聚烯烃月度逻辑添加图片结果
This commit is contained in:
workpc
parent
9647067f65
commit
e44f3f7ecf
@ -88,6 +88,13 @@ bdwdname = [
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'次三月',
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'次三月',
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'次四月',
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'次四月',
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]
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]
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# 数据库预测结果表八大维度列名
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price_columns = [
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'day_price', 'week_price', 'second_week_price', 'next_week_price',
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'next_month_price', 'next_february_price', 'next_march_price', 'next_april_price'
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]
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modelsindex = [
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modelsindex = [
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{
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{
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"NHITS": "SELF0000275",
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"NHITS": "SELF0000275",
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@ -459,7 +466,7 @@ print("数据库连接成功", host, dbname, dbusername)
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# 数据截取日期
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# 数据截取日期
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start_year = 2000 # 数据开始年份
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start_year = 2000 # 数据开始年份
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end_time = '' # 数据截取日期
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end_time = '2025-07-22' # 数据截取日期
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freq = 'M' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日
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freq = 'M' # 时间频率，"D": 天 "W": 周"M": 月"Q": 季度"A": 年 "H": 小时 "T": 分钟 "S": 秒 "B": 工作日
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delweekenday = True if freq == 'B' else False # 是否删除周末数据
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delweekenday = True if freq == 'B' else False # 是否删除周末数据
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is_corr = False # 特征是否参与滞后领先提升相关系数
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is_corr = False # 特征是否参与滞后领先提升相关系数
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26
lib/tools.py
26
lib/tools.py
@ -864,8 +864,7 @@ def find_best_models(date='', global_config=None):
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# 获取真实价格数据
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# 获取真实价格数据
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try:
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try:
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true_price = pd.read_csv(os.path.join(
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true_price = pd.read_csv('juxitingdataset/指标数据.csv')[['ds', 'y']]
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global_config['dataset'], '指标数据.csv'))[['ds', 'y']]
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except FileNotFoundError:
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except FileNotFoundError:
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global_config['logger'].error(
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global_config['logger'].error(
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f"未找到文件: {os.path.join(global_config['dataset'], '指标数据.csv')}")
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f"未找到文件: {os.path.join(global_config['dataset'], '指标数据.csv')}")
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@ -1082,23 +1081,31 @@ def plot_pp_predict_result(y_hat, global_config):
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import seaborn as sns
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import seaborn as sns
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# 获取y的真实值
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# 获取y的真实值
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y = pd.read_csv(os.path.join(
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# y = pd.read_csv(os.path.join(
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global_config['dataset'], '指标数据.csv'))[['ds', 'y']]
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# global_config['dataset'], '指标数据.csv'))[['ds', 'y']]
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y = pd.read_csv('juxitingdataset/指标数据.csv')[['ds', 'y']]
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y['ds'] = pd.to_datetime(y['ds'])
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y['ds'] = pd.to_datetime(y['ds'])
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y = y[y['ds'] < y_hat['ds'].iloc[0]][-30:]
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y = y[y['ds'] < y_hat['ds'].iloc[0]][-30:]
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# 取y的最后一行数据追加到y_hat（将真实值最后一行作为预测值起点）
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if not y.empty:
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# 获取y的最后一行并将'y'列重命名为'predictresult'以匹配y_hat结构
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y_last_row = y.tail(1).rename(columns={'y': 'predictresult'})
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# 追加到y_hat
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y_y_hat = pd.concat([y_last_row, y_hat], ignore_index=True)
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# 创建图表和子图布局，为表格预留空间
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# 创建图表和子图布局，为表格预留空间
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fig, ax = plt.subplots(figsize=(16, 9))
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fig, ax = plt.subplots(figsize=(16, 9))
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# 对日期列进行排序，确保日期大的在右边
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# 对日期列进行排序，确保日期大的在右边
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y_hat = y_hat.sort_values(by='ds')
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y_y_hat = y_y_hat.sort_values(by='ds')
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y = y.sort_values(by='ds')
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y = y.sort_values(by='ds')
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# 绘制 y_hat 的折线图，颜色为橙色
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# 绘制 y_hat 的折线图，颜色为橙色
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sns.lineplot(x=y_hat['ds'], y=y_hat['predictresult'],
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sns.lineplot(x=y_y_hat['ds'], y=y_y_hat['predictresult'],
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color='orange', label='y_hat', ax=ax, linestyle='--')
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color='orange', label='预测值', ax=ax, linestyle='--')
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# 绘制 y 的折线图，颜色为蓝色
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# 绘制 y 的折线图，颜色为蓝色
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sns.lineplot(x=y['ds'], y=y['y'], color='blue', label='y', ax=ax)
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sns.lineplot(x=y['ds'], y=y['y'], color='blue', label='真实值', ax=ax)
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# date_str = pd.Timestamp(y_hat["ds"].iloc[0]).strftime('%Y-%m-%d')
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# date_str = pd.Timestamp(y_hat["ds"].iloc[0]).strftime('%Y-%m-%d')
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ax.set_title(f'{global_config["end_time"]} PP期货八大维度 预测价格走势')
