repo stringlengths 1 99 | file stringlengths 13 215 | code stringlengths 12 59.2M | file_length int64 12 59.2M | avg_line_length float64 3.82 1.48M | max_line_length int64 12 2.51M | extension_type stringclasses 1
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HGB | HGB-master/LP/benchmark/methods/MAGNN/utils/pytorchtools.py | import numpy as np
import torch
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, patience, verbose=False, delta=0, save_path='checkpoint.pt'):
"""
Args:
patience (int): How long to wait after last time... | 1,824 | 36.244898 | 111 | py |
HGB | HGB-master/LP/benchmark/methods/MAGNN/utils/tools.py | import torch
import dgl
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score, normalized_mutual_info_score, adjusted_rand_score
from sklearn.cluster import KMeans
from sklearn.svm import LinearSVC
def idx_to_one_hot(idx_arr):
one_hot = np.zeros((idx_arr.shap... | 11,415 | 46.173554 | 174 | py |
HGB | HGB-master/LP/benchmark/methods/MAGNN/model/MAGNN_lp.py | import torch
import torch.nn as nn
import numpy as np
from model.base_MAGNN import MAGNN_ctr_ntype_specific
# for link prediction task
class MAGNN_lp_layer(nn.Module):
def __init__(self,
num_metapaths_list,
num_edge_type,
etypes_lists,
in_dim,
... | 5,609 | 41.824427 | 115 | py |
HGB | HGB-master/LP/benchmark/methods/MAGNN/model/MAGNN_nc_mb.py | import torch
import torch.nn as nn
import numpy as np
from model.base_MAGNN import MAGNN_ctr_ntype_specific
# support for mini-batched forward
# only support one layer for one ctr_ntype
class MAGNN_nc_mb_layer(nn.Module):
def __init__(self,
num_metapaths,
num_edge_type,
... | 4,483 | 38.333333 | 119 | py |
HGB | HGB-master/LP/benchmark/methods/MAGNN/model/MAGNN_nc.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from model.base_MAGNN import MAGNN_ctr_ntype_specific
fc_switch = False
# multi-layer support
class MAGNN_nc_layer(nn.Module):
def __init__(self,
num_metapaths_list,
num_edge_type,
... | 5,888 | 41.985401 | 148 | py |
HGB | HGB-master/LP/benchmark/methods/MAGNN/model/base_MAGNN.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl.function as fn
from dgl.nn.pytorch import edge_softmax
class MAGNN_metapath_specific(nn.Module):
def __init__(self,
etypes,
out_dim,
num_heads,
rnn_type='gru',
... | 11,914 | 46.66 | 168 | py |
HGB | HGB-master/LP/benchmark/methods/RGCN/utils.py | import numpy as np
import torch
import scipy as sp
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, patience, verbose=False, delta=0, save_path='checkpoint.pt'):
"""
Args:
patience (int): How long to ... | 3,127 | 29.368932 | 111 | py |
HGB | HGB-master/LP/benchmark/methods/RGCN/model.py | import torch as th
import torch.nn as nn
from dgl.nn.pytorch import GraphConv, GATConv
import torch.nn.functional as F
class BaseRGCN(nn.Module):
def __init__(self, in_dims, h_dim, out_dim, num_rels, num_bases,
num_hidden_layers=1, dropout=0,
use_self_loop=False):
super(B... | 4,676 | 32.170213 | 79 | py |
HGB | HGB-master/LP/benchmark/methods/RGCN/link_predict.py | import dgl
from model import BaseRGCN
from utils import load_data
from utils import EarlyStopping
import utils
import numpy as np
import torch.nn.functional as F
import torch.nn as nn
import torch
from collections import defaultdict
import argparse
import time
import sys
from dgl.nn.pytorch import RelGraphConv
import p... | 16,667 | 42.293506 | 125 | py |
HGB | HGB-master/LP/benchmark/methods/HetGNN/main.py | import torch
import torch.optim as optim
torch.set_num_threads(2)
from args import read_args
from torch.autograd import Variable
import numpy as np
import random
import pickle
import os
import data_generator
import tools
import sys
os.environ['CUDA_VISIBLE_DEVICES'] = '9'
sys.path.append(f'../../')
from scripts.data_l... | 5,558 | 38.147887 | 113 | py |
HGB | HGB-master/LP/benchmark/methods/HetGNN/data_generator.py | import six.moves.cPickle as pickle
import numpy as np
import string
import re
import random
import math
from collections import Counter
from itertools import *
import sys
import os
import json
from collections import defaultdict
import torch as th
class input_data(object):
def __init__(self, args, dl):
sel... | 13,882 | 48.405694 | 117 | py |
HGB | HGB-master/LP/benchmark/methods/HetGNN/tools.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from args import read_args
