原油绘图逻辑同步为聚烯烃绘图逻辑
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workpc
parent
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commit
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300
codeback.py
300
codeback.py
@ -500,4 +500,302 @@
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# plt.ylabel('价格')
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# plt.ylabel('价格')
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# plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight')
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# plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight')
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# plt.close()
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# plt.close()
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####################################################特征处理
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####################################################上传服务
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# def upload_warning_info(last_update_times_df,y_last_update_time):
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# logger.info(f'上传预警信息')
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# try:
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# warning_data_df = last_update_times_df[last_update_times_df['warning_date']<y_last_update_time][['stop_update_period','warning_date','last_update_time','update_period','feature']]
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# warning_data_df.columns = ['停更周期','预警日期','最后更新时间','更新周期','特征名称']
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# if len(warning_data_df) > 0:
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# content = '原油特征指标预警信息：\n\n'
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# warning_data_df = warning_data_df.sort_values(by='停更周期',ascending=False)
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# fixed_length = 20
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# warning_data_df['特征名称'] = warning_data_df['特征名称'].str.replace(" ", "")
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# content = warning_data_df.to_string(index=False, col_space=fixed_length)
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# else:
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# logger.info(f'没有需要上传的预警信息')
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# content = '没有需要维护的特征指标'
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# warning_date = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
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# warning_data['data']['WARNING_DATE'] = warning_date
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# warning_data['data']['WARNING_CONTENT'] = content
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# upload_warning_data(warning_data)
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# logger.info(f'上传预警信息成功')
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# except Exception as e:
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# logger.error(f'上传预警信息失败：{e}')
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#######################################绘图逻辑
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# def model_losss(sqlitedb):
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# global dataset
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# # 预测数据处理 predict
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# df_combined = loadcsv(os.path.join(dataset,"cross_validation.csv"))
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# df_combined = dateConvert(df_combined)
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# # 删除空列
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# df_combined.dropna(axis=1,inplace=True)
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# # 删除缺失值,预测过程不能有缺失值
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# df_combined.dropna(inplace=True)
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# # 其他列转为数值类型
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# df_combined = df_combined.astype({col: 'float32' for col in df_combined.columns if col not in ['cutoff','ds'] })
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# # 使用 groupby 和 transform 结合 lambda 函数来获取每个分组中 cutoff 的最小值，并创建一个新的列来存储这个最大值
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# df_combined['max_cutoff'] = df_combined.groupby('ds')['cutoff'].transform('min')
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# # 然后筛选出那些 cutoff 等于 max_cutoff 的行，这样就得到了每个分组中 cutoff 最大的行，并保留了其他列
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# df_combined = df_combined[df_combined['cutoff'] == df_combined['max_cutoff']]
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# # 删除模型生成的cutoff列
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# df_combined.drop(columns=['cutoff', 'max_cutoff'], inplace=True)
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# # 获取模型名称
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# modelnames = df_combined.columns.to_list()[2:]
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# if 'y' in modelnames:
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# modelnames.remove('y')
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# df_combined3 = df_combined.copy() # 备份df_combined,后面画图需要
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# # 计算波动率
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# df_combined3['volatility'] = df_combined3['y'].pct_change().round(4)
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# # 计算近60日的波动率 10% 90%分位数
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# df_combined3['quantile_10'] = df_combined3['volatility'].rolling(60).quantile(0.1)
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# df_combined3['quantile_90'] = df_combined3['volatility'].rolling(60).quantile(0.9)
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# df_combined3 = df_combined3.round(4)
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# # 计算分位数对应的价格
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# df_combined3['quantile_10_price'] = df_combined3['y'] * (1 + df_combined3['quantile_10'])
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# df_combined3['quantile_90_price'] = df_combined3['y'] * (1 + df_combined3['quantile_90'])
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# # 遍历行
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# def find_min_max_within_quantile(row):
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# # 获取分位数10%和90%的值
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# q10 = row['quantile_10_price']
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# q90 = row['quantile_90_price']
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# # 判断flot值是否为空值
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# if pd.isna(q10) or pd.isna(q90):
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# return pd.Series([None, None, None, None], index=['min_within_quantile','max_within_quantile','min_model','max_model'])
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# # 初始化最小和最大值为None
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# min_value = None
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# max_value = None
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# min_value_model = ''
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# max_value_model = ''
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# # 遍历指定列，找出在分位数范围内的最大最小值
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# for model in modelnames:
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# value = row[model]
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# if value >= q10 and value <= q90:
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# if min_value is None or value < min_value:
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# min_value = value
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# min_value_model = model
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# if max_value is None or value > max_value:
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# max_value = value
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# max_value_model = model
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# # 返回最大最小值
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# return pd.Series([min_value, max_value,min_value_model,max_value_model], index=['min_within_quantile', 'max_within_quantile','min_model','max_model'])
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# # 应用函数到每一行
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# df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)
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# # 去除有空值的行
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# # df_combined3.dropna(inplace=True)
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# # 保存到数据库
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# df_combined3.to_sql('testandpredict_groupby', sqlitedb.connection, if_exists='replace', index=False)
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# df_combined3.to_csv(os.path.join(dataset,"testandpredict_groupby.csv"),index=False)
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# # 空的列表存储每个模型的MSE、RMSE、MAE、MAPE、SMAPE
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# cellText = []
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# # 遍历模型名称，计算模型评估指标
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# for model in modelnames:
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# modelmse = mse(df_combined['y'], df_combined[model])
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# modelrmse = rmse(df_combined['y'], df_combined[model])
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# modelmae = mae(df_combined['y'], df_combined[model])
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# # modelmape = mape(df_combined['y'], df_combined[model])
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# # modelsmape = smape(df_combined['y'], df_combined[model])
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# # modelr2 = r2_score(df_combined['y'], df_combined[model])
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# cellText.append([model,round(modelmse, 3), round(modelrmse, 3), round(modelmae, 3)])
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# model_results3 = pd.DataFrame(cellText,columns=['模型(Model)','平均平方误差(MSE)', '均方根误差(RMSE)', '平均绝对误差(MAE)'])
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# # 按MSE降序排列
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# model_results3 = model_results3.sort_values(by='平均平方误差(MSE)', ascending=True)
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# model_results3.to_csv(os.path.join(dataset,"model_evaluation.csv"),index=False)
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# modelnames = model_results3['模型(Model)'].tolist()
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# allmodelnames = modelnames.copy()
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# # 保存5个最佳模型的名称
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# if len(modelnames) > 5:
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# modelnames = modelnames[0:5]
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# with open(os.path.join(dataset,"best_modelnames.txt"), 'w') as f:
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# f.write(','.join(modelnames) + '\n')
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# # 预测值与真实值对比图
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# plt.rcParams['font.sans-serif'] = ['SimHei']
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# plt.figure(figsize=(15, 10))
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# # 设置有5个子图的画布
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# for n,model in enumerate(modelnames):
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# plt.subplot(3, 2, n+1)
