# -*- coding: utf-8 -*-

# Author: XuMing <[email protected]>

# Brief:

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

# Initializing Classifiers

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')

# Loading some example data

X, y = iris_data()

X = X[:,[0, 2]]

# Plotting Decision Regions

gs = gridspec.GridSpec(2, 2)

fig = plt.figure(figsize=(10, 8))

for clf, lab, grd in zip([clf1, clf2, clf3, eclf],

['Logistic Regression', 'Random Forest', 'RBF kernel SVM', 'Ensemble'],

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.savefig('1.png')

plt.show()