From b8342cdbbad85e79a40bd0d572c84fdc4cef0a7e Mon Sep 17 00:00:00 2001 From: Mitzi Morris Date: Fri, 4 Oct 2024 16:38:21 -0400 Subject: [PATCH 1/8] unit tests passing --- .../compute_potential_scale_reduction.hpp | 236 -------------- src/stan/analyze/mcmc/split_chains.hpp | 56 ++++ src/stan/mcmc/chains.hpp | 37 ++- ...compute_potential_scale_reduction_test.cpp | 308 ------------------ src/test/unit/mcmc/chains_test.cpp | 16 +- 5 files changed, 76 insertions(+), 577 deletions(-) diff --git a/src/stan/analyze/mcmc/compute_potential_scale_reduction.hpp b/src/stan/analyze/mcmc/compute_potential_scale_reduction.hpp index f4fcb971bc7..e40253ebb49 100644 --- a/src/stan/analyze/mcmc/compute_potential_scale_reduction.hpp +++ b/src/stan/analyze/mcmc/compute_potential_scale_reduction.hpp @@ -17,156 +17,6 @@ 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. - * - * @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. @@ -248,33 +98,9 @@ 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. - * - * @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. @@ -298,42 +124,6 @@ inline double compute_potential_scale_reduction( return compute_potential_scale_reduction(draws, sizes); } -/** - * 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. - * - * @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 @@ -366,32 +156,6 @@ inline double compute_split_potential_scale_reduction( return compute_potential_scale_reduction(split_draws, half_sizes); } -/** - * 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. - * - * @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 diff --git a/src/stan/analyze/mcmc/split_chains.hpp b/src/stan/analyze/mcmc/split_chains.hpp index 24726291d62..a7fb1b8d616 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 +9,61 @@ 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_samples = chains[0].rows(); + size_t half = std::floor(num_samples / 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() + half, 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_samples = samples.rows(); + size_t half = std::floor(num_samples / 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() + half, + 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/mcmc/chains.hpp b/src/stan/mcmc/chains.hpp index a553fd36cdf..6f309df05ae 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); @@ -595,8 +595,8 @@ 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); @@ -608,22 +608,26 @@ 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( @@ -631,9 +635,6 @@ class chains { 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/test/unit/analyze/mcmc/compute_potential_scale_reduction_test.cpp b/src/test/unit/analyze/mcmc/compute_potential_scale_reduction_test.cpp index 41a102e51df..47f31fc8566 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); @@ -499,22 +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"}; @@ -526,20 +235,3 @@ 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/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)); From 439d5f52d752a00de49872ce7268f450b8afb4de Mon Sep 17 00:00:00 2001 From: Mitzi Morris Date: Fri, 4 Oct 2024 16:41:39 -0400 Subject: [PATCH 2/8] refactored functions, unit tests --- src/stan/analyze/mcmc/check_chains.hpp | 43 +++++ src/stan/analyze/mcmc/rank_normalization.hpp | 61 ++++++++ .../mcmc/split_rank_normalized_ess.hpp | 148 ++++++++++++++++++ .../mcmc/split_rank_normalized_rhat.hpp | 86 ++++++++++ .../compute_split_rank_normalized_rhat.cpp | 82 ++++++++++ 5 files changed, 420 insertions(+) create mode 100644 src/stan/analyze/mcmc/check_chains.hpp create mode 100644 src/stan/analyze/mcmc/rank_normalization.hpp create mode 100644 src/stan/analyze/mcmc/split_rank_normalized_ess.hpp create mode 100644 src/stan/analyze/mcmc/split_rank_normalized_rhat.hpp create mode 100644 src/test/unit/analyze/mcmc/compute_split_rank_normalized_rhat.cpp 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/rank_normalization.hpp b/src/stan/analyze/mcmc/rank_normalization.hpp new file mode 100644 index 00000000000..d53a9acf56a --- /dev/null +++ b/src/stan/analyze/mcmc/rank_normalization.hpp @@ -0,0 +1,61 @@ +#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. 