Find the optimal separating hyperplane using an SVC for classes that are unbalanced.
We first find the separating plane with a plain SVC and then plot (dashed) the separating hyperplane with automatically correction for unbalanced classes.
Note
This example will also work by replacing SVC(kernel="linear") with SGDClassifier(loss="hinge"). Setting the loss parameter of the SGDClassifier equal to hinge will yield behaviour such as that of a SVC with a linear kernel.
For example try instead of the SVC:
1 | clf = SGDClassifier(n_iter=100, alpha=0.01) |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | print(__doc__)import numpy as npimport matplotlib.pyplot as pltfrom sklearn import svm#from sklearn.linear_model import SGDClassifier# we create 40 separable pointsrng = np.random.RandomState(0)n_samples_1 = 1000n_samples_2 = 100X = np.r_[1.5 * rng.randn(n_samples_1, 2), 0.5 * rng.randn(n_samples_2, 2) + [2, 2]]y = [0] * (n_samples_1) + [1] * (n_samples_2)# fit the model and get the separating hyperplaneclf = svm.SVC(kernel='linear', C=1.0)clf.fit(X, y)w = clf.coef_[0]a = -w[0] / w[1]xx = np.linspace(-5, 5)yy = a * xx - clf.intercept_[0] / w[1]# get the separating hyperplane using weighted classeswclf = svm.SVC(kernel='linear', class_weight={1: 10})wclf.fit(X, y)ww = wclf.coef_[0]wa = -ww[0] / ww[1]wyy = wa * xx - wclf.intercept_[0] / ww[1]# plot separating hyperplanes and samplesh0 = plt.plot(xx, yy, 'k-', label='no weights')h1 = plt.plot(xx, wyy, 'k--', label='with weights')plt.scatter(X[:, 0], X[:, 1], c=y, cmap=plt.cm.Paired)plt.legend()plt.axis('tight')plt.show() |
Total running time of the script: (0 minutes 0.077 seconds)
Download Python source code:
plot_separating_hyperplane_unbalanced.py
Download IPython notebook:
plot_separating_hyperplane_unbalanced.ipynb
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