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Diffstat (limited to 'third_party/abseil_cpp/absl/random/log_uniform_int_distribution_test.cc')
| -rw-r--r-- | third_party/abseil_cpp/absl/random/log_uniform_int_distribution_test.cc | 280 |
1 files changed, 0 insertions, 280 deletions
diff --git a/third_party/abseil_cpp/absl/random/log_uniform_int_distribution_test.cc b/third_party/abseil_cpp/absl/random/log_uniform_int_distribution_test.cc deleted file mode 100644 index 5e780d96d3..0000000000 --- a/third_party/abseil_cpp/absl/random/log_uniform_int_distribution_test.cc +++ /dev/null @@ -1,280 +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/log_uniform_int_distribution.h" - -#include <cstddef> -#include <cstdint> -#include <iterator> -#include <random> -#include <sstream> -#include <string> -#include <vector> - -#include "gmock/gmock.h" -#include "gtest/gtest.h" -#include "absl/base/internal/raw_logging.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 { - -template <typename IntType> -class LogUniformIntDistributionTypeTest : public ::testing::Test {}; - -using IntTypes = ::testing::Types<int8_t, int16_t, int32_t, int64_t, // - uint8_t, uint16_t, uint32_t, uint64_t>; -TYPED_TEST_CASE(LogUniformIntDistributionTypeTest, IntTypes); - -TYPED_TEST(LogUniformIntDistributionTypeTest, SerializeTest) { - using param_type = - typename absl::log_uniform_int_distribution<TypeParam>::param_type; - using Limits = std::numeric_limits<TypeParam>; - - constexpr int kCount = 1000; - absl::InsecureBitGen gen; - for (const auto& param : { - param_type(0, 1), // - param_type(0, 2), // - param_type(0, 2, 10), // - param_type(9, 32, 4), // - param_type(1, 101, 10), // - param_type(1, Limits::max() / 2), // - param_type(0, Limits::max() - 1), // - param_type(0, Limits::max(), 2), // - param_type(0, Limits::max(), 10), // - param_type(Limits::min(), 0), // - param_type(Limits::lowest(), Limits::max()), // - param_type(Limits::min(), Limits::max()), // - }) { - // Validate parameters. - const auto min = param.min(); - const auto max = param.max(); - const auto base = param.base(); - absl::log_uniform_int_distribution<TypeParam> before(min, max, base); - EXPECT_EQ(before.min(), param.min()); - EXPECT_EQ(before.max(), param.max()); - EXPECT_EQ(before.base(), param.base()); - - { - absl::log_uniform_int_distribution<TypeParam> via_param(param); - EXPECT_EQ(via_param, before); - } - - // Validate stream serialization. - std::stringstream ss; - ss << before; - - absl::log_uniform_int_distribution<TypeParam> after(3, 6, 17); - - EXPECT_NE(before.max(), after.max()); - EXPECT_NE(before.base(), after.base()); - EXPECT_NE(before.param(), after.param()); - EXPECT_NE(before, after); - - ss >> after; - - EXPECT_EQ(before.min(), after.min()); - EXPECT_EQ(before.max(), after.max()); - EXPECT_EQ(before.base(), after.base()); - EXPECT_EQ(before.param(), after.param()); - EXPECT_EQ(before, after); - - // Smoke test. - auto sample_min = after.max(); - auto sample_max = after.min(); - for (int i = 0; i < kCount; i++) { - auto sample = after(gen); - EXPECT_GE(sample, after.min()); - EXPECT_LE(sample, after.max()); - if (sample > sample_max) sample_max = sample; - if (sample < sample_min) sample_min = sample; - } - ABSL_INTERNAL_LOG(INFO, - absl::StrCat("Range: ", +sample_min, ", ", +sample_max)); - } -} - -using log_uniform_i32 = absl::log_uniform_int_distribution<int32_t>; - -class LogUniformIntChiSquaredTest - : public testing::TestWithParam<log_uniform_i32::param_type> { - public: - // The ChiSquaredTestImpl provides a chi-squared goodness of fit test for - // data generated by the log-uniform-int distribution. - double ChiSquaredTestImpl(); - - // 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}; -}; - -double LogUniformIntChiSquaredTest::ChiSquaredTestImpl() { - using absl::random_internal::kChiSquared; - - const auto& param = GetParam(); - - // Check the distribution of L=log(log_uniform_int_distribution, base), - // expecting that L is roughly uniformly distributed, that is: - // - // P[L=0] ~= P[L=1] ~= ... ~= P[L=log(max)] - // - // For a total of X entries, each bucket should contain some number of samples - // in the interval [X/k - a, X/k + a]. - // - // Where `a` is approximately sqrt(X/k). This is validated by bucketing - // according to the log function and using a chi-squared test for uniformity. - - const bool is_2 = (param.base() == 2); - const double base_log = 1.0 / std::log(param.base()); - const auto bucket_index = [base_log, is_2, ¶m](int32_t x) { - uint64_t y = static_cast<uint64_t>(x) - param.min(); - return (y == 0) ? 