import os
import re
import time
import torch
import torch.nn as nn
import torch.optim as optim
import pandas as pd
import numpy as np
import joblib # 添加缺失的导入
from datetime import datetime
from torch.utils.data import Dataset, DataLoader, random_split
from tqdm import tqdm
import pickle
import mysql.connector
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from gensim.models import Word2Vec
import spacy
nlp = spacy.load("en_core_web_sm", disable=["parser", "ner"])
STOPWORDS = spacy.lang.en.stop_words.STOP_WORDS
def clean_text(text):
return re.sub(r'[^a-zA-Z0-9\s]', '', str(text)).strip().lower()
def tokenize(text):
"""优化tokenize函数，移除冗余检查"""
doc = nlp(clean_text(text))
return [token.text for token in doc if token.text not in STOPWORDS] # 移除isalnum检查
def preprocess(text):
tokens = tokenize(text)
return " ".join(tokens)
class SemanticMatchModel(nn.Module):
def __init__(self, input_dim):
super().__init__()
self.fc1 = nn.Linear(input_dim, 256)
self.bn1 = nn.BatchNorm1d(256)
self.fc2 = nn.Linear(256, 128)
self.bn2 = nn.BatchNorm1d(128)
self.fc3 = nn.Linear(128, 64)
self.bn3 = nn.BatchNorm1d(64)
self.fc4 = nn.Linear(64, 1)
self.dropout = nn.Dropout(0.3)
self.relu = nn.ReLU()
self.sigmoid = nn.Sigmoid()
def forward(self, x):
x = self.relu(self.bn1(self.fc1(x)))
x = self.dropout(x)
x = self.relu(self.bn2(self.fc2(x)))
x = self.dropout(x)
x = self.relu(self.bn3(self.fc3(x)))
x = self.dropout(x)
x = self.sigmoid(self.fc4(x))
return x
class QADataset(Dataset):
def __init__(self, qa_pairs, tfidf_vectorizer, negative_ratio=1.0):
self.qa_pairs = qa_pairs
self.vectorizer = tfidf_vectorizer
self.samples = []
# 构建正样本
for i, (q, a) in enumerate(self.qa_pairs):
self.samples.append((q, a, 1))
# 优化负样本构建逻辑
if negative_ratio > 0:
total_pairs = len(self.qa_pairs)
all_answers = [a for _, a in self.qa_pairs]
# 预先生成负样本索引
neg_indices = np.random.choice(
len(all_answers),
size=int(total_pairs * negative_ratio),
replace=True
)
for idx, (q, a) in enumerate(self.qa_pairs):
sample_count = int(negative_ratio)
start = idx * sample_count
end = start + sample_count
for j in range(start, end):
if j < len(neg_indices):
neg_a = all_answers[neg_indices[j]]
# 确保不是当前答案
if neg_a != a:
self.samples.append((q, neg_a, 0))
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
q, a, label = self.samples[idx]
q_vec = self.vectorizer.transform([preprocess(q)]).toarray()[0]
a_vec = self.vectorizer.transform([preprocess(a)]).toarray()[0]
pair_vec = np.concatenate((q_vec, a_vec))
return torch.tensor(pair_vec, dtype=torch.float32), torch.tensor(label, dtype=torch.float32)
class KnowledgeBase:
def __init__(self,
host='localhost',
user='root',
password='hy188747',
database='ubuntu_qa',
table='qa_pair',
model_dir=r"D:\NLP-PT\PT4\model",
negative_ratio=1.0):
print("🔄 初始化知识库...")
self.host = host
self.user = user
self.password = password
self.database = database
self.table = table
self.model_dir = model_dir
self.negative_ratio = negative_ratio
os.makedirs(self.model_dir, exist_ok=True)
self.qa_pairs = []
self.q_texts = []
self.a_texts = []
self.semantic_model = None
self.word2vec_model = None
self.tfidf_vectorizer = None
self.tfidf_matrix = None
# 调整初始化顺序
self.load_data_from_mysql()
self.load_or_cache_processed_questions()
self.load_cached_tfidf()
self.load_cached_word2vec_model()
# 最后加载模型（确保依赖项已初始化）
model_path = os.path.join(self.model_dir, 'semantic_match_model.pth')
if os.path.exists(model_path):
self.load_model()
else:
print("⚠ 语义匹配模型未训练，请先训练模型。")
def load_data_from_mysql(self):
print("🔄 正在连接 MySQL，加载问答数据...")
