modules/audio_processing/agc/clipping_predictor_evaluator_unittest.cc

/*
 *  Copyright (c) 2021 The WebRTC project authors. All Rights Reserved.
 *
 *  Use of this source code is governed by a BSD-style license
 *  that can be found in the LICENSE file in the root of the source
 *  tree. An additional intellectual property rights grant can be found
 *  in the file PATENTS.  All contributing project authors may
 *  be found in the AUTHORS file in the root of the source tree.
 */

#include "modules/audio_processing/agc/clipping_predictor_evaluator.h"

#include <cstdint>
#include <memory>
#include <tuple>
#include <vector>

#include "absl/types/optional.h"
#include "rtc_base/numerics/safe_conversions.h"
#include "rtc_base/random.h"
#include "test/gmock.h"
#include "test/gtest.h"

namespace webrtc {
namespace {

using testing::Eq;
using testing::Optional;

constexpr bool kDetected = true;
constexpr bool kNotDetected = false;

constexpr bool kPredicted = true;
constexpr bool kNotPredicted = false;

int SumTrueFalsePositivesNegatives(
    const ClippingPredictorEvaluator& evaluator) {
  return evaluator.true_positives() + evaluator.true_negatives() +
         evaluator.false_positives() + evaluator.false_negatives();
}

// Checks the metrics after init - i.e., no call to `Observe()`.
TEST(ClippingPredictorEvaluatorTest, Init) {
  ClippingPredictorEvaluator evaluator(/*history_size=*/3);
  EXPECT_EQ(evaluator.true_positives(), 0);
  EXPECT_EQ(evaluator.true_negatives(), 0);
  EXPECT_EQ(evaluator.false_positives(), 0);
  EXPECT_EQ(evaluator.false_negatives(), 0);
}

class ClippingPredictorEvaluatorParameterization
    : public ::testing::TestWithParam<std::tuple<int, int>> {
 protected:
  uint64_t seed() const {
    return rtc::checked_cast<uint64_t>(std::get<0>(GetParam()));
  }
  int history_size() const { return std::get<1>(GetParam()); }
};

// Checks that after each call to `Observe()` at most one metric changes.
TEST_P(ClippingPredictorEvaluatorParameterization, AtMostOneMetricChanges) {
  constexpr int kNumCalls = 123;
  Random random_generator(seed());
  ClippingPredictorEvaluator evaluator(history_size());

  for (int i = 0; i < kNumCalls; ++i) {
    SCOPED_TRACE(i);
    // Read metrics before `Observe()` is called.
    const int last_tp = evaluator.true_positives();
    const int last_tn = evaluator.true_negatives();
    const int last_fp = evaluator.false_positives();
    const int last_fn = evaluator.false_negatives();
    // `Observe()` a random observation.
    bool clipping_detected = random_generator.Rand<bool>();
    bool clipping_predicted = random_generator.Rand<bool>();
    evaluator.Observe(clipping_detected, clipping_predicted);

    // Check that at most one metric has changed.
    int num_changes = 0;
    num_changes += last_tp == evaluator.true_positives() ? 0 : 1;
    num_changes += last_tn == evaluator.true_negatives() ? 0 : 1;
    num_changes += last_fp == evaluator.false_positives() ? 0 : 1;
    num_changes += last_fn == evaluator.false_negatives() ? 0 : 1;
    EXPECT_GE(num_changes, 0);
    EXPECT_LE(num_changes, 1);
  }
}

// Checks that after each call to `Observe()` each metric either remains
// unchanged or grows.
TEST_P(ClippingPredictorEvaluatorParameterization, MetricsAreWeaklyMonotonic) {
  constexpr int kNumCalls = 123;
  Random random_generator(seed());
  ClippingPredictorEvaluator evaluator(history_size());

  for (int i = 0; i < kNumCalls; ++i) {
    SCOPED_TRACE(i);
    // Read metrics before `Observe()` is called.
    const int last_tp = evaluator.true_positives();
    const int last_tn = evaluator.true_negatives();
    const int last_fp = evaluator.false_positives();
    const int last_fn = evaluator.false_negatives();
    // `Observe()` a random observation.
    bool clipping_detected = random_generator.Rand<bool>();
    bool clipping_predicted = random_generator.Rand<bool>();
    evaluator.Observe(clipping_detected, clipping_predicted);

