From 9890ac0d3f944dcaabce702a8a189be311e2372b Mon Sep 17 00:00:00 2001 From: Nicusor Serban <48496524+serban-nicusor-toptal@users.noreply.github.com> Date: Wed, 5 Jun 2024 18:15:18 +0200 Subject: [PATCH] Update master to Release/v2.35.0 (#3292) --- .github/ISSUE_TEMPLATE.md | 2 +- Jenkinsfile | 62 +- RELEASE-NOTES.txt | 11 + lib/stan_math | 2 +- make/tests | 5 +- runTests.py | 2 - src/doxygen/doxygen.cfg | 2 +- .../compute_potential_scale_reduction.hpp | 236 +++ src/stan/mcmc/chains.hpp | 25 +- .../hmc/hamiltonians/base_hamiltonian.hpp | 4 +- src/stan/model/model_base.hpp | 8 +- src/stan/model/model_base_crtp.hpp | 4 +- src/stan/model/model_header.hpp | 3 - src/stan/optimization/bfgs.hpp | 4 +- src/stan/optimization/newton.hpp | 2 +- src/stan/services/diagnose/diagnose.hpp | 2 +- .../services/experimental/advi/fullrank.hpp | 14 +- .../services/experimental/advi/meanfield.hpp | 14 +- src/stan/services/optimize/bfgs.hpp | 135 +- src/stan/services/optimize/laplace_sample.hpp | 85 +- src/stan/services/optimize/lbfgs.hpp | 121 +- src/stan/services/optimize/newton.hpp | 4 +- src/stan/services/pathfinder/multi.hpp | 59 +- src/stan/services/pathfinder/psis.hpp | 13 +- src/stan/services/pathfinder/single.hpp | 31 +- src/stan/services/sample/fixed_param.hpp | 62 +- src/stan/services/sample/hmc_nuts_dense_e.hpp | 62 +- .../sample/hmc_nuts_dense_e_adapt.hpp | 75 +- src/stan/services/sample/hmc_nuts_diag_e.hpp | 30 +- .../services/sample/hmc_nuts_diag_e_adapt.hpp | 77 +- src/stan/services/sample/hmc_nuts_unit_e.hpp | 55 +- .../services/sample/hmc_nuts_unit_e_adapt.hpp | 61 +- .../services/sample/hmc_static_dense_e.hpp | 21 +- .../sample/hmc_static_dense_e_adapt.hpp | 23 +- .../services/sample/hmc_static_diag_e.hpp | 21 +- .../sample/hmc_static_diag_e_adapt.hpp | 24 +- .../services/sample/hmc_static_unit_e.hpp | 15 +- .../sample/hmc_static_unit_e_adapt.hpp | 18 +- src/stan/services/sample/standalone_gqs.hpp | 90 +- src/stan/services/util/create_rng.hpp | 21 +- .../util/create_unit_e_dense_inv_metric.hpp | 20 +- .../util/create_unit_e_diag_inv_metric.hpp | 15 +- src/stan/services/util/gq_writer.hpp | 12 +- src/stan/services/util/mcmc_writer.hpp | 7 +- src/stan/variational/advi.hpp | 1 - src/stan/version.hpp | 4 +- .../good/services/bernoulli.data.R | 2 - .../good/variational/gradient_warn.data.R | 4 - .../good/variational/gradient_warn.data.json | 6 + .../good/variational/hier_logistic.data.R | 1745 ----------------- .../good/variational/hier_logistic.data.json | 1007 ++++++++++ .../test-models/performance/logistic.data.R | 52 - .../performance/logistic.data.json | 135 ++ ...compute_potential_scale_reduction_test.cpp | 349 +++- src/test/unit/io/random_var_context_test.cpp | 6 +- src/test/unit/mcmc/chains_test.cpp | 65 +- src/test/unit/mcmc/hmc/base_hmc_test.cpp | 26 +- .../hamiltonians/base_hamiltonian_test.cpp | 7 +- .../hmc/hamiltonians/dense_e_metric_test.cpp | 17 +- .../hmc/hamiltonians/diag_e_metric_test.cpp | 18 +- .../hmc/hamiltonians/softabs_metric_test.cpp | 17 +- .../hmc/hamiltonians/unit_e_metric_test.cpp | 17 +- .../hmc/integrators/expl_leapfrog2_test.cpp | 24 +- .../hmc/integrators/expl_leapfrog_test.cpp | 46 +- .../hmc/integrators/impl_leapfrog2_test.cpp | 45 +- .../hmc/integrators/impl_leapfrog_test.cpp | 63 +- .../unit/mcmc/hmc/nuts/base_nuts_test.cpp | 57 +- .../unit/mcmc/hmc/nuts/derived_nuts_test.cpp | 29 +- .../unit/mcmc/hmc/nuts/instantiation_test.cpp | 21 +- .../unit/mcmc/hmc/nuts/softabs_nuts_test.cpp | 30 +- .../unit/mcmc/hmc/nuts/unit_e_nuts_test.cpp | 30 +- .../nuts_classic/base_nuts_classic_test.cpp | 29 +- .../derived_nuts_classic_test.cpp | 16 +- .../hmc/nuts_classic/instantiation_test.cpp | 20 +- .../mcmc/hmc/static/base_static_hmc_test.cpp | 20 +- .../base_static_uniform_test.cpp | 18 +- .../derived_static_uniform_test.cpp | 89 +- .../unit/mcmc/hmc/xhmc/base_xhmc_test.cpp | 36 +- .../unit/mcmc/hmc/xhmc/softabs_xhmc_test.cpp | 16 +- .../unit/mcmc/hmc/xhmc/unit_e_xhmc_test.cpp | 26 +- .../model/array_functions_roundtrip_test.cpp | 3 +- src/test/unit/model/model_base_test.cpp | 4 +- .../optimize/laplace_jacobian_test.cpp | 1 - .../services/optimize/laplace_sample_test.cpp | 56 +- .../pathfinder/eight_schools_test.cpp | 2 +- .../services/pathfinder/normal_glm_test.cpp | 31 +- src/test/unit/services/pathfinder/util.hpp | 6 +- .../sample/hmc_nuts_dense_inv_metric_test.cpp | 42 +- .../sample/hmc_nuts_diag_inv_metric_test.cpp | 17 +- .../hmc_nuts_unit_e_adapt_parallel_test.cpp | 2 +- .../hmc_static_dense_inv_metric_test.cpp | 41 +- .../hmc_static_diag_inv_metric_test.cpp | 17 +- .../sample/standalone_gqs_parallel_test.cpp | 8 +- .../services/sample/standalone_gqs_test.cpp | 6 +- .../unit/services/util/create_rng_test.cpp | 12 +- .../util/generate_transitions_test.cpp | 4 +- .../unit/services/util/gq_writer_test.cpp | 4 +- .../unit/services/util/initialize_test.cpp | 3 +- .../unit/services/util/inv_metric_test.cpp | 27 +- .../unit/services/util/mcmc_writer_test.cpp | 4 +- .../util/run_adaptive_sampler_test.cpp | 5 +- .../unit/services/util/run_sampler_test.cpp | 3 +- .../unit/variational/advi_messages_test.cpp | 25 +- .../advi_multivar_no_constraint_test.cpp | 24 +- .../advi_multivar_with_constraint_test.cpp | 24 +- .../advi_univar_no_constraint_test.cpp | 24 +- .../advi_univar_with_constraint_test.cpp | 24 +- .../unit/variational/eta_adapt_big_test.cpp | 21 +- .../unit/variational/eta_adapt_fail_test.cpp | 21 +- .../eta_adapt_mock_models_test.cpp | 4 - .../unit/variational/eta_adapt_small_test.cpp | 28 +- .../unit/variational/gradient_warn_test.cpp | 22 +- .../variational/hier_logistic_cp_test.cpp | 13 +- .../unit/variational/hier_logistic_test.cpp | 21 +- .../stochastic_gradient_ascent_test.cpp | 4 - src/test/unit/version_test.cpp | 8 +- 116 files changed, 3193 insertions(+), 3020 deletions(-) delete mode 100644 src/test/test-models/good/services/bernoulli.data.R delete mode 100644 src/test/test-models/good/variational/gradient_warn.data.R create mode 100644 src/test/test-models/good/variational/gradient_warn.data.json delete mode 100644 src/test/test-models/good/variational/hier_logistic.data.R create mode 100644 src/test/test-models/good/variational/hier_logistic.data.json delete mode 100644 src/test/test-models/performance/logistic.data.R create mode 100644 src/test/test-models/performance/logistic.data.json diff --git a/.github/ISSUE_TEMPLATE.md b/.github/ISSUE_TEMPLATE.md index 8251d825621..c3ac6a5e208 100644 --- a/.github/ISSUE_TEMPLATE.md +++ b/.github/ISSUE_TEMPLATE.md @@ -24,4 +24,4 @@ Describe what you expect the output to be. Knowing the correct behavior is also Provide any additional information here. #### Current Version: -v2.34.1 +v2.35.0 diff --git a/Jenkinsfile b/Jenkinsfile index c58282a5bfe..47b3aacbbc4 100644 --- a/Jenkinsfile +++ b/Jenkinsfile @@ -96,7 +96,7 @@ pipeline { preserveStashes(buildCount: 7) parallelsAlwaysFailFast() buildDiscarder(logRotator(numToKeepStr: '20', daysToKeepStr: '30')) - disableConcurrentBuilds(abortPrevious: env.BRANCH_NAME != "downstream_tests" || env.BRANCH_NAME != "downstream_hotfix") + disableConcurrentBuilds(abortPrevious: env.BRANCH_NAME != "downstream_tests" && env.BRANCH_NAME != "downstream_hotfix") } environment { GCC = 'g++' @@ -192,19 +192,17 @@ pipeline { } post { always { - - recordIssues id: "lint_doc_checks", - name: "Linting & Doc checks", - enabledForFailure: true, - aggregatingResults : true, - tools: [ - cppLint(id: "cpplint", name: "Linting & Doc checks@CPPLINT") - ], - blameDisabled: false, - qualityGates: [[threshold: 1, type: 'TOTAL', unstable: true]], - healthy: 10, unhealthy: 100, minimumSeverity: 'HIGH', - referenceJobName: env.BRANCH_NAME - + recordIssues( + id: "lint_doc_checks", + name: "Linting & Doc checks", + enabledForFailure: true, + aggregatingResults : true, + tools: [ + cppLint(id: "cpplint", name: "Linting & Doc checks@CPPLINT") + ], + qualityGates: [[threshold: 1, type: 'TOTAL', unstable: true]], + healthy: 10, unhealthy: 100, minimumSeverity: 'HIGH' + ) deleteDir() } } @@ -266,8 +264,8 @@ pipeline { SET \"PATH=C:\\PROGRA~1\\R\\R-4.1.2\\bin;%PATH%\" SET \"PATH=C:\\PROGRA~1\\Microsoft^ MPI\\Bin;%PATH%\" SET \"MPI_HOME=C:\\PROGRA~1\\Microsoft^ MPI\\Bin\" - mingw32-make.exe -f lib/stan_math/make/standalone math-libs - mingw32-make.exe -j${PARALLEL} test-headers + make.exe -f lib/stan_math/make/standalone math-libs + make.exe -j${PARALLEL} test-headers """ setupCXX(false, WIN_CXX, stanc3_bin_url()) runTestsWin("src/test/unit") @@ -450,7 +448,7 @@ pipeline { SET \"PATH=C:\\PROGRA~1\\Microsoft^ MPI\\Bin;%PATH%\" SET \"MPI_HOME=C:\\PROGRA~1\\Microsoft^ MPI\\Bin\" cd performance-tests-cmdstan/cmdstan - mingw32-make.exe -j${PARALLEL} build + make.exe -j${PARALLEL} build cd .. python ./runPerformanceTests.py -j${PARALLEL} ${integration_tests_flags()}--runs=0 stanc3/test/integration/good python ./runPerformanceTests.py -j${PARALLEL} ${integration_tests_flags()}--runs=0 example-models @@ -498,21 +496,21 @@ pipeline { post { always { node("linux") { - recordIssues id: "pipeline", - name: "Entire pipeline results", - enabledForFailure: true, - aggregatingResults : false, - filters: [ - excludeFile('lib/.*') - ], - tools: [ - gcc4(id: "pipeline_gcc4", name: "GNU C Compiler"), - clang(id: "pipeline_clang", name: "LLVM/Clang") - ], - blameDisabled: false, - qualityGates: [[threshold: 30, type: 'TOTAL', unstable: true]], - healthy: 10, unhealthy: 100, minimumSeverity: 'HIGH', - referenceJobName: env.BRANCH_NAME + recordIssues( + id: "pipeline", + name: "Entire pipeline results", + enabledForFailure: true, + aggregatingResults : false, + filters: [ + excludeFile('lib/.*') + ], + tools: [ + gcc4(id: "pipeline_gcc4", name: "GNU C Compiler"), + clang(id: "pipeline_clang", name: "LLVM/Clang") + ], + qualityGates: [[threshold: 30, type: 'TOTAL', unstable: true]], + healthy: 10, unhealthy: 100, minimumSeverity: 'HIGH' + ) } } success { 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 b89e92c8547..8a09048f518 160000 --- a/lib/stan_math +++ b/lib/stan_math @@ -1 +1 @@ -Subproject commit b89e92c85478bbf4636b706e432ec7e93ea9c879 +Subproject commit 8a09048f5187662687e04c14d7737cf4d20a18a1 diff --git a/make/tests b/make/tests index 6eff2a94d75..2cf99e23178 100644 --- a/make/tests +++ b/make/tests @@ -85,12 +85,15 @@ HEADER_TESTS := $(addsuffix -test,$(call findfiles,src/stan,*.hpp)) ifeq ($(OS),Windows_NT) DEV_NULL = nul + ifeq ($(IS_UCRT),true) + UCRT_NULL_FLAG = -S + endif else DEV_NULL = /dev/null endif %.hpp-test : %.hpp test/dummy.cpp - $(COMPILE.cpp) -O0 -include $^ -o $(DEV_NULL) + $(COMPILE.cpp) -O0 -include $^ $(UCRT_NULL_FLAG) -o $(DEV_NULL) test/dummy.cpp: @mkdir -p test diff --git a/runTests.py b/runTests.py index 26e8098c561..2947e11a056 100755 --- a/runTests.py +++ b/runTests.py @@ -80,8 +80,6 @@ def doCommand(command, exit_on_failure=True): """Run command as a shell command and report/exit on errors.""" print("------------------------------------------------------------") print("%s" % command) - if isWin() and command.startswith("make "): - command = command.replace("make ", "mingw32-make ") p1 = subprocess.Popen(command, shell=True) p1.wait() if exit_on_failure and (not (p1.returncode is None) and not (p1.returncode == 0)): diff --git a/src/doxygen/doxygen.cfg b/src/doxygen/doxygen.cfg index 68763e2c632..35053b4ba80 100644 --- a/src/doxygen/doxygen.cfg +++ b/src/doxygen/doxygen.cfg @@ -38,7 +38,7 @@ PROJECT_NAME = "Stan" # could be handy for archiving the generated documentation or if some version # control system is used. -PROJECT_NUMBER = 2.34.1 +PROJECT_NUMBER = 2.35.0 # Using the PROJECT_BRIEF tag one can provide an optional one line description # for a project that appears at the top of each page and should give viewer a diff --git a/src/stan/analyze/mcmc/compute_potential_scale_reduction.hpp b/src/stan/analyze/mcmc/compute_potential_scale_reduction.hpp index 5ae43c8205c..f4fcb971bc7 100644 --- a/src/stan/analyze/mcmc/compute_potential_scale_reduction.hpp +++ b/src/stan/analyze/mcmc/compute_potential_scale_reduction.hpp @@ -8,6 +8,7 @@ #include #include #include +#include #include #include #include @@ -16,6 +17,156 @@ 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. @@ -101,6 +252,29 @@ inline double compute_potential_scale_reduction( 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. @@ -124,6 +298,42 @@ 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 @@ -156,6 +366,32 @@ 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/mcmc/chains.hpp b/src/stan/mcmc/chains.hpp index a0c8e789727..a553fd36cdf 100644 --- a/src/stan/mcmc/chains.hpp +++ b/src/stan/mcmc/chains.hpp @@ -13,8 +13,6 @@ #include #include #include -#include -#include #include #include #include @@ -44,7 +42,7 @@ using Eigen::Dynamic; * *

Storage Order: Storage is column/last-index major. */ -template +template class chains { private: std::vector param_names_; @@ -597,6 +595,22 @@ class chains { return split_effective_sample_size(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; + } + + return analyze::compute_split_potential_scale_reduction_rank(draws, sizes); + } + double split_potential_scale_reduction(const int index) const { int n_chains = num_chains(); std::vector draws(n_chains); @@ -612,6 +626,11 @@ class chains { return analyze::compute_split_potential_scale_reduction(draws, sizes); } + 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)); } diff --git a/src/stan/mcmc/hmc/hamiltonians/base_hamiltonian.hpp b/src/stan/mcmc/hmc/hamiltonians/base_hamiltonian.hpp index ab47e01068d..abb575bfb60 100644 --- a/src/stan/mcmc/hmc/hamiltonians/base_hamiltonian.hpp +++ b/src/stan/mcmc/hmc/hamiltonians/base_hamiltonian.hpp @@ -52,7 +52,7 @@ class base_hamiltonian { void update_potential(Point& z, callbacks::logger& logger) { try { z.V = -stan::model::log_prob_propto(model_, z.q); - } catch (const std::exception& e) { + } catch (const std::domain_error& e) { this->write_error_msg_(e, logger); z.V = std::numeric_limits::infinity(); } @@ -62,7 +62,7 @@ class base_hamiltonian { try { stan::model::gradient(model_, z.q, z.V, z.g, logger); z.V = -z.V; - } catch (const std::exception& e) { + } catch (const std::domain_error& e) { this->write_error_msg_(e, logger); z.V = std::numeric_limits::infinity(); } diff --git a/src/stan/model/model_base.hpp b/src/stan/model/model_base.hpp index bb9a50c20db..1f6de972f98 100644 --- a/src/stan/model/model_base.hpp +++ b/src/stan/model/model_base.hpp @@ -7,7 +7,7 @@ #include #endif #include -#include +#include #include #include #include @@ -367,8 +367,7 @@ class model_base : public prob_grad { * in output * @param[in,out] msgs msgs stream to which messages are written */ - virtual void write_array(boost::ecuyer1988& base_rng, - Eigen::VectorXd& params_r, + virtual void write_array(stan::rng_t& base_rng, Eigen::VectorXd& params_r, Eigen::VectorXd& params_constrained_r, bool include_tparams = true, bool include_gqs = true, std::ostream* msgs = 0) const = 0; @@ -618,8 +617,7 @@ class model_base : public prob_grad { * in output * @param[in,out] msgs msgs stream to which messages are written */ - virtual void write_array(boost::ecuyer1988& base_rng, - std::vector& params_r, + virtual void write_array(stan::rng_t& base_rng, std::vector& params_r, std::vector& params_i, std::vector& params_r_constrained, bool include_tparams = true, bool include_gqs = true, diff --git a/src/stan/model/model_base_crtp.hpp b/src/stan/model/model_base_crtp.hpp index 5a64ced8c07..1868b3e0567 100644 --- a/src/stan/model/model_base_crtp.hpp +++ b/src/stan/model/model_base_crtp.hpp @@ -133,7 +133,7 @@ class model_base_crtp : public stan::model::model_base { msgs); } - void write_array(boost::ecuyer1988& rng, Eigen::VectorXd& theta, + void write_array(stan::rng_t& rng, Eigen::VectorXd& theta, Eigen::VectorXd& vars, bool include_tparams = true, bool include_gqs = true, std::ostream* msgs = 0) const override { @@ -202,7 +202,7 @@ class model_base_crtp : public stan::model::model_base { theta, theta_i, msgs); } - void write_array(boost::ecuyer1988& rng, std::vector& theta, + void write_array(stan::rng_t& rng, std::vector& theta, std::vector& theta_i, std::vector& vars, bool include_tparams = true, bool include_gqs = true, std::ostream* msgs = 0) const override { diff --git a/src/stan/model/model_header.hpp b/src/stan/model/model_header.hpp index 75ac6c19cbd..34948377f74 100644 --- a/src/stan/model/model_header.hpp +++ b/src/stan/model/model_header.hpp @@ -13,9 +13,6 @@ #include #include -#include -#include - #include #include #include diff --git a/src/stan/optimization/bfgs.hpp b/src/stan/optimization/bfgs.hpp index 53e7fa075bd..86a5a20ccae 100644 --- a/src/stan/optimization/bfgs.hpp +++ b/src/stan/optimization/bfgs.hpp @@ -309,7 +309,7 @@ class ModelAdaptor { try { f = -log_prob_propto(_model, _x, _params_i, _msgs); - } catch (const std::exception &e) { + } catch (const std::domain_error &e) { if (_msgs) (*_msgs) << e.what() << std::endl; return 1; @@ -341,7 +341,7 @@ class ModelAdaptor { try { f = -log_prob_grad(_model, _x, _params_i, _g, _msgs); - } catch (const std::exception &e) { + } catch (const std::domain_error &e) { if (_msgs) (*_msgs) << e.what() << std::endl; return 1; diff --git a/src/stan/optimization/newton.hpp b/src/stan/optimization/newton.hpp index 624a8c8dbc1..b6f24d3eb08 100644 --- a/src/stan/optimization/newton.hpp +++ b/src/stan/optimization/newton.hpp @@ -60,7 +60,7 @@ double newton_step(M& model, std::vector& params_r, try { f1 = stan::model::log_prob_grad(model, new_params_r, params_i, gradient); - } catch (std::exception& e) { + } catch (std::domain_error& e) { // FIXME: this is not a good way to handle a general exception f1 = -1e100; } diff --git a/src/stan/services/diagnose/diagnose.hpp b/src/stan/services/diagnose/diagnose.hpp index 