return B
#%%
import pandas as pd
from src.io import readcsv
alphas = np.geomspace(0.0005, 0.05, 20)
n_init = 100
basename = f"-n_init={n_init}-left-only"
exp_name = "144.0-BDP-revamp-gm-sf"
perm_df = readcsv("permuatations" + basename, foldername=exp_name, index_col=0)
meta = readcsv("meta" + basename, foldername=exp_name, index_col=0)
# adj_df = pd.DataFrame(adj, index=meta.index, columns=meta.index)
adj_df = readcsv("adj" + basename, foldername=exp_name, index_col=0)
adj = adj_df.values
alpha = 0.00021
alpha = np.round(alpha, decimals=5)
str_alpha = f"a{alpha}"
perm_inds = perm_df[str_alpha]
perm_adj = adj[np.ix_(perm_inds, perm_inds)]
perm_meta = meta.iloc[perm_inds].copy()
fig, ax = plt.subplots(1, 1, figsize=(20, 20))
adjplot(
perm_adj,
meta=perm_meta,
# ##
# parameters for the experiment
metric = "bic"
bic_ratio = 1
d = 8 # embedding dimension
method = "iso"
# parameters for plotting
lowest_level = 7
width = 0.5
gap = 10
basename = f"-method={method}-d={d}-bic_ratio={bic_ratio}"
title = f"Method={method}, d={d}, BIC ratio={bic_ratio}"
exp = "137.0-BDP-omni-clust"
# load data
pair_meta = readcsv("meta" + basename, foldername=exp, index_col=0)
pair_meta["lvl0_labels"] = pair_meta["lvl0_labels"].astype(str)
pair_adj = readcsv("adj" + basename, foldername=exp, index_col=0)
pair_mg = MetaGraph(pair_adj.values, pair_meta)
pair_meta = pair_mg.meta
lp_inds, rp_inds = get_paired_inds(pair_meta)
left_adj = pair_adj[np.ix_(lp_inds, lp_inds)]
right_adj = pair_adj[np.ix_(rp_inds, rp_inds)]
calc_blockmodel_df
level = 7
class_key = f"lvl{level}_labels"
metric = "bic"
bic_ratio = 1
d = 8 # embedding dimension
method = "color_iso"
basename = f"-method={method}-d={d}-bic_ratio={bic_ratio}"
title = f"Method={method}, d={d}, BIC ratio={bic_ratio}"
exp = "137.2-BDP-omni-clust"
# load data
pair_meta = readcsv("meta" + basename, foldername=exp, index_col=0)
pair_meta["lvl0_labels"] = pair_meta["lvl0_labels"].astype(str)
pair_adj = readcsv("adj" + basename, foldername=exp, index_col=0)
pair_adj = pair_adj.values
mg = MetaGraph(pair_adj, pair_meta)
meta = mg.meta
def sort_mg(mg, level_names):
meta = mg.meta
sort_class = level_names + ["merge_class"]
class_order = ["sf"]
total_sort_by = []
for sc in sort_class:
for co in class_order:
class_value = meta.groupby(sc)[co].mean()
