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mitzimorrismitzimorris
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Merge pull request #3303 from stan-dev/bugfix/3071-fix-unit-test
Fix and add unit tests for issue 3071 - adaptive sampler runs on models w/ zero params.
2 parents cd8b2e0 + e546442 commit 2f8ae28

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‎src/test/test-models/good/services/zero_params.stan‎

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transformed data {
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int N = 2;
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}
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generated quantities {
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real theta = beta_rng(1, 1);
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real eta = beta_rng(10, 10);
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}

‎src/test/unit/services/sample/hmc_nuts_dense_e_adapt_no_params_test.cpp‎

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#include <stan/services/sample/hmc_nuts_dense_e.hpp>
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#include <gtest/gtest.h>
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#include <stan/io/empty_var_context.hpp>
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#include <test/test-models/good/services/zero_params.hpp>
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#include <test/unit/services/instrumented_callbacks.hpp>
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#include <iostream>
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auto&& blah = stan::math::init_threadpool_tbb();
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static constexpr size_t num_chains = 4;
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class ServicesSampleHMCNutsDenseENoParams : public testing::Test {
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public:
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ServicesSampleHMCNutsDenseENoParams() : model(data_context, 0, &model_log) {
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for (int i = 0; i < num_chains; ++i) {
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init.push_back(stan::test::unit::instrumented_writer{});
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parameter.push_back(stan::test::unit::instrumented_writer{});
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diagnostic.push_back(stan::test::unit::instrumented_writer{});
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context.push_back(std::make_shared<stan::io::empty_var_context>());
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}
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}
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stan::io::empty_var_context data_context;
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std::stringstream model_log;
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stan::test::unit::instrumented_logger logger;
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std::vector<stan::test::unit::instrumented_writer> init;
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std::vector<stan::test::unit::instrumented_writer> parameter;
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std::vector<stan::test::unit::instrumented_writer> diagnostic;
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std::vector<std::shared_ptr<stan::io::empty_var_context>> context;
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stan_model model;
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};
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TEST_F(ServicesSampleHMCNutsDenseENoParams, call_count) {
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unsigned int random_seed = 0;
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unsigned int chain = 1;
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double init_radius = 0;
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int num_warmup = 200;
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int num_samples = 400;
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int num_thin = 5;
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bool save_warmup = true;
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int refresh = 0;
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double stepsize = 0.1;
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double stepsize_jitter = 0;
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int max_depth = 8;
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double delta = .1;
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double gamma = .1;
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double kappa = .1;
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double t0 = .1;
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unsigned int init_buffer = 50;
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unsigned int term_buffer = 50;
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unsigned int window = 100;
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stan::test::unit::instrumented_interrupt interrupt;
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EXPECT_EQ(interrupt.call_count(), 0);
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int return_code = stan::services::sample::hmc_nuts_dense_e(
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model, num_chains, context, random_seed, chain, init_radius, num_warmup,
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num_samples, num_thin, save_warmup, refresh, stepsize, stepsize_jitter,
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max_depth, interrupt, logger, init, parameter, diagnostic);
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EXPECT_EQ(0, return_code);
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int num_output_lines = (num_warmup + num_samples) / num_thin;
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EXPECT_EQ((num_warmup + num_samples) * num_chains, interrupt.call_count());
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for (int i = 0; i < num_chains; ++i) {
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EXPECT_EQ(1, parameter[i].call_count("vector_string"));
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EXPECT_EQ(num_output_lines, parameter[i].call_count("vector_double"));
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EXPECT_EQ(1, diagnostic[i].call_count("vector_string"));
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EXPECT_EQ(num_output_lines, diagnostic[i].call_count("vector_double"));
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}
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}
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TEST_F(ServicesSampleHMCNutsDenseENoParams, parameter_checks) {
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unsigned int random_seed = 0;
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unsigned int chain = 1;
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double init_radius = 0;
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int num_warmup = 200;
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int num_samples = 400;
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int num_thin = 5;
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bool save_warmup = true;
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int refresh = 0;
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double stepsize = 0.1;
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double stepsize_jitter = 0;
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int max_depth = 8;
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double delta = .1;
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double gamma = .1;
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double kappa = .1;
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double t0 = .1;
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unsigned int init_buffer = 50;
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unsigned int term_buffer = 50;
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unsigned int window = 100;
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stan::test::unit::instrumented_interrupt interrupt;
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EXPECT_EQ(interrupt.call_count(), 0);
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int return_code = stan::services::sample::hmc_nuts_dense_e(
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model, num_chains, context, random_seed, chain, init_radius, num_warmup,
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num_samples, num_thin, save_warmup, refresh, stepsize, stepsize_jitter,
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max_depth, interrupt, logger, init, parameter, diagnostic);
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for (size_t i = 0; i < num_chains; ++i) {
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std::vector<std::vector<std::string>> parameter_names;
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parameter_names = parameter[i].vector_string_values();
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std::vector<std::vector<double>> parameter_values;
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parameter_values = parameter[i].vector_double_values();
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std::vector<std::string> strings;
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strings = parameter[i].string_values();
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std::vector<std::vector<std::string>> diagnostic_names;
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diagnostic_names = diagnostic[i].vector_string_values();
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std::vector<std::vector<double>> diagnostic_values;
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diagnostic_values = diagnostic[i].vector_double_values();
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// Expect message at end of warmup
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EXPECT_EQ("Adaptation terminated", strings[0]);
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// Expectations of sampler and model variables names.
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ASSERT_EQ(9, parameter_names[0].size());
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EXPECT_EQ("lp__", parameter_names[0][0]);
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EXPECT_EQ("accept_stat__", parameter_names[0][1]);
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EXPECT_EQ("stepsize__", parameter_names[0][2]);
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EXPECT_EQ("treedepth__", parameter_names[0][3]);
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EXPECT_EQ("n_leapfrog__", parameter_names[0][4]);
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EXPECT_EQ("divergent__", parameter_names[0][5]);
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EXPECT_EQ("energy__", parameter_names[0][6]);
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EXPECT_EQ("theta", parameter_names[0][7]);
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EXPECT_EQ("eta", parameter_names[0][8]);
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// Expect one name per parameter value.
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EXPECT_EQ(parameter_names[0].size(), parameter_values[0].size());
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EXPECT_EQ(diagnostic_names[0].size(), diagnostic_values[0].size());
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EXPECT_EQ((num_warmup + num_samples) / num_thin, parameter_values.size());
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// Expect one call to set parameter names, and one set of output per
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// iteration.
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EXPECT_EQ("lp__", diagnostic_names[0][0]);
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EXPECT_EQ("accept_stat__", diagnostic_names[0][1]);
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}
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EXPECT_EQ(return_code, 0);
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}
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TEST_F(ServicesSampleHMCNutsDenseENoParams, output_regression) {
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unsigned int random_seed = 0;
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unsigned int chain = 1;
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double init_radius = 0;
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int num_warmup = 200;
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int num_samples = 400;
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int num_thin = 5;
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bool save_warmup = true;
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int refresh = 0;
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double stepsize = 0.1;
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double stepsize_jitter = 0;
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int max_depth = 8;
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double delta = .1;
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double gamma = .1;
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double kappa = .1;
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double t0 = .1;
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unsigned int init_buffer = 50;
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unsigned int term_buffer = 50;
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unsigned int window = 100;
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stan::test::unit::instrumented_interrupt interrupt;
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EXPECT_EQ(interrupt.call_count(), 0);
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stan::services::sample::hmc_nuts_dense_e(
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model, num_chains, context, random_seed, chain, init_radius, num_warmup,
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num_samples, num_thin, save_warmup, refresh, stepsize, stepsize_jitter,
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max_depth, interrupt, logger, init, parameter, diagnostic);
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for (auto&& init_it : init) {
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std::vector<std::string> init_values;
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init_values = init_it.string_values();
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EXPECT_EQ(0, init_values.size());
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}
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EXPECT_EQ(num_chains, logger.find_info("Elapsed Time:"));
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EXPECT_EQ(num_chains, logger.find_info("seconds (Warm-up)"));
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EXPECT_EQ(num_chains, logger.find_info("seconds (Sampling)"));
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EXPECT_EQ(num_chains, logger.find_info("seconds (Total)"));
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EXPECT_EQ(0, logger.call_count_error());
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}

