algorithm_platform/models_processing/model_train/model_training_ml.py

import lightgbm as lgb
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error, mean_absolute_error
from flask import Flask, request
import time
import traceback
import logging
from common.database_dml import get_data_from_mongo, insert_pickle_model_into_mongo
from common.processing_data_common import missing_features, str_to_list
from sklearn.pipeline import Pipeline
from sklearn.svm import SVR
from sklearn.preprocessing import MinMaxScaler

app = Flask('model_training_ml——service')

"""
基于model_training_lightgbm.py
机器学习通用训练方法，特点
1. 保存模型同时，保存模型特征
2. 支持模型训练样本权重(需要在预处理部分生成权重特征)

参数格式如下

"""


def train_lgb(data_split, categorical_features, model_params, num_boost_round, sample_weight=None):
    X_train, X_test, y_train, y_test = data_split
    # 创建LightGBM数据集
    lgb_train = lgb.Dataset(X_train, y_train, categorical_feature=categorical_features, weight=sample_weight)
    lgb_eval = lgb.Dataset(X_test, y_test, reference=lgb_train)
    # 设置参数
    params = {
        'objective': 'regression',
        'metric': 'rmse',
        'boosting_type': 'gbdt',
        'verbose': 1
    }
    print(type(model_params))
    params.update(model_params)
    # 训练模型
    print('Starting training...')
    gbm = lgb.train(params,
                    lgb_train,
                    num_boost_round=num_boost_round,
                    valid_sets=[lgb_train, lgb_eval],
                    )
    y_pred = gbm.predict(X_test, num_iteration=gbm.best_iteration)
    return gbm, y_pred


def train_svr(data_split, model_params, sample_weight=None):
    X_train, X_test, y_train, y_test = data_split

    svr = Pipeline([('scaler', MinMaxScaler()),
                    ('model', SVR(**model_params))])

    # 训练模型
    print('Starting training...')
    svr.fit(X_train, y_train, model__sample_weight=sample_weight)
    y_pred = svr.predict(X_test)
    return svr, y_pred


def build_model(df, args):
    np.random.seed(42)
    # lightgbm预测下
    numerical_features, categorical_features, label, model_name, num_boost_round, model_params, col_time = str_to_list(
        args['numerical_features']), str_to_list(args['categorical_features']), args['label'], args['model_name'], int(
        args['num_boost_round']), eval(args['model_params']), args['col_time']
    # 样本权重
    sample_weight = None
    if 'sample_weight' in args.keys():
        sample_weight = args['sample_weight']

    features = numerical_features + categorical_features
    print("features:************", features)
    if 'is_limit' in df.columns:
        df = df[df['is_limit'] == False]
    # 清洗特征平均缺失率大于20%的天
    df = missing_features(df, features, col_time)
    df = df[~np.isnan(df[label])]
    # 拆分数据为训练集和测试集
    X_train, X_test, y_train, y_test = train_test_split(df[features], df[label], test_size=0.2, random_state=42,
                                                        shuffle=False)

    model_type = args['model_type']
    # 区分常规机器学习模型和lgb，这里只实例化svr，后续可扩展
    if model_type == "lightgbm":
        model, y_pred = train_lgb([X_train, X_test, y_train, y_test], categorical_features, model_params,
                                  num_boost_round, sample_weight=sample_weight)
    elif model_type == "SVR":
        model, y_pred = train_svr([X_train, X_test, y_train, y_test], model_params, sample_weight=sample_weight)
    else:
        raise ValueError(f"Invalid model_type, must be one of [lightgbm, SVR]")

    # 评估
    mse = mean_squared_error(y_test, y_pred)
    rmse = np.sqrt(mse)
    mae = mean_absolute_error(y_test, y_pred)
    print(f'The test rmse is: {rmse},"The test mae is:"{mae}')
    return model, features


@app.route('/model_training_ml', methods=['POST'])
def model_training_ml():
    # 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)
        power_df = get_data_from_mongo(args)
        model, features = build_model(power_df, args)
        insert_pickle_model_into_mongo(model, args, features=features)
        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_training_ml log")
    from waitress import serve

    serve(app, host="0.0.0.0", port=10125)
    print("server start!")