雍安环境使用5个模型画预测图
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workpc
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@ -176,7 +176,7 @@ table_name = 'v_tbl_crude_oil_warning'
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### 开关
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is_train = False # 是否训练
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is_debug = False # 是否调试
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is_eta = False # 是否使用eta接口
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is_eta = True # 是否使用eta接口
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is_market = True # 是否通过市场信息平台获取特征 ,在is_eta 为true 的情况下生效
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is_timefurture = True # 是否使用时间特征
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is_fivemodels = False # 是否使用之前保存的最佳的5个模型
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@ -107,7 +107,6 @@ ClassifyId = 1214
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################################################################################################################ 变量定义--雍安测试环境
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login_pushreport_url = "http://192.168.100.115:9090/dom-api/api/server/login"
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upload_url = "http://192.168.100.115:9090/dom-api/api/analysis/reportInfo/researchUploadReportSave"
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# upload_url = "http://192.168.100.109:8080/jingbo/api/analysis/reportInfo/researchUploadReportSave" # zhaoqiwei
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upload_warning_url = "http://192.168.100.115:9090/dom-api/api/basicBuiness/crudeOilWarning/save"
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query_data_list_item_nos_url = "http://192.168.100.115:9090/dom-api/api/warehouse/dwDataItem/queryDataListItemNos"
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@ -169,7 +168,8 @@ dbusername ='root'
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password = '123456'
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dbname = 'jingbo_test'
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table_name = 'v_tbl_crude_oil_warning'
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# 表名前缀
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table_name_prefix = 'yongan_'
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### 开关
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is_train = False # 是否训练
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@ -41,8 +41,9 @@ plt.rcParams['font.sans-serif'] = ['SimHei'] # 用来正常显示中文标签
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plt.rcParams['axes.unicode_minus'] = False # 用来正常显示负号
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# from config_jingbo_pro import *
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from config_jingbo import *
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# from config_juxiting import *
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# from config_jingbo import *
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# from config_yongan import *
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from config_juxiting import *
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File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@ -1,7 +1,7 @@
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# 读取配置
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from lib.dataread_yongan import *
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from lib.dataread import *
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from lib.tools import SendMail,exception_logger
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from models.nerulforcastmodels import ex_Model,model_losss,model_losss_juxiting,brent_export_pdf,tansuanli_export_pdf,pp_export_pdf,model_losss_juxiting
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from models.nerulforcastmodels import ex_Model,brent_export_pdf,tansuanli_export_pdf,pp_export_pdf,model_losss_yongan
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import glob
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import torch
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@ -243,7 +243,7 @@ def predict_main():
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# logger.info('模型训练完成')
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# logger.info('训练数据绘图ing')
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# model_results3 = model_losss(sqlitedb,end_time=end_time)
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# model_results3 = model_losss_yongan(sqlitedb,end_time=end_time,table_name_prefix=table_name_prefix)
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# logger.info('训练数据绘图end')
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# 模型报告
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@ -226,6 +226,413 @@ def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patien
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return
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# 雍安环境预测评估指数
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@exception_logger
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def model_losss_yongan(sqlitedb,end_time,table_name_prefix):
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global dataset
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global rote
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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_name = most_model[0]
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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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df_combined = sqlitedb.select_data('accuracy',where_condition=f"created_dt <= '{end_time}'")
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df_combined4 = df_combined.copy() # 备份df_combined,后面画图需要
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# 删除缺失值大于80%的列
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logger.info(df_combined.shape)
