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utils.py
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utils.py
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from torch.utils.data import Dataset
import torch
import numpy as np
import os
import torch.optim as optim
import torch.nn.functional as F
import math
from numpy.linalg import norm
from sklearn import preprocessing
from torch import Tensor
from pathlib import Path
from torch.nn import init
import logging
import time
import random
from numpy import linalg as LA
from ogb.nodeproppred import PygNodePropPredDataset
from torch_geometric.datasets import Planetoid, Amazon, LINKXDataset
from torch_geometric.utils import to_undirected
from torch_geometric.transforms import ToUndirected
from torch_geometric.utils import add_remaining_self_loops
from torch_geometric.seed import seed_everything
from Hetero_dataset import HeteroDataset
from LINKX_dataset import LINKXDataset
logger = None
seeds=[ 8073, 49184, 94208, 1681, 25443, 27880, 75161, 84677,
32340, 38995, 78096, 37432, 70984, 841, 62755, 23832, 49295,
63475, 30897]
def degree(row,num_nodes):
out = torch.zeros((num_nodes, ), dtype=row.dtype)
one = torch.ones((row.size(0), ), dtype=out.dtype)
return out.scatter_add_(0, row, one)
def setup_logger(name):
global logger
logger = logging.getLogger(name)
def set_logger(args,logger,dt,name="edge"):
check_dir(f"{args.analysis_path}/{args.dataset}/{name}/")
print(
f"****** log in: {args.analysis_path}/{args.dataset}/{name}/{dt}_Batch_{args.num_batch_removes}_Num_{args.num_removes}_lam_{args.lam}_lr_{args.lr}_mode_{args.weight_mode}_rmax_{args.rmax}_std_{args.std}_axis_{args.axis_num}_r_{args.r}_edge_idx_{args.edge_idx_start}_seed_{args.seed}.log ******"
)
file_handler = logging.FileHandler(
f"{args.analysis_path}/{args.dataset}/{name}/{dt}_Batch_{args.num_batch_removes}_Num_{args.num_removes}_lam_{args.lam}_lr_{args.lr}_mode_{args.weight_mode}_rmax_{args.rmax}_std_{args.std}_axis_{args.axis_num}_r_{args.r}_edge_idx_{args.edge_idx_start}_seed_{args.seed}.log"
)
file_handler.setLevel(logging.DEBUG)
# console_handler = logging.StreamHandler()
# console_handler.setLevel(logging.DEBUG)
formatter = logging.Formatter(
"%(asctime)s - %(name)s - %(levelname)s - %(message)s"
)
file_handler.setFormatter(formatter)
logger.addHandler(file_handler)
# logger.addHandler(console_handler)
def random_planetoid_splits(
data, num_classes, percls_trn=20, val_lb=500, test_lb=1000, Flag=0
):
# Set new random planetoid splits:
# * round(train_rate*len(data)/num_classes) * num_classes labels for training
# * val_rate*len(data) labels for validation
# * rest labels for testing
if Flag == 0:
indices = []
for i in range(num_classes):
index = (data.y == i).nonzero().view(-1)
index = index[torch.randperm(index.size(0))]
indices.append(index)
train_index = torch.cat([i[:percls_trn] for i in indices], dim=0)
rest_index = torch.cat([i[percls_trn:] for i in indices], dim=0)
rest_index = rest_index[torch.randperm(rest_index.size(0))]
data.train_mask = index_to_mask(train_index, size=data.num_nodes)
data.val_mask = index_to_mask(rest_index[:val_lb], size=data.num_nodes)
data.test_mask = index_to_mask(rest_index[val_lb:], size=data.num_nodes)
else:
all_index = torch.randperm(data.y.shape[0])
data.val_mask = index_to_mask(all_index[:val_lb], size=data.num_nodes)
data.test_mask = index_to_mask(
all_index[val_lb : (val_lb + test_lb)], size=data.num_nodes
)
data.train_mask = index_to_mask(
all_index[(val_lb + test_lb) :], size=data.num_nodes
)
return data
def index_to_mask(index, size):