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ax.set_title(f'{global_config["end_time"]} PP期货八大维度 预测价格走势')
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@ -1129,7 +1136,8 @@ def plot_pp_predict_result(y_hat, global_config):
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table.set_fontsize(14)
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table.set_fontsize(14)
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plt.tight_layout(rect=[0, 0.1, 1, 1]) # 调整布局，为表格留出空间
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plt.tight_layout(rect=[0, 0.1, 1, 1]) # 调整布局，为表格留出空间
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plt.savefig('pp_predict_result.png')
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plt.savefig(os.path.join(
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global_config['dataset'], 'pp_predict_result.png'))
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if __name__ == '__main__':
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if __name__ == '__main__':
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@ -554,14 +554,7 @@ if __name__ == '__main__':
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# logger.info(f'预测失败：{e}')
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# logger.info(f'预测失败：{e}')
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# continue
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# continue
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# predict_main()
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predict_main()
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# push_market_value()
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# push_market_value()
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# sql_inset_predict(global_config)
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# sql_inset_predict(global_config)
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from lib.tools import find_best_models
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best_bdwd_price = find_best_models(
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date='2025-07-22', global_config=global_config)
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y_hat = pd.DataFrame(best_bdwd_price).T[['date', 'predictresult']]
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y_hat['ds'] = pd.to_datetime(y_hat['date'])
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# 绘制PP期货预测结果的图表
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plot_pp_predict_result(y_hat, global_config)
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@ -3,7 +3,7 @@
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from lib.dataread import *
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from lib.dataread import *
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from config_juxiting_yuedu import *
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from config_juxiting_yuedu import *
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from lib.tools import SendMail, convert_df_to_pydantic_pp, exception_logger, get_modelsname
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from lib.tools import SendMail, convert_df_to_pydantic_pp, exception_logger, get_modelsname
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from models.nerulforcastmodels import ex_Model, model_losss_juxiting, pp_export_pdf
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from models.nerulforcastmodels import ex_Model, model_losss_juxiting, pp_bdwd_png, pp_export_pdf
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import datetime
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import datetime
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import torch
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import torch
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torch.set_float32_matmul_precision("high")
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torch.set_float32_matmul_precision("high")
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@ -24,6 +24,7 @@ global_config.update({
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'settings': settings,
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'settings': settings,
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'bdwdname': bdwdname,
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'bdwdname': bdwdname,
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'columnsrename': columnsrename,
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'columnsrename': columnsrename,
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'price_columns': price_columns,
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# 模型参数
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# 模型参数
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@ -291,210 +292,218 @@ def predict_main():
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返回:
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返回:
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None
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None
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"""
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"""
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# end_time = global_config['end_time']
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end_time = global_config['end_time']
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# signature = BinanceAPI(APPID, SECRET)
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signature = BinanceAPI(APPID, SECRET)
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# etadata = EtaReader(signature=signature,
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etadata = EtaReader(signature=signature,
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# classifylisturl=global_config['classifylisturl'],
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classifylisturl=global_config['classifylisturl'],
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# classifyidlisturl=global_config['classifyidlisturl'],
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classifyidlisturl=global_config['classifyidlisturl'],
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# edbcodedataurl=global_config['edbcodedataurl'],
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edbcodedataurl=global_config['edbcodedataurl'],
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# edbcodelist=global_config['edbcodelist'],
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edbcodelist=global_config['edbcodelist'],
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# edbdatapushurl=global_config['edbdatapushurl'],
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edbdatapushurl=global_config['edbdatapushurl'],
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# edbdeleteurl=global_config['edbdeleteurl'],
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edbdeleteurl=global_config['edbdeleteurl'],
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# edbbusinessurl=global_config['edbbusinessurl'],
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edbbusinessurl=global_config['edbbusinessurl'],
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# classifyId=global_config['ClassifyId'],
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classifyId=global_config['ClassifyId'],
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# )
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)
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# # 获取数据
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# 获取数据
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# if is_eta:
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if is_eta:
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# logger.info('从eta获取数据...')
