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liurui 2024-11-07 10:45:51 +08:00
parent f4eea44c8c
commit bf831258e6
2 changed files with 36 additions and 28 deletions
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8
main.py
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@ -138,15 +138,23 @@ def predict_main():
is_eta=is_eta,
)
logger.info('模型训练完成')
# # 模型评估
logger.info('训练数据绘图ing')
model_results3 = model_losss_juxiting(sqlitedb)
logger.info('训练数据绘图end')
# 模型报告
logger.info('制作报告ing')
title = f'{settings}--{now}-预测报告' # 报告标题
brent_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
reportname=reportname,sqlitedb=sqlitedb),
# pp_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,
# reportname=reportname),
logger.info('制作报告end')
logger.info('模型训练完成')
# tansuanli_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,end_time=end_time,reportname=reportname)

56
models/nerulforcastmodels.py
View File

@ -510,36 +510,14 @@ def model_losss_juxiting(sqlitedb):
# 使用最佳五个模型进行绘图
# best_models = pd.read_csv(os.path.join(dataset,'best_modelnames.txt'),header=None).values.flatten().tolist()
# def find_min_max_within_quantile(row):
# row = row[best_models]
# q10 = row.min()
# q90 = row.max()
# # 获取 row行10%分位值对应的模型名称
# min_model = row[row == q10].idxmin()
# max_model = row[row == q90].idxmin()
# # # 判断flot值是否为空值
# # if pd.isna(q10) or pd.isna(q90):
# return pd.Series([q10, q90,min_model,max_model], index=['min_within_quantile','max_within_quantile','min_model','max_model'])
# # 遍历行
# df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)
# df_combined = df_combined.round(4)
# print(df_combined3)
# 通道使用预测模型的80%置信度
best_models = pd.read_csv(os.path.join(dataset,'best_modelnames.txt'),header=None).values.flatten().tolist()
def find_min_max_within_quantile(row):
row.drop(['ds','y'], inplace=True)
# 获取分位数10%和90%的值
q10 = row.quantile(0.1)
q90 = row.quantile(0.9)
row = row[best_models]
q10 = row.min()
q90 = row.max()
# 获取 row行10%分位值对应的模型名称
min_model = row[row == q10].index[0]
max_model = row[row == q90].index[0]
min_model = row[row == q10].idxmin()
max_model = row[row == q90].idxmin()
# # 判断flot值是否为空值
# if pd.isna(q10) or pd.isna(q90):
@ -549,6 +527,28 @@ def model_losss_juxiting(sqlitedb):
df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)
df_combined = df_combined.round(4)
print(df_combined3)
# # 通道使用预测模型的80%置信度
# def find_min_max_within_quantile(row):
# row.drop(['ds','y'], inplace=True)
# # 获取分位数10%和90%的值
# q10 = row.quantile(0.1)
# q90 = row.quantile(0.9)
# # 获取 row行10%分位值对应的模型名称
# min_model = row[row == q10].index[0]
# max_model = row[row == q90].index[0]
# # # 判断flot值是否为空值
# # if pd.isna(q10) or pd.isna(q90):
# return pd.Series([q10, q90,min_model,max_model], index=['min_within_quantile','max_within_quantile','min_model','max_model'])
# # 遍历行
# df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)
# df_combined = df_combined.round(4)
# print(df_combined3)