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utils.py
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utils.py
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import math
import sys
import numpy as np
import scipy.sparse as sp
import torch
import _pickle as pkl
import networkx as nx
import scipy.io as sio
def bin_kl_div(target, input, eps=1e-10):
# if sum(q < 0) or sum(q > 1) or p < 0 or p > 1:
# assert "bin_kl_div: illegal input probability!"
input_ = 1-input
target_ = 1-target
kl = target*(torch.log(target+eps) - torch.log(input+eps)) + target_*(torch.log(target_+eps) - torch.log(input_+eps))
return kl.mean()
def get_sharp_common_z(zs, w, temp=0.5):
sum = 0.
for i in range(len(zs)):
sum = sum + w[i] * zs[i]
avg_z = sum / len(zs)
sharp_z = (torch.pow(avg_z, 1. / temp) / torch.sum(torch.pow(avg_z, 1. / temp), dim=1, keepdim=True)).detach()
return sharp_z
def elbo_kl_loss(q, belief, target_prob,eps=1e-10):
neg_ent = -q*torch.log(q+eps) - (1-q)*torch.log(1-q+eps)
const_prob = math.log(sum(belief)) - torch.log(sum(target_prob)+eps)
kl_loss = neg_ent+const_prob
kl_loss = kl_loss.mean()
return kl_loss
def normalize_spadj(mx):
"""Row-normalize sparse matrix"""
rowsum = np.array(mx.sum(1))
r_inv_sqrt = np.power(rowsum, -0.5).flatten()
r_inv_sqrt[np.isinf(r_inv_sqrt)] = 0.
r_mat_inv_sqrt = sp.diags(r_inv_sqrt)
return mx.dot(r_mat_inv_sqrt).transpose().dot(r_mat_inv_sqrt)
def normalize_weight(weights, p=1 / 2, eps=1e-12):
'''
:param weights: a list [w1, w2, w3]
:param p: default=1
:param eps:
:return:
'''
ws = np.array(weights)
ws = np.power(ws, p) # label soft
ws = ws / ws.max()
# r = max(np.power(np.power(ws, p).sum(), 1/p), eps)
# ws = ws / r
return ws
def normalize_spfeatures(mx):
"""Row-normalize sparse matrix"""
rowsum = np.array(mx.sum(1))
r_inv = np.power(rowsum, -1).flatten()
r_inv[np.isinf(r_inv)] = 0.
r_mat_inv = sp.diags(r_inv)
mx = r_mat_inv.dot(mx)
return mx
def normalize_features(x):
rowsum = np.array(x.sum(1))
r_inv = np.power(rowsum, -1).flatten()
r_inv[np.isinf(r_inv)] = 0.
r_inv = r_inv.reshape((x.shape[0], -1))
x = x * r_inv
return x
def normalize_adj(x):
# rowsum = np.array(x.sum(1))
# colsum = np.array(x.sum(0))
# r_inv = np.power(rowsum, -0.5).flatten()
# r_inv[np.isinf(r_inv)] = 0.
# c_inv = np.power(colsum, -0.5).flatten()
# c_inv[np.isinf(c_inv)] = 0.
# r_inv = r_inv.reshape((x.shape[0], -1))
# c_inv = c_inv.reshape((-1, x.shape[1]))
# x = x * r_inv * c_inv
rowsum = np.array(x.sum(1))
r_inv = np.power(rowsum, -1.).flatten()
r_inv[np.isinf(r_inv)] = 0.
r_inv = r_inv.reshape((x.shape[0], -1))
x = x * r_inv
return x
def parse_index_file(filename):
"""Parse index file."""
index = []
for line in open(filename):
index.append(int(line.strip()))
return index
def load_planetoid(dataset, path):
path = path + dataset
print('data loading.....')
