八大维度数据存储到数据库，修改表设计
This commit is contained in:
workpc
parent
b2c958a4fd
commit
352d30dfa9
@ -7,11 +7,20 @@ from decimal import Decimal
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class PredictionResult(BaseModel):
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prediction_date: datetime
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bdwd: Optional[str] = None
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feature_factor_frequency: str
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strategy_id: int
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predicted_price: Decimal
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oil_code: Optional[str] = None
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oil_name: Optional[str] = None
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data_date: Optional[datetime] = None
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market_price: Optional[Decimal] = None
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day_price: Optional[Decimal] = None
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week_price: Optional[Decimal] = None
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second_week_price: Optional[Decimal] = None
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next_week_price: Optional[Decimal] = None
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next_month_price: Optional[Decimal] = None
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next_february_price: Optional[Decimal] = None
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next_march_price: Optional[Decimal] = None
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next_april_price: Optional[Decimal] = None
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model_evaluation_id: int
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model_id: int
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tenant_code: Optional[str] = None
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19
lib/tools.py
19
lib/tools.py
@ -576,6 +576,8 @@ class MySQLDB:
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return 0
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try:
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cursor = self.connection.cursor()
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logging.info(f"Executing batch insert SQL: {query}")
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logging.info(f"Batch insert parameters: {params_list}")
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cursor.executemany(query, params_list)
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self.connection.commit()
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affected_rows = cursor.rowcount
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@ -680,7 +682,7 @@ def get_modelsname(df, global_config):
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tb = 'v_tbl_predict_models'
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sql = f'select model_name,id from {tb} '
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modelsname = global_config['db_mysql'].execute_query(sql)
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model_id_name_dict = {row['model_name']: row['id'] for row in modelsname}
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model_id_name_dict = {row['id']: row['model_name'] for row in modelsname}
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model_name_list = [row['model_name'] for row in modelsname]
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model_name_list = set(columns) & set(model_name_list)
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model_name_list = list(model_name_list)
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@ -688,18 +690,19 @@ def get_modelsname(df, global_config):
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return model_name_list, model_id_name_dict
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def convert_df_to_pydantic(df_predict, model_id_name_dict, bdwd, global_config):
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def convert_df_to_pydantic(df_predict, model_id_name_dict, global_config):
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reverse_model_id_name_dict = {
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value: key for key, value in model_id_name_dict.items()}
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results = []
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data = global_config['DEFAULT_CONFIG'].copy()
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data['prediction_date'] = df_predict['created_dt'].values[0]
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if isinstance(data['prediction_date'], np.datetime64):
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data['prediction_date'] = pd.Timestamp(
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data['prediction_date']).to_pydatetime()
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data['data_date'] = df_predict['created_dt'].values[0]
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if isinstance(data['data_date'], np.datetime64):
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data['data_date'] = pd.Timestamp(
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data['data_date']).to_pydatetime()
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for c in df_predict.columns:
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if c not in ['ds', 'created_dt']:
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data['model_id'] = model_id_name_dict[c]
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data['bdwd'] = bdwd
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data['model_id'] = reverse_model_id_name_dict[c]
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data['predicted_price'] = Decimal(
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round(df_predict[c].values[0], 2))
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result = PredictionResult(**data)
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@ -161,58 +161,80 @@ def sql_inset_predict(global_config):
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# 获取本周预测结果
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this_week_df = df[df['ds'] == df['ds'].max()]
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wd = ['next_day', 'this_week']
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wd = ['day_price', 'week_price']
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model_name_list, model_id_name_dict = get_modelsname(df, global_config)
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for df, w in zip([next_day_df, this_week_df], wd):
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PRICE_COLUMNS = [
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'day_price', 'week_price', 'second_week_price', 'next_week_price',
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'next_month_price', 'next_february_price', 'next_march_price', 'next_april_price'
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]
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params_list = []
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for df, price_type in zip([next_day_df, this_week_df], wd):
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update_columns = [
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"feature_factor_frequency = VALUES(feature_factor_frequency)",