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df_combined = df_combined.loc[:, df_combined.isnull().mean() < 0.8]
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logger.info(df_combined.shape)
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# 删除缺失值
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df_combined.dropna(inplace=True)
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logger.info(df_combined.shape)
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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 ['CREAT_DATE','ds','created_dt'] })
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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')['CREAT_DATE'].transform('max')
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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['CREAT_DATE'] == 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=['CREAT_DATE', 'max_cutoff','created_dt','min_within_quantile','max_within_quantile','id','min_price','max_price','LOW_PRICE','HIGH_PRICE','mean'], inplace=True)
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# 获取模型名称
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modelnames = df_combined.columns.to_list()[1:]
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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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# 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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if is_fivemodels:
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pass
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else:
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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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for n,model in enumerate(modelnames[:5]):
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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 = pd.read_csv(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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def first_row_to_database(df):
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# # 取第一行数据存储到数据库中
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first_row = df.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 col in first_row.columns:
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sqlitedb.add_column_if_not_exists('trueandpredict',col,'TEXT')
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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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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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first_row_to_database(df_predict)
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df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)
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# 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
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names = []
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names_df = df_combined3.copy()
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for col in allmodelnames:
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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]
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names.append(f'{col}-{most_model_name}-误差比例')
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names_df = names_df[names]
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def add_rote_column(row):
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columns = []
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for r in names_df.columns:
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if row[r] <= rote:
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columns.append(r.split('-')[0])
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return pd.Series([columns], index=['columns'])
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names_df['columns'] = names_df.apply(add_rote_column, axis=1)
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def add_upper_lower_bound(row):
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print(row['columns'])
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print(type(row['columns']))
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# 计算上边界值
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upper_bound = df_combined3.loc[row.name,row['columns']].max()
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# 计算下边界值
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lower_bound = df_combined3.loc[row.name,row['columns']].min()
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return pd.Series([lower_bound, upper_bound], index=['min_within_quantile', 'max_within_quantile'])
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df_combined3[['min_within_quantile','max_within_quantile']] = names_df.apply(add_upper_lower_bound, axis=1)
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def find_closest_values(row):
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x = row.y
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if x is None or np.isnan(x):
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return pd.Series([None, None], index=['min_price','max_price'])
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# row = row.drop('ds')
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row = row.values.tolist()
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row.sort()
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print(row)
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# x 在row中的索引
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index = row.index(x)
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if index == 0:
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return pd.Series([row[index+1], row[index+2]], index=['min_price','max_price'])
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elif index == len(row)-1:
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return pd.Series([row[index-2], row[index-1]], index=['min_price','max_price'])
