Plot decision function of a weighted dataset, where the size of points is proportional to its weight.
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 | print(__doc__)import numpy as npimport matplotlib.pyplot as pltfrom sklearn import linear_model# we create 20 pointsnp.random.seed(0)X = np.r_[np.random.randn(10, 2) + [1, 1], np.random.randn(10, 2)]y = [1] * 10 + [-1] * 10sample_weight = 100 * np.abs(np.random.randn(20))# and assign a bigger weight to the last 10 samplessample_weight[:10] *= 10# plot the weighted data pointsxx, yy = np.meshgrid(np.linspace(-4, 5, 500), np.linspace(-4, 5, 500))plt.figure()plt.scatter(X[:, 0], X[:, 1], c=y, s=sample_weight, alpha=0.9, cmap=plt.cm.bone)## fit the unweighted modelclf = linear_model.SGDClassifier(alpha=0.01, n_iter=100)clf.fit(X, y)Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])Z = Z.reshape(xx.shape)no_weights = plt.contour(xx, yy, Z, levels=[0], linestyles=['solid'])## fit the weighted modelclf = linear_model.SGDClassifier(alpha=0.01, n_iter=100)clf.fit(X, y, sample_weight=sample_weight)Z = clf.decision_function(np.c_[xx.ravel(), yy.ravel()])Z = Z.reshape(xx.shape)samples_weights = plt.contour(xx, yy, Z, levels=[0], linestyles=['dashed'])plt.legend([no_weights.collections[0], samples_weights.collections[0]], ["no weights", "with weights"], loc="lower left")plt.xticks(())plt.yticks(())plt.show() |
Total running time of the script: (0 minutes 0.107 seconds)
Download Python source code:
plot_sgd_weighted_samples.py
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plot_sgd_weighted_samples.ipynb
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