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"oil_code = VALUES(oil_code)",
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"oil_name = VALUES(oil_name)",
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"data_date = VALUES(data_date)",
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"market_price = VALUES(market_price)",
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f"{price_type} = VALUES({price_type})",
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"model_evaluation_id = VALUES(model_evaluation_id)",
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"tenant_code = VALUES(tenant_code)",
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"version_num = VALUES(version_num)",
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"delete_flag = VALUES(delete_flag)",
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"update_user = VALUES(update_user)",
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"update_date = VALUES(update_date)"
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]
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insert_query = f"""
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INSERT INTO v_tbl_predict_prediction_results (
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feature_factor_frequency, strategy_id, oil_code, oil_name, data_date,
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market_price, day_price, week_price, second_week_price, next_week_price,
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next_month_price, next_february_price, next_march_price, next_april_price,
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model_evaluation_id, model_id, tenant_code, version_num, delete_flag,
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create_user, create_date, update_user, update_date
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) VALUES (
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%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s
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)
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ON DUPLICATE KEY UPDATE
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{', '.join(update_columns)}
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"""
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next_day_df = df[['ds', 'created_dt'] + model_name_list]
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pydantic_results = convert_df_to_pydantic(
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next_day_df, model_id_name_dict, w, global_config)
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next_day_df, model_id_name_dict, global_config)
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if pydantic_results:
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insert_query = """
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INSERT INTO v_tbl_predict_prediction_results (
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prediction_date, bdwd,
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feature_factor_frequency, strategy_id, predicted_price,
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model_evaluation_id, model_id, tenant_code,
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version_num, delete_flag, create_user, create_date, update_user, update_date
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) VALUES (
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%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s
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)
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ON DUPLICATE KEY UPDATE
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feature_factor_frequency = VALUES(feature_factor_frequency),
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predicted_price = VALUES(predicted_price),
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model_evaluation_id = VALUES(model_evaluation_id),
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tenant_code = VALUES(tenant_code),
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version_num = VALUES(version_num),
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delete_flag = VALUES(delete_flag),
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create_user = VALUES(create_user),
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create_date = VALUES(create_date),
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update_user = VALUES(update_user),
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update_date = VALUES(update_date)
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"""
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params_list = []
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for result in pydantic_results:
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price_values = [None] * len(PRICE_COLUMNS)
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price_index = PRICE_COLUMNS.index(price_type)
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price_values[price_index] = next_day_df[model_id_name_dict[result.model_id]].values[0]
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params = (
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result.prediction_date,
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result.bdwd,
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result.feature_factor_frequency,
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result.strategy_id,
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result.predicted_price,
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result.oil_code,
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result.oil_name,
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next_day_df['created_dt'].values[0],
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result.market_price,
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*price_values,
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result.model_evaluation_id,
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result.model_id,
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result.tenant_code,
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result.version_num,
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result.delete_flag,
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1,
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'0',
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result.create_user,
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result.create_date,
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result.update_user,
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result.update_date
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)
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params_list.append(params)
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affected_rows = config.db_mysql.execute_batch_insert(
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insert_query, params_list)
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print(f"成功插入 {affected_rows} 条记录")
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config.db_mysql.close()
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affected_rows = config.db_mysql.execute_batch_insert(
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insert_query, params_list)
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config.logger.info(f"成功插入或更新 {affected_rows} 条记录")