mask = torch.zeros(size, dtype=torch.bool, device=index.device)
mask[index] = 1
return mask
def load_data(path,dataset,self_loop=True,undirected=True):
if dataset in ["cora", "citeseer", "pubmed"]:
data = Planetoid(root=path, name=dataset, split="full")
data = data[0]
if undirected:
data.edge_index = to_undirected(data.edge_index)
elif dataset in ["ogbn-arxiv", "ogbn-products", "ogbn-papers100M"]:
data = PygNodePropPredDataset(name=dataset, root=path)
split_idx = data.get_idx_split()
data = data[0]
data.train_mask = torch.zeros(data.x.shape[0], dtype=torch.bool)
data.train_mask[split_idx["train"]] = True
data.val_mask = torch.zeros(data.x.shape[0], dtype=torch.bool)
data.val_mask[split_idx["valid"]] = True
data.test_mask = torch.zeros(data.x.shape[0], dtype=torch.bool)
data.test_mask[split_idx["test"]] = True
data.y = data.y.squeeze(-1)
# logger.info(f"original edge_index: {data.edge_index.shape}")
if undirected:
data.edge_index = to_undirected(data.edge_index)
elif dataset in ["computers", "photo"]:
origin_data = Amazon(path, dataset)
data = origin_data[0]
data = random_planetoid_splits(
data, num_classes=origin_data.num_classes, val_lb=500, test_lb=1000, Flag=1
)
if undirected:
data.edge_index = to_undirected(data.edge_index)
elif dataset in [
"penn94",
"genius",
"wiki",
"pokec",
"arxiv-year",
"twitch-gamer",
"snap-patents",
"twitch-de",
"deezer-europe",
]:
data = LINKXDataset(root=path, name=dataset)
if dataset != "arxiv-year" and dataset != "snap-patents":
if undirected:
data.data["edge_index"] = to_undirected(data.data["edge_index"])
data = data[0]
elif dataset in ["questions", "minesweeper", "tolokers"]:
data = HeteroDataset(
root=path, name=dataset, transform=ToUndirected()
)
data = data[0]
else:
raise ("Error: Not supported dataset yet.")
if self_loop:
edge_index, _ = add_remaining_self_loops(data.edge_index)
else:
edge_index=data.edge_index
edge_index = edge_index.numpy().astype(np.int32)
# logger.debug(f"edge_index: {edge_index[:,:10]}")
return data,edge_index
def get_prop_weight(weight_mode,prop_step,decay):
weights = []
if weight_mode == "decay":
weight = 1.0
for _ in range(prop_step):
weights.append(decay * weight)
weight *= 1 - decay
elif weight_mode == "avg":
for _ in range(prop_step):
weights.append(float(1) / prop_step)
elif weight_mode == "test":
weights.extend([0 for _ in range(prop_step - 1)])
weights.append(1)
elif weight_mode == "hetero":
for i in range(prop_step):
weights.append(pow(-1, i))
return weights
def preprocess_data(X, axis_num=1,ord=2):
"""
input:
X: (n,d), torch.Tensor
"""
X_np = X.numpy()
scaler = preprocessing.StandardScaler().fit(X_np)
X_scaled = scaler.transform(X_np)
X_norm = norm(X_scaled,ord=ord, axis=axis_num)
X_scaled = X_scaled / X_norm.max()
X_scaled = X_scaled.astype(np.float64)
X_scaled = np.nan_to_num(X_scaled)
return torch.from_numpy(X_scaled)
class SimpleDataset(Dataset):
def __init__(self, x, y):
self.x = x
self.y = y
assert self.x.size(0) == self.y.size(0)
def __len__(self):
return self.x.size(0)
def __getitem__(self, idx):
return self.x[idx], self.y[idx]
def com_accuracy(y_pred, y):
pred = y_pred.data.max(1)[1]
pred = pred.reshape(pred.size(0), 1)
correct = pred.eq(y.data).cpu().sum()
accuracy = correct.to(dtype=torch.long) / len(y)
return accuracy
def rand_train_test_idx(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 get_idx_split(name,label,split_type='random', train_prop=.6, valid_prop=.2):
"""
train_prop: The proportion of dataset for train split. Between 0 and 1.
valid_prop: The proportion of dataset for validation split. Between 0 and 1.
