import numpy as np
import matplotlib.pyplot as plt
from sklearn import datasets
X, y = datasets.make_moons(noise=0.25, random_state=666)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
plt.scatter(X[y==0,0],X[y==0,1])
plt.scatter(X[y==1,0],X[y==1,1])
plt.show()
from sklearn.linear_model import LogisticRegression
log_clf = LogisticRegression()
log_clf.fit(X_train, y_train)
log_clf.score(X_test, y_test)
from sklearn.svm import SVC
svm_clf = SVC()
svm_clf.fit(X_train, y_train)
svm_clf.score(X_test, y_test)
from sklearn.tree import DecisionTreeClassifier
dt_clf = DecisionTreeClassifier()
dt_clf.fit(X_train, y_train)
dt_clf.score(X_test, y_test)
y_predict1 = log_clf.predict(X_test)
y_predict2 = svm_clf.predict(X_test)
y_predict3 = dt_clf.predict(X_test)
y_predict = np.array((y_predict1+y_predict2+y_predict3) >= 2, dtype='int')
from sklearn.metrics import accuracy_score
accuracy_score(y_test, y_predict)
from sklearn.ensemble import VotingClassifier
voting_clf = VotingClassifier(estimators=[
('log_clf', LogisticRegression()),
('svm_clf', SVC()),
('dt_clf', DecisionTreeClassifier())
], voting='hard')
voting_clf.fit(X_train, y_train)
voting_clf.score(X_test, y_test)
