线性支持向量机(鸢尾花分类)

一、导入模块

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
from matplotlib.font_manager import FontProperties
from sklearn import datasets
from sklearn.svm import SVC
%matplotlib inline
font = FontProperties(fname='/Library/Fonts/Heiti.ttc')

二、获取数据

iris_data = datasets.load_iris()
X = iris_data.data[:, [2, 3]]
y = iris_data.target
label_list = ['山鸢尾', '杂色鸢尾', '维吉尼亚鸢尾']

三、构建决策边界

def plot_decision_regions(X, y, classifier=None):
    marker_list = ['o', 'x', 's']
    color_list = ['r', 'b', 'g']
    cmap = ListedColormap(color_list[:len(np.unique(y))])

    x1_min, x1_max = X[:, 0].min()-1, X[:, 0].max()+1
    x2_min, x2_max = X[:, 1].min()-1, X[:, 1].max()+1
    t1 = np.linspace(x1_min, x1_max, 666)
    t2 = np.linspace(x2_min, x2_max, 666)

    x1, x2 = np.meshgrid(t1, t2)
    y_hat = classifier.predict(np.array([x1.ravel(), x2.ravel()]).T)
    y_hat = y_hat.reshape(x1.shape)
    plt.contourf(x1, x2, y_hat, alpha=0.2, cmap=cmap)
    plt.xlim(x1_min, x1_max)
    plt.ylim(x2_min, x2_max)

    for ind, clas in enumerate(np.unique(y)):
        plt.scatter(X[y == clas, 0], X[y == clas, 1], alpha=0.8, s=50,
                    c=color_list[ind], marker=marker_list[ind], label=label_list[clas])

四、线性可分支持向量机

4.1 训练模型

svm = SVC(kernel='linear', random_state=1)
svm.fit(X, y)

SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',
  kernel='linear', max_iter=-1, probability=False, random_state=1,
  shrinking=True, tol=0.001, verbose=False)

4.2 可视化

plot_decision_regions(X, y, classifier=svm)
plt.xlabel('花瓣长度（cm）', fontproperties=font)
plt.ylabel('花瓣宽度（cm）', fontproperties=font)
plt.title('线性可分支持向量机(鸢尾花分类)', fontproperties=font, fontsize=20)
plt.legend(prop=font)
plt.show()

png

五、线性支持向量机

5.1 训练模型(C=0.01)

svm = SVC(kernel='linear', C=0.01, random_state=1)
svm.fit(X, y)

SVC(C=0.01, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',
  kernel='linear', max_iter=-1, probability=False, random_state=1,
  shrinking=True, tol=0.001, verbose=False)

5.2 可视化

plot_decision_regions(X, y, classifier=svm)
plt.xlabel('花瓣长度（cm）', fontproperties=font)
plt.ylabel('花瓣宽度（cm）', fontproperties=font)
plt.title('线性支持向量机(鸢尾花分类)', fontproperties=font, fontsize=20)
plt.legend(prop=font)
plt.show()

png

5.3 训练模型(C=100)

svm = SVC(kernel='linear', C=100, random_state=1)
svm.fit(X, y)

SVC(C=100, cache_size=200, class_weight=None, coef0=0.0,
  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',
  kernel='linear', max_iter=-1, probability=False, random_state=1,
  shrinking=True, tol=0.001, verbose=False)

5.4 可视化

plot_decision_regions(X, y, classifier=svm)
plt.xlabel('花瓣长度（cm）', fontproperties=font)
plt.ylabel('花瓣宽度（cm）', fontproperties=font)
plt.title('线性支持向量机(鸢尾花分类)', fontproperties=font, fontsize=20)
plt.legend(prop=font)
plt.show()

png