Pushing the docs to dev/ for branch: master, commit 325a7f9e6cb7c954897b058a5983c0bdaf3aa50b

Author: sklearn-ci

dev/_downloads/auto_examples_jupyter.zip

@@ -26,7 +26,7 @@
       },
       "outputs": [],
       "source": [
-        "print(__doc__)\n\nimport itertools\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import svm, datasets\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix\n\n# import some data to play with\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\nclass_names = iris.target_names\n\n# Split the data into a training set and a test set\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n\n# Run classifier, using a model that is too regularized (C too low) to see\n# the impact on the results\nclassifier = svm.SVC(kernel='linear', C=0.01)\ny_pred = classifier.fit(X_train, y_train).predict(X_test)\n\n\ndef plot_confusion_matrix(cm, classes,\n                          normalize=False,\n                          title='Confusion matrix',\n                          cmap=plt.cm.Blues):\n    \"\"\"\n    This function prints and plots the confusion matrix.\n    Normalization can be applied by setting `normalize=True`.\n    \"\"\"\n    if normalize:\n        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n        print(\"Normalized confusion matrix\")\n    else:\n        print('Confusion matrix, without normalization')\n\n    print(cm)\n\n    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n    plt.title(title)\n    plt.colorbar()\n    tick_marks = np.arange(len(classes))\n    plt.xticks(tick_marks, classes, rotation=45)\n    plt.yticks(tick_marks, classes)\n\n    fmt = '.2f' if normalize else 'd'\n    thresh = cm.max() / 2.\n    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n        plt.text(j, i, format(cm[i, j], fmt),\n                 horizontalalignment=\"center\",\n                 color=\"white\" if cm[i, j] > thresh else \"black\")\n\n    plt.tight_layout()\n    plt.ylabel('True label')\n    plt.xlabel('Predicted label')\n\n# Compute confusion matrix\ncnf_matrix = confusion_matrix(y_test, y_pred)\nnp.set_printoptions(precision=2)\n\n# Plot non-normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=class_names,\n                      title='Confusion matrix, without normalization')\n\n# Plot normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,\n                      title='Normalized confusion matrix')\n\nplt.show()"
+        "print(__doc__)\n\nimport itertools\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import svm, datasets\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix\n\n# import some data to play with\niris = datasets.load_iris()\nX = iris.data\ny = iris.target\nclass_names = iris.target_names\n\n# Split the data into a training set and a test set\nX_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n\n# Run classifier, using a model that is too regularized (C too low) to see\n# the impact on the results\nclassifier = svm.SVC(kernel='linear', C=0.01)\ny_pred = classifier.fit(X_train, y_train).predict(X_test)\n\n\ndef plot_confusion_matrix(cm, classes,\n                          normalize=False,\n                          title='Confusion matrix',\n                          cmap=plt.cm.Blues):\n    \"\"\"\n    This function prints and plots the confusion matrix.\n    Normalization can be applied by setting `normalize=True`.\n    \"\"\"\n    if normalize:\n        cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n        print(\"Normalized confusion matrix\")\n    else:\n        print('Confusion matrix, without normalization')\n\n    print(cm)\n\n    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n    plt.title(title)\n    plt.colorbar()\n    tick_marks = np.arange(len(classes))\n    plt.xticks(tick_marks, classes, rotation=45)\n    plt.yticks(tick_marks, classes)\n\n    fmt = '.2f' if normalize else 'd'\n    thresh = cm.max() / 2.\n    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n        plt.text(j, i, format(cm[i, j], fmt),\n                 horizontalalignment=\"center\",\n                 color=\"white\" if cm[i, j] > thresh else \"black\")\n\n    plt.ylabel('True label')\n    plt.xlabel('Predicted label')\n    plt.tight_layout()\n\n\n# Compute confusion matrix\ncnf_matrix = confusion_matrix(y_test, y_pred)\nnp.set_printoptions(precision=2)\n\n# Plot non-normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=class_names,\n                      title='Confusion matrix, without normalization')\n\n# Plot normalized confusion matrix\nplt.figure()\nplot_confusion_matrix(cnf_matrix, classes=class_names, normalize=True,\n                      title='Normalized confusion matrix')\n\nplt.show()"
       ]
     }
   ],

dev/_downloads/auto_examples_python.zip

@@ -79,9 +79,10 @@ def plot_confusion_matrix(cm, classes,
                  horizontalalignment="center",
                  color="white" if cm[i, j] > thresh else "black")
 
-    plt.tight_layout()
     plt.ylabel('True label')
     plt.xlabel('Predicted label')
+    plt.tight_layout()
+
 
 # Compute confusion matrix
 cnf_matrix = confusion_matrix(y_test, y_pred)