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/split_rank_normalized_ess.hpp b/src/stan/analyze/mcmc/split_rank_normalized_ess.hpp new file mode 100644 index 00000000000..6a264901478 --- /dev/null +++ b/src/stan/analyze/mcmc/split_rank_normalized_ess.hpp @@ -0,0 +1,148 @@ +#ifndef STAN_ANALYZE_MCMC_SPLIT_RANK_NORMALIZED_ESS_HPP +#define STAN_ANALYZE_MCMC_SPLIT_RANK_NORMALIZED_ESS_HPP + +#include +#include +#include +#include +#include +#include +#include +#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. + * The value returned is the minimum of ESS and (sample_sz * log10(sample_sz). + * Sample autocovariance is computed using Stan math library implmentation. + * See https://arxiv.org/abs/1903.08008, section 3.2 for discussion. + * + * @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 num_draws = chains.rows(); + Eigen::MatrixXd acov(num_draws, 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) { + Eigen::Map chain_col(chains.col(i).data(), + num_draws); + Eigen::Map cov_col(acov.col(i).data(), num_draws); + stan::math::autocovariance(chain_col, cov_col); + chain_mean(i) = chain_col.mean(); + chain_var(i) = cov_col(0) * num_draws / (num_draws - 1); + } + + // compute var_plus, eqn (3) + double w_chain_var = math::mean(chain_var); // W (within chain var) + double var_plus = w_chain_var * (num_draws - 1) / num_draws; // \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(num_draws); + Eigen::VectorXd acov_t(num_chains); + double rho_hat_even = 1.0; + rho_hat_t(0) = rho_hat_even; // lag 0 + double rho_hat_odd = 1 - (w_chain_var - acov.row(1).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 < num_draws - 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 num_samples = num_chains * num_draws; + // 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(num_samples)); + return (num_samples / tau_hat); +} + +/** + * 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 + * + * 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 potential scale reduction + */ +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..b0716688010 --- /dev/null +++ b/src/stan/analyze/mcmc/split_rank_normalized_rhat.hpp @@ -0,0 +1,86 @@ +#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 +#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); +} + +/** + * 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/test/unit/analyze/mcmc/compute_split_rank_normalized_rhat.cpp b/src/test/unit/analyze/mcmc/compute_split_rank_normalized_rhat.cpp new file mode 100644 index 00000000000..adc6cf16a6d --- /dev/null +++ b/src/test/unit/analyze/mcmc/compute_split_rank_normalized_rhat.cpp @@ -0,0 +1,82 @@ +#include +#include +#include +#include +#include +#include +#include + +class RankNormalizedRhat : public testing::Test { + public: + void SetUp() { + 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 (!eight_schools_1_stream || !eight_schools_2_stream) { + FAIL() << "Failed to open one or more test files"; + } + eight_schools_1_stream.seekg(0, std::ios::beg); + eight_schools_2_stream.seekg(0, std::ios::beg); + 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() { + blocker1_stream.close(); + blocker2_stream.close(); + } + + std::stringstream out; + std::ifstream eight_schools_1_stream, eight_schools_2_stream; + stan::io::stan_csv eight_schools_1, eight_schools_2; +}; + + +TEST_F(RankNormalizedRhat, compute_split_rank_normalized_rhat) { + stan::mcmc::chains<> chains(eight_schools_1); + + // test against R implementation in pkg posterior + Eigen::VectorXd rhat_8_schools_1_bulk(10); + rhat_8_schools_1_bulk << 1.0012958313, 1.0046136496, 1.0085723580, + 1.0248629375, 1.0111456620, 1.0004458336, 0.9987162973, 1.0339773469, + 0.9985612618, 1.0281667351; + + Eigen::VectorXd rhat_8_schools_1_tail(10); + rhat_8_schools_1_tail << 1.005676523, 