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",
)
#%%
from src.data import load_left
from graspy.models import DCSBMEstimator
graph, labels = load_left()
dcsbm = DCSBMEstimator(directed=True, loops=False, degree_directed=True)
dcsbm.fit(graph, y=labels)
dcsbm.mse(graph)
#%%
from src.models import GridSearchUS
from src.models import select_rdpg
n_init = 3
n_components_try = range(1, 5)
param_grid = dict(n_components=n_components_try)
select_rdpg(graph, param_grid)
#%%
from graspy.utils import cartprod
import numpy as np
s = range(20, 25)
f = np.random.uniform(size=5)
out = cartprod(s, f)
from itertools import product
out = product(s, f)
for i, j in product(s, f):
print(i)