    // Check that metrics are weakly monotonic.
    EXPECT_GE(evaluator.true_positives(), last_tp);
    EXPECT_GE(evaluator.true_negatives(), last_tn);
    EXPECT_GE(evaluator.false_positives(), last_fp);
    EXPECT_GE(evaluator.false_negatives(), last_fn);
  }
}

// Checks that after each call to `Observe()` the growth speed of each metrics
// is bounded.
TEST_P(ClippingPredictorEvaluatorParameterization, BoundedMetricsGrowth) {
  constexpr int kNumCalls = 123;
  Random random_generator(seed());
  ClippingPredictorEvaluator evaluator(history_size());

  for (int i = 0; i < kNumCalls; ++i) {
    SCOPED_TRACE(i);
    // Read metrics before `Observe()` is called.
    const int last_tp = evaluator.true_positives();
    const int last_tn = evaluator.true_negatives();
    const int last_fp = evaluator.false_positives();
    const int last_fn = evaluator.false_negatives();
    // `Observe()` a random observation.
    bool clipping_detected = random_generator.Rand<bool>();
    bool clipping_predicted = random_generator.Rand<bool>();
    evaluator.Observe(clipping_detected, clipping_predicted);

    // Check that TPs grow by at most `history_size() + 1`. Such an upper bound
    // is reached when multiple predictions are matched by a single detection.
    EXPECT_LE(evaluator.true_positives() - last_tp, history_size() + 1);
    // Check that TNs, FPs and FNs grow by at most one. `max_growth`.
    EXPECT_LE(evaluator.true_negatives() - last_tn, 1);
    EXPECT_LE(evaluator.false_positives() - last_fp, 1);
    EXPECT_LE(evaluator.false_negatives() - last_fn, 1);
  }
}

// Checks that `Observe()` returns a prediction interval if and only if one or
// more true positives are found.
TEST_P(ClippingPredictorEvaluatorParameterization,
       PredictionIntervalIfAndOnlyIfTruePositives) {
  constexpr int kNumCalls = 123;
  Random random_generator(seed());
  ClippingPredictorEvaluator evaluator(history_size());

  for (int i = 0; i < kNumCalls; ++i) {
    SCOPED_TRACE(i);
    // Read true positives before `Observe()` is called.
    const int last_tp = evaluator.true_positives();
    // `Observe()` a random observation.
    bool clipping_detected = random_generator.Rand<bool>();
    bool clipping_predicted = random_generator.Rand<bool>();
    absl::optional<int> prediction_interval =
        evaluator.Observe(clipping_detected, clipping_predicted);

    // Check that the prediction interval is returned when a true positive is
    // found.
    if (evaluator.true_positives() == last_tp) {
      EXPECT_FALSE(prediction_interval.has_value());
    } else {
      EXPECT_TRUE(prediction_interval.has_value());
    }
  }
}

INSTANTIATE_TEST_SUITE_P(
    ClippingPredictorEvaluatorTest,
    ClippingPredictorEvaluatorParameterization,
    ::testing::Combine(::testing::Values(4, 8, 15, 16, 23, 42),
                       ::testing::Values(1, 10, 21)));

// Checks that, observing a detection and a prediction after init, produces a
// true positive.
TEST(ClippingPredictorEvaluatorTest, OneTruePositiveAfterInit) {
  ClippingPredictorEvaluator evaluator(/*history_size=*/3);
  evaluator.Observe(kDetected, kPredicted);
  EXPECT_EQ(evaluator.true_positives(), 1);

  EXPECT_EQ(evaluator.true_negatives(), 0);
  EXPECT_EQ(evaluator.false_positives(), 0);
  EXPECT_EQ(evaluator.false_negatives(), 0);
}