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else:
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return pd.Series([row[index-1], row[index+1]], index=['min_price','max_price'])
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def find_most_common_model():
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# 最多频率的模型名称
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min_model_max_frequency_model = df_combined3['min_model'].tail(60).value_counts().idxmax()
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max_model_max_frequency_model = df_combined3['max_model'].tail(60).value_counts().idxmax()
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if min_model_max_frequency_model == max_model_max_frequency_model:
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# 取60天第二多的模型
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max_model_max_frequency_model = df_combined3['max_model'].tail(60).value_counts().nlargest(2).index[1]
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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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# find_most_common_model()
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df_combined3['ds'] = pd.to_datetime(df_combined3['ds'])
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df_combined3['ds'] = df_combined3['ds'].dt.strftime('%Y-%m-%d')
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df_predict2 = df_combined3.tail(horizon)
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# 保存到数据库
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if not sqlitedb.check_table_exists(f'{table_name_prefix}accuracy'):
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columns = ','.join(df_combined3.columns.to_list()+['id','CREAT_DATE','min_price','max_price','LOW_PRICE','HIGH_PRICE','mean'])
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sqlitedb.create_table('accuracy',columns=columns)
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existing_data = sqlitedb.select_data(table_name = "accuracy")
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if not existing_data.empty:
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max_id = existing_data['id'].astype(int).max()
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df_predict2['id'] = range(max_id + 1, max_id + 1 + len(df_predict2))
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else:
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df_predict2['id'] = range(1, 1 + len(df_predict2))
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df_predict2['CREAT_DATE'] = end_time
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save_to_database(sqlitedb,df_predict2,"accuracy",end_time)
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# 上周准确率计算
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accuracy_df = sqlitedb.select_data(table_name = "accuracy")
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predict_y = accuracy_df.copy()
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# ids = predict_y[predict_y['min_price'].isnull()]['id'].tolist()
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ids = predict_y['id'].tolist()
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# 准确率基准与绘图上下界逻辑一致
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# predict_y[['min_price','max_price']] = predict_y[['min_within_quantile','max_within_quantile']]
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# 模型评估前五均值
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# predict_y['min_price'] = predict_y[modelnames].mean(axis=1) -1
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# predict_y['max_price'] = predict_y[modelnames].mean(axis=1) +1
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# 模型评估前十均值
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predict_y['min_price'] = predict_y[allmodelnames[0:10]].mean(axis=1) -1.5
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predict_y['mean'] = predict_y[allmodelnames[0:10]].mean(axis=1)
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predict_y['max_price'] = predict_y[allmodelnames[0:10]].mean(axis=1) +1.5
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# 模型评估前十最大最小
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# allmodelnames 和 predict_y 列 重复的
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# allmodelnames = [col for col in allmodelnames if col in predict_y.columns]
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# predict_y['min_price'] = predict_y[allmodelnames[0:10]].min(axis=1)
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# predict_y['max_price'] = predict_y[allmodelnames[0:10]].max(axis=1)
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for id in ids:
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row = predict_y[predict_y['id'] == id]
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try:
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sqlitedb.update_data('accuracy',f"min_price = {row['min_price'].values[0]},max_price = {row['max_price'].values[0]},mean={row['mean'].values[0]}",f"id = {id}")
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except:
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logger.error(f'更新accuracy表中的min_price,max_price,mean值失败，row={row}')
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df = accuracy_df.copy()
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df['ds'] = pd.to_datetime(df['ds'])
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df = df.reindex()
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# 判断预测值在不在布伦特最高最低价范围内，准确率为1，否则为0
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def is_within_range(row):
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for model in allmodelnames:
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if row['LOW_PRICE'] <= row[col] <= row['HIGH_PRICE']:
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return 1
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else:
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return 0