1.009670999, 1.00184184, 1.002222679, + 1.004148161, 1.003218528, 1.009195353, 1.001426744, 1.003984381, + 1.025817745; + + for (size_t i = 0; i < 10; ++i) { + auto rhats = chains.split_rank_normalized_rhat(i + 7); + EXPECT_NEAR(rhats.first, rhat_8_schools_1_bulk(i), 0.05); + EXPECT_NEAR(rhats.second, rhat_8_schools_1_tail(i), 0.05); + } +} + +TEST_F(RankNormalizedRhat, const_fail) { + std::ifstream bernoulli_const_1_stream, bernoulli_const_2_stream; + stan::io::stan_csv bernoulli_const_1, bernoulli_const_2; + bernoulli_const_1_stream.open( + "src/test/unit/mcmc/test_csv_files/bernoulli_const_1.csv", + std::ifstream::in); + bernoulli_const_1 + = stan::io::stan_csv_reader::parse(bernoulli_const_1_stream, &out); + bernoulli_const_1_stream.close(); + bernoulli_const_2_stream.open( + "src/test/unit/mcmc/test_csv_files/bernoulli_const_2.csv", + std::ifstream::in); + bernoulli_const_2 + = stan::io::stan_csv_reader::parse(bernoulli_const_2_stream, &out); + bernoulli_const_2_stream.close(); + stan::mcmc::chains<> chains(bernoulli_const_1); + chains.add(bernoulli_const_2); + + auto rhat = chains.split_rank_normalized_rhat("zeta"); + EXPECT_TRUE(std::isnan(rhat.first)); + EXPECT_TRUE(std::isnan(rhat.second)); +} From a954edcf6def6aeacb19605203bbf7a92ea2ef41 Mon Sep 17 00:00:00 2001 From: Mitzi Morris Date: Fri, 4 Oct 2024 17:00:43 -0400 Subject: [PATCH 3/8] unit tests passing --- ...mpute_split_rank_normalized_rhat_test.cpp} | 47 +- .../mcmc/test_csv_files/bernoulli_const_1.csv | 1057 +++++++++++++++++ .../mcmc/test_csv_files/bernoulli_const_2.csv | 1057 +++++++++++++++++ .../mcmc/test_csv_files/eight_schools_1.csv | 557 +++++++++ 4 files changed, 2684 insertions(+), 34 deletions(-) rename src/test/unit/analyze/mcmc/{compute_split_rank_normalized_rhat.cpp => compute_split_rank_normalized_rhat_test.cpp} (59%) create mode 100644 src/test/unit/mcmc/test_csv_files/bernoulli_const_1.csv create mode 100644 src/test/unit/mcmc/test_csv_files/bernoulli_const_2.csv create mode 100644 src/test/unit/mcmc/test_csv_files/eight_schools_1.csv diff --git a/src/test/unit/analyze/mcmc/compute_split_rank_normalized_rhat.cpp b/src/test/unit/analyze/mcmc/compute_split_rank_normalized_rhat_test.cpp similarity index 59% rename from src/test/unit/analyze/mcmc/compute_split_rank_normalized_rhat.cpp rename to src/test/unit/analyze/mcmc/compute_split_rank_normalized_rhat_test.cpp index adc6cf16a6d..ee5f130cd67 100644 --- a/src/test/unit/analyze/mcmc/compute_split_rank_normalized_rhat.cpp +++ b/src/test/unit/analyze/mcmc/compute_split_rank_normalized_rhat_test.cpp @@ -6,38 +6,16 @@ #include #include -class RankNormalizedRhat : public testing::Test { - public: - void SetUp() { - 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 (!eight_schools_1_stream || !eight_schools_2_stream) { - FAIL() << "Failed to open one or more test files"; - } - eight_schools_1_stream.seekg(0, std::ios::beg); - eight_schools_2_stream.seekg(0, std::ios::beg); - 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() { - blocker1_stream.close(); - blocker2_stream.close(); - } - +TEST(RankNormalizedRhat, compute_split_rank_normalized_rhat) { std::stringstream out; - std::ifstream eight_schools_1_stream, eight_schools_2_stream; - stan::io::stan_csv eight_schools_1, eight_schools_2; -}; - - -TEST_F(RankNormalizedRhat, compute_split_rank_normalized_rhat) { + std::ifstream eight_schools_1_stream; + stan::io::stan_csv eight_schools_1; + 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(); stan::mcmc::chains<> chains(eight_schools_1); // test against R implementation in pkg posterior @@ -52,13 +30,14 @@ TEST_F(RankNormalizedRhat, compute_split_rank_normalized_rhat) { 1.025817745; for (size_t i = 0; i < 10; ++i) { - auto rhats = chains.split_rank_normalized_rhat(i + 7); + auto rhats = chains.split_potential_scale_reduction_rank(i + 7); EXPECT_NEAR(rhats.first, rhat_8_schools_1_bulk(i), 0.05); EXPECT_NEAR(rhats.second, rhat_8_schools_1_tail(i), 