聚烯烃周度调试
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@ -301,42 +301,42 @@ def predict_main():
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# 判断当前日期是不是周一
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is_weekday = datetime.datetime.now().weekday() == 0
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if is_weekday:
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logger.info('今天是周一，更新预测模型')
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# 计算最近60天预测残差最低的模型名称
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model_results = sqlitedb.select_data(
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'trueandpredict', order_by="ds DESC", limit="60")
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# 删除空值率为90%以上的列
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if len(model_results) > 10:
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model_results = model_results.dropna(
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thresh=len(model_results)*0.1, axis=1)
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# 删除空行
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model_results = model_results.dropna()
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modelnames = model_results.columns.to_list()[2:-2]
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for col in model_results[modelnames].select_dtypes(include=['object']).columns:
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model_results[col] = model_results[col].astype(np.float32)
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# 计算每个预测值与真实值之间的偏差率
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for model in modelnames:
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model_results[f'{model}_abs_error_rate'] = abs(
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model_results['y'] - model_results[model]) / model_results['y']
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# 获取每行对应的最小偏差率值
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min_abs_error_rate_values = model_results.apply(
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lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
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# 获取每行对应的最小偏差率值对应的列名
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min_abs_error_rate_column_name = model_results.apply(
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lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
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# 将列名索引转换为列名
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min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
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lambda x: x.split('_')[0])
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# 取出现次数最多的模型名称
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most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
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logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
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# 保存结果到数据库
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if not sqlitedb.check_table_exists('most_model'):
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sqlitedb.create_table(
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'most_model', columns="ds datetime, most_common_model TEXT")
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sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
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'%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
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# if is_weekday:
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# logger.info('今天是周一，更新预测模型')
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# # 计算最近60天预测残差最低的模型名称
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# model_results = sqlitedb.select_data(
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# 'trueandpredict', order_by="ds DESC", limit="60")
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# # 删除空值率为90%以上的列
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# if len(model_results) > 10:
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# model_results = model_results.dropna(
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# thresh=len(model_results)*0.1, axis=1)
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# # 删除空行
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# model_results = model_results.dropna()
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# modelnames = model_results.columns.to_list()[2:-2]
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# for col in model_results[modelnames].select_dtypes(include=['object']).columns:
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# model_results[col] = model_results[col].astype(np.float32)
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# # 计算每个预测值与真实值之间的偏差率
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# for model in modelnames:
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# model_results[f'{model}_abs_error_rate'] = abs(
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# model_results['y'] - model_results[model]) / model_results['y']
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# # 获取每行对应的最小偏差率值
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# min_abs_error_rate_values = model_results.apply(
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# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)
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# # 获取每行对应的最小偏差率值对应的列名
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# min_abs_error_rate_column_name = model_results.apply(
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# lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)
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# # 将列名索引转换为列名
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# min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(
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# lambda x: x.split('_')[0])
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# # 取出现次数最多的模型名称
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# most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
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# logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
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# # 保存结果到数据库
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# if not sqlitedb.check_table_exists('most_model'):
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# sqlitedb.create_table(
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# 'most_model', columns="ds datetime, most_common_model TEXT")
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# sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime(
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# '%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
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if is_corr:
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df = corr_feature(df=df)
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@ -375,17 +375,17 @@ def predict_main():
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logger.info('训练数据绘图end')
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# # 模型报告
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logger.info('制作报告ing')
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title = f'{settings}--{end_time}-预测报告' # 报告标题
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reportname = f'聚烯烃PP大模型周度预测--{end_time}.pdf' # 报告文件名
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reportname = reportname.replace(':', '-') # 替换冒号
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pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
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reportname=reportname, sqlitedb=sqlitedb),
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# logger.info('制作报告ing')
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# title = f'{settings}--{end_time}-预测报告' # 报告标题
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# reportname = f'聚烯烃PP大模型周度预测--{end_time}.pdf' # 报告文件名
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# reportname = reportname.replace(':', '-') # 替换冒号
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# pp_export_pdf(dataset=dataset, num_models=5 if is_fivemodels else 22, time=end_time,
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# reportname=reportname, sqlitedb=sqlitedb),
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logger.info('制作报告end')
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logger.info('模型训练完成')
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# logger.info('制作报告end')
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# logger.info('模型训练完成')
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push_market_value()
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# push_market_value()
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# # LSTM 单变量模型
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# ex_Lstm(df,input_seq_len=input_size,output_seq_len=horizon,is_debug=is_debug,dataset=dataset)
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@ -412,12 +412,11 @@ def predict_main():
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if __name__ == '__main__':
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# global end_time
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# 遍历2024-11-25 到 2024-12-3 之间的工作日日期
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# for i_time in pd.date_range('2022-1-1', '2025-3-26', freq='M'):
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# try:
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# global_config['end_time'] = i_time.strftime('%Y-%m-%d')
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# predict_main()
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# except Exception as e:
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# logger.info(f'预测失败：{e}')
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# continue
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for i_time in pd.date_range('2025-3-1', '2025-5-26', freq='W'):
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try:
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global_config['end_time'] = i_time.strftime('%Y-%m-%d')
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predict_main()
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except Exception as e:
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logger.info(f'预测失败：{e}')
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continue
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# predict_main()
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