def __init__(self, num_nodes, embedding_dim, features_dim, n_hidden,
n_classes, n_layers, activation, dropout, aggregator_type,
**kwargs):
super(GraphSAGE, self).__init__()
# embedding layer
self.embedding = nn.Embedding(num_nodes, embedding_dim=embedding_dim)
self.layers = nn.ModuleList()
in_feats = embedding_dim + features_dim
# input layer
self.layers.append(
SAGEConv(in_feats,
n_hidden,
aggregator_type,
feat_drop=dropout,
activation=activation))
# hidden layers
for i in range(n_layers - 1):
self.layers.append(
SAGEConv(n_hidden,
n_hidden,
aggregator_type,
feat_drop=dropout,
activation=activation))
# output layer
self.layers.append(
SAGEConv(n_hidden,
n_classes,
aggregator_type,
feat_drop=dropout,
activation=None)) # activation None
def __init__(self, in_feats, n_hidden, n_classes, n_layers, activation,
dropout, aggregator_type, num_genes):
super(GraphSAGE, self).__init__()
self.num_genes = num_genes
self.layers = nn.ModuleList()
# input layer
self.layers.append(
SAGEConv(in_feats,
n_hidden,
aggregator_type,
feat_drop=dropout,
activation=activation))
# hidden layers
for i in range(n_layers - 1):
self.layers.append(
SAGEConv(n_hidden,
n_hidden,
aggregator_type,
feat_drop=dropout,
activation=activation))
# self.dropout = nn.Dropout(p=0.5)
# output layer
self.linear1 = nn.Linear(n_hidden * 3, n_hidden)
self.dense1_bn = nn.BatchNorm1d(n_hidden)
self.linear2 = nn.Linear(n_hidden, n_classes)
def __init__(self, g, in_feats, n_hidden, n_classes, n_layers, activation,
dropout, aggregator_type):
super(GraphSAGE, self).__init__()
self.layers = nn.ModuleList()
self.g = g
# input layer
self.layers.append(
SAGEConv(in_feats,
n_hidden,
aggregator_type,
feat_drop=dropout,
activation=activation))
# hidden layers
for i in range(n_layers - 1):
self.layers.append(
SAGEConv(n_hidden,
n_hidden,
aggregator_type,
feat_drop=dropout,
activation=activation))
# output layer
self.layers.append(
SAGEConv(n_hidden,
n_classes,
aggregator_type,
feat_drop=dropout,
activation=None)) # activation None
def __init__(self, in_dim, hidden_dim, n_classes):
# noted, lstm and pool have the best performnace reported in GraphSage paper, the typical number of hidden layers is 2
super(Net, self).__init__()
self.layers = nn.ModuleList([
SAGEConv(
in_dim,
hidden_dim,
aggregator_type='lstm',
feat_drop=0.5,
activation=F.relu
), # need to change if we have more node or edge features
SAGEConv(hidden_dim,
hidden_dim,
aggregator_type='lstm',
feat_drop=0.5,
activation=F.relu),
# SAGEConv(hidden_dim, hidden_dim, aggregator_type='mean'),
SAGEConv(hidden_dim,
n_classes,
aggregator_type='lstm',
activation=None)
# SAGEConv(in_dim, hidden_dim, aggregator_type='pool', feat_drop=0.5, activation=F.relu), # need to change if we have more node or edge features
# SAGEConv(hidden_dim, hidden_dim, aggregator_type='pool', feat_drop=0.5,activation=F.relu),
# # SAGEConv(hidden_dim, hidden_dim, aggregator_type='mean'),
# SAGEConv(hidden_dim, n_classes, aggregator_type='pool', activation=None)
])
def __init__(self, layer_sizes):
super().__init__()
input_size, hidden_size, embedding_size = layer_sizes
self.convs = nn.ModuleList([
SAGEConv(input_size, hidden_size, 'mean'),
SAGEConv(hidden_size, hidden_size, 'mean'),
SAGEConv(hidden_size, embedding_size, 'mean')
])
self.skip_lins = nn.ModuleList([
nn.Linear(input_size, hidden_size, bias=False),
nn.Linear(input_size, hidden_size, bias=False),
])
self.layer_norms = nn.ModuleList([
LayerNorm(hidden_size),
LayerNorm(hidden_size),
LayerNorm(embedding_size),
])
