algorithm_platform/models_processing/model_predict/model_prediction_lightgbm.py

import pandas as pd
from pymongo import MongoClient
import pickle
from flask import Flask,request
import time
import logging
import traceback
from common.database_dml import get_data_from_mongo,insert_data_into_mongo
from common.alert import send_message
from datetime import date, timedelta
app = Flask('model_prediction_lightgbm——service')
def str_to_list(arg):
    if arg == '':
        return []
    else:
        return arg.split(',')


def forecast_data_distribution(pre_data,args):
    col_time = args['col_time']
    tomorrow = (date.today() + timedelta(days=1)).strftime('%Y-%m-%d')
    field_mapping = {'clearsky_ghi': 'clearskyGhi', 'dni_calcd': 'dniCalcd','surface_pressure':'surfacePressure'}
    # 根据字段映射重命名列
    pre_data = pre_data.rename(columns=field_mapping)

    if len(pre_data)==0:
        send_message('lightgbm预测组件', args['farmId'], '请注意：获取NWP数据为空，预测文件无法生成！')
        result = pd.DataFrame({'farm_id':[], col_time:[], 'power_forecast':[]})

    elif len(pre_data[pre_data[col_time].str.contains(tomorrow)])<96:
        send_message('lightgbm预测组件', args['farmId'], "日前数据记录缺失，不足96条，用DQ代替并补值！")
        start_time = pre_data[col_time].min()
        end_time = pre_data[col_time].max()
        date_range = pd.date_range(start=start_time, end=end_time, freq='15T').strftime('%Y-%m-%d %H:%M:%S').tolist()
        df_date = pd.DataFrame({col_time:date_range})
        result = pd.merge(df_date,pre_data,how='left',on=col_time).sort_values(by=col_time).fillna(method='ffill').fillna(method='bfill')
        result = result[['farm_id', 'date_time', 'power_forecast']]
    else:
        df = pre_data.sort_values(by=col_time).fillna(method='ffill').fillna(method='bfill')
        mongodb_connection, mongodb_database, mongodb_model_table, model_name = "mongodb://root:sdhjfREWFWEF23e@192.168.1.43:30000/", \
        args['mongodb_database'], args['mongodb_model_table'], args['model_name']
        client = MongoClient(mongodb_connection)
        db = client[mongodb_database]
        collection = db[mongodb_model_table]
        model_data = collection.find_one({"model_name": model_name})
        if model_data is not None:
            model_binary = model_data['model']  # 确保这个字段是存储模型的二进制数据
            # 反序列化模型
            model = pickle.loads(model_binary)
            diff = set(model.feature_name()) - set(pre_data.columns)
            if len(diff) > 0:
                send_message('lightgbm预测组件', args['farmId'], f'NWP特征列缺失,使用DQ代替！features:{diff}')
                result = pre_data[['farm_id', 'date_time', 'power_forecast']]
            else:
                df['power_forecast'] = model.predict(df[model.feature_name()])
                df.loc[df['power_forecast'] < 0, 'power_forecast'] = 0
                # 添加小时列 把光夜间置为0
                df["hour"] = pd.to_datetime(df["date_time"]).dt.hour
                df.loc[(df["hour"] >= 20) | (df["hour"] < 6), 'power_forecast'] = 0
                print("model predict result  successfully!")
                result = df[['farm_id', 'date_time', 'power_forecast']]
        else:
            send_message('lightgbm预测组件', args['farmId'], "日前数据记录缺失，不足96条，用DQ代替并补值！")
            result = pre_data[['farm_id', 'date_time', 'power_forecast']]
    result['power_forecast'] = round(result['power_forecast'],2)
    return result


def model_prediction(df,args):
    mongodb_connection,mongodb_database,mongodb_model_table,model_name,col_reserve = "mongodb://root:sdhjfREWFWEF23e@192.168.1.43:30000/",args['mongodb_database'],args['mongodb_model_table'],args['model_name'],str_to_list(args['col_reserve'])
    client = MongoClient(mongodb_connection)
    db = client[mongodb_database]
    collection = db[mongodb_model_table]
    model_data = collection.find_one({"model_name": model_name})
    if 'is_limit' in df.columns:
        df = df[df['is_limit'] == False]

    if model_data is not None:
        model_binary = model_data['model']  # 确保这个字段是存储模型的二进制数据
        # 反序列化模型 
        model = pickle.loads(model_binary)
        df['predict'] = model.predict(df[model.feature_name()])
        df.loc[df['predict']<0,'predict']=0
        df['model'] = model_name
        print("model predict result  successfully!")
    features_reserve = col_reserve + ['model','predict']
    return df[set(features_reserve)]


@app.route('/model_prediction_lightgbm', methods=['POST'])
def model_prediction_lightgbm():
    # 获取程序开始时间  
    start_time = time.time()  
    result = {}
    success = 0
    print("Program starts execution!")
    try:
        args = request.values.to_dict()
        print('args',args)
        logger.info(args)
        forecast_file = int(args['forecast_file'])
        power_df = get_data_from_mongo(args)
        if forecast_file == 1:
            predict_data = forecast_data_distribution(power_df, args)
        else:
            predict_data = model_prediction(power_df, args)
        insert_data_into_mongo(predict_data,args)
        success = 1
    except Exception as e:
        my_exception = traceback.format_exc()
        my_exception.replace("\n","\t")
        result['msg'] = my_exception
    end_time = time.time() 
   
    result['success'] = success
    result['args'] = args
    result['start_time'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(start_time))
    result['end_time'] = time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(end_time))
    print("Program execution ends!")
    return result


if __name__=="__main__":  
    print("Program starts execution!")
    logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
    logger = logging.getLogger("model_prediction_lightgbm log")
    from waitress import serve
    serve(app, host="0.0.0.0", port=10090)
    print("server start!")