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config.db_mysql.close()
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def predict_main():
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@ -563,5 +585,6 @@ if __name__ == '__main__':
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# end_time = i_time.strftime('%Y-%m-%d')
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# predict_main()
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predict_main()
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# predict_main()
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# push_market_value()
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sql_inset_predict(global_config=global_config)
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@ -163,68 +163,158 @@ def sql_inset_predict(global_config):
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df['created_dt'] = pd.to_datetime(df['created_dt'])
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df['ds'] = pd.to_datetime(df['ds'])
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# 获取次月预测结果
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next_month_df = df[df['ds'] == df['ds'].min()]
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next_month_price_df = df[df['ds'] == df['ds'].min()]
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# 获取次二月预测结果
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next_two_months_df = df.iloc[[1]]
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next_february_price_df = df.iloc[[1]]
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# 获取次三月预测结果
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next_three_months_df = df.iloc[[2]]
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next_march_price_df = df.iloc[[2]]
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# 获取次四月预测结果
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next_four_months_df = df[df['ds'] == df['ds'].max()]
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next_april_price_df = df[df['ds'] == df['ds'].max()]
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wd = ['next_month', 'next_two_months',
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'next_three_months', 'next_four_months']
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wd = ['next_month_price', 'next_february_price',
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'next_march_price', 'next_april_price']
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model_name_list, model_id_name_dict = get_modelsname(df, global_config)
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for df, w in zip([next_month_df, next_two_months_df, next_three_months_df, next_four_months_df], wd):
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PRICE_COLUMNS = [
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'day_price', 'week_price', 'second_week_price', 'next_week_price',
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'next_month_price', 'next_february_price', 'next_march_price', 'next_april_price'
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]
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params_list = []
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for df, price_type in zip([next_month_price_df, next_february_price_df, next_march_price_df, next_april_price_df], wd):
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update_columns = [
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"feature_factor_frequency = VALUES(feature_factor_frequency)",
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"oil_code = VALUES(oil_code)",
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"oil_name = VALUES(oil_name)",
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"data_date = VALUES(data_date)",
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"market_price = VALUES(market_price)",
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f"{price_type} = VALUES({price_type})",
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"model_evaluation_id = VALUES(model_evaluation_id)",
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"tenant_code = VALUES(tenant_code)",
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"version_num = VALUES(version_num)",
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"delete_flag = VALUES(delete_flag)",
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"update_user = VALUES(update_user)",
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"update_date = VALUES(update_date)"
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]
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insert_query = f"""
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INSERT INTO v_tbl_predict_prediction_results (
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feature_factor_frequency, strategy_id, oil_code, oil_name, data_date,
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market_price, day_price, week_price, second_week_price, next_week_price,
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next_month_price, next_february_price, next_march_price, next_april_price,
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model_evaluation_id, model_id, tenant_code, version_num, delete_flag,
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create_user, create_date, update_user, update_date
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) VALUES (
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%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s
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)
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ON DUPLICATE KEY UPDATE
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{', '.join(update_columns)}
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"""
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next_day_df = df[['ds', 'created_dt'] + model_name_list]
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pydantic_results = convert_df_to_pydantic(
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next_day_df, model_id_name_dict, w, global_config)
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next_day_df, model_id_name_dict, global_config)
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if pydantic_results:
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insert_query = """
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INSERT INTO v_tbl_predict_prediction_results (
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prediction_date, bdwd,
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feature_factor_frequency, strategy_id, predicted_price,
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model_evaluation_id, model_id, tenant_code,
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version_num, delete_flag, create_user, create_date, update_user, update_date
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) VALUES (
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%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s
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)
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ON DUPLICATE KEY UPDATE
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feature_factor_frequency = VALUES(feature_factor_frequency),
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predicted_price = VALUES(predicted_price),
