// 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
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