Note
Click here to download the full example code or to run this example in your browser via Binder
SGD: Penalties¶
Contours of where the penalty is equal to 1 for the three penalties L1, L2 and elastic-net.
All of the above are supported by SGDClassifier
and SGDRegressor.
print(__doc__)
import numpy as np
import matplotlib.pyplot as plt
l1_color = "navy"
l2_color = "c"
elastic_net_color = "darkorange"
line = np.linspace(-1.5, 1.5, 1001)
xx, yy = np.meshgrid(line, line)
l2 = xx ** 2 + yy ** 2
l1 = np.abs(xx) + np.abs(yy)
rho = 0.5
elastic_net = rho * l1 + (1 - rho) * l2
plt.figure(figsize=(10, 10), dpi=100)
ax = plt.gca()
elastic_net_contour = plt.contour(xx, yy, elastic_net, levels=[1],
colors=elastic_net_color)
l2_contour = plt.contour(xx, yy, l2, levels=[1], colors=l2_color)
l1_contour = plt.contour(xx, yy, l1, levels=[1], colors=l1_color)
ax.set_aspect("equal")
ax.spines['left'].set_position('center')
ax.spines['right'].set_color('none')
ax.spines['bottom'].set_position('center')
ax.spines['top'].set_color('none')
plt.clabel(elastic_net_contour, inline=1, fontsize=18,
fmt={1.0: 'elastic-net'}, manual=[(-1, -1)])
plt.clabel(l2_contour, inline=1, fontsize=18,
fmt={1.0: 'L2'}, manual=[(-1, -1)])
plt.clabel(l1_contour, inline=1, fontsize=18,
fmt={1.0: 'L1'}, manual=[(-1, -1)])
plt.tight_layout()
plt.show()
Total running time of the script: ( 0 minutes 0.635 seconds)
Estimated memory usage: 8 MB
