新颖性检测

import numpy as np

import matplotlib.pyplot as plt

import matplotlib.font_manager

from sklearn import svm

plt.rcParams['font.sans-serif'] = ['SimHei']

plt.rcParams['axes.unicode_minus'] = False

xx, yy = np.meshgrid(np.linspace(-5, 5, 500), np.linspace(-5, 5, 500))

# 获取训练数据

X = 0.3 * np.random.randn(100, 2)

X_train = np.r_[X + 2, X - 2]

# 获取一些常规的新颖观察值

X = 0.3 * np.random.randn(20, 2)

X_test = np.r_[X + 2, X - 2]

# 获取一些异常的新颖观察值

X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))

# 拟合模型

clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1)

clf.fit(X_train)

y_pred_train = clf.predict(X_train)

y_pred_test = clf.predict(X_test)

y_pred_outliers = clf.predict(X_outliers)

n_error_train = y_pred_train[y_pred_train == -1].size

n_error_test = y_pred_test[y_pred_test == -1].size

n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size

# 绘制直线，点和最接近平面的向量

Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])

Z = Z.reshape(xx.shape)

plt.title("新颖性检测")

plt.contourf(xx, yy, Z, levels=np.linspace(Z.min(), 0, 7), cmap=plt.cm.PuBu)

a = plt.contour(xx, yy, Z, levels=[0], linewidths=2, colors='darkred')

plt.contourf(xx, yy, Z, levels=[0, Z.max()], colors='palevioletred')

s = 40

b1 = plt.scatter(X_train[:, 0], X_train[:, 1], c='white', s=s, edgecolors='k')

b2 = plt.scatter(X_test[:, 0], X_test[:, 1], c='blueviolet', s=s,

                edgecolors='k')

c = plt.scatter(X_outliers[:, 0], X_outliers[:, 1], c='gold', s=s,

                edgecolors='k')

plt.axis('tight')

plt.xlim((-5, 5))

plt.ylim((-5, 5))

plt.legend([a.collections[0], b1, b2, c],

          ["learned frontier", "training observations",

            "new regular observations", "new abnormal observations"],

          loc="upper left",

          prop=matplotlib.font_manager.FontProperties(size=11))

plt.xlabel(

    "error train: %d/200 ; errors novel regular: %d/40 ; "

    "errors novel abnormal: %d/40"

    % (n_error_train, n_error_test, n_error_outliers))

plt.show()