514 lines
20 KiB
Python
514 lines
20 KiB
Python
"""
|
||
药店销售预测系统 - 核心预测器类
|
||
支持多店铺销售预测功能
|
||
"""
|
||
|
||
import os
|
||
import pandas as pd
|
||
import numpy as np
|
||
import torch
|
||
import time
|
||
import matplotlib.pyplot as plt
|
||
from datetime import datetime
|
||
|
||
from trainers import (
|
||
train_product_model_with_mlstm,
|
||
train_product_model_with_kan,
|
||
train_product_model_with_tcn,
|
||
train_product_model_with_transformer
|
||
)
|
||
from predictors.model_predictor import load_model_and_predict
|
||
from utils.data_utils import prepare_data, prepare_sequences
|
||
from utils.multi_store_data_utils import (
|
||
load_multi_store_data,
|
||
get_store_product_sales_data,
|
||
aggregate_multi_store_data
|
||
)
|
||
from analysis.metrics import evaluate_model
|
||
from core.config import DEVICE, DEFAULT_MODEL_DIR, DEFAULT_DATA_PATH
|
||
|
||
class PharmacyPredictor:
|
||
"""
|
||
药店销售预测系统核心类，用于训练模型和进行预测
|
||
"""
|
||
def __init__(self, data_path=None, model_dir=DEFAULT_MODEL_DIR):
|
||
"""
|
||
初始化预测器
|
||
|
||
参数:
|
||
data_path: 数据文件路径，默认使用多店铺CSV文件
|
||
model_dir: 模型保存目录
|
||
"""
|
||
# 设置默认数据路径为多店铺CSV文件
|
||
if data_path is None:
|
||
data_path = 'pharmacy_sales_multi_store.csv'
|
||
|
||
self.data_path = data_path
|
||
self.model_dir = model_dir
|
||
self.device = DEVICE
|
||
|
||
if not os.path.exists(model_dir):
|
||
os.makedirs(model_dir)
|
||
|
||
print(f"使用设备: {self.device}")
|
||
|
||
# 尝试加载多店铺数据
|
||
try:
|
||
self.data = load_multi_store_data(data_path)
|
||
print(f"已加载多店铺数据，来源: {data_path}")
|
||
except Exception as e:
|
||
print(f"加载数据失败: {e}")
|
||
self.data = None
|
||
|
||
def train_model(self, product_id, model_type='transformer', epochs=100, batch_size=32,
|
||
learning_rate=0.001, sequence_length=30, forecast_horizon=7,
|
||
hidden_size=64, num_layers=2, dropout=0.1, use_optimized=False,
|
||
store_id=None, training_mode='product', aggregation_method='sum',
|
||
socketio=None, task_id=None, version=None, continue_training=False,
|
||
progress_callback=None):
|
||
"""
|
||
训练预测模型 - 支持多店铺训练
|
||
|
||
参数:
|
||
product_id: 产品ID
|
||
model_type: 模型类型 ('transformer', 'mlstm', 'kan', 'tcn', 'optimized_kan')
|
||
epochs: 训练轮次
|
||
batch_size: 批次大小
|
||
learning_rate: 学习率
|
||
sequence_length: 输入序列长度
|
||
forecast_horizon: 预测天数
|
||
hidden_size: 隐藏层大小
|
||
num_layers: 层数
|
||
dropout: Dropout比例
|
||
use_optimized: 是否使用优化版KAN（仅当model_type为'kan'时有效）
|
||
store_id: 店铺ID（仅当training_mode为'store'时使用）
|
||
training_mode: 训练模式 ('product', 'store', 'global')
|
||
aggregation_method: 聚合方法 ('sum', 'mean', 'median') - 仅用于全局训练
|
||
|
||
返回:
|
||
metrics: 模型评估指标
|
||
"""
|
||
# 创建统一的输出函数
|
||
def log_message(message, log_type='info'):
|
||
"""统一的日志输出函数"""
|
||
print(message, flush=True) # 始终输出到控制台
|
||
|
||
# 如果有进度回调，也发送到回调
|
||
if progress_callback:
|
||
try:
|
||
progress_callback({
|
||
'log_type': log_type,
|
||
'message': message
|
||
})
|
||
except Exception as e:
|
||
print(f"进度回调失败: {e}", flush=True)
|
||
|
||
if self.data is None:
|
||
log_message("没有可用的数据，请先加载或生成数据", 'error')
|
||
return None
|
||
|
||
# 根据训练模式准备数据
|
||
