discrete_distribution_test.cc (8294B)
1 // Copyright 2017 The Abseil Authors. 2 // 3 // Licensed under the Apache License, Version 2.0 (the "License"); 4 // you may not use this file except in compliance with the License. 5 // You may obtain a copy of the License at 6 // 7 // https://www.apache.org/licenses/LICENSE-2.0 8 // 9 // Unless required by applicable law or agreed to in writing, software 10 // distributed under the License is distributed on an "AS IS" BASIS, 11 // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 // See the License for the specific language governing permissions and 13 // limitations under the License. 14 15 #include "absl/random/discrete_distribution.h" 16 17 #include <cmath> 18 #include <cstddef> 19 #include <cstdint> 20 #include <iterator> 21 #include <numeric> 22 #include <random> 23 #include <sstream> 24 #include <string> 25 #include <vector> 26 27 #include "gmock/gmock.h" 28 #include "gtest/gtest.h" 29 #include "absl/log/log.h" 30 #include "absl/random/internal/chi_square.h" 31 #include "absl/random/internal/distribution_test_util.h" 32 #include "absl/random/internal/pcg_engine.h" 33 #include "absl/random/internal/sequence_urbg.h" 34 #include "absl/random/random.h" 35 #include "absl/strings/str_cat.h" 36 #include "absl/strings/strip.h" 37 38 namespace { 39 40 template <typename IntType> 41 class DiscreteDistributionTypeTest : public ::testing::Test {}; 42 43 using IntTypes = ::testing::Types<int8_t, uint8_t, int16_t, uint16_t, int32_t, 44 uint32_t, int64_t, uint64_t>; 45 TYPED_TEST_SUITE(DiscreteDistributionTypeTest, IntTypes); 46 47 TYPED_TEST(DiscreteDistributionTypeTest, ParamSerializeTest) { 48 using param_type = 49 typename absl::discrete_distribution<TypeParam>::param_type; 50 51 absl::discrete_distribution<TypeParam> empty; 52 EXPECT_THAT(empty.probabilities(), testing::ElementsAre(1.0)); 53 54 absl::discrete_distribution<TypeParam> before({1.0, 2.0, 1.0}); 55 56 // Validate that the probabilities sum to 1.0. We picked values which 57 // can be represented exactly to avoid floating-point roundoff error. 58 double s = 0; 59 for (const auto& x : before.probabilities()) { 60 s += x; 61 } 62 EXPECT_EQ(s, 1.0); 63 EXPECT_THAT(before.probabilities(), testing::ElementsAre(0.25, 0.5, 0.25)); 64 65 // Validate the same data via an initializer list. 66 { 67 std::vector<double> data({1.0, 2.0, 1.0}); 68 69 absl::discrete_distribution<TypeParam> via_param{ 70 param_type(std::begin(data), std::end(data))}; 71 72 EXPECT_EQ(via_param, before); 73 } 74 75 std::stringstream ss; 76 ss << before; 77 absl::discrete_distribution<TypeParam> after; 78 79 EXPECT_NE(before, after); 80 81 ss >> after; 82 83 EXPECT_EQ(before, after); 84 } 85 86 TYPED_TEST(DiscreteDistributionTypeTest, Constructor) { 87 auto fn = [](double x) { return x; }; 88 { 89 absl::discrete_distribution<int> unary(0, 1.0, 9.0, fn); 90 EXPECT_THAT(unary.probabilities(), testing::ElementsAre(1.0)); 91 } 92 93 { 94 absl::discrete_distribution<int> unary(2, 1.0, 9.0, fn); 95 // => fn(1.0 + 0 * 4 + 2) => 3 96 // => fn(1.0 + 1 * 4 + 2) => 7 97 EXPECT_THAT(unary.probabilities(), testing::ElementsAre(0.3, 0.7)); 98 } 99 } 100 101 TEST(DiscreteDistributionTest, InitDiscreteDistribution) { 102 using testing::_; 103 using testing::Pair; 104 105 { 106 std::vector<double> p({1.0, 2.0, 3.0}); 107 std::vector<std::pair<double, size_t>> q = 108 absl::random_internal::InitDiscreteDistribution(&p); 109 110 EXPECT_THAT(p, testing::ElementsAre(1 / 6.0, 2 / 6.0, 3 / 6.0)); 111 112 // Each bucket is p=1/3, so bucket 0 will send half it's traffic 113 // to bucket 2, while the rest will retain all of their traffic. 114 EXPECT_THAT(q, testing::ElementsAre(Pair(0.5, 2), // 115 Pair(1.0, _), // 116 Pair(1.0, _))); 117 } 118 119 { 120 std::vector<double> p({1.0, 2.0, 3.0, 5.0, 2.0}); 121 122 std::vector<std::pair<double, size_t>> q = 123 absl::random_internal::InitDiscreteDistribution(&p); 124 125 EXPECT_THAT(p, testing::ElementsAre(1 / 13.0, 2 / 13.0, 3 / 13.0, 5 / 13.0, 126 2 / 13.0)); 127 128 // A more complex bucketing solution: Each bucket has p=0.2 129 // So buckets 0, 1, 4 will send their alternate traffic elsewhere, which 130 // happens to be bucket 3. 