def simulate_affiliation_dpe():
nrange = [400] #50*2**np.arange(3)
drange = np.arange(1,5)
embed = [Embed.dot_product_embed,
Embed.dot_product_embed_unscaled,
Embed.normalized_laplacian_embed,
Embed.normalized_laplacian_embed_scaled]
k = 2
p = .15
q = .1
for n in nrange:
G = rg.affiliation_model(n, k, p, q)
for d in drange:
print n*k,d,
for e in embed:
Embed.cluster_vertices_kmeans(G, e, d, k, 'kmeans')
print num_diffs_w_perms_graph(G, 'block', 'kmeans'),
print
plot.matshow(nx.adj_matrix(G))
plot.show()
def embedding_vs_dimension_performance():
n = 50
drange = np.arange(1,5)
embed = [Embed.dot_product_embed,
Embed.dot_product_embed_unscaled,
Embed.normalized_laplacian_embed,
Embed.normalized_laplacian_embed_scaled]
nmc = 10
k = 2
p = .5
q = .1
all_params = list(IterGrid({'d':drange, 'embed':embed}))
[param.update({'num_diff':np.zeros(nmc),'rand_idx':np.zeros(nmc)}) for param in all_params]
for mc in np.arange(nmc):
print mc
G = rg.affiliation_model(n, k, p, q)
truth = nx.get_node_attributes(G, 'block').values()
for param in all_params:
pred = Embed.cluster_vertices_kmeans(G, param['embed'], param['d'], 2)
param['num_diff'][mc] = num_diff_w_perms(truth, pred)
param['rand_idx'][mc] = metrics.adjusted_rand_score(truth, pred)
return all_params