import numpy as np
import string
import re
import math
import sys
args = read_args()
class HetAgg(nn.Module):
def __init__(self, args, feature_list, neigh_list_train, train_id_list, d... | 8,675 | 44.1875 | 139 | py |
HGB | HGB-master/LP/benchmark/methods/GNN/utils.py | from sklearn.metrics import (auc, f1_score, precision_recall_curve, roc_auc_score)
import scipy.sparse as sp
import argparse
from torch.utils.data import Dataset
import torch as th
import numpy as np
import random
import sys
from collections import defaultdict
sys.path.append('../../')
from scripts.data_loader import ... | 6,631 | 37.55814 | 106 | py |
HGB | HGB-master/LP/benchmark/methods/GNN/GNN.py | import torch as th
from torch import nn
from torch_geometric.nn import GCNConv, GATConv
import torch.nn.functional as F
class DisMult(th.nn.Module):
def __init__(self, rel_num, dim):
super(DisMult, self).__init__()
self.dim = dim
self.weights = nn.Parameter(th.FloatTensor(size=(rel_num, dim,... | 3,747 | 35.038462 | 108 | py |
HGB | HGB-master/LP/benchmark/methods/GNN/homoGNN.py | import torch as th
import numpy as np
from torch.utils.data import DataLoader
from torch import nn
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '5'
import pickle
import sys
from collections import defaultdict
from utils import *
from GNN import GCN, GAT
sys.path.append('../../')
from scripts.data_loader import data_... | 7,316 | 43.345455 | 115 | py |
HGB | HGB-master/LP/benchmark/methods/MAGNN_ini/run_DBLP.py | import time
import argparse
import torch
import torch.nn.functional as F
import numpy as np
from utils.pytorchtools import EarlyStopping
from utils.data import load_DBLP_data
from utils.tools import index_generator, evaluate_results_nc, parse_minibatch
from model import MAGNN_nc_mb
# Params
out_dim = 4
dropout_rate ... | 10,569 | 50.813725 | 139 | py |
HGB | HGB-master/LP/benchmark/methods/MAGNN_ini/run_IMDB.py | import time
import argparse
import torch.nn.functional as F
import torch.sparse
import numpy as np
import dgl
from utils.pytorchtools import EarlyStopping
from utils.data import load_IMDB_data
from utils.tools import evaluate_results_nc
from model import MAGNN_nc
# Params
out_dim = 3
dropout_rate = 0.5
lr = 0.005
we... | 8,734 | 47.259669 | 133 | py |
HGB | HGB-master/LP/benchmark/methods/MAGNN_ini/run_LastFM.py | import time
import argparse
import torch
import torch.nn.functional as F
import numpy as np
from sklearn.metrics import roc_auc_score, average_precision_score
from utils.pytorchtools import EarlyStopping
from utils.data import load_LastFM_data
from utils.tools import index_generator, parse_minibatch_LastFM
from model... | 13,354 | 57.318777 | 198 | py |
HGB | HGB-master/LP/benchmark/methods/MAGNN_ini/test_LastFM.py | import time
import argparse
import torch
import torch.nn.functional as F
import numpy as np
from sklearn.metrics import roc_auc_score, average_precision_score
from utils.pytorchtools import EarlyStopping
from utils.data import load_LastFM_data
from utils.tools import index_generator, parse_minibatch_LastFM
from model... | 8,476 | 46.357542 | 198 | py |
HGB | HGB-master/LP/benchmark/methods/MAGNN_ini/utils/pytorchtools.py | import numpy as np
import torch
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, patience, verbose=False, delta=0, save_path='checkpoint.pt'):
"""
Args:
patience (int): How long to wait after last time... | 1,824 | 36.244898 | 111 | py |
HGB | HGB-master/LP/benchmark/methods/MAGNN_ini/utils/tools.py | import torch
import dgl
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score, normalized_mutual_info_score, adjusted_rand_score
from sklearn.cluster import KMeans
from sklearn.svm import LinearSVC
def idx_to_one_hot(idx_arr):
one_hot = np.zeros((idx_arr.shap... | 11,415 | 46.173554 | 174 | py |
HGB | HGB-master/LP/benchmark/methods/MAGNN_ini/model/MAGNN_lp.py | import torch
import torch.nn as nn
import numpy as np
from model.base_MAGNN import MAGNN_ctr_ntype_specific
# for link prediction task
class MAGNN_lp_layer(nn.Module):
def __init__(self,
num_metapaths_list,
num_edge_type,
etypes_lists,
in_dim,
... | 5,609 | 41.824427 | 115 | py |
HGB | HGB-master/LP/benchmark/methods/MAGNN_ini/model/MAGNN_nc_mb.py | import torch
import torch.nn as nn
import numpy as np
from model.base_MAGNN import MAGNN_ctr_ntype_specific
