| // 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/log/log.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_SUITE(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; |
| } |
| LOG(INFO) << "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) { |
| LOG(INFO) << "values"; |
| for (size_t i = 0; i < buckets.size(); i++) { |
| LOG(INFO) << i << ": " << buckets[i]; |
| } |
| LOG(INFO) << "trials=" << trials << "\n" |
| << kChiSquared << "(data, " << dof << ") = " << chi_square << " (" |
| << p << ")\n" |
| << kChiSquared << " @ 0.98 = " << 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 |