def testMatrixSimilarity(self):
numExamples = 5
numFeatures = 3
V1 = numpy.random.rand(numExamples, numFeatures)
matcher = GraphMatch(alpha=0.0)
C = matcher.matrixSimilarity(V1, V1)
Cdiag = numpy.diag(C)
nptst.assert_array_almost_equal(Cdiag, numpy.ones(Cdiag.shape[0]))
V1[:, 2] *= 10
C2 = matcher.matrixSimilarity(V1, V1)
Cdiag = numpy.diag(C2)
nptst.assert_array_almost_equal(Cdiag, numpy.ones(Cdiag.shape[0]))
nptst.assert_array_almost_equal(C, C2)
#print("Running match")
J = numpy.ones((numExamples, numFeatures))
Z = numpy.zeros((numExamples, numFeatures))
C2 = matcher.matrixSimilarity(J, Z)
#This should be 1 ideally
nptst.assert_array_almost_equal(C2, numpy.ones(C2.shape))
C2 = matcher.matrixSimilarity(J, J)
nptst.assert_array_almost_equal(C2, numpy.ones(C2.shape))
def profileSimulateGraphMatch(self):
N, matchAlpha, breakDist, purtScale = HIVModelUtils.toyABCParams()
startDate, endDate, recordStep, M, targetGraph = HIVModelUtils.toySimulationParams()
theta, stdTheta = HIVModelUtils.toyTheta()
featureInds= numpy.ones(targetGraph.vlist.getNumFeatures(), numpy.bool)
featureInds[HIVVertices.dobIndex] = False
featureInds[HIVVertices.infectionTimeIndex] = False
featureInds[HIVVertices.hiddenDegreeIndex] = False
featureInds[HIVVertices.stateIndex] = False
featureInds = numpy.arange(featureInds.shape[0])[featureInds]
#QCV is fastest and most accurate
#PATH is slowests but quite accurate
#RANK is very fast by less accurate than PATH
#U is fastest but least accurate
matcher = GraphMatch("QCV", alpha=matchAlpha, featureInds=featureInds, useWeightM=False)
matcher.lambdaM = 50
matcher.init = "rand"
graphMetrics = HIVGraphMetrics2(targetGraph, breakDist, matcher, float(endDate))
def run():
times, infectedIndices, removedIndices, graph = HIVModelUtils.simulate(theta, startDate, endDate, recordStep, M, graphMetrics)
print("Mean distance " + str(graphMetrics.meanDistance()))
ProfileUtils.profile('run()', globals(), locals())
def testVertexSimilarities(self):
matcher = GraphMatch(alpha=0.0)
C = matcher.vertexSimilarities(self.graph1, self.graph1)
Cdiag = numpy.diag(C)
nptst.assert_array_almost_equal(Cdiag, numpy.ones(Cdiag.shape[0]))
#Now compute trace(C)/||C||
#print(numpy.trace(C)/numpy.linalg.norm(C))
#Test use of feature inds
matcher = GraphMatch(alpha=0.0, featureInds=numpy.array([0]))
C = matcher.vertexSimilarities(self.graph1, self.graph2)
#Now, let's vary the non-used feature
self.graph1.vlist[:, 1] = 0
C2 = matcher.vertexSimilarities(self.graph1, self.graph2)
nptst.assert_array_equal(C, C2)
self.graph2.vlist[:, 1] = 0
C2 = matcher.vertexSimilarities(self.graph1, self.graph2)
nptst.assert_array_equal(C, C2)
#Vary used feature
self.graph1.vlist[:, 0] = 0
C2 = matcher.vertexSimilarities(self.graph1, self.graph2)
self.assertTrue((C != C2).any())
def __init__(self, realGraph, maxSize=100, matcher=None, startTime=None, alpha=0.5):
"""
A class to model metrics about and between HIVGraphs such as summary
statistics and distances. In this case we perform graph matching
using the QCV algorithm and other graph matching methods.
:param realGraph: The target epidemic graph
:param epsilon: The max mean objective before we break the simulation
:param matcher: The graph matcher object to compute graph objective.
