Delete AUC metrics from MultiClassHead. This removed AUC metrics from...

Delete AUC metrics from MultiClassHead. This removed AUC metrics from DNNClassifier, LinearClassifier and DNNLinearCombinedClassifier when n_classes > 2.
Change: 149656850

tensorflow/contrib/learn/python/learn/estimators/head.py

@@ -1033,9 +1033,6 @@ class _MultiClassHead(_SingleHead):
       # "accuracy/threshold_0.500000_mean" metric for binary classification.
       metrics[_summary_key(self.head_name, mkey.ACCURACY)] = (
           metrics_lib.streaming_accuracy(classes, labels, weights))
-      metrics[_summary_key(self.head_name, mkey.AUC)] = (
-          _streaming_auc_with_class_id_label(
-              probabilities, labels, weights, self.logits_dimension))
 
       for class_id in self._metric_class_ids:
         # TODO(ptucker): Add per-class accuracy, precision, recall.
@@ -1051,9 +1048,6 @@ class _MultiClassHead(_SingleHead):
         metrics[_summary_key(
             self.head_name, mkey.CLASS_LOGITS_MEAN % class_id)] = (
                 _predictions_streaming_mean(logits, weights, class_id))
-        metrics[_summary_key(self.head_name, mkey.CLASS_AUC % class_id)] = (
-            _class_streaming_auc(probabilities, labels, weights, class_id,
-                                 self.logits_dimension))
 
     return metrics
 
@@ -1773,20 +1767,6 @@ def _class_labels_streaming_mean(labels, weights, class_id):
       weights=weights)
 
 
-def _class_streaming_auc(predictions, labels, weights, class_id,
-                         num_classes):
-  indicator_labels = _class_id_labels_to_indicator(
-      labels, num_classes=num_classes)
-  return _streaming_auc(predictions, indicator_labels, weights, class_id)
-
-
-def _streaming_auc_with_class_id_label(predictions, labels, weights,
-                                       num_classes):
-  indicator_labels = _class_id_labels_to_indicator(
-      labels, num_classes=num_classes)
-  return _streaming_auc(predictions, indicator_labels, weights)
-
-
 def _streaming_auc(predictions, labels, weights=None, class_id=None):
   predictions = ops.convert_to_tensor(predictions)
   labels = ops.convert_to_tensor(labels)

tensorflow/contrib/learn/python/learn/estimators/head_test.py

@@ -883,11 +883,7 @@ class MultiClassHeadTest(test.TestCase):
   def _expected_eval_metrics(self, expected_loss):
     return {
         "accuracy": 0.,
-        "auc": 1. / 4,
         "loss": expected_loss,
-        "auc/class0": 1.,
-        "auc/class1": 1.,
-        "auc/class2": 0.,
         "labels/actual_label_mean/class0": 0. / 1,
         "labels/actual_label_mean/class1": 0. / 1,
         "labels/actual_label_mean/class2": 1. / 1,
@@ -957,11 +953,7 @@ class MultiClassHeadTest(test.TestCase):
       expected_loss = 1.0986123
       _assert_metrics(self, expected_loss, {
           "accuracy": 0.,
-          "auc": 2. / 4,
           "loss": expected_loss,
-          "auc/class0": 1.,
-          "auc/class1": 1.,
-          "auc/class2": 0.,
           "labels/actual_label_mean/class0": 0. / 1,
           "labels/actual_label_mean/class1": 0. / 1,
           "labels/actual_label_mean/class2": 1. / 1,
@@ -1023,11 +1015,7 @@ class MultiClassHeadTest(test.TestCase):
       expected_loss = 3.1698461
       expected_eval_metrics = {
           "accuracy": 0.,
-          "auc": 9.99999e-07,
           "loss": expected_loss,
-          "auc/class0": 1.,
-          "auc/class1": 1.,
-          "auc/class2": 0.,
           "labels/actual_label_mean/class0": 0. / 1,
           "labels/actual_label_mean/class1": 0. / 1,
           "labels/actual_label_mean/class2": 1. / 1,