Source code for deeprobust.graph.utils

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}')