self.activations = nn.ModuleList([
nn.PReLU(),
nn.PReLU(),
nn.PReLU(),
])
def __init__(self,
in_feats,
n_hidden,
n_classes,
n_layers,
activation,
dropout,
aggregator_type):
super(GraphSAGE, self).__init__()
self.layers = nn.ModuleList()
# self.dropout = mlpcell.Dropout(dropout)
self.dropout = nn.Dropout(dropout)
self.activation = activation
# self.layers.append(SAGEConvOpt(in_feats, n_hidden, 'mean', feat_drop=dropout, bias=True, norm=None, activation=activation))
# for i in range(1, n_layers - 1):
# self.layers.append(SAGEConvOpt(n_hidden, n_hidden, 'mean', feat_drop=dropout, bias=True, norm=None, activation=activation))
# self.layers.append(SAGEConvOpt(n_hidden, n_classes, 'mean', feat_drop=0.0, bias=True)) # activation None
# # input layer
self.layers.append(SAGEConv(in_feats, n_hidden, aggregator_type))
# # hidden layers
for i in range(1, n_layers - 1):
self.layers.append(SAGEConv(n_hidden, n_hidden, aggregator_type))
# # output layer
self.layers.append(SAGEConv(n_hidden, n_classes, aggregator_type)) # activation None
def __init__(self, in_dim, hidden_dim, n_classes):
super(Net, self).__init__()
self.layers = nn.ModuleList([
SAGEConv(in_dim, hidden_dim, aggregator_type='lstm'),
# SAGEConv(hidden_dim, hidden_dim, aggregator_type='mean'),
SAGEConv(hidden_dim, hidden_dim, aggregator_type='lstm')
])
self.linear = nn.Linear(hidden_dim, n_classes) # predice classes
def __init__(self, in_dim, hidden_dim, n_classes):
super(Net, self).__init__()
self.layers = nn.ModuleList([
SAGEConv(
in_dim, hidden_dim, aggregator_type='lstm'
), # need to change if we have more node or edge features
SAGEConv(hidden_dim, hidden_dim, aggregator_type='lstm'),
SAGEConv(hidden_dim, hidden_dim, aggregator_type='lstm')
])
self.linear = nn.Linear(hidden_dim, n_classes) # predice classes
def __init__(self, in_feats, n_hidden, n_classes, n_layers, activation,
dropout, aggregator_type):
super(GraphSAGE, self).__init__()
self.layers = nn.ModuleList()
self.dropout = nn.Dropout(dropout)
self.activation = activation
self.layers.append(SAGEConv(in_feats, n_hidden, aggregator_type))
# hidden layers
for i in range(n_layers - 1):
self.layers.append(SAGEConv(n_hidden, n_hidden, aggregator_type))
# output layer
self.layers.append(SAGEConv(n_hidden, n_classes, aggregator_type))
def __init__(self, in_dim, hidden_dim, n_classes):
super(Net, self).__init__()
self.layers = nn.ModuleList([
# please see the model implementation in the following
# https://docs.dgl.ai/_modules/dgl/nn/pytorch/conv/sageconv.html#SAGEConv
SAGEConv(
in_dim, hidden_dim, aggregator_type='mean'
), # need to change if we have more node or edge features
SAGEConv(hidden_dim, hidden_dim, aggregator_type='mean'),
SAGEConv(hidden_dim, hidden_dim, aggregator_type='mean')
])
self.linear = nn.Linear(hidden_dim, n_classes) # predice classes
def __init__(self,
annotation_size,
out_feats,
n_steps,
device,
n_etypes=12,
gnn_type='sage',
sage_type='gcn',
tok_embedding=0,
residual=False):
super(Graph_NN, self).__init__()
self.annotation_size = annotation_size
self.out_feats = out_feats
self.layers = nn.ModuleList()
# control signals
self.tok_embedding_flag = tok_embedding
self.residual = residual
self.gnn_type = gnn_type
if tok_embedding == 1:
self.tok_embedding = nn.Linear(out_feats, out_feats)
elif tok_embedding == 2:
self.tok_embedding = nn.Sequential(
nn.Embedding(annotation_size, out_feats), )
#ggnn
if gnn_type == 'ggnn':
self.ggnn = GatedGraphConv(in_feats=out_feats,
out_feats=out_feats,
n_steps=n_steps,
n_etypes=n_etypes)
#graphsage
