.. _sphx_glr_auto_examples_linear_model_plot_sgd_weighted_samples.py: ===================== SGD: Weighted samples ===================== Plot decision function of a weighted dataset, where the size of points is proportional to its weight. .. image:: /auto_examples/linear_model/images/sphx_glr_plot_sgd_weighted_samples_001.png :align: center .. code-block:: python 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 0.064 seconds) .. container:: sphx-glr-footer .. container:: sphx-glr-download :download:`Download Python source code: plot_sgd_weighted_samples.py ` .. container:: sphx-glr-download :download:`Download Jupyter notebook: plot_sgd_weighted_samples.ipynb ` .. rst-class:: sphx-glr-signature `Generated by Sphinx-Gallery `_