# support for mini-batched forward
# only support one layer for one ctr_ntype
class MAGNN_nc_mb_layer(nn.Module):
def __init__(self,
num_metapaths,
num_edge_type,
... | 4,483 | 38.333333 | 119 | py |
HGB | HGB-master/LP/benchmark/methods/MAGNN_ini/model/MAGNN_nc.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from model.base_MAGNN import MAGNN_ctr_ntype_specific
fc_switch = False
# multi-layer support
class MAGNN_nc_layer(nn.Module):
def __init__(self,
num_metapaths_list,
num_edge_type,
... | 5,888 | 41.985401 | 148 | py |
HGB | HGB-master/LP/benchmark/methods/MAGNN_ini/model/base_MAGNN.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl.function as fn
from dgl.nn.pytorch import edge_softmax
class MAGNN_metapath_specific(nn.Module):
def __init__(self,
etypes,
out_dim,
num_heads,
rnn_type='gru',
... | 11,914 | 46.66 | 168 | py |
HGB | HGB-master/LP/benchmark/methods/HGT/model.py | import dgl
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class DistMult(nn.Module):
def __init__(self, num_rel, dim):
super(DistMult, self).__init__()
self.W = nn.Parameter(torch.FloatTensor(size=(num_rel, dim, dim)))
nn.init.xavier_normal_(self.W, gain=1.4... | 6,631 | 43.213333 | 117 | py |
HGB | HGB-master/LP/benchmark/methods/HGT/link_predict.py | import scipy.io
import urllib.request
import dgl
import math
import numpy as np
from model import *
import torch
from data_loader import data_loader
from utils.data import load_data
from utils.pytorchtools import EarlyStopping
import argparse
import time
from collections import defaultdict
ap = argparse.ArgumentParser... | 12,942 | 43.477663 | 226 | py |
HGB | HGB-master/LP/benchmark/methods/HGT/utils/pytorchtools.py | import numpy as np
import torch
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, patience, verbose=False, delta=0, save_path='checkpoint.pt'):
"""
Args:
patience (int): How long to wait after last time... | 1,824 | 36.244898 | 111 | py |
HGB | HGB-master/LP/benchmark/methods/HGT/utils/tools.py | import torch
import dgl
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score, normalized_mutual_info_score, adjusted_rand_score
from sklearn.cluster import KMeans
from sklearn.svm import LinearSVC
def idx_to_one_hot(idx_arr):
one_hot = np.zeros((idx_arr.shap... | 11,404 | 46.128099 | 174 | py |
HGB | HGB-master/LP/benchmark/methods/GATNE/main_pytorch.py | import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from numpy import random
from torch.nn.parameter import Parameter
import os
import pickle
from utils import *
import sys
os.environ['CUDA_VISIBLE_DEVICES'] = '2'
sys.path.append('../../')
from scripts.data_loader import ... | 13,587 | 38.61516 | 116 | py |
HGB | HGB-master/LP/GATNE/src/homGNN.py | import sys
import random
import torch as th
import numpy as np
from torch.utils.data import Dataset, DataLoader
from torch import nn
from torch_geometric.nn import GCNConv, SAGEConv, TopKPooling, GATConv
import torch.nn.functional as F
from sklearn.metrics import (auc, f1_score, precision_recall_curve,
... | 14,515 | 36.802083 | 119 | py |
HGB | HGB-master/LP/GATNE/src/main_pytorch.py | import math
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from numpy import random
from torch.nn.parameter import Parameter
from utils import *
import os
os.environ['CUDA_VISIBEL_DEVICES']='9'
def get_batches(pairs, neighbors, batch_size):
n_batches = (len(pairs) + (batch_... | 11,238 | 36.588629 | 169 | py |
HGB | HGB-master/NC/GTN/main.py | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from model import GTN
import pdb
import pickle
import argparse
from utils import f1_score
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', type=str,
help='... | 6,053 | 47.047619 | 134 | py |
HGB | HGB-master/NC/GTN/main_sparse.py | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from model_sparse import GTN
from matplotlib import pyplot as plt
import pdb
from torch_geometric.utils import dense_to_sparse, f1_score, accuracy
from torch_geometric.data import Data
import torch_sparse
import pickle
#from mem impor... | 6,694 | 44.544218 | 125 | py |
HGB | HGB-master/NC/GTN/utils.py | from __future__ import division
import torch
def accuracy(pred, target):
r"""Computes the accuracy of correct predictions.
Args:
pred (Tensor): The predictions.
target (Tensor): The targets.