:param startTime: A start time of the simulation
:param alpha: This is the weight factor for the average. Weights decay faster with a larger alpha.
"""
self.objectives = []
self.graphObjs = []
self.graphSizes = []
self.labelObjs = []
self.times = []
self.realGraph = realGraph
self.maxSize = maxSize
self.startTime = startTime
self.computationalTimes = []
self.alpha = alpha
if matcher == None:
self.matcher = GraphMatch("QCV")
else:
self.matcher = matcher
def testVertexSimilarities(self):
matcher = GraphMatch(alpha=0.0)
C = matcher.vertexSimilarities(self.graph1, self.graph1)
Cdiag = numpy.diag(C)
nptst.assert_array_almost_equal(Cdiag, numpy.ones(Cdiag.shape[0]))
#Now compute trace(C)/||C||
#print(numpy.trace(C)/numpy.linalg.norm(C))
#Test use of feature inds
matcher = GraphMatch(alpha=0.0, featureInds=numpy.array([0]))
C = matcher.vertexSimilarities(self.graph1, self.graph2)
#Now, let's vary the non-used feature
self.graph1.vlist[:, 1] = 0
C2 = matcher.vertexSimilarities(self.graph1, self.graph2)
nptst.assert_array_equal(C, C2)
self.graph2.vlist[:, 1] = 0
C2 = matcher.vertexSimilarities(self.graph1, self.graph2)
nptst.assert_array_equal(C, C2)
#Vary used feature
self.graph1.vlist[:, 0] = 0
C2 = matcher.vertexSimilarities(self.graph1, self.graph2)
self.assertTrue((C != C2).any())
def testMatrixSimilarity(self):
numExamples = 5
numFeatures = 3
V1 = numpy.random.rand(numExamples, numFeatures)
matcher = GraphMatch(alpha=0.0)
C = matcher.matrixSimilarity(V1, V1)
Cdiag = numpy.diag(C)
nptst.assert_array_almost_equal(Cdiag, numpy.ones(Cdiag.shape[0]))
V1[:, 2] *= 10
C2 = matcher.matrixSimilarity(V1, V1)
Cdiag = numpy.diag(C2)
nptst.assert_array_almost_equal(Cdiag, numpy.ones(Cdiag.shape[0]))
nptst.assert_array_almost_equal(C, C2)
#print("Running match")
J = numpy.ones((numExamples, numFeatures))
Z = numpy.zeros((numExamples, numFeatures))
C2 = matcher.matrixSimilarity(J, Z)
#This should be 1 ideally
nptst.assert_array_almost_equal(C2, numpy.ones(C2.shape))
C2 = matcher.matrixSimilarity(J, J)
nptst.assert_array_almost_equal(C2, numpy.ones(C2.shape))
def testMatch(self):
matcher = GraphMatch(algorithm="U", alpha=0.3)
permutation, distance, time = matcher.match(self.graph1, self.graph2)
#Checked output file - seems correct
distance2 = GraphMatch(alpha=0.0).distance(self.graph1, self.graph2, permutation)
self.assertAlmostEquals(distance[0], distance2)
#Now test case in which alpha is different
matcher = GraphMatch(algorithm="U", alpha=0.5)
permutation, distance, time = matcher.match(self.graph1, self.graph2)
distance2 = GraphMatch(alpha=0.0).distance(self.graph1, self.graph2, permutation)
self.assertAlmostEquals(distance[0], distance2)
#Test normalised distance
alpha = 0.0
permutation, distance, time = GraphMatch(algorithm="U", alpha=alpha).match(self.graph1, self.graph2)
distance2 = GraphMatch(alpha=alpha).distance(self.graph1, self.graph2, permutation, True)
self.assertAlmostEquals(distance[1], distance2)
alpha = 1.0
permutation, distance, time = GraphMatch(algorithm="U", alpha=alpha).match(self.graph1, self.graph2)
distance2 = GraphMatch(alpha=alpha).distance(self.graph1, self.graph2, permutation, True)
self.assertAlmostEquals(distance[1], distance2, 5)
#Test empty graph
alpha = 0.0
graph1 = SparseGraph(VertexList(0, 0))
graph2 = SparseGraph(VertexList(0, 0))
permutation, distance, time = GraphMatch(algorithm="U", alpha=alpha).match(graph1, graph2)
