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@@ -16,7 +16,6 @@
 from itertools import cycle
 
 import matplotlib.pyplot as plt
-import numpy as np
 
 from sklearn import datasets
 from sklearn.linear_model import enet_path, lasso_path
@@ -49,41 +48,37 @@
 
 plt.figure(1)
 colors = cycle(["b", "r", "g", "c", "k"])
-neg_log_alphas_lasso = -np.log10(alphas_lasso)
-neg_log_alphas_enet = -np.log10(alphas_enet)
 for coef_l, coef_e, c in zip(coefs_lasso, coefs_enet, colors):
-    l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c)
-    l2 = plt.plot(neg_log_alphas_enet, coef_e, linestyle="--", c=c)
+    l1 = plt.semilogx(alphas_lasso, coef_l, c=c)
+    l2 = plt.semilogx(alphas_enet, coef_e, linestyle="--", c=c)
 
-plt.xlabel("-Log(alpha)")
+plt.xlabel("alpha")
 plt.ylabel("coefficients")
 plt.title("Lasso and Elastic-Net Paths")
-plt.legend((l1[-1], l2[-1]), ("Lasso", "Elastic-Net"), loc="lower left")
+plt.legend((l1[-1], l2[-1]), ("Lasso", "Elastic-Net"), loc="lower right")
 plt.axis("tight")
 
 
 plt.figure(2)
-neg_log_alphas_positive_lasso = -np.log10(alphas_positive_lasso)
 for coef_l, coef_pl, c in zip(coefs_lasso, coefs_positive_lasso, colors):
-    l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c)
-    l2 = plt.plot(neg_log_alphas_positive_lasso, coef_pl, linestyle="--", c=c)
+    l1 = plt.semilogy(alphas_lasso, coef_l, c=c)
+    l2 = plt.semilogy(alphas_positive_lasso, coef_pl, linestyle="--", c=c)
 
-plt.xlabel("-Log(alpha)")
+plt.xlabel("alpha")
 plt.ylabel("coefficients")
 plt.title("Lasso and positive Lasso")
-plt.legend((l1[-1], l2[-1]), ("Lasso", "positive Lasso"), loc="lower left")
+plt.legend((l1[-1], l2[-1]), ("Lasso", "positive Lasso"), loc="lower right")
 plt.axis("tight")
 
 
 plt.figure(3)
-neg_log_alphas_positive_enet = -np.log10(alphas_positive_enet)
 for coef_e, coef_pe, c in zip(coefs_enet, coefs_positive_enet, colors):
-    l1 = plt.plot(neg_log_alphas_enet, coef_e, c=c)
-    l2 = plt.plot(neg_log_alphas_positive_enet, coef_pe, linestyle="--", c=c)
+    l1 = plt.semilogx(alphas_enet, coef_e, c=c)
+    l2 = plt.semilogx(alphas_positive_enet, coef_pe, linestyle="--", c=c)
 