names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph']
objects = []
for i in range(len(names)):
with open("{}/ind.{}.{}".format(path, dataset, names[i]), 'rb') as f:
if sys.version_info > (3, 0):
objects.append(pkl.load(f, encoding='latin1'))
else:
objects.append(pkl.load(f))
x, y, tx, ty, allx, ally, graph = tuple(objects)
test_idx_reorder = parse_index_file("{}/ind.{}.test.index".format(path, dataset))
test_idx_range = np.sort(test_idx_reorder)
if dataset == 'citeseer':
# Fix citeseer dataset (there are some isolated nodes in the graph)
# Find isolated nodes, add them as zero-vecs into the right position
test_idx_range_full = range(min(test_idx_reorder), max(test_idx_reorder) + 1)
tx_extended = sp.lil_matrix((len(test_idx_range_full), x.shape[1]))
tx_extended[test_idx_range - min(test_idx_range), :] = tx
tx = tx_extended
ty_extended = np.zeros((len(test_idx_range_full), y.shape[1]))
ty_extended[test_idx_range - min(test_idx_range), :] = ty
ty = ty_extended
features_unorm = sp.vstack((allx, tx)).tolil()
features_unorm[test_idx_reorder, :] = features_unorm[test_idx_range, :]
features = normalize_spfeatures(features_unorm)
adj_noeye = nx.adjacency_matrix(nx.from_dict_of_lists(graph))
adj_temp = adj_noeye
# norm
adj = adj_noeye + adj_noeye.T.multiply(adj_noeye.T > adj_noeye) - adj_noeye.multiply(adj_noeye.T > adj_noeye)
adj = adj + sp.eye(adj.shape[0])
adj = normalize_spadj(adj)
# D1 = np.array(adj.sum(axis=1)) ** (-0.5)
# D2 = np.array(adj.sum(axis=0)) ** (-0.5)
# D1 = sp.diags(D1[:, 0], format='csr')
# D2 = sp.diags(D2[0, :], format='csr')
# adj = adj.dot(D1)
# adj = D2.dot(adj)
labels = np.vstack((ally, ty))
labels[test_idx_reorder, :] = labels[test_idx_range, :]
adj = torch.FloatTensor(np.array(adj.todense()))
# adj = sparse_mx_to_torch_sparse_tensor(adj)
features = torch.FloatTensor(np.array(features.todense()))
labels = torch.LongTensor(np.argmax(labels, -1))
adj_labels = torch.FloatTensor(np.array(adj_noeye.todense()) != 0)
feature_labels = torch.FloatTensor(np.array(features_unorm.todense()))
print('complete data loading')
return labels, adj, features, adj_labels, feature_labels
def load_multi(dataset, root):
# load the data: x, tx, allx, graph
if dataset == 'acm':
path = root + 'ACM3025.mat'
elif dataset == 'dblp':
path = root + 'DBLP4057.mat'
elif dataset == 'imdb':
path = root + 'imdb5k.mat'
data = sio.loadmat(path)
# print(dataset)
# print(data)
# rownetworks = np.array([(data['PLP'] - np.eye(N)).tolist()]) #, (data['PLP'] - np.eye(N)).tolist() , (data['PTP'] - np.eye(N)).tolist()])
if dataset == "acm":
truelabels, truefeatures = data['label'], data['feature'].astype(float)
N = truefeatures.shape[0] # nodes num
rownetworks = np.array([(data['PAP']).tolist(), (data['PLP']).tolist()])
elif dataset == "dblp":
truelabels, truefeatures = data['label'], data['features'].astype(float)
N = truefeatures.shape[0]
rownetworks = np.array([(data['net_APA']).tolist(), (data['net_APCPA']).tolist(), (data['net_APTPA']).tolist()])
rownetworks[2] += np.eye(rownetworks[2].shape[0])
# rownetworks = rownetworks[:2]
elif dataset == 'imdb':
truelabels, truefeatures = data['label'], data['feature'].astype(float)
N = truefeatures.shape[0]
rownetworks = np.array([(data['MAM']).tolist(), (data['MDM']).tolist(), (data['MYM']).tolist()])
# rownetworks = rownetworks[:2]
numView = rownetworks.shape[0]
adjs_labels = []
adjs = []
feature_labels = torch.FloatTensor(np.array(truefeatures)).contiguous()
features = torch.FloatTensor(normalize_features(truefeatures)).contiguous()
for i in range(numView):
adjs_labels.append(torch.FloatTensor(np.array(rownetworks[i])))
adjs.append(torch.FloatTensor(normalize_adj(np.array(rownetworks[i]))))
labels = torch.LongTensor(np.argmax(truelabels, -1)).contiguous()
return labels, adjs, features, adjs_labels, feature_labels, numView
def eliminate_self_loops1(A):
"""Remove self-loops from the adjacency matrix."""
A = A.tolil()
A.setdiag(0)
A = A.tocsr()
A.eliminate_zeros()
return A
def eliminate_self_loops(G):
G.adj_matrix = eliminate_self_loops1(G.adj_matrix)
return G
def create_subgraph(sparse_graph, _sentinel=None, nodes_to_remove=None, nodes_to_keep=None):
"""Create a graph with the specified subset of nodes.
Exactly one of (nodes_to_remove, nodes_to_keep) should be provided, while the other stays None.
Note that to avoid confusion, it is required to pass node indices as named arguments to this function.
