Esempio n. 1
0
 def test_katz_sim(self):
   G = self.G
   katz_sim, nodelist = katz(G)
   nt.assert_equal(len(nodelist), 6)
   for i in range(6):
     nt.assert_true(i in nodelist)
   nt.assert_equal(len(katz_sim), 6)
   for i in range(self.G.katz_sim.shape[0]):
     for j in range(self.G.katz_sim.shape[1]):
       print i, ',', j
       nt.assert_almost_equal(self.G.katz_sim[i,j], katz_sim[i,j], places=4)
 def test_katz_sim(self):
     G = self.G
     katz_sim, nodelist = katz(G)
     nt.assert_equal(len(nodelist), 6)
     for i in range(6):
         nt.assert_true(i in nodelist)
     nt.assert_equal(len(katz_sim), 6)
     for i in range(self.G.katz_sim.shape[0]):
         for j in range(self.G.katz_sim.shape[1]):
             print i, ',', j
             nt.assert_almost_equal(self.G.katz_sim[i, j],
                                    katz_sim[i, j],
                                    places=4)
Esempio n. 3
0
### VERTEX SIMILARITY

graphs = pickle.load(open('data/graphs_networkx.pkl', 'rb'))
results = dict()

for graph_type, G in graphs.iteritems():
    print "Calculating for %s" % (graph_type)
    results[graph_type] = dict()
    print "ASCOS ---------------------------"
    results[graph_type]["ascos"] = ascos(G)
    print "COSINE ---------------------------"
    results[graph_type]["cosine"] = cosine(G)
    print "JACCARD --------------------------"
    results[graph_type]["jaccard"] = jaccard(G)
    print "KATZ -----------------------------"
    results[graph_type]["katz"] = katz(G)
    print "LHN ------------------------------"
    results[graph_type]["lhn"] = lhn(G)
    print "RSS2 -----------------------------"
    results[graph_type]["rss2"] = rss2(G)
    print "DICE -----------------------------"
    results[graph_type]["dice"] = dice(G)
    print "INVERSE LOG WEIGHTED --------------"
    results[graph_type]["inverse_log_weighted"] = inverse_log_weighted(G)

pickle.dump(results, open("data/sim_metrics.pkl", "wb"))

### IMAGE SIMILARITY

# Let's make a concept by concept data frame
contrast_lookup = pandas.read_csv("data/contrast_by_concept_binary_df.tsv",
### VERTEX SIMILARITY

graphs = pickle.load(open('data/graphs_networkx.pkl','rb'))
results = dict()

for graph_type,G in graphs.iteritems():
    print "Calculating for %s" %(graph_type)
    results[graph_type] = dict()
    print "ASCOS ---------------------------"
    results[graph_type]["ascos"] = ascos(G)
    print "COSINE ---------------------------"
    results[graph_type]["cosine"] = cosine(G)
    print "JACCARD --------------------------"
    results[graph_type]["jaccard"] = jaccard(G)
    print "KATZ -----------------------------"
    results[graph_type]["katz"] = katz(G)
    print "LHN ------------------------------"
    results[graph_type]["lhn"] = lhn(G)
    print "RSS2 -----------------------------"
    results[graph_type]["rss2"] = rss2(G)
    print "DICE -----------------------------"
    results[graph_type]["dice"] = dice(G)
    print "INVERSE LOG WEIGHTED --------------"
    results[graph_type]["inverse_log_weighted"] = inverse_log_weighted(G)

pickle.dump(results,open("data/sim_metrics.pkl","wb"))

### IMAGE SIMILARITY

# Let's make a concept by concept data frame
contrast_lookup = pandas.read_csv("data/contrast_by_concept_binary_df.tsv",sep="\t",index_col=0)