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diff --git a/‎dev/_sources/auto_examples/applications/plot_face_recognition.rst.txt
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diff --git a/‎dev/_sources/auto_examples/applications/plot_model_complexity_influence.rst.txt
Lines changed: 15 additions & 15 deletions b/‎dev/_sources/auto_examples/applications/plot_model_complexity_influence.rst.txt
Lines changed: 15 additions & 15 deletions
@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\n# Author: Olivier Grisel, Gael Varoquaux, Alexandre Gramfort\n# License: BSD 3 clause\n\nimport time\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.linear_model import LassoCV, LassoLarsCV, LassoLarsIC\nfrom sklearn import datasets\n\n# This is to avoid division by zero while doing np.log10\nEPSILON = 1e-4\n\nX, y = datasets.load_diabetes(return_X_y=True)\n\nrng = np.random.RandomState(42)\nX = np.c_[X, rng.randn(X.shape[0], 14)]  # add some bad features\n\n# normalize data as done by Lars to allow for comparison\nX /= np.sqrt(np.sum(X ** 2, axis=0))\n\n# #############################################################################\n# LassoLarsIC: least angle regression with BIC/AIC criterion\n\nmodel_bic = LassoLarsIC(criterion='bic')\nt1 = time.time()\nmodel_bic.fit(X, y)\nt_bic = time.time() - t1\nalpha_bic_ = model_bic.alpha_\n\nmodel_aic = LassoLarsIC(criterion='aic')\nmodel_aic.fit(X, y)\nalpha_aic_ = model_aic.alpha_\n\n\ndef plot_ic_criterion(model, name, color):\n    alpha_ = model.alpha_ + EPSILON\n    alphas_ = model.alphas_ + EPSILON\n    criterion_ = model.criterion_\n    plt.plot(-np.log10(alphas_), criterion_, '--', color=color,\n             linewidth=3, label='%s criterion' % name)\n    plt.axvline(-np.log10(alpha_), color=color, linewidth=3,\n                label='alpha: %s estimate' % name)\n    plt.xlabel('-log(alpha)')\n    plt.ylabel('criterion')\n\n\nplt.figure()\nplot_ic_criterion(model_aic, 'AIC', 'b')\nplot_ic_criterion(model_bic, 'BIC', 'r')\nplt.legend()\nplt.title('Information-criterion for model selection (training time %.3fs)'\n          % t_bic)\n\n# #############################################################################\n# LassoCV: coordinate descent\n\n# Compute paths\nprint(\"Computing regularization path using the coordinate descent lasso...\")\nt1 = time.time()\nmodel = LassoCV(cv=20).fit(X, y)\nt_lasso_cv = time.time() - t1\n\n# Display results\nm_log_alphas = -np.log10(model.alphas_ + EPSILON)\n\nplt.figure()\nymin, ymax = 2300, 3800\nplt.plot(m_log_alphas, model.mse_path_, ':')\nplt.plot(m_log_alphas, model.mse_path_.mean(axis=-1), 'k',\n         label='Average across the folds', linewidth=2)\nplt.axvline(-np.log10(model.alpha_ + EPSILON), linestyle='--', color='k',\n            label='alpha: CV estimate')\n\nplt.legend()\n\nplt.xlabel('-log(alpha)')\nplt.ylabel('Mean square error')\nplt.title('Mean square error on each fold: coordinate descent '\n          '(train time: %.2fs)' % t_lasso_cv)\nplt.axis('tight')\nplt.ylim(ymin, ymax)\n\n# #############################################################################\n# LassoLarsCV: least angle regression\n\n# Compute paths\nprint(\"Computing regularization path using the Lars lasso...