55 lines
1.3 KiB
Python
55 lines
1.3 KiB
Python
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"""
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==============
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SGD: Penalties
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==============
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Contours of where the penalty is equal to 1
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for the three penalties L1, L2 and elastic-net.
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All of the above are supported by :class:`~sklearn.linear_model.SGDClassifier`
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and :class:`~sklearn.linear_model.SGDRegressor`.
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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l1_color = "navy"
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l2_color = "c"
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elastic_net_color = "darkorange"
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line = np.linspace(-1.5, 1.5, 1001)
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xx, yy = np.meshgrid(line, line)
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l2 = xx**2 + yy**2
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l1 = np.abs(xx) + np.abs(yy)
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rho = 0.5
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elastic_net = rho * l1 + (1 - rho) * l2
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plt.figure(figsize=(10, 10), dpi=100)
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ax = plt.gca()
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elastic_net_contour = plt.contour(
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xx, yy, elastic_net, levels=[1], colors=elastic_net_color
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)
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l2_contour = plt.contour(xx, yy, l2, levels=[1], colors=l2_color)
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l1_contour = plt.contour(xx, yy, l1, levels=[1], colors=l1_color)
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ax.set_aspect("equal")
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ax.spines["left"].set_position("center")
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ax.spines["right"].set_color("none")
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ax.spines["bottom"].set_position("center")
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ax.spines["top"].set_color("none")
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plt.clabel(
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elastic_net_contour,
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inline=1,
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fontsize=18,
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fmt={1.0: "elastic-net"},
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manual=[(-1, -1)],
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)
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plt.clabel(l2_contour, inline=1, fontsize=18, fmt={1.0: "L2"}, manual=[(-1, -1)])
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plt.clabel(l1_contour, inline=1, fontsize=18, fmt={1.0: "L1"}, manual=[(-1, -1)])
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plt.tight_layout()
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plt.show()
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