// Checks that, observing a detection but no prediction after init, produces a
// false negative.
TEST(ClippingPredictorEvaluatorTest, OneFalseNegativeAfterInit) {
  ClippingPredictorEvaluator evaluator(/*history_size=*/3);
  evaluator.Observe(kDetected, kNotPredicted);
  EXPECT_EQ(evaluator.false_negatives(), 1);

  EXPECT_EQ(evaluator.true_positives(), 0);
  EXPECT_EQ(evaluator.true_negatives(), 0);
  EXPECT_EQ(evaluator.false_positives(), 0);
}

// Checks that, observing no detection but a prediction after init, produces a
// false positive after expiration.
TEST(ClippingPredictorEvaluatorTest, OneFalsePositiveAfterInit) {
  ClippingPredictorEvaluator evaluator(/*history_size=*/3);
  evaluator.Observe(kNotDetected, kPredicted);
  EXPECT_EQ(evaluator.false_positives(), 0);
  evaluator.Observe(kNotDetected, kNotPredicted);
  evaluator.Observe(kNotDetected, kNotPredicted);
  evaluator.Observe(kNotDetected, kNotPredicted);
  EXPECT_EQ(evaluator.false_positives(), 1);

  EXPECT_EQ(evaluator.true_positives(), 0);
  EXPECT_EQ(evaluator.true_negatives(), 0);
  EXPECT_EQ(evaluator.false_negatives(), 0);
}

// Checks that, observing no detection and no prediction after init, produces a
// true negative.
TEST(ClippingPredictorEvaluatorTest, OneTrueNegativeAfterInit) {
  ClippingPredictorEvaluator evaluator(/*history_size=*/3);
  evaluator.Observe(kNotDetected, kNotPredicted);
  EXPECT_EQ(evaluator.true_negatives(), 1);

  EXPECT_EQ(evaluator.true_positives(), 0);
  EXPECT_EQ(evaluator.false_positives(), 0);
  EXPECT_EQ(evaluator.false_negatives(), 0);
}

// Checks that the evaluator detects true negatives when clipping is neither
// predicted nor detected.
TEST(ClippingPredictorEvaluatorTest, NeverDetectedAndNotPredicted) {
  ClippingPredictorEvaluator evaluator(/*history_size=*/3);
  evaluator.Observe(kNotDetected, kNotPredicted);
  evaluator.Observe(kNotDetected, kNotPredicted);
  evaluator.Observe(kNotDetected, kNotPredicted);
  evaluator.Observe(kNotDetected, kNotPredicted);
  EXPECT_EQ(evaluator.true_negatives(), 4);

  EXPECT_EQ(evaluator.true_positives(), 0);
  EXPECT_EQ(evaluator.false_positives(), 0);
  EXPECT_EQ(evaluator.false_negatives(), 0);
}

// Checks that the evaluator detects a false negative when clipping is detected
// but not predicted.
TEST(ClippingPredictorEvaluatorTest, DetectedButNotPredicted) {
  ClippingPredictorEvaluator evaluator(/*history_size=*/3);
  evaluator.Observe(kNotDetected, kNotPredicted);
  evaluator.Observe(kNotDetected, kNotPredicted);
  evaluator.Observe(kNotDetected, kNotPredicted);
  evaluator.Observe(kDetected, kNotPredicted);
  EXPECT_EQ(evaluator.false_negatives(), 1);

  EXPECT_EQ(evaluator.true_positives(), 0);
  EXPECT_EQ(evaluator.true_negatives(), 3);
  EXPECT_EQ(evaluator.false_positives(), 0);
}

// Checks that the evaluator does not detect a false positive when clipping is
// predicted but not detected until the observation period expires.
TEST(ClippingPredictorEvaluatorTest,
     PredictedOnceAndNeverDetectedBeforeDeadline) {
  ClippingPredictorEvaluator evaluator(/*history_size=*/3);
  evaluator.Observe(kNotDetected, kPredicted);
  evaluator.Observe(kNotDetected, kNotPredicted);
  EXPECT_EQ(evaluator.false_positives(), 0);
  evaluator.Observe(kNotDetected, kNotPredicted);
  EXPECT_EQ(evaluator.false_positives(), 0);
  evaluator.Observe(kNotDetected, kNotPredicted);
  EXPECT_EQ(evaluator.false_positives(), 1);

