xgboost模型训练到部署

比眉伴天荒 2022-12-29 14:04 131阅读 0赞

训练代码：

from numpy import loadtxt
    from xgboost import XGBClassifier
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import accuracy_score
    
    dataset = loadtxt('data.csv', delimiter=",")
    X=dataset[:,0:8]
    Y=dataset[:,8]
    seed = 7
    test_size = 0.33
    X_train, X_test, y_train, y_test = train_test_split(X, Y, test_size=test_size, random_state=seed)
    model = XGBClassifier()
    eval_set = [(X_test, y_test)]
    model.fit(X_train, y_train, early_stopping_rounds=10, eval_metric="logloss", eval_set=eval_set, verbose=False)
    
    model.get_booster().save_model('xgb.h5')

训练成功后保存一个模型文件用于后面的部署，部署采用flask

from numpy import loadtxt
    from xgboost import XGBClassifier
    from sklearn.model_selection import train_test_split
    from sklearn.metrics import accuracy_score
    from flask import Flask, render_template, request, redirect, url_for, flash, jsonify
    import numpy as np
    import xgboost as xgb
    
    new_model = xgb.Booster(model_file='xgb.h5')#加载模型
    app = Flask(__name__)
    
    @app.route('/predict', methods=['POST'])
    def xg_predict():
         if request.method == 'POST':
             get_input=request.get_json()['input']#从请求中获取数据
             get_input=xgb.DMatrix(get_input)#转换成xg的数据格式
             predicted =  new_model.predict(get_input)#预测
             print(predicted)
             return jsonify(predicted)#将预测结果返回给请求者
    
    if __name__ == "__main__":
         app.run(debug=True,host='0.0.0.0')

数据可以自己制造，利用[这个制造就行][Link 1]

部署后就可以远程请求了

[Link 1]: https://blog.csdn.net/zhou_438/article/details/111568200