Pushing the docs to dev/ for branch: master, commit 3c6f2b09061a49a0ca281f6b651ae3dc61d7e34f

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -24,7 +24,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "from __future__ import print_function\nprint(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import datasets\nfrom sklearn.linear_model import LassoCV\nfrom sklearn.linear_model import Lasso\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import cross_val_score\n\ndiabetes = datasets.load_diabetes()\nX = diabetes.data[:150]\ny = diabetes.target[:150]\n\nlasso = Lasso(random_state=0)\nalphas = np.logspace(-4, -0.5, 30)\n\nscores = list()\nscores_std = list()\n\nn_folds = 3\n\nfor alpha in alphas:\n    lasso.alpha = alpha\n    this_scores = cross_val_score(lasso, X, y, cv=n_folds, n_jobs=1)\n    scores.append(np.mean(this_scores))\n    scores_std.append(np.std(this_scores))\n\nscores, scores_std = np.array(scores), np.array(scores_std)\n\nplt.figure().set_size_inches(8, 6)\nplt.semilogx(alphas, scores)\n\n# plot error lines showing +/- std. errors of the scores\nstd_error = scores_std / np.sqrt(n_folds)\n\nplt.semilogx(alphas, scores + std_error, 'b--')\nplt.semilogx(alphas, scores - std_error, 'b--')\n\n# alpha=0.2 controls the translucency of the fill color\nplt.fill_between(alphas, scores + std_error, scores - std_error, alpha=0.2)\n\nplt.ylabel('CV score +/- std error')\nplt.xlabel('alpha')\nplt.axhline(np.max(scores), linestyle='--', color='.5')\nplt.xlim([alphas[0], alphas[-1]])"
+        "from __future__ import print_function\nprint(__doc__)\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import datasets\nfrom sklearn.linear_model import LassoCV\nfrom sklearn.linear_model import Lasso\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import GridSearchCV\n\ndiabetes = datasets.load_diabetes()\nX = diabetes.data[:150]\ny = diabetes.target[:150]\n\nlasso = Lasso(random_state=0)\nalphas = np.logspace(-4, -0.5, 30)\n\ntuned_parameters = [{'alpha': alphas}]\nn_folds = 3\n\nclf = GridSearchCV(lasso, tuned_parameters, cv=n_folds, refit=False)\nclf.fit(X, y)\nscores = clf.cv_results_['mean_test_score']\nscores_std = clf.cv_results_['std_test_score']\nplt.figure().set_size_inches(8, 6)\nplt.semilogx(alphas, scores)\n\n# plot error lines showing +/- std. errors of the scores\nstd_error = scores_std / np.sqrt(n_folds)\n\nplt.semilogx(alphas, scores + std_error, 'b--')\nplt.semilogx(alphas, scores - std_error, 'b--')\n\n# alpha=0.2 controls the translucency of the fill color\nplt.fill_between(alphas, scores + std_error, scores - std_error, alpha=0.2)\n\nplt.ylabel('CV score +/- std error')\nplt.xlabel('alpha')\nplt.axhline(np.max(scores), linestyle='--', color='.5')\nplt.xlim([alphas[0], alphas[-1]])"
       ], 
       "outputs": [], 
       "metadata": {

dev/_downloads/auto_examples_python.zip

@@ -19,7 +19,7 @@
 from sklearn.linear_model import LassoCV
 from sklearn.linear_model import Lasso
 from sklearn.model_selection import KFold
-from sklearn.model_selection import cross_val_score
+from sklearn.model_selection import GridSearchCV
 
 diabetes = datasets.load_diabetes()
 X = diabetes.data[:150]
@@ -28,19 +28,13 @@
 lasso = Lasso(random_state=0)
 alphas = np.logspace(-4, -0.5, 30)
 
-scores = list()
-scores_std = list()
-
+tuned_parameters = [{'alpha': alphas}]
 n_folds = 3
 
-for alpha in alphas:
-    lasso.alpha = alpha
-    this_scores = cross_val_score(lasso, X, y, cv=n_folds, n_jobs=1)
-    scores.append(np.mean(this_scores))
-    scores_std.append(np.std(this_scores))
-
-scores, scores_std = np.array(scores), np.array(scores_std)
-
+clf = GridSearchCV(lasso, tuned_parameters, cv=n_folds, refit=False)
+clf.fit(X, y)
+scores = clf.cv_results_['mean_test_score']
+scores_std = clf.cv_results_['std_test_score']
 plt.figure().set_size_inches(8, 6)
 plt.semilogx(alphas, scores)