Parameters
----------
sparse_graph : SparseGraph
Input graph.
_sentinel : None
Internal, to prevent passing positional arguments. Do not use.
nodes_to_remove : array-like of int
Indices of nodes that have to removed.
nodes_to_keep : array-like of int
Indices of nodes that have to be kept.
Returns
-------
sparse_graph : SparseGraph
Graph with specified nodes removed.
"""
# Check that arguments are passed correctly
if _sentinel is not None:
raise ValueError("Only call `create_subgraph` with named arguments',"
" (nodes_to_remove=...) or (nodes_to_keep=...)")
if nodes_to_remove is None and nodes_to_keep is None:
raise ValueError("Either nodes_to_remove or nodes_to_keep must be provided.")
elif nodes_to_remove is not None and nodes_to_keep is not None:
raise ValueError("Only one of nodes_to_remove or nodes_to_keep must be provided.")
elif nodes_to_remove is not None:
nodes_to_keep = [i for i in range(sparse_graph.num_nodes()) if i not in nodes_to_remove]
elif nodes_to_keep is not None:
nodes_to_keep = sorted(nodes_to_keep)
else:
raise RuntimeError("This should never happen.")
sparse_graph.adj_matrix = sparse_graph.adj_matrix[nodes_to_keep][:, nodes_to_keep]
if sparse_graph.attr_matrix is not None:
sparse_graph.attr_matrix = sparse_graph.attr_matrix[nodes_to_keep]
if sparse_graph.labels is not None:
sparse_graph.labels = sparse_graph.labels[nodes_to_keep]
if sparse_graph.node_names is not None:
sparse_graph.node_names = sparse_graph.node_names[nodes_to_keep]
return sparse_graph
def largest_connected_components(sparse_graph, n_components=1):
"""Select the largest connected components in the graph.
Parameters
----------
sparse_graph : SparseGraph
Input graph.
n_components : int, default 1
Number of largest connected components to keep.
Returns
-------
sparse_graph : SparseGraph
Subgraph of the input graph where only the nodes in largest n_components are kept.
"""
_, component_indices = sp.csgraph.connected_components(sparse_graph.adj_matrix)
component_sizes = np.bincount(component_indices)
components_to_keep = np.argsort(component_sizes)[::-1][:n_components] # reverse order to sort descending
nodes_to_keep = [
idx for (idx, component) in enumerate(component_indices) if component in components_to_keep
]
return create_subgraph(sparse_graph, nodes_to_keep=nodes_to_keep)
class SparseGraph:
"""Attributed labeled graph stored in sparse matrix form.
"""
def __init__(self, adj_matrix, attr_matrix=None, labels=None,
node_names=None, attr_names=None, class_names=None, metadata=None):
"""Create an attributed graph.
Parameters
----------
adj_matrix : sp.csr_matrix, shape [num_nodes, num_nodes]
Adjacency matrix in CSR format.
attr_matrix : sp.csr_matrix or np.ndarray, shape [num_nodes, num_attr], optional
Attribute matrix in CSR or numpy format.
labels : np.ndarray, shape [num_nodes], optional
Array, where each entry represents respective node's label(s).
node_names : np.ndarray, shape [num_nodes], optional
Names of nodes (as strings).
attr_names : np.ndarray, shape [num_attr]
Names of the attributes (as strings).
class_names : np.ndarray, shape [num_classes], optional
Names of the class labels (as strings).
metadata : object
Additional metadata such as text.
"""
# Make sure that the dimensions of matrices / arrays all agree
if sp.isspmatrix(adj_matrix):
adj_matrix = adj_matrix.tocsr().astype(np.float32)
else:
raise ValueError("Adjacency matrix must be in sparse format (got {0} instead)"
.format(type(adj_matrix)))
if adj_matrix.shape[0] != adj_matrix.shape[1]:
raise ValueError("Dimensions of the adjacency matrix don't agree")
if attr_matrix is not None:
if sp.isspmatrix(attr_matrix):
attr_matrix = attr_matrix.tocsr().astype(np.float32)
elif isinstance(attr_matrix, np.ndarray):
attr_matrix = attr_matrix.astype(np.float32)
else:
raise ValueError("Attribute matrix must be a sp.spmatrix or a np.ndarray (got {0} instead)"
.format(type(attr_matrix)))
if attr_matrix.shape[0] != adj_matrix.shape[0]:
raise ValueError("Dimensions of the adjacency and attribute matrices don't agree")
if labels is not None:
if labels.shape[0] != adj_matrix.shape[0]:
raise ValueError("Dimensions of the adjacency matrix and the label vector don't agree")
if node_names is not None:
if len(node_names) != adj_matrix.shape[0]:
raise ValueError("Dimensions of the adjacency matrix and the node names don't agree")
if attr_names is not None:
if len(attr_names) != attr_matrix.shape[1]:
raise ValueError("Dimensions of the attribute matrix and the attribute names don't agree")
self.adj_matrix = adj_matrix
self.attr_matrix = attr_matrix
self.labels = labels
self.node_names = node_names
self.attr_names = attr_names
self.class_names = class_names
self.metadata = metadata
def num_nodes(self):
"""Get the number of nodes in the graph."""