  EXPECT_EQ(evaluator.true_positives(), 0);
  EXPECT_EQ(evaluator.true_negatives(), 0);
  EXPECT_EQ(evaluator.false_negatives(), 0);
}

// Checks that the evaluator detects a false positive when clipping is predicted
// but detected after the observation period expires.
TEST(ClippingPredictorEvaluatorTest, PredictedOnceButDetectedAfterDeadline) {
  ClippingPredictorEvaluator evaluator(/*history_size=*/3);
  evaluator.Observe(kNotDetected, kPredicted);
  evaluator.Observe(kNotDetected, kNotPredicted);
  evaluator.Observe(kNotDetected, kNotPredicted);
  evaluator.Observe(kNotDetected, kNotPredicted);
  evaluator.Observe(kDetected, kNotPredicted);
  EXPECT_EQ(evaluator.false_positives(), 1);

  EXPECT_EQ(evaluator.true_positives(), 0);
  EXPECT_EQ(evaluator.true_negatives(), 0);
  EXPECT_EQ(evaluator.false_negatives(), 1);
}

// Checks that a prediction followed by a detection counts as true positive.
TEST(ClippingPredictorEvaluatorTest, PredictedOnceAndThenImmediatelyDetected) {
  ClippingPredictorEvaluator evaluator(/*history_size=*/3);
  evaluator.Observe(kNotDetected, kPredicted);
  EXPECT_EQ(evaluator.false_positives(), 0);
  evaluator.Observe(kDetected, kNotPredicted);
  EXPECT_EQ(evaluator.true_positives(), 1);

  EXPECT_EQ(evaluator.true_negatives(), 0);
  EXPECT_EQ(evaluator.false_positives(), 0);
  EXPECT_EQ(evaluator.false_negatives(), 0);
}

// Checks that a prediction followed by a delayed detection counts as true
// positive if the delay is within the observation period.
TEST(ClippingPredictorEvaluatorTest, PredictedOnceAndDetectedBeforeDeadline) {
  ClippingPredictorEvaluator evaluator(/*history_size=*/3);
  evaluator.Observe(kNotDetected, kPredicted);
  EXPECT_EQ(evaluator.false_positives(), 0);
  evaluator.Observe(kNotDetected, kNotPredicted);
  EXPECT_EQ(evaluator.false_positives(), 0);
  evaluator.Observe(kDetected, kNotPredicted);
  EXPECT_EQ(evaluator.true_positives(), 1);

  EXPECT_EQ(evaluator.true_negatives(), 0);
  EXPECT_EQ(evaluator.false_positives(), 0);
  EXPECT_EQ(evaluator.false_negatives(), 0);
}

// Checks that a prediction followed by a delayed detection counts as true
// positive if the delay equals the observation period.
TEST(ClippingPredictorEvaluatorTest, PredictedOnceAndDetectedAtDeadline) {
  ClippingPredictorEvaluator evaluator(/*history_size=*/3);
  evaluator.Observe(kNotDetected, kPredicted);
  EXPECT_EQ(evaluator.false_positives(), 0);
  evaluator.Observe(kNotDetected, kNotPredicted);
  EXPECT_EQ(evaluator.false_positives(), 0);
  evaluator.Observe(kNotDetected, kNotPredicted);
  EXPECT_EQ(evaluator.false_positives(), 0);
  evaluator.Observe(kDetected, kNotPredicted);
  EXPECT_EQ(evaluator.true_positives(), 1);

  EXPECT_EQ(evaluator.true_negatives(), 0);
  EXPECT_EQ(evaluator.false_positives(), 0);
  EXPECT_EQ(evaluator.false_negatives(), 0);
}