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logger.info('从eta获取数据...')
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# df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_data(
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df_zhibiaoshuju, df_zhibiaoliebiao = etadata.get_eta_api_pp_data(
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# data_set=data_set, dataset=dataset) # 原始数据，未处理
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data_set=data_set, dataset=dataset) # 原始数据，未处理
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# if is_market:
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if is_market:
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# logger.info('从市场信息平台获取数据...')
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logger.info('从市场信息平台获取数据...')
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# try:
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try:
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# # 如果是测试环境，最高价最低价取excel文档
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# 如果是测试环境，最高价最低价取excel文档
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# if server_host == '192.168.100.53':
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if server_host == '192.168.100.53':
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# logger.info('从excel文档获取最高价最低价')
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logger.info('从excel文档获取最高价最低价')
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# df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
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df_zhibiaoshuju = get_high_low_data(df_zhibiaoshuju)
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# else:
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else:
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# logger.info('从市场信息平台获取数据')
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logger.info('从市场信息平台获取数据')
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# df_zhibiaoshuju = get_market_data(
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df_zhibiaoshuju = get_market_data(
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# end_time, df_zhibiaoshuju)
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end_time, df_zhibiaoshuju)
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# except:
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except:
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# logger.info('最高最低价拼接失败')
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logger.info('最高最低价拼接失败')
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# # 保存到xlsx文件的sheet表
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# 保存到xlsx文件的sheet表
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# with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
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with pd.ExcelWriter(os.path.join(dataset, data_set)) as file:
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# df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
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df_zhibiaoshuju.to_excel(file, sheet_name='指标数据', index=False)
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# df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
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df_zhibiaoliebiao.to_excel(file, sheet_name='指标列表', index=False)
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# # 数据处理
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# 数据处理
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# df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
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df = datachuli_juxiting(df_zhibiaoshuju, df_zhibiaoliebiao, y=global_config['y'], dataset=dataset, add_kdj=add_kdj, is_timefurture=is_timefurture,
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# end_time=end_time)
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end_time=end_time)
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# else:
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else:
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# # 读取数据
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# 读取数据
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# logger.info('读取本地数据：' + os.path.join(dataset, data_set))
