diff --git a/RELEASE-NOTES.txt b/RELEASE-NOTES.txt index 302667a1f4e..f1d6a1c990e 100644 --- a/RELEASE-NOTES.txt +++ b/RELEASE-NOTES.txt @@ -5,6 +5,17 @@ Note: these are the release notes for the stan-dev/stan repository. Further changes may arise at the interface level (stan-dev/{rstan, pystan, cmdstan}) and math library level (stan-dev/math). +v2.35.0 (3 June 2024) +====================================================================== + + - The algorithms no longer catch `std::exception` unconditionally. The Math library uses `std::domain_error` for recoverable errors, and these are the ones which are caught. (#3259) + - Allow laplace sampling without evaluating `log_prob` for each draw. (#3261) + - Allow laplace sampling to save the Hessian as a diagnostic output. (#3261) + - Stan's RNG usages now uses a type definition `stan::rng_t` rather than hard coding a specific Boost RNG. (#3263) + - Switched the pRNG used by default in the services and tests to be `boost::mixmax`. Note that this means seeds from previous versions will lead to different numerical results in this version. (#3264) + - Add a new ranked R-hat diagnostic from [Vehtari](https://arxiv.org/abs/1903.08008). (#3266) + - Fixed an issue where Pathfinder would sometimes return more draws than requested. (#3279) + v2.34.1 (23 January 2024) ====================================================================== diff --git a/lib/stan_math b/lib/stan_math index e1fcbe8dca2..7ada875b1a3 160000 --- a/lib/stan_math +++ b/lib/stan_math @@ -1 +1 @@ -Subproject commit e1fcbe8dca2bfa85b1e109b2bfb1cf65fe0363b7 +Subproject commit 7ada875b1a3a20fd78ce4eabd29664dc0d1fc42e diff --git a/makefile b/makefile index 7fd6542b617..40ff060e652 100644 --- a/makefile +++ b/makefile @@ -2,16 +2,12 @@ # Stan # ----------------- # -# To customize your build, set make variables in either: -# ~/.config/stan/make.local -# make/local -# Variables in make/local is loaded after ~/.config/stan/make.local +# To customize your build, set make variables in make/local ## 'help' is the default make target. help: --include $(HOME)/.config/stan/make.local # user-defined variables -include make/local # user-defined variables MATH ?= lib/stan_math/ diff --git a/src/stan/analyze/mcmc/check_chains.hpp b/src/stan/analyze/mcmc/check_chains.hpp new file mode 100644 index 00000000000..6f816982b3f --- /dev/null +++ b/src/stan/analyze/mcmc/check_chains.hpp @@ -0,0 +1,43 @@ +#ifndef STAN_ANALYZE_MCMC_CHECK_CHAINS_HPP +#define STAN_ANALYZE_MCMC_CHECK_CHAINS_HPP + +#include +#include +#include +#include +#include +#include + +namespace stan { +namespace analyze { + +/** + * Checks that values across all matrix columns finite and non-identical. + * + * @param chains matrix of draws, one column per chain + * @return bool true if OK, false otherwise + */ +inline bool is_finite_and_varies(const Eigen::MatrixXd chains) { + size_t num_chains = chains.cols(); + size_t num_samples = chains.rows(); + Eigen::VectorXd first_draws = Eigen::VectorXd::Zero(num_chains); + for (std::size_t i = 0; i < num_chains; ++i) { + first_draws(i) = chains.col(i)(0); + for (int j = 0; j < num_samples; ++j) { + if (!std::isfinite(chains.col(i)(j))) + return false; + } + if (chains.col(i).isApproxToConstant(first_draws(i))) { + return false; + } + } + if (num_chains > 1 && first_draws.isApproxToConstant(first_draws(0))) { + return false; + } + return true; +} + +} // namespace analyze +} // namespace stan + +#endif diff --git a/src/stan/analyze/mcmc/compute_effective_sample_size.hpp b/src/stan/analyze/mcmc/compute_effective_sample_size.hpp index 39eb636c714..e06c89f7a1b 100644 --- a/src/stan/analyze/mcmc/compute_effective_sample_size.hpp +++ b/src/stan/analyze/mcmc/compute_effective_sample_size.hpp @@ -12,6 +12,8 @@ namespace stan { namespace analyze { /** + * \deprecated use split_rank_normalized_ess instead + * * Computes the effective sample size (ESS) for the specified * parameter across all kept samples. The value returned is the * minimum of ESS and the number_total_draws * @@ -138,6 +140,8 @@ inline double compute_effective_sample_size(std::vector draws, } /** + * \deprecated use split_rank_normalized_ess instead + * * Computes the effective sample size (ESS) for the specified * parameter across all kept samples. The value returned is the * minimum of ESS and the number_total_draws * @@ -164,6 +168,8 @@ inline double compute_effective_sample_size(std::vector draws, } /** + * \deprecated use split_rank_normalized_ess instead + * * Computes the split effective sample size (ESS) for the specified * parameter across all kept samples. The value returned is the * minimum of ESS and the number_total_draws * @@ -199,6 +205,8 @@ inline double compute_split_effective_sample_size( } /** + * \deprecated use split_rank_normalized_ess instead + * * Computes the split effective sample size (ESS) for the specified * parameter across all kept samples. The value returned is the * minimum of ESS and the number_total_draws * diff --git a/src/stan/analyze/mcmc/compute_potential_scale_reduction.hpp b/src/stan/analyze/mcmc/compute_potential_scale_reduction.hpp index f4fcb971bc7..baa686e63d8 100644 --- a/src/stan/analyze/mcmc/compute_potential_scale_reduction.hpp +++ b/src/stan/analyze/mcmc/compute_potential_scale_reduction.hpp @@ -18,156 +18,8 @@ namespace stan { namespace analyze { /** - * Computes normalized average ranks for draws. Transforming them to normal - * scores using inverse normal transformation and a fractional offset. Based on - * paper https://arxiv.org/abs/1903.08008 - * @param chains stores chains in columns - * @return normal scores for average ranks of draws - */ -inline Eigen::MatrixXd rank_transform(const Eigen::MatrixXd& chains) { - const Eigen::Index rows = chains.rows(); - const Eigen::Index cols = chains.cols(); - const Eigen::Index size = rows * cols; - - std::vector> value_with_index(size); - - for (Eigen::Index i = 0; i < size; ++i) { - value_with_index[i] = {chains(i), i}; - } - - std::sort(value_with_index.begin(), value_with_index.end()); - - Eigen::MatrixXd rank_matrix = Eigen::MatrixXd::Zero(rows, cols); - - // Assigning average ranks - for (Eigen::Index i = 0; i < size; ++i) { - // Handle ties by averaging ranks - Eigen::Index j = i + 1; - double sum_ranks = j; - Eigen::Index count = 1; - - while (j < size && value_with_index[j].first == value_with_index[i].first) { - sum_ranks += j + 1; // Rank starts from 1 - ++j; - ++count; - } - double avg_rank = sum_ranks / count; - boost::math::normal_distribution dist; - for (std::size_t k = i; k < j; ++k) { - double p = (avg_rank - 3.0 / 8.0) / (size - 2.0 * 3.0 / 8.0 + 1.0); - const Eigen::Index index = value_with_index[k].second; - rank_matrix(index) = boost::math::quantile(dist, p); - } - i = j - 1; // Skip over tied elements - } - return rank_matrix; -} - -/** - * Computes square root of marginal posterior variance of the estimand by the - * weigted average of within-chain variance W and between-chain variance B. + * \deprecated use `rhat` instead * - * @param chains stores chains in columns - * @return square root of ((N-1)/N)W + B/N - */ -inline double rhat(const Eigen::MatrixXd& chains) { - const Eigen::Index num_chains = chains.cols(); - const Eigen::Index num_draws = chains.rows(); - - Eigen::RowVectorXd within_chain_means = chains.colwise().mean(); - double across_chain_mean = within_chain_means.mean(); - double between_variance - = num_draws - * (within_chain_means.array() - across_chain_mean).square().sum() - / (num_chains - 1); - double within_variance = - // Divide each row by chains and get sum of squares for each chain - // (getting a vector back) - ((chains.rowwise() - within_chain_means) - .array() - .square() - .colwise() - // divide each sum of square by num_draws, sum the sum of squares, - // and divide by num chains - .sum() - / (num_draws - 1.0)) - .sum() - / num_chains; - - return sqrt((between_variance / within_variance + num_draws - 1) / num_draws); -} - -/** - * Computes the potential scale reduction (Rhat) using rank based diagnostic for - * the specified parameter across all kept samples. Based on paper - * https://arxiv.org/abs/1903.08008 - * - * Current implementation assumes draws are stored in contiguous - * blocks of memory. Chains are trimmed from the back to match the - * length of the shortest chain. - * - * @param chain_begins stores pointers to arrays of chains - * @param chain_sizes stores sizes of chains - * @return potential scale reduction for the specified parameter - */ -inline std::pair compute_potential_scale_reduction_rank( - const std::vector& chain_begins, - const std::vector& chain_sizes) { - std::vector nonzero_chain_begins; - std::vector nonzero_chain_sizes; - nonzero_chain_begins.reserve(chain_begins.size()); - nonzero_chain_sizes.reserve(chain_sizes.size()); - for (size_t i = 0; i < chain_sizes.size(); ++i) { - if (chain_sizes[i]) { - nonzero_chain_begins.push_back(chain_begins[i]); - nonzero_chain_sizes.push_back(chain_sizes[i]); - } - } - if (!nonzero_chain_sizes.size()) { - return {std::numeric_limits::quiet_NaN(), - std::numeric_limits::quiet_NaN()}; - } - std::size_t num_nonzero_chains = nonzero_chain_sizes.size(); - std::size_t min_num_draws = nonzero_chain_sizes[0]; - for (std::size_t chain = 1; chain < num_nonzero_chains; ++chain) { - min_num_draws = std::min(min_num_draws, nonzero_chain_sizes[chain]); - } - - // check if chains are constant; all equal to first draw's value - bool are_all_const = false; - Eigen::VectorXd init_draw = Eigen::VectorXd::Zero(num_nonzero_chains); - Eigen::MatrixXd draws_matrix(min_num_draws, num_nonzero_chains); - - for (std::size_t chain = 0; chain < num_nonzero_chains; chain++) { - Eigen::Map> draws( - nonzero_chain_begins[chain], nonzero_chain_sizes[chain]); - - for (std::size_t n = 0; n < min_num_draws; n++) { - if (!std::isfinite(draws(n))) { - return {std::numeric_limits::quiet_NaN(), - std::numeric_limits::quiet_NaN()}; - } - draws_matrix(n, chain) = draws(n); - } - - init_draw(chain) = draws(0); - are_all_const |= !draws.isApproxToConstant(draws(0)); - } - // If all chains are constant then return NaN - if (are_all_const && init_draw.isApproxToConstant(init_draw(0))) { - return {std::numeric_limits::quiet_NaN(), - std::numeric_limits::quiet_NaN()}; - } - - double rhat_bulk = rhat(rank_transform(draws_matrix)); - double rhat_tail = rhat(rank_transform( - (draws_matrix.array() - math::quantile(draws_matrix.reshaped(), 0.5)) - .abs())); - - return std::make_pair(rhat_bulk, rhat_tail); -} - -/** * Computes the potential scale reduction (Rhat) for the specified * parameter across all kept samples. * @@ -248,34 +100,12 @@ inline double compute_potential_scale_reduction( * num_chains / (num_chains - 1); double var_within = chain_var.mean(); - // rewrote [(n-1)*W/n + B/n]/W as (n-1+ B/W)/n return sqrt((var_between / var_within + num_draws - 1) / num_draws); } /** - * Computes the potential scale reduction (Rhat) using rank based diagnostic for - * the specified parameter across all kept samples. Based on paper - * https://arxiv.org/abs/1903.08008 - * - * See more details in Stan reference manual section "Potential - * Scale Reduction". http://mc-stan.org/users/documentation - * - * Current implementation assumes draws are stored in contiguous - * blocks of memory. Chains are trimmed from the back to match the - * length of the shortest chain. Argument size will be broadcast to - * same length as draws. + * \deprecated use split_rank_normalized_rhat instead * - * @param chain_begins stores pointers to arrays of chains - * @param size stores sizes of chains - * @return potential scale reduction for the specified parameter - */ -inline std::pair compute_potential_scale_reduction_rank( - const std::vector& chain_begins, size_t size) { - std::vector sizes(chain_begins.size(), size); - return compute_potential_scale_reduction_rank(chain_begins, sizes); -} - -/** * Computes the potential scale reduction (Rhat) for the specified * parameter across all kept samples. * @@ -299,42 +129,8 @@ inline double compute_potential_scale_reduction( } /** - * Computes the potential scale reduction (Rhat) using rank based diagnostic for - * the specified parameter across all kept samples. Based on paper - * https://arxiv.org/abs/1903.08008 - * - * When the number of total draws N is odd, the (N+1)/2th draw is ignored. - * - * See more details in Stan reference manual section "Potential - * Scale Reduction". http://mc-stan.org/users/documentation + * \deprecated use split_rank_normalized_rhat instead * - * Current implementation assumes draws are stored in contiguous - * blocks of memory. Chains are trimmed from the back to match the - * length of the shortest chain. - * - * @param chain_begins stores pointers to arrays of chains - * @param chain_sizes stores sizes of chains - * @return potential scale reduction for the specified parameter - */ -inline std::pair compute_split_potential_scale_reduction_rank( - const std::vector& chain_begins, - const std::vector& chain_sizes) { - size_t num_chains = chain_sizes.size(); - size_t num_draws = chain_sizes[0]; - for (size_t chain = 1; chain < num_chains; ++chain) { - num_draws = std::min(num_draws, chain_sizes[chain]); - } - - std::vector split_draws - = split_chains(chain_begins, chain_sizes); - - size_t half = std::floor(num_draws / 2.0); - std::vector half_sizes(2 * num_chains, half); - - return compute_potential_scale_reduction_rank(split_draws, half_sizes); -} - -/** * Computes the split potential scale reduction (Rhat) for the * specified parameter across all kept samples. When the number of * total draws N is odd, the (N+1)/2th draw is ignored. @@ -367,32 +163,8 @@ inline double compute_split_potential_scale_reduction( } /** - * Computes the potential scale reduction (Rhat) using rank based diagnostic for - * the specified parameter across all kept samples. Based on paper - * https://arxiv.org/abs/1903.08008 - * - * When the number of total draws N is odd, the (N+1)/2th draw is ignored. - * - * See more details in Stan reference manual section "Potential - * Scale Reduction". http://mc-stan.org/users/documentation - * - * Current implementation assumes draws are stored in contiguous - * blocks of memory. Chains are trimmed from the back to match the - * length of the shortest chain. Argument size will be broadcast to - * same length as draws. + * \deprecated use split_rank_normalized_rhat instead * - * @param chain_begins stores pointers to arrays of chains - * @param size stores sizes of chains - * @return potential scale reduction for the specified parameter - */ -inline std::pair compute_split_potential_scale_reduction_rank( - const std::vector& chain_begins, size_t size) { - size_t num_chains = chain_begins.size(); - std::vector sizes(num_chains, size); - return compute_split_potential_scale_reduction_rank(chain_begins, sizes); -} - -/** * Computes the split potential scale reduction (Rhat) for the * specified parameter across all kept samples. When the number of * total draws N is odd, the (N+1)/2th draw is ignored. diff --git a/src/stan/analyze/mcmc/ess.hpp b/src/stan/analyze/mcmc/ess.hpp new file mode 100644 index 00000000000..33342ce68a8 --- /dev/null +++ b/src/stan/analyze/mcmc/ess.hpp @@ -0,0 +1,108 @@ +#ifndef STAN_ANALYZE_MCMC_ESS_HPP +#define STAN_ANALYZE_MCMC_ESS_HPP + +#include +#include +#include +#include +#include +#include + +namespace stan { +namespace analyze { + +/** + * Computes the effective sample size (ESS) for the specified + * parameter across all chains. The number of draws per chain must be > 3, + * and the values across all draws must be finite and not constant. + * See https://arxiv.org/abs/1903.08008, section 3.2 for discussion. + * + * Sample autocovariance is computed using the implementation in this namespace + * which normalizes lag-k autocorrelation estimators by N instead of (N - k), + * yielding biased but more stable estimators as discussed in Geyer (1992); see + * https://projecteuclid.org/euclid.ss/1177011137. + * + * @param chains matrix of draws across all chains + * @return effective sample size for the specified parameter + */ +double ess(const Eigen::MatrixXd& chains) { + const Eigen::Index num_chains = chains.cols(); + const Eigen::Index draws_per_chain = chains.rows(); + Eigen::MatrixXd acov(draws_per_chain, num_chains); + Eigen::VectorXd chain_mean(num_chains); + Eigen::VectorXd chain_var(num_chains); + + // compute the per-chain autocovariance + for (size_t i = 0; i < num_chains; ++i) { + chain_mean(i) = chains.col(i).mean(); + Eigen::Map draw_col(chains.col(i).data(), + draws_per_chain); + Eigen::VectorXd cov_col(draws_per_chain); + autocovariance(draw_col, cov_col); + acov.col(i) = cov_col; + chain_var(i) = cov_col(0) * draws_per_chain / (draws_per_chain - 1); + } + + // compute var_plus, eqn (3) + double w_chain_var = math::mean(chain_var); // W (within chain var) + double var_plus + = w_chain_var * (draws_per_chain - 1) / draws_per_chain; // \hat{var}^{+} + if (num_chains > 1) { + var_plus += math::variance(chain_mean); // B (between chain var) + } + + // Geyer's initial positive sequence, eqn (11) + Eigen::VectorXd rho_hat_t = Eigen::VectorXd::Zero(draws_per_chain); + double rho_hat_even = 1.0; + rho_hat_t(0) = rho_hat_even; // lag 0 + + Eigen::VectorXd acov_t(num_chains); + for (size_t i = 0; i < num_chains; ++i) { + acov_t(i) = acov(1, i); + } + double rho_hat_odd = 1 - (w_chain_var - acov_t.mean()) / var_plus; + rho_hat_t(1) = rho_hat_odd; // lag 1 + + // compute autocorrelation at lag t for pair (t, t+1) + // paired autocorrelation is guaranteed to be positive, monotone and convex + size_t t = 1; + while (t < draws_per_chain - 4 && (rho_hat_even + rho_hat_odd > 0) + && !std::isnan(rho_hat_even + rho_hat_odd)) { + for (size_t i = 0; i < num_chains; ++i) { + acov_t(i) = acov.col(i)(t + 1); + } + rho_hat_even = 1 - (w_chain_var - acov_t.mean()) / var_plus; + for (size_t i = 0; i < num_chains; ++i) { + acov_t(i) = acov.col(i)(t + 2); + } + rho_hat_odd = 1 - (w_chain_var - acov_t.mean()) / var_plus; + if ((rho_hat_even + rho_hat_odd) >= 0) { + rho_hat_t(t + 1) = rho_hat_even; + rho_hat_t(t + 2) = rho_hat_odd; + } + // convert initial positive sequence into an initial monotone sequence + if (rho_hat_t(t + 1) + rho_hat_t(t + 2) > rho_hat_t(t - 1) + rho_hat_t(t)) { + rho_hat_t(t + 1) = (rho_hat_t(t - 1) + rho_hat_t(t)) / 2; + rho_hat_t(t + 2) = rho_hat_t(t + 1); + } + t += 2; + } + + auto max_t = t; // max lag, used for truncation + // see discussion p. 8, par "In extreme antithetic cases, " + if (rho_hat_even > 0) { + rho_hat_t(max_t + 1) = rho_hat_even; + } + + double draws_total = num_chains * draws_per_chain; + // eqn (13): Geyer's truncation rule, w/ modification + double tau_hat = -1 + 2 * rho_hat_t.head(max_t).sum() + rho_hat_t(max_t + 1); + // safety check for negative values and with max ess equal to ess*log10(ess) + tau_hat = std::max(tau_hat, 1 / std::log10(draws_total)); + return (draws_total / tau_hat); +} + +} // namespace analyze +} // namespace stan + +#endif diff --git a/src/stan/analyze/mcmc/mcse.hpp b/src/stan/analyze/mcmc/mcse.hpp new file mode 100644 index 00000000000..040b94d9ea7 --- /dev/null +++ b/src/stan/analyze/mcmc/mcse.hpp @@ -0,0 +1,67 @@ +#ifndef STAN_ANALYZE_MCMC_MCSE_HPP +#define STAN_ANALYZE_MCMC_MCSE_HPP + +#include +#include +#include +#include +#include +#include +#include + +namespace stan { +namespace analyze { + +/** + * 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 chains matrix of draws across all chains + * @return mcse + */ +inline double mcse_mean(const Eigen::MatrixXd& chains) { + const Eigen::Index num_draws = chains.rows(); + if (chains.rows() < 4 || !is_finite_and_varies(chains)) + return std::numeric_limits::quiet_NaN(); + + double sample_var + = (chains.array() - chains.mean()).square().sum() / (chains.size() - 1); + return std::sqrt(sample_var / ess(chains)); +} + +/** + * 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: + * https://github.com/stan-dev/posterior/blob/98bf52329d68f3307ac4ecaaea659276ee1de8df/R/convergence.R#L478-L496 + * + * @param chains matrix of draws across all chains + * @return mcse + */ +inline double mcse_sd(const Eigen::MatrixXd& chains) { + if (chains.rows() < 4 || !is_finite_and_varies(chains)) + return std::numeric_limits::quiet_NaN(); + + // center the data, take abs value + Eigen::MatrixXd draws_ctr = (chains.array() - chains.mean()).abs().matrix(); + + // posterior pkg fn `ess_mean` computes on split chains + double ess_mean = ess(split_chains(draws_ctr)); + + // estimated variance (2nd moment) + double Evar = draws_ctr.array().square().mean(); + + // variance of variance, adjusted for ESS + double fourth_moment = draws_ctr.array().pow(4).mean(); + double varvar = (fourth_moment - std::pow(Evar, 2)) / ess_mean; + + // variance of standard deviation - use Taylor series approximation + double varsd = varvar / Evar / 4.0; + return std::sqrt(varsd); +} + +} // namespace analyze +} // namespace stan + +#endif diff --git a/src/stan/analyze/mcmc/rank_normalization.hpp b/src/stan/analyze/mcmc/rank_normalization.hpp new file mode 100644 index 00000000000..f645b153bd2 --- /dev/null +++ b/src/stan/analyze/mcmc/rank_normalization.hpp @@ -0,0 +1,62 @@ +#ifndef STAN_ANALYZE_MCMC_RANK_NORMALIZATION_HPP +#define STAN_ANALYZE_MCMC_RANK_NORMALIZATION_HPP + +#include +#include +#include +#include +#include +#include + +namespace stan { +namespace analyze { + +/** + * Computes normalized average ranks for pooled draws. The values across + * all draws be finite and not constant. Normal scores computed using + * inverse normal transformation and a fractional offset. Based on paper + * https://arxiv.org/abs/1903.08008 + * + * @param chains matrix of draws, one column per chain + * @return normal scores for average ranks of draws + */ +inline Eigen::MatrixXd rank_transform(const Eigen::MatrixXd& chains) { + const Eigen::Index rows = chains.rows(); + const Eigen::Index cols = chains.cols(); + const Eigen::Index size = rows * cols; + + std::vector> value_with_index(size); + for (Eigen::Index i = 0; i < size; ++i) { + value_with_index[i] = {chains(i), i}; + } + + std::sort(value_with_index.begin(), value_with_index.end()); + Eigen::MatrixXd rank_matrix = Eigen::MatrixXd::Zero(rows, cols); + // Assigning average ranks + for (Eigen::Index i = 0; i < size; ++i) { + // Handle ties by averaging ranks + Eigen::Index j = i + 1; + double sum_ranks = j; + Eigen::Index count = 1; + + while (j < size && value_with_index[j].first == value_with_index[i].first) { + sum_ranks += j + 1; // Rank starts from 1 + ++j; + ++count; + } + double avg_rank = sum_ranks / count; + boost::math::normal_distribution dist; + for (std::size_t k = i; k < j; ++k) { + double p = (avg_rank - 0.375) / (size + 0.25); + const Eigen::Index index = value_with_index[k].second; + rank_matrix(index) = boost::math::quantile(dist, p); + } + i = j - 1; // Skip over tied elements + } + return rank_matrix; +} + +} // namespace analyze +} // namespace stan + +#endif diff --git a/src/stan/analyze/mcmc/rhat.hpp b/src/stan/analyze/mcmc/rhat.hpp new file mode 100644 index 00000000000..ed5ed5cfc7b --- /dev/null +++ b/src/stan/analyze/mcmc/rhat.hpp @@ -0,0 +1,50 @@ +#ifndef STAN_ANALYZE_MCMC_RHAT_HPP +#define STAN_ANALYZE_MCMC_RHAT_HPP + +#include +#include +#include +#include +#include + +namespace stan { +namespace analyze { + +/** + * Computes square root of marginal posterior variance of the estimand by the + * weighted average of within-chain variance W and between-chain variance B. + * + * @param chains stores chains in columns + * @return square root of ((N-1)/N)W + B/N + */ +inline double rhat(const Eigen::MatrixXd& chains) { + const Eigen::Index num_chains = chains.cols(); + const Eigen::Index num_draws = chains.rows(); + + Eigen::RowVectorXd within_chain_means = chains.colwise().mean(); + double across_chain_mean = within_chain_means.mean(); + double between_variance + = num_draws + * (within_chain_means.array() - across_chain_mean).square().sum() + / (num_chains - 1); + double within_variance = + // Divide each row by chains and get sum of squares for each chain + // (getting a vector back) + ((chains.rowwise() - within_chain_means) + .array() + .square() + .colwise() + // divide each sum of square by num_draws, sum the sum of squares, + // and divide by num chains + .sum() + / (num_draws - 1.0)) + .sum() + / num_chains; + + return sqrt((between_variance / within_variance + num_draws - 1) / num_draws); +} + +} // namespace analyze +} // namespace stan + +#endif diff --git a/src/stan/analyze/mcmc/split_chains.hpp b/src/stan/analyze/mcmc/split_chains.hpp index 24726291d62..fa89f6dc293 100644 --- a/src/stan/analyze/mcmc/split_chains.hpp +++ b/src/stan/analyze/mcmc/split_chains.hpp @@ -1,6 +1,7 @@ #ifndef STAN_ANALYZE_MCMC_SPLIT_CHAINS_HPP #define STAN_ANALYZE_MCMC_SPLIT_CHAINS_HPP +#include #include #include #include @@ -8,6 +9,63 @@ namespace stan { namespace analyze { +/** + * Splits each chain into two chains of equal length. When the + * number of total draws N is odd, the (N+1)/2th draw is ignored. + * + * @param chains vector of per-chain sample matrices + * @param index matrix column for parameter of interest + * @return samples matrix, shape (num_iters/2, num_chains*2) + */ +inline Eigen::MatrixXd split_chains(const std::vector& chains, + const int index) { + size_t num_chains = chains.size(); + size_t num_draws = chains[0].rows(); + size_t half = std::floor(num_draws / 2.0); + size_t tail_start = std::floor((num_draws + 1) / 2.0); + + Eigen::MatrixXd split_draws_matrix(half, num_chains * 2); + int split_i = 0; + for (std::size_t i = 0; i < num_chains; ++i) { + Eigen::Map head_block(chains[i].col(index).data(), + half); + Eigen::Map tail_block( + chains[i].col(index).data() + tail_start, half); + + split_draws_matrix.col(split_i) = head_block; + split_draws_matrix.col(split_i + 1) = tail_block; + split_i += 2; + } + return split_draws_matrix; +} + +/** + * Splits each chain into two chains of equal length. When the + * number of total draws N is odd, the (N+1)/2th draw is ignored. + * + * @param samples matrix of per-chain samples, shape (num_iters, num_chains) + * @return samples matrix reshaped as (num_iters/2, num_chains*2) + */ +inline Eigen::MatrixXd split_chains(const Eigen::MatrixXd& samples) { + size_t num_chains = samples.cols(); + size_t num_draws = samples.rows(); + size_t half = std::floor(num_draws / 2.0); + size_t tail_start = std::floor((num_draws + 1) / 2.0); + + Eigen::MatrixXd split_draws_matrix(half, num_chains * 2); + int split_i = 0; + for (std::size_t i = 0; i < num_chains; ++i) { + Eigen::Map head_block(samples.col(i).data(), half); + Eigen::Map tail_block( + samples.col(i).data() + tail_start, half); + + split_draws_matrix.col(split_i) = head_block; + split_draws_matrix.col(split_i + 1) = tail_block; + split_i += 2; + } + return split_draws_matrix; +} + /** * Splits each chain into two chains of equal length. When the * number of total