import numpy as np
import scipy.sparse as sp
import torch
from sklearn.model_selection import train_test_split
import torch.sparse as ts
import torch.nn.functional as F
import warnings
[docs]def encode_onehot(labels):
"""Convert label to onehot format.
Parameters
----------
labels : numpy.array
node labels
Returns
-------
numpy.array
onehot labels
"""
eye = np.eye(labels.max() + 1)
onehot_mx = eye[labels]
return onehot_mx
[docs]def tensor2onehot(labels):
"""Convert label tensor to label onehot tensor.
Parameters
----------
labels : torch.LongTensor
node labels
Returns
-------
torch.LongTensor
onehot labels tensor
"""
eye = torch.eye(labels.max() + 1)
onehot_mx = eye[labels]
return onehot_mx.to(labels.device)
[docs]def preprocess(adj, features, labels, preprocess_adj=False, preprocess_feature=False, sparse=False, device='cpu'):
"""Convert adj, features, labels from array or sparse matrix to
torch Tensor, and normalize the input data.
Parameters
----------
adj : scipy.sparse.csr_matrix
the adjacency matrix.
features : scipy.sparse.csr_matrix
node features
labels : numpy.array
node labels
preprocess_adj : bool
whether to normalize the adjacency matrix
preprocess_feature : bool
whether to normalize the feature matrix
sparse : bool
whether to return sparse tensor
device : str
'cpu' or 'cuda'
"""
if preprocess_adj:
adj = normalize_adj(adj)
if preprocess_feature:
features = normalize_feature(features)
labels = torch.LongTensor(labels)
if sparse:
adj = sparse_mx_to_torch_sparse_tensor(adj)
features = sparse_mx_to_torch_sparse_tensor(features)
else:
features = torch.FloatTensor(np.array(features.todense()))
adj = torch.FloatTensor(adj.todense())
return adj.to(device), features.to(device), labels.to(device)
[docs]def to_tensor(adj, features, labels=None, device='cpu'):
"""Convert adj, features, labels from array or sparse matrix to
torch Tensor.
Parameters
----------
adj : scipy.sparse.csr_matrix
the adjacency matrix.
features : scipy.sparse.csr_matrix
node features
labels : numpy.array
node labels
device : str
'cpu' or 'cuda'
"""
if sp.issparse(adj):
adj = sparse_mx_to_torch_sparse_tensor(adj)
else:
adj = torch.FloatTensor(adj)
if sp.issparse(features):
features = sparse_mx_to_torch_sparse_tensor(features)
else:
features = torch.FloatTensor(np.array(features))
if labels is None:
return adj.to(device), features.to(device)
else:
labels = torch.LongTensor(labels)
return adj.to(device), features.to(device), labels.to(device)
[docs]def normalize_feature(mx):
"""Row-normalize sparse matrix or dense matrix
Parameters
----------
mx : scipy.sparse.csr_matrix or numpy.array
matrix to be normalized
Returns
-------
scipy.sprase.lil_matrix
normalized matrix
"""
if type(mx) is not sp.lil.lil_matrix:
try:
mx = mx.tolil()
except AttributeError:
pass
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
[docs]def normalize_adj(mx):
"""Normalize sparse adjacency matrix,
A' = (D + I)^-1/2 * ( A + I ) * (D + I)^-1/2
Row-normalize sparse matrix
Parameters
----------
mx : scipy.sparse.csr_matrix
matrix to be normalized
Returns
-------
scipy.sprase.lil_matrix
normalized matrix
"""
# TODO: maybe using coo format would be better?
if type(mx) is not sp.lil.lil_matrix:
mx = mx.tolil()
if mx[0, 0] == 0 :
mx = mx + sp.eye(mx.shape[0])
rowsum = np.array(mx.sum(1))
r_inv = np.power(rowsum, -1/2).flatten()
r_inv[np.isinf(r_inv)] = 0.