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# plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值')
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# plt.plot(df_combined3['ds'], df_combined3[model], label=model)
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# plt.legend()
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# plt.xlabel('日期')
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# plt.ylabel('价格')
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# plt.title(model+'拟合')
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# plt.subplots_adjust(hspace=0.5)
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# plt.savefig(os.path.join(dataset,'预测值与真实值对比图.png'), bbox_inches='tight')
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# plt.close()
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# # 历史数据+预测数据
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# # 拼接未来时间预测
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# df_predict = loadcsv(os.path.join(dataset,'predict.csv'))
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# df_predict.drop('unique_id',inplace=True,axis=1)
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# df_predict.dropna(axis=1,inplace=True)
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# try:
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# df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y-%m-%d')
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# except ValueError :
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# df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y/%m/%d')
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# # 取第一行数据存储到数据库中
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# first_row = df_predict.head(1)
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# first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')
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# # 将预测结果保存到数据库
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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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# else:
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# for row in first_row.itertuples(index=False):
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# row_dict = row._asdict()
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# columns=row_dict.keys()
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# for col in columns:
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# sqlitedb.add_column_if_not_exists('trueandpredict',col,'TEXT')
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# check_query = sqlitedb.select_data('trueandpredict',where_condition = f"ds = '{row.ds}'")
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# if len(check_query) > 0:
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# set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
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# sqlitedb.update_data('trueandpredict',set_clause,where_condition = f"ds = '{row.ds}'")
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# continue
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# sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=columns)
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# # 最多频率的模型名称
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# num = df_combined3.shape[0] if df_combined3.shape[0] < 60 else 60
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# min_model_max_frequency_model = df_combined3['min_model'][-num:].value_counts().idxmax()
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# max_model_max_frequency_model = df_combined3['max_model'][-num:].value_counts().idxmax()
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# df_predict['min_model'] = min_model_max_frequency_model
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# df_predict['max_model'] = max_model_max_frequency_model
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# df_predict['min_within_quantile'] = df_predict[min_model_max_frequency_model]
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# df_predict['max_within_quantile'] = df_predict[max_model_max_frequency_model]
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# df_predict2 = df_predict.copy()
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# df_predict2['ds'] = df_predict2['ds'].dt.strftime('%Y-%m-%d 00:00:00')
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# # 将预测结果保存到数据库
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# # 判断表存在
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# if not sqlitedb.check_table_exists('testandpredict_groupby'):
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# df_predict2.to_sql('testandpredict_groupby',sqlitedb.connection,index=False)
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# else:
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# for row in df_predict2.itertuples(index=False):
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# row_dict = row._asdict()
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# check_query = sqlitedb.select_data('testandpredict_groupby',where_condition = f"ds = '{row.ds}'")
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# if len(check_query) > 0:
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# set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
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# sqlitedb.update_data('testandpredict_groupby',set_clause,where_condition = f"ds = '{row.ds}'")
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# continue
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# sqlitedb.insert_data('testandpredict_groupby',tuple(row_dict.values()),columns=row_dict.keys())
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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 allmodelnames:
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# df_combined3[f'{model}_abs_error_rate'] = abs(df_combined3['y'] - df_combined3[model]) / df_combined3['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 = df_combined3.apply(lambda row: row[[f'{model}_abs_error_rate' for model in allmodelnames]].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 = df_combined3.apply(lambda row: row[[f'{model}_abs_error_rate' for model in allmodelnames]].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(lambda x: x.split('_')[0])
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# # 获取最小偏差率对应的模型的预测值
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# min_abs_error_rate_predictions = df_combined3.apply(lambda row: row[min_abs_error_rate_column_name[row.name]], axis=1)
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# # 将最小偏差率对应的模型的预测值添加到DataFrame中
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# df_combined3['min_abs_error_rate_prediction'] = min_abs_error_rate_predictions
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# df_combined3['min_abs_error_rate_column_name'] = min_abs_error_rate_column_name
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# df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)
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# # 判断 df 的数值列转为float
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# for col in df_combined3.columns:
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# try:
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# if col != 'ds':
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# df_combined3[col] = df_combined3[col].astype(float)
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# df_combined3[col] = df_combined3[col].round(2)
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# except ValueError:
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# pass
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# df_combined3.to_csv(os.path.join(dataset,"df_combined3.csv"),index=False)
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# # 历史价格+预测价格
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# # df_combined3 = df_combined3[-50:] # 取50个数据点画图
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# # 历史价格
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# plt.figure(figsize=(20, 10))
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# plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值')
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# # 颜色填充
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# plt.fill_between(df_combined3['ds'], df_combined3['min_within_quantile'], df_combined3['max_within_quantile'], alpha=0.2)
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# # plt.plot(df_combined3['ds'], df_combined3['min_abs_error_rate_prediction'], label='最小绝对误差', linestyle='--', color='orange')
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# # 网格
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# plt.grid(True)
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# # 显示历史值
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# for i, j in zip(df_combined3['ds'], df_combined3['y']):
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# plt.text(i, j, str(j), ha='center', va='bottom')
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# # 数据库查询最佳模型名称
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# # most_model = [sqlitedb.select_data('most_model',columns=['most_common_model'],order_by='ds desc',limit=1).values[0][0]]
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# most_model = modelnames[0:5]
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# for model in most_model:
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# plt.plot(df_combined3['ds'], df_combined3[model], label=model,marker='o')
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# # 当前日期画竖虚线
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# plt.axvline(x=df_combined3['ds'].iloc[-horizon], color='r', linestyle='--')
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# plt.legend()
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# plt.xlabel('日期')
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# plt.ylabel('价格')
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# plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight')
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# plt.close()
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# # 预测值表格
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# fig, ax = plt.subplots(figsize=(20, 6))
|
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|
# ax.axis('off') # 关闭坐标轴
|
||||||
|
# # 数值保留2位小数
|
||||||
|
# df_combined3 = df_combined3.round(2)
|
||||||
|
# df_combined3 = df_combined3[-horizon:]
|
||||||
|
# df_combined3['Day'] = [f'Day_{i}' for i in range(1,horizon+1)]
|
||||||
|
# # Day列放到最前面
|
||||||
|
# df_combined3 = df_combined3[['Day'] + list(df_combined3.columns[:-1])]
|
||||||
|
# table = ax.table(cellText=df_combined3.values, colLabels=df_combined3.columns, loc='center')
|
||||||
|
# #加宽表格
|
||||||
|
# table.auto_set_font_size(False)
|
||||||
|
# table.set_fontsize(10)
|
||||||
|
|
||||||
|
# # 设置表格样式，列数据最小的用绿色标识
|
||||||
|
# plt.savefig(os.path.join(dataset,'预测值表格.png'), bbox_inches='tight')
|
||||||
|
# plt.close()
|
||||||
|
# # plt.show()
|
||||||
|
|
||||||
|
# # 可视化评估结果
|
||||||
|
# plt.rcParams['font.sans-serif'] = ['SimHei']
|
||||||
|
# fig, ax = plt.subplots(figsize=(20, 10))
|
||||||
|
# ax.axis('off') # 关闭坐标轴
|
||||||
|
# table = ax.table(cellText=model_results3.values, colLabels=model_results3.columns, loc='center')
|
||||||
|
# # 加宽表格
|
||||||
|
# table.auto_set_font_size(False)
|
||||||
|
# table.set_fontsize(10)
|
||||||
|
|
||||||
|
# # 设置表格样式，列数据最小的用绿色标识
|
||||||
|
# plt.savefig(os.path.join(dataset,'模型评估.png'), bbox_inches='tight')
|
||||||
|
# plt.close()
|
||||||
|
# return model_results3
|
||||||
|
|||||||
@ -124,6 +124,7 @@ def upload_warning_data(warning_data):
|
|||||||
warning_data = warning_data
|
warning_data = warning_data
|
||||||
headers = {"Authorization": token}
|
headers = {"Authorization": token}
|
||||||
logger.info("预警上传中...")