try:
conn = mysql.connector.connect(
host=self.host,
user=self.user,
password=self.password,
database=self.database
)
cursor = conn.cursor()
query = f"SELECT question_text, answer_text FROM {self.table}"
cursor.execute(query)
rows = cursor.fetchall()
self.qa_pairs = [(row[0], row[1]) for row in rows]
self.q_texts = [pair[0] for pair in self.qa_pairs]
self.a_texts = [pair[1] for pair in self.qa_pairs]
print(f"✅ 成功从 MySQL 加载 {len(self.qa_pairs)} 条问答数据。")
except Exception as e:
print(f"❌ 数据库连接失败: {e}")
self.qa_pairs = []
finally:
if conn.is_connected():
conn.close()
def load_or_cache_processed_questions(self):
cache_path = os.path.join(self.model_dir, 'processed_questions.pkl')
if os.path.exists(cache_path):
print("🔄 使用缓存预处理后的分词文本。")
with open(cache_path, 'rb') as f:
self.processed_q_list = pickle.load(f)
else:
print("🔄 正在预处理问题文本（首次较慢）...")
self.processed_q_list = [preprocess(q) for q in tqdm(self.q_texts)]
with open(cache_path, 'wb') as f:
pickle.dump(self.processed_q_list, f)
print("✅ 预处理缓存已保存。")
def load_cached_tfidf(self):
cache_tfidf_matrix = os.path.join(self.model_dir, 'tfidf_matrix.npz')
cache_qa_list = os.path.join(self.model_dir, 'tfidf_qa.pkl')
tfidf_path = os.path.join(self.model_dir, 'tfidf_vectorizer.pkl')
if os.path.exists(tfidf_path) and os.path.exists(cache_tfidf_matrix) and os.path.exists(cache_qa_list):
print("🔄 加载 TF-IDF 缓存版本。")
self.tfidf_vectorizer = joblib.load(tfidf_path)
self.tfidf_matrix = np.load(cache_tfidf_matrix)['tfidf']
with open(cache_qa_list, 'rb') as f:
self.tfidf_qa = pickle.load(f)
else:
print("🔄 创建并构建 TF-IDF（首次较慢）...")
self.tfidf_vectorizer = TfidfVectorizer(
tokenizer=lambda x: x.split(),
lowercase=False,
max_features=10000
)
self.tfidf_qa = self.processed_q_list
self.tfidf_matrix = self.tfidf_vectorizer.fit_transform(self.tfidf_qa).toarray()
print("✅ TF-IDF 构建完成。")
joblib.dump(self.tfidf_vectorizer, tfidf_path)
np.savez_compressed(cache_tfidf_matrix, tfidf=self.tfidf_matrix)
with open(cache_qa_list, 'wb') as f:
pickle.dump(self.tfidf_qa, f)
def load_cached_word2vec_model(self):
word2vec_path = os.path.join(self.model_dir, 'word2vec.model')
if os.path.exists(word2vec_path):
print("🔄 加载缓存中的 Word2Vec 模型...")
self.word2vec_model = Word2Vec.load(word2vec_path)
else:
print("🔄 训练 Word2Vec 模型（首次较慢）...")
tokenized_questions = [preprocess(q).split() for q in self.q_texts]
self.word2vec_model = Word2Vec(
sentences=tokenized_questions,
vector_size=100,
window=5,
min_count=1,
workers=4,
epochs=10
)
self.word2vec_model.save(word2vec_path)
print("✅ Word2Vec 模型训练完成并保存。")
def sentence_to_vec(self, sentence):
"""修复空向量问题"""
tokens = preprocess(sentence).split()
if not tokens:
return np.zeros(100) # 默认向量大小
if self.word2vec_model:
vecs = [self.word2vec_model.wv[w] for w in tokens if w in self.word2vec_model.wv]
return np.mean(vecs, axis=0) if vecs else np.zeros(self.word2vec_model.vector_size)
else:
vec = self.tfidf_vectorizer.transform([preprocess(sentence)]).toarray()[0]
return vec
def build_model(self, epochs=10, batch_size=128, lr=1e-3):
# 创建数据集
full_dataset = QADataset(self.qa_pairs, self.tfidf_vectorizer, negative_ratio=self.negative_ratio)
# 划分训练集/验证集
train_size = int(len(full_dataset) * 0.8)
val_size = len(full_dataset) - train_size
train_dataset, val_dataset = random_split(full_dataset, [train_size, val_size])
# 创建数据加载器
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True, num_workers=2)
val_loader = DataLoader(val_dataset, batch_size=batch_size, shuffle=False, num_workers=2)
# 初始化模型
sample_input, _ = full_dataset[0]
input_dim = sample_input.shape[0]
self.semantic_model = SemanticMatchModel(input_dim)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.semantic_model.to(device)
criterion = nn.BCELoss()
optimizer = optim.Adam(self.semantic_model.parameters(), lr=lr)
scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'max', patience=2, factor=0.5)
# 训练模型
best_val_acc = 0.0
print("\n开始模型训练...")
