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iris_stack.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+################## load packages #####################
+from sklearn import datasets
+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
+
+
+################## load data #####################
+iris = datasets.load_iris()
+x, y = iris.data[:, 1:3], iris.target
+
+
+################## define classifier #####################
+clf1 = KNeighborsClassifier(n_neighbors = 1)
+
+clf2 = RandomForestClassifier(random_state = 1)
+
+clf3 = GaussianNB()
+
+lr = LogisticRegression()
+
+sclf = StackingClassifier(classifiers = [clf1, clf2, clf3], meta_classifier = lr)
+
+
+################## class result #####################
+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))

iris_stack2.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+################## load packages #####################
+from sklearn import datasets
+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
+
+
+################## load data #####################
+iris = datasets.load_iris()
+x, y = iris.data[:, 1:3], iris.target
+
+
+################## define classifier #####################
+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)
+
+
+################## class result #####################
+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))

iris_stack3.py

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+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+
+################## load packages #####################
+from sklearn import datasets
+from sklearn.linear_model import LogisticRegression
+from mlxtend.classifier import StackingClassifier
+from mlxtend.feature_selection import ColumnSelector
+from sklearn.pipeline import make_pipeline
+
+
+################## load data #####################
+iris = datasets.load_iris()
+x, y = iris.data, iris.target
+
+
+################## define classifier #####################
+
+pipe1 = make_pipeline(ColumnSelector(cols=(0, 1)),
+                      LogisticRegression())
+pipe2 = make_pipeline(ColumnSelector(cols=(2, 3)),
+                      LogisticRegression())
+
+sclf = StackingClassifier(classifiers=[pipe1, pipe2], 
+                          meta_classifier=LogisticRegression())
+
+################## fit and predict #####################
+sclf.fit(x, y)
+
+print(sclf.predict(x))
+
+########### predict class probability ###########
+print(sclf.predict_proba(x))