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load_data.py
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load_data.py
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import torch
import numpy as np
import pickle as pkl
import scipy.io as sio
import scipy.sparse as sp
def preprocess_features(features):
"""Row-normalize feature matrix and convert to tuple representation"""
rowsum = np.array(features.sum(1))
r_inv = np.power(rowsum, -1).flatten()
r_inv[np.isinf(r_inv)] = 0.
r_mat_inv = sp.diags(r_inv)
features = r_mat_inv.dot(features)
return features.todense()
def normalize_adj(adj):
"""Symmetrically normalize adjacency matrix."""
adj = sp.coo_matrix(adj)
rowsum = np.array(adj.sum(1))
d_inv_sqrt = np.power(rowsum, -0.5).flatten()
d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.
d_mat_inv_sqrt = sp.diags(d_inv_sqrt)
return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo()
def ACM(dataname='ACM'):
"""load dataset ACM
Returns:
gnd(ndarray): [nnodes,]
"""
if dataname == "ACM":
# Load data
dataset = "./dataset/" + 'ACM3025'
data = sio.loadmat('{}.mat'.format(dataset))
if (dataset == 'large_cora'):
X = data['X']
A = data['G']
gnd = data['labels']
gnd = gnd[0, :]
else:
X = data['feature']
A = data['PAP']
B = data['PLP']
# C = data['PMP']
# D = data['PTP']
if sp.issparse(X):
X = X.todense()
A = np.array(A)
B = np.array(B)
X = np.array(X)
Adj = []
Adj.append(A)
Adj.append(B)
gnd = data['label']
gnd = gnd.T
gnd = np.argmax(gnd, axis=0)
return X, Adj, gnd
def amazon():
data = pkl.load(open("./dataset/amazon.pkl", "rb"))
label = data['label'].argmax(1)
# dense
ivi = torch.from_numpy(data["IVI"]).float()
ibi = torch.from_numpy(data["IBI"]).float()
ioi = torch.from_numpy(data["IOI"]).float()
adj = []
adj.append(ivi)
adj.append(ibi)
adj.append(ioi)
features = torch.from_numpy(data['feature']).float()
return features, adj, label
def DBLP_L():
data = pkl.load(open("./dataset/dblp.pkl", "rb"))
label = data['label'].argmax(1)
pap = np.array(data["PAP"])
ppp = np.array(data["PPrefP"])
A = []
A.append(pap)
A.append(ppp)
features = np.array(data['feature'])
return features, A, label
def aminer(ratio=[20, 40, 60], type_num=[6564, 13329, 35890]):
"""load aminer
Args:
ratio (list, optional): _description_. Defaults to [20, 40, 60].
type_num (list, optional): _description_. Defaults to [6564, 13329, 35890].
Returns:
label(ndarray): [nnodes, ]
"""
path = "./dataset/aminer/"
label = np.load(path + "labels.npy").astype('int32')
pap = sp.load_npz(path + "pap.npz")
prp = sp.load_npz(path + "prp.npz")
pos = sp.load_npz(path + "pos.npz")
adj_pap = pap.todense().astype(int)
adj_prp = prp.todense().astype(int)
adj_pos = pos.todense().astype(int)
adj = []
adj.append(torch.from_numpy(adj_pap))
adj.append(torch.from_numpy(adj_prp))
feat_p = sp.eye(type_num[0])
feat_a = sp.eye(type_num[1])
feat_r = sp.eye(type_num[2])
feat_p = torch.FloatTensor(preprocess_features(feat_p))
feat_a = torch.FloatTensor(preprocess_features(feat_a))
feat_r = torch.FloatTensor(preprocess_features(feat_r))
return feat_p, adj, label
if __name__ == '__main__':
aminer()