return self.adj_matrix.shape[0]
def num_edges(self):
"""Get the number of edges in the graph.
For undirected graphs, (i, j) and (j, i) are counted as single edge.
"""
if self.is_directed():
return int(self.adj_matrix.nnz)
else:
return int(self.adj_matrix.nnz / 2)
def get_neighbors(self, idx):
"""Get the indices of neighbors of a given node.
Parameters
----------
idx : int
Index of the node whose neighbors are of interest.
"""
return self.adj_matrix[idx].indices
def is_directed(self):
"""Check if the graph is directed (adjacency matrix is not symmetric)."""
return (self.adj_matrix != self.adj_matrix.T).sum() != 0
def to_undirected(self):
"""Convert to an undirected graph (make adjacency matrix symmetric)."""
if self.is_weighted():
raise ValueError("Convert to unweighted graph first.")
else:
self.adj_matrix = self.adj_matrix + self.adj_matrix.T
self.adj_matrix[self.adj_matrix != 0] = 1
return self
def is_weighted(self):
"""Check if the graph is weighted (edge weights other than 1)."""
return np.any(np.unique(self.adj_matrix[self.adj_matrix != 0].A1) != 1)
def to_unweighted(self):
"""Convert to an unweighted graph (set all edge weights to 1)."""
self.adj_matrix.data = np.ones_like(self.adj_matrix.data)
return self
# Quality of life (shortcuts)
def standardize(self):
"""Select the LCC of the unweighted/undirected/no-self-loop graph.
All changes are done inplace.
"""
G = self.to_unweighted().to_undirected()
G = eliminate_self_loops(G)
G = largest_connected_components(G, 1)
return G
def unpack(self):
"""Return the (A, X, z) triplet."""
return self.adj_matrix, self.attr_matrix, self.labels
def load_npz_to_sparse_graph(file_name):
"""Load a SparseGraph from a Numpy binary file.
Parameters
----------
file_name : str
Name of the file to load.
Returns
-------
sparse_graph : SparseGraph
Graph in sparse matrix format.
"""
with np.load(file_name) as loader:
loader = dict(loader)
adj_matrix = sp.csr_matrix((loader['adj_data'], loader['adj_indices'], loader['adj_indptr']),
shape=loader['adj_shape'])
if 'attr_data' in loader:
# Attributes are stored as a sparse CSR matrix
attr_matrix = sp.csr_matrix((loader['attr_data'], loader['attr_indices'], loader['attr_indptr']),
shape=loader['attr_shape'])
elif 'attr_matrix' in loader:
# Attributes are stored as a (dense) np.ndarray
attr_matrix = loader['attr_matrix']
else:
attr_matrix = None
if 'labels_data' in loader:
# Labels are stored as a CSR matrix
labels = sp.csr_matrix((loader['labels_data'], loader['labels_indices'], loader['labels_indptr']),
shape=loader['labels_shape'])
elif 'labels' in loader:
# Labels are stored as a numpy array
labels = loader['labels']
else:
labels = None
node_names = loader.get('node_names')
attr_names = loader.get('attr_names')
class_names = loader.get('class_names')
metadata = loader.get('metadata')
return SparseGraph(adj_matrix, attr_matrix, labels, node_names, attr_names, class_names, metadata)
def mine_Amazon(dataset, root):
X = []
if dataset == 'amazon_photos':
path = root + 'amazon_electronics_photo.npz'
elif dataset == 'amazon_computers':
path = root + 'amazon_electronics_computers.npz'
Amazon = load_npz_to_sparse_graph(path)
Adj = sp.csr_matrix(Amazon.standardize().adj_matrix).A
Attr = sp.csr_matrix(Amazon.standardize().attr_matrix).A
Gnd = sp.csr_matrix(Amazon.standardize().labels).A
Gnd = Gnd.T.squeeze()
Attr = np.array(Attr)
X.append(Attr)
feature2 = Attr.dot(Attr.T)
# feature2 = (feature2 - feature2.min(axis=0)) / (feature2.max(axis=0) - feature2.min(axis=0))
# feature2 = np.where(feature2 > 0.5, 1., 0.)
X.append(feature2)
X.append(np.array(Adj))
return X, Gnd
def load_data(dataset, path):
"""Load data."""