|
logger.info("预警上传中...")
|
||||||
|
logger.info(f"upload_warning_url:{upload_warning_url}")
|
||||||
logger.info(f"token:{token}")
|
logger.info(f"token:{token}")
|
||||||
logger.info(f"warning_data:{warning_data}" )
|
logger.info(f"warning_data:{warning_data}" )
|
||||||
upload_res = requests.post(url=upload_warning_url, headers=headers, json=warning_data, timeout=(3, 15))
|
upload_res = requests.post(url=upload_warning_url, headers=headers, json=warning_data, timeout=(3, 15))
|
||||||
@ -133,31 +134,6 @@ def upload_warning_data(warning_data):
|
|||||||
logger.info("预警上传失败")
|
logger.info("预警上传失败")
|
||||||
return None
|
return None
|
||||||
|
|
||||||
|
|
||||||
# def upload_warning_info(last_update_times_df,y_last_update_time):
|
|
||||||
# logger.info(f'上传预警信息')
|
|
||||||
# try:
|
|
||||||
# warning_data_df = last_update_times_df[last_update_times_df['warning_date']<y_last_update_time][['stop_update_period','warning_date','last_update_time','update_period','feature']]
|
|
||||||
# warning_data_df.columns = ['停更周期','预警日期','最后更新时间','更新周期','特征名称']
|
|
||||||
# if len(warning_data_df) > 0:
|
|
||||||
# content = '原油特征指标预警信息：\n\n'
|
|
||||||
# warning_data_df = warning_data_df.sort_values(by='停更周期',ascending=False)
|
|
||||||
# fixed_length = 20
|
|
||||||
# warning_data_df['特征名称'] = warning_data_df['特征名称'].str.replace(" ", "")
|
|
||||||
# content = warning_data_df.to_string(index=False, col_space=fixed_length)
|
|
||||||
|
|
||||||
# else:
|
|
||||||
# logger.info(f'没有需要上传的预警信息')
|
|
||||||
# content = '没有需要维护的特征指标'
|
|
||||||
# warning_date = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
|
|
||||||
# warning_data['data']['WARNING_DATE'] = warning_date
|
|
||||||
# warning_data['data']['WARNING_CONTENT'] = content
|
|
||||||
|
|
||||||
# upload_warning_data(warning_data)
|
|
||||||
# logger.info(f'上传预警信息成功')
|
|
||||||
# except Exception as e:
|
|
||||||
# logger.error(f'上传预警信息失败：{e}')
|
|
||||||
|
|
||||||
|
|
||||||
def upload_warning_info(df_count):
|
def upload_warning_info(df_count):
|
||||||
logger.info(f'上传预警信息')
|
logger.info(f'上传预警信息')
|
||||||
@ -165,7 +141,7 @@ def upload_warning_info(df_count):
|
|||||||
warning_date = datetime.datetime.now().strftime('%Y-%m-%d')
|
warning_date = datetime.datetime.now().strftime('%Y-%m-%d')
|
||||||
content = f'{warning_date}有{df_count}个停更'
|
content = f'{warning_date}有{df_count}个停更'
|
||||||
warning_data['data']['WARNING_DATE'] = warning_date
|
warning_data['data']['WARNING_DATE'] = warning_date
|
||||||
warning_data['data']['WARNING_CONTENT'] = content
|
warning_data['data']['WARNING_CONTENT'] = content + '2'
|
||||||
upload_warning_data(warning_data)
|
upload_warning_data(warning_data)
|
||||||
logger.info(f'上传预警信息成功')
|
logger.info(f'上传预警信息成功')
|
||||||
except Exception as e:
|
except Exception as e:
|
||||||
|
|||||||
@ -139,8 +139,7 @@ def predict_main():
|
|||||||
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
|
||||||
|
|
||||||
try:
|
try:
|
||||||
# if is_weekday:
|
if is_weekday:
|
||||||
if True:
|
|
||||||
logger.info('今天是周一，发送特征预警')
|
logger.info('今天是周一，发送特征预警')
|
||||||
# 上传预警信息到数据库
|
# 上传预警信息到数据库
|
||||||
warning_data_df = df_zhibiaoliebiao.copy()
|
warning_data_df = df_zhibiaoliebiao.copy()
|
||||||
@ -197,7 +196,7 @@ def predict_main():
|
|||||||
logger.info('模型训练完成')
|
logger.info('模型训练完成')
|
||||||
|
|
||||||
logger.info('训练数据绘图ing')
|
logger.info('训练数据绘图ing')
|
||||||
model_results3 = model_losss_juxiting(sqlitedb)
|
model_results3 = model_losss(sqlitedb)
|
||||||
logger.info('训练数据绘图end')
|
logger.info('训练数据绘图end')
|
||||||
|
|
||||||
# 模型报告
|
# 模型报告
|
||||||
|
|||||||
@ -221,6 +221,10 @@ def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patien
|
|||||||
# 原油计算预测评估指数
|
# 原油计算预测评估指数
|
||||||
def model_losss(sqlitedb):
|
def model_losss(sqlitedb):
|
||||||
global dataset
|
global dataset
|
||||||
|
global rote
|
||||||
|
most_model = [sqlitedb.select_data('most_model',columns=['most_common_model'],order_by='ds desc',limit=1).values[0][0]]
|
||||||
|
most_model_name = most_model[0]
|
||||||
|
|
||||||
# 预测数据处理 predict
|
# 预测数据处理 predict
|
||||||
df_combined = loadcsv(os.path.join(dataset,"cross_validation.csv"))
|
df_combined = loadcsv(os.path.join(dataset,"cross_validation.csv"))
|
||||||
df_combined = dateConvert(df_combined)
|
df_combined = dateConvert(df_combined)
|
||||||
@ -231,69 +235,18 @@ def model_losss(sqlitedb):
|
|||||||
# 其他列转为数值类型
|
# 其他列转为数值类型
|
||||||
df_combined = df_combined.astype({col: 'float32' for col in df_combined.columns if col not in ['cutoff','ds'] })
|
df_combined = df_combined.astype({col: 'float32' for col in df_combined.columns if col not in ['cutoff','ds'] })
|
||||||
# 使用 groupby 和 transform 结合 lambda 函数来获取每个分组中 cutoff 的最小值，并创建一个新的列来存储这个最大值
|
# 使用 groupby 和 transform 结合 lambda 函数来获取每个分组中 cutoff 的最小值，并创建一个新的列来存储这个最大值
|
||||||
df_combined['max_cutoff'] = df_combined.groupby('ds')['cutoff'].transform('min')
|
df_combined['max_cutoff'] = df_combined.groupby('ds')['cutoff'].transform('max')
