From 7946efebcb046fb8088139f1beeecfdc5f4dac6d Mon Sep 17 00:00:00 2001 From: Mitzi Morris Date: Fri, 11 Oct 2024 15:15:05 -0400 Subject: [PATCH] adding chainset and unit tests --- src/stan/mcmc/chainset.hpp | 504 ++++ src/test/unit/mcmc/chainset_test.cpp | 334 +++ .../mcmc/test_csv_files/bernoulli_500.csv | 557 +++++ .../mcmc/test_csv_files/bernoulli_default.csv | 1057 +++++++++ .../mcmc/test_csv_files/bernoulli_thin.csv | 1057 +++++++++ .../mcmc/test_csv_files/bernoulli_warmup.csv | 2057 +++++++++++++++++ .../mcmc/test_csv_files/bernoulli_zeta.csv | 1057 +++++++++ .../test_csv_files/eight_schools_empty.csv | 57 + 8 files changed, 6680 insertions(+) create mode 100644 src/stan/mcmc/chainset.hpp create mode 100644 src/test/unit/mcmc/chainset_test.cpp create mode 100644 src/test/unit/mcmc/test_csv_files/bernoulli_500.csv create mode 100644 src/test/unit/mcmc/test_csv_files/bernoulli_default.csv create mode 100644 src/test/unit/mcmc/test_csv_files/bernoulli_thin.csv create mode 100644 src/test/unit/mcmc/test_csv_files/bernoulli_warmup.csv create mode 100644 src/test/unit/mcmc/test_csv_files/bernoulli_zeta.csv create mode 100644 src/test/unit/mcmc/test_csv_files/eight_schools_empty.csv diff --git a/src/stan/mcmc/chainset.hpp b/src/stan/mcmc/chainset.hpp new file mode 100644 index 0000000000..3846212e95 --- /dev/null +++ b/src/stan/mcmc/chainset.hpp @@ -0,0 +1,504 @@ +#ifndef STAN_MCMC_CHAINSET_HPP +#define STAN_MCMC_CHAINSET_HPP + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +namespace stan { +namespace mcmc { +using Eigen::Dynamic; + +/** + * An mcmc::chainset object manages the post-warmup draws + * across a set of MCMC chains, which all have the same number of samples. + * + * @note samples are stored in column major, i.e., each column corresponds to + * an output variable (element). + * + */ +class chainset { + private: + size_t num_samples_; + std::vector param_names_; + std::vector chains_; + + public: + /* Construct a chainset from a single sample. + * Throws execption if sample is empty. + */ + explicit chainset(const stan::io::stan_csv& stan_csv) { + if (chains_.size() > 0) { + throw std::invalid_argument("Cannot re-initialize chains object"); + } + if (stan_csv.header.size() == 0 || stan_csv.samples.rows() == 0) { + throw std::invalid_argument("Error: empty sample"); + } + param_names_ = stan_csv.header; + num_samples_ = stan_csv.samples.rows(); + chains_.push_back(stan_csv.samples); + } + + /* Construct a chainset from a set of samples. + * Throws execption if sample column names and shapes don't match. + */ + explicit chainset(const std::vector& stan_csv) { + if (stan_csv.empty()) + return; + if (chains_.size() > 0) { + throw std::invalid_argument("Cannot re-initialize chains object"); + } + if (stan_csv[0].header.size() == 0 || stan_csv[0].samples.rows() == 0) { + throw std::invalid_argument("Error: empty sample"); + } + param_names_ = stan_csv[0].header; + num_samples_ = stan_csv[0].samples.rows(); + chains_.push_back(stan_csv[0].samples); + std::stringstream ss; + for (size_t i = 1; i < stan_csv.size(); ++i) { + if (stan_csv[i].header.size() != param_names_.size()) { + ss << "Error: chain " << (i + 1) << " missing or extra columns"; + throw std::invalid_argument(ss.str()); + } + for (int j = 0; j < param_names_.size(); j++) { + if (param_names_[j] != stan_csv[i].header[j]) { + ss << "Error: chain " << (i + 1) << " header column " << (j + 1) + << " doesn't match chain 1 header, found: " + << stan_csv[i].header[j] << " expecting: " << param_names_[j]; + throw std::invalid_argument(ss.str()); + } + } + if (stan_csv[i].samples.rows() != num_samples_) { + ss << "Error: chain " << (i + 1) << ", missing or extra rows."; + throw std::invalid_argument(ss.str()); + } + chains_.push_back(stan_csv[i].samples); + } + } + + /** + * Report number of chains in chainset. + * @return chainset size. + */ + inline int num_chains() const { return chains_.size(); } + + /** + * Report number of parameters per chain. + * @return size of parameter names vector. + */ + inline int num_params() const { return param_names_.size(); } + + /** + * Report number of samples (draws) per chain. + * @return rows per chain + */ + inline int num_samples() const { return num_samples_; } + + /** + * Get parameter names. + * @return vector of parameter names. + */ + const std::vector& param_names() const { return param_names_; } + + /** + * Get name of parameter at specified column index. + * Throws exception if index is out of bounds. + * + * @param index column index + * @return parameter name + */ + const std::string& param_name(int index) const { + if (index < 0 || index >= param_names_.size()) { + std::stringstream ss; + ss << "Bad index " << index << ", should be between 0 and " + << (param_names_.size() - 1); + throw std::invalid_argument(ss.str()); + } + return param_names_[index]; + } + + /** + * Get column index for specified parameter name. + * Throws exception if name not found. + * + * @param name parameter name + * @return column index + */ + int index(const std::string& name) const { + auto it = std::find(param_names_.begin(), param_names_.end(), name); + if (it == param_names_.end()) { + std::stringstream ss; + ss << "Unknown parameter name " << name; + throw std::invalid_argument(ss.str()); + } + return std::distance(param_names_.begin(), it); + } + + /** + * Assemble samples (draws) from specified column index across all chains + * into a matrix of samples X chain. + * Throws exception if column index is out of bounds. + * + * @param index column index + * @return matrix of draws across all chains + */ + Eigen::MatrixXd samples(const int index) const { + Eigen::MatrixXd result(num_samples(), chains_.size()); + if (index < 0 || index >= param_names_.size()) { + std::stringstream ss; + ss << "Bad index " << index << ", should be between 0 and " + << (param_names_.size() - 1); + throw std::invalid_argument(ss.str()); + } + for (int i = 0; i < chains_.size(); ++i) { + result.col(i) = chains_[i].col(index); + } + return result; + } + + /** + * Assemble samples (draws) from specified parameter name across all chains + * into a matrix of samples X chain. + * Throws exception if parameter name is not found. + * + * @param name parameter name + * @return matrix of draws across all chains + */ + Eigen::MatrixXd samples(const std::string& name) const { + return samples(index(name)); + } + + /** + * Compute mean value for specified parameter