if dataset in ['cora', 'citeseer', 'pubmed']:
labels, adjs, feature, adjs_labels, feature_label = load_planetoid(dataset, path)
# feature1_label = torch.matmul(feature_label, feature_label.t())
# feature1 = normalize_features(feature1_label)
features = [feature, feature]
feature_labels = [feature_label, feature_label]
# shared_feature = torch.cat(features, dim=-1)
# shared_feature_label = torch.cat(feature_labels, dim=1)
shared_feature = feature
shared_feature_label = feature_label
adjs = [adjs, adjs.clone()]
adjs_labels = [adjs_labels, adjs_labels.clone()]
num_graph = len(adjs)
elif dataset in ['acm', 'dblp', 'imdb']:
labels, adjs, feature, adjs_labels, feature_label, num_graph = load_multi(dataset, path)
features = []
feature_labels = []
for _ in range(num_graph):
features.append(feature)
feature_labels.append(feature_label)
shared_feature = feature
shared_feature_label = feature_label
elif dataset in ['amazon_photos', 'amazon_computers']:
X, Gnd = mine_Amazon(dataset, path)
num_graph = len(X) - 1
labels = torch.LongTensor(np.asarray(Gnd)).contiguous()
adjs_label = torch.FloatTensor(np.array(X[-1])).contiguous()
adj = torch.FloatTensor(normalize_adj(np.array(X[-1]))).contiguous()
features = []
feature_labels = []
adjs = []
adjs_labels = []
for i in range(num_graph):
feature_label = torch.FloatTensor(np.array(X[i])).contiguous().bool().float()
feature = torch.FloatTensor(normalize_features(np.array(X[i]))).contiguous()
feature_labels.append(feature_label)
features.append(feature)
adjs.append(adj)
adjs_labels.append(adjs_label)
shared_feature = torch.cat(features, dim=1)
shared_feature_label = torch.cat(feature_labels, dim=1)
## refer to SDSNE
# elif dataset in ['bbcsport_2view', '3sources']:
# from utils_SDSNE import load_data as load_data_sdsne
# labels, adjs, features, adjs_labels, feature_labels, shared_feature, shared_feature_label, num_graph\
# = load_data_sdsne(dataFile=path + dataset + '.mat', k=14, sigma=0.5)
else:
assert 'Dataset is not exist.'
return labels, adjs, features, adjs_labels, feature_labels, shared_feature, shared_feature_label, num_graph
def load_graph(dataset, k):
if k:
path = 'graph/{}{}_graph.txt'.format(dataset, k)
else:
path = 'graph/{}_graph.txt'.format(dataset)
data = np.loadtxt('data/{}.txt'.format(dataset))
n, _ = data.shape
idx = np.array([i for i in range(n)], dtype=np.int32)
idx_map = {j: i for i, j in enumerate(idx)}
edges_unordered = np.genfromtxt(path, dtype=np.int32)
edges = np.array(list(map(idx_map.get, edges_unordered.flatten())),
dtype=np.int32).reshape(edges_unordered.shape)
adj = sp.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),
shape=(n, n), dtype=np.float32)
# build symmetric adjacency matrix
adj_noeye = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)
adj = adj_noeye + sp.eye(adj_noeye.shape[0])
adj = normalize(adj)
adj = sparse_mx_to_torch_sparse_tensor(adj)
adj_label = sparse_mx_to_torch_sparse_tensor(adj_noeye)
return adj, adj_label
def normalize(mx):
"""Row-normalize sparse matrix"""
rowsum = np.array(mx.sum(1))
r_inv = np.power(rowsum, -1).flatten()
r_inv[np.isinf(r_inv)] = 0.
r_mat_inv = sp.diags(r_inv)
mx = r_mat_inv.dot(mx)
return mx
def sparse_mx_to_torch_sparse_tensor(sparse_mx):
"""Convert a scipy sparse matrix to a torch sparse tensor."""
sparse_mx = sparse_mx.tocoo().astype(np.float32)
indices = torch.from_numpy(
np.vstack((sparse_mx.row, sparse_mx.col)).astype(np.int64))
values = torch.from_numpy(sparse_mx.data)
shape = torch.Size(sparse_mx.shape)
return torch.sparse.FloatTensor(indices, values, shape)