25b3907643c..caf5872169e 100644 --- a/src/stan/services/diagnose/diagnose.hpp +++ b/src/stan/services/diagnose/diagnose.hpp @@ -43,7 +43,7 @@ int diagnose(Model& model, const stan::io::var_context& init, double epsilon, double error, callbacks::interrupt& interrupt, callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& parameter_writer) { - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector disc_vector; std::vector cont_vector = util::initialize( diff --git a/src/stan/services/experimental/advi/fullrank.hpp b/src/stan/services/experimental/advi/fullrank.hpp index 6eb8181ac20..5fba2e4e026 100644 --- a/src/stan/services/experimental/advi/fullrank.hpp +++ b/src/stan/services/experimental/advi/fullrank.hpp @@ -10,7 +10,6 @@ #include #include #include -#include #include #include @@ -60,7 +59,7 @@ int fullrank(Model& model, const stan::io::var_context& init, callbacks::writer& diagnostic_writer) { util::experimental_message(logger); - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector disc_vector; std::vector cont_vector; @@ -84,11 +83,16 @@ int fullrank(Model& model, const stan::io::var_context& init, = Eigen::Map(&cont_vector[0], cont_vector.size(), 1); stan::variational::advi + stan::rng_t> cmd_advi(model, cont_params, rng, grad_samples, elbo_samples, eval_elbo, output_samples); - cmd_advi.run(eta, adapt_engaged, adapt_iterations, tol_rel_obj, - max_iterations, logger, parameter_writer, diagnostic_writer); + try { + cmd_advi.run(eta, adapt_engaged, adapt_iterations, tol_rel_obj, + max_iterations, logger, parameter_writer, diagnostic_writer); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return stan::services::error_codes::OK; } diff --git a/src/stan/services/experimental/advi/meanfield.hpp b/src/stan/services/experimental/advi/meanfield.hpp index 87772dda7b2..49bee285058 100644 --- a/src/stan/services/experimental/advi/meanfield.hpp +++ b/src/stan/services/experimental/advi/meanfield.hpp @@ -10,7 +10,6 @@ #include #include #include -#include #include #include @@ -60,7 +59,7 @@ int meanfield(Model& model, const stan::io::var_context& init, callbacks::writer& diagnostic_writer) { util::experimental_message(logger); - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector disc_vector; std::vector cont_vector; @@ -83,11 +82,16 @@ int meanfield(Model& model, const stan::io::var_context& init, = Eigen::Map(&cont_vector[0], cont_vector.size(), 1); stan::variational::advi + stan::rng_t> cmd_advi(model, cont_params, rng, grad_samples, elbo_samples, eval_elbo, output_samples); - cmd_advi.run(eta, adapt_engaged, adapt_iterations, tol_rel_obj, - max_iterations, logger, parameter_writer, diagnostic_writer); + try { + cmd_advi.run(eta, adapt_engaged, adapt_iterations, tol_rel_obj, + max_iterations, logger, parameter_writer, diagnostic_writer); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return stan::services::error_codes::OK; } diff --git a/src/stan/services/optimize/bfgs.hpp b/src/stan/services/optimize/bfgs.hpp index 12f1df56596..37cd0a58973 100644 --- a/src/stan/services/optimize/bfgs.hpp +++ b/src/stan/services/optimize/bfgs.hpp @@ -57,7 +57,7 @@ int bfgs(Model& model, const stan::io::var_context& init, bool save_iterations, int refresh, callbacks::interrupt& interrupt, callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& parameter_writer) { - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector disc_vector; std::vector cont_vector; @@ -96,7 +96,16 @@ int bfgs(Model& model, const stan::io::var_context& init, if (save_iterations) { std::vector values; std::stringstream msg; - model.write_array(rng, cont_vector, disc_vector, values, true, true, &msg); + try { + model.write_array(rng, cont_vector, disc_vector, values, true, true, + &msg); + } catch (const std::exception& e) { + if (msg.str().length() > 0) { + logger.info(msg); + } + logger.error(e.what()); + return error_codes::SOFTWARE; + } if (msg.str().length() > 0) logger.info(msg); @@ -105,66 +114,82 @@ int bfgs(Model& model, const stan::io::var_context& init, } int ret = 0; - while (ret == 0) { - interrupt(); - if (refresh > 0 - && (bfgs.iter_num() == 0 || ((bfgs.iter_num() + 1) % refresh == 0))) - logger.info( - " Iter" - " log prob" - " ||dx||" - " ||grad||" - " alpha" - " alpha0" - " # evals" - " Notes "); - - ret = bfgs.step(); - lp = bfgs.logp(); - bfgs.params_r(cont_vector); - - if (refresh > 0 - && (ret != 0 || !bfgs.note().empty() || bfgs.iter_num() == 0 - || ((bfgs.iter_num() + 1) % refresh == 0))) { - std::stringstream msg; - msg << " " << std::setw(7) << bfgs.iter_num() << " "; - msg << " " << std::setw(12) << std::setprecision(6) << lp << " "; - msg << " " << std::setw(12) << std::setprecision(6) - << bfgs.prev_step_size() << " "; - msg << " " << std::setw(12) << std::setprecision(6) - << bfgs.curr_g().norm() << " "; - msg << " " << std::setw(10) << std::setprecision(4) << bfgs.alpha() - << " "; - msg << " " << std::setw(10) << std::setprecision(4) << bfgs.alpha0() - << " "; - msg << " " << std::setw(7) << bfgs.grad_evals() << " "; - msg << " " << bfgs.note() << " "; - logger.info(msg); - } - - if (bfgs_ss.str().length() > 0) { - logger.info(bfgs_ss); - bfgs_ss.str(""); - } - - if (save_iterations) { - std::vector values; - std::stringstream msg; - model.write_array(rng, cont_vector, disc_vector, values, true, true, - &msg); - // This if is here to match the pre-refactor behavior - if (msg.str().length() > 0) + try { + while (ret == 0) { + interrupt(); + if (refresh > 0 + && (bfgs.iter_num() == 0 || ((bfgs.iter_num() + 1) % refresh == 0))) + logger.info( + " Iter" + " log prob" + " ||dx||" + " ||grad||" + " alpha" + " alpha0" + " # evals" + " Notes "); + + ret = bfgs.step(); + + lp = bfgs.logp(); + bfgs.params_r(cont_vector); + + if (refresh > 0 + && (ret != 0 || !bfgs.note().empty() || bfgs.iter_num() == 0 + || ((bfgs.iter_num() + 1) % refresh == 0))) { + std::stringstream msg; + msg << " " << std::setw(7) << bfgs.iter_num() << " "; + msg << " " << std::setw(12) << std::setprecision(6) << lp << " "; + msg << " " << std::setw(12) << std::setprecision(6) + << bfgs.prev_step_size() << " "; + msg << " " << std::setw(12) << std::setprecision(6) + << bfgs.curr_g().norm() << " "; + msg << " " << std::setw(10) << std::setprecision(4) << bfgs.alpha() + << " "; + msg << " " << std::setw(10) << std::setprecision(4) << bfgs.alpha0() + << " "; + msg << " " << std::setw(7) << bfgs.grad_evals() << " "; + msg << " " << bfgs.note() << " "; logger.info(msg); - - values.insert(values.begin(), lp); - parameter_writer(values); + } + + if (bfgs_ss.str().length() > 0) { + logger.info(bfgs_ss); + bfgs_ss.str(""); + } + + if (save_iterations) { + std::vector values; + std::stringstream msg; + model.write_array(rng, cont_vector, disc_vector, values, true, true, + &msg); + + // This if is here to match the pre-refactor behavior + if (msg.str().length() > 0) + logger.info(msg); + + values.insert(values.begin(), lp); + parameter_writer(values); + } } + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; } if (!save_iterations) { std::vector values; std::stringstream msg; - model.write_array(rng, cont_vector, disc_vector, values, true, true, &msg); + try { + model.write_array(rng, cont_vector, disc_vector, values, true, true, + &msg); + } catch (const std::exception& e) { + if (msg.str().length() > 0) { + logger.info(msg); + } + logger.error(e.what()); + return error_codes::SOFTWARE; + } if (msg.str().length() > 0) logger.info(msg); values.insert(values.begin(), lp); diff --git a/src/stan/services/optimize/laplace_sample.hpp b/src/stan/services/optimize/laplace_sample.hpp index 6ec1dfacad7..600fc52cd56 100644 --- a/src/stan/services/optimize/laplace_sample.hpp +++ b/src/stan/services/optimize/laplace_sample.hpp @@ -4,6 +4,7 @@ #include #include #include +#include #include #include #include @@ -17,9 +18,10 @@ namespace internal { template void laplace_sample(const Model& model, const Eigen::VectorXd& theta_hat, - int draws, unsigned int random_seed, int refresh, - callbacks::interrupt& interrupt, callbacks::logger& logger, - callbacks::writer& sample_writer) { + int draws, bool calculate_lp, unsigned int random_seed, + int refresh, callbacks::interrupt& interrupt, + callbacks::logger& logger, callbacks::writer& sample_writer, + callbacks::structured_writer& hessian_writer) { if (draws <= 0) { throw std::domain_error("Number of draws must be > 0; found draws = " + std::to_string(draws)); @@ -72,6 +74,13 @@ void laplace_sample(const Model& model, const Eigen::VectorXd& theta_hat, if (refresh > 0 && log_density_msgs.peek() != std::char_traits::eof()) logger.info(log_density_msgs); + interrupt(); + hessian_writer.begin_record(); + hessian_writer.write("lp_mode", log_p); + hessian_writer.write("gradient", grad); + hessian_writer.write("Hessian", hessian); + hessian_writer.end_record(); + // calculate Cholesky factor and inverse interrupt(); if (refresh > 0) { @@ -88,7 +97,7 @@ void laplace_sample(const Model& model, const Eigen::VectorXd& theta_hat, } // generate draws std::stringstream refresh_msg; - boost::ecuyer1988 rng = util::create_rng(random_seed, 0); + stan::rng_t rng = util::create_rng(random_seed, 0); Eigen::VectorXd draw_vec; // declare draw_vec, msgs here to avoid re-alloc for (int m = 0; m < draws; ++m) { interrupt(); // allow interpution each iteration @@ -109,7 +118,13 @@ void laplace_sample(const Model& model, const Eigen::VectorXd& theta_hat, logger.info(write_array_msgs); // output draw, log_p, log_q std::vector draw(&draw_vec(0), &draw_vec(0) + draw_size); - double log_p = log_density_fun(unc_draw).val(); + + double log_p; + if (calculate_lp) { + log_p = log_density_fun(unc_draw).val(); + } else { + log_p = std::numeric_limits::quiet_NaN(); + } draw.insert(draw.begin(), log_p); Eigen::VectorXd diff = unc_draw - theta_hat; double log_q = diff.transpose() * half_hessian * diff; @@ -139,6 +154,8 @@ void laplace_sample(const Model& model, const Eigen::VectorXd& theta_hat, * @param[in] theta_hat unconstrained mode at which to center the * Laplace approximation * @param[in] draws number of draws to generate + * @param[in] calculate_lp whether to calculate the log probability of the + * approximate draws * @param[in] random_seed seed for generating random numbers in the * Stan program and in sampling * @param[in] refresh period between iterations at which updates are @@ -148,23 +165,69 @@ void laplace_sample(const Model& model, const Eigen::VectorXd& theta_hat, * sampler and from Stan programs * @param[in,out] sample_writer callback for writing parameter names * and then draws + * @param[in,out] hessian_writer callback for writing the log probability, + * gradient, and Hessian at the mode for diagnostic purposes * @return a return code, with 0 indicating success */ template int laplace_sample(const Model& model, const Eigen::VectorXd& theta_hat, - int draws, unsigned int random_seed, int refresh, - callbacks::interrupt& interrupt, callbacks::logger& logger, - callbacks::writer& sample_writer) { + int draws, bool calculate_lp, unsigned int random_seed, + int refresh, callbacks::interrupt& interrupt, + callbacks::logger& logger, callbacks::writer& sample_writer, + callbacks::structured_writer& hessian_writer) { try { - internal::laplace_sample(model, theta_hat, draws, random_seed, - refresh, interrupt, logger, - sample_writer); + internal::laplace_sample(model, theta_hat, draws, calculate_lp, + random_seed, refresh, interrupt, logger, + sample_writer, hessian_writer); } catch (const std::exception& e) { logger.error(e.what()); return error_codes::CONFIG; } return error_codes::OK; } + +/** + * Take the specified number of draws from the Laplace approximation + * for the model at the specified unconstrained mode, writing the + * draws, unnormalized log density, and unnormalized density of the + * approximation to the sample writer and writing messages to the + * logger, returning a return code of zero if successful. + * + * Interrupts are called between compute-intensive operations. To + * turn off all console messages sent to the logger, set refresh to 0. + * If an exception is thrown by the model, the return value is + * non-zero, and if refresh > 0, its message is given to the logger as + * an error. + * + * @tparam jacobian `true` to include Jacobian adjustment for + * constrained parameters + * @tparam Model a Stan model + * @param[in] model model from which to sample + * @param[in] theta_hat unconstrained mode at which to center the + * Laplace approximation + * @param[in] draws number of draws to generate + * @param[in] random_seed seed for generating random numbers in the + * Stan program and in sampling + * @param[in] refresh period between iterations at which updates are + * given, with a value of 0 turning off all messages + * @param[in] interrupt callback for interrupting sampling + * @param[in,out] logger callback for writing console messages from + * sampler and from Stan programs + * @param[in,out] sample_writer callback for writing parameter names + * and then draws + * @return a return code, with 0 indicating success + */ +template +int laplace_sample(const Model& model, const Eigen::VectorXd& theta_hat, + int draws, unsigned int random_seed, int refresh, + callbacks::interrupt& interrupt, callbacks::logger& logger, + callbacks::writer& sample_writer) { + callbacks::structured_writer dummy_hessian_writer; + return laplace_sample(model, theta_hat, draws, true, random_seed, + refresh, interrupt, logger, sample_writer, + dummy_hessian_writer); +} + } // namespace services } // namespace stan diff --git a/src/stan/services/optimize/lbfgs.hpp b/src/stan/services/optimize/lbfgs.hpp index 9955e8f8633..bf50e9088f6 100644 --- a/src/stan/services/optimize/lbfgs.hpp +++ b/src/stan/services/optimize/lbfgs.hpp @@ -59,7 +59,7 @@ int lbfgs(Model& model, const stan::io::var_context& init, int refresh, callbacks::interrupt& interrupt, callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& parameter_writer) { - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector disc_vector; std::vector cont_vector; @@ -109,65 +109,80 @@ int lbfgs(Model& model, const stan::io::var_context& init, } int ret = 0; - while (ret == 0) { - interrupt(); - if (refresh > 0 - && (lbfgs.iter_num() == 0 || ((lbfgs.iter_num() + 1) % refresh == 0))) - logger.info( - " Iter" - " log prob" - " ||dx||" - " ||grad||" - " alpha" - " alpha0" - " # evals" - " Notes "); - - ret = lbfgs.step(); - lp = lbfgs.logp(); - lbfgs.params_r(cont_vector); - - if (refresh > 0 - && (ret != 0 || !lbfgs.note().empty() || lbfgs.iter_num() == 0 - || ((lbfgs.iter_num() + 1) % refresh == 0))) { - std::stringstream msg; - msg << " " << std::setw(7) << lbfgs.iter_num() << " "; - msg << " " << std::setw(12) << std::setprecision(6) << lp << " "; - msg << " " << std::setw(12) << std::setprecision(6) - << lbfgs.prev_step_size() << " "; - msg << " " << std::setw(12) << std::setprecision(6) - << lbfgs.curr_g().norm() << " "; - msg << " " << std::setw(10) << std::setprecision(4) << lbfgs.alpha() - << " "; - msg << " " << std::setw(10) << std::setprecision(4) << lbfgs.alpha0() - << " "; - msg << " " << std::setw(7) << lbfgs.grad_evals() << " "; - msg << " " << lbfgs.note() << " "; - logger.info(msg); - } - - if (lbfgs_ss.str().length() > 0) { - logger.info(lbfgs_ss); - lbfgs_ss.str(""); - } - - if (save_iterations) { - std::vector values; - std::stringstream msg; - model.write_array(rng, cont_vector, disc_vector, values, true, true, - &msg); - if (msg.str().length() > 0) + try { + while (ret == 0) { + interrupt(); + if (refresh > 0 + && (lbfgs.iter_num() == 0 || ((lbfgs.iter_num() + 1) % refresh == 0))) + logger.info( + " Iter" + " log prob" + " ||dx||" + " ||grad||" + " alpha" + " alpha0" + " # evals" + " Notes "); + + ret = lbfgs.step(); + + lp = lbfgs.logp(); + lbfgs.params_r(cont_vector); + + if (refresh > 0 + && (ret != 0 || !lbfgs.note().empty() || lbfgs.iter_num() == 0 + || ((lbfgs.iter_num() + 1) % refresh == 0))) { + std::stringstream msg; + msg << " " << std::setw(7) << lbfgs.iter_num() << " "; + msg << " " << std::setw(12) << std::setprecision(6) << lp << " "; + msg << " " << std::setw(12) << std::setprecision(6) + << lbfgs.prev_step_size() << " "; + msg << " " << std::setw(12) << std::setprecision(6) + << lbfgs.curr_g().norm() << " "; + msg << " " << std::setw(10) << std::setprecision(4) << lbfgs.alpha() + << " "; + msg << " " << std::setw(10) << std::setprecision(4) << lbfgs.alpha0() + << " "; + msg << " " << std::setw(7) << lbfgs.grad_evals() << " "; + msg << " " << lbfgs.note() << " "; logger.info(msg); - - values.insert(values.begin(), lp); - parameter_writer(values); + } + + if (lbfgs_ss.str().length() > 0) { + logger.info(lbfgs_ss); + lbfgs_ss.str(""); + } + + if (save_iterations) { + std::vector values; + std::stringstream msg; + model.write_array(rng, cont_vector, disc_vector, values, true, true, + &msg); + if (msg.str().length() > 0) + logger.info(msg); + + values.insert(values.begin(), lp); + parameter_writer(values); + } } + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; } if (!save_iterations) { std::vector values; std::stringstream msg; - model.write_array(rng, cont_vector, disc_vector, values, true, true, &msg); + try { + model.write_array(rng, cont_vector, disc_vector, values, true, true, + &msg); + } catch (const std::exception& e) { + if (msg.str().length() > 0) { + logger.info(msg); + } + logger.error(e.what()); + return error_codes::SOFTWARE; + } if (msg.str().length() > 0) logger.info(msg); diff --git a/src/stan/services/optimize/newton.hpp b/src/stan/services/optimize/newton.hpp index 65d29f9088e..db64f6e46c3 100644 --- a/src/stan/services/optimize/newton.hpp +++ b/src/stan/services/optimize/newton.hpp @@ -44,7 +44,7 @@ int newton(Model& model, const