:rtype: int
"""
return (pred == target).sum().item() / target.numel()
def true_positi... | 3,565 | 22.460526 | 66 | py |
HGB | HGB-master/NC/GTN/model.py | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import math
from matplotlib import pyplot as plt
import pdb
class GTN(nn.Module):
def __init__(self, num_edge, num_channels, w_in, w_out, num_class,num_layers,norm):
super(GTN, self).__init__()
self.num_edge... | 4,825 | 33.471429 | 118 | py |
HGB | HGB-master/NC/GTN/GNN.py | import torch.nn as nn
import dgl
from dgl.nn.pytorch import GraphConv
import dgl.function as fn
from dgl.nn.pytorch import edge_softmax, GATConv
class GAT(nn.Module):
def __init__(self,
g,
num_layers,
in_dim,
num_hidden,
num_cl... | 2,550 | 31.291139 | 84 | py |
HGB | HGB-master/NC/GTN/main_gnn.py | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
#from model import GTN
import pdb
import pickle
import argparse
from utils import f1_score
import dgl
from GNN import GCN, GAT
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--dataset', ty... | 6,794 | 45.862069 | 144 | py |
HGB | HGB-master/NC/GTN/model_sparse.py | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import math
from matplotlib import pyplot as plt
import pdb
from torch_geometric.utils import dense_to_sparse, f1_score
from gcn import GCNConv
from torch_scatter import scatter_add
import torch_sparse
import torch_sparse_old
from tor... | 6,068 | 39.192053 | 133 | py |
HGB | HGB-master/NC/GTN/gcn.py | import torch
from torch.nn import Parameter
from torch_scatter import scatter_add
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import remove_self_loops, add_self_loops
from inits import glorot, zeros
import pdb
class GCNConv(MessagePassing):
r"""The graph convolutional operator from th... | 4,197 | 35.504348 | 79 | py |
HGB | HGB-master/NC/GTN/messagepassing.py | import inspect
import torch
from torch_geometric.utils import scatter_
special_args = [
'edge_index', 'edge_index_i', 'edge_index_j', 'size', 'size_i', 'size_j'
]
__size_error_msg__ = ('All tensors which should get mapped to the same source '
'or target nodes must be of same size in dimensio... | 5,593 | 40.746269 | 79 | py |
HGB | HGB-master/NC/MAGNN/run_DBLP_gnn.py | import time
import argparse
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from utils.pytorchtools import EarlyStopping
from utils.data import load_DBLP_data
from utils.tools import index_generator, evaluate_results_nc, parse_minibatch
#from model import MAGNN_nc_mb
from sklear... | 12,373 | 47.14786 | 217 | py |
HGB | HGB-master/NC/MAGNN/run_DBLP.py | import time
import argparse
import torch
import torch.nn.functional as F
import numpy as np
from utils.pytorchtools import EarlyStopping
from utils.data import load_DBLP_data
from utils.tools import index_generator, evaluate_results_nc, parse_minibatch
from model import MAGNN_nc_mb
# Params
out_dim = 4
dropout_rate ... | 10,569 | 50.813725 | 139 | py |
HGB | HGB-master/NC/MAGNN/GNN.py | import torch
import torch.nn as nn
import dgl
from dgl.nn.pytorch import GraphConv
import dgl.function as fn
from dgl.nn.pytorch import edge_softmax, GATConv
class GAT(nn.Module):
def __init__(self,
g,
num_layers,
in_dim,
num_hidden,
... | 3,626 | 34.213592 | 115 | py |
HGB | HGB-master/NC/MAGNN/run_IMDB.py | import time
import argparse
import torch.nn.functional as F
import torch.sparse
import numpy as np
import dgl
from utils.pytorchtools import EarlyStopping
from utils.data import load_IMDB_data
from utils.tools import evaluate_results_nc
from model import MAGNN_nc
# Params
out_dim = 3
dropout_rate = 0.5
lr = 0.005
we... | 8,734 | 47.259669 | 133 | py |
HGB | HGB-master/NC/MAGNN/run_LastFM.py | import time
import argparse
import torch
import torch.nn.functional as F
import numpy as np
from sklearn.metrics import roc_auc_score, average_precision_score
from utils.pytorchtools import EarlyStopping
from utils.data import load_LastFM_data
from utils.tools import index_generator, parse_minibatch_LastFM
from model... | 13,301 | 57.087336 | 145 | py |
HGB | HGB-master/NC/MAGNN/utils/pytorchtools.py | import numpy as np
import torch
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, patience, verbose=False, delta=0, save_path='checkpoint.pt'):
"""
Args:
patience (int): How long to wait after last time... | 1,824 | 36.244898 | 111 | py |
HGB | HGB-master/NC/MAGNN/utils/tools.py | import torch
import dgl
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score, normalized_mutual_info_score, adjusted_rand_score
from sklearn.cluster import KMeans
from sklearn.svm import LinearSVC
def idx_to_one_hot(idx_arr):
one_hot = np.zeros((idx_arr.shap... | 11,415 | 46.173554 | 174 | py |
HGB | HGB-master/NC/MAGNN/model/MAGNN_lp.py | import torch
import torch.nn as nn
import numpy as np
from model.base_MAGNN import MAGNN_ctr_ntype_specific
# for link prediction task
class MAGNN_lp_layer(nn.Module):
def __init__(self,
num_metapaths_list,
num_edge_type,
etypes_lists,
in_dim,
... | 5,609 | 41.824427 | 115 | py |
HGB | HGB-master/NC/MAGNN/model/MAGNN_nc_mb.py | import torch
import torch.nn as nn
import numpy as np
from model.base_MAGNN import MAGNN_ctr_ntype_specific
# support for mini-batched forward
# only support one layer for one ctr_ntype
class MAGNN_nc_mb_layer(nn.Module):
def __init__(self,
num_metapaths,
num_edge_type,
... | 4,483 | 38.333333 | 119 | py |
HGB | HGB-master/NC/MAGNN/model/MAGNN_nc.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from model.base_MAGNN import MAGNN_ctr_ntype_specific
fc_switch = False
# multi-layer support
class MAGNN_nc_layer(nn.Module):
def __init__(self,
num_metapaths_list,
num_edge_type,
... | 5,888 | 41.985401 | 148 | py |