"""
if split_type == 'random':
ignore_negative = False if name == 'ogbn-proteins' else True
train_idx, valid_idx, test_idx = rand_train_test_idx(
label, train_prop=train_prop, valid_prop=valid_prop, ignore_negative=ignore_negative)
train_mask = torch.zeros(label.shape[0], dtype=torch.bool)
val_mask = torch.zeros(label.shape[0], dtype=torch.bool)
test_mask = torch.zeros(label.shape[0], dtype=torch.bool)
train_mask[train_idx] = 1
val_mask[valid_idx] = 1
test_mask[test_idx] = 1
return train_mask, val_mask, test_mask
def get_split(data,X,train_mode,Y_binary,dataset_name="None"):
if dataset_name in ["wiki"]:
train_mask, val_mask, test_mask = get_idx_split(dataset_name,data.y)
else:
if len(data.train_mask.shape) > 1:
# hetero datasets, multi split
train_mask = data.train_mask[:, 0].clone().detach()
val_mask = data.val_mask[:, 0].clone().detach()
test_mask = data.test_mask[:, 0].clone().detach()
else:
train_mask = data.train_mask.clone().detach()
val_mask = data.val_mask.clone().detach()
test_mask = data.test_mask.clone().detach()
X_train, X_val, X_test = (X[train_mask], X[val_mask], X[test_mask])
# label prepare
if train_mode == "binary":
if "+" in Y_binary:
# two classes are specified
class1 = int(Y_binary.split("+")[0])
class2 = int(Y_binary.split("+")[1])
Y = data.y.clone().detach().float()
Y[data.y == class1] = 1
Y[data.y == class2] = -1
else:
# one vs rest
class1 = int(Y_binary)
Y = data.y.clone().detach().float()
Y[data.y == class1] = 1
Y[data.y != class1] = -1
y_train, y_val, y_test = (
Y[train_mask],
Y[val_mask],
Y[test_mask],
)
else:
y_train = F.one_hot(data.y[train_mask],num_classes=data.y.max().item()+1) * 2 - 1
y_train = y_train.float()
y_val = data.y[val_mask]
y_test = data.y[test_mask]
return X_train, X_val, X_test, y_train, y_val, y_test, train_mask, val_mask, test_mask
def get_split_large(data,train_mode,Y_binary,dataset_name="None"):
if dataset_name in ["wiki"]:
train_mask, val_mask, test_mask = get_idx_split(dataset_name,data.y)
else:
if len(data.train_mask.shape) > 1:
# hetero datasets, multi split
train_mask = data.train_mask[:, 0].clone().detach()
val_mask = data.val_mask[:, 0].clone().detach()
test_mask = data.test_mask[:, 0].clone().detach()
else:
train_mask = data.train_mask.clone().detach()
val_mask = data.val_mask.clone().detach()
test_mask = data.test_mask.clone().detach()
# label prepare
if train_mode == "binary":
if "+" in Y_binary:
# two classes are specified
class1 = int(Y_binary.split("+")[0])
class2 = int(Y_binary.split("+")[1])
Y = data.y.clone().detach().float()
Y[data.y == class1] = 1
Y[data.y == class2] = -1
else:
# one vs rest
class1 = int(Y_binary)
Y = data.y.clone().detach().float()
Y[data.y == class1] = 1
Y[data.y != class1] = -1
y_train, y_val, y_test = (
Y[train_mask],
Y[val_mask],
Y[test_mask],
)
else:
y_train = F.one_hot(data.y[train_mask],num_classes=data.y.max().item()+1) * 2 - 1
y_train = y_train.float()
y_val = data.y[val_mask]
y_test = data.y[test_mask]
return y_train, y_val, y_test, train_mask, val_mask, test_mask
def get_deep_split(data,X,train_mode,Y_binary,dataset_name="None"):
if dataset_name in ["wiki"]:
train_mask, val_mask, test_mask = get_idx_split(dataset_name,data.y)