-plt.xlabel("-Log(alpha)")
+plt.xlabel("alpha")
 plt.ylabel("coefficients")
 plt.title("Elastic-Net and positive Elastic-Net")
-plt.legend((l1[-1], l2[-1]), ("Elastic-Net", "positive Elastic-Net"), loc="lower left")
+plt.legend((l1[-1], l2[-1]), ("Elastic-Net", "positive Elastic-Net"), loc="lower right")
 plt.axis("tight")
 plt.show()
@@ -15,7 +15,7 @@
       },
       "outputs": [],
       "source": [
-        "# Author: Alexandre Gramfort <[email protected]>\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom itertools import cycle\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import datasets\nfrom sklearn.linear_model import enet_path, lasso_path\n\nX, y = datasets.load_diabetes(return_X_y=True)\n\n\nX /= X.std(axis=0)  # Standardize data (easier to set the l1_ratio parameter)\n\n# Compute paths\n\neps = 5e-3  # the smaller it is the longer is the path\n\nprint(\"Computing regularization path using the lasso...\")\nalphas_lasso, coefs_lasso, _ = lasso_path(X, y, eps=eps)\n\nprint(\"Computing regularization path using the positive lasso...\")\nalphas_positive_lasso, coefs_positive_lasso, _ = lasso_path(\n    X, y, eps=eps, positive=True\n)\nprint(\"Computing regularization path using the elastic net...\")\nalphas_enet, coefs_enet, _ = enet_path(X, y, eps=eps, l1_ratio=0.8)\n\nprint(\"Computing regularization path using the positive elastic net...\")\nalphas_positive_enet, coefs_positive_enet, _ = enet_path(\n    X, y, eps=eps, l1_ratio=0.8, positive=True\n)\n\n# Display results\n\nplt.figure(1)\ncolors = cycle([\"b\", \"r\", \"g\", \"c\", \"k\"])\nneg_log_alphas_lasso = -np.log10(alphas_lasso)\nneg_log_alphas_enet = -np.log10(alphas_enet)\nfor coef_l, coef_e, c in zip(coefs_lasso, coefs_enet, colors):\n    l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c)\n    l2 = plt.plot(neg_log_alphas_enet, coef_e, linestyle=\"--\", c=c)\n\nplt.xlabel(\"-Log(alpha)\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Lasso and Elastic-Net Paths\")\nplt.legend((l1[-1], l2[-1]), (\"Lasso\", \"Elastic-Net\"), loc=\"lower left\")\nplt.axis(\"tight\")\n\n\nplt.figure(2)\nneg_log_alphas_positive_lasso = -np.log10(alphas_positive_lasso)\nfor coef_l, coef_pl, c in zip(coefs_lasso, coefs_positive_lasso, colors):\n    l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c)\n    l2 = plt.plot(neg_log_alphas_positive_lasso, coef_pl, linestyle=\"--\", c=c)\n\nplt.xlabel(\"-Log(alpha)\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Lasso and positive Lasso\")\nplt.legend((l1[-1], l2[-1]), (\"Lasso\", \"positive Lasso\"), loc=\"lower left\")\nplt.axis(\"tight\")\n\n\nplt.figure(3)\nneg_log_alphas_positive_enet = -np.log10(alphas_positive_enet)\nfor coef_e, coef_pe, c in zip(coefs_enet, coefs_positive_enet, colors):\n    l1 = plt.plot(neg_log_alphas_enet, coef_e, c=c)\n    l2 = plt.plot(neg_log_alphas_positive_enet, coef_pe, linestyle=\"--\", c=c)\n\nplt.xlabel(\"-Log(alpha)\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Elastic-Net and positive Elastic-Net\")\nplt.legend((l1[-1], l2[-1]), (\"Elastic-Net\", \"positive Elastic-Net\"), loc=\"lower left\")\nplt.axis(\"tight\")\nplt.show()"