draws N is odd, the (N+1)/2th draw is ignored. diff --git a/src/stan/analyze/mcmc/split_rank_normalized_ess.hpp b/src/stan/analyze/mcmc/split_rank_normalized_ess.hpp new file mode 100644 index 00000000000..e0c6316cc74 --- /dev/null +++ b/src/stan/analyze/mcmc/split_rank_normalized_ess.hpp @@ -0,0 +1,60 @@ +#ifndef STAN_ANALYZE_MCMC_SPLIT_RANK_NORMALIZED_ESS_HPP +#define STAN_ANALYZE_MCMC_SPLIT_RANK_NORMALIZED_ESS_HPP + +#include +#include +#include +#include +#include +#include + +namespace stan { +namespace analyze { + +/** + * Computes the split effective sample size (split ESS) using rank based + * diagnostic for a set of per-chain draws. Based on paper + * https://arxiv.org/abs/1903.08008 Computes bulk ESS over entire sample, + * and tail ESS over the 0.05 and 0.95 quantiles. + * + * When the number of total draws N is odd, the last draw is ignored. + * + * See more details in Stan reference manual section "Potential + * Scale Reduction". http://mc-stan.org/users/documentation + + * @param chains matrix of per-chain draws, num_iters X chain + * @return pair ESS_bulk, ESS_tail + */ +inline std::pair split_rank_normalized_ess( + const Eigen::MatrixXd& chains) { + Eigen::MatrixXd split_draws_matrix = split_chains(chains); + if (!is_finite_and_varies(split_draws_matrix) + || split_draws_matrix.rows() < 4) { + return std::make_pair(std::numeric_limits::quiet_NaN(), + std::numeric_limits::quiet_NaN()); + } + double ess_bulk = ess(rank_transform(split_draws_matrix)); + Eigen::MatrixXd q05 = (split_draws_matrix.array() + <= math::quantile(split_draws_matrix.reshaped(), 0.05)) + .cast(); + double ess_tail_05 = ess(q05); + Eigen::MatrixXd q95 = (split_draws_matrix.array() + >= math::quantile(split_draws_matrix.reshaped(), 0.95)) + .cast(); + double ess_tail_95 = ess(q95); + + double ess_tail; + if (std::isnan(ess_tail_05)) { + ess_tail = ess_tail_95; + } else if (std::isnan(ess_tail_95)) { + ess_tail = ess_tail_05; + } else { + ess_tail = std::min(ess_tail_05, ess_tail_95); + } + return std::make_pair(ess_bulk, ess_tail); +} + +} // namespace analyze +} // namespace stan + +#endif diff --git a/src/stan/analyze/mcmc/split_rank_normalized_rhat.hpp b/src/stan/analyze/mcmc/split_rank_normalized_rhat.hpp new file mode 100644 index 00000000000..50cce0b64d6 --- /dev/null +++ b/src/stan/analyze/mcmc/split_rank_normalized_rhat.hpp @@ -0,0 +1,47 @@ +#ifndef STAN_ANALYZE_MCMC_SPLIT_RANK_NORMALIZED_RHAT_HPP +#define STAN_ANALYZE_MCMC_SPLIT_RANK_NORMALIZED_RHAT_HPP + +#include +#include +#include +#include +#include +#include +#include + +namespace stan { +namespace analyze { + +/** + * Computes the split potential scale reduction (split Rhat) using rank based + * diagnostic for a set of per-chain draws. Based on paper + * https://arxiv.org/abs/1903.08008 + * + * When the number of total draws N is odd, the last draw is ignored. + * + * See more details in Stan reference manual section "Potential + * Scale Reduction". http://mc-stan.org/users/documentation + + * @param chains matrix of per-chain samples, num_iters X chain + * @return potential scale reduction for the specified parameter + */ +inline std::pair split_rank_normalized_rhat( + const Eigen::MatrixXd& chains) { + Eigen::MatrixXd split_draws_matrix = split_chains(chains); + if (!is_finite_and_varies(split_draws_matrix)) { + return std::make_pair(std::numeric_limits::quiet_NaN(), + std::numeric_limits::quiet_NaN()); + } + double rhat_bulk = rhat(rank_transform(split_draws_matrix)); + // zero-center the draws at the median + double rhat_tail = rhat( + rank_transform((split_draws_matrix.array() + - math::quantile(split_draws_matrix.reshaped(), 0.5)) + .abs())); + return std::make_pair(rhat_bulk, rhat_tail); +} + +} // namespace analyze +} // namespace stan + +#endif diff --git a/src/stan/io/deserializer.hpp b/src/stan/io/deserializer.hpp index ae3490010c8..f8f1a2d8c7f 100644 --- a/src/stan/io/deserializer.hpp +++ b/src/stan/io/deserializer.hpp @@ -382,11 +382,8 @@ class deserializer { template inline auto read_constrain_lb(const LB& lb, LP& lp, Sizes... sizes) { - if (Jacobian) { - return stan::math::lb_constrain(this->read(sizes...), lb, lp); - } else { - return stan::math::lb_constrain(this->read(sizes...), lb); - } + return stan::math::lb_constrain(this->read(sizes...), lb, + lp); } /** @@ -408,11 +405,8 @@ class deserializer { template inline auto read_constrain_ub(const UB& ub, LP& lp, Sizes... sizes) { - if (Jacobian) { - return stan::math::ub_constrain(this->read(sizes...), ub, lp); - } else { - return stan::math::ub_constrain(this->read(sizes...), ub); - } + return stan::math::ub_constrain(this->read(sizes...), ub, + lp); } /** @@ -437,11 +431,8 @@ class deserializer { typename... Sizes> inline auto read_constrain_lub(const LB& lb, const UB& ub, LP& lp, Sizes... sizes) { - if (Jacobian) { - return stan::math::lub_constrain(this->read(sizes...), lb, ub, lp); - } else { - return stan::math::lub_constrain(this->read(sizes...), lb, ub); - } + return stan::math::lub_constrain(this->read(sizes...), lb, + ub, lp); } /** @@ -470,14 +461,8 @@ class deserializer { inline auto read_constrain_offset_multiplier(const Offset& offset, const Mult& multiplier, LP& lp, Sizes... sizes) { - using stan::math::offset_multiplier_constrain; - if (Jacobian) { - return offset_multiplier_constrain(this->read(sizes...), offset, - multiplier, lp); - } else { - return offset_multiplier_constrain(this->read(sizes...), offset, - multiplier); - } + return stan::math::offset_multiplier_constrain( + this->read(sizes...), offset, multiplier, lp); } /** @@ -501,12 +486,8 @@ class deserializer { template * = nullptr> inline auto read_constrain_unit_vector(LP& lp, Sizes... sizes) { - using stan::math::unit_vector_constrain; - if (Jacobian) { - return math::eval(unit_vector_constrain(this->read(sizes...), lp)); - } else { - return math::eval(unit_vector_constrain(this->read(sizes...))); - } + return stan::math::eval(stan::math::unit_vector_constrain( + this->read(sizes...), lp)); } /** @@ -562,13 +543,9 @@ class deserializer { template * = nullptr> inline auto read_constrain_simplex(LP& lp, size_t size) { - using stan::math::simplex_constrain; stan::math::check_positive("read_simplex", "size", size); - if (Jacobian) { - return simplex_constrain(this->read(size - 1), lp); - } else { - return simplex_constrain(this->read(size - 1)); - } + return stan::math::simplex_constrain(this->read(size - 1), + lp); } /** @@ -604,6 +581,64 @@ class deserializer { return ret; } + /** + * Return the next zero sum vector of the specified size (using one fewer + * unconstrained scalars), incrementing the specified reference with the + * log absolute Jacobian determinant (no adjustment, in this case). + * + *

See stan::math::sum_to_zero_constrain(Eigen::Matrix,T&). + * + * @tparam Ret The type to return. + * @tparam Jacobian Whether to increment the log of the absolute Jacobian + * determinant of the transform. + * @tparam LP Type of log probability. + * @tparam Sizes A parameter pack of integral types. + * @param lp The reference to the variable holding the log + * @param size Number of cells in zero sum vector to generate. + * @return The next zero sum of the specified size. + * @throws std::invalid_argument if number of dimensions (`k`) is zero + */ + template * = nullptr> + inline auto read_constrain_sum_to_zero(LP& lp, size_t size) { + stan::math::check_positive("read_sum_to_zero", "size", size); + return stan::math::sum_to_zero_constrain( + this->read(size - 1), lp); + } + + /** + * Return the next zero sum vector of the specified size (using one fewer + * unconstrained scalars), incrementing the specified reference with the + * log absolute Jacobian determinant (no adjustment, in this case). + * + *

See stan::math::sum_to_zero_constrain(Eigen::Matrix,T&). + * + * @tparam Ret The type to return. + * @tparam Jacobian Whether to increment the log of the absolute Jacobian + * determinant of the transform. + * @tparam LP Type of log probability. + * @tparam Sizes A parameter pack of integral types. + * @param lp The reference to the variable holding the log + * probability to increment. + * @param vecsize The size of the return vector. + * @param sizes Pack of integrals to use to construct the return's type. + * @return The next zero sum of the specified size. + * @throws std::invalid_argument if number of dimensions (`k`) is zero + */ + template * = nullptr> + inline auto read_constrain_sum_to_zero(LP& lp, const size_t vecsize, + Sizes... sizes) { + std::decay_t ret; + ret.reserve(vecsize); + for (size_t i = 0; i < vecsize; ++i) { + ret.emplace_back( + this->read_constrain_sum_to_zero, Jacobian>( + lp, sizes...)); + } + return ret; + } + /** * Return the next ordered vector of the specified * size, incrementing the specified reference with the log @@ -624,12 +659,8 @@ class deserializer { template * = nullptr> inline auto read_constrain_ordered(LP& lp, Sizes... sizes) { - using stan::math::ordered_constrain; - if (Jacobian) { - return ordered_constrain(this->read(sizes...), lp); - } else { - return ordered_constrain(this->read(sizes...)); - } + return stan::math::ordered_constrain(this->read(sizes...), + lp); } /** @@ -684,12 +715,8 @@ class deserializer { template * = nullptr> inline auto read_constrain_positive_ordered(LP& lp, Sizes... sizes) { - using stan::math::positive_ordered_constrain; - if (Jacobian) { - return positive_ordered_constrain(this->read(sizes...), lp); - } else { - return positive_ordered_constrain(this->read(sizes...)); - } + return stan::math::positive_ordered_constrain( + this->read(sizes...), lp); } /** @@ -745,17 +772,10 @@ class deserializer { require_matrix_t* = nullptr> inline auto read_constrain_cholesky_factor_cov(LP& lp, Eigen::Index M, Eigen::Index N) { - if (Jacobian) { - return stan::math::cholesky_factor_constrain( - this->read>((N * (N + 1)) / 2 - + (M - N) * N), - M, N, lp); - } else { - return stan::math::cholesky_factor_constrain( - this->read>((N * (N + 1)) / 2 - + (M - N) * N), - M, N); - } + return stan::math::cholesky_factor_constrain( + this->read>((N * (N + 1)) / 2 + + (M - N) * N), + M, N, lp); } /** @@ -811,16 +831,9 @@ class deserializer { template * = nullptr> inline auto read_constrain_cholesky_factor_corr(LP& lp, Eigen::Index K) { - using stan::math::cholesky_corr_constrain; - if (Jacobian) { - return cholesky_corr_constrain( - this->read>((K * (K - 1)) / 2), - K, lp); - } else { - return cholesky_corr_constrain( - this->read>((K * (K - 1)) / 2), - K); - } + return stan::math::cholesky_corr_constrain( + this->read>((K * (K - 1)) / 2), K, + lp); } /** @@ -875,18 +888,9 @@ class deserializer { template * = nullptr> inline auto read_constrain_cov_matrix(LP& lp, Eigen::Index k) { - using stan::math::cov_matrix_constrain; - if (Jacobian) { - return cov_matrix_constrain( - this->read>(k - + (k * (k - 1)) / 2), - k, lp); - } else { - return cov_matrix_constrain( - this->read>(k - + (k * (k - 1)) / 2), - k); - } + return stan::math::cov_matrix_constrain( + this->read>(k + (k * (k - 1)) / 2), + k, lp); } /** @@ -939,16 +943,9 @@ class deserializer { require_not_std_vector_t* = nullptr, require_matrix_t* = nullptr> inline auto read_constrain_corr_matrix(LP& lp, Eigen::Index k) { - using stan::math::corr_matrix_constrain; - if (Jacobian) { - return corr_matrix_constrain( - this->read>((k * (k - 1)) / 2), - k, lp); - } else { - return corr_matrix_constrain( - this->read>((k * (k - 1)) / 2), - k); - } + return stan::math::corr_matrix_constrain( + this->read>((k * (k - 1)) / 2), k, + lp); } /** @@ -981,6 +978,118 @@ class deserializer { return ret; } + /** + * Return the next object transformed to a matrix with simplexes along the + * columns + * + *

See stan::math::stochastic_column_constrain(T,T&). + * + * @tparam Ret The type to return. + * @tparam Jacobian Whether to increment the log of the absolute Jacobian + * determinant of the transform. + * @tparam LP Type of log probability. + * @param lp The reference to the variable holding the log + * probability to increment. + * @param rows Rows of matrix + * @param cols Cols of matrix + */ + template * = nullptr, + require_matrix_t* = nullptr> + inline auto read_constrain_stochastic_column(LP& lp, Eigen::Index rows, + Eigen::Index cols) { + return stan::math::stochastic_column_constrain( + this->read>(rows - 1, cols), lp); + } + + /** + * Specialization of \ref read_constrain_stochastic_column for `std::vector` + * return types. + * + *

See stan::math::stochastic_column_constrain(T,T&). + * + * @tparam Ret The type to return. + * @tparam Jacobian Whether to increment the log of the absolute Jacobian + * determinant of the transform. + * @tparam LP Type of log probability. + * @tparam Sizes A parameter pack of integral types. + * @param lp The reference to the variable holding the log + * probability to increment. + * @param vecsize The size of the return vector. + * @param sizes Pack of integrals to use to construct the return's type. + * @return Standard vector of matrices transformed to have simplixes along the + * columns. + */ + template * = nullptr> + inline auto read_constrain_stochastic_column(LP& lp, const size_t vecsize, + Sizes... sizes) { + std::decay_t ret; + ret.reserve(vecsize); + for (size_t i = 0; i < vecsize; ++i) { + ret.emplace_back( + this->read_constrain_stochastic_column, Jacobian>( + lp, sizes...)); + } + return ret; + } + + /** + * Return the next object transformed to a matrix with simplexes along the + * rows + * + *

See stan::math::stochastic_row_constrain(T,T&). + * + * @tparam Ret The type to return. + * @tparam Jacobian Whether to increment the log of the absolute Jacobian + * determinant of the transform. + * @tparam LP Type of log probability. + * @param lp The reference to the variable holding the log + * probability to increment. + * @param rows Rows of matrix + * @param cols Cols of matrix + */ + template * = nullptr, + require_matrix_t* = nullptr> + inline auto read_constrain_stochastic_row(LP& lp, Eigen::Index rows, + Eigen::Index cols) { + return stan::math::stochastic_row_constrain( + this->read>(rows, cols - 1), lp); + } + + /** + * Specialization of \ref read_constrain_stochastic_row for `std::vector` + * return types. + * + *

See stan::math::stochastic_row_constrain(T,T&). + * + * @tparam Ret The type to return. + * @tparam Jacobian Whether to increment the log of the absolute Jacobian + * determinant of the transform. + * @tparam LP Type of log probability. + * @tparam Sizes A parameter pack of integral types. + * @param lp The reference to the variable holding the log + * probability to increment. + * @param vecsize The size of the return vector. + * @param sizes Pack of integrals to use to construct the return's type. + * @return Standard vector of matrices transformed to have simplixes along the + * columns. + */ + template * = nullptr> + inline auto read_constrain_stochastic_row(LP& lp, const size_t vecsize, + Sizes... sizes) { + std::decay_t ret; + ret.reserve(vecsize); + for (size_t i = 0; i < vecsize; ++i) { + ret.emplace_back( + this->read_constrain_stochastic_row, Jacobian>( + lp, sizes...)); + } + return ret; + } + /** * Read a serialized lower bounded variable and unconstrain it * @@ -1109,6 +1218,37 @@ class deserializer { return ret; } + /** + * Read a serialized sum_to_zero vector and unconstrain it + * + * @tparam Ret Type of output + * @return Unconstrained vector + */ + template * = nullptr> + inline auto read_free_sum_to_zero(size_t size) { + return stan::math::sum_to_zero_free(this->read(size)); + } + + /** + * Read serialized zero-sum vectors and unconstrain them + * + * @tparam Ret Type of output + * @tparam Sizes Types of dimensions of output + * @param vecsize Vector size + * @param sizes dimensions + * @return Unconstrained vectors + */ + template * = nullptr> + inline auto read_free_sum_to_zero(size_t vecsize, Sizes... sizes) { + std::decay_t ret; + ret.reserve(vecsize); + for (size_t i = 0; i < vecsize; ++i) { + ret.emplace_back(read_free_sum_to_zero>(sizes...)); + } + return ret; + } + /** * Read a serialized ordered and unconstrain it * @@ -1301,6 +1441,73 @@ class deserializer { } return ret; } + + /** + * Read a serialized column simplex matrix and unconstrain it + * + * @tparam Ret Type of output + * @param rows Rows of matrix + * @param cols Cols of matrix + * @return Unconstrained matrix + */ + template * = nullptr> + inline auto read_free_stochastic_column(size_t rows, size_t cols) { + return stan::math::stochastic_column_free(this->read(rows, cols)); + } + + /** + * Read serialized column simplex matrices and unconstrain them + * + * @tparam Ret Type of output + * @tparam Sizes Types of dimensions of output + * @param vecsize Vector size + * @param sizes dimensions + * @return Unconstrained matrices + */ + template * = nullptr> + inline auto read_free_stochastic_column(size_t vecsize, Sizes... sizes) { + std::decay_t ret; + ret.reserve(vecsize); + for (size_t i = 0; i < vecsize; ++i) { + ret.emplace_back( + read_free_stochastic_column>(sizes...)); + } + return ret; + } + + /** + * Read a serialized row simplex matrix and unconstrain it + * + * @tparam Ret Type of output + * @param rows Rows of matrix + * @param cols Cols of matrix + * @return Unconstrained matrix + */ + template * = nullptr> + inline auto read_free_stochastic_row(size_t rows, size_t cols) { + return stan::math::stochastic_row_free(this->read(rows, cols)); + } + + /** + * Read serialized row simplex matrices and unconstrain them + * + * @tparam Ret Type of output + * @tparam Sizes Types of dimensions of output + * @param vecsize Vector size + * @param sizes dimensions + * @return Unconstrained matrices + */ + template * = nullptr> + inline auto read_free_stochastic_row(size_t vecsize, Sizes... sizes) { + std::decay_t ret; + ret.reserve(vecsize); + for (size_t i = 0; i < vecsize; ++i) { + ret.emplace_back(read_free_stochastic_row>(sizes...)); + } + return ret; + } }; } // namespace io diff --git a/src/stan/io/serializer.hpp b/src/stan/io/serializer.hpp index 08817b68b4a..97a3c8ba3b3 100644 --- a/src/stan/io/serializer.hpp +++ b/src/stan/io/serializer.hpp @@ -347,6 +347,31 @@ class serializer { } } + /** + * Write a serialized sum-to-zero vector and unconstrain it + * + * @tparam Vec An Eigen type with either fixed rows or columns at compile + * time. + * @param x The vector to read from. + */ + template * = nullptr> + inline void write_free_sum_to_zero(const Vec& x) { + this->write(stan::math::sum_to_zero_free(x)); + } + + /** + * Write serialized zero sum vectors and unconstrain them + * + * @tparam StdVec A `std:vector` + * @param x An std vector. + */ + template * = nullptr> + inline void write_free_sum_to_zero(const StdVec& x) { + for (const auto& ret_i : x) { + this->write_free_sum_to_zero(ret_i); + } + } + /** * Write a serialized ordered and unconstrain it * @@ -492,6 +517,54 @@ class serializer { this->write_free_corr_matrix(ret_i); } } + + /** + * Read a serialized column simplex matrix and unconstrain it + * + * @tparam Mat An Eigen matrix + * @param x A column stochastic eigen matrix + */ + template * = nullptr> + inline void write_free_stochastic_column(Mat&& x) { + this->write(stan::math::stochastic_column_free(x)); + } + + /** + * Read serialized column simplex matrices and unconstrain them + * + * @tparam StdVec A standard vector of Eigen matrices + * @param x A vector of column stochastic Eigen matrices + */ + template * = nullptr> + inline void write_free_stochastic_column(StdVec&& x) { + for (auto&& x_i : x) { + this->write_free_stochastic_column(x_i); + } + } + + /** + * Read a serialized row simplex matrix and unconstrain it + * + * @tparam Mat An Eigen matrix + * @param x A row stochastic eigen matrix + */ + template * = nullptr> + inline void write_free_stochastic_row(Mat&& x) { + this->write(stan::math::stochastic_row_free(x)); + } + + /** + * Read serialized row simplex matrices and unconstrain them + * + * @tparam StdVec A standard vector of Eigen matrices + * @param x A vector of row stochastic Eigen matrices + */ + template * = nullptr> + inline void write_free_stochastic_row(StdVec&& x) { + for (auto&& x_i : x) { + this->write_free_stochastic_row(x_i); + } + } }; } // namespace io diff --git a/src/stan/io/stan_csv_reader.hpp b/src/stan/io/stan_csv_reader.hpp index 5ded3d49c9d..22a11947a44 100644 --- a/src/stan/io/stan_csv_reader.hpp +++ b/src/stan/io/stan_csv_reader.hpp @@ -29,8 +29,6 @@ inline void prettify_stan_csv_name(std::string& variable) { } } -// FIXME: should consolidate with the options from -// the command line in stan::lang struct stan_csv_metadata { int stan_version_major; int stan_version_minor; @@ -47,6 +45,7 @@ struct stan_csv_metadata { bool save_warmup; size_t thin; bool append_samples; + std::string method; std::string algorithm; std::string engine; int max_depth; @@ -64,8 +63,9 @@ struct stan_csv_metadata { num_samples(0), num_warmup(0), save_warmup(false), - thin(0), + thin(1), append_samples(false), + method(""), algorithm(""), engine(""), max_depth(10) {} @@ -101,13 +101,12 @@ class stan_csv_reader { stan_csv_reader() {} ~stan_csv_reader() {} - static bool read_metadata(std::istream& in, stan_csv_metadata& metadata, - std::ostream* out) { + static void read_metadata(std::istream& in, stan_csv_metadata& metadata) { std::stringstream ss; std::string line; if (in.peek() != '#') - return false; + return; while (in.peek() == '#') { std::getline(in, line); ss << line << '\n'; @@ -161,9 +160,15 @@ class stan_csv_reader { metadata.model = value; } else if (name.compare("num_samples") == 0) { std::stringstream(value) >> metadata.num_samples; + } else if (name.compare("output_samples") == 0) { // ADVI config name + std::stringstream(value) >> metadata.num_samples; } else if (name.compare("num_warmup") == 0) { std::stringstream(value) >> metadata.num_warmup; } else if (name.compare("save_warmup") == 0) { + // cmdstan args can be "true" and "false", was "1", "0" + if (value.compare("true") == 0) { + value = "1"; + } std::stringstream(value) >> metadata.save_warmup; } else if (name.compare("thin") == 0) { std::stringstream(value) >> metadata.thin; @@ -177,6 +182,8 @@ class stan_csv_reader { metadata.random_seed = false; } else if (name.compare("append_samples") == 0) { std::stringstream(value) >> metadata.append_samples; + } else if (name.compare("method") == 0) { + metadata.method = value; } else if (name.compare("algorithm") == 0) { metadata.algorithm = value; } else if (name.compare("engine") == 0) { @@ -185,14 +192,10 @@ class stan_csv_reader { std::stringstream(value) >> metadata.max_depth; } } - if (ss.good() == true) - return false; - - return true; } // read_metadata static bool read_header(std::istream& in, std::vector& header, - std::ostream* out, bool prettify_name = true) { + bool prettify_name = true) { std::string line; if (!std::isalpha(in.peek())) @@ -216,66 +219,57 @@ class stan_csv_reader { return true; } - static bool read_adaptation(std::istream& in, stan_csv_adaptation& adaptation, - std::ostream* out) { + static void read_adaptation(std::istream& in, + stan_csv_adaptation& adaptation) { std::stringstream ss; std::string line; int lines = 0; - if (in.peek() != '#' || in.good() == false) - return false; - + return; while (in.peek() == '#') { std::getline(in, line); ss << line << std::endl; lines++; } ss.seekg(std::ios_base::beg); + if (lines < 2) + return; - if (lines < 4) - return false; - - char comment; // Buffer for comment indicator, # + std::getline(ss, line); // comment adaptation terminated - // Skip first two lines - std::getline(ss, line); - - // Stepsize - std::getline(ss, line, '='); + // parse stepsize + std::getline(ss, line, '='); // stepsize boost::trim(line); ss >> adaptation.step_size; + if (lines == 2) // ADVI reports stepsize, no metric + return; - // Metric parameters - std::getline(ss, line); - std::getline(ss, line); - std::getline(ss, line); + std::getline(ss, line); // consume end of stepsize line + std::getline(ss, line); // comment elements of mass matrix + std::getline(ss, line); // diagonal metric or row 1 of dense metric int rows = lines - 3; int cols = std::count(line.begin(), line.end(), ',') + 1; adaptation.metric.resize(rows, cols); + char comment; // Buffer for comment indicator, # + // parse metric, row by row, element by element for (int row = 0; row < rows; row++) { std::stringstream line_ss; line_ss.str(line); line_ss >> comment; - for (int col = 0; col < cols; col++) { std::string token; std::getline(line_ss, token, ','); boost::trim(token); std::stringstream(token) >> adaptation.metric(row, col); } - std::getline(ss, line); // Read in next line + std::getline(ss, line); } - - if (ss.good()) - return false; - else - return true; } static bool read_samples(std::istream& in, Eigen::MatrixXd& samples, - stan_csv_timing& timing, std::ostream* out) { + stan_csv_timing& timing) { std::stringstream ss; std::string line; @@ -283,14 +277,13 @@ class stan_csv_reader { int cols = -1; if (in.peek() == '#' || in.good() == false) - return false; + return false; // need at least one data row while (in.good()) { bool comment_line = (in.peek() == '#'); bool empty_line = (in.peek() == '\n'); std::getline(in, line); - if (empty_line) continue; if (!line.length()) @@ -316,11 +309,10 @@ class stan_csv_reader { if (cols == -1) { cols = current_cols; } else if (cols != current_cols) { - if (out) - *out << "Error: expected " << cols << " columns, but found " - << current_cols << " instead for row " << rows + 1 - << std::endl; - return false; + std::stringstream msg; + msg << "Error: expected " << cols << " columns, but found " + << current_cols << " instead for row " << rows + 1; + throw std::invalid_argument(msg.str()); } rows++; } @@ -348,36 +340,45 @@ class stan_csv_reader { /** * Parses the file. * + * Throws exception if contents can't be parsed into header + data rows. + * + * Emits warning message + * * @param[in] in input stream to parse * @param[out] out output stream to send messages */ static stan_csv parse(std::istream& in, std::ostream* out) { stan_csv data; + std::string line; - if (!read_metadata(in, data.metadata, out)) { - if (out) - *out << "Warning: non-fatal error reading metadata" << std::endl; + read_metadata(in, data.metadata); + if (!read_header(in, data.header)) { + throw std::invalid_argument("Error: no column names found in csv file"); } - if (!read_header(in, data.header, out)) { - if (out) - *out << "Error: error reading header" << std::endl; - throw std::invalid_argument("Error with header of input file in parse"); + // skip warmup draws, if any + if (data.metadata.algorithm != "fixed_param" && data.metadata.num_warmup > 0 + && data.metadata.save_warmup) { + while (in.peek() != '#') { + std::getline(in, line); + } } - if (!read_adaptation(in, data.adaptation, out)) { - if (out) - *out << "Warning: non-fatal error reading adaptation data" << std::endl; + if (data.metadata.algorithm != "fixed_param") { + read_adaptation(in, data.adaptation); } data.timing.warmup = 