# 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()


# TODO(priyag): Add TPU Strategy to this once metrics aggregate correctly using
# TowerLocalVariables on TPUs. Submit http://cl/208914352.
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],
      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_one_shot_iterator()
      if isinstance(distribution, tpu_strategy.TPUStrategy):
        def step_fn(ctx, inputs):
          value, update = distribution.call_for_each_tower(
              metric_fn, inputs)
          ctx.set_non_tensor_output(name="value", output=value)
          return distribution.group(update)

        ctx = distribution.run_steps_on_dataset(
            step_fn, iterator, iterations=distribution.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 towers.
        batches_per_update = (
            distribution.num_towers * distribution.steps_per_run)
      else:
        value, update = distribution.call_for_each_tower(
            metric_fn, iterator.get_next())
        update = distribution.group(update)
        # TODO(josh11b): Once we switch to using a global batch size for input,
        # replace "distribution.num_towers" with "1".
        batches_per_update = distribution.num_towers

      self.evaluate(distribution.initialize())
      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

      self.evaluate(distribution.finalize())

  @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()