Models.py

#!/usr/bin/python
#-*-coding:utf-8-*-
'''@Date:18-10-13'''
'''@Time:下午10:02'''
'''@author: Duncan'''

import numpy as np
from sklearn.model_selection import KFold

# stacking方法
def get_stacking(clf, x_train, y_train, x_test, n_folds=10):
    """
    这个函数是stacking的核心，使用交叉验证的方法得到次级训练集
    x_train, y_train, x_test 的值应该为numpy里面的数组类型 numpy.ndarray .
    如果输入为pandas的DataFrame类型则会把报错"""
    train_num, test_num = x_train.shape[0], x_test.shape[0]
    second_level_train_set = np.zeros((train_num,))
    second_level_test_set = np.zeros((test_num,))
    test_nfolds_sets = np.zeros((test_num, n_folds))
    kf = KFold(n_splits=n_folds)

    for i,(train_index, test_index) in enumerate(kf.split(x_train)):
        x_tra, y_tra = x_train[train_index], y_train[train_index]
        x_tst, y_tst =  x_train[test_index], y_train[test_index]

        clf.fit(x_tra, y_tra)

        second_level_train_set[test_index] = clf.predict(x_tst)
        test_nfolds_sets[:,i] = clf.predict(x_test)

    second_level_test_set[:] = test_nfolds_sets.mean(axis=1)
    return second_level_train_set, second_level_test_set