动手学深度学习PyTorch版-word2vec

word2vec

PTB数据集

import collections
import math
import random
import sys
import time
import os
import numpy as np
import torch
from torch import nn
import torch.utils.data as Data

with open('/home/kesci/input/ptb_train1020/ptb.train.txt', 'r') as f:
    lines = f.readlines() # 该数据集中句子以换行符为分割
    raw_dataset = [st.split() for st in lines] # st是sentence的缩写，单词以空格为分割
print('# sentences: %d' % len(raw_dataset))

# 对于数据集的前3个句子，打印每个句子的词数和前5个词
# 句尾符为 '' ，生僻词全用 '' 表示，数字则被替换成了 'N'
for st in raw_dataset[:3]:
    print('# tokens:', len(st), st[:5])

索引建立

counter = collections.Counter([tk for st in raw_dataset for tk in st]) # tk是token的缩写
counter = dict(filter(lambda x: x[1] >= 5, counter.items())) # 只保留在数据集中至少出现5次的词

idx_to_token = [tk for tk, _ in counter.items()]
token_to_idx = {tk: idx for idx, tk in enumerate(idx_to_token)}
dataset = [[token_to_idx[tk] for tk in st if tk in token_to_idx]
           for st in raw_dataset] # raw_dataset中的单词在这一步被转换为对应的idx
num_tokens = sum([len(st) for st in dataset])
'# tokens: %d' % num_tokens

二次采样

def discard(idx):
    '''
    @params:
        idx: 单词的下标
    @return: True/False 表示是否丢弃该单词
    '''
    return random.uniform(0, 1) < 1 - math.sqrt(
        1e-4 / counter[idx_to_token[idx]] * num_tokens)

subsampled_dataset = [[tk for tk in st if not discard(tk)] for st in dataset]
print('# tokens: %d' % sum([len(st) for st in subsampled_dataset]))

def compare_counts(token):
    return '# %s: before=%d, after=%d' % (token, sum(
        [st.count(token_to_idx[token]) for st in dataset]), sum(
        [st.count(token_to_idx[token]) for st in subsampled_dataset]))

print(compare_counts('the'))
print(compare_counts('join'))

中心词和背景词提取

def get_centers_and_contexts(dataset, max_window_size):
    '''
    @params:
        dataset: 数据集为句子的集合，每个句子则为单词的集合，此时单词已经被转换为相应数字下标
        max_window_size: 背景词的词窗大小的最大值
    @return:
        centers: 中心词的集合
        contexts: 背景词窗的集合，与中心词对应，每个背景词窗则为背景词的集合
    '''
    centers, contexts = [], []
    for st in dataset:
        if len(st) < 2:  # 每个句子至少要有2个词才可能组成一对“中心词-背景词”
            continue
        centers += st
        for center_i in range(len(st)):
            window_size = random.randint(1, max_window_size) # 随机选取背景词窗大小
            indices = list(range(max(0, center_i - window_size),
                                 min(len(st), center_i + 1 + window_size)))
            indices.remove(center_i)  # 将中心词排除在背景词之外
            contexts.append([st[idx] for idx in indices])
    return centers, contexts

all_centers, all_contexts = get_centers_and_contexts(subsampled_dataset, 5)

tiny_dataset = [list(range(7)), list(range(7, 10))]
print('dataset', tiny_dataset)
for center, context in zip(*get_centers_and_contexts(tiny_dataset, 2)):
    print('center', center, 'has contexts', context)

Skip-Gram模型

embed = nn.Embedding(num_embeddings=10, embedding_dim=4)
print(embed.weight)

x = torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.long)
print(embed(x))

X = torch.ones((2, 1, 4))
Y = torch.ones((2, 4, 6))
print(torch.bmm(X, Y).shape)

def skip_gram(center, contexts_and_negatives, embed_v, embed_u):
    '''
    @params:
        center: 中心词下标，形状为 (n, 1) 的整数张量
        contexts_and_negatives: 背景词和噪音词下标，形状为 (n, m) 的整数张量
        embed_v: 中心词的 embedding 层
        embed_u: 背景词的 embedding 层
    @return:
        pred: 中心词与背景词（或噪音词）的内积，之后可用于计算概率 p(w_o|w_c)
    '''
    v = embed_v(center) # shape of (n, 1, d)
    u = embed_u(contexts_and_negatives) # shape of (n, m, d)
    pred = torch.bmm(v, u.permute(0, 2, 1)) # bmm((n, 1, d), (n, d, m)) => shape of (n, 1, m)
    return pred

负采样近似

def get_negatives(all_contexts, sampling_weights, K):
    '''
    @params:
        all_contexts: [[w_o1, w_o2, ...], [...], ... ]
        sampling_weights: 每个单词的噪声词采样概率
        K: 随机采样个数
    @return:
        all_negatives: [[w_n1, w_n2, ...], [...], ...]
    '''
    all_negatives, neg_candidates, i = [], [], 0
    population = list(range(len(sampling_weights)))
    for contexts in all_contexts:
        negatives = []
        while len(negatives) < len(contexts) * K:
            if i == len(neg_candidates):
                # 根据每个词的权重（sampling_weights）随机生成k个词的索引作为噪声词。
                # 为了高效计算，可以将k设得稍大一点
                i, neg_candidates = 0, random.choices(
                    population, sampling_weights, k=int(1e5))
            neg, i = neg_candidates[i], i + 1
            # 噪声词不能是背景词
            if neg not in set(contexts):
                negatives.append(neg)
        all_negatives.append(negatives)
    return all_negatives

sampling_weights = [counter[w]**0.75 for w in idx_to_token]
all_negatives = get_negatives(all_contexts, sampling_weights, 5)