|
||||||
|
|
||||||
# 然后筛选出那些 cutoff 等于 max_cutoff 的行，这样就得到了每个分组中 cutoff 最大的行，并保留了其他列
|
# 然后筛选出那些 cutoff 等于 max_cutoff 的行，这样就得到了每个分组中 cutoff 最大的行，并保留了其他列
|
||||||
df_combined = df_combined[df_combined['cutoff'] == df_combined['max_cutoff']]
|
df_combined = df_combined[df_combined['cutoff'] == df_combined['max_cutoff']]
|
||||||
# 删除模型生成的cutoff列
|
# 删除模型生成的cutoff列
|
||||||
df_combined.drop(columns=['cutoff', 'max_cutoff'], inplace=True)
|
df_combined.drop(columns=['cutoff', 'max_cutoff'], inplace=True)
|
||||||
# 获取模型名称
|
# 获取模型名称
|
||||||
modelnames = df_combined.columns.to_list()[2:]
|
modelnames = df_combined.columns.to_list()[1:]
|
||||||
if 'y' in modelnames:
|
if 'y' in modelnames:
|
||||||
modelnames.remove('y')
|
modelnames.remove('y')
|
||||||
df_combined3 = df_combined.copy() # 备份df_combined,后面画图需要
|
df_combined3 = df_combined.copy() # 备份df_combined,后面画图需要
|
||||||
|
|
||||||
# 计算波动率
|
|
||||||
df_combined3['volatility'] = df_combined3['y'].pct_change().round(4)
|
|
||||||
# 计算近60日的波动率 10% 90%分位数
|
|
||||||
df_combined3['quantile_10'] = df_combined3['volatility'].rolling(60).quantile(0.1)
|
|
||||||
df_combined3['quantile_90'] = df_combined3['volatility'].rolling(60).quantile(0.9)
|
|
||||||
df_combined3 = df_combined3.round(4)
|
|
||||||
# 计算分位数对应的价格
|
|
||||||
df_combined3['quantile_10_price'] = df_combined3['y'] * (1 + df_combined3['quantile_10'])
|
|
||||||
df_combined3['quantile_90_price'] = df_combined3['y'] * (1 + df_combined3['quantile_90'])
|
|
||||||
|
|
||||||
# 遍历行
|
|
||||||
def find_min_max_within_quantile(row):
|
|
||||||
# 获取分位数10%和90%的值
|
|
||||||
q10 = row['quantile_10_price']
|
|
||||||
q90 = row['quantile_90_price']
|
|
||||||
|
|
||||||
# 判断flot值是否为空值
|
|
||||||
if pd.isna(q10) or pd.isna(q90):
|
|
||||||
return pd.Series([None, None, None, None], index=['min_within_quantile','max_within_quantile','min_model','max_model'])
|
|
||||||
|
|
||||||
# 初始化最小和最大值为None
|
|
||||||
min_value = None
|
|
||||||
max_value = None
|
|
||||||
min_value_model = ''
|
|
||||||
max_value_model = ''
|
|
||||||
|
|
||||||
|
|
||||||
# 遍历指定列，找出在分位数范围内的最大最小值
|
|
||||||
for model in modelnames:
|
|
||||||
value = row[model]
|
|
||||||
if value >= q10 and value <= q90:
|
|
||||||
if min_value is None or value < min_value:
|
|
||||||
min_value = value
|
|
||||||
min_value_model = model
|
|
||||||
|
|
||||||
if max_value is None or value > max_value:
|
|
||||||
max_value = value
|
|
||||||
max_value_model = model
|
|
||||||
|
|
||||||
# 返回最大最小值
|
|
||||||
return pd.Series([min_value, max_value,min_value_model,max_value_model], index=['min_within_quantile', 'max_within_quantile','min_model','max_model'])
|
|
||||||
|
|
||||||
# 应用函数到每一行
|
|
||||||
df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)
|
|
||||||
|
|
||||||
# 去除有空值的行
|
|
||||||
# df_combined3.dropna(inplace=True)
|
|
||||||
# 保存到数据库
|
|
||||||
df_combined3.to_sql('testandpredict_groupby', sqlitedb.connection, if_exists='replace', index=False)
|
|
||||||
df_combined3.to_csv(os.path.join(dataset,"testandpredict_groupby.csv"),index=False)
|
|
||||||
|
|
||||||
|
|
||||||
# 空的列表存储每个模型的MSE、RMSE、MAE、MAPE、SMAPE
|
# 空的列表存储每个模型的MSE、RMSE、MAE、MAPE、SMAPE
|
||||||
cellText = []
|
cellText = []
|
||||||
@ -319,12 +272,11 @@ def model_losss(sqlitedb):
|
|||||||
modelnames = modelnames[0:5]
|
modelnames = modelnames[0:5]
|
||||||
with open(os.path.join(dataset,"best_modelnames.txt"), 'w') as f:
|
with open(os.path.join(dataset,"best_modelnames.txt"), 'w') as f:
|
||||||
f.write(','.join(modelnames) + '\n')
|
f.write(','.join(modelnames) + '\n')
|
||||||
|
|
||||||
# 预测值与真实值对比图
|
# 预测值与真实值对比图
|
||||||
plt.rcParams['font.sans-serif'] = ['SimHei']
|
plt.rcParams['font.sans-serif'] = ['SimHei']
|
||||||
plt.figure(figsize=(15, 10))
|
plt.figure(figsize=(15, 10))
|
||||||
# 设置有5个子图的画布
|
for n,model in enumerate(modelnames[:5]):
|
||||||
for n,model in enumerate(modelnames):
|
|
||||||
plt.subplot(3, 2, n+1)
|
plt.subplot(3, 2, n+1)
|
||||||
plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值')
|
plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值')
|
||||||
plt.plot(df_combined3['ds'], df_combined3[model], label=model)
|
plt.plot(df_combined3['ds'], df_combined3[model], label=model)
|
||||||
@ -336,9 +288,10 @@ def model_losss(sqlitedb):
|
|||||||
plt.savefig(os.path.join(dataset,'预测值与真实值对比图.png'), bbox_inches='tight')
|
plt.savefig(os.path.join(dataset,'预测值与真实值对比图.png'), bbox_inches='tight')
|
||||||
plt.close()
|
plt.close()
|
||||||
|
|
||||||
# 历史数据+预测数据
|
|
||||||
# 拼接未来时间预测
|
# # 历史数据+预测数据
|
||||||
df_predict = loadcsv(os.path.join(dataset,'predict.csv'))
|
# # 拼接未来时间预测
|
||||||
|
df_predict = pd.read_csv(os.path.join(dataset,'predict.csv'))
|
||||||
df_predict.drop('unique_id',inplace=True,axis=1)
|
df_predict.drop('unique_id',inplace=True,axis=1)
|
||||||
df_predict.dropna(axis=1,inplace=True)
|
df_predict.dropna(axis=1,inplace=True)
|
||||||
|
|
||||||
@ -347,63 +300,97 @@ def model_losss(sqlitedb):