r_mat_inv = sp.diags(r_inv)
mx = r_mat_inv.dot(mx)
mx = mx.dot(r_mat_inv)
return mx
[docs]def normalize_sparse_tensor(adj, fill_value=1):
"""Normalize sparse tensor. Need to import torch_scatter
"""
edge_index = adj._indices()
edge_weight = adj._values()
num_nodes= adj.size(0)
edge_index, edge_weight = add_self_loops(
edge_index, edge_weight, fill_value, num_nodes)
row, col = edge_index
from torch_scatter import scatter_add
deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
deg_inv_sqrt = deg.pow(-0.5)
deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
values = deg_inv_sqrt[row] * edge_weight * deg_inv_sqrt[col]
shape = adj.shape
return torch.sparse.FloatTensor(edge_index, values, shape)
def add_self_loops(edge_index, edge_weight=None, fill_value=1, num_nodes=None):
# num_nodes = maybe_num_nodes(edge_index, num_nodes)
loop_index = torch.arange(0, num_nodes, dtype=torch.long,
device=edge_index.device)
loop_index = loop_index.unsqueeze(0).repeat(2, 1)
if edge_weight is not None:
assert edge_weight.numel() == edge_index.size(1)
loop_weight = edge_weight.new_full((num_nodes, ), fill_value)
edge_weight = torch.cat([edge_weight, loop_weight], dim=0)
edge_index = torch.cat([edge_index, loop_index], dim=1)
return edge_index, edge_weight
[docs]def normalize_adj_tensor(adj, sparse=False):
"""Normalize adjacency tensor matrix.
"""
device = torch.device("cuda" if adj.is_cuda else "cpu")
if sparse:
# warnings.warn('If you find the training process is too slow, you can uncomment line 207 in deeprobust/graph/utils.py. Note that you need to install torch_sparse')
# TODO if this is too slow, uncomment the following code,
# but you need to install torch_scatter
# return normalize_sparse_tensor(adj)
adj = to_scipy(adj)
mx = normalize_adj(adj)
return sparse_mx_to_torch_sparse_tensor(mx).to(device)
else:
mx = adj + torch.eye(adj.shape[0]).to(device)
rowsum = mx.sum(1)
r_inv = rowsum.pow(-1/2).flatten()
r_inv[torch.isinf(r_inv)] = 0.
r_mat_inv = torch.diag(r_inv)
mx = r_mat_inv @ mx
mx = mx @ r_mat_inv
return mx
[docs]def degree_normalize_adj(mx):
"""Row-normalize sparse matrix"""
mx = mx.tolil()
if mx[0, 0] == 0 :
mx = mx + sp.eye(mx.shape[0])
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 = mx.dot(r_mat_inv)
mx = r_mat_inv.dot(mx)
return mx
[docs]def degree_normalize_sparse_tensor(adj, fill_value=1):
"""degree_normalize_sparse_tensor.
"""
edge_index = adj._indices()
edge_weight = adj._values()
num_nodes= adj.size(0)
edge_index, edge_weight = add_self_loops(
edge_index, edge_weight, fill_value, num_nodes)
row, col = edge_index
from torch_scatter import scatter_add
deg = scatter_add(edge_weight, row, dim=0, dim_size=num_nodes)
deg_inv_sqrt = deg.pow(-1)
deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
values = deg_inv_sqrt[row] * edge_weight
shape = adj.shape
return torch.sparse.FloatTensor(edge_index, values, shape)
[docs]def degree_normalize_adj_tensor(adj, sparse=True):
"""degree_normalize_adj_tensor.
"""
device = torch.device("cuda" if adj.is_cuda else "cpu")
if sparse:
# return degree_normalize_sparse_tensor(adj)
adj = to_scipy(adj)
mx = degree_normalize_adj(adj)
return sparse_mx_to_torch_sparse_tensor(mx).to(device)
else:
mx = adj + torch.eye(adj.shape[0]).to(device)
rowsum = mx.sum(1)
r_inv = rowsum.pow(-1).flatten()
r_inv[torch.isinf(r_inv)] = 0.
r_mat_inv = torch.diag(r_inv)
mx = r_mat_inv @ mx
return mx
[docs]def accuracy(output, labels):
"""Return accuracy of output compared to labels.
Parameters
----------
output : torch.Tensor
output from model
labels : torch.Tensor or numpy.array
node labels
Returns
-------
float
accuracy
"""
if not hasattr(labels, '__len__'):
labels = [labels]
if type(labels) is not torch.Tensor:
labels = torch.LongTensor(labels)
preds = output.max(1)[1].type_as(labels)
correct = preds.eq(labels).double()
correct = correct.sum()
return correct / len(labels)
def loss_acc(output, labels, targets, avg_loss=True):
if type(labels) is not torch.Tensor:
labels = torch.LongTensor(labels)
preds = output.max(1)[1].type_as(labels)
correct = preds.eq(labels).double()[targets]
loss = F.nll_loss(output[targets], labels[targets], reduction='mean' if avg_loss else 'none')
if avg_loss:
return loss, correct.sum() / len(targets)
return loss, correct
# correct = correct.sum()
# return loss, correct / len(labels)
[docs]def classification_margin(output, true_label):
"""Calculate classification margin for outputs.