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model_evaluation_id = VALUES(model_evaluation_id),
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tenant_code = VALUES(tenant_code),
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version_num = VALUES(version_num),
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delete_flag = VALUES(delete_flag),
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create_user = VALUES(create_user),
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create_date = VALUES(create_date),
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update_user = VALUES(update_user),
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update_date = VALUES(update_date)
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"""
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params_list = []
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for result in pydantic_results:
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price_values = [None] * len(PRICE_COLUMNS)
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price_index = PRICE_COLUMNS.index(price_type)
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price_values[price_index] = next_day_df[model_id_name_dict[result.model_id]].values[0]
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params = (
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result.prediction_date,
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result.bdwd,
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result.feature_factor_frequency,
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result.strategy_id,
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result.predicted_price,
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result.oil_code,
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result.oil_name,
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next_day_df['created_dt'].values[0],
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result.market_price,
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*price_values,
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result.model_evaluation_id,
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result.model_id,
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result.tenant_code,
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result.version_num,
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result.delete_flag,
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1,
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'0',
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result.create_user,
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result.create_date,
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result.update_user,
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result.update_date
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)
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params_list.append(params)
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affected_rows = config.db_mysql.execute_batch_insert(
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insert_query, params_list)
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print(f"成功插入 {affected_rows} 条记录")
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config.db_mysql.close()
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affected_rows = config.db_mysql.execute_batch_insert(
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insert_query, params_list)
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config.logger.info(f"成功插入或更新 {affected_rows} 条记录")
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config.db_mysql.close()
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# def sql_inset_predict(global_config):
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# df = pd.read_csv(os.path.join(config.dataset, 'predict.csv'))
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# df['created_dt'] = pd.to_datetime(df['created_dt'])
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# df['ds'] = pd.to_datetime(df['ds'])
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# # 获取次月预测结果
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# next_month_df = df[df['ds'] == df['ds'].min()]
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# # 获取次二月预测结果
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# next_two_months_df = df.iloc[[1]]
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# # 获取次三月预测结果
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# next_three_months_df = df.iloc[[2]]
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# # 获取次四月预测结果
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# next_four_months_df = df[df['ds'] == df['ds'].max()]
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# wd = ['next_month', 'next_two_months',
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# 'next_three_months', 'next_four_months']
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# model_name_list, model_id_name_dict = get_modelsname(df, global_config)
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# for df, w in zip([next_month_df, next_two_months_df, next_three_months_df, next_four_months_df], wd):
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# next_day_df = df[['ds', 'created_dt'] + model_name_list]
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# pydantic_results = convert_df_to_pydantic(
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# next_day_df, model_id_name_dict, w, global_config)
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# if pydantic_results:
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# insert_query = """
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# INSERT INTO v_tbl_predict_prediction_results (
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# prediction_date, bdwd,
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# feature_factor_frequency, strategy_id, predicted_price,
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# model_evaluation_id, model_id, tenant_code,
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# version_num, delete_flag, create_user, create_date, update_user, update_date
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# ) VALUES (
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# %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s
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# )
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# ON DUPLICATE KEY UPDATE
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# feature_factor_frequency = VALUES(feature_factor_frequency),
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# predicted_price = VALUES(predicted_price),
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# model_evaluation_id = VALUES(model_evaluation_id),
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# tenant_code = VALUES(tenant_code),
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# version_num = VALUES(version_num),
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# delete_flag = VALUES(delete_flag),
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# create_user = VALUES(create_user),
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# create_date = VALUES(create_date),
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# update_user = VALUES(update_user),
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# update_date = VALUES(update_date)
