Note
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Plot decision function of a weighted dataset, where the size of points is proportional to its weight.
print(__doc__) import numpy as np import matplotlib.pyplot as plt from sklearn import linear_model # we create 20 points np.random.seed(0) X = np.r_[np.random.randn(10, 2) + [1, 1], np.random.randn(10, 2)] y = [1] * 10 + [-1] * 10 sample_weight = 100 * np.abs(np.random.randn(20)) # and assign a bigger weight to the last 10 samples sample_weight[:10] *= 10 # plot the weighted data points xx, 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, edgecolor='black') # fit the unweighted model clf = linear_model.SGDClassifier(alpha=0.01, max_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 model clf = linear_model.SGDClassifier(alpha=0.01, max_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 0.097 seconds)
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http://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_weighted_samples.html