Pushing the docs to dev/ for branch: master, commit cb41c52625c7181db357cef6c0fdb30cec52cce2

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\n# Author: Fabian Pedregosa <[email protected]>\n#         Alexandre Gramfort <[email protected]>\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import linear_model\nfrom sklearn import datasets\n\ndiabetes = datasets.load_diabetes()\nX = diabetes.data\ny = diabetes.target\n\nprint(\"Computing regularization path using the LARS ...\")\nalphas, _, coefs = linear_model.lars_path(X, y, method='lasso', verbose=True)\n\nxx = np.sum(np.abs(coefs.T), axis=1)\nxx /= xx[-1]\n\nplt.plot(xx, coefs.T)\nymin, ymax = plt.ylim()\nplt.vlines(xx, ymin, ymax, linestyle='dashed')\nplt.xlabel('|coef| / max|coef|')\nplt.ylabel('Coefficients')\nplt.title('LASSO Path')\nplt.axis('tight')\nplt.show()"
+        "print(__doc__)\n\n# Author: Fabian Pedregosa <[email protected]>\n#         Alexandre Gramfort <[email protected]>\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import linear_model\nfrom sklearn import datasets\n\ndiabetes = datasets.load_diabetes()\nX = diabetes.data\ny = diabetes.target\n\nprint(\"Computing regularization path using the LARS ...\")\n_, _, coefs = linear_model.lars_path(X, y, method='lasso', verbose=True)\n\nxx = np.sum(np.abs(coefs.T), axis=1)\nxx /= xx[-1]\n\nplt.plot(xx, coefs.T)\nymin, ymax = plt.ylim()\nplt.vlines(xx, ymin, ymax, linestyle='dashed')\nplt.xlabel('|coef| / max|coef|')\nplt.ylabel('Coefficients')\nplt.title('LASSO Path')\nplt.axis('tight')\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -27,7 +27,7 @@
 y = diabetes.target
 
 print("Computing regularization path using the LARS ...")
-alphas, _, coefs = linear_model.lars_path(X, y, method='lasso', verbose=True)
+_, _, coefs = linear_model.lars_path(X, y, method='lasso', verbose=True)
 
 xx = np.sum(np.abs(coefs.T), axis=1)
 xx /= xx[-1]