HGB | HGB-master/NC/MAGNN/model/base_MAGNN.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl.function as fn
from dgl.nn.pytorch import edge_softmax
class MAGNN_metapath_specific(nn.Module):
def __init__(self,
etypes,
out_dim,
num_heads,
rnn_type='gru',
... | 11,914 | 46.66 | 168 | py |
HGB | HGB-master/NC/RGCN/model.py | import torch as th
import torch.nn as nn
from dgl.nn.pytorch import GraphConv, GATConv
import torch.nn.functional as F
import numpy as np
class GAT(nn.Module):
def __init__(self,
in_dim,
num_hidden,
num_classes,
num_layers,
activ... | 6,321 | 32.273684 | 109 | py |
HGB | HGB-master/NC/RGCN/entity_classify.py | """
Modeling Relational Data with Graph Convolutional Networks
Paper: https://arxiv.org/abs/1703.06103
Code: https://github.com/tkipf/relational-gcn
Difference compared to tkipf/relation-gcn
* l2norm applied to all weights
* remove nodes that won't be touched
"""
import argparse
import numpy as np
import time
import ... | 9,677 | 36.657588 | 116 | py |
HGB | HGB-master/NC/HetGNN/code/tools.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from args import read_args
import numpy as np
import string
import re
import math
args = read_args()
class HetAgg(nn.Module):
def __init__(self, args, feature_list, a_neigh_list_train, p_neigh_list_train, v_neigh_l... | 13,023 | 37.877612 | 114 | py |
HGB | HGB-master/NC/HetGNN/code/HetGNN.py | import torch
import torch.optim as optim
import data_generator
import tools
from args import read_args
from torch.autograd import Variable
import numpy as np
import random
torch.set_num_threads(2)
import os
os.environ['CUDA_VISIBLE_DEVICES']='0'
class model_class(object):
def __init__(self, args):
super(model_clas... | 4,105 | 30.343511 | 108 | py |
HGB | HGB-master/NC/HetGNN/code/homoGNN.py | import numpy as np
import torch as th
import torch.nn as nn
from torch_geometric.nn import GCNConv, SAGEConv, TopKPooling, GATConv
import torch.nn.functional as F
import re
import random
import csv
import argparse
import datetime
from sklearn.metrics import f1_score
from itertools import *
from scipy.sparse import coo_... | 14,996 | 38.465789 | 116 | py |
HGB | HGB-master/NC/benchmark/methods/baseline/GNN.py | import torch
import torch.nn as nn
import dgl
from dgl.nn.pytorch import GraphConv
import dgl.function as fn
from dgl.nn.pytorch import edge_softmax, GATConv
from conv import myGATConv
class myGAT(nn.Module):
def __init__(self,
g,
edge_dim,
num_etypes,
... | 8,528 | 38.486111 | 169 | py |
HGB | HGB-master/NC/benchmark/methods/baseline/run.py | import sys
sys.path.append('../../')
import time
import argparse
import torch
import torch.nn.functional as F
import numpy as np
from utils.pytorchtools import EarlyStopping
from utils.data import load_data
from GNN import GCN, GAT, RGAT
import dgl
def sp_to_spt(mat):
coo = mat.tocoo()
values = coo.data
... | 7,070 | 41.089286 | 158 | py |
HGB | HGB-master/NC/benchmark/methods/baseline/conv.py | """Torch modules for graph attention networks(GAT)."""
# pylint: disable= no-member, arguments-differ, invalid-name
import torch as th
from torch import nn
from dgl import function as fn
from dgl.nn.pytorch import edge_softmax
from dgl._ffi.base import DGLError
from dgl.nn.pytorch.utils import Identity
from dgl.utils ... | 6,486 | 44.363636 | 92 | py |
HGB | HGB-master/NC/benchmark/methods/baseline/run_multi.py | import sys
sys.path.append('../../')
import time
import argparse
import os
import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from utils.pytorchtools import EarlyStopping
from utils.data import load_data
#from utils.tools import index_generator, evaluate_results_nc, parse_minibatch
f... | 7,689 | 41.252747 | 187 | py |
HGB | HGB-master/NC/benchmark/methods/baseline/run_new.py | import sys
sys.path.append('../../')
import time
import argparse
import os
import torch
import torch.nn.functional as F
import numpy as np
from utils.pytorchtools import EarlyStopping
from utils.data import load_data
#from utils.tools import index_generator, evaluate_results_nc, parse_minibatch
from GNN import myGAT
i... | 7,947 | 41.731183 | 189 | py |
HGB | HGB-master/NC/benchmark/methods/baseline/utils/pytorchtools.py | import numpy as np
import torch
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, patience, verbose=False, delta=0, save_path='checkpoint.pt'):
"""
Args:
patience (int): How long to wait after last time... | 1,824 | 36.244898 | 111 | py |
HGB | HGB-master/NC/benchmark/methods/baseline/utils/tools.py | import torch
import dgl
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score, normalized_mutual_info_score, adjusted_rand_score
from sklearn.cluster import KMeans
from sklearn.svm import LinearSVC
def idx_to_one_hot(idx_arr):
one_hot = np.zeros((idx_arr.shap... | 11,404 | 46.128099 | 174 | py |
HGB | HGB-master/NC/benchmark/methods/GTN/main.py | import sys
sys.path.append('../../')
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from model import GTN
import pdb
import pickle
import argparse
from utils import f1_score
from scripts.data_loader import data_loader
import scipy.sparse as sp
def load_data(args):
dataset, fu... | 8,779 | 43.120603 | 134 | py |
HGB | HGB-master/NC/benchmark/methods/GTN/main_sparse.py | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from model_sparse import GTN
from matplotlib import pyplot as plt
import pdb
from torch_geometric.utils import dense_to_sparse, f1_score, accuracy
from torch_geometric.data import Data
import torch_sparse
import pickle
#from mem impor... | 6,694 | 44.544218 | 125 | py |
HGB | HGB-master/NC/benchmark/methods/GTN/utils.py | from __future__ import division
import torch
def accuracy(pred, target):
r"""Computes the accuracy of correct predictions.