0.05); } } -TEST_F(RankNormalizedRhat, const_fail) { +TEST(RankNormalizedRhat, const_fail) { + std::stringstream out; std::ifstream bernoulli_const_1_stream, bernoulli_const_2_stream; stan::io::stan_csv bernoulli_const_1, bernoulli_const_2; bernoulli_const_1_stream.open( @@ -76,7 +55,7 @@ TEST_F(RankNormalizedRhat, const_fail) { stan::mcmc::chains<> chains(bernoulli_const_1); chains.add(bernoulli_const_2); - auto rhat = chains.split_rank_normalized_rhat("zeta"); + auto rhat = chains.split_potential_scale_reduction_rank("zeta"); EXPECT_TRUE(std::isnan(rhat.first)); EXPECT_TRUE(std::isnan(rhat.second)); } diff --git a/src/test/unit/mcmc/test_csv_files/bernoulli_const_1.csv b/src/test/unit/mcmc/test_csv_files/bernoulli_const_1.csv new file mode 100644 index 00000000000..b4a727ec857 --- /dev/null +++ b/src/test/unit/mcmc/test_csv_files/bernoulli_const_1.csv @@ -0,0 +1,1057 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-07-20 18:56:30 UTC +# method = sample (Default) +# sample +# num_samples = 1000 (Default) +# num_warmup = 1000 (Default) +# save_warmup = false (Default) +# thin = 1 (Default) +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 1 (Default) +# data +# file = examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 3635036313 (Default) +# output +# file = bernoulli_out.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0-25-gbb9ce42 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,zeta +# Adaptation terminated +# Step size = 0.932037 +# Diagonal elements of inverse mass matrix: +# 0.591014 +-7.81355,0.761218,0.932037,1,3,0,8.13669,0.15 +-8.71049,0.894869,0.932037,1,1,0,8.72268,0.15 +-7.3531,0.957519,0.932037,1,3,0,8.53469,0.15 +-7.02007,1,0.932037,1,1,0,7.29505,0.15 +-8.49033,0.716959,0.932037,1,3,0,9.20407,0.15 +-7.18724,1,0.932037,1,1,0,8.06459,0.15 +-10.0621,0.636916,0.932037,2,3,0,10.0852,0.15 +-8.11851,1,0.932037,1,1,0,9.65805,0.15 +-6.86936,0.948809,0.932037,2,3,0,8.60097,0.15 +-7.13756,0.935492,0.932037,2,3,0,7.43717,0.15 +-7.09122,1,0.932037,1,1,0,7.19518,0.15 +-6.91669,1,0.932037,1,1,0,7.06383,0.15 +-6.76371,0.840542,0.932037,2,3,0,8.08838,0.15 +-6.84805,0.983351,0.932037,1,3,0,6.84861,0.15 +-8.3727,0.653843,0.932037,2,3,0,9.53079,0.15 +-7.25928,0.806553,0.932037,1,3,0,9.43963,0.15 +-6.95987,0.853051,0.932037,2,3,0,9.71446,0.15 +-6.95334,1,0.932037,1,1,0,7.00708,0.15 +-6.98417,0.993206,0.932037,1,1,0,7.02663,0.15 +-8.9535,0.727406,0.932037,1,3,0,9.27425,0.15 +-7.08162,0.995936,0.932037,2,3,0,9.00617,0.15 +-7.16691,0.98306,0.932037,1,1,0,7.22238,0.15 +-6.8898,1,0.932037,1,1,0,7.10684,0.15 +-7.8902,0.846547,0.932037,1,3,0,7.96857,0.15 +-7.24239,1,0.932037,1,1,0,7.78446,0.15 +-7.94748,0.888836,0.932037,1,1,0,7.94857,0.15 +-6.83694,0.864212,0.932037,2,3,0,8.91495,0.15 +-6.83694,0.425574,0.932037,1,3,0,9.01249,0.15 +-6.80131,0.985852,0.932037,1,3,0,6.93894,0.15 +-6.74824,0.978446,0.932037,2,3,0,6.95179,0.15 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+-7.58846,0.835161,0.932037,1,3,0,7.98997,0.15 +-6.84168,1,0.932037,2,3,0,7.32922,0.15 +-6.79898,0.907476,0.932037,2,3,0,7.23342,0.15 +-7.18968,0.940405,0.932037,2,3,0,7.1897,0.15 +-7.30081,0.811278,0.932037,2,3,0,8.27114,0.15 +-7.44395,0.975262,0.932037,1,1,0,7.52376,0.15 +-7.10913,1,0.932037,1,1,0,7.3984,0.15 +-6.74902,0.994264,0.932037,1,3,0,7.08219,0.15 +-7.95169,0.736282,0.932037,1,3,0,8.05657,0.15 +# +# 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_const_2.csv b/src/test/unit/mcmc/test_csv_files/bernoulli_const_2.csv new file mode 100644 index 00000000000..b4a727ec857 --- /dev/null +++ b/src/test/unit/mcmc/test_csv_files/bernoulli_const_2.csv @@ -0,0 +1,1057 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-07-20 18:56:30 UTC +# method = sample (Default) +# sample +# num_samples = 1000 (Default) +# num_warmup = 1000 (Default) +# save_warmup = false (Default) +# thin = 1 (Default) +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 1 (Default) +# data +# file = examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 3635036313 (Default) +# output +# file = bernoulli_out.