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collect_watts-strogatz.py
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collect_watts-strogatz.py
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import json
import os
import numpy as np
from joblib import Parallel, delayed
from numpy.linalg import eigh
from lcs import *
data_dir = "Data/watts-strogatz/"
def collect_parameters(dir):
clist = set()
blist = set()
plist = set()
rlist = set()
for f in os.listdir(dir):
d = f.split(".json")[0].split("_")
c = int(d[0])
b = float(d[1])
p = float(d[2])
r = int(d[3])
clist.add(c)
blist.add(b)
plist.add(p)
rlist.add(r)
c_dict = {c: i for i, c in enumerate(sorted(clist))}
b_dict = {b: i for i, b in enumerate(sorted(blist))}
p_dict = {p: i for i, p in enumerate(sorted(plist))}
r_dict = {r: i for i, r in enumerate(sorted(rlist))}
return c_dict, b_dict, p_dict, r_dict
def get_metrics(f, dir, c_dict, b_dict, p_dict, r_dict):
fname = os.path.join(dir, f)
d = f.split(".json")[0].split("_")
c = int(d[0])
b = float(d[1])
p = float(d[2])
r = int(d[3])
i = c_dict[c]
j = b_dict[b]
k = p_dict[p]
l = r_dict[r]
with open(fname, "r") as file:
data = json.loads(file.read())
A = np.array(data["A"])
samples = np.array(data["samples"])
m = dict()
m["rho"] = density(A)
m["rho-samples"] = density(samples.mean(axis=0))
m["mean-degree"] = degrees(A).mean()
m["mean-squared-degree"] = (degrees(A) ** 2).mean()
m["pf-eigenvalue"] = eigh(A)[0][-1]
m["ps"] = posterior_similarity(samples, A)
m["sps"] = samplewise_posterior_similarity(samples, A)
m["fs"] = f_score(samples, A)
m["fs-norm-random"] = f_score(samples, A, normalize=True, rho_guess=0.5)
m["fs-norm-density"] = f_score(samples, A, normalize=True, rho_guess=m["rho"])
m["fce"] = fraction_of_correct_entries(samples, A)
m["fce-norm-random"] = fraction_of_correct_entries(
samples, A, normalize=True, rho_guess=0.5
)
m["fce-norm-density"] = fraction_of_correct_entries(
samples, A, normalize=True, rho_guess=m["rho"]
)
m["precision"] = precision(samples, A)
m["recall"] = recall(samples, A)
m["auroc"] = auroc(samples, A)
m["auprc"] = auprc(samples, A)
print((i, j, k, l), flush=True)
return i, j, k, l, m
# get number of available cores
n_processes = len(os.sched_getaffinity(0))
c_dict, b_dict, p_dict, r_dict = collect_parameters(data_dir)
n_c = len(c_dict)
n_b = len(b_dict)
n_p = len(p_dict)
n_r = len(r_dict)
data = {}
data["beta"] = list(b_dict)
data["p"] = list(p_dict)
data["rho"] = np.zeros((n_c, n_b, n_p, n_r))
data["rho-samples"] = np.zeros((n_c, n_b, n_p, n_r))
data["mean-degree"] = np.zeros((n_c, n_b, n_p, n_r))
data["mean-squared-degree"] = np.zeros((n_c, n_b, n_p, n_r))
data["pf-eigenvalue"] = np.zeros((n_c, n_b, n_p, n_r))
data["ps"] = np.zeros((n_c, n_b, n_p, n_r))
data["sps"] = np.zeros((n_c, n_b, n_p, n_r))
data["fs"] = np.zeros((n_c, n_b, n_p, n_r))
data["fs-norm-random"] = np.zeros((n_c, n_b, n_p, n_r))
data["fs-norm-density"] = np.zeros((n_c, n_b, n_p, n_r))
data["fce"] = np.zeros((n_c, n_b, n_p, n_r))
data["fce-norm-random"] = np.zeros((n_c, n_b, n_p, n_r))
data["fce-norm-density"] = np.zeros((n_c, n_b, n_p, n_r))
data["precision"] = np.zeros((n_c, n_b, n_p, n_r))
data["recall"] = np.zeros((n_c, n_b, n_p, n_r))
data["auroc"] = np.zeros((n_c, n_b, n_p, n_r))
data["auprc"] = np.zeros((n_c, n_b, n_p, n_r))
arglist = []
for f in os.listdir(data_dir):
arglist.append((f, data_dir, c_dict, b_dict, p_dict, r_dict))
results = Parallel(n_jobs=n_processes)(delayed(get_metrics)(*arg) for arg in arglist)
for i, j, k, l, m in results:
for key, val in m.items():
data[key][i, j, k, l] = val
for key, val in data.items():
if not isinstance(val, list):
data[key] = val.tolist()
datastring = json.dumps(data)
with open("Data/watts-strogatz.json", "w") as output_file:
output_file.write(datastring)