0; data.timing.sampling = 0; - if (!read_samples(in, data.samples, data.timing, out)) { - if (out) - *out << "Warning: non-fatal error reading samples" << std::endl; + if (data.metadata.method == "variational") { + std::getline(in, line); // discard variational estimate } + if (!read_samples(in, data.samples, data.timing)) { + if (out) + *out << "No draws found" << std::endl; + } return data; } }; diff --git a/src/stan/mcmc/chains.hpp b/src/stan/mcmc/chains.hpp index a553fd36cdf..fdccc53cf5d 100644 --- a/src/stan/mcmc/chains.hpp +++ b/src/stan/mcmc/chains.hpp @@ -5,6 +5,7 @@ #include #include #include +#include #include #include #include @@ -558,7 +559,6 @@ class chains { return autocovariance(chain, index(name)); } - // FIXME: reimplement using autocorrelation. double effective_sample_size(const int index) const { int n_chains = num_chains(); std::vector draws(n_chains); @@ -588,6 +588,7 @@ class chains { = samples_(chain).col(index).bottomRows(n_kept_samples).data(); sizes[chain] = n_kept_samples; } + return analyze::compute_split_effective_sample_size(draws, sizes); } @@ -595,8 +596,7 @@ class chains { return split_effective_sample_size(index(name)); } - std::pair split_potential_scale_reduction_rank( - const int index) const { + double split_potential_scale_reduction(const int index) const { int n_chains = num_chains(); std::vector draws(n_chains); std::vector sizes(n_chains); @@ -608,32 +608,32 @@ class chains { sizes[chain] = n_kept_samples; } - return analyze::compute_split_potential_scale_reduction_rank(draws, sizes); + return analyze::compute_split_potential_scale_reduction(draws, sizes); } - double split_potential_scale_reduction(const int index) const { + double split_potential_scale_reduction(const std::string& name) const { + return split_potential_scale_reduction(index(name)); + } + + std::pair split_potential_scale_reduction_rank( + const int index) const { int n_chains = num_chains(); - std::vector draws(n_chains); - std::vector sizes(n_chains); - int n_kept_samples = 0; - for (int chain = 0; chain < n_chains; ++chain) { - n_kept_samples = num_kept_samples(chain); - draws[chain] - = samples_(chain).col(index).bottomRows(n_kept_samples).data(); - sizes[chain] = n_kept_samples; + int n_kept_samples = std::numeric_limits::max(); + for (size_t i = 0; i < n_chains; ++i) { + n_kept_samples = std::min(n_kept_samples, num_kept_samples(i)); } - - return analyze::compute_split_potential_scale_reduction(draws, sizes); + Eigen::MatrixXd chains(n_kept_samples, n_chains); + for (size_t i = 0; i < n_chains; ++i) { + auto bottom_rows = samples_(i).col(index).bottomRows(n_kept_samples); + chains.col(i) = bottom_rows.eval(); + } + return analyze::split_rank_normalized_rhat(chains); } std::pair split_potential_scale_reduction_rank( const std::string& name) const { return split_potential_scale_reduction_rank(index(name)); } - - double split_potential_scale_reduction(const std::string& name) const { - return split_potential_scale_reduction(index(name)); - } }; } // namespace mcmc diff --git a/src/stan/mcmc/chainset.hpp b/src/stan/mcmc/chainset.hpp new file mode 100644 index 00000000000..9eb9369621c --- /dev/null +++ b/src/stan/mcmc/chainset.hpp @@ -0,0 +1,486 @@ +#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 +#include + +namespace stan { +namespace mcmc { + +/** + * A 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. + * + * Calls stan::math::quantile which throws + * std::invalid_argument If any element of samples_vec is NaN, or size 0. + * and std::domain_error If `p<0` or `p>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] + 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. + * + * @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. + * + * @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); + 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. + * + * @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 mcse + */ + double mcse_mean(const int index) const { + return analyze::mcse_mean(samples(index)); + } + + /** + * 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 mcse + */ + 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 mcse_sd + */ + double mcse_sd(const int index) const { + return analyze::mcse_sd(samples(index)); + } + + /** + * 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 mcse_sd + */ + 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/stan/mcmc/hmc/base_hmc.hpp b/src/stan/mcmc/hmc/base_hmc.hpp index 80c26f75957..0d93b81a301 100644 --- a/src/stan/mcmc/hmc/base_hmc.hpp +++ b/src/stan/mcmc/hmc/base_hmc.hpp @@ -93,6 +93,12 @@ class base_hmc : public base_mcmc { void init_stepsize(callbacks::logger& logger) { ps_point z_init(this->z_); + // step size is meaningless in zero-dimensional space + if (this->z_.q.size() == 0) { + this->nom_epsilon_ = std::numeric_limits::quiet_NaN(); + return; + } + // Skip initialization for extreme step sizes if (this->nom_epsilon_ == 0 || this->nom_epsilon_ > 1e7 || std::isnan(this->nom_epsilon_)) diff --git a/src/stan/mcmc/hmc/hamiltonians/dense_e_point.hpp b/src/stan/mcmc/hmc/hamiltonians/dense_e_point.hpp index ac5b4c3eafd..5f3dcc9c89b 100644 --- a/src/stan/mcmc/hmc/hamiltonians/dense_e_point.hpp +++ b/src/stan/mcmc/hmc/hamiltonians/dense_e_point.hpp @@ -43,6 +43,8 @@ class dense_e_point : public ps_point { */ inline void write_metric(stan::callbacks::writer& writer) { writer("Elements of inverse mass matrix:"); + if (inv_e_metric_.rows() == 0) + writer(""); for (int i = 0; i < inv_e_metric_.rows(); ++i) { std::stringstream inv_e_metric_ss; inv_e_metric_ss << inv_e_metric_(i, 0); diff --git a/src/stan/mcmc/hmc/hamiltonians/diag_e_point.hpp b/src/stan/mcmc/hmc/hamiltonians/diag_e_point.hpp index f5a15de2cba..9b3ec53741c 100644 --- a/src/stan/mcmc/hmc/hamiltonians/diag_e_point.hpp +++ b/src/stan/mcmc/hmc/hamiltonians/diag_e_point.hpp @@ -44,7 +44,8 @@ class diag_e_point : public ps_point { inline void write_metric(stan::callbacks::writer& writer) { writer("Diagonal elements of inverse mass matrix:"); std::stringstream inv_e_metric_ss; - inv_e_metric_ss << inv_e_metric_(0); + if (inv_e_metric_.size() > 0) + inv_e_metric_ss << inv_e_metric_(0); for (int i = 1; i < inv_e_metric_.size(); ++i) inv_e_metric_ss << ", " << inv_e_metric_(i); writer(inv_e_metric_ss.str()); diff --git a/src/stan/model/indexing/rvalue.hpp b/src/stan/model/indexing/rvalue.hpp index 7a07726c336..3090ece9cc2 100644 --- a/src/stan/model/indexing/rvalue.hpp +++ b/src/stan/model/indexing/rvalue.hpp @@ -144,6 +144,7 @@ inline auto rvalue(Vec&& v, const char* name, index_uni idx) { * Types: vector[multi] = vector * * @tparam EigVec Eigen type with either dynamic rows or columns, but not both. + * @tparam MultiIndex A multi index. * @param[in] v Eigen vector type. * @param[in] name Name of variable * @param[in] idx Sequence of integers. @@ -151,18 +152,23 @@ inline auto rvalue(Vec&& v, const char* name, index_uni idx) { * @throw std::invalid_argument If the value size isn't the same as * the indexed size. */ -template * = nullptr> -inline auto rvalue(EigVec&& v, const char* name, const index_multi& idx) { +template * = nullptr, + require_same_t* = nullptr> +inline auto rvalue(EigVec&& v, const char* name, MultiIndex&& idx) { + using fwd_t = decltype(stan::math::to_ref(std::forward(v))); + const auto v_size = v.size(); + for (auto idx_i : idx.ns_) { + math::check_range("vector[multi] indexing", name, v_size, idx_i); + } return stan::math::make_holder( - [name, &idx](auto& v_ref) { - return plain_type_t::NullaryExpr( - idx.ns_.size(), [name, &idx, &v_ref](Eigen::Index i) { - math::check_range("vector[multi] indexing", name, v_ref.size(), - idx.ns_[i]); - return v_ref.coeff(idx.ns_[i] - 1); - }); + [](auto&& v_ref, auto&& idx_inner) { + Eigen::Map> idx2(idx_inner.ns_.data(), + idx_inner.ns_.size()); + return std::forward(v_ref)(idx2 - 1); }, - stan::math::to_ref(v)); + std::forward(stan::math::to_ref(std::forward(v))), + std::forward(idx)); } /** @@ -257,26 +263,27 @@ inline auto rvalue(Mat&& x, const char* name, index_uni idx) { * Types: matrix[multi] = matrix * * @tparam EigMat Eigen type with dynamic rows and columns. + * @tparam MultiIndex A multi index. * @param[in] x Eigen type * @param[in] name Name of variable * @param[in] idx A multi index for selecting a set of rows. * @throw std::out_of_range If any of the indices are out of bounds. */ -template * = nullptr> -inline plain_type_t rvalue(EigMat&& x, const char* name, - const index_multi& idx) { +template * = nullptr, + require_same_t* = nullptr> +inline auto rvalue(EigMat&& x, const char* name, MultiIndex&& idx) { for (int i = 0; i < idx.ns_.size(); ++i) { math::check_range("matrix[multi] row indexing", name, x.rows(), idx.ns_[i]); } return stan::math::make_holder( - [&idx](auto& x_ref) { - return plain_type_t::NullaryExpr( - idx.ns_.size(), x_ref.cols(), - [&idx, &x_ref](Eigen::Index i, Eigen::Index j) { - return x_ref.coeff(idx.ns_[i] - 1, j); - }); + [](auto&& x_ref, auto&& idx_inner) { + using vec_map = Eigen::Map>; + return x_ref((vec_map(idx_inner.ns_.data(), idx_inner.ns_.size()) - 1), + Eigen::all); }, - stan::math::to_ref(x)); + stan::math::to_ref(std::forward(x)), + std::forward(idx)); } /** @@ -419,29 +426,34 @@ inline auto rvalue(Mat&& x, const char* name, index_uni row_idx, * Types: matrix[uni, multi] = row vector * * @tparam EigMat Eigen type with dynamic rows and columns. + * @tparam MultiIndex A multi index. * @param[in] x Matrix to index. * @param[in] name Name of variable * @param[in] row_idx uni index for selecting rows. * @param[in] col_idx multi index for selecting cols. * @throw std::out_of_range If any of the indices are out of bounds. */ -template * = nullptr> -inline Eigen::Matrix, 1, Eigen::Dynamic> rvalue( - EigMat&& x, const char* name, index_uni row_idx, - const index_multi& col_idx) { +template * = nullptr, + require_same_t* = nullptr> +inline auto rvalue(EigMat&& x, const char* name, index_uni row_idx, + MultiIndex&& col_idx) { math::check_range("matrix[uni, multi] row indexing", name, x.rows(), row_idx.n_); + const auto x_cols = x.cols(); + for (auto idx_i : col_idx.ns_) { + math::check_range("matrix[uni, multi] column indexing", name, x_cols, + idx_i); + } return stan::math::make_holder( - [name, row_idx, &col_idx](auto& x_ref) { - return Eigen::Matrix, 1, Eigen::Dynamic>:: - NullaryExpr(col_idx.ns_.size(), [name, row_i = row_idx.n_ - 1, - &col_idx, &x_ref](Eigen::Index i) { - math::check_range("matrix[uni, multi] column indexing", name, - x_ref.cols(), col_idx.ns_[i]); - return x_ref.coeff(row_i, col_idx.ns_[i] - 1); - }); + [row_idx](auto&& x_ref, auto&& col_idx_inner) { + using vec_map = Eigen::Map>; + return x_ref( + row_idx.n_ - 1, + (vec_map(col_idx_inner.ns_.data(), col_idx_inner.ns_.size()) - 1)); }, - stan::math::to_ref(x)); + stan::math::to_ref(std::forward(x)), + std::forward(col_idx)); } /** @@ -451,31 +463,33 @@ inline Eigen::Matrix, 1, Eigen::Dynamic> rvalue( * Types: matrix[multi, uni] = vector * * @tparam EigMat Eigen type with dynamic rows and columns. + * @tparam MultiIndex A multi index. * @param[in] x Matrix to index. * @param[in] name Name of variable * @param[in] row_idx multi index for selecting rows. * @param[in] col_idx uni index for selecting cols. * @throw std::out_of_range If any of the indices are out of bounds. */ -template * = nullptr> -inline Eigen::Matrix, Eigen::Dynamic, 1> rvalue( - EigMat&& x, const char* name, const index_multi& row_idx, - index_uni col_idx) { +template * = nullptr, + require_same_t* = nullptr> +inline auto rvalue(EigMat&& x, const char* name, MultiIndex&& row_idx, + index_uni col_idx) { math::check_range("matrix[multi, uni] column indexing", name, x.cols(), col_idx.n_); - + const auto x_rows = x.rows(); + for (auto idx_i : row_idx.ns_) { + math::check_range("matrix[uni, multi] row indexing", name, x_rows, idx_i); + } return stan::math::make_holder( - [name, &row_idx, col_idx](auto& x_ref) { - return Eigen::Matrix, Eigen::Dynamic, 1>:: - NullaryExpr(row_idx.ns_.size(), - [name, &row_idx, col_i = col_idx.n_ - 1, - &x_ref](Eigen::Index i) { - math::check_range("matrix[multi, uni] row indexing", - name, x_ref.rows(), row_idx.ns_[i]); - return x_ref.coeff(row_idx.ns_[i] - 1, col_i); - }); + [col_idx](auto&& x_ref, auto&& row_idx_inner) { + using vec_map = Eigen::Map>; + return x_ref( + (vec_map(row_idx_inner.ns_.data(), row_idx_inner.ns_.size()) - 1), + col_idx.n_ - 1); }, - stan::math::to_ref(x)); + stan::math::to_ref(std::forward(x)), + std::forward(row_idx)); } /** @@ -485,32 +499,40 @@ inline Eigen::Matrix, Eigen::Dynamic, 1> rvalue( * Types: matrix[multi, multi] = matrix * * @tparam EigMat An eigen matrix + * @tparam RowMultiIndex A multi index. + * @tparam ColMultiIndex A multi index. * @param[in] x Matrix to index. * @param[in] name String form of expression being evaluated. * @param[in] row_idx multi index for selecting rows. * @param[in] col_idx multi index for selecting cols. * @return Result of indexing matrix. */ -template * = nullptr> -inline plain_type_t rvalue(EigMat&& x, const char* name, - const index_multi& row_idx, - const index_multi& col_idx) { - const auto& x_ref = stan::math::to_ref(x); +template * = nullptr, + require_same_t* = nullptr, + require_same_t* = nullptr> +inline auto rvalue(EigMat&& x, const char* name, RowMultiIndex&& row_idx, + ColMultiIndex&& col_idx) { const Eigen::Index rows = row_idx.ns_.size(); const Eigen::Index cols = col_idx.ns_.size(); - plain_type_t x_ret(rows, cols); - for (Eigen::Index j = 0; j < cols; ++j) { - for (Eigen::Index i = 0; i < rows; ++i) { - const Eigen::Index m = row_idx.ns_[i]; - const Eigen::Index n = col_idx.ns_[j]; - math::check_range("matrix[multi,multi] row indexing", name, x_ref.rows(), - m); - math::check_range("matrix[multi,multi] column indexing", name, - x_ref.cols(), n); - x_ret.coeffRef(i, j) = x_ref.coeff(m - 1, n - 1); - } + const auto x_rows = x.rows(); + const auto x_cols = x.cols(); + for (auto idx_i : row_idx.ns_) { + math::check_range("matrix[uni, multi] row indexing", name, x_rows, idx_i); } - return x_ret; + for (auto idx_j : col_idx.ns_) { + math::check_range("matrix[uni, multi] col indexing", name, x_cols, idx_j); + } + return stan::math::make_holder( + [](auto&& x_ref, auto&& row_idx_inner, auto&& col_idx_inner) { + using vec_map = Eigen::Map>; + return x_ref( + (vec_map(row_idx_inner.ns_.data(), row_idx_inner.ns_.size()) - 1), + (vec_map(col_idx_inner.ns_.data(), col_idx_inner.ns_.size()) - 1)); + }, + stan::math::to_ref(std::forward(x)), + std::forward(row_idx), + std::forward(col_idx)); } /** @@ -541,28 +563,37 @@ inline auto rvalue(Mat&& x, const char* name, const Idx& row_idx, * Types: matrix[Idx, multi] = matrix * * @tparam EigMat An eigen matrix - * @param[in] x Eigen matrix. + * @tparam Idx An index type + * @tparam MultiIndex A multi index + * @param[in] x Eigen matrix * @param[in] name String form of expression being evaluated. * @param[in] row_idx index for selecting rows. * @param[in] col_idx multi index for selecting cols. * @return Result of indexing matrix. */ -template * = nullptr, - require_not_same_t, index_uni>* = nullptr> -inline plain_type_t rvalue(EigMat&& x, const char* name, - const Idx& row_idx, - const index_multi& col_idx) { - const auto& x_ref = stan::math::to_ref(x); - const int rows = rvalue_index_size(row_idx, x_ref.rows()); - plain_type_t x_ret(rows, col_idx.ns_.size()); - for (int j = 0; j < col_idx.ns_.size(); ++j) { - const Eigen::Index n = col_idx.ns_[j]; - math::check_range("matrix[..., multi] column indexing", name, x_ref.cols(), - n); - x_ret.col(j) = rvalue(x_ref.col(n - 1), name, row_idx); + require_not_same_t, index_uni>* = nullptr, + require_not_same_t, index_multi>* = nullptr, + require_same_t* = nullptr> +inline auto rvalue(EigMat&& x, const char* name, Idx&& row_idx, + MultiIndex&& col_idx) { + const auto x_cols = x.cols(); + for (auto idx_j : col_idx.ns_) { + math::check_range("matrix[..., multi] column indexing", name, x_cols, + idx_j); } - return x_ret; + return stan::math::make_holder( + [name](auto&& x_ref, auto&& row_idx_inner, auto&& col_idx_inner) { + using vec_map = Eigen::Map>; + return rvalue( + x_ref(Eigen::all, + (vec_map(col_idx_inner.ns_.data(), col_idx_inner.ns_.size()) + - 1)), + name, std::forward(row_idx_inner)); + }, + stan::math::to_ref(std::forward(x)), std::forward(row_idx), + std::forward(col_idx)); } /** @@ -572,15 +603,16 @@ inline plain_type_t rvalue(EigMat&& x, const char* name, * * @tparam Mat An eigen matrix or `var_value` whose inner type is an Eigen * matrix. + * @tparam Idx An index type * @param[in] x type * @param[in] name Name of variable * @param[in] row_idx index for selecting rows. * @throw std::out_of_range If any of the indices are out of bounds. */ template * = nullptr> -inline auto rvalue(Mat&& x, const char* name, const Idx& row_idx, +inline auto rvalue(Mat&& x, const char* name, Idx&& row_idx, index_omni /*col_idx*/) { - return rvalue(std::forward(x), name, row_idx); + return rvalue(std::forward(x), name, std::forward(row_idx)); } /** @@ -599,12 +631,12 @@ inline auto rvalue(Mat&& x, const char* name, const Idx& row_idx, * @throw std::out_of_range If any of the indices are out of bounds. */ template * = nullptr> -inline auto rvalue(Mat&& x, const char* name, const Idx& row_idx, +inline auto rvalue(Mat&& x, const char* name, Idx&& row_idx, index_min col_idx) { const Eigen::Index col_size = x.cols() - (col_idx.min_ - 1); math::check_range("matrix[..., min] column indexing", name, x.cols(), col_idx.min_); - return rvalue(x.rightCols(col_size), name, row_idx); + return rvalue(x.rightCols(col_size), name, std::forward(row_idx)); } /** @@ -623,14 +655,14 @@ inline auto rvalue(Mat&& x, const char* name, const Idx& row_idx, * @throw std::out_of_range If any of the indices are out of bounds. */ template * = nullptr> -inline auto rvalue(Mat&& x, const char* name, const Idx& row_idx, +inline auto rvalue(Mat&& x, const char* name, Idx&& row_idx, index_max col_idx) { if (col_idx.max_ > 0) { math::check_range("matrix[..., max] column indexing", name, x.cols(), col_idx.max_); - return rvalue(x.leftCols(col_idx.max_), name, row_idx); + return rvalue(x.leftCols(col_idx.max_), name, std::forward(row_idx)); } else { - return rvalue(x.leftCols(0), name, row_idx); + return rvalue(x.leftCols(0), name, std::forward(row_idx)); } } @@ -650,7 +682,7 @@ inline auto rvalue(Mat&& x, const char* name, const Idx& row_idx, * @return Result of indexing matrix. */ template * = nullptr> -inline auto rvalue(Mat&& x, const char* name, const Idx& row_idx, +inline auto rvalue(Mat&& x, const char* name, Idx&& row_idx, index_min_max col_idx) { math::check_range("matrix[..., min_max] min column indexing", name, x.cols(), col_idx.min_); @@ -659,9 +691,9 @@ inline auto rvalue(Mat&& x, const char* name, const Idx& row_idx, math::check_range("matrix[..., min_max] max column indexing", name, x.cols(), col_idx.max_); return rvalue(x.middleCols(col_start, col_idx.max_ - col_start), name, - row_idx); + std::forward(row_idx)); } else { - return rvalue(x.middleCols(col_start, 0), name, row_idx); + return rvalue(x.middleCols(col_start, 0), name, std::forward(row_idx)); } } @@ -674,7 +706,7 @@ inline auto rvalue(Mat&& x, const char* name, const Idx& row_idx, * Types: std::vector[uni | Idx] : T[Idx] * * @tparam T Type of list elements. - * @tparam Idx Index list type for indexes after first index. + * @tparam Idxs Index list type for indexes after first index. * @param[in] v Container of list elements. * @param[in] name String form of expression being evaluated. * @param[in] idx1 first index. @@ -685,16 +717,16 @@ template * = nullptr, require_not_t>* = nullptr> inline auto rvalue(StdVec&& v, const char* name, index_uni idx1, - const Idxs&... idxs) { + Idxs&&... idxs) { math::check_range("array[uni, ...] index", name, v.size(), idx1.n_); - return rvalue(std::move(v[idx1.n_ - 1]), name, idxs...); + return rvalue(std::move(v[idx1.n_ - 1]), name, std::forward(idxs)...); } template * = nullptr> inline auto rvalue(StdVec& v, const char* name, index_uni idx1, - const Idxs&... idxs) { + Idxs&&... idxs) { math::check_range("array[uni, ...] index", name, v.size(), idx1.n_); - return rvalue(v[idx1.n_ - 1], name, idxs...); + return rvalue(v[idx1.n_ - 1], name, std::forward(idxs)...); } /** @@ -738,7 +770,7 @@ inline auto rvalue(StdVec&& v, const char* name, index_uni idx) { * * @tparam T Type of list elements. * @tparam Idx1 Index list type for first index. - * @tparam Idx2 Index list type for second index index. + * @tparam Idxs Index list type for second index index. * @param[in] v Container of list elements. * @param[in] name String form of expression being evaluated. * @param[in] idx1 first index @@ -749,7 +781,7 @@ template * = nullptr, require_not_same_t* = nullptr> inline auto rvalue(StdVec&& v, const char* name, const Idx1& idx1, - const Idxs&... idxs) { + Idxs&&... idxs) { using inner_type = plain_type_t; const auto index_size = rvalue_index_size(idx1, v.size()); diff --git a/src/stan/model/model_base.hpp b/src/stan/model/model_base.hpp index 1f6de972f98..eab24bec11e 100644 --- a/src/stan/model/model_base.hpp +++ b/src/stan/model/model_base.hpp @@ -1,11 +1,11 @@ #ifndef STAN_MODEL_MODEL_BASE_HPP #define STAN_MODEL_MODEL_BASE_HPP -#include -#include #ifdef STAN_MODEL_FVAR_VAR #include #endif +#include +#include #include #include #include diff --git a/src/stan/model/model_base_crtp.hpp b/src/stan/model/model_base_crtp.hpp index 1868b3e0567..cfb54a91ad5 100644 --- a/src/stan/model/model_base_crtp.hpp +++ b/src/stan/model/model_base_crtp.hpp @@ -1,10 +1,10 @@ #ifndef STAN_MODEL_MODEL_BASE_CRTP_HPP #define STAN_MODEL_MODEL_BASE_CRTP_HPP -#include #ifdef STAN_MODEL_FVAR_VAR #include #endif +#include #include #include #include diff --git a/src/stan/model/model_header.hpp b/src/stan/model/model_header.hpp index 34948377f74..a3896ecc6cb 100644 --- a/src/stan/model/model_header.hpp +++ b/src/stan/model/model_header.hpp @@ -1,14 +1,14 @@ #ifndef STAN_MODEL_MODEL_HEADER_HPP #define STAN_MODEL_MODEL_HEADER_HPP +#include +#include #include #include #include #include -#include -#include #include #include #include diff --git a/src/stan/services/sample/hmc_nuts_dense_e.hpp b/src/stan/services/sample/hmc_nuts_dense_e.hpp index 0fb818b19cb..84dcb2baa78 100644 --- a/src/stan/services/sample/hmc_nuts_dense_e.hpp +++ b/src/stan/services/sample/hmc_nuts_dense_e.hpp @@ -237,7 +237,8 @@ int hmc_nuts_dense_e(Model& model, size_t num_chains, util::run_sampler(samplers[i], model, cont_vectors[i], num_warmup, num_samples, num_thin, refresh, save_warmup, rngs[i], interrupt, logger, sample_writer[i], - diagnostic_writer[i], init_chain_id + i); + diagnostic_writer[i], init_chain_id + i, + num_chains); } }, tbb::simple_partitioner()); diff --git a/src/stan/services/sample/hmc_nuts_diag_e.hpp b/src/stan/services/sample/hmc_nuts_diag_e.hpp index bb789c01519..80a9d24aad9 100644 --- a/src/stan/services/sample/hmc_nuts_diag_e.hpp +++ b/src/stan/services/sample/hmc_nuts_diag_e.hpp @@ -232,7 +232,8 @@ int hmc_nuts_diag_e(Model& model, size_t num_chains, util::run_sampler(samplers[i], model, cont_vectors[i], num_warmup, num_samples, num_thin, refresh, save_warmup, rngs[i], interrupt, logger, sample_writer[i], - diagnostic_writer[i], init_chain_id + i); + diagnostic_writer[i], init_chain_id + i, + num_chains); } }, tbb::simple_partitioner()); diff --git a/src/stan/services/util/validate_dense_inv_metric.hpp b/src/stan/services/util/validate_dense_inv_metric.hpp index ad75fc517a7..412c7932081 100644 --- a/src/stan/services/util/validate_dense_inv_metric.hpp +++ b/src/stan/services/util/validate_dense_inv_metric.hpp @@ -18,8 +18,9 @@ namespace util { inline void validate_dense_inv_metric(const Eigen::MatrixXd& inv_metric, callbacks::logger& logger) { try { - stan::math::check_pos_definite("check_pos_definite", "inv_metric", - inv_metric); + if (inv_metric.size() > 0) + stan::math::check_pos_definite("check_pos_definite", "inv_metric", + inv_metric); } catch (const std::domain_error& e) { logger.error("Inverse Euclidean metric not positive definite."); throw std::domain_error("Initialization failure"); diff --git a/src/test/test-models/good/model/accumulate.stan b/src/test/test-models/good/model/accumulate.stan new file mode 100644 index 00000000000..42468751450 --- /dev/null +++ b/src/test/test-models/good/model/accumulate.stan @@ -0,0 +1,9 @@ +data { + int N; +} +parameters { + vector[N] y; +} +model { + target += y; +} diff --git a/src/test/test-models/good/services/zero_params.stan b/src/test/test-models/good/services/zero_params.stan new file mode 100644 index 00000000000..46cb64ecfe8 --- /dev/null +++ b/src/test/test-models/good/services/zero_params.stan @@ -0,0 +1,7 @@ +transformed data { + int N = 2; +} +generated quantities { + real theta = beta_rng(1, 1); + real eta = beta_rng(10, 10); +} diff --git a/src/test/unit/analyze/mcmc/check_chains_test.cpp b/src/test/unit/analyze/mcmc/check_chains_test.cpp new file mode 100644 index 00000000000..7d4c9107b14 --- /dev/null +++ b/src/test/unit/analyze/mcmc/check_chains_test.cpp @@ -0,0 +1,48 @@ +#include +#include +#include +#include +#include +#include +#include + +TEST(CheckChains, good_and_bad) { + std::stringstream out; + std::ifstream eight_schools_1_stream, eight_schools_2_stream; + stan::io::stan_csv eight_schools_1, eight_schools_2; + eight_schools_1_stream.open( + "src/test/unit/mcmc/test_csv_files/eight_schools_1.csv", + std::ifstream::in); + eight_schools_1 + = stan::io::stan_csv_reader::parse(eight_schools_1_stream, &out); + eight_schools_1_stream.close(); + + eight_schools_2_stream.open( + "src/test/unit/mcmc/test_csv_files/eight_schools_2.csv", + std::ifstream::in); + eight_schools_2 + = stan::io::stan_csv_reader::parse(eight_schools_2_stream, &out); + eight_schools_2_stream.close(); + + Eigen::MatrixXd chain_1(eight_schools_1.samples.rows(), 1); + Eigen::MatrixXd chains(eight_schools_1.samples.rows(), 2); + + // stepsize - constant after adaptation + chain_1.col(0) = eight_schools_1.samples.col(2); + EXPECT_FALSE(stan::analyze::is_finite_and_varies(chain_1)); + + chains.col(0) = eight_schools_1.samples.col(2); + chains.col(1) = eight_schools_2.samples.col(2); + EXPECT_FALSE(stan::analyze::is_finite_and_varies(chains)); + + for (size_t i = 0; i < 10; ++i) { + chains.col(0) = eight_schools_1.samples.col(i + 7); + chains.col(1) = eight_schools_2.samples.col(i + 7); + EXPECT_TRUE(stan::analyze::is_finite_and_varies(chains)); + } + + // above test shows that column 7 is OK - make it non-finite + chain_1.col(0) = eight_schools_1.samples.col(7); + chain_1(0, 0) = std::numeric_limits::infinity(); + EXPECT_FALSE(stan::analyze::is_finite_and_varies(chain_1)); +} diff --git a/src/test/unit/analyze/mcmc/compute_potential_scale_reduction_test.cpp b/src/test/unit/analyze/mcmc/compute_potential_scale_reduction_test.cpp index 41a102e51df..65d5c15a819 100644 --- a/src/test/unit/analyze/mcmc/compute_potential_scale_reduction_test.cpp +++ b/src/test/unit/analyze/mcmc/compute_potential_scale_reduction_test.cpp @@ -58,64 +58,6 @@ TEST_F(ComputeRhat, compute_potential_scale_reduction) { << ", parameter: " << chains.param_name(index); } } -TEST_F(ComputeRhat, compute_potential_scale_reduction_rank) { - std::stringstream out; - stan::io::stan_csv blocker1 - = stan::io::stan_csv_reader::parse(blocker1_stream, &out); - stan::io::stan_csv blocker2 - = stan::io::stan_csv_reader::parse(blocker2_stream, &out); - EXPECT_EQ("", out.str()); - - stan::mcmc::chains<> chains(blocker1); - chains.add(blocker2); - - // Eigen::VectorXd rhat(48); - // rhat - // << 1.00067,1.00497,1.00918,1.00055,1.0015,1.00088,1.00776,1.00042,1.00201,0.99956,0.99984,1.00054,1.00403,1.00516,1.00591,1.00627,1.00134,1.00895,1.00079,1.00368,1.00092,1.00133,1.01005,1.00107,1.00151,1.00229,1.0,1.00008,1.00315,1.00277,1.00247,1.00003,1.001,1.01267,1.00011,1.00066,1.00091,1.00237,1.00019,1.00104,1.00341,0.99981,1.00033,0.99967,1.00306,1.00072,1.00191,1.00658; - - Eigen::VectorXd rhat_bulk(48); - rhat_bulk << 1.00067, 0.99979, 0.99966, 1.00055, 1.0011, 1.00088, 1.00032, - 0.99997, 1.00201, 0.99956, 0.99956, 0.9995, 1.00292, 1.00516, 1.00591, - 0.99975, 1.00088, 1.00895, 1.00079, 0.99953, 1.00092, 1.00044, 1.01005, - 0.9996, 1.00151, 0.99966, 0.99965, 0.99963, 1.00315, 1.00277, 1.00247, - 1.00003, 0.99994, 1.00116, 0.99952, 1.0005, 1.00091, 1.00213, 1.00019, - 0.99977, 1.0003, 0.99981, 1.00003, 0.99967, 1.00306, 1.00072, 0.9996, - 0.99979; - Eigen::VectorXd rhat_tail(48); - rhat_tail << 1.00063, 1.00497, 1.00918, 0.99965, 1.0015, 0.99962, 1.00776, - 1.00042, 0.99963, 0.99951, 0.99984, 1.00054, 1.00403, 1.00107, 1.00287, - 1.00627, 1.00134, 0.99957, 0.99997, 1.00368, 1.00053, 1.00133, 1.00589, - 1.00107, 1.00031, 1.00229, 1.0, 1.00008, 1.0001, 1.00116, 1.00219, - 0.99992, 1.001, 1.01267, 1.00011, 1.00066, 1.00065, 1.00237, 0.9995, - 1.00104, 1.00341, 0.99958, 1.00033, 0.9996, 0.99957, 1.00058, 1.00191, - 1.00658; - - // replicates calls to stan::analyze::compute_effective_sample_size - // for any interface *without* access to chains class - Eigen::Matrix samples( - chains.num_chains()); - std::vector draws(chains.num_chains()); - std::vector sizes(chains.num_chains()); - for (int index = 4; index < chains.num_params(); index++) { - for (int chain = 0; chain < chains.num_chains(); ++chain) { - samples(chain) = chains.samples(chain, index); - draws[chain] = &samples(chain)(0); - sizes[chain] = samples(chain).size(); - } - double computed_bulk_rhat, computed_tail_rhat; - std::tie(computed_bulk_rhat, computed_tail_rhat) - = stan::analyze::compute_potential_scale_reduction_rank(draws, sizes); - double expected_bulk_rhat = rhat_bulk(index - 4); - double expected_tail_rhat = rhat_tail(index - 4); - - ASSERT_NEAR(expected_bulk_rhat, computed_bulk_rhat, 1e-4) - << "Bulk Rhat mismatch for index: " << index - << ", parameter: " << chains.param_name(index); - ASSERT_NEAR(expected_tail_rhat, computed_tail_rhat, 1e-4) - << "Tail Rhat mismatch for index: " << index - << ", parameter: " << chains.param_name(index); - } -} TEST_F(ComputeRhat, compute_potential_scale_reduction_convenience) { std::stringstream out; @@ -156,60 +98,6 @@ TEST_F(ComputeRhat, compute_potential_scale_reduction_convenience) { } } -TEST_F(ComputeRhat, compute_potential_scale_reduction_rank_convenience) { - std::stringstream out; - stan::io::stan_csv blocker1 - = stan::io::stan_csv_reader::parse(blocker1_stream, &out); - stan::io::stan_csv blocker2 - = stan::io::stan_csv_reader::parse(blocker2_stream, &out); - EXPECT_EQ("", out.str()); - - stan::mcmc::chains<> chains(blocker1); - chains.add(blocker2); - - Eigen::VectorXd rhat_bulk(48); - rhat_bulk << 1.00067, 0.99979, 0.99966, 1.00055, 1.0011, 1.00088, 1.00032, - 0.99997, 1.00201, 0.99956, 0.99956, 0.9995, 1.00292, 1.00516, 1.00591, - 0.99975, 1.00088, 1.00895, 1.00079, 0.99953, 1.00092, 1.00044, 1.01005, - 0.9996, 1.00151, 0.99966, 0.99965, 0.99963, 1.00315, 1.00277, 1.00247, - 1.00003, 0.99994, 1.00116, 0.99952, 1.0005, 1.00091, 1.00213, 1.00019, - 0.99977, 1.0003, 0.99981, 1.00003, 0.99967, 1.00306, 1.00072, 0.9996, - 0.99979; - Eigen::VectorXd rhat_tail(48); - rhat_tail << 1.00063, 1.00497, 1.00918, 0.99965, 1.0015, 0.99962, 1.00776, - 1.00042, 0.99963, 0.99951, 0.99984, 1.00054, 1.00403, 1.00107, 1.00287, - 1.00627, 1.00134, 0.99957, 0.99997, 1.00368, 1.00053, 1.00133, 1.00589, - 1.00107, 1.00031, 1.00229, 1.0, 1.00008, 1.0001, 1.00116, 1.00219, - 0.99992, 1.001, 1.01267, 1.00011, 1.00066, 1.00065, 1.00237, 0.9995, - 1.00104, 1.00341, 0.99958, 1.00033, 0.9996, 0.99957, 1.00058, 1.00191, - 1.00658; - - Eigen::Matrix samples( - chains.num_chains()); - std::vector draws(chains.num_chains()); - std::vector sizes(chains.num_chains()); - for (int index = 4; index < chains.num_params(); index++) { - for (int chain = 0; chain < chains.num_chains(); ++chain) { - samples(chain) = chains.samples(chain, index); - draws[chain] = &samples(chain)(0); - } - size_t size = samples(0).size(); - - double computed_bulk_rhat, computed_tail_rhat; - std::tie(computed_bulk_rhat, computed_tail_rhat) - = stan::analyze::compute_potential_scale_reduction_rank(draws, size); - double expected_bulk_rhat = rhat_bulk(index - 4); - double expected_tail_rhat = rhat_tail(index - 4); - - ASSERT_NEAR(expected_bulk_rhat, computed_bulk_rhat, 1e-4) - << "Bulk Rhat mismatch for index: " << index - << ", parameter: " << chains.param_name(index); - ASSERT_NEAR(expected_tail_rhat, computed_tail_rhat, 1e-4) - << "Tail Rhat mismatch for index: " << index - << ", parameter: " << chains.param_name(index); - } -} - TEST_F(ComputeRhat, chains_compute_split_potential_scale_reduction) { std::stringstream out; stan::io::stan_csv blocker1 @@ -244,55 +132,6 @@ TEST_F(ComputeRhat, chains_compute_split_potential_scale_reduction) { } } -TEST_F(ComputeRhat, chains_compute_split_potential_scale_reduction_rank) { - std::stringstream out; - stan::io::stan_csv blocker1 - = stan::io::stan_csv_reader::parse(blocker1_stream, &out); - stan::io::stan_csv blocker2 - = stan::io::stan_csv_reader::parse(blocker2_stream, &out); - EXPECT_EQ("", out.str()); - - stan::mcmc::chains<> chains(blocker1); - chains.add(blocker2); - - Eigen::VectorXd rhat_bulk(48); - rhat_bulk << 1.0078, 1.0109, 0.99919, 1.001, 1.00401, 1.00992, 1.00182, - 1.00519, 1.00095, 1.00351, 1.00554, 1.00075, 1.00595, 1.00473, 1.00546, - 1.01304, 1.00166, 1.0074, 1.00178, 1.00588, 1.00406, 1.00129, 1.00976, - 1.0013, 1.00193, 1.00104, 0.99938, 1.00025, 1.01082, 1.0019, 1.00354, - 1.0043, 1.00111, 1.00281, 1.00436, 1.00515, 1.00325, 1.0089, 1.00222, - 1.00118, 1.00191, 1.00283, 1.0003, 1.00216, 1.00335, 1.00133, 1.00023, - 1.0109; - Eigen::VectorXd rhat_tail(48); - rhat_tail << 1.00097, 1.00422, 1.00731, 1.00333, 1.00337, 0.99917, 1.00734, - 1.00633, 1.00074, 1.00906, 1.01019, 1.00074, 1.00447, 1.00383, 1.00895, - 1.00389, 1.00052, 1.00188, 1.00236, 1.00284, 1.00414, 1.00303, 1.00327, - 1.00295, 1.00037, 1.0044, 1.00488, 1.00178, 1.00475, 1.00082, 1.00413, - 1.01303, 1.0024, 1.01148, 1.00098, 1.00078, 1.00712, 1.00595, 1.00124, - 1.00112, 1.00381, 1.0006, 1.00188, 1.00225, 1.0026, 1.0009, 1.00209, - 1.00464; - - for (int index = 4; index < chains.num_params(); index++) { - double computed_bulk_rhat, computed_tail_rhat; - std::tie(computed_bulk_rhat, computed_tail_rhat) - = chains.split_potential_scale_reduction_rank(index); - double expected_bulk_rhat = rhat_bulk(index - 4); - double expected_tail_rhat = rhat_tail(index - 4); - - ASSERT_NEAR(expected_bulk_rhat, computed_bulk_rhat, 1e-4) - << "Bulk Rhat mismatch for index: " << index - << ", parameter: " << chains.param_name(index); - ASSERT_NEAR(expected_tail_rhat, computed_tail_rhat, 1e-4) - << "Tail Rhat mismatch for index: " << index - << ", parameter: " << chains.param_name(index); - } - for (int index = 0; index < chains.num_params(); index++) { - std::string name = chains.param_name(index); - ASSERT_EQ(chains.split_potential_scale_reduction_rank(index), - chains.split_potential_scale_reduction_rank(name)); - } -} - TEST_F(ComputeRhat, compute_split_potential_scale_reduction) { std::stringstream out; stan::io::stan_csv blocker1 @@ -335,64 +174,6 @@ TEST_F(ComputeRhat, compute_split_potential_scale_reduction) { } } -TEST_F(ComputeRhat, compute_split_potential_scale_reduction_rank) { - std::stringstream out; - stan::io::stan_csv blocker1 - = stan::io::stan_csv_reader::parse(blocker1_stream, &out); - stan::io::stan_csv blocker2 - = stan::io::stan_csv_reader::parse(blocker2_stream, &out); - EXPECT_EQ("", out.str()); - - stan::mcmc::chains<> chains(blocker1); - - chains.add(blocker2); - - Eigen::VectorXd rhat_bulk(48); - rhat_bulk << 1.0078, 1.0109, 0.99919, 1.001, 1.00401, 1.00992, 1.00182, - 1.00519, 1.00095, 1.00351, 1.00554, 1.00075, 1.00595, 1.00473, 1.00546, - 1.01304, 1.00166, 1.0074, 1.00178, 1.00588, 1.00406, 1.00129, 1.00976, - 1.0013, 1.00193, 1.00104, 0.99938, 1.00025, 1.01082, 1.0019, 1.00354, - 1.0043, 1.00111, 1.00281, 1.00436, 1.00515, 1.00325, 1.0089, 1.00222, - 1.00118, 1.00191, 1.00283, 1.0003, 1.00216, 1.00335, 1.00133, 1.00023, - 1.0109; - Eigen::VectorXd rhat_tail(48); - rhat_tail << 1.00097, 1.00422, 1.00731, 1.00333, 1.00337, 0.99917, 1.00734, - 1.00633, 1.00074, 1.00906, 1.01019, 1.00074, 1.00447, 1.00383, 1.00895, - 1.00389, 1.00052, 1.00188, 1.00236, 1.00284, 1.00414, 1.00303, 1.00327, - 1.00295, 1.00037, 1.0044, 1.00488, 1.00178, 1.00475, 1.00082, 1.00413, - 1.01303, 1.0024, 1.01148, 1.00098, 1.00078, 1.00712, 1.00595, 1.00124, - 1.00112, 1.00381, 1.0006, 1.00188, 1.00225, 1.0026, 1.0009, 1.00209, - 1.00464; - - // replicates calls to stan::analyze::compute_effective_sample_size - // for any interface *without* access to chains class - Eigen::Matrix samples( - chains.num_chains()); - std::vector draws(chains.num_chains()); - std::vector sizes(chains.num_chains()); - for (int index = 4; index < chains.num_params(); index++) { - for (int chain = 0; chain < chains.num_chains(); ++chain) { - samples(chain) = chains.samples(chain, index); - draws[chain] = &samples(chain)(0); - sizes[chain] = samples(chain).size(); - } - - double computed_bulk_rhat, computed_tail_rhat; - std::tie(computed_bulk_rhat, computed_tail_rhat) - = stan::analyze::compute_split_potential_scale_reduction_rank(draws, - sizes); - double expected_bulk_rhat = rhat_bulk(index - 4); - double expected_tail_rhat = rhat_tail(index - 4); - - ASSERT_NEAR(expected_bulk_rhat, computed_bulk_rhat, 1e-4) - << "Bulk Rhat mismatch for index: " << index - << ", parameter: " << chains.param_name(index); - ASSERT_NEAR(expected_tail_rhat, computed_tail_rhat, 1e-4) - << "Tail Rhat mismatch for index: " << index - << ", parameter: " << chains.param_name(index); - } -} - TEST_F(ComputeRhat, compute_split_potential_scale_reduction_convenience) { std::stringstream out; stan::io::stan_csv blocker1 @@ -432,62 +213,6 @@ TEST_F(ComputeRhat, compute_split_potential_scale_reduction_convenience) { } } -TEST_F(ComputeRhat, compute_split_potential_scale_reduction_convenience_rank) { - std::stringstream out; - stan::io::stan_csv blocker1 - = stan::io::stan_csv_reader::parse(blocker1_stream, &out); - stan::io::stan_csv blocker2 - = stan::io::stan_csv_reader::parse(blocker2_stream, &out); - EXPECT_EQ("", out.str()); - - stan::mcmc::chains<> chains(blocker1); - chains.add(blocker2); - - Eigen::VectorXd rhat_bulk(48); - rhat_bulk << 1.0078, 1.0109, 0.99919, 1.001, 1.00401, 1.00992, 1.00182, - 1.00519, 1.00095, 1.00351, 1.00554, 1.00075, 1.00595, 1.00473, 1.00546, - 1.01304, 1.00166, 1.0074, 1.00178, 1.00588, 1.00406, 1.00129, 1.00976, - 1.0013, 1.00193, 1.00104, 0.99938, 1.00025, 1.01082, 1.0019, 1.00354, - 1.0043, 1.00111, 1.00281, 1.00436, 1.00515, 1.00325, 1.0089, 1.00222, - 1.00118, 1.00191, 1.00283, 1.0003, 1.00216, 1.00335, 1.00133, 1.00023, - 1.0109; - Eigen::VectorXd rhat_tail(48); - rhat_tail << 1.00097, 1.00422, 1.00731, 1.00333, 1.00337, 0.99917, 1.00734, - 1.00633, 1.00074, 1.00906, 1.01019, 1.00074, 1.00447, 1.00383, 1.00895, - 1.00389, 1.00052, 1.00188, 1.00236, 1.00284, 1.00414, 1.00303, 1.00327, - 1.00295, 1.00037, 1.0044, 1.00488, 1.00178, 1.00475, 1.00082, 1.00413, - 1.01303, 1.0024, 1.01148, 1.00098, 1.00078, 1.00712, 1.00595, 1.00124, - 1.00112, 1.00381, 1.0006, 1.00188, 1.00225, 1.0026, 1.0009, 1.00209, - 1.00464; - - Eigen::Matrix samples( - chains.num_chains()); - std::vector draws(chains.num_chains()); - std::vector sizes(chains.num_chains()); - - for (int index = 4; index < chains.num_params(); index++) { - for (int chain = 0; chain < chains.num_chains(); ++chain) { - samples(chain) = chains.samples(chain, index); - draws[chain] = &samples(chain)(0); - } - size_t size = samples(0).size(); - - double computed_bulk_rhat, computed_tail_rhat; - std::tie(computed_bulk_rhat, computed_tail_rhat) - = stan::analyze::compute_split_potential_scale_reduction_rank(draws, - size); - double expected_bulk_rhat = rhat_bulk(index - 4); - double expected_tail_rhat = rhat_tail(index - 4); - - ASSERT_NEAR(expected_bulk_rhat, computed_bulk_rhat, 1e-4) - << "Bulk Rhat mismatch for index: " << index - << ", parameter: " << chains.param_name(index); - ASSERT_NEAR(expected_tail_rhat, computed_tail_rhat, 1e-4) - << "Tail Rhat mismatch for index: " << index - << ", parameter: " << chains.param_name(index); - } -} - TEST_F(ComputeRhat, compute_potential_scale_reduction_constant) { std::vector param_names{"a"}; stan::mcmc::chains<> chains(param_names); @@ -499,23 +224,6 @@ TEST_F(ComputeRhat, compute_potential_scale_reduction_constant) { << "rhat for index: " << 1 << ", parameter: " << chains.param_name(1); } -TEST_F(ComputeRhat, compute_potential_scale_reduction_rank_constant) { - std::vector param_names{"a"}; - stan::mcmc::chains<> chains(param_names); - Eigen::Matrix draws; - draws << 1.0, 1.0; - chains.add(draws); - - double computed_bulk_rhat, computed_tail_rhat; - std::tie(computed_bulk_rhat, computed_tail_rhat) - = chains.split_potential_scale_reduction_rank(0); - - ASSERT_TRUE(std::isnan(computed_bulk_rhat)) - << "rhat for index: " << 1 << ", parameter: " << chains.param_name(1); - ASSERT_TRUE(std::isnan(computed_tail_rhat)) - << "rhat for index: " << 1 << ", parameter: " << chains.param_name(1); -} - TEST_F(ComputeRhat, compute_potential_scale_reduction_nan) { std::vector param_names{"a"}; stan::mcmc::chains<> chains(param_names); @@ -526,20 +234,3 @@ TEST_F(ComputeRhat, compute_potential_scale_reduction_nan) { ASSERT_TRUE(std::isnan(chains.split_potential_scale_reduction(0))) << "rhat for index: " << 1 << ", parameter: " << chains.param_name(1); } - -TEST_F(ComputeRhat, compute_potential_scale_reduction_rank_nan) { - std::vector param_names{"a"}; - stan::mcmc::chains<> chains(param_names); - Eigen::Matrix draws; - draws << 1.0, std::numeric_limits::quiet_NaN(); - chains.add(draws); - - double computed_bulk_rhat, computed_tail_rhat; - std::tie(computed_bulk_rhat, computed_tail_rhat) - = chains.split_potential_scale_reduction_rank(0); - - ASSERT_TRUE(std::isnan(computed_bulk_rhat)) - << "rhat for index: " << 1 << ", parameter: " << chains.param_name(1); - ASSERT_TRUE(std::isnan(computed_tail_rhat)) - << "rhat for index: " << 1 << ", parameter: " << chains.param_name(1); -} diff --git a/src/test/unit/analyze/mcmc/ess_basic_test.cpp b/src/test/unit/analyze/mcmc/ess_basic_test.cpp new file mode 100644 index 00000000000..2a1e910f59f --- /dev/null +++ b/src/test/unit/analyze/mcmc/ess_basic_test.cpp @@ -0,0 +1,53 @@ +#include +#include +#include +#include +#include +#include +#include + +class EssBasic : public testing::Test { + public: + void SetUp() { + chains_lp.resize(1000, 4); + chains_theta.resize(1000, 4); + draws_theta.resize(4); + draws_lp.resize(4); + sizes.resize(4); + for (size_t i = 0; i < 4; ++i) { + std::stringstream fname; + fname << "src/test/unit/analyze/mcmc/test_csv_files/bern" << (i + 1) + << ".csv"; + std::ifstream bern_stream(fname.str(), std::ifstream::in); + stan::io::stan_csv bern_csv + = stan::io::stan_csv_reader::parse(bern_stream, &out); + bern_stream.close(); + chains_lp.col(i) = bern_csv.samples.col(0); + chains_theta.col(i) = bern_csv.samples.col(7); + draws_lp[i] = chains_lp.col(i).data(); + draws_theta[i] = chains_theta.col(i).data(); + sizes[i] = 1000; + } + } + + void TearDown() {} + + std::stringstream out; + Eigen::MatrixXd chains_lp; + Eigen::MatrixXd chains_theta; + std::vector draws_theta; + std::vector draws_lp; + std::vector sizes; +}; + +TEST_F(EssBasic, test_basic_ess) { + // computed via cmdstan 2.35.0 stansummary + double ess_lp_expect = 1335.41366787; + double ess_theta_expect = 1377.5030282; + + auto ess_basic_lp = stan::analyze::ess(chains_lp); + auto ess_basic_theta = stan::analyze::ess(chains_theta); + + EXPECT_NEAR(ess_lp_expect, ess_basic_lp, 1e-8); + EXPECT_NEAR(ess_theta_expect, ess_basic_theta, 1e-8); +} diff --git a/src/test/unit/analyze/mcmc/mcse_test.cpp b/src/test/unit/analyze/mcmc/mcse_test.cpp new file mode 100644 index 00000000000..5b962ec3eb6 --- /dev/null +++ b/src/test/unit/analyze/mcmc/mcse_test.cpp @@ -0,0 +1,73 @@ +#include +#include +#include +#include +#include +#include +#include + +class MonteCarloStandardError : public testing::Test { + public: + void SetUp() { + chains_lp.resize(1000, 4); + chains_theta.resize(1000, 4); + chains_divergent.resize(1000, 4); + for (size_t i = 0; i < 4; ++i) { + std::stringstream fname; + fname << "src/test/unit/analyze/mcmc/test_csv_files/bern" << (i + 1) + << ".csv"; + std::ifstream bern_stream(fname.str(), std::ifstream::in); + stan::io::stan_csv bern_csv + = stan::io::stan_csv_reader::parse(bern_stream, &out); + bern_stream.close(); + chains_lp.col(i) = bern_csv.samples.col(0); + chains_theta.col(i) = bern_csv.samples.col(7); + chains_divergent.col(i) = bern_csv.samples.col(5); + } + } + + void TearDown() {} + + std::stringstream out; + Eigen::MatrixXd chains_lp; + Eigen::MatrixXd chains_theta; + Eigen::MatrixXd chains_divergent; +}; + +TEST_F(MonteCarloStandardError, test_mcse) { + // computed via R pkg posterior + double mcse_mean_lp_expect = 0.02016; + double mcse_mean_theta_expect = 0.00323; + + double mcse_sd_lp_expect = 0.03553; + double mcse_sd_theta_expect = 0.00216; + EXPECT_NEAR(mcse_mean_lp_expect, stan::analyze::mcse_mean(chains_lp), 1e-4); + EXPECT_NEAR(mcse_mean_theta_expect, stan::analyze::mcse_mean(chains_theta), + 1e-4); + + EXPECT_NEAR(mcse_sd_lp_expect, stan::analyze::mcse_sd(chains_lp), 1e-4); + EXPECT_NEAR(mcse_sd_theta_expect, stan::analyze::mcse_sd(chains_theta), 1e-4); +} + +TEST_F(MonteCarloStandardError, const_fail) { + auto mcse_mean = stan::analyze::mcse_mean(chains_divergent); + auto mcse_sd = stan::analyze::mcse_sd(chains_divergent); + EXPECT_TRUE(std::isnan(mcse_mean)); + EXPECT_TRUE(std::isnan(mcse_sd)); +} + +TEST_F(MonteCarloStandardError, inf_fail) { + chains_theta(0, 0) = std::numeric_limits::infinity(); + auto mcse_mean = stan::analyze::mcse_mean(chains_theta); + auto mcse_sd = stan::analyze::mcse_sd(chains_theta); + EXPECT_TRUE(std::isnan(mcse_mean)); + EXPECT_TRUE(std::isnan(mcse_sd)); +} + +TEST_F(MonteCarloStandardError, short_chains_fail) { + chains_theta.resize(3, 4); + auto mcse_mean = stan::analyze::mcse_mean(chains_theta); + auto mcse_sd = stan::analyze::mcse_sd(chains_theta); + EXPECT_TRUE(std::isnan(mcse_mean)); + EXPECT_TRUE(std::isnan(mcse_sd)); +} diff --git a/src/test/unit/analyze/mcmc/rank_normalization_test.cpp b/src/test/unit/analyze/mcmc/rank_normalization_test.cpp new file mode 100644 index 00000000000..f4776d3443c --- /dev/null +++ b/src/test/unit/analyze/mcmc/rank_normalization_test.cpp @@ -0,0 +1,85 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include + +class RankNormalization : public testing::Test { + public: + void SetUp() { + chains_theta.resize(1000, 4); + for (size_t i = 0; i < 4; ++i) { + std::stringstream fname; + fname << "src/test/unit/analyze/mcmc/test_csv_files/bern" << (i + 1) + << ".csv"; + std::ifstream bern_stream(fname.str(), std::ifstream::in); + stan::io::stan_csv bern_csv + = stan::io::stan_csv_reader::parse(bern_stream, &out); + bern_stream.close(); + chains_theta.col(i) = bern_csv.samples.col(7); + } + } + + void TearDown() {} + + std::stringstream out; + Eigen::MatrixXd chains_theta; +}; + +TEST_F(RankNormalization, test_min_max) { + Eigen::Index maxRow, maxCol; + Eigen::Index minRow, minCol; + double max_val = chains_theta.maxCoeff(&maxRow, &maxCol); + double min_val = chains_theta.minCoeff(&minRow, &minCol); + chains_theta(maxRow, maxCol) = 0.9999; + chains_theta(minRow, minCol) = 0.001; + + auto rank_norm_theta = stan::analyze::rank_transform(chains_theta); + + Eigen::Index maxRowRankNorm, maxColRankNorm; + Eigen::Index minRowRankNorm, minColRankNorm; + max_val = rank_norm_theta.maxCoeff(&maxRowRankNorm, &maxColRankNorm); + min_val = rank_norm_theta.minCoeff(&minRowRankNorm, &minColRankNorm); + EXPECT_EQ(maxRow, maxRowRankNorm); + EXPECT_EQ(minRow, minRowRankNorm); + EXPECT_EQ(maxCol, maxColRankNorm); + EXPECT_EQ(minCol, minColRankNorm); +} + +TEST_F(RankNormalization, test_symmetry) { + Eigen::MatrixXd foo(8, 2); + int val = 0; + for (size_t col = 0; col < 2; ++col) { + for (size_t row = 0; row < 8; ++row) { + val++; + foo(row, col) = val; + } + } + auto bar = stan::analyze::rank_transform(foo); + size_t rev_col = 2; + for (size_t col = 0; col < 2; ++col) { + --rev_col; + size_t rev_row = 8; + for (size_t row = 0; row < 8; ++row) { + --rev_row; + EXPECT_NEAR(bar(row, col), -bar(rev_row, rev_col), 1e-10); + } + } + + Eigen::MatrixXd baz(2, 8); + for (size_t col = 0; col < 8; ++col) { + int val = 0; + for (size_t row = 0; row < 2; ++row) { + val++; + baz(row, col) = val; + } + } + auto boz = stan::analyze::rank_transform(baz); + for (size_t col = 0; col < 8; ++col) { + EXPECT_NEAR(boz(0, col), -boz(1, col), 1e-10); + } +} diff --git a/src/test/unit/analyze/mcmc/rhat_basic_test.cpp b/src/test/unit/analyze/mcmc/rhat_basic_test.cpp new file mode 100644 index 00000000000..a825ecb8200 --- /dev/null +++ b/src/test/unit/analyze/mcmc/rhat_basic_test.cpp @@ -0,0 +1,54 @@ +#include +#include +#include +#include +#include +#include + +class RhatBasic : public testing::Test { + public: + void SetUp() { + chains_lp.resize(1000, 4); + chains_theta.resize(1000, 4); + draws_theta.resize(4); + draws_lp.resize(4); + sizes.resize(4); + for (size_t i = 0; i < 4; ++i) { + std::stringstream fname; + fname << "src/test/unit/analyze/mcmc/test_csv_files/bern" << (i + 1) + << ".csv"; + std::ifstream bern_stream(fname.str(), std::ifstream::in); + stan::io::stan_csv bern_csv + = stan::io::stan_csv_reader::parse(bern_stream, &out); + bern_stream.close(); + chains_lp.col(i) = bern_csv.samples.col(0); + chains_theta.col(i) = bern_csv.samples.col(7); + draws_lp[i] = chains_lp.col(i).data(); + draws_theta[i] = chains_theta.col(i).data(); + sizes[i] = 1000; + } + } + + void TearDown() {} + + std::stringstream out; + Eigen::MatrixXd chains_lp; + Eigen::MatrixXd chains_theta; + std::vector draws_theta; + std::vector draws_lp; + std::vector sizes; +}; + +TEST_F(RhatBasic, test_basic_rhat) { + // computed via cmdstan 2.35.0 stansummary + double rhat_lp_basic_expect = 1.00035489482; + double rhat_theta_basic_expect = 1.00721797217; + + auto rhat_basic_lp + = stan::analyze::rhat(stan::analyze::split_chains(chains_lp)); + auto rhat_basic_theta + = stan::analyze::rhat(stan::analyze::split_chains(chains_theta)); + + EXPECT_NEAR(rhat_lp_basic_expect, rhat_basic_lp, 1e-10); + EXPECT_NEAR(rhat_theta_basic_expect, rhat_basic_theta, 1e-10); +} diff --git a/src/test/unit/analyze/mcmc/split_chains_test.cpp b/src/test/unit/analyze/mcmc/split_chains_test.cpp index 55fb46725f4..a027937da7a 100644 --- a/src/test/unit/analyze/mcmc/split_chains_test.cpp +++ b/src/test/unit/analyze/mcmc/split_chains_test.cpp @@ -109,3 +109,49 @@ TEST_F(SplitChains, split_chains_convenience) { } } } + +TEST_F(SplitChains, split_draws_matrix_odd_rows) { + // When the number of total draws N is odd, the (N+1)/2th draw is ignored. + Eigen::MatrixXd foo(7, 2); + int val = 0; + for (size_t col = 0; col < 2; ++col) { + for (size_t row = 0; row < 7; ++row) { + val += 1; + foo(row, col) = val; + } + } + auto bar = stan::analyze::split_chains(foo); + EXPECT_EQ(4, bar.cols()); + EXPECT_EQ(3, bar.rows()); + EXPECT_EQ(bar(0, 0), 1); + EXPECT_EQ(bar(1, 0), 2); + EXPECT_EQ(bar(2, 0), 3); + EXPECT_EQ(bar(0, 1), 5); + EXPECT_EQ(bar(1, 1), 6); + EXPECT_EQ(bar(2, 1), 7); + EXPECT_EQ(bar(0, 2), 8); + EXPECT_EQ(bar(1, 2), 9); + EXPECT_EQ(bar(2, 2), 10); + EXPECT_EQ(bar(0, 3), 12); + EXPECT_EQ(bar(1, 3), 13); + EXPECT_EQ(bar(2, 3), 14); + + Eigen::MatrixXd baz(4, 2); + for (size_t col = 0; col < 2; ++col) { + for (size_t row = 0; row < 4; ++row) { + val += 1; + baz(row, col) = val; + } + } + auto boz = stan::analyze::split_chains(baz); + EXPECT_EQ(4, boz.cols()); + EXPECT_EQ(2, boz.rows()); + EXPECT_EQ(boz(0, 0), 15); + EXPECT_EQ(boz(1, 0), 16); + EXPECT_EQ(boz(0, 1), 17); + EXPECT_EQ(boz(1, 1), 18); + EXPECT_EQ(boz(0, 2), 19); + EXPECT_EQ(boz(1, 2), 20); + EXPECT_EQ(boz(0, 3), 21); + EXPECT_EQ(boz(1, 3), 22); +} diff --git a/src/test/unit/analyze/mcmc/split_rank_normalized_ess_test.cpp b/src/test/unit/analyze/mcmc/split_rank_normalized_ess_test.cpp new file mode 100644 index 00000000000..44c823f5f28 --- /dev/null +++ b/src/test/unit/analyze/mcmc/split_rank_normalized_ess_test.cpp @@ -0,0 +1,73 @@ +#include +#include +#include +#include +#include +#include +#include + +class RankNormalizedEss : public testing::Test { + public: + void SetUp() { + chains_lp.resize(1000, 4); + chains_theta.resize(1000, 4); + chains_divergent.resize(1000, 4); + for (size_t i = 0; i < 4; ++i) { + std::stringstream fname; + fname << "src/test/unit/analyze/mcmc/test_csv_files/bern" << (i + 1) + << ".csv"; + std::ifstream bern_stream(fname.str(), std::ifstream::in); + stan::io::stan_csv bern_csv + = stan::io::stan_csv_reader::parse(bern_stream, &out); + bern_stream.close(); + chains_lp.col(i) = bern_csv.samples.col(0); + chains_theta.col(i) = bern_csv.samples.col(7); + chains_divergent.col(i) = bern_csv.samples.col(5); + } + } + + void TearDown() {} + + std::stringstream out; + Eigen::MatrixXd chains_lp; + Eigen::MatrixXd chains_theta; + Eigen::MatrixXd chains_divergent; +}; + +TEST_F(RankNormalizedEss, test_bulk_tail_ess) { + // computed via R pkg posterior + double ess_lp_bulk_expect = 1512.7684; + double ess_lp_tail_expect = 1591.9707; + + double ess_theta_bulk_expect = 1407.5124; + double ess_theta_tail_expect = 1291.7131; + + auto ess_lp = stan::analyze::split_rank_normalized_ess(chains_lp); + auto ess_theta = stan::analyze::split_rank_normalized_ess(chains_theta); + + EXPECT_NEAR(ess_lp_bulk_expect, ess_lp.first, 1e-4); + EXPECT_NEAR(ess_lp_tail_expect, ess_lp.second, 1e-4); + + EXPECT_NEAR(ess_theta_bulk_expect, ess_theta.first, 1e-4); + EXPECT_NEAR(ess_theta_tail_expect, ess_theta.second, 1e-4); +} + +TEST_F(RankNormalizedEss, const_fail) { + auto ess = stan::analyze::split_rank_normalized_ess(chains_divergent); + EXPECT_TRUE(std::isnan(ess.first)); + EXPECT_TRUE(std::isnan(ess.second)); +} + +TEST_F(RankNormalizedEss, inf_fail) { + chains_theta(0, 