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import numpy as np
import torch
import pickle
import time
from itertools import combinations
from scipy.special import comb
from multiprocessing import Pool
def schuffle(W, x):
n = W.shape[0]
idx=np.random.permutation(n)
W_new=W[idx,:][:,idx]
x_new=x[idx]
return W_new , x_new
def block_model_adj(com, p_r, q):
n=len(com)
W=np.zeros((n, n), dtype=np.int64)
for i in range(n):
for j in range(i+1,n):
if com[i] == com[j]:
prob = p_r[com[i]]
else:
prob = q
if np.random.binomial(1, prob) == 1:
W[i,j] = 1
W[j,i] = 1
return W
def block_model(sel_r, clust_size_min, clust_size_max, p_r, q):
com = []
for r in sel_r:
clust_size = np.random.randint(clust_size_min, clust_size_max, size=1)[0]
com.append(np.repeat(r, clust_size, axis=0))
com = np.concatenate(com)
W = block_model_adj(com, p_r, q)
return W, com
def adj_to_edge_list(W):
n = W.shape[0]
e = int(np.sum(W))
edge_index = np.zeros((2, e), dtype=np.int64)
counter = 0
for i in range(n):
for j in range(n):
if W[i,j] == 1:
edge_index[0, counter] = i
edge_index[1, counter] = j
counter += 1
assert counter == e
return edge_index
SMB_ENVIRONMENT_CONFIG = {
'r1': 4,
'r2': 3,
's1': 3,
's2': 2,
'p_r': np.array([0.9, 0.8, 0.7, 0.6, 0.5, 0.4, 0.3]),
'size_min': 20,
'size_max': 40,
'q': 0.1,
'ntypes': 8,
}
DOMAIN_SELECTION_DICT = dict(zip([frozenset(sel_r1) for sel_r1 in combinations(range(SMB_ENVIRONMENT_CONFIG['r1']), SMB_ENVIRONMENT_CONFIG['s1'])], range(comb(SMB_ENVIRONMENT_CONFIG['r1'], SMB_ENVIRONMENT_CONFIG['s1'], exact=True))))
CLASS_SELECTION_DICT = dict(zip([frozenset(sel_r2) for sel_r2 in combinations(range(SMB_ENVIRONMENT_CONFIG['r2']), SMB_ENVIRONMENT_CONFIG['s2'])], range(comb(SMB_ENVIRONMENT_CONFIG['r2'], SMB_ENVIRONMENT_CONFIG['s2'], exact=True))))
def encode_domain_selection(sel_r1):
return DOMAIN_SELECTION_DICT[frozenset(sel_r1)]
def encode_class_selection(sel_r2):
return CLASS_SELECTION_DICT[frozenset(sel_r2)]
class SBMEnvironmentGraph():
def __init__(self, config=SMB_ENVIRONMENT_CONFIG):
# parameters
self.r1 = SMB_ENVIRONMENT_CONFIG['r1']
self.r2 = SMB_ENVIRONMENT_CONFIG['r2']
self.s1 = SMB_ENVIRONMENT_CONFIG['s1']
self.s2 = SMB_ENVIRONMENT_CONFIG['s2']
assert self.s1 > 0 and self.s1 < self.r1
assert self.s2 > 0 and self.s2 < self.r2
self.p_r = SMB_ENVIRONMENT_CONFIG['p_r']
self.p_r1 = self.p_r[0::2]
self.p_r2 = self.p_r[1::2]
assert len(self.p_r1) == self.r1
assert len(self.p_r2) == self.r2
self.size_min = SMB_ENVIRONMENT_CONFIG['size_min']
self.size_max = SMB_ENVIRONMENT_CONFIG['size_max']
self.q = SMB_ENVIRONMENT_CONFIG['q']
self.ntypes = SMB_ENVIRONMENT_CONFIG['ntypes']
# data
self.x = None
self.y = None
self.edge_index = None
self.domain_id = None
# generate
self.generate()
def validate(self):
assert isinstance(self.x, torch.Tensor)
assert isinstance(self.y, torch.Tensor)
assert isinstance(self.edge_index, torch.Tensor)
assert isinstance(self.domain_id, np.ndarray)
assert self.x.ndim == 1
assert self.y.ndim == 1
assert self.edge_index.ndim == 2 and self.edge_index.size(0) == 2
assert self.domain_id.ndim == 1
assert self.x.dtype == torch.int64
assert self.y.dtype == torch.int64
assert self.edge_index.dtype == torch.int64
assert self.domain_id.dtype == np.int64
def get(self):
self.validate()
graph_data = {
'x': self.x,
'y': self.y,
'edge_index': self.edge_index,
}
return graph_data, self.domain_id
def stat(self):
self.validate()
stat = {
'# of nodes': self.x.size(0),
'# of edges': self.edge_index.size(1)//2,
'avg degree': self.edge_index.size(1)/self.x.size(0)/2,
}
return stat
def generate(self):
# select environment (domain defining) communities
sel_r1 = np.random.choice(self.r1, self.s1, replace=False)
sel_r1 = np.sort(sel_r1)
# select the target (class defining) communities
sel_r2 = np.random.choice(self.r2, self.s2, replace=False)
sel_r2 = np.sort(sel_r2)
# combine the sel and edge prob lists
sel_r2 = sel_r2 + self.r1
sel_r = np.concatenate([sel_r1, sel_r2])
p_r = np.concatenate([self.p_r1, self.p_r2])
# generate adj and com list
W, com = block_model(sel_r, self.size_min, self.size_max, self.p_r, self.q)
# conversion
self.x = torch.from_numpy(com).long()
self.y = torch.tensor(sel_r2).long()
self.edge_index = torch.from_numpy(adj_to_edge_list(W)).long()
self.domain_id = np.array(sel_r1, dtype=np.int64)
def generate_func(i):
return SBMEnvironmentGraph()
def generate_sbm_graph_list(n_samples, n_processes=1):
np.random.seed(0)
start_time = time.time()
sbm_graph_list = []
with Pool(processes=n_processes) as pool:
for i, sbmg in enumerate(pool.imap_unordered(generate_func, range(n_samples))):
sbm_graph_list.append(sbmg)
print(f'Progress: {i/n_samples*100:.2f}%', end='\r', flush=True)
print(' '*50, end='\r')
print(f'Total generation time: {time.time() - start_time:.2f} secs.')
return sbm_graph_list
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