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data_utils.py
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data_utils.py
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import os
from collections import defaultdict
import random
import torch
import torch.nn.functional as F
import numpy as np
from scipy import sparse as sp
from torch_geometric.utils import degree, to_scipy_sparse_matrix
from torch_sparse import SparseTensor
def rand_train_test_idx_502525(label, train_prop=.5, valid_prop=.25, ignore_negative=True):
""" randomly splits label into train/valid/test splits """
if ignore_negative:
labeled_nodes = torch.where(label != -1)[0]
else:
labeled_nodes = label
n = labeled_nodes.shape[0]
train_num = int(n * train_prop)
valid_num = int(n * valid_prop)
perm = torch.as_tensor(np.random.permutation(n))
train_indices = perm[:train_num]
val_indices = perm[train_num:train_num + valid_num]
test_indices = perm[train_num + valid_num:]
if not ignore_negative:
return train_indices, val_indices, test_indices
train_idx = labeled_nodes[train_indices]
valid_idx = labeled_nodes[val_indices]
test_idx = labeled_nodes[test_indices]
return train_idx, valid_idx, test_idx
def rand_train_test_idx_602020(label, train_prop=.6, valid_prop=.2, ignore_negative=True):
""" randomly splits label into train/valid/test splits """
# random.seed(2022)
if ignore_negative:
labeled_nodes = torch.where(label != -1)[0]
else:
labeled_nodes = label
n = labeled_nodes.shape[0]
train_num = int(n * train_prop)
valid_num = int(n * valid_prop)
all_idx = np.arange(n)
random.shuffle(all_idx)
train_indices = all_idx[:train_num]
val_indices = all_idx[train_num:train_num+valid_num]
test_indices = all_idx[train_num+valid_num:]
if not ignore_negative:
return train_indices, val_indices, test_indices
train_idx = labeled_nodes[train_indices]
valid_idx = labeled_nodes[val_indices]
test_idx = labeled_nodes[test_indices]
return train_idx, valid_idx, test_idx
def load_fixed_splits(data_dir, dataset, name, protocol):
splits_lst = []
if name in ['cora', 'citeseer', 'pubmed'] and protocol == 'semi':
splits = {}
splits['train'] = torch.as_tensor(dataset.train_idx)
splits['valid'] = torch.as_tensor(dataset.valid_idx)
splits['test'] = torch.as_tensor(dataset.test_idx)
splits_lst.append(splits)
elif name in ['cora', 'citeseer', 'pubmed', 'chameleon', 'squirrel', 'film', 'cornell', 'texas', 'wisconsin']:
for i in range(10):
splits_file_path = '{}/geom-gcn/splits/{}'.format(data_dir, name) + '_split_0.6_0.2_' + str(i) + '.npz'
splits = {}
with np.load(splits_file_path) as splits_file:
splits['train'] = torch.BoolTensor(splits_file['train_mask'])
splits['valid'] = torch.BoolTensor(splits_file['val_mask'])
splits['test'] = torch.BoolTensor(splits_file['test_mask'])
splits_lst.append(splits)
else:
raise NotImplementedError
return splits_lst
def class_rand_splits(label, label_num_per_class):
train_idx, non_train_idx = [], []
idx = torch.arange(label.shape[0])
class_list = label.squeeze().unique()
valid_num, test_num = 500, 1000
for i in range(class_list.shape[0]):
c_i = class_list[i]
idx_i = idx[label.squeeze() == c_i]
n_i = idx_i.shape[0]
rand_idx = idx_i[torch.randperm(n_i)]
train_idx += rand_idx[:label_num_per_class].tolist()
non_train_idx += rand_idx[label_num_per_class:].tolist()
train_idx = torch.as_tensor(train_idx)
non_train_idx = torch.as_tensor(non_train_idx)
non_train_idx = non_train_idx[torch.randperm(non_train_idx.shape[0])]
valid_idx, test_idx = non_train_idx[:valid_num], non_train_idx[valid_num:valid_num + test_num]
return train_idx, valid_idx, test_idx
def even_quantile_labels(vals, nclasses, verbose=True):
""" partitions vals into nclasses by a quantile based split,
where the first class is less than the 1/nclasses quantile,
second class is less than the 2/nclasses quantile, and so on
vals is np array
returns an np array of int class labels
"""
label = -1 * np.ones(vals.shape[0], dtype=np.int)
interval_lst = []
lower = -np.inf
for k in range(nclasses - 1):
upper = np.quantile(vals, (k + 1) / nclasses)
interval_lst.append((lower, upper))
inds = (vals >= lower) * (vals < upper)
label[inds] = k
lower = upper
label[vals >= lower] = nclasses - 1
interval_lst.append((lower, np.inf))
if verbose:
print('Class Label Intervals:')
for class_idx, interval in enumerate(interval_lst):
print(f'Class {class_idx}: [{interval[0]}, {interval[1]})]')
return label
def to_planetoid(dataset):
"""
Takes in a NCDataset and returns the dataset in H2GCN Planetoid form, as follows:
x => the feature vectors of the training instances as scipy.sparse.csr.csr_matrix object;
tx => the feature vectors of the test instances as scipy.sparse.csr.csr_matrix object;
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;
y => the one-hot labels of the labeled training instances as numpy.ndarray object;
ty => the one-hot labels of the test instances as numpy.ndarray object;
ally => the labels for instances in ind.dataset_str.allx as numpy.ndarray object;
graph => a dict in the format {index: [index_of_neighbor_nodes]} as collections.defaultdict
object;
split_idx => The ogb dictionary that contains the train, valid, test splits
"""
split_idx = dataset.get_idx_split('random', 0.25)
train_idx, valid_idx, test_idx = split_idx["train"], split_idx["valid"], split_idx["test"]
graph, label = dataset[0]
label = torch.squeeze(label)
print("generate x")
x = graph['node_feat'][train_idx].numpy()
x = sp.csr_matrix(x)
tx = graph['node_feat'][test_idx].numpy()
tx = sp.csr_matrix(tx)
allx = graph['node_feat'].numpy()
allx = sp.csr_matrix(allx)
y = F.one_hot(label[train_idx]).numpy()
ty = F.one_hot(label[test_idx]).numpy()
ally = F.one_hot(label).numpy()
edge_index = graph['edge_index'].T
graph = defaultdict(list)
for i in range(0, label.shape[0]):
graph[i].append(i)
for start_edge, end_edge in edge_index:
graph[start_edge.item()].append(end_edge.item())
return x, tx, allx, y, ty, ally, graph, split_idx
def to_sparse_tensor(edge_index, edge_feat, num_nodes):
""" converts the edge_index into SparseTensor
"""
num_edges = edge_index.size(1)
(row, col), N, E = edge_index, num_nodes, num_edges
perm = (col * N + row).argsort()
row, col = row[perm], col[perm]
value = edge_feat[perm]
adj_t = SparseTensor(row=col, col=row, value=value,
sparse_sizes=(N, N), is_sorted=True)