nptst.assert_array_equal(permutation, numpy.array([], numpy.int))
self.assertEquals(distance, [0, 0, 0])
#Test where 1 graph is empty
permutation, distance, time = GraphMatch(algorithm="U", alpha=alpha).match(graph1, self.graph1)
self.assertEquals(numpy.linalg.norm(self.graph1.getWeightMatrix())**2, distance[0])
self.assertEquals(distance[1], 1)
self.assertEquals(distance[2], 1)
permutation, distance, time = GraphMatch(algorithm="U", alpha=alpha).match(self.graph1, graph1)
self.assertEquals(numpy.linalg.norm(self.graph1.getWeightMatrix())**2, distance[0])
self.assertEquals(distance[1], 1)
self.assertEquals(distance[2], 1)
alpha = 1.0
permutation, distance, time = GraphMatch(algorithm="U", alpha=alpha).match(graph1, self.graph1)
self.assertEquals(numpy.linalg.norm(self.graph1.getWeightMatrix())**2, distance[0])
V2 = self.graph1.vlist.getVertices()
V1 = numpy.zeros(V2.shape)
C = GraphMatch(algorithm="U", alpha=alpha).matrixSimilarity(V1, V2)
dist = numpy.trace(C)/numpy.linalg.norm(C)
self.assertAlmostEquals(distance[1], -dist, 4)
self.assertAlmostEquals(distance[2], -dist, 4)
permutation, distance, time = GraphMatch(algorithm="U", alpha=alpha).match(self.graph1, graph1)
self.assertEquals(numpy.linalg.norm(self.graph1.getWeightMatrix())**2, distance[0])
self.assertAlmostEquals(distance[1], -dist, 4)
self.assertAlmostEquals(distance[2], -dist, 4)
#Test one graph which is a subgraph of another
p = 0.2
k = 10
numVertices = 20
generator = SmallWorldGenerator(p, k)
graph = SparseGraph(VertexList(numVertices, 2))
graph = generator.generate(graph)
subgraphInds = numpy.random.permutation(numVertices)[0:10]
subgraph = graph.subgraph(subgraphInds)
matcher = GraphMatch(algorithm="U", alpha=0.0)
permutation, distance, time = matcher.match(graph, subgraph)
distance = matcher.distance(graph, subgraph, permutation, True, True)
self.assertTrue(distance < 1)
def testDistance(self):
permutation = numpy.arange(self.numVertices)
dist = GraphMatch(alpha=0.0).distance(self.graph1, self.graph1, permutation)
self.assertEquals(dist, 0.0)
dist = GraphMatch(alpha=0.0).distance(self.graph1, self.graph2, permutation)
self.assertAlmostEquals(dist, 50.0)
permutation = numpy.arange(self.numVertices)
permutation[8] = 9
permutation[9] = 8
dist = GraphMatch(alpha=0.0).distance(self.graph1, self.graph2, permutation)
self.assertAlmostEquals(dist, 54.0)
#Try graphs of unequal size
graph3 = self.graph1.subgraph(range(8))
permutation = numpy.arange(self.numVertices)
dist1 = GraphMatch(alpha=0.0).distance(self.graph1, graph3, permutation)
dist1a = GraphMatch(alpha=0.0).distance(graph3, self.graph1, permutation)
self.assertEquals(dist1, dist1a)
graph3 = self.graph1.subgraph(range(5))
dist2 = GraphMatch(alpha=0.0).distance(self.graph1, graph3, permutation)
dist2a = GraphMatch(alpha=0.0).distance(graph3, self.graph1, permutation)
self.assertEquals(dist2, dist2a)
self.assertTrue(dist1 < dist2)
#Test case where alpha!=0
alpha = 1.0
permutation = numpy.arange(self.numVertices)
distance = GraphMatch(alpha=alpha).distance(self.graph1, self.graph2, permutation, False)
C = GraphMatch(alpha=alpha).vertexSimilarities(self.graph1, self.graph2)
distance2 = -numpy.trace(C)
self.assertEquals(distance, distance2)
#Check case where we want non negativve distance even when alpha!=0
distance = GraphMatch(alpha=alpha).distance(self.graph1, self.graph2, permutation, True, True)
