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Diffstat (limited to 'third_party/abseil_cpp/absl/random/discrete_distribution_test.cc')
| -rw-r--r-- | third_party/abseil_cpp/absl/random/discrete_distribution_test.cc | 250 |
1 files changed, 250 insertions, 0 deletions
diff --git a/third_party/abseil_cpp/absl/random/discrete_distribution_test.cc b/third_party/abseil_cpp/absl/random/discrete_distribution_test.cc new file mode 100644 index 0000000000..6d007006ef --- /dev/null +++ b/third_party/abseil_cpp/absl/random/discrete_distribution_test.cc @@ -0,0 +1,250 @@ +// 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/discrete_distribution.h" + +#include <cmath> +#include <cstddef> +#include <cstdint> +#include <iterator> +#include <numeric> +#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/strip.h" + +namespace { + +template <typename IntType> +class DiscreteDistributionTypeTest : public ::testing::Test {}; + +using IntTypes = ::testing::Types<int8_t, uint8_t, int16_t, uint16_t, int32_t, + uint32_t, int64_t, uint64_t>; +TYPED_TEST_SUITE(DiscreteDistributionTypeTest, IntTypes); + +TYPED_TEST(DiscreteDistributionTypeTest, ParamSerializeTest) { + using param_type = + typename absl::discrete_distribution<TypeParam>::param_type; + + absl::discrete_distribution<TypeParam> empty; + EXPECT_THAT(empty.probabilities(), testing::ElementsAre(1.0)); + + absl::discrete_distribution<TypeParam> before({1.0, 2.0, 1.0}); + + // Validate that the probabilities sum to 1.0. We picked values which + // can be represented exactly to avoid floating-point roundoff error. + double s = 0; + for (const auto& x : before.probabilities()) { + s += x; + } + EXPECT_EQ(s, 1.0); + EXPECT_THAT(before.probabilities(), testing::ElementsAre(0.25, 0.5, 0.25)); + + // Validate the same data via an initializer list. + { + std::vector<double> data({1.0, 2.0, 1.0}); + + absl::discrete_distribution<TypeParam> via_param{ + param_type(std::begin(data), std::end(data))}; + + EXPECT_EQ(via_param, before); + } + + std::stringstream ss; + ss << before; + absl::discrete_distribution<TypeParam> after; + + EXPECT_NE(before, after); + + ss >> after; + + EXPECT_EQ(before, after); +} + +TYPED_TEST(DiscreteDistributionTypeTest, Constructor) { + auto fn = [](double x) { return x; }; + { + absl::discrete_distribution<int> unary(0, 1.0, 9.0, fn); + EXPECT_THAT(unary.probabilities(), testing::ElementsAre(1.0)); + } + + { + absl::discrete_distribution<int> unary(2, 1.0, 9.0, fn); + // => fn(1.0 + 0 * 4 + 2) => 3 + // => fn(1.0 + 1 * 4 + 2) => 7 + EXPECT_THAT(unary.probabilities(), testing::ElementsAre(0.3, 0.7)); + } +} + +TEST(DiscreteDistributionTest, InitDiscreteDistribution) { + using testing::Pair; + + { + std::vector<double> p({1.0, 2.0, 3.0}); + std::vector<std::pair<double, size_t>> q = + absl::random_internal::InitDiscreteDistribution(&p); + + EXPECT_THAT(p, testing::ElementsAre(1 / 6.0, 2 / 6.0, 3 / 6.0)); + + // Each bucket is p=1/3, so bucket 0 will send half it's traffic + // to bucket 2, while the rest will retain all of their traffic. + EXPECT_THAT(q, testing::ElementsAre(Pair(0.5, 2), // + Pair(1.0, 1), // + Pair(1.0, 2))); + } + + { + std::vector<double> p({1.0, 2.0, 3.0, 5.0, 2.0}); + + std::vector<std::pair<double, size_t>> q = + absl::random_internal::InitDiscreteDistribution(&p); + + EXPECT_THAT(p, testing::ElementsAre(1 / 13.0, 2 / 13.0, 3 / 13.0, 5 / 13.0, + 2 / 13.0)); + + // A more complex bucketing solution: Each bucket has p=0.2 + // So buckets 0, 1, 4 will send their alternate traffic elsewhere, which + // happens to be bucket 3. + // However, summing up that