def dcsbm_objective(adj, labels):
# class1_var = np.var(input[class1_inds])
# class2_var = np.var(input[class2_inds])
dcsbm = SBMEstimator()
dcsbm.fit(adj, y=labels)
objective = dcsbm.score(adj)
return objective
temp_dict = base_dict.copy()
temp_dict["Metric"] = "GMM likelihood"
temp_dict["Score"] = score
out_dicts.append(temp_dict)
# GMM BIC
score = gmm.model_.bic(latent)
temp_dict = base_dict.copy()
temp_dict["Metric"] = "GMM BIC"
temp_dict["Score"] = score
out_dicts.append(temp_dict)
# SBM likelihood
sbm = SBMEstimator(directed=True, loops=False)
sbm.fit(bin_adj, y=pred_labels)
score = sbm.score(bin_adj)
temp_dict = base_dict.copy()
temp_dict["Metric"] = "SBM likelihood"
temp_dict["Score"] = score
out_dicts.append(temp_dict)
# DCSBM likelihood
dcsbm = DCSBMEstimator(directed=True, loops=False)
dcsbm.fit(bin_adj, y=pred_labels)
score = dcsbm.score(bin_adj)
temp_dict = base_dict.copy()
temp_dict["Metric"] = "DCSBM likelihood"
temp_dict["Score"] = score
out_dicts.append(temp_dict)
# ARI of the subset with labels
np.random.seed(8888)
n_init = 200
clip = 1 / (right_graph.size - right_graph.shape[0])
heatmap_kws = dict(vmin=0,
vmax=1,
font_scale=1.5,
hier_label_fontsize=20,
cbar=False)
fig, ax = plt.subplots(4, 2, figsize=(15, 30))
# A priori SBM
ap_estimator = SBMEstimator()
ap_estimator.fit(right_graph, y=right_labels)
lik = ap_estimator.score(right_graph, clip=clip)
heatmap(
right_graph,
inner_hier_labels=right_labels,
title="Right MB (by cell type)",
ax=ax[0, 0],
**heatmap_kws,
)
heatmap(
ap_estimator.p_mat_,
inner_hier_labels=right_labels,
title=f"A priori SBM, lik = {lik:.2f}",
ax=ax[0, 1],
**heatmap_kws,
)