self.assertTrue(distance >= 0)
permutation = numpy.arange(self.numVertices)
distance = GraphMatch(alpha=alpha).distance(self.graph1, self.graph1, permutation, True, True)
self.assertEquals(distance, 0)
#Check case where both graphs are empty
graph1 = SparseGraph(VertexList(0, 0))
graph2 = SparseGraph(VertexList(0, 0))
permutation = numpy.array([], numpy.int)
distance = GraphMatch(alpha=alpha).distance(graph1, graph1, permutation, True, True)
self.assertEquals(distance, 0)
#Now, just one graph is empty
#Distance is always 1 due to normalisations
alpha = 0.0
permutation = numpy.arange(10, dtype=numpy.int)
distance = GraphMatch(alpha=alpha).distance(self.graph1, graph1, permutation, True, True)
self.assertEquals(distance, 1.0)
permutation = numpy.arange(10, dtype=numpy.int)
distance = GraphMatch(alpha=alpha).distance(self.graph2, graph1, permutation, True, True)
self.assertEquals(distance, 1.0)
#distance = GraphMatch(alpha=alpha).distance(self.graph1, graph1, permutation, False, False)
#self.assertEquals(distance, numpy.linalg.norm(self.graph1.getWeightMatrix())**2)
alpha = 0.9
matcher = GraphMatch("U", alpha=alpha)
permutation, distanceVector, time = matcher.match(self.graph2, graph1)
distance = matcher.distance(self.graph2, graph1, permutation, True, True)
self.assertEquals(distance, 1.0)
alpha = 1.0
permutation = numpy.arange(10, dtype=numpy.int)
distance = GraphMatch(alpha=alpha).distance(self.graph1, graph1, permutation, True, True)
self.assertEquals(distance, 1.0)
permutation = numpy.arange(10, dtype=numpy.int)
distance = GraphMatch(alpha=alpha).distance(self.graph2, graph1, permutation, True, True)
self.assertEquals(distance, 1.0)
alpha = 0.5
permutation = numpy.arange(10, dtype=numpy.int)
distance = GraphMatch(alpha=alpha).distance(self.graph2, graph1, permutation, True, True)
self.assertEquals(distance, 1.0)
#Test on unequal graphs and compare against distance from graphm
alpha = 0.5
matcher = GraphMatch(alpha=alpha)
permutation, distanceVector, time = matcher.match(self.graph1, self.graph2)
distance = matcher.distance(self.graph1, self.graph2, permutation, True, False)
self.assertAlmostEquals(distanceVector[1], distance, 3)
def testAddGraph(self):
maxSize = 100
metrics = HIVGraphMetrics2(self.graph, maxSize)
metrics.addGraph(self.graph)
self.assertAlmostEquals(metrics.objectives[0], -0.834, 3)
self.assertAlmostEquals(metrics.meanObjective(), -0.834, 3)
#Start a new graph
#Compute distances directly
matcher = GraphMatch("QCV")
graph = HIVGraph(self.graph.size)
dists = []
metrics = HIVGraphMetrics2(self.graph, maxSize)
graph.vlist.setInfected(1, 0.0)
graph.vlist.setDetected(1, 0.1, 0)
graph.endEventTime = 1
metrics.addGraph(graph)
t = graph.endTime()
subgraph1 = graph.subgraph(graph.removedIndsAt(t))
subgraph2 = self.graph.subgraph(graph.removedIndsAt(t))
permutation, distance, time = matcher.match(subgraph1, subgraph2)
lastObj, lastGraphObj, lastLabelObj = matcher.distance(subgraph1, subgraph2, permutation, True, False, True)
self.assertEquals(metrics.objectives[-1], lastObj)
self.assertFalse(metrics.shouldBreak())
graph.vlist.setInfected(2, 2.0)
graph.vlist.setDetected(2, 2.0, 0)
metrics.addGraph(graph)
t = graph.endTime()
subgraph1 = graph.subgraph(graph.removedIndsAt(t))
subgraph2 = self.graph.subgraph(graph.removedIndsAt(t))
permutation, distance, time = matcher.match(subgraph1, subgraph2)