if training_mode == 'product':
|
||
# 按产品训练：使用所有店铺的该产品数据
|
||
product_data = self.data[self.data['product_id'] == product_id].copy()
|
||
if product_data.empty:
|
||
log_message(f"找不到产品 {product_id} 的数据", 'error')
|
||
return None
|
||
log_message(f"按产品训练模式: 产品 {product_id}, 数据量: {len(product_data)}")
|
||
|
||
elif training_mode == 'store':
|
||
# 按店铺训练：使用特定店铺的特定产品数据
|
||
if not store_id:
|
||
log_message("店铺训练模式需要指定 store_id", 'error')
|
||
return None
|
||
try:
|
||
product_data = get_store_product_sales_data(
|
||
store_id=store_id,
|
||
product_id=product_id,
|
||
file_path=self.data_path
|
||
)
|
||
log_message(f"按店铺训练模式: 店铺 {store_id}, 产品 {product_id}, 数据量: {len(product_data)}")
|
||
except Exception as e:
|
||
log_message(f"获取店铺产品数据失败: {e}", 'error')
|
||
return None
|
||
|
||
elif training_mode == 'global':
|
||
# 全局训练：聚合所有店铺的产品数据
|
||
try:
|
||
product_data = aggregate_multi_store_data(
|
||
product_id=product_id,
|
||
aggregation_method=aggregation_method,
|
||
file_path=self.data_path
|
||
)
|
||
log_message(f"全局训练模式: 产品 {product_id}, 聚合方法 {aggregation_method}, 数据量: {len(product_data)}")
|
||
except Exception as e:
|
||
log_message(f"聚合全局数据失败: {e}", 'error')
|
||
return None
|
||
else:
|
||
log_message(f"不支持的训练模式: {training_mode}", 'error')
|
||
return None
|
||
|
||
# 根据训练模式构建模型标识符
|
||
if training_mode == 'store':
|
||
model_identifier = f"{store_id}_{product_id}"
|
||
elif training_mode == 'global':
|
||
model_identifier = f"global_{product_id}_{aggregation_method}"
|
||
else:
|
||
model_identifier = product_id
|
||
|
||
# 调用相应的训练函数
|
||
try:
|
||
log_message(f"🤖 开始调用 {model_type} 训练器")
|
||
if model_type == 'transformer':
|
||
model_result, metrics, actual_version = train_product_model_with_transformer(
|
||
product_id,
|
||
store_id=store_id,
|
||
training_mode=training_mode,
|
||
aggregation_method=aggregation_method,
|
||
epochs=epochs,
|
||
model_dir=self.model_dir,
|
||
version=version,
|
||
socketio=socketio,
|
||
task_id=task_id,
|
||
continue_training=continue_training
|
||
)
|
||
log_message(f"✅ {model_type} 训练器返回: metrics={type(metrics)}, version={actual_version}", 'success')
|
||
elif model_type == 'mlstm':
|
||
_, metrics, _, _ = train_product_model_with_mlstm(
|
||
product_id,
|
||
store_id=store_id,
|
||
training_mode=training_mode,
|
||
aggregation_method=aggregation_method,
|
||
epochs=epochs,
|
||
model_dir=self.model_dir,
|
||
socketio=socketio,
|
||
task_id=task_id,
|
||
progress_callback=progress_callback
|
||
)
|
||
elif model_type == 'kan':
|
||
_, metrics = train_product_model_with_kan(
|
||
product_id,
|
||
store_id=store_id,
|
||
training_mode=training_mode,
|
||
aggregation_method=aggregation_method,
|
||
epochs=epochs,
|
||
use_optimized=use_optimized,
|
||
model_dir=self.model_dir
|
||
)
|
||
elif model_type == 'optimized_kan':
|
||
_, metrics = train_product_model_with_kan(
|
||
product_id,
|
||
store_id=store_id,
|
||
training_mode=training_mode,
|
||
aggregation_method=aggregation_method,
|
||
epochs=epochs,
|
||
use_optimized=True,
|
||
model_dir=self.model_dir
|
||
)
|
||
elif model_type == 'tcn':
|
||