\")\nt1 = time.time()\nmodel = LassoLarsCV(cv=20).fit(X, y)\nt_lasso_lars_cv = time.time() - t1\n\n# Display results\nm_log_alphas = -np.log10(model.cv_alphas_ + EPSILON)\n\nplt.figure()\nplt.plot(m_log_alphas, model.mse_path_, ':')\nplt.plot(m_log_alphas, model.mse_path_.mean(axis=-1), 'k',\n         label='Average across the folds', linewidth=2)\nplt.axvline(-np.log10(model.alpha_), linestyle='--', color='k',\n            label='alpha CV')\nplt.legend()\n\nplt.xlabel('-log(alpha)')\nplt.ylabel('Mean square error')\nplt.title('Mean square error on each fold: Lars (train time: %.2fs)'\n          % t_lasso_lars_cv)\nplt.axis('tight')\nplt.ylim(ymin, ymax)\n\nplt.show()"
+        "print(__doc__)\n\n# Author: Olivier Grisel, Gael Varoquaux, Alexandre Gramfort\n# License: BSD 3 clause\n\nimport time\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.linear_model import LassoCV, LassoLarsCV, LassoLarsIC\nfrom sklearn import datasets\n\n# This is to avoid division by zero while doing np.log10\nEPSILON = 1e-4\n\nX, y = datasets.load_diabetes(return_X_y=True)\n\nrng = np.random.RandomState(42)\nX = np.c_[X, rng.randn(X.shape[0], 14)]  # add some bad features\n\n# normalize data as done by Lars to allow for comparison\nX /= np.sqrt(np.sum(X ** 2, axis=0))\n\n# #############################################################################\n# LassoLarsIC: least angle regression with BIC/AIC criterion\n\nmodel_bic = LassoLarsIC(criterion='bic')\nt1 = time.time()\nmodel_bic.fit(X, y)\nt_bic = time.time() - t1\nalpha_bic_ = model_bic.alpha_\n\nmodel_aic = LassoLarsIC(criterion='aic')\nmodel_aic.fit(X, y)\nalpha_aic_ = model_aic.alpha_\n\n\ndef plot_ic_criterion(model, name, color):\n    criterion_ = model.criterion_\n    plt.semilogx(model.alphas_ + EPSILON, criterion_, '--', color=color,\n                 linewidth=3, label='%s criterion' % name)\n    plt.axvline(model.alpha_ + EPSILON, color=color, linewidth=3,\n                label='alpha: %s estimate' % name)\n    plt.xlabel(r'$\\alpha$')\n    plt.ylabel('criterion')\n\n\nplt.figure()\nplot_ic_criterion(model_aic, 'AIC', 'b')\nplot_ic_criterion(model_bic, 'BIC', 'r')\nplt.legend()\nplt.title('Information-criterion for model selection (training time %.3fs)'\n          % t_bic)\n\n# #############################################################################\n# LassoCV: coordinate descent\n\n# Compute paths\nprint(\"Computing regularization path using the coordinate descent lasso...\")\nt1 = time.time()\nmodel = LassoCV(cv=20).fit(X, y)\nt_lasso_cv = time.time() - t1\n\n# Display results\nplt.figure()\nymin, ymax = 2300, 3800\nplt.semilogx(model.alphas_ + EPSILON, model.mse_path_, ':')\nplt.plot(model.alphas_ + EPSILON, model.mse_path_.mean(axis=-1), 'k',\n         label='Average across the folds', linewidth=2)\nplt.axvline(model.alpha_ + EPSILON, linestyle='--', color='k',\n            label='alpha: CV estimate')\n\nplt.legend()\n\nplt.xlabel(r'$\\alpha$')\nplt.ylabel('Mean square error')\nplt.title('Mean square error on each fold: coordinate descent '\n          '(train time: %.2fs)' % t_lasso_cv)\nplt.axis('tight')\nplt.ylim(ymin, ymax)\n\n# #############################################################################\n# LassoLarsCV: least angle regression\n\n# Compute paths\nprint(\"Computing regularization path using the Lars lasso...