lastObj, lastGraphObj, lastLabelObj = matcher.distance(subgraph1, subgraph2, permutation, True, False, True)
self.assertEquals(metrics.objectives[-1], lastObj)
self.assertFalse(metrics.shouldBreak())
graph.vlist.setInfected(7, 3.0)
graph.vlist.setDetected(7, 3.0, 0)
metrics.addGraph(graph)
t = graph.endTime()
subgraph1 = graph.subgraph(graph.removedIndsAt(t))
subgraph2 = self.graph.subgraph(graph.removedIndsAt(t))
permutation, distance, time = matcher.match(subgraph1, subgraph2)
lastObj, lastGraphObj, lastLabelObj = matcher.distance(subgraph1, subgraph2, permutation, True, False, True)
self.assertEquals(metrics.objectives[-1], lastObj)
self.assertFalse(metrics.shouldBreak())
#Test case where one graph has zero size
graph1 = HIVGraph(10)
graph2 = HIVGraph(10)
graph1.vlist[:, HIVVertices.stateIndex] = HIVVertices.removed
metrics = HIVGraphMetrics2(graph2, maxSize)
metrics.addGraph(graph1)
#Problem is that distance is 1 when one graph is zero
self.assertEquals(len(metrics.objectives), 0)
class HIVGraphMetrics2(object):
def __init__(self, realGraph, maxSize=100, matcher=None, startTime=None, alpha=0.5):
"""
A class to model metrics about and between HIVGraphs such as summary
statistics and distances. In this case we perform graph matching
using the QCV algorithm and other graph matching methods.
:param realGraph: The target epidemic graph
:param epsilon: The max mean objective before we break the simulation
:param matcher: The graph matcher object to compute graph objective.
:param startTime: A start time of the simulation
:param alpha: This is the weight factor for the average. Weights decay faster with a larger alpha.
"""
self.objectives = []
self.graphObjs = []
self.graphSizes = []
self.labelObjs = []
self.times = []
self.realGraph = realGraph
self.maxSize = maxSize
self.startTime = startTime
self.computationalTimes = []
self.alpha = alpha
if matcher == None:
self.matcher = GraphMatch("QCV")
else:
self.matcher = matcher
def addGraph(self, simulatedGraph):
"""
Compute the objective between this graph and the realGraph at the time
of the last event of this one.
"""
t = simulatedGraph.endTime()
#Only select vertices added after startTime and before t
inds = numpy.setdiff1d(simulatedGraph.removedIndsAt(t), simulatedGraph.removedIndsAt(self.startTime))
subgraph = simulatedGraph.subgraph(inds)
inds = numpy.setdiff1d(self.realGraph.removedIndsAt(t), self.realGraph.removedIndsAt(self.startTime))
subTargetGraph = self.realGraph.subgraph(inds)
#logging.debug("Simulated size " + str(subgraph.size) + " and real size " + str(subTargetGraph.size))
self.graphSizes.append(subgraph.size)
#Only add objective if the real graph has nonzero size
if subTargetGraph.size != 0 and subgraph.size <= self.maxSize:
permutation, distance, time = self.matcher.match(subgraph, subTargetGraph)
lastObj, lastGraphObj, lastLabelObj = self.matcher.distance(subgraph, subTargetGraph, permutation, True, False, True)
self.computationalTimes.append(time)
self.objectives.append(lastObj)
self.graphObjs.append(lastGraphObj)
self.labelObjs.append(lastLabelObj)
self.times.append(t)
else:
logging.debug("Not adding objective at time " + str(t) + " with simulated size " + str(subgraph.size) + " and real size " + str(subTargetGraph.size))
def meanObjective(self, objectives=None):
"""
This is the moving average objective of the graph matches so far.