|
|||||||
except ValueError :
|
except ValueError :
|
||||||
df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y/%m/%d')
|
df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y/%m/%d')
|
||||||
|
|
||||||
# 取第一行数据存储到数据库中
|
def first_row_to_database(df):
|
||||||
first_row = df_predict.head(1)
|
# # 取第一行数据存储到数据库中
|
||||||
first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')
|
first_row = df.head(1)
|
||||||
# 将预测结果保存到数据库
|
first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')
|
||||||
if not sqlitedb.check_table_exists('trueandpredict'):
|
# 将预测结果保存到数据库
|
||||||
first_row.to_sql('trueandpredict',sqlitedb.connection,index=False)
|
if not sqlitedb.check_table_exists('trueandpredict'):
|
||||||
else:
|
first_row.to_sql('trueandpredict',sqlitedb.connection,index=False)
|
||||||
for row in first_row.itertuples(index=False):
|
else:
|
||||||
row_dict = row._asdict()
|
for col in first_row.columns:
|
||||||
columns=row_dict.keys()
|
sqlitedb.add_column_if_not_exists('trueandpredict',col,'TEXT')
|
||||||
for col in columns:
|
for row in first_row.itertuples(index=False):
|
||||||
sqlitedb.add_column_if_not_exists('trueandpredict',col,'TEXT')
|
row_dict = row._asdict()
|
||||||
check_query = sqlitedb.select_data('trueandpredict',where_condition = f"ds = '{row.ds}'")
|
columns=row_dict.keys()
|
||||||
if len(check_query) > 0:
|
check_query = sqlitedb.select_data('trueandpredict',where_condition = f"ds = '{row.ds}'")
|
||||||
set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
|
if len(check_query) > 0:
|
||||||
sqlitedb.update_data('trueandpredict',set_clause,where_condition = f"ds = '{row.ds}'")
|
set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
|
||||||
continue
|
sqlitedb.update_data('trueandpredict',set_clause,where_condition = f"ds = '{row.ds}'")
|
||||||
sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=columns)
|
continue
|
||||||
# 最多频率的模型名称
|
sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=columns)
|
||||||
num = df_combined3.shape[0] if df_combined3.shape[0] < 60 else 60
|
|
||||||
min_model_max_frequency_model = df_combined3['min_model'][-num:].value_counts().idxmax()
|
first_row_to_database(df_predict)
|
||||||
max_model_max_frequency_model = df_combined3['max_model'][-num:].value_counts().idxmax()
|
|
||||||
df_predict['min_model'] = min_model_max_frequency_model
|
|
||||||
df_predict['max_model'] = max_model_max_frequency_model
|
|
||||||
df_predict['min_within_quantile'] = df_predict[min_model_max_frequency_model]
|
|
||||||
df_predict['max_within_quantile'] = df_predict[max_model_max_frequency_model]
|
|
||||||
df_predict2 = df_predict.copy()
|
|
||||||
df_predict2['ds'] = df_predict2['ds'].dt.strftime('%Y-%m-%d 00:00:00')
|
|
||||||
# 将预测结果保存到数据库
|
|
||||||
# 判断表存在
|
|
||||||
if not sqlitedb.check_table_exists('testandpredict_groupby'):
|
|
||||||
df_predict2.to_sql('testandpredict_groupby',sqlitedb.connection,index=False)
|
|
||||||
else:
|
|
||||||
for row in df_predict2.itertuples(index=False):
|
|
||||||
row_dict = row._asdict()
|
|
||||||
check_query = sqlitedb.select_data('testandpredict_groupby',where_condition = f"ds = '{row.ds}'")
|
|
||||||
if len(check_query) > 0:
|
|
||||||
set_clause = ", ".join([f"{key} = '{value}'" for key, value in row_dict.items()])
|
|
||||||
sqlitedb.update_data('testandpredict_groupby',set_clause,where_condition = f"ds = '{row.ds}'")
|
|
||||||
continue
|
|
||||||
sqlitedb.insert_data('testandpredict_groupby',tuple(row_dict.values()),columns=row_dict.keys())
|
|
||||||
# 计算每个预测值与真实值之间的偏差率
|
|
||||||
for model in allmodelnames:
|
|
||||||
df_combined3[f'{model}_abs_error_rate'] = abs(df_combined3['y'] - df_combined3[model]) / df_combined3['y']
|
|
||||||
|
|
||||||
# 获取每行对应的最小偏差率值
|
|
||||||
min_abs_error_rate_values = df_combined3.apply(lambda row: row[[f'{model}_abs_error_rate' for model in allmodelnames]].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 = df_combined3.apply(lambda row: row[[f'{model}_abs_error_rate' for model in allmodelnames]].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])
|
|
||||||
# 获取最小偏差率对应的模型的预测值
|
|
||||||
min_abs_error_rate_predictions = df_combined3.apply(lambda row: row[min_abs_error_rate_column_name[row.name]], axis=1)
|
|
||||||
# 将最小偏差率对应的模型的预测值添加到DataFrame中
|
|
||||||
df_combined3['min_abs_error_rate_prediction'] = min_abs_error_rate_predictions
|
|
||||||
df_combined3['min_abs_error_rate_column_name'] = min_abs_error_rate_column_name
|
|
||||||
df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)
|
df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)
|
||||||
|
|
||||||
|
# 计算每个模型与最佳模型的绝对误差比例，根据设置的阈值rote筛选预测值显示最大最小值
|
||||||
|
names = []
|
||||||
|
names_df = df_combined3.copy()
|
||||||
|
for col in allmodelnames:
|
||||||
|
names_df[f'{col}-{most_model_name}-误差比例'] = abs(names_df[col] - names_df[most_model_name]) / names_df[most_model_name]
|
||||||
|
names.append(f'{col}-{most_model_name}-误差比例')
|
||||||
|
|
||||||