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logger.info('读取本地数据：' + os.path.join(dataset, data_set))
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# df, df_zhibiaoliebiao = getdata_zhoudu_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
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df, df_zhibiaoliebiao = getdata_zhoudu_juxiting(filename=os.path.join(dataset, data_set), y=y, dataset=dataset, add_kdj=add_kdj,
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# is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
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is_timefurture=is_timefurture, end_time=end_time) # 原始数据，未处理
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# # 更改预测列名称
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# 更改预测列名称
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# df.rename(columns={y: 'y'}, inplace=True)
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df.rename(columns={y: 'y'}, inplace=True)
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# if is_edbnamelist:
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if is_edbnamelist:
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# df = df[edbnamelist]
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df = df[edbnamelist]
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# df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
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df.to_csv(os.path.join(dataset, '指标数据.csv'), index=False)
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# # 保存最新日期的y值到数据库
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# 保存最新日期的y值到数据库
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# # 取第一行数据存储到数据库中
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# 取第一行数据存储到数据库中
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# first_row = df[['ds', 'y']].tail(1)
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first_row = df[['ds', 'y']].tail(1)
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# # 判断y的类型是否为float
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# 判断y的类型是否为float
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# if not isinstance(first_row['y'].values[0], float):
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if not isinstance(first_row['y'].values[0], float):
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# logger.info(f'{end_time}预测目标数据为空，跳过')
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logger.info(f'{end_time}预测目标数据为空，跳过')
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# return None
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return None
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# # 将最新真实值保存到数据库
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# 将最新真实值保存到数据库
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# if not sqlitedb.check_table_exists('trueandpredict'):
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if not sqlitedb.check_table_exists('trueandpredict'):
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# first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
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first_row.to_sql('trueandpredict', sqlitedb.connection, index=False)
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# else:
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else:
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# for row in first_row.itertuples(index=False):
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for row in first_row.itertuples(index=False):
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# row_dict = row._asdict()
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row_dict = row._asdict()
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# config.logger.info(f'要保存的真实值：{row_dict}')
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config.logger.info(f'要保存的真实值：{row_dict}')
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# # 判断ds是否为字符串类型,如果不是则转换为字符串类型
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# 判断ds是否为字符串类型,如果不是则转换为字符串类型
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# if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
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if isinstance(row_dict['ds'], (pd.Timestamp, datetime.datetime)):
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row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