0) = std::numeric_limits::infinity(); + auto ess = stan::analyze::split_rank_normalized_ess(chains_theta); + EXPECT_TRUE(std::isnan(ess.first)); + EXPECT_TRUE(std::isnan(ess.second)); +} + +TEST_F(RankNormalizedEss, short_chains_fail) { + chains_theta.resize(3, 4); + auto ess = stan::analyze::split_rank_normalized_ess(chains_theta); + EXPECT_TRUE(std::isnan(ess.first)); + EXPECT_TRUE(std::isnan(ess.second)); +} diff --git a/src/test/unit/analyze/mcmc/split_rank_normalized_rhat_test.cpp b/src/test/unit/analyze/mcmc/split_rank_normalized_rhat_test.cpp new file mode 100644 index 00000000000..c9fd1ddb8d5 --- /dev/null +++ b/src/test/unit/analyze/mcmc/split_rank_normalized_rhat_test.cpp @@ -0,0 +1,63 @@ +#include +#include +#include +#include +#include +#include +#include +#include +#include + +class RankNormalizedRhat : public testing::Test { + public: + void SetUp() { + chains_lp.resize(1000, 4); + chains_theta.resize(1000, 4); + chains_divergent.resize(1000, 4); + for (size_t i = 0; i < 4; ++i) { + std::stringstream fname; + fname << "src/test/unit/analyze/mcmc/test_csv_files/bern" << (i + 1) + << ".csv"; + std::ifstream bern_stream(fname.str(), std::ifstream::in); + stan::io::stan_csv bern_csv + = stan::io::stan_csv_reader::parse(bern_stream, &out); + bern_stream.close(); + chains_lp.col(i) = bern_csv.samples.col(0); + chains_theta.col(i) = bern_csv.samples.col(7); + chains_divergent.col(i) = bern_csv.samples.col(5); + } + } + + void TearDown() {} + + std::stringstream out; + Eigen::MatrixXd chains_lp; + Eigen::MatrixXd chains_theta; + Eigen::MatrixXd chains_divergent; +}; + +TEST_F(RankNormalizedRhat, test_bulk_tail_rhat) { + // computed via R pkg posterior + double rhat_lp_expect = 1.00073; + double rhat_theta_expect = 1.006789; + + auto rhat_lp = stan::analyze::split_rank_normalized_rhat(chains_lp); + auto rhat_theta = stan::analyze::split_rank_normalized_rhat(chains_theta); + + EXPECT_NEAR(rhat_lp_expect, std::max(rhat_lp.first, rhat_lp.second), 1e-5); + EXPECT_NEAR(rhat_theta_expect, std::max(rhat_theta.first, rhat_theta.second), + 1e-5); +} + +TEST_F(RankNormalizedRhat, const_fail) { + auto rhat = stan::analyze::split_rank_normalized_rhat(chains_divergent); + EXPECT_TRUE(std::isnan(rhat.first)); + EXPECT_TRUE(std::isnan(rhat.second)); +} + +TEST_F(RankNormalizedRhat, inf_fail) { + chains_theta(0, 0) = std::numeric_limits::infinity(); + auto rhat = stan::analyze::split_rank_normalized_rhat(chains_theta); + EXPECT_TRUE(std::isnan(rhat.first)); + EXPECT_TRUE(std::isnan(rhat.second)); +} diff --git a/src/test/unit/analyze/mcmc/test_csv_files/bern1.csv b/src/test/unit/analyze/mcmc/test_csv_files/bern1.csv new file mode 100644 index 00000000000..4daa3fc0f76 --- /dev/null +++ b/src/test/unit/analyze/mcmc/test_csv_files/bern1.csv @@ -0,0 +1,1057 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-10-17 18:13:44 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 = /Users/mitzi/.cmdstan/cmdstan-2.35.0/examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 86520 +# output +# file = /Users/mitzi/github/stan-dev/cmdstanpy/test_csv_files/bernoulli-20241017141344_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 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,theta +# Adaptation terminated +# Step size = 0.883328 +# Diagonal elements of inverse mass matrix: +# 0.512256 +-6.80778,0.991604,0.883328,2,3,0,6.81551,0.208532 +-6.80778,0.676659,0.883328,1,3,0,9.01984,0.208532 +-11.053,0.593184,0.883328,3,7,0,11.0857,0.0272877 +-10.8526,1,0.883328,1,1,0,11.3097,0.0293594 +-10.9957,0.926907,0.883328,2,7,0,11.355,0.661812 +-7.85065,1,0.883328,1,1,0,10.0946,0.457561 +-7.05998,1,0.883328,1,1,0,7.63241,0.356179 +-6.74806,0.998884,0.883328,2,3,0,7.0771,0.251099 +-6.77871,0.995524,0.883328,2,3,0,6.78513,0.281783 +-6.77684,1,0.883328,1,1,0,6.78461,0.28078 +-7.52399,0.771045,0.883328,1,3,0,8.22042,0.119146 +-8.05976,0.94003,0.883328,1,1,0,8.06699,0.0905514 +-6.75013,0.966227,0.883328,1,3,0,8.50437,0.241949 +-6.75182,0.99883,0.883328,1,3,0,6.75883,0.261 +-6.77286,0.985494,0.883328,2,3,0,6.84503,0.222854 +-6.74967,0.998274,0.883328,1,3,0,6.7864,0.257228 +-6.79941,0.964756,0.883328,2,3,0,6.97244,0.211425 +-6.91419,0.957459,0.883328,1,3,0,7.12266,0.326215 +-6.78633,0.974476,0.883328,1,3,0,7.07508,0.216511 +-6.78573,0.995433,0.883328,2,3,0,6.84195,0.216764 +-7.85073,0.799746,0.883328,1,3,0,8.22993,0.100264 +-7.98366,0.988934,0.883328,2,7,0,7.98371,0.470442 +-7.21863,1,0.883328,2,3,0,7.7957,0.382034 +-7.21863,0.213664,0.883328,2,3,0,13.1076,0.382034 +-6.80613,0.951776,0.883328,1,3,0,7.51243,0.209083 +-6.7796,0.989672,0.883328,1,3,0,6.90754,0.282255 +-6.92089,0.94385,0.883328,2,3,0,7.12942,0.181708 +-7.47031,0.931841,0.883328,2,3,0,7.67456,0.122833 +-6.85826,0.941179,0.883328,1,3,0,8.17326,0.311509 +-6.81941,1,0.883328,1,1,0,6.85828,0.299087 +-6.81618,1,0.883328,2,7,0,6.81777,0.205848 +-6.82389,0.92142,0.883328,2,3,0,7.4697,0.203538 +-6.76346,0.99761,0.883328,2,3,0,6.84805,0.228476 +-7.43915,0.862823,0.883328,1,3,0,7.66908,0.12507 +-7.08252,1,0.883328,1,1,0,7.38678,0.158088 +-8.35252,0.851521,0.883328,1,3,0,8.52579,0.0791085 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+-7.30099,1,0.883328,1,1,0,7.44888,0.135996 +-7.2148,1,0.883328,1,1,0,7.34275,0.143844 +-7.03391,1,0.883328,1,1,0,7.19824,0.164253 +-7.20134,0.973883,0.883328,1,1,0,7.2152,0.145158 +-6.75167,0.944383,0.883328,2,3,0,7.80575,0.239427 +# +# Elapsed Time: 0.003 seconds (Warm-up) +# 0.009 seconds (Sampling) +# 0.012 seconds (Total) +# diff --git a/src/test/unit/analyze/mcmc/test_csv_files/bern2.csv b/src/test/unit/analyze/mcmc/test_csv_files/bern2.csv new file mode 100644 index 00000000000..a92c9624aca --- /dev/null +++ b/src/test/unit/analyze/mcmc/test_csv_files/bern2.csv @@ -0,0 +1,1057 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-10-17 18:13:44 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 = 2 +# data +# file = /Users/mitzi/.cmdstan/cmdstan-2.35.0/examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 86520 +# output +# file = /Users/mitzi/github/stan-dev/cmdstanpy/test_csv_files/bernoulli-20241017141344_2.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 = 0.911571 +# Diagonal elements of inverse mass matrix: +# 0.568283 +-9.4699,1,0.911571,2,3,0,9.88038,0.0495852 +-8.60156,1,0.911571,1,1,0,9.43576,0.0708923 +-6.90138,0.862296,0.911571,2,7,0,9.70197,0.323078 +-7.07893,0.945255,0.911571,1,1,0,7.10214,0.359544 +-8.54466,0.605769,0.911571,1,1,0,8.5574,0.518234 +-6.76418,0.871155,0.911571,2,3,0,9.48349,0.22799 +-6.74884,0.966719,0.911571,2,3,0,6.98528,0.244954 +-7.20361,0.927301,0.911571,2,3,0,7.27293,0.144935 +-7.1124,0.971088,0.911571,2,3,0,7.66262,0.154583 +-6.7508,0.893501,0.911571,2,3,0,8.57743,0.240753 +-6.82289,0.980223,0.911571,1,3,0,6.85006,0.300313 +-7.64253,0.825605,0.911571,1,3,0,7.64649,0.435767 +-6.78465,0.988398,0.911571,1,3,0,7.70179,0.217225 +-6.74918,1,0.911571,1,3,0,6.77909,0.244013 +-7.07853,0.772008,0.911571,2,3,0,8.122,0.158572 +-6.87654,0.983311,0.911571,2,3,0,7.28121,0.19046 +-6.85124,1,0.911571,1,1,0,6.88925,0.196265 +-7.95745,0.817925,0.911571,1,3,0,8.13031,0.0951186 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seconds (Warm-up) +# 0.01 seconds (Sampling) +# 0.013 seconds (Total) +# diff --git a/src/test/unit/analyze/mcmc/test_csv_files/bern3.csv b/src/test/unit/analyze/mcmc/test_csv_files/bern3.csv new file mode 100644 index 00000000000..0f79beab7b6 --- /dev/null +++ b/src/test/unit/analyze/mcmc/test_csv_files/bern3.csv @@ -0,0 +1,1057 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-10-17 18:13:44 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 = 3 +# data +# file = /Users/mitzi/.cmdstan/cmdstan-2.35.0/examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 86520 +# output +# file = /Users/mitzi/github/stan-dev/cmdstanpy/test_csv_files/bernoulli-20241017141344_3.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 = 0.889047 +# Diagonal elements of inverse mass matrix: +# 0.568698 +-8.55171,0.835828,0.889047,2,3,0,8.79251,0.0724419 +-9.14968,0.951918,0.889047,1,1,0,9.22155,0.0563692 +-6.74914,0.962544,0.889047,1,3,0,9.97683,0.244117 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b/src/test/unit/analyze/mcmc/test_csv_files/bern4.csv new file mode 100644 index 00000000000..0067fcc7e58 --- /dev/null +++ b/src/test/unit/analyze/mcmc/test_csv_files/bern4.csv @@ -0,0 +1,1057 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-10-17 18:13:44 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 = 4 +# data +# file = /Users/mitzi/.cmdstan/cmdstan-2.35.0/examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 86520 +# output +# file = /Users/mitzi/github/stan-dev/cmdstanpy/test_csv_files/bernoulli-20241017141344_4.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 = 0.933117 +# Diagonal elements of inverse mass matrix: +# 0.524582 +-7.19125,0.977404,0.933117,1,1,0,7.22452,0.146161 +-9.45173,0.793941,0.933117,2,3,0,9.72516,0.0499428 +-8.92419,1,0.933117,1,1,0,9.52673,0.0618385 +-8.93603,0.999061,0.933117,1,1,0,9.22216,0.0615352 +-8.55245,1,0.933117,1,1,0,9.03101,0.0724187 +-7.16094,0.95946,0.933117,1,3,0,8.4516,0.149269 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+-6.91761,1,0.933117,1,1,0,7.00394,0.327033 +-6.99244,0.977434,0.933117,1,1,0,7.02894,0.343347 +-6.79696,1,0.933117,1,1,0,6.93551,0.290395 +-8.40777,0.520849,0.933117,2,7,0,9.76679,0.0771788 +-8.63826,0.992893,0.933117,2,3,0,8.80062,0.0697792 +-9.48645,0.935444,0.933117,1,1,0,9.52197,0.0492622 +-8.75046,1,0.933117,1,1,0,9.48909,0.0665149 +-8.60584,1,0.933117,1,1,0,8.94626,0.0707612 +-9.75047,0.91488,0.933117,1,1,0,9.75585,0.0444312 +-9.45161,1,0.933117,1,1,0,9.94452,0.0499452 +-9.79435,0.963408,0.933117,2,7,0,9.79513,0.60087 +-8.16888,1,0.933117,2,7,0,9.45458,0.0860266 +-7.56289,1,0.933117,1,1,0,8.10492,0.116595 +-7.69908,0.981513,0.933117,1,1,0,7.80135,0.108363 +-7.24226,1,0.933117,1,1,0,7.63715,0.141241 +-6.85242,0.943646,0.933117,1,3,0,7.62494,0.309792 +-6.88501,0.94844,0.933117,1,3,0,7.14732,0.188666 +-6.81858,1,0.933117,1,1,0,6.87503,0.205114 +-6.76475,0.883611,0.933117,2,3,0,7.80614,0.227616 +-6.84914,0.991632,0.933117,2,3,0,6.85204,0.308806 +-7.51701,0.763008,0.933117,1,3,0,8.27668,0.119614 +-6.76761,0.90412,0.933117,2,3,0,8.5988,0.225818 +-6.78041,0.996967,0.933117,1,1,0,6.78272,0.219121 +-6.78041,0.747946,0.933117,1,3,0,8.15598,0.219121 +-6.76517,0.983521,0.933117,2,3,0,6.89815,0.273612 +-7.16731,0.878915,0.933117,1,3,0,7.43158,0.148604 +-7.27032,0.982755,0.933117,1,1,0,7.32544,0.138683 +-7.27032,0.920183,0.933117,1,1,0,7.74757,0.138683 +-6.75154,1,0.933117,2,3,0,7.21971,0.239613 +-6.82245,0.888446,0.933117,2,3,0,7.43196,0.203958 +-6.76453,0.999512,0.933117,1,3,0,6.80906,0.227757 +-6.76453,0.714532,0.933117,1,3,0,8.3038,0.227757 +-7.60373,0.814369,0.933117,1,3,0,7.82324,0.431442 +-6.75037,0.949438,0.933117,2,3,0,7.93797,0.241497 +-6.77988,0.993874,0.933117,1,3,0,6.78171,0.219367 +-6.8034,0.994568,0.933117,1,1,0,6.80661,0.210015 +-6.81117,0.989403,0.933117,2,3,0,6.91379,0.207425 +-6.8538,0.990618,0.933117,1,1,0,6.86008,0.195644 +-6.83511,1,0.933117,2,3,0,6.86524,0.2004 +-6.80345,0.83411,0.933117,2,3,0,8.3546,0.209997 +-6.78035,1,0.933117,1,1,0,6.80146,0.219146 +-8.01724,0.756505,0.933117,1,3,0,8.38808,0.0924092 +-6.94623,0.98036,0.933117,1,3,0,7.93218,0.177297 +-6.85075,0.975231,0.933117,1,3,0,7.20496,0.309291 +-6.7745,0.97555,0.933117,2,3,0,7.00287,0.279475 +-6.9289,0.968507,0.933117,1,3,0,6.9293,0.32968 +-7.19901,0.919324,0.933117,1,1,0,7.21973,0.379081 +-6.94004,1,0.933117,1,1,0,7.14248,0.332219 +-6.76595,0.988338,0.933117,1,3,0,7.00223,0.226843 +-7.23783,0.8856,0.933117,1,3,0,7.40236,0.384869 +-6.76687,1,0.933117,1,3,0,7.12189,0.274774 +-6.82158,0.984886,0.933117,1,1,0,6.8218,0.299856 +-7.37609,0.885258,0.933117,1,3,0,7.37759,0.403927 +-8.98535,0.580092,0.933117,1,1,0,9.04422,0.550362 +-6.83412,1,0.933117,2,7,0,8.35385,0.304093 +-8.90448,0.68737,0.933117,2,7,0,8.90499,0.0623476 +-6.75358,0.910195,0.933117,1,3,0,9.47606,0.236981 +-6.82047,0.986371,0.933117,1,3,0,6.82499,0.204546 +-7.39061,0.724513,0.933117,2,3,0,8.86995,0.128709 +-7.14655,0.975365,0.933117,2,3,0,7.82271,0.1508 +-6.91308,1,0.933117,1,1,0,7.10286,0.183144 +-7.66687,0.897663,0.933117,1,3,0,7.71526,0.110219 +-7.14537,1,0.933117,1,1,0,7.58353,0.150927 +-7.14619,0.914067,0.933117,1,3,0,7.97007,0.370825 +-7.15179,0.810884,0.933117,1,3,0,8.0999,0.150238 +-7.05621,1,0.933117,1,1,0,7.17359,0.161348 +-7.00444,1,0.933117,1,1,0,7.08692,0.168318 +-9.45746,0.611648,0.933117,1,3,0,10.5904,0.580969 +-8.53314,1,0.933117,1,1,0,9.60608,0.517339 +-7.03412,1,0.933117,2,3,0,8.356,0.164225 +-6.85584,0.958689,0.933117,2,7,0,7.33865,0.310802 +-6.79446,0.981094,0.933117,1,3,0,6.97699,0.213254 +-6.79756,0.98851,0.933117,1,3,0,6.89726,0.29065 +-6.92037,0.944995,0.933117,1,3,0,7.11814,0.181803 +-7.58146,0.908818,0.933117,1,3,0,7.61075,0.115411 +-6.88438,0.987375,0.933117,1,3,0,7.49441,0.188796 +-6.89473,0.997839,0.933117,1,1,0,6.92264,0.186684 +-6.85119,1,0.933117,1,1,0,6.8991,0.196278 +-7.00353,0.989641,0.933117,2,3,0,7.005,0.168448 +-7.04386,0.992385,0.933117,1,1,0,7.0871,0.162939 +-7.61613,0.919816,0.933117,1,3,0,7.61852,0.113254 +-6.74817,1,0.933117,2,3,0,7.47342,0.247859 +-7.47698,0.838765,0.933117,1,3,0,7.58682,0.416618 +-9.18756,0.559437,0.933117,1,1,0,9.26535,0.563898 +-7.87144,1,0.933117,2,7,0,8.89897,0.0992328 +-6.77539,0.925126,0.933117,1,3,0,8.29713,0.279974 +-7.8816,0.819045,0.933117,2,3,0,7.8955,0.0987325 +-6.78475,1,0.933117,2,3,0,7.77764,0.217185 +-6.80888,0.98569,0.933117,1,3,0,6.89968,0.2952 +-7.14637,0.931089,0.933117,1,3,0,7.14649,0.370854 +-6.74851,0.983012,0.933117,2,3,0,7.25722,0.246125 +-6.76365,0.996602,0.933117,1,3,0,6.76531,0.228345 +# +# Elapsed Time: 0.003 seconds (Warm-up) +# 0.009 seconds (Sampling) +# 0.012 seconds (Total) +# diff --git a/src/test/unit/analyze/mcmc/test_csv_files/cmdstan-2-35-0-stansummary.csv b/src/test/unit/analyze/mcmc/test_csv_files/cmdstan-2-35-0-stansummary.csv new file mode 100644 index 00000000000..7537b4cb0bd --- /dev/null +++ b/src/test/unit/analyze/mcmc/test_csv_files/cmdstan-2-35-0-stansummary.csv @@ -0,0 +1,18 @@ +name,Mean,MCSE,StdDev,5%,50%,95%,N_Eff,N_Eff/s,R_hat +"lp__",-7.2867579275,0.0202120721354,0.738616062616,-8.7825,-6.99001,-6.7503,1335.41366787,35142.4649438,1.00035489482 +"accept_stat__",0.92587778475,0.00177759974411,0.116280973122,0.672417,0.974476,1,4279.0696651,112607.09645,1.00155471132 +"stepsize__",0.90426575,0.0139260619795,0.0197240339958,0.883328,0.911571,0.933117,2.00601805416,52.7899487937,6.91946682912e+12 +"treedepth__",1.3515,0.00798399449516,0.479066932987,1,1,2,3600.40978064,94747.6258062,1.00017741661 +"n_leapfrog__",2.515,0.0230857538371,1.31423240019,1,3,3,3240.82976278,85284.9937574,1.00632559786 +"divergent__",0,nan,0,0,0,0,nan,nan,nan +"energy__",7.76890199,0.0271304159659,1.00790019677,6.80125,7.46009,9.72873,1380.13686038,36319.3910627,1.00204956973 +"theta",0.251297410482,0.00327489095264,0.121546686658,0.0769898,0.237539,0.473863,1377.5030282,36250.0796895,1.00721797217 +# Inference for Stan model: bernoulli_model +# 4 chains: each with iter=(1000,1000,1000,1000); warmup=(0,0,0,0); thin=(1,1,1,1); 4000 iterations saved. +# +# Warmup took (0.0030, 0.0030, 0.0040, 0.0030) seconds, 0.013 seconds total +# Sampling took (0.0090, 0.010, 0.010, 0.0090) seconds, 0.038 seconds total +# Samples were drawn using hmc with nuts. +# For each parameter, N_Eff is a crude measure of effective sample size, +# and R_hat is the potential scale reduction factor on split chains (at +# convergence, R_hat=1). diff --git a/src/test/unit/io/deserializer_free_test.cpp b/src/test/unit/io/deserializer_free_test.cpp index f5e0c34fa17..925b1c7e93b 100644 --- a/src/test/unit/io/deserializer_free_test.cpp +++ b/src/test/unit/io/deserializer_free_test.cpp @@ -6,9 +6,13 @@ // lb -template -void read_free_lb_test(Sizes... sizes) { - double lb = 0.5; +namespace stan { +namespace test { +template +void deserializer_test_impl(DeserializeRead&& deserialize_read, + DeserializeFree&& deserialize_free, + const std::tuple& sizes, Args&&... args) { Eigen::VectorXd theta1 = Eigen::VectorXd::Random(100); Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta1.size()); Eigen::VectorXd theta3 = Eigen::VectorXd::Random(theta1.size()); @@ -17,16 +21,17 @@ void read_free_lb_test(Sizes... sizes) { // Read an constrained variable stan::io::deserializer deserializer1(theta1, theta_i); double lp = 0.0; - Ret vec_ref = deserializer1.read_constrain_lb(lb, lp, sizes...); - + Ret vec_ref + = stan::math::apply(deserialize_read, sizes, deserializer1, args..., lp); // Serialize a constrained variable stan::io::serializer serializer(theta2); serializer.write(vec_ref); - // Read a serialized constrained variable and unconstrain it // This is the code that is being tested stan::io::deserializer deserializer2(theta2, theta_i); - Ret uvec_ref = deserializer2.read_free_lb(lb, sizes...); + Ret uvec_ref + = stan::math::apply(deserialize_free, sizes, deserializer2, args...); + // deserialize_free(deserializer2, sizes...); // Serialize the unconstrained variable stan::io::serializer serializer2(theta3); @@ -44,161 +49,131 @@ void read_free_lb_test(Sizes... sizes) { theta3.segment(0, used1)); } -TEST(deserializer_vector, read_free_lb) { - read_free_lb_test(); - read_free_lb_test(4); - read_free_lb_test>(2, 4); - read_free_lb_test>>(3, 2, 4); +template class Deserializer, + typename... Args, typename... Sizes> +void deserializer_test(const std::tuple& sizes, Args&&... args) { + deserializer_test_impl(Deserializer::read(), + Deserializer::free(), sizes, args...); } -// ub - -template -void read_free_ub_test(Sizes... sizes) { - double ub = 0.5; - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(100); - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta1.size()); - Eigen::VectorXd theta3 = Eigen::VectorXd::Random(theta1.size()); - std::vector theta_i; +} // namespace test +} // namespace stan + +template +struct LbConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_lb(args...); + }; + } + static auto free() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_free_lb(args...); + }; + } +}; - // Read an constrained variable - stan::io::deserializer deserializer1(theta1, theta_i); - double lp = 0.0; - Ret vec_ref = deserializer1.read_constrain_ub(ub, lp, sizes...); - - // Serialize a constrained variable - stan::io::serializer serializer(theta2); - serializer.write(vec_ref); - - // Read a serialized constrained variable and unconstrain it - // This is the code that is being tested - stan::io::deserializer deserializer2(theta2, theta_i); - Ret uvec_ref = deserializer2.read_free_ub(ub, sizes...); - - // Serialize the unconstrained variable - stan::io::serializer serializer2(theta3); - serializer2.write(uvec_ref); - - size_t used1 = theta1.size() - deserializer1.available(); - size_t used3 = theta3.size() - serializer2.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used3); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta3.segment(0, used1)); +TEST(deserializer_vector, read_free_lb) { + using stan::test::deserializer_test; + deserializer_test(std::make_tuple(), 0.5); + deserializer_test(std::make_tuple(4), 0.5); + deserializer_test, LbConstrain>( + std::make_tuple(2, 4), 0.5); + deserializer_test>, LbConstrain>( + std::make_tuple(3, 2, 4), 0.5); } +// ub +template +struct UbConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_ub(args...); + }; + } + static auto free() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_free_ub(args...); + }; + } +}; + TEST(deserializer_vector, read_free_ub) { - read_free_ub_test(); - read_free_ub_test(4); - read_free_ub_test>(2, 4); - read_free_ub_test>>(3, 2, 4); + using stan::test::deserializer_test; + deserializer_test(std::make_tuple(), 0.5); + deserializer_test(std::make_tuple(4), 0.5); + deserializer_test, UbConstrain>( + std::make_tuple(2, 4), 0.5); + deserializer_test>, UbConstrain>( + std::make_tuple(3, 2, 4), 0.5); } // lub - -template -void read_free_lub_test(Sizes... sizes) { - double lb = 0.2; - double ub = 0.5; - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(100); - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta1.size()); - Eigen::VectorXd theta3 = Eigen::VectorXd::Random(theta1.size()); - std::vector theta_i; - - // Read an constrained variable - stan::io::deserializer deserializer1(theta1, theta_i); - double lp = 0.0; - Ret vec_ref - = deserializer1.read_constrain_lub(lb, ub, lp, sizes...); - - // Serialize a constrained variable - stan::io::serializer serializer(theta2); - serializer.write(vec_ref); - - // Read a serialized constrained variable and unconstrain it - // This is the code that is being tested - stan::io::deserializer deserializer2(theta2, theta_i); - Ret uvec_ref = deserializer2.read_free_lub(lb, ub, sizes...); - - // Serialize the unconstrained variable - stan::io::serializer serializer2(theta3); - serializer2.write(uvec_ref); - - size_t used1 = theta1.size() - deserializer1.available(); - size_t used3 = theta3.size() - serializer2.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used3); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta3.segment(0, used1)); -} +template +struct LubConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_lub(args...); + }; + } + static auto free() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_free_lub(args...); + }; + } +}; TEST(deserializer_vector, read_free_lub) { - read_free_lub_test(); - read_free_lub_test(4); - read_free_lub_test>(2, 4); - read_free_lub_test>>(3, 2, 4); + using stan::test::deserializer_test; + deserializer_test(std::make_tuple(), 0.2, 0.5); + deserializer_test(std::make_tuple(4), 0.2, + 0.5); + deserializer_test, LubConstrain>( + std::make_tuple(2, 4), 0.2, 0.5); + deserializer_test>, LubConstrain>( + std::make_tuple(3, 2, 4), 0.2, 0.5); } // offset multiplier - -template -void read_free_offset_multiplier_test(Sizes... sizes) { - double offset = 0.5; - double multiplier = 0.35; - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(100); - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta1.size()); - Eigen::VectorXd theta3 = Eigen::VectorXd::Random(theta1.size()); - std::vector theta_i; - - // Read an constrained variable - stan::io::deserializer deserializer1(theta1, theta_i); - double lp = 0.0; - Ret vec_ref = deserializer1.read_constrain_offset_multiplier( - offset, multiplier, lp, sizes...); - - // Serialize a constrained variable - stan::io::serializer serializer(theta2); - serializer.write(vec_ref); - - // Read a serialized constrained variable and unconstrain it - // This is the code that is being tested - stan::io::deserializer deserializer2(theta2, theta_i); - Ret uvec_ref = deserializer2.read_free_offset_multiplier( - offset, multiplier, sizes...); - - // Serialize the unconstrained variable - stan::io::serializer serializer2(theta3); - serializer2.write(uvec_ref); - - size_t used1 = theta1.size() - deserializer1.available(); - size_t used3 = theta3.size() - serializer2.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used3); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta3.segment(0, used1)); -} +template +struct OffsetMultConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_offset_multiplier(args...); + }; + } + static auto free() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_free_offset_multiplier(args...); + }; + } +}; TEST(deserializer_vector, read_free_offset_multiplier) { - read_free_offset_multiplier_test(); - read_free_offset_multiplier_test(4); - read_free_offset_multiplier_test>(2, 4); - read_free_offset_multiplier_test>>( - 3, 2, 4); + using stan::test::deserializer_test; + deserializer_test(std::make_tuple(), 0.2, 0.5); + deserializer_test(std::make_tuple(4), + 0.2, 0.5); + deserializer_test, OffsetMultConstrain>( + std::make_tuple(2, 4), 0.2, 0.5); + deserializer_test>, + OffsetMultConstrain>(std::make_tuple(3, 2, 4), 0.2, 0.5); } // unit vector +template +struct UnitVecConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_unit_vector(args...); + }; + } + static auto free() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_free_unit_vector(args...); + }; + } +}; template void read_free_unit_vector_test(Sizes... sizes) { @@ -242,6 +217,7 @@ void read_free_unit_vector_test(Sizes... sizes) { } TEST(deserializer_vector, read_free_unit_vector) { + using stan::test::deserializer_test; read_free_unit_vector_test(4); read_free_unit_vector_test>(2, 4); read_free_unit_vector_test>>(3, 2, @@ -249,330 +225,249 @@ TEST(deserializer_vector, read_free_unit_vector) { } // simplex +template +struct SimplexConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_simplex(args...); + }; + } + static auto free() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_free_simplex(args...); + }; + } +}; -template -void read_free_simplex_test(Sizes... sizes) { - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(100); - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta1.size()); - Eigen::VectorXd theta3 = Eigen::VectorXd::Random(theta1.size()); - std::vector theta_i; - - // Read an constrained variable - stan::io::deserializer deserializer1(theta1, theta_i); - double lp = 0.0; - Ret vec_ref = deserializer1.read_constrain_simplex(lp, sizes...); - - // Serialize a constrained variable - stan::io::serializer serializer(theta2); - serializer.write(vec_ref); - - // Read a serialized constrained variable and unconstrain it - // This is the code that is being tested - stan::io::deserializer deserializer2(theta2, theta_i); - Ret uvec_ref = deserializer2.read_free_simplex(sizes...); - - // Serialize the unconstrained variable - stan::io::serializer serializer2(theta3); - serializer2.write(uvec_ref); - - size_t used1 = theta1.size() - deserializer1.available(); - size_t used3 = theta3.size() - serializer2.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used3); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta3.segment(0, used1)); +TEST(deserializer_vector, read_free_simplex) { + using stan::test::deserializer_test; + deserializer_test(std::make_tuple(4)); + deserializer_test, SimplexConstrain>( + std::make_tuple(2, 4)); + deserializer_test>, + SimplexConstrain>(std::make_tuple(3, 2, 4)); } -TEST(deserializer_vector, read_free_simplex) { - read_free_simplex_test(4); - read_free_simplex_test>(2, 4); - read_free_simplex_test>>(3, 2, 4); +// sum_to_zero +template +struct SumToZeroConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_sum_to_zero(args...); + }; + } + static auto free() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_free_sum_to_zero(args...); + }; + } +}; + +TEST(deserializer_vector, read_free_sum_to_zero) { + using stan::test::deserializer_test; + deserializer_test(std::make_tuple(4)); + deserializer_test, SumToZeroConstrain>( + std::make_tuple(2, 4)); + deserializer_test>, + SumToZeroConstrain>(std::make_tuple(3, 2, 4)); } // ordered - -template -void read_free_ordered_test(Sizes... sizes) { - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(100); - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta1.size()); - Eigen::VectorXd theta3 = Eigen::VectorXd::Random(theta1.size()); - std::vector theta_i; - - // Read an constrained variable - stan::io::deserializer deserializer1(theta1, theta_i); - double lp = 0.0; - Ret vec_ref = deserializer1.read_constrain_ordered(lp, sizes...); - - // Serialize a constrained variable - stan::io::serializer serializer(theta2); - serializer.write(vec_ref); - - // Read a serialized constrained variable and unconstrain it - // This is the code that is being tested - stan::io::deserializer deserializer2(theta2, theta_i); - Ret uvec_ref = deserializer2.read_free_ordered(sizes...); - - // Serialize the unconstrained variable - stan::io::serializer serializer2(theta3); - serializer2.write(uvec_ref); - - size_t used1 = theta1.size() - deserializer1.available(); - size_t used3 = theta3.size() - serializer2.