# -*- coding: utf-8 -*- """ 训练与对比实验模块 功能： 1. 单模型训练与评估 2. 模型对比实验（LR vs MLP） 3. 向量化对比实验（BoW vs TF-IDF） 4. 超参数对比实验 """ import numpy as np import time from datetime import datetime from model_numpy import create_model from dataset import load_data from config import * def train_single_model(X_train, y_train, X_test, y_test, model_type, vectorizer_type, epochs, batch_size, lr, hidden_size): """ 训练单个模型并返回结果 """ print(f"\n{'='*60}") print(f"训练配置:") print(f" 模型: {model_type.upper()}") print(f" 向量: {vectorizer_type.upper()}") print(f" 学习率: {lr}") if model_type == 'mlp': print(f" 隐藏层大小: {hidden_size}") print(f" 训练轮数: {epochs}") print(f"{'='*60}") # 重新加载数据（确保向量化方式正确） X_tr, y_tr, X_te, y_te, _ = load_data( DATA_DIR, MAX_FEATURES, MAX_SEQ_LEN, vectorizer_type ) # 计算类别权重（解决数据不平衡问题） pos_count = sum(y_tr) neg_count = len(y_tr) - pos_count n_samples = len(y_tr) n_classes = 2 if USE_CLASS_WEIGHT: # 权重 = n_samples / (n_classes * n_class_i) # 负面样本少，权重更大 weight_pos = n_samples / (n_classes * pos_count) weight_neg = n_samples / (n_classes * neg_count) class_weight = {0: weight_neg, 1: weight_pos} print(f" 类别权重: 正面={weight_pos:.2f}, 负面={weight_neg:.2f}") else: class_weight = None print(f" 类别权重: 不使用") # 创建模型 model = create_model( model_type=model_type, input_size=X_tr.shape[1], hidden_size=hidden_size, num_classes=NUM_CLASSES, learning_rate=lr, keep_prob=KEEP_PROB, class_weight=class_weight ) # 训练 start_time = time.time() model.fit(X_tr, y_tr, X_te, y_te, epochs=epochs, batch_size=batch_size, verbose=True) train_time = time.time() - start_time # 最终评估 train_acc = model.accuracy(X_tr, y_tr) test_acc = model.accuracy(X_te, y_te) print(f"\n最终结果:") print(f" 训练准确率: {train_acc:.4f}") print(f" 测试准确率: {test_acc:.4f}") print(f" 训练时间: {train_time:.2f}秒") # 保存模型权重 # 生成模型文件名（包含配置标识和时间戳） weight_suffix = "weighted" if USE_CLASS_WEIGHT else "noweight" timestamp = datetime.now().strftime("%m%d_%H%M%S") model_path = f"model_{model_type}_{vectorizer_type}_{weight_suffix}_{timestamp}" model.save(model_path) print(f" 权重文件: {model_path}_*.npy") return { 'model_type': model_type, 'vectorizer_type': vectorizer_type, 'train_acc': train_acc, 'test_acc': test_acc, 'train_time': train_time, 'lr': lr, 'hidden_size': hidden_size } def run_comparison_experiments(): """ 运行对比实验 实验设计： 1. 不同向量化方法的效果对比 (BoW vs TF-IDF) 2. 不同模型的效果对比 (LR vs MLP) 3. 超参数对比 (学习率, 隐藏层大小) """ print("\n" + "=" * 70) print("文本分类对比实验") print("=" * 70) results = [] # ===== 实验1: 向量化方法对比 (固定模型=LR) ===== print("\n\n" + "▶ " * 20) print("实验1: 向量化方法对比 (模型=LR, 学习率=0.05)") print("▶ " * 20) for vec_type in COMPARE_VECTORS: result = train_single_model( None, None, None, None, model_type='lr', vectorizer_type=vec_type, epochs=50, batch_size=BATCH_SIZE, lr=0.05, hidden_size=HIDDEN_SIZE ) results.append(result) # ===== 实验2: 模型复杂度对比 (固定向量=TF-IDF) ===== print("\n\n" + "▶ " * 20) print("实验2: 模型复杂度对比 (向量=TF-IDF)") print("▶ " * 20) for model_type in COMPARE_MODELS: result = train_single_model( None, None, None, None, model_type=model_type, vectorizer_type='tfidf', epochs=50, batch_size=BATCH_SIZE, lr=LEARNING_RATE, hidden_size=HIDDEN_SIZE ) results.append(result) # ===== 实验3: 学习率对比 (MLP) ===== print("\n\n" + "▶ " * 20) print("实验3: 学习率对比 (模型=MLP, 向量=TF-IDF)") print("▶ " * 20) for lr in [0.01, 0.1]: result = train_single_model( None, None, None, None, model_type='mlp', vectorizer_type='tfidf', epochs=50, batch_size=BATCH_SIZE, lr=lr, hidden_size=64 ) results.append(result) # ===== 实验4: 隐藏层大小对比 (MLP) ===== print("\n\n" + "▶ " * 20) print("实验4: 隐藏层大小对比 (模型=MLP, 向量=TF-IDF)") print("▶ " * 20) for hidden_size in [32, 128]: result = train_single_model( None, None, None, None, model_type='mlp', vectorizer_type='tfidf', epochs=50, batch_size=BATCH_SIZE, lr=LEARNING_RATE, hidden_size=hidden_size ) results.append(result) # ===== 汇总报告 ===== print("\n\n" + "=" * 70) print("实验结果汇总") print("=" * 70) print(f"\n{'配置':<40} {'训练准确率':<12} {'测试准确率':<12} {'训练时间(秒)':<10}") print("-" * 76) for r in results: config = f"{r['model_type'].upper()} + {r['vectorizer_type'].upper()}" if r['lr'] != LEARNING_RATE: config += f", lr={r['lr']}" if r['hidden_size'] != HIDDEN_SIZE: config += f", h={r['hidden_size']}" print(f"{config:<40} {r['train_acc']:<12.4f} {r['test_acc']:<12.4f} {r['train_time']:<10.2f}") # 分析 print("\n" + "=" * 70) print("结果分析") print("=" * 70) # 找出最佳配置 best = max(results, key=lambda x: x['test_acc']) print(f"\n最佳测试准确率: {best['test_acc']:.4f}") print(f"最佳配置: {best['model_type'].upper()} + {best['vectorizer_type'].upper()}") # BoW vs TF-IDF 分析 bow_results = [r for r in results if r['vectorizer_type'] == 'bow' and r['model_type'] == 'lr'] tfidf_results = [r for r in results if r['vectorizer_type'] == 'tfidf' and r['model_type'] == 'lr'] if bow_results and tfidf_results: bow_acc = bow_results[0]['test_acc'] tfidf_acc = tfidf_results[0]['test_acc'] print(f"\n向量化方法影响:") print(f" BoW测试准确率: {bow_acc:.4f}") print(f" TF-IDF测试准确率: {tfidf_acc:.4f}") print(f" 差异: {abs(tfidf_acc - bow_acc):.4f} ({'TF-IDF更优' if tfidf_acc > bow_acc else 'BoW更优'})") # LR vs MLP 分析 lr_results = [r for r in results if r['model_type'] == 'lr' and r['vectorizer_type'] == 'tfidf'] mlp_results = [r for r in results if r['model_type'] == 'mlp' and r['vectorizer_type'] == 'tfidf'] if lr_results and mlp_results: lr_acc = lr_results[0]['test_acc'] mlp_acc = mlp_results[0]['test_acc'] print(f"\n模型复杂度影响:") print(f" LR测试准确率: {lr_acc:.4f}") print(f" MLP测试准确率: {mlp_acc:.4f}") print(f" 差异: {abs(mlp_acc - lr_acc):.4f} ({'MLP更优' if mlp_acc > lr_acc else 'LR更优'})") return results def main(): """主函数""" print("=" * 70) print("文本分类实验 - 纯NumPy实现") print("数据集: ChnSentiCorp (中文酒店评论)") print("=" * 70) if RUN_COMPARISON: # 运行对比实验 run_comparison_experiments() else: # 运行单个模型 train_single_model( None, None, None, None, model_type=MODEL_TYPE, vectorizer_type=VECTORIZER_TYPE, epochs=NUM_EPOCHS, batch_size=BATCH_SIZE, lr=LEARNING_RATE, hidden_size=HIDDEN_SIZE ) print("\n" + "=" * 70) print("实验完成!") print("=" * 70) if __name__ == '__main__': main()