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elif not isinstance(row_dict['ds'], str):
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try:
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row_dict['ds'] = pd.to_datetime(
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row_dict['ds']).strftime('%Y-%m-%d')
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except:
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logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
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# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
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# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
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# elif not isinstance(row_dict['ds'], str):
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# row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
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check_query = sqlitedb.select_data(
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'trueandpredict', where_condition=f"ds = '{row.ds}'")
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if len(check_query) > 0:
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set_clause = ", ".join(
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[f"{key} = '{value}'" for key, value in row_dict.items()])
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sqlitedb.update_data(
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'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
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continue
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sqlitedb.insert_data('trueandpredict', tuple(
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row_dict.values()), columns=row_dict.keys())
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# 更新accuracy表的y值
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if not sqlitedb.check_table_exists('accuracy'):
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pass
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else:
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update_y = sqlitedb.select_data(
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'accuracy', where_condition="y is null")
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if len(update_y) > 0:
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logger.info('更新accuracy表的y值')
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# 找到update_y 中ds且df中的y的行
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update_y = update_y[update_y['ds'] <= end_time]
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logger.info(f'要更新y的信息：{update_y}')
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# try:
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# try:
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# row_dict['ds'] = pd.to_datetime(
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for row in update_y.itertuples(index=False):
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# row_dict['ds']).strftime('%Y-%m-%d')
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try:
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# except:
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row_dict = row._asdict()
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# logger.warning(f"无法解析的时间格式: {row_dict['ds']}")
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yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
|
||||||
# # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d')
|
LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
|
||||||
# # row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')
|
HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
|
||||||
# check_query = sqlitedb.select_data(
|
sqlitedb.update_data(
|
||||||
# 'trueandpredict', where_condition=f"ds = '{row.ds}'")
|
'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
||||||
# if len(check_query) > 0:
|
except:
|
||||||
# set_clause = ", ".join(
|
logger.info(f'更新accuracy表的y值失败：{row_dict}')
|
||||||
# [f"{key} = '{value}'" for key, value in row_dict.items()])
|
# except Exception as e:
|
||||||
# sqlitedb.update_data(
|
# logger.info(f'更新accuracy表的y值失败：{e}')
|
||||||
# 'trueandpredict', set_clause, where_condition=f"ds = '{row.ds}'")
|
|
||||||
# continue
|
|
||||||
# sqlitedb.insert_data('trueandpredict', tuple(
|
|
||||||
# row_dict.values()), columns=row_dict.keys())
|
|
||||||
|
|
||||||
# # 更新accuracy表的y值
|
# 判断当前日期是不是周一
|
||||||
# if not sqlitedb.check_table_exists('accuracy'):
|
is_weekday = datetime.datetime.now().weekday() == 0
|
||||||
# pass
|
if is_weekday:
|
||||||
# else:
|
logger.info('今天是周一，更新预测模型')
|
||||||
# update_y = sqlitedb.select_data(
|
# 计算最近60天预测残差最低的模型名称
|
||||||
# 'accuracy', where_condition="y is null")
|
model_results = sqlitedb.select_data(
|
||||||
# if len(update_y) > 0:
|
'trueandpredict', order_by="ds DESC", limit="60")
|
||||||
# logger.info('更新accuracy表的y值')
|
# 删除空值率为90%以上的列
|
||||||
# # 找到update_y 中ds且df中的y的行
|
if len(model_results) > 10:
|
||||||
# update_y = update_y[update_y['ds'] <= end_time]
|
model_results = model_results.dropna(
|
||||||
# logger.info(f'要更新y的信息：{update_y}')
|
thresh=len(model_results)*0.1, axis=1)
|
||||||
# # try:
|
# 删除空行
|
||||||
# for row in update_y.itertuples(index=False):
|
model_results = model_results.dropna()
|
||||||
# try:
|
modelnames = model_results.columns.to_list()[2:-1]
|
||||||
# row_dict = row._asdict()
|
for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
||||||
# yy = df[df['ds'] == row_dict['ds']]['y'].values[0]
|
model_results[col] = model_results[col].astype(np.float32)
|
||||||
# LOW = df[df['ds'] == row_dict['ds']]['Brentzdj'].values[0]
|
# 计算每个预测值与真实值之间的偏差率
|
||||||
# HIGH = df[df['ds'] == row_dict['ds']]['Brentzgj'].values[0]
|
for model in modelnames:
|
||||||
# sqlitedb.update_data(
|
model_results[f'{model}_abs_error_rate'] = abs(
|
||||||
# 'accuracy', f"y = {yy},LOW_PRICE = {LOW},HIGH_PRICE = {HIGH}", where_condition=f"ds = '{row_dict['ds']}'")
|
model_results['y'] - model_results[model]) / model_results['y']
|
||||||
# except:
|
# 获取每行对应的最小偏差率值
|
||||||
# logger.info(f'更新accuracy表的y值失败：{row_dict}')
|
min_abs_error_rate_values = model_results.apply(
|
||||||
# # except Exception as e:
|
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
||||||
# # logger.info(f'更新accuracy表的y值失败：{e}')
|
# 获取每行对应的最小偏差率值对应的列名
|
||||||
|
min_abs_error_rate_column_name = model_results.apply(
|
||||||
|
lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
||||||
|
# 将列名索引转换为列名
|
||||||
|
min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
|
||||||
|
lambda x: x.split('_')[0])
|
||||||
|
# 取出现次数最多的模型名称
|
||||||
|
most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
||||||
|
logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
||||||
|
# 保存结果到数据库
|
||||||
|
if not sqlitedb.check_table_exists('most_model'):
|
||||||
|
sqlitedb.create_table(
|
||||||
|
'most_model', columns="ds datetime, most_common_model TEXT")
|
||||||
|
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
|
||||||
|
'%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||||
|
|
||||||
# # 判断当前日期是不是周一
|
if is_corr:
|
||||||
# is_weekday = datetime.datetime.now().weekday() == 0
|
df = corr_feature(df=df)
|
||||||
# if is_weekday:
|
|
||||||
# logger.info('今天是周一，更新预测模型')
|
|
||||||
# # 计算最近60天预测残差最低的模型名称
|
|
||||||
# model_results = sqlitedb.select_data(
|
|
||||||
# 'trueandpredict', order_by="ds DESC", limit="60")
|
|
||||||
# # 删除空值率为90%以上的列
|
|
||||||
# if len(model_results) > 10:
|
|
||||||
# model_results = model_results.dropna(
|
|
||||||
# thresh=len(model_results)*0.1, axis=1)
|
|
||||||
# # 删除空行
|
|
||||||
# model_results = model_results.dropna()
|
|
||||||
# modelnames = model_results.columns.to_list()[2:-1]
|
|
||||||
# for col in model_results[modelnames].select_dtypes(include=['object']).columns:
|
|
||||||
# model_results[col] = model_results[col].astype(np.float32)
|
|
||||||
# # 计算每个预测值与真实值之间的偏差率
|
|
||||||
# for model in modelnames:
|
|
||||||
# model_results[f'{model}_abs_error_rate'] = abs(
|
|
||||||
# model_results['y'] - model_results[model]) / model_results['y']
|
|
||||||
# # 获取每行对应的最小偏差率值
|
|
||||||
# min_abs_error_rate_values = model_results.apply(
|
|
||||||
# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
|
|
||||||
# # 获取每行对应的最小偏差率值对应的列名
|
|
||||||
# min_abs_error_rate_column_name = model_results.apply(
|
|
||||||
# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
|
|
||||||
# # 将列名索引转换为列名
|
|
||||||
# min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
|
|
||||||
# lambda x: x.split('_')[0])
|
|
||||||
# # 取出现次数最多的模型名称
|
|
||||||
