58 lines
1.4 KiB
Python
58 lines
1.4 KiB
Python
"""
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================================================
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SGD: Separating hyperplane with weighted classes
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================================================
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Fit linear SVMs with and without class weighting.
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Allows to handle problems with unbalanced classes.
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"""
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print __doc__
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import numpy as np
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import pylab as pl
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from sklearn.linear_model import SGDClassifier
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# we create 40 separable points
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np.random.seed(0)
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n_samples_1 = 1000
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n_samples_2 = 100
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X = np.r_[1.5 * np.random.randn(n_samples_1, 2),
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0.5 * np.random.randn(n_samples_2, 2) + [2, 2]]
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y = np.array([0] * (n_samples_1) + [1] * (n_samples_2), dtype=np.float64)
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idx = np.arange(y.shape[0])
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np.random.shuffle(idx)
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X = X[idx]
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y = y[idx]
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mean = X.mean(axis=0)
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std = X.std(axis=0)
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X = (X - mean) / std
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# fit the model and get the separating hyperplane
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clf = SGDClassifier(n_iter=100, alpha=0.01)
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clf.fit(X, y)
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w = clf.coef_.ravel()
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a = -w[0] / w[1]
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xx = np.linspace(-5, 5)
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yy = a * xx - clf.intercept_ / w[1]
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# get the separating hyperplane using weighted classes
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wclf = SGDClassifier(n_iter=100, alpha=0.01, class_weight={1: 10})
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wclf.fit(X, y)
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ww = wclf.coef_.ravel()
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wa = -ww[0] / ww[1]
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wyy = wa * xx - wclf.intercept_ / ww[1]
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# plot separating hyperplanes and samples
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pl.set_cmap(pl.cm.Paired)
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h0 = pl.plot(xx, yy, 'k-', label='no weights')
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h1 = pl.plot(xx, wyy, 'k--', label='with weights')
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pl.scatter(X[:, 0], X[:, 1], c=y)
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pl.legend()
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pl.axis('tight')
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pl.show()
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