stan::io::var_context& init, callbacks::interrupt& interrupt, callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& parameter_writer) { - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector disc_vector; std::vector cont_vector; @@ -62,7 +62,7 @@ int newton(Model& model, const stan::io::var_context& init, lp = model.template log_prob(cont_vector, disc_vector, &message); logger.info(message); - } catch (const std::exception& e) { + } catch (const std::domain_error& e) { logger.info(""); logger.info( "Informational Message: The current" diff --git a/src/stan/services/pathfinder/multi.hpp b/src/stan/services/pathfinder/multi.hpp index f7836b73414..1707d5d332a 100644 --- a/src/stan/services/pathfinder/multi.hpp +++ b/src/stan/services/pathfinder/multi.hpp @@ -82,7 +82,7 @@ namespace pathfinder { * sample. If `false`, (`num_draws` - `num_elbo_draws`) of the joint log * probability calculations will be `NA` and psis resampling will not be * performed. - * @param[in] psis_resampling If `true`, psis resampling is performed over the + * @param[in] psis_resample If `true`, psis resampling is performed over the * samples returned by all of the individual pathfinders and `num_multi_draws` * samples are written to `parameter_writer`. If `false`, no psis resampling is * performed and (`num_paths` * `num_draws`) samples are written to @@ -117,28 +117,33 @@ inline int pathfinder_lbfgs_multi( individual_samples; individual_samples.resize(num_paths); std::atomic lp_calls{0}; - tbb::parallel_for( - tbb::blocked_range(0, num_paths), [&](tbb::blocked_range r) { - for (int iter = r.begin(); iter < r.end(); ++iter) { - auto pathfinder_ret - = stan::services::pathfinder::pathfinder_lbfgs_single( - model, *(init[iter]), random_seed, stride_id + iter, - init_radius, history_size, init_alpha, tol_obj, tol_rel_obj, - tol_grad, tol_rel_grad, tol_param, num_iterations, - num_elbo_draws, num_draws, save_iterations, refresh, - interrupt, logger, init_writers[iter], - single_path_parameter_writer[iter], - single_path_diagnostic_writer[iter], calculate_lp); - if (unlikely(std::get<0>(pathfinder_ret) != error_codes::OK)) { - logger.error(std::string("Pathfinder iteration: ") - + std::to_string(iter) + " failed."); - return; + try { + tbb::parallel_for( + tbb::blocked_range(0, num_paths), [&](tbb::blocked_range r) { + for (int iter = r.begin(); iter < r.end(); ++iter) { + auto pathfinder_ret + = stan::services::pathfinder::pathfinder_lbfgs_single( + model, *(init[iter]), random_seed, stride_id + iter, + init_radius, history_size, init_alpha, tol_obj, tol_rel_obj, + tol_grad, tol_rel_grad, tol_param, num_iterations, + num_elbo_draws, num_draws, save_iterations, refresh, + interrupt, logger, init_writers[iter], + single_path_parameter_writer[iter], + single_path_diagnostic_writer[iter], calculate_lp); + if (unlikely(std::get<0>(pathfinder_ret) != error_codes::OK)) { + logger.error(std::string("Pathfinder iteration: ") + + std::to_string(iter) + " failed."); + return; + } + individual_lp_ratios[iter] = std::move(std::get<1>(pathfinder_ret)); + individual_samples[iter] = std::move(std::get<2>(pathfinder_ret)); + lp_calls += std::get<3>(pathfinder_ret); } - individual_lp_ratios[iter] = std::move(std::get<1>(pathfinder_ret)); - individual_samples[iter] = std::move(std::get<2>(pathfinder_ret)); - lp_calls += std::get<3>(pathfinder_ret); - } - }); + }); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } // if any pathfinders failed, we want to remove their empty results individual_lp_ratios.erase( @@ -194,11 +199,9 @@ inline int pathfinder_lbfgs_multi( 3 * std::sqrt(num_returned_samples)); Eigen::Array weight_vals = stan::services::psis::psis_weights(lp_ratios, tail_len, logger); - boost::ecuyer1988 rng - = util::create_rng(random_seed, stride_id); - boost::variate_generator< - boost::ecuyer1988&, - boost::random::discrete_distribution> + stan::rng_t rng = util::create_rng(random_seed, stride_id); + boost::variate_generator> rand_psis_idx( rng, boost::random::discrete_distribution( boost::iterator_range( @@ -233,7 +236,7 @@ inline int pathfinder_lbfgs_multi( parameter_writer(total_time_str); } parameter_writer(); - return 0; + return error_codes::OK; } } // namespace pathfinder } // namespace services diff --git a/src/stan/services/pathfinder/psis.hpp b/src/stan/services/pathfinder/psis.hpp index 1684daf5cfd..b4da2550639 100644 --- a/src/stan/services/pathfinder/psis.hpp +++ b/src/stan/services/pathfinder/psis.hpp @@ -5,6 +5,8 @@ #include #include #include +#include +#include namespace stan { namespace services { @@ -261,10 +263,13 @@ inline Eigen::Array psis_weights( llr_weights.coeffRef(idx.coeff(i)) = smoothed.first.coeff(i); } if (smoothed.second > 0.7) { - logger.warn(std::string("Pareto k value (") + - std::to_string(smoothed.second) + ") is greater than 0.7." - " Importance resampling was not able to improve the approximation," - " which may indicate that the approximation itself is poor."); + std::stringstream s; + s << "Pareto k value (" << std::setprecision(2) << smoothed.second + << ") is greater than 0.7. Importance resampling was not able to " + << "improve the approximation, which may indicate that the " + << "approximation itself is poor."; + + logger.warn(s.str()); } } } diff --git a/src/stan/services/pathfinder/single.hpp b/src/stan/services/pathfinder/single.hpp index 5b163c519e8..5b3bbf34d12 100644 --- a/src/stan/services/pathfinder/single.hpp +++ b/src/stan/services/pathfinder/single.hpp @@ -221,7 +221,7 @@ inline elbo_est_t est_approx_draws(LPF&& lp_fun, ConstrainF&& constrain_fun, size_t num_samples, const EigVec& alpha, const std::string& iter_msg, Logger&& logger, bool calculate_lp = true) { - boost::variate_generator> + boost::variate_generator> rand_unit_gaus(rng, boost::normal_distribution<>()); const auto num_params = taylor_approx.x_center.size(); size_t lp_fun_calls = 0; @@ -250,7 +250,7 @@ inline elbo_est_t est_approx_draws(LPF&& lp_fun, ConstrainF&& constrain_fun, approx_samples_col = approx_samples.col(i); ++lp_fun_calls; lp_mat.coeffRef(i, 1) = lp_fun(approx_samples_col, pathfinder_ss); - } catch (const std::exception& e) { + } catch (const std::domain_error& e) { lp_mat.coeffRef(i, 1) = -std::numeric_limits::infinity(); } log_stream(logger, pathfinder_ss, iter_msg); @@ -530,7 +530,7 @@ auto pathfinder_impl(RNG&& rng, LPFun&& lp_fun, ConstrainFun&& constrain_fun, lp_fun, constrain_fun, rng, taylor_appx, num_elbo_draws, alpha, iter_msg, logger), taylor_appx); - } catch (const std::exception& e) { + } catch (const std::domain_error& e) { logger.warn(iter_msg + "ELBO estimation failed " + " with error: " + e.what()); return std::make_pair(internal::elbo_est_t{}, internal::taylor_approx_t{}); @@ -607,8 +607,7 @@ inline auto pathfinder_lbfgs_single( callbacks::writer& init_writer, ParamWriter& parameter_writer, DiagnosticWriter& diagnostic_writer, bool calculate_lp = true) { const auto start_pathfinder_time = std::chrono::steady_clock::now(); - boost::ecuyer1988 rng - = util::create_rng(random_seed, stride_id); + stan::rng_t rng = util::create_rng(random_seed, stride_id); std::vector disc_vector; std::vector cont_vector; @@ -821,7 +820,16 @@ inline auto pathfinder_lbfgs_single( logger.info(lbfgs_ss); lbfgs_ss.str(""); } - throw e; + if (ReturnLpSamples) { + // we want to terminate multi-path pathfinder during these unrecoverable + // exceptions + throw; + } else { + logger.error(e.what()); + return internal::ret_pathfinder( + error_codes::SOFTWARE, Eigen::Array(0), + Eigen::Matrix(0, 0), 0); + } } } if (unlikely(save_iterations)) { @@ -901,7 +909,7 @@ inline auto pathfinder_lbfgs_single( approx_samples_constrained_col) .matrix(); } - } catch (const std::exception& e) { + } catch (const std::domain_error& e) { std::string err_msg = e.what(); logger.warn(path_num + "Final sampling approximation failed with error: " + err_msg); @@ -925,12 +933,13 @@ inline auto pathfinder_lbfgs_single( lp_ratio = std::move(elbo_best.lp_ratio); } } else { + // output only first num_draws from what we computed for ELBO constrained_draws_mat - = Eigen::Matrix( - names.size(), elbo_draws.cols()); + = Eigen::Matrix(names.size(), + num_draws); Eigen::VectorXd approx_samples_constrained_col; Eigen::VectorXd unconstrained_col; - for (Eigen::Index i = 0; i < elbo_draws.cols(); ++i) { + for (Eigen::Index i = 0; i < num_draws; ++i) { constrained_draws_mat.col(i).head(2) = elbo_lp_mat.row(i).matrix(); unconstrained_col = elbo_draws.col(i); constrained_draws_mat.col(i).tail(num_unconstrained_params) @@ -938,7 +947,7 @@ inline auto pathfinder_lbfgs_single( approx_samples_constrained_col) .matrix(); } - lp_ratio = std::move(elbo_best.lp_ratio); + lp_ratio = std::move(elbo_best.lp_ratio.head(num_draws)); } parameter_writer(constrained_draws_mat); parameter_writer(); diff --git a/src/stan/services/sample/fixed_param.hpp b/src/stan/services/sample/fixed_param.hpp index efe75c2f402..17e04a621e2 100644 --- a/src/stan/services/sample/fixed_param.hpp +++ b/src/stan/services/sample/fixed_param.hpp @@ -49,7 +49,7 @@ int fixed_param(Model& model, const stan::io::var_context& init, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer) { - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector disc_vector; std::vector cont_vector; @@ -74,9 +74,14 @@ int fixed_param(Model& model, const stan::io::var_context& init, writer.write_diagnostic_names(s, sampler, model); auto start = std::chrono::steady_clock::now(); - util::generate_transitions(sampler, num_samples, 0, num_samples, num_thin, - refresh, true, false, writer, s, model, rng, - interrupt, logger); + try { + util::generate_transitions(sampler, num_samples, 0, num_samples, num_thin, + refresh, true, false, writer, s, model, rng, + interrupt, logger); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } auto end = std::chrono::steady_clock::now(); double sample_delta_t = std::chrono::duration_cast(end - start) @@ -134,7 +139,7 @@ int fixed_param(Model& model, const std::size_t num_chains, init_writer[0], sample_writers[0], diagnostic_writers[0]); } - std::vector rngs; + std::vector rngs; std::vector cont_vectors; std::vector writers; std::vector samples; @@ -156,27 +161,32 @@ int fixed_param(Model& model, const std::size_t num_chains, writers[i].write_diagnostic_names(samples[i], samplers[i], model); } - tbb::parallel_for( - tbb::blocked_range(0, num_chains, 1), - [&samplers, &writers, &samples, &model, &rngs, &interrupt, &logger, - num_samples, num_thin, refresh, chain, - num_chains](const tbb::blocked_range& r) { - for (size_t i = r.begin(); i != r.end(); ++i) { - auto start = std::chrono::steady_clock::now(); - util::generate_transitions(samplers[i], num_samples, 0, num_samples, - num_thin, refresh, true, false, writers[i], - samples[i], model, rngs[i], interrupt, - logger, chain + i, num_chains); - auto end = std::chrono::steady_clock::now(); - double sample_delta_t - = std::chrono::duration_cast(end - - start) - .count() - / 1000.0; - writers[i].write_timing(0.0, sample_delta_t); - } - }, - tbb::simple_partitioner()); + try { + tbb::parallel_for( + tbb::blocked_range(0, num_chains, 1), + [&samplers, &writers, &samples, &model, &rngs, &interrupt, &logger, + num_samples, num_thin, refresh, chain, + num_chains](const tbb::blocked_range& r) { + for (size_t i = r.begin(); i != r.end(); ++i) { + auto start = std::chrono::steady_clock::now(); + util::generate_transitions( + samplers[i], num_samples, 0, num_samples, num_thin, refresh, + true, false, writers[i], samples[i], model, rngs[i], interrupt, + logger, chain + i, num_chains); + auto end = std::chrono::steady_clock::now(); + double sample_delta_t + = std::chrono::duration_cast(end + - start) + .count() + / 1000.0; + writers[i].write_timing(0.0, sample_delta_t); + } + }, + tbb::simple_partitioner()); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_codes::OK; } diff --git a/src/stan/services/sample/hmc_nuts_dense_e.hpp b/src/stan/services/sample/hmc_nuts_dense_e.hpp index b2a4142eb89..0fb818b19cb 100644 --- a/src/stan/services/sample/hmc_nuts_dense_e.hpp +++ b/src/stan/services/sample/hmc_nuts_dense_e.hpp @@ -56,7 +56,7 @@ int hmc_nuts_dense_e(Model& model, const stan::io::var_context& init, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer) { - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector disc_vector; std::vector cont_vector; @@ -73,7 +73,7 @@ int hmc_nuts_dense_e(Model& model, const stan::io::var_context& init, return error_codes::CONFIG; } - stan::mcmc::dense_e_nuts sampler(model, rng); + stan::mcmc::dense_e_nuts sampler(model, rng); sampler.set_metric(inv_metric); @@ -81,9 +81,14 @@ int hmc_nuts_dense_e(Model& model, const stan::io::var_context& init, sampler.set_stepsize_jitter(stepsize_jitter); sampler.set_max_depth(max_depth); - util::run_sampler(sampler, model, cont_vector, num_warmup, num_samples, - num_thin, refresh, save_warmup, rng, interrupt, logger, - sample_writer, diagnostic_writer); + try { + util::run_sampler(sampler, model, cont_vector, num_warmup, num_samples, + num_thin, refresh, save_warmup, rng, interrupt, logger, + sample_writer, diagnostic_writer); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_codes::OK; } @@ -123,11 +128,9 @@ int hmc_nuts_dense_e(Model& model, const stan::io::var_context& init, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer) { - stan::io::dump dmp + auto default_metric = util::create_unit_e_dense_inv_metric(model.num_params_r()); - stan::io::var_context& unit_e_metric = dmp; - - return hmc_nuts_dense_e(model, init, unit_e_metric, random_seed, chain, + return hmc_nuts_dense_e(model, init, default_metric, random_seed, chain, init_radius, num_warmup, num_samples, num_thin, save_warmup, refresh, stepsize, stepsize_jitter, max_depth, interrupt, logger, init_writer, @@ -197,11 +200,11 @@ int hmc_nuts_dense_e(Model& model, size_t num_chains, stepsize, stepsize_jitter, max_depth, interrupt, logger, init_writer[0], sample_writer[0], diagnostic_writer[0]); } - std::vector rngs; + std::vector rngs; rngs.reserve(num_chains); std::vector> cont_vectors; cont_vectors.reserve(num_chains); - using sample_t = stan::mcmc::dense_e_nuts; + using sample_t = stan::mcmc::dense_e_nuts; std::vector samplers; samplers.reserve(num_chains); try { @@ -223,20 +226,25 @@ int hmc_nuts_dense_e(Model& model, size_t num_chains, logger.error(e.what()); return error_codes::CONFIG; } - tbb::parallel_for( - tbb::blocked_range(0, num_chains, 1), - [num_warmup, num_samples, num_thin, refresh, save_warmup, num_chains, - init_chain_id, &samplers, &model, &rngs, &interrupt, &logger, - &sample_writer, &cont_vectors, - &diagnostic_writer](const tbb::blocked_range& r) { - for (size_t i = r.begin(); i != r.end(); ++i) { - 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); - } - }, - tbb::simple_partitioner()); + try { + tbb::parallel_for( + tbb::blocked_range(0, num_chains, 1), + [num_warmup, num_samples, num_thin, refresh, save_warmup, num_chains, + init_chain_id, &samplers, &model, &rngs, &interrupt, &logger, + &sample_writer, &cont_vectors, + &diagnostic_writer](const tbb::blocked_range& r) { + for (size_t i = r.begin(); i != r.end(); ++i) { + 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); + } + }, + tbb::simple_partitioner()); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_codes::OK; } @@ -297,10 +305,10 @@ int hmc_nuts_dense_e(Model& model, size_t num_chains, max_depth, interrupt, logger, init_writer[0], sample_writer[0], diagnostic_writer[0]); } - std::vector> unit_e_metrics; + std::vector> unit_e_metrics; unit_e_metrics.reserve(num_chains); for (size_t i = 0; i < num_chains; ++i) { - unit_e_metrics.emplace_back(std::make_unique( + unit_e_metrics.emplace_back(std::make_unique( util::create_unit_e_dense_inv_metric(model.num_params_r()))); } return hmc_nuts_dense_e(model, num_chains, init, unit_e_metrics, random_seed, diff --git a/src/stan/services/sample/hmc_nuts_dense_e_adapt.hpp b/src/stan/services/sample/hmc_nuts_dense_e_adapt.hpp index 66d0f954f43..ce1befff874 100644 --- a/src/stan/services/sample/hmc_nuts_dense_e_adapt.hpp +++ b/src/stan/services/sample/hmc_nuts_dense_e_adapt.hpp @@ -66,7 +66,7 @@ int hmc_nuts_dense_e_adapt( callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer, callbacks::structured_writer& metric_writer) { - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector cont_vector; @@ -82,7 +82,7 @@ int hmc_nuts_dense_e_adapt( return error_codes::CONFIG; } - stan::mcmc::adapt_dense_e_nuts sampler(model, rng); + stan::mcmc::adapt_dense_e_nuts sampler(model, rng); sampler.set_metric(inv_metric); @@ -98,11 +98,15 @@ int hmc_nuts_dense_e_adapt( sampler.set_window_params(num_warmup, init_buffer, term_buffer, window, logger); - - util::run_adaptive_sampler(sampler, model, cont_vector, num_warmup, - num_samples, num_thin, refresh, save_warmup, rng, - interrupt, logger, sample_writer, - diagnostic_writer, metric_writer); + try { + util::run_adaptive_sampler(sampler, model, cont_vector, num_warmup, + num_samples, num_thin, refresh, save_warmup, rng, + interrupt, logger, sample_writer, + diagnostic_writer, metric_writer); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_codes::OK; } @@ -206,11 +210,10 @@ int hmc_nuts_dense_e_adapt( callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer, callbacks::structured_writer& metric_writer) { - stan::io::dump dmp + auto default_metric = util::create_unit_e_dense_inv_metric(model.num_params_r()); - stan::io::var_context& unit_e_metric = dmp; return hmc_nuts_dense_e_adapt( - model, init, unit_e_metric, random_seed, chain, init_radius, num_warmup, + model, init, default_metric, random_seed, chain, init_radius, num_warmup, num_samples, num_thin, save_warmup, refresh, stepsize, stepsize_jitter, max_depth, delta, gamma, kappa, t0, init_buffer, term_buffer, window, interrupt, logger, init_writer, sample_writer, diagnostic_writer, @@ -259,12 +262,11 @@ int hmc_nuts_dense_e_adapt( unsigned int window, callbacks::interrupt& interrupt, callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer) { - stan::io::dump dmp + auto default_metric = util::create_unit_e_dense_inv_metric(model.num_params_r()); - stan::io::var_context& unit_e_metric = dmp; callbacks::structured_writer dummy_metric_writer; return hmc_nuts_dense_e_adapt( - model, init, unit_e_metric, random_seed, chain, init_radius, num_warmup, + model, init, default_metric, random_seed, chain, init_radius, num_warmup, num_samples, num_thin, save_warmup, refresh, stepsize, stepsize_jitter, max_depth, delta, gamma, kappa, t0, init_buffer, term_buffer, window, interrupt, logger, init_writer, sample_writer, diagnostic_writer, @@ -347,8 +349,8 @@ int hmc_nuts_dense_e_adapt( init_buffer, term_buffer, window, interrupt, logger, init_writer[0], sample_writer[0], diagnostic_writer[0], metric_writer[0]); } - using sample_t = stan::mcmc::adapt_dense_e_nuts; - std::vector rngs; + using sample_t = stan::mcmc::adapt_dense_e_nuts; + std::vector rngs; rngs.reserve(num_chains); std::vector> cont_vectors; cont_vectors.reserve(num_chains); @@ -381,21 +383,26 @@ int hmc_nuts_dense_e_adapt( logger.error(e.what()); return error_codes::CONFIG; } - tbb::parallel_for( - tbb::blocked_range(0, num_chains, 1), - [num_warmup, num_samples, num_thin, refresh, save_warmup, num_chains, - init_chain_id, &samplers, &model, &rngs, &interrupt, &logger, - &sample_writer, &cont_vectors, &diagnostic_writer, - &metric_writer](const tbb::blocked_range& r) { - for (size_t i = r.begin(); i != r.end(); ++i) { - util::run_adaptive_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], metric_writer[i], - init_chain_id + i, num_chains); - } - }, - tbb::simple_partitioner()); + try { + tbb::parallel_for( + tbb::blocked_range(0, num_chains, 1), + [num_warmup, num_samples, num_thin, refresh, save_warmup, num_chains, + init_chain_id, &samplers, &model, &rngs, &interrupt, &logger, + &sample_writer, &cont_vectors, &diagnostic_writer, + &metric_writer](const tbb::blocked_range& r) { + for (size_t i = r.begin(); i != r.end(); ++i) { + util::run_adaptive_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], metric_writer[i], + init_chain_id + i, num_chains); + } + }, + tbb::simple_partitioner()); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_codes::OK; } @@ -543,10 +550,10 @@ int hmc_nuts_dense_e_adapt( std::vector& sample_writer, std::vector& diagnostic_writer, std::vector& metric_writer) { - std::vector> unit_e_metric; + std::vector> unit_e_metric; unit_e_metric.reserve(num_chains); for (size_t i = 0; i < num_chains; ++i) { - unit_e_metric.emplace_back(std::make_unique( + unit_e_metric.emplace_back(std::make_unique( util::create_unit_e_dense_inv_metric(model.num_params_r()))); } if (num_chains == 1) { @@ -623,10 +630,10 @@ int hmc_nuts_dense_e_adapt( std::vector& init_writer, std::vector& sample_writer, std::vector& diagnostic_writer) { - std::vector> unit_e_metric; + std::vector> unit_e_metric; unit_e_metric.reserve(num_chains); for (size_t i = 0; i < num_chains; ++i) { - unit_e_metric.emplace_back(std::make_unique( + unit_e_metric.emplace_back(std::make_unique( util::create_unit_e_dense_inv_metric(model.num_params_r()))); } std::vector dummy_metric_writer( diff --git a/src/stan/services/sample/hmc_nuts_diag_e.hpp b/src/stan/services/sample/hmc_nuts_diag_e.hpp index 7cdc2de3cc4..bb789c01519 100644 --- a/src/stan/services/sample/hmc_nuts_diag_e.hpp +++ b/src/stan/services/sample/hmc_nuts_diag_e.hpp @@ -56,7 +56,7 @@ int hmc_nuts_diag_e(Model& model, const stan::io::var_context& init, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer) { - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector disc_vector; std::vector cont_vector; @@ -72,17 +72,21 @@ int hmc_nuts_diag_e(Model& model, const stan::io::var_context& init, return error_codes::CONFIG; } - stan::mcmc::diag_e_nuts sampler(model, rng); + stan::mcmc::diag_e_nuts sampler(model, rng); sampler.set_metric(inv_metric); sampler.set_nominal_stepsize(stepsize); sampler.set_stepsize_jitter(stepsize_jitter); sampler.set_max_depth(max_depth); - util::run_sampler(sampler, model, cont_vector, num_warmup, num_samples, - num_thin, refresh, save_warmup, rng, interrupt, logger, - sample_writer, diagnostic_writer); - + try { + util::run_sampler(sampler, model, cont_vector, num_warmup, num_samples, + num_thin, refresh, save_warmup, rng, interrupt, logger, + sample_writer, diagnostic_writer); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_codes::OK; } @@ -121,11 +125,9 @@ int hmc_nuts_diag_e(Model& model, const stan::io::var_context& init, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer) { - stan::io::dump dmp + auto default_metric = util::create_unit_e_diag_inv_metric(model.num_params_r()); - stan::io::var_context& unit_e_metric = dmp; - - return hmc_nuts_diag_e(model, init, unit_e_metric, random_seed, chain, + return hmc_nuts_diag_e(model, init, default_metric, random_seed, chain, init_radius, num_warmup, num_samples, num_thin, save_warmup, refresh, stepsize, stepsize_jitter, max_depth, interrupt, logger, init_writer, @@ -194,11 +196,11 @@ int hmc_nuts_diag_e(Model& model, size_t num_chains, stepsize, stepsize_jitter, max_depth, interrupt, logger, init_writer[0], sample_writer[0], diagnostic_writer[0]); } - std::vector rngs; + std::vector rngs; rngs.reserve(num_chains); std::vector> cont_vectors; cont_vectors.reserve(num_chains); - using sample_t = stan::mcmc::diag_e_nuts; + using sample_t = stan::mcmc::diag_e_nuts; std::vector samplers; samplers.reserve(num_chains); try { @@ -293,10 +295,10 @@ int hmc_nuts_diag_e(Model& model, size_t num_chains, max_depth, interrupt, logger, init_writer[0], sample_writer[0], diagnostic_writer[0]); } - std::vector> unit_e_metrics; + std::vector> unit_e_metrics; unit_e_metrics.reserve(num_chains); for (size_t i = 0; i < num_chains; ++i) { - unit_e_metrics.emplace_back(std::make_unique( + unit_e_metrics.emplace_back(std::make_unique( util::create_unit_e_diag_inv_metric(model.num_params_r()))); } return hmc_nuts_diag_e(model, num_chains, init, unit_e_metrics, random_seed, diff --git a/src/stan/services/sample/hmc_nuts_diag_e_adapt.hpp b/src/stan/services/sample/hmc_nuts_diag_e_adapt.hpp index fc1d1f69d5d..0cd79933a82 100644 --- a/src/stan/services/sample/hmc_nuts_diag_e_adapt.hpp +++ b/src/stan/services/sample/hmc_nuts_diag_e_adapt.hpp @@ -66,7 +66,7 @@ int hmc_nuts_diag_e_adapt( callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer, callbacks::structured_writer& metric_writer) { - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector cont_vector; @@ -83,7 +83,7 @@ int hmc_nuts_diag_e_adapt( return error_codes::CONFIG; } - stan::mcmc::adapt_diag_e_nuts sampler(model, rng); + stan::mcmc::adapt_diag_e_nuts sampler(model, rng); sampler.set_metric(inv_metric); sampler.set_nominal_stepsize(stepsize); @@ -99,11 +99,15 @@ int hmc_nuts_diag_e_adapt( sampler.set_window_params(num_warmup, init_buffer, term_buffer, window, logger); - util::run_adaptive_sampler(sampler, model, cont_vector, num_warmup, - num_samples, num_thin, refresh, save_warmup, rng, - interrupt, logger, sample_writer, - diagnostic_writer, metric_writer); - + try { + util::run_adaptive_sampler(sampler, model, cont_vector, num_warmup, + num_samples, num_thin, refresh, save_warmup, rng, + interrupt, logger, sample_writer, + diagnostic_writer, metric_writer); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_codes::OK; } @@ -206,11 +210,10 @@ int hmc_nuts_diag_e_adapt( callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer, callbacks::structured_writer& metric_writer) { - stan::io::dump dmp + auto default_metric = util::create_unit_e_diag_inv_metric(model.num_params_r()); - stan::io::var_context& unit_e_metric = dmp; return hmc_nuts_diag_e_adapt( - model, init, unit_e_metric, random_seed, chain, init_radius, num_warmup, + model, init, default_metric, random_seed, chain, init_radius, num_warmup, num_samples, num_thin, save_warmup, refresh, stepsize, stepsize_jitter, max_depth, delta, gamma, kappa, t0, init_buffer, term_buffer, window, interrupt, logger, init_writer, sample_writer, diagnostic_writer, @@ -259,12 +262,11 @@ int hmc_nuts_diag_e_adapt( unsigned int window, callbacks::interrupt& interrupt, callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer) { - stan::io::dump dmp + auto default_metric = util::create_unit_e_diag_inv_metric(model.num_params_r()); - stan::io::var_context& unit_e_metric = dmp; callbacks::structured_writer dummy_metric_writer; return hmc_nuts_diag_e_adapt( - model, init, unit_e_metric, random_seed, chain, init_radius, num_warmup, + model, init, default_metric, random_seed, chain, init_radius, num_warmup, num_samples, num_thin, save_warmup, refresh, stepsize, stepsize_jitter, max_depth, delta, gamma, kappa, t0, init_buffer, term_buffer, window, interrupt, logger, init_writer, sample_writer, diagnostic_writer, @@ -347,8 +349,8 @@ int hmc_nuts_diag_e_adapt( init_buffer, term_buffer, window, interrupt, logger, init_writer[0], sample_writer[0], diagnostic_writer[0], metric_writer[0]); } - using sample_t = stan::mcmc::adapt_diag_e_nuts; - std::vector rngs; + using sample_t = stan::mcmc::adapt_diag_e_nuts; + std::vector rngs; rngs.reserve(num_chains); std::vector> cont_vectors; cont_vectors.reserve(num_chains); @@ -381,21 +383,26 @@ int hmc_nuts_diag_e_adapt( logger.error(e.what()); return error_codes::CONFIG; } - tbb::parallel_for( - tbb::blocked_range(0, num_chains, 1), - [num_warmup, num_samples, num_thin, refresh, save_warmup, num_chains, - init_chain_id, &samplers, &model, &rngs, &interrupt, &logger, - &sample_writer, &cont_vectors, &diagnostic_writer, - &metric_writer](const tbb::blocked_range& r) { - for (size_t i = r.begin(); i != r.end(); ++i) { - util::run_adaptive_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], metric_writer[i], - init_chain_id + i, num_chains); - } - }, - tbb::simple_partitioner()); + try { + tbb::parallel_for( + tbb::blocked_range(0, num_chains, 1), + [num_warmup, num_samples, num_thin, refresh, save_warmup, num_chains, + init_chain_id, &samplers, &model, &rngs, &interrupt, &logger, + &sample_writer, &cont_vectors, &diagnostic_writer, + &metric_writer](const tbb::blocked_range& r) { + for (size_t i = r.begin(); i != r.end(); ++i) { + util::run_adaptive_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], metric_writer[i], + init_chain_id + i, num_chains); + } + }, + tbb::simple_partitioner()); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_codes::OK; } @@ -543,10 +550,10 @@ int hmc_nuts_diag_e_adapt( std::vector& sample_writer, std::vector& diagnostic_writer, std::vector& metric_writer) { - std::vector> unit_e_metric; + std::vector> unit_e_metric; unit_e_metric.reserve(num_chains); for (size_t i = 0; i < num_chains; ++i) { - unit_e_metric.emplace_back(std::make_unique( + unit_e_metric.emplace_back(std::make_unique( util::create_unit_e_diag_inv_metric(model.num_params_r()))); } if (num_chains == 1) { @@ -581,8 +588,6 @@ int hmc_nuts_diag_e_adapt( * length as this value. * @param[in] init A std vector of init var contexts for initialization of each * chain. - * @param[in] init_inv_metric var context exposing an initial diagonal - * inverse Euclidean metric (must be positive definite) * @param[in] random_seed random seed for the random number generator * @param[in] init_chain_id first chain id. The pseudo random number generator * will advance by for each chain by an integer sequence from `init_chain_id` to @@ -625,10 +630,10 @@ int hmc_nuts_diag_e_adapt( std::vector& init_writer, std::vector& sample_writer, std::vector& diagnostic_writer) { - std::vector> unit_e_metric; + std::vector> unit_e_metric; unit_e_metric.reserve(num_chains); for (size_t i = 0; i < num_chains; ++i) { - unit_e_metric.emplace_back(std::make_unique( + unit_e_metric.emplace_back(std::make_unique( util::create_unit_e_diag_inv_metric(model.num_params_r()))); } std::vector dummy_metric_writer( diff --git a/src/stan/services/sample/hmc_nuts_unit_e.hpp b/src/stan/services/sample/hmc_nuts_unit_e.hpp index e1d85079fc9..c7a2ca7c1bd 100644 --- a/src/stan/services/sample/hmc_nuts_unit_e.hpp +++ b/src/stan/services/sample/hmc_nuts_unit_e.hpp @@ -52,7 +52,7 @@ int hmc_nuts_unit_e(Model& model, const stan::io::var_context& init, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer) { - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector disc_vector; std::vector cont_vector; @@ -64,15 +64,19 @@ int hmc_nuts_unit_e(Model& model, const stan::io::var_context& init, logger.error(e.what()); return error_codes::CONFIG; } - stan::mcmc::unit_e_nuts sampler(model, rng); + stan::mcmc::unit_e_nuts sampler(model, rng); sampler.set_nominal_stepsize(stepsize); sampler.set_stepsize_jitter(stepsize_jitter); sampler.set_max_depth(max_depth); - util::run_sampler(sampler, model, cont_vector, num_warmup, num_samples, - num_thin, refresh, save_warmup, rng, interrupt, logger, - sample_writer, diagnostic_writer); - + try { + util::run_sampler(sampler, model, cont_vector, num_warmup, num_samples, + num_thin, refresh, save_warmup, rng, interrupt, logger, + sample_writer, diagnostic_writer); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_codes::OK; } @@ -133,8 +137,8 @@ int hmc_nuts_unit_e(Model& model, size_t num_chains, max_depth, interrupt, logger, init_writer[0], sample_writer[0], diagnostic_writer[0]); } - using sample_t = stan::mcmc::unit_e_nuts; - std::vector rngs; + using sample_t = stan::mcmc::unit_e_nuts; + std::vector rngs; rngs.reserve(num_chains); std::vector> cont_vectors; cont_vectors.reserve(num_chains); @@ -155,21 +159,26 @@ int hmc_nuts_unit_e(Model& model, size_t num_chains, logger.error(e.what()); return error_codes::CONFIG; } - tbb::parallel_for( - tbb::blocked_range(0, num_chains, 1), - [num_warmup, num_samples, num_thin, refresh, save_warmup, num_chains, - init_chain_id, &samplers, &model, &rngs, &interrupt, &logger, - &sample_writer, &cont_vectors, - &diagnostic_writer](const tbb::blocked_range& r) { - for (size_t i = r.begin(); i != r.end(); ++i) { - 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, - num_chains); - } - }, - tbb::simple_partitioner()); + try { + tbb::parallel_for( + tbb::blocked_range(0, num_chains, 1), + [num_warmup, num_samples, num_thin, refresh, save_warmup, num_chains, + init_chain_id, &samplers, &model, &rngs, &interrupt, &logger, + &sample_writer, &cont_vectors, + &diagnostic_writer](const tbb::blocked_range& r) { + for (size_t i = r.begin(); i != r.end(); ++i) { + 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, + num_chains); + } + }, + tbb::simple_partitioner()); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_codes::OK; } diff --git a/src/stan/services/sample/hmc_nuts_unit_e_adapt.hpp b/src/stan/services/sample/hmc_nuts_unit_e_adapt.hpp index 6af9c742349..af0e7bee1db 100644 --- a/src/stan/services/sample/hmc_nuts_unit_e_adapt.hpp +++ b/src/stan/services/sample/hmc_nuts_unit_e_adapt.hpp @@ -59,7 +59,7 @@ int hmc_nuts_unit_e_adapt( callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer, callbacks::structured_writer& metric_writer) { - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector disc_vector; std::vector cont_vector; @@ -72,7 +72,7 @@ int hmc_nuts_unit_e_adapt( return error_codes::CONFIG; } - stan::mcmc::adapt_unit_e_nuts sampler(model, rng); + stan::mcmc::adapt_unit_e_nuts sampler(model, rng); sampler.set_nominal_stepsize(stepsize); sampler.set_stepsize_jitter(stepsize_jitter); sampler.set_max_depth(max_depth); @@ -83,11 +83,15 @@ int hmc_nuts_unit_e_adapt( sampler.get_stepsize_adaptation().set_kappa(kappa); sampler.get_stepsize_adaptation().set_t0(t0); - util::run_adaptive_sampler(sampler, model, cont_vector, num_warmup, - num_samples, num_thin, refresh, save_warmup, rng, - interrupt, logger, sample_writer, - diagnostic_writer, metric_writer); - + try { + util::run_adaptive_sampler(sampler, model, cont_vector, num_warmup, + num_samples, num_thin, refresh, save_warmup, rng, + interrupt, logger, sample_writer, + diagnostic_writer, metric_writer); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_codes::OK; } @@ -200,8 +204,8 @@ int hmc_nuts_unit_e_adapt( max_depth, delta, gamma, kappa, t0, interrupt, logger, init_writer[0], sample_writer[0], diagnostic_writer[0], metric_writer[0]); } - using sample_t = stan::mcmc::adapt_unit_e_nuts; - std::vector rngs; + using sample_t = stan::mcmc::adapt_unit_e_nuts; + std::vector rngs; rngs.reserve(num_chains); std::vector> cont_vectors; cont_vectors.reserve(num_chains); @@ -228,21 +232,26 @@ int hmc_nuts_unit_e_adapt( logger.error(e.what()); return error_codes::CONFIG; } - tbb::parallel_for( - tbb::blocked_range(0, num_chains, 1), - [num_warmup, num_samples, num_thin, refresh, save_warmup, num_chains, - init_chain_id, &samplers, &model, &rngs, &interrupt, &logger, - &sample_writer, &cont_vectors, &diagnostic_writer, - &metric_writer](const tbb::blocked_range& r) { - for (size_t i = r.begin(); i != r.end(); ++i) { - util::run_adaptive_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], metric_writer[i], - init_chain_id + i, num_chains); - } - }, - tbb::simple_partitioner()); + try { + tbb::parallel_for( + tbb::blocked_range(0, num_chains, 1), + [num_warmup, num_samples, num_thin, refresh, save_warmup, num_chains, + init_chain_id, &samplers, &model, &rngs, &interrupt, &logger, + &sample_writer, &cont_vectors, &diagnostic_writer, + &metric_writer](const tbb::blocked_range& r) { + for (size_t i = r.begin(); i != r.end(); ++i) { + util::run_adaptive_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], metric_writer[i], + init_chain_id + i, num_chains); + } + }, + tbb::simple_partitioner()); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_codes::OK; } @@ -263,7 +272,9 @@ int hmc_nuts_unit_e_adapt( * @param[in] init An std vector of init var contexts for initialization of each * chain. * @param[in] random_seed random seed for the random number generator - * @param[in] chain chain id to advance the pseudo random number generator + * @param[in] init_chain_id first chain id. The pseudo random number generator + * will advance for each chain by an integer sequence from `init_chain_id` to + * `init_chain_id + num_chains - 1` * @param[in] init_radius radius to initialize * @param[in] num_warmup Number of warmup samples * @param[in] num_samples Number of samples diff --git a/src/stan/services/sample/hmc_static_dense_e.hpp b/src/stan/services/sample/hmc_static_dense_e.hpp index 45ecb496ba3..c0931617383 100644 --- a/src/stan/services/sample/hmc_static_dense_e.hpp +++ b/src/stan/services/sample/hmc_static_dense_e.hpp @@ -53,7 +53,7 @@ int hmc_static_dense_e( double stepsize_jitter, double int_time, callbacks::interrupt& interrupt, callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer) { - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector disc_vector; std::vector cont_vector; @@ -70,15 +70,20 @@ int hmc_static_dense_e( return error_codes::CONFIG; } - stan::mcmc::dense_e_static_hmc sampler(model, rng); + stan::mcmc::dense_e_static_hmc sampler(model, rng); sampler.set_metric(inv_metric); sampler.set_nominal_stepsize_and_T(stepsize, int_time); sampler.set_stepsize_jitter(stepsize_jitter); - util::run_sampler(sampler, model, cont_vector, num_warmup, num_samples, - num_thin, refresh, save_warmup, rng, interrupt, logger, - sample_writer, diagnostic_writer); + try { + util::run_sampler(sampler, model, cont_vector, num_warmup, num_samples, + num_thin, refresh, save_warmup, rng, interrupt, logger, + sample_writer, diagnostic_writer); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_codes::OK; } @@ -116,11 +121,9 @@ int hmc_static_dense_e( double stepsize_jitter, double int_time, callbacks::interrupt& interrupt, callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer) { - stan::io::dump dmp + auto default_metric = util::create_unit_e_dense_inv_metric(model.num_params_r()); - stan::io::var_context& unit_e_metric = dmp; - - return hmc_static_dense_e(model, init, unit_e_metric, random_seed, chain, + return hmc_static_dense_e(model, init, default_metric, random_seed, chain, init_radius, num_warmup, num_samples, num_thin, save_warmup, refresh, stepsize, stepsize_jitter, int_time, interrupt, logger, init_writer, diff --git a/src/stan/services/sample/hmc_static_dense_e_adapt.hpp b/src/stan/services/sample/hmc_static_dense_e_adapt.hpp index 98fcdbcefcf..b56082620ad 100644 --- a/src/stan/services/sample/hmc_static_dense_e_adapt.hpp +++ b/src/stan/services/sample/hmc_static_dense_e_adapt.hpp @@ -65,7 +65,7 @@ int hmc_static_dense_e_adapt( unsigned int window, callbacks::interrupt& interrupt, callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer) { - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector disc_vector; std::vector cont_vector; @@ -82,8 +82,7 @@ int hmc_static_dense_e_adapt( return error_codes::CONFIG; } - stan::mcmc::adapt_dense_e_static_hmc sampler(model, - rng); + stan::mcmc::adapt_dense_e_static_hmc sampler(model, rng); sampler.set_metric(inv_metric); sampler.set_nominal_stepsize_and_T(stepsize, int_time); @@ -99,10 +98,15 @@ int hmc_static_dense_e_adapt( logger); callbacks::structured_writer dummy_metric_writer; - util::run_adaptive_sampler(sampler, model, cont_vector, num_warmup, - num_samples, num_thin, refresh, save_warmup, rng, - interrupt, logger, sample_writer, - diagnostic_writer, dummy_metric_writer); + try { + util::run_adaptive_sampler(sampler, model, cont_vector, num_warmup, + num_samples, num_thin, refresh, save_warmup, rng, + interrupt, logger, sample_writer, + diagnostic_writer, dummy_metric_writer); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_codes::OK; } @@ -149,12 +153,11 @@ int hmc_static_dense_e_adapt( unsigned int window, callbacks::interrupt& interrupt, callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer) { - stan::io::dump dmp + auto default_metric = util::create_unit_e_dense_inv_metric(model.num_params_r()); - stan::io::var_context& unit_e_metric = dmp; return hmc_static_dense_e_adapt( - model, init, unit_e_metric, random_seed, chain, init_radius, num_warmup, + model, init, default_metric, random_seed, chain, init_radius, num_warmup, num_samples, num_thin, save_warmup, refresh, stepsize, stepsize_jitter, int_time, delta, gamma, kappa, t0, init_buffer, term_buffer, window, interrupt, logger, init_writer, sample_writer, diagnostic_writer); diff --git a/src/stan/services/sample/hmc_static_diag_e.hpp b/src/stan/services/sample/hmc_static_diag_e.hpp index 1e5d1f9b120..b19c211047a 100644 --- a/src/stan/services/sample/hmc_static_diag_e.hpp +++ b/src/stan/services/sample/hmc_static_diag_e.hpp @@ -56,7 +56,7 @@ int hmc_static_diag_e(Model& model, const stan::io::var_context& init, callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer) { - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector disc_vector; std::vector cont_vector; @@ -73,15 +73,19 @@ int hmc_static_diag_e(Model& model, const stan::io::var_context& init, return error_codes::CONFIG; } - stan::mcmc::diag_e_static_hmc sampler(model, rng); + stan::mcmc::diag_e_static_hmc sampler(model, rng); sampler.set_metric(inv_metric); sampler.set_nominal_stepsize_and_T(stepsize, int_time); sampler.set_stepsize_jitter(stepsize_jitter); - - util::run_sampler(sampler, model, cont_vector, num_warmup, num_samples, - num_thin, refresh, save_warmup, rng, interrupt, logger, - sample_writer, diagnostic_writer); + try { + util::run_sampler(sampler, model, cont_vector, num_warmup, num_samples, + num_thin, refresh, save_warmup, rng, interrupt, logger, + sample_writer, diagnostic_writer); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_codes::OK; } @@ -121,11 +125,10 @@ int hmc_static_diag_e(Model& model, const stan::io::var_context& init, callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer) { - stan::io::dump dmp + auto default_metric = util::create_unit_e_diag_inv_metric(model.num_params_r()); - stan::io::var_context& unit_e_metric = dmp; - return hmc_static_diag_e(model, init, unit_e_metric, random_seed, chain, + return hmc_static_diag_e(model, init, default_metric, random_seed, chain, init_radius, num_warmup, num_samples, num_thin, save_warmup, refresh, stepsize, stepsize_jitter, int_time, interrupt, logger, init_writer, diff --git a/src/stan/services/sample/hmc_static_diag_e_adapt.hpp b/src/stan/services/sample/hmc_static_diag_e_adapt.hpp index 83b282ee733..e4041b59b3d 100644 --- a/src/stan/services/sample/hmc_static_diag_e_adapt.hpp +++ b/src/stan/services/sample/hmc_static_diag_e_adapt.hpp @@ -64,7 +64,7 @@ int hmc_static_diag_e_adapt( unsigned int window, callbacks::interrupt& interrupt, callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer) { - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector disc_vector; std::vector cont_vector; @@ -80,8 +80,7 @@ int hmc_static_diag_e_adapt( return error_codes::CONFIG; } - stan::mcmc::adapt_diag_e_static_hmc sampler(model, - rng); + stan::mcmc::adapt_diag_e_static_hmc sampler(model, rng); sampler.set_metric(inv_metric); sampler.set_nominal_stepsize_and_T(stepsize, int_time); @@ -97,10 +96,16 @@ int hmc_static_diag_e_adapt( logger); callbacks::structured_writer dummy_metric_writer; - util::run_adaptive_sampler(sampler, model, cont_vector, num_warmup, - num_samples, num_thin, refresh, save_warmup, rng, - interrupt, logger, sample_writer, - diagnostic_writer, dummy_metric_writer); + + try { + util::run_adaptive_sampler(sampler, model, cont_vector, num_warmup, + num_samples, num_thin, refresh, save_warmup, rng, + interrupt, logger, sample_writer, + diagnostic_writer, dummy_metric_writer); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_codes::OK; } @@ -147,12 +152,11 @@ int hmc_static_diag_e_adapt( unsigned int window, callbacks::interrupt& interrupt, callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer) { - stan::io::dump dmp + auto default_metric = util::create_unit_e_diag_inv_metric(model.num_params_r()); - stan::io::var_context& unit_e_metric = dmp; return hmc_static_diag_e_adapt( - model, init, unit_e_metric, random_seed, chain, init_radius, num_warmup, + model, init, default_metric, random_seed, chain, init_radius, num_warmup, num_samples, num_thin, save_warmup, refresh, stepsize, stepsize_jitter, int_time, delta, gamma, kappa, t0, init_buffer, term_buffer, window, interrupt, logger, init_writer, sample_writer, diagnostic_writer); diff --git a/src/stan/services/sample/hmc_static_unit_e.hpp b/src/stan/services/sample/hmc_static_unit_e.hpp index d46cc6dc1fd..8e2b8428fbe 100644 --- a/src/stan/services/sample/hmc_static_unit_e.hpp +++ b/src/stan/services/sample/hmc_static_unit_e.hpp @@ -52,7 +52,7 @@ int hmc_static_unit_e(Model& model, const stan::io::var_context& init, callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer) { - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector disc_vector; std::vector cont_vector; @@ -64,13 +64,18 @@ int hmc_static_unit_e(Model& model, const stan::io::var_context& init, logger.error(e.what()); return error_codes::CONFIG; } - stan::mcmc::unit_e_static_hmc sampler(model, rng); + stan::mcmc::unit_e_static_hmc sampler(model, rng); sampler.set_nominal_stepsize_and_T(stepsize, int_time); sampler.set_stepsize_jitter(stepsize_jitter); - util::run_sampler(sampler, model, cont_vector, num_warmup, num_samples, - num_thin, refresh, save_warmup, rng, interrupt, logger, - sample_writer, diagnostic_writer); + try { + util::run_sampler(sampler, model, cont_vector, num_warmup, num_samples, + num_thin, refresh, save_warmup, rng, interrupt, logger, + sample_writer, diagnostic_writer); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_codes::OK; } diff --git a/src/stan/services/sample/hmc_static_unit_e_adapt.hpp b/src/stan/services/sample/hmc_static_unit_e_adapt.hpp index 96459d57a12..bf4bf5c17e5 100644 --- a/src/stan/services/sample/hmc_static_unit_e_adapt.hpp +++ b/src/stan/services/sample/hmc_static_unit_e_adapt.hpp @@ -57,7 +57,7 @@ int hmc_static_unit_e_adapt( double kappa, double t0, callbacks::interrupt& interrupt, callbacks::logger& logger, callbacks::writer& init_writer, callbacks::writer& sample_writer, callbacks::writer& diagnostic_writer) { - boost::ecuyer1988 rng = util::create_rng(random_seed, chain); + stan::rng_t rng = util::create_rng(random_seed, chain); std::vector disc_vector; std::vector cont_vector; @@ -69,8 +69,7 @@ int hmc_static_unit_e_adapt( logger.error(e.what()); return error_codes::CONFIG; } - stan::mcmc::adapt_unit_e_static_hmc sampler(model, - rng); + stan::mcmc::adapt_unit_e_static_hmc sampler(model, rng); sampler.set_nominal_stepsize_and_T(stepsize, int_time); sampler.set_stepsize_jitter(stepsize_jitter); @@ -81,10 +80,15 @@ int hmc_static_unit_e_adapt( sampler.get_stepsize_adaptation().set_t0(t0); callbacks::structured_writer dummy_metric_writer; - util::run_adaptive_sampler(sampler, model, cont_vector, num_warmup, - num_samples, num_thin, refresh, save_warmup, rng, - interrupt, logger, sample_writer, - diagnostic_writer, dummy_metric_writer); + try { + util::run_adaptive_sampler(sampler, model, cont_vector, num_warmup, + num_samples, num_thin, refresh, save_warmup, rng, + interrupt, logger, sample_writer, + diagnostic_writer, dummy_metric_writer); + } catch (const std::exception& e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_codes::OK; } diff --git a/src/stan/services/sample/standalone_gqs.hpp b/src/stan/services/sample/standalone_gqs.hpp index 85f7ec5a5b2..c792acd08f1 100644 --- a/src/stan/services/sample/standalone_gqs.hpp +++ b/src/stan/services/sample/standalone_gqs.hpp @@ -63,23 +63,27 @@ int standalone_generate(const Model &model, const Eigen::MatrixXd &draws, util::gq_writer writer(sample_writer, logger, p_names.size()); writer.write_gq_names(model); - boost::ecuyer1988 rng = util::create_rng(seed, 1); + stan::rng_t rng = util::create_rng(seed, 1); std::vector unconstrained_params_r; std::vector row(draws.cols()); - - for (size_t i = 0; i < draws.rows(); ++i) { - Eigen::Map(&row[0], draws.cols()) = draws.row(i); - try { - model.unconstrain_array(row, unconstrained_params_r, &msg); - } catch (const std::exception &e) { - if (msg.str().length() > 0) - logger.error(msg); - logger.error(e.what()); - return error_codes::DATAERR; + try { + for (size_t i = 0; i < draws.rows(); ++i) { + Eigen::Map(&row[0], draws.cols()) = draws.row(i); + try { + model.unconstrain_array(row, unconstrained_params_r, &msg); + } catch (const std::exception &e) { + if (msg.str().length() > 0) + logger.error(msg); + logger.error(e.what()); + return error_codes::DATAERR; + } + interrupt(); // call out to interrupt and fail + writer.write_gq_values(model, rng, unconstrained_params_r); } - interrupt(); // call out to interrupt and fail - writer.write_gq_values(model, rng, unconstrained_params_r); + } catch (const std::exception &e) { + logger.error(e.what()); + return error_codes::SOFTWARE; } return error_codes::OK; } @@ -125,7 +129,7 @@ int standalone_generate(const Model &model, const int num_chains, } std::vector writers; writers.reserve(num_chains); - std::vector rngs; + std::vector rngs; rngs.reserve(num_chains); for (int i = 0; i < num_chains; ++i) { if (draws[i].size() == 0) { @@ -147,34 +151,40 @@ int standalone_generate(const Model &model, const int num_chains, rngs.emplace_back(util::create_rng(seed, i + 1)); } bool error_any = false; - tbb::parallel_for( - tbb::blocked_range(0, num_chains, 1), - [&draws, &model, &logger, &interrupt, &writers, &rngs, - &error_any](const tbb::blocked_range &r) { - Eigen::VectorXd unconstrained_params_r(draws[0].cols()); - Eigen::VectorXd row(draws[0].cols()); - std::stringstream msg; - for (size_t slice_idx = r.begin(); slice_idx != r.end(); ++slice_idx) { - for (size_t i = 0; i < draws[slice_idx].rows(); ++i) { - if (error_any) - return; - try { - row = draws[slice_idx].row(i); - model.unconstrain_array(row, unconstrained_params_r, &msg); - } catch (const std::exception &e) { - if (msg.str().length() > 0) - logger.error(msg); - logger.error(e.what()); - error_any = true; - return; + try { + tbb::parallel_for( + tbb::blocked_range(0, num_chains, 1), + [&draws, &model, &logger, &interrupt, &writers, &rngs, + &error_any](const tbb::blocked_range &r) { + Eigen::VectorXd unconstrained_params_r(draws[0].cols()); + Eigen::VectorXd row(draws[0].cols()); + std::stringstream msg; + for (size_t slice_idx = r.begin(); slice_idx != r.end(); + ++slice_idx) { + for (size_t i = 0; i < draws[slice_idx].rows(); ++i) { + if (error_any) + return; + try { + row = draws[slice_idx].row(i); + model.unconstrain_array(row, unconstrained_params_r, &msg); + } catch (const std::domain_error &e) { + if (msg.str().length() > 0) + logger.error(msg); + logger.error(e.what()); + error_any = true; + return; + } + interrupt(); // call out to interrupt and fail + writers[slice_idx].write_gq_values(model, rngs[slice_idx], + unconstrained_params_r); } - interrupt(); // call out to interrupt and fail - writers[slice_idx].write_gq_values(model, rngs[slice_idx], - unconstrained_params_r); } - } - }, - tbb::simple_partitioner()); + }, + tbb::simple_partitioner()); + } catch (const std::exception &e) { + logger.error(e.what()); + return error_codes::SOFTWARE; + } return error_any ? error_codes::DATAERR : error_codes::OK; } diff --git a/src/stan/services/util/create_rng.hpp b/src/stan/services/util/create_rng.hpp index cbc742c6686..a88d1626953 100644 --- a/src/stan/services/util/create_rng.hpp +++ b/src/stan/services/util/create_rng.hpp @@ -1,9 +1,12 @@ #ifndef STAN_SERVICES_UTIL_CREATE_RNG_HPP #define STAN_SERVICES_UTIL_CREATE_RNG_HPP -#include +#include namespace stan { + +using rng_t = boost::random::mixmax; + namespace services { namespace util { @@ -18,19 +21,15 @@ namespace util { * that the draws used to initialized transformed data are not * duplicated. * - * @tparam RngType type of RNG to return, default is `boost::ecuyer1988` * @param[in] seed the random seed * @param[in] chain the chain id - * @return an RNG instance + * @return an stan::rng_t instance */ -template -inline RngType create_rng(unsigned int seed, unsigned int chain) { - using boost::uintmax_t; - static constexpr uintmax_t DISCARD_STRIDE = static_cast(1) << 50; - RngType rng(seed); - // always discard at least 1 to avoid issue with small seeds for certain RNG - // distributions. See stan#3167 and boostorg/random#92 - rng.discard(std::max(static_cast(1), DISCARD_STRIDE * chain)); +inline rng_t create_rng(unsigned int seed, unsigned int chain) { + // RNG state is 128 bits, but user only provides 64 total bits + // Additionally, there are issues if all 128 bits are 0, hence + // the 1 as the second argument + rng_t rng(0, 1, seed, chain); return rng; } diff --git a/src/stan/services/util/create_unit_e_dense_inv_metric.hpp b/src/stan/services/util/create_unit_e_dense_inv_metric.hpp index ee9afc670a6..d8448ada2c9 100644 --- a/src/stan/services/util/create_unit_e_dense_inv_metric.hpp +++ b/src/stan/services/util/create_unit_e_dense_inv_metric.hpp @@ -1,8 +1,7 @@ #ifndef STAN_SERVICES_UTIL_CREATE_UNIT_E_DENSE_INV_METRIC_HPP #define STAN_SERVICES_UTIL_CREATE_UNIT_E_DENSE_INV_METRIC_HPP -#include -#include +#include #include namespace stan { @@ -16,14 +15,15 @@ namespace util { * @param[in] num_params expected number of dense elements * @return var_context */ -inline stan::io::dump create_unit_e_dense_inv_metric(size_t num_params) { - auto num_params_str = std::to_string(num_params); - std::string dims("),.Dim=c(" + num_params_str + ", " + num_params_str + "))"); - Eigen::IOFormat RFmt(Eigen::StreamPrecision, Eigen::DontAlignCols, ", ", ",", - "", "", "inv_metric <- structure(c(", dims); - std::stringstream txt; - txt << Eigen::MatrixXd::Identity(num_params, num_params).format(RFmt); - return stan::io::dump(txt); +inline auto create_unit_e_dense_inv_metric(size_t num_params) { + std::vector names{"inv_metric"}; + std::vector vals(num_params * num_params, 0.0); + for (size_t i = 0; i < num_params; ++i) { + vals[i * num_params + i] = 1.0; + } + std::vector> dimss{{num_params, num_params}}; + + return stan::io::array_var_context(names, vals, dimss); } } // namespace util } // namespace services diff --git a/src/stan/services/util/create_unit_e_diag_inv_metric.hpp b/src/stan/services/util/create_unit_e_diag_inv_metric.hpp index e14a80e3283..6db1d211473 100644 --- a/src/stan/services/util/create_unit_e_diag_inv_metric.hpp +++ b/src/stan/services/util/create_unit_e_diag_inv_metric.hpp @@ -1,7 +1,7 @@ #ifndef STAN_SERVICES_UTIL_CREATE_UNIT_E_DIAG_INV_METRIC_HPP #define STAN_SERVICES_UTIL_CREATE_UNIT_E_DIAG_INV_METRIC_HPP -#include +#include #include namespace stan { @@ -15,13 +15,12 @@ namespace util { * @param[in] num_params expected number of diagonal elements * @return var_context */ -inline stan::io::dump create_unit_e_diag_inv_metric(size_t num_params) { - std::string dims("),.Dim=c(" + std::to_string(num_params) + "))"); - Eigen::IOFormat RFmt(Eigen::StreamPrecision, Eigen::DontAlignCols, ", ", ",", - "", "", "inv_metric <- structure(c(", dims); - std::stringstream txt; - txt << Eigen::VectorXd::Ones(num_params).format(RFmt); - return stan::io::dump(txt); +inline auto create_unit_e_diag_inv_metric(size_t num_params) { + std::vector names{"inv_metric"}; + std::vector vals(num_params, 1.0); + std::vector> dimss{{num_params}}; + + return stan::io::array_var_context(names, vals, dimss); } } // namespace util } // namespace services diff --git a/src/stan/services/util/gq_writer.hpp b/src/stan/services/util/gq_writer.hpp index c844a54dd22..5cb8e6eb78a 100644 --- a/src/stan/services/util/gq_writer.hpp +++ b/src/stan/services/util/gq_writer.hpp @@ -79,10 +79,15 @@ class gq_writer { model.write_array(rng, draw, params_i, values, false, true, &ss); if (ss.str().length() > 0) logger_.info(ss); + } catch (const std::domain_error& e) { + if (ss.str().length() > 0) + logger_.info(ss); + logger_.info(e.what()); } catch (const std::exception& e) { if (ss.str().length() > 0) logger_.info(ss); logger_.info(e.what()); + throw; } std::vector gq_values(values.begin() + num_constrained_params_, @@ -110,11 +115,16 @@ class gq_writer { if (ss.str().length() > 0) { logger_.info(ss); } - } catch (const std::exception& e) { + } catch (const std::domain_error& e) { if (ss.str().length() > 0) { logger_.info(ss); } logger_.info(e.what()); + } catch (const std::exception& e) { + if (ss.str().length() > 0) + logger_.info(ss); + logger_.info(e.what()); + throw; } sample_writer_(values); } diff --git a/src/stan/services/util/mcmc_writer.hpp b/src/stan/services/util/mcmc_writer.hpp index d51f168c9bc..7f5d5661858 100644 --- a/src/stan/services/util/mcmc_writer.hpp +++ b/src/stan/services/util/mcmc_writer.hpp @@ -111,11 +111,16 @@ class mcmc_writer { sample.cont_params().data() + sample.cont_params().size()); model.write_array(rng, cont_params, params_i, model_values, true, true, &ss); - } catch (const std::exception& e) { + } catch (const std::domain_error& e) { if (ss.str().length() > 0) logger_.info(ss); ss.str(""); logger_.info(e.what()); + } catch (const std::exception& e) { + if (ss.str().length() > 0) + logger_.info(ss); + logger_.info(e.what()); + throw; } if (ss.str().length() > 0) logger_.info(ss); diff --git a/src/stan/variational/advi.hpp b/src/stan/variational/advi.hpp index 87391382f03..681ce65e82c 100644 --- a/src/stan/variational/advi.hpp +++ b/src/stan/variational/advi.hpp @@ -5,7 +5,6 @@ #include #include #include -#include #include #include #include diff --git a/src/stan/version.hpp b/src/stan/version.hpp index 65e3bd71161..e1c12af4157 100644 --- a/src/stan/version.hpp +++ b/src/stan/version.hpp @@ -12,8 +12,8 @@ #endif #define STAN_MAJOR 2 -#define STAN_MINOR 34 -#define STAN_PATCH 1 +#define STAN_MINOR 35 +#define STAN_PATCH 0 namespace stan { diff --git a/src/test/test-models/good/services/bernoulli.data.R b/src/test/test-models/good/services/bernoulli.data.R deleted file mode 100644 index 5443d8ba764..00000000000 --- a/src/test/test-models/good/services/bernoulli.data.R +++ /dev/null @@ -1,2 +0,0 @@ -N <- 10 -y <- c(0,1,0,0,0,0,0,0,0,1) \ No newline at end of file diff --git a/src/test/test-models/good/variational/gradient_warn.data.R b/src/test/test-models/good/variational/gradient_warn.data.R deleted file mode 100644 index f464de0ace9..00000000000 --- a/src/test/test-models/good/variational/gradient_warn.data.R +++ /dev/null @@ -1,4 +0,0 @@ -N <- 10 -p <- c(63.1, 108.3, 1.0, 46.0, 22.9, 14.8, 28.8, 52.5, 60.1, 81.3) -Ngrps <- 5 -grp_index <- c(1, 1, 1, 1, 1, 1, 2, 3, 4, 5) diff --git a/src/test/test-models/good/variational/gradient_warn.data.json b/src/test/test-models/good/variational/gradient_warn.data.json new file mode 100644 index 00000000000..f56038bf0c1 --- /dev/null +++ b/src/test/test-models/good/variational/gradient_warn.data.json @@ -0,0 +1,6 @@ +{ + "Ngrps": 5, + "N": 10, + "grp_index": [1, 1, 1, 1, 1, 1, 2, 3, 4, 5], + "p": [63.1, 108.3, 1, 46, 22.9, 14.8, 28.8, 52.5, 60.1, 81.3] +} diff --git a/src/test/test-models/good/variational/hier_logistic.data.R b/src/test/test-models/good/variational/hier_logistic.data.R deleted file mode 100644 index f7c16eca391..00000000000 --- a/src/test/test-models/good/variational/hier_logistic.data.R +++ /dev/null @@ -1,1745 +0,0 @@ -ids <- -c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, -4, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, -9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 12, 12, 12, 12, 12, 12, -12, 12, 12, 12, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 15, 15, 15, 15, 15, 15, 15, 15, -15, 15, 16, 16, 16, 16, 16, 16, 16, 16, 16, 16, 17, 17, 17, 17, 17, 17, 17, 17, 17, 17, 18, 18, 18, 18, 18, 18, 18, 18, 18, 18, -19, 19, 19, 19, 19, 19, 19, 19, 19, 19, 20, 20, 20, 20, 20, 20, 20, 20, 20, 20, 21, 21, 21, 21, 21, 21, 21, 21, 21, 21, 22, 22, -22, 22, 22, 22, 22, 22, 22, 22, 23, 23, 23, 23, 23, 23, 23, 23, 23, 23, 24, 24, 24, 24, 24, 24, 24, 24, 24, 24, 25, 25, 25, 25, -25, 25, 25, 25, 25, 25, 26, 26, 26, 26, 26, 26, 26, 26, 26, 26, 27, 27, 27, 27, 27, 27, 27, 27, 27, 27, 28, 28, 28, 28, 28, 28, -28, 28, 28, 28, 29, 29, 29, 29, 29, 29, 29, 29, 29, 29, 30, 30, 30, 30, 30, 30, 30, 30, 30, 30, 31, 31, 31, 31, 31, 31, 31, 31, -31, 31, 32, 32, 32, 32, 32, 32, 32, 32, 32, 32, 33, 33, 33, 33, 33, 33, 33, 33, 33, 33, 34, 34, 34, 34, 34, 34, 34, 34, 34, 34, -35, 35, 35, 35, 35, 35, 35, 35, 35, 35, 36, 36, 36, 36, 36, 36, 36, 36, 36, 36, 37, 37, 37, 37, 37, 37, 37, 37, 37, 37, 38, 38, -38, 38, 38, 38, 38, 38, 38, 38, 39, 39, 39, 39, 39, 39, 39, 39, 39, 39, 40, 40, 40, 40, 40, 40, 40, 40, 40, 40, 41, 41, 41, 41, -41, 41, 41, 41, 41, 41, 42, 42, 42, 42, 42, 42, 42, 42, 42, 42, 43, 43, 43, 43, 43, 43, 43, 43, 43, 43, 44, 44, 44, 44, 44, 44, -44, 44, 44, 44, 45, 45, 45, 45, 45, 45, 45, 45, 45, 45, 46, 46, 46, 46, 46, 46, 46, 46, 46, 46, 47, 47, 47, 47, 47, 47, 47, 47, -47, 47, 48, 48, 48, 48, 48, 48, 48, 48, 48, 48, 49, 49, 49, 49, 49, 49, 49, 49, 49, 49, 50, 50, 50, 50, 50, 50, 50, 50, 50, 50, -51, 51, 51, 51, 51, 51, 51, 51, 51, 51, 52, 52, 52, 52, 52, 52, 52, 52, 52, 52, 53, 53, 53, 53, 53, 53, 53, 53, 53, 53, 54, 54, -54, 54, 54, 54, 54, 54, 54, 54, 55, 55, 55, 55, 55, 55, 55, 55, 55, 55, 56, 56, 56, 56, 56, 56, 56, 56, 56, 56, 57, 57, 57, 57, -57, 57, 57, 57, 57, 57, 58, 58, 58, 58, 58, 58, 58, 58, 58, 58, 59, 59, 59, 59, 59, 59, 59, 59, 59, 59, 60, 60, 60, 60, 60, 60, -60, 60, 60, 60, 61, 61, 61, 61, 61, 61, 61, 61, 61, 61, 62, 62, 62, 62, 62, 62, 62, 62, 62, 62, 63, 63, 63, 63, 63, 63, 63, 63, -63, 63, 64, 64, 64, 64, 64, 64, 64, 64, 64, 64, 65, 65, 65, 65, 65, 65, 65, 65, 65, 65, 66, 66, 66, 66, 66, 66, 66, 66, 66, 66, -67, 67, 67, 67, 67, 67, 67, 67, 67, 67, 68, 68, 68, 68, 68, 68, 68, 68, 68, 68, 69, 69, 69, 69, 69, 69, 69, 69, 69, 69, 70, 70, -70, 70, 70, 70, 70, 70, 70, 70, 71, 71, 71, 71, 71, 71, 71, 71, 71, 71, 72, 72, 72, 72, 72, 72, 72, 72, 72, 72, 73, 73, 73, 73, -73, 73, 73, 73, 73, 73, 74, 74, 74, 74, 74, 74, 74, 74, 74, 74, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 76, 76, 76, 76, 76, 76, -76, 76, 76, 76, 77, 77, 77, 77, 77, 77, 77, 77, 77, 77, 78, 78, 78, 78, 78, 78, 78, 78, 78, 78, 79, 79, 79, 79, 79, 79, 79, 79, -79, 79, 80, 80, 80, 80, 80, 80, 80, 80, 80, 80, 81, 81, 81, 81, 81, 81, 81, 81, 81, 81, 82, 82, 82, 82, 82, 82, 82, 82, 82, 82, -83, 83, 83, 83, 83, 83, 83, 83, 83, 83, 84, 84, 84, 84, 84, 84, 84, 84, 84, 84, 85, 85, 85, 85, 85, 85, 85, 85, 85, 85, 86, 86, -86, 86, 86, 86, 86, 86, 86, 86, 87, 87, 87, 87, 87, 87, 87, 87, 87, 87, 88, 88, 88, 88, 88, 88, 88, 88, 88, 88, 89, 89, 89, 89, -89, 89, 89, 89, 89, 89, 90, 90, 90, 90, 90, 90, 90, 90, 90, 90, 91, 91, 91, 91, 91, 91, 91, 91, 91, 91, 92, 92, 92, 92, 92, 92, -92, 92, 92, 92, 93, 93, 93, 93, 93, 93, 93, 93, 93, 93, 94, 94, 94, 94, 94, 94, 94, 94, 94, 94, 95, 95, 95, 95, 95, 95, 95, 95, -95, 95, 96, 96, 96, 96, 96, 96, 96, 96, 96, 96, 97, 97, 97, 97, 97, 97, 97, 97, 97, 97, 98, 98, 98, 98, 98, 98, 98, 98, 98, 98, -99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100) -X <- -structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2.57917716199775, -2.38744893329618, 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0, 0, 1], + "N": 1000 +} diff --git a/src/test/test-models/performance/logistic.data.R b/src/test/test-models/performance/logistic.data.R deleted file mode 100644 index f5dd5907e40..00000000000 --- a/src/test/test-models/performance/logistic.data.R +++ /dev/null @@ -1,52 +0,0 @@ -N <- -128 -M <- -2 -y <- -c(0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, -1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, -1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, -1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, -1, 1, 0) -x <- -structure(c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, -1, 1, 1, 1, 1, 1, 1.3297992629225, 1.2724293214294, 0.414641434456408, --1.53995004190371, -0.928567034713538, -0.29472044679056, -0.00576717274753696, -2.40465338885795, 0.76359346114046, -0.799009248989368, -1.14765700923635, --0.289461573688223, -0.299215117897316, -0.411510832795067, 0.252223448156132, --0.891921127284569, 0.435683299355719, -1.23753842192996, -0.224267885278309, -0.377395645981701, 0.133336360814841, 0.804189509744908, -0.0571067743838088, -0.503607972233726, 1.08576936214569, -0.69095383969683, -1.28459935387219, -0.046726172188352, -0.235706556439501, -0.542888255010254, -0.433310317456782, --0.649471646796233, 0.726750747385451, 1.1519117540872, 0.992160365445798, --0.429513109491881, 1.23830410085338, -0.279346281854269, 1.75790308981071, -0.560746090888056, -0.452783972553158, -0.832043296117832, -1.16657054708471, --1.0655905803883, -1.563782051071, 1.15653699715018, 0.83204712857239, --0.227328691424755, 0.266137361672105, -0.376702718583628, 2.44136462889459, --0.795339117255372, -0.0548774737115786, 0.250141322854153, 0.618243293566247, --0.172623502645857, -2.22390027400994, -1.26361438497058, 0.358728895971352, --0.0110454784656636, -0.940649162618608, -0.115825322156954, --0.814968708869917, 0.242263480859686, -1.4250983947325, 0.36594112304922, -0.248412648872596, 0.0652881816716207, 0.0191563916602738, 0.257338377155533, --0.649010077708898, -0.119168762418038, 0.66413569989411, 1.10096910219409, -0.14377148075807, -0.117753598165951, -0.912068366948338, -1.43758624082998, --0.797089525071965, 1.25408310644997, 0.77214218580453, -0.21951562675344, --0.424810283377287, -0.418980099421959, 0.996986860909106, -0.275778029088027, -1.2560188173061, 0.646674390495345, 1.29931230256343, -0.873262111744435, -0.00837095999603331, -0.880871723252545, 0.59625901661066, 0.119717641289537, --0.282173877322451, 1.45598840106634, 0.229019590694692, 0.996543928544126, -0.781859184600258, -0.776776621764597, -0.615989907707918, 0.0465803028049967, --1.13038577760069, 0.576718781896486, -1.28074943178832, 1.62544730346494, --0.500696596002705, 1.67829720781629, -0.412519887482398, -0.97228683550556, -0.0253828675878054, 0.0274753367451927, -1.68018272239593, 1.05375086302862, --1.11959910457218, 0.335617209968815, 0.494795767113158, 0.138052708711737, --0.118792025778828, 0.197684262345795, -1.06869271125479, -0.803213217364741, --1.11376513631953, 1.58009168370384, 1.49781876103841, 0.262645458662762, --1.23290119957126, -0.00372353379218051), .Dim = c(128L, 2L)) diff --git a/src/test/test-models/performance/logistic.data.json b/src/test/test-models/performance/logistic.data.json new file mode 100644 index 00000000000..592e89be51b --- /dev/null +++ b/src/test/test-models/performance/logistic.data.json @@ -0,0 +1,135 @@ +{ + "x": [ + [1, 1.3297992629225], + [1, 1.2724293214294], + [1, 0.414641434456408], + [1, -1.53995004190371], + [1, -0.928567034713538], + [1, -0.29472044679056], + [1, -0.00576717274753696], + [1, 2.40465338885795], + [1, 0.76359346114046], + [1, -0.799009248989368], + [1, -1.14765700923635], + [1, -0.289461573688223], + [1, -0.299215117897316], + [1, -0.411510832795067], + [1, 0.252223448156132], + [1, -0.891921127284569], + [1, 0.435683299355719], + [1, -1.23753842192996], + [1, -0.224267885278309], + [1, 0.377395645981701], + [1, 0.133336360814841], + [1, 0.804189509744908], + [1, -0.0571067743838088], + [1, 0.503607972233726], + [1, 1.08576936214569], + [1, -0.69095383969683], + [1, -1.28459935387219], + [1, 0.046726172188352], + [1, -0.235706556439501], + [1, -0.542888255010254], + [1, -0.433310317456782], + [1, -0.649471646796233], + [1, 0.726750747385451], + [1, 1.1519117540872], + [1, 0.992160365445798], + [1, -0.429513109491881], + [1, 1.23830410085338], + [1, -0.279346281854269], + [1, 1.75790308981071], + [1, 0.560746090888056], + [1, -0.452783972553158], + [1, -0.832043296117832], + [1, -1.16657054708471], + [1, -1.0655905803883], + [1, -1.563782051071], + [1, 1.15653699715018], + [1, 0.83204712857239], + [1, -0.227328691424755], + [1, 0.266137361672105], + [1, -0.376702718583628], + [1, 2.44136462889459], + [1, -0.795339117255372], + [1, -0.0548774737115786], + [1, 0.250141322854153], + [1, 0.618243293566247], + [1, -0.172623502645857], + [1, -2.22390027400994], + [1, -1.26361438497058], + [1, 0.358728895971352], + [1, -0.0110454784656636], + [1, -0.940649162618608], + [1, -0.115825322156954], + [1, -0.814968708869917], + [1, 0.242263480859686], + [1, -1.4250983947325], + [1, 0.36594112304922], + [1, 0.248412648872596], + [1, 0.0652881816716207], + [1, 0.0191563916602738], + [1, 0.257338377155533], + [1, -0.649010077708898], + [1, -0.119168762418038], + [1, 0.66413569989411], + [1, 1.10096910219409], + [1, 0.14377148075807], + [1, -0.117753598165951], + [1, -0.912068366948338], + [1, -1.43758624082998], + [1, -0.797089525071965], + [1, 1.25408310644997], + [1, 0.77214218580453], + [1, -0.21951562675344], + [1, -0.424810283377287], + [1, -0.418980099421959], + [1, 0.996986860909106], + [1, -0.275778029088027], + [1, 1.2560188173061], + [1, 0.646674390495345], + [1, 1.29931230256343], + [1, -0.873262111744435], + [1, 0.00837095999603331], + [1, -0.880871723252545], + [1, 0.59625901661066], + [1, 0.119717641289537], + [1, -0.282173877322451], + [1, 1.45598840106634], + [1, 0.229019590694692], + [1, 0.996543928544126], + [1, 0.781859184600258], + [1, -0.776776621764597], + [1, -0.615989907707918], + [1, 0.0465803028049967], + [1, -1.13038577760069], + [1, 0.576718781896486], + [1, -1.28074943178832], + [1, 1.62544730346494], + [1, -0.500696596002705], + [1, 1.67829720781629], + [1, -0.412519887482398], + [1, -0.97228683550556], + [1, 0.0253828675878054], + [1, 0.0274753367451927], + [1, -1.68018272239593], + [1, 1.05375086302862], + [1, -1.11959910457218], + [1, 0.335617209968815], + [1, 0.494795767113158], + [1, 0.138052708711737], + [1, -0.118792025778828], + [1, 0.197684262345795], + [1, -1.06869271125479], + [1, -0.803213217364741], + [1, -1.11376513631953], + [1, 1.58009168370384], + [1, 1.49781876103841], + [1, 0.262645458662762], + [1, -1.23290119957126], + [1, -0.00372353379218051] + ], + "y": [0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 0], + "M": 2, + "N": 128 +} 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 9e4df6d09f2..41a102e51df 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 @@ -27,18 +27,17 @@ TEST_F(ComputeRhat, compute_potential_scale_reduction) { 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 << 1.00718, 1.00473, 0.999203, 1.00061, 1.00378, 1.01031, 1.00173, - 1.0045, 1.00111, 1.00337, 1.00546, 1.00105, 1.00558, 1.00463, 1.00534, - 1.01244, 1.00174, 1.00718, 1.00186, 1.00554, 1.00436, 1.00147, 1.01017, - 1.00162, 1.00143, 1.00058, 0.999221, 1.00012, 1.01028, 1.001, 1.00305, - 1.00435, 1.00055, 1.00246, 1.00447, 1.0048, 1.00209, 1.01159, 1.00202, - 1.00077, 1.0021, 1.00262, 1.00308, 1.00197, 1.00246, 1.00085, 1.00047, - 1.00735; + rhat << 1.00042, 1.00036, 0.99955, 1.00047, 1.00119, 1.00089, 1.00018, + 1.00019, 1.00226, 0.99954, 0.9996, 0.99951, 1.00237, 1.00515, 1.00566, + 0.99957, 1.00099, 1.00853, 1.0008, 0.99961, 1.0006, 1.00046, 1.01023, + 0.9996, 1.0011, 0.99967, 0.99973, 0.99958, 1.00242, 1.00213, 1.00244, + 0.99998, 0.99969, 1.00079, 0.99955, 1.0009, 1.00136, 1.00288, 1.00036, + 0.99989, 1.00077, 0.99997, 1.00194, 0.99972, 1.00257, 1.00109, 1.00004, + 0.99955; // replicates calls to stan::analyze::compute_effective_sample_size // for any interface *without* access to chains class @@ -54,11 +53,69 @@ TEST_F(ComputeRhat, compute_potential_scale_reduction) { } ASSERT_NEAR(rhat(index - 4), stan::analyze::compute_potential_scale_reduction(draws, sizes), - 1.0) + 1e-4) << "rhat for index: " << index << ", 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; @@ -72,13 +129,13 @@ TEST_F(ComputeRhat, compute_potential_scale_reduction_convenience) { chains.add(blocker2); Eigen::VectorXd rhat(48); - rhat << 1.00718, 1.00473, 0.999203, 1.00061, 1.00378, 1.01031, 1.00173, - 1.0045, 1.00111, 1.00337, 1.00546, 1.00105, 1.00558, 1.00463, 1.00534, - 1.01244, 1.00174, 1.00718, 1.00186, 1.00554, 1.00436, 1.00147, 1.01017, - 1.00162, 1.00143, 1.00058, 0.999221, 1.00012, 1.01028, 1.001, 1.00305, - 1.00435, 1.00055, 1.00246, 1.00447, 1.0048, 1.00209, 1.01159, 1.00202, - 1.00077, 1.0021, 1.00262, 1.00308, 1.00197, 1.00246, 1.00085, 1.00047, - 1.00735; + rhat << 1.00042, 1.00036, 0.99955, 1.00047, 1.00119, 1.00089, 1.00018, + 1.00019, 1.00226, 0.99954, 0.9996, 0.99951, 1.00237, 1.00515, 1.00566, + 0.99957, 1.00099, 1.00853, 1.0008, 0.99961, 1.0006, 1.00046, 1.01023, + 0.9996, 1.0011, 0.99967, 0.99973, 0.99958, 1.00242, 1.00213, 1.00244, + 0.99998, 0.99969, 1.00079, 0.99955, 1.0009, 1.00136, 1.00288, 1.00036, + 0.99989, 1.00077, 0.99997, 1.00194, 0.99972, 1.00257, 1.00109, 1.00004, + 0.99955; Eigen::Matrix samples( chains.num_chains()); @@ -89,16 +146,71 @@ TEST_F(ComputeRhat, compute_potential_scale_reduction_convenience) { samples(chain) = chains.samples(chain, index); draws[chain] = &samples(chain)(0); } + size_t size = samples(0).size(); ASSERT_NEAR(rhat(index - 4), stan::analyze::compute_potential_scale_reduction(draws, size), - 1.0) + 1e-4) << "rhat for index: " << index << ", parameter: " << chains.param_name(index); } } -TEST_F(ComputeRhat, chains_compute_potential_scale_reduction) { +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 = stan::io::stan_csv_reader::parse(blocker1_stream, &out); @@ -132,6 +244,55 @@ TEST_F(ComputeRhat, chains_compute_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 @@ -168,12 +329,70 @@ TEST_F(ComputeRhat, compute_split_potential_scale_reduction) { ASSERT_NEAR( rhat(index - 4), stan::analyze::compute_split_potential_scale_reduction(draws, sizes), - 1.0) + 1e-4) << "rhat for index: " << index << ", parameter: " << chains.param_name(index); } } +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 @@ -207,12 +426,68 @@ TEST_F(ComputeRhat, compute_split_potential_scale_reduction_convenience) { ASSERT_NEAR( rhat(index - 4), stan::analyze::compute_split_potential_scale_reduction(draws, size), - 1.0) + 1e-4) << "rhat for index: " << index << ", parameter: " << chains.param_name(index); } } +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); @@ -224,6 +499,23 @@ 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); @@ -234,3 +526,20 @@ 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/io/random_var_context_test.cpp b/src/test/unit/io/random_var_context_test.cpp index 18bfeb7f2b0..64ea370f17a 100644 --- a/src/test/unit/io/random_var_context_test.cpp +++ b/src/test/unit/io/random_var_context_test.cpp @@ -1,7 +1,7 @@ #include #include +#include #include -#include // L'Ecuyer RNG #include #include #include @@ -95,12 +95,12 @@ class random_var_context : public testing::Test { random_var_context() : empty_context(), model(empty_context, 0, static_cast(0)), - rng(0), + rng(stan::services::util::create_rng(0, 0)), throwing_model() {} stan::io::empty_var_context empty_context; stan_model model; - boost::ecuyer1988 rng; + stan::rng_t rng; test::mock_throwing_model_in_write_array throwing_model; }; diff --git a/src/test/unit/mcmc/chains_test.cpp b/src/test/unit/mcmc/chains_test.cpp index 987279de66e..f2d90aaf0d6 100644 --- a/src/test/unit/mcmc/chains_test.cpp +++ b/src/test/unit/mcmc/chains_test.cpp @@ -1,7 +1,6 @@ #include #include #include -#include #include #include #include @@ -875,3 +874,67 @@ TEST_F(McmcChains, blocker_split_potential_scale_reduction) { chains.split_potential_scale_reduction(name)); } } + +TEST_F(McmcChains, blocker_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(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, + 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); + + // 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++) { + 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/hmc/base_hmc_test.cpp b/src/test/unit/mcmc/hmc/base_hmc_test.cpp index 00742532075..ce3d8f51f45 100644 --- a/src/test/unit/mcmc/hmc/base_hmc_test.cpp +++ b/src/test/unit/mcmc/hmc/base_hmc_test.cpp @@ -1,22 +1,20 @@ #include #include #include -#include +#include #include #include #include -typedef boost::ecuyer1988 rng_t; - namespace stan { namespace mcmc { -class mock_hmc - : public base_hmc { +class mock_hmc : public base_hmc { public: - mock_hmc(const mock_model& m, rng_t& rng) - : base_hmc(m, rng) { - } + mock_hmc(const mock_model& m, stan::rng_t& rng) + : base_hmc( + m, rng) {} sample transition(sample& init_sample, callbacks::logger& logger) { this->seed(init_sample.cont_params()); @@ -33,7 +31,7 @@ class mock_hmc } // namespace stan TEST(McmcBaseHMC, point_construction) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); Eigen::VectorXd q(2); q(0) = 5; @@ -47,14 +45,14 @@ TEST(McmcBaseHMC, point_construction) { } TEST(McmcBaseHMC, point_access_from_const_hmc) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); const stan::mcmc::mock_hmc sampler(stan::mcmc::mock_model(2), base_rng); EXPECT_EQ(2, sampler.z().q.size()); EXPECT_EQ(2, sampler.z().g.size()); } TEST(McmcBaseHMC, seed) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); Eigen::VectorXd q(2); q(0) = 5; @@ -70,7 +68,7 @@ TEST(McmcBaseHMC, seed) { } TEST(McmcBaseHMC, set_nominal_stepsize) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); Eigen::VectorXd q(2); q(0) = 5; @@ -88,7 +86,7 @@ TEST(McmcBaseHMC, set_nominal_stepsize) { } TEST(McmcBaseHMC, set_stepsize_jitter) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); Eigen::VectorXd q(2); q(0) = 5; @@ -108,7 +106,7 @@ TEST(McmcBaseHMC, set_stepsize_jitter) { TEST(McmcBaseHMC, streams) { stan::test::capture_std_streams(); - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); Eigen::VectorXd q(2); q(0) = 5; diff --git a/src/test/unit/mcmc/hmc/hamiltonians/base_hamiltonian_test.cpp b/src/test/unit/mcmc/hmc/hamiltonians/base_hamiltonian_test.cpp index 4926f6daef0..1e70c182594 100644 --- a/src/test/unit/mcmc/hmc/hamiltonians/base_hamiltonian_test.cpp +++ b/src/test/unit/mcmc/hmc/hamiltonians/base_hamiltonian_test.cpp @@ -3,12 +3,10 @@ #include #include #include -#include +#include #include #include -typedef boost::ecuyer1988 rng_t; - TEST(BaseHamiltonian, update_potential_gradient) { stan::io::empty_var_context data_var_context; @@ -16,7 +14,8 @@ TEST(BaseHamiltonian, update_potential_gradient) { funnel_model_namespace::funnel_model model(data_var_context, 0, &model_output); - stan::mcmc::mock_hamiltonian + stan::mcmc::mock_hamiltonian metric(model); stan::mcmc::ps_point z(11); z.q.setOnes(); diff --git a/src/test/unit/mcmc/hmc/hamiltonians/dense_e_metric_test.cpp b/src/test/unit/mcmc/hmc/hamiltonians/dense_e_metric_test.cpp index 31f5bcbcd75..fc351755f3f 100644 --- a/src/test/unit/mcmc/hmc/hamiltonians/dense_e_metric_test.cpp +++ b/src/test/unit/mcmc/hmc/hamiltonians/dense_e_metric_test.cpp @@ -1,5 +1,5 @@ #include -#include +#include #include #include #include @@ -8,10 +8,8 @@ #include #include -typedef boost::ecuyer1988 rng_t; - TEST(McmcDenseEMetric, sample_p) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); Eigen::Matrix2d m(2, 2); m(0, 0) = 3.0; @@ -23,7 +21,7 @@ TEST(McmcDenseEMetric, sample_p) { stan::mcmc::mock_model model(2); - stan::mcmc::dense_e_metric metric(model); + stan::mcmc::dense_e_metric metric(model); stan::mcmc::dense_e_point z(2); z.set_metric(m_inv); @@ -62,8 +60,6 @@ TEST(McmcDenseEMetric, sample_p) { } TEST(McmcDenseEMetric, gradients) { - rng_t base_rng(0); - Eigen::VectorXd q = Eigen::VectorXd::Ones(11); stan::mcmc::dense_e_point z(q.size()); @@ -79,7 +75,7 @@ TEST(McmcDenseEMetric, gradients) { funnel_model_namespace::funnel_model model(data_var_context, 0, &model_output); - stan::mcmc::dense_e_metric + stan::mcmc::dense_e_metric metric(model); double epsilon = 1e-6; @@ -156,8 +152,6 @@ TEST(McmcDenseEMetric, gradients) { TEST(McmcDenseEMetric, streams) { stan::test::capture_std_streams(); - rng_t base_rng(0); - Eigen::VectorXd q(2); q(0) = 5; q(1) = 1; @@ -165,7 +159,8 @@ TEST(McmcDenseEMetric, streams) { stan::mcmc::mock_model model(q.size()); // typedef to use within Google Test macros - typedef stan::mcmc::dense_e_metric dense_e; + typedef stan::mcmc::dense_e_metric + dense_e; EXPECT_NO_THROW(dense_e metric(model)); diff --git a/src/test/unit/mcmc/hmc/hamiltonians/diag_e_metric_test.cpp b/src/test/unit/mcmc/hmc/hamiltonians/diag_e_metric_test.cpp index 1d11e26db95..1e56a41b46f 100644 --- a/src/test/unit/mcmc/hmc/hamiltonians/diag_e_metric_test.cpp +++ b/src/test/unit/mcmc/hmc/hamiltonians/diag_e_metric_test.cpp @@ -1,5 +1,5 @@ #include -#include +#include #include #include #include @@ -8,17 +8,15 @@ #include #include -typedef boost::ecuyer1988 rng_t; - TEST(McmcDiagEMetric, sample_p) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); Eigen::VectorXd q(2); q(0) = 5; q(1) = 1; stan::mcmc::mock_model model(q.size()); - stan::mcmc::diag_e_metric metric(model); + stan::mcmc::diag_e_metric metric(model); stan::mcmc::diag_e_point z(q.size()); int n_samples = 1000; @@ -44,8 +42,6 @@ TEST(McmcDiagEMetric, sample_p) { } TEST(McmcDiagEMetric, gradients) { - rng_t base_rng(0); - Eigen::VectorXd q = Eigen::VectorXd::Ones(11); stan::mcmc::diag_e_point z(q.size()); @@ -61,8 +57,8 @@ TEST(McmcDiagEMetric, gradients) { std::stringstream debug, info, warn, error, fatal; stan::callbacks::stream_logger logger(debug, info, warn, error, fatal); - stan::mcmc::diag_e_metric metric( - model); + stan::mcmc::diag_e_metric + metric(model); double epsilon = 1e-6; @@ -137,8 +133,6 @@ TEST(McmcDiagEMetric, gradients) { TEST(McmcDiagEMetric, streams) { stan::test::capture_std_streams(); - rng_t base_rng(0); - Eigen::VectorXd q(2); q(0) = 5; q(1) = 1; @@ -146,7 +140,7 @@ TEST(McmcDiagEMetric, streams) { stan::mcmc::mock_model model(q.size()); // typedef to use within Google Test macros - typedef stan::mcmc::diag_e_metric diag_e; + typedef stan::mcmc::diag_e_metric diag_e; EXPECT_NO_THROW(diag_e metric(model)); diff --git a/src/test/unit/mcmc/hmc/hamiltonians/softabs_metric_test.cpp b/src/test/unit/mcmc/hmc/hamiltonians/softabs_metric_test.cpp index c1a477c1a1c..194abacc0f6 100644 --- a/src/test/unit/mcmc/hmc/hamiltonians/softabs_metric_test.cpp +++ b/src/test/unit/mcmc/hmc/hamiltonians/softabs_metric_test.cpp @@ -1,27 +1,24 @@ #include #include +#include #include #include #include #include -#include - #include #include -typedef boost::ecuyer1988 rng_t; - TEST(McmcSoftAbs, sample_p) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); Eigen::VectorXd q(2); q(0) = 5; q(1) = 1; stan::mcmc::mock_model model(q.size()); - stan::mcmc::softabs_metric metric(model); + stan::mcmc::softabs_metric metric(model); stan::mcmc::softabs_point z(q.size()); int n_samples = 1000; @@ -60,8 +57,6 @@ TEST(McmcSoftAbs, sample_p) { } TEST(McmcSoftAbs, gradients) { - rng_t base_rng(0); - Eigen::VectorXd q = Eigen::VectorXd::Ones(11); stan::mcmc::softabs_point z(q.size()); @@ -77,7 +72,7 @@ TEST(McmcSoftAbs, gradients) { funnel_model_namespace::funnel_model model(data_var_context, 0, &model_output); - stan::mcmc::softabs_metric + stan::mcmc::softabs_metric metric(model); double epsilon = 1e-6; @@ -152,7 +147,6 @@ TEST(McmcSoftAbs, gradients) { TEST(McmcSoftAbs, streams) { stan::test::capture_std_streams(); - rng_t base_rng(0); Eigen::VectorXd q(2); q(0) = 5; @@ -160,7 +154,8 @@ TEST(McmcSoftAbs, streams) { stan::mcmc::mock_model model(q.size()); // for use in Google Test macros below - typedef stan::mcmc::softabs_metric softabs; + typedef stan::mcmc::softabs_metric + softabs; EXPECT_NO_THROW(softabs metric(model)); diff --git a/src/test/unit/mcmc/hmc/hamiltonians/unit_e_metric_test.cpp b/src/test/unit/mcmc/hmc/hamiltonians/unit_e_metric_test.cpp index 635386f55fc..51262a16dd7 100644 --- a/src/test/unit/mcmc/hmc/hamiltonians/unit_e_metric_test.cpp +++ b/src/test/unit/mcmc/hmc/hamiltonians/unit_e_metric_test.cpp @@ -1,5 +1,5 @@ #include -#include +#include #include #include #include @@ -8,17 +8,15 @@ #include #include -typedef boost::ecuyer1988 rng_t; - TEST(McmcUnitEMetric, sample_p) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); Eigen::VectorXd q(2); q(0) = 5; q(1) = 1; stan::mcmc::mock_model model(q.size()); - stan::mcmc::unit_e_metric metric(model); + stan::mcmc::unit_e_metric metric(model); stan::mcmc::unit_e_point z(q.size()); int n_samples = 1000; @@ -44,8 +42,6 @@ TEST(McmcUnitEMetric, sample_p) { } TEST(McmcUnitEMetric, gradients) { - rng_t base_rng(0); - Eigen::VectorXd q = Eigen::VectorXd::Ones(11); stan::mcmc::unit_e_point z(q.size()); @@ -61,8 +57,8 @@ TEST(McmcUnitEMetric, gradients) { funnel_model_namespace::funnel_model model(data_var_context, 0, &model_output); - stan::mcmc::unit_e_metric metric( - model); + stan::mcmc::unit_e_metric + metric(model); double epsilon = 1e-6; @@ -136,7 +132,6 @@ TEST(McmcUnitEMetric, gradients) { TEST(McmcUnitEMetric, streams) { stan::test::capture_std_streams(); - rng_t base_rng(0); Eigen::VectorXd q(2); q(0) = 5; @@ -144,7 +139,7 @@ TEST(McmcUnitEMetric, streams) { stan::mcmc::mock_model model(q.size()); // for use in Google Test macros below - typedef stan::mcmc::unit_e_metric unit_e; + typedef stan::mcmc::unit_e_metric unit_e; EXPECT_NO_THROW(unit_e metric(model)); diff --git a/src/test/unit/mcmc/hmc/integrators/expl_leapfrog2_test.cpp b/src/test/unit/mcmc/hmc/integrators/expl_leapfrog2_test.cpp index 489eddf5d28..175a4b2e0b7 100644 --- a/src/test/unit/mcmc/hmc/integrators/expl_leapfrog2_test.cpp +++ b/src/test/unit/mcmc/hmc/integrators/expl_leapfrog2_test.cpp @@ -7,13 +7,9 @@ #include #include #include -#include // L'Ecuyer RNG - -typedef boost::ecuyer1988 rng_t; +#include TEST(McmcHmcIntegratorsExplLeapfrog, energy_conservation) { - rng_t base_rng(0); - stan::io::empty_var_context data_var_context; std::stringstream model_output; @@ -22,12 +18,12 @@ TEST(McmcHmcIntegratorsExplLeapfrog, energy_conservation) { gauss_model_namespace::gauss_model model(data_var_context, 0, &model_output); - stan::mcmc::expl_leapfrog< - stan::mcmc::unit_e_metric > + stan::mcmc::expl_leapfrog > integrator; - stan::mcmc::unit_e_metric metric( - model); + stan::mcmc::unit_e_metric + metric(model); stan::mcmc::unit_e_point z(1); z.q(0) = 1; @@ -61,8 +57,6 @@ TEST(McmcHmcIntegratorsExplLeapfrog, energy_conservation) { } TEST(McmcHmcIntegratorsExplLeapfrog, symplecticness) { - rng_t base_rng(0); - stan::io::empty_var_context data_var_context; std::stringstream model_output; @@ -71,12 +65,12 @@ TEST(McmcHmcIntegratorsExplLeapfrog, symplecticness) { gauss_model_namespace::gauss_model model(data_var_context, 0, &model_output); - stan::mcmc::expl_leapfrog< - stan::mcmc::unit_e_metric > + stan::mcmc::expl_leapfrog > integrator; - stan::mcmc::unit_e_metric metric( - model); + stan::mcmc::unit_e_metric + metric(model); // Create a circle of points const int n_points = 1000; diff --git a/src/test/unit/mcmc/hmc/integrators/expl_leapfrog_test.cpp b/src/test/unit/mcmc/hmc/integrators/expl_leapfrog_test.cpp index 6dd6eaf419e..ff0a0c197ec 100644 --- a/src/test/unit/mcmc/hmc/integrators/expl_leapfrog_test.cpp +++ b/src/test/unit/mcmc/hmc/integrators/expl_leapfrog_test.cpp @@ -5,18 +5,16 @@ #include #include -#include +#include #include #include -#include // L'Ecuyer RNG +#include #include // namespace //************************************************************ -typedef boost::ecuyer1988 rng_t; - class McmcHmcIntegratorsExplLeapfrogF : public testing::Test { public: McmcHmcIntegratorsExplLeapfrogF() @@ -25,10 +23,10 @@ class McmcHmcIntegratorsExplLeapfrogF : public testing::Test { diag_e_integrator() {} void SetUp() { - static const std::string DATA("mu <- 0.0\ny <- 0\n"); + static const std::string DATA("{\"mu\":0.0, \"y\":0}"); std::stringstream data_stream(DATA); // setup hamiltonian - stan::io::dump data_var_context(data_stream); + stan::json::json_data data_var_context(data_stream); model = new command_model_namespace::command_model(data_var_context); debug.str(""); @@ -44,12 +42,12 @@ class McmcHmcIntegratorsExplLeapfrogF : public testing::Test { stan::callbacks::stream_logger logger; // integrator under test - stan::mcmc::expl_leapfrog< - stan::mcmc::unit_e_metric > + stan::mcmc::expl_leapfrog > unit_e_integrator; - stan::mcmc::expl_leapfrog< - stan::mcmc::diag_e_metric > + stan::mcmc::expl_leapfrog > diag_e_integrator; // model @@ -72,7 +70,7 @@ TEST_F(McmcHmcIntegratorsExplLeapfrogF, begin_update_p) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -104,7 +102,7 @@ TEST_F(McmcHmcIntegratorsExplLeapfrogF, update_q) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -136,7 +134,7 @@ TEST_F(McmcHmcIntegratorsExplLeapfrogF, end_update_p) { EXPECT_NEAR(z.g(0), 1.67264975797776, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -168,7 +166,7 @@ TEST_F(McmcHmcIntegratorsExplLeapfrogF, evolve_1) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -200,7 +198,7 @@ TEST_F(McmcHmcIntegratorsExplLeapfrogF, evolve_2) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -232,7 +230,7 @@ TEST_F(McmcHmcIntegratorsExplLeapfrogF, evolve_3) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -264,7 +262,7 @@ TEST_F(McmcHmcIntegratorsExplLeapfrogF, evolve_4) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -296,7 +294,7 @@ TEST_F(McmcHmcIntegratorsExplLeapfrogF, evolve_5) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -327,7 +325,7 @@ TEST_F(McmcHmcIntegratorsExplLeapfrogF, evolve_6) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -359,7 +357,7 @@ TEST_F(McmcHmcIntegratorsExplLeapfrogF, evolve_7) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -391,7 +389,7 @@ TEST_F(McmcHmcIntegratorsExplLeapfrogF, evolve_8) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -424,7 +422,7 @@ TEST_F(McmcHmcIntegratorsExplLeapfrogF, evolve_9) { EXPECT_NEAR(z.g(0), 1.27097196280777, 1e-15); // setup hamiltonian - stan::mcmc::diag_e_metric + stan::mcmc::diag_e_metric hamiltonian(*model); // setup epsilon @@ -446,8 +444,8 @@ TEST_F(McmcHmcIntegratorsExplLeapfrogF, evolve_9) { TEST_F(McmcHmcIntegratorsExplLeapfrogF, streams) { stan::test::capture_std_streams(); - typedef stan::mcmc::expl_leapfrog< - stan::mcmc::unit_e_metric > + typedef stan::mcmc::expl_leapfrog > integrator; EXPECT_NO_THROW(integrator i); diff --git a/src/test/unit/mcmc/hmc/integrators/impl_leapfrog2_test.cpp b/src/test/unit/mcmc/hmc/integrators/impl_leapfrog2_test.cpp index 47606be8eb4..23f72a40fa7 100644 --- a/src/test/unit/mcmc/hmc/integrators/impl_leapfrog2_test.cpp +++ b/src/test/unit/mcmc/hmc/integrators/impl_leapfrog2_test.cpp @@ -1,4 +1,5 @@ #include +#include #include #include #include @@ -6,17 +7,11 @@ #include -#include // L'Ecuyer RNG - #include #include -typedef boost::ecuyer1988 rng_t; - TEST(McmcHmcIntegratorsImplLeapfrog, unit_e_energy_conservation) { - rng_t base_rng(0); - stan::io::empty_var_context data_var_context; std::stringstream model_output; @@ -25,12 +20,12 @@ TEST(McmcHmcIntegratorsImplLeapfrog, unit_e_energy_conservation) { gauss_model_namespace::gauss_model model(data_var_context, 0, &model_output); - stan::mcmc::impl_leapfrog< - stan::mcmc::unit_e_metric > + stan::mcmc::impl_leapfrog > integrator; - stan::mcmc::unit_e_metric metric( - model); + stan::mcmc::unit_e_metric + metric(model); stan::mcmc::unit_e_point z(1); z.q(0) = 1; @@ -64,8 +59,6 @@ TEST(McmcHmcIntegratorsImplLeapfrog, unit_e_energy_conservation) { } TEST(McmcHmcIntegratorsImplLeapfrog, unit_e_symplecticness) { - rng_t base_rng(0); - stan::io::empty_var_context data_var_context; std::stringstream model_output; @@ -74,12 +67,12 @@ TEST(McmcHmcIntegratorsImplLeapfrog, unit_e_symplecticness) { gauss_model_namespace::gauss_model model(data_var_context, 0, &model_output); - stan::mcmc::impl_leapfrog< - stan::mcmc::unit_e_metric > + stan::mcmc::impl_leapfrog > integrator; - stan::mcmc::unit_e_metric metric( - model); + stan::mcmc::unit_e_metric + metric(model); // Create a circle of points const int n_points = 1000; @@ -151,8 +144,6 @@ TEST(McmcHmcIntegratorsImplLeapfrog, unit_e_symplecticness) { } TEST(McmcHmcIntegratorsImplLeapfrog, softabs_energy_conservation) { - rng_t base_rng(0); - stan::io::empty_var_context data_var_context; std::stringstream model_output; @@ -161,12 +152,12 @@ TEST(McmcHmcIntegratorsImplLeapfrog, softabs_energy_conservation) { gauss_model_namespace::gauss_model model(data_var_context, 0, &model_output); - stan::mcmc::impl_leapfrog< - stan::mcmc::softabs_metric > + stan::mcmc::impl_leapfrog > integrator; - stan::mcmc::softabs_metric metric( - model); + stan::mcmc::softabs_metric + metric(model); stan::mcmc::softabs_point z(1); z.q(0) = 1; @@ -200,8 +191,6 @@ TEST(McmcHmcIntegratorsImplLeapfrog, softabs_energy_conservation) { } TEST(McmcHmcIntegratorsImplLeapfrog, softabs_symplecticness) { - rng_t base_rng(0); - stan::io::empty_var_context data_var_context; std::stringstream model_output; @@ -210,12 +199,12 @@ TEST(McmcHmcIntegratorsImplLeapfrog, softabs_symplecticness) { gauss_model_namespace::gauss_model model(data_var_context, 0, &model_output); - stan::mcmc::impl_leapfrog< - stan::mcmc::softabs_metric > + stan::mcmc::impl_leapfrog > integrator; - stan::mcmc::softabs_metric metric( - model); + stan::mcmc::softabs_metric + metric(model); // Create a circle of points const int n_points = 1000; diff --git a/src/test/unit/mcmc/hmc/integrators/impl_leapfrog_test.cpp b/src/test/unit/mcmc/hmc/integrators/impl_leapfrog_test.cpp index 8aba1b21dff..1af9dceb16e 100644 --- a/src/test/unit/mcmc/hmc/integrators/impl_leapfrog_test.cpp +++ b/src/test/unit/mcmc/hmc/integrators/impl_leapfrog_test.cpp @@ -1,5 +1,6 @@ -#include +#include #include +#include #include #include #include @@ -8,8 +9,6 @@ #include #include -#include // L'Ecuyer RNG - #include #include @@ -17,8 +16,6 @@ // namespace //************************************************************ -typedef boost::ecuyer1988 rng_t; - class McmcHmcIntegratorsImplLeapfrogF : public testing::Test { public: McmcHmcIntegratorsImplLeapfrogF() @@ -27,10 +24,10 @@ class McmcHmcIntegratorsImplLeapfrogF : public testing::Test { diag_e_integrator() {} void SetUp() { - static const std::string DATA("mu <- 0.0\ny <- 0\n"); + static const std::string DATA("{\"mu\":0.0, \"y\":0}"); std::stringstream data_stream(DATA); // setup hamiltonian - stan::io::dump data_var_context(data_stream); + stan::json::json_data data_var_context(data_stream); model = new command_model_namespace::command_model(data_var_context); debug.str(""); @@ -46,16 +43,16 @@ class McmcHmcIntegratorsImplLeapfrogF : public testing::Test { stan::callbacks::stream_logger logger; // integrator under test - stan::mcmc::impl_leapfrog< - stan::mcmc::unit_e_metric > + stan::mcmc::impl_leapfrog > unit_e_integrator; - stan::mcmc::impl_leapfrog< - stan::mcmc::diag_e_metric > + stan::mcmc::impl_leapfrog > diag_e_integrator; stan::mcmc::impl_leapfrog > + command_model_namespace::command_model, stan::rng_t> > softabs_integrator; // model @@ -75,7 +72,7 @@ TEST_F(McmcHmcIntegratorsImplLeapfrogF, begin_update_p) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -107,7 +104,8 @@ TEST_F(McmcHmcIntegratorsImplLeapfrogF, softabs_begin_update_p) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::softabs_metric + stan::mcmc::softabs_metric hamiltonian(*model); // setup epsilon @@ -141,7 +139,7 @@ TEST_F(McmcHmcIntegratorsImplLeapfrogF, update_q) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -173,7 +171,8 @@ TEST_F(McmcHmcIntegratorsImplLeapfrogF, softabs_update_q) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::softabs_metric + stan::mcmc::softabs_metric hamiltonian(*model); // setup epsilon @@ -207,7 +206,7 @@ TEST_F(McmcHmcIntegratorsImplLeapfrogF, end_update_p) { EXPECT_NEAR(z.g(0), 1.67264975797776, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -239,7 +238,8 @@ TEST_F(McmcHmcIntegratorsImplLeapfrogF, softabs_end_update_p) { EXPECT_NEAR(z.g(0), 1.67264975797776, 1e-15); // setup hamiltonian - stan::mcmc::softabs_metric + stan::mcmc::softabs_metric hamiltonian(*model); // setup epsilon @@ -273,7 +273,7 @@ TEST_F(McmcHmcIntegratorsImplLeapfrogF, evolve_1) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -305,7 +305,7 @@ TEST_F(McmcHmcIntegratorsImplLeapfrogF, evolve_2) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -337,7 +337,7 @@ TEST_F(McmcHmcIntegratorsImplLeapfrogF, evolve_3) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -369,7 +369,7 @@ TEST_F(McmcHmcIntegratorsImplLeapfrogF, evolve_4) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -401,7 +401,7 @@ TEST_F(McmcHmcIntegratorsImplLeapfrogF, evolve_5) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -432,7 +432,7 @@ TEST_F(McmcHmcIntegratorsImplLeapfrogF, evolve_6) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -464,7 +464,7 @@ TEST_F(McmcHmcIntegratorsImplLeapfrogF, evolve_7) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -496,7 +496,7 @@ TEST_F(McmcHmcIntegratorsImplLeapfrogF, evolve_8) { EXPECT_NEAR(z.g(0), 1.99987371079118, 1e-15); // setup hamiltonian - stan::mcmc::unit_e_metric + stan::mcmc::unit_e_metric hamiltonian(*model); // setup epsilon @@ -529,7 +529,7 @@ TEST_F(McmcHmcIntegratorsImplLeapfrogF, evolve_9) { EXPECT_NEAR(z.g(0), 1.27097196280777, 1e-15); // setup hamiltonian - stan::mcmc::diag_e_metric + stan::mcmc::diag_e_metric hamiltonian(*model); // setup epsilon @@ -561,7 +561,8 @@ TEST_F(McmcHmcIntegratorsImplLeapfrogF, softabs_evolve) { EXPECT_NEAR(z.g(0), 1.27097196280777, 1e-15); // setup hamiltonian - stan::mcmc::softabs_metric + stan::mcmc::softabs_metric hamiltonian(*model); // setup epsilon @@ -585,8 +586,8 @@ TEST_F(McmcHmcIntegratorsImplLeapfrogF, softabs_evolve) { TEST_F(McmcHmcIntegratorsImplLeapfrogF, streams) { stan::test::capture_std_streams(); - typedef stan::mcmc::impl_leapfrog< - stan::mcmc::unit_e_metric > + typedef stan::mcmc::impl_leapfrog > integrator; EXPECT_NO_THROW(integrator i); @@ -600,7 +601,7 @@ TEST_F(McmcHmcIntegratorsImplLeapfrogF, softabs_streams) { stan::test::capture_std_streams(); typedef stan::mcmc::impl_leapfrog > + command_model_namespace::command_model, stan::rng_t> > integrator; EXPECT_NO_THROW(integrator i); diff --git a/src/test/unit/mcmc/hmc/nuts/base_nuts_test.cpp b/src/test/unit/mcmc/hmc/nuts/base_nuts_test.cpp index 50527c03cda..f58ccf0d60c 100644 --- a/src/test/unit/mcmc/hmc/nuts/base_nuts_test.cpp +++ b/src/test/unit/mcmc/hmc/nuts/base_nuts_test.cpp @@ -3,20 +3,18 @@ #include #include #include -#include +#include #include -typedef boost::ecuyer1988 rng_t; - namespace stan { namespace mcmc { -class mock_nuts - : public base_nuts { +class mock_nuts : public base_nuts { public: - mock_nuts(const mock_model& m, rng_t& rng) - : base_nuts(m, - rng) {} + mock_nuts(const mock_model& m, stan::rng_t& rng) + : base_nuts( + m, rng) {} bool compute_criterion(Eigen::VectorXd& p_sharp_minus, Eigen::VectorXd& p_sharp_plus, Eigen::VectorXd& rho) { @@ -24,13 +22,13 @@ class mock_nuts } }; -class rho_inspector_mock_nuts - : public base_nuts { +class rho_inspector_mock_nuts : public base_nuts { public: std::vector rho_values; - rho_inspector_mock_nuts(const mock_model& m, rng_t& rng) - : base_nuts(m, - rng) {} + rho_inspector_mock_nuts(const mock_model& m, stan::rng_t& rng) + : base_nuts( + m, rng) {} bool compute_criterion(Eigen::VectorXd& p_sharp_minus, Eigen::VectorXd& p_sharp_plus, Eigen::VectorXd& rho) { @@ -40,13 +38,14 @@ class rho_inspector_mock_nuts }; class edge_inspector_mock_nuts - : public base_nuts { + : public base_nuts { public: std::vector p_sharp_minus_values; std::vector p_sharp_plus_values; - edge_inspector_mock_nuts(const mock_model& m, rng_t& rng) - : base_nuts(m, - rng) {} + edge_inspector_mock_nuts(const mock_model& m, stan::rng_t& rng) + : base_nuts( + m, rng) {} bool compute_criterion(Eigen::VectorXd& p_sharp_minus, Eigen::VectorXd& p_sharp_plus, Eigen::VectorXd& rho) { @@ -92,18 +91,18 @@ class divergent_hamiltonian : public base_hamiltonian { }; class divergent_nuts : public base_nuts { + expl_leapfrog, stan::rng_t> { public: - divergent_nuts(const mock_model& m, rng_t& rng) - : base_nuts( - m, rng) {} + divergent_nuts(const mock_model& m, stan::rng_t& rng) + : base_nuts(m, rng) {} }; } // namespace mcmc } // namespace stan TEST(McmcNutsBaseNuts, set_max_depth_test) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); Eigen::VectorXd q(2); q(0) = 5; @@ -123,7 +122,7 @@ TEST(McmcNutsBaseNuts, set_max_depth_test) { } TEST(McmcNutsBaseNuts, set_max_delta_test) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); Eigen::VectorXd q(2); q(0) = 5; @@ -138,7 +137,7 @@ TEST(McmcNutsBaseNuts, set_max_delta_test) { } TEST(McmcNutsBaseNuts, build_tree_test) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); int model_size = 1; double init_momentum = 1.5; @@ -199,7 +198,7 @@ TEST(McmcNutsBaseNuts, build_tree_test) { } TEST(McmcNutsBaseNuts, rho_aggregation_test) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); int model_size = 1; double init_momentum = 1.5; @@ -267,7 +266,7 @@ TEST(McmcNutsBaseNuts, rho_aggregation_test) { } TEST(McmcNutsBaseNuts, divergence_test) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); int model_size = 1; double init_momentum = 1.5; @@ -334,7 +333,7 @@ TEST(McmcNutsBaseNuts, divergence_test) { } TEST(McmcNutsBaseNuts, transition) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); int model_size = 1; double init_momentum = 1.5; @@ -363,7 +362,7 @@ TEST(McmcNutsBaseNuts, transition) { EXPECT_EQ((2 << (sampler.get_max_depth() - 1)) - 1, sampler.n_leapfrog_); EXPECT_FALSE(sampler.divergent_); - EXPECT_EQ(21 * init_momentum, s.cont_params()(0)); + EXPECT_EQ(23 * init_momentum, s.cont_params()(0)); EXPECT_EQ(0, s.log_prob()); EXPECT_EQ(1, s.accept_stat()); EXPECT_EQ("", debug.str()); @@ -374,7 +373,7 @@ TEST(McmcNutsBaseNuts, transition) { } TEST(McmcNutsBaseNuts, transition_egde_momenta) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(42424253, 0); int model_size = 1; double init_momentum = 1.5; diff --git a/src/test/unit/mcmc/hmc/nuts/derived_nuts_test.cpp b/src/test/unit/mcmc/hmc/nuts/derived_nuts_test.cpp index 1bf6e3dbb1c..a6d0669ed6b 100644 --- a/src/test/unit/mcmc/hmc/nuts/derived_nuts_test.cpp +++ b/src/test/unit/mcmc/hmc/nuts/derived_nuts_test.cpp @@ -5,17 +5,14 @@ #include #include #include +#include #include -#include - #include -typedef boost::ecuyer1988 rng_t; - TEST(McmcNutsDerivedNuts, compute_criterion_unit_e) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); int model_size = 1; @@ -26,8 +23,8 @@ TEST(McmcNutsDerivedNuts, compute_criterion_unit_e) { Eigen::VectorXd rho(model_size); stan::mcmc::mock_model model(model_size); - stan::mcmc::unit_e_nuts sampler(model, - base_rng); + stan::mcmc::unit_e_nuts sampler( + model, base_rng); start.q(0) = 1; start.p(0) = 1; @@ -55,7 +52,7 @@ TEST(McmcNutsDerivedNuts, compute_criterion_unit_e) { } TEST(McmcNutsDerivedNuts, compute_criterion_diag_e) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); int model_size = 1; @@ -66,8 +63,8 @@ TEST(McmcNutsDerivedNuts, compute_criterion_diag_e) { Eigen::VectorXd rho(model_size); stan::mcmc::mock_model model(model_size); - stan::mcmc::diag_e_nuts sampler(model, - base_rng); + stan::mcmc::diag_e_nuts sampler( + model, base_rng); start.q(0) = 1; start.p(0) = 1; @@ -95,7 +92,7 @@ TEST(McmcNutsDerivedNuts, compute_criterion_diag_e) { } TEST(McmcNutsDerivedNuts, compute_criterion_dense_e) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); int model_size = 1; @@ -106,8 +103,8 @@ TEST(McmcNutsDerivedNuts, compute_criterion_dense_e) { Eigen::VectorXd rho(model_size); stan::mcmc::mock_model model(model_size); - stan::mcmc::dense_e_nuts sampler(model, - base_rng); + stan::mcmc::dense_e_nuts sampler( + model, base_rng); start.q(0) = 1; start.p(0) = 1; @@ -135,7 +132,7 @@ TEST(McmcNutsDerivedNuts, compute_criterion_dense_e) { } TEST(McmcNutsDerivedNuts, compute_criterion_softabs) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); int model_size = 1; @@ -146,8 +143,8 @@ TEST(McmcNutsDerivedNuts, compute_criterion_softabs) { Eigen::VectorXd rho(model_size); stan::mcmc::mock_model model(model_size); - stan::mcmc::softabs_nuts sampler(model, - base_rng); + stan::mcmc::softabs_nuts sampler( + model, base_rng); start.q(0) = 1; start.p(0) = 1; diff --git a/src/test/unit/mcmc/hmc/nuts/instantiation_test.cpp b/src/test/unit/mcmc/hmc/nuts/instantiation_test.cpp index 241d2e38f69..bd0070944fd 100644 --- a/src/test/unit/mcmc/hmc/nuts/instantiation_test.cpp +++ b/src/test/unit/mcmc/hmc/nuts/instantiation_test.cpp @@ -6,16 +6,14 @@ #include #include #include -#include +#include #include #include #include -typedef boost::ecuyer1988 rng_t; - TEST(McmcNuts, instantiaton_test) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4839294, 0); std::stringstream output; stan::callbacks::stream_writer writer(output); @@ -25,21 +23,24 @@ TEST(McmcNuts, instantiaton_test) { stan::io::empty_var_context data_var_context; gauss3D_model_namespace::gauss3D_model model(data_var_context); - stan::mcmc::unit_e_nuts + stan::mcmc::unit_e_nuts unit_e_sampler(model, base_rng); - stan::mcmc::diag_e_nuts + stan::mcmc::diag_e_nuts diag_e_sampler(model, base_rng); - stan::mcmc::dense_e_nuts + stan::mcmc::dense_e_nuts dense_e_sampler(model, base_rng); - stan::mcmc::adapt_unit_e_nuts + stan::mcmc::adapt_unit_e_nuts adapt_unit_e_sampler(model, base_rng); - stan::mcmc::adapt_diag_e_nuts + stan::mcmc::adapt_diag_e_nuts adapt_diag_e_sampler(model, base_rng); - stan::mcmc::adapt_dense_e_nuts + stan::mcmc::adapt_dense_e_nuts adapt_dense_e_sampler(model, base_rng); } diff --git a/src/test/unit/mcmc/hmc/nuts/softabs_nuts_test.cpp b/src/test/unit/mcmc/hmc/nuts/softabs_nuts_test.cpp index 7d4506b788b..8eaa84df6b3 100644 --- a/src/test/unit/mcmc/hmc/nuts/softabs_nuts_test.cpp +++ b/src/test/unit/mcmc/hmc/nuts/softabs_nuts_test.cpp @@ -1,16 +1,14 @@ #include #include #include -#include +#include #include #include #include -typedef boost::ecuyer1988 rng_t; - TEST(McmcSoftAbsNuts, build_tree_test) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4839294, 0); stan::mcmc::softabs_point z_init(3); z_init.q(0) = 1; @@ -26,7 +24,7 @@ TEST(McmcSoftAbsNuts, build_tree_test) { stan::io::empty_var_context data_var_context; gauss3D_model_namespace::gauss3D_model model(data_var_context); - stan::mcmc::softabs_nuts + stan::mcmc::softabs_nuts sampler(model, base_rng); sampler.z() = z_init; @@ -97,7 +95,7 @@ TEST(McmcSoftAbsNuts, build_tree_test) { } TEST(McmcSoftAbsNuts, tree_boundary_test) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4839294, 0); stan::mcmc::softabs_point z_init(3); z_init.q(0) = 1; @@ -116,7 +114,7 @@ TEST(McmcSoftAbsNuts, tree_boundary_test) { model_t model(data_var_context); // Compute expected tree boundaries - typedef stan::mcmc::softabs_metric metric_t; + typedef stan::mcmc::softabs_metric metric_t; metric_t metric(model); stan::mcmc::impl_leapfrog softabs_integrator; @@ -169,7 +167,7 @@ TEST(McmcSoftAbsNuts, tree_boundary_test) { Eigen::VectorXd p_sharp_backward_4 = metric.dtau_dp(z_test); // Check expected tree boundaries to those dynamically generated by NUTS - stan::mcmc::softabs_nuts sampler(model, base_rng); + stan::mcmc::softabs_nuts sampler(model, base_rng); sