我目前正在开发一个数据挖掘项目,该项目正在创建一个18000x18000的相似性矩阵
以下是构建矩阵的两种方法
def CreateSimilarityMatrix(dbSubsetData, distancePairsList):
global matrix
matrix = [ [0.0 for y in range(dbSubsetData.shape[0])] for x in range(dbSubsetData.shape[0])]
for i in range(len(dbSubsetData)): #record1
SimilarityArray = []
start = time.time()
for j in range(i+1, len(dbSubsetData)): #record2
Similarity = GetDistanceBetweenTwoRecords(dbSubsetData, i, j, distancePairsList)
#The similarities are all very small numbers which might be why the preference value needs to be so precise.
#Let's multiply the value by a scalar 10 to give the values more range.
matrix[i][j] = Similarity * 10.0
matrix[j][i] = Similarity * 10.0
end = time.time()
return matrix
def GetDistanceBetweenTwoRecords(dbSubsetData, i, j, distancePairsList):
Record1 = dbSubsetData.iloc[i]
Record2 = dbSubsetData.iloc[j]
columns = dbSubsetData.columns
distancer = 0.0
distancec = 0.0
for i in range(len(Record1)):
columnName = columns[i]
Record1Value = Record1[i]
Record2Value = Record2[i]
if(Record1Value != Record2Value):
ob = distancePairsList[distancePairsDict[columnName]-1]
if(ob.attributeType == "String"):
strValue = Record1Value+":"+Record2Value
strValue2 = Record2Value+":"+Record1Value
if strValue in ob.distancePairs:
val = ((ob.distancePairs[strValue])**2)
val = val * -1
distancec = distancec + val
elif strValue2 in ob.distancePairs:
val = ((ob.distancePairs[strValue2])**2)
val = val * -1
distancec = distancec + val
elif(ob.attributeType == "Number"):
val = ((Record1Value - Record2Value)*ob.getSignificance())**2
val = val * -1
distancer = distancer + val
distance = distancer + distancec
return distance
每次迭代循环18000x19次(每行18000次,每个属性19次)。迭代总数为(18000x18000x19)/ 2,因为它是对称的,因此我只需要做矩阵的一半。这将花费大约36个小时来完成,这是一个我显然想要削减的时间框架。
我认为多处理就是诀窍。由于每一行都是独立生成数字并将它们拟合到矩阵中,因此我可以使用CreateSimilarityMatrix运行多进程。所以我在创建我的流程的函数中创建了这个
matrix = [ [0.0 for y in range(SubsetDBNormalizedAttributes.shape[0])] for x in range(SubsetDBNormalizedAttributes.shape[0])]
if __name__ == '__main__':
procs = []
for i in range(4):
proc = Process(target=CreateSimilarityMatrix, args=(SubsetDBNormalizedAttributes, distancePairsList, i, 4))
procs.append(proc)
proc.start()
proc.join()
创建相似度矩阵现在更改为
def CreateSimilarityMatrix(dbSubsetData, distancePairsList, counter=0, iteration=1):
global Matrix
for i in range(counter, len(dbSubsetData), iteration): #record1
SimilarityArray = []
start = time.time()
for j in range(i+1, len(dbSubsetData)): #record2
Similarity = GetDistanceBetweenTwoRecords(dbSubsetData, i, j, distancePairsList)
#print("Similarity Between Records",i,":",j," is ", Similarity)
#The similarities are all very small numbers which might be why the preference value needs to be so precise.
#Let's multiply the value by a scalar 10 to give the values more range.
Matrix[i][j] = Similarity * 10.0
Matrix[j][i] = Similarity * 10.0
end = time.time()
print("Iteration",i,"took",end-start,"(s)")
目前这是s-l-o-w。这真的很慢。启动一个进程需要几分钟,然后启动下一个进程需要几分钟。我以为这些应该同时运行?我的流程申请是否不正确?
如果您正在使用CPython,那么有一种称为全局解释器锁(GIL)的东西,它使得实际多线程变得困难,同时使事情变得更快,并且可以反而大大减慢速度。
如果你正在处理矩阵,使用numpy,这肯定比普通的Python快很多。