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tests for degenerate distributions
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@rileyjmurray
rileyjmurray committed Jul 4, 2024
1 parent adab6c6 commit a191110
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Showing 2 changed files with 61 additions and 41 deletions.
2 changes: 0 additions & 2 deletions RandBLAS/util.hh
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Original file line number Diff line number Diff line change
Expand Up @@ -288,8 +288,6 @@ RNGState<RNG> sample_indices_iid(
}
auto random_unif01 = uneg11_to_uneg01<TF>(rv_array[rv_index]);
int64_t sample_index = std::lower_bound(cdf, cdf + n, random_unif01) - cdf;
// ^ uses binary search to set sample_index to the smallest value for which
// random_unif01 < cdf[sample_index].
samples[i] = sample_index;
rv_index += 1;
}
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100 changes: 61 additions & 39 deletions test/test_basic_rng/test_sample_indices.cc
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Original file line number Diff line number Diff line change
Expand Up @@ -94,14 +94,14 @@ class TestSampleIndices : public ::testing::Test
return;
}

static void test_iid_kolmogorov_smirnov(int64_t N, double significance, int64_t num_samples, uint32_t seed) {
static void test_iid_kolmogorov_smirnov(int64_t N, int exponent, double significance, int64_t num_samples, uint32_t seed) {
using RandBLAS_StatTests::KolmogorovSmirnovConstants::critical_value_rep_mutator;
auto critical_value = critical_value_rep_mutator(num_samples, significance);

// Make the true CDF
std::vector<float> true_cdf{};
for (int i = 0; i < N; ++i)
true_cdf.push_back(1.0/((float)i + 1.0));
true_cdf.push_back(std::pow(1.0/((float)i + 1.0), exponent));
RandBLAS::util::weights_to_cdf(N, true_cdf.data());

RNGState state(seed);
Expand All @@ -111,6 +111,42 @@ class TestSampleIndices : public ::testing::Test
index_set_kolmogorov_smirnov_tester(samples, true_cdf, critical_value);
return;
}

static void test_iid_degenerate_distributions(uint32_t seed) {
int64_t N = 100;
int64_t num_samples = N*N;
std::vector<int64_t> samples(num_samples, -1);
RNGState state(seed);

using RandBLAS::util::weights_to_cdf;
using RandBLAS::util::sample_indices_iid;

// Test case 1: distribution is nonuniform, with mass only on even elements != 10.
std::vector<float> true_cdf(N, 0.0);
for (int i = 0; i < N; i = i + 2)
true_cdf[i] = 1.0f / ((float) i + 1.0f);
true_cdf[10] = 0.0;
weights_to_cdf(N, true_cdf.data());
sample_indices_iid(N, true_cdf.data(), num_samples, samples.data(), state);
for (auto s : samples) {
ASSERT_FALSE(s == 10 || s % 2 == 1) << "s = " << s;
}

// Test case 2: distribution is trivial (a delta function),
// and a negative weight needs to be clipped without error.
std::fill(true_cdf.begin(), true_cdf.end(), 0.0);
std::fill(samples.begin(), samples.end(), -1);
true_cdf[17] = 99.0f;
true_cdf[3] = -std::numeric_limits<float>::epsilon()/10;
randblas_require(true_cdf[3] < 0);
weights_to_cdf(N, true_cdf.data());
ASSERT_GE(true_cdf[17], 0.0f);
sample_indices_iid(N, true_cdf.data(), num_samples, samples.data(), state);
for (auto s : samples) {
ASSERT_EQ(s, 17);
}
return;
}

};

Expand All @@ -136,6 +172,11 @@ TEST_F(TestSampleIndices, smoke_big) {
test_iid_uniform_smoke(huge_N, 1000, i);
}

TEST_F(TestSampleIndices, support_of_degenerate_distributions) {
for (uint32_t i = 789; i < 799; ++i)
test_iid_degenerate_distributions(i);
}

TEST_F(TestSampleIndices, iid_uniform_ks_generous) {
double s = 1e-6;
test_iid_uniform_kolmogorov_smirnov(100, s, 100000, 0);
Expand All @@ -160,49 +201,30 @@ TEST_F(TestSampleIndices, iid_uniform_ks_skeptical) {

TEST_F(TestSampleIndices, iid_ks_generous) {
double s = 1e-6;
test_iid_kolmogorov_smirnov(100, s, 100000, 0);
test_iid_kolmogorov_smirnov(10000, s, 1000, 0);
test_iid_kolmogorov_smirnov(1000000, s, 1000, 0);
test_iid_kolmogorov_smirnov(100, 1, s, 100000, 0);
test_iid_kolmogorov_smirnov(10000, 1, s, 1000, 0);
test_iid_kolmogorov_smirnov(1000000, 1, s, 1000, 0);
test_iid_kolmogorov_smirnov(100, 3, s, 100000, 0);
test_iid_kolmogorov_smirnov(10000, 3, s, 1000, 0);
test_iid_kolmogorov_smirnov(1000000, 3, s, 1000, 0);
}

TEST_F(TestSampleIndices, iid_ks_moderate) {
float s = 1e-4;
test_iid_kolmogorov_smirnov(100, s, 100000, 0);
test_iid_kolmogorov_smirnov(10000, s, 1000, 0);
test_iid_kolmogorov_smirnov(1000000, s, 1000, 0);
test_iid_kolmogorov_smirnov(100, 1, s, 100000, 0);
test_iid_kolmogorov_smirnov(10000, 1, s, 1000, 0);
test_iid_kolmogorov_smirnov(1000000, 1, s, 1000, 0);
test_iid_kolmogorov_smirnov(100, 3, s, 100000, 0);
test_iid_kolmogorov_smirnov(10000, 3, s, 1000, 0);
test_iid_kolmogorov_smirnov(1000000, 3, s, 1000, 0);
}

TEST_F(TestSampleIndices, iid_ks_skeptical) {
float s = 1e-2;
test_iid_kolmogorov_smirnov(100, s, 100000, 0);
test_iid_kolmogorov_smirnov(10000, s, 1000, 0);
test_iid_kolmogorov_smirnov(1000000, s, 1000, 0);
test_iid_kolmogorov_smirnov(100, 1, s, 100000, 0);
test_iid_kolmogorov_smirnov(10000, 1, s, 1000, 0);
test_iid_kolmogorov_smirnov(1000000, 1, s, 1000, 0);
test_iid_kolmogorov_smirnov(100, 3, s, 100000, 0);
test_iid_kolmogorov_smirnov(10000, 3, s, 1000, 0);
test_iid_kolmogorov_smirnov(1000000, 3, s, 1000, 0);
}



// class TestSampleIndices : public ::testing::Test
// {
// protected:

// virtual void SetUp(){};

// virtual void TearDown(){};

// template<typename T>
// static void test_basic(

// ) {
// return;
// }

// };


// TEST_F(TestSampleIndices, smoke)
// {
// // do something
// }



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