def test_rss2_sim_no_weight(self):
G = self.G
rss2_sim = rss2(G, disregard_weight=True)
nt.assert_equal(len(rss2_sim), 8)
for i in range(8):
assert(i in rss2_sim)
for i in self.G.rss2_sim.keys():
nt.assert_equal(len(self.G.rss2_sim[i]), len(rss2_sim[i]))
for j in self.G.rss2_sim[i].keys():
nt.assert_almost_equal(rss2_sim[i][j], self.G.rss2_sim[i][j], places=4)
def test_rss2_sim_with_weight(self):
H = self.H
rss2_sim = rss2(H)
nt.assert_equal(len(rss2_sim), 5)
for i in range(5):
assert(i in rss2_sim)
for i in self.H.rss2_sim.keys():
nt.assert_equal(len(self.H.rss2_sim[i]), len(rss2_sim[i]))
for j in self.H.rss2_sim[i].keys():
print i, ',', j
nt.assert_almost_equal(rss2_sim[i][j], self.H.rss2_sim[i][j], places=4)
def test_rss2_sim_no_weight(self):
G = self.G
rss2_sim = rss2(G, disregard_weight=True)
nt.assert_equal(len(rss2_sim), 8)
for i in range(8):
assert (i in rss2_sim)
for i in self.G.rss2_sim.keys():
nt.assert_equal(len(self.G.rss2_sim[i]), len(rss2_sim[i]))
for j in self.G.rss2_sim[i].keys():
nt.assert_almost_equal(rss2_sim[i][j],
self.G.rss2_sim[i][j],
places=4)
def test_rss2_sim_with_weight(self):
H = self.H
rss2_sim = rss2(H)
nt.assert_equal(len(rss2_sim), 5)
for i in range(5):
assert (i in rss2_sim)
for i in self.H.rss2_sim.keys():
nt.assert_equal(len(self.H.rss2_sim[i]), len(rss2_sim[i]))
for j in self.H.rss2_sim[i].keys():
print i, ',', j
nt.assert_almost_equal(rss2_sim[i][j],
self.H.rss2_sim[i][j],
places=4)
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)
images = pandas.read_csv("data/contrast_defined_images_filtered.tsv",
sep="\t",
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)
images = pandas.read_csv("data/contrast_defined_images_filtered.tsv",sep="\t",index_col=0)
def get_concepts(image1):
image1_concepts = images['cognitive_contrast_cogatlas_id'][images['image_id']==image1].tolist()[0]