// Checks that a prediction followed by a multiple adjacent detections within
// the deadline counts as a single true positive and that, after the deadline,
// a detection counts as a false negative.
TEST(ClippingPredictorEvaluatorTest, PredictedOnceAndDetectedMultipleTimes) {
  ClippingPredictorEvaluator evaluator(/*history_size=*/3);
  evaluator.Observe(kNotDetected, kPredicted);
  evaluator.Observe(kNotDetected, kNotPredicted);
  // Multiple detections.
  evaluator.Observe(kDetected, kNotPredicted);
  EXPECT_EQ(evaluator.true_positives(), 1);
  evaluator.Observe(kDetected, kNotPredicted);
  EXPECT_EQ(evaluator.true_positives(), 1);
  // A detection outside of the observation period counts as false negative.
  evaluator.Observe(kDetected, kNotPredicted);
  EXPECT_EQ(evaluator.false_negatives(), 1);
  EXPECT_EQ(SumTrueFalsePositivesNegatives(evaluator), 2);

  EXPECT_EQ(evaluator.true_negatives(), 0);
  EXPECT_EQ(evaluator.false_positives(), 0);
}

// Checks that a false positive is added when clipping is detected after a too
// early prediction.
TEST(ClippingPredictorEvaluatorTest,
     PredictedMultipleTimesAndDetectedOnceAfterDeadline) {
  ClippingPredictorEvaluator evaluator(/*history_size=*/3);
  evaluator.Observe(kNotDetected, kPredicted);  // ---+
  evaluator.Observe(kNotDetected, kPredicted);  //    |
  evaluator.Observe(kNotDetected, kPredicted);  //    |
  evaluator.Observe(kNotDetected, kPredicted);  // <--+ Not matched.
  // The time to match a detection after the first prediction expired.
  EXPECT_EQ(evaluator.false_positives(), 1);
  evaluator.Observe(kDetected, kNotPredicted);
  // The detection above does not match the first prediction because it happened
  // after the deadline of the 1st prediction.
  EXPECT_EQ(evaluator.false_positives(), 1);

  EXPECT_EQ(evaluator.true_positives(), 3);
  EXPECT_EQ(evaluator.true_negatives(), 0);
  EXPECT_EQ(evaluator.false_negatives(), 0);
}

// Checks that multiple consecutive predictions match the first detection
// observed before the expected detection deadline expires.
TEST(ClippingPredictorEvaluatorTest, PredictedMultipleTimesAndDetectedOnce) {
  ClippingPredictorEvaluator evaluator(/*history_size=*/3);
  evaluator.Observe(kNotDetected, kPredicted);  // --+
  evaluator.Observe(kNotDetected, kPredicted);  //   | --+
  evaluator.Observe(kNotDetected, kPredicted);  //   |   | --+
  evaluator.Observe(kDetected, kNotPredicted);  // <-+ <-+ <-+
  EXPECT_EQ(evaluator.true_positives(), 3);
  // The following observations do not generate any true negatives as they
  // belong to the observation period of the last prediction - for which a
  // detection has already been matched.
  const int true_negatives = evaluator.true_negatives();
  evaluator.Observe(kNotDetected, kNotPredicted);
  evaluator.Observe(kNotDetected, kNotPredicted);
  EXPECT_EQ(evaluator.true_negatives(), true_negatives);

  EXPECT_EQ(evaluator.true_negatives(), 0);
  EXPECT_EQ(evaluator.false_positives(), 0);
  EXPECT_EQ(evaluator.false_negatives(), 0);
}

// Checks that multiple consecutive predictions match the multiple detections
// observed before the expected detection deadline expires.
TEST(ClippingPredictorEvaluatorTest,
     PredictedMultipleTimesAndDetectedMultipleTimes) {
  ClippingPredictorEvaluator evaluator(/*history_size=*/3);
  evaluator.Observe(kNotDetected, kPredicted);  // --+
  evaluator.Observe(kNotDetected, kPredicted);  //   | --+
  evaluator.Observe(kNotDetected, kPredicted);  //   |   | --+
  evaluator.Observe(kDetected, kNotPredicted);  // <-+ <-+ <-+
  evaluator.Observe(kDetected, kNotPredicted);  //     <-+ <-+
  EXPECT_EQ(evaluator.true_positives(), 3);
  // The following observation does not generate a true negative as it belongs
  // to the observation period of the last prediction - for which two detections
  // have already been matched.
  const int true_negatives = evaluator.true_negatives();
  evaluator.Observe(kNotDetected, kNotPredicted);
  EXPECT_EQ(evaluator.true_negatives(), true_negatives);

