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Diffstat (limited to 'third_party/abseil_cpp/absl/random/exponential_distribution_test.cc')
| -rw-r--r-- | third_party/abseil_cpp/absl/random/exponential_distribution_test.cc | 430 |
1 files changed, 0 insertions, 430 deletions
diff --git a/third_party/abseil_cpp/absl/random/exponential_distribution_test.cc b/third_party/abseil_cpp/absl/random/exponential_distribution_test.cc deleted file mode 100644 index 8e9e69b64b..0000000000 --- a/third_party/abseil_cpp/absl/random/exponential_distribution_test.cc +++ /dev/null @@ -1,430 +0,0 @@ -// Copyright 2017 The Abseil Authors. -// -// 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 -// -// https://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. - -#include "absl/random/exponential_distribution.h" - -#include <algorithm> -#include <cmath> -#include <cstddef> -#include <cstdint> -#include <iterator> -#include <limits> -#include <random> -#include <sstream> -#include <string> -#include <type_traits> -#include <vector> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/base/internal/raw_logging.h" -#include "absl/base/macros.h" -#include "absl/random/internal/chi_square.h" -#include "absl/random/internal/distribution_test_util.h" -#include "absl/random/internal/pcg_engine.h" -#include "absl/random/internal/sequence_urbg.h" -#include "absl/random/random.h" -#include "absl/strings/str_cat.h" -#include "absl/strings/str_format.h" -#include "absl/strings/str_replace.h" -#include "absl/strings/strip.h" - -namespace { - -using absl::random_internal::kChiSquared; - -template <typename RealType> -class ExponentialDistributionTypedTest : public ::testing::Test {}; - -#if defined(__EMSCRIPTEN__) -using RealTypes = ::testing::Types<float, double>; -#else -using RealTypes = ::testing::Types<float, double, long double>; -#endif // defined(__EMSCRIPTEN__) -TYPED_TEST_CASE(ExponentialDistributionTypedTest, RealTypes); - -TYPED_TEST(ExponentialDistributionTypedTest, SerializeTest) { - using param_type = - typename absl::exponential_distribution<TypeParam>::param_type; - - const TypeParam kParams[] = { - // Cases around 1. - 1, // - std::nextafter(TypeParam(1), TypeParam(0)), // 1 - epsilon - std::nextafter(TypeParam(1), TypeParam(2)), // 1 + epsilon - // Typical cases. - TypeParam(1e-8), TypeParam(1e-4), TypeParam(1), TypeParam(2), - TypeParam(1e4), TypeParam(1e8), TypeParam(1e20), TypeParam(2.5), - // Boundary cases. - std::numeric_limits<TypeParam>::max(), - std::numeric_limits<TypeParam>::epsilon(), - std::nextafter(std::numeric_limits<TypeParam>::min(), - TypeParam(1)), // min + epsilon - std::numeric_limits<TypeParam>::min(), // smallest normal - // There are some errors dealing with denorms on apple platforms. - std::numeric_limits<TypeParam>::denorm_min(), // smallest denorm - std::numeric_limits<TypeParam>::min() / 2, // denorm - std::nextafter(std::numeric_limits<TypeParam>::min(), - TypeParam(0)), // denorm_max - }; - - constexpr int kCount = 1000; - absl::InsecureBitGen gen; - - for (const TypeParam lambda : kParams) { - // Some values may be invalid; skip those. - if (!std::isfinite(lambda)) continue; - ABSL_ASSERT(lambda > 0); - - const param_type param(lambda); - - absl::exponential_distribution<TypeParam> before(lambda); - EXPECT_EQ(before.lambda(), param.lambda()); - - { - absl::exponential_distribution<TypeParam> via_param(param); - EXPECT_EQ(via_param, before); - EXPECT_EQ(via_param.param(), before.param()); - } - - // Smoke test. - auto sample_min = before.max(); - auto sample_max = before.min(); - for (int i = 0; i < kCount; i++) { - auto sample = before(gen); - EXPECT_GE(sample, before.min()) << before; - EXPECT_LE(sample, before.max()) << before; - if (sample > sample_max) sample_max = sample; - if (sample < sample_min) sample_min = sample; - } - if (!std::is_same<TypeParam, long double>::value) { - ABSL_INTERNAL_LOG(INFO, - absl::StrFormat("Range {%f}: %f, %f, lambda=%f", lambda, - sample_min, sample_max, lambda)); - } - - std::stringstream ss; - ss << before; - - if (!std::isfinite(lambda)) { - // Streams do not deserialize inf/nan correctly. - continue; - } - // Validate stream serialization. - absl::exponential_distribution<TypeParam> after(34.56f); - - EXPECT_NE(before.lambda(), after.lambda()); - EXPECT_NE(before.param(), after.param()); - EXPECT_NE(before, after); - - ss >> after; - -#if defined(__powerpc64__) || defined(__PPC64__) || defined(__powerpc__) || \ - defined(__ppc__) || defined(__PPC__) - if (std::is_same<TypeParam, long double>::value) { - // Roundtripping