|
names_df = names_df[names]
|
||||||
|
def add_rote_column(row):
|
||||||
|
columns = []
|
||||||
|
for r in names_df.columns:
|
||||||
|
if row[r] <= rote:
|
||||||
|
columns.append(r.split('-')[0])
|
||||||
|
return pd.Series([columns], index=['columns'])
|
||||||
|
names_df['columns'] = names_df.apply(add_rote_column, axis=1)
|
||||||
|
|
||||||
|
def add_upper_lower_bound(row):
|
||||||
|
print(row['columns'])
|
||||||
|
print(type(row['columns']))
|
||||||
|
# 计算上边界值
|
||||||
|
upper_bound = df_combined3.loc[row.name,row['columns']].max()
|
||||||
|
# 计算下边界值
|
||||||
|
lower_bound = df_combined3.loc[row.name,row['columns']].min()
|
||||||
|
return pd.Series([lower_bound, upper_bound], index=['min_within_quantile', 'max_within_quantile'])
|
||||||
|
df_combined3[['min_within_quantile','max_within_quantile']] = names_df.apply(add_upper_lower_bound, axis=1)
|
||||||
|
|
||||||
|
|
||||||
|
def find_most_common_model():
|
||||||
|
# 最多频率的模型名称
|
||||||
|
min_model_max_frequency_model = df_combined3['min_model'].tail(20).value_counts().idxmax()
|
||||||
|
max_model_max_frequency_model = df_combined3['max_model'].tail(20).value_counts().idxmax()
|
||||||
|
if min_model_max_frequency_model == max_model_max_frequency_model:
|
||||||
|
# 取20天第二多的模型
|
||||||
|
max_model_max_frequency_model = df_combined3['max_model'].tail(20).value_counts().nlargest(2).index[1]
|
||||||
|
|
||||||
|
df_predict['min_model'] = min_model_max_frequency_model
|
||||||
|
df_predict['max_model'] = max_model_max_frequency_model
|
||||||
|
df_predict['min_within_quantile'] = df_predict[min_model_max_frequency_model]
|
||||||
|
df_predict['max_within_quantile'] = df_predict[max_model_max_frequency_model]
|
||||||
|
|
||||||
|
|
||||||
|
# find_most_common_model()
|
||||||
|
|
||||||
|
df_predict2 = df_predict.copy()
|
||||||
|
df_predict2['ds'] = pd.to_datetime(df_predict2['ds'])
|
||||||
|
df_predict2['ds'] = df_predict2['ds'].dt.strftime('%Y-%m-%d 00:00:00')
|
||||||
|
|
||||||
|
|
||||||
|
def _add_abs_error_rate():
|
||||||
|
# 计算每个预测值与真实值之间的偏差率
|
||||||
|
for model in allmodelnames:
|
||||||
|
df_combined3[f'{model}_abs_error_rate'] = abs(df_combined3['y'] - df_combined3[model]) / df_combined3['y']
|
||||||
|
|
||||||
|
# 获取每行对应的最小偏差率值
|
||||||
|
min_abs_error_rate_values = df_combined3.apply(lambda row: row[[f'{model}_abs_error_rate' for model in allmodelnames]].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 = df_combined3.apply(lambda row: row[[f'{model}_abs_error_rate' for model in allmodelnames]].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])
|
||||||
|
# 获取最小偏差率对应的模型的预测值
|
||||||
|
min_abs_error_rate_predictions = df_combined3.apply(lambda row: row[min_abs_error_rate_column_name[row.name]], axis=1)
|
||||||
|
# 将最小偏差率对应的模型的预测值添加到DataFrame中
|
||||||
|
df_combined3['min_abs_error_rate_prediction'] = min_abs_error_rate_predictions
|
||||||
|
df_combined3['min_abs_error_rate_column_name'] = min_abs_error_rate_column_name
|
||||||
|
|
||||||
|
# _add_abs_error_rate()
|
||||||
|
|
||||||
# 判断 df 的数值列转为float
|
# 判断 df 的数值列转为float
|
||||||
for col in df_combined3.columns:
|
for col in df_combined3.columns:
|
||||||
try:
|
try:
|
||||||
@ -412,69 +399,84 @@ def model_losss(sqlitedb):
|
|||||||
df_combined3[col] = df_combined3[col].round(2)
|
df_combined3[col] = df_combined3[col].round(2)
|
||||||
except ValueError:
|
except ValueError:
|
||||||
pass
|
pass
|
||||||
df_combined3.to_csv(os.path.join(dataset,"df_combined3.csv"),index=False)
|
df_combined3.to_csv(os.path.join(dataset,"testandpredict_groupby.csv"),index=False)
|
||||||
|
|
||||||
|
|
||||||
# 历史价格+预测价格
|
# 历史价格+预测价格
|
||||||
# df_combined3 = df_combined3[-50:] # 取50个数据点画图
|
sqlitedb.drop_table('testandpredict_groupby')
|
||||||
# 历史价格
|
df_combined3.to_sql('testandpredict_groupby',sqlitedb.connection,index=False)
|
||||||
plt.figure(figsize=(20, 10))
|
|
||||||
plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值')
|
|
||||||
# 颜色填充
|
|
||||||
plt.fill_between(df_combined3['ds'], df_combined3['min_within_quantile'], df_combined3['max_within_quantile'], alpha=0.2)
|
|
||||||
# plt.plot(df_combined3['ds'], df_combined3['min_abs_error_rate_prediction'], label='最小绝对误差', linestyle='--', color='orange')
|
|
||||||
# 网格
|
|
||||||
plt.grid(True)
|
|
||||||
# 显示历史值
|
|
||||||
for i, j in zip(df_combined3['ds'], df_combined3['y']):
|
|
||||||
plt.text(i, j, str(j), ha='center', va='bottom')
|
|
||||||
|
|
||||||
# 数据库查询最佳模型名称
|
|
||||||
# most_model = [sqlitedb.select_data('most_model',columns=['most_common_model'],order_by='ds desc',limit=1).values[0][0]]
|
|
||||||
most_model = modelnames[0:5]
|
|
||||||
for model in most_model:
|
|
||||||
plt.plot(df_combined3['ds'], df_combined3[model], label=model,marker='o')
|
|
||||||
# 当前日期画竖虚线
|
|
||||||
plt.axvline(x=df_combined3['ds'].iloc[-horizon], color='r', linestyle='--')
|
|
||||||
plt.legend()
|
|
||||||
plt.xlabel('日期')
|
|
||||||
plt.ylabel('价格')
|
|
||||||
|
|
||||||
plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight')
|
def _plt_predict_ture(df):
|
||||||
plt.close()
|
lens = df.shape[0] if df.shape[0] < 180 else 90
|
||||||
|
df = df[-lens:] # 取180个数据点画图
|
||||||
# 预测值表格
|
# 历史价格
|
||||||
fig, ax = plt.subplots(figsize=(20, 6))
|
plt.figure(figsize=(20, 10))
|
||||||
ax.axis('off') # 关闭坐标轴
|
plt.plot(df['ds'], df['y'], label='真实值')
|
||||||
# 数值保留2位小数
|
# 颜色填充
|
||||||
df_combined3 = df_combined3.round(2)
|
plt.fill_between(df['ds'], df['max_within_quantile'], df['min_within_quantile'], alpha=0.2)
|
||||||
df_combined3 = df_combined3[-horizon:]
|
# markers = ['o', 's', '^', 'D', 'v', '*', 'p', 'h', 'H', '+', 'x', 'd']
|
||||||
df_combined3['Day'] = [f'Day_{i}' for i in range(1,horizon+1)]
|
# random_marker = random.choice(markers)
|
||||||
# Day列放到最前面
|
# for model in allmodelnames:
|
||||||
df_combined3 = df_combined3[['Day'] + list(df_combined3.columns[:-1])]
|
# for model in ['BiTCN','RNN']:
|
||||||
table = ax.table(cellText=df_combined3.values, colLabels=df_combined3.columns, loc='center')
|
# plt.plot(df['ds'], df[model], label=model,marker=random_marker)
|
||||||
#加宽表格
|
# plt.plot(df_combined3['ds'], df_combined3['min_abs_error_rate_prediction'], label='最小绝对误差', linestyle='--', color='orange')
|
||||||
table.auto_set_font_size(False)
|
# 网格
|
||||||
table.set_fontsize(10)
|
plt.grid(True)
|
||||||
|
# 显示历史值
|
||||||
|
for i, j in zip(df['ds'], df['y']):
|
||||||
|
plt.text(i, j, str(j), ha='center', va='bottom')
|
||||||
|
|
||||||
# 设置表格样式，列数据最小的用绿色标识
|
for model in most_model:
|
||||||
plt.savefig(os.path.join(dataset,'预测值表格.png'), bbox_inches='tight')
|
plt.plot(df['ds'], df[model], label=model,marker='o')
|
||||||
plt.close()
|
# 当前日期画竖虚线
|
||||||
# plt.show()
|
plt.axvline(x=df['ds'].iloc[-horizon], color='r', linestyle='--')
|
||||||
|
plt.legend()
|
||||||
# 可视化评估结果
|
plt.xlabel('日期')
|
||||||
plt.rcParams['font.sans-serif'] = ['SimHei']
|
plt.ylabel('价格')
|
||||||
fig, ax = plt.subplots(figsize=(20, 10))
|
|
||||||
ax.axis('off') # 关闭坐标轴
|
plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight')
|
||||||
table = ax.table(cellText=model_results3.values, colLabels=model_results3.columns, loc='center')
|
plt.close()
|
||||||
# 加宽表格
|
|
||||||
table.auto_set_font_size(False)
|
def _plt_predict_table(df):
|
||||||
table.set_fontsize(10)
|
# 预测值表格
|
||||||
|
fig, ax = plt.subplots(figsize=(20, 6))
|
||||||
|
ax.axis('off') # 关闭坐标轴
|
||||||
|
# 数值保留2位小数
|
||||||
|
df = df.round(2)
|
||||||
|
df = df[-horizon:]
|
||||||
|
df['Day'] = [f'Day_{i}' for i in range(1,horizon+1)]
|
||||||
|
# Day列放到最前面
|
||||||
|
df = df[['Day'] + list(df.columns[:-1])]
|
||||||
|
table = ax.table(cellText=df.values, colLabels=df.columns, loc='center')
|
||||||
|
#加宽表格
|
||||||
|
table.auto_set_font_size(False)
|
||||||
|
table.set_fontsize(10)
|
||||||
|
|
||||||
|
# 设置表格样式，列数据最小的用绿色标识
|
||||||
|
plt.savefig(os.path.join(dataset,'预测值表格.png'), bbox_inches='tight')
|
||||||
|
plt.close()
|
||||||
|
|
||||||
|
def _plt_model_results3():
|
||||||
|
# 可视化评估结果
|
||||||
|
plt.rcParams['font.sans-serif'] = ['SimHei']
|
||||||
|
fig, ax = plt.subplots(figsize=(20, 10))
|
||||||
|
ax.axis('off') # 关闭坐标轴
|
||||||
|
table = ax.table(cellText=model_results3.values, colLabels=model_results3.columns, loc='center')
|
||||||
|
# 加宽表格
|
||||||
|
table.auto_set_font_size(False)
|
||||||
|
table.set_fontsize(10)
|
||||||
|
|
||||||
|
# 设置表格样式，列数据最小的用绿色标识
|
||||||
|
plt.savefig(os.path.join(dataset,'模型评估.png'), bbox_inches='tight')
|
||||||
|
plt.close()
|
||||||
|
|
||||||
|
_plt_predict_ture(df_combined3)
|
||||||
|
_plt_predict_table(df_combined3)
|
||||||
|
_plt_model_results3()
|
||||||
|
|
||||||
# 设置表格样式，列数据最小的用绿色标识
|
|
||||||
plt.savefig(os.path.join(dataset,'模型评估.png'), bbox_inches='tight')
|
|
||||||
plt.close()
|
|
||||||
return model_results3
|
return model_results3
|
||||||
|
|
||||||
|
|
||||||
# 聚烯烃计算预测评估指数
|
# 聚烯烃计算预测评估指数
|
||||||
def model_losss_juxiting(sqlitedb):
|
def model_losss_juxiting(sqlitedb):
|
||||||
global dataset
|
global dataset
|
||||||
|
|||||||
@ -2,7 +2,7 @@
|
|||||||
"cells": [
|
"cells": [
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 1,
|
"execution_count": 8,
|
||||||
"id": "31c0e11d-c87a-4e95-92a0-d1d09625e255",
|
"id": "31c0e11d-c87a-4e95-92a0-d1d09625e255",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
@ -15,7 +15,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 2,
|
"execution_count": 9,
|
||||||
"id": "83c81b9e",
|
"id": "83c81b9e",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
@ -25,7 +25,7 @@
|
|||||||
"'http://192.168.100.53:8080/jingbo-dev/api/server/login'"
|
"'http://192.168.100.53:8080/jingbo-dev/api/server/login'"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"execution_count": 2,
|
"execution_count": 9,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"output_type": "execute_result"
|
"output_type": "execute_result"
|
||||||
}
|
}
|
||||||
@ -44,7 +44,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 3,
