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
from data_processing.data_operation.weight import WEIGHT_REGISTER

app = Flask('model_training_ml——service')

def get_sample_weight(df, label, args):
    # 样本权重
    if 'sample_weight' in args.keys():
        if args['sample_weight'] in WEIGHT_REGISTER.keys():
            sample_weight = WEIGHT_REGISTER[args['sample_weight']](df[label].values.reshape(-1), **args)
        elif args['sample_weight'] in df.columns.tolist():
            sample_weight = df[args['sample_weight']].values.reshape(-1)
        else:
            sample_weight = None
            print('sample_weight is neither in the predefined weights nor a column of the DataFrame, not applicable')
    return sample_weight

def train_lgb(data_split, categorical_features, model_params, num_boost_round=100, 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
    }
    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)
    # 参数
    numerical_features, categorical_features, label, model_name, model_params, col_time = str_to_list(
        args['numerical_features']), str_to_list(args['categorical_features']), args['label'], args['model_name'], eval(
        args['model_params']), args['col_time']

    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
    df_train, df_test = train_test_split(df, test_size=0.2, random_state=42,
                                                        shuffle=False)
    X_train, y_train = df_train[features].values, df_train[label].values
    X_test, y_test = df_test[features].values, df_test[label].values

    # 获取样本权重
    sample_weight = get_sample_weight(df_train, label=label, args=args)

    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":
        num_boost_round = int(args['num_boost_round'])
        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!")