_, metrics, _, _ = train_product_model_with_tcn(
|
||
product_id,
|
||
store_id=store_id,
|
||
training_mode=training_mode,
|
||
aggregation_method=aggregation_method,
|
||
epochs=epochs,
|
||
model_dir=self.model_dir,
|
||
socketio=socketio,
|
||
task_id=task_id
|
||
)
|
||
else:
|
||
log_message(f"不支持的模型类型: {model_type}", 'error')
|
||
return None
|
||
|
||
# 检查和打印返回的metrics
|
||
log_message(f"📊 训练完成，检查返回的metrics: {metrics}")
|
||
|
||
# 在返回的metrics中添加训练信息
|
||
if metrics:
|
||
log_message(f"✅ metrics不为空，添加训练信息")
|
||
metrics.update({
|
||
'training_mode': training_mode,
|
||
'store_id': store_id,
|
||
'product_id': product_id,
|
||
'model_identifier': model_identifier,
|
||
'aggregation_method': aggregation_method if training_mode == 'global' else None
|
||
})
|
||
log_message(f"📈 最终返回的metrics: {metrics}", 'success')
|
||
else:
|
||
log_message(f"⚠️ metrics为空或None", 'warning')
|
||
|
||
return metrics
|
||
|
||
except Exception as e:
|
||
log_message(f"模型训练失败: {e}", 'error')
|
||
return None
|
||
|
||
def predict(self, product_id, model_type, future_days=7, start_date=None, analyze_result=False, version=None,
|
||
store_id=None, training_mode='product', aggregation_method='sum'):
|
||
"""
|
||
使用已训练的模型进行预测 - 支持多店铺预测
|
||
|
||
参数:
|
||
product_id: 产品ID
|
||
model_type: 模型类型
|
||
future_days: 预测未来天数
|
||
start_date: 预测起始日期
|
||
analyze_result: 是否分析预测结果
|
||
version: 模型版本，如果为None则使用最新版本
|
||
store_id: 店铺ID（仅当training_mode为'store'时使用）
|
||
training_mode: 训练模式 ('product', 'store', 'global')
|
||
aggregation_method: 聚合方法 ('sum', 'mean', 'median') - 仅用于全局预测
|
||
|
||
返回:
|
||
预测结果和分析（如果analyze_result为True）
|
||
"""
|
||
# 根据训练模式构建模型标识符
|
||
if training_mode == 'store' and store_id:
|
||
model_identifier = f"{store_id}_{product_id}"
|
||
elif training_mode == 'global':
|
||
model_identifier = f"global_{product_id}_{aggregation_method}"
|
||
else:
|
||
model_identifier = product_id
|
||
|
||
return load_model_and_predict(
|
||
model_identifier,
|
||
model_type,
|
||
future_days=future_days,
|
||
start_date=start_date,
|
||
analyze_result=analyze_result,
|
||
version=version
|
||
)
|
||
|
||
def train_optimized_kan_model(self, product_id, epochs=100, batch_size=32,
|
||
learning_rate=0.001, sequence_length=30, forecast_horizon=7,
|
||
hidden_size=64, num_layers=2, dropout=0.1):
|
||
"""
|
||
训练优化版KAN模型（便捷方法）
|
||
|
||
参数与train_model相同，但固定model_type为'kan'且use_optimized为True
|
||
"""
|
||
return self.train_model(
|
||
product_id=product_id,
|
||
model_type='kan',
|
||
epochs=epochs,
|
||
batch_size=batch_size,
|
||
learning_rate=learning_rate,
|
||
sequence_length=sequence_length,
|
||
forecast_horizon=forecast_horizon,
|
||
hidden_size=hidden_size,
|
||
num_layers=num_layers,
|
||
dropout=dropout,
|
||
use_optimized=True
|
||
)
|
||
|
||
def compare_kan_models(self, product_id, epochs=100, batch_size=32,
|
||
learning_rate=0.001, sequence_length=30, forecast_horizon=7,
|
||
hidden_size=64, num_layers=2, dropout=0.1):
|
||
"""
|
||
比较原始KAN和优化版KAN模型性能
|
||
|
||
参数与train_model相同
|
||
|
||
返回:
|
||
比较结果字典
|
||
"""
|
||
print(f"开始比较产品 {product_id} 的原始KAN和优化版KAN模型性能...")