across all chains. + * + * @param index parameter index + * @return mean parameter value + */ + double mean(const int index) const { return samples(index).mean(); } + + /** + * Compute mean value for specified parameter across all chains. + * + * @param name parameter name + * @return mean parameter value + */ + double mean(const std::string& name) const { return mean(index(name)); } + + /** + * Compute sample variance for specified parameter across all chains. + * 1 / (N - 1) * sum((theta_n - mean(theta))^2) + * + * @param index parameter index + * @return sample variance + */ + double variance(const int index) const { + Eigen::MatrixXd draws = samples(index); + return (draws.array() - draws.mean()).square().sum() / (draws.size() - 1); + } + + /** + * Compute sample variance for specified parameter across all chains. + * 1 / (N - 1) * sum((theta_n - mean(theta))^2) + * + * @param name parameter name + * @return sample variance + */ + double variance(const std::string& name) const { + return variance(index(name)); + } + + /** + * Compute standard deviation for specified parameter across all chains. + * + * @param index parameter index + * @return sample sd + */ + double sd(const int index) const { return std::sqrt(variance(index)); } + + /** + * Compute standard deviation for specified parameter across all chains. + * + * @param name parameter name + * @return sample sd + */ + double sd(const std::string& name) const { return sd(index(name)); } + + /** + * Compute median value of specified parameter across all chains. + * + * @param index parameter index + * @return median + */ + double median(const int index) const { return (quantile(index, 0.5)); } + + /** + * Compute median value of specified parameter across all chains. + * + * @param name parameter name + * @return median + */ + double median(const std::string& name) const { return median(index(name)); } + + /** + * Compute maximum absolute deviation (mad) for specified parameter. + * + * Follows R implementation: constant * median(abs(x - center)) + * where the value of center is median(x) and the constant is 1.4826, + * a scale factor for asymptotically normal consistency: `1/qnorm(3/4)`. + * (R stats version 3.6.2) + * + * @param index parameter index + * @return sample mad + */ + double max_abs_deviation(const int index) const { + Eigen::MatrixXd draws = samples(index); + auto center = median(index); + Eigen::MatrixXd abs_dev = (draws.array() - center).abs(); + Eigen::Map map(abs_dev.data(), abs_dev.size()); + return 1.4826 * stan::math::quantile(map, 0.5); + } + + /** + * Compute maximum absolute deviation (mad) for specified parameter. + * + * Follows R implementation: constant * median(abs(x - center)) + * where the value of center is median(x) and the constant is 1.4826, + * a scale factor for asymptotically normal consistency: `1/qnorm(3/4)`. + * (R stats version 3.6.2) + * + * @param name parameter name + * @return sample mad + */ + double max_abs_deviation(const std::string& name) const { + return max_abs_deviation(index(name)); + } + + /** + * Compute the quantile value of the specified parameter + * at the specified probability. + * + * Throws exception if specified probability is not between 0 and 1. + * + * @param index parameter index + * @param prob probability + * @return parameter value at quantile + */ + double quantile(const int index, const double prob) const { + // Ensure the probability is within [0, 1] + if (prob <= 0.0 || prob >= 1.0) { + throw std::out_of_range("Probability must be between 0 and 1."); + } + Eigen::MatrixXd draws = samples(index); + Eigen::Map map(draws.data(), draws.size()); + return stan::math::quantile(map, prob); + } + + /** + * Compute the quantile value of the specified parameter + * at the specified probability. + * + * Throws exception if specified probability is not between 0 and 1. + * + * @param name parameter name + * @param prob probability + * @return parameter value at quantile + */ + double quantile(const std::string& name, const double prob) const { + return quantile(index(name), prob); + } + + /** + * Compute the quantile values of the specified parameter + * for a set of specified probabilities. + * + * Throws exception if any probability is not between 0 and 1. + * + * @param index parameter index + * @param probs vector of probabilities + * @return vector of parameter values for quantiles + */ + Eigen::VectorXd quantiles(const int index, + const Eigen::VectorXd& probs) const { + if (probs.size() == 0) + return Eigen::VectorXd::Zero(0); + if (probs.minCoeff() <= 0.0 || probs.maxCoeff() >= 1.0) { + throw std::out_of_range("Probabilities must be between 0 and 1."); + } + Eigen::MatrixXd draws = samples(index); + Eigen::Map map(draws.data(), draws.size()); + std::vector probs_vec(probs.data(), probs.data() + probs.size()); + std::vector quantiles = stan::math::quantile(map, probs_vec); + return Eigen::Map(quantiles.data(), quantiles.size()); + } + + /** + * Compute the quantile values of the specified parameter + * for a set of specified probabilities. + * + * Throws exception if any probability is not between 0 and 1. + * + * @param name parameter name + * @param probs vector of probabilities + * @return vector of parameter values for quantiles + */ + Eigen::VectorXd quantiles(const std::string& name, + const Eigen::VectorXd& probs) const { + return quantiles(index(name), probs); + } + + /** + * Computes the split potential scale reduction (split Rhat) using rank based + * diagnostic for a set of per-chain draws, for bulk and tail Rhat. + * Based on paper https://arxiv.org/abs/1903.08008 + * + * @param index parameter index + * @return pair (bulk_rhat, tail_rhat) + */ + std::pair split_rank_normalized_rhat(const int index) const { + return analyze::split_rank_normalized_rhat(samples(index)); + } + + /** + * Computes the split potential scale reduction (split Rhat) using rank based + * diagnostic for a set of per-chain draws, for bulk and tail Rhat. + * Based on paper https://arxiv.org/abs/1903.08008 + * + * @param name parameter name + * @return pair (bulk_rhat, tail_rhat) + */ + std::pair split_rank_normalized_rhat( + const std::string& name) const { + return split_rank_normalized_rhat(index(name)); + } + + /** + * Computes the effective sample size (ESS) for the specified + * parameter across all chains, according to the algorithm presented in + * https://arxiv.org/abs/1903.08008, section 3.2 for folded (split) + * rank-normalized ESS for both bulk and tail ESS. + * + * @param index parameter index + * @return pair (bulk_ess, tail_ess) + */ + std::pair split_rank_normalized_ess(const int index) const { + return analyze::split_rank_normalized_ess(samples(index)); + } + + /** + * Computes the effective sample size (ESS) for the specified + * parameter across all chains, according to the algorithm presented in + * https://arxiv.org/abs/1903.08008, section 3.2 for folded (split) + * rank-normalized ESS for both bulk and tail ESS. + * + * @param name parameter name + * @return pair (bulk_ess, tail_ess) + */ + std::pair split_rank_normalized_ess( + const std::string& name) const { + return split_rank_normalized_ess(index(name)); + } + + /** + * Computes the mean Monte Carlo error estimate for the central 90% interval. + * See https://arxiv.org/abs/1903.08008, section 4.4. + * Follows implementation in the R posterior package + * + * @param index parameter index + * @return pair (bulk_ess, tail_ess) + */ + double mcse_mean(const int index) const { + double ess_bulk = analyze::split_rank_normalized_ess(samples(index)).first; + return sd(index) / std::sqrt(ess_bulk); + } + + /** + * Computes the mean Monte Carlo error estimate for the central 90% interval. + * See https://arxiv.org/abs/1903.08008, section 4.4. + * Follows implementation in the R posterior package. + * + * @param name parameter name + * @return pair (bulk_ess, tail_ess) + */ + double mcse_mean(const std::string& name) const { + return mcse_mean(index(name)); + } + + /** + * Computes the standard deviation of the Monte Carlo error estimate + * https://arxiv.org/abs/1903.08008, section 4.4. + * Follows implementation in the R posterior package. + * + * @param index parameter index + * @return pair (bulk_ess, tail_ess) + */ + double mcse_sd(const int index) const { + Eigen::MatrixXd s = samples(index); + Eigen::MatrixXd s2 = s.array().square(); + double ess_s = analyze::split_rank_normalized_ess(s).first; + double ess_s2 = analyze::split_rank_normalized_ess(s2).first; + double ess_sd = std::min(ess_s, ess_s2); + return sd(index) + * std::sqrt(stan::math::e() * std::pow(1 - 1 / ess_sd, ess_sd - 1) + - 1); + } + + /** + * Computes the standard deviation of the Monte Carlo error estimate + * https://arxiv.org/abs/1903.08008, section 4.4. + * Follows implementation in the R posterior package + * + * @param name parameter name + * @return pair (bulk_ess, tail_ess) + */ + double mcse_sd(const std::string& name) const { return mcse_sd(index(name)); } + + /** + * Compute autocorrelation for one column of one chain. + * Throws exception if column index is out of bounds. + * Autocorrelation is computed using Stan math library implmentation. + * + * @param chain chain index + * @param index column index + * @return vector of chain autocorrelation at all lags + */ + Eigen::VectorXd autocorrelation(const int chain, const int index) const { + Eigen::MatrixXd s = samples(index); + Eigen::Map chain_col(samples(chain).data(), + num_samples()); + Eigen::VectorXd autocorr_col(num_samples()); + stan::math::autocorrelation(s.col(chain), autocorr_col); + return autocorr_col; + } + + /** + * Compute autocorrelation for one column of one chain. + * Throws exception if column index is out of bounds. + * Autocorrelation is computed using Stan math library implmentation. + * + * @param chain chain index + * @param name column name + * @return vector of chain autocorrelation at all lags + */ + Eigen::VectorXd autocorrelation(const int chain, + const std::string name) const { + return autocorrelation(chain, index(name)); + } +}; + +} // namespace mcmc +} // namespace stan + +#endif diff --git a/src/test/unit/mcmc/chainset_test.cpp b/src/test/unit/mcmc/chainset_test.cpp new file mode 100644 index 0000000000..9662c3159c --- /dev/null +++ b/src/test/unit/mcmc/chainset_test.cpp @@ -0,0 +1,334 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +class McmcChains : public testing::Test { + public: + void SetUp() override { + bernoulli_500_stream.open( + "src/test/unit/mcmc/test_csv_files/bernoulli_500.csv", + std::ifstream::in); + bernoulli_default_stream.open( + "src/test/unit/mcmc/test_csv_files/bernoulli_default.csv", + std::ifstream::in); + bernoulli_thin_stream.open( + "src/test/unit/mcmc/test_csv_files/bernoulli_thin.csv", + std::ifstream::in); + bernoulli_warmup_stream.open( + "src/test/unit/mcmc/test_csv_files/bernoulli_warmup.csv", + std::ifstream::in); + bernoulli_zeta_stream.open( + "src/test/unit/mcmc/test_csv_files/bernoulli_zeta.csv", + std::ifstream::in); + eight_schools_1_stream.open( + "src/test/unit/mcmc/test_csv_files/eight_schools_1.csv", + std::ifstream::in); + eight_schools_2_stream.open( + "src/test/unit/mcmc/test_csv_files/eight_schools_2.csv", + std::ifstream::in); + eight_schools_5iters_1_stream.open( + "src/test/unit/mcmc/test_csv_files/eight_schools_5iters_1.csv", + std::ifstream::in); + eight_schools_5iters_2_stream.open( + "src/test/unit/mcmc/test_csv_files/eight_schools_5iters_2.csv", + std::ifstream::in); + + if (!bernoulli_500_stream || !bernoulli_default_stream + || !bernoulli_thin_stream || !bernoulli_warmup_stream + || !bernoulli_zeta_stream || !eight_schools_1_stream + || !eight_schools_2_stream || !eight_schools_5iters_1_stream + || !eight_schools_5iters_2_stream) { + FAIL() << "Failed to open one or more test files"; + } + bernoulli_500_stream.seekg(0, std::ios::beg); + bernoulli_default_stream.seekg(0, std::ios::beg); + bernoulli_thin_stream.seekg(0, std::ios::beg); + bernoulli_warmup_stream.seekg(0, std::ios::beg); + bernoulli_zeta_stream.seekg(0, std::ios::beg); + eight_schools_1_stream.seekg(0, std::ios::beg); + eight_schools_2_stream.seekg(0, std::ios::beg); + eight_schools_5iters_1_stream.seekg(0, std::ios::beg); + eight_schools_5iters_2_stream.seekg(0, std::ios::beg); + + bernoulli_500 + = stan::io::stan_csv_reader::parse(bernoulli_500_stream, &out); + bernoulli_default + = stan::io::stan_csv_reader::parse(bernoulli_default_stream, &out); + bernoulli_thin + = stan::io::stan_csv_reader::parse(bernoulli_thin_stream, &out); + bernoulli_warmup + = stan::io::stan_csv_reader::parse(bernoulli_warmup_stream, &out); + bernoulli_zeta + = stan::io::stan_csv_reader::parse(bernoulli_zeta_stream, &out); + eight_schools_1 + = stan::io::stan_csv_reader::parse(eight_schools_1_stream, &out); + eight_schools_2 + = stan::io::stan_csv_reader::parse(eight_schools_2_stream, &out); + eight_schools_5iters_1 + = stan::io::stan_csv_reader::parse(eight_schools_5iters_1_stream, &out); + eight_schools_5iters_2 + = stan::io::stan_csv_reader::parse(eight_schools_5iters_2_stream, &out); + } + + void TearDown() override { + bernoulli_500_stream.close(); + bernoulli_default_stream.close(); + bernoulli_thin_stream.close(); + bernoulli_warmup_stream.close(); + bernoulli_zeta_stream.close(); + eight_schools_1_stream.close(); + eight_schools_2_stream.close(); + eight_schools_5iters_1_stream.close(); + eight_schools_5iters_2_stream.close(); + } + + std::stringstream out; + + std::ifstream bernoulli_500_stream, bernoulli_default_stream, + bernoulli_thin_stream, bernoulli_warmup_stream, bernoulli_zeta_stream, + eight_schools_1_stream, eight_schools_2_stream, + eight_schools_5iters_1_stream, eight_schools_5iters_2_stream; + + stan::io::stan_csv bernoulli_500, bernoulli_default, bernoulli_thin, + bernoulli_warmup, bernoulli_zeta, eight_schools_1, eight_schools_2, + eight_schools_5iters_1, eight_schools_5iters_2; +}; + +TEST_F(McmcChains, constructor) { + stan::mcmc::chainset chain_1(eight_schools_1); + EXPECT_EQ(1, chain_1.num_chains()); + EXPECT_EQ(eight_schools_1.header.size(), chain_1.num_params()); + EXPECT_EQ( + eight_schools_1.metadata.num_samples / eight_schools_1.metadata.thin, + chain_1.num_samples()); + + std::vector eight_schools; + eight_schools.push_back(eight_schools_1); + eight_schools.push_back(eight_schools_2); + stan::mcmc::chainset chain_2(eight_schools); + EXPECT_EQ(2, chain_2.num_chains()); + EXPECT_EQ(eight_schools_1.header.size(), chain_2.num_params()); + EXPECT_EQ( + eight_schools_1.metadata.num_samples / eight_schools_1.metadata.thin, + chain_2.num_samples()); + + std::vector bernoulli; + bernoulli.push_back(bernoulli_default); + bernoulli.push_back(bernoulli_thin); + bernoulli.push_back(bernoulli_warmup); + stan::mcmc::chainset chain_3(bernoulli); + EXPECT_EQ(3, chain_3.num_chains()); + EXPECT_EQ(bernoulli_default.header.size(), chain_3.num_params()); + EXPECT_EQ(bernoulli_default.metadata.num_samples, chain_3.num_samples()); +} + +TEST_F(McmcChains, addFail) { + std::vector bad; + bad.push_back(bernoulli_default); + bad.push_back(bernoulli_500); + EXPECT_THROW(stan::mcmc::chainset fail(bad), std::invalid_argument); + + bad.clear(); + bad.push_back(bernoulli_default); + bad.push_back(eight_schools_1); + EXPECT_THROW(stan::mcmc::chainset fail(bad), std::invalid_argument); + + bad.clear(); + bad.push_back(bernoulli_default); + bad.push_back(bernoulli_zeta); + EXPECT_THROW(stan::mcmc::chainset fail(bad), std::invalid_argument); +} + +TEST_F(McmcChains, paramNameIndex) { + stan::mcmc::chainset chains_csv(eight_schools_1); + EXPECT_EQ(1, chains_csv.num_chains()); + for (int i = 0; i < eight_schools_1.header.size(); i++) { + EXPECT_EQ(eight_schools_1.header[i], chains_csv.param_name(i)); + EXPECT_EQ(i, chains_csv.index(eight_schools_1.header[i])); + } + EXPECT_THROW(chains_csv.param_name(-5000), std::invalid_argument); + EXPECT_THROW(chains_csv.param_name(5000), std::invalid_argument); + EXPECT_THROW(chains_csv.index("foo"), std::invalid_argument); +} + +TEST_F(McmcChains, eight_schools_samples) { + std::vector eight_schools; + eight_schools.push_back(eight_schools_1); + eight_schools.push_back(eight_schools_2); + stan::mcmc::chainset chain_2(eight_schools); + Eigen::MatrixXd mu_all = chain_2.samples("mu"); + EXPECT_EQ(chain_2.num_chains() * chain_2.num_samples(), mu_all.size()); + mu_all = chain_2.samples(7); + EXPECT_EQ(chain_2.num_chains() * chain_2.num_samples(), mu_all.size()); + + EXPECT_THROW(chain_2.samples(5000), std::invalid_argument); + EXPECT_THROW(chain_2.samples("foo"), std::invalid_argument); +} + +TEST_F(McmcChains, split_rank_normalized_rhat) { + stan::mcmc::chainset chain_1(eight_schools_1); + EXPECT_EQ(1, chain_1.num_chains()); + + // test against R implementation in pkg posterior + Eigen::VectorXd rhat_8_schools_1_bulk(10); + rhat_8_schools_1_bulk << 1.0012958313, 1.0046136496, 1.0085723580, + 1.0248629375, 1.0111456620, 1.0004458336, 0.9987162973, 1.0339773469, + 0.9985612618, 1.0281667351; + + Eigen::VectorXd rhat_8_schools_1_tail(10); + rhat_8_schools_1_tail << 1.005676523, 1.009670999, 1.00184184, 1.002222679, + 1.004148161, 1.003218528, 1.009195353, 1.001426744, 1.003984381, + 1.025817745; + + for (size_t i = 0; i < 10; ++i) { + auto rhats = chain_1.split_rank_normalized_rhat(i + 7); + EXPECT_NEAR(rhats.first, rhat_8_schools_1_bulk(i), 0.05); + EXPECT_NEAR(rhats.second, rhat_8_schools_1_tail(i), 0.05); + } +} + +TEST_F(McmcChains, split_rank_normalized_ess) { + std::vector eight_schools; + eight_schools.push_back(eight_schools_1); + eight_schools.push_back(eight_schools_2); + stan::mcmc::chainset chain_2(eight_schools); + EXPECT_EQ(2, chain_2.num_chains()); + + // test against R implementation in pkg posterior (via cmdstanr) + Eigen::VectorXd ess_8_schools_bulk(10); + ess_8_schools_bulk << 348, 370, 600, 638, 765, 608, 629, 274, 517, 112; + Eigen::VectorXd ess_8_schools_tail(10); + ess_8_schools_tail << 845, 858, 874, 726, 620, 753, 826, 628, 587, 108; + + for (size_t i = 0; i < 10; ++i) { + auto ess = chain_2.split_rank_normalized_ess(i + 7); + EXPECT_NEAR(ess.first, ess_8_schools_bulk(i), 5); + EXPECT_NEAR(ess.second, ess_8_schools_tail(i), 5); + } +} + +TEST_F(McmcChains, ess_short_chains) { + std::vector eight_schools_5iters; + eight_schools_5iters.push_back(eight_schools_5iters_1); + eight_schools_5iters.push_back(eight_schools_5iters_2); + stan::mcmc::chainset chain_2(eight_schools_5iters); + EXPECT_EQ(2, chain_2.num_chains()); + + for (size_t i = 0; i < 10; ++i) { + auto ess = chain_2.split_rank_normalized_ess(i + 7); + EXPECT_TRUE(std::isnan(ess.first)); + EXPECT_TRUE(std::isnan(ess.second)); + } +} + +TEST_F(McmcChains, summary_stats) { + std::vector eight_schools; + eight_schools.push_back(eight_schools_1); + eight_schools.push_back(eight_schools_2); + stan::mcmc::chainset chain_2(eight_schools); + EXPECT_EQ(2, chain_2.num_chains()); + + // test against R implementation in pkg posterior (via cmdstanr) + Eigen::VectorXd s8_mean(10), s8_median(10), s8_sd(10), s8_mad(10), s8_q5(10), + s8_q95(10); + s8_mean << 7.95, 12.54, 7.82, 5.33, 7.09, 4.12, 5.72, 11.65, 8.80, 8.26; + s8_median << 8.00, 11.27, 7.39, 5.44, 6.64, 4.54, 5.93, 11.38, 8.28, 7.05; + s8_sd << 5.48, 9.57, 6.85, 8.39, 6.91, 6.57, 6.85, 7.76, 8.40, 5.53; + s8_mad << 5.49, 8.79, 6.39, 7.38, 5.98, 6.25, 6.59, 