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used3); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta3.segment(0, used1)); -} +template +struct OrderedConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_ordered(args...); + }; + } + static auto free() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_free_ordered(args...); + }; + } +}; TEST(deserializer_vector, read_free_ordered) { - read_free_ordered_test(4); - read_free_ordered_test>(2, 4); - read_free_ordered_test>>(3, 2, 4); + using stan::test::deserializer_test; + deserializer_test(std::make_tuple(4)); + deserializer_test, OrderedConstrain>( + std::make_tuple(2, 4)); + deserializer_test>, + OrderedConstrain>(std::make_tuple(3, 2, 4)); } // positive_ordered - -template -void read_free_positive_ordered_test(Sizes... sizes) { - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(100); - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta1.size()); - Eigen::VectorXd theta3 = Eigen::VectorXd::Random(theta1.size()); - std::vector theta_i; - - // Read an constrained variable - stan::io::deserializer deserializer1(theta1, theta_i); - double lp = 0.0; - Ret vec_ref - = deserializer1.read_constrain_positive_ordered(lp, sizes...); - - // Serialize a constrained variable - stan::io::serializer serializer(theta2); - serializer.write(vec_ref); - - // Read a serialized constrained variable and unconstrain it - // This is the code that is being tested - stan::io::deserializer deserializer2(theta2, theta_i); - Ret uvec_ref = deserializer2.read_free_positive_ordered(sizes...); - - // Serialize the unconstrained variable - stan::io::serializer serializer2(theta3); - serializer2.write(uvec_ref); - - size_t used1 = theta1.size() - deserializer1.available(); - size_t used3 = theta3.size() - serializer2.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used3); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta3.segment(0, used1)); -} +template +struct PositiveOrderedConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_positive_ordered(args...); + }; + } + static auto free() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_free_positive_ordered(args...); + }; + } +}; TEST(deserializer_vector, read_free_positive_ordered) { - read_free_positive_ordered_test(4); - read_free_positive_ordered_test>(2, 4); - read_free_positive_ordered_test>>( - 3, 2, 4); + using stan::test::deserializer_test; + deserializer_test( + std::make_tuple(4)); + deserializer_test, PositiveOrderedConstrain>( + std::make_tuple(2, 4)); + deserializer_test>, + PositiveOrderedConstrain>(std::make_tuple(3, 2, 4)); } // cholesky_factor_cov - -template -void read_free_cholesky_factor_cov_test(Sizes... sizes) { - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(100); - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta1.size()); - Eigen::VectorXd theta3 = Eigen::VectorXd::Random(theta1.size()); - std::vector theta_i; - - // Read an constrained variable - stan::io::deserializer deserializer1(theta1, theta_i); - double lp = 0.0; - Ret vec_ref = deserializer1.read_constrain_cholesky_factor_cov( - lp, sizes...); - - // Serialize a constrained variable - stan::io::serializer serializer(theta2); - serializer.write(vec_ref); - - // Read a serialized constrained variable and unconstrain it - // This is the code that is being tested - stan::io::deserializer deserializer2(theta2, theta_i); - Ret uvec_ref = deserializer2.read_free_cholesky_factor_cov(sizes...); - - // Serialize the unconstrained variable - stan::io::serializer serializer2(theta3); - serializer2.write(uvec_ref); - - size_t used1 = theta1.size() - deserializer1.available(); - size_t used3 = theta3.size() - serializer2.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used3); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta3.segment(0, used1)); -} - +template +struct CholFacCovConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_cholesky_factor_cov( + args...); + }; + } + static auto free() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_free_cholesky_factor_cov(args...); + }; + } +}; TEST(deserializer_vector, read_free_cholesky_factor_cov) { - read_free_cholesky_factor_cov_test(4, 3); - read_free_cholesky_factor_cov_test>(2, 4, 3); - read_free_cholesky_factor_cov_test>>( - 3, 2, 4, 3); - - read_free_cholesky_factor_cov_test(2, 2); - read_free_cholesky_factor_cov_test>(2, 2, 2); - read_free_cholesky_factor_cov_test>>( - 3, 2, 2, 2); + using stan::test::deserializer_test; + deserializer_test( + std::make_tuple(4, 3)); + deserializer_test, CholFacCovConstrain>( + std::make_tuple(2, 4, 3)); + deserializer_test>, + CholFacCovConstrain>(std::make_tuple(3, 2, 4, 3)); + + deserializer_test( + std::make_tuple(2, 2)); + deserializer_test, CholFacCovConstrain>( + std::make_tuple(2, 2, 2)); + deserializer_test>, + CholFacCovConstrain>(std::make_tuple(3, 2, 2, 2)); } // cholesky_factor_corr - -template -void read_free_cholesky_factor_corr_test(Sizes... sizes) { - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(100); - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta1.size()); - Eigen::VectorXd theta3 = Eigen::VectorXd::Random(theta1.size()); - std::vector theta_i; - - // Read an constrained variable - stan::io::deserializer deserializer1(theta1, theta_i); - double lp = 0.0; - Ret vec_ref = deserializer1.read_constrain_cholesky_factor_corr( - lp, sizes...); - - // Serialize a constrained variable - stan::io::serializer serializer(theta2); - serializer.write(vec_ref); - - // Read a serialized constrained variable and unconstrain it - // This is the code that is being tested - stan::io::deserializer deserializer2(theta2, theta_i); - Ret uvec_ref = deserializer2.read_free_cholesky_factor_corr(sizes...); - - // Serialize the unconstrained variable - stan::io::serializer serializer2(theta3); - serializer2.write(uvec_ref); - - size_t used1 = theta1.size() - deserializer1.available(); - size_t used3 = theta3.size() - serializer2.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used3); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta3.segment(0, used1)); -} - +template +struct CholFacCorrConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_cholesky_factor_corr( + args...); + }; + } + static auto free() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_free_cholesky_factor_corr(args...); + }; + } +}; TEST(deserializer_vector, read_free_cholesky_factor_corr) { - read_free_cholesky_factor_corr_test(2); - read_free_cholesky_factor_corr_test>(2, 2); - read_free_cholesky_factor_corr_test< - std::vector>>(3, 2, 2); + using stan::test::deserializer_test; + deserializer_test(std::make_tuple(2)); + deserializer_test, CholFacCorrConstrain>( + std::make_tuple(2, 2)); + deserializer_test>, + CholFacCorrConstrain>(std::make_tuple(3, 2, 2)); } // cov_matrix - -template -void read_free_cov_matrix_test(Sizes... sizes) { - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(100); - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta1.size()); - Eigen::VectorXd theta3 = Eigen::VectorXd::Random(theta1.size()); - std::vector theta_i; - - // Read an constrained variable - stan::io::deserializer deserializer1(theta1, theta_i); - double lp = 0.0; - Ret vec_ref - = deserializer1.read_constrain_cov_matrix(lp, sizes...); - - // Serialize a constrained variable - stan::io::serializer serializer(theta2); - serializer.write(vec_ref); - - // Read a serialized constrained variable and unconstrain it - // This is the code that is being tested - stan::io::deserializer deserializer2(theta2, theta_i); - Ret uvec_ref = deserializer2.read_free_cov_matrix(sizes...); - - // Serialize the unconstrained variable - stan::io::serializer serializer2(theta3); - serializer2.write(uvec_ref); - - size_t used1 = theta1.size() - deserializer1.available(); - size_t used3 = theta3.size() - serializer2.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used3); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta3.segment(0, used1)); -} - +template +struct CovMatConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_cov_matrix(args...); + }; + } + static auto free() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_free_cov_matrix(args...); + }; + } +}; TEST(deserializer_vector, read_free_cov_matrix) { - read_free_cov_matrix_test(2); - read_free_cov_matrix_test>(2, 2); - read_free_cov_matrix_test>>(3, 2, 2); + using stan::test::deserializer_test; + deserializer_test(std::make_tuple(2)); + deserializer_test, CovMatConstrain>( + std::make_tuple(2, 2)); + deserializer_test>, CovMatConstrain>( + std::make_tuple(3, 2, 2)); } // corr_matrix +template +struct CorrMatConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_corr_matrix(args...); + }; + } + static auto free() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_free_corr_matrix(args...); + }; + } +}; +TEST(deserializer_vector, read_free_corr_matrix) { + using stan::test::deserializer_test; + deserializer_test(std::make_tuple(2)); + deserializer_test, CorrMatConstrain>( + std::make_tuple(2, 2)); + deserializer_test>, + CorrMatConstrain>(std::make_tuple(3, 2, 2)); +} -template -void read_free_corr_matrix_test(Sizes... sizes) { - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(100); - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta1.size()); - Eigen::VectorXd theta3 = Eigen::VectorXd::Random(theta1.size()); - std::vector theta_i; - - // Read an constrained variable - stan::io::deserializer deserializer1(theta1, theta_i); - double lp = 0.0; - Ret vec_ref - = deserializer1.read_constrain_corr_matrix(lp, sizes...); - - // Serialize a constrained variable - stan::io::serializer serializer(theta2); - serializer.write(vec_ref); - - // Read a serialized constrained variable and unconstrain it - // This is the code that is being tested - stan::io::deserializer deserializer2(theta2, theta_i); - Ret uvec_ref = deserializer2.read_free_corr_matrix(sizes...); - - // Serialize the unconstrained variable - stan::io::serializer serializer2(theta3); - serializer2.write(uvec_ref); - - size_t used1 = theta1.size() - deserializer1.available(); - size_t used3 = theta3.size() - serializer2.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used3); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta3.segment(0, used1)); +// stochastic_column +template +struct StochasticCol { + static auto read() { + return [](stan::io::deserializer& deserializer, auto& lp, + auto... sizes) { + return deserializer.read_constrain_stochastic_column( + lp, sizes...); + }; + } + static auto free() { + return [](stan::io::deserializer& deserializer, auto... sizes) { + return deserializer.read_free_stochastic_column(sizes...); + }; + } +}; +TEST(deserializer_vector, read_stochastic_column_matrix) { + using stan::test::deserializer_test; + deserializer_test(std::make_tuple(3, 3)); + deserializer_test, StochasticCol>( + std::make_tuple(2, 3, 3)); + deserializer_test>, StochasticCol>( + std::make_tuple(3, 2, 3, 3)); } -TEST(deserializer_vector, read_free_corr_matrix) { - read_free_corr_matrix_test(2); - read_free_corr_matrix_test>(2, 2); - read_free_corr_matrix_test>>(3, 2, - 2); +template +struct StochasticRow { + static auto read() { + return [](stan::io::deserializer& deserializer, auto& lp, + auto... sizes) { + return deserializer.read_constrain_stochastic_row(lp, + sizes...); + }; + } + static auto free() { + return [](stan::io::deserializer& deserializer, auto... sizes) { + return deserializer.read_free_stochastic_row(sizes...); + }; + } +}; +TEST(deserializer_vector, read_stochastic_row_matrix) { + using stan::test::deserializer_test; + deserializer_test(std::make_tuple(3, 3)); + deserializer_test, StochasticRow>( + std::make_tuple(2, 3, 3)); + deserializer_test>, StochasticRow>( + std::make_tuple(3, 2, 3, 3)); } diff --git a/src/test/unit/io/deserializer_stdvector_test.cpp b/src/test/unit/io/deserializer_stdvector_test.cpp index ad2a884a61f..e934f6e42e2 100644 --- a/src/test/unit/io/deserializer_stdvector_test.cpp +++ b/src/test/unit/io/deserializer_stdvector_test.cpp @@ -129,6 +129,52 @@ TEST(deserializer_array, simplex) { } } +// sum_to_zero + +TEST(deserializer_array, sum_to_zero) { + std::vector theta_i; + std::vector theta; + for (size_t i = 0; i < 100U; ++i) + theta.push_back(static_cast(i)); + + stan::io::deserializer deserializer1(theta, theta_i); + stan::io::deserializer deserializer2(theta, theta_i); + + // no jac + { + double lp_ref = 0.0; + double lp = 0.0; + auto y + = deserializer1 + .read_constrain_sum_to_zero, false>( + lp, 4, 3); + for (size_t i = 0; i < 4; ++i) { + stan::test::expect_near_rel( + "test_std_vector_deserializer", y[i], + deserializer2.read_constrain_sum_to_zero( + lp_ref, 3)); + } + EXPECT_FLOAT_EQ(lp_ref, lp); + } + + // jac + { + double lp_ref = 0.0; + double lp = 0.0; + auto y + = deserializer1 + .read_constrain_sum_to_zero, true>( + lp, 4, 3); + for (size_t i = 0; i < 4; ++i) { + stan::test::expect_near_rel( + "test_std_vector_deserializer", y[i], + deserializer2.read_constrain_sum_to_zero( + lp_ref, 3)); + } + EXPECT_FLOAT_EQ(lp_ref, lp); + } +} + // ordered TEST(deserializer_array, ordered) { diff --git a/src/test/unit/io/deserializer_test.cpp b/src/test/unit/io/deserializer_test.cpp index 08d9afeb2fe..71940e6c904 100644 --- a/src/test/unit/io/deserializer_test.cpp +++ b/src/test/unit/io/deserializer_test.cpp @@ -672,6 +672,48 @@ TEST(deserializer_vector, simplex_jacobian) { EXPECT_FLOAT_EQ(lp_ref, lp); } +// sum_to_zero + +TEST(deserializer_vector, sum_to_zero_constrain) { + std::vector theta_i; + std::vector theta; + theta.push_back(3.0); + theta.push_back(-1.0); + theta.push_back(-2.0); + theta.push_back(0.0); + stan::io::deserializer deserializer(theta, theta_i); + double lp = 0; + Eigen::VectorXd reference + = stan::math::sum_to_zero_constrain(stan::math::to_vector(theta)); + Eigen::VectorXd phi( + deserializer.read_constrain_sum_to_zero( + lp, theta.size() + 1)); + for (size_t i = 0; i < phi.size(); ++i) { + EXPECT_FLOAT_EQ(reference(i), phi[i]); + } +} + +TEST(deserializer_vector, sum_to_zero_jacobian) { + std::vector theta_i; + std::vector theta; + theta.push_back(3.0); + theta.push_back(-1.0); + theta.push_back(-2.0); + theta.push_back(0.0); + stan::io::deserializer deserializer(theta, theta_i); + double lp = 0.0; + double lp_ref = 0.0; + Eigen::VectorXd reference + = stan::math::sum_to_zero_constrain(stan::math::to_vector(theta), lp_ref); + Eigen::VectorXd phi( + deserializer.read_constrain_sum_to_zero( + lp, theta.size() + 1)); + for (size_t i = 0; i < phi.size(); ++i) { + EXPECT_FLOAT_EQ(reference(i), phi[i]); + } + EXPECT_FLOAT_EQ(lp_ref, lp); +} + // ordered TEST(deserializer_vector, ordered_constrain) { diff --git a/src/test/unit/io/deserializer_var_test.cpp b/src/test/unit/io/deserializer_var_test.cpp index de62573b639..cdfa70c0b17 100644 --- a/src/test/unit/io/deserializer_var_test.cpp +++ b/src/test/unit/io/deserializer_var_test.cpp @@ -597,6 +597,49 @@ TEST(deserializer_vector, simplex_jacobian) { EXPECT_FLOAT_EQ(lp_ref.val(), lp.val()); } +// sum_to_zero + +TEST(deserializer_vector, sum_to_zero_constrain) { + std::vector theta_i; + std::vector theta; + theta.push_back(3.0); + theta.push_back(-1.0); + theta.push_back(-2.0); + theta.push_back(0.0); + stan::io::deserializer deserializer(theta, theta_i); + stan::math::var lp = 0; + Eigen::Matrix reference + = stan::math::sum_to_zero_constrain(stan::math::to_vector(theta)); + Eigen::Matrix phi( + deserializer.read_constrain_sum_to_zero< + Eigen::Matrix, false>( + lp, theta.size() + 1)); + for (size_t i = 0; i < phi.size(); ++i) { + EXPECT_FLOAT_EQ(reference(i).val(), phi[i].val()); + } +} + +TEST(deserializer_vector, sum_to_zero_jacobian) { + std::vector theta_i; + std::vector theta; + theta.push_back(3.0); + theta.push_back(-1.0); + theta.push_back(-2.0); + theta.push_back(0.0); + stan::io::deserializer deserializer(theta, theta_i); + stan::math::var lp = 0.0; + stan::math::var lp_ref = 0.0; + Eigen::Matrix reference + = stan::math::sum_to_zero_constrain(stan::math::to_vector(theta), lp_ref); + Eigen::Matrix phi( + deserializer.read_constrain_sum_to_zero< + Eigen::Matrix, true>( + lp, theta.size() + 1)); + for (size_t i = 0; i < phi.size(); ++i) { + EXPECT_FLOAT_EQ(reference(i).val(), phi[i].val()); + } + EXPECT_FLOAT_EQ(lp_ref.val(), lp.val()); +} // ordered TEST(deserializer_vector, ordered_constrain) { diff --git a/src/test/unit/io/deserializer_varmat_test.cpp b/src/test/unit/io/deserializer_varmat_test.cpp index 0f583402f5c..34d08a66c60 100644 --- a/src/test/unit/io/deserializer_varmat_test.cpp +++ b/src/test/unit/io/deserializer_varmat_test.cpp @@ -325,6 +325,44 @@ TEST(deserializer, read_constrain_simplex_jacobian) { EXPECT_FLOAT_EQ(lp_ref.val(), lp.val()); } +// sum_to_zero + +TEST(deserializer, read_constrain_sum_to_zero_constrain) { + std::vector theta_i; + std::vector theta; + theta.push_back(-2.0); + theta.push_back(3.0); + theta.push_back(-1.0); + theta.push_back(0.0); + stan::io::deserializer deserializer(theta, theta_i); + stan::math::var lp = 0.0; + auto reference + = stan::math::sum_to_zero_constrain(stan::math::to_vector(theta)); + auto y = deserializer.read_constrain_sum_to_zero( + lp, theta.size() + 1); + EXPECT_TRUE((std::is_same::value)); + stan::test::expect_near_rel("deserializer tests", reference.val(), y.val()); +} + +TEST(deserializer, read_constrain_sum_to_zero_jacobian) { + std::vector theta_i; + std::vector theta; + theta.push_back(-2.0); + theta.push_back(3.0); + theta.push_back(-1.0); + theta.push_back(0.0); + stan::io::deserializer deserializer(theta, theta_i); + stan::math::var lp_ref = 0.0; + stan::math::var lp = 0.0; + auto reference + = stan::math::sum_to_zero_constrain(stan::math::to_vector(theta), lp_ref); + auto y = deserializer.read_constrain_sum_to_zero( + lp, theta.size() + 1); + EXPECT_TRUE((std::is_same::value)); + stan::test::expect_near_rel("deserializer tests", reference.val(), y.val()); + EXPECT_FLOAT_EQ(lp_ref.val(), lp.val()); +} + // ordered TEST(deserializer, read_constrain_ordered_constrain) { diff --git a/src/test/unit/io/serializer_test.cpp b/src/test/unit/io/serializer_test.cpp index f6ab9fc742d..909df7949c0 100644 --- a/src/test/unit/io/serializer_test.cpp +++ b/src/test/unit/io/serializer_test.cpp @@ -291,96 +291,28 @@ TEST(serializer, eos_exception) { } } -template -void write_free_lb_test(Sizes... sizes) { - double lb = 0.5; +namespace stan { +namespace test { +template +void serializer_test_impl(DeserializeRead&& deserialize_read, + SerializeFree&& serialize_free, + const std::tuple& sizes, Args&&... args) { constexpr size_t theta_size = 100; Eigen::VectorXd theta1 = Eigen::VectorXd::Random(theta_size); - std::vector theta_i; - - // Read an constrained variable - stan::io::deserializer deserializer(theta1, theta_i); - double lp = 0.0; - Ret vec_ref = deserializer.read_constrain_lb(lb, lp, sizes...); - - // Serialize a constrained variable - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta_size); - stan::io::serializer serializer(theta2); - serializer.write_free_lb(lb, vec_ref); - - size_t used1 = theta1.size() - deserializer.available(); - size_t used2 = theta2.size() - serializer.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used2); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta2.segment(0, used1)); -} - -TEST(serializer_vectorized, write_free_lb) { - write_free_lb_test(); - write_free_lb_test(4); - write_free_lb_test>(2, 4); - write_free_lb_test>>(3, 2, 4); -} - -template -void write_free_ub_test(Sizes... sizes) { - double ub = 0.5; - constexpr size_t theta_size = 100; - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(theta_size); - std::vector theta_i; - - // Read an constrained variable - stan::io::deserializer deserializer(theta1, theta_i); - double lp = 0.0; - Ret vec_ref = deserializer.read_constrain_ub(ub, lp, sizes...); - - // Serialize a constrained variable Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta_size); - stan::io::serializer serializer(theta2); - serializer.write_free_ub(ub, vec_ref); - - size_t used1 = theta1.size() - deserializer.available(); - size_t used2 = theta2.size() - serializer.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used2); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta2.segment(0, used1)); -} - -TEST(serializer_vectorized, write_free_ub) { - write_free_ub_test(); - write_free_ub_test(4); - write_free_ub_test>(2, 4); - write_free_ub_test>>(3, 2, 4); -} - -template -void write_free_lub_test(Sizes... sizes) { - double ub = 0.5; - double lb = 0.1; - constexpr size_t theta_size = 100; - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(theta_size); std::vector theta_i; // Read an constrained variable stan::io::deserializer deserializer(theta1, theta_i); double lp = 0.0; Ret vec_ref - = deserializer.read_constrain_lub(lb, ub, lp, sizes...); + = stan::math::apply(deserialize_read, sizes, deserializer, args..., lp); // Serialize a constrained variable - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta_size); stan::io::serializer serializer(theta2); - serializer.write_free_lub(lb, ub, vec_ref); + // serializer.write_free_lb(lb, vec_ref); + serialize_free(serializer, args..., vec_ref); size_t used1 = theta1.size() - deserializer.available(); size_t used2 = theta2.size() - serializer.available(); @@ -394,51 +326,116 @@ void write_free_lub_test(Sizes... sizes) { theta2.segment(0, used1)); } -TEST(serializer_vectorized, write_free_lub) { - write_free_lub_test(); - write_free_lub_test(4); - write_free_lub_test>(2, 4); - write_free_lub_test>>(3, 2, 4); +template class Serializer, typename... Args, + typename... Sizes> +void serializer_test(const std::tuple& sizes, Args&&... args) { + serializer_test_impl(Serializer::read(), Serializer::free(), + sizes, args...); } -template -void write_free_offset_multiplier_test(Sizes... sizes) { - double offset = 0.5; - double multiplier = 0.35; - constexpr size_t theta_size = 100; - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(theta_size); - std::vector theta_i; +} // namespace test +} // namespace stan - // Read an constrained variable - stan::io::deserializer deserializer(theta1, theta_i); - double lp = 0.0; - Ret vec_ref = deserializer.read_constrain_offset_multiplier( - offset, multiplier, lp, sizes...); - - // Serialize a constrained variable - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta_size); - stan::io::serializer serializer(theta2); - serializer.write_free_offset_multiplier(offset, multiplier, vec_ref); +template +struct LbConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_lb(args...); + }; + } + static auto free() { + return [](stan::io::serializer& serial, auto&&... args) { + return serial.write_free_lb(args...); + }; + } +}; - size_t used1 = theta1.size() - deserializer.available(); - size_t used2 = theta2.size() - serializer.available(); +TEST(serializer_vectorized, write_free_lb) { + using stan::test::serializer_test; + serializer_test(std::make_tuple(), 0.5); + serializer_test(std::make_tuple(4), 0.5); + serializer_test, LbConstrain>( + std::make_tuple(2, 4), 0.5); + serializer_test>, LbConstrain>( + std::make_tuple(3, 2, 4), 0.5); +} + +// ub +template +struct UbConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_ub(args...); + }; + } + static auto free() { + return [](stan::io::serializer& serial, auto&&... args) { + return serial.write_free_ub(args...); + }; + } +}; - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used2); +TEST(serializer_vectorized, write_free_ub) { + using stan::test::serializer_test; + serializer_test(std::make_tuple(), 0.5); + serializer_test(std::make_tuple(4), 0.5); + serializer_test, UbConstrain>( + std::make_tuple(2, 4), 0.5); + serializer_test>, UbConstrain>( + std::make_tuple(3, 2, 4), 0.5); +} + +// lub +template +struct LubConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_lub(args...); + }; + } + static auto free() { + return [](stan::io::serializer& serial, auto&&... args) { + return serial.write_free_lub(args...); + }; + } +}; - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta2.segment(0, used1)); -} +TEST(serializer_vectorized, write_free_lub) { + using stan::test::serializer_test; + serializer_test(std::make_tuple(), 0.2, 0.5); + serializer_test(std::make_tuple(4), 0.2, 0.5); + serializer_test, LubConstrain>( + std::make_tuple(2, 4), 0.2, 0.5); + serializer_test>, LubConstrain>( + std::make_tuple(3, 2, 4), 0.2, 0.5); +} + +// offset multiplier +template +struct OffsetMultConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_offset_multiplier(args...); + }; + } + static auto free() { + return [](stan::io::serializer& serial, auto&&... args) { + return serial.write_free_offset_multiplier(args...); + }; + } +}; TEST(serializer_vectorized, write_free_offset_multiplier) { - write_free_offset_multiplier_test(); - write_free_offset_multiplier_test(4); - write_free_offset_multiplier_test>(2, 4); - write_free_offset_multiplier_test>>( - 3, 2, 4); + using stan::test::serializer_test; + serializer_test(std::make_tuple(), 0.2, 0.5); + serializer_test(std::make_tuple(4), 0.2, + 0.5); + serializer_test, OffsetMultConstrain>( + std::make_tuple(2, 4), 0.2, 0.5); + serializer_test>, + OffsetMultConstrain>(std::make_tuple(3, 2, 4), 0.2, 0.5); } + template void write_free_unit_vector_test(Sizes... sizes) { Eigen::VectorXd theta1 = Eigen::VectorXd::Random(100); @@ -477,259 +474,253 @@ TEST(serializer_vectorized, write_free_unit_vector) { 4); } -template -void write_free_simplex_test(Sizes... sizes) { - constexpr size_t theta_size = 100; - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(theta_size); - std::vector theta_i; - - // Read an constrained variable - stan::io::deserializer deserializer(theta1, theta_i); - double lp = 0.0; - Ret vec_ref = deserializer.read_constrain_simplex(lp, sizes...); - - // Serialize a constrained variable - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta_size); - stan::io::serializer serializer(theta2); - serializer.write_free_simplex(vec_ref); - - size_t used1 = theta1.size() - deserializer.available(); - size_t used2 = theta2.size() - serializer.