def evalCommunityDetection(algo, G):
""" Evaluate a community detection algorithm """
t = stopwatch.Timer()
algo.run()
zeta = algo.getPartition()
t.stop()
results = [["time [s]", t.elapsed],
["# communities", zeta.numberOfSubsets()],
["modularity", Modularity().getQuality(zeta, G)]]
print(tabulate.tabulate(results))
def inspectPartitions(partition, graph):
if not have_tabulate:
raise MissingDependencyError("tabulate")
partitionSizes = partition.subsetSizes()
mod = Modularity().getQuality(partition, graph)
props = [
["# partitions", partition.numberOfSubsets()],
["min partition size", min(partitionSizes)],
["max partition size", max(partitionSizes)],
["avg. partition size", sum(partitionSizes) / len(partitionSizes)],
["imbalance", computeImbalance(partition, graph)],
["edge cut", computeEdgeCut(partition, graph)],
["edge cut (portion)", computeEdgeCut(partition, graph) / graph.numberOfEdges() ],
["modularity", mod],
]
print(tabulate.tabulate(props))
def inspectCommunities(zeta, G):
""" Display information about communities
:param zeta communities
:param G graph
"""
communitySizes = zeta.subsetSizes()
mod = Modularity().getQuality(zeta, G)
commProps = [
["# communities", zeta.numberOfSubsets()],
["min community size", min(communitySizes)],
["max community size", max(communitySizes)],
["avg. community size",
sum(communitySizes) / len(communitySizes)],
#["imbalance", zeta.getImbalance()],
["modularity", mod],
]
print(tabulate.tabulate(commProps))