机器学习mlxtend_01

# -*- coding: utf-8 -*-
"""
Created on Wed Oct 24 09:53:29 2018

@author: User
"""

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
import itertools
from sklearn.linear_model import LogisticRegression
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from mlxtend.classifier import EnsembleVoteClassifier
from mlxtend.data import iris_data
from mlxtend.plotting import plot_decision_regions

clf1 = LogisticRegression(random_state = 0)
clf2 = RandomForestClassifier(random_state=0)
clf3 = SVC(random_state = 0, probability=True)
eclf = EnsembleVoteClassifier(clfs=[clf1, clf2, clf3], weights=[2, 1, 1], voting='soft')
X, y =iris_data()
X=X[:, [0, 2]]

gs = gridspec.GridSpec(2, 2)
fig = plt.figure(figsize=(10, 8))
labels = ['Logistic Regression', 'Random Forest', 'RBF kernel SVM', 'Ensemble']
for clf, lab, grd in zip([clf1, clf2, clf3, eclf],
                         labels,
                         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, legend=2)
    plt.title(lab)
plt.show()