KA_ensemble.py 文件源码

def ka_bagging_2class_or_reg_lgbm(X_train, y_train, seed, bag_round, params
                                 , X_test, using_notebook=True, num_boost_round=0):
    '''
        early version
    '''
    # create array object to hold predictions
    baggedpred=np.zeros(shape=X_test.shape[0]).astype(np.float32)
    #loop for as many times as we want bags
    if using_notebook:
        for n in tqdm_notebook(range(0, bag_round)):
            #shuffle first, aids in increasing variance and forces different results
            X_train, y_train=shuffle(X_train, y_train, random_state=seed+n)
            params['seed'] = seed + n
            model = lightgbm.train(params, lightgbm.Dataset(X_train, y_train), num_boost_round=num_boost_round)
            pred = model.predict(X_test)
            baggedpred += pred/bag_round

    return baggedpred