+        "# Author: Alexandre Gramfort <[email protected]>\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom itertools import cycle\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn import datasets\nfrom sklearn.linear_model import enet_path, lasso_path\n\nX, y = datasets.load_diabetes(return_X_y=True)\n\n\nX /= X.std(axis=0)  # Standardize data (easier to set the l1_ratio parameter)\n\n# Compute paths\n\neps = 5e-3  # the smaller it is the longer is the path\n\nprint(\"Computing regularization path using the lasso...\")\nalphas_lasso, coefs_lasso, _ = lasso_path(X, y, eps=eps)\n\nprint(\"Computing regularization path using the positive lasso...\")\nalphas_positive_lasso, coefs_positive_lasso, _ = lasso_path(\n    X, y, eps=eps, positive=True\n)\nprint(\"Computing regularization path using the elastic net...\")\nalphas_enet, coefs_enet, _ = enet_path(X, y, eps=eps, l1_ratio=0.8)\n\nprint(\"Computing regularization path using the positive elastic net...\")\nalphas_positive_enet, coefs_positive_enet, _ = enet_path(\n    X, y, eps=eps, l1_ratio=0.8, positive=True\n)\n\n# Display results\n\nplt.figure(1)\ncolors = cycle([\"b\", \"r\", \"g\", \"c\", \"k\"])\nfor coef_l, coef_e, c in zip(coefs_lasso, coefs_enet, colors):\n    l1 = plt.semilogx(alphas_lasso, coef_l, c=c)\n    l2 = plt.semilogx(alphas_enet, coef_e, linestyle=\"--\", c=c)\n\nplt.xlabel(\"alpha\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Lasso and Elastic-Net Paths\")\nplt.legend((l1[-1], l2[-1]), (\"Lasso\", \"Elastic-Net\"), loc=\"lower right\")\nplt.axis(\"tight\")\n\n\nplt.figure(2)\nfor coef_l, coef_pl, c in zip(coefs_lasso, coefs_positive_lasso, colors):\n    l1 = plt.semilogy(alphas_lasso, coef_l, c=c)\n    l2 = plt.semilogy(alphas_positive_lasso, coef_pl, linestyle=\"--\", c=c)\n\nplt.xlabel(\"alpha\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Lasso and positive Lasso\")\nplt.legend((l1[-1], l2[-1]), (\"Lasso\", \"positive Lasso\"), loc=\"lower right\")\nplt.axis(\"tight\")\n\n\nplt.figure(3)\nfor coef_e, coef_pe, c in zip(coefs_enet, coefs_positive_enet, colors):\n    l1 = plt.semilogx(alphas_enet, coef_e, c=c)\n    l2 = plt.semilogx(alphas_positive_enet, coef_pe, linestyle=\"--\", c=c)\n\nplt.xlabel(\"alpha\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Elastic-Net and positive Elastic-Net\")\nplt.legend((l1[-1], l2[-1]), (\"Elastic-Net\", \"positive Elastic-Net\"), loc=\"lower right\")\nplt.axis(\"tight\")\nplt.show()"
       ]
     }
   ],
Original file line number	Diff line number	Diff line change
`@@ -15,7 +15,7 @@`
`15`	`15`	`},`
`16`	`16`	`"outputs": [],`
`17`	`17`	`"source": [`
`18`		- "# Author: Alexandre Gramfort <[email protected]>\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom itertools import cycle\n\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom sklearn import datasets\nfrom sklearn.linear_model import enet_path, lasso_path\n\nX, y = datasets.load_diabetes(return_X_y=True)\n\n\nX /= X.std(axis=0) # Standardize data (easier to set the l1_ratio parameter)\n\n# Compute paths\n\neps = 5e-3 # the smaller it is the longer is the path\n\nprint(\"Computing regularization path using the lasso...\")\nalphas_lasso, coefs_lasso, _ = lasso_path(X, y, eps=eps)\n\nprint(\"Computing regularization path using the positive lasso...\")\nalphas_positive_lasso, coefs_positive_lasso, _ = lasso_path(\n X, y, eps=eps, positive=True\n)\nprint(\"Computing regularization path using the elastic net...\")\nalphas_enet, coefs_enet, _ = enet_path(X, y, eps=eps, l1_ratio=0.8)\n\nprint(\"Computing regularization path using the positive elastic net...