59 lines
1.8 KiB
Python
59 lines
1.8 KiB
Python
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"""
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=====================================
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Plot the support vectors in LinearSVC
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=====================================
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Unlike SVC (based on LIBSVM), LinearSVC (based on LIBLINEAR) does not provide
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the support vectors. This example demonstrates how to obtain the support
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vectors in LinearSVC.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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from sklearn.datasets import make_blobs
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from sklearn.inspection import DecisionBoundaryDisplay
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from sklearn.svm import LinearSVC
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X, y = make_blobs(n_samples=40, centers=2, random_state=0)
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plt.figure(figsize=(10, 5))
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for i, C in enumerate([1, 100]):
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# "hinge" is the standard SVM loss
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clf = LinearSVC(C=C, loss="hinge", random_state=42).fit(X, y)
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# obtain the support vectors through the decision function
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decision_function = clf.decision_function(X)
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# we can also calculate the decision function manually
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# decision_function = np.dot(X, clf.coef_[0]) + clf.intercept_[0]
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# The support vectors are the samples that lie within the margin
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# boundaries, whose size is conventionally constrained to 1
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support_vector_indices = np.where(np.abs(decision_function) <= 1 + 1e-15)[0]
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support_vectors = X[support_vector_indices]
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plt.subplot(1, 2, i + 1)
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plt.scatter(X[:, 0], X[:, 1], c=y, s=30, cmap=plt.cm.Paired)
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ax = plt.gca()
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DecisionBoundaryDisplay.from_estimator(
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clf,
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X,
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ax=ax,
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grid_resolution=50,
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plot_method="contour",
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colors="k",
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levels=[-1, 0, 1],
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alpha=0.5,
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linestyles=["--", "-", "--"],
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)
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plt.scatter(
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support_vectors[:, 0],
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support_vectors[:, 1],
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s=100,
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linewidth=1,
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facecolors="none",
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edgecolors="k",
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)
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plt.title("C=" + str(C))
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plt.tight_layout()
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plt.show()
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