Pushing the docs to dev/ for branch: master, commit cee48cdf8f67bca9408855d2cd5a20fabec60155

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -78,7 +78,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "# 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_)\n\nplt.figure()\nplt.plot(m_log_alphas, model.cv_mse_path_, ':')\nplt.plot(m_log_alphas, model.cv_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()"
+        "# 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_)\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()"
       ], 
       "outputs": [], 
       "metadata": {

dev/_downloads/auto_examples_python.zip

@@ -138,8 +138,8 @@ def plot_ic_criterion(model, name, color):
 m_log_alphas = -np.log10(model.cv_alphas_)
 
 plt.figure()
-plt.plot(m_log_alphas, model.cv_mse_path_, ':')
-plt.plot(m_log_alphas, model.cv_mse_path_.mean(axis=-1), 'k',
+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',
             label='alpha CV')