Args:
pred (Tensor): The predictions.
target (Tensor): The targets.
:rtype: int
"""
return (pred == target).sum().item() / target.numel()
def true_positi... | 3,565 | 22.460526 | 66 | py |
HGB | HGB-master/NC/benchmark/methods/GTN/model.py | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import math
from matplotlib import pyplot as plt
import pdb
class GTN(nn.Module):
def __init__(self, num_edge, num_channels, w_ins, w_out, num_class,num_layers,norm):
super(GTN, self).__init__()
self.num_edg... | 5,021 | 33.634483 | 118 | py |
HGB | HGB-master/NC/benchmark/methods/GTN/model_sparse.py | import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
import math
from matplotlib import pyplot as plt
import pdb
from torch_geometric.utils import dense_to_sparse, f1_score
from gcn import GCNConv
from torch_scatter import scatter_add
import torch_sparse
import torch_sparse_old
from tor... | 6,068 | 39.192053 | 133 | py |
HGB | HGB-master/NC/benchmark/methods/GTN/gcn.py | import torch
from torch.nn import Parameter
from torch_scatter import scatter_add
from torch_geometric.nn import MessagePassing
from torch_geometric.utils import remove_self_loops, add_self_loops
from inits import glorot, zeros
import pdb
class GCNConv(MessagePassing):
r"""The graph convolutional operator from th... | 4,197 | 35.504348 | 79 | py |
HGB | HGB-master/NC/benchmark/methods/GTN/messagepassing.py | import inspect
import torch
from torch_geometric.utils import scatter_
special_args = [
'edge_index', 'edge_index_i', 'edge_index_j', 'size', 'size_i', 'size_j'
]
__size_error_msg__ = ('All tensors which should get mapped to the same source '
'or target nodes must be of same size in dimensio... | 5,593 | 40.746269 | 79 | py |
HGB | HGB-master/NC/benchmark/methods/GTN/main_multi.py | import sys
sys.path.append('../../')
import torch
import numpy as np
import torch.nn as nn
import torch.nn.functional as F
from model import GTN
import pdb
import pickle
import argparse
from utils import f1_score
from scripts.data_loader import data_loader
import scipy.sparse as sp
def load_data(args):
dataset, fu... | 9,130 | 43.325243 | 127 | py |
HGB | HGB-master/NC/benchmark/methods/MAGNN/run_DBLP.py | import sys
sys.path.append('../../')
import time
import argparse
import torch
import torch.nn.functional as F
import numpy as np
from utils.pytorchtools import EarlyStopping
from utils.data import load_DBLP_data
from utils.tools import index_generator, evaluate_results_nc, parse_minibatch
from model import MAGNN_nc_m... | 11,478 | 50.245536 | 139 | py |
HGB | HGB-master/NC/benchmark/methods/MAGNN/run_IMDB.py | import time
import argparse
import torch.nn.functional as F
import torch.sparse
import numpy as np
import dgl
from utils.pytorchtools import EarlyStopping
from utils.data import load_IMDB_data
from utils.tools import evaluate_results_nc
from model import MAGNN_nc
# Params
out_dim = 3
dropout_rate = 0.5
lr = 0.005
we... | 8,734 | 47.259669 | 133 | py |
HGB | HGB-master/NC/benchmark/methods/MAGNN/run_ACM.py | import sys
sys.path.append('../../')
import time
import argparse
import torch
import torch.nn.functional as F
import numpy as np
from utils.pytorchtools import EarlyStopping
from utils.data import load_ACM_data
from utils.tools import index_generator, evaluate_results_nc, parse_minibatch
from model import MAGNN_nc_mb... | 11,505 | 50.366071 | 139 | py |
HGB | HGB-master/NC/benchmark/methods/MAGNN/run_LastFM.py | import time
import argparse
import torch
import torch.nn.functional as F
import numpy as np
from sklearn.metrics import roc_auc_score, average_precision_score
from utils.pytorchtools import EarlyStopping
from utils.data import load_LastFM_data
from utils.tools import index_generator, parse_minibatch_LastFM
from model... | 13,301 | 57.087336 | 145 | py |
HGB | HGB-master/NC/benchmark/methods/MAGNN/run_Freebase.py | import sys
sys.path.append('../../')
import time
import argparse
import torch
import torch.nn.functional as F
import numpy as np
from utils.pytorchtools import EarlyStopping
from utils.data import load_Freebase_data
from utils.tools import index_generator, evaluate_results_nc, parse_minibatch
from model import MAGNN_... | 11,818 | 49.943966 | 139 | py |
HGB | HGB-master/NC/benchmark/methods/MAGNN/run_IMDB_new.py | import sys
sys.path.append('../../')
import time
import argparse
import torch.nn.functional as F
