clustListExact = exactClusterer.clusterFromIterator(graphIterator, False)
logging.debug("Running approximate method")
clustListApprox = []
for i in range(len(k2s)):
graphIterator = ThreeClustIterator(p, numClusters, r).getIterator()
clustListApprox.append(iascClusterers[i].clusterFromIterator(graphIterator, False))
logging.debug("Running Nystrom method")
graphIterator = ThreeClustIterator(p, numClusters, r).getIterator()
clustListNystrom = nystromClusterer.clusterFromIterator(graphIterator, False)
if do_Nings:
logging.debug("Running Nings method")
graphIterator = ThreeClustIterator(p, numClusters, r).getIterator()
clustListNings = ningsClusterer.cluster(graphIterator)
logging.debug("Running random SVD method")
graphIterator = ThreeClustIterator(p, numClusters, r).getIterator()
clustListRandSVD = randSvdCluster.clusterFromIterator(graphIterator, False)
# computer rand index error for each iteration
# error: proportion of pairs of vertices (x,y) s.t.
# (cl(x) == cl(y)) != (learned_cl(x) == learned_cl(y))
for it in range(len(ThreeClustIterator().subgraphIndicesList)):
indicesList = ThreeClustIterator().subgraphIndicesList[it]
numUsedVertices = len(indicesList)
for i in range(len(k2s)):
clustErrApprox[t, it, r, i] += GraphUtils.randIndex(clustListApprox[i][it], indicesList)
clustErrExact[t, it, r] += GraphUtils.randIndex(clustListExact[it], indicesList)
def testCluster(self):
print "< testCluster >"
numVertices = 8
graph = SparseGraph(GeneralVertexList(numVertices))
graph.addEdge(0, 1)
graph.addEdge(0, 2)
graph.addEdge(1, 2)
graph.addEdge(3, 4)
graph.addEdge(3, 5)
graph.addEdge(4, 5)
graph.addEdge(0, 3)
W = graph.getWeightMatrix()
graphIterator = []
graphIterator.append(W[0:6, 0:6].copy())
W[1, 6] += 1
W[6, 1] += 1
graphIterator.append(W[0:7, 0:7].copy())
W[4, 7] += 1
W[7, 4] += 1
graphIterator.append(W.copy())
graphIterator = iter(graphIterator)
k = 2
clusterer = NingSpectralClustering(k)
clustersList = clusterer.cluster(toSparseGraphListIterator(graphIterator))
#Why are the bottom rows of Q still zero?
#Try example in which only edges change
numVertices = 7
graph = SparseGraph(GeneralVertexList(numVertices))
graph.addEdge(0, 1)
graph.addEdge(0, 2)
graph.addEdge(1, 2)
graph.addEdge(3, 4)
WList = []
W = graph.getWeightMatrix()
WList.append(W[0:5, 0:5].copy())
graph.addEdge(3, 5)
graph.addEdge(4, 5)
W = graph.getWeightMatrix()
WList.append(W[0:6, 0:6].copy())
graph.addEdge(0, 6)
graph.addEdge(1, 6)
graph.addEdge(2, 6)
W = graph.getWeightMatrix()
WList.append(W[0:7, 0:7].copy())
iterator = iter(WList)
clustersList = clusterer.cluster(toSparseGraphListIterator(iterator))
#Seems to work, amazingly
#print(clustersList)
#Try removing rows/cols
W2 = W[0:5, 0:5]
W3 = W[0:4, 0:4]
WList = [W, W2, W3]
iterator = iter(WList)
clustersList = clusterer.cluster(toSparseGraphListIterator(iterator))
#nptst.assert_array_equal(clustersList[0][0:5], clustersList[1])
nptst.assert_array_equal(clustersList[1][0:4], clustersList[2])
#Make sure 1st clustering (without updates) is correct
L = GraphUtils.normalisedLaplacianRw(scipy.sparse.csr_matrix(W))
numpy.random.seed(21)
lmbda, Q = scipy.sparse.linalg.eigs(L, min(k, L.shape[0]-1), which="SM", ncv = min(20*k, L.shape[0]), v0=numpy.random.rand(L.shape[0]))
V = VqUtils.whiten(Q)
centroids, distortion = vq.kmeans(V, k, iter=20)
clusters, distortion = vq.vq(V, centroids)
#This should be equal but the eigenvector computation is unstable
#even with repeated runs (and no way to set the seed)
nptst.assert_array_equal(clusters, clustersList[0])
graphIterator = getGraphIterator()
clustListNystrom, timeListNystrom, eigenQualityNystrom = nystromClusterer.clusterFromIterator(
graphIterator, verbose=True
)
if args.runRandomisedSvd:
# run with Nystrom approximation
logging.info("Running randomised svd method")
graphIterator = getGraphIterator()
clustListRSvd, timeListRSvd, eigenQualityRSvd = RSvdClusterer.clusterFromIterator(graphIterator, verbose=True)
if args.runNing:
# run with Ning's incremental approximation
logging.info("Running Nings method")
graphIterator = getGraphIterator()
clustListNings, timeListNings, eigenQualityNings = ningsClusterer.cluster(graphIterator, verbose=True)
# print clusters
if args.runExact:
logging.info("learned clustering with exact eigenvalue decomposition")
for i in range(len(clustersExact)):
clusters = clustersExact[i]
print(clusters)
if args.runIASC:
logging.info("learned clustering with our approximation approach")
for i in range(len(clustListApprox)):
clusters = clustListApprox[i]
print(clusters)
if args.runNing:
logging.info("learned clustering with Nings approximation approach")
for i in range(len(clustListNings)):
logging.debug("Running approximate method")
resApproxList = []
for i in range(len(k2s)):
graphIterator = ThreeClustIterator(p, numClusters, r).getIterator()
resApproxList.append(iascClusterers[i].clusterFromIterator(graphIterator, True))
logging.debug("Running Nystrom method")
resNystromList = []
for i in range(len(k3s)):
graphIterator = ThreeClustIterator(p, numClusters, r).getIterator()
resNystromList.append(nystromClusterers[i].clusterFromIterator(graphIterator, True))
if do_Nings:
logging.debug("Running Nings method")
graphIterator = ThreeClustIterator(p, numClusters, r).getIterator()
resNings = ningsClusterer.cluster(graphIterator, True)
logging.debug("Running random SVD method")
resRandSVDList = []
for i in range(len(k4s)):
graphIterator = ThreeClustIterator(p, numClusters, r).getIterator()
resRandSVDList.append(randSvdClusterers[i].clusterFromIterator(graphIterator, True))
# computer rand index error for each iteration
# error: proportion of pairs of vertices (x,y) s.t.
# (cl(x) == cl(y)) != (learned_cl(x) == learned_cl(y))
for it in range(len(ThreeClustIterator().subgraphIndicesList)):
indicesList = ThreeClustIterator().subgraphIndicesList[it]
numUsedVertices = len(indicesList)
for k in range(len(k2s)):