start_time = time.time()
for epoch in range(epochs):
self.semantic_model.train()
total_loss, total_correct, total_samples = 0.0, 0, 0
for X_batch, y_batch in tqdm(train_loader, desc=f"Epoch {epoch + 1}/{epochs} - 训练中"):
X_batch, y_batch = X_batch.to(device), y_batch.to(device)
optimizer.zero_grad()
outputs = self.semantic_model(X_batch).squeeze()
loss = criterion(outputs, y_batch)
loss.backward()
optimizer.step()
total_loss += loss.item() * len(y_batch)
preds = (outputs >= 0.5).float()
total_correct += (preds == y_batch).sum().item()
total_samples += len(y_batch)
train_loss = total_loss / total_samples
train_acc = total_correct / total_samples
# 验证阶段
self.semantic_model.eval()
val_loss, val_correct, val_samples = 0.0, 0, 0
with torch.no_grad():
for X_val, y_val in val_loader:
X_val, y_val = X_val.to(device), y_val.to(device)
outputs_val = self.semantic_model(X_val).squeeze()
loss_val = criterion(outputs_val, y_val)
val_loss += loss_val.item() * len(y_val)
preds_val = (outputs_val >= 0.5).float()
val_correct += (preds_val == y_val).sum().item()
val_samples += len(y_val)
val_loss /= val_samples
val_acc = val_correct / val_samples
# 更新学习率
scheduler.step(val_acc)
print(f"Epoch [{epoch + 1}/{epochs}] | "
f"Train Loss: {train_loss:.4f}, Train Acc: {train_acc:.4f} | "
f"Val Loss: {val_loss:.4f}, Val Acc: {val_acc:.4f}")
# 保存最优模型
if val_acc > best_val_acc:
best_val_acc = val_acc
model_path = os.path.join(self.model_dir, 'semantic_match_model.pth')
torch.save(self.semantic_model.state_dict(), model_path)
print(f"✅ 新的最优模型已保存 (Val Acc: {best_val_acc:.4f})")
end_time = time.time()
print(f"\n训练完成，共耗时 {end_time - start_time:.2f} 秒。")
# 加载最优模型权重
model_path = os.path.join(self.model_dir, 'semantic_match_model.pth')
self.semantic_model.load_state_dict(torch.load(model_path, map_location=device))
self.semantic_model.eval()
def load_model(self):
"""加载训练好的语义匹配模型"""
input_dim = self.tfidf_matrix.shape[1] * 2
model_path = os.path.join(self.model_dir, 'semantic_match_model.pth')
self.semantic_model = SemanticMatchModel(input_dim)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.semantic_model.load_state_dict(torch.load(model_path, map_location=device))
self.semantic_model.to(device)
self.semantic_model.eval()
print("✅ 语义匹配模型加载完成。")
def retrieve(self, query, semantic_topk=100):
# 粗检
query_tfidf = self.tfidf_vectorizer.transform([preprocess(query)]).toarray()[0]
tfidf_scores = cosine_similarity([query_tfidf], self.tfidf_matrix).flatten()
query_sent_vec = self.sentence_to_vec(query)
sent_vecs = np.array([self.sentence_to_vec(q) for q in self.q_texts])
sent_scores = cosine_similarity([query_sent_vec], sent_vecs).flatten()
# 归一化句子向量相似度到[0,1]
sent_scores = (sent_scores + 1) / 2
sim_scores = tfidf_scores + sent_scores
# 确保有足够的数据
if len(sim_scores) == 0:
return ("抱歉，知识库中没有找到相关信息。", 0.0)
topk_indices = np.argpartition(sim_scores, -semantic_topk)[-semantic_topk:]
topk_indices = topk_indices[np.argsort(sim_scores[topk_indices])[::-1]]
