Internal change

PiperOrigin-RevId: 373487968
Change-Id: Ife7a5f2681c8e07f4c945fd3d7935b0fe9d8dfad

Author: tensorflower-gardener

tensorflow/python/keras/metrics.py

@@ -973,8 +973,6 @@ def __init__(self,
     self.init_thresholds = thresholds
     self.thresholds = metrics_utils.parse_init_thresholds(
         thresholds, default_threshold=0.5)
-    self._evenly_distribute_thresholds = (
-        metrics_utils.evenly_distributed_thresholds(self.thresholds))
     self.accumulator = self.add_weight(
         'accumulator',
         shape=(len(self.thresholds),),
@@ -998,7 +996,6 @@ def update_state(self, y_true, y_pred, sample_weight=None):
         y_true,
         y_pred,
         thresholds=self.thresholds,
-        evenly_distribute_thresholds=self._evenly_distribute_thresholds,
         sample_weight=sample_weight)
 
   def result(self):
@@ -1298,8 +1295,6 @@ def __init__(self,
     default_threshold = 0.5 if top_k is None else metrics_utils.NEG_INF
     self.thresholds = metrics_utils.parse_init_thresholds(
         thresholds, default_threshold=default_threshold)
-    self._evenly_distribute_thresholds = (
-        metrics_utils.evenly_distributed_thresholds(self.thresholds))
     self.true_positives = self.add_weight(
         'true_positives',
         shape=(len(self.thresholds),),
@@ -1331,7 +1326,6 @@ def update_state(self, y_true, y_pred, sample_weight=None):
         y_true,
         y_pred,
         thresholds=self.thresholds,
-        evenly_distribute_thresholds=self._evenly_distribute_thresholds,
         top_k=self.top_k,
         class_id=self.class_id,
         sample_weight=sample_weight)
@@ -1427,8 +1421,6 @@ def __init__(self,
     default_threshold = 0.5 if top_k is None else metrics_utils.NEG_INF
     self.thresholds = metrics_utils.parse_init_thresholds(
         thresholds, default_threshold=default_threshold)
-    self._evenly_distribute_thresholds = (
-        metrics_utils.evenly_distributed_thresholds(self.thresholds))
     self.true_positives = self.add_weight(
         'true_positives',
         shape=(len(self.thresholds),),
@@ -1460,7 +1452,6 @@ def update_state(self, y_true, y_pred, sample_weight=None):
         y_true,
         y_pred,
         thresholds=self.thresholds,
-        evenly_distribute_thresholds=self._evenly_distribute_thresholds,
         top_k=self.top_k,
         class_id=self.class_id,
         sample_weight=sample_weight)
@@ -1524,12 +1515,10 @@ def __init__(self,
     # Compute `num_thresholds` thresholds in [0, 1]
     if num_thresholds == 1:
       self.thresholds = [0.5]
-      self._evenly_distribute_thresholds = False
     else:
       thresholds = [(i + 1) * 1.0 / (num_thresholds - 1)
                     for i in range(num_thresholds - 2)]
       self.thresholds = [0.0] + thresholds + [1.0]
-      self._evenly_distribute_thresholds = True
 
   def update_state(self, y_true, y_pred, sample_weight=None):
     """Accumulates confusion matrix statistics.
@@ -1554,7 +1543,6 @@ def update_state(self, y_true, y_pred, sample_weight=None):
         y_true,
         y_pred,
         thresholds=self.thresholds,
-        evenly_distribute_thresholds=self._evenly_distribute_thresholds,
         class_id=self.class_id,
         sample_weight=sample_weight)
 
@@ -2091,9 +2079,6 @@ def __init__(self,
       # If specified, use the supplied thresholds.
       self.num_thresholds = len(thresholds) + 2
       thresholds = sorted(thresholds)
-      self._evenly_distribute_thresholds = (
-          metrics_utils.evenly_distributed_thresholds(
-              np.array([0.0] + thresholds + [1.0])))
     else:
       if num_thresholds <= 1:
         raise ValueError('`num_thresholds` must be > 1.')
@@ -2103,7 +2088,6 @@ def __init__(self,
       self.num_thresholds = num_thresholds
       thresholds = [(i + 1) * 1.0 / (num_thresholds - 1)
                     for i in range(num_thresholds - 2)]
-      self._evenly_distribute_thresholds = True
 
     # Add an endpoint "threshold" below zero and above one for either
     # threshold method to account for floating point imprecisions.
@@ -2256,7 +2240,6 @@ def update_state(self, y_true, y_pred, sample_weight=None):
           y_true,
           y_pred,
           self._thresholds,
-          evenly_distribute_thresholds=self._evenly_distribute_thresholds,
           sample_weight=sample_weight,
           multi_label=self.multi_label,
           label_weights=label_weights)

tensorflow/python/keras/metrics_confusion_matrix_test.py

@@ -33,11 +33,6 @@
 from tensorflow.python.platform import test
 from tensorflow.python.platform import tf_logging
 
-try:
-  import memory_profiler  # pylint:disable=g-import-not-at-top
-except ImportError:
-  memory_profiler = None
-
 
