Pushing the docs to dev/ for branch: master, commit be305cebaf41f9f4616e540b686cf506c1b96875

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -78,7 +78,7 @@
       "execution_count": null, 
       "cell_type": "code", 
       "source": [
-        "plt.figure(figsize=(10, 6))\nplt.subplots_adjust(left=0.02, right=0.98)\n\n# plot the covariances\ncovs = [('Empirical', emp_cov), ('Ledoit-Wolf', lw_cov_),\n        ('GraphLasso', cov_), ('True', cov)]\nvmax = cov_.max()\nfor i, (name, this_cov) in enumerate(covs):\n    plt.subplot(2, 4, i + 1)\n    plt.imshow(this_cov, interpolation='nearest', vmin=-vmax, vmax=vmax,\n               cmap=plt.cm.RdBu_r)\n    plt.xticks(())\n    plt.yticks(())\n    plt.title('%s covariance' % name)\n\n\n# plot the precisions\nprecs = [('Empirical', linalg.inv(emp_cov)), ('Ledoit-Wolf', lw_prec_),\n         ('GraphLasso', prec_), ('True', prec)]\nvmax = .9 * prec_.max()\nfor i, (name, this_prec) in enumerate(precs):\n    ax = plt.subplot(2, 4, i + 5)\n    plt.imshow(np.ma.masked_equal(this_prec, 0),\n               interpolation='nearest', vmin=-vmax, vmax=vmax,\n               cmap=plt.cm.RdBu_r)\n    plt.xticks(())\n    plt.yticks(())\n    plt.title('%s precision' % name)\n    ax.set_axis_bgcolor('.7')\n\n# plot the model selection metric\nplt.figure(figsize=(4, 3))\nplt.axes([.2, .15, .75, .7])\nplt.plot(model.cv_alphas_, np.mean(model.grid_scores, axis=1), 'o-')\nplt.axvline(model.alpha_, color='.5')\nplt.title('Model selection')\nplt.ylabel('Cross-validation score')\nplt.xlabel('alpha')\n\nplt.show()"
+        "plt.figure(figsize=(10, 6))\nplt.subplots_adjust(left=0.02, right=0.98)\n\n# plot the covariances\ncovs = [('Empirical', emp_cov), ('Ledoit-Wolf', lw_cov_),\n        ('GraphLasso', cov_), ('True', cov)]\nvmax = cov_.max()\nfor i, (name, this_cov) in enumerate(covs):\n    plt.subplot(2, 4, i + 1)\n    plt.imshow(this_cov, interpolation='nearest', vmin=-vmax, vmax=vmax,\n               cmap=plt.cm.RdBu_r)\n    plt.xticks(())\n    plt.yticks(())\n    plt.title('%s covariance' % name)\n\n\n# plot the precisions\nprecs = [('Empirical', linalg.inv(emp_cov)), ('Ledoit-Wolf', lw_prec_),\n         ('GraphLasso', prec_), ('True', prec)]\nvmax = .9 * prec_.max()\nfor i, (name, this_prec) in enumerate(precs):\n    ax = plt.subplot(2, 4, i + 5)\n    plt.imshow(np.ma.masked_equal(this_prec, 0),\n               interpolation='nearest', vmin=-vmax, vmax=vmax,\n               cmap=plt.cm.RdBu_r)\n    plt.xticks(())\n    plt.yticks(())\n    plt.title('%s precision' % name)\n    ax.set_axis_bgcolor('.7')\n\n# plot the model selection metric\nplt.figure(figsize=(4, 3))\nplt.axes([.2, .15, .75, .7])\nplt.plot(model.cv_alphas_, np.mean(model.grid_scores_, axis=1), 'o-')\nplt.axvline(model.alpha_, color='.5')\nplt.title('Model selection')\nplt.ylabel('Cross-validation score')\nplt.xlabel('alpha')\n\nplt.show()"
       ], 
       "outputs": [], 
       "metadata": {

dev/_downloads/auto_examples_python.zip

@@ -126,7 +126,7 @@
 # plot the model selection metric
 plt.figure(figsize=(4, 3))
 plt.axes([.2, .15, .75, .7])
-plt.plot(model.cv_alphas_, np.mean(model.grid_scores, axis=1), 'o-')
+plt.plot(model.cv_alphas_, np.mean(model.grid_scores_, axis=1), 'o-')
 plt.axvline(model.alpha_, color='.5')
 plt.title('Model selection')
 plt.ylabel('Cross-validation score')