‎src/test/unit/services/util/inv_metric_test.cpp‎

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ASSERT_NEAR(1.0, diag_vals[99], 0.0001);
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}
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TEST(inv_metric, create_dense_sz0) {
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auto default_metric = stan::services::util::create_unit_e_dense_inv_metric(0);
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stan::io::var_context& inv_inv_metric = default_metric;
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std::vector<double> diag_vals = inv_inv_metric.vals_r("inv_metric");
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EXPECT_EQ(0, diag_vals.size());
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}
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TEST(inv_metric, create_dense_sz2) {
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auto default_metric = stan::services::util::create_unit_e_dense_inv_metric(2);
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stan::io::var_context& inv_inv_metric = default_metric;
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TEST(inv_metric, read_dense_sz0) {
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stan::callbacks::logger logger;
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stan::io::dump dmp = stan::services::util::create_unit_e_dense_inv_metric(0);
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auto zero_metric = stan::services::util::create_unit_e_dense_inv_metric(0);
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Eigen::MatrixXd inv_inv_metric
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= stan::services::util::read_dense_inv_metric(dmp, 0, logger);
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= stan::services::util::read_dense_inv_metric(zero_metric, 0, logger);
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EXPECT_EQ(0, inv_inv_metric.size());
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EXPECT_EQ(0, inv_inv_metric.rows());
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EXPECT_EQ(0, inv_inv_metric.cols());

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