# Pre-process some important attributes.
adj_t.storage.rowptr()
adj_t.storage.csr2csc()
return adj_t
def normalize(edge_index):
""" normalizes the edge_index
"""
adj_t = edge_index.set_diag()
deg = adj_t.sum(dim=1).to(torch.float)
deg_inv_sqrt = deg.pow(-0.5)
deg_inv_sqrt[deg_inv_sqrt == float('inf')] = 0
adj_t = deg_inv_sqrt.view(-1, 1) * adj_t * deg_inv_sqrt.view(1, -1)
return adj_t
def gen_normalized_adjs(dataset):
""" returns the normalized adjacency matrix
"""
row, col = dataset.graph['edge_index']
N = dataset.graph['num_nodes']
adj = SparseTensor(row=row, col=col, sparse_sizes=(N, N))
deg = adj.sum(dim=1).to(torch.float)
D_isqrt = deg.pow(-0.5)
D_isqrt[D_isqrt == float('inf')] = 0
DAD = D_isqrt.view(-1,1) * adj * D_isqrt.view(1,-1)
DA = D_isqrt.view(-1,1) * D_isqrt.view(-1,1) * adj
AD = adj * D_isqrt.view(1,-1) * D_isqrt.view(1,-1)
return DAD, DA, AD
def convert_to_adj(edge_index,n_node):
'''convert from pyg format edge_index to n by n adj matrix'''
adj=torch.zeros((n_node,n_node))
row,col=edge_index
adj[row,col]=1
return adj
def adj_mul(adj_i, adj, N):
adj_i_sp = torch.sparse_coo_tensor(adj_i, torch.ones(adj_i.shape[1], dtype=torch.float).to(adj.device), (N, N))
adj_sp = torch.sparse_coo_tensor(adj, torch.ones(adj.shape[1], dtype=torch.float).to(adj.device), (N, N))
adj_j = torch.sparse.mm(adj_i_sp, adj_sp)
adj_j = adj_j.coalesce().indices()
return adj_j
import subprocess
def get_gpu_memory_map():
"""Get the current gpu usage.
Returns
-------
usage: dict
Keys are device ids as integers.
Values are memory usage as integers in MB.
"""
result = subprocess.check_output(
[
'nvidia-smi', '--query-gpu=memory.used',
'--format=csv,nounits,noheader'
], encoding='utf-8')
# Convert lines into a dictionary
gpu_memory = np.array([int(x) for x in result.strip().split('\n')])
# gpu_memory_map = dict(zip(range(len(gpu_memory)), gpu_memory))
return gpu_memory
def laplacian_positional_encoding(g, pos_enc_dim):
"""
Graph positional encoding v/ Laplacian eigenvectors
"""
# Laplacian
# Calculate the adjacency matrix A using PyG's to_scipy_sparse_matrix function
A = to_scipy_sparse_matrix(g.graph['edge_index'], num_nodes = g.graph['num_nodes']).tocsr()
# Calculate the degree matrix N using PyG's degree function
N = sp.diags((torch.clamp(degree(g.graph['edge_index'][1],num_nodes = g.graph['num_nodes'],dtype=torch.int), min=1).numpy())** -0.5, dtype=float)
# Calculate the Laplacian matrix L
L = sp.eye(g.graph['num_nodes']) - N * A * N
# Eigenvectors with scipy
#EigVal, EigVec = sp.linalg.eigs(L, k=pos_enc_dim+1, which='SR')
EigVal, EigVec = sp.linalg.eigs(L, k=pos_enc_dim+1, which='SR', tol=1e-2) # for 40 PEs
EigVec = EigVec[:, EigVal.argsort()] # increasing order
lap_pos_enc = torch.from_numpy(EigVec[:,1:pos_enc_dim+1]).float()
return lap_pos_enc
dataset_drive_url = {
'snap-patents' : '1ldh23TSY1PwXia6dU0MYcpyEgX-w3Hia',
'pokec' : '1dNs5E7BrWJbgcHeQ_zuy5Ozp2tRCWG0y',
'yelp-chi': '1fAXtTVQS4CfEk4asqrFw9EPmlUPGbGtJ',
}
splits_drive_url = {
'snap-patents' : '12xbBRqd8mtG_XkNLH8dRRNZJvVM4Pw-N',
'pokec' : '1ZhpAiyTNc0cE_hhgyiqxnkKREHK7MK-_',
}