tempPositive
-                temp2 = tailEntityVector - tempPositive
-                temp3 = relationVector + tempPositive - tempNegtative
-                temp4 = headEntityVectorWithCorruptedTriplet - tempNegtative
-                temp5 = tailEntityVectorWithCorruptedTriplet + tempNegtative
-
-                headEntityVector = temp1
-                tailEntityVector = temp2
-                relationVector = temp3
-                headEntityVectorWithCorruptedTriplet = temp4
-                tailEntityVectorWithCorruptedTriplet = temp5
+                if self.L1:
+                    tempPositive = 2 * self.learingRate * (tailEntityVectorBeforeBatch - headEntityVectorBeforeBatch - relationVectorBeforeBatch)
+                    tempNegtative = 2 * self.learingRate * (tailEntityVectorWithCorruptedTripletBeforeBatch - headEntityVectorWithCorruptedTripletBeforeBatch - relationVectorBeforeBatch)
+                    tempPositiveL1 = []
+                    tempNegtativeL1 = []
+                    for i in range(self.dim):#不知道有没有pythonic的写法（比如列表推倒或者numpy的函数）？
+                        if tempPositive[i] >= 0:
+                            tempPositiveL1.append(1)
+                        else:
+                            tempPositiveL1.append(-1)
+                        if tempNegtative[i] >= 0:
+                            tempNegtativeL1.append(1)
+                        else:
+                            tempNegtativeL1.append(-1)
+                    tempPositive = array(tempPositiveL1)  
+                    tempNegtative = array(tempNegtativeL1)
+
+                else:
+                    tempPositive = 2 * self.learingRate * (tailEntityVectorBeforeBatch - headEntityVectorBeforeBatch - relationVectorBeforeBatch)
+                    tempNegtative = 2 * self.learingRate * (tailEntityVectorWithCorruptedTripletBeforeBatch - headEntityVectorWithCorruptedTripletBeforeBatch - relationVectorBeforeBatch)
+    
+                headEntityVector = headEntityVector + tempPositive
+                tailEntityVector = tailEntityVector - tempPositive
+                relationVector = relationVector + tempPositive - tempNegtative
+                headEntityVectorWithCorruptedTriplet = headEntityVectorWithCorruptedTriplet - tempNegtative
+                tailEntityVectorWithCorruptedTriplet = tailEntityVectorWithCorruptedTriplet + tempNegtative
 
                 #只归一化这几个刚更新的向量，而不是按原论文那些一口气全更新了
-                self.entityList[tripletWithCorruptedTriplet[0][0]] = norm(headEntityVector.tolist())
-                self.entityList[tripletWithCorruptedTriplet[0][1]] = norm(tailEntityVector.tolist())
-                self.relationList[tripletWithCorruptedTriplet[0][2]] = norm(relationVector.tolist())
-                self.entityList[tripletWithCorruptedTriplet[1][0]] = norm(headEntityVectorWithCorruptedTriplet.tolist())
-                self.entityList[tripletWithCorruptedTriplet[1][1]] = norm(tailEntityVectorWithCorruptedTriplet.tolist())
-            i += 1
-
+                copyEntityList[tripletWithCorruptedTriplet[0][0]] = norm(headEntityVector)
+                copyEntityList[tripletWithCorruptedTriplet[0][1]] = norm(tailEntityVector)
+                copyRelationList[tripletWithCorruptedTriplet[0][2]] = norm(relationVector)
+                copyEntityList[tripletWithCorruptedTriplet[1][0]] = norm(headEntityVectorWithCorruptedTriplet)
+                copyEntityList[tripletWithCorruptedTriplet[1][1]] = norm(tailEntityVectorWithCorruptedTriplet)
+                
+        self.entityList = copyEntityList
+        self.relationList = copyRelationList
+        
     def writeEntilyVector(self, dir):
         print("写入实体")
         entityVectorFile = open(dir, 'w')
         for entity in self.entityList.keys():
             entityVectorFile.write(entity+"\t")
-            entityVectorFile.write(str(self.entityList[entity]))
+            entityVectorFile.write(str(self.entityList[entity].tolist()))
             entityVectorFile.write("\n")
         entityVectorFile.close()
 
@@ -133,24 +160,27 @@ class TransE:
         relationVectorFile = open(dir, 'w')
         for relation in self.relationList.keys():
             relationVectorFile.write(relation + "\t")
-            relationVectorFile.write(str(self.relationList[relation]))
+            relationVectorFile.write(str(self.relationList[relation].tolist()))
             relationVectorFile.write("\n")
         relationVectorFile.close()
 
 def init(dim):
-    return uniform(-6/(dim**0.5),6/(dim**0.5))
+    return uniform(-6/(dim**0.5), 6/(dim**0.5))
 
-def distance(h, t, r):
+def distanceL1(h, t ,r):
     s = h + r - t
-    narray = array(s)
-    narray2 = narray*narray
-    sum = narray2.sum()
+    sum = fabs(s).sum()
     return sum
 
+def distanceL2(h, t, r):
+    s = h + r - t
+    sum = (s*s).sum()
+    return sum
+ 
 def norm(list):
     '''
     归一化
-    :param 向量:
+    :param 向量
     :return: 向量的平方和的开方后的向量
     '''
     var = linalg.norm(list)
@@ -158,7 +188,7 @@ def norm(list):
     while i < len(list):
         list[i] = list[i]/var
         i += 1
-    return list
+    return array(list)
 
 def openDetailsAndId(dir,sp="\t"):
     idNum = 0
@@ -192,9 +222,10 @@ if __name__ == '__main__':
     dirTrain = "C:\\data\\train.txt"
     tripleNum, tripleList = openTrain(dirTrain)
     print("打开TransE")
-    transE = TransE(entityList,relationList,tripleList,dim = 30)
+    transE = TransE(entityList,relationList,tripleList, margin=1, dim = 100)
     print("TranE初始化")
     transE.initialize()
-    transE.transE(300000)
+    transE.transE(15000)
     transE.writeRelationVector("c:\\relationVector.txt")
     transE.writeEntilyVector("c:\\entityVector.txt")
+