Machine learning 8 (서포트벡터머신)

SVM 모델

import numpy as np
from sklearn.datasets import load_iris
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler, PolynomialFeatures
from sklearn.svm import LinearSVC, SVC

iris = load_iris()
X = iris['data'][:, 2:] # petal length, petal width
y = (iris['target'] == 2).astype(np.float64) # Virginica 이진 분류

pipeline1 = Pipeline([
                ('변환기이름1', 변환기1()),
                ('변환기이름2', 변환기2())
            ])
pipeline.fit_transfrom()

pipeline2 = Pipeline([
                ('변환기이름1', 변환기1()),
                ('변환기이름2', 변환기2()),
                ('모델이름', 모델객체())
            ])
pipeline2.fit()

svm_clf = Pipeline([
            ('scaler', StandardScaler()),
            ('linear_svc', LinearSVC(C=1, loss='hinge', random_state=42))    
])
svm_clf.fit(X, y)

Pipeline(steps=[('scaler', StandardScaler()),
                ('linear_svc', LinearSVC(C=1, loss='hinge', random_state=42))])

svm_clf.predict([[5.5, 1.7]]) # Virginca로 예측

array([1.])

비선형 분류

import matplotlib.pyplot as plt
from sklearn.datasets import make_moons

X, y = make_moons(n_samples=100, noise=0.15, random_state=42)

plt.plot(X[:, 0][y==0], X[:, 1][y==0], 'b.')
plt.plot(X[:, 0][y==1], X[:, 1][y==1], 'r.')
plt.xlabel('X1')
plt.ylabel('X2')
plt.show()

polynomial_svm_clf = Pipeline([
            ('poly_features', PolynomialFeatures(degree=3)),
            ('scaler', StandardScaler()),
            ('linear_svc', LinearSVC(C=10, loss='hinge', max_iter=2000, random_state=42))    
])
polynomial_svm_clf.fit(X, y)

Pipeline(steps=[('poly_features', PolynomialFeatures(degree=3)),
                ('scaler', StandardScaler()),
                ('linear_svc',
                 LinearSVC(C=10, loss='hinge', max_iter=2000,
                           random_state=42))])

polynomial_svm_clf.predict([[5.5, 1.7]]) # Virginca로 예측

array([1], dtype=int64)

다항식 커널

poly_kernel_svm_clf = Pipeline([
            ('scaler', StandardScaler()),
            ('svm_clf', SVC(kernel='poly', degree=3, coef0=1, C=5))    
])
poly_kernel_svm_clf.fit(X, y)

Pipeline(steps=[('scaler', StandardScaler()),
                ('svm_clf', SVC(C=5, coef0=1, kernel='poly'))])

유사도 특성

rbf_kernel_svm_clf = Pipeline([
            ('scaler', StandardScaler()),
            ('svm_clf', SVC(kernel='rbf', gamma=5, C=0.001))  # gamma, C 모두 값이 작아질수록 규제
])
rbf_kernel_svm_clf.fit(X, y)

Pipeline(steps=[('scaler', StandardScaler()),
                ('svm_clf', SVC(C=0.001, gamma=5))])

Reference

• 핸즈온 머신러닝 (오렐리앙 제롱 저)