feat: add TextRNN

TextCNN-THUCNews/Classification/scripts/verify_embedding_order.py

@@ -1,235 +0,0 @@
-# coding: UTF-8
-"""
-验证embedding保存顺序与原始数据集顺序一致性的脚本
-"""
-import os
-import sys
-import numpy as np
-import yaml
-import torch
-from tqdm import tqdm
-
-# 添加当前目录到路径
-sys.path.append(os.path.dirname(os.path.abspath(__file__)))
-
-from model import Model, Config
-from dataset_utils import build_dataset, build_iterator
-
-
-def verify_embedding_order(config_path, epoch_num=1):
-    """
-    验证指定epoch的embedding顺序是否与原始数据集一致
-    
-    Args:
-        config_path: 配置文件路径
-        epoch_num: 要验证的epoch数
-    """
-    print("=" * 60)
-    print("验证Embedding保存顺序与原始数据集一致性")
-    print("=" * 60)
-    
-    # 加载配置
-    with open(config_path, 'r', encoding='utf-8') as f:
-        train_config = yaml.safe_load(f)
-    
-    # 初始化配置
-    config = Config(train_config['dataset_path'], train_config.get('embedding', 'random'))
-    config.batch_size = train_config.get('batch_size', 128)
-    
-    # 构建数据集
-    print("1. 构建数据集...")
-    vocab, train_data, dev_data, test_data = build_dataset(config, train_config.get('use_word', False))
-    config.n_vocab = len(vocab)
-    
-    print(f"   训练数据集大小: {len(train_data)}")
-    print(f"   验证数据集大小: {len(dev_data)}")
-    print(f"   测试数据集大小: {len(test_data)}")
-    print(f"   总数据集大小: {len(train_data) + len(dev_data) + len(test_data)}")
-    
-    # 检查保存的embedding文件是否存在
-    epochs_dir = train_config.get('epochs_dir', '../epochs')
-    epoch_dir = os.path.join(epochs_dir, f'epoch_{epoch_num}')
-    embeddings_path = os.path.join(epoch_dir, 'embeddings.npy')
-    predictions_path = os.path.join(epoch_dir, 'predictions.npy')
-    dataset_info_path = os.path.join(epoch_dir, 'dataset_info.json')
-    
-    if not os.path.exists(embeddings_path):
-        print(f"❌ 找不到embedding文件: {embeddings_path}")
-        print("请先运行训练脚本生成embedding文件")
-        return False
-    
-    print(f"2. 加载epoch {epoch_num}的embedding文件...")
-    saved_embeddings = np.load(embeddings_path)
-    saved_predictions = np.load(predictions_path)
-    
-    print(f"   保存的embedding形状: {saved_embeddings.shape}")
-    print(f"   保存的prediction形状: {saved_predictions.shape}")
-    
-    # 加载数据集信息
-    dataset_info = []
-    if os.path.exists(dataset_info_path):
-        import json
-        with open(dataset_info_path, 'r', encoding='utf-8') as f:
-            dataset_info = json.load(f)
-        print("   数据集信息:")
-        for info in dataset_info:
-            print(f"     {info['name']}: {info['size']} 个样本, 索引 {info['start_idx']}-{info['end_idx']-1}")
-    else:
-        print("   ⚠️ 未找到dataset_info.json，假设只有训练数据")
-    
-    # 加载模型
-    print("3. 加载模型...")
-    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
-    config.device = device
-    
-    model = Model(config).to(device)
-    model_path = os.path.join(epoch_dir, 'model.pt')
-    model.load_state_dict(torch.load(model_path, map_location=device))
-    model.eval()
-    
-    # 重新生成embedding（使用新的迭代器，按train/dev/test顺序）
-    print("4. 重新生成embedding以验证顺序...")
-    
-    # 按顺序处理三个数据集
-    datasets = [
-        ('train', train_data),
-        ('dev', dev_data), 
-        ('test', test_data)
-    ]
-    
-    fresh_features = []
-    fresh_predictions = []
-    
-    with torch.no_grad():
-        for dataset_name, dataset in datasets:
-            print(f"   重新生成 {dataset_name} 数据集特征...")
-            
-            data_iter = build_iterator(dataset, config)
-            dataset_features = []
-            dataset_predictions = []
-            
-            for texts, labels in tqdm(data_iter, desc=f"重新生成{dataset_name}特征"):
-                features = model.feature(texts)
-                predictions = model.get_prediction(texts)
-                
-                dataset_features.append(features)
-                dataset_predictions.append(predictions)
-            
-            if dataset_features:
-                dataset_embeddings = np.vstack(dataset_features)
-                dataset_preds = np.vstack(dataset_predictions)