 @combinations.generate(combinations.combine(mode=['graph', 'eager']))
 class FalsePositivesTest(test.TestCase, parameterized.TestCase):
@@ -1781,104 +1776,5 @@ def test_reset_state(self):
       self.assertAllEqual(auc_obj.true_positives, np.zeros((5, 2)))
 
 
-class ThresholdsTest(test.TestCase, parameterized.TestCase):
-
-  @parameterized.parameters([
-      metrics.TruePositives(),
-      metrics.TrueNegatives(),
-      metrics.FalsePositives(),
-      metrics.FalseNegatives(),
-      metrics.Precision(),
-      metrics.Recall(),
-      metrics.SensitivityAtSpecificity(0.5),
-      metrics.SpecificityAtSensitivity(0.5),
-      metrics.PrecisionAtRecall(0.5),
-      metrics.RecallAtPrecision(0.5),
-      metrics.AUC()])
-  def test_with_default_thresholds(self, metric_obj):
-    # By default, the thresholds will be evenly distributed if there are more
-    # than 1. In case there is only 1 thresholds, then we expect
-    # _evenly_distribute_thresholds to be false.
-    expected = len(metric_obj.thresholds) > 1
-    self.assertEqual(metric_obj._evenly_distribute_thresholds, expected)
-
-  @parameterized.parameters([
-      metrics.TruePositives,
-      metrics.TrueNegatives,
-      metrics.FalsePositives,
-      metrics.FalseNegatives,
-      metrics.Precision,
-      metrics.Recall])
-  def test_with_manual_thresholds(self, metric_cls):
-    even_thresholds = [0.0, 0.25, 0.5, 0.75, 1.0]
-    metric_obj = metric_cls(thresholds=even_thresholds)
-    self.assertTrue(metric_obj._evenly_distribute_thresholds)
-
-    uneven_thresholds = [0.0, 0.45, 1.0]
-    metric_obj = metric_cls(thresholds=uneven_thresholds)
-    self.assertFalse(metric_obj._evenly_distribute_thresholds)
-
-  def test_manual_thresholds_auc(self):
-    # The AUC metric handles manual thresholds input differently (it will add
-    # 0.0 and 1.0 for user).
-    even_thresholds = [0.25, 0.5, 0.75]
-    auc = metrics.AUC(thresholds=even_thresholds)
-    self.assertTrue(auc._evenly_distribute_thresholds)
-
-    # Test for save model
-    cloned = metrics.AUC.from_config(auc.get_config())
-    self.assertTrue(cloned._evenly_distribute_thresholds)
-
-    uneven_thresholds = [0.45,]
-    auc = metrics.AUC(thresholds=uneven_thresholds)
-    self.assertFalse(auc._evenly_distribute_thresholds)
-
-    cloned = metrics.AUC.from_config(auc.get_config())
-    self.assertFalse(cloned._evenly_distribute_thresholds)
-
-
-@combinations.generate(combinations.combine(mode=['eager']))
-class AUCMemoryTest(test.TestCase, parameterized.TestCase):
-  # This test is added to measure the memory footprint for
-  # metrics_utils._update_confusion_matrix_variables_optimized().
-
-  def test_memory_usage(self):
-    if memory_profiler is None:
-      self.skipTest('Skip test since memory_profiler is not available.')
-
-    self.y_true = np.random.randint(2, size=(1024, 1024))
-    self.y_pred = np.random.rand(1024, 1024)
-
-    memeory_usage_1 = memory_profiler.memory_usage((self.even_thresholds_auc))
-    memeory_usage_2 = memory_profiler.memory_usage((self.uneven_thresholds_auc))
-    # memory usage is a list of number which sampled when running the function.
-    # The pure memory consumption is approximately max(usage) - min(usage)
-    memeory_usage_1 = max(memeory_usage_1) - min(memeory_usage_1)
-    memeory_usage_2 = max(memeory_usage_2) - min(memeory_usage_2)
-
-    # Since we expect the new approach should have memory footprint as O(T + N)
-    # and old apporach has O(T * N). When N = 200 here, the ratio between 1 and
-    # 2 should be at least 50 (some room for other overhead).
-    self.assertLess(memeory_usage_1 * 50, memeory_usage_2)
-
-  def even_thresholds_auc(self):
-    auc = metrics.AUC(num_thresholds=200)
-    self.assertTrue(auc._evenly_distribute_thresholds)
-
-    auc(self.y_true, self.y_pred)
-
-  def uneven_thresholds_auc(self):
-    num_thresholds = 200
-    thresholds = [x / (num_thresholds - 1) for x in range(num_thresholds)]
-    thresholds[100] += 1 / 200
-    thresholds = thresholds[1:-1]
-
-    auc = metrics.AUC(thresholds=thresholds)
-    self.assertFalse(auc._evenly_distribute_thresholds)
-    self.assertEqual(auc.num_thresholds, num_thresholds)
-
-    auc(self.y_true, self.y_pred)
-
-
 if __name__ == '__main__':
   test.main()