stacking的实现-mxtend库「建议收藏」

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关于Stacked的实现库-mlxtend的学习
StackingClassifier
StackingCVClassifier

关于Stacked的实现库-mlxtend的学习

环境情况：
##################################################################
python 3.6
mlxtend 0.13.0
scikit-learn 0.19.0
##################################################################

该mlxtend库的的stacking实现，通过结合sklearn中的模型进行。
推荐使用StackingCV方法。

StackingClassifier

该函数主要介绍stacking基本原理，是简单实现。

基本算法说明

算法伪代码

例子1 简单实现stacking

from sklearn import datasets

iris = datasets.load_iris()
X, y = iris.data[:, 1:3], iris.target

from sklearn import model_selection
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 StackingClassifier
import numpy as np

clf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GaussianNB()
lr = LogisticRegression()
sclf = StackingClassifier(classifiers=[clf1, clf2, clf3], 
                          meta_classifier=lr)

print('3-fold cross validation:\n')

for clf, label in zip([clf1, clf2, clf3, sclf], 
                      ['KNN', 
                       'Random Forest', 
                       'Naive Bayes',
                       'StackingClassifier']):

    scores = model_selection.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: 0.91 (+/- 0.06) [Random Forest]
Accuracy: 0.92 (+/- 0.03) [Naive Bayes]
Accuracy: 0.95 (+/- 0.03) [StackingClassifier]

例子2 sub-model的输出是probabilities

基于第一层的概率，进行第二层的计算

clf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GaussianNB()
lr = LogisticRegression()
sclf = StackingClassifier(classifiers=[clf1, clf2, clf3],
                          use_probas=True,
                          average_probas=False,
                          meta_classifier=lr)

print('3-fold cross validation:\n')

for clf, label in zip([clf1, clf2, clf3, sclf], 
                      ['KNN', 
                       'Random Forest', 
                       'Naive Bayes',
                       'StackingClassifier']):

    scores = model_selection.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: 0.91 (+/- 0.06) [Random Forest]
Accuracy: 0.92 (+/- 0.03) [Naive Bayes]
Accuracy: 0.94 (+/- 0.03) [StackingClassifier]

例子3 结合GridSearch

结合网格搜索交叉验证

各个模型参数
各个基学习器+参数

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 StackingClassifier

# Initializing models

clf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GaussianNB()
lr = LogisticRegression()
sclf = StackingClassifier(classifiers=[clf1, clf2, clf3], 
                          meta_classifier=lr)

params = { 
   'kneighborsclassifier__n_neighbors': [1, 5],
          'randomforestclassifier__n_estimators': [10, 50],
          'meta-logisticregression__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_)

0.667 +/- 0.00 {'kneighborsclassifier__n_neighbors': 1, 'meta-logisticregression__C': 0.1, 'randomforestclassifier__n_estimators': 10}
0.667 +/- 0.00 {'kneighborsclassifier__n_neighbors': 1, 'meta-logisticregression__C': 0.1, 'randomforestclassifier__n_estimators': 50}
0.927 +/- 0.02 {'kneighborsclassifier__n_neighbors': 1, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 10}
0.913 +/- 0.03 {'kneighborsclassifier__n_neighbors': 1, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 50}
0.667 +/- 0.00 {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 0.1, 'randomforestclassifier__n_estimators': 10}
0.667 +/- 0.00 {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 0.1, 'randomforestclassifier__n_estimators': 50}
0.933 +/- 0.02 {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 10}
0.940 +/- 0.02 {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 50}
Best parameters: {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 50}
Accuracy: 0.94

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 StackingClassifier

# Initializing models

clf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GaussianNB()
lr = LogisticRegression()
sclf = StackingClassifier(classifiers=[clf1, clf2, clf3], 
                          meta_classifier=lr)

params = { 
   'kneighborsclassifier__n_neighbors': [1, 5],
          'classifiers':[(clf1,clf1),(clf2, clf3)]}

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_)

0.907 +/- 0.03 {'classifiers': (KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=1, n_neighbors=1, p=2,
           weights='uniform'), KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=1, n_neighbors=1, p=2,
           weights='uniform')), 'kneighborsclassifier__n_neighbors': 1}
0.907 +/- 0.03 {'classifiers': (KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=1, n_neighbors=1, p=2,
           weights='uniform'), KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',
           metric_params=None, n_jobs=1, n_neighbors=1, p=2,
           weights='uniform')), 'kneighborsclassifier__n_neighbors': 5}
0.933 +/- 0.02 {'classifiers': (RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
            oob_score=False, random_state=1, verbose=0, warm_start=False), GaussianNB(priors=None)), 'kneighborsclassifier__n_neighbors': 1}
0.933 +/- 0.02 {'classifiers': (RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
            oob_score=False, random_state=1, verbose=0, warm_start=False), GaussianNB(priors=None)), 'kneighborsclassifier__n_neighbors': 5}
Best parameters: {'classifiers': (RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',
            max_depth=None, max_features='auto', max_leaf_nodes=None,
            min_impurity_decrease=0.0, min_impurity_split=None,
            min_samples_leaf=1, min_samples_split=2,
            min_weight_fraction_leaf=0.0, n_estimators=10, n_jobs=1,
            oob_score=False, random_state=1, verbose=0, warm_start=False), GaussianNB(priors=None)), 'kneighborsclassifier__n_neighbors': 1}
Accuracy: 0.93

