67 lines
2.1 KiB
Python
67 lines
2.1 KiB
Python
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"""
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=========================================================
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SVM Tie Breaking Example
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=========================================================
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Tie breaking is costly if ``decision_function_shape='ovr'``, and therefore it
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is not enabled by default. This example illustrates the effect of the
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``break_ties`` parameter for a multiclass classification problem and
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``decision_function_shape='ovr'``.
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The two plots differ only in the area in the middle where the classes are
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tied. If ``break_ties=False``, all input in that area would be classified as
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one class, whereas if ``break_ties=True``, the tie-breaking mechanism will
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create a non-convex decision boundary in that area.
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"""
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# Code source: Andreas Mueller, Adrin Jalali
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# License: BSD 3 clause
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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.svm import SVC
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X, y = make_blobs(random_state=27)
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fig, sub = plt.subplots(2, 1, figsize=(5, 8))
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titles = ("break_ties = False", "break_ties = True")
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for break_ties, title, ax in zip((False, True), titles, sub.flatten()):
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svm = SVC(
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kernel="linear", C=1, break_ties=break_ties, decision_function_shape="ovr"
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).fit(X, y)
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xlim = [X[:, 0].min(), X[:, 0].max()]
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ylim = [X[:, 1].min(), X[:, 1].max()]
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xs = np.linspace(xlim[0], xlim[1], 1000)
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ys = np.linspace(ylim[0], ylim[1], 1000)
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xx, yy = np.meshgrid(xs, ys)
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pred = svm.predict(np.c_[xx.ravel(), yy.ravel()])
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colors = [plt.cm.Accent(i) for i in [0, 4, 7]]
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points = ax.scatter(X[:, 0], X[:, 1], c=y, cmap="Accent")
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classes = [(0, 1), (0, 2), (1, 2)]
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line = np.linspace(X[:, 1].min() - 5, X[:, 1].max() + 5)
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ax.imshow(
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-pred.reshape(xx.shape),
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cmap="Accent",
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alpha=0.2,
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extent=(xlim[0], xlim[1], ylim[1], ylim[0]),
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)
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for coef, intercept, col in zip(svm.coef_, svm.intercept_, classes):
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line2 = -(line * coef[1] + intercept) / coef[0]
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ax.plot(line2, line, "-", c=colors[col[0]])
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ax.plot(line2, line, "--", c=colors[col[1]])
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ax.set_xlim(xlim)
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ax.set_ylim(ylim)
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ax.set_title(title)
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ax.set_aspect("equal")
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plt.show()
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