图神经网络

# coding=utf-8
import os
import numpy as np
import tensorflow as tf
import tf_geometric as tfg
import pickle
from tf_geometric.utils import tf_utils
from collections import Counter
from tqdm import tqdm
from sklearn.model_selection import train_test_split
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow import keras

data_dir = "rt-polarity"  # 数据集路径，需要在运行前导入当前目录

texts = []
labels = []
for fname, label in [["rt-polarity.pos", 1], ["rt-polarity.neg", 0]]:
    fpath = os.path.join(data_dir, fname)
    with open(fpath, "r", encoding="utf-8") as f:
        for line in f:
            texts.append(line.strip())
            labels.append(label)

train_texts, test_texts, train_labels, test_labels = train_test_split(texts, labels, test_size=0.2)

tokenizer = Tokenizer()
tokenizer.fit_on_texts(train_texts)

train_sequences = tokenizer.texts_to_sequences(train_texts)
test_sequences = tokenizer.texts_to_sequences(test_texts)


class PMIModel(object):
    def __init__(self):
        self.word_counter = None
        self.pair_counter = None

    def get_pair_id(self, word0, word1):
        pair_id = tuple(sorted([word0, word1]))
        return pair_id

    def fit(self, sequences, window_size):
        self.word_counter = Counter()
        self.pair_counter = Counter()
        num_windows = 0
        for sequence in tqdm(sequences):
            for offset in range(len(sequence) - window_size):
                window = sequence[offset:offset + window_size]
                num_windows += 1
                for i, word0 in enumerate(window):
                    self.word_counter[word0] += 1
                    for j, word1 in enumerate(window[i + 1:]):
                        pair_id = self.get_pair_id(word0, word1)
                        self.pair_counter[pair_id] += 1

        for word, count in self.word_counter.items():
            self.word_counter[word] = count / num_windows
        for pair_id, count in self.pair_counter.items():
            self.pair_counter[pair_id] = count / num_windows

    def transform(self, word0, word1):
        prob_a = self.word_counter[word0]
        prob_b = self.word_counter[word1]
        pair_id = self.get_pair_id(word0, word1)
        prob_pair = self.pair_counter[pair_id]

        if prob_a == 0 or prob_b == 0 or prob_pair == 0:
            return 0

        pmi = np.log(prob_pair / (prob_a * prob_b))
        pmi = np.maximum(pmi, 0.0)
        return pmi


def build_word_graph(num_words, pmi_model, embedding_size):
    x = tf.Variable(tf.random.truncated_normal([num_words, embedding_size], stddev=1 / np.sqrt(embedding_size)), dtype=tf.float32)
    edges = []
    edge_weight = []
    for (word0, word1) in pmi_model.pair_counter.keys():
        pmi = pmi_model.transform(word0, word1)
        if pmi > 0:
            edges.append([word0, word1])
            edge_weight.append(pmi)
            edges.append([word1, word0])
            edge_weight.append(pmi)
    edge_index = np.array(edges).T
    return tfg.Graph(x=x, edge_index=edge_index, edge_weight=edge_weight)


def build_combined_graph(word_graph, sequences, embedding_size):
    num_words = word_graph.num_nodes
    x = tf.zeros([len(sequences), embedding_size], dtype=tf.float32)
    edges = []
    edge_weight = []
    for i, sequence in enumerate(sequences):
        doc_node_index = num_words + i
        for word in sequence:
            edges.append([doc_node_index, word])  # only directed edge
            edge_weight.append(1.0)  # use BOW instaead of TF-IDF

    edge_index = np.array(edges).T
    x = tf.concat([word_graph.x, x], axis=0)
    edge_index = np.concatenate([word_graph.edge_index, edge_index], axis=1)
    edge_weight = np.concatenate([word_graph.edge_weight, edge_weight], axis=0)
    return tfg.Graph(x=x, edge_index=edge_index, edge_weight=edge_weight)