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0-25-gbb9ce42 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,zeta +# Adaptation terminated +# Step size = 0.932037 +# Diagonal elements of inverse mass matrix: +# 0.591014 +-7.81355,0.761218,0.932037,1,3,0,8.13669,0.15 +-8.71049,0.894869,0.932037,1,1,0,8.72268,0.15 +-7.3531,0.957519,0.932037,1,3,0,8.53469,0.15 +-7.02007,1,0.932037,1,1,0,7.29505,0.15 +-8.49033,0.716959,0.932037,1,3,0,9.20407,0.15 +-7.18724,1,0.932037,1,1,0,8.06459,0.15 +-10.0621,0.636916,0.932037,2,3,0,10.0852,0.15 +-8.11851,1,0.932037,1,1,0,9.65805,0.15 +-6.86936,0.948809,0.932037,2,3,0,8.60097,0.15 +-7.13756,0.935492,0.932037,2,3,0,7.43717,0.15 +-7.09122,1,0.932037,1,1,0,7.19518,0.15 +-6.91669,1,0.932037,1,1,0,7.06383,0.15 +-6.76371,0.840542,0.932037,2,3,0,8.08838,0.15 +-6.84805,0.983351,0.932037,1,3,0,6.84861,0.15 +-8.3727,0.653843,0.932037,2,3,0,9.53079,0.15 +-7.25928,0.806553,0.932037,1,3,0,9.43963,0.15 +-6.95987,0.853051,0.932037,2,3,0,9.71446,0.15 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diff --git a/src/test/unit/mcmc/test_csv_files/eight_schools_1.csv b/src/test/unit/mcmc/test_csv_files/eight_schools_1.csv new file mode 100644 index 00000000000..e89e6fe189a --- /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, 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+-16.2031,0.951159,0.182121,4,15,0,19.977,5.95471,13.3501,10.0575,8.95337,6.01971,11.6715,3.74652,8.84567,4.37327,3.66562 +-15.432,0.964331,0.182121,4,15,0,22.562,6.38961,8.91075,6.61703,5.5607,7.4082,-1.10682,10.9796,3.87121,6.50734,2.82847 +-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) +# From 4a7011077bf67517b5b24168f6ffaf2193567823 Mon Sep 17 00:00:00 2001 From: Mitzi Morris Date: Mon, 7 Oct 2024 13:28:59 -0400 Subject: [PATCH 4/8] refactored code, added unit tests --- .../mcmc/split_rank_normalized_ess_test.cpp | 70 +++ ...pp => split_rank_normalized_rhat_test.cpp} | 14 +- .../mcmc/test_csv_files/eight_schools_2.csv | 557 ++++++++++++++++++ .../test_csv_files/eight_schools_5iters_1.csv | 62 ++ .../test_csv_files/eight_schools_5iters_2.csv | 62 ++ 5 files changed, 759 insertions(+), 6 deletions(-) create mode 100644 src/test/unit/analyze/mcmc/split_rank_normalized_ess_test.cpp rename src/test/unit/analyze/mcmc/{compute_split_rank_normalized_rhat_test.cpp => split_rank_normalized_rhat_test.cpp} (78%) create mode 100644 src/test/unit/mcmc/test_csv_files/eight_schools_2.csv create mode 100644 src/test/unit/mcmc/test_csv_files/eight_schools_5iters_1.csv create mode 100644 src/test/unit/mcmc/test_csv_files/eight_schools_5iters_2.csv 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..475ac522df5 --- /dev/null +++ b/src/test/unit/analyze/mcmc/split_rank_normalized_ess_test.cpp @@ -0,0 +1,70 @@ +#include +#include +#include +#include +#include +#include +#include + +TEST(RankNormalizedEss, compute_split_rank_normalized_ess) { + 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(); + + // test against R implementation in pkg posterior (via cmdstanr) + Eigen::VectorXd ess_8_schools_bulk(10); + ess_8_schools_bulk << 348, 370, 600, 638, 765, 608, 629, 274, 517, 112; + Eigen::VectorXd ess_8_schools_tail(10); + ess_8_schools_tail << 845, 858, 874, 726, 620, 753, 826, 628, 587, 108; + + + Eigen::MatrixXd chains(eight_schools_1.samples.rows(), 2); + 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); + auto ess = stan::analyze::split_rank_normalized_ess(chains); + EXPECT_NEAR(ess.first, ess_8_schools_bulk(i), 5); + EXPECT_NEAR(ess.second, ess_8_schools_tail(i), 5); + } +} + +TEST(RankNormalizedEss, short_chains_fail) { + std::stringstream out; + std::ifstream eight_schools_5iters_1_stream, eight_schools_5iters_2_stream; + stan::io::stan_csv eight_schools_5iters_1, eight_schools_5iters_2; + eight_schools_5iters_1_stream.open( + "src/test/unit/mcmc/test_csv_files/eight_schools_5iters_1.csv", + std::ifstream::in); + eight_schools_5iters_1 + = stan::io::stan_csv_reader::parse(eight_schools_5iters_1_stream, &out); + eight_schools_5iters_1_stream.close(); + eight_schools_5iters_2_stream.open( + "src/test/unit/mcmc/test_csv_files/eight_schools_5iters_2.csv", + std::ifstream::in); + eight_schools_5iters_2 + = stan::io::stan_csv_reader::parse(eight_schools_5iters_2_stream, &out); + eight_schools_5iters_2_stream.close(); + + + Eigen::MatrixXd chains(eight_schools_5iters_1.samples.rows(), 2); + for (size_t i = 0; i < 10; ++i) { + chains.col(0) = eight_schools_5iters_1.samples.col(i + 7); + chains.col(1) = eight_schools_5iters_2.samples.col(i + 7); + auto ess = stan::analyze::split_rank_normalized_ess(chains); + EXPECT_TRUE(std::isnan(ess.first)); + EXPECT_TRUE(std::isnan(ess.second)); + } +} diff --git a/src/test/unit/analyze/mcmc/compute_split_rank_normalized_rhat_test.cpp b/src/test/unit/analyze/mcmc/split_rank_normalized_rhat_test.cpp similarity index 78% rename from src/test/unit/analyze/mcmc/compute_split_rank_normalized_rhat_test.cpp rename to src/test/unit/analyze/mcmc/split_rank_normalized_rhat_test.cpp index ee5f130cd67..db967a87bc4 100644 --- a/src/test/unit/analyze/mcmc/compute_split_rank_normalized_rhat_test.cpp +++ b/src/test/unit/analyze/mcmc/split_rank_normalized_rhat_test.cpp @@ -1,4 +1,4 @@ -#include +#include #include #include #include @@ -16,7 +16,6 @@ TEST(RankNormalizedRhat, compute_split_rank_normalized_rhat) { eight_schools_1 = stan::io::stan_csv_reader::parse(eight_schools_1_stream, &out); eight_schools_1_stream.close(); - stan::mcmc::chains<> chains(eight_schools_1); // test against R implementation in pkg posterior Eigen::VectorXd rhat_8_schools_1_bulk(10); @@ -29,8 +28,10 @@ TEST(RankNormalizedRhat, compute_split_rank_normalized_rhat) { 1.004148161, 1.003218528, 1.009195353, 1.001426744, 1.003984381, 1.025817745; + Eigen::MatrixXd chains(eight_schools_1.samples.rows(), 1); for (size_t i = 0; i < 10; ++i) { - auto rhats = chains.split_potential_scale_reduction_rank(i + 7); + chains.col(0) = eight_schools_1.samples.col(i + 7); + auto rhats = stan::analyze::split_rank_normalized_rhat(chains); EXPECT_NEAR(rhats.first, rhat_8_schools_1_bulk(i), 0.05); EXPECT_NEAR(rhats.second, rhat_8_schools_1_tail(i), 0.05); } @@ -52,10 +53,11 @@ TEST(RankNormalizedRhat, const_fail) { bernoulli_const_2 = stan::io::stan_csv_reader::parse(bernoulli_const_2_stream, &out); bernoulli_const_2_stream.close(); - stan::mcmc::chains<> chains(bernoulli_const_1); - chains.add(bernoulli_const_2); - auto rhat = chains.split_potential_scale_reduction_rank("zeta"); + Eigen::MatrixXd chains(bernoulli_const_1.samples.rows(), 2); + chains.col(0) = bernoulli_const_1.samples.col(bernoulli_const_1.samples.cols() - 1); + chains.col(1) = bernoulli_const_2.samples.col(bernoulli_const_2.samples.cols() - 1); + auto rhat = stan::analyze::split_rank_normalized_rhat(chains); EXPECT_TRUE(std::isnan(rhat.first)); EXPECT_TRUE(std::isnan(rhat.second)); } 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 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seconds (Warm-up) +# 0.014 seconds (Sampling) +# 0.047 seconds (Total) +# diff --git a/src/test/unit/mcmc/test_csv_files/eight_schools_5iters_1.csv b/src/test/unit/mcmc/test_csv_files/eight_schools_5iters_1.csv new file mode 100644 index 00000000000..c210031c92e --- /dev/null +++ b/src/test/unit/mcmc/test_csv_files/eight_schools_5iters_1.csv @@ -0,0 +1,62 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = eight_schools_model +# start_datetime = 2024-08-02 00:41:06 UTC +# method = sample (Default) +# sample +# num_samples = 5 +# 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 = 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 = 1272150304 +# output +# file = /Users/mitzi/github/stan-dev/cmdstan/examples/eight_schools/test_csv_files/eight_schools-202408012041-1-5c2994.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-202408012041-1-71b713.