# SBM
heatmap(sbm.p_mat_, ax=ax[0, 0], title="Probability matrix", **heatmap_kws)
heatmap(np.squeeze(sbm.sample()),
ax=ax[0, 1],
title="Random sample",
**heatmap_kws)
ax[0, 0].set_ylabel("SBM", **side_label_kws)
# DCSBM
heatmap(dcsbm.p_mat_, ax=ax[1, 0], **heatmap_kws)
heatmap(np.squeeze(dcsbm.sample()), ax=ax[1, 1], **heatmap_kws)
ax[1, 0].set_ylabel("DCSBM", **side_label_kws)
# RDPG
heatmap(rdpg.p_mat_, ax=ax[2, 0], **heatmap_kws)
heatmap(np.squeeze(rdpg.sample()), ax=ax[2, 1], **heatmap_kws)
ax[2, 0].set_ylabel("RDPG", **side_label_kws)
plt.tight_layout()
# Add colorbar
sm = plt.cm.ScalarMappable(cmap=cmap, norm=norm)
sm.set_array(rdpg.p_mat_)
cbar = fig.colorbar(sm,
ax=ax,
orientation="horizontal",
pad=0.01,
shrink=0.8,
fraction=0.1)
cbar.ax.tick_params(labelsize=16)
plt.savefig("DCSBMProbabilityMatrix", bbox_inches='tight')
heatmap(dcsbme.sample()[0],
inner_hier_labels=labels,
title="DCSBM sample",
font_scale=1.5,
sort_nodes=True)
plt.savefig("DCSBMSample", bbox_inches='tight')
rdpge = RDPGEstimator(loops=False)
rdpge.fit(adj, y=labels)
heatmap(rdpge.p_mat_,
inner_hier_labels=labels,
vmin=0,
vmax=1,
font_scale=1.5,
title="RDPG probability matrix",
sort_nodes=True
)
plt.savefig("RDPGProbabilityMatrix", bbox_inches='tight')
heatmap(rdpge.sample()[0],
inner_hier_labels=labels,
font_scale=1.5,
title="RDPG sample",
sort_nodes=True)
plt.savefig("RDPGSample", bbox_inches='tight')