列子4-针对不同整体选用不同模型

from sklearn.datasets import load_iris
from mlxtend.classifier import StackingClassifier
from mlxtend.feature_selection import ColumnSelector
from sklearn.pipeline import make_pipeline, Pipeline
from sklearn.linear_model import LogisticRegression

iris = load_iris()
X = iris.data
y = iris.target

pipe1 = make_pipeline(ColumnSelector(cols=(0, 2)),
                      LogisticRegression())
pipe2 = make_pipeline(ColumnSelector(cols=(1, 2, 3)),
                      LogisticRegression())

sclf = StackingClassifier(classifiers=[pipe1, pipe2], 
                          meta_classifier=LogisticRegression())

sclf.fit(X, y)

StackingClassifier(average_probas=False,
          classifiers=[Pipeline(memory=None,
     steps=[('columnselector', ColumnSelector(cols=(0, 2), drop_axis=False)), ('logisticregression', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          pen...='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False))])],
          meta_classifier=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False),
          store_train_meta_features=False, use_clones=True,
          use_features_in_secondary=False, use_probas=False, verbose=0)

StackingClassifier(average_probas=False,
          classifiers=[Pipeline(steps=[('columnselector', ColumnSelector(cols=(0, 2))), ('logisticregression', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False))])],
          meta_classifier=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False),
          use_features_in_secondary=False, use_probas=False, verbose=0)

StackingClassifier(average_probas=False,
          classifiers=[Pipeline(memory=None,
     steps=[('columnselector', ColumnSelector(cols=(0, 2), drop_axis=False)), ('logisticregression', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False))])],
          meta_classifier=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False),
          store_train_meta_features=False, use_clones=True,
          use_features_in_secondary=False, use_probas=False, verbose=0)

StackingCVClassifier

在一般应用当中，常常需要将数据集划分几份，即采用K折样本划分的方式。下面是对k折交叉验证的方式的说明

例子1 简单stackingCV 分类

from sklearn import datasets

iris = datasets.load_iris()
X, y = iris.data[:, 1:3], iris.target

from sklearn import model_selection
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
import numpy as np

RANDOM_SEED = 42

clf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=RANDOM_SEED)
clf3 = GaussianNB()
lr = LogisticRegression()

# The StackingCVClassifier uses scikit-learn's check_cv
# internally, which doesn't support a random seed. Thus
# NumPy's random seed need to be specified explicitely for
# deterministic behavior
np.random.seed(RANDOM_SEED)
sclf = StackingCVClassifier(classifiers=[clf1, clf2, clf3], 
                            meta_classifier=lr)

print('3-fold cross validation:\n')

for clf, label in zip([clf1, clf2, clf3, sclf], 
                      ['KNN', 
                       'Random Forest', 
                       'Naive Bayes',
                       'StackingClassifier']):

    scores = model_selection.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: 0.90 (+/- 0.03) [Random Forest]
Accuracy: 0.92 (+/- 0.03) [Naive Bayes]
Accuracy: 0.93 (+/- 0.02) [StackingClassifier]

import matplotlib.pyplot as plt
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()

例子2 sub-model的输出是probabilities

clf1 = KNeighborsClassifier(n_neighbors=1)
clf2 = RandomForestClassifier(random_state=1)
clf3 = GaussianNB()
lr = LogisticRegression()

# The StackingCVClassifier uses scikit-learn's check_cv
# internally, which doesn't support a random seed. Thus
# NumPy's random seed need to be specified explicitely for
# deterministic behavior
np.random.seed(RANDOM_SEED)
sclf = StackingCVClassifier(classifiers=[clf1, clf2, clf3],
                            use_probas=True,
                            meta_classifier=lr)

print('3-fold cross validation:\n')

for clf, label in zip([clf1, clf2, clf3, sclf], 
                      ['KNN', 
                       'Random Forest', 
                       'Naive Bayes',
                       'StackingClassifier']):

    scores = model_selection.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: 0.91 (+/- 0.06) [Random Forest]
Accuracy: 0.92 (+/- 0.03) [Naive Bayes]
Accuracy: 0.95 (+/- 0.04) [StackingClassifier]