alternate traffic gives bucket 3 too much + // traffic, so it will send some traffic to bucket 2. + constexpr double b0 = 1.0 / 13.0 / 0.2; + constexpr double b1 = 2.0 / 13.0 / 0.2; + constexpr double b3 = (5.0 / 13.0 / 0.2) - ((1 - b0) + (1 - b1) + (1 - b1)); + + EXPECT_THAT(q, testing::ElementsAre(Pair(b0, 3), // + Pair(b1, 3), // + Pair(1.0, 2), // + Pair(b3, 2), // + Pair(b1, 3))); + } +} + +TEST(DiscreteDistributionTest, ChiSquaredTest50) { + using absl::random_internal::kChiSquared; + + constexpr size_t kTrials = 10000; + constexpr int kBuckets = 50; // inclusive, so actally +1 + + // 1-in-100000 threshold, but remember, there are about 8 tests + // in this file. And the test could fail for other reasons. + // Empirically validated with --runs_per_test=10000. + const int kThreshold = + absl::random_internal::ChiSquareValue(kBuckets, 0.99999); + + std::vector<double> weights(kBuckets, 0); + std::iota(std::begin(weights), std::end(weights), 1); + absl::discrete_distribution<int> dist(std::begin(weights), std::end(weights)); + + // 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); + + std::vector<int32_t> counts(kBuckets, 0); + for (size_t i = 0; i < kTrials; i++) { + auto x = dist(rng); + counts[x]++; + } + + // Scale weights. + double sum = 0; + for (double x : weights) { + sum += x; + } + for (double& x : weights) { + x = kTrials * (x / sum); + } + + double chi_square = + absl::random_internal::ChiSquare(std::begin(counts), std::end(counts), + std::begin(weights), std::end(weights)); + + if (chi_square > kThreshold) { + double p_value = + absl::random_internal::ChiSquarePValue(chi_square, kBuckets); + + // Chi-squared test failed. Output does not appear to be uniform. + std::string msg; + for (size_t i = 0; i < counts.size(); i++) { + absl::StrAppend(&msg, i, ": ", counts[i], " vs ", weights[i], "\n"); + } + absl::StrAppend(&msg, kChiSquared, " p-value ", p_value, "\n"); + absl::StrAppend(&msg, "High ", kChiSquared, " value: ", chi_square, " > ", + kThreshold); + ABSL_RAW_LOG(INFO, "%s", msg.c_str()); + FAIL() << msg; + } +} + +TEST(DiscreteDistributionTest, StabilityTest) { + // absl::discrete_distribution stabilitiy relies on + // absl::uniform_int_distribution and absl::bernoulli_distribution. + absl::random_internal::sequence_urbg urbg( + {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, + 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, + 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, + 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull}); + + std::vector<int> output(6); + + { + absl::discrete_distribution<int32_t> dist({1.0, 2.0, 3.0, 5.0, 2.0}); + EXPECT_EQ(0, dist.min()); + EXPECT_EQ(4, dist.max()); + for (auto& v : output) { + v = dist(urbg); + } + EXPECT_EQ(12, urbg.invocations()); + } + + // With 12 calls to urbg, each call into discrete_distribution consumes + // precisely 2 values: one for the uniform call, and a second for the + // bernoulli. + // + // Given the alt mapping: 0=>3, 1=>3, 2=>2, 3=>2, 4=>3, we can + // + // uniform: 443210143131 + // bernoulli: b0 000011100101 + // bernoulli: b1 001111101101 + // bernoulli: b2 111111111111 + // bernoulli: b3 001111101111 + // bernoulli: b4 001111101101 + // ... + EXPECT_THAT(output, testing::ElementsAre(3, 3, 1, 3, 3, 3)); + + { + urbg.reset(); + absl::discrete_distribution<int64_t> dist({1.0, 2.0, 3.0, 5.0, 2.0}); + EXPECT_EQ(0, dist.min()); + EXPECT_EQ(4, dist.max()); + for (auto& v : output) { + v = dist(urbg); + } + EXPECT_EQ(12, urbg.invocations()); + } + EXPECT_THAT(output, testing::ElementsAre(3, 3, 0, 3, 0, 4)); +} + +} // namespace |