`probs_true_label - probs_best_second_class`
Parameters
----------
output: torch.Tensor
output vector (1 dimension)
true_label: int
true label for this node
Returns
-------
list
classification margin for this node
"""
probs = torch.exp(output)
probs_true_label = probs[true_label].clone()
probs[true_label] = 0
probs_best_second_class = probs[probs.argmax()]
return (probs_true_label - probs_best_second_class).item()
[docs]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)
sparserow=torch.LongTensor(sparse_mx.row).unsqueeze(1)
sparsecol=torch.LongTensor(sparse_mx.col).unsqueeze(1)
sparseconcat=torch.cat((sparserow, sparsecol),1)
sparsedata=torch.FloatTensor(sparse_mx.data)
return torch.sparse.FloatTensor(sparseconcat.t(),sparsedata,torch.Size(sparse_mx.shape))
# slower version....
# 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)
[docs]def to_scipy(tensor):
"""Convert a dense/sparse tensor to scipy matrix"""
if is_sparse_tensor(tensor):
values = tensor._values()
indices = tensor._indices()
return sp.csr_matrix((values.cpu().numpy(), indices.cpu().numpy()), shape=tensor.shape)
else:
indices = tensor.nonzero().t()
values = tensor[indices[0], indices[1]]
return sp.csr_matrix((values.cpu().numpy(), indices.cpu().numpy()), shape=tensor.shape)
[docs]def is_sparse_tensor(tensor):
"""Check if a tensor is sparse tensor.
Parameters
----------
tensor : torch.Tensor
given tensor
Returns
-------
bool
whether a tensor is sparse tensor
"""
# if hasattr(tensor, 'nnz'):
if tensor.layout == torch.sparse_coo:
return True
else:
return False
[docs]def get_train_val_test(nnodes, val_size=0.1, test_size=0.8, stratify=None, seed=None):
"""This setting follows nettack/mettack, where we split the nodes
into 10% training, 10% validation and 80% testing data
Parameters
----------
nnodes : int
number of nodes in total
val_size : float
size of validation set
test_size : float
size of test set
stratify :
data is expected to split in a stratified fashion. So stratify should be labels.
seed : int or None
random seed
Returns
-------
idx_train :
node training indices
idx_val :
node validation indices
idx_test :
node test indices
"""
assert stratify is not None, 'stratify cannot be None!'
if seed is not None:
np.random.seed(seed)
idx = np.arange(nnodes)
train_size = 1 - val_size - test_size
idx_train_and_val, idx_test = train_test_split(idx,
random_state=None,
train_size=train_size + val_size,
test_size=test_size,
stratify=stratify)
if stratify is not None:
stratify = stratify[idx_train_and_val]
idx_train, idx_val = train_test_split(idx_train_and_val,
random_state=None,
train_size=(train_size / (train_size + val_size)),
test_size=(val_size / (train_size + val_size)),
stratify=stratify)
return idx_train, idx_val, idx_test
[docs]def get_train_test(nnodes, test_size=0.8, stratify=None, seed=None):
"""This function returns training and test set without validation.
It can be used for settings of different label rates.
Parameters
----------
nnodes : int
number of nodes in total
test_size : float
size of test set
stratify :
data is expected to split in a stratified fashion. So stratify should be labels.
seed : int or None
random seed
Returns
-------
idx_train :
node training indices
idx_test :
node test indices
"""
assert stratify is not None, 'stratify cannot be None!'
if seed is not None:
np.random.seed(seed)
idx = np.arange(nnodes)
train_size = 1 - test_size
idx_train, idx_test = train_test_split(idx, random_state=None,
train_size=train_size,
test_size=test_size,
stratify=stratify)
return idx_train, idx_test
[docs]def get_train_val_test_gcn(labels, seed=None):
"""This setting follows gcn, where we randomly sample 20 instances for each class
as training data, 500 instances as validation data, 1000 instances as test data.
Note here we are not using fixed splits. When random seed changes, the splits
will also change.
Parameters
----------
labels : numpy.array
node labels
seed : int or None
random seed
Returns
-------
idx_train :
node training indices
idx_val :
node validation indices
idx_test :
node test indices
"""
if seed is not None:
np.random.seed(seed)
idx = np.arange(len(labels))
nclass = labels.max() + 1
idx_train = []
idx_unlabeled = []
for i in range(nclass):
labels_i = idx[labels==i]
labels_i = np.random.permutation(labels_i)
idx_train = np.hstack((idx_train, labels_i[: 20])).astype(np.int)
idx_unlabeled = np.hstack((idx_unlabeled, labels_i[20: ])).astype(np.int)
idx_unlabeled = np.random.permutation(idx_unlabeled)
idx_val = idx_unlabeled[: 500]
idx_test = idx_unlabeled[500: 1500]
return idx_train, idx_val, idx_test
[docs]def get_train_test_labelrate(labels, label_rate):
"""Get train test according to given label rate.
