XGBoost 1.3.1
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          XGBoost 1.3.1

          XGBoost

          XGBoost框架下,自定义作业支持发布保存模型为picklejoblib格式,并且在发布至模型仓库时需要选择相应的模型文件。使用下面代码进行模型训练时,训练程序可以自行加载数据,训练数据选择空文件夹即可。

          pickle格式示例代码

          # -*- coding:utf-8 -*-
          """ xgboost train demo """
          import xgboost as xgb
          import numpy as np
          def save_model(model):
              """ save model with pickle format """
              import pickle
              with open('output/clf.pickle', 'wb') as f:
                  pickle.dump(model, f)
          def save_model_joblib(model):
              """ save model with joblib format """
              import joblib
              joblib.dump(model, 'output/clf.pkl')
          def main():
              """ main """
              rawData = np.array([[2, 4], [3, 4], [1, 2], [4, 5], [7, 8]])
              label = np.array([6, 7, 3, 9, 15])
              dtrain = xgb.DMatrix(rawData, label=label)
              deval = xgb.DMatrix(np.array([[3, 5], [3, 6]]), label=np.array([8, 9]))
              param = {'max_depth': 2, 'eta': 1, 'silent': 1, 'objective': 'reg:linear'}
              evallist = [(deval, 'eval'), (dtrain, 'train')]
              num_round = 10
              bst = xgb.train(param, dtrain, num_round, evallist)
              dtest = xgb.DMatrix(np.array([[2, 4], [7, 8]]))
              ypred = bst.predict(dtest)
              print(ypred)
              save_model_joblib(bst)
          if __name__ == '__main__':
              main()
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