-                
-                fresh_features.append(dataset_embeddings)
-                fresh_predictions.append(dataset_preds)
-                
-                print(f"     {dataset_name} 重新生成形状: {dataset_embeddings.shape}")
-    
-    fresh_embeddings = np.vstack(fresh_features)
-    fresh_predictions_array = np.vstack(fresh_predictions)
-    
-    print(f"   重新生成的总embedding形状: {fresh_embeddings.shape}")
-    print(f"   重新生成的总prediction形状: {fresh_predictions_array.shape}")
-    
-    # 比较两次生成的结果
-    print("5. 比较结果...")
-    
-    # 检查形状是否一致
-    if saved_embeddings.shape != fresh_embeddings.shape:
-        print("❌ 形状不一致!")
-        print(f"   保存的: {saved_embeddings.shape}")
-        print(f"   重新生成的: {fresh_embeddings.shape}")
-        return False
-    
-    # 检查数值是否一致（允许小的浮点误差）
-    embedding_diff = np.abs(saved_embeddings - fresh_embeddings)
-    max_embedding_diff = np.max(embedding_diff)
-    mean_embedding_diff = np.mean(embedding_diff)
-    
-    prediction_diff = np.abs(saved_predictions - fresh_predictions_array)
-    max_prediction_diff = np.max(prediction_diff)
-    mean_prediction_diff = np.mean(prediction_diff)
-    
-    print(f"   Embedding最大差异: {max_embedding_diff:.10f}")
-    print(f"   Embedding平均差异: {mean_embedding_diff:.10f}")
-    print(f"   Prediction最大差异: {max_prediction_diff:.10f}")
-    print(f"   Prediction平均差异: {mean_prediction_diff:.10f}")
-    
-    # 判断是否一致（允许1e-6的误差）
-    tolerance = 1e-6
-    embedding_consistent = max_embedding_diff < tolerance
-    prediction_consistent = max_prediction_diff < tolerance
-    
-    if embedding_consistent and prediction_consistent:
-        print("✅ 验证通过！Embedding和Prediction的保存顺序与原始数据集完全一致")
-        
-        # 额外验证：检查不同数据集的样本对应关系
-        print("\n6. 额外验证各数据集样本的对应关系...")
-        
-        # 合并所有数据集用于验证
-        all_data = train_data + dev_data + test_data
-        
-        # 验证前几个样本和各数据集边界处的样本
-        test_indices = []
-        
-        # 添加前5个样本
-        test_indices.extend(range(min(5, len(all_data))))
-        
-        # 如果有数据集信息，添加边界处的样本
-        if dataset_info:
-            for info in dataset_info:
-                # 添加每个数据集开始和结束的样本
-                start_idx = info['start_idx']
-                end_idx = info['end_idx'] - 1
-                test_indices.extend([start_idx, end_idx])
-        
-        # 去重并排序
-        test_indices = sorted(list(set(test_indices)))
-        test_indices = [i for i in test_indices if i < len(all_data)]
-        
-        for i in test_indices:
-            original_label = all_data[i][1]
-            
-            # 从保存的prediction中获取预测结果
-            saved_pred_logits = saved_predictions[i]
-            saved_pred_class = np.argmax(saved_pred_logits)
-            
-            # 确定样本属于哪个数据集
-            dataset_name = "unknown"
-            if dataset_info:
-                for info in dataset_info:
-                    if info['start_idx'] <= i < info['end_idx']:
-                        dataset_name = info['name']
-                        break
-            
-            print(f"   样本{i}: {dataset_name}数据集, 原始标签={original_label}, 预测类别={saved_pred_class}")
-        
-        return True
-    else:
-        print("❌ 验证失败！数据顺序可能不一致")
-        if not embedding_consistent:
-            print(f"   Embedding不一致，最大差异: {max_embedding_diff}")
-        if not prediction_consistent:
-            print(f"   Prediction不一致，最大差异: {max_prediction_diff}")
-        return False
-
-
-def main():
-    """主函数"""
-    import argparse
-    
-    parser = argparse.ArgumentParser(description='验证embedding保存顺序')
-    parser.add_argument('--config', type=str, default='train.yaml', help='配置文件路径')
-    parser.add_argument('--epoch', type=int, default=1, help='要验证的epoch数')
-    
-    args = parser.parse_args()
-    
-    success = verify_embedding_order(args.config, args.epoch)
-    
-    if success:
-        print("\n🎉 验证成功！可以放心使用保存的embedding进行可视化分析。")
-    else:
-        print("\n⚠️ 验证失败！请检查训练脚本的embedding保存逻辑。")
-
-
-if __name__ == '__main__':
-    main()