例子3 结合GridSearch

结合sklearn中GridSearchCV功能。
param中的sub-model分类器参数名称，“sklearn分类器名称__参数”。例如’kneighborsclassifier__n_neighbors’。
meta-model参数名称，“meta-sklearn分类器名称__参数”。例如’meta-logisticregression__C’。

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()

# The StackingCVClassifier uses scikit-learn's check_cv
# internally, which doesn't support a random seed. Thus
# NumPy's random seed need to be specified explicitely for
# deterministic behavior
np.random.seed(RANDOM_SEED)
sclf = StackingCVClassifier(classifiers=[clf1, clf2, clf3], 
                            meta_classifier=lr)

params = { 
   'kneighborsclassifier__n_neighbors': [1, 5],
          'randomforestclassifier__n_estimators': [10, 50],
          'meta-logisticregression__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_)

0.673 +/- 0.01 {'kneighborsclassifier__n_neighbors': 1, 'meta-logisticregression__C': 0.1, 'randomforestclassifier__n_estimators': 10}
0.667 +/- 0.00 {'kneighborsclassifier__n_neighbors': 1, 'meta-logisticregression__C': 0.1, 'randomforestclassifier__n_estimators': 50}
0.920 +/- 0.02 {'kneighborsclassifier__n_neighbors': 1, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 10}
0.893 +/- 0.02 {'kneighborsclassifier__n_neighbors': 1, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 50}
0.667 +/- 0.00 {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 0.1, 'randomforestclassifier__n_estimators': 10}
0.667 +/- 0.00 {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 0.1, 'randomforestclassifier__n_estimators': 50}
0.947 +/- 0.02 {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 10}
0.947 +/- 0.02 {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 50}
Best parameters: {'kneighborsclassifier__n_neighbors': 5, 'meta-logisticregression__C': 10.0, 'randomforestclassifier__n_estimators': 10}
Accuracy: 0.95

例子4 在不同特征子集上用分类算法

from sklearn.datasets import load_iris
from mlxtend.classifier import StackingCVClassifier
from mlxtend.feature_selection import ColumnSelector
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import LogisticRegression

iris = load_iris()
X = iris.data
y = iris.target

pipe1 = make_pipeline(ColumnSelector(cols=(0, 2)),
                      LogisticRegression())
pipe2 = make_pipeline(ColumnSelector(cols=(1, 2, 3)),
                      LogisticRegression())

sclf = StackingCVClassifier(classifiers=[pipe1, pipe2], 
                            meta_classifier=LogisticRegression())

sclf.fit(X, y)

StackingCVClassifier(classifiers=[Pipeline(memory=None,
     steps=[('columnselector', ColumnSelector(cols=(0, 2), drop_axis=False)), ('logisticregression', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          pen...='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False))])],
           cv=2,
           meta_classifier=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False),
           shuffle=True, store_train_meta_features=False, stratify=True,
           use_clones=True, use_features_in_secondary=False,
           use_probas=False, verbose=0)

sclf = StackingCVClassifier(classifiers=[Pipeline(steps=[('columnselector', ColumnSelector(cols=(0, 2))), ('logisticregression', LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False))])],
           cv=2,
           meta_classifier=LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,
          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,
          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,
          verbose=0, warm_start=False),
           shuffle=True, stratify=True, use_features_in_secondary=False,
           use_probas=False, verbose=0)

scores = model_selection.cross_val_score(sclf, X, y, 
                                              cv=3, scoring='accuracy')
print(scores.mean())

0.920343137255

该库是机器学习的辅助工具包，提供机器学习流程的丰富功能，待深入了解。同时，上面涉及的两个方法的具体各个参数，可以查看该库的官网。

参考来源：

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stacking的实现-mxtend库「建议收藏」

Table of Contents

关于Stacked的实现库-mlxtend的学习

StackingClassifier

例子1 简单实现stacking

例子2 sub-model的输出是probabilities

例子3 结合GridSearch

列子4-针对不同整体选用不同模型

StackingCVClassifier

例子1 简单stackingCV 分类

例子2 sub-model的输出是probabilities

例子3 结合GridSearch

例子4 在不同特征子集上用分类算法

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stacking的实现-mxtend库「建议收藏」

Table of Contents

关于Stacked的实现库-mlxtend的学习

StackingClassifier

例子1 简单实现stacking

例子2 sub-model的输出是probabilities

例子3 结合GridSearch

列子4-针对不同整体选用不同模型

StackingCVClassifier

例子1 简单stackingCV 分类

例子2 sub-model的输出是probabilities

例子3 结合GridSearch

例子4 在不同特征子集上用分类算法

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