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used2); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta2.segment(0, used1)); -} +// simplex +template +struct SimplexConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_simplex(args...); + }; + } + static auto free() { + return [](stan::io::serializer& serial, auto&&... args) { + return serial.write_free_simplex(args...); + }; + } +}; TEST(serializer_vectorized, write_free_simplex) { - write_free_simplex_test(4); - write_free_simplex_test>(2, 4); - write_free_simplex_test>>(3, 2, 4); + using stan::test::serializer_test; + serializer_test(std::make_tuple(4)); + serializer_test, SimplexConstrain>( + std::make_tuple(2, 4)); + serializer_test>, SimplexConstrain>( + std::make_tuple(3, 2, 4)); +} + +// sum_to_zero +template +struct SumToZeroConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_sum_to_zero(args...); + }; + } + static auto free() { + return [](stan::io::serializer& serial, auto&&... args) { + return serial.write_free_sum_to_zero(args...); + }; + } +}; + +TEST(serializer_vectorized, write_free_sum_to_zero) { + using stan::test::serializer_test; + serializer_test(std::make_tuple(4)); + serializer_test, SumToZeroConstrain>( + std::make_tuple(2, 4)); + serializer_test>, + SumToZeroConstrain>(std::make_tuple(3, 2, 4)); } // ordered -template -void write_free_ordered_test(Sizes... sizes) { - // Read an constrained variable - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(100); - std::vector theta_i; - stan::io::deserializer deserializer(theta1, theta_i); - double lp = 0.0; - Ret vec_ref = deserializer.read_constrain_ordered(lp, sizes...); - - // Serialize a constrained variable - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta1.size()); - stan::io::serializer serializer(theta2); - serializer.write_free_ordered(vec_ref); - - size_t used1 = theta1.size() - deserializer.available(); - size_t used2 = theta2.size() - serializer.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used2); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta2.segment(0, used1)); -} +template +struct OrderedConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_ordered(args...); + }; + } + static auto free() { + return [](stan::io::serializer& serial, auto&&... args) { + return serial.write_free_ordered(args...); + }; + } +}; TEST(serializer_vectorized, write_free_ordered) { - write_free_ordered_test(4); - write_free_ordered_test>(2, 4); - write_free_ordered_test>>(3, 2, 4); + using stan::test::serializer_test; + serializer_test(std::make_tuple(4)); + serializer_test, OrderedConstrain>( + std::make_tuple(2, 4)); + serializer_test>, OrderedConstrain>( + std::make_tuple(3, 2, 4)); } // positive_ordered - -template -void write_free_positive_ordered_test(Sizes... sizes) { - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(100); - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta1.size()); - std::vector theta_i; - - // Read an constrained variable - stan::io::deserializer deserializer(theta1, theta_i); - double lp = 0.0; - Ret vec_ref - = deserializer.read_constrain_positive_ordered(lp, sizes...); - - // Serialize a constrained variable - stan::io::serializer serializer(theta2); - serializer.write_free_positive_ordered(vec_ref); - - size_t used1 = theta1.size() - deserializer.available(); - size_t used2 = theta2.size() - serializer.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used2); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta2.segment(0, used1)); -} +template +struct PositiveOrderedConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_positive_ordered(args...); + }; + } + static auto free() { + return [](stan::io::serializer& serial, auto&&... args) { + return serial.write_free_positive_ordered(args...); + }; + } +}; TEST(serializer_vectorized, write_free_positive_ordered) { - write_free_positive_ordered_test(4); - write_free_positive_ordered_test>(2, 4); - write_free_positive_ordered_test>>( - 3, 2, 4); + using stan::test::serializer_test; + serializer_test( + std::make_tuple(4)); + serializer_test, PositiveOrderedConstrain>( + std::make_tuple(2, 4)); + serializer_test>, + PositiveOrderedConstrain>(std::make_tuple(3, 2, 4)); } // cholesky_factor_cov - -template -void write_free_cholesky_factor_cov_test(Sizes... sizes) { - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(100); - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta1.size()); - std::vector theta_i; - - // Read an constrained variable - stan::io::deserializer deserializer(theta1, theta_i); - double lp = 0.0; - Ret vec_ref = deserializer.read_constrain_cholesky_factor_cov( - lp, sizes...); - - // Serialize a constrained variable - stan::io::serializer serializer(theta2); - serializer.write_free_cholesky_factor_cov(vec_ref); - - size_t used1 = theta1.size() - deserializer.available(); - size_t used2 = theta2.size() - serializer.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used2); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta2.segment(0, used1)); -} +template +struct CholFacCovConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_cholesky_factor_cov( + args...); + }; + } + static auto free() { + return [](stan::io::serializer& serial, auto&&... args) { + return serial.write_free_cholesky_factor_cov(args...); + }; + } +}; TEST(serializer_vectorized, write_free_cholesky_factor_cov) { - write_free_cholesky_factor_cov_test(4, 3); - write_free_cholesky_factor_cov_test>(2, 4, 3); - write_free_cholesky_factor_cov_test< - std::vector>>(3, 2, 4, 3); - - write_free_cholesky_factor_cov_test(2, 2); - write_free_cholesky_factor_cov_test>(2, 2, 2); - write_free_cholesky_factor_cov_test< - std::vector>>(3, 2, 2, 2); + using stan::test::serializer_test; + serializer_test(std::make_tuple(4, 3)); + serializer_test, CholFacCovConstrain>( + std::make_tuple(2, 4, 3)); + serializer_test>, + CholFacCovConstrain>(std::make_tuple(3, 2, 4, 3)); + + serializer_test(std::make_tuple(2, 2)); + serializer_test, CholFacCovConstrain>( + std::make_tuple(2, 2, 2)); + serializer_test>, + CholFacCovConstrain>(std::make_tuple(3, 2, 2, 2)); } // cholesky_factor_corr - -template -void write_free_cholesky_factor_corr_test(Sizes... sizes) { - // Read an constrained variable - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(100); - std::vector theta_i; - stan::io::deserializer deserializer(theta1, theta_i); - double lp = 0.0; - Ret vec_ref = deserializer.read_constrain_cholesky_factor_corr( - lp, sizes...); - - // Serialize a constrained variable - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta1.size()); - stan::io::serializer serializer(theta2); - serializer.write_free_cholesky_factor_corr(vec_ref); - - size_t used1 = theta1.size() - deserializer.available(); - size_t used2 = theta2.size() - serializer.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used2); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta2.segment(0, used1)); -} +template +struct CholFacCorrConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_cholesky_factor_corr( + args...); + }; + } + static auto free() { + return [](stan::io::serializer& serial, auto&&... args) { + return serial.write_free_cholesky_factor_corr(args...); + }; + } +}; TEST(serializer_vectorized, write_free_cholesky_factor_corr) { - write_free_cholesky_factor_corr_test(2); - write_free_cholesky_factor_corr_test>(2, 2); - write_free_cholesky_factor_corr_test< - std::vector>>(3, 2, 2); + using stan::test::serializer_test; + serializer_test(std::make_tuple(2)); + serializer_test, CholFacCorrConstrain>( + std::make_tuple(2, 2)); + serializer_test>, + CholFacCorrConstrain>(std::make_tuple(3, 2, 2)); } // cov_matrix - -template -void write_free_cov_matrix_test(Sizes... sizes) { - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(100); - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta1.size()); - std::vector theta_i; - - // Read an constrained variable - stan::io::deserializer deserializer(theta1, theta_i); - double lp = 0.0; - Ret vec_ref - = deserializer.read_constrain_cov_matrix(lp, sizes...); - - // Serialize a constrained variable - stan::io::serializer serializer(theta2); - serializer.write_free_cov_matrix(vec_ref); - - size_t used1 = theta1.size() - deserializer.available(); - size_t used2 = theta2.size() - serializer.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used2); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta2.segment(0, used1)); -} +template +struct CovMatConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_cov_matrix(args...); + }; + } + static auto free() { + return [](stan::io::serializer& serializer, auto&&... args) { + return serializer.write_free_cov_matrix(args...); + }; + } +}; TEST(serializer_vectorized, write_free_cov_matrix) { - write_free_cov_matrix_test(2); - write_free_cov_matrix_test>(2, 2); - write_free_cov_matrix_test>>(3, 2, - 2); + using stan::test::serializer_test; + serializer_test(std::make_tuple(2)); + serializer_test, CovMatConstrain>( + std::make_tuple(2, 2)); + serializer_test>, CovMatConstrain>( + std::make_tuple(3, 2, 2)); } // corr_matrix - -template -void write_free_corr_matrix_test(Sizes... sizes) { - Eigen::VectorXd theta1 = Eigen::VectorXd::Random(100); - Eigen::VectorXd theta2 = Eigen::VectorXd::Random(theta1.size()); - std::vector theta_i; - - // Read an constrained variable - stan::io::deserializer deserializer(theta1, theta_i); - double lp = 0.0; - Ret vec_ref - = deserializer.read_constrain_corr_matrix(lp, sizes...); - - // Serialize a constrained variable - stan::io::serializer serializer(theta2); - serializer.write_free_corr_matrix(vec_ref); - - size_t used1 = theta1.size() - deserializer.available(); - size_t used2 = theta2.size() - serializer.available(); - - // Number of variables read should equal number of variables written - EXPECT_EQ(used1, used2); - - // Make sure the variables written back are the same - stan::test::expect_near_rel("deserializer read free", - theta1.segment(0, used1), - theta2.segment(0, used1)); -} +template +struct CorrMatConstrain { + static auto read() { + return [](stan::io::deserializer& deserializer, auto&&... args) { + return deserializer.read_constrain_corr_matrix(args...); + }; + } + static auto free() { + return [](stan::io::serializer& serial, auto&&... args) { + return serial.write_free_corr_matrix(args...); + }; + } +}; TEST(serializer_vectorized, write_free_corr_matrix) { - write_free_corr_matrix_test(2); - write_free_corr_matrix_test>(2, 2); - write_free_corr_matrix_test>>(3, 2, - 2); + using stan::test::serializer_test; + serializer_test(std::make_tuple(2)); + serializer_test, CorrMatConstrain>( + std::make_tuple(2, 2)); + serializer_test>, CorrMatConstrain>( + std::make_tuple(3, 2, 2)); +} + +// stochastic_column +template +struct StochasticCol { + static auto read() { + return [](stan::io::deserializer& deserializer, auto& lp, + auto... sizes) { + return deserializer.read_constrain_stochastic_column( + lp, sizes...); + }; + } + static auto free() { + return [](stan::io::serializer& serializer, auto&&... args) { + return serializer.write_free_stochastic_column(args...); + }; + } +}; +TEST(deserializer_vector, read_stochastic_column_matrix) { + using stan::test::serializer_test; + serializer_test(std::make_tuple(3, 3)); + serializer_test, StochasticCol>( + std::make_tuple(2, 3, 3)); + serializer_test>, StochasticCol>( + std::make_tuple(3, 2, 3, 3)); +} + +template +struct StochasticRow { + static auto read() { + return [](stan::io::deserializer& deserializer, auto& lp, + auto... sizes) { + return deserializer.read_constrain_stochastic_row(lp, + sizes...); + }; + } + static auto free() { + return [](stan::io::serializer& serializer, auto&&... args) { + return serializer.write_free_stochastic_row(args...); + }; + } +}; +TEST(deserializer_vector, read_stochastic_row_matrix) { + using stan::test::serializer_test; + serializer_test(std::make_tuple(3, 3)); + serializer_test, StochasticRow>( + std::make_tuple(2, 3, 3)); + serializer_test>, StochasticRow>( + std::make_tuple(3, 2, 3, 3)); } diff --git a/src/test/unit/io/stan_csv_reader_test.cpp b/src/test/unit/io/stan_csv_reader_test.cpp index 8ee33547d2d..864bd1512e8 100644 --- a/src/test/unit/io/stan_csv_reader_test.cpp +++ b/src/test/unit/io/stan_csv_reader_test.cpp @@ -1,5 +1,6 @@ #include #include +#include #include #include #include @@ -22,6 +23,13 @@ class StanIoStanCsvReader : public testing::Test { "src/test/unit/io/test_csv_files/blocker_nondiag.0.csv"); eight_schools_stream.open( "src/test/unit/io/test_csv_files/eight_schools.csv"); + + bernoulli_thin_stream.open( + "src/test/unit/io/test_csv_files/bernoulli_thin.csv"); + bernoulli_warmup_stream.open( + "src/test/unit/io/test_csv_files/bernoulli_warmup.csv"); + fixed_param_stream.open( + "src/test/unit/io/test_csv_files/fixed_param_output.csv"); } void TearDown() { @@ -33,10 +41,11 @@ class StanIoStanCsvReader : public testing::Test { header3_stream.close(); adaptation1_stream.close(); samples1_stream.close(); - epil0_stream.close(); - blocker_nondiag0_stream.close(); + bernoulli_thin_stream.close(); + bernoulli_warmup_stream.close(); + fixed_param_stream.close(); } std::ifstream blocker0_stream, epil0_stream; @@ -46,12 +55,14 @@ class StanIoStanCsvReader : public testing::Test { std::ifstream metadata3_stream, header2_stream; std::ifstream eight_schools_stream; std::ifstream header3_stream; + std::ifstream bernoulli_thin_stream; + std::ifstream bernoulli_warmup_stream; + std::ifstream fixed_param_stream; }; TEST_F(StanIoStanCsvReader, read_metadata1) { stan::io::stan_csv_metadata metadata; - EXPECT_TRUE( - stan::io::stan_csv_reader::read_metadata(metadata1_stream, metadata, 0)); + stan::io::stan_csv_reader::read_metadata(metadata1_stream, metadata); EXPECT_EQ(2, metadata.stan_version_major); EXPECT_EQ(9, metadata.stan_version_minor); @@ -77,8 +88,7 @@ TEST_F(StanIoStanCsvReader, read_metadata1) { TEST_F(StanIoStanCsvReader, read_metadata3) { stan::io::stan_csv_metadata metadata; - EXPECT_TRUE( - stan::io::stan_csv_reader::read_metadata(metadata3_stream, metadata, 0)); + stan::io::stan_csv_reader::read_metadata(metadata3_stream, metadata); EXPECT_EQ(2, metadata.stan_version_major); EXPECT_EQ(9, metadata.stan_version_minor); @@ -104,8 +114,7 @@ TEST_F(StanIoStanCsvReader, read_metadata3) { TEST_F(StanIoStanCsvReader, read_header1) { std::vector header; - EXPECT_TRUE( - stan::io::stan_csv_reader::read_header(header1_stream, header, 0)); + EXPECT_TRUE(stan::io::stan_csv_reader::read_header(header1_stream, header)); ASSERT_EQ(55, header.size()); EXPECT_EQ("lp__", header[0]); @@ -132,8 +141,7 @@ TEST_F(StanIoStanCsvReader, read_header1) { TEST_F(StanIoStanCsvReader, read_header2) { std::vector header; - EXPECT_TRUE( - stan::io::stan_csv_reader::read_header(header2_stream, header, 0)); + EXPECT_TRUE(stan::io::stan_csv_reader::read_header(header2_stream, header)); ASSERT_EQ(5, header.size()); EXPECT_EQ("d", header[0]); @@ -147,8 +155,7 @@ TEST_F(StanIoStanCsvReader, read_header2) { TEST_F(StanIoStanCsvReader, read_header_tuples) { std::vector header; - EXPECT_TRUE( - stan::io::stan_csv_reader::read_header(header3_stream, header, 0)); + EXPECT_TRUE(stan::io::stan_csv_reader::read_header(header3_stream, header)); ASSERT_EQ(46, header.size()); @@ -181,8 +188,7 @@ TEST_F(StanIoStanCsvReader, read_header_tuples) { TEST_F(StanIoStanCsvReader, read_adaptation1) { stan::io::stan_csv_adaptation adaptation; - EXPECT_TRUE(stan::io::stan_csv_reader::read_adaptation(adaptation1_stream, - adaptation, 0)); + stan::io::stan_csv_reader::read_adaptation(adaptation1_stream, adaptation); EXPECT_FLOAT_EQ(0.118745, adaptation.step_size); ASSERT_EQ(47, adaptation.metric.size()); @@ -240,7 +246,7 @@ TEST_F(StanIoStanCsvReader, read_samples1) { stan::io::stan_csv_timing timing; EXPECT_TRUE(stan::io::stan_csv_reader::read_samples(samples1_stream, samples, - timing, 0)); + timing)); ASSERT_EQ(5, samples.rows()); ASSERT_EQ(55, samples.cols()); @@ -537,3 +543,51 @@ TEST_F(StanIoStanCsvReader, ParseEightSchools) { EXPECT_EQ("", out.str()); } + +TEST_F(StanIoStanCsvReader, skip_warmup) { + stan::io::stan_csv bernoulli_warmup; + std::stringstream out; + bernoulli_warmup + = stan::io::stan_csv_reader::parse(bernoulli_warmup_stream, &out); + ASSERT_EQ(1000, bernoulli_warmup.samples.rows()); + ASSERT_EQ(1000, bernoulli_warmup.metadata.num_warmup); + ASSERT_EQ(1000, bernoulli_warmup.metadata.num_samples); + ASSERT_NE(0, bernoulli_warmup.adaptation.step_size); +} + +TEST_F(StanIoStanCsvReader, thinned_data) { + stan::io::stan_csv bernoulli_thin; + std::stringstream out; + bernoulli_thin + = stan::io::stan_csv_reader::parse(bernoulli_thin_stream, &out); + ASSERT_EQ(1000, bernoulli_thin.samples.rows()); +} + +TEST_F(StanIoStanCsvReader, fixed_param) { + stan::io::stan_csv fixed_param; + std::stringstream out; + fixed_param = stan::io::stan_csv_reader::parse(fixed_param_stream, &out); + ASSERT_EQ(10, fixed_param.samples.rows()); +} + +TEST_F(StanIoStanCsvReader, no_samples) { + std::ifstream no_samples_stream; + no_samples_stream.open( + "src/test/unit/io/test_csv_files/bernoulli_no_samples.csv"); + std::stringstream out; + stan::io::stan_csv no_samples + = stan::io::stan_csv_reader::parse(no_samples_stream, &out); + no_samples_stream.close(); + ASSERT_EQ(out.str(), "No draws found\n"); +} + +TEST_F(StanIoStanCsvReader, variational) { + std::ifstream variational_stream; + variational_stream.open( + "src/test/unit/io/test_csv_files/bernoulli_variational.csv"); + std::stringstream out; + stan::io::stan_csv variational + = stan::io::stan_csv_reader::parse(variational_stream, &out); + variational_stream.close(); + ASSERT_EQ(1000, variational.metadata.num_samples); +} diff --git a/src/test/unit/io/test_csv_files/bernoulli_no_samples.csv b/src/test/unit/io/test_csv_files/bernoulli_no_samples.csv new file mode 100644 index 00000000000..3244c03f7c4 --- /dev/null +++ b/src/test/unit/io/test_csv_files/bernoulli_no_samples.csv @@ -0,0 +1,57 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-10-03 16:54:16 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/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 1517767693 (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 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,theta +# Adaptation terminated +# Step size = 1.22726 +# Diagonal elements of inverse mass matrix: +# 0.489285 +# +# Elapsed Time: 0.005 seconds (Warm-up) +# 0 seconds (Sampling) +# 0.005 seconds (Total) +# diff --git a/src/test/unit/io/test_csv_files/bernoulli_thin.csv b/src/test/unit/io/test_csv_files/bernoulli_thin.csv new file mode 100644 index 00000000000..b0874d8ead9 --- /dev/null +++ b/src/test/unit/io/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 +-6.99262,0.974315,0.893542,1,1,0,6.99262,0.170032 +-7.177,0.949564,0.893542,2,7,0,7.19028,0.147604 +-7.02071,0.934256,0.893542,2,3,0,7.30094,0.348894 +-6.75447,0.998729,0.893542,2,3,0,6.76944,0.264366 +-6.99389,0.954672,0.893542,1,3,0,7.04803,0.169846 +-8.10147,0.867196,0.893542,1,3,0,8.27154,0.0887819 +-7.83949,1,0.893542,1,1,0,8.36851,0.100831 +-7.20348,0.899977,0.893542,1,3,0,7.65125,0.379758 +-7.12214,0.820496,0.893542,1,3,0,8.08646,0.153481 +-6.75619,1,0.893542,2,3,0,7.17582,0.266194 +-6.80721,0.968917,0.893542,1,3,0,7.36674,0.294557 +-7.03198,1,0.893542,1,1,0,7.76254,0.351031 +-6.85284,0.960446,0.893542,1,3,0,7.15062,0.195876 +-6.77208,0.987924,0.893542,1,3,0,7.03182,0.278062 +-6.91359,1,0.893542,2,3,0,8.13328,0.18305 +-6.81561,0.956574,0.893542,2,7,0,7.45532,0.297719 +-6.76032,1,0.893542,2,7,0,6.80503,0.230743 +-6.76597,0.998846,0.893542,1,1,0,6.76725,0.22683 +-6.7594,0.993657,0.893542,1,3,0,6.82523,0.231466 +-7.25866,0.839089,0.893542,2,3,0,8.4433,0.139735 +-7.1134,0.971276,0.893542,1,1,0,7.1556,0.365446 +-7.52094,0.727841,0.893542,1,3,0,8.73414,0.11935 +-7.67112,0.993503,0.893542,1,1,0,7.78017,0.109972 +-6.75103,0.998383,0.893542,2,3,0,6.83417,0.259776 +-8.62702,0.934538,0.893542,2,3,0,9.21545,0.0701174 +-6.85649,0.909432,0.893542,1,3,0,8.95293,0.310994 +-6.87955,0.968041,0.893542,1,3,0,6.94534,0.317443 +-6.84001,0.965601,0.893542,1,3,0,7.07766,0.199102 +-7.88997,1,0.893542,2,3,0,8.09487,0.0983237 +-7.06055,0.900192,0.893542,1,3,0,8.68948,0.356282 +-6.74805,0.996745,0.893542,1,3,0,6.97881,0.250856 +-7.19725,1,0.893542,1,1,0,7.28556,0.145563 +-6.78602,0.995476,0.893542,1,1,0,6.78705,0.285469 +-6.79545,0.966991,0.893542,2,7,0,8.83762,0.289747 +-6.85701,0.986415,0.893542,2,3,0,6.9222,0.194874 +-8.13779,0.808085,0.893542,2,7,0,8.15821,0.0872819 +-6.77475,1,0.893542,2,3,0,6.80685,0.221869 +-6.755,1,0.893542,1,1,0,6.76348,0.235428 +-7.72282,0.995936,0.893542,2,3,0,7.81505,0.107027 +-6.85068,0.991723,0.893542,2,7,0,7.67011,0.309272 +-6.78351,0.96595,0.893542,1,3,0,7.29485,0.217724 +-7.01992,0.916257,0.893542,1,3,0,7.27688,0.166148 +-6.75058,0.983401,0.893542,1,3,0,7.38448,0.259018 +-8.06771,0.664252,0.893542,1,3,0,9.09338,0.0902102 +-6.82585,1,0.893542,2,3,0,7.7661,0.202972 +-7.00359,0.957838,0.893542,1,1,0,7.00467,0.345568 +-7.16065,0.965784,0.893542,2,7,0,7.16072,0.373134 +-6.74805,0.99626,0.893542,1,3,0,6.98927,0.24901 +-6.90231,1,0.893542,2,3,0,7.05968,0.185192 +-7.20636,0.93361,0.893542,1,3,0,9.402,0.144665 +-7.84106,1,0.893542,1,1,0,8.44831,0.456603 +-6.75385,0.986687,0.893542,1,3,0,7.15744,0.236668 +-7.73869,0.848859,0.893542,1,1,0,7.76537,0.446112 +-9.04616,0.923365,0.893542,2,3,0,9.28102,0.554505 +-7.16253,1,0.893542,1,1,0,7.26087,0.149103 +-6.75061,0.953379,0.893542,2,3,0,7.55278,0.241086 +-6.87201,0.983579,0.893542,2,3,0,6.89287,0.315394 +-8.37334,0.780237,0.893542,2,3,0,8.91045,0.504589 +-7.31022,1,0.893542,1,1,0,8.08813,0.39512 +-7.13369,0.836937,0.893542,1,3,0,7.87458,0.152199 +-7.55607,0.978527,0.893542,1,1,0,7.60506,0.117035 +-6.7799,0.995845,0.893542,2,3,0,6.81306,0.219357 +-6.87584,0.978011,0.893542,1,1,0,6.87584,0.316441 +-6.90665,1,0.893542,2,3,0,7.03415,0.184358 +-8.366,0.954237,0.893542,2,7,0,8.64308,0.503988 +-7.31371,1,0.893542,2,3,0,7.59964,0.395598 +-7.42729,1,0.893542,1,1,0,8.24438,0.410478 +-9.83769,0.367101,0.893542,1,3,0,12.9683,0.0429592 +-11.7092,0.971359,0.893542,1,1,0,11.7472,0.0215426 +-7.94546,0.93523,0.893542,2,7,0,11.9231,0.466816 +-7.74281,0.747755,0.893542,1,3,0,8.44191,0.105922 +-6.84047,0.920699,0.893542,2,3,0,7.68591,0.198981 +-8.60661,0.827904,0.893542,1,1,0,8.82479,0.522995 +-7.29777,1,0.893542,2,3,0,7.39398,0.136273 +-6.97269,1,0.893542,1,1,0,7.22693,0.339296 +-6.86249,1,0.893542,1,1,0,7.01724,0.312728 +-6.97125,0.913468,0.893542,1,3,0,7.58656,0.173264 +-7.8638,0.989915,0.893542,1,1,0,7.9762,0.0996114 +-6.86197,1,0.893542,2,3,0,7.63776,0.193713 +-7.22206,0.850919,0.893542,1,3,0,7.71338,0.143146 +-7.22681,0.99046,0.893542,1,1,0,7.27628,0.142693 +-7.09726,1,0.893542,1,1,0,7.36621,0.156335 +-9.96351,0.978531,0.893542,2,3,0,11.1987,0.0409345 +-7.34497,1,0.893542,2,3,0,10.5413,0.132321 +-6.81305,0.938689,0.893542,2,3,0,7.24819,0.206825 +-6.81323,0.996598,0.893542,1,1,0,6.81843,0.206767 +-6.83142,0.971003,0.893542,1,3,0,6.96602,0.201408 +-6.83125,1,0.893542,1,1,0,6.94813,0.30315 +-6.7882,0.950841,0.893542,2,3,0,7.15238,0.215732 +-9.17868,0.966705,0.893542,1,1,0,9.2473,0.0557102 +-10.1185,0.944434,0.893542,1,1,0,10.1239,0.0385895 +-7.53536,0.731681,0.893542,2,3,0,8.955,0.423587 +-7.17139,0.844452,0.893542,2,3,0,7.87684,0.148181 +-7.10769,0.986547,0.893542,1,1,0,7.17427,0.364485 +-6.94717,0.935748,0.893542,2,3,0,7.17961,0.17714 +-6.84534,0.989334,0.893542,1,1,0,6.84994,0.307647 +-6.8368,0.954327,0.893542,1,3,0,7.1096,0.304961 +-6.74879,0.996744,0.893542,1,3,0,6.85754,0.245111 +-6.99263,0.97013,0.893542,2,3,0,7.04979,0.170031 +-8.34668,1,0.893542,1,1,0,9.31823,0.502398 +-7.78992,0.97634,0.893542,1,1,0,7.8495,0.103392 +-6.96885,0.97489,0.893542,1,3,0,7.75062,0.173638 +-7.13219,0.920703,0.893542,1,3,0,7.2178,0.368554 +-6.74834,0.99754,0.893542,1,3,0,8.20523,0.246878 +-6.82781,0.98513,0.893542,1,3,0,6.83699,0.301999 +-8.51144,0.8241,0.893542,2,3,0,8.59822,0.0737269 +-6.8833,0.990012,0.893542,2,3,0,7.1302,0.189023 +-7.727,0.933842,0.893542,2,7,0,7.72929,0.444881 +-6.92808,1,0.893542,2,3,0,7.01247,0.180418 +-7.46481,0.853946,0.893542,1,3,0,7.74876,0.123222 +-7.52531,0.818528,0.893542,1,1,0,8.12529,0.422404 +-7.53784,1,0.893542,2,7,0,7.5663,0.118227 +-8.66406,0.93062,0.893542,1,1,0,8.66529,0.0690104 +-6.87277,0.98575,0.893542,1,3,0,7.37422,0.19128 +-6.74819,0.998319,0.893542,1,3,0,6.8526,0.247725 +-7.47646,0.972692,0.893542,2,3,0,7.5325,0.416555 +-6.82543,0.998174,0.893542,1,1,0,6.83612,0.203094 +-6.94406,0.988418,0.893542,1,1,0,6.95306,0.177661 +-7.50427,0.900985,0.893542,1,3,0,7.62359,0.120477 +-8.30957,0.959888,0.893542,1,1,0,8.34722,0.0806548 +-6.85627,0.480224,0.893542,2,3,0,11.4286,0.195052 +-7.8289,1,0.893542,1,1,0,8.32911,0.101369 +-6.82852,1,0.893542,1,1,0,6.94737,0.302239 +-6.94381,0.895885,0.893542,1,3,0,7.67155,0.177703 +-6.80431,0.871567,0.893542,2,3,0,8.18055,0.293417 +-7.84658,0.975609,0.893542,1,1,0,7.90755,0.100473 +-6.74811,1,0.893542,2,3,0,6.783,0.248387 +-7.37909,1,0.893542,1,1,0,7.56009,0.404317 +-7.36159,0.918547,0.893542,1,1,0,7.38981,0.402026 +-6.75283,0.999402,0.893542,1,3,0,6.79322,0.237882 +-6.97729,1,0.893542,2,7,0,6.98137,0.172332 +-6.91629,0.999631,0.893542,1,1,0,6.94684,0.182549 +-6.76816,0.999578,0.893542,2,7,0,6.917,0.275627 +-6.86094,0.941705,0.893542,2,3,0,8.84018,0.193952 +-7.25168,0.653707,0.893542,1,1,0,8.33062,0.38688 +-6.75039,0.96706,0.893542,2,3,0,6.98851,0.258663 +-6.77982,0.981375,0.893542,2,7,0,7.00643,0.28237 +-7.07903,0.897172,0.893542,1,3,0,7.42532,0.158511 +-10.6092,0.672748,0.893542,2,3,0,10.6214,0.0321139 +-6.83435,0.969853,0.893542,3,7,0,10.164,0.200606 +-6.95401,0.973201,0.893542,2,3,0,6.98236,0.176011 +-8.17193,0.985546,0.893542,2,3,0,8.19935,0.0859048 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b/src/test/unit/io/test_csv_files/bernoulli_variational.csv new file mode 100644 index 00000000000..613de2e786b --- /dev/null +++ b/src/test/unit/io/test_csv_files/bernoulli_variational.csv @@ -0,0 +1,1039 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-10-03 17:01:50 UTC +# method = variational +# variational +# algorithm = meanfield (Default) +# meanfield +# iter = 10000 (Default) +# grad_samples = 1 (Default) +# elbo_samples = 100 (Default) +# eta = 1 (Default) +# adapt +# engaged = true (Default) +# iter = 50 (Default) +# tol_rel_obj = 0.01 (Default) +# eval_elbo = 100 (Default) +# output_samples = 1000 (Default) +# id = 1 (Default) +# data +# file = examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 1518221616 (Default) +# output +# file = output.csv (Default) +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = 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+# 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 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+-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/io/test_csv_files/fixed_param_output.csv b/src/test/unit/io/test_csv_files/fixed_param_output.csv new file mode 100644 index 00000000000..f18f70b8223 --- /dev/null +++ b/src/test/unit/io/test_csv_files/fixed_param_output.csv @@ -0,0 +1,55 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = linear_model_dgp_model +# start_datetime = 