73 lines
2.0 KiB
Python
73 lines
2.0 KiB
Python
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"""
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=====================
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SVM: Weighted samples
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=====================
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Plot decision function of a weighted dataset, where the size of points
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is proportional to its weight.
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The sample weighting rescales the C parameter, which means that the classifier
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puts more emphasis on getting these points right. The effect might often be
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subtle.
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To emphasize the effect here, we particularly weight outliers, making the
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deformation of the decision boundary very visible.
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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 import svm
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def plot_decision_function(classifier, sample_weight, axis, title):
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# plot the decision function
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xx, yy = np.meshgrid(np.linspace(-4, 5, 500), np.linspace(-4, 5, 500))
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Z = classifier.decision_function(np.c_[xx.ravel(), yy.ravel()])
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Z = Z.reshape(xx.shape)
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# plot the line, the points, and the nearest vectors to the plane
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axis.contourf(xx, yy, Z, alpha=0.75, cmap=plt.cm.bone)
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axis.scatter(
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X[:, 0],
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X[:, 1],
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c=y,
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s=100 * sample_weight,
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alpha=0.9,
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cmap=plt.cm.bone,
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edgecolors="black",
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)
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axis.axis("off")
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axis.set_title(title)
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# we create 20 points
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np.random.seed(0)
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X = np.r_[np.random.randn(10, 2) + [1, 1], np.random.randn(10, 2)]
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y = [1] * 10 + [-1] * 10
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sample_weight_last_ten = abs(np.random.randn(len(X)))
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sample_weight_constant = np.ones(len(X))
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# and bigger weights to some outliers
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sample_weight_last_ten[15:] *= 5
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sample_weight_last_ten[9] *= 15
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# Fit the models.
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# This model does not take into account sample weights.
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clf_no_weights = svm.SVC(gamma=1)
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clf_no_weights.fit(X, y)
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# This other model takes into account some dedicated sample weights.
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clf_weights = svm.SVC(gamma=1)
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clf_weights.fit(X, y, sample_weight=sample_weight_last_ten)
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fig, axes = plt.subplots(1, 2, figsize=(14, 6))
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plot_decision_function(
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clf_no_weights, sample_weight_constant, axes[0], "Constant weights"
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)
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plot_decision_function(clf_weights, sample_weight_last_ten, axes[1], "Modified weights")
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plt.show()
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