stacking算法原理
1：对于Model1，将训练集D分为k份，对于每一份，用剩余数据集训练模型，然后预测出这一份的结果
2：重复上面步骤，直到每一份都预测出来。得到次级模型的训练集
3：得到k份测试集，平均后得到次级模型的测试集
4：对于Model2、Model3…..重复以上情况，得到M维数据
5：选定次级模型，进行训练预测，一般这最后一层用的是LR。
优缺点：
优点：
1、 采用交叉验证方法构造，稳健性强；
2、 可以结合多个模型判断结果，进行次级训练，效果好；
缺点：
1、构造复杂，难以得到相应规则，商用上难以解释。
代码：
import numpy as np
from sklearn.model_selection import KFold
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
#我们这里使用5个分类算法，为了体现stacking的思想，就不加参数了
from sklearn.ensemble import (RandomForestClassifier, AdaBoostClassifier,
GradientBoostingClassifier, ExtraTreesClassifier)
from sklearn.svm import SVC
rf_model = RandomForestClassifier()
adb_model = AdaBoostClassifier()
gdbc_model = GradientBoostingClassifier()
et_model = ExtraTreesClassifier()
svc_model = SVC()
#在这里我们使用train_test_split来人为的制造一些数据
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
iris = load_iris()
train_x, test_x, train_y, test_y = train_test_split(iris.data, iris.target, test_size=0.2)
train_sets = []
test_sets = []
for clf in [rf_model, adb_model, gdbc_model, et_model, svc_model]:
train_set, test_set = get_stacking(clf, train_x, train_y, test_x)
train_sets.append(train_set)
test_sets.append(test_set)
meta_train = np.concatenate([result_set.reshape(-1,1) for result_set in train_sets], axis=1)
meta_test = np.concatenate([y_test_set.reshape(-1,1) for y_test_set in test_sets], axis=1)
#使用决策树作为我们的次级分类器
from sklearn.tree import DecisionTreeClassifier
dt_model = DecisionTreeClassifier()
dt_model.fit(meta_train, train_y)
df_predict = dt_model.predict(meta_test)
print(df_predict)