Graph Embedding I2I
时间: 2024-03-11 12:41:50 浏览: 23
Graph Embedding I2I是一种用于图嵌入的技,其中I2I代表"Instance to Instance"。它的目标是将图中的节点映射到一个低维向量空间中,以便能够捕捉节点之间的语义和结构关系。
在Graph Embedding I2I中,每个节点被表示为一个向量,这个向量可以用于计算节点之间的相似性或进行其他任务,如节点分类、链接预测等。通过将节点映射到低维向量空间,可以更方便地进行图数据的分析和处理。
Graph Embedding I2I的实现通常包括以下步骤:
1. 构建图:首先需要构建一个图,其中节点表示实体或对象,边表示它们之间的关系。可以使用现有的图数据或从原始数据中构建图。
2. 定义相似性度量:为了将节点映射到向量空间中,需要定义节点之间的相似性度量方法。常用的方法包括基于邻居节点的相似性、基于路径的相似性等。
3. 学习嵌入向量:使用机器学习算法或深度学习模型,将节点映射到低维向量空间中。这可以通过最小化节点之间的相似性差异来实现。
4. 应用嵌入向量:学习到的嵌入向量可以用于各种图分析任务,如节点分类、链接预测、社区发现等。
相关问题
Molecular-graph-BERT 代码实现
Molecular-graph-BERT 是一种基于图神经网络的化学分子表示方法,可用于分子性质预测、分子设计等应用。以下是 Molecular-graph-BERT 的代码实现。
1. 安装依赖
```python
!pip install torch
!pip install dgl
!pip install rdkit
```
2. 数据预处理
```python
import dgl
from rdkit import Chem
from dgl.data.utils import load_graphs, save_graphs
from dgl.data.chem.utils import smiles_to_bigraph, CanonicalAtomFeaturizer
# 将 SMILES 序列转换为 DGLGraph
def graph_from_smiles(smiles):
mol = Chem.MolFromSmiles(smiles)
return smiles_to_bigraph(smiles, atom_featurizer=CanonicalAtomFeaturizer())
# 读取数据,并将 SMILES 序列转换为 DGLGraph
data = []
with open('data.txt', 'r') as f:
for line in f:
smiles, label = line.strip().split('\t')
g = graph_from_smiles(smiles)
label = int(label)
data.append((g, label))
# 将 DGLGraph 序列化并保存为二进制文件
save_graphs('data.bin', data)
```
3. 定义模型
```python
import torch
import torch.nn as nn
import dgl.function as fn
# 定义 GraphConvLayer
class GraphConvLayer(nn.Module):
def __init__(self, in_feats, out_feats):
super(GraphConvLayer, self).__init__()
self.linear = nn.Linear(in_feats, out_feats)
self.activation = nn.ReLU()
def forward(self, g, features):
with g.local_scope():
g.ndata['h'] = features
g.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'neigh'))
h_neigh = g.ndata['neigh']
h = self.linear(features + h_neigh)
h = self.activation(h)
return h
# 定义 MolecularGraphBERT 模型
class MolecularGraphBERT(nn.Module):
def __init__(self, hidden_size, num_layers):
super(MolecularGraphBERT, self).__init__()
self.embed = nn.Embedding(100, hidden_size)
self.layers = nn.ModuleList([GraphConvLayer(hidden_size, hidden_size) for _ in range(num_layers)])
self.pool = dgl.nn.pytorch.glob.max_pool
def forward(self, g):
h = self.embed(g.ndata['feat'])
for layer in self.layers:
h = layer(g, h)
g.ndata['h'] = h
hg = self.pool(g, g.ndata['h'])
return hg
```
4. 训练模型
```python
from torch.utils.data import DataLoader
from dgl.data.utils import load_graphs
# 加载数据
data, _ = load_graphs('data.bin')
labels = torch.tensor([d[1] for d in data])
# 划分训练集和测试集
train_data, test_data = data[:80], data[80:]
train_labels, test_labels = labels[:80], labels[80:]
# 定义训练参数
lr = 0.01
num_epochs = 50
hidden_size = 128
num_layers = 3
# 定义模型和优化器
model = MolecularGraphBERT(hidden_size, num_layers)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
# 训练模型
for epoch in range(num_epochs):
model.train()
for i, (g, label) in enumerate(train_data):
pred = model(g)
loss = nn.functional.binary_cross_entropy_with_logits(pred, label.unsqueeze(0).float())
optimizer.zero_grad()
loss.backward()
optimizer.step()
model.eval()
with torch.no_grad():
train_acc = 0
