def run_fit(seed, param_grid, directed, n_init, n_jobs):
# run left
graph = load_drosophila_left()
if not directed:
graph = symmetrize(graph, method="avg")
graph = binarize(graph)
ddcsbm_left_df = select_dcsbm(
graph,
param_grid,
directed=directed,
degree_directed=False,
n_jobs=n_jobs,
n_init=n_init,
)
save_obj(ddcsbm_left_df, file_obs, "ddcsbm_left_df")
# run right
graph = load_drosophila_right()
if not directed:
graph = symmetrize(graph, method="avg")
graph = binarize(graph)
ddcsbm_right_df = select_dcsbm(
graph,
param_grid,
directed=directed,
degree_directed=False,
n_jobs=n_jobs,
n_init=n_init,
)
save_obj(ddcsbm_right_df, file_obs, "ddcsbm_right_df")
return 0
def run_fit(seed, param_grid, directed, n_init, n_jobs):
graph = load_drosophila_left()
if not directed:
graph = symmetrize(graph, method="avg")
graph = binarize(graph)
np.random.seed(seed)
dcsbm_out_df = select_dcsbm(
graph,
param_grid,
directed=directed,
degree_directed=False,
n_jobs=n_jobs,
n_init=n_init,
)
ddcsbm_out_df = select_dcsbm(
graph,
param_grid,
directed=directed,
degree_directed=True,
n_jobs=n_jobs,
n_init=n_init,
)
save_obj(dcsbm_out_df, file_obs, "dcsbm_out_df")
save_obj(ddcsbm_out_df, file_obs, "ddcsbm_out_df")
return 0
def run_fit(
seed,
n_components_try_range,
n_components_try_rdpg,
n_block_try_range,
directed,
n_init,
embed_kws_try_range,
n_jobs,
):
graph = load_drosophila_left()
if not directed:
graph = symmetrize(graph, method="avg")
graph = binarize(graph)
np.random.seed(seed)
param_grid = {
"n_components": n_components_try_range,
"n_blocks": n_block_try_range,
"embed_kws": embed_kws_try_range,
}
out_df = select_dcsbm(
graph,
param_grid,
directed=directed,
degree_directed=False,
n_jobs=n_jobs,
n_init=n_init,
)
print(out_df.head())
save_obj(out_df, file_obs, "grid_search_out")
return 0
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)
#
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#
#
#