"""
nclass = labels.max() + 1
train_size = int(round(len(labels) * label_rate / nclass))
print("=== train_size = %s ===" % train_size)
idx_train, idx_val, idx_test = get_splits_each_class(labels, train_size=train_size)
return idx_train, idx_test
[docs]def get_splits_each_class(labels, train_size):
"""We randomly sample n instances for class, where n = train_size.
"""
idx = np.arange(len(labels))
nclass = labels.max() + 1
idx_train = []
idx_val = []
idx_test = []
for i in range(nclass):
labels_i = idx[labels==i]
labels_i = np.random.permutation(labels_i)
idx_train = np.hstack((idx_train, labels_i[: train_size])).astype(np.int)
idx_val = np.hstack((idx_val, labels_i[train_size: 2*train_size])).astype(np.int)
idx_test = np.hstack((idx_test, labels_i[2*train_size: ])).astype(np.int)
return np.random.permutation(idx_train), np.random.permutation(idx_val), \
np.random.permutation(idx_test)
def unravel_index(index, array_shape):
rows = index // array_shape[1]
cols = index % array_shape[1]
return rows, cols
def get_degree_squence(adj):
try:
return adj.sum(0)
except:
return ts.sum(adj, dim=1).to_dense()
[docs]def likelihood_ratio_filter(node_pairs, modified_adjacency, original_adjacency, d_min, threshold=0.004):
"""
Filter the input node pairs based on the likelihood ratio test proposed by Zügner et al. 2018, see
https://dl.acm.org/citation.cfm?id=3220078. In essence, for each node pair return 1 if adding/removing the edge
between the two nodes does not violate the unnoticeability constraint, and return 0 otherwise. Assumes unweighted
and undirected graphs.
"""
N = int(modified_adjacency.shape[0])
# original_degree_sequence = get_degree_squence(original_adjacency)
# current_degree_sequence = get_degree_squence(modified_adjacency)
original_degree_sequence = original_adjacency.sum(0)
current_degree_sequence = modified_adjacency.sum(0)
concat_degree_sequence = torch.cat((current_degree_sequence, original_degree_sequence))
# Compute the log likelihood values of the original, modified, and combined degree sequences.
ll_orig, alpha_orig, n_orig, sum_log_degrees_original = degree_sequence_log_likelihood(original_degree_sequence, d_min)
ll_current, alpha_current, n_current, sum_log_degrees_current = degree_sequence_log_likelihood(current_degree_sequence, d_min)
ll_comb, alpha_comb, n_comb, sum_log_degrees_combined = degree_sequence_log_likelihood(concat_degree_sequence, d_min)
# Compute the log likelihood ratio
current_ratio = -2 * ll_comb + 2 * (ll_orig + ll_current)
# Compute new log likelihood values that would arise if we add/remove the edges corresponding to each node pair.
new_lls, new_alphas, new_ns, new_sum_log_degrees = updated_log_likelihood_for_edge_changes(node_pairs,
modified_adjacency, d_min)
# Combination of the original degree distribution with the distributions corresponding to each node pair.
n_combined = n_orig + new_ns
new_sum_log_degrees_combined = sum_log_degrees_original + new_sum_log_degrees
alpha_combined = compute_alpha(n_combined, new_sum_log_degrees_combined, d_min)
new_ll_combined = compute_log_likelihood(n_combined, alpha_combined, new_sum_log_degrees_combined, d_min)
new_ratios = -2 * new_ll_combined + 2 * (new_lls + ll_orig)
# Allowed edges are only those for which the resulting likelihood ratio measure is < than the threshold
allowed_edges = new_ratios < threshold
if allowed_edges.is_cuda:
filtered_edges = node_pairs[allowed_edges.cpu().numpy().astype(np.bool)]
else:
filtered_edges = node_pairs[allowed_edges.numpy().astype(np.bool)]
allowed_mask = torch.zeros(modified_adjacency.shape)
allowed_mask[filtered_edges.T] = 1
allowed_mask += allowed_mask.t()
return allowed_mask, current_ratio
[docs]def degree_sequence_log_likelihood(degree_sequence, d_min):
"""
Compute the (maximum) log likelihood of the Powerlaw distribution fit on a degree distribution.
