def run_fit(seed, directed):
# run left
graph, labels = load_left()
print(labels)
if not directed:
graph = symmetrize(graph, method="avg")
# fit SBM
sbm = SBMEstimator(directed=True, loops=False)
sbm_left_df = fit_a_priori(sbm, graph, labels)
print(sbm_left_df["n_params"])
save_obj(sbm_left_df, file_obs, "sbm_left_df")
# fit DCSBM
dcsbm = DCSBMEstimator(directed=True, loops=False, degree_directed=False)
dcsbm_left_df = fit_a_priori(dcsbm, graph, labels)
save_obj(dcsbm_left_df, file_obs, "dcsbm_left_df")
# fit dDCSBM
ddcsbm = DCSBMEstimator(directed=True, loops=False, degree_directed=True)
ddcsbm_left_df = fit_a_priori(ddcsbm, graph, labels)
save_obj(ddcsbm_left_df, file_obs, "ddcsbm_left_df")
# run right
graph, labels = load_right()
if not directed:
graph = symmetrize(graph, method="avg")
# fit SBM
sbm = SBMEstimator(directed=True, loops=False)
sbm_right_df = fit_a_priori(sbm, graph, labels)
save_obj(sbm_right_df, file_obs, "sbm_right_df")
# fit DCSBM
dcsbm = DCSBMEstimator(directed=True, loops=False, degree_directed=False)
dcsbm_right_df = fit_a_priori(dcsbm, graph, labels)
save_obj(dcsbm_right_df, file_obs, "dcsbm_right_df")
# fit dDCSBM
ddcsbm = DCSBMEstimator(directed=True, loops=False, degree_directed=True)
ddcsbm_right_df = fit_a_priori(ddcsbm, graph, labels)
save_obj(ddcsbm_right_df, file_obs, "ddcsbm_right_df")
return 0
hue=pred_labels,
ax=a,
palette=cmap2,
**scatter_kws,
)
else:
for j, label in enumerate(np.unique(right_labels)):
inds = np.where(right_labels == label)[0]
plot_data = latent[inds][:, i]
sns.distplot(plot_data, color=cmap1[j], ax=a)
plt.tight_layout()
save("multipanel_dcsbm", fmt="png")
#%%
estimator = DCSBMEstimator()
ap_results = fit_a_priori(estimator, left_adj, left_labels)
param_grid = dict(n_blocks=list(range(1, 10)))
sweep_results = select_dcsbm(left_adj,
param_grid,
n_init=25,
n_jobs=-2,
metric=None)
sweep_results
best_results = get_best(sweep_results,
"n_params",
score_name=score,
small_better=False)