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# """
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# params_list = []
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# for result in pydantic_results:
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# params = (
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# result.prediction_date,
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# result.bdwd,
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# result.feature_factor_frequency,
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# result.strategy_id,
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# result.predicted_price,
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# result.model_evaluation_id,
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# result.model_id,
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# result.tenant_code,
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# result.version_num,
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# result.delete_flag,
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# result.create_user,
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# result.create_date,
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# result.update_user,
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# result.update_date
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# )
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# params_list.append(params)
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# affected_rows = config.db_mysql.execute_batch_insert(
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# insert_query, params_list)
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# print(f"成功插入 {affected_rows} 条记录")
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# config.db_mysql.close()
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def predict_main():
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@ -508,4 +598,5 @@ if __name__ == '__main__':
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# logger.info(f'预测失败：{e}')
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# continue
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predict_main()
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# predict_main()
|
||||
sql_inset_predict(global_config=global_config)
|
||||
|
||||
@ -145,63 +145,85 @@ def sql_inset_predict(global_config):
|
||||
df = pd.read_csv(os.path.join(config.dataset, 'predict.csv'))
|
||||
df['created_dt'] = pd.to_datetime(df['created_dt'])
|
||||
df['ds'] = pd.to_datetime(df['ds'])
|
||||
# 获取次日预测结果
|
||||
next_week_df = df[df['ds'] == df['ds'].min()]
|
||||
# 获取本周预测结果
|
||||
next_two_weeks_df = df[df['ds'] == df['ds'].max()]
|
||||
# 获取次周预测结果
|
||||
second_week_price_df = df[df['ds'] == df['ds'].min()]
|
||||
# 获取隔周周预测结果
|
||||
next_week_price_df = df[df['ds'] == df['ds'].max()]
|
||||
|
||||
wd = ['next_week', 'next_two_weeks']
|
||||
wd = ['second_week_price', 'next_week_price']
|
||||
model_name_list, model_id_name_dict = get_modelsname(df, global_config)
|
||||
|
||||
for df, w in zip([next_week_df, next_two_weeks_df], wd):
|
||||
PRICE_COLUMNS = [
|
||||
'day_price', 'week_price', 'second_week_price', 'next_week_price',
|
||||
'next_month_price', 'next_february_price', 'next_march_price', 'next_april_price'
|
||||
]
|
||||
|
||||
params_list = []
|
||||
for df, price_type in zip([second_week_price_df, next_week_price_df], wd):
|
||||
|
||||
update_columns = [
|
||||
"feature_factor_frequency = VALUES(feature_factor_frequency)",
|
||||
"oil_code = VALUES(oil_code)",
|
||||
"oil_name = VALUES(oil_name)",
|
||||
"data_date = VALUES(data_date)",
|
||||
"market_price = VALUES(market_price)",
|
||||
f"{price_type} = VALUES({price_type})",
|
||||
"model_evaluation_id = VALUES(model_evaluation_id)",
|
||||
"tenant_code = VALUES(tenant_code)",
|
||||
"version_num = VALUES(version_num)",
|
||||
"delete_flag = VALUES(delete_flag)",
|
||||
"update_user = VALUES(update_user)",
|
||||
"update_date = VALUES(update_date)"
|
||||
]
|
||||
|
||||
insert_query = f"""
|
||||
INSERT INTO v_tbl_predict_prediction_results (
|
||||
feature_factor_frequency, strategy_id, oil_code, oil_name, data_date,
|
||||
market_price, day_price, week_price, second_week_price, next_week_price,
|
||||
next_month_price, next_february_price, next_march_price, next_april_price,
|
||||
model_evaluation_id, model_id, tenant_code, version_num, delete_flag,
|
||||
create_user, create_date, update_user, update_date
|
||||
) VALUES (
|
||||
%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s
|
||||
)
|
||||
ON DUPLICATE KEY UPDATE
|
||||
{', '.join(update_columns)}
|
||||
"""
|
||||
|
||||
next_day_df = df[['ds', 'created_dt'] + model_name_list]
|
||||
|
||||
pydantic_results = convert_df_to_pydantic(
|
||||
next_day_df, model_id_name_dict, w, global_config)
|
||||
next_day_df, model_id_name_dict, global_config)
|
||||
if pydantic_results:
|
||||
insert_query = """
|
||||
INSERT INTO v_tbl_predict_prediction_results (
|
||||
prediction_date, bdwd,
|
||||
feature_factor_frequency, strategy_id, predicted_price,
|
||||
model_evaluation_id, model_id, tenant_code,
|
||||
version_num, delete_flag, create_user, create_date, update_user, update_date
|
||||
) VALUES (
|
||||
%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s
|
||||
)
|
||||
ON DUPLICATE KEY UPDATE
|
||||
feature_factor_frequency = VALUES(feature_factor_frequency),
|
||||
predicted_price = VALUES(predicted_price),
|
||||
model_evaluation_id = VALUES(model_evaluation_id),
|
||||
tenant_code = VALUES(tenant_code),
|
||||
version_num = VALUES(version_num),
|
||||
delete_flag = VALUES(delete_flag),
|
||||
create_user = VALUES(create_user),
|
||||
create_date = VALUES(create_date),
|
||||
update_user = VALUES(update_user),
|
||||
update_date = VALUES(update_date)
|
||||
"""
|
||||
params_list = []
|
||||
|
||||
for result in pydantic_results:
|
||||
price_values = [None] * len(PRICE_COLUMNS)
|
||||
price_index = PRICE_COLUMNS.index(price_type)
|
||||
price_values[price_index] = next_day_df[model_id_name_dict[result.model_id]].values[0]
|
||||
|
||||
params = (
|
||||
result.prediction_date,
|
||||
result.bdwd,
|
||||
result.feature_factor_frequency,
|
||||
result.strategy_id,
|
||||
result.predicted_price,
|
||||
result.oil_code,
|
||||
result.oil_name,
|
||||
next_day_df['created_dt'].values[0],
|
||||
result.market_price,
|
||||
*price_values,
|
||||
result.model_evaluation_id,
|
||||
result.model_id,
|
||||
result.tenant_code,
|
||||
result.version_num,
|
||||
result.delete_flag,
|
||||
1,
|
||||
'0',
|
||||
result.create_user,
|
||||
result.create_date,
|
||||
result.update_user,
|
||||
result.update_date
|
||||
)
|
||||
params_list.append(params)
|
||||
affected_rows = config.db_mysql.execute_batch_insert(
|
||||
insert_query, params_list)
|
||||
print(f"成功插入 {affected_rows} 条记录")
|
||||
config.db_mysql.close()
|
||||
affected_rows = config.db_mysql.execute_batch_insert(
|
||||
insert_query, params_list)
|
||||
config.logger.info(f"成功插入或更新 {affected_rows} 条记录")
|
||||
config.db_mysql.close()
|
||||
|
||||
|
||||
def predict_main():
|
||||
@ -478,4 +500,5 @@ if __name__ == '__main__':
|
||||
# logger.info(f'预测失败：{e}')
|
||||
# continue
|
||||
|
||||
predict_main()
|
||||
# predict_main()
|
||||
sql_inset_predict(global_config=global_config)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user