"""
# Determine which degrees are to be considered, i.e. >= d_min.
D_G = degree_sequence[(degree_sequence >= d_min.item())]
try:
sum_log_degrees = torch.log(D_G).sum()
except:
sum_log_degrees = np.log(D_G).sum()
n = len(D_G)
alpha = compute_alpha(n, sum_log_degrees, d_min)
ll = compute_log_likelihood(n, alpha, sum_log_degrees, d_min)
return ll, alpha, n, sum_log_degrees
[docs]def updated_log_likelihood_for_edge_changes(node_pairs, adjacency_matrix, d_min):
""" Adopted from https://github.com/danielzuegner/nettack
"""
# For each node pair find out whether there is an edge or not in the input adjacency matrix.
edge_entries_before = adjacency_matrix[node_pairs.T]
degree_sequence = adjacency_matrix.sum(1)
D_G = degree_sequence[degree_sequence >= d_min.item()]
sum_log_degrees = torch.log(D_G).sum()
n = len(D_G)
deltas = -2 * edge_entries_before + 1
d_edges_before = degree_sequence[node_pairs]
d_edges_after = degree_sequence[node_pairs] + deltas[:, None]
# Sum the log of the degrees after the potential changes which are >= d_min
sum_log_degrees_after, new_n = update_sum_log_degrees(sum_log_degrees, n, d_edges_before, d_edges_after, d_min)
# Updated estimates of the Powerlaw exponents
new_alpha = compute_alpha(new_n, sum_log_degrees_after, d_min)
# Updated log likelihood values for the Powerlaw distributions
new_ll = compute_log_likelihood(new_n, new_alpha, sum_log_degrees_after, d_min)
return new_ll, new_alpha, new_n, sum_log_degrees_after
def update_sum_log_degrees(sum_log_degrees_before, n_old, d_old, d_new, d_min):
# Find out whether the degrees before and after the change are above the threshold d_min.
old_in_range = d_old >= d_min
new_in_range = d_new >= d_min
d_old_in_range = d_old * old_in_range.float()
d_new_in_range = d_new * new_in_range.float()
# Update the sum by subtracting the old values and then adding the updated logs of the degrees.
sum_log_degrees_after = sum_log_degrees_before - (torch.log(torch.clamp(d_old_in_range, min=1))).sum(1) \
+ (torch.log(torch.clamp(d_new_in_range, min=1))).sum(1)
# Update the number of degrees >= d_min
new_n = n_old - (old_in_range!=0).sum(1) + (new_in_range!=0).sum(1)
new_n = new_n.float()
return sum_log_degrees_after, new_n
def compute_alpha(n, sum_log_degrees, d_min):
try:
alpha = 1 + n / (sum_log_degrees - n * torch.log(d_min - 0.5))
except:
alpha = 1 + n / (sum_log_degrees - n * np.log(d_min - 0.5))
return alpha
def compute_log_likelihood(n, alpha, sum_log_degrees, d_min):
# Log likelihood under alpha
try:
ll = n * torch.log(alpha) + n * alpha * torch.log(d_min) + (alpha + 1) * sum_log_degrees
except:
ll = n * np.log(alpha) + n * alpha * np.log(d_min) + (alpha + 1) * sum_log_degrees
return ll
[docs]def ravel_multiple_indices(ixs, shape, reverse=False):
"""
"Flattens" multiple 2D input indices into indices on the flattened matrix, similar to np.ravel_multi_index.
Does the same as ravel_index but for multiple indices at once.
Parameters
----------
ixs: array of ints shape (n, 2)
The array of n indices that will be flattened.
shape: list or tuple of ints of length 2
The shape of the corresponding matrix.
Returns
-------
array of n ints between 0 and shape[0]*shape[1]-1
The indices on the flattened matrix corresponding to the 2D input indices.
"""
if reverse:
return ixs[:, 1] * shape[1] + ixs[:, 0]
return ixs[:, 0] * shape[1] + ixs[:, 1]
[docs]def visualize(your_var):
"""visualize computation graph"""
from graphviz import Digraph
import torch
from torch.autograd import Variable
from torchviz import make_dot
make_dot(your_var).view()
def reshape_mx(mx, shape):
indices = mx.nonzero()
return sp.csr_matrix((mx.data, (indices[0], indices[1])), shape=shape)
# def check_path(file_path):
# if not osp.exists(file_path):
# os.system(f'mkdir -p {file_path}')