Datasets:

code-philia
/

mtpnet_text

# coding: UTF-8
import os
import sys
import numpy as np
import yaml
import argparse

# 添加当前目录到路径
sys.path.append(os.path.dirname(os.path.abspath(__file__)))

from model import Config
from dataset_utils import build_dataset, get_labels_from_dataset


def extract_labels(config_path, output_dir):
    """
    提取数据集标签并保存
    
    Args:
        config_path: 配置文件路径
        output_dir: 输出目录
    """
    # 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'))
    
    # 构建数据集
    print("正在构建数据集...")
    vocab, train_data, dev_data, test_data = build_dataset(config, train_config.get('use_word', False))
    
    # 提取标签
    print("正在提取标签...")
    train_labels = get_labels_from_dataset(train_data)
    dev_labels = get_labels_from_dataset(dev_data)
    test_labels = get_labels_from_dataset(test_data)
    
    # 合并所有标签（按训练、验证、测试的顺序）
    all_labels = np.concatenate([train_labels, dev_labels, test_labels])
    
    # 确保输出目录存在
    os.makedirs(output_dir, exist_ok=True)
    
    # 保存标签
    labels_path = os.path.join(output_dir, 'labels.npy')
    np.save(labels_path, all_labels)
    
    # 保存各个数据集的标签（可选）
    np.save(os.path.join(output_dir, 'train_labels.npy'), train_labels)
    np.save(os.path.join(output_dir, 'dev_labels.npy'), dev_labels)
    np.save(os.path.join(output_dir, 'test_labels.npy'), test_labels)
    
    # 输出统计信息
    print("标签提取完成！")
    print(f"总标签数量: {len(all_labels)}")
    print(f"训练集标签数量: {len(train_labels)}")
    print(f"验证集标签数量: {len(dev_labels)}")
    print(f"测试集标签数量: {len(test_labels)}")
    print(f"类别数量: {len(np.unique(all_labels))}")
    print(f"类别分布: {np.bincount(all_labels)}")
    print(f"标签已保存到: {labels_path}")
    
    # 保存类别名称映射
    class_names_path = os.path.join(output_dir, 'class_names.txt')
    with open(class_names_path, 'w', encoding='utf-8') as f:
        for i, class_name in enumerate(config.class_list):
            f.write(f"{i}\t{class_name}\n")
    print(f"类别名称映射已保存到: {class_names_path}")


def main():
    parser = argparse.ArgumentParser(description='提取数据集标签')
    parser.add_argument('--config', type=str, default='train.yaml', 
                       help='训练配置文件路径')
    parser.add_argument('--output', type=str, default='../dataset', 
                       help='输出目录')
    
    args = parser.parse_args()
    
    extract_labels(args.config, args.output)


if __name__ == '__main__':
    main()

	# coding: UTF-8
	import os
	import sys
	import numpy as np
	import yaml
	import argparse

	# 添加当前目录到路径
	sys.path.append(os.path.dirname(os.path.abspath(__file__)))

	from model import Config
	from dataset_utils import build_dataset, get_labels_from_dataset


	def extract_labels(config_path, output_dir):
	"""
	提取数据集标签并保存

	Args:
	config_path: 配置文件路径
	output_dir: 输出目录
	"""
	# 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'))

	# 构建数据集
	print("正在构建数据集...")
	vocab, train_data, dev_data, test_data = build_dataset(config, train_config.get('use_word', False))

	# 提取标签
	print("正在提取标签...")
	train_labels = get_labels_from_dataset(train_data)
	dev_labels = get_labels_from_dataset(dev_data)
	test_labels = get_labels_from_dataset(test_data)

	# 合并所有标签（按训练、验证、测试的顺序）
	all_labels = np.concatenate([train_labels, dev_labels, test_labels])

	# 确保输出目录存在
	os.makedirs(output_dir, exist_ok=True)

	# 保存标签
	labels_path = os.path.join(output_dir, 'labels.npy')
	np.save(labels_path, all_labels)

	# 保存各个数据集的标签（可选）
	np.save(os.path.join(output_dir, 'train_labels.npy'), train_labels)
	np.save(os.path.join(output_dir, 'dev_labels.npy'), dev_labels)
	np.save(os.path.join(output_dir, 'test_labels.npy'), test_labels)

	# 输出统计信息
	print("标签提取完成！")
	print(f"总标签数量: {len(all_labels)}")
	print(f"训练集标签数量: {len(train_labels)}")
	print(f"验证集标签数量: {len(dev_labels)}")
	print(f"测试集标签数量: {len(test_labels)}")
	print(f"类别数量: {len(np.unique(all_labels))}")
	print(f"类别分布: {np.bincount(all_labels)}")
	print(f"标签已保存到: {labels_path}")

	# 保存类别名称映射
	class_names_path = os.path.join(output_dir, 'class_names.txt')
	with open(class_names_path, 'w', encoding='utf-8') as f:
	for i, class_name in enumerate(config.class_list):
	f.write(f"{i}\t{class_name}\n")
	print(f"类别名称映射已保存到: {class_names_path}")


	def main():
	parser = argparse.ArgumentParser(description='提取数据集标签')
	parser.add_argument('--config', type=str, default='train.yaml',
	help='训练配置文件路径')
	parser.add_argument('--output', type=str, default='../dataset',
	help='输出目录')

	args = parser.parse_args()

	extract_labels(args.config, args.output)


	if __name__ == '__main__':
	main()