0 - : is_2 ? static_cast<int>(1 + std::log2(y)) - : static_cast<int>(1 + std::log(y) * base_log); - }; - const int max_bucket = bucket_index(param.max()); // inclusive - const size_t trials = 15 + (max_bucket + 1) * 10; - - log_uniform_i32 dist(param); - - std::vector<int64_t> buckets(max_bucket + 1); - for (size_t i = 0; i < trials; ++i) { - const auto sample = dist(rng_); - // Check the bounds. - ABSL_ASSERT(sample <= dist.max()); - ABSL_ASSERT(sample >= dist.min()); - // Convert the output of the generator to one of num_bucket buckets. - int bucket = bucket_index(sample); - ABSL_ASSERT(bucket <= max_bucket); - ++buckets[bucket]; - } - - // The null-hypothesis is that the distribution is uniform with respect to - // log-uniform-int bucketization. - const int dof = buckets.size() - 1; - const double expected = trials / static_cast<double>(buckets.size()); - - const double threshold = absl::random_internal::ChiSquareValue(dof, 0.98); - - double chi_square = absl::random_internal::ChiSquareWithExpected( - std::begin(buckets), std::end(buckets), expected); - - const double p = absl::random_internal::ChiSquarePValue(chi_square, dof); - - if (chi_square > threshold) { - ABSL_INTERNAL_LOG(INFO, "values"); - for (size_t i = 0; i < buckets.size(); i++) { - ABSL_INTERNAL_LOG(INFO, absl::StrCat(i, ": ", buckets[i])); - } - ABSL_INTERNAL_LOG(INFO, - absl::StrFormat("trials=%d\n" - "%s(data, %d) = %f (%f)\n" - "%s @ 0.98 = %f", - trials, kChiSquared, dof, chi_square, p, - kChiSquared, threshold)); - } - return p; -} - -TEST_P(LogUniformIntChiSquaredTest, MultiTest) { - const int kTrials = 5; - int failures = 0; - for (int i = 0; i < kTrials; i++) { - double p_value = ChiSquaredTestImpl(); - if (p_value < 0.005) { - 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); -} - -// Generate the parameters for the test. -std::vector<log_uniform_i32::param_type> GenParams() { - using Param = log_uniform_i32::param_type; - using Limits = std::numeric_limits<int32_t>; - - return std::vector<Param>{ - Param{0, 1, 2}, - Param{1, 1, 2}, - Param{0, 2, 2}, - Param{0, 3, 2}, - Param{0, 4, 2}, - Param{0, 9, 10}, - Param{0, 10, 10}, - Param{0, 11, 10}, - Param{1, 10, 10}, - Param{0, (1 << 8) - 1, 2}, - Param{0, (1 << 8), 2}, - Param{0, (1 << 30) - 1, 2}, - Param{-1000, 1000, 10}, - Param{0, Limits::max(), 2}, - Param{0, Limits::max(), 3}, - Param{0, Limits::max(), 10}, - Param{Limits::min(), 0}, - Param{Limits::min(), Limits::max(), 2}, - }; -} - -std::string ParamName( - const ::testing::TestParamInfo<log_uniform_i32::param_type>& info) { - const auto& p = info.param; - std::string name = - absl::StrCat("min_", p.min(), "__max_", p.max(), "__base_", p.base()); - return absl::StrReplaceAll(name, {{"+", "_"}, {"-", "_"}, {".", "_"}}); -} - -INSTANTIATE_TEST_SUITE_P(All, LogUniformIntChiSquaredTest, - ::testing::ValuesIn(GenParams()), ParamName); - -// NOTE: absl::log_uniform_int_distribution is not guaranteed to be stable. -TEST(LogUniformIntDistributionTest, StabilityTest) { - using testing::ElementsAre; - // absl::uniform_int_distribution stability relies on - // absl::random_internal::LeadingSetBit, std::log, std::pow. - absl::random_internal::sequence_urbg urbg( - {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, - 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, - 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, - 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull}); - - std::vector<int> output(6); - - { - absl::log_uniform_int_distribution<int32_t> dist(0, 256); - std::generate(std::begin(output), std::end(output), - [&] { return dist(urbg); }); - EXPECT_THAT(output, ElementsAre(256, 66, 4, 6, 57, 103)); - } - urbg.reset(); - { - absl::log_uniform_int_distribution<int32_t> dist(0, 256, 10); - std::generate(std::begin(output), std::end(output), - [&] { return dist(urbg); }); - EXPECT_THAT(output, ElementsAre(8, 4, 0, 0, 0, 69)); - } -} - -} // namespace |