def __init__(self):
self.exprTreeEmbedder = ExpressionTreeEmbedder()
def __init__(self):
self.exprTreeEmbedder = ExpressionTreeEmbedder()
class ConditionalAnomalyCalculator:
def __init__(self):
self.exprTreeEmbedder = ExpressionTreeEmbedder()
def conditionalAnomaly(self, contextsForFunction):
for context in contextsForFunction.itervalues():
termDocMatrix = self.exprTreeEmbedder.embed(
context, context.symbol)
if termDocMatrix == None:
continue
# Results are written into the context object
# That's not really good.
self.calculateDeviationVector(context, termDocMatrix)
def calculateDeviationVector(self, context, termDocMatrix):
originLocation = context.origin.location
matrix = termDocMatrix.matrix.tocsc()
originDocIndex = termDocMatrix.doc2Index[originLocation]
originFunctionVector = matrix[:, originDocIndex]
xd = originFunctionVector.todense()
vectors = []
distances = [] # distance in API space
cDistances = [] # distance in expression space
# Add self to model
# vectors.append(originFunctionVector.T)
# distances.append(0.0)
# cDistances.append(0.0)
#
for neighbour in context.neighbours:
l = neighbour.location
vectors.append(matrix[:, termDocMatrix.doc2Index[l]].T)
distances.append(neighbour.distance)
neighbourVec = vectors[-1].todense().T
cDistances.append(self.cosineDistance(xd, neighbourVec))
mue = self.calculateWeightedMean(vectors, distances)
context.gammaC = (1.0 / (len(cDistances))) * numpy.sum(cDistances)
# alpha = [(1-d) for d in distances]
# N = numpy.sum(alpha)
# context.gammaC = (1.0/N) * numpy.sum([alpha[i]*cDistances[i] for i in xrange(len(cDistances))])
context.gammaA = float(numpy.sum(distances)) / len(distances)
context.mue = mue
context.originVector = originFunctionVector.T
context.deviationVec = originFunctionVector.T - mue
context.index2Term = termDocMatrix.index2Term
# From the deviation vector, take all negative coefficients
# Then calculate the length of the resulting vector
# context.gammaC = self.euclideanDev(context.deviationVec)
context.gammaC = self.maxDev(context.deviationVec)
# Finally, weigh by (1-gammaA) to account for the quality of neighbours
# context.gammaC *= (1.0 - context.gammaA)
def cosineDistance(self, x, y):
from scipy.spatial.distance import cosine
from numpy.linalg import norm
if norm(x) == 0 or norm(y) == 0:
if norm(x) == norm(y):
return 0.0
else:
return 1.0
return cosine(x, y)
def euclideanDev(self, deviationVec):
missing = [i**2 for i in deviationVec.toarray()[0, :] if i < 0]
return numpy.sum(missing)
def maxDev(self, deviationVec):
# missing = [i**2 for i in deviationVec.toarray()[0,:] if i < 0]
missing = [float(-i) for i in deviationVec.toarray()[0, :] if i < 0]
if len(missing) == 0:
return 0
return numpy.max(missing)
def calculateWeightedMean(self, vectors, distances):
# N = numpy.sum([1-d for d in distances])
# mue = numpy.sum([(1-d) * v for (v,d) in zip(vectors, distances)])
N = len(distances)
mue = numpy.sum([v for (v, d) in zip(vectors, distances)])
mue *= (1.0 / N)
return mue
class ConditionalAnomalyCalculator:
def __init__(self):
self.exprTreeEmbedder = ExpressionTreeEmbedder()
def conditionalAnomaly(self, contextsForFunction):
for context in contextsForFunction.itervalues():
termDocMatrix = self.exprTreeEmbedder.embed(context, context.symbol)
if termDocMatrix == None:
continue
# Results are written into the context object
# That's not really good.
self.calculateDeviationVector(context, termDocMatrix)
def calculateDeviationVector(self, context, termDocMatrix):
originLocation = context.origin.location
matrix = termDocMatrix.matrix.tocsc()
originDocIndex = termDocMatrix.doc2Index[originLocation]
originFunctionVector = matrix[:,originDocIndex]
xd = originFunctionVector.todense()
vectors = []
distances = [] # distance in API space
cDistances = [] # distance in expression space
# Add self to model
# vectors.append(originFunctionVector.T)
# distances.append(0.0)
# cDistances.append(0.0)
#
for neighbour in context.neighbours:
l = neighbour.location
vectors.append(matrix[:,termDocMatrix.doc2Index[l]].T)
distances.append(neighbour.distance)
neighbourVec = vectors[-1].todense().T
cDistances.append(self.cosineDistance(xd, neighbourVec))
mue = self.calculateWeightedMean(vectors, distances)
context.gammaC = (1.0/(len(cDistances))) * numpy.sum(cDistances)
# alpha = [(1-d) for d in distances]
# N = numpy.sum(alpha)
# context.gammaC = (1.0/N) * numpy.sum([alpha[i]*cDistances[i] for i in xrange(len(cDistances))])
context.gammaA = float(numpy.sum(distances)) / len(distances)
context.mue = mue
context.originVector = originFunctionVector.T
context.deviationVec = originFunctionVector.T - mue
context.index2Term = termDocMatrix.index2Term
# From the deviation vector, take all negative coefficients
# Then calculate the length of the resulting vector
# context.gammaC = self.euclideanDev(context.deviationVec)
context.gammaC = self.maxDev(context.deviationVec)
# Finally, weigh by (1-gammaA) to account for the quality of neighbours
# context.gammaC *= (1.0 - context.gammaA)
def cosineDistance(self, x ,y):
from scipy.spatial.distance import cosine
from numpy.linalg import norm
if norm(x) == 0 or norm(y) == 0:
if norm(x) == norm(y):
return 0.0
else:
return 1.0
return cosine(x,y)
def euclideanDev(self, deviationVec):
missing = [i**2 for i in deviationVec.toarray()[0,:] if i < 0]
return numpy.sum(missing)
def maxDev(self, deviationVec):
# missing = [i**2 for i in deviationVec.toarray()[0,:] if i < 0]
missing = [float(-i) for i in deviationVec.toarray()[0,:] if i < 0]
if len(missing) == 0:
return 0
return numpy.max(missing)
def calculateWeightedMean(self, vectors, distances):
# N = numpy.sum([1-d for d in distances])
# mue = numpy.sum([(1-d) * v for (v,d) in zip(vectors, distances)])
N = len(distances)
mue = numpy.sum([v for (v,d) in zip(vectors, distances)])
mue *= (1.0/N)
return mue