TextRNN-THUCNews/Classification/README.md

@@ -0,0 +1,110 @@
+# TextRNN 可视化实验脚本
+
+基于原始 Chinese-Text-Classification-Pytorch 仓库重构的 TextRNN 训练脚本，专门用于可视化实验。
+
+## 目录结构
+
+```
+Classification/
+├── scripts/           # 脚本文件
+│   ├── model.py      # 模型定义，包含feature、get_prediction、prediction函数
+│   ├── train.py      # 训练脚本，支持多卡训练
+│   ├── train.yaml    # 训练配置文件
+│   ├── dataset_utils.py  # 数据集处理工具
+│   └── get_label.py  # 标签提取脚本
+├── dataset/          # 数据集文件
+│   ├── train.txt     # 训练数据
+│   ├── dev.txt       # 验证数据
+│   ├── test.txt      # 测试数据
+│   ├── class.txt     # 类别列表
+│   ├── vocab.pkl     # 词汇表
+│   └── labels.npy    # 提取的标签
+└── epochs/           # 按epoch存放模型文件和特征向量
+    ├── epoch_1/
+    │   ├── model.pt      # 模型权重
+    │   ├── embeddings.npy # 特征向量
+    │   └── predictions.npy # 预测值
+    └── epoch_2/
+        └── ...
+```
+
+## 功能说明
+
+### 1. model.py
+- **Model类**: TextRNN模型实现
+- **feature()**: 提取中间层特征向量（dropout层输出），用于可视化
+- **get_prediction()**: 获取模型最终层输出向量（logits）
+- **prediction()**: 根据中间特征向量预测结果
+
+### 2. train.py
+- 支持多GPU训练
+- 每个epoch自动保存模型、特征向量、预测值到 `epochs/epoch_N/`
+- 支持配置文件驱动训练
+- 实时显示训练进度和验证结果
+
+### 3. dataset_utils.py
+- 数据集加载和预处理
+- 词汇表构建
+- 数据迭代器实现
+
+### 4. get_label.py
+- 提取数据集标签并保存为 `labels.npy`
+- 生成类别名称映射文件
+
+## 使用方法
+
+### 1. 准备数据集
+将THUCNews数据集放入 `dataset/` 目录：
+```bash
+# 数据格式：每行一个样本，用tab分隔文本和标签
+text1\t0
+text2\t1
+...
+```
+
+### 2. 提取标签
+```bash
+cd scripts
+python get_label.py --config train.yaml --output ../dataset
+```
+
+### 3. 训练模型
+```bash
+cd scripts
+python train.py --config train.yaml
+```
+
+### 4. 配置文件说明
+编辑 `scripts/train.yaml` 来调整训练参数：
+```yaml
+dataset_path: "../dataset"    # 数据集路径
+num_epochs: 20               # 训练轮数
+batch_size: 128              # 批次大小
+learning_rate: 0.001         # 学习率
+use_word: false              # false=字符级，true=词级
+epochs_dir: "../epochs"      # 模型保存路径
+```
+
+## 可视化数据
+
+训练完成后，每个epoch的数据保存在 `epochs/epoch_N/` 中：
+- `model.pt`: 模型权重文件
+- `embeddings.npy`: 特征向量矩阵 (N_samples, feature_dim)
+- `predictions.npy`: 预测值矩阵 (N_samples, num_classes)
+
+这些数据可以直接用于可视化分析，如t-SNE降维、特征分布分析等。
+
+## 多GPU训练
+
+脚本自动检测可用GPU数量并启用多GPU训练：
+```python
+# 自动使用所有可用GPU
+if torch.cuda.device_count() > 1:
+    model = nn.DataParallel(model)
+```
+
+## 依赖要求
+
+```bash
+pip install torch numpy scikit-learn tqdm pyyaml
+```

TextRNN-THUCNews/Classification/dataset/THUCNews/data/class.txt

@@ -0,0 +1,10 @@
+finance
+realty
+stocks
+education
+science
+society
+politics
+sports
+game
+entertainment

TextRNN-THUCNews/Classification/dataset/THUCNews/data/embedding_SougouNews.npz

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TextRNN-THUCNews/Classification/dataset/THUCNews/data/embedding_Tencent.npz

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TextRNN-THUCNews/Classification/dataset/THUCNews/data/vocab.pkl

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TextRNN-THUCNews/Classification/dataset/info.json

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+{
+  "model": "TextRNN",
+  "classes": [
+    "finance",
+    "realty",
+    "stocks",
+    "education",
+    "science",
+    "society",
+    "politics",
+    "sports",
+    "game",
+    "entertainment"
+  ]
+}

TextRNN-THUCNews/Classification/dataset/labels.npy

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