# 精检
if self.semantic_model:
device = next(self.semantic_model.parameters()).device
with torch.no_grad():
batch_inputs = []
for i in topk_indices:
q = preprocess(self.q_texts[i])
a = preprocess(self.a_texts[i])
q_vec = self.tfidf_vectorizer.transform([q]).toarray()[0]
a_vec = self.tfidf_vectorizer.transform([a]).toarray()[0]
pair_input = np.concatenate((q_vec, a_vec))
batch_inputs.append(pair_input)
if batch_inputs:
batch_inputs = torch.tensor(np.stack(batch_inputs), dtype=torch.float32).to(device)
batch_scores = self.semantic_model(batch_inputs).squeeze().cpu().numpy()
semantic_scores = batch_scores
else:
semantic_scores = np.zeros(len(topk_indices))
# 综合得分（添加归一化权重）
coarse_scores = sim_scores[topk_indices] / 2.0 # 归一化到[0,1]
final_scores = 0.3 * coarse_scores + 0.7 * semantic_scores
best_idx_in_topk = np.argmax(final_scores)
best_idx = topk_indices[best_idx_in_topk]
return self.qa_pairs[best_idx], final_scores[best_idx_in_topk]
else:
best_idx = topk_indices[0] if topk_indices.size > 0 else 0
return self.qa_pairs[best_idx], sim_scores[best_idx]
def recommend_similar(self, query, topk=3):
"""修复索引越界问题"""
query_tfidf = self.tfidf_vectorizer.transform([preprocess(query)]).toarray()[0]
scores = cosine_similarity([query_tfidf], self.tfidf_matrix).flatten()
# 安全获取topk索引
if len(scores) == 0:
return []
if len(scores) < topk:
topk = len(scores)
topk_idx = np.argpartition(scores, -topk)[-topk:]
topk_idx = topk_idx[np.argsort(scores[topk_idx])[::-1]]
return [(self.q_texts[i], self.a_texts[i]) for i in topk_idx]
class FeedbackRecorder:
def __init__(self, file_path='unanswered_questions.csv'):
self.file_path = file_path
if not os.path.exists(self.file_path):
with open(self.file_path, 'w', newline='', encoding='utf-8') as f:
import csv
csv.writer(f).writerow(['time', 'question'])
def record_question(self, question):
with open(self.file_path, 'a', newline='', encoding='utf-8') as f:
import csv
writer = csv.writer(f)
writer.writerow([datetime.now().isoformat(), question])
def main():
kb = KnowledgeBase(
host='localhost',
user='root',
password='hy188747',
database='ubuntu_qa',
table='qa_pair',
model_dir=r"D:\NLP-PT\PT4\model",
negative_ratio=1.0
)
if input("是否重新训练语义匹配模型？(y/n): ").strip().lower() == 'y':
kb.build_model(
epochs=5,
batch_size=128,
lr=1e-3
)
recorder = FeedbackRecorder()
print("\n🎯 智能知识问答系统已启动（输入'q'退出聊天）\n")
while True:
query = input("🧐 问题：")
if query.strip().lower() == 'q':
break
try:
result, score = kb.retrieve(query)
if result:
print(f"💡 回答：{result[1]}")
print(f"📊 匹配信心分数: {score:.4f}\n")
else:
print("⚠ 没有找到合适的答案，已将你的问题记录下来。")
recorder.record_question(query)
print("🔥 相似问题推荐：")
similar_questions = kb.recommend_similar(query, topk=3)
for q, a in similar_questions:
print(f"Q: {q}\nA: {a}\n")
except Exception as e:
print(f"❌ 检索过程中发生错误: {e}")
if __name__ == "__main__":
main()
本文详细解析了PyTorch中next(self.parameters())及其相关函数的使用，包括如何利用这些函数进行模型参数的初始化，如创建零张量，并介绍了其在神经网络训练中的作用。
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