SGD: Weighted samples

Plot decision function of a weighted dataset, where the size of points is proportional to its weight.

Python source code: plot_sgd_weighted_samples.py

print __doc__

import numpy as np
import pylab as pl
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))
pl.set_cmap(pl.cm.bone)
pl.scatter(X[:, 0], X[:, 1], c=y, s=sample_weight, alpha=0.9)

## fit the unweighted model
clf = 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)
pl.contour(xx, yy, Z, levels=[0], linestyles=['solid'])

## fit the weighted model
clf = 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)
pl.contour(xx, yy, Z, levels=[0], linestyles=['dashed'])

pl.legend(["no weights", "with weights"],
          loc="lower left")

pl.axis('off')
pl.show()