  EXPECT_EQ(evaluator.true_negatives(), 0);
  EXPECT_EQ(evaluator.false_positives(), 0);
  EXPECT_EQ(evaluator.false_negatives(), 0);
}

// Checks that multiple consecutive predictions match all the detections
// observed before the expected detection deadline expires.
TEST(ClippingPredictorEvaluatorTest, PredictedMultipleTimesAndAllDetected) {
  ClippingPredictorEvaluator evaluator(/*history_size=*/3);
  evaluator.Observe(kNotDetected, kPredicted);  // --+
  evaluator.Observe(kNotDetected, kPredicted);  //   | --+
  evaluator.Observe(kNotDetected, kPredicted);  //   |   | --+
  evaluator.Observe(kDetected, kNotPredicted);  // <-+ <-+ <-+
  evaluator.Observe(kDetected, kNotPredicted);  //     <-+ <-+
  evaluator.Observe(kDetected, kNotPredicted);  //         <-+
  EXPECT_EQ(evaluator.true_positives(), 3);
  EXPECT_EQ(evaluator.true_negatives(), 0);
  EXPECT_EQ(evaluator.false_positives(), 0);
  EXPECT_EQ(evaluator.false_negatives(), 0);
}

// Checks that multiple non-consecutive predictions match all the detections
// observed before the expected detection deadline expires.
TEST(ClippingPredictorEvaluatorTest,
     PredictedMultipleTimesWithGapAndAllDetected) {
  ClippingPredictorEvaluator evaluator(/*history_size=*/3);
  evaluator.Observe(kNotDetected, kPredicted);     // --+
  evaluator.Observe(kNotDetected, kNotPredicted);  //   |
  evaluator.Observe(kNotDetected, kPredicted);     //   | --+
  evaluator.Observe(kDetected, kNotPredicted);     // <-+ <-+
  evaluator.Observe(kDetected, kNotPredicted);     //     <-+
  evaluator.Observe(kDetected, kNotPredicted);     //     <-+
  EXPECT_EQ(evaluator.true_positives(), 2);
  EXPECT_EQ(evaluator.true_negatives(), 0);
  EXPECT_EQ(evaluator.false_positives(), 0);
  EXPECT_EQ(evaluator.false_negatives(), 0);
}

class ClippingPredictorEvaluatorPredictionIntervalParameterization
    : public ::testing::TestWithParam<std::tuple<int, int>> {
 protected:
  int num_extra_observe_calls() const { return std::get<0>(GetParam()); }
  int history_size() const { return std::get<1>(GetParam()); }
};

// Checks that the minimum prediction interval is returned if clipping is
// correctly predicted as soon as detected - i.e., no anticipation.
TEST_P(ClippingPredictorEvaluatorPredictionIntervalParameterization,
       MinimumPredictionInterval) {
  ClippingPredictorEvaluator evaluator(history_size());
  for (int i = 0; i < num_extra_observe_calls(); ++i) {
    EXPECT_EQ(evaluator.Observe(kNotDetected, kNotPredicted), absl::nullopt);
  }
  absl::optional<int> prediction_interval =
      evaluator.Observe(kDetected, kPredicted);
  EXPECT_THAT(prediction_interval, Optional(Eq(0)));
}

// Checks that a prediction interval between the minimum and the maximum is
// returned if clipping is correctly predicted before it is detected but not as
// early as possible.
TEST_P(ClippingPredictorEvaluatorPredictionIntervalParameterization,
       IntermediatePredictionInterval) {
  ClippingPredictorEvaluator evaluator(history_size());
  for (int i = 0; i < num_extra_observe_calls(); ++i) {
    EXPECT_EQ(evaluator.Observe(kNotDetected, kNotPredicted), absl::nullopt);
  }
  EXPECT_EQ(evaluator.Observe(kNotDetected, kPredicted), absl::nullopt);
  EXPECT_EQ(evaluator.Observe(kNotDetected, kPredicted), absl::nullopt);
  EXPECT_EQ(evaluator.Observe(kNotDetected, kPredicted), absl::nullopt);
  absl::optional<int> prediction_interval =
      evaluator.Observe(kDetected, kPredicted);
  EXPECT_THAT(prediction_interval, Optional(Eq(3)));
}