floating point values requires sufficient precision to - // reconstruct the exact value. It turns out that long double has some - // errors doing this on ppc, particularly for values - // near {1.0 +/- epsilon}. - if (lambda <= std::numeric_limits<double>::max() && - lambda >= std::numeric_limits<double>::lowest()) { - EXPECT_EQ(static_cast<double>(before.lambda()), - static_cast<double>(after.lambda())) - << ss.str(); - } - continue; - } -#endif - - EXPECT_EQ(before.lambda(), after.lambda()) // - << ss.str() << " " // - << (ss.good() ? "good " : "") // - << (ss.bad() ? "bad " : "") // - << (ss.eof() ? "eof " : "") // - << (ss.fail() ? "fail " : ""); - } -} - -// http://www.itl.nist.gov/div898/handbook/eda/section3/eda3667.htm - -class ExponentialModel { - public: - explicit ExponentialModel(double lambda) - : lambda_(lambda), beta_(1.0 / lambda) {} - - double lambda() const { return lambda_; } - - double mean() const { return beta_; } - double variance() const { return beta_ * beta_; } - double stddev() const { return std::sqrt(variance()); } - double skew() const { return 2; } - double kurtosis() const { return 6.0; } - - double CDF(double x) { return 1.0 - std::exp(-lambda_ * x); } - - // The inverse CDF, or PercentPoint function of the distribution - double InverseCDF(double p) { - ABSL_ASSERT(p >= 0.0); - ABSL_ASSERT(p < 1.0); - return -beta_ * std::log(1.0 - p); - } - - private: - const double lambda_; - const double beta_; -}; - -struct Param { - double lambda; - double p_fail; - int trials; -}; - -class ExponentialDistributionTests : public testing::TestWithParam<Param>, - public ExponentialModel { - public: - ExponentialDistributionTests() : ExponentialModel(GetParam().lambda) {} - - // SingleZTest provides a basic z-squared test of the mean vs. expected - // mean for data generated by the poisson distribution. - template <typename D> - bool SingleZTest(const double p, const size_t samples); - - // SingleChiSquaredTest provides a basic chi-squared test of the normal - // distribution. - template <typename D> - double SingleChiSquaredTest(); - - // We use a fixed bit generator for distribution accuracy tests. This allows - // these tests to be deterministic, while still testing the qualify of the - // implementation. - absl::random_internal::pcg64_2018_engine rng_{0x2B7E151628AED2A6}; -}; - -template <typename D> -bool ExponentialDistributionTests::SingleZTest(const double p, - const size_t samples) { - D dis(lambda()); - - std::vector<double> data; - data.reserve(samples); - for (size_t i = 0; i < samples; i++) { - const double x = dis(rng_); - data.push_back(x); - } - - const auto m = absl::random_internal::ComputeDistributionMoments(data); - const double max_err = absl::random_internal::MaxErrorTolerance(p); - const double z = absl::random_internal::ZScore(mean(), m); - const bool pass = absl::random_internal::Near("z", z, 0.0, max_err); - - if (!pass) { - ABSL_INTERNAL_LOG( - INFO, absl::StrFormat("p=%f max_err=%f\n" - " lambda=%f\n" - " mean=%f vs. %f\n" - " stddev=%f vs. %f\n" - " skewness=%f vs. %f\n" - " kurtosis=%f vs. %f\n" - " z=%f vs. 0", - p, max_err, lambda(), m.mean, mean(), - std::sqrt(m.variance), stddev(), m.skewness, - skew(), m.kurtosis, kurtosis(), z)); - } - return pass; -} - -template <typename D> -double ExponentialDistributionTests::SingleChiSquaredTest() { - const size_t kSamples = 10000; - const int kBuckets = 50; - - // The InverseCDF is the percent point function of the distribution, and can - // be used to assign buckets roughly uniformly. - std::vector<double> cutoffs; - const double kInc = 1.0 / static_cast<double>(kBuckets); - for (double p = kInc; p < 1.0; p += kInc) { - cutoffs.push_back(InverseCDF(p)); - } - if (cutoffs.back() != std::numeric_limits<double>::infinity()) { - cutoffs.push_back(std::numeric_limits<double>::infinity()); - } - - D dis(lambda()); - - std::vector<int32_t> counts(cutoffs.size(), 0); - for (int j = 0; j < kSamples; j++) { - const double x = dis(rng_); - auto it = std::upper_bound(cutoffs.begin(), cutoffs.end(), x); - counts[std::distance(cutoffs.begin(), it)]++; - } - - // Null-hypothesis is that the distribution is exponentially distributed - // with the provided lambda (not estimated from the data). - const int dof = static_cast<int>(counts.size()) - 1; - - // Our threshold for logging is 1-in-50. - const double threshold = absl::random_internal::ChiSquareValue(dof, 0.98); - - const double expected = - static_cast<double>(kSamples) / static_cast<double>(counts.size()); - - double chi_square = absl::random_internal::ChiSquareWithExpected( - std::begin(counts), std::end(counts), expected); - double p = absl::random_internal::ChiSquarePValue(chi_square, dof); - - if (chi_square > threshold) { - for (int i = 0; i < cutoffs.size(); i++) { - ABSL_INTERNAL_LOG( - INFO, absl::StrFormat("%d : (%f) = %d", i, cutoffs[i], counts[i])); - } - - ABSL_INTERNAL_LOG(INFO, - absl::StrCat("lambda ", lambda(), "\n", // - " expected ", expected, "\n", // - kChiSquared, " ", chi_square, " (", p, ")\n", - kChiSquared, " @ 0.98 = ", threshold)); - } - return p; -} - -TEST_P(ExponentialDistributionTests, ZTest) { - const size_t kSamples = 10000; - const auto& param = GetParam(); - const int expected_failures = - std::max(1, static_cast<int>(std::ceil(param.trials * param.p_fail))); - const double p = absl::random_internal::RequiredSuccessProbability( - param.p_fail, param.trials); - - int failures = 0; - for (int i = 0; i < param.trials; i++) { - failures += SingleZTest<absl::exponential_distribution<double>>(p, kSamples) - ? 0 - : 1; - } - EXPECT_LE(failures, expected_failures); -} - -TEST_P(ExponentialDistributionTests, ChiSquaredTest) { - const int kTrials = 20; - int failures = 0; - - for (int i = 0; i < kTrials; i++) { - double p_value = - SingleChiSquaredTest<absl::exponential_distribution<double>>(); - if (p_value < 0.005) { // 1/200 - failures++; - } - } - - // There is a 0.10% chance of producing at least one failure, so raise the - // failure threshold high enough to allow for a flake rate < 10,000. - EXPECT_LE(failures, 4); -} - -std::vector<Param> GenParams() { - return { - Param{1.0, 0.02, 100}, - Param{2.5, 0.02, 100}, - Param{10, 0.02, 100}, - // large - Param{1e4, 0.02, 100}, - Param{1e9, 0.02, 100}, - // small - Param{0.1, 0.02, 100}, - Param{1e-3, 0.02, 100}, - Param{1e-5, 0.02, 100}, - }; -} - -std::string ParamName(const ::testing::TestParamInfo<Param>& info) { - const auto& p = info.param; - std::string name = absl::StrCat("lambda_", absl::SixDigits(p.lambda)); - return absl::StrReplaceAll(name, {{"+", "_"}, {"-", "_"}, {".", "_"}}); -} - -INSTANTIATE_TEST_CASE_P(All, ExponentialDistributionTests, - ::testing::ValuesIn(GenParams()), ParamName); - -// NOTE: absl::exponential_distribution is not guaranteed to be stable. -TEST(ExponentialDistributionTest, StabilityTest) { - // absl::exponential_distribution stability relies on std::log1p and - // absl::uniform_real_distribution. - absl::random_internal::sequence_urbg urbg( - {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, - 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, - 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, - 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull}); - - std::vector<int> output(14); - - { - absl::exponential_distribution<double> dist; - std::generate(std::begin(output), std::end(output), - [&] { return static_cast<int>(10000.0 * dist(urbg)); }); - - EXPECT_EQ(14, urbg.invocations()); - EXPECT_THAT(output, - testing::ElementsAre(0, 71913, 14375, 5039, 1835, 861, 25936, - 804, 126, 12337, 17984, 27002, 0, 71913)); - } - - urbg.reset(); - { - absl::exponential_distribution<float> dist; - std::generate(std::begin(output), std::end(output), - [&] { return static_cast<int>(10000.0f * dist(urbg)); }); - - EXPECT_EQ(14, urbg.invocations()); - EXPECT_THAT(output, - testing::ElementsAre(0, 71913, 14375, 5039, 1835, 861, 25936, - 804, 126, 12337, 17984, 27002, 0, 71913)); - } -} - -TEST(ExponentialDistributionTest, AlgorithmBounds) { - // Relies on absl::uniform_real_distribution, so some of these comments - // reference that. - absl::exponential_distribution<double> dist; - - { - // This returns the smallest value >0 from absl::uniform_real_distribution. - absl::random_internal::sequence_urbg urbg({0x0000000000000001ull}); - double a = dist(urbg); - EXPECT_EQ(a, 5.42101086242752217004e-20); - } - - { - // This returns a value very near 0.5 from absl::uniform_real_distribution. - absl::random_internal::sequence_urbg urbg({0x7fffffffffffffefull}); - double a = dist(urbg); - EXPECT_EQ(a, 0.693147180559945175204); - } - - { - // This returns the largest value <1 from absl::uniform_real_distribution. - // WolframAlpha: ~39.1439465808987766283058547296341915292187253 - absl::random_internal::sequence_urbg urbg({0xFFFFFFFFFFFFFFeFull}); - double a = dist(urbg); - EXPECT_EQ(a, 36.7368005696771007251); - } - { - // This *ALSO* returns the largest value <1. - absl::random_internal::sequence_urbg urbg({0xFFFFFFFFFFFFFFFFull}); - double a = dist(urbg); - EXPECT_EQ(a, 36.7368005696771007251); - } -} - -} // namespace |