\")\nt1 = time.time()\nmodel = LassoLarsCV(cv=20).fit(X, y)\nt_lasso_lars_cv = time.time() - t1\n\n# Display results\nplt.figure()\nplt.semilogx(model.cv_alphas_ + EPSILON, model.mse_path_, ':')\nplt.semilogx(model.cv_alphas_ + EPSILON, model.mse_path_.mean(axis=-1), 'k',\n             label='Average across the folds', linewidth=2)\nplt.axvline(model.alpha_, linestyle='--', color='k',\n            label='alpha CV')\nplt.legend()\n\nplt.xlabel(r'$\\alpha$')\nplt.ylabel('Mean square error')\nplt.title('Mean square error on each fold: Lars (train time: %.2fs)'\n          % t_lasso_lars_cv)\nplt.axis('tight')\nplt.ylim(ymin, ymax)\n\nplt.show()"
       ]
     }
   ],
 
@@ -80,14 +80,12 @@
 
 
 def plot_ic_criterion(model, name, color):
-    alpha_ = model.alpha_ + EPSILON
-    alphas_ = model.alphas_ + EPSILON
     criterion_ = model.criterion_
-    plt.plot(-np.log10(alphas_), criterion_, '--', color=color,
-             linewidth=3, label='%s criterion' % name)
-    plt.axvline(-np.log10(alpha_), color=color, linewidth=3,
+    plt.semilogx(model.alphas_ + EPSILON, criterion_, '--', color=color,
+                 linewidth=3, label='%s criterion' % name)
+    plt.axvline(model.alpha_ + EPSILON, color=color, linewidth=3,
                 label='alpha: %s estimate' % name)
-    plt.xlabel('-log(alpha)')
+    plt.xlabel(r'$\alpha$')
     plt.ylabel('criterion')
 
 
@@ -108,19 +106,17 @@ def plot_ic_criterion(model, name, color):
 t_lasso_cv = time.time() - t1
 
 # Display results
-m_log_alphas = -np.log10(model.alphas_ + EPSILON)
-
 plt.figure()
 ymin, ymax = 2300, 3800
-plt.plot(m_log_alphas, model.mse_path_, ':')
-plt.plot(m_log_alphas, model.mse_path_.mean(axis=-1), 'k',
+plt.semilogx(model.alphas_ + EPSILON, model.mse_path_, ':')
+plt.plot(model.alphas_ + EPSILON, model.mse_path_.mean(axis=-1), 'k',
          label='Average across the folds', linewidth=2)
-plt.axvline(-np.log10(model.alpha_ + EPSILON), linestyle='--', color='k',
+plt.axvline(model.alpha_ + EPSILON, linestyle='--', color='k',
             label='alpha: CV estimate')
 
 plt.legend()
 
-plt.xlabel('-log(alpha)')
+plt.xlabel(r'$\alpha$')
 plt.ylabel('Mean square error')
 plt.title('Mean square error on each fold: coordinate descent '
           '(train time: %.2fs)' % t_lasso_cv)
@@ -137,17 +133,15 @@ def plot_ic_criterion(model, name, color):
 t_lasso_lars_cv = time.time() - t1
 
 # Display results
-m_log_alphas = -np.log10(model.cv_alphas_ + EPSILON)
-
 plt.figure()
-plt.plot(m_log_alphas, model.mse_path_, ':')
-plt.plot(m_log_alphas, model.mse_path_.mean(axis=-1), 'k',
-         label='Average across the folds', linewidth=2)
-plt.axvline(-np.log10(model.alpha_), linestyle='--', color='k',
+plt.semilogx(model.cv_alphas_ + EPSILON, model.mse_path_, ':')
+plt.semilogx(model.cv_alphas_ + EPSILON, model.mse_path_.mean(axis=-1), 'k',
+             label='Average across the folds', linewidth=2)
+plt.axvline(model.alpha_, linestyle='--', color='k',
             label='alpha CV')
 plt.legend()
 
-plt.xlabel('-log(alpha)')
+plt.xlabel(r'$\alpha$')
 plt.ylabel('Mean square error')
 plt.title('Mean square error on each fold: Lars (train time: %.2fs)'
           % t_lasso_lars_cv)
Original file line number	Diff line number	Diff line change
`@@ -26,7 +26,7 @@`
`26`	`26`	`},`
`27`	`27`	`"outputs": [],`
