from sklearn.datasets import load_iris
from mlxtend.feature_selection import ColumnSelector
from sklearn.pipeline import make_pipeline
iris = load_iris()
X = iris.data[:100]
y = iris.target[:100]
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)
decision_scores = sclf.decision_function(X)
print("Val auc Score of Stacking: %f" %
(roc_auc_score(y,
sclf.predict_proba(X)[:, 1])))
fig, axe = plt.subplots(2, 2, figsize=(30, 20))
rlb.ComprehensiveIndicatorFigure(y, decision_scores, axe[0], 1)
rlb.ComprehensiveIndicatorSkLibFigure(y, decision_scores, axe[1])
# In[]:
# 5、ROC Curve with decision_function
from sklearn import model_selection
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
