原油绘图逻辑同步为聚烯烃绘图逻辑

This commit is contained in:
workpc 2024-12-05 17:59:18 +08:00
parent c893350b7d
commit 89b4a2c7a6
5 changed files with 488 additions and 213 deletions

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@ -500,4 +500,302 @@
# plt.ylabel('价格') # plt.ylabel('价格')
# plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight') # plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight')
# plt.close() # plt.close()
####################################################特征处理
####################################################上传服务
# 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}')
#######################################绘图逻辑
# def model_losss(sqlitedb):
# global dataset
# # 预测数据处理 predict
# df_combined = loadcsv(os.path.join(dataset,"cross_validation.csv"))
# df_combined = dateConvert(df_combined)
# # 删除空列
# df_combined.dropna(axis=1,inplace=True)
# # 删除缺失值,预测过程不能有缺失值
# df_combined.dropna(inplace=True)
# # 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'] })
# # 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')
# # 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']]
# # 删除模型生成的cutoff列
# df_combined.drop(columns=['cutoff', 'max_cutoff'], inplace=True)
# # 获取模型名称
# modelnames = df_combined.columns.to_list()[2:]
# if 'y' in modelnames:
# modelnames.remove('y')
# 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
# cellText = []
# # 遍历模型名称,计算模型评估指标
# for model in modelnames:
# modelmse = mse(df_combined['y'], df_combined[model])
# modelrmse = rmse(df_combined['y'], df_combined[model])
# modelmae = mae(df_combined['y'], df_combined[model])
# # modelmape = mape(df_combined['y'], df_combined[model])
# # modelsmape = smape(df_combined['y'], df_combined[model])
# # modelr2 = r2_score(df_combined['y'], df_combined[model])
# cellText.append([model,round(modelmse, 3), round(modelrmse, 3), round(modelmae, 3)])
# 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)'])
# # 按MSE降序排列
# model_results3 = model_results3.sort_values(by='平均平方误差(MSE)', ascending=True)
# model_results3.to_csv(os.path.join(dataset,"model_evaluation.csv"),index=False)
# modelnames = model_results3['模型(Model)'].tolist()
# allmodelnames = modelnames.copy()
# # 保存5个最佳模型的名称
# if len(modelnames) > 5:
# modelnames = modelnames[0:5]
# with open(os.path.join(dataset,"best_modelnames.txt"), 'w') as f:
# f.write(','.join(modelnames) + '\n')
# # 预测值与真实值对比图
# plt.rcParams['font.sans-serif'] = ['SimHei']
# plt.figure(figsize=(15, 10))
# # 设置有5个子图的画布
# for n,model in enumerate(modelnames):
# 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[model], label=model)
# plt.legend()
# plt.xlabel('日期')
# plt.ylabel('价格')
# plt.title(model+'拟合')
# plt.subplots_adjust(hspace=0.5)
# plt.savefig(os.path.join(dataset,'预测值与真实值对比图.png'), bbox_inches='tight')
# plt.close()
# # 历史数据+预测数据
# # 拼接未来时间预测
# df_predict = loadcsv(os.path.join(dataset,'predict.csv'))
# df_predict.drop('unique_id',inplace=True,axis=1)
# df_predict.dropna(axis=1,inplace=True)
# try:
# df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y-%m-%d')
# except ValueError :
# 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
# first_row = df_predict.head(1)
# first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')
# # 将预测结果保存到数据库
# if not sqlitedb.check_table_exists('trueandpredict'):
# first_row.to_sql('trueandpredict',sqlitedb.connection,index=False)
# else:
# for row in first_row.itertuples(index=False):
# row_dict = row._asdict()
# columns=row_dict.keys()
# for col in columns:
# sqlitedb.add_column_if_not_exists('trueandpredict',col,'TEXT')
# check_query = sqlitedb.select_data('trueandpredict',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('trueandpredict',set_clause,where_condition = f"ds = '{row.ds}'")
# continue
# sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=columns)
# # 最多频率的模型名称
# 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()
# 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)
# # 判断 df 的数值列转为float
# for col in df_combined3.columns:
# try:
# if col != 'ds':
# df_combined3[col] = df_combined3[col].astype(float)
# df_combined3[col] = df_combined3[col].round(2)
# except ValueError:
# pass
# df_combined3.to_csv(os.path.join(dataset,"df_combined3.csv"),index=False)
# # 历史价格+预测价格
# # df_combined3 = df_combined3[-50:] # 取50个数据点画图
# # 历史价格
# 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')
# plt.close()
# # 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_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

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@ -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}{df_count}个停更' content = f'{warning_date}{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:

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@ -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

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@ -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

View File

@ -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"
] ]
}, },
{ {