`28`	`28`	`"source": [`
`29`		- "print(__doc__)\n\n# Author: Olivier Grisel, Gael Varoquaux, Alexandre Gramfort\n# License: BSD 3 clause\n\nimport time\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.linear_model import LassoCV, LassoLarsCV, LassoLarsIC\nfrom sklearn import datasets\n\n# This is to avoid division by zero while doing np.log10\nEPSILON = 1e-4\n\nX, y = datasets.load_diabetes(return_X_y=True)\n\nrng = np.random.RandomState(42)\nX = np.c_[X, rng.randn(X.shape[0], 14)] # add some bad features\n\n# normalize data as done by Lars to allow for comparison\nX /= np.sqrt(np.sum(X ** 2, axis=0))\n\n# #############################################################################\n# LassoLarsIC: least angle regression with BIC/AIC criterion\n\nmodel_bic = LassoLarsIC(criterion='bic')\nt1 = time.time()\nmodel_bic.fit(X, y)\nt_bic = time.time() - t1\nalpha_bic_ = model_bic.alpha_\n\nmodel_aic = LassoLarsIC(criterion='aic')\nmodel_aic.fit(X, y)\nalpha_aic_ = model_aic.alpha_\n\n\ndef plot_ic_criterion(model, name, color):\n alpha_ = model.alpha_ + EPSILON\n alphas_ = model.alphas_ + EPSILON\n criterion_ = model.criterion_\n plt.plot(-np.log10(alphas_), criterion_, '--', color=color,\n linewidth=3, label='%s criterion' % name)\n plt.axvline(-np.log10(alpha_), color=color, linewidth=3,\n label='alpha: %s estimate' % name)\n plt.xlabel('-log(alpha)')\n plt.ylabel('criterion')\n\n\nplt.figure()\nplot_ic_criterion(model_aic, 'AIC', 'b')\nplot_ic_criterion(model_bic, 'BIC', 'r')\nplt.legend()\nplt.title('Information-criterion for model selection (training time %.3fs)'\n % t_bic)\n\n# #############################################################################\n# LassoCV: coordinate descent\n\n# Compute paths\nprint(\"Computing regularization path using the coordinate descent lasso...\")\nt1 = time.time()\nmodel = LassoCV(cv=20).fit(X, y)\nt_lasso_cv = time.time() - t1\n\n# Display results\nm_log_alphas = -np.log10(model.alphas_ + EPSILON)\n\nplt.figure()\nymin, ymax = 2300, 3800\nplt.plot(m_log_alphas, model.mse_path_, ':')\nplt.plot(m_log_alphas, model.mse_path_.mean(axis=-1), 'k',\n label='Average across the folds', linewidth=2)\nplt.axvline(-np.log10(model.alpha_ + EPSILON), linestyle='--', color='k',\n label='alpha: CV estimate')\n\nplt.legend()\n\nplt.xlabel('-log(alpha)')\nplt.ylabel('Mean square error')\nplt.title('Mean square error on each fold: coordinate descent '\n '(train time: %.2fs)' % t_lasso_cv)\nplt.axis('tight')\nplt.ylim(ymin, ymax)\n\n# #############################################################################\n# LassoLarsCV: least angle regression\n\n# Compute paths\nprint(\"Computing regularization path using the Lars lasso...\")\nt1 = time.time()\nmodel = LassoLarsCV(cv=20).fit(X, y)\nt_lasso_lars_cv = time.time() - t1\n\n# Display results\nm_log_alphas = -np.log10(model.cv_alphas_ + EPSILON)\n\nplt.figure()\nplt.plot(m_log_alphas, model.mse_path_, ':')\nplt.plot(m_log_alphas, model.mse_path_.mean(axis=-1), 'k',\n label='Average across the folds', linewidth=2)\nplt.axvline(-np.log10(model.alpha_), linestyle='--', color='k',\n label='alpha CV')\nplt.legend()\n\nplt.xlabel('-log(alpha)')\nplt.ylabel('Mean square error')\nplt.title('Mean square error on each fold: Lars (train time: %.2fs)'\n % t_lasso_lars_cv)\nplt.axis('tight')\nplt.ylim(ymin, ymax)\n\nplt.show()"
