examples/train_robot_thp_with_features.py

"""
训练RobotTHP模型（带语义特征）

展示如何在EasyTPP框架中使用RobotTHP模型，并加载语义特征、偏差特征等
"""

import torch
from torch.utils.data import DataLoader

from easy_tpp.config_factory import DataSpecConfig
from easy_tpp.model import TorchRobotTHP
from easy_tpp.preprocess.robert_dataset import RobertTPPDataset
from easy_tpp.preprocess.robert_tokenizer import RobertEventTokenizer
from easy_tpp.preprocess.data_collator import TPPDataCollator


def prepare_robert_data():
    """
    准备评论罗伯特数据（示例）
    
    实际使用时，应该从JSON文件加载并处理
    """
    # 示例数据
    time_seqs = [
        [0.0, 10.5, 25.3, 45.2],
        [0.0, 5.2, 12.8]
    ]
    type_seqs = [
        [0, 1, 2, 1],  # post, bot_comment, user_comment, user_comment
        [0, 1, 2]
    ]
    time_delta_seqs = [
        [0.0, 10.5, 14.8, 19.9],
        [0.0, 5.2, 7.6]
    ]
    
    # 语义向量（示例：768维BERT向量）
    semantic_vectors = [
        [[0.1] * 768, [0.2] * 768, [0.3] * 768, [0.4] * 768],
        [[0.1] * 768, [0.2] * 768, [0.3] * 768]
    ]
    
    # 偏差特征（示例：3维 [语境偏差, 情感偏差, 困惑度]）
    deviation_features = [
        [[0.0, 0.0, 0.0], [0.7, 0.5, 0.3], [0.2, 0.1, 0.1], [0.3, 0.2, 0.1]],
        [[0.0, 0.0, 0.0], [0.6, 0.4, 0.2], [0.1, 0.1, 0.1]]
    ]
    
    # 自发/被@标记（-1=不适用, 0=被@, 1=自发）
    is_spontaneous = [
        [-1.0, 1.0, -1.0, -1.0],  # 原帖不适用, 罗伯特自发, 用户评论不适用
        [-1.0, 0.0, -1.0]  # 原帖不适用, 罗伯特被@, 用户评论不适用
    ]
    
    return {
        'time_seqs': time_seqs,
        'type_seqs': type_seqs,
        'time_delta_seqs': time_delta_seqs,
        'semantic_vectors': semantic_vectors,
        'deviation_features': deviation_features,
        'is_spontaneous': is_spontaneous
    }


def create_data_loader(data_dict, config, use_semantic=True, use_deviation=True):
    """
    创建数据加载器
    
    Args:
        data_dict: 数据字典
        config: 数据配置
        use_semantic: 是否使用语义特征
        use_deviation: 是否使用偏差特征
    
    Returns:
        DataLoader: 数据加载器
    """
    # 创建数据集
    dataset = RobertTPPDataset(data_dict)
    
    # 创建分词器
    tokenizer = RobertEventTokenizer(
        config,
        use_semantic=use_semantic,
        use_deviation=use_deviation,
        semantic_dim=768
    )
    
    # 创建数据整理器
    padding = True if tokenizer.padding_strategy is None else tokenizer.padding_strategy
    truncation = False if tokenizer.truncation_strategy is None else tokenizer.truncation_strategy
    
    data_collator = TPPDataCollator(
        tokenizer=tokenizer,
        return_tensors='pt',
        max_length=tokenizer.model_max_length,
        padding=padding,
        truncation=truncation
    )
    
    # 创建数据加载器
    data_loader = DataLoader(
        dataset,
        collate_fn=data_collator,
        batch_size=config.batch_size,
        shuffle=True
    )
    
    return data_loader


def main():
    """主函数"""
    print("=" * 60)
    print("训练RobotTHP模型（带语义特征）")
    print("=" * 60)
    
    # 1. 准备数据
    print("\n1. 准备数据...")
    data_dict = prepare_robert_data()
    print(f"   序列数: {len(data_dict['time_seqs'])}")
    
    # 2. 创建配置
    print("\n2. 创建配置...")
    config = DataSpecConfig.parse_from_yaml_config({
        'num_event_types': 4,
        'batch_size': 2,
        'pad_token_id': 4
    })
    
    # 3. 创建数据加载器
    print("\n3. 创建数据加载器...")
    data_loader = create_data_loader(
        data_dict,
        config,
        use_semantic=True,
        use_deviation=True
    )
    
    # 4. 创建模型配置
    print("\n4. 创建模型...")
    from easy_tpp.config_factory import ModelConfig
    
    model_config = ModelConfig.parse_from_yaml_config({
        'hidden_size': 128,
        'num_layers': 3,
        'num_heads': 6,
        'dropout_rate': 0.1,
        'num_event_types': 4,
        'num_event_types_pad': 5,
        'pad_token_id': 4,
        'semantic_dim': 768,
        'use_semantic': True,
        'use_deviation': True,
        'use_structure_mask': False,
        'loss_integral_num_sample_per_step': 20,
        'use_mc_samples': True,
        'gpu': -1
    })
    
    model = TorchRobotTHP(model_config)
    print(f"   模型参数数量: {sum(p.numel() for p in model.parameters()):,}")
    
    # 5. 测试一个批次
    print("\n5. 测试数据加载...")
    for batch in data_loader:
        # batch是BatchEncoding对象，需要转换为tuple/list
        batch_values = batch.values()
        
        print(f"   批次大小: {len(batch_values[0])}")
        print(f"   序列长度: {batch_values[0].shape[1]}")
        print(f"   时间序列形状: {batch_values[0].shape}")
        print(f"   事件类型形状: {batch_values[2].shape}")
        
        if len(batch_values) > 5:
            print(f"   语义向量形状: {batch_values[5].shape if batch_values[5] is not None else 'None'}")
        if len(batch_values) > 6:
            print(f"   偏差特征形状: {batch_values[6].shape if batch_values[6] is not None else 'None'}")
        if len(batch_values) > 7:
            print(f"   自发标记形状: {batch_values[7].shape if batch_values[7] is not None else 'None'}")
        
        # 6. 测试前向传播
        print("\n6. 测试前向传播...")
        model.eval()
        with torch.no_grad():
            loss, num_events = model.loglike_loss(batch_values)
            print(f"   损失值: {loss.item():.4f}")
            print(f"   事件数: {num_events}")
        
        break
    
    print("\n✅ 测试完成！")
    print("\n使用说明:")
    print("1. 将你的JSON数据转换为上述格式")
    print("2. 使用RobertTPPDataset和RobertEventTokenizer加载数据")
    print("3. 在EasyTPP配置文件中设置model_id为RobotTHP")
    print("4. 运行训练即可")


if __name__ == '__main__':
    main()