\")\nalphas_positive_enet, coefs_positive_enet, _ = enet_path(\n X, y, eps=eps, l1_ratio=0.8, positive=True\n)\n\n# Display results\n\nplt.figure(1)\ncolors = cycle([\"b\", \"r\", \"g\", \"c\", \"k\"])\nneg_log_alphas_lasso = -np.log10(alphas_lasso)\nneg_log_alphas_enet = -np.log10(alphas_enet)\nfor coef_l, coef_e, c in zip(coefs_lasso, coefs_enet, colors):\n l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c)\n l2 = plt.plot(neg_log_alphas_enet, coef_e, linestyle=\"--\", c=c)\n\nplt.xlabel(\"-Log(alpha)\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Lasso and Elastic-Net Paths\")\nplt.legend((l1[-1], l2[-1]), (\"Lasso\", \"Elastic-Net\"), loc=\"lower left\")\nplt.axis(\"tight\")\n\n\nplt.figure(2)\nneg_log_alphas_positive_lasso = -np.log10(alphas_positive_lasso)\nfor coef_l, coef_pl, c in zip(coefs_lasso, coefs_positive_lasso, colors):\n l1 = plt.plot(neg_log_alphas_lasso, coef_l, c=c)\n l2 = plt.plot(neg_log_alphas_positive_lasso, coef_pl, linestyle=\"--\", c=c)\n\nplt.xlabel(\"-Log(alpha)\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Lasso and positive Lasso\")\nplt.legend((l1[-1], l2[-1]), (\"Lasso\", \"positive Lasso\"), loc=\"lower left\")\nplt.axis(\"tight\")\n\n\nplt.figure(3)\nneg_log_alphas_positive_enet = -np.log10(alphas_positive_enet)\nfor coef_e, coef_pe, c in zip(coefs_enet, coefs_positive_enet, colors):\n l1 = plt.plot(neg_log_alphas_enet, coef_e, c=c)\n l2 = plt.plot(neg_log_alphas_positive_enet, coef_pe, linestyle=\"--\", c=c)\n\nplt.xlabel(\"-Log(alpha)\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Elastic-Net and positive Elastic-Net\")\nplt.legend((l1[-1], l2[-1]), (\"Elastic-Net\", \"positive Elastic-Net\"), loc=\"lower left\")\nplt.axis(\"tight\")\nplt.show()"
	`18`	+ "# Author: Alexandre Gramfort <[email protected]>\n# SPDX-License-Identifier: BSD-3-Clause\n\nfrom itertools import cycle\n\nimport matplotlib.pyplot as plt\n\nfrom sklearn import datasets\nfrom sklearn.linear_model import enet_path, lasso_path\n\nX, y = datasets.load_diabetes(return_X_y=True)\n\n\nX /= X.std(axis=0) # Standardize data (easier to set the l1_ratio parameter)\n\n# Compute paths\n\neps = 5e-3 # the smaller it is the longer is the path\n\nprint(\"Computing regularization path using the lasso...\")\nalphas_lasso, coefs_lasso, _ = lasso_path(X, y, eps=eps)\n\nprint(\"Computing regularization path using the positive lasso...\")\nalphas_positive_lasso, coefs_positive_lasso, _ = lasso_path(\n X, y, eps=eps, positive=True\n)\nprint(\"Computing regularization path using the elastic net...\")\nalphas_enet, coefs_enet, _ = enet_path(X, y, eps=eps, l1_ratio=0.8)\n\nprint(\"Computing regularization path using the positive elastic net...\")\nalphas_positive_enet, coefs_positive_enet, _ = enet_path(\n X, y, eps=eps, l1_ratio=0.8, positive=True\n)\n\n# Display results\n\nplt.figure(1)\ncolors = cycle([\"b\", \"r\", \"g\", \"c\", \"k\"])\nfor coef_l, coef_e, c in zip(coefs_lasso, coefs_enet, colors):\n l1 = plt.semilogx(alphas_lasso, coef_l, c=c)\n l2 = plt.semilogx(alphas_enet, coef_e, linestyle=\"--\", c=c)\n\nplt.xlabel(\"alpha\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Lasso and Elastic-Net Paths\")\nplt.legend((l1[-1], l2[-1]), (\"Lasso\", \"Elastic-Net\"), loc=\"lower right\")\nplt.axis(\"tight\")\n\n\nplt.figure(2)\nfor coef_l, coef_pl, c in zip(coefs_lasso, coefs_positive_lasso, colors):\n l1 = plt.semilogy(alphas_lasso, coef_l, c=c)\n l2 = plt.semilogy(alphas_positive_lasso, coef_pl, linestyle=\"--\", c=c)\n\nplt.xlabel(\"alpha\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Lasso and positive Lasso\")\nplt.legend((l1[-1], l2[-1]), (\"Lasso\", \"positive Lasso\"), loc=\"lower right\")\nplt.axis(\"tight\")\n\n\nplt.figure(3)\nfor coef_e, coef_pe, c in zip(coefs_enet, coefs_positive_enet, colors):\n l1 = plt.semilogx(alphas_enet, coef_e, c=c)\n l2 = plt.semilogx(alphas_positive_enet, coef_pe, linestyle=\"--\", c=c)\n\nplt.xlabel(\"alpha\")\nplt.ylabel(\"coefficients\")\nplt.title(\"Elastic-Net and positive Elastic-Net\")\nplt.legend((l1[-1], l2[-1]), (\"Elastic-Net\", \"positive Elastic-Net\"), loc=\"lower right\")\nplt.axis(\"tight\")\nplt.show()"
`19`	`19`	`]`
`20`	`20`	`}`
`21`	`21`	`],`