if gnn_type == 'sage':
self.layers.append(
SAGEConv(out_feats,
out_feats,
sage_type,
feat_drop=0.1,
activation=F.relu))
for i in range(n_steps - 1):
self.layers.append(
SAGEConv(out_feats,
out_feats,
sage_type,
feat_drop=0.1,
activation=F.relu))
self.device = device
def __init__(self,
g,
in_feats,
n_classes,
n_layers=1,
n_hidden=16,
activation=F.relu,
dropout=0.5,
aggregator_type='pool'):
super(GraphSAGE, self).__init__()
self.layers = nn.ModuleList()
self.g = g
# input layer
self.layers.append(SAGEConv(in_feats, n_hidden, aggregator_type, feat_drop=dropout, activation=activation))
# output layer
self.layers.append(SAGEConv(n_hidden, n_classes, aggregator_type, feat_drop=dropout, activation=None)) # activation None
def __init__(self, in_channels, hidden_channels, out_channels, num_layers,
dropout, aggr_type):
super(Graph_Predictor, self).__init__()
self.conv = nn.ModuleList()
self.conv.append(SAGEConv(in_channels, hidden_channels, aggr_type))
for _ in range(num_layers - 1):
self.conv.append(
SAGEConv(hidden_channels, hidden_channels, aggr_type))
self.lin = nn.ModuleList()
for _ in range(num_layers - 1):
self.lin.append(nn.Linear(hidden_channels, hidden_channels))
self.lin.append(nn.Linear(hidden_channels, out_channels))
self.pooling = AvgPooling()
self.dropout = nn.Dropout(dropout)
def __init__(self, in_feats, n_hidden, n_classes, n_layers, activation,
dropout, aggregator_type):
super(JKNet_model, self).__init__()
self.layers = nn.ModuleList()
# input layer
self.layers.append(
SAGEConv(in_feats,
n_hidden,
aggregator_type,
feat_drop=0.,
activation=activation))
# hidden layers
for i in range(n_layers - 1):
self.layers.append(
SAGEConv(n_hidden,
n_hidden,
aggregator_type,
feat_drop=dropout,
activation=activation))
# output layer
self.layer_output = nn.Linear(n_hidden * n_layers, n_classes)
def __init__(self,
in_dim,
hidden_dim_1,
fc_hidden_1,
fc_hidden_2,
hidden_dim_2,
num_classes,
feat_drop=0,
use_cuda=False):
"""
Constructor for the GraphAttConvBinaryClassifier class
Parameters:
in_dim (int): Dimension of features for each node
hidden_dim (int): Dimension of hidden embeddings
num_classes (int): Number of output classes
use_cuda (bool): Indicates whether GPU should be utilized or not
"""
super(GraphSageBinaryClassifier, self).__init__()
# Model layers
self.conv1 = SAGEConv(in_dim,
hidden_dim_1,
feat_drop=feat_drop,
aggregator_type='mean')
self.conv2 = SAGEConv(hidden_dim_1,
hidden_dim_2,
feat_drop=feat_drop,
aggregator_type='mean')
self.conv3 = SAGEConv(hidden_dim_2,
fc_hidden_1,
feat_drop=feat_drop,
aggregator_type='mean')
self.fc_1 = nn.Linear(fc_hidden_1, fc_hidden_2)
self.fc_2 = nn.Linear(fc_hidden_2, num_classes)
self.out = nn.LogSoftmax(dim=1)
self.use_cuda = use_cuda
def __init__(self, input_dimension: int, dimensions: _typing.Sequence[int],
act: _typing.Optional[str], dropout: _typing.Optional[float],
agg: str):
super(_SAGE, self).__init__()
if agg not in ("gcn", "pool", "mean", "lstm"):
raise ValueError("Unsupported aggregator type")
self.__convolution_layers: torch.nn.ModuleList = torch.nn.ModuleList()
for layer, _dimension in enumerate(dimensions):
self.__convolution_layers.append(
SAGEConv(
input_dimension if layer == 0 else dimensions[layer - 1],
_dimension, agg))
self._act: _typing.Optional[str] = act
self._dropout: _typing.Optional[float] = dropout
def __init__(self,
hid_dim,
n_heads,
pf_dim,
dropout,
device,
mem_dim,
gnn_layer_num=2):
super().__init__()
self.layer_norm = nn.LayerNorm(hid_dim)
if mem_dim != 0:
self.self_attention = MultiHeadAttentionLayer_wMem(hid_dim,