import torch.sparse
import numpy as np
import dgl
from utils.pytorchtools import EarlyStopping
from utils.data import load_IMDB_data_new
from utils.tools import evaluate_results_nc
from model import MAGNN_nc
import torch
... | 9,039 | 46.830688 | 133 | py |
HGB | HGB-master/NC/benchmark/methods/MAGNN/utils/pytorchtools.py | import numpy as np
import torch
class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, patience, verbose=False, delta=0, save_path='checkpoint.pt'):
"""
Args:
patience (int): How long to wait after last time... | 1,824 | 36.244898 | 111 | py |
HGB | HGB-master/NC/benchmark/methods/MAGNN/utils/tools.py | import torch
import dgl
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import f1_score, normalized_mutual_info_score, adjusted_rand_score
from sklearn.cluster import KMeans
from sklearn.svm import LinearSVC
def idx_to_one_hot(idx_arr):
one_hot = np.zeros((idx_arr.shap... | 11,415 | 46.173554 | 174 | py |
HGB | HGB-master/NC/benchmark/methods/MAGNN/model/MAGNN_lp.py | import torch
import torch.nn as nn
import numpy as np
from model.base_MAGNN import MAGNN_ctr_ntype_specific
# for link prediction task
class MAGNN_lp_layer(nn.Module):
def __init__(self,
num_metapaths_list,
num_edge_type,
etypes_lists,
in_dim,
... | 5,609 | 41.824427 | 115 | py |
HGB | HGB-master/NC/benchmark/methods/MAGNN/model/MAGNN_nc_mb.py | import torch
import torch.nn as nn
import numpy as np
from model.base_MAGNN import MAGNN_ctr_ntype_specific
# support for mini-batched forward
# only support one layer for one ctr_ntype
class MAGNN_nc_mb_layer(nn.Module):
def __init__(self,
num_metapaths,
num_edge_type,
... | 4,483 | 38.333333 | 119 | py |
HGB | HGB-master/NC/benchmark/methods/MAGNN/model/MAGNN_nc.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
from model.base_MAGNN import MAGNN_ctr_ntype_specific
fc_switch = False
# multi-layer support
class MAGNN_nc_layer(nn.Module):
def __init__(self,
num_metapaths_list,
num_edge_type,
... | 5,888 | 41.985401 | 148 | py |
HGB | HGB-master/NC/benchmark/methods/MAGNN/model/base_MAGNN.py | import torch
import torch.nn as nn
import torch.nn.functional as F
import dgl.function as fn
from dgl.nn.pytorch import edge_softmax
class MAGNN_metapath_specific(nn.Module):
def __init__(self,
etypes,
out_dim,
num_heads,
rnn_type='gru',
... | 11,914 | 46.66 | 168 | py |
HGB | HGB-master/NC/benchmark/methods/RGCN/model.py | import torch as th
import torch.nn as nn
from dgl.nn.pytorch import GraphConv, GATConv
import torch.nn.functional as F
class BaseRGCN(nn.Module):
def __init__(self, in_dims, h_dim, out_dim, num_rels, num_bases,
num_hidden_layers=1, dropout=0,
use_self_loop=False, use_cuda=False):... | 4,725 | 32.28169 | 79 | py |
HGB | HGB-master/NC/benchmark/methods/RGCN/entity_classify.py | """
Modeling Relational Data with Graph Convolutional Networks
Paper: https://arxiv.org/abs/1703.06103
Code: https://github.com/tkipf/relational-gcn
Difference compared to tkipf/relation-gcn
* l2norm applied to all weights
* remove nodes that won't be touched
"""
from numpy.lib.function_base import append
from model i... | 13,282 | 36.416901 | 161 | py |
HGB | HGB-master/NC/benchmark/methods/HetGNN/code/DBLP/main.py | import torch
import torch.optim as optim
torch.set_num_threads(2)
from args import read_args
from torch.autograd import Variable
import numpy as np
import random
import pickle
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
import data_generator
import tools
import sys
sys.path.append('../../')
from scripts.data_l... | 4,839 | 37.72 | 116 | py |
HGB | HGB-master/NC/benchmark/methods/HetGNN/code/DBLP/tools.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from args import read_args
import numpy as np
import string
import re
import math
import sys
args = read_args()
class HetAgg(nn.Module):
def __init__(self, args, feature_list, a_neigh_list_train, p_neigh_list_t... | 16,705 | 48.426036 | 120 | py |
HGB | HGB-master/NC/benchmark/methods/HetGNN/code/ACM/main.py | import torch
import torch.optim as optim
torch.set_num_threads(2)
from args import read_args
from torch.autograd import Variable
import numpy as np
import random
import pickle
import os
import data_generator
import tools
import sys
os.environ['CUDA_VISIBLE_DEVICES'] = '6'
sys.path.append(f'../../')
from scripts.data_... | 4,850 | 36.898438 | 113 | py |