|
||
|
||
# 训练原始KAN模型
|
||
print("\n训练原始KAN模型...")
|
||
kan_metrics = self.train_model(
|
||
product_id=product_id,
|
||
model_type='kan',
|
||
epochs=epochs,
|
||
batch_size=batch_size,
|
||
learning_rate=learning_rate,
|
||
sequence_length=sequence_length,
|
||
forecast_horizon=forecast_horizon,
|
||
hidden_size=hidden_size,
|
||
num_layers=num_layers,
|
||
dropout=dropout,
|
||
use_optimized=False
|
||
)
|
||
|
||
# 训练优化版KAN模型
|
||
print("\n训练优化版KAN模型...")
|
||
optimized_kan_metrics = self.train_model(
|
||
product_id=product_id,
|
||
model_type='kan',
|
||
epochs=epochs,
|
||
batch_size=batch_size,
|
||
learning_rate=learning_rate,
|
||
sequence_length=sequence_length,
|
||
forecast_horizon=forecast_horizon,
|
||
hidden_size=hidden_size,
|
||
num_layers=num_layers,
|
||
dropout=dropout,
|
||
use_optimized=True
|
||
)
|
||
|
||
# 比较结果
|
||
comparison = {
|
||
'kan': kan_metrics,
|
||
'optimized_kan': optimized_kan_metrics
|
||
}
|
||
|
||
# 打印比较结果
|
||
print("\n模型性能比较:")
|
||
print(f"{'指标':<10} {'原始KAN':<15} {'优化版KAN':<15} {'改进百分比':<15}")
|
||
print("-" * 55)
|
||
|
||
for metric in ['mse', 'rmse', 'mae', 'mape']:
|
||
if metric in kan_metrics and metric in optimized_kan_metrics:
|
||
kan_value = kan_metrics[metric]
|
||
opt_value = optimized_kan_metrics[metric]
|
||
improvement = (kan_value - opt_value) / kan_value * 100 if kan_value != 0 else 0
|
||
print(f"{metric.upper():<10} {kan_value:<15.4f} {opt_value:<15.4f} {improvement:<15.2f}%")
|
||
|
||
# R²值越高越好，所以计算改进的方式不同
|
||
if 'r2' in kan_metrics and 'r2' in optimized_kan_metrics:
|
||
kan_r2 = kan_metrics['r2']
|
||
opt_r2 = optimized_kan_metrics['r2']
|
||
improvement = (opt_r2 - kan_r2) / (1 - kan_r2) * 100 if kan_r2 != 1 else 0
|
||
print(f"{'R²':<10} {kan_r2:<15.4f} {opt_r2:<15.4f} {improvement:<15.2f}%")
|
||
|
||
# 训练时间
|
||
if 'training_time' in kan_metrics and 'training_time' in optimized_kan_metrics:
|
||
kan_time = kan_metrics['training_time']
|
||
opt_time = optimized_kan_metrics['training_time']
|
||
time_diff = (opt_time - kan_time) / kan_time * 100 if kan_time != 0 else 0
|
||
print(f"{'时间(秒)':<10} {kan_time:<15.2f} {opt_time:<15.2f} {time_diff:<15.2f}%")
|
||
|
||
return comparison
|
||
|
||
def list_available_models(self, product_id=None, store_id=None, training_mode=None):
|
||
"""
|
||
列出可用的已训练模型 - 支持多店铺模型
|
||
|
||
参数:
|
||
product_id: 产品ID，如果为None则列出所有模型
|
||
store_id: 店铺ID，用于筛选店铺专属模型
|
||
training_mode: 训练模式筛选 ('product', 'store', 'global')
|
||
|
||
返回:
|
||
可用模型列表
|
||
"""
|
||
if not os.path.exists(self.model_dir):
|
||
print(f"模型目录 {self.model_dir} 不存在")
|
||
return []
|
||
|
||
model_files = os.listdir(self.model_dir)
|
||
|
||
models = []
|
||
for file in model_files:
|
||
if file.endswith('.pth'):
|
||
try:
|
||
# 解析模型文件名
|
||
model_info = self._parse_model_filename(file)
|
||
if model_info:
|
||
# 应用过滤条件
|
||
if product_id and model_info.get('product_id') != product_id:
|
||
continue
|
||
if store_id and model_info.get('store_id') != store_id:
|
||
continue
|
||
if training_mode and model_info.get('training_mode') != training_mode:
|
||
continue
|
||
|
||
model_info['file_name'] = file
|
||
model_info['file_path'] = os.path.join(self.model_dir, file)
|
||
models.append(model_info)
|
||
except Exception as e:
|
||
print(f"解析模型文件名失败: {file}, 错误: {e}")
|
||
continue
|
||
|
||
return models
|
||
|
||
def _parse_model_filename(self, filename):
|
||
"""
|
||
解析模型文件名，提取模型信息
|
||
|
||
参数:
|
||
filename: 模型文件名
|
||
|
||
返回:
|
||
dict: 模型信息字典
|
||
"""
|
||
# 移除文件扩展名
|
||
name = filename.replace('.pth', '')
|
||
|
||
# 解析新的多店铺模型命名格式
|
||
if '_model_product_' in name:
|
||
parts = name.split('_model_product_')
|
||
model_type = parts[0]
|
||
product_part = parts[1]
|
||
|
||
# 检查是否是店铺模型 (格式: model_type_model_product_store_id_product_id)
|
||
if len(product_part.split('_')) > 1:
|
||
store_id = product_part.split('_')[0]
|
||
product_id = '_'.join(product_part.split('_')[1:])
|
||
training_mode = 'store'
|
||
# 检查是否是全局模型 (格式: model_type_model_product_global_product_id_method)
|
||
elif product_part.startswith('global_'):
|
||
parts = product_part.split('_')
|
||
if len(parts) >= 3:
|
||
product_id = '_'.join(parts[1:-1])
|
||
aggregation_method = parts[-1]
|
||
store_id = None
|
||
training_mode = 'global'
|
||
else:
|
||
product_id = product_part
|
||
store_id = None
|
||
training_mode = 'product'
|
||
else:
|
||
# 常规产品模型
|
||
product_id = product_part
|
||
store_id = None
|
||
training_mode = 'product'
|
||
|
||
# 处理优化版KAN模型
|
||
if 'optimized' in model_type:
|
||
model_type = 'optimized_kan'
|
||
|
||
return {
|
||
'model_type': model_type,
|
||
'product_id': product_id,
|
||
'store_id': store_id,
|
||
'training_mode': training_mode,
|
||
'aggregation_method': aggregation_method if training_mode == 'global' and 'aggregation_method' in locals() else None
|
||
}
|
||
|
||
# 处理旧格式的向后兼容性
|
||
elif "kan_optimized_model" in name:
|
||
model_type = "optimized_kan"
|
||
product_id = name.split('_product_')[1] if '_product_' in name else 'unknown'
|
||
return {
|
||
'model_type': model_type,
|
||
'product_id': product_id,
|
||
'store_id': None,
|
||
'training_mode': 'product',
|
||
'aggregation_method': None
|
||
}
|
||
|
||
return None
|
||
|
||
def delete_model(self, product_id, model_type):
|
||
"""
|
||
删除已训练的模型
|
||
|
||
参数:
|
||
product_id: 产品ID
|
||
model_type: 模型类型
|
||
|
||
返回:
|
||
是否成功删除
|
||
"""
|
||
model_suffix = '_optimized' if model_type == 'optimized_kan' else ''
|
||
model_name = f"{model_type}{model_suffix}_model_product_{product_id}.pth"
|
||
model_path = os.path.join(self.model_dir, model_name)
|
||
|
||
if os.path.exists(model_path):
|
||
os.remove(model_path)
|
||
print(f"已删除模型: {model_path}")
|
||
return True
|
||
else:
|
||
print(f"模型文件 {model_path} 不存在")
|
||
return False |