def run_fit(seed):
np.random.seed(seed)
# load
left_graph, left_labels = load_left()
right_graph, right_labels = load_right()
# fit SBM left, predict right
sbm_fit_left = SBMEstimator(directed=True, loops=False)
sbm_fit_left.fit(left_graph, y=left_labels)
right_pred_mse = mse_on_other(sbm_fit_left, right_graph, right_labels)
right_pred_likelihood = likelihood_on_other(sbm_fit_left, right_graph,
right_labels)
right_pred_sc_likelihood = likelihood_on_other(
sbm_fit_left,
right_graph,
right_labels,
clip=1 / (right_graph.size - right_graph.shape[0]),
)
right_pred_dict = {
"n_params": sbm_fit_left._n_parameters(),
"mse": right_pred_mse,
"likelihood": right_pred_likelihood,
"zc_likelihood": right_pred_likelihood,
"sc_likelihood": right_pred_sc_likelihood,
}
right_pred_df = pd.DataFrame(right_pred_dict, index=[0])
print(right_pred_df)
save_obj(right_pred_df, file_obs, "right_pred_sbm_df")
# fit SBM right, predict left
sbm_fit_right = SBMEstimator(directed=True, loops=False)
sbm_fit_right.fit(right_graph, y=right_labels)
left_pred_mse = mse_on_other(sbm_fit_right, left_graph, left_labels)
left_pred_likelihood = likelihood_on_other(sbm_fit_right, left_graph,
left_labels)
left_pred_sc_likelihood = likelihood_on_other(
sbm_fit_right,
left_graph,
left_labels,
clip=1 / (left_graph.size - left_graph.shape[0]),
)
left_pred_dict = {
"n_params": sbm_fit_right._n_parameters(),
"mse": left_pred_mse,
"likelihood": left_pred_likelihood,
"zc_likelihood": left_pred_likelihood,
"sc_likelihood": left_pred_sc_likelihood,
}
left_pred_df = pd.DataFrame(left_pred_dict, index=[0])
print(left_pred_df)
save_obj(left_pred_df, file_obs, "left_pred_sbm_df")
# sbm_fit_right = SBMEstimator(directed=True, loops=False)
# sbm_fit_right.fit(right_graph, y=right_labels)
# right_b = sbm_fit_right.block_p_
# # save_obj(sbm_left_df, file_obs, "sbm_left_df")
return 0
def test_SBM_nparams(self):
e = self.estimator.fit(self.graph, y=self.labels)
assert e._n_parameters() == (4)
e = SBMEstimator()
e.fit(self.graph)
assert e._n_parameters() == (4 + 1)
e = SBMEstimator(directed=False)
e.fit(self.graph)
assert e._n_parameters() == (1 + 3)
palette=cmap,
**plt_kws,
)
plt.xlabel("# Params (SBM params for SBMs)")
plt.ylabel("MSE")
plt.title(f"Drosophila old MB left, directed ({experiment}:{run})")
plt.savefig(save_dir / "rank_sbm_Klines.pdf", format="pdf", facecolor="w")
#%%
from graspy.models import SBMEstimator
from graspy.datasets import load_drosophila_left, load_drosophila_right
from graspy.utils import binarize
sbm = SBMEstimator(directed=True, loops=False)
left_adj, left_labels = load_drosophila_left(return_labels=True)
left_adj = binarize(left_adj)
sbm.fit(left_adj, y=left_labels)
sbm.mse(left_adj)
sbm._n_parameters()
right_adj, right_labels = load_drosophila_right(return_labels=True)
er = SBMEstimator(directed=True, loops=False, n_blocks=2)
er.fit(left_adj)
er.mse(left_adj)
heatmap(left_adj,
inner_hier_labels=er.vertex_assignments_,
outer_hier_labels=left_labels)
#%%
