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data.py
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data.py
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import numpy as np
import pickle as pkl
import networkx as nx
import scipy.sparse as sp
from scipy.sparse.linalg.eigen.arpack import eigsh
import sys
def parse_index_file(filename):
"""
Parse index file.
"""
index = []
for line in open(filename):
index.append(int(line.strip()))
return index
def sample_mask(idx, l):
"""
Create mask.
"""
mask = np.zeros(l)
mask[idx] = 1
return np.array(mask, dtype=np.bool)
def load_data(dataset_str):
"""
Loads input data from gcn/data directory
ind.dataset_str.x => the feature vectors of the training instances as scipy.sparse.csr.csr_matrix object;
ind.dataset_str.tx => the feature vectors of the test instances as scipy.sparse.csr.csr_matrix object;
ind.dataset_str.allx => the feature vectors of both labeled and unlabeled training instances
(a superset of ind.dataset_str.x) as scipy.sparse.csr.csr_matrix object;
ind.dataset_str.y => the one-hot labels of the labeled training instances as numpy.ndarray object;
ind.dataset_str.ty => the one-hot labels of the test instances as numpy.ndarray object;
ind.dataset_str.ally => the labels for instances in ind.dataset_str.allx as numpy.ndarray object;
ind.dataset_str.graph => a dict in the format {index: [index_of_neighbor_nodes]} as collections.defaultdict
object;
ind.dataset_str.test.index => the indices of test instances in graph, for the inductive setting as list object.
All objects above must be saved using python pickle module.
:param dataset_str: Dataset name
:return: All data input files loaded (as well the training/test data).
"""
names = ['x', 'y', 'tx', 'ty', 'allx', 'ally', 'graph']
objects = []
for i in range(len(names)):
with open("data/ind.{}.{}".format(dataset_str, 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("data/ind.{}.test.index".format(dataset_str))
test_idx_range = np.sort(test_idx_reorder)
if dataset_str == '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 = sp.vstack((allx, tx)).tolil()
features[test_idx_reorder, :] = features[test_idx_range, :]
adj = nx.adjacency_matrix(nx.from_dict_of_lists(graph))
labels = np.vstack((ally, ty))
labels[test_idx_reorder, :] = labels[test_idx_range, :]
idx_test = test_idx_range.tolist()
idx_train = range(len(y))
idx_val = range(len(y), len(y)+500)
train_mask = sample_mask(idx_train, labels.shape[0])
val_mask = sample_mask(idx_val, labels.shape[0])
test_mask = sample_mask(idx_test, labels.shape[0])
y_train = np.zeros(labels.shape)
y_val = np.zeros(labels.shape)
y_test = np.zeros(labels.shape)
y_train[train_mask, :] = labels[train_mask, :]
y_val[val_mask, :] = labels[val_mask, :]
y_test[test_mask, :] = labels[test_mask, :]
return adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask
def sparse_to_tuple(sparse_mx):
"""
Convert sparse matrix to tuple representation.
"""
def to_tuple(mx):
if not sp.isspmatrix_coo(mx):
mx = mx.tocoo()
coords = np.vstack((mx.row, mx.col)).transpose()
values = mx.data
shape = mx.shape
return coords, values, shape
if isinstance(sparse_mx, list):
for i in range(len(sparse_mx)):
sparse_mx[i] = to_tuple(sparse_mx[i])
else:
sparse_mx = to_tuple(sparse_mx)
return sparse_mx
def preprocess_features(features):
"""
Row-normalize feature matrix and convert to tuple representation
"""
rowsum = np.array(features.sum(1)) # get sum of each row, [2708, 1]
r_inv = np.power(rowsum, -1).flatten() # 1/rowsum, [2708]
r_inv[np.isinf(r_inv)] = 0. # zero inf data
r_mat_inv = sp.diags(r_inv) # sparse diagonal matrix, [2708, 2708]
features = r_mat_inv.dot(features) # D^-1:[2708, 2708]@X:[2708, 2708]
return sparse_to_tuple(features) # [coordinates, data, shape], []
def normalize_adj(adj):
"""Symmetrically normalize adjacency matrix."""
adj = sp.coo_matrix(adj)
rowsum = np.array(adj.sum(1)) # D
d_inv_sqrt = np.power(rowsum, -0.5).flatten() # D^-0.5
d_inv_sqrt[np.isinf(d_inv_sqrt)] = 0.
d_mat_inv_sqrt = sp.diags(d_inv_sqrt) # D^-0.5
return adj.dot(d_mat_inv_sqrt).transpose().dot(d_mat_inv_sqrt).tocoo() # D^-0.5AD^0.5
def preprocess_adj(adj):
"""Preprocessing of adjacency matrix for simple GCN model and conversion to tuple representation."""
adj_normalized = normalize_adj(adj + sp.eye(adj.shape[0]))
return sparse_to_tuple(adj_normalized)
def chebyshev_polynomials(adj, k):
"""
Calculate Chebyshev polynomials up to order k. Return a list of sparse matrices (tuple representation).
"""
print("Calculating Chebyshev polynomials up to order {}...".format(k))
adj_normalized = normalize_adj(adj)
laplacian = sp.eye(adj.shape[0]) - adj_normalized
largest_eigval, _ = eigsh(laplacian, 1, which='LM')
scaled_laplacian = (2. / largest_eigval[0]) * laplacian - sp.eye(adj.shape[0])
t_k = list()
t_k.append(sp.eye(adj.shape[0]))
t_k.append(scaled_laplacian)
def chebyshev_recurrence(t_k_minus_one, t_k_minus_two, scaled_lap):
s_lap = sp.csr_matrix(scaled_lap, copy=True)
return 2 * s_lap.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], scaled_laplacian))
return sparse_to_tuple(t_k)