# -*- 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.neighbors import KNeighborsClassifier

from sklearn.naive_bayes import GaussianNB

from sklearn.svm import SVC

from sklearn.ensemble import RandomForestClassifier

from sklearn.model_selection import cross_val_score

from mlxtend.classifier import EnsembleVoteClassifier, StackingClassifier

from mlxtend.data import iris_data

from mlxtend.plotting import plot_decision_regions

# Loading some example data

X, y = iris_data()

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

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')

# Plotting Decision Regions

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',

'StackingClassifier'],

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)

scores = cross_val_score(clf, X, y, cv=3, scoring='accuracy')

print("Accuracy: %0.2f (+/- %0.2f) [%s]"

% (scores.mean(), scores.std(), lab))

plt.savefig('2.png')

plt.show()