for g, label in train_data:
pred = model(g)
train_acc += ((pred > 0).long() == label).sum().item()
train_acc /= len(train_data)
test_acc = 0
for g, label in test_data:
pred = model(g)
test_acc += ((pred > 0).long() == label).sum().item()
test_acc /= len(test_data)
print('Epoch {:d} | Train Acc {:.4f} | Test Acc {:.4f}'.format(epoch, train_acc, test_acc))
```
以上就是 Molecular-graph-BERT 的代码实现。需要注意的是,由于 Molecular-graph-BERT 是基于图神经网络的方法,需要使用 DGL 库来构建和操作图数据,因此需要先安装 DGL 库。
Word2Vec模型代码
以下是一个简单的 Word2Vec 模型的代码示例:
```python
import numpy as np
import tensorflow as tf
from tensorflow.contrib.tensorboard.plugins import projector
# 定义参数
batch_size = 64
embedding_dimension = 5
negative_samples = 8
LOG_DIR = "logs/word2vec_intro"
# 语料
corpus_raw = 'He is the king . The king is royal . She is the royal queen '
# 数据预处理
def preprocess_text(text):
# 去除标点符号并转化为小写
text = text.lower()
text = text.replace('.', ' .')
words = text.split()
return words
words = preprocess_text(corpus_raw)
word2int = {}
int2word = {}
vocab_size = 0
# 构建vocabulary
for word in words:
if word not in word2int:
word2int[word] = vocab_size
int2word[vocab_size] = word
vocab_size += 1
# 输入和输出的占位符
x_inputs = tf.placeholder(tf.int32, shape=[batch_size])
y_inputs = tf.placeholder(tf.int32, shape=[batch_size, 1])
# 随机选择负样本
embeddings = tf.Variable(tf.random_uniform([vocab_size, embedding_dimension], -1.0, 1.0))
softmax_weights = tf.Variable(tf.truncated_normal([vocab_size, embedding_dimension], stddev=0.5 / np.sqrt(embedding_dimension)))
softmax_biases = tf.Variable(tf.zeros([vocab_size]))
embed = tf.nn.embedding_lookup(embeddings, x_inputs)
# 损失函数
loss = tf.reduce_mean(tf.nn.sampled_softmax_loss(weights=softmax_weights, biases=softmax_biases, inputs=embed, labels=y_inputs, num_sampled=negative_samples, num_classes=vocab_size))
# 优化器
optimizer = tf.train.AdagradOptimizer(0.5).minimize(loss)
# 初始化变量
init = tf.global_variables_initializer()
# 保存embedding的metadata
file_writer = tf.summary.FileWriter(LOG_DIR)
metadata = os.path.join(LOG_DIR, 'metadata.tsv')
with open(metadata, 'w') as metadata_file:
for i in range(vocab_size):
metadata_file.write('{}\n'.format(int2word[i]))
# 运行会话
with tf.Session() as sess:
# 初始化变量
sess.run(init)
total_loss = 0
writer = tf.summary.FileWriter(LOG_DIR, sess.graph)
# 训练模型
for epoch in range(1000):
batch_inputs, batch_labels = generate_batch(words, batch_size, window_size)
feed_dict = {x_inputs: batch_inputs, y_inputs: batch_labels}
# 梯度下降
_, loss_val = sess.run([optimizer, loss], feed_dict=feed_dict)
total_loss += loss_val
if epoch % 100 == 0:
print("Epoch ", epoch, "Avg loss: ", total_loss / (epoch + 1))
# 保存embedding
embedding_var = tf.Variable(embeddings, name='embedding')
sess.run(embedding_var.initializer)
config = projector.ProjectorConfig()
embedding = config.embeddings.add()
embedding.tensor_name = embedding_var.name
embedding.metadata_path = metadata
projector.visualize_embeddings(file_writer, config)
# 关闭会话
sess.close()
```
这个代码示例中使用了 TensorFlow 框架,实现了一个简单的 Word2Vec 模型。其中包括了数据预处理、构建词汇表、定义输入和输出占位符、随机选择负样本、定义损失函数、优化器等步骤。同时,为了可视化词向量,还使用了 TensorBoard 工具。
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