7.79, 7.59, 4.66; + s8_q5 << -0.46, -0.39, -3.04, -8.90, -3.31, -7.58, -5.84, 0.10, -4.15, 2.08; + s8_q95 << 17.01, 30.47, 19.25, 19.02, 18.72, 14.49, 16.04, 25.77, 22.71, + 18.74; + Eigen::VectorXd probs(3); + probs << 0.05, 0.5, 0.95; + + for (size_t i = 0; i < 10; ++i) { + auto mean = chain_2.mean(i + 7); + EXPECT_NEAR(mean, s8_mean(i), 0.05); + auto median = chain_2.median(i + 7); + EXPECT_NEAR(median, s8_median(i), 0.05); + auto sd = chain_2.sd(i + 7); + EXPECT_NEAR(sd, s8_sd(i), 0.05); + auto mad = chain_2.max_abs_deviation(i + 7); + EXPECT_NEAR(mad, s8_mad(i), 0.05); + auto q_5 = chain_2.quantile(i + 7, 0.05); + EXPECT_NEAR(q_5, s8_q5(i), 0.5); + auto q_95 = chain_2.quantile(i + 7, 0.95); + EXPECT_NEAR(q_95, s8_q95(i), 0.5); + auto qs_5_50_95 = chain_2.quantiles(i + 7, probs); + EXPECT_EQ(3, qs_5_50_95.size()); + EXPECT_NEAR(qs_5_50_95(0), s8_q5(i), 0.5); + EXPECT_NEAR(qs_5_50_95(1), s8_median(i), 0.05); + EXPECT_NEAR(qs_5_50_95(2), s8_q95(i), 0.5); + } +} + +TEST_F(McmcChains, mcse) { + std::vector eight_schools; + eight_schools.push_back(eight_schools_1); + eight_schools.push_back(eight_schools_2); + stan::mcmc::chainset chain_2(eight_schools); + EXPECT_EQ(2, chain_2.num_chains()); + + // test against R implementation in pkg posterior + Eigen::VectorXd s8_mcse_mean(10), s8_mcse_sd(10); + s8_mcse_mean << 0.288379, 0.4741815, 0.2741001, 0.3294614, 0.2473758, + 0.2665048, 0.2701363, 0.4740092, 0.3621771, 0.3832464; + s8_mcse_sd << 0.1841825, 0.2854258, 0.192332, 0.2919369, 0.2478025, 0.2207478, + 0.2308452, 0.2522107, 0.2946896, 0.3184745; + + for (size_t i = 0; i < 10; ++i) { + auto mcse_mean = chain_2.mcse_mean(i + 7); + EXPECT_NEAR(mcse_mean, s8_mcse_mean(i), 0.5); + auto mcse_sd = chain_2.mcse_sd(i + 7); + EXPECT_NEAR(mcse_sd, s8_mcse_sd(i), 0.7); + } +} + +TEST_F(McmcChains, const_fail) { + std::ifstream bernoulli_const_1_stream, bernoulli_const_2_stream; + stan::io::stan_csv bernoulli_const_1, bernoulli_const_2; + bernoulli_const_1_stream.open( + "src/test/unit/mcmc/test_csv_files/bernoulli_const_1.csv", + std::ifstream::in); + bernoulli_const_1 + = stan::io::stan_csv_reader::parse(bernoulli_const_1_stream, &out); + bernoulli_const_1_stream.close(); + bernoulli_const_2_stream.open( + "src/test/unit/mcmc/test_csv_files/bernoulli_const_2.csv", + std::ifstream::in); + bernoulli_const_2 + = stan::io::stan_csv_reader::parse(bernoulli_const_2_stream, &out); + bernoulli_const_2_stream.close(); + std::vector bernoulli_const; + bernoulli_const.push_back(bernoulli_const_1); + bernoulli_const.push_back(bernoulli_const_2); + stan::mcmc::chainset chain_2(bernoulli_const); + EXPECT_EQ(2, chain_2.num_chains()); + auto rhat = chain_2.split_rank_normalized_rhat("zeta"); + EXPECT_TRUE(std::isnan(rhat.first)); + EXPECT_TRUE(std::isnan(rhat.second)); + auto ess = chain_2.split_rank_normalized_ess("zeta"); + EXPECT_TRUE(std::isnan(ess.first)); + EXPECT_TRUE(std::isnan(ess.second)); +} + +TEST_F(McmcChains, autocorrelation) { + stan::mcmc::chainset chain_1(eight_schools_1); + EXPECT_EQ(1, chain_1.num_chains()); + + Eigen::VectorXd mu_ac_posterior(10); + mu_ac_posterior << 1.00000000000, 0.19487668999, 0.05412049365, 0.07834048575, + 0.04145609855, 0.04353962161, -0.00977255885, 0.00005175308, + 0.01791577080, 0.01245035817; + auto mu_ac = chain_1.autocorrelation(0, "mu"); + for (size_t i = 0; i < 10; ++i) { + EXPECT_NEAR(mu_ac_posterior(i), mu_ac(i), 0.05); + } +} diff --git a/src/test/unit/mcmc/test_csv_files/bernoulli_500.csv b/src/test/unit/mcmc/test_csv_files/bernoulli_500.csv new file mode 100644 index 0000000000..f7c479a86e --- /dev/null +++ b/src/test/unit/mcmc/test_csv_files/bernoulli_500.csv @@ -0,0 +1,557 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-07-20 18:55:04 UTC +# method = sample (Default) +# sample +# num_samples = 500 +# num_warmup = 1000 (Default) +# save_warmup = false (Default) +# thin = 1 (Default) +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 1 (Default) +# data +# file = examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 3634950897 (Default) +# output +# file = bernoulli_1.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0-25-gbb9ce42 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,theta +# Adaptation terminated +# Step size = 0.908714 +# Diagonal elements of inverse mass matrix: +# 0.533354 +-6.77412,0.987824,0.908714,2,3,0,6.8785,0.279254 +-6.89323,0.977402,0.908714,1,3,0,6.89338,0.321021 +-6.78182,1,0.908714,2,3,0,6.8661,0.218479 +-6.78182,0.773777,0.908714,1,3,0,7.98348,0.218479 +-7.21517,0.892133,0.908714,1,3,0,7.43887,0.381517 +-9.70031,0.447016,0.908714,1,1,0,9.70243,0.59546 +-7.64929,1,0.908714,2,3,0,9.25311,0.111255 +-7.80318,0.993575,0.908714,2,3,0,7.9051,0.102697 +-6.75708,0.98594,0.908714,1,3,0,7.94373,0.233437 +-6.74807,1,0.908714,2,3,0,6.75624,0.251184 +-6.76508,0.961627,0.908714,2,3,0,6.97493,0.227399 +-6.76508,0.694403,0.908714,1,3,0,8.48868,0.227399 +-7.46,0.856941,0.908714,1,3,0,7.64341,0.123565 +-6.84767,0.988752,0.908714,1,3,0,7.39531,0.197147 +-6.76112,0.937435,0.908714,2,3,0,7.4025,0.270583 +-6.76723,0.993298,0.908714,2,3,0,6.80703,0.226049 +-6.75849,1,0.908714,1,1,0,6.7661,0.232214 +-6.75856,0.916923,0.908714,2,3,0,7.37221,0.232155 +-6.76412,0.905032,0.908714,2,3,0,7.46252,0.228033 +-6.75246,0.98558,0.908714,2,3,0,6.86086,0.261903 +-7.14063,0.806926,0.908714,2,3,0,7.89708,0.15144 +-7.65515,0.922931,0.908714,1,1,0,7.65525,0.110908 +-6.85804,0.996431,0.908714,2,3,0,7.69222,0.194631 +-6.7956,0.992136,0.908714,2,3,0,6.94247,0.212823 +-6.75011,0.941177,0.908714,2,3,0,7.20539,0.241987 +-6.83571,0.977386,0.908714,1,3,0,6.86933,0.304611 +-7.04002,0.894555,0.908714,1,3,0,7.42018,0.163442 +-6.91021,0.955935,0.908714,1,3,0,7.47326,0.325253 +-6.75575,1,0.908714,2,3,0,6.89311,0.234677 +-6.74979,0.982019,0.908714,2,3,0,6.87707,0.25749 +-6.75266,0.999273,0.908714,1,1,0,6.75273,0.262168 +-6.75854,0.989314,0.908714,2,3,0,6.81651,0.232168 +-7.35696,0.872376,0.908714,1,3,0,7.52038,0.131354 +-7.17209,1,0.908714,1,1,0,7.3618,0.148109 +-6.76514,0.974208,0.908714,1,3,0,7.33856,0.273592 +-6.81293,0.933943,0.908714,2,3,0,7.16788,0.206861 +-6.78344,0.990552,0.908714,2,3,0,6.90852,0.284208 +-6.75189,1,0.908714,1,3,0,6.77606,0.261105 +-6.91381,0.79852,0.908714,2,3,0,8.08135,0.183007 +-7.36903,0.965086,0.908714,2,3,0,7.37005,0.403004 +-7.24594,1,0.908714,1,1,0,7.45634,0.38605 +-7.10956,1,0.908714,1,1,0,7.2858,0.3648 +-7.1841,0.701565,0.908714,2,3,0,8.86758,0.376798 +-6.75487,0.962329,0.908714,2,3,0,7.41982,0.264819 +-6.91264,0.952408,0.908714,1,3,0,7.01286,0.183226 +-6.74841,0.935239,0.908714,2,3,0,7.4253,0.253501 +-7.01744,0.836819,0.908714,2,3,0,7.72885,0.166491 +-6.76439,0.998239,0.908714,1,3,0,6.993,0.227849 +-6.77316,0.99526,0.908714,2,3,0,6.8081,0.222694 +-7.23311,0.91075,0.908714,1,3,0,7.31151,0.142098 +-6.93024,1,0.908714,2,3,0,7.17757,0.180038 +-7.0312,0.98107,0.908714,1,1,0,7.04685,0.164616 +-6.76558,0.997454,0.908714,2,3,0,7.04984,0.22708 +-8.32852,0.675198,0.908714,1,3,0,8.99917,0.0799677 +-7.54902,1,0.908714,1,1,0,8.23363,0.117493 +-7.19487,1,0.908714,1,1,0,7.50636,0.1458 +-8.13201,0.809671,0.908714,1,3,0,9.46989,0.484014 +-7.47984,1,0.908714,1,1,0,8.05615,0.416966 +-7.85717,0.884683,0.908714,1,1,0,8.02328,0.45821 +-6.76221,0.977312,0.908714,2,3,0,8.06601,0.229347 +-6.8572,0.99033,0.908714,2,3,0,6.86077,0.311201 +-6.82348,1,0.908714,1,1,0,6.86184,0.300519 +-7.97359,0.617908,0.908714,1,3,0,9.12086,0.0943758 +-7.45034,1,0.908714,1,1,0,7.91667,0.124258 +-6.93542,0.985719,0.908714,1,3,0,7.36867,0.179135 +-7.60792,0.840752,0.908714,1,3,0,8.27659,0.431913 +-7.5992,1,0.908714,1,1,0,7.86308,0.43093 +-7.5992,0.252049,0.908714,1,1,0,10.4877,0.43093 +-7.35221,1,0.908714,1,1,0,7.66226,0.400786 +-7.82219,0.85935,0.908714,1,1,0,7.93875,0.454706 +-8.08711,0.972194,0.908714,2,3,0,8.37798,0.479988 +-7.0257,1,0.908714,2,3,0,7.75859,0.349846 +-7.2537,0.977501,0.908714,2,3,0,7.30002,0.387173 +-6.93473,0.786102,0.908714,2,3,0,8.43393,0.331018 +-6.77979,1,0.908714,1,3,0,6.89224,0.282354 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+-6.80467,0.99303,0.908714,1,3,0,7.49888,0.209581 +-6.86328,0.995849,0.908714,2,3,0,6.86601,0.193412 +-6.7527,1,0.908714,2,3,0,6.84135,0.262221 +-6.89757,0.968179,0.908714,1,3,0,6.90889,0.322123 +-6.7829,0.971235,0.908714,2,3,0,7.09147,0.283942 +-6.76687,1,0.908714,1,1,0,6.78086,0.274774 +-7.56409,0.874398,0.908714,2,3,0,7.57617,0.116518 +# +# Elapsed Time: 0.005 seconds (Warm-up) +# 0.005 seconds (Sampling) +# 0.01 seconds (Total) +# diff --git a/src/test/unit/mcmc/test_csv_files/bernoulli_default.csv b/src/test/unit/mcmc/test_csv_files/bernoulli_default.csv new file mode 100644 index 0000000000..d45074864f --- /dev/null +++ b/src/test/unit/mcmc/test_csv_files/bernoulli_default.csv @@ -0,0 +1,1057 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-07-20 18:56:30 UTC +# method = sample (Default) +# sample +# num_samples = 1000 (Default) +# num_warmup = 1000 (Default) +# save_warmup = false (Default) +# thin = 1 (Default) +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 1 (Default) +# data +# file = examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 3635036313 (Default) +# output +# file = bernoulli_out.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0-25-gbb9ce42 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,theta +# Adaptation terminated +# Step size = 0.932037 +# Diagonal elements of inverse mass matrix: +# 0.591014 +-7.81355,0.761218,0.932037,1,3,0,8.13669,0.102158 +-8.71049,0.894869,0.932037,1,1,0,8.72268,0.0676546 +-7.3531,0.957519,0.932037,1,3,0,8.53469,0.131664 +-7.02007,1,0.932037,1,1,0,7.29505,0.166128 +-8.49033,0.716959,0.932037,1,3,0,9.20407,0.513986 +-7.18724,1,0.932037,1,1,0,8.06459,0.377282 +-10.0621,0.636916,0.932037,2,3,0,10.0852,0.615706 +-8.11851,1,0.932037,1,1,0,9.65805,0.482811 +-6.86936,0.948809,0.932037,2,3,0,8.60097,0.192035 +-7.13756,0.935492,0.932037,2,3,0,7.43717,0.151774 +-7.09122,1,0.932037,1,1,0,7.19518,0.157048 +-6.91669,1,0.932037,1,1,0,7.06383,0.182475 +-6.76371,0.840542,0.932037,2,3,0,8.08838,0.228308 +-6.84805,0.983351,0.932037,1,3,0,6.84861,0.197051 +-8.3727,0.653843,0.932037,2,3,0,9.53079,0.504537 +-7.25928,0.806553,0.932037,1,3,0,9.43963,0.139678 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+-6.74804,0.996585,0.932037,2,3,0,6.81732,0.249269 +-9.50952,0.509265,0.932037,1,3,0,9.67564,0.584141 +-8.27778,1,0.932037,1,1,0,9.48419,0.496645 +-7.2527,1,0.932037,2,3,0,8.04703,0.140278 +-6.75205,0.907555,0.932037,2,3,0,8.25548,0.238895 +-7.3411,0.857468,0.932037,1,3,0,7.42326,0.399305 +-7.07094,1,0.932037,1,1,0,7.32247,0.358137 +-7.04767,0.556206,0.932037,2,3,0,9.56585,0.353943 +-6.87625,1,0.932037,2,3,0,6.9975,0.190522 +-7.12535,0.945777,0.932037,1,1,0,7.12652,0.153122 +-7.12535,0.913353,0.932037,1,1,0,7.53634,0.153122 +-6.78755,1,0.932037,2,3,0,7.04258,0.286194 +-7.7545,0.780448,0.932037,1,3,0,7.76329,0.447766 +-6.77502,0.859809,0.932037,2,3,0,8.49845,0.221729 +-7.19668,0.726665,0.932037,2,3,0,8.64609,0.145619 +-9.64819,0.775506,0.932037,2,3,0,10.0305,0.0462333 +-6.86272,0.7808,0.932037,1,3,0,10.9403,0.312792 +-6.99329,0.696273,0.932037,2,3,0,8.30815,0.343519 +-6.99329,0.700745,0.932037,1,1,0,7.63428,0.343519 +-7.11558,0.957857,0.932037,1,1,0,7.18108,0.365809 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+-6.74902,0.994264,0.932037,1,3,0,7.08219,0.24445 +-7.95169,0.736282,0.932037,1,3,0,8.05657,0.467411 +# +# Elapsed Time: 0.005 seconds (Warm-up) +# 0.012 seconds (Sampling) +# 0.017 seconds (Total) +# diff --git a/src/test/unit/mcmc/test_csv_files/bernoulli_thin.csv b/src/test/unit/mcmc/test_csv_files/bernoulli_thin.csv new file mode 100644 index 0000000000..b0874d8ead --- /dev/null +++ b/src/test/unit/mcmc/test_csv_files/bernoulli_thin.csv @@ -0,0 +1,1057 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-07-23 15:13:19 UTC +# method = sample (Default) +# sample +# num_samples = 2000 +# num_warmup = 1000 (Default) +# save_warmup = false (Default) +# thin = 2 +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 1 (Default) +# data +# file = examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 3880845880 (Default) +# output +# file = bernoulli_thin.