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 = 0.255122 +# Diagonal elements of inverse mass matrix: +# 29.0476, 61.52, 39.9696, 71.637, 55.2727, 46.9849, 47.056, 46.955, 63.8751, 0.452837 +-20.7877,0.992501,0.255122,4,31,0,26.7532,15.418,22.6964,2.33549,-0.47199,10.617,0.327037,10.1927,16.3596,19.9623,9.72387 +-20.3617,0.786426,0.255122,3,15,0,23.3769,15.4589,17.628,4.63432,10.408,11.706,2.19791,13.2514,13.176,24.6951,11.005 +-20.0581,0.970637,0.255122,3,15,0,23.1533,10.6479,9.46195,13.6135,12.0037,13.4518,-0.981723,14.7449,10.6646,23.8779,6.28949 +-27.7784,0.685455,0.255122,4,31,0,32.1245,18.1249,29.0012,16.4194,21.7475,18.8411,-5.54255,-0.590031,2.02352,1.58803,26.5075 +-20.4323,0.976331,0.255122,4,15,0,31.0216,6.67189,0.393845,2.87533,-5.4399,-0.80737,7.71426,5.1214,17.8469,7.69329,9.05484 +# +# Elapsed Time: 0.03 seconds (Warm-up) +# 0 seconds (Sampling) +# 0.03 seconds (Total) +# diff --git a/src/test/unit/mcmc/test_csv_files/eight_schools_5iters_2.csv b/src/test/unit/mcmc/test_csv_files/eight_schools_5iters_2.csv new file mode 100644 index 00000000000..5ac7176c6f9 --- /dev/null +++ b/src/test/unit/mcmc/test_csv_files/eight_schools_5iters_2.csv @@ -0,0 +1,62 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = eight_schools_model +# start_datetime = 2024-08-02 00:41:07 UTC +# method = sample (Default) +# sample +# num_samples = 5 +# 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 = 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 = 1272150304 +# output +# file = /Users/mitzi/github/stan-dev/cmdstan/examples/eight_schools/test_csv_files/eight_schools-202408012041-2-5c2994.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-202408012041-2-71b713.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 = 0.212532 +# Diagonal elements of inverse mass matrix: +# 36.7915, 71.7469, 39.8211, 74.4468, 50.0889, 46.2225, 50.7702, 47.6696, 63.508, 0.719632 +-25.6347,0.977811,0.212532,4,15,0,30.7373,3.48962,18.9434,-8.37292,-16.7421,0.568964,9.96071,17.3906,8.33767,4.11305,13.9073 +-26.2724,0.957923,0.212532,5,31,0,33.4452,1.10426,19.5777,7.16892,-4.27551,1.20374,1.75746,15.5193,-6.81069,-11.3926,16.3893 +-25.8267,0.989673,0.212532,4,15,0,28.8245,1.07736,17.5014,13.9165,0.358151,2.27947,-3.36194,14.716,-5.54053,-11.8808,8.61073 +-29.6451,0.640928,0.212532,5,31,0,35.3758,8.49943,13.8662,0.409198,7.39161,8.57074,6.92426,24.1421,4.40711,-10.1655,31.0153 +-21.8872,0.886503,0.212532,4,15,0,32.6965,7.28245,18.5208,12.251,6.33112,7.20102,3.02724,23.5692,10.1139,4.93785,5.93738 +# +# Elapsed Time: 0.03 seconds (Warm-up) +# 0 seconds (Sampling) +# 0.03 seconds (Total) +# From 1355de82fdf26e9b9192a3dd8a3dd08e295781f5 Mon Sep 17 00:00:00 2001 From: Mitzi Morris Date: Mon, 7 Oct 2024 13:30:58 -0400 Subject: [PATCH 5/8] lint fix --- src/stan/mcmc/chains.hpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/src/stan/mcmc/chains.hpp b/src/stan/mcmc/chains.hpp index 6f309df05ae..1cb7ed66fed 100644 --- a/src/stan/mcmc/chains.hpp +++ b/src/stan/mcmc/chains.hpp @@ -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); } @@ -595,7 +596,6 @@ class chains { return split_effective_sample_size(index(name)); } - double split_potential_scale_reduction(const int index) const { int n_chains = num_chains(); std::vector draws(n_chains); @@ -634,7 +634,6 @@ class chains { const std::string& name) const { return split_potential_scale_reduction_rank(index(name)); } - }; } // namespace mcmc From c184c9e3cc06bb4ab0f6fb791bac78a05baa2bf4 Mon Sep 17 00:00:00 2001 From: Stan Jenkins Date: Mon, 7 Oct 2024 13:43:16 -0400 Subject: [PATCH 6/8] [Jenkins] auto-formatting by clang-format version 10.0.0-4ubuntu1 --- src/stan/mcmc/chains.hpp | 2 +- .../mcmc/compute_potential_scale_reduction_test.cpp | 1 - .../unit/analyze/mcmc/split_rank_normalized_ess_test.cpp | 4 +--- .../unit/analyze/mcmc/split_rank_normalized_rhat_test.cpp | 8 +++++--- 4 files changed, 7 insertions(+), 8 deletions(-) diff --git a/src/stan/mcmc/chains.hpp b/src/stan/mcmc/chains.hpp index 1cb7ed66fed..fdccc53cf5d 100644 --- a/src/stan/mcmc/chains.hpp +++ b/src/stan/mcmc/chains.hpp @@ -625,7 +625,7 @@ class chains { 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(); + chains.col(i) = bottom_rows.eval(); } return analyze::split_rank_normalized_rhat(chains); } 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 47f31fc8566..