# most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
|
|
||||||
# logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
|
|
||||||
# # 保存结果到数据库
|
|
||||||
# if not sqlitedb.check_table_exists('most_model'):
|
|
||||||
# sqlitedb.create_table(
|
|
||||||
# 'most_model', columns="ds datetime, most_common_model TEXT")
|
|
||||||
# sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
|
|
||||||
# '%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
|
||||||
|
|
||||||
# if is_corr:
|
df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
|
||||||
# df = corr_feature(df=df)
|
logger.info(f"开始训练模型...")
|
||||||
|
row, col = df.shape
|
||||||
|
|
||||||
# df1 = df.copy() # 备份一下，后面特征筛选完之后加入ds y 列用
|
now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
||||||
# logger.info(f"开始训练模型...")
|
ex_Model(df,
|
||||||
# row, col = df.shape
|
horizon=global_config['horizon'],
|
||||||
|
input_size=global_config['input_size'],
|
||||||
|
train_steps=global_config['train_steps'],
|
||||||
|
val_check_steps=global_config['val_check_steps'],
|
||||||
|
early_stop_patience_steps=global_config['early_stop_patience_steps'],
|
||||||
|
is_debug=global_config['is_debug'],
|
||||||
|
dataset=global_config['dataset'],
|
||||||
|
is_train=global_config['is_train'],
|
||||||
|
is_fivemodels=global_config['is_fivemodels'],
|
||||||
|
val_size=global_config['val_size'],
|
||||||
|
test_size=global_config['test_size'],
|
||||||
|
settings=global_config['settings'],
|
||||||
|
now=now,
|
||||||
|
etadata=etadata,
|
||||||
|
modelsindex=global_config['modelsindex'],
|
||||||
|
data=data,
|
||||||
|
is_eta=global_config['is_eta'],
|
||||||
|
end_time=global_config['end_time'],
|
||||||
|
)
|
||||||
|
|
||||||
# now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
|
logger.info('模型训练完成')
|
||||||
# ex_Model(df,
|
|
||||||
# horizon=global_config['horizon'],
|
|
||||||
# input_size=global_config['input_size'],
|
|
||||||
# train_steps=global_config['train_steps'],
|
|
||||||
# val_check_steps=global_config['val_check_steps'],
|
|
||||||
# early_stop_patience_steps=global_config['early_stop_patience_steps'],
|
|
||||||
# is_debug=global_config['is_debug'],
|
|
||||||
# dataset=global_config['dataset'],
|
|
||||||
# is_train=global_config['is_train'],
|
|
||||||
# is_fivemodels=global_config['is_fivemodels'],
|
|
||||||
# val_size=global_config['val_size'],
|
|
||||||
# test_size=global_config['test_size'],
|
|
||||||
# settings=global_config['settings'],
|
|
||||||
# now=now,
|
|
||||||
# etadata=etadata,
|
|
||||||
# modelsindex=global_config['modelsindex'],
|
|
||||||
# data=data,
|
|
||||||
# is_eta=global_config['is_eta'],
|
|
||||||
# end_time=global_config['end_time'],
|
|
||||||
# )
|
|
||||||
|
|
||||||
# logger.info('模型训练完成')
|
logger.info('训练数据绘图ing')
|
||||||
|
model_results3 = model_losss_juxiting(
|
||||||
|
sqlitedb, end_time=global_config['end_time'], is_fivemodels=global_config['is_fivemodels'])
|
||||||
|
logger.info('训练数据绘图end')
|
||||||
|
|
||||||
# logger.info('训练数据绘图ing')
|
push_market_value()
|
||||||
# model_results3 = model_losss_juxiting(sqlitedb, end_time=global_config['end_time'],is_fivemodels=global_config['is_fivemodels'])
|
|
||||||
# logger.info('训练数据绘图end')
|
|
||||||
|
|
||||||
# push_market_value()
|
|
||||||
# # 模型报告
|
|
||||||
# logger.info('制作报告ing')
|
|
||||||
# title = f'{settings}--{end_time}-预测报告' # 报告标题
|
|
||||||
# reportname = f'聚烯烃PP大模型月度预测--{end_time}.pdf' # 报告文件名
|
|
||||||
# reportname = reportname.replace(':', '-') # 替换冒号
|
|
||||||
# pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
|
|
||||||
# reportname=reportname, sqlitedb=sqlitedb),
|
|
||||||
|
|
||||||
# logger.info('制作报告end')
|
|
||||||
# logger.info('模型训练完成')
|
|
||||||
sql_inset_predict(global_config)
|
sql_inset_predict(global_config)
|
||||||
|
|
||||||
|
模型报告
|
||||||
|
logger.info('制作报告ing')
|
||||||
|
title = f'{settings}--{end_time}-预测报告' # 报告标题
|
||||||
|
reportname = f'聚烯烃PP大模型月度预测--{end_time}.pdf' # 报告文件名
|
||||||
|
reportname = reportname.replace(':', '-') # 替换冒号
|
||||||
|
pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
|
||||||
|
reportname=reportname, sqlitedb=sqlitedb),
|
||||||
|
|
||||||
|
logger.info('制作报告end')
|
||||||
|
logger.info('模型训练完成')
|
||||||
|
|
||||||
|
# 图片报告
|
||||||
|
logger.info('图片报告ing')
|
||||||
|
pp_bdwd_png(global_config=global_config)
|
||||||
|
logger.info('图片报告end')