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,theta +# Adaptation terminated +# Step size = 0.893542 +# Diagonal elements of inverse mass matrix: +# 0.483503 +-8.91945,1,0.893542,1,1,0,9.73374,0.0619604 +-8.90814,1,0.893542,1,1,0,9.95846,0.545008 +-7.21915,1,0.893542,1,1,0,7.56642,0.382112 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b/src/test/unit/mcmc/test_csv_files/bernoulli_warmup.csv new file mode 100644 index 0000000000..799f5c8260 --- /dev/null +++ b/src/test/unit/mcmc/test_csv_files/bernoulli_warmup.csv @@ -0,0 +1,2057 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-07-22 12:26:39 UTC +# method = sample (Default) +# sample +# num_samples = 1000 (Default) +# num_warmup = 1000 (Default) +# save_warmup = true +# thin = 1 (Default) +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 1 (Default) +# data +# file = examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 3784445287 (Default) +# output +# file = output.csv (Default) +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0-25-gbb9ce42 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,theta +-9.8506,1,0.5,1,1,0,11.6894,0.604056 +-9.8506,0,14.3855,0,1,1,9.9718,0.604056 +-9.8506,1.07054e-09,2.43117,1,1,0,10.1705,0.604056 +-8.24874,1,0.239791,2,3,0,9.78315,0.49418 +-8.54651,0.976787,0.324332,1,1,0,8.57154,0.518378 +-7.21218,0.977577,0.47329,2,3,0,8.85175,0.38107 +-6.74823,1,0.750786,1,3,0,7.08766,0.247464 +-6.74823,0.0568166,1.34037,1,1,0,7.21871,0.247464 +-6.88855,0.99524,0.127193,3,15,0,7.76928,0.319814 +-7.18954,0.980866,0.230706,3,9,0,7.91162,0.146333 +-7.00517,0.999406,0.406612,2,3,0,7.22475,0.168215 +-7.00517,0.908606,0.766132,1,3,0,7.56833,0.168215 +-6.75143,1,1.08692,1,1,0,6.86451,0.239771 +-6.87144,0.77589,2.05487,1,1,0,7.04035,0.191574 +-6.7634,0.633501,1.92269,1,3,0,6.7636,0.272338 +-6.76111,1,1.1598,1,3,0,6.76265,0.23015 +-6.80263,0.90372,2.17832,1,1,0,6.9112,0.210282 +-6.80263,0.0115697,3.02409,1,1,0,7.38493,0.210282 +-6.74812,0.999483,0.279874,2,7,0,6.83911,0.248267 +-6.79674,0.990221,0.524639,1,3,0,6.8362,0.2903 +-6.79674,0.70284,0.947731,1,1,0,7.09021,0.2903 +-6.79674,0.970796,0.725248,1,1,0,6.86262,0.2903 +-6.85069,0.950437,1.22339,1,1,0,6.91591,0.309273 +-6.85069,0.0172536,1.92853,1,1,0,7.04452,0.309273 +-8.64872,0.977805,0.204974,4,15,0,8.65035,0.0694662 +-6.76625,0.999785,0.352164,3,7,0,8.64791,0.226652 +-7.04444,0.971063,0.638147,2,3,0,7.06282,0.353351 +-9.92209,0.0721566,1.05618,2,3,0,10.5028,0.0415885 +-9.86371,1,0.141791,1,1,0,9.92203,0.0425312 +-7.89631,0.998185,0.255292,2,5,0,10.4841,0.0980156 +-8.58774,0.971383,0.452922,3,7,0,9.25298,0.0713172 +-8.34876,1,0.740085,1,1,0,8.76482,0.0792422 +-6.81748,1,1.29574,1,1,0,7.18455,0.298398 +-6.81748,2.00258e-21,2.24798,1,1,0,9.56968,0.298398 +-6.81248,0.991645,0.272988,2,7,0,7.15155,0.207006 +-6.85997,0.987319,0.46496,3,7,0,7.06891,0.312004 +-6.85997,0.220416,0.776452,1,3,0,9.96316,0.312004 +-7.79145,0.984064,0.176692,2,5,0,7.96719,0.451582 +-6.99048,0.972469,0.292427,1,3,0,8.47322,0.342953 +-7.06807,0.988568,0.466233,1,1,0,7.08132,0.357627 +-7.08394,0.993545,0.768751,1,1,0,7.20573,0.360419 +-7.26741,0.94431,1.27388,1,3,0,7.27623,0.138945 +-7.48585,0.77512,1.85318,1,1,0,8.38914,0.121745 +-7.48585,1.62756e-06,1.76397,1,1,0,7.58761,0.121745 +-7.10351,0.961717,0.250226,2,7,0,10.7736,0.363778 +-7.22772,0.995816,0.381071,2,3,0,7.23134,0.383382 +-7.22772,0.740624,0.626526,1,1,0,7.99246,0.383382 +-6.76514,0.9988,0.553652,3,7,0,7.23247,0.273593 +-7.24015,0.753856,0.908029,2,3,0,7.63644,0.141438 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+-8.952,1,0.398939,2,3,0,9.27498,0.548062 +-9.18855,0.999125,0.616734,2,7,0,9.20778,0.0554879 +-7.24774,1,0.947222,1,1,0,8.69856,0.140734 +-7.24774,0.202087,1.45078,1,1,0,7.29856,0.140734 +-7.26566,0.998816,0.427919,1,1,0,7.29767,0.139102 +-6.91413,0.931556,0.65354,1,3,0,7.86673,0.3262 +-6.87128,1,0.86646,2,3,0,6.89856,0.19161 +-6.768,1,1.3159,1,1,0,6.78045,0.275525 +-6.768,8.67729e-08,1.98999,1,1,0,7.59461,0.275525 +-7.9148,0.904272,0.402673,2,5,0,8.38631,0.0971256 +-7.06381,0.980558,0.503805,2,3,0,8.46016,0.160389 +-7.39013,0.978768,0.731889,2,3,0,7.40663,0.128747 +-6.76543,1,1.05559,2,3,0,7.04327,0.273794 +-6.76543,0.159028,1.5818,1,1,0,7.09493,0.273794 +-6.91956,0.94612,0.454522,1,3,0,7.39536,0.181949 +-6.76739,0.950092,0.613207,2,3,0,7.38962,0.225951 +-6.85207,0.970879,0.831322,1,1,0,6.85346,0.196063 +-12.4758,0.00604068,1.16985,1,1,0,13.3062,0.721196 +-11.7387,1,0.2566,2,3,0,12.4659,0.693413 +-10.2243,1,0.382607,1,1,0,11.6102,0.624313 +-10.8714,0.865432,0.568409,1,1,0,11.5235,0.656119 +-8.28112,1,0.651815,2,7,0,10.3982,0.0817023 +-7.07017,1,0.962578,1,1,0,7.92797,0.159598 +-7.00702,1,1.41659,1,1,0,7.28115,0.167951 +-7.00702,0.0463564,2.07767,1,1,0,8.55227,0.167951 +-7.51448,0.811914,0.513168,2,5,0,9.45773,0.421124 +-6.80546,1,0.53134,3,7,0,7.47906,0.293871 +-6.75523,0.9975,0.778651,2,3,0,6.82148,0.2352 +-7.98668,0.510767,1.13214,1,1,0,7.99159,0.0937796 +-8.57516,0.940216,0.672299,1,1,0,8.61556,0.0717072 +-10.9654,0.756415,0.878176,1,1,0,10.9668,0.0281728 +-10.9654,2.39406e-53,3.2776,2,7,0,11.462,0.0281728 +-10.9654,0,7.65341,0,1,1,11.0171,0.0281728 +-11.2278,0.98842,0.754621,1,1,0,11.6163,0.0256105 +-9.18505,0.81209,0.762058,1,3,0,11.0807,0.0555666 +-7.31509,1,0.601188,2,3,0,9.13989,0.134796 +-7.37962,0.974851,0.917142,2,3,0,7.39422,0.404387 +-7.93864,0.71181,1.42246,2,3,0,7.93864,0.0959959 +-8.52121,0.863499,1.02619,2,3,0,8.52132,0.516409 +-8.72953,0.796954,1.21163,1,1,0,10.711,0.532192 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+-7.21715,0.677044,0.825931,1,1,0,8.31299,0.381813 +# Adaptation terminated +# Step size = 0.83124 +# Diagonal elements of inverse mass matrix: +# 0.525769 +-6.77384,0.964073,0.83124,1,3,0,7.46753,0.222338 +-6.863,0.968947,0.83124,1,3,0,7.01137,0.312874 +-6.83479,0.964185,0.83124,1,3,0,7.16339,0.200486 +-6.80509,0.978314,0.83124,1,3,0,7.03713,0.293726 +-6.79148,0.786013,0.83124,2,3,0,8.42285,0.214411 +-6.79943,0.998548,0.83124,1,1,0,6.80547,0.211417 +-6.85456,0.967137,0.83124,2,3,0,7.15794,0.310427 +-7.11014,0.963078,0.83124,2,7,0,7.14083,0.154842 +-6.75222,0.990723,0.83124,1,3,0,7.18664,0.238666 +-7.40454,0.858025,0.83124,1,3,0,7.7421,0.127645 +-7.17574,0.883441,0.83124,1,3,0,8.96348,0.375502 +-7.6387,0.88609,0.83124,1,1,0,7.67194,0.435344 +-7.41421,0.638614,0.83124,1,3,0,10.1123,0.126916 +-7.80019,0.957369,0.83124,1,1,0,7.80975,0.102852 +-7.88627,0.991536,0.83124,1,1,0,7.99586,0.0985041 +-6.75872,1,0.83124,2,3,0,7.70683,0.232017 +-7.11378,0.930982,0.83124,1,3,0,7.23357,0.154425 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+-6.74891,0.911224,0.83124,2,3,0,7.43307,0.244756 +-7.42023,0.912902,0.83124,2,3,0,7.43121,0.126465 +-7.34223,0.999701,0.83124,2,7,0,7.4227,0.399457 +-6.75652,0.992019,0.83124,2,3,0,7.41195,0.266528 +-6.76283,0.994113,0.83124,2,7,0,6.7964,0.228913 +-6.80605,0.983335,0.83124,1,3,0,6.88718,0.294104 +-6.97427,0.917773,0.83124,1,3,0,7.34947,0.172796 +-6.78479,0.97611,0.83124,1,3,0,7.23119,0.284872 +-6.7651,1,0.83124,1,1,0,6.78103,0.273564 +-6.74958,1,0.83124,2,3,0,6.76193,0.243075 +-7.02863,0.969077,0.83124,2,3,0,7.05541,0.3504 +-6.91736,0.953693,0.83124,2,3,0,7.44255,0.326972 +-6.918,0.618501,0.83124,2,3,0,9.85086,0.182235 +-6.84677,1,0.83124,1,1,0,6.90933,0.197372 +-6.96252,0.980836,0.83124,1,1,0,6.96279,0.174638 +# +# Elapsed Time: 0.01 seconds (Warm-up) +# 0.008 seconds (Sampling) +# 0.018 seconds (Total) +# diff --git a/src/test/unit/mcmc/test_csv_files/bernoulli_zeta.csv b/src/test/unit/mcmc/test_csv_files/bernoulli_zeta.csv new file mode 100644 index 0000000000..5688883ef0 --- /dev/null +++ b/src/test/unit/mcmc/test_csv_files/bernoulli_zeta.csv @@ -0,0 +1,1057 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-07-20 18:56:30 UTC +# method = sample (Default) +# sample +# num_samples = 1000 (Default) +# num_warmup = 1000 (Default) +# save_warmup = false (Default) +# thin = 1 (Default) +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 1 (Default) +# data +# file = examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 3635036313 (Default) +# output +# file = bernoulli_out.