def run_fit(seed, directed, n_components_range):
# run left
left_graph, labels = load_left()
if not directed:
left_graph = symmetrize(left_graph, method="avg")
# run right
right_graph, labels = load_right()
if not directed:
right_graph = symmetrize(right_graph, method="avg")
outs = []
for n_components in n_components_range:
ldt = LatentDistributionTest(n_components=n_components,
n_bootstraps=500)
ldt.fit(left_graph, right_graph)
result = {}
result["p-value"] = ldt.p_
result["sample-t"] = ldt.sample_T_statistic_
result["n_components"] = n_components
outs.append(result)
print(f"Done with {n_components}")
out_df = pd.DataFrame(outs)
save_obj(out_df, file_obs, "ldt_df")
return 0
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 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
def run_fit(seed, directed, n_components_range):
# run left
left_graph, labels = load_left()
if not directed:
left_graph = symmetrize(left_graph, method="avg")
# run right
right_graph, labels = load_right()
if not directed:
right_graph = symmetrize(right_graph, method="avg")
def fit(n_components):
# np.random.seed(seed)
return fit_ldt(left_graph, right_graph, n_components)
outs = Parallel(n_jobs=-2, verbose=5)(delayed(fit)(n) for n in n_components_range)
out_df = pd.DataFrame(outs)
save_obj(out_df, file_obs, "ldt_df")
return 0
#%%
from src.data import load_left, load_right
from graspy.plot import heatmap
from graspy.utils import binarize
import matplotlib.pyplot as plt
left_adj, left_labels = load_left()
right_adj, right_labels = load_right()
heatmap_kws = dict(cbar=False, font_scale=2)
fig, ax = plt.subplots(1, 2, figsize=(18, 10))
heatmap(left_adj,
inner_hier_labels=left_labels,
ax=ax[0],
title="Left",
**heatmap_kws)
heatmap(right_adj,
inner_hier_labels=right_labels,
ax=ax[1],
title="Right",
**heatmap_kws)
plt.tight_layout()
plt.savefig(
"./maggot_models/reports/figures/raw_heatmaps/heatmaps.pdf",
facecolor="w",
format="pdf",
)
param_name="param_n_components",
score_name=score_name)
#%%
from pathlib import Path
import matplotlib.pyplot as plt
def save(name, fmt="pdf"):
path = Path(
"/Users/bpedigo/JHU_code/maggot_models/maggot_models/notebooks/outs")
plt.savefig(path / str(name + "." + fmt), fmt=fmt, facecolor="w")
plt.style.use("seaborn-white")
right_graph, right_labels = load_right()
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)