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Stacking集成学习在各类机器学习竞赛当中得到了广泛的应用，尤其是在结构化的机器学习竞赛当中表现非常好。今天我们就来介绍下stacking这个在机器学习模型融合当中的大杀器的原理。并在博文的后面附有相关代码实现。

## 三.stacking模型代码实现（基本实现）

#### 由于sklearn并没有直接对Stacking的方法，因此我们需要下载mlxtend工具包(pip install mlxtend)

```# 1. 简单堆叠3折CV分类
from sklearn import datasets
iris = datasets.load_iris()
X, y = iris.data[:, 1:3], iris.target
from sklearn.model_selection import cross_val_score
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from mlxtend.classifier import StackingCVClassifier
RANDOM_SEED = 42
clf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=RANDOM_SEED)
clf3 = GaussianNB()
lr = LogisticRegression()
# Starting from v0.16.0, StackingCVRegressor supports
# `random_state` to get deterministic result.
sclf = StackingCVClassifier(classifiers=[clf1, clf2, clf3],  # 第一层分类器
meta_classifier=lr,   # 第二层分类器，并非表示第二次stacking，而是通过logistic regression对新的训练特征数据进行训练，得到predicted label
random_state=RANDOM_SEED)
print('3-fold cross validation:
')
for clf, label in zip([clf1, clf2, clf3, sclf], ['KNN', 'Random Forest', 'Naive Bayes','StackingClassifier']):
scores = cross_val_score(clf, X, y, cv=3, scoring='accuracy')
print("Accuracy: %0.2f (+/- %0.2f) [%s]" % (scores.mean(), scores.std(), label))```

```3-fold cross validation:
Accuracy: 0.91 (+/- 0.01) [KNN]
Accuracy: 0.95 (+/- 0.01) [Random Forest]
Accuracy: 0.91 (+/- 0.02) [Naive Bayes]
Accuracy: 0.93 (+/- 0.02) [StackingClassifier]```

```# 我们画出决策边界
from mlxtend.plotting import plot_decision_regions
import matplotlib.gridspec as gridspec
import itertools
gs = gridspec.GridSpec(2, 2)
fig = plt.figure(figsize=(10,8))
for clf, lab, grd in zip([clf1, clf2, clf3, sclf],
['KNN',
'Random Forest',
'Naive Bayes',
'StackingCVClassifier'],
itertools.product([0, 1], repeat=2)):
clf.fit(X, y)
ax = plt.subplot(gs[grd[0], grd[1]])
fig = plot_decision_regions(X=X, y=y, clf=clf)
plt.title(lab)
plt.show()```

```# 使用概率作为元特征
clf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GaussianNB()
lr = LogisticRegression()
sclf = StackingCVClassifier(classifiers=[clf1, clf2, clf3],
use_probas=True,
average_probas = False,
meta_classifier=lr,
random_state=42)
#average_probas = False 表示不对概率进行平均
print('3-fold cross validation:
')
for clf, label in zip([clf1, clf2, clf3, sclf],
['KNN',
'Random Forest',
'Naive Bayes',
'StackingClassifier']):
scores = cross_val_score(clf, X, y,
cv=3, scoring='accuracy')
print("Accuracy: %0.2f (+/- %0.2f) [%s]"
% (scores.mean(), scores.std(), label))```

```3-fold cross validation:
Accuracy: 0.91 (+/- 0.01) [KNN]
Accuracy: 0.95 (+/- 0.01) [Random Forest]
Accuracy: 0.91 (+/- 0.02) [Naive Bayes]
Accuracy: 0.95 (+/- 0.02) [StackingClassifier]```

## 四.Stacking配合grid search网格搜索实现超参数调整

```# 3. 堆叠5折CV分类与网格搜索(结合网格搜索调参优化)
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.naive_bayes import GaussianNB
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from mlxtend.classifier import StackingCVClassifier
# Initializing models
clf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=RANDOM_SEED)
clf3 = GaussianNB()
lr = LogisticRegression()
sclf = StackingCVClassifier(classifiers=[clf1, clf2, clf3],
meta_classifier=lr,
random_state=42)
params = {'kneighborsclassifier__n_neighbors': [1, 5],
'randomforestclassifier__n_estimators': [10, 50],
'meta_classifier__C': [0.1, 10.0]}
grid = GridSearchCV(estimator=sclf,
param_grid=params,
cv=5,
refit=True)
grid.fit(X, y)
cv_keys = ('mean_test_score', 'std_test_score', 'params')
for r, _ in enumerate(grid.cv_results_['mean_test_score']):
print("%0.3f +/- %0.2f %r"
% (grid.cv_results_[cv_keys[0]][r],
grid.cv_results_[cv_keys[1]][r] / 2.0,
grid.cv_results_[cv_keys[2]][r]))
print('Best parameters: %s' % grid.best_params_)
print('Accuracy: %.2f' % grid.best_score_)```

```0.947 +/- 0.03 {'kneighborsclassifier__n_neighbors': 1, 'meta_classifier__C': 0.1, 'randomforestclassifier__n_estimators': 10}
0.933 +/- 0.02 {'kneighborsclassifier__n_neighbors': 1, 'meta_classifier__C': 0.1, 'randomforestclassifier__n_estimators': 50}
0.940 +/- 0.02 {'kneighborsclassifier__n_neighbors': 1, 'meta_classifier__C': 10.0, 'randomforestclassifier__n_estimators': 10}
0.940 +/- 0.02 {'kneighborsclassifier__n_neighbors': 1, 'meta_classifier__C': 10.0, 'randomforestclassifier__n_estimators': 50}
0.953 +/- 0.02 {'kneighborsclassifier__n_neighbors': 5, 'meta_classifier__C': 0.1, 'randomforestclassifier__n_estimators': 10}
0.953 +/- 0.02 {'kneighborsclassifier__n_neighbors': 5, 'meta_classifier__C': 0.1, 'randomforestclassifier__n_estimators': 50}
0.953 +/- 0.02 {'kneighborsclassifier__n_neighbors': 5, 'meta_classifier__C': 10.0, 'randomforestclassifier__n_estimators': 10}
0.953 +/- 0.02 {'kneighborsclassifier__n_neighbors': 5, 'meta_classifier__C': 10.0, 'randomforestclassifier__n_estimators': 50}
Best parameters: {'kneighborsclassifier__n_neighbors': 5, 'meta_classifier__C': 0.1, 'randomforestclassifier__n_estimators': 10}
Accuracy: 0.95```

1.https://github.com/Geeksongs/ensemble-learning/blob/main/CH5-%E9%9B%86%E6%88%90%E5%AD%A6%E4%B9%A0%E4%B9%8Bblending%E4%B8%8Estacking/Stacking.ipynb

2.https://www.cnblogs.com/Christina-Notebook/p/10063146.html