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0-25-gbb9ce42 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,zeta +# Adaptation terminated +# Step size = 0.932037 +# Diagonal elements of inverse mass matrix: +# 0.591014 +-7.81355,0.761218,0.932037,1,3,0,8.13669,0.102158 +-8.71049,0.894869,0.932037,1,1,0,8.72268,0.0676546 +-7.3531,0.957519,0.932037,1,3,0,8.53469,0.131664 +-7.02007,1,0.932037,1,1,0,7.29505,0.166128 +-8.49033,0.716959,0.932037,1,3,0,9.20407,0.513986 +-7.18724,1,0.932037,1,1,0,8.06459,0.377282 +-10.0621,0.636916,0.932037,2,3,0,10.0852,0.615706 +-8.11851,1,0.932037,1,1,0,9.65805,0.482811 +-6.86936,0.948809,0.932037,2,3,0,8.60097,0.192035 +-7.13756,0.935492,0.932037,2,3,0,7.43717,0.151774 +-7.09122,1,0.932037,1,1,0,7.19518,0.157048 +-6.91669,1,0.932037,1,1,0,7.06383,0.182475 +-6.76371,0.840542,0.932037,2,3,0,8.08838,0.228308 +-6.84805,0.983351,0.932037,1,3,0,6.84861,0.197051 +-8.3727,0.653843,0.932037,2,3,0,9.53079,0.504537 +-7.25928,0.806553,0.932037,1,3,0,9.43963,0.139678 +-6.95987,0.853051,0.932037,2,3,0,9.71446,0.175062 +-6.95334,1,0.932037,1,1,0,7.00708,0.17612 +-6.98417,0.993206,0.932037,1,1,0,7.02663,0.171288 +-8.9535,0.727406,0.932037,1,3,0,9.27425,0.0610911 +-7.08162,0.995936,0.932037,2,3,0,9.00617,0.158197 +-7.16691,0.98306,0.932037,1,1,0,7.22238,0.148646 +-6.8898,1,0.932037,1,1,0,7.10684,0.18768 +-7.8902,0.846547,0.932037,1,3,0,7.96857,0.0983126 +-7.24239,1,0.932037,1,1,0,7.78446,0.14123 +-7.94748,0.888836,0.932037,1,1,0,7.94857,0.0955821 +-6.83694,0.864212,0.932037,2,3,0,8.91495,0.305006 +-6.83694,0.425574,0.932037,1,3,0,9.01249,0.305006 +-6.80131,0.985852,0.932037,1,3,0,6.93894,0.210747 +-6.74824,0.978446,0.932037,2,3,0,6.95179,0.252645 +-6.74832,0.945401,0.932037,2,3,0,7.0277,0.24694 +-6.85442,0.972221,0.932037,1,3,0,6.87104,0.310384 +-6.86406,0.996818,0.932037,1,1,0,6.89618,0.313175 +-7.11238,0.918504,0.932037,1,1,0,7.12695,0.365274 +-7.11238,0.431368,0.932037,1,1,0,8.53302,0.365274 +-6.79843,1,0.932037,1,1,0,7.00834,0.291015 +-6.74804,0.996585,0.932037,2,3,0,6.81732,0.249269 +-9.50952,0.509265,0.932037,1,3,0,9.67564,0.584141 +-8.27778,1,0.932037,1,1,0,9.48419,0.496645 +-7.2527,1,0.932037,2,3,0,8.04703,0.140278 +-6.75205,0.907555,0.932037,2,3,0,8.25548,0.238895 +-7.3411,0.857468,0.932037,1,3,0,7.42326,0.399305 +-7.07094,1,0.932037,1,1,0,7.32247,0.358137 +-7.04767,0.556206,0.932037,2,3,0,9.56585,0.353943 +-6.87625,1,0.932037,2,3,0,6.9975,0.190522 +-7.12535,0.945777,0.932037,1,1,0,7.12652,0.153122 +-7.12535,0.913353,0.932037,1,1,0,7.53634,0.153122 +-6.78755,1,0.932037,2,3,0,7.04258,0.286194 +-7.7545,0.780448,0.932037,1,3,0,7.76329,0.447766 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+-7.21981,0.951312,0.932037,1,1,0,7.23197,0.143362 +-7.47612,0.930523,0.932037,2,3,0,8.12075,0.122424 +-6.81131,0.990375,0.932037,2,3,0,7.55719,0.207381 +-6.80278,0.920389,0.932037,2,3,0,7.43635,0.210229 +-6.88803,0.979103,0.932037,1,1,0,6.89063,0.188041 +-7.02902,0.795987,0.932037,2,3,0,8.6993,0.164908 +-6.74805,0.993012,0.932037,1,3,0,7.01716,0.250947 +-6.93322,0.952905,0.932037,1,3,0,6.95228,0.330673 +-6.76781,0.904559,0.932037,2,3,0,7.33629,0.275396 +-6.76232,0.985564,0.932037,2,3,0,6.83656,0.271521 +-6.87137,0.974516,0.932037,1,3,0,6.87148,0.31522 +-7.21556,0.953187,0.932037,2,3,0,7.22785,0.381575 +-6.76367,0.998041,0.932037,1,3,0,7.1984,0.228336 +-6.82308,0.981349,0.932037,1,3,0,6.86896,0.30038 +-6.82308,0.352125,0.932037,1,3,0,10.8402,0.30038 +-6.99474,0.738309,0.932037,2,3,0,8.0486,0.34381 +-6.99474,0.963463,0.932037,1,1,0,7.1311,0.34381 +-6.8637,0.713515,0.932037,2,3,0,8.29356,0.313071 +-6.94178,0.974091,0.932037,1,1,0,6.96832,0.332611 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+-8.19963,0.701948,0.932037,1,3,0,8.62984,0.0848102 +-6.87827,1,0.932037,1,3,0,8.08649,0.190089 +-6.90011,0.99488,0.932037,1,1,0,6.92793,0.185622 +-7.58846,0.835161,0.932037,1,3,0,7.98997,0.429714 +-6.84168,1,0.932037,2,3,0,7.32922,0.306509 +-6.79898,0.907476,0.932037,2,3,0,7.23342,0.291247 +-7.18968,0.940405,0.932037,2,3,0,7.1897,0.377657 +-7.30081,0.811278,0.932037,2,3,0,8.27114,0.136012 +-7.44395,0.975262,0.932037,1,1,0,7.52376,0.124721 +-7.10913,1,0.932037,1,1,0,7.3984,0.154957 +-6.74902,0.994264,0.932037,1,3,0,7.08219,0.24445 +-7.95169,0.736282,0.932037,1,3,0,8.05657,0.467411 +# +# Elapsed Time: 0.005 seconds (Warm-up) +# 0.012 seconds (Sampling) +# 0.017 seconds (Total) +# diff --git a/src/test/unit/mcmc/test_csv_files/eight_schools_empty.csv b/src/test/unit/mcmc/test_csv_files/eight_schools_empty.csv new file mode 100644 index 0000000000..1e93414470 --- /dev/null +++ b/src/test/unit/mcmc/test_csv_files/eight_schools_empty.csv @@ -0,0 +1,57 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = eight_schools_model +# start_datetime = 2024-07-20 18:52:24 UTC +# method = sample (Default) +# sample +# num_samples = 0 +# num_warmup = 1000 (Default) +# save_warmup = false (Default) +# thin = 1 (Default) +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 1 (Default) +# data +# file = examples/eight_schools/eight_schools.data.json +# init = 2 (Default) +# random +# seed = 3634790287 (Default) +# output +# file = eight_schools_1.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0-25-gbb9ce42 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,tau +# Adaptation terminated +# Step size = 0.21285 +# Diagonal elements of inverse mass matrix: +# 22.9615, 70.1007, 38.6355, 46.2857, 44.7487, 36.401, 46.9537, 45.2008, 61.2207, 0.755335 +# +# Elapsed Time: 0.046 seconds (Warm-up) +# 0.023 seconds (Sampling) +# 0.069 seconds (Total) +#