HGB | HGB-master/NC/benchmark/methods/HetGNN/code/ACM/data_generator.py | import six.moves.cPickle as pickle
import numpy as np
import string
import re
import random
import math
from collections import Counter
from itertools import *
import sys
import os
import json
from collections import defaultdict
import torch as th
class input_data(object):
def __init__(self, args, dl):
sel... | 12,625 | 48.513725 | 119 | py |
HGB | HGB-master/NC/benchmark/methods/HetGNN/code/ACM/tools.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from args import read_args
import numpy as np
import string
import re
import math
import sys
args = read_args()
class HetAgg(nn.Module):
def __init__(self, args, feature_list, neigh_list_train, train_id_list, d... | 8,585 | 44.428571 | 139 | py |
HGB | HGB-master/NC/benchmark/methods/HetGNN/code/IMDB/main.py | import torch
import torch.optim as optim
torch.set_num_threads(2)
from args import read_args
from torch.autograd import Variable
import numpy as np
import random
import pickle
import os
import data_generator
import tools
import sys
os.environ['CUDA_VISIBLE_DEVICES'] = '7'
sys.path.append(f'../../')
from scripts.data_... | 4,850 | 36.898438 | 113 | py |
HGB | HGB-master/NC/benchmark/methods/HetGNN/code/IMDB/data_generator.py | import six.moves.cPickle as pickle
import numpy as np
import string
import re
import random
import math
from collections import Counter
from itertools import *
import sys
import os
import json
from collections import defaultdict
import torch as th
from tqdm import tqdm
class input_data(object):
def __init__(self, ... | 13,863 | 49.414545 | 128 | py |
HGB | HGB-master/NC/benchmark/methods/HetGNN/code/IMDB/tools.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import Variable
from args import read_args
import numpy as np
import string
import re
import math
import sys
from collections import defaultdict
args = read_args()
class HetAgg(nn.Module):
def __init__(self, args, feature_list... | 8,660 | 45.069149 | 130 | py |
HGB | HGB-master/NC/benchmark/methods/HAN/main.py | import torch
from sklearn.metrics import f1_score
import dgl
from utils import load_data, EarlyStopping
import torch.nn.functional as F
from model_hetero import HAN, HAN_freebase
def score(logits, labels):
_, indices = torch.max(logits, dim=1)
prediction = indices.long().cpu().numpy()
labels = labels.cpu(... | 4,447 | 36.378151 | 113 | py |
HGB | HGB-master/NC/benchmark/methods/HAN/model_hetero.py | """This model shows an example of using dgl.metapath_reachable_graph on the original heterogeneous
graph.
Because the original HAN implementation only gives the preprocessed homogeneous graph, this model
could not reproduce the result in HAN as they did not provide the preprocessing code, and we
constructed another da... | 4,565 | 34.123077 | 98 | py |
HGB | HGB-master/NC/benchmark/methods/HAN/model_hetero_multi.py | """This model shows an example of using dgl.metapath_reachable_graph on the original heterogeneous
graph.
Because the original HAN implementation only gives the preprocessed homogeneous graph, this model
could not reproduce the result in HAN as they did not provide the preprocessing code, and we
constructed another da... | 4,541 | 33.938462 | 98 | py |
HGB | HGB-master/NC/benchmark/methods/HAN/utils.py | import datetime
import dgl
import errno
import numpy as np
import os
import pickle
import random
import torch
import copy
import torch as th
from dgl.data.utils import download, get_download_dir, _get_dgl_url
from pprint import pprint
from scipy import sparse
from scipy import io as sio
import sys
sys.path.append('.... | 13,732 | 34.485788 | 119 | py |
HGB | HGB-master/NC/benchmark/methods/HAN/main_multi.py | import torch
from sklearn.metrics import f1_score
import dgl
from utils import load_data, EarlyStopping
import torch.nn.functional as F
from model_hetero_multi import HAN
import numpy as np
def evaluate(model, g, features, labels, mask, loss_func, dl):
return loss
def main(args):
g, features, labels, num_cla... | 3,362 | 34.03125 | 81 | py |
HGB | HGB-master/NC/benchmark/methods/GNN/GNN.py | import torch
import torch.nn as nn
import dgl
from dgl.nn.pytorch import GraphConv
import dgl.function as fn
from dgl.nn.pytorch import edge_softmax, GATConv
class GAT(nn.Module):
def __init__(self,
g,
in_dims,
num_hidden,
num_classes,
... | 3,208 | 34.263736 | 102 | py |
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