else:
if len(data.train_mask.shape) > 1:
# hetero datasets, multi split
train_mask = data.train_mask[:, 0].clone().detach()
val_mask = data.val_mask[:, 0].clone().detach()
test_mask = data.test_mask[:, 0].clone().detach()
else:
train_mask = data.train_mask.clone().detach()
val_mask = data.val_mask.clone().detach()
test_mask = data.test_mask.clone().detach()
X_train, X_val, X_test = (X[train_mask], X[val_mask], X[test_mask])
y_train = data.y[train_mask]
y_val = data.y[val_mask]
y_test = data.y[test_mask]
return X_train, X_val, X_test, y_train, y_val, y_test, train_mask, val_mask, test_mask
def get_deep_split_large(data,train_mode,Y_binary,dataset_name="None"):
if dataset_name in ["wiki"]:
train_mask, val_mask, test_mask = get_idx_split(dataset_name,data.y)
else:
if len(data.train_mask.shape) > 1:
# hetero datasets, multi split
train_mask = data.train_mask[:, 0].clone().detach()
val_mask = data.val_mask[:, 0].clone().detach()
test_mask = data.test_mask[:, 0].clone().detach()
else:
train_mask = data.train_mask.clone().detach()
val_mask = data.val_mask.clone().detach()
test_mask = data.test_mask.clone().detach()
y_train = data.y[train_mask]
y_val = data.y[val_mask]
y_test = data.y[test_mask]
return y_train, y_val, y_test, train_mask, val_mask, test_mask
def check_propagation(groundtruth, result):
print(groundtruth.shape, result.shape)
assert len(groundtruth) == len(result)
l1error = np.sum(np.abs(groundtruth - result)) / len(groundtruth)
maxl1error = max(
[
np.sum(np.abs(groundtruth[i] - result[i]))
for i in range(groundtruth.shape[0])
]
)
maxerror = max(
[
np.max(np.abs(groundtruth[i] - result[i]))
for i in range(groundtruth.shape[0])
]
)
index = np.unravel_index(
np.argmax(np.abs(groundtruth - result)),
(groundtruth.shape[0], groundtruth.shape[1]),
)
print("max error at: ", index)
print("max error: ", groundtruth[index], result[index])
print("max l1-error: ", maxl1error)
print("max error: ", maxerror)
return maxl1error, maxerror
def check_dir(path):
if not os.path.exists(path):
os.makedirs(path)
def get_budget(std, eps, c):
return std * eps / c
def get_worst_Gbound_edge(deg1,deg2,train_size,feat_dim,lam,rmax,num_nodes,prop_step):
c=1
c_1=1
gamma_1=1/4
gamma_2=1/4
epsilon_1=math.sqrt(num_nodes)*prop_step*rmax
multi1=c*gamma_1/lam*feat_dim+c_1*math.sqrt(feat_dim*train_size)
multi2=epsilon_1+2*gamma_1*feat_dim/train_size*(4/math.sqrt(deg1)+4/math.sqrt(deg2))
return multi1*multi2
def get_worst_Gbound_node(degs,train_size,feat_dim,lam,rmax,num_nodes,prop_step):
c=1
c_1=1
gamma_1=1/4
gamma_2=1/4
epsilon_1=math.sqrt(num_nodes)*prop_step*rmax
degsum=0
for _deg in degs:
degsum+=4/math.sqrt(_deg)
multi1=c*gamma_1/lam*feat_dim+c_1*math.sqrt(feat_dim*train_size)
multi2=epsilon_1+2*gamma_1*feat_dim/train_size*degsum
return multi1*multi2
def get_worst_Gbound_feat(_deg,train_size,feat_dim,lam,rmax,num_nodes,prop_step):
c=1
c_1=1
gamma_1=1/4
gamma_2=1/4
epsilon_1=math.sqrt(num_nodes)*prop_step*rmax
multi1=c*gamma_1/lam*feat_dim+c_1*math.sqrt(feat_dim*train_size)
multi2=epsilon_1+2*gamma_1*feat_dim/train_size*math.sqrt(_deg)
return multi1*multi2
def get_c(delta):
return np.sqrt(2 * np.log(1.5 / delta))