n_heads,
dropout,
device,
m=mem_dim)
else:
self.self_attention = MultiHeadAttentionLayer(
hid_dim, n_heads, dropout, device)
self.positionwise_feedforward = PositionwiseFeedforwardLayer(
hid_dim, pf_dim, dropout)
self.layers = nn.ModuleList()
self.dropout = nn.Dropout(dropout)
self.layers.append(
SAGEConv(hid_dim, hid_dim, "gcn", feat_drop=0, activation=F.relu))
for i in range(gnn_layer_num - 1):
self.layers.append(
SAGEConv(hid_dim,
hid_dim,
"gcn",
feat_drop=0,
activation=F.relu))
self.hid_dim = hid_dim
self.dropout = nn.Dropout(dropout)
def __init__(self, args):
super(GraphSAGE, self).__init__()
self.args = args
self.num_layer = int(self.args["num_layers"])
missing_keys = list(
set([
"features_num", "num_class", "num_layers", "hidden", "dropout",
"act", "agg"
]) - set(self.args.keys()))
if len(missing_keys) > 0:
raise Exception("Missing keys: %s." % ",".join(missing_keys))
if not self.num_layer == len(self.args["hidden"]) + 1:
LOGGER.warn(
"Warning: layer size does not match the length of hidden units"
)
if self.args["agg"] not in ("gcn", "pool", "mean", "lstm"):
self.args["agg"] = "gcn"
self.convs = torch.nn.ModuleList()
self.convs.append(
SAGEConv(self.args["features_num"],
self.args["hidden"][0],
aggregator_type=self.args["agg"]))
for i in range(self.num_layer - 2):
self.convs.append(
SAGEConv(self.args["hidden"][i],
self.args["hidden"][i + 1],
aggregator_type=self.args["agg"]))
self.convs.append(
SAGEConv(self.args["hidden"][-1],
self.args["num_class"],
aggregator_type=self.args["agg"]))
def __init__(self,
in_feats: tuple,
out_feats,
weight=True,
device=None,
dropout_rate=0.0):
super(GCMCGraphSage, self).__init__()
self._in_feats = in_feats
self._out_feats = out_feats
self.device = device
self.dropout = nn.Dropout(dropout_rate)
if weight:
self.feat1 = nn.Parameter(th.Tensor(in_feats[0], 10))
self.feat2 = nn.Parameter(th.Tensor(in_feats[1], 10))
else:
self.register_parameter('weight', None)
self.reset_parameters()
from dgl.nn.pytorch.conv import SAGEConv
self.sage = SAGEConv(10, out_feats, aggregator_type='mean')
def __init__(self,
in_feats,
n_hidden,
n_classes,
n_layers,
activation,
dropout,
aggregator_type):
super(GraphSAGE, self).__init__()
self.droplayer = nn.Dropout(p=dropout)
# input layer
self.inplayer = nn.Linear(in_feats, n_hidden)
self.layers = nn.ModuleList()
# hidden layers
for i in range(n_layers):
self.layers.append(SAGEConv(n_hidden, n_hidden, aggregator_type, feat_drop=dropout, activation=activation))
# output layer
self.outlayer = nn.Linear(n_hidden, n_classes)
def __init__(self, config):
super(Procedure, self).__init__()
self.config = config
self.node_embedding = torch.nn.Embedding(config['node_number'],
config['node_hidden_size'])
self.times_embedding = torch.nn.Embedding(
config['n_times_types'], config['appearance_time_hidden_size'])
self.interval_embedding = torch.nn.Embedding(
config['n_intervals'], config['interval_hidden_size'])
self.connection_embedding = torch.nn.Embedding(
config['n_connection_types'], config['connection_hidden_size'])
self.score_MLP = score_MLP(config)
self.GNN = SAGEConv(config['node_hidden_size'],
config['node_hidden_size'], 'mean')
if torch.cuda.is_available():
self.node_embedding = self.node_embedding.cuda()
self.connection_embedding = self.connection_embedding.cuda()
self.score_MLP = self.score_MLP.cuda()
def __init__(
self,
g,
nentity, # input feat
n_hidden, # hidden dimension
n_classes, # Output feature size; i.e, the number of dimensions of :math:`h_i^{(l+1)}`.
