准确率计算数据入库
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liurui
parent
363da754b2
commit
af408caef1
@ -197,10 +197,10 @@ warning_data = {
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### 开关
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is_train = True # 是否训练
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is_debug = False # 是否调试
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is_debug = True # 是否调试
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is_eta = False # 是否使用eta接口
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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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is_fivemodels = True # 是否使用之前保存的最佳的5个模型
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is_edbcode = True # 特征使用edbcoding列表中的
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is_edbnamelist = False # 自定义特征，对应上面的edbnamelist
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is_update_eta = False # 预测结果上传到eta
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@ -109,34 +109,34 @@ def predict_main():
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import datetime
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# 判断当前日期是不是周一
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is_weekday = datetime.datetime.now().weekday() == 0
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if is_weekday:
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logger.info('今天是周一，更新预测模型')
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# 计算最近60天预测残差最低的模型名称
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model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
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# 删除空值率为40%以上的列
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if len(model_results) > 10:
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model_results = model_results.dropna(thresh=len(model_results)*0.6,axis=1)
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# 删除空行
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model_results = model_results.dropna()
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modelnames = model_results.columns.to_list()[2:]
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for col in model_results[modelnames].select_dtypes(include=['object']).columns:
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model_results[col] = model_results[col].astype(np.float32)
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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 modelnames:
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model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['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 = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].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 = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].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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# 取出现次数最多的模型名称
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most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
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logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
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# 保存结果到数据库
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if not sqlitedb.check_table_exists('most_model'):
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sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT")
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sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
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# if is_weekday:
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# logger.info('今天是周一，更新预测模型')
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# # 计算最近60天预测残差最低的模型名称
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# model_results = sqlitedb.select_data('trueandpredict', order_by="ds DESC", limit="60")
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# # 删除空值率为40%以上的列
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# if len(model_results) > 10:
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# model_results = model_results.dropna(thresh=len(model_results)*0.6,axis=1)
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# # 删除空行
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# model_results = model_results.dropna()
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# modelnames = model_results.columns.to_list()[2:]
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# for col in model_results[modelnames].select_dtypes(include=['object']).columns:
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# model_results[col] = model_results[col].astype(np.float32)
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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 modelnames:
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# model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['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 = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].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 = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].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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# # 取出现次数最多的模型名称
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# most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()
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# logger.info(f"最近60天预测残差最低的模型名称：{most_common_model}")
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# # 保存结果到数据库
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# if not sqlitedb.check_table_exists('most_model'):
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# sqlitedb.create_table('most_model', columns="ds datetime, most_common_model TEXT")
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# sqlitedb.insert_data('most_model', (datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'), most_common_model,), columns=('ds', 'most_common_model',))
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try:
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if is_weekday:
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@ -172,25 +172,25 @@ def predict_main():
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row, col = df.shape
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now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')
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ex_Model(df,
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horizon=horizon,
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input_size=input_size,
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train_steps=train_steps,
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val_check_steps=val_check_steps,
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early_stop_patience_steps=early_stop_patience_steps,
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is_debug=is_debug,
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dataset=dataset,
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is_train=is_train,
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is_fivemodels=is_fivemodels,
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val_size=val_size,
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test_size=test_size,
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settings=settings,
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now=now,
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etadata=etadata,
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modelsindex=modelsindex,
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data=data,
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is_eta=is_eta,
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)
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# ex_Model(df,
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# horizon=horizon,
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# input_size=input_size,
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# train_steps=train_steps,
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# val_check_steps=val_check_steps,
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# early_stop_patience_steps=early_stop_patience_steps,
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# is_debug=is_debug,
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# dataset=dataset,
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# is_train=is_train,
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# is_fivemodels=is_fivemodels,
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# val_size=val_size,
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# test_size=test_size,
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# settings=settings,
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# now=now,
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# etadata=etadata,
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# modelsindex=modelsindex,
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# data=data,
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# is_eta=is_eta,
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# )
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logger.info('模型训练完成')
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@ -201,17 +201,6 @@ def ex_Model(df,horizon,input_size,train_steps,val_check_steps,early_stop_patien
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df_predict.to_csv(os.path.join(dataset,"predict.csv"),index=False)
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df_predict2 = df_predict.copy()
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# 保存到数据库
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if not sqlitedb.check_table_exists('accuracy'):
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sqlitedb.create_table('accuracy', columns="id int,PREDICT_DATE datetime,CREAT_DATE datetime, MIN_PRICE TEXT,MAX_PRICE TEXT,HIGH_PRICE TEXT,LOW_PRICE TEXT,RIGHT_ROTE ")
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existing_data = sqlitedb.execute_query("SELECT * FROM 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.to_sql(table_name, con=sqlitedb.connect, if_exists='append', index=False)
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# 把预测值上传到eta
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if is_update_eta:
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@ -384,6 +373,25 @@ def model_losss(sqlitedb):
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df_predict2['ds'] = pd.to_datetime(df_predict2['ds'])
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df_predict2['ds'] = df_predict2['ds'].dt.strftime('%Y-%m-%d 00:00:00')
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# 保存到数据库
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if not sqlitedb.check_table_exists('accuracy'):
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sqlitedb.create_table('accuracy', columns="id int,PREDICT_DATE datetime,CREAT_DATE datetime, MIN_PRICE TEXT,MAX_PRICE TEXT,HIGH_PRICE TEXT,LOW_PRICE TEXT,RIGHT_ROTE ")
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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'] = datetime.datetime.now().strftime('%Y-%m-%d')
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df_predict2['PREDICT_DATE'] = df_predict2['ds']
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df_predict2['MIN_PRICE'] = df_predict2['min_within_quantile']
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df_predict2['MAX_PRICE'] = df_predict2['max_within_quantile']
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print(df_predict2.clumns)
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df_predict2 = df_predict2[['id','PREDICT_DATE','CREAT_DATE','MIN_PRICE','MAX_PRICE']]
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df_predict2.to_sql(table_name, con=sqlitedb.connection, if_exists='append', index=False)
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def _add_abs_error_rate():
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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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