|
||||||
|
|
||||||
# # LSTM 单变量模型
|
# # LSTM 单变量模型
|
||||||
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
|
# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
|
||||||
|
|
||||||
|
|||||||
@ -6,7 +6,7 @@ import seaborn as sns
|
|||||||
import matplotlib.pyplot as plt
|
import matplotlib.pyplot as plt
|
||||||
import matplotlib.dates as mdates
|
import matplotlib.dates as mdates
|
||||||
import datetime
|
import datetime
|
||||||
from lib.tools import Graphs, mse, rmse, mae, exception_logger
|
from lib.tools import Graphs, find_best_models, mse, plot_pp_predict_result, rmse, mae, exception_logger
|
||||||
from lib.tools import save_to_database, get_week_date
|
from lib.tools import save_to_database, get_week_date
|
||||||
from lib.dataread import *
|
from lib.dataread import *
|
||||||
from neuralforecast import NeuralForecast
|
from neuralforecast import NeuralForecast
|
||||||
@ -165,7 +165,8 @@ def ex_Model(df, horizon, input_size, train_steps, val_check_steps, early_stop_p
|
|||||||
|
|
||||||
# VanillaTransformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
|
# VanillaTransformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
|
||||||
# Autoformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
|
# Autoformer(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ), //报错了
|
||||||
NBEATS(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard', ),
|
NBEATS(h=horizon, input_size=input_size, max_steps=train_steps,
|
||||||
|
val_check_steps=val_check_steps, scaler_type='standard', ),
|
||||||
# NBEATSx(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',activation='ReLU', ), //报错
|
# NBEATSx(h=horizon, input_size=input_size, max_steps=train_steps, val_check_steps=val_check_steps, scaler_type='standard',activation='ReLU', ), //报错
|
||||||
# HINT(h=horizon),
|
# HINT(h=horizon),
|
||||||
|
|
||||||
@ -2359,7 +2360,8 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
|
|||||||
stime = df3['ds'].iloc[0]
|
stime = df3['ds'].iloc[0]
|
||||||
etime = df3['ds'].iloc[-1]
|
etime = df3['ds'].iloc[-1]
|
||||||
# 添加偏差率表格
|
# 添加偏差率表格
|
||||||
fivemodels = '、'.join(eval_df['模型(Model)'].values[:5]) # 字符串形式，后面写入字符串使用
|
fivemodels = '、'.join(
|
||||||
|
eval_df['模型(Model)'].values[:5]) # 字符串形式，后面写入字符串使用
|
||||||
content.append(Graphs.draw_text(
|
content.append(Graphs.draw_text(
|
||||||
f'预测使用了{num_models}个模型进行训练，使用评估结果MAE前五的模型分别是 {fivemodels} ，模型上一预测区间 {stime} -- {etime}的偏差率(%)分别是：'))
|
f'预测使用了{num_models}个模型进行训练，使用评估结果MAE前五的模型分别是 {fivemodels} ，模型上一预测区间 {stime} -- {etime}的偏差率(%)分别是：'))
|
||||||
# # 添加偏差率表格
|
# # 添加偏差率表格
|
||||||
@ -2370,7 +2372,8 @@ def brent_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, in
|
|||||||
content.append(Graphs.draw_table(col_width, *data))
|
content.append(Graphs.draw_table(col_width, *data))
|
||||||
|
|
||||||
content.append(Graphs.draw_little_title('上一周预测准确率：'))
|
content.append(Graphs.draw_little_title('上一周预测准确率：'))
|
||||||
df4 = sqlitedb.select_data('accuracy_rote', order_by='结束日期 desc', limit=1)
|
df4 = sqlitedb.select_data(
|
||||||
|
'accuracy_rote', order_by='结束日期 desc', limit=1)
|
||||||
df4 = df4.T
|
df4 = df4.T
|
||||||
df4 = df4.reset_index()
|
df4 = df4.reset_index()
|
||||||
df4 = df4.T
|
df4 = df4.T
|
||||||
@ -3524,6 +3527,17 @@ def pp_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, input
|
|||||||
print(f"请求超时: {e}")
|
print(f"请求超时: {e}")
|
||||||
|
|
||||||
|
|
||||||
|
@exception_logger
|
||||||
|
def pp_bdwd_png(global_config):
|
||||||
|
best_bdwd_price = find_best_models(
|
||||||
|
date=global_config['end_time'], global_config=global_config)
|
||||||
|
# y_hat = pd.DataFrame(best_bdwd_price).T[['date', 'predictresult']][-4:]
|
||||||
|
y_hat = pd.DataFrame(best_bdwd_price).T[['date', 'predictresult']]
|
||||||
|
y_hat['ds'] = pd.to_datetime(y_hat['date'])
|
||||||
|
# 绘制PP期货预测结果的图表
|
||||||
|
plot_pp_predict_result(y_hat, global_config)
|
||||||
|
|
||||||
|
|
||||||
def pp_export_pdf_v1(num_indicators=475, num_models=21, num_dayindicator=202, inputsize=5, dataset='dataset', time='2024-07-30', reportname='report.pdf'):
|
def pp_export_pdf_v1(num_indicators=475, num_models=21, num_dayindicator=202, inputsize=5, dataset='dataset', time='2024-07-30', reportname='report.pdf'):
|
||||||
global y
|
global y
|
||||||
# 创建内容对应的空列表
|
# 创建内容对应的空列表
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user