103 lines
2.7 KiB
Python
103 lines
2.7 KiB
Python
"""
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==========================================
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One-class SVM with non-linear kernel (RBF)
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==========================================
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An example using a one-class SVM for novelty detection.
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:ref:`One-class SVM <svm_outlier_detection>` is an unsupervised
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algorithm that learns a decision function for novelty detection:
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classifying new data as similar or different to the training set.
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"""
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# %%
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import numpy as np
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from sklearn import svm
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# Generate train data
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X = 0.3 * np.random.randn(100, 2)
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X_train = np.r_[X + 2, X - 2]
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# Generate some regular novel observations
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X = 0.3 * np.random.randn(20, 2)
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X_test = np.r_[X + 2, X - 2]
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# Generate some abnormal novel observations
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X_outliers = np.random.uniform(low=-4, high=4, size=(20, 2))
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# fit the model
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clf = svm.OneClassSVM(nu=0.1, kernel="rbf", gamma=0.1)
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clf.fit(X_train)
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y_pred_train = clf.predict(X_train)
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y_pred_test = clf.predict(X_test)
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y_pred_outliers = clf.predict(X_outliers)
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n_error_train = y_pred_train[y_pred_train == -1].size
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n_error_test = y_pred_test[y_pred_test == -1].size
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n_error_outliers = y_pred_outliers[y_pred_outliers == 1].size
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# %%
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import matplotlib.font_manager
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import matplotlib.lines as mlines
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import matplotlib.pyplot as plt
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from sklearn.inspection import DecisionBoundaryDisplay
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_, ax = plt.subplots()
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# generate grid for the boundary display
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xx, yy = np.meshgrid(np.linspace(-5, 5, 10), np.linspace(-5, 5, 10))
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X = np.concatenate([xx.reshape(-1, 1), yy.reshape(-1, 1)], axis=1)
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DecisionBoundaryDisplay.from_estimator(
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clf,
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X,
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response_method="decision_function",
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plot_method="contourf",
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ax=ax,
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cmap="PuBu",
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)
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DecisionBoundaryDisplay.from_estimator(
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clf,
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X,
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response_method="decision_function",
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plot_method="contourf",
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ax=ax,
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levels=[0, 10000],
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colors="palevioletred",
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)
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DecisionBoundaryDisplay.from_estimator(
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clf,
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X,
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response_method="decision_function",
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plot_method="contour",
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ax=ax,
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levels=[0],
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colors="darkred",
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linewidths=2,
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)
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s = 40
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b1 = ax.scatter(X_train[:, 0], X_train[:, 1], c="white", s=s, edgecolors="k")
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b2 = ax.scatter(X_test[:, 0], X_test[:, 1], c="blueviolet", s=s, edgecolors="k")
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c = ax.scatter(X_outliers[:, 0], X_outliers[:, 1], c="gold", s=s, edgecolors="k")
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plt.legend(
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[mlines.Line2D([], [], color="darkred"), b1, b2, c],
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[
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"learned frontier",
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"training observations",
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"new regular observations",
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"new abnormal observations",
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],
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loc="upper left",
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prop=matplotlib.font_manager.FontProperties(size=11),
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)
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ax.set(
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xlabel=(
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f"error train: {n_error_train}/200 ; errors novel regular: {n_error_test}/40 ;"
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f" errors novel abnormal: {n_error_outliers}/40"
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),
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title="Novelty Detection",
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xlim=(-5, 5),
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ylim=(-5, 5),
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)
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plt.show()
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