"""
if objectives == None:
objectives = numpy.array(self.objectives)
if objectives.shape[0]!=0:
weights = self.alpha * (1 - self.alpha)**(numpy.arange(objectives.shape[0], 0, -1)-1)
logging.debug("weights="+str(weights))
return numpy.average(objectives, weights=weights)
else:
return float("inf")
def meanGraphObjective(self):
"""
This is the mean graph objective of the graph matches so far.
"""
graphObjs = numpy.array(self.graphObjs)
if graphObjs.shape[0]!=0:
return graphObjs.mean()
else:
return float("inf")
def meanLabelObjective(self):
"""
This is the mean label objective of the graph matches so far.
"""
labelObjs = numpy.array(self.labelObjs)
if labelObjs.shape[0]!=0:
return labelObjs.mean()
else:
return float("inf")
def shouldBreak(self):
"""
We break when the graph size exceeds a threshold
"""
if self.graphSizes[-1] > self.maxSize:
logging.debug("Breaking as size has become too large: " + str(self.graphSizes[-1]) + " > " + str(self.maxSize))
return self.graphSizes[-1] > self.maxSize
def testMatch(self):
matcher = GraphMatch(algorithm="U", alpha=0.3)
permutation, distance, time = matcher.match(self.graph1, self.graph2)
#Checked output file - seems correct
distance2 = GraphMatch(alpha=0.0).distance(self.graph1, self.graph2,
permutation)
self.assertAlmostEquals(distance[0], distance2)
#Now test case in which alpha is different
matcher = GraphMatch(algorithm="U", alpha=0.5)
permutation, distance, time = matcher.match(self.graph1, self.graph2)
distance2 = GraphMatch(alpha=0.0).distance(self.graph1, self.graph2,
permutation)
self.assertAlmostEquals(distance[0], distance2)
#Test normalised distance
alpha = 0.0
permutation, distance, time = GraphMatch(algorithm="U",
alpha=alpha).match(
self.graph1, self.graph2)
distance2 = GraphMatch(alpha=alpha).distance(self.graph1, self.graph2,
permutation, True)
self.assertAlmostEquals(distance[1], distance2)
alpha = 1.0
permutation, distance, time = GraphMatch(algorithm="U",
alpha=alpha).match(
self.graph1, self.graph2)
distance2 = GraphMatch(alpha=alpha).distance(self.graph1, self.graph2,
permutation, True)
self.assertAlmostEquals(distance[1], distance2, 5)
#Test empty graph
alpha = 0.0
graph1 = SparseGraph(VertexList(0, 0))
graph2 = SparseGraph(VertexList(0, 0))
permutation, distance, time = GraphMatch(algorithm="U",
alpha=alpha).match(
graph1, graph2)
nptst.assert_array_equal(permutation, numpy.array([], numpy.int))
self.assertEquals(distance, [0, 0, 0])
#Test where 1 graph is empty
permutation, distance, time = GraphMatch(algorithm="U",
alpha=alpha).match(
graph1, self.graph1)
self.assertEquals(
numpy.linalg.norm(self.graph1.getWeightMatrix())**2, distance[0])
self.assertEquals(distance[1], 1)
self.assertEquals(distance[2], 1)
permutation, distance, time = GraphMatch(algorithm="U",
alpha=alpha).match(
self.graph1, graph1)
self.assertEquals(
numpy.linalg.norm(self.graph1.getWeightMatrix())**2, distance[0])
self.assertEquals(distance[1], 1)
self.assertEquals(distance[2], 1)
alpha = 1.0
permutation, distance, time = GraphMatch(algorithm="U",
alpha=alpha).match(
graph1, self.graph1)
self.assertEquals(
numpy.linalg.norm(self.graph1.getWeightMatrix())**2, distance[0])
V2 = self.graph1.vlist.getVertices()
