278 lines
8.7 KiB
Python
278 lines
8.7 KiB
Python
"""
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药店销售预测系统 - 全局配置参数
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"""
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import torch
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import matplotlib
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matplotlib.use('Agg') # 设置matplotlib后端为Agg，适用于无头服务器环境
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import matplotlib.pyplot as plt
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import os
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import re
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import glob
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# 解决画图中文显示问题
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plt.rcParams['font.sans-serif'] = ['SimHei']
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plt.rcParams['axes.unicode_minus'] = False
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# 获取设备（GPU或CPU）
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def get_device():
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"""获取可用的计算设备（GPU或CPU）"""
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if torch.cuda.is_available():
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return torch.device('cuda')
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else:
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return torch.device('cpu')
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# 全局设备
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DEVICE = get_device()
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# 数据相关配置
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DEFAULT_DATA_PATH = 'pharmacy_sales.xlsx'
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DEFAULT_MODEL_DIR = 'saved_models'
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DEFAULT_FEATURES = ['sales', 'price', 'weekday', 'month', 'is_holiday', 'is_weekend', 'is_promotion', 'temperature']
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# 时间序列参数
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LOOK_BACK = 5 # 使用过去5天数据（适应小数据集）
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FORECAST_HORIZON = 3 # 预测未来3天销量（适应小数据集）
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# 训练参数
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DEFAULT_EPOCHS = 50 # 训练轮次
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DEFAULT_BATCH_SIZE = 32 # 批大小
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DEFAULT_LEARNING_RATE = 0.001 # 学习率
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# 模型参数
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NUM_FEATURES = 8 # 输入特征数
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EMBED_DIM = 32 # 嵌入维度
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DENSE_DIM = 32 # 隐藏层神经元数
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NUM_HEADS = 4 # 注意力头数
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DROPOUT_RATE = 0.1 # 丢弃率
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NUM_BLOCKS = 3 # 编码器解码器数
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HIDDEN_SIZE = 64 # 隐藏层大小
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NUM_LAYERS = 2 # 层数
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# 支持的模型类型
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SUPPORTED_MODELS = ['mlstm', 'kan', 'transformer', 'tcn', 'optimized_kan']
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# 版本管理配置
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MODEL_VERSION_PREFIX = 'v' # 版本前缀
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DEFAULT_VERSION = 'v1' # 默认版本号
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# WebSocket配置
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WEBSOCKET_NAMESPACE = '/training' # WebSocket命名空间
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TRAINING_UPDATE_INTERVAL = 1 # 训练进度更新间隔（秒）
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# 创建模型保存目录
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os.makedirs(DEFAULT_MODEL_DIR, exist_ok=True)
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def get_next_model_version(product_id: str, model_type: str) -> str:
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"""
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获取指定产品和模型类型的下一个版本号
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Args:
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product_id: 产品ID
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model_type: 模型类型
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Returns:
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下一个版本号，格式如 'v2', 'v3' 等
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"""
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# 新格式：带版本号的文件
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pattern_new = f"{model_type}_model_product_{product_id}_v*.pth"
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existing_files_new = glob.glob(os.path.join(DEFAULT_MODEL_DIR, pattern_new))
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# 旧格式：不带版本号的文件（兼容性支持）
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pattern_old = f"{model_type}_model_product_{product_id}.pth"
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old_file_path = os.path.join(DEFAULT_MODEL_DIR, pattern_old)
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has_old_format = os.path.exists(old_file_path)
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# 如果没有任何格式的文件，返回默认版本
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if not existing_files_new and not has_old_format:
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return DEFAULT_VERSION
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# 提取新格式文件的版本号
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versions = []
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for file_path in existing_files_new:
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filename = os.path.basename(file_path)
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version_match = re.search(rf"_v(\d+)\.pth$", filename)
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if version_match:
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versions.append(int(version_match.group(1)))
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# 如果存在旧格式文件，将其视为v1
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if has_old_format:
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versions.append(1)
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print(f"检测到旧格式模型文件: {old_file_path}，将其视为版本v1")
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if versions:
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next_version_num = max(versions) + 1
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return f"v{next_version_num}"
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else:
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return DEFAULT_VERSION
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def get_model_file_path(product_id: str, model_type: str, version: str = None) -> str:
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"""
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生成模型文件路径
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Args:
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product_id: 产品ID
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model_type: 模型类型
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version: 版本号，如果为None则获取下一个版本
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Returns:
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模型文件的完整路径
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"""
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if version is None:
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version = get_next_model_version(product_id, model_type)
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# 特殊处理v1版本：检查是否存在旧格式文件
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if version == "v1":
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# 检查旧格式文件是否存在
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old_format_filename = f"{model_type}_model_product_{product_id}.pth"
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old_format_path = os.path.join(DEFAULT_MODEL_DIR, old_format_filename)
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if os.path.exists(old_format_path):
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print(f"找到旧格式模型文件: {old_format_path}，将其作为v1版本")
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return old_format_path
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# 使用新格式文件名
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filename = f"{model_type}_model_product_{product_id}_{version}.pth"
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return os.path.join(DEFAULT_MODEL_DIR, filename)
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def get_model_versions(product_id: str, model_type: str) -> list:
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"""
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获取指定产品和模型类型的所有版本
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Args:
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product_id: 产品ID
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model_type: 模型类型
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Returns:
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版本列表，按版本号排序
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"""
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# 新格式：带版本号的文件
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pattern_new = f"{model_type}_model_product_{product_id}_v*.pth"
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existing_files_new = glob.glob(os.path.join(DEFAULT_MODEL_DIR, pattern_new))
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# 旧格式：不带版本号的文件（兼容性支持）
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pattern_old = f"{model_type}_model_product_{product_id}.pth"
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old_file_path = os.path.join(DEFAULT_MODEL_DIR, pattern_old)
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has_old_format = os.path.exists(old_file_path)
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versions = []
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# 处理新格式文件
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for file_path in existing_files_new:
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filename = os.path.basename(file_path)
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version_match = re.search(rf"_v(\d+)\.pth$", filename)
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if version_match:
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version_num = int(version_match.group(1))
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versions.append(f"v{version_num}")
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# 如果存在旧格式文件，将其视为v1
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if has_old_format:
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if "v1" not in versions: # 避免重复添加
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versions.append("v1")
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print(f"检测到旧格式模型文件: {old_file_path}，将其视为版本v1")
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# 按版本号排序
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versions.sort(key=lambda v: int(v[1:]))
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return versions
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def get_latest_model_version(product_id: str, model_type: str) -> str:
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"""
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获取指定产品和模型类型的最新版本
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Args:
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product_id: 产品ID
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model_type: 模型类型
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Returns:
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最新版本号，如果没有则返回None
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"""
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versions = get_model_versions(product_id, model_type)
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return versions[-1] if versions else None
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def save_model_version_info(product_id: str, model_type: str, version: str, file_path: str, metrics: dict = None):
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"""
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保存模型版本信息到数据库
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Args:
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product_id: 产品ID
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model_type: 模型类型
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version: 版本号
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file_path: 模型文件路径
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metrics: 模型性能指标
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"""
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import sqlite3
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import json
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from datetime import datetime
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try:
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conn = sqlite3.connect('prediction_history.db')
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cursor = conn.cursor()
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# 插入模型版本记录
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cursor.execute('''
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INSERT INTO model_versions (
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product_id, model_type, version, file_path, created_at, metrics, is_active
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) VALUES (?, ?, ?, ?, ?, ?, ?)
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''', (
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product_id,
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model_type,
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version,
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file_path,
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datetime.now().isoformat(),
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json.dumps(metrics) if metrics else None,
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1 # 新模型默认为激活状态
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))
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conn.commit()
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conn.close()
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print(f"已保存模型版本信息: {product_id}_{model_type}_{version}")
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except Exception as e:
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print(f"保存模型版本信息失败: {str(e)}")
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def get_model_version_info(product_id: str, model_type: str, version: str = None):
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"""
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从数据库获取模型版本信息
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Args:
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product_id: 产品ID
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model_type: 模型类型
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version: 版本号，如果为None则获取最新版本
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Returns:
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模型版本信息字典
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"""
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import sqlite3
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import json
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try:
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conn = sqlite3.connect('prediction_history.db')
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conn.row_factory = sqlite3.Row
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cursor = conn.cursor()
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if version:
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cursor.execute('''
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SELECT * FROM model_versions
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WHERE product_id = ? AND model_type = ? AND version = ?
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ORDER BY created_at DESC LIMIT 1
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''', (product_id, model_type, version))
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else:
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cursor.execute('''
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SELECT * FROM model_versions
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WHERE product_id = ? AND model_type = ?
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ORDER BY created_at DESC LIMIT 1
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''', (product_id, model_type))
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row = cursor.fetchone()
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conn.close()
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if row:
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result = dict(row)
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if result['metrics']:
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result['metrics'] = json.loads(result['metrics'])
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return result
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return None
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except Exception as e:
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print(f"获取模型版本信息失败: {str(e)}")
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return None |