Pushing the docs to dev/ for branch: master, commit d62d59f7800a722746523a215d1c61b434c25aa4

Author: sklearn-ci

dev/_downloads/3409d9766d352cc9f9b169d4a799a87a/auto_examples_python.zip

@@ -220,8 +220,8 @@
 # estimators using :func:`~utils.estimator_checks.check_estimator`. For
 # instance, the ``check_estimator(LinearSVC)`` passes.
 #
-# We now provide a ``pytest`` specific decorator which allows the ``pytest``
-# ro run all checks independently and report the checks that are failing.
+# We now provide a ``pytest`` specific decorator which allows ``pytest``
+# to run all checks independently and report the checks that are failing.
 
 from sklearn.linear_model import LogisticRegression
 from sklearn.tree import DecisionTreeRegressor
@@ -231,3 +231,28 @@
 @parametrize_with_checks([LogisticRegression, DecisionTreeRegressor])
 def test_sklearn_compatible_estimator(estimator, check):
     check(estimator)
+
+############################################################################
+# ROC AUC now supports multiclass classification
+# ----------------------------------------------
+# The :func:`roc_auc_score` function can also be used in multi-class
+# classification. Two averaging strategies are currently supported: the
+# one-vs-one algorithm computes the average of the pairwise ROC AUC scores, and
+# the one-vs-rest algorithm computes the average of the ROC AUC scores for each
+# class against all other classes. In both cases, the predicted labels are
+# provided in an array with values from 0 to ``n_classes``, and the scores
+# correspond to the probability estimates that a sample belongs to a particular
+# class. The OvO and OvR algorithms supports weighting uniformly
+# (``average='macro'``) and weighting by the prevalence
+# (``average='weighted'``).
+#
+# Read more in the :ref:`User Guide <roc_metrics>`.
+
+
+from sklearn.datasets import make_classification
+from sklearn.svm import SVC
+from sklearn.metrics import roc_auc_score
+
+X, y = make_classification(n_classes=4, n_informative=16)
+clf = SVC(decision_function_shape='ovo', probability=True).fit(X, y)
+print(roc_auc_score(y, clf.predict_proba(X), multi_class='ovo'))

dev/_downloads/7ee55c12f8d3eb1dd8d2005d9dd7b6f1/plot_release_highlights_0_22_0.py

@@ -166,7 +166,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "Checking scikit-learn compatibility of an estimator\n---------------------------------------------------\nDevelopers can check the compatibility of their scikit-learn compatible\nestimators using :func:`~utils.estimator_checks.check_estimator`. For\ninstance, the ``check_estimator(LinearSVC)`` passes.\n\nWe now provide a ``pytest`` specific decorator which allows the ``pytest``\nro run all checks independently and report the checks that are failing.\n\n"
+        "Checking scikit-learn compatibility of an estimator\n---------------------------------------------------\nDevelopers can check the compatibility of their scikit-learn compatible\nestimators using :func:`~utils.estimator_checks.check_estimator`. For\ninstance, the ``check_estimator(LinearSVC)`` passes.\n\nWe now provide a ``pytest`` specific decorator which allows ``pytest``\nto run all checks independently and report the checks that are failing.\n\n"
       ]
     },
     {
@@ -179,6 +179,24 @@
       "source": [
         "from sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.utils.estimator_checks import parametrize_with_checks\n\n\n@parametrize_with_checks([LogisticRegression, DecisionTreeRegressor])\ndef test_sklearn_compatible_estimator(estimator, check):\n    check(estimator)"
       ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {},
+      "source": [
+        "ROC AUC now supports multiclass classification\n----------------------------------------------\nThe :func:`roc_auc_score` function can also be used in multi-class\nclassification. Two averaging strategies are currently supported: the\none-vs-one algorithm computes the average of the pairwise ROC AUC scores, and\nthe one-vs-rest algorithm computes the average of the ROC AUC scores for each\nclass against all other classes. In both cases, the predicted labels are\nprovided in an array with values from 0 to ``n_classes``, and the scores\ncorrespond to the probability estimates that a sample belongs to a particular\nclass. The OvO and OvR algorithms supports weighting uniformly\n(``average='macro'``) and weighting by the prevalence\n(``average='weighted'``).\n\nRead more in the `User Guide <roc_metrics>`.\n\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "collapsed": false
+      },
+      "outputs": [],
+      "source": [
+        "from sklearn.datasets import make_classification\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import roc_auc_score\n\nX, y = make_classification(n_classes=4, n_informative=16)\nclf = SVC(decision_function_shape='ovo', probability=True).fit(X, y)\nprint(roc_auc_score(y, clf.predict_proba(X), multi_class='ovo'))"
+      ]
     }
   ],
   "metadata": {