V1 = numpy.zeros(V2.shape)
C = GraphMatch(algorithm="U", alpha=alpha).matrixSimilarity(V1, V2)
dist = numpy.trace(C) / numpy.linalg.norm(C)
self.assertAlmostEquals(distance[1], -dist, 4)
self.assertAlmostEquals(distance[2], -dist, 4)
permutation, distance, time = GraphMatch(algorithm="U",
alpha=alpha).match(
self.graph1, graph1)
self.assertEquals(
numpy.linalg.norm(self.graph1.getWeightMatrix())**2, distance[0])
self.assertAlmostEquals(distance[1], -dist, 4)
self.assertAlmostEquals(distance[2], -dist, 4)
#Test one graph which is a subgraph of another
p = 0.2
k = 10
numVertices = 20
generator = SmallWorldGenerator(p, k)
graph = SparseGraph(VertexList(numVertices, 2))
graph = generator.generate(graph)
subgraphInds = numpy.random.permutation(numVertices)[0:10]
subgraph = graph.subgraph(subgraphInds)
matcher = GraphMatch(algorithm="U", alpha=0.0)
permutation, distance, time = matcher.match(graph, subgraph)
distance = matcher.distance(graph, subgraph, permutation, True, True)
self.assertTrue(distance < 1)
def testDistance2(self):
permutation = numpy.arange(self.numVertices)
dist = GraphMatch(alpha=0.0).distance2(self.graph1, self.graph1,
permutation)
self.assertEquals(dist, 0.0)
dist = GraphMatch(alpha=0.0).distance2(self.graph1, self.graph2,
permutation)
dist2 = GraphMatch(alpha=0.0).distance(self.graph1, self.graph2,
permutation, True)
self.assertAlmostEquals(dist, dist2)
permutation = numpy.arange(self.numVertices)
permutation[8] = 9
permutation[9] = 8
dist = GraphMatch(alpha=0.0).distance2(self.graph1, self.graph2,
permutation)
dist2 = GraphMatch(alpha=0.0).distance(self.graph1, self.graph2,
permutation, True)
self.assertAlmostEquals(dist, dist2)
#Try graphs of unequal size
graph3 = self.graph1.subgraph(range(8))
permutation = numpy.arange(self.numVertices)
dist1 = GraphMatch(alpha=0.0).distance2(self.graph1, graph3,
permutation)
dist1a = GraphMatch(alpha=0.0).distance2(graph3, self.graph1,
permutation)
self.assertEquals(dist1, dist1a)
graph3 = self.graph1.subgraph(range(5))
dist2 = GraphMatch(alpha=0.0).distance2(self.graph1, graph3,
permutation)
dist2a = GraphMatch(alpha=0.0).distance2(graph3, self.graph1,
permutation)
self.assertEquals(dist2, dist2a)
self.assertTrue(dist1 < dist2)
#Test case where alpha!=0
alpha = 1.0
permutation = numpy.arange(self.numVertices)
distance = GraphMatch(alpha=alpha).distance2(self.graph1, self.graph1,
permutation)
self.assertEquals(distance, 0.0)
#Check distances are between 0 and 1
for i in range(100):
alpha = numpy.random.rand()
permutation = numpy.random.permutation(self.numVertices)
distance = GraphMatch(alpha=alpha).distance2(
self.graph1, self.graph1, permutation)
self.assertTrue(0 <= distance <= 1)
def testDistance(self):
permutation = numpy.arange(self.numVertices)
dist = GraphMatch(alpha=0.0).distance(self.graph1, self.graph1,
permutation)
self.assertEquals(dist, 0.0)
dist = GraphMatch(alpha=0.0).distance(self.graph1, self.graph2,
permutation)
self.assertAlmostEquals(dist, 50.0)
permutation = numpy.arange(self.numVertices)
permutation[8] = 9
permutation[9] = 8
dist = GraphMatch(alpha=0.0).distance(self.graph1, self.graph2,
permutation)
self.assertAlmostEquals(dist, 54.0)
