text: # Copyright 2018 The TensorFlow Authors. All Rights Reserved.
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #     http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  # ==============================================================================
  """Tests for V1 metrics."""
  from __future__ import absolute_import
  from __future__ import division
  from __future__ import print_function
  
  from absl.testing import parameterized
  
  from tensorflow.contrib.distribute.python import combinations
  from tensorflow.contrib.distribute.python import tpu_strategy
  from tensorflow.python.data.ops import dataset_ops
  from tensorflow.python.eager import test
  from tensorflow.python.framework import ops
  from tensorflow.python.ops import math_ops
  from tensorflow.python.ops import metrics
  from tensorflow.python.ops import variables
  
  
  def _labeled_dataset_fn():
    # First four batches of x: labels, predictions -> (labels == predictions)
    #  0: 0, 0 -> True;   1: 1, 1 -> True;   2: 2, 2 -> True;   3: 3, 0 -> False
    #  4: 4, 1 -> False;  5: 0, 2 -> False;  6: 1, 0 -> False;  7: 2, 1 -> False
    #  8: 3, 2 -> False;  9: 4, 0 -> False; 10: 0, 1 -> False; 11: 1, 2 -> False
    # 12: 2, 0 -> False; 13: 3, 1 -> False; 14: 4, 2 -> False; 15: 0, 0 -> True
    return dataset_ops.Dataset.range(1000).map(
        lambda x: {"labels": x % 5, "predictions": x % 3}).batch(
            4, drop_remainder=True)
  
  
  def _boolean_dataset_fn():
    # First four batches of labels, predictions: {TP, FP, TN, FN}
    # with a threshold of 0.5:
    #   T, T -> TP;  F, T -> FP;   T, F -> FN
    #   F, F -> TN;  T, T -> TP;   F, T -> FP
    #   T, F -> FN;  F, F -> TN;   T, T -> TP
    #   F, T -> FP;  T, F -> FN;   F, F -> TN
    return dataset_ops.Dataset.from_tensor_slices({
        "labels": [True, False, True, False],
        "predictions": [True, True, False, False]}).repeat().batch(
            3, drop_remainder=True)
  
  
  def _threshold_dataset_fn():
    # First four batches of labels, predictions: {TP, FP, TN, FN}
    # with a threshold of 0.5:
    #   True, 1.0 -> TP;  False, .75 -> FP;   True, .25 -> FN
    #  False, 0.0 -> TN;   True, 1.0 -> TP;  False, .75 -> FP
    #   True, .25 -> FN;  False, 0.0 -> TN;   True, 1.0 -> TP
    #  False, .75 -> FP;   True, .25 -> FN;  False, 0.0 -> TN
    return dataset_ops.Dataset.from_tensor_slices({
        "labels": [True, False, True, False],
        "predictions": [1.0, 0.75, 0.25, 0.]}).repeat().batch(
            3, drop_remainder=True)
  
  
  def _regression_dataset_fn():
    return dataset_ops.Dataset.from_tensor_slices({
        "labels": [1., .5, 1., 0.],
        "predictions": [1., .75, .25, 0.]}).repeat()
  
  
  def all_combinations():
    return combinations.combine(
        distribution=[combinations.default_strategy,
                      combinations.one_device_strategy,
                      combinations.mirrored_strategy_with_gpu_and_cpu,
                      combinations.mirrored_strategy_with_two_gpus,
                      combinations.core_mirrored_strategy_with_gpu_and_cpu,
                      combinations.core_mirrored_strategy_with_two_gpus],
        mode=["graph"])
  
  
  def tpu_combinations():
    return combinations.combine(distribution=[combinations.tpu_strategy_one_step,
                                              combinations.tpu_strategy],
                                mode=["graph"])
  
  
  # TODO(josh11b): Test metrics.recall_at_top_k, metrics.average_precision_at_k,
  # metrics.precision_at_k
  class MetricsV1Test(test.TestCase, parameterized.TestCase):
  
    def _test_metric(self, distribution, dataset_fn, metric_fn, expected_fn):
      with ops.Graph().as_default(), distribution.scope():
        iterator = distribution.distribute_dataset(
            dataset_fn).make_initializable_iterator()
        if isinstance(distribution, tpu_strategy.TPUStrategy):
          def step_fn(ctx, inputs):
            value, update = distribution.extended.call_for_each_replica(
                metric_fn, args=(inputs,))
            ctx.set_non_tensor_output(name="value", output=value)
            return distribution.group(update)
  
          ctx = distribution.extended.experimental_run_steps_on_iterator(
              step_fn, iterator, iterations=distribution.extended.steps_per_run)
          update = ctx.run_op
          value = ctx.non_tensor_outputs["value"]
          # In each run, we run multiple steps, and each steps consumes as many
          # batches as number of replicas.
          batches_per_update = (
              distribution.num_replicas_in_sync *
              distribution.extended.steps_per_run)
        else:
          value, update = distribution.extended.call_for_each_replica(
              metric_fn, args=(iterator.get_next(),))
          update = distribution.group(update)
          # TODO(josh11b): Once we switch to using a global batch size for input,
          # replace "distribution.num_replicas_in_sync" with "1".
          batches_per_update = distribution.num_replicas_in_sync
  