2024-07-23 18:24:49 UTC +# method = sample (Default) +# sample +# num_samples = 10 +# 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 = fixed_param +# num_chains = 1 (Default) +# id = 1 (Default) +# data +# file = linear_data.json +# init = 2 (Default) +# random +# seed = 3892335730 (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 +# stancflags = --filename-in-msg=linear_model_dgp.stan 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+# +# Elapsed Time: 0 seconds (Warm-up) +# 0.001 seconds (Sampling) +# 0.001 seconds (Total) +# diff --git a/src/test/unit/mcmc/chains_test.cpp b/src/test/unit/mcmc/chains_test.cpp index f2d90aaf0d6..4e3d578313c 100644 --- a/src/test/unit/mcmc/chains_test.cpp +++ b/src/test/unit/mcmc/chains_test.cpp @@ -886,15 +886,6 @@ TEST_F(McmcChains, blocker_split_potential_scale_reduction_rank) { stan::mcmc::chains<> chains(blocker1); chains.add(blocker2); - // Eigen::VectorXd rhat(48); - // rhat - // << 1.0078, 1.0109, 1.00731, 1.00333, 1.00401, 1.00992, 1.00734, 1.00633, - // 1.00095, 1.00906, 1.01019, 1.00075, 1.00595, 1.00473, 1.00895, 1.01304, - // 1.00166, 1.0074, 1.00236, 1.00588, 1.00414, 1.00303, 1.00976, 1.00295, - // 1.00193, 1.0044, 1.00488, 1.00178, 1.01082, 1.0019, 1.00413, 1.01303, - // 1.0024, 1.01148, 1.00436, 1.00515, 1.00712, 1.0089, 1.00222, 1.00118, - // 1.00381, 1.00283, 1.00188, 1.00225, 1.00335, 1.00133, 1.00209, 1.0109; - Eigen::VectorXd rhat_bulk(48); rhat_bulk << 1.0078, 1.0109, 0.99919, 1.001, 1.00401, 1.00992, 1.00182, 1.00519, 1.00095, 1.00351, 1.00554, 1.00075, 1.00595, 1.00473, 1.00546, @@ -925,14 +916,9 @@ TEST_F(McmcChains, blocker_split_potential_scale_reduction_rank) { ASSERT_NEAR(expected_tail_rhat, computed_tail_rhat, 1e-4) << "Tail Rhat mismatch for index: " << index << ", parameter: " << chains.param_name(index); - - // ASSERT_NEAR(rhat(index - 4), - // chains.split_potential_scale_reduction_rank(index), 1e-4) - // << "rhat for index: " << index - // << ", parameter: " << chains.param_name(index); } - for (int index = 0; index < chains.num_params(); index++) { + for (int index = 4; index < chains.num_params(); index++) { std::string name = chains.param_name(index); ASSERT_EQ(chains.split_potential_scale_reduction_rank(index), chains.split_potential_scale_reduction_rank(name)); diff --git a/src/test/unit/mcmc/chainset_test.cpp b/src/test/unit/mcmc/chainset_test.cpp new file mode 100644 index 00000000000..56476524850 --- /dev/null +++ b/src/test/unit/mcmc/chainset_test.cpp @@ -0,0 +1,209 @@ +#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); + 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); + + if (!bernoulli_500_stream || !bernoulli_default_stream + || !bernoulli_thin_stream || !bernoulli_warmup_stream + || !eight_schools_1_stream || !eight_schools_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); + eight_schools_1_stream.seekg(0, std::ios::beg); + eight_schools_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); + 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); + } + + void TearDown() override { + bernoulli_500_stream.close(); + bernoulli_default_stream.close(); + bernoulli_thin_stream.close(); + bernoulli_warmup_stream.close(); + eight_schools_1_stream.close(); + eight_schools_2_stream.close(); + } + + std::stringstream out; + + std::ifstream bernoulli_500_stream, bernoulli_default_stream, + bernoulli_thin_stream, bernoulli_warmup_stream, eight_schools_1_stream, + eight_schools_2_stream; + + stan::io::stan_csv bernoulli_500, bernoulli_default, bernoulli_thin, + bernoulli_warmup, eight_schools_1, eight_schools_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); +} + +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, summary_stats) { + std::stringstream out; + std::vector bern_csvs(4); + for (size_t i = 0; i < 4; ++i) { + std::stringstream fname; + fname << "src/test/unit/analyze/mcmc/test_csv_files/bern" << (i + 1) + << ".csv"; + std::ifstream bern_stream(fname.str(), std::ifstream::in); + stan::io::stan_csv bern_csv + = stan::io::stan_csv_reader::parse(bern_stream, &out); + bern_stream.close(); + bern_csvs[i] = bern_csv; + } + stan::mcmc::chainset bern_chains(bern_csvs); + EXPECT_EQ(4, bern_chains.num_chains()); + + Eigen::MatrixXd theta = bern_chains.samples("theta"); + // default summary statistics - via R pkg posterior + double theta_mean_expect = 0.251297; + EXPECT_NEAR(theta_mean_expect, bern_chains.mean("theta"), 1e-5); + + double theta_median_expect = 0.237476; + EXPECT_NEAR(theta_median_expect, bern_chains.median("theta"), 1e-5); + + double theta_sd_expect = 0.121546; + EXPECT_NEAR(theta_sd_expect, bern_chains.sd("theta"), 1e-5); + + double theta_mad_expect = 0.12309; + EXPECT_NEAR(theta_mad_expect, bern_chains.max_abs_deviation("theta"), 1e-5); + + double theta_mcse_mean_expect = 0.003234; + EXPECT_NEAR(theta_mcse_mean_expect, bern_chains.mcse_mean("theta"), 1e-4); + + double theta_mcse_sd_expect = 0.002164; + EXPECT_NEAR(theta_mcse_sd_expect, bern_chains.mcse_sd("theta"), 1e-4); + + Eigen::VectorXd probs(6); + probs << 0.0, 0.01, 0.05, 0.95, 0.99, 1.0; + Eigen::VectorXd quantiles_expect(6); + quantiles_expect << 0.004072, 0.046281, 0.077169, 0.473885, 0.574524, + 0.698401; + Eigen::VectorXd theta_quantiles = bern_chains.quantiles("theta", probs); + for (size_t i = 0; i < probs.size(); ++i) { + EXPECT_NEAR(quantiles_expect(i), theta_quantiles(i), 1e-5); + } + + double theta_rhat_expect = 1.00679; + auto rhat = bern_chains.split_rank_normalized_rhat("theta"); + EXPECT_NEAR(theta_rhat_expect, std::max(rhat.first, rhat.second), 1e-5); + + double theta_ess_bulk_expect = 1407.5124; + double theta_ess_tail_expect = 1291.7131; + auto ess = bern_chains.split_rank_normalized_ess("theta"); + EXPECT_NEAR(theta_ess_bulk_expect, ess.first, 1e-4); + EXPECT_NEAR(theta_ess_tail_expect, ess.second, 1e-4); + + // autocorrelation - first 10 lags + Eigen::VectorXd theta_ac_expect(10); + theta_ac_expect << 1.00000, 0.42204, 0.20683, 0.08383, 0.037326, 0.02507, + 0.02003, 0.01347, 0.00476, 0.029495; + auto theta_ac = bern_chains.autocorrelation(0, "theta"); + for (size_t i = 0; i < 10; ++i) { + EXPECT_NEAR(theta_ac(i), theta_ac_expect(i), 0.0005); + } +} 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 00000000000..f7c479a86ee --- /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 +-6.74895,0.998922,0.908714,1,3,0,6.78592,0.244633 +-6.93759,0.731848,0.908714,2,3,0,8.66514,0.17876 +-7.61391,0.908155,0.908714,1,3,0,7.64703,0.113391 +-7.36814,1,0.908714,1,1,0,7.62491,0.130463 +-8.28709,0.92711,0.908714,2,7,0,8.30461,0.49743 +-7.35295,1,0.908714,2,3,0,8.06187,0.400884 +-7.54375,0.940763,0.908714,1,1,0,7.69381,0.424567 +-6.75119,0.974925,0.908714,2,3,0,7.73022,0.240141 +-6.75836,0.95575,0.908714,2,3,0,7.0279,0.23232 +-6.75759,1,0.908714,1,1,0,6.76028,0.232976 +-7.84043,0.766398,0.908714,1,3,0,8.25276,0.100783 +-7.36878,1,0.908714,1,1,0,7.78742,0.130413 +-6.92704,0.987905,0.908714,1,3,0,7.29386,0.180603 +-6.87173,1,0.908714,1,1,0,6.93002,0.19151 +-6.87173,0.792653,0.908714,1,3,0,8.27612,0.19151 +-6.76869,0.995507,0.908714,2,3,0,6.91339,0.22518 +-6.76869,0.717822,0.908714,1,3,0,8.35048,0.22518 +-7.02303,0.821164,0.908714,2,3,0,7.978,0.165721 +-6.88921,1,0.908714,1,1,0,7.00276,0.1878 +-7.03623,0.937861,0.908714,1,3,0,7.42379,0.351828 +-10.0552,0.518188,0.908714,2,7,0,10.1628,0.0395289 +-7.03905,1,0.908714,2,3,0,9.71561,0.16357 +-7.06047,0.973981,0.908714,2,3,0,7.42468,0.160808 +-7.12565,0.996219,0.908714,2,3,0,7.17004,0.153088 +-7.31585,0.989771,0.908714,2,3,0,7.34328,0.134732 +-6.95355,0.922218,0.908714,1,3,0,7.97785,0.33521 +-7.348,0.886923,0.908714,1,1,0,7.36349,0.400227 +-6.77188,1,0.908714,1,3,0,7.21394,0.277942 +-6.76938,1,0.908714,1,1,0,6.77619,0.276409 +-6.82464,0.974309,0.908714,2,3,0,6.93407,0.300918 +-6.82568,0.970555,0.908714,1,3,0,7.00774,0.20302 +-6.79962,1,0.908714,1,1,0,6.82614,0.211349 +-6.75759,0.877431,0.908714,2,3,0,7.82851,0.232976 +-6.75086,0.998853,0.908714,1,3,0,6.76664,0.259492 +-7.33482,0.846044,0.908714,1,3,0,7.63709,0.133151 +-7.16299,1,0.908714,1,1,0,7.34291,0.149055 +-8.75594,0.821593,0.908714,1,3,0,8.95305,0.0663607 +-8.15624,1,0.908714,1,1,0,8.74352,0.0865339 +-7.81872,1,0.908714,1,1,0,8.18978,0.101892 +-6.80465,0.995317,0.908714,2,3,0,7.84167,0.209584 +-6.75107,0.961176,0.908714,2,3,0,7.07814,0.259841 +-6.75107,0.667664,0.908714,1,3,0,8.23261,0.259841 +-7.18165,0.905003,0.908714,1,3,0,7.23074,0.376419 +-7.40419,0.932419,0.908714,1,1,0,7.49683,0.407552 +-7.4394,0.988812,0.908714,1,1,0,7.62997,0.411994 +-6.75917,0.993332,0.908714,1,3,0,7.47631,0.231653 +-7.77958,0.781858,0.908714,1,3,0,8.14744,0.103939 +-7.62945,1,0.908714,1,1,0,7.86107,0.112444 +-7.32531,0.874756,0.908714,1,3,0,9.12726,0.397178 +-7.58534,0.973387,0.908714,2,3,0,7.7175,0.429359 +-7.3058,1,0.908714,1,1,0,7.61966,0.394511 +-6.86476,0.950946,0.908714,2,3,0,7.68758,0.193074 +-6.75907,0.999201,0.908714,1,3,0,6.848,0.231736 +-6.81488,0.981992,0.908714,1,3,0,6.86672,0.297452 +-7.12749,0.877474,0.908714,1,3,0,7.53085,0.152884 +-6.93162,0.939572,0.908714,1,3,0,7.6458,0.330306 +-6.93162,0.579221,0.908714,1,3,0,9.27146,0.330306 +-6.83618,1,0.908714,1,1,0,6.91448,0.30476 +-6.91338,0.917038,0.908714,2,3,0,7.31475,0.32602 +-7.47192,0.764242,0.908714,1,3,0,8.40039,0.122719 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+-9.04738,1,0.908714,1,1,0,9.42482,0.0587723 +-7.35321,0.939807,0.908714,1,3,0,8.98756,0.131654 +-6.82224,0.994909,0.908714,2,3,0,7.407,0.204021 +-6.97304,0.969418,0.908714,1,1,0,6.97304,0.172986 +-6.74861,1,0.908714,2,3,0,6.94018,0.254313 +-7.0768,0.63347,0.908714,2,3,0,9.34757,0.158784 +-7.18864,0.993672,0.908714,2,3,0,7.22464,0.146424 +-7.60022,0.8546,0.908714,1,3,0,8.75127,0.431045 +-6.90251,1,0.908714,1,1,0,7.38904,0.323359 +-7.41937,0.855759,0.908714,1,1,0,7.42212,0.409481 +-9.79054,0.461021,0.908714,1,1,0,9.81688,0.600653 +-7.16347,1,0.908714,2,3,0,8.942,0.373579 +-7.01203,1,0.908714,1,1,0,7.16854,0.347221 +-6.83641,0.955169,0.908714,1,3,0,7.27663,0.200051 +-6.79662,0.984361,0.908714,1,3,0,6.96527,0.290248 +-7.02116,0.894083,0.908714,2,3,0,7.42908,0.348982 +-6.75609,1,0.908714,1,3,0,6.96946,0.266101 +-6.82245,0.986625,0.908714,1,3,0,6.82387,0.300159 +-7.7373,0.683024,0.908714,1,3,0,8.66388,0.106225 +-7.52727,1,0.908714,1,1,0,7.78194,0.118927 +-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 00000000000..d45074864f0 --- /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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+-6.74902,0.994264,0.932037,1,3,0,7.08219,0.24445 +-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 00000000000..b0874d8ead9 --- /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 00000000000..799f5c8260f --- /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 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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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/dev/null +++ b/src/test/unit/mcmc/test_csv_files/eight_schools_1.csv @@ -0,0 +1,557 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = eight_schools_model +# start_datetime = 2024-08-01 14:37:47 UTC +# method = sample (Default) +# sample +# num_samples = 1000 (Default) +# 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 = true +# 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 = 2 +# data +# file = /Users/mitzi/github/stan-dev/cmdstan/examples/eight_schools/eight_schools.data.json +# init = 2 (Default) +# random +# seed = 2075043197 +# output +# file = /Users/mitzi/github/stan-dev/cmdstan/examples/eight_schools/test_csv_files/eight_schools-202408011037-2-509896.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = /Users/mitzi/github/stan-dev/cmdstan/examples/eight_schools/test_csv_files/eight_schools-profile-202408011037-2-8881e2.csv +# save_cmdstan_config = true +# 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 = 0.182121 +# Diagonal elements of inverse mass matrix: +# 27.4582, 75.4878, 47.3922, 57.6197, 46.4329, 43.3031, 53.4421, 50.9888, 64.8955, 0.56423 +-9.86509,0.645896,0.182121,2,7,0,13.0863,2.42733,4.75786,2.30752,2.565,2.59007,1.95177,3.13204,0.788537,0.123147,1.97795 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+-20.0475,0.649902,0.182121,4,15,0,22.0061,3.42992,15.8588,3.02822,5.17707,2.80142,1.40652,9.07183,20.8198,-1.2328,10.3522 +-19.3212,0.997575,0.182121,4,15,0,21.5268,5.09931,17.4029,15.7512,1.19264,-0.762529,1.57646,5.11005,12.0962,1.38057,9.8779 +-24.6914,0.536628,0.182121,4,15,0,29.1446,1.30014,27.001,-0.687212,6.79555,1.79753,-3.27025,6.06327,19.9418,12.4043,26.7752 +# +# Elapsed Time: 0.029 seconds (Warm-up) +# 0.021 seconds (Sampling) +# 0.05 seconds (Total) +# diff --git a/src/test/unit/mcmc/test_csv_files/eight_schools_2.csv b/src/test/unit/mcmc/test_csv_files/eight_schools_2.csv new file mode 100644 index 00000000000..64de3747fe0 --- /dev/null +++ b/src/test/unit/mcmc/test_csv_files/eight_schools_2.csv @@ -0,0 +1,557 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = eight_schools_model +# start_datetime = 2024-08-01 14:37:47 UTC +# method = sample (Default) +# sample +# num_samples = 1000 (Default) +# 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 = true +# 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 = /Users/mitzi/github/stan-dev/cmdstan/examples/eight_schools/eight_schools.data.json +# init = 2 (Default) +# random +# seed = 2075043197 +# output +# file = /Users/mitzi/github/stan-dev/cmdstan/examples/eight_schools/test_csv_files/eight_schools-202408011037-1-509896.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = /Users/mitzi/github/stan-dev/cmdstan/examples/eight_schools/test_csv_files/eight_schools-profile-202408011037-1-8881e2.csv +# save_cmdstan_config = true +# 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 = 0.336745 +# Diagonal elements of inverse mass matrix: +# 33.7664, 89.1003, 44.7183, 77.6697, 52.9281, 45.1871, 50.4784, 57.663, 73.1941, 0.341311 +-22.6886,0.880578,0.336745,4,15,0,32.7969,13.2473,15.7896,11.5358,-5.68628,13.5109,-7.53635,13.0527,12.4528,18.6467,8.02557 +-19.3145,0.954769,0.336745,3,15,0,22.6261,6.1528,0.797012,8.52562,2.65594,8.49469,-1.71417,9.97396,1.99237,3.18343,8.16682 +-12.4894,0.0202636,0.336745,2,3,0,15.3711,10.5769,12.2114,9.1497,9.7484,6.92357,10.3499,6.30889,14.4636,11.6266,2.97153 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+-19.3531,0.919406,0.336745,4,31,0,25.4856,6.2005,5.43483,15.3251,-5.53421,12.1012,4.35679,7.90481,3.58084,9.13552,5.98859 +-23.3089,1,0.336745,4,15,0,27.4982,1.18657,3.30229,13.5647,-17.8207,2.99393,-2.37234,-8.71695,5.11045,6.24978,7.61964 +-19.4431,0.283094,0.336745,4,15,0,25.5995,6.50781,5.35509,-0.859629,2.89783,11.0083,7.55646,0.372878,-1.95316,7.3891,4.86381 +-26.9759,0.691295,0.336745,4,15,0,30.5906,2.72051,43.2935,15.6525,21.2698,5.32216,6.08599,-0.98289,6.2702,15.158,20.8478 +-23.5924,0.989251,0.336745,4,15,0,30.085,4.04984,30.3739,20.2051,7.58934,6.57008,1.49937,11.5953,10.7501,10.8386,17.3521 +-24.41,0.888797,0.336745,4,15,0,28.3563,6.12028,14.0367,22.5807,-6.12814,5.83412,4.03114,-5.08366,2.05762,25.9845,11.0976 +-22.1974,0.994768,0.336745,4,15,0,31.0575,14.2237,28.7059,10.3603,14.0616,-4.87943,9.36401,12.0302,17.444,5.40906,11.6105 +-14.1696,0.709837,0.336745,3,11,0,19.7331,15.9681,18.7514,15.2656,15.1527,10.4797,15.012,15.2679,17.025,17.9529,3.40531 +-14.4907,0.021047,0.336745,3,7,0,16.4405,14.9764,14.8696,11.7967,15.7974,14.2084,10.5986,14.9225,18.9482,19.4772,3.27572 +-15.4055,0.300138,0.336745,3,7,0,23.4673,11.7121,14.1051,12.3432,11.3707,15.3393,12.9864,6.31346,9.41294,14.2159,4.98951 +-15.8079,0.364128,0.336745,4,15,0,20.2539,9.08011,9.15961,4.19594,0.872986,5.496,6.15755,10.9845,10.335,12.6892,5.00765 +-18.7649,0.714286,0.336745,2,7,1,22.4775,9.48837,14.4946,15.0065,12.8769,9.37905,0.641803,0.195459,11.1618,0.710823,8.53976 +-25.1487,0.937918,0.336745,4,15,0,30.4718,6.33705,27.4899,-5.6571,9.1077,4.15108,20.7318,9.5952,11.9771,3.09614,11.6798 +-32.196,0.986945,0.336745,4,15,0,35.0963,6.15156,-11.3464,19.7338,-4.24472,31.2506,-10.0496,-3.88973,21.6028,26.0876,27.406 +-21.9544,0.967773,0.336745,4,15,0,25.9732,16.5322,12.0791,23.3136,10.0659,20.8625,-0.079561,10.6124,13.8721,18.7243,10.6837 +-20.0075,0.539987,0.336745,2,7,0,25.0897,11.5944,12.5285,4.35598,-0.656948,-2.78016,5.01872,7.28059,11.6611,7.20103,8.22928 +-22.2454,0.31691,0.336745,4,15,0,27.322,14.1913,36.8083,13.9573,9.45807,7.2093,7.85113,9.052,27.7697,18.3323,11.1118 +-25.0136,0.895256,0.336745,4,15,0,32.1723,-0.456606,9.14915,1.12047,3.24415,3.20007,-11.4082,13.5019,23.2876,-11.9034,15.3264 +-20.6791,0.960778,0.336745,3,15,0,35.9081,11.7339,8.95894,12.2122,-3.5993,8.65168,11.7904,-3.5005,15.6875,6.87793,8.96095 +-21.6769,0.930388,0.336745,4,15,0,27.2034,3.67072,11.4748,7.77133,-7.38866,11.1409,-1.48472,-3.87525,9.39501,22.5217,7.85932 +-18.1091,0.99531,0.336745,3,7,0,23.3359,9.64035,9.85188,19.0552,6.04828,5.67318,0.327966,7.83185,8.01275,14.7202,5.24851 +-17.6802,0.991146,0.336745,3,7,0,20.826,8.12887,8.98983,13.1344,5.61432,9.38492,-1.69121,9.88144,2.99302,7.0212,6.65635 +-21.7026,0.755985,0.336745,3,15,0,27.8246,6.90567,4.31983,13.9175,-0.00257747,14.5546,-9.91783,8.30026,20.3456,9.19659,9.13339 +-21.8721,0.929872,0.336745,3,7,0,27.4204,12.3844,8.37973,24.9725,9.81605,17.4002,4.36655,4.88499,23.6801,16.2054,9.61569 +-18.8908,1,0.336745,3,7,0,24.5854,3.194,9.75998,9.06204,0.183302,0.196596,-5.97357,5.16562,3.37759,-4.88835,5.07644 +-22.7828,0.86184,0.336745,3,7,0,26.2674,5.59897,4.71278,6.90791,5.8789,12.7258,1.17602,-6.6017,13.1816,10.5098,18.5821 +-22.5734,0.764378,0.336745,4,15,0,31.6748,13.3428,23.6905,3.23508,8.74927,6.00672,0.387502,-5.14408,13.5627,10.2369,19.5973 +-23.0557,0.717645,0.336745,3,15,0,25.8071,8.478,-2.018,4.18658,22.3128,-2.80499,6.61421,3.79011,7.33186,12.4917,8.49459 +-27.1179,0.916112,0.336745,4,15,0,33.4767,20.7388,40.3445,4.41775,-8.14155,26.5261,5.5807,4.92906,29.6332,14.1235,13.3984 +-29.363,0.951986,0.336745,4,15,0,37.2485,16.2518,25.1177,3.25609,19.0689,1.29517,-2.73295,-15.3183,40.6719,14.2395,14.7113 +-27.0516,0.68738,0.336745,4,15,0,36.8853,20.3164,22.4672,6.27826,7.74054,4.5806,1.53811,2.00511,40.2226,16.4054,26.6207 +-21.3013,0.576033,0.336745,4,15,0,26.9604,9.82982,13.7271,16.0268,24.1489,14.2994,3.03415,13.2682,17.841,16.864,8.61276 +-17.1328,0.393815,0.336745,4,15,0,29.2668,9.13415,8.69133,0.753641,10.0112,15.7982,6.94527,9.1939,11.3976,4.99216,3.63798 +-17.1669,0.51697,0.336745,3,15,0,19.9239,11.0228,19.2251,16.2691,7.80126,8.73697,5.05567,6.52144,17.9187,13.3159,7.51632 +-19.9621,0.512936,0.336745,3,15,0,26.0303,12.9022,13.0419,18.7603,7.02067,1.30278,2.60538,10.3783,16.0523,11.2631,4.81099 +-22.7311,0.990752,0.336745,4,15,0,26.833,13.4324,23.8206,5.898,16.9165,-2.62158,13.0999,-2.00217,11.3853,11.1324,13.2378 +-19.0636,1,0.336745,3,7,0,25.298,8.39533,15.4892,8.56764,12.137,-2.18516,10.3939,-1.42548,13.6689,4.43648,7.22636 +-15.6881,0.480904,0.336745,3,7,0,19.9769,7.72875,4.61929,7.79223,2.06302,2.28203,5.41732,5.37156,4.33698,4.47377,3.67658 +# +# Elapsed Time: 0.033 seconds (Warm-up) +# 0.014 seconds (Sampling) +# 0.047 seconds (Total) +# diff --git a/src/test/unit/model/compile_test.cpp b/src/test/unit/model/compile_test.cpp new file mode 100644 index 00000000000..abd9f383df7 --- /dev/null +++ b/src/test/unit/model/compile_test.cpp @@ -0,0 +1,2 @@ +// test model which previously failed with -DSTAN_MODEL_FVAR_VAR +#include diff --git a/src/test/unit/services/sample/hmc_nuts_dense_e_adapt_no_params_test.cpp b/src/test/unit/services/sample/hmc_nuts_dense_e_adapt_no_params_test.cpp new file mode 100644 index 00000000000..a2e5781a60e --- /dev/null +++ b/src/test/unit/services/sample/hmc_nuts_dense_e_adapt_no_params_test.cpp @@ -0,0 +1,177 @@ +#include +#include +#include +#include +#include +#include + +auto&& blah = stan::math::init_threadpool_tbb(); + +static constexpr size_t num_chains = 4; +class ServicesSampleHMCNutsDenseENoParams : public testing::Test { + public: + ServicesSampleHMCNutsDenseENoParams() : model(data_context, 0, &model_log) { + for (int i = 0; i < num_chains; ++i) { + init.push_back(stan::test::unit::instrumented_writer{}); + parameter.push_back(stan::test::unit::instrumented_writer{}); + diagnostic.push_back(stan::test::unit::instrumented_writer{}); + context.push_back(std::make_shared()); + } + } + stan::io::empty_var_context data_context; + std::stringstream model_log; + stan::test::unit::instrumented_logger logger; + std::vector init; + std::vector parameter; + std::vector diagnostic; + std::vector> context; + stan_model model; +}; + +TEST_F(ServicesSampleHMCNutsDenseENoParams, call_count) { + unsigned int random_seed = 0; + unsigned int chain = 1; + double init_radius = 0; + int num_warmup = 200; + int num_samples = 400; + int num_thin = 5; + bool save_warmup = true; + int refresh = 0; + double stepsize = 0.1; + double stepsize_jitter = 0; + int max_depth = 8; + double delta = .1; + double gamma = .1; + double kappa = .1; + double t0 = .1; + unsigned int init_buffer = 50; + unsigned int term_buffer = 50; + unsigned int window = 100; + stan::test::unit::instrumented_interrupt interrupt; + EXPECT_EQ(interrupt.call_count(), 0); + + int return_code = stan::services::sample::hmc_nuts_dense_e( + model, num_chains, context, random_seed, chain, init_radius, num_warmup, + num_samples, num_thin, save_warmup, refresh, stepsize, stepsize_jitter, + max_depth, interrupt, logger, init, parameter, diagnostic); + + EXPECT_EQ(0, return_code); + + int num_output_lines = (num_warmup + num_samples) / num_thin; + EXPECT_EQ((num_warmup + num_samples) * num_chains, interrupt.call_count()); + for (int i = 0; i < num_chains; ++i) { + EXPECT_EQ(1, parameter[i].call_count("vector_string")); + EXPECT_EQ(num_output_lines, parameter[i].call_count("vector_double")); + EXPECT_EQ(1, diagnostic[i].call_count("vector_string")); + EXPECT_EQ(num_output_lines, diagnostic[i].call_count("vector_double")); + } +} + +TEST_F(ServicesSampleHMCNutsDenseENoParams, parameter_checks) { + unsigned int random_seed = 0; + unsigned int chain = 1; + double init_radius = 0; + int num_warmup = 200; + int num_samples = 400; + int num_thin = 5; + bool save_warmup = true; + int refresh = 0; + double stepsize = 0.1; + double stepsize_jitter = 0; + int max_depth = 8; + double delta = .1; + double gamma = .1; + double kappa = .1; + double t0 = .1; + unsigned int init_buffer = 50; + unsigned int term_buffer = 50; + unsigned int window = 100; + stan::test::unit::instrumented_interrupt interrupt; + EXPECT_EQ(interrupt.call_count(), 0); + + int return_code = stan::services::sample::hmc_nuts_dense_e( + model, num_chains, context, random_seed, chain, init_radius, num_warmup, + num_samples, num_thin, save_warmup, refresh, stepsize, stepsize_jitter, + max_depth, interrupt, logger, init, parameter, diagnostic); + + for (size_t i = 0; i < num_chains; ++i) { + std::vector> parameter_names; + parameter_names = parameter[i].vector_string_values(); + std::vector> parameter_values; + parameter_values = parameter[i].vector_double_values(); + std::vector strings; + strings = parameter[i].string_values(); + std::vector> diagnostic_names; + diagnostic_names = diagnostic[i].vector_string_values(); + std::vector> diagnostic_values; + diagnostic_values = diagnostic[i].vector_double_values(); + + // Expect message at end of warmup + EXPECT_EQ("Adaptation terminated", strings[0]); + + // Expectations of sampler and model variables names. + ASSERT_EQ(9, parameter_names[0].size()); + EXPECT_EQ("lp__", parameter_names[0][0]); + EXPECT_EQ("accept_stat__", parameter_names[0][1]); + EXPECT_EQ("stepsize__", parameter_names[0][2]); + EXPECT_EQ("treedepth__", parameter_names[0][3]); + EXPECT_EQ("n_leapfrog__", parameter_names[0][4]); + EXPECT_EQ("divergent__", parameter_names[0][5]); + EXPECT_EQ("energy__", parameter_names[0][6]); + EXPECT_EQ("theta", parameter_names[0][7]); + EXPECT_EQ("eta", parameter_names[0][8]); + + // Expect one name per parameter value. + EXPECT_EQ(parameter_names[0].size(), parameter_values[0].size()); + EXPECT_EQ(diagnostic_names[0].size(), diagnostic_values[0].size()); + + EXPECT_EQ((num_warmup + num_samples) / num_thin, parameter_values.size()); + + // Expect one call to set parameter names, and one set of output per + // iteration. + EXPECT_EQ("lp__", diagnostic_names[0][0]); + EXPECT_EQ("accept_stat__", diagnostic_names[0][1]); + } + EXPECT_EQ(return_code, 0); +} + +TEST_F(ServicesSampleHMCNutsDenseENoParams, output_regression) { + unsigned int random_seed = 0; + unsigned int chain = 1; + double init_radius = 0; + int num_warmup = 200; + int num_samples = 400; + int num_thin = 5; + bool save_warmup = true; + int refresh = 0; + double stepsize = 0.1; + double stepsize_jitter = 0; + int max_depth = 8; + double delta = .1; + double gamma = .1; + double kappa = .1; + double t0 = .1; + unsigned int init_buffer = 50; + unsigned int term_buffer = 50; + unsigned int window = 100; + stan::test::unit::instrumented_interrupt interrupt; + EXPECT_EQ(interrupt.call_count(), 0); + + stan::services::sample::hmc_nuts_dense_e( + model, num_chains, context, random_seed, chain, init_radius, num_warmup, + num_samples, num_thin, save_warmup, refresh, stepsize, stepsize_jitter, + max_depth, interrupt, logger, init, parameter, diagnostic); + + for (auto&& init_it : init) { + std::vector init_values; + init_values = init_it.string_values(); + + EXPECT_EQ(0, init_values.size()); + } + + EXPECT_EQ(num_chains, logger.find_info("Elapsed Time:")); + EXPECT_EQ(num_chains, logger.find_info("seconds (Warm-up)")); + EXPECT_EQ(num_chains, logger.find_info("seconds (Sampling)")); + EXPECT_EQ(num_chains, logger.find_info("seconds (Total)")); + EXPECT_EQ(0, logger.call_count_error()); +} diff --git a/src/test/unit/services/util/inv_metric_test.cpp b/src/test/unit/services/util/inv_metric_test.cpp index 0c2f3888e13..92892a64058 100644 --- a/src/test/unit/services/util/inv_metric_test.cpp +++ b/src/test/unit/services/util/inv_metric_test.cpp @@ -27,6 +27,13 @@ TEST(inv_metric, create_diag_sz100) { ASSERT_NEAR(1.0, diag_vals[99], 0.0001); } +TEST(inv_metric, create_dense_sz0) { + auto default_metric = stan::services::util::create_unit_e_dense_inv_metric(0); + stan::io::var_context& inv_inv_metric = default_metric; + std::vector diag_vals = inv_inv_metric.vals_r("inv_metric"); + EXPECT_EQ(0, diag_vals.size()); +} + TEST(inv_metric, create_dense_sz2) { auto default_metric = stan::services::util::create_unit_e_dense_inv_metric(2); stan::io::var_context& inv_inv_metric = default_metric; @@ -120,6 +127,16 @@ TEST(inv_metric, read_dense_OK) { ASSERT_NEAR(0.8274, inv_inv_metric(8), 0.000001); } +TEST(inv_metric, read_dense_sz0) { + stan::callbacks::logger logger; + auto zero_metric = stan::services::util::create_unit_e_dense_inv_metric(0); + Eigen::MatrixXd inv_inv_metric + = stan::services::util::read_dense_inv_metric(zero_metric, 0, logger); + EXPECT_EQ(0, inv_inv_metric.size()); + EXPECT_EQ(0, inv_inv_metric.rows()); + EXPECT_EQ(0, inv_inv_metric.cols()); +} + TEST(inv_metric, read_dense_bad1) { stan::callbacks::logger logger; std::string txt