	`29`	+ "print(__doc__)\n\n# Author: Olivier Grisel, Gael Varoquaux, Alexandre Gramfort\n# License: BSD 3 clause\n\nimport time\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn.linear_model import LassoCV, LassoLarsCV, LassoLarsIC\nfrom sklearn import datasets\n\n# This is to avoid division by zero while doing np.log10\nEPSILON = 1e-4\n\nX, y = datasets.load_diabetes(return_X_y=True)\n\nrng = np.random.RandomState(42)\nX = np.c_[X, rng.randn(X.shape[0], 14)] # add some bad features\n\n# normalize data as done by Lars to allow for comparison\nX /= np.sqrt(np.sum(X ** 2, axis=0))\n\n# #############################################################################\n# LassoLarsIC: least angle regression with BIC/AIC criterion\n\nmodel_bic = LassoLarsIC(criterion='bic')\nt1 = time.time()\nmodel_bic.fit(X, y)\nt_bic = time.time() - t1\nalpha_bic_ = model_bic.alpha_\n\nmodel_aic = LassoLarsIC(criterion='aic')\nmodel_aic.fit(X, y)\nalpha_aic_ = model_aic.alpha_\n\n\ndef plot_ic_criterion(model, name, color):\n criterion_ = model.criterion_\n plt.semilogx(model.alphas_ + EPSILON, criterion_, '--', color=color,\n linewidth=3, label='%s criterion' % name)\n plt.axvline(model.alpha_ + EPSILON, color=color, linewidth=3,\n label='alpha: %s estimate' % name)\n plt.xlabel(r'$\\alpha$')\n plt.ylabel('criterion')\n\n\nplt.figure()\nplot_ic_criterion(model_aic, 'AIC', 'b')\nplot_ic_criterion(model_bic, 'BIC', 'r')\nplt.legend()\nplt.title('Information-criterion for model selection (training time %.3fs)'\n % t_bic)\n\n# #############################################################################\n# LassoCV: coordinate descent\n\n# Compute paths\nprint(\"Computing regularization path using the coordinate descent lasso...\")\nt1 = time.time()\nmodel = LassoCV(cv=20).fit(X, y)\nt_lasso_cv = time.time() - t1\n\n# Display results\nplt.figure()\nymin, ymax = 2300, 3800\nplt.semilogx(model.alphas_ + EPSILON, model.mse_path_, ':')\nplt.plot(model.alphas_ + EPSILON, model.mse_path_.mean(axis=-1), 'k',\n label='Average across the folds', linewidth=2)\nplt.axvline(model.alpha_ + EPSILON, linestyle='--', color='k',\n label='alpha: CV estimate')\n\nplt.legend()\n\nplt.xlabel(r'$\\alpha$')\nplt.ylabel('Mean square error')\nplt.title('Mean square error on each fold: coordinate descent '\n '(train time: %.2fs)' % t_lasso_cv)\nplt.axis('tight')\nplt.ylim(ymin, ymax)\n\n# #############################################################################\n# LassoLarsCV: least angle regression\n\n# Compute paths\nprint(\"Computing regularization path using the Lars lasso...\")\nt1 = time.time()\nmodel = LassoLarsCV(cv=20).fit(X, y)\nt_lasso_lars_cv = time.time() - t1\n\n# Display results\nplt.figure()\nplt.semilogx(model.cv_alphas_ + EPSILON, model.mse_path_, ':')\nplt.semilogx(model.cv_alphas_ + EPSILON, model.mse_path_.mean(axis=-1), 'k',\n label='Average across the folds', linewidth=2)\nplt.axvline(model.alpha_, linestyle='--', color='k',\n label='alpha CV')\nplt.legend()\n\nplt.xlabel(r'$\\alpha$')\nplt.ylabel('Mean square error')\nplt.title('Mean square error on each fold: Lars (train time: %.2fs)'\n % t_lasso_lars_cv)\nplt.axis('tight')\nplt.ylim(ymin, ymax)\n\nplt.show()"
`30`	`30`	`]`
`31`	`31`	`}`
`32`	`32`	`],`