        self.evaluate(iterator.initializer)
        self.evaluate(variables.local_variables_initializer())
  
        batches_consumed = 0
        for i in range(4):
          self.evaluate(update)
          batches_consumed += batches_per_update
          self.assertAllClose(expected_fn(batches_consumed),
                              self.evaluate(value),
                              0.001,
                              msg="After update #" + str(i+1))
          if batches_consumed >= 4:  # Consume 4 input batches in total.
            break
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testMean(self, distribution):
      def _dataset_fn():
        return dataset_ops.Dataset.range(1000).map(math_ops.to_float).batch(
            4, drop_remainder=True)
  
      def _expected_fn(num_batches):
        # Mean(0..3) = 1.5, Mean(0..7) = 3.5, Mean(0..11) = 5.5, etc.
        return num_batches * 2 - 0.5
  
      self._test_metric(distribution, _dataset_fn, metrics.mean, _expected_fn)
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testAccuracy(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.accuracy(labels, predictions)
  
      def _expected_fn(num_batches):
        return [3./4, 3./8, 3./12, 4./16][num_batches - 1]
  
      self._test_metric(
          distribution, _labeled_dataset_fn, _metric_fn, _expected_fn)
  
    # TODO(priyag, jhseu): Enable TPU for this test once scatter_add is added
    # for TPUMirroredVariable.
    @combinations.generate(all_combinations())
    def testMeanPerClassAccuracy(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.mean_per_class_accuracy(
            labels, predictions, num_classes=5)
  
      def _expected_fn(num_batches):
        mean = lambda x: sum(x) / len(x)
        return [mean([1., 1., 1., 0., 0.]),
                mean([0.5, 0.5, 0.5, 0., 0.]),
                mean([1./3, 1./3, 0.5, 0., 0.]),
                mean([0.5, 1./3, 1./3, 0., 0.])][num_batches - 1]
  
      self._test_metric(
          distribution, _labeled_dataset_fn, _metric_fn, _expected_fn)
  
    # NOTE(priyag): This metric doesn't work on TPUs yet.
    @combinations.generate(all_combinations())
    def testMeanIOU(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.mean_iou(
            labels, predictions, num_classes=5)
  
      def _expected_fn(num_batches):
        mean = lambda x: sum(x) / len(x)
        return [mean([1./2, 1./1, 1./1, 0.]),  # no class 4 in first batch
                mean([1./4, 1./4, 1./3, 0., 0.]),
                mean([1./6, 1./6, 1./5, 0., 0.]),
                mean([2./8, 1./7, 1./7, 0., 0.])][num_batches - 1]
  
      self._test_metric(
          distribution, _labeled_dataset_fn, _metric_fn, _expected_fn)
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testMeanTensor(self, distribution):
      def _dataset_fn():
        dataset = dataset_ops.Dataset.range(1000).map(math_ops.to_float)
        # Want to produce a fixed, known shape, so drop remainder when batching.
        dataset = dataset.batch(4, drop_remainder=True)
        return dataset
  
      def _expected_fn(num_batches):
        # Mean(0, 4, ..., 4 * num_batches - 4) == 2 * num_batches - 2
        # Mean(1, 5, ..., 4 * num_batches - 3) == 2 * num_batches - 1
        # Mean(2, 6, ..., 4 * num_batches - 2) == 2 * num_batches
        # Mean(3, 7, ..., 4 * num_batches - 1) == 2 * num_batches + 1
        first = 2. * num_batches - 2.
        return [first, first + 1., first + 2., first + 3.]
  
      self._test_metric(
          distribution, _dataset_fn, metrics.mean_tensor, _expected_fn)
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testAUCROC(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.auc(labels, predictions, num_thresholds=8, curve="ROC",
                           summation_method="careful_interpolation")
  
      def _expected_fn(num_batches):
        return [0.5, 7./9, 0.8, 0.75][num_batches - 1]
  
      self._test_metric(
          distribution, _threshold_dataset_fn, _metric_fn, _expected_fn)
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testAUCPR(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.auc(labels, predictions, num_thresholds=8, curve="PR",
                           summation_method="careful_interpolation")
  
      def _expected_fn(num_batches):
        return [0.797267, 0.851238, 0.865411, 0.797267][num_batches - 1]
  
      self._test_metric(
          distribution, _threshold_dataset_fn, _metric_fn, _expected_fn)
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testFalseNegatives(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.false_negatives(labels, predictions)
  
      def _expected_fn(num_batches):
        return [1., 1., 2., 3.][num_batches - 1]
  
      self._test_metric(
          distribution, _boolean_dataset_fn, _metric_fn, _expected_fn)
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testFalseNegativesAtThresholds(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.false_negatives_at_thresholds(labels, predictions, [.5])
  