n_layers, # gcn layer
activation, # activation str format
dropout,
aggregator_type,
dist_func,
ent_ini, # num of training nodes
in_feats=64,
freeze=False):
super(GraphSAGE, self).__init__()
self.layers = nn.ModuleList()
self.freeze = freeze
if activation == 'relu':
act_fun = Fn.relu
elif activation == 'gelu':
act_fun = Fn.gelu
elif activation == 'sigmoid':
act_fun = Fn.sigmoid
elif activation == 'elu':
act_fun = Fn.elu
elif activation == 'glu':
act_fun = Fn.glu
elif activation == 'none':
act_fun = None
else:
raise ValueError(
'the graph activation function should be [relu gelu sigmoid elu glu or none] but instead of {}'
.format(activation))
self.activation = act_fun
# input layer
self.layers.append(
SAGEConv(in_feats,
n_hidden,
aggregator_type,
feat_drop=dropout,
activation=act_fun))
# hidden layers
for i in range(n_layers - 1):
self.layers.append(
SAGEConv(n_hidden,
n_hidden,
aggregator_type,
feat_drop=dropout,
activation=act_fun))
# output layer
self.layers.append(
SAGEConv(n_hidden,
n_classes,
aggregator_type,
feat_drop=dropout,
activation=act_fun)) # activation None
self.predictor = ScorePredictor(dist_func)
if ent_ini is None:
self.entity_embedding = nn.Parameter(
torch.zeros(nentity, self.entity_dim))
nn.init.xavier_uniform_(tensor=self.entity_embedding,
gain=activation)
self.entity_dim = in_feats
else:
embed_li = []
hidden_dim = len(ent_ini[0])
for i in range(nentity):
embed_li.append(ent_ini[i])
embed_li = numpy.asarray(embed_li)
embed_li = torch.from_numpy(embed_li).float()
self.entity_embedding = nn.Parameter(embed_li,
requires_grad=not freeze)
# self.entity_dim = hidden_dim
# self.entity_embedding = nn.Embedding.from_pretrained(embed_li, freeze=freeze)
self.g = g
self.g.ndata['feature'] = self.entity_embedding
def __init__(self,
use_KG,
input_node_dim,
gnn_model,
num_gnn_layers,
n_hidden,
dropout,
use_attention=True,
n_entities=None,
n_relations=None,
relation_dim=None,
reg_lambda_kg=0.01,
reg_lambda_gnn=0.01,
res_type="Bi"):
super(Model, self).__init__()
self._use_KG = use_KG
self._n_entities = n_entities
self._n_relations = n_relations
self._gnn_model = gnn_model
self._use_attention = use_attention
self._reg_lambda_kg = reg_lambda_kg
self._reg_lambda_gnn = reg_lambda_gnn
### for input node embedding
self.entity_embed = nn.Embedding(n_entities,
input_node_dim) ### e_h, e_t
self.relation_embed = nn.Embedding(n_relations, relation_dim) ### e_r
self.W_R = nn.Parameter(
th.Tensor(n_relations, input_node_dim, relation_dim)) ### W_r
nn.init.xavier_uniform_(self.W_R, gain=nn.init.calculate_gain('relu'))
self.layers = nn.ModuleList()
for i in range(num_gnn_layers):
r = int(math.pow(2, i))
act = None if i + 1 == num_gnn_layers else F.relu
if i == 0:
if gnn_model == "kgat":
self.layers.append(
KGATConv(input_node_dim, n_hidden // r, dropout))
elif gnn_model == "graphsage":
self.layers.append(
SAGEConv(input_node_dim,
n_hidden // r,
aggregator_type="mean",
feat_drop=dropout,
activation=act))
else:
raise NotImplementedError
else:
r2 = int(math.pow(2, i - 1))
if gnn_model == "kgat":
self.layers.append(
KGATConv(n_hidden // r2, n_hidden // r, dropout))
elif gnn_model == "graphsage":
self.layers.append(
SAGEConv(n_hidden // r2,
n_hidden // r,
aggregator_type="mean",
feat_drop=dropout,
activation=act))
else:
raise NotImplementedError
def __init__(self, nfeat, nhid, nclass, dropout):
super(GraphSage, self).__init__()
self.dropout = dropout
self.conv1 = SAGEConv(nfeat, nhid,aggregator_type='mean')
self.conv2 = SAGEConv(nhid, nclass,aggregator_type='mean')
# -*- coding: utf-8 -*- """ Created on Thu Nov 26 16:56:12 2020 @author: user """ import dgl from dgl.nn.pytorch.conv import SAGEConv g = dgl.graph(data=(train[:, 0], train[:, 1])) GNN = SAGEConv(8, 8, 'mean') a = GNN(g, node_embedding.weight)