批量读取数据

class MyDataset(torch.utils.data.Dataset):
    def __init__(self, centers, contexts, negatives):
        assert len(centers) == len(contexts) == len(negatives)
        self.centers = centers
        self.contexts = contexts
        self.negatives = negatives
        
    def __getitem__(self, index):
        return (self.centers[index], self.contexts[index], self.negatives[index])

    def __len__(self):
        return len(self.centers)
    
def batchify(data):
    '''
    用作DataLoader的参数collate_fn
    @params:
        data: 长为batch_size的列表，列表中的每个元素都是__getitem__得到的结果
    @outputs:
        batch: 批量化后得到 (centers, contexts_negatives, masks, labels) 元组
            centers: 中心词下标，形状为 (n, 1) 的整数张量
            contexts_negatives: 背景词和噪声词的下标，形状为 (n, m) 的整数张量
            masks: 与补齐相对应的掩码，形状为 (n, m) 的0/1整数张量
            labels: 指示中心词的标签，形状为 (n, m) 的0/1整数张量
    '''
    max_len = max(len(c) + len(n) for _, c, n in data)
    centers, contexts_negatives, masks, labels = [], [], [], []
    for center, context, negative in data:
        cur_len = len(context) + len(negative)
        centers += [center]
        contexts_negatives += [context + negative + [0] * (max_len - cur_len)]
        masks += [[1] * cur_len + [0] * (max_len - cur_len)] # 使用掩码变量mask来避免填充项对损失函数计算的影响
        labels += [[1] * len(context) + [0] * (max_len - len(context))]
        batch = (torch.tensor(centers).view(-1, 1), torch.tensor(contexts_negatives),
            torch.tensor(masks), torch.tensor(labels))
    return batch

batch_size = 512
num_workers = 0 if sys.platform.startswith('win32') else 4

dataset = MyDataset(all_centers, all_contexts, all_negatives)
data_iter = Data.DataLoader(dataset, batch_size, shuffle=True,
                            collate_fn=batchify, 
                            num_workers=num_workers)
for batch in data_iter:
    for name, data in zip(['centers', 'contexts_negatives', 'masks',
                           'labels'], batch):
        print(name, 'shape:', data.shape)
    break

训练模型

class SigmoidBinaryCrossEntropyLoss(nn.Module):
    def __init__(self):
        super(SigmoidBinaryCrossEntropyLoss, self).__init__()
    def forward(self, inputs, targets, mask=None):
        '''
        @params:
            inputs: 经过sigmoid层后为预测D=1的概率
            targets: 0/1向量，1代表背景词，0代表噪音词
        @return:
            res: 平均到每个label的loss
        '''
        inputs, targets, mask = inputs.float(), targets.float(), mask.float()
        res = nn.functional.binary_cross_entropy_with_logits(inputs, targets, reduction="none", weight=mask)
        res = res.sum(dim=1) / mask.float().sum(dim=1)
        return res

loss = SigmoidBinaryCrossEntropyLoss()

pred = torch.tensor([[1.5, 0.3, -1, 2], [1.1, -0.6, 2.2, 0.4]])
label = torch.tensor([[1, 0, 0, 0], [1, 1, 0, 0]]) # 标签变量label中的1和0分别代表背景词和噪声词
mask = torch.tensor([[1, 1, 1, 1], [1, 1, 1, 0]])  # 掩码变量
print(loss(pred, label, mask))

def sigmd(x):
    return - math.log(1 / (1 + math.exp(-x)))
print('%.4f' % ((sigmd(1.5) + sigmd(-0.3) + sigmd(1) + sigmd(-2)) / 4)) # 注意1-sigmoid(x) = sigmoid(-x)
print('%.4f' % ((sigmd(1.1) + sigmd(-0.6) + sigmd(-2.2)) / 3))

embed_size = 100
net = nn.Sequential(nn.Embedding(num_embeddings=len(idx_to_token), embedding_dim=embed_size),
                    nn.Embedding(num_embeddings=len(idx_to_token), embedding_dim=embed_size))

def train(net, lr, num_epochs):
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    print("train on", device)
    net = net.to(device)
    optimizer = torch.optim.Adam(net.parameters(), lr=lr)
    for epoch in range(num_epochs):
        start, l_sum, n = time.time(), 0.0, 0
        for batch in data_iter:
            center, context_negative, mask, label = [d.to(device) for d in batch]
            
            pred = skip_gram(center, context_negative, net[0], net[1])
            
            l = loss(pred.view(label.shape), label, mask).mean() # 一个batch的平均loss
            optimizer.zero_grad()
            l.backward()
            optimizer.step()
            l_sum += l.cpu().item()
            n += 1
        print('epoch %d, loss %.2f, time %.2fs'
              % (epoch + 1, l_sum / n, time.time() - start))

train(net, 0.01, 5)

模型测试

def get_similar_tokens(query_token, k, embed):
    '''
    @params:
        query_token: 给定的词语
        k: 近义词的个数
        embed: 预训练词向量
    '''
    W = embed.weight.data
    x = W[token_to_idx[query_token]]
    # 添加的1e-9是为了数值稳定性
    cos = torch.matmul(W, x) / (torch.sum(W * W, dim=1) * torch.sum(x * x) + 1e-9).sqrt()
    _, topk = torch.topk(cos, k=k+1)
    topk = topk.cpu().numpy()
    for i in topk[1:]:  # 除去输入词
        print('cosine sim=%.3f: %s' % (cos[i], (idx_to_token[i])))
        
get_similar_tokens('chip', 3, net[0])