      def _expected_fn(num_batches):
        return [[1.], [1.], [2.], [3.]][num_batches - 1]
  
      self._test_metric(
          distribution, _threshold_dataset_fn, _metric_fn, _expected_fn)
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testTrueNegatives(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.true_negatives(labels, predictions)
  
      def _expected_fn(num_batches):
        return [0., 1., 2., 3.][num_batches - 1]
  
      self._test_metric(
          distribution, _boolean_dataset_fn, _metric_fn, _expected_fn)
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testTrueNegativesAtThresholds(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.true_negatives_at_thresholds(labels, predictions, [.5])
  
      def _expected_fn(num_batches):
        return [[0.], [1.], [2.], [3.]][num_batches - 1]
  
      self._test_metric(
          distribution, _threshold_dataset_fn, _metric_fn, _expected_fn)
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testFalsePositives(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.false_positives(labels, predictions)
  
      def _expected_fn(num_batches):
        return [1., 2., 2., 3.][num_batches - 1]
  
      self._test_metric(
          distribution, _boolean_dataset_fn, _metric_fn, _expected_fn)
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testFalsePositivesAtThresholds(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.false_positives_at_thresholds(labels, predictions, [.5])
  
      def _expected_fn(num_batches):
        return [[1.], [2.], [2.], [3.]][num_batches - 1]
  
      self._test_metric(
          distribution, _threshold_dataset_fn, _metric_fn, _expected_fn)
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testTruePositives(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.true_positives(labels, predictions)
  
      def _expected_fn(num_batches):
        return [1., 2., 3., 3.][num_batches - 1]
  
      self._test_metric(
          distribution, _boolean_dataset_fn, _metric_fn, _expected_fn)
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testTruePositivesAtThresholds(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.true_positives_at_thresholds(labels, predictions, [.5])
  
      def _expected_fn(num_batches):
        return [[1.], [2.], [3.], [3.]][num_batches - 1]
  
      self._test_metric(
          distribution, _threshold_dataset_fn, _metric_fn, _expected_fn)
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testPrecision(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.precision(labels, predictions)
  
      def _expected_fn(num_batches):
        return [0.5, 0.5, 0.6, 0.5][num_batches - 1]
  
      self._test_metric(
          distribution, _boolean_dataset_fn, _metric_fn, _expected_fn)
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testPrecisionAtThreshold(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.precision_at_thresholds(labels, predictions, [0.5])
  
      def _expected_fn(num_batches):
        return [[0.5], [0.5], [0.6], [0.5]][num_batches - 1]
  
      self._test_metric(
          distribution, _threshold_dataset_fn, _metric_fn, _expected_fn)
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testRecall(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.recall(labels, predictions)
  
      def _expected_fn(num_batches):
        return [0.5, 2./3, 0.6, 0.5][num_batches - 1]
  
      self._test_metric(
          distribution, _boolean_dataset_fn, _metric_fn, _expected_fn)
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testRecallAtThreshold(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.recall_at_thresholds(labels, predictions, [0.5])
  
      def _expected_fn(num_batches):
        return [[0.5], [2./3], [0.6], [0.5]][num_batches - 1]
  
      self._test_metric(
          distribution, _threshold_dataset_fn, _metric_fn, _expected_fn)
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testMeanSquaredError(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.mean_squared_error(labels, predictions)
  
      def _expected_fn(num_batches):
        return [0., 1./32, 0.208333, 0.15625][num_batches - 1]
  
      self._test_metric(
          distribution, _regression_dataset_fn, _metric_fn, _expected_fn)
  
    @combinations.generate(all_combinations() + tpu_combinations())
    def testRootMeanSquaredError(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.root_mean_squared_error(labels, predictions)
  
      def _expected_fn(num_batches):
        return [0., 0.176777, 0.456435, 0.395285][num_batches - 1]
  
      self._test_metric(
          distribution, _regression_dataset_fn, _metric_fn, _expected_fn)
  
    @combinations.generate(all_combinations())
    def testSensitivityAtSpecificity(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.sensitivity_at_specificity(labels, predictions, 0.8)
  
      def _expected_fn(num_batches):
        return [0.5, 2./3, 0.6, 0.5][num_batches - 1]
  
      self._test_metric(
          distribution, _threshold_dataset_fn, _metric_fn, _expected_fn)
  
    @combinations.generate(all_combinations())
    def testSpecificityAtSensitivity(self, distribution):
      def _metric_fn(x):
        labels = x["labels"]
        predictions = x["predictions"]
        return metrics.specificity_at_sensitivity(labels, predictions, 0.95)
  
      def _expected_fn(num_batches):
        return [0., 1./3, 0.5, 0.5][num_batches - 1]
  
      self._test_metric(
          distribution, _threshold_dataset_fn, _metric_fn, _expected_fn)
  
  
  if __name__ == "__main__":
    test.main()
  