/
synthesis.py
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/
synthesis.py
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import numpy as np
import torch
from scipy import sparse
from scipy.stats import multivariate_normal
from torch_geometric.utils import erdos_renyi_graph, from_scipy_sparse_matrix
def feature_graph():
edge_index = erdos_renyi_graph(2400, 0.01)
torch.save(edge_index, './synthdata/feature/edge_index.pt')
dim = 20
mask_convariance_maxtix = np.diag([1 for _ in range(dim)])
center1 = 2.5 * np.random.random(size=dim) - 1
center2 = 2.5 * np.random.random(size=dim) - 1
center3 = 2.5 * np.random.random(size=dim) - 1
data1 = multivariate_normal.rvs(mean=center1,
cov=mask_convariance_maxtix,
size=800)
data2 = multivariate_normal.rvs(mean=center2,
cov=mask_convariance_maxtix,
size=800)
data3 = multivariate_normal.rvs(mean=center3,
cov=mask_convariance_maxtix,
size=800)
data = np.vstack((data1, data2, data3))
label = np.array([0 for _ in range(800)] + [1 for _ in range(800)] +
[2 for _ in range(800)])
permutation = np.random.permutation(label.shape[0])
data = data[permutation, :]
label = label[permutation]
x, y = torch.from_numpy(data), torch.from_numpy(label)
x, y = x.float(), y.long()
torch.save(x, './synthdata/feature/x.pt')
torch.save(y, './synthdata/feature/y.pt')
def topology_graph():
adj = np.zeros((2400, 2400))
for i in range(800):
for j in range(i + 1, 800):
z = np.random.randint(0, 100, dtype=int)
if z > 96: # 0.03
adj[i, j] = 1
adj[j, i] = 1
for i in range(800, 1600):
for j in range(i + 1, 1600):
z = np.random.randint(0, 100, dtype=int)
if z > 96: # 0.03
adj[i, j] = 1
adj[j, i] = 1
for i in range(1600, 2400):
for j in range(i + 1, 2400):
z = np.random.randint(0, 100, dtype=int)
if z > 96: # 0.03
adj[i, j] = 1
adj[j, i] = 1
for i in range(800):
for j in range(800, 1600):
z = np.random.randint(0, 10000, dtype=int)
if z > 9999: # 0.0001
adj[i, j] = 1
adj[j, i] = 1
for i in range(800):
for j in range(1600, 2400):
z = np.random.randint(0, 10000, dtype=int)
if z > 9999: # 0.00001
adj[i, j] = 1
adj[j, i] = 1
for i in range(800, 1600):
for j in range(1600, 2400):
z = np.random.randint(0, 10000, dtype=int)
if z > 9999: # 0.00001
adj[i, j] = 1
adj[j, i] = 1
arr_sparse = sparse.coo_matrix(adj)
edge_index, _ = from_scipy_sparse_matrix(arr_sparse)
edge_index = edge_index.long()
torch.save(edge_index, './synthdata/topology/edge_index.pt')
dim = 20
mask_convariance_maxtix = np.diag([1 for _ in range(dim)])
center1 = 2.5 * np.random.random(size=dim) - 1
center2 = 2.5 * np.random.random(size=dim) - 1
center3 = 2.5 * np.random.random(size=dim) - 1
data1 = multivariate_normal.rvs(mean=center1,
cov=mask_convariance_maxtix,
size=800)
data2 = multivariate_normal.rvs(mean=center2,
cov=mask_convariance_maxtix,
size=800)
data3 = multivariate_normal.rvs(mean=center3,
cov=mask_convariance_maxtix,
size=800)
data = np.vstack((data1, data2, data3))
label = np.array([0 for _ in range(800)] + [1 for _ in range(800)] +
[2 for _ in range(800)])
x, y = torch.from_numpy(data), torch.from_numpy(label)
x, y = x.float(), y.long()
torch.save(x, './synthdata/topology/x.pt')
torch.save(y, './synthdata/topology/y.pt')
if __name__ == '__main__':
# feature_graph()
topology_graph()