131 // However, summing up that alternate traffic gives bucket 3 too much 132 // traffic, so it will send some traffic to bucket 2. 133 constexpr double b0 = 1.0 / 13.0 / 0.2; 134 constexpr double b1 = 2.0 / 13.0 / 0.2; 135 constexpr double b3 = (5.0 / 13.0 / 0.2) - ((1 - b0) + (1 - b1) + (1 - b1)); 136 137 EXPECT_THAT(q, testing::ElementsAre(Pair(b0, 3), // 138 Pair(b1, 3), // 139 Pair(1.0, _), // 140 Pair(b3, 2), // 141 Pair(b1, 3))); 142 } 143 } 144 145 TEST(DiscreteDistributionTest, ChiSquaredTest50) { 146 using absl::random_internal::kChiSquared; 147 148 constexpr size_t kTrials = 10000; 149 constexpr int kBuckets = 50; // inclusive, so actually +1 150 151 // 1-in-100000 threshold, but remember, there are about 8 tests 152 // in this file. And the test could fail for other reasons. 153 // Empirically validated with --runs_per_test=10000. 154 const int kThreshold = 155 absl::random_internal::ChiSquareValue(kBuckets, 0.99999); 156 157 std::vector<double> weights(kBuckets, 0); 158 std::iota(std::begin(weights), std::end(weights), 1); 159 absl::discrete_distribution<int> dist(std::begin(weights), std::end(weights)); 160 161 // We use a fixed bit generator for distribution accuracy tests. This allows 162 // these tests to be deterministic, while still testing the qualify of the 163 // implementation. 164 absl::random_internal::pcg64_2018_engine rng(0x2B7E151628AED2A6); 165 166 std::vector<int32_t> counts(kBuckets, 0); 167 for (size_t i = 0; i < kTrials; i++) { 168 auto x = dist(rng); 169 counts[x]++; 170 } 171 172 // Scale weights. 173 double sum = 0; 174 for (double x : weights) { 175 sum += x; 176 } 177 for (double& x : weights) { 178 x = kTrials * (x / sum); 179 } 180 181 double chi_square = 182 absl::random_internal::ChiSquare(std::begin(counts), std::end(counts), 183 std::begin(weights), std::end(weights)); 184 185 if (chi_square > kThreshold) { 186 double p_value = 187 absl::random_internal::ChiSquarePValue(chi_square, kBuckets); 188 189 // Chi-squared test failed. Output does not appear to be uniform. 190 std::string msg; 191 for (size_t i = 0; i < counts.size(); i++) { 192 absl::StrAppend(&msg, i, ": ", counts[i], " vs ", weights[i], "\n"); 193 } 194 absl::StrAppend(&msg, kChiSquared, " p-value ", p_value, "\n"); 195 absl::StrAppend(&msg, "High ", kChiSquared, " value: ", chi_square, " > ", 196 kThreshold); 197 LOG(INFO) << msg; 198 FAIL() << msg; 199 } 200 } 201 202 TEST(DiscreteDistributionTest, StabilityTest) { 203 // absl::discrete_distribution stability relies on 204 // absl::uniform_int_distribution and absl::bernoulli_distribution. 205 absl::random_internal::sequence_urbg urbg( 206 {0x0003eb76f6f7f755ull, 0xFFCEA50FDB2F953Bull, 0xC332DDEFBE6C5AA5ull, 207 0x6558218568AB9702ull, 0x2AEF7DAD5B6E2F84ull, 0x1521B62829076170ull, 208 0xECDD4775619F1510ull, 0x13CCA830EB61BD96ull, 0x0334FE1EAA0363CFull, 209 0xB5735C904C70A239ull, 0xD59E9E0BCBAADE14ull, 0xEECC86BC60622CA7ull}); 210 211 std::vector<int> output(6); 212 213 { 214 absl::discrete_distribution<int32_t> dist({1.0, 2.0, 3.0, 5.0, 2.0}); 215 EXPECT_EQ(0, dist.min()); 216 EXPECT_EQ(4, dist.max()); 217 for (auto& v : output) { 218 v = dist(urbg); 219 } 220 EXPECT_EQ(12, urbg.invocations()); 221 } 222 223 // With 12 calls to urbg, each call into discrete_distribution consumes 224 // precisely 2 values: one for the uniform call, and a second for the 225 // bernoulli. 226 // 227 // Given the alt mapping: 0=>3, 1=>3, 2=>2, 3=>2, 4=>3, we can 228 // 229 // uniform: 443210143131 230 // bernoulli: b0 000011100101 231 // bernoulli: b1 001111101101 232 // bernoulli: b2 111111111111 233 // bernoulli: b3 001111101111 234 // bernoulli: b4 001111101101 235 // ... 236 EXPECT_THAT(output, testing::ElementsAre(3, 3, 1, 3, 3, 3)); 237 238 { 239 urbg.reset(); 240 absl::discrete_distribution<int64_t> dist({1.0, 2.0, 3.0, 5.0, 2.0}); 241 EXPECT_EQ(0, dist.min()); 242 EXPECT_EQ(4, dist.max()); 243 for (auto& v : output) { 244 v = dist(urbg); 245 } 246 EXPECT_EQ(12, urbg.invocations()); 247 } 248 EXPECT_THAT(output, testing::ElementsAre(3, 3, 0, 3, 0, 4)); 249 } 250 251 } // namespace