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utils.py
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from __future__ import print_function
import scipy.sparse as sp
import numpy as np
from scipy.sparse.linalg.eigen.arpack import eigsh, ArpackNoConvergence
def encode_onehot(labels):
classes = set(labels)
classes_dict = {c: np.identity(len(classes))[i, :] for i, c in enumerate(classes)}
labels_onehot = np.array(list(map(classes_dict.get, labels)), dtype=np.int32)
return labels_onehot
def load_cora_data(path="cora_data/", dataset="cora"):
"""Load citation network dataset"""
print('Loading {} dataset...'.format(dataset))
idx_features_labels = np.genfromtxt("{}{}.content".format(path, dataset), dtype=np.dtype(str))
features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32)
labels = encode_onehot(idx_features_labels[:, -1])
# build graph
idx = np.array(idx_features_labels[:, 0], dtype=np.int32)
idx_map = {j: i for i, j in enumerate(idx)}
edges_unordered = np.genfromtxt("{}{}.cites".format(path, dataset), 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=(labels.shape[0], labels.shape[0]), dtype=np.float32)
# build symmetric adjacency matrix
adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)
print('Dataset has {} nodes, {} edges, {} features.'.format(adj.shape[0], edges.shape[0], features.shape[1]))
return features.todense(), adj, labels
def load_web_data(path="web_data/", dataset="cornell"):
"""Load citation network dataset"""
print('Loading {} dataset...'.format(dataset))
idx_features_labels = np.genfromtxt("{}{}.content".format(path, dataset), dtype=np.dtype(str))
features = sp.csr_matrix(idx_features_labels[:, 1:-1], dtype=np.float32)
labels = encode_onehot(idx_features_labels[:, -1])
# build graph
idx = np.array(idx_features_labels[:, 0], dtype=np.dtype(str))
idx_map = {j: i for i, j in enumerate(idx)}
edges_unordered = np.genfromtxt("{}{}.cites".format(path, dataset), dtype=np.dtype(str))
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=(labels.shape[0], labels.shape[0]), dtype=np.float32)
# build symmetric adjacency matrix
adj = adj + adj.T.multiply(adj.T > adj) - adj.multiply(adj.T > adj)
print('Dataset has {} nodes, {} edges, {} features.'.format(adj.shape[0], edges.shape[0], features.shape[1]))
return features.todense(), adj, labels
def normalize_adj(adj, symmetric=True):
if symmetric:
d = sp.diags(np.power(np.array(adj.sum(1)), -0.5).flatten(), 0)
a_norm = adj.dot(d).transpose().dot(d).tocsr()
else:
d = sp.diags(np.power(np.array(adj.sum(1)), -1).flatten(), 0)
a_norm = d.dot(adj).tocsr()
return a_norm
def preprocess_adj(adj, symmetric=True):
adj = adj + sp.eye(adj.shape[0])
adj = normalize_adj(adj, symmetric)
return adj
def sample_mask(idx, l):
mask = np.zeros(l)
mask[idx] = 1
return np.array(mask, dtype=np.bool)
def get_splits(y):
split_val = len(y) // 4
idx_train = range(split_val * 4)
idx_val = range(split_val * 2, split_val * 3)
idx_test = range(split_val * 3, split_val * 4)
y_train = np.zeros(y.shape, dtype=np.int32)
y_val = np.zeros(y.shape, dtype=np.int32)
y_test = np.zeros(y.shape, dtype=np.int32)
y_train[idx_train] = y[idx_train]
y_val[idx_val] = y[idx_val]
y_test[idx_test] = y[idx_test]
train_mask = sample_mask(idx_train, y.shape[0])
return y_train, y_val, y_test, idx_train, idx_val, idx_test, train_mask
def categorical_crossentropy(preds, labels):
return np.mean(-np.log(np.extract(labels, preds)))
def accuracy(preds, labels):
return np.mean(np.equal(np.argmax(labels, 1), np.argmax(preds, 1)))
def evaluate_preds(preds, labels, indices):
split_loss = list()
split_acc = list()
for y_split, idx_split in zip(labels, indices):
split_loss.append(categorical_crossentropy(preds[idx_split], y_split[idx_split]))
split_acc.append(accuracy(preds[idx_split], y_split[idx_split]))
return split_loss, split_acc
def normalized_laplacian(adj, symmetric=True):
adj_normalized = normalize_adj(adj, symmetric)
laplacian = sp.eye(adj.shape[0]) - adj_normalized
return laplacian
def rescale_laplacian(laplacian):
try:
print('Calculating largest eigenvalue of normalized graph Laplacian...')
largest_eigval = eigsh(laplacian, 1, which='LM', return_eigenvectors=False)[0]
except ArpackNoConvergence:
print('Eigenvalue calculation did not converge! Using largest_eigval=2 instead.')
largest_eigval = 2
scaled_laplacian = (2. / largest_eigval) * laplacian - sp.eye(laplacian.shape[0])
return scaled_laplacian
def chebyshev_polynomial(X, k):
"""Calculate Chebyshev polynomials up to order k. Return a list of sparse matrices."""
print("Calculating Chebyshev polynomials up to order {}...".format(k))
T_k = list()
T_k.append(sp.eye(X.shape[0]).tocsr())
T_k.append(X)
def chebyshev_recurrence(T_k_minus_one, T_k_minus_two, X):
X_ = sp.csr_matrix(X, copy=True)
return 2 * X_.dot(T_k_minus_one) - T_k_minus_two
for i in range(2, k+1):
T_k.append(chebyshev_recurrence(T_k[-1], T_k[-2], X))
return T_k
def sparse_to_tuple(sparse_mx):
if not sp.isspmatrix_coo(sparse_mx):
sparse_mx = sparse_mx.tocoo()
coords = np.vstack((sparse_mx.row, sparse_mx.col)).transpose()
values = sparse_mx.data
shape = sparse_mx.shape
return coords, values, shape