#Try graphs of unequal size
graph3 = self.graph1.subgraph(range(8))
permutation = numpy.arange(self.numVertices)
dist1 = GraphMatch(alpha=0.0).distance(self.graph1, graph3,
permutation)
dist1a = GraphMatch(alpha=0.0).distance(graph3, self.graph1,
permutation)
self.assertEquals(dist1, dist1a)
graph3 = self.graph1.subgraph(range(5))
dist2 = GraphMatch(alpha=0.0).distance(self.graph1, graph3,
permutation)
dist2a = GraphMatch(alpha=0.0).distance(graph3, self.graph1,
permutation)
self.assertEquals(dist2, dist2a)
self.assertTrue(dist1 < dist2)
#Test case where alpha!=0
alpha = 1.0
permutation = numpy.arange(self.numVertices)
distance = GraphMatch(alpha=alpha).distance(self.graph1, self.graph2,
permutation, False)
C = GraphMatch(alpha=alpha).vertexSimilarities(self.graph1,
self.graph2)
distance2 = -numpy.trace(C)
self.assertEquals(distance, distance2)
#Check case where we want non negativve distance even when alpha!=0
distance = GraphMatch(alpha=alpha).distance(self.graph1, self.graph2,
permutation, True, True)
self.assertTrue(distance >= 0)
permutation = numpy.arange(self.numVertices)
distance = GraphMatch(alpha=alpha).distance(self.graph1, self.graph1,
permutation, True, True)
self.assertEquals(distance, 0)
#Check case where both graphs are empty
graph1 = SparseGraph(VertexList(0, 0))
graph2 = SparseGraph(VertexList(0, 0))
permutation = numpy.array([], numpy.int)
distance = GraphMatch(alpha=alpha).distance(graph1, graph1,
permutation, True, True)
self.assertEquals(distance, 0)
#Now, just one graph is empty
#Distance is always 1 due to normalisations
alpha = 0.0
permutation = numpy.arange(10, dtype=numpy.int)
distance = GraphMatch(alpha=alpha).distance(self.graph1, graph1,
permutation, True, True)
self.assertEquals(distance, 1.0)
permutation = numpy.arange(10, dtype=numpy.int)
distance = GraphMatch(alpha=alpha).distance(self.graph2, graph1,
permutation, True, True)
self.assertEquals(distance, 1.0)
#distance = GraphMatch(alpha=alpha).distance(self.graph1, graph1, permutation, False, False)
#self.assertEquals(distance, numpy.linalg.norm(self.graph1.getWeightMatrix())**2)
alpha = 0.9
matcher = GraphMatch("U", alpha=alpha)
permutation, distanceVector, time = matcher.match(self.graph2, graph1)
distance = matcher.distance(self.graph2, graph1, permutation, True,
True)
self.assertEquals(distance, 1.0)
alpha = 1.0
permutation = numpy.arange(10, dtype=numpy.int)
distance = GraphMatch(alpha=alpha).distance(self.graph1, graph1,
permutation, True, True)
self.assertEquals(distance, 1.0)
permutation = numpy.arange(10, dtype=numpy.int)
distance = GraphMatch(alpha=alpha).distance(self.graph2, graph1,
permutation, True, True)
self.assertEquals(distance, 1.0)
alpha = 0.5
permutation = numpy.arange(10, dtype=numpy.int)
distance = GraphMatch(alpha=alpha).distance(self.graph2, graph1,
permutation, True, True)
self.assertEquals(distance, 1.0)
#Test on unequal graphs and compare against distance from graphm
alpha = 0.5
matcher = GraphMatch(alpha=alpha)
permutation, distanceVector, time = matcher.match(
self.graph1, self.graph2)
distance = matcher.distance(self.graph1, self.graph2, permutation,
True, False)
self.assertAlmostEquals(distanceVector[1], distance, 3)