visual_outlier.py

import numpy as np
import matplotlib.pyplot as plt
from matplotlib.font_manager import FontProperties
from pysvm import OneClassSVM

try:
    import seaborn as sns
    sns.set()
except:
    pass

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

X = 0.3 * np.random.randn(100, 2)
X_train = np.r_[X + 2, X - 2]

X = 0.3 * np.random.randn(20, 2)
X_test = np.r_[X + 2, X - 2]

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

clf = 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

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

plt.title("Novelty Detection")
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 data", "test regular data",
    "test abnormal data"
],
           loc="upper left",
           prop=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.savefig("../src/oc_svm.png")