Pushing the docs to dev/ for branch: master, commit 2ccc946157d40bbb8bb17b70e98df6af49d5f40c

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -15,7 +15,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n==============\nSGD: Penalties\n==============\n\nPlot the contours of the three penalties.\n\nAll of the above are supported by\n:class:`sklearn.linear_model.stochastic_gradient`.\n\n\n"
+        "\n==============\nSGD: Penalties\n==============\n\nContours of where the penalty is equal to 1\nfor the three penalties L1, L2 and elastic-net.\n\nAll of the above are supported by\n:class:`sklearn.linear_model.stochastic_gradient`.\n\n\n"
       ]
     },
     {
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "from __future__ import division\nprint(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\ndef l1(xs):\n    return np.array([np.sqrt((1 - np.sqrt(x ** 2.0)) ** 2.0) for x in xs])\n\n\ndef l2(xs):\n    return np.array([np.sqrt(1.0 - x ** 2.0) for x in xs])\n\n\ndef el(xs, z):\n    return np.array([(2 - 2 * x - 2 * z + 4 * x * z -\n                      (4 * z ** 2\n                       - 8 * x * z ** 2\n                       + 8 * x ** 2 * z ** 2\n                       - 16 * x ** 2 * z ** 3\n                       + 8 * x * z ** 3 + 4 * x ** 2 * z ** 4) ** (1. / 2)\n                      - 2 * x * z ** 2) / (2 - 4 * z) for x in xs])\n\n\ndef cross(ext):\n    plt.plot([-ext, ext], [0, 0], \"k-\")\n    plt.plot([0, 0], [-ext, ext], \"k-\")\n\nxs = np.linspace(0, 1, 100)\n\nalpha = 0.501  # 0.5 division throuh zero\n\ncross(1.2)\n\nl1_color = \"navy\"\nl2_color = \"c\"\nelastic_net_color = \"darkorange\"\nlw = 2\n\nplt.plot(xs, l1(xs), color=l1_color, label=\"L1\", lw=lw)\nplt.plot(xs, -1.0 * l1(xs), color=l1_color, lw=lw)\nplt.plot(-1 * xs, l1(xs), color=l1_color, lw=lw)\nplt.plot(-1 * xs, -1.0 * l1(xs), color=l1_color, lw=lw)\n\nplt.plot(xs, l2(xs), color=l2_color, label=\"L2\", lw=lw)\nplt.plot(xs, -1.0 * l2(xs), color=l2_color, lw=lw)\nplt.plot(-1 * xs, l2(xs), color=l2_color, lw=lw)\nplt.plot(-1 * xs, -1.0 * l2(xs), color=l2_color, lw=lw)\n\nplt.plot(xs, el(xs, alpha), color=elastic_net_color, label=\"Elastic Net\", lw=lw)\nplt.plot(xs, -1.0 * el(xs, alpha), color=elastic_net_color, lw=lw)\nplt.plot(-1 * xs, el(xs, alpha), color=elastic_net_color, lw=lw)\nplt.plot(-1 * xs, -1.0 * el(xs, alpha), color=elastic_net_color, lw=lw)\n\nplt.xlabel(r\"$w_0$\")\nplt.ylabel(r\"$w_1$\")\nplt.legend()\n\nplt.axis(\"equal\")\nplt.show()"
+        "print(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nl1_color = \"navy\"\nl2_color = \"c\"\nelastic_net_color = \"darkorange\"\n\nline = np.linspace(-1.5, 1.5, 1001)\nxx, yy = np.meshgrid(line, line)\n\nl2 = xx ** 2 + yy ** 2\nl1 = np.abs(xx) + np.abs(yy)\nrho = 0.5\nelastic_net = rho * l1 + (1 - rho) * l2\n\nplt.figure(figsize=(10, 10), dpi=100)\nax = plt.gca()\n\nelastic_net_contour = plt.contour(xx, yy, elastic_net, levels=[1],\n                                  colors=elastic_net_color)\nl2_contour = plt.contour(xx, yy, l2, levels=[1], colors=l2_color)\nl1_contour = plt.contour(xx, yy, l1, levels=[1], colors=l1_color)\nax.set_aspect(\"equal\")\nax.spines['left'].set_position('center')\nax.spines['right'].set_color('none')\nax.spines['bottom'].set_position('center')\nax.spines['top'].set_color('none')\n\nplt.clabel(elastic_net_contour, inline=1, fontsize=18,\n           fmt={1.0: 'elastic-net'}, manual=[(-1, -1)])\nplt.clabel(l2_contour, inline=1, fontsize=18,\n           fmt={1.0: 'L2'}, manual=[(-1, -1)])\nplt.clabel(l1_contour, inline=1, fontsize=18,\n           fmt={1.0: 'L1'}, manual=[(-1, -1)])\n\nplt.tight_layout()\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -3,70 +3,49 @@
 SGD: Penalties
 ==============
 
-Plot the contours of the three 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
 :class:`sklearn.linear_model.stochastic_gradient`.
 
 """
-from __future__ import division
 print(__doc__)
 
 import numpy as np
 import matplotlib.pyplot as plt
 
-
-def l1(xs):
-    return np.array([np.sqrt((1 - np.sqrt(x ** 2.0)) ** 2.0) for x in xs])
-
-
-def l2(xs):
-    return np.array([np.sqrt(1.0 - x ** 2.0) for x in xs])
-
-
-def el(xs, z):
-    return np.array([(2 - 2 * x - 2 * z + 4 * x * z -
-                      (4 * z ** 2
-                       - 8 * x * z ** 2
-                       + 8 * x ** 2 * z ** 2
-                       - 16 * x ** 2 * z ** 3
-                       + 8 * x * z ** 3 + 4 * x ** 2 * z ** 4) ** (1. / 2)
-                      - 2 * x * z ** 2) / (2 - 4 * z) for x in xs])
-
-
-def cross(ext):
-    plt.plot([-ext, ext], [0, 0], "k-")
-    plt.plot([0, 0], [-ext, ext], "k-")
-
-xs = np.linspace(0, 1, 100)
-
-alpha = 0.501  # 0.5 division throuh zero
-
-cross(1.2)
-
 l1_color = "navy"
 l2_color = "c"
 elastic_net_color = "darkorange"
-lw = 2
-
-plt.plot(xs, l1(xs), color=l1_color, label="L1", lw=lw)
-plt.plot(xs, -1.0 * l1(xs), color=l1_color, lw=lw)
-plt.plot(-1 * xs, l1(xs), color=l1_color, lw=lw)
-plt.plot(-1 * xs, -1.0 * l1(xs), color=l1_color, lw=lw)
-
-plt.plot(xs, l2(xs), color=l2_color, label="L2", lw=lw)
-plt.plot(xs, -1.0 * l2(xs), color=l2_color, lw=lw)
-plt.plot(-1 * xs, l2(xs), color=l2_color, lw=lw)
-plt.plot(-1 * xs, -1.0 * l2(xs), color=l2_color, lw=lw)
-
-plt.plot(xs, el(xs, alpha), color=elastic_net_color, label="Elastic Net", lw=lw)
-plt.plot(xs, -1.0 * el(xs, alpha), color=elastic_net_color, lw=lw)
-plt.plot(-1 * xs, el(xs, alpha), color=elastic_net_color, lw=lw)
-plt.plot(-1 * xs, -1.0 * el(xs, alpha), color=elastic_net_color, lw=lw)
-
-plt.xlabel(r"$w_0$")
-plt.ylabel(r"$w_1$")
-plt.legend()
 
-plt.axis("equal")
+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()