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 @@ -224,7 +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_nan) { std::vector param_names{"a"}; stan::mcmc::chains<> chains(param_names); 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 index 475ac522df5..654a2fd1686 100644 --- a/src/test/unit/analyze/mcmc/split_rank_normalized_ess_test.cpp +++ b/src/test/unit/analyze/mcmc/split_rank_normalized_ess_test.cpp @@ -23,14 +23,13 @@ TEST(RankNormalizedEss, compute_split_rank_normalized_ess) { eight_schools_2 = stan::io::stan_csv_reader::parse(eight_schools_2_stream, &out); eight_schools_2_stream.close(); - + // test against R implementation in pkg posterior (via cmdstanr) Eigen::VectorXd ess_8_schools_bulk(10); ess_8_schools_bulk << 348, 370, 600, 638, 765, 608, 629, 274, 517, 112; Eigen::VectorXd ess_8_schools_tail(10); ess_8_schools_tail << 845, 858, 874, 726, 620, 753, 826, 628, 587, 108; - Eigen::MatrixXd chains(eight_schools_1.samples.rows(), 2); for (size_t i = 0; i < 10; ++i) { chains.col(0) = eight_schools_1.samples.col(i + 7); @@ -57,7 +56,6 @@ TEST(RankNormalizedEss, short_chains_fail) { eight_schools_5iters_2 = stan::io::stan_csv_reader::parse(eight_schools_5iters_2_stream, &out); eight_schools_5iters_2_stream.close(); - Eigen::MatrixXd chains(eight_schools_5iters_1.samples.rows(), 2); for (size_t i = 0; i < 10; ++i) { 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 index db967a87bc4..f0a46f2530b 100644 --- a/src/test/unit/analyze/mcmc/split_rank_normalized_rhat_test.cpp +++ b/src/test/unit/analyze/mcmc/split_rank_normalized_rhat_test.cpp @@ -53,10 +53,12 @@ TEST(RankNormalizedRhat, const_fail) { bernoulli_const_2 = stan::io::stan_csv_reader::parse(bernoulli_const_2_stream, &out); bernoulli_const_2_stream.close(); - + Eigen::MatrixXd chains(bernoulli_const_1.samples.rows(), 2); - chains.col(0) = bernoulli_const_1.samples.col(bernoulli_const_1.samples.cols() - 1); - chains.col(1) = bernoulli_const_2.samples.col(bernoulli_const_2.samples.cols() - 1); + chains.col(0) + = bernoulli_const_1.samples.col(bernoulli_const_1.samples.cols() - 1); + chains.col(1) + = bernoulli_const_2.samples.col(bernoulli_const_2.samples.cols() - 1); auto rhat = stan::analyze::split_rank_normalized_rhat(chains); EXPECT_TRUE(std::isnan(rhat.first)); EXPECT_TRUE(std::isnan(rhat.second)); From 1ebd1e2d3441776df090b430f7a6f174beb8568f Mon Sep 17 00:00:00 2001 From: Mitzi Morris Date: Mon, 7 Oct 2024 16:55:01 -0400 Subject: [PATCH 7/8] more unit tests --- .../unit/analyze/mcmc/check_chains_test.cpp | 48 +++++++++++++++++++ 1 file changed, 48 insertions(+) create mode 100644 src/test/unit/analyze/mcmc/check_chains_test.cpp 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..a9498f9b814 --- /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 - 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)); +} From f67a14241ccac890cfbfabe67787d49e60b748bc Mon Sep 17 00:00:00 2001 From: Stan Jenkins Date: Mon, 7 Oct 2024 16:55:36 -0400 Subject: [PATCH 8/8] [Jenkins] auto-formatting by clang-format version 10.0.0-4ubuntu1 --- src/test/unit/analyze/mcmc/check_chains_test.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/test/unit/analyze/mcmc/check_chains_test.cpp b/src/test/unit/analyze/mcmc/check_chains_test.cpp index a9498f9b814..4f3f9c579b8 100644 --- a/src/test/unit/analyze/mcmc/check_chains_test.cpp +++ b/src/test/unit/analyze/mcmc/check_chains_test.cpp @@ -43,6 +43,6 @@ TEST(CheckChains, good_and_bad) { // above test shows that column 7 is - make it non-finite chain_1.col(0) = eight_schools_1.samples.col(7); - chain_1(0,0) = std::numeric_limits::infinity(); + chain_1(0, 0) = std::numeric_limits::infinity(); EXPECT_FALSE(stan::analyze::is_finite_and_varies(chain_1)); }