聚烯烃图形报告调试完成
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workpc
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@ -756,7 +756,7 @@ def calculate_correlation(df):
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# 去掉ds y
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df = df.drop(columns=['ds', 'y'])
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# 计算相关系数
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corr = df.corrwith(yy)
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corr = df.corrwith(yy).round(4)
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# 输出相关系数
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print(corr)
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# 保存结果
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50
lib/tools.py
50
lib/tools.py
@ -1074,7 +1074,7 @@ def find_best_models(date='', global_config=None):
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return best_models
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def plot_pp_predict_result(y_hat, global_config):
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def plot_pp_predict_result(y_hat, global_config,wd='yuedu'):
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"""
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绘制PP期货预测结果的图表
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"""
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@ -1082,11 +1082,18 @@ def plot_pp_predict_result(y_hat, global_config):
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import seaborn as sns
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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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# 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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if wd == 'yuedu':
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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']][-12:]
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xgx_df = pd.read_csv(os.path.join(
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global_config['dataset'], '相关系数.csv'))
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else:
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y = pd.read_csv('juxitingdataset/指标数据.csv')[['ds', 'y']][-30:]
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xgx_df = pd.read_csv('juxitingdataset/相关系数.csv')
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xgx_df = xgx_df.rename(columns={xgx_df.columns[0]: '指标', xgx_df.columns[1]: '系数'})
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top_10_correlations = xgx_df.sort_values(by='系数',ascending=False)[1:11]
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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]]
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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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@ -1096,7 +1103,8 @@ def plot_pp_predict_result(y_hat, global_config):
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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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fig, ax = plt.subplots(figsize=(16, 9))
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fig = plt.figure(figsize=(16, 22))
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ax = fig.add_axes([0.05, 0.55, 0.9, 0.25]) # 16:9核心参数
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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_y_hat = y_y_hat.sort_values(by='ds')
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@ -1132,13 +1140,33 @@ def plot_pp_predict_result(y_hat, global_config):
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# 在图表下方添加表格
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table = ax.table(cellText=data, colLabels=columns,
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loc='bottom', bbox=[0, -0.6, 1, 0.2])
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loc='bottom', bbox=[0, -0.4, 1, 0.2],cellLoc='center')
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table.auto_set_font_size(False)
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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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table.set_fontsize(12)
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# 相关系数表格 - 准备数据（指标名称+相关系数两列）
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table_data = top_10_correlations[['指标', '系数']].values.tolist() # 提取表格数据
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table_data.insert(0, ['指标名称', '相关系数']) # 添加表头
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# 在当前图表下方绘制表格（调整bbox参数控制位置和大小）
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table = ax.table(
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cellText=table_data, # 表格数据
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loc='bottom', # 表格位置（底部）
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bbox=[0, -0.9, 1, 0.4], # [左, 下, 宽, 高]，调整下边界(-0.7)和高度(0.5)控制表格位置
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cellLoc='left'
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)
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table.auto_set_font_size(False) # 关闭自动字体大小
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table.set_fontsize(10) # 设置表格字体大小
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table.scale(1.1, 1.5) # 调整表格缩放比例（宽度, 高度）
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plt.tight_layout() # 自动调整整体布局
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plt.savefig(os.path.join(
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global_config['dataset'], 'pp_predict_result.png'))
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global_config['dataset'], f'pp_{wd}correlation.png'),
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bbox_inches='tight',
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pad_inches=1.0 # 增加边距
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)
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if __name__ == '__main__':
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@ -528,13 +528,19 @@ 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('2025-7-28', '2025-7-29', freq='B'):
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try:
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global_config['end_time'] = i_time.strftime('%Y-%m-%d')
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global_config['db_mysql'].connect()
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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('2025-7-28', '2025-7-29', freq='B'):
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# try:
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# global_config['end_time'] = i_time.strftime('%Y-%m-%d')
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# global_config['db_mysql'].connect()
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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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predict_main()
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# 图片报告
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# global_config['end_time'] = '2025-07-31'
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# logger.info('图片报告ing')
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# pp_bdwd_png(global_config=global_config)
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# logger.info('图片报告end')
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@ -3539,11 +3539,34 @@ def pp_export_pdf(num_indicators=475, num_models=21, num_dayindicator=202, input
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def pp_bdwd_png(global_config):
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best_bdwd_price = find_best_models(
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date=global_config['end_time'], global_config=global_config)
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# y_hat = pd.DataFrame(best_bdwd_price).T[['date', 'predictresult']][-4:]
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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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y_hat_yuedu = pd.DataFrame(best_bdwd_price).T[['date', 'predictresult']][-4:]
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y_hat_yuedu['ds'] = pd.to_datetime(y_hat_yuedu['date'])
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# 绘制PP期货月度预测结果的图表
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plot_pp_predict_result(y_hat_yuedu, global_config)
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y_hat_zhoudu = pd.DataFrame(best_bdwd_price).T[['date', 'predictresult']][2:4]
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y_hat_zhoudu['ds'] = pd.to_datetime(y_hat_zhoudu['date'])
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y_hat_zhoudu.drop(columns=['date'],inplace=True)
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print(y_hat_zhoudu)
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# 获取本周最佳模型的五日预测价格
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five_days_predict_price = pd.read_csv('juxitingdataset/predict.csv')
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week_price_modelname = best_bdwd_price['week_price']['model_name']
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five_days_predict_price = five_days_predict_price[['ds',week_price_modelname]]
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five_days_predict_price['ds'] = pd.to_datetime(five_days_predict_price['ds'])
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five_days_predict_price.rename(columns={week_price_modelname:'predictresult'},inplace=True)
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# 设置索引 次日 次二日 次三日 次四日 次五日
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index_labels = ["次日", "次二日", "次三日", "次四日", "次五日"]
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five_days_predict_price.index = index_labels
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y_hat_riduzhoudu = pd.concat([y_hat_zhoudu, five_days_predict_price], axis=0)
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y_hat_riduzhoudu = y_hat_riduzhoudu.sort_values(by='ds')
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print(y_hat_riduzhoudu)
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# 绘制PP期货日度周度预测结果的图表
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plot_pp_predict_result(y_hat_riduzhoudu, global_config,'zhoudu')
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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'):
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@ -17,8 +17,8 @@ def run_predictions(target_date):
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# 依次执行每个脚本
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for script in scripts:
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# command = [r"C:\Users\Hello\.conda\envs\predict\python", script] # liuruipc
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# command = [r"C:/Users/EDY/.conda/envs/priceforecast/python.exe", script] #168pc
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command = [r"C:/Users/Hello/.conda/envs/jaigeyuce/python.exe", script] #yitijipc
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command = [r"C:/Users/EDY/.conda/envs/priceforecast/python.exe", script] #168pc
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# command = [r"C:/Users/Hello/.conda/envs/jaigeyuce/python.exe", script] #yitijipc
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subprocess.run(command, check=True)
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