103 lines
2.7 KiB
Python
103 lines
2.7 KiB
Python
|
"""
|
||
|
|
==========================================
|
||
|
|
One-class SVM with non-linear kernel (RBF)
|
||
|
|
==========================================
|
||
|
|
|
||
|
|
An example using a one-class SVM for novelty detection.
|
||
|
|
|
||
|
|
:ref:`One-class SVM <svm_outlier_detection>` is an unsupervised
|
||
|
|
algorithm that learns a decision function for novelty detection:
|
||
|
|
classifying new data as similar or different to the training set.
|
||
|
|
|
||
|
|
"""
|
||
|
|
|
||
|
|
# %%
|
||
|
|
import numpy as np
|
||
|
|
|
||
|
|
from sklearn import svm
|
||
|
|
|
||
|
|
# Generate train data
|
||
|
|
X = 0.3 * np.random.randn(100, 2)
|
||
|
|
X_train = np.r_[X + 2, X - 2]
|
||
|
|
# Generate some regular novel observations
|
||
|
|
X = 0.3 * np.random.randn(20, 2)
|
||
|
|
X_test = np.r_[X + 2, X - 2]
|
||
|
|
# Generate some abnormal novel observations
|
||
|
|
X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))
|
||
|
|
|
||
|
|
# fit the model
|
||
|
|
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
|
||
|
|
|
||
|
|
# %%
|
||
|
|
import matplotlib.font_manager
|
||
|
|
import matplotlib.lines as mlines
|
||
|
|
import matplotlib.pyplot as plt
|
||
|
|
|
||
|
|
from sklearn.inspection import DecisionBoundaryDisplay
|
||
|
|
|
||
|
|
_, ax = plt.subplots()
|
||
|
|
|
||
|
|
# generate grid for the boundary display
|
||
|
|
xx, yy = np.meshgrid(np.linspace(-5, 5, 10), np.linspace(-5, 5, 10))
|
||
|
|
X = np.concatenate([xx.reshape(-1, 1), yy.reshape(-1, 1)], axis=1)
|
||
|
|
DecisionBoundaryDisplay.from_estimator(
|
||
|
|
clf,
|
||
|
|
X,
|
||
|
|
response_method="decision_function",
|
||
|
|
plot_method="contourf",
|
||
|
|
ax=ax,
|
||
|
|
cmap="PuBu",
|
||
|
|
)
|
||
|
|
DecisionBoundaryDisplay.from_estimator(
|
||
|
|
clf,
|
||
|
|
X,
|
||
|
|
response_method="decision_function",
|
||
|
|
plot_method="contourf",
|
||
|
|
ax=ax,
|
||
|
|
levels=[0, 10000],
|
||
|
|
colors="palevioletred",
|
||
|
|
)
|
||
|
|
DecisionBoundaryDisplay.from_estimator(
|
||
|
|
clf,
|
||
|
|
X,
|
||
|
|
response_method="decision_function",
|
||
|
|
plot_method="contour",
|
||
|
|
ax=ax,
|
||
|
|
levels=[0],
|
||
|
|
colors="darkred",
|
||
|
|
linewidths=2,
|
||
|
|
)
|
||
|
|
|
||
|
|
s = 40
|
||
|
|
b1 = ax.scatter(X_train[:, 0], X_train[:, 1], c="white", s=s, edgecolors="k")
|
||
|
|
b2 = ax.scatter(X_test[:, 0], X_test[:, 1], c="blueviolet", s=s, edgecolors="k")
|
||
|
|
c = ax.scatter(X_outliers[:, 0], X_outliers[:, 1], c="gold", s=s, edgecolors="k")
|
||
|
|
plt.legend(
|
||
|
|
[mlines.Line2D([], [], color="darkred"), b1, b2, c],
|
||
|
|
[
|
||
|
|
"learned frontier",
|
||
|
|
"training observations",
|
||
|
|
"new regular observations",
|
||
|
|
"new abnormal observations",
|
||
|
|
],
|
||
|
|
loc="upper left",
|
||
|
|
prop=matplotlib.font_manager.FontProperties(size=11),
|
||
|
|
)
|
||
|
|
ax.set(
|
||
|
|
xlabel=(
|
||
|
|
f"error train: {n_error_train}/200 ; errors novel regular: {n_error_test}/40 ;"
|
||
|
|
f" errors novel abnormal: {n_error_outliers}/40"
|
||
|
|
),
|
||
|
|
title="Novelty Detection",
|
||
|
|
xlim=(-5, 5),
|
||
|
|
ylim=(-5, 5),
|
||
|
|
)
|
||
|
|
plt.show()
|