# building PMI model is time consuming, using cache to optimize
pmi_cache_path = "cached_pmi_model.p"
if os.path.exists(pmi_cache_path):
    with open(pmi_cache_path, "rb") as f:
        pmi_model = pickle.load(f)
else:
    pmi_model = PMIModel()
    pmi_model.fit(train_sequences, window_size=6)
    with open(pmi_cache_path, "wb") as f:
        pickle.dump(pmi_model, f)

embedding_size = 150
num_words = len(tokenizer.word_index) + 1
word_graph = build_word_graph(num_words, pmi_model, embedding_size)
train_combined_graph = build_combined_graph(word_graph, train_sequences, embedding_size)
test_combined_graph = build_combined_graph(word_graph, test_sequences, embedding_size)

print(word_graph)
print(train_combined_graph)
print(test_combined_graph)

num_classes = 2


# 构建GCN模型
class GCNModel(tf.keras.Model):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.gcn0 = tfg.layers.GCN(100, activation=tf.nn.relu)
        self.gcn1 = tfg.layers.GCN(num_classes)
        self.dropout = keras.layers.Dropout(0.5)

    def call(self, inputs, training=None, mask=None, cache=None):
        x, edge_index, edge_weight = inputs
        h = self.gcn0([x, edge_index, edge_weight], cache=cache)
        h = self.dropout(h, training=training)
        h = self.gcn1([h, edge_index, edge_weight], cache=cache)
        return h


model = GCNModel()
model.gcn0.cache_normed_edge(train_combined_graph)
model.gcn0.cache_normed_edge(test_combined_graph)


@tf_utils.function
def forward(graph, training=False):
    logits = model([graph.x, graph.edge_index, graph.edge_weight], cache=graph.cache, training=training)
    logits = logits[num_words:]
    return logits


def compute_loss(logits, labels):
    losses = tf.nn.softmax_cross_entropy_with_logits(
        logits=logits,
        labels=tf.one_hot(labels, depth=num_classes)
    )
    mean_loss = tf.reduce_mean(losses)
    return mean_loss


optimizer = tf.keras.optimizers.Adam(learning_rate=5e-2)
for step in tqdm(range(1000)):
    with tf.GradientTape() as tape:
        logits = forward(train_combined_graph, training=True)
        mean_loss = compute_loss(logits, train_labels)

    vars = tape.watched_variables()
    grads = tape.gradient(mean_loss, vars)
    optimizer.apply_gradients(zip(grads, vars))

    if step % 10 == 0:
        logits = forward(test_combined_graph)
        preds = tf.argmax(logits, axis=-1)
        corrects = tf.cast(tf.equal(preds, test_labels), tf.float32)
        accuracy = tf.reduce_mean(corrects)
        print("step = {}\tloss = {}\ttest_accuracy = {}".format(step, mean_loss, accuracy))

import networkx as nx
from node2vec import Node2Vec
# Embed edges using Hadamard method
from node2vec.edges import HadamardEmbedder
import matplotlib.pyplot as plt

graph = nx.fast_gnp_random_graph(n=20, p=0.2)  # 建图，可以给出自己的graph
node2vec = Node2Vec(graph, dimensions=64, walk_length=30, num_walks=200, workers=4)  # Precompute probabilities and generate walks
model = node2vec.fit(window=10, min_count=1, batch_words=4)  # Any keywords acceptable by gensim.Word2Vec can be passed
print(graph)
nx.draw(graph)
plt.savefig('network.png')  # draw the network figure

print(model.wv.most_similar('2'))  # Look for most similar nodes. Output node names are always strings

model.wv.save_word2vec_format('embedding.txt')  # Save embeddings for later use
# model.save('embedding_model')  # Save model for later use

# 获取边的嵌入
edges_embs = HadamardEmbedder(keyed_vectors=model.wv)
print(edges_embs[('1', '2')])  # Look for embeddings on the fly - here we pass normal tuples