|
"execution_count": 10,
|
||||||
"id": "2b330ee3-c006-4ab1-8558-59c51ac8d86f",
|
"id": "2b330ee3-c006-4ab1-8558-59c51ac8d86f",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
@ -59,7 +59,7 @@
|
|||||||
" 'funcOperation': '获取token'}"
|
" 'funcOperation': '获取token'}"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
"execution_count": 3,
|
"execution_count": 10,
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"output_type": "execute_result"
|
"output_type": "execute_result"
|
||||||
}
|
}
|
||||||
@ -70,7 +70,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 4,
|
"execution_count": 11,
|
||||||
"id": "dcb6100a-ed2b-4077-a1a9-361c6cb565f9",
|
"id": "dcb6100a-ed2b-4077-a1a9-361c6cb565f9",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
@ -87,7 +87,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 5,
|
"execution_count": 12,
|
||||||
"id": "22c0c7c4",
|
"id": "22c0c7c4",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
@ -95,7 +95,7 @@
|
|||||||
"name": "stdout",
|
"name": "stdout",
|
||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"{'confirmFlg': False, 'data': {'accessToken': 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzM0MjMwMjQsImp0aSI6IjhjZWE4YWQ4YWU3YTQyMmY4ODkxYWY4N2RhNmFmNGI5In0.Doq76Zh4PWFr6U0ICJsWpcpFX7tALvIadgXKkt_IHTc', 'md5Token': '091cf636ce5a735ef287a312b1c5d410'}, 'status': True}\n"
|
"{'confirmFlg': False, 'data': {'accessToken': 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzM0MjU1NTYsImp0aSI6IjczNjVlNTZmNTZiYjQ5YjhhNjE3MzhiNDJhMWVmOTJjIn0.pUUzeBqbcHv2B3Z2ZQ6pDdBscWeVGlai3LvVU-Hm03E', 'md5Token': 'f288634c14d5e93fc9c0b7a423a8ba33'}, 'status': True}\n"
|
||||||
]
|
]
|
||||||
}
|
}
|
||||||
],
|
],
|
||||||
@ -105,7 +105,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 6,
|
"execution_count": 13,
|
||||||
"id": "12077ead",
|
"id": "12077ead",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [],
|
"outputs": [],
|
||||||
@ -115,7 +115,7 @@
|
|||||||
},
|
},
|
||||||
{
|
{
|
||||||
"cell_type": "code",
|
"cell_type": "code",
|
||||||
"execution_count": 7,
|
"execution_count": 14,
|
||||||
"id": "a7ae21d1",
|
"id": "a7ae21d1",
|
||||||
"metadata": {},
|
"metadata": {},
|
||||||
"outputs": [
|
"outputs": [
|
||||||
@ -127,8 +127,8 @@
|
|||||||
"INFO:my_logger:上传预警信息\n",
|
"INFO:my_logger:上传预警信息\n",
|
||||||
"预警上传中...\n",
|
"预警上传中...\n",
|
||||||
"INFO:my_logger:预警上传中...\n",
|
"INFO:my_logger:预警上传中...\n",
|
||||||
"token:eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzM0MjMwMjQsImp0aSI6IjdiNTdhNDUxNWUwOTQzYWZhNWEwYTUxNzllM2Y0MDQ1In0.7KTHvBMEpsRPM9esVdp3MPLz_5WCjuK1vZvwkhbhfy0\n",
|
"token:eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzM0MjU1NTYsImp0aSI6Ijk2ZjJlNDg4NDgzMzQyYThhYmMyYzVhYjg2NGNhNDhhIn0.Vl6wmKDRxPdZANwEEWAQ4wBPbJKC2YWVi0Gm51ZzjE0\n",
|
||||||
"INFO:my_logger:token:eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzM0MjMwMjQsImp0aSI6IjdiNTdhNDUxNWUwOTQzYWZhNWEwYTUxNzllM2Y0MDQ1In0.7KTHvBMEpsRPM9esVdp3MPLz_5WCjuK1vZvwkhbhfy0\n",
|
"INFO:my_logger:token:eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzM0MjU1NTYsImp0aSI6Ijk2ZjJlNDg4NDgzMzQyYThhYmMyYzVhYjg2NGNhNDhhIn0.Vl6wmKDRxPdZANwEEWAQ4wBPbJKC2YWVi0Gm51ZzjE0\n",
|
||||||
"warning_data:{'funcModule': '原油特征停更预警', 'funcOperation': '原油特征停更预警', 'data': {'WARNING_TYPE_NAME': '特征数据停更预警', 'WARNING_CONTENT': '2024-12-05有34个停更', 'WARNING_DATE': '2024-12-05'}}\n",
|
"warning_data:{'funcModule': '原油特征停更预警', 'funcOperation': '原油特征停更预警', 'data': {'WARNING_TYPE_NAME': '特征数据停更预警', 'WARNING_CONTENT': '2024-12-05有34个停更', 'WARNING_DATE': '2024-12-05'}}\n",
|
||||||
"INFO:my_logger:warning_data:{'funcModule': '原油特征停更预警', 'funcOperation': '原油特征停更预警', 'data': {'WARNING_TYPE_NAME': '特征数据停更预警', 'WARNING_CONTENT': '2024-12-05有34个停更', 'WARNING_DATE': '2024-12-05'}}\n"
|
"INFO:my_logger:warning_data:{'funcModule': '原油特征停更预警', 'funcOperation': '原油特征停更预警', 'data': {'WARNING_TYPE_NAME': '特征数据停更预警', 'WARNING_CONTENT': '2024-12-05有34个停更', 'WARNING_DATE': '2024-12-05'}}\n"
|
||||||
]
|
]
|
||||||
@ -137,7 +137,7 @@
|
|||||||
"name": "stdout",
|
"name": "stdout",
|
||||||
"output_type": "stream",
|
"output_type": "stream",
|
||||||
"text": [
|
"text": [
|
||||||
"{'confirmFlg': False, 'data': {'accessToken': 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzM0MjMwMjQsImp0aSI6IjdiNTdhNDUxNWUwOTQzYWZhNWEwYTUxNzllM2Y0MDQ1In0.7KTHvBMEpsRPM9esVdp3MPLz_5WCjuK1vZvwkhbhfy0', 'md5Token': '33e47710d77c32c7f3db2c83cd2bd621'}, 'status': True}\n"
|
"{'confirmFlg': False, 'data': {'accessToken': 'eyJ0eXAiOiJKV1QiLCJhbGciOiJIUzI1NiJ9.eyJzdWIiOiJhcGlfdGVzdCIsInRoIjoiOGE0NTc3ZGJkOTE5Njc1NzU4ZDU3OTk5YTFlODkxZmUiLCJsdCI6ImFwaSIsImlzcyI6IiIsInRtIjoiUEMiLCJleHAiOjE3MzM0MjU1NTYsImp0aSI6Ijk2ZjJlNDg4NDgzMzQyYThhYmMyYzVhYjg2NGNhNDhhIn0.Vl6wmKDRxPdZANwEEWAQ4wBPbJKC2YWVi0Gm51ZzjE0', 'md5Token': '99b49d2d29f44041f46ecd03a3987961'}, 'status': True}\n"
|
||||||
]
|
]
|
||||||
},
|
},
|
||||||
{
|
{
|
||||||
|
|||||||
Loading…
Reference in New Issue
Block a user