// Checks that the maximum prediction interval is returned if clipping is
// correctly predicted as early as possible.
TEST_P(ClippingPredictorEvaluatorPredictionIntervalParameterization,
       MaximumPredictionInterval) {
  ClippingPredictorEvaluator evaluator(history_size());
  for (int i = 0; i < num_extra_observe_calls(); ++i) {
    EXPECT_EQ(evaluator.Observe(kNotDetected, kNotPredicted), absl::nullopt);
  }
  for (int i = 0; i < history_size(); ++i) {
    EXPECT_EQ(evaluator.Observe(kNotDetected, kPredicted), absl::nullopt);
  }
  absl::optional<int> prediction_interval =
      evaluator.Observe(kDetected, kPredicted);
  EXPECT_THAT(prediction_interval, Optional(Eq(history_size())));
}

// Checks that `Observe()` returns the prediction interval as soon as a true
// positive is found and never again while ongoing detections are matched to a
// previously observed prediction.
TEST_P(ClippingPredictorEvaluatorPredictionIntervalParameterization,
       PredictionIntervalReturnedOnce) {
  ASSERT_LT(num_extra_observe_calls(), history_size());
  ClippingPredictorEvaluator evaluator(history_size());
  // Observe predictions before detection.
  for (int i = 0; i < num_extra_observe_calls(); ++i) {
    EXPECT_EQ(evaluator.Observe(kNotDetected, kPredicted), absl::nullopt);
  }
  // Observe a detection.
  absl::optional<int> prediction_interval =
      evaluator.Observe(kDetected, kPredicted);
  EXPECT_TRUE(prediction_interval.has_value());
  // `Observe()` does not return a prediction interval anymore during ongoing
  // detections observed while a detection is still expected.
  for (int i = 0; i < history_size(); ++i) {
    EXPECT_EQ(evaluator.Observe(kDetected, kNotPredicted), absl::nullopt);
  }
}

INSTANTIATE_TEST_SUITE_P(
    ClippingPredictorEvaluatorTest,
    ClippingPredictorEvaluatorPredictionIntervalParameterization,
    ::testing::Combine(::testing::Values(0, 3, 5), ::testing::Values(7, 11)));

// Checks that, when a detection is expected, the expectation is removed if and
// only if `Reset()` is called after a prediction is observed.
TEST(ClippingPredictorEvaluatorTest, NoFalsePositivesAfterReset) {
  constexpr int kHistorySize = 2;

  ClippingPredictorEvaluator with_reset(kHistorySize);
  with_reset.Observe(kNotDetected, kPredicted);
  with_reset.Reset();
  with_reset.Observe(kNotDetected, kNotPredicted);
  with_reset.Observe(kNotDetected, kNotPredicted);
  EXPECT_EQ(with_reset.true_positives(), 0);
  EXPECT_EQ(with_reset.true_negatives(), 2);
  EXPECT_EQ(with_reset.false_positives(), 0);
  EXPECT_EQ(with_reset.false_negatives(), 0);

  ClippingPredictorEvaluator no_reset(kHistorySize);
  no_reset.Observe(kNotDetected, kPredicted);
  no_reset.Observe(kNotDetected, kNotPredicted);
  no_reset.Observe(kNotDetected, kNotPredicted);
  EXPECT_EQ(no_reset.true_positives(), 0);
  EXPECT_EQ(no_reset.true_negatives(), 0);
  EXPECT_EQ(no_reset.false_positives(), 1);
  EXPECT_EQ(no_reset.false_negatives(), 0);
}

}  // namespace
}  // namespace webrtc