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95 lines
3.2 KiB
95 lines
3.2 KiB
2 years ago
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import os
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import random
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import numpy as np
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import scipy.sparse as sp
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from src import config
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def load_luo_data(dataset):
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dp = np.loadtxt('../../data/RawData/luo/mat_drug_protein.txt'.format(dataset), dtype=int)
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dd = np.loadtxt('../../data/RawData/luo/mat_drug_drug.txt'.format(dataset), dtype=int)
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pp = np.loadtxt('../../data/RawData/luo/mat_protein_protein.txt'.format(dataset), dtype=int)
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adj = np.vstack((np.hstack((dd, dp)), np.hstack((dp.T, pp))))
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return sp.csr_matrix(adj + sp.eye(adj.shape[0])), dd.shape[0]
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def load_yam_data(dataset):
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dp = np.loadtxt('../../data/RawData/Yamanishi/{}_admat_dgc.txt'.format(dataset), dtype=str, delimiter='\t')[1:, 1:].astype(np.int).T
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dd = np.loadtxt('../../data/RawData/Yamanishi/{}_simmat_dc.txt'.format(dataset), dtype=str, delimiter='\t')[1:, 1:].astype(np.float)
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pp = np.loadtxt('../../data/RawData/Yamanishi/{}_simmat_dg.txt'.format(dataset), dtype=str, delimiter='\t')[1:, 1:].astype(np.float)
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dd = np.where(dd < 0.5, 0, 1)
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pp = np.where(pp < 0.5, 0, 1)
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adj = np.vstack((np.hstack((dd, dp)), np.hstack((dp.T, pp))))
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return sp.csr_matrix(adj), dd.shape[0]
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def is_symmetry(adj):
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for i in range(adj.shape[0]):
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for j in range(adj.shape[1]):
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if adj[i][j] != adj[j][i]:
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return False
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return True
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def is_1_diag(adj):
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if sum(np.diagonal(adj)) != adj.shape[0]:
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return False
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return True
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def change_unbalanced(adj, percent, dp_line, dataset):
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"""
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note: percent控制屏蔽掉的节点所占的百分比
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:param adj:
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:param percent:
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:return: 返回去除部分已知关联的邻接矩阵
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"""
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# 判断是否对称
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# assert is_symmetry(adj.A)
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adj = adj - sp.dia_matrix((adj.diagonal()[np.newaxis, :], [0]), shape=adj.shape) + sp.eye(adj.shape[0])
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# 判断对角线是否全为1
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assert is_1_diag(adj.A)
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adj = (sp.triu(adj) + sp.triu(adj).T - sp.eye(adj.shape[0])).A
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row = list(range(0, dp_line))
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col = list(range(dp_line, adj.shape[0]))
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idx = []
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for i in row:
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for j in col:
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if i != j and adj[i][j] == 1:
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idx.append((i, j))
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num = int(np.floor(percent * len(idx)))
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count = 0
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# random.seed(config.seed)
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while count < num:
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row, col = random.choice(idx)
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idx.remove((row, col))
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adj[row][col] = 0
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adj[col][row] = 0
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count += 1
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# idx = []
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# for i in range(adj.shape[0]):
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# for j in range(i + 1, adj.shape[0]):
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# if adj[i][j] == 1:
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# idx.append((i, j))
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# num = int(np.floor(percent * len(idx)))
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# count = 0
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# # random.seed(config.seed)
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# while count < num:
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# row, col = random.choice(idx)
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# idx.remove((row, col))
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# adj[row][col] = 0
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# adj[col][row] = 0
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# count += 1
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# 保存改变不平衡性后新的dp
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new_dp = adj[0:dp_line, dp_line:]
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# if not os.path.exists('../../data/partitioned_data/{0}/feature'.format(dataset)):
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# os.mkdir('../../data/partitioned_data/{0}/feature'.format(dataset))
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# np.savetxt('../../data/partitioned_data/{0}/feature/{0}_new_admat_dgc.txt'.format(dataset), new_dp, fmt='%d', delimiter='\t')
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return sp.csr_matrix(adj.astype(np.int))
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