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# 定义一个函数来计算准确率
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def calculate_accuracy(row):
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# 比较真实最高最低，和预测最高最低 计算准确率
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# 全子集情况：
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if (row['max_price'] >= row['HIGH_PRICE'] and row['min_price'] <= row['LOW_PRICE']) or \
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(row['max_price'] <= row['HIGH_PRICE'] and row['min_price'] >= row['LOW_PRICE']):
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return 1
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# 无交集情况：
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if row['max_price'] < row['LOW_PRICE'] or \
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row['min_price'] > row['HIGH_PRICE']:
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return 0
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# 有交集情况：
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else:
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sorted_prices = sorted([row['LOW_PRICE'], row['min_price'], row['max_price'], row['HIGH_PRICE']])
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middle_diff = sorted_prices[2] - sorted_prices[1]
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price_range = row['HIGH_PRICE'] - row['LOW_PRICE']
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accuracy = middle_diff / price_range
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return accuracy
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columns = ['HIGH_PRICE','LOW_PRICE','min_price','max_price']
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df[columns] = df[columns].astype(float)
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df['ACCURACY'] = df.apply(calculate_accuracy, axis=1)
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# df['ACCURACY'] = df.apply(is_within_range, axis=1)
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# 计算准确率并保存结果
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def _get_accuracy_rate(df,create_dates,ds_dates,endtime):
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df3 = df.copy()
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df3 = df3[df3['CREAT_DATE'].isin(create_dates)]
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df3 = df3[df3['ds'].isin(ds_dates)]
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accuracy_rote = 0
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for i,group in df3.groupby('CREAT_DATE'):
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accuracy_rote += (group['ACCURACY'].sum()/len(group))*weight_dict[len(group)-1]
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accuracy_rote = round(accuracy_rote,2)
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df4 = pd.DataFrame(columns=['开始日期','结束日期','准确率'])
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df4.loc[len(df4)] = {'开始日期':ds_dates[0],'结束日期':ds_dates[-1],'准确率':accuracy_rote}
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df4.to_sql("accuracy_rote", con=sqlitedb.connection, if_exists='append', index=False)
|
||||
create_dates,ds_dates = get_week_date(end_time)
|
||||
_get_accuracy_rate(df,create_dates,ds_dates,end_time)
|
||||
|
||||
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
|
||||
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,"testandpredict_groupby.csv"),index=False)
|
||||
|
||||
|
||||
# 历史价格+预测价格
|
||||
sqlitedb.drop_table('testandpredict_groupby')
|
||||
df_combined3.to_sql('testandpredict_groupby',sqlitedb.connection,index=False)
|
||||
|
||||
def _plt_predict_ture(df):
|
||||
lens = df.shape[0] if df.shape[0] < 180 else 90
|
||||
df = df[-lens:] # 取180个数据点画图
|
||||
# 历史价格
|
||||
plt.figure(figsize=(20, 10))
|
||||
plt.plot(df['ds'], df['y'], label='真实值')
|
||||
# 颜色填充
|
||||
plt.fill_between(df['ds'], df['max_within_quantile'], df['min_within_quantile'], alpha=0.2)
|
||||
markers = ['o', 's', '^', 'D', 'v', '*', 'p', 'h', 'H', '+', 'x', 'd']
|
||||
random_marker = random.choice(markers)
|
||||
for model in modelnames:
|
||||
plt.plot(df['ds'][-horizon:], df[model][-horizon:], label=model,marker=random_marker)
|
||||
# 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['ds'], df['y']):
|
||||
plt.text(i, j, str(j), ha='center', va='bottom')
|
||||
|
||||
# 当前日期画竖虚线
|
||||
plt.axvline(x=df['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()
|
||||
|
||||
|
||||
def _plt_modeltopten_predict_ture(df):
|
||||
df['max_cutoff'] = df.groupby('ds')['CREAT_DATE'].transform('max')
|
||||
df = df[df['CREAT_DATE'] == df['max_cutoff']]
|
||||
df['mean'] = df['mean'].astype(float)
|
||||
lens = df.shape[0] if df.shape[0] < 180 else 180
|
||||
df = df[-lens:] # 取180个数据点画图
|
||||
# 历史价格
|
||||
plt.figure(figsize=(20, 10))
|
||||
plt.plot(df['ds'], df['y'], label='真实值')
|
||||
plt.plot(df['ds'], df['mean'], label='模型前十均值', linestyle='--', color='orange')
|
||||
# 颜色填充
|
||||
plt.fill_between(df['ds'], df['max_price'], df['min_price'], alpha=0.2)
|
||||
# markers = ['o', 's', '^', 'D', 'v', '*', 'p', 'h', 'H', '+', 'x', 'd']
|
||||
# random_marker = random.choice(markers)
|
||||
# for model in allmodelnames:
|
||||
# for model in ['BiTCN','RNN']:
|
||||
# plt.plot(df['ds'], df[model], label=model,marker=random_marker)
|
||||
# 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['ds'], df['y']):
|
||||
plt.text(i, j, str(j), ha='center', va='bottom')
|
||||
|
||||
# 当前日期画竖虚线
|
||||
plt.axvline(x=df['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1.png'), bbox_inches='tight')
|
||||
plt.close()
|
||||
|
||||
|
||||
def _plt_predict_table(df):
|
||||
# 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_modeltopten_predict_ture(df_combined4)
|
||||
_plt_predict_table(df_combined3)
|
||||
_plt_model_results3()
|
||||
|
||||
return model_results3
|
||||
|
||||
|
||||
# 原油计算预测评估指数
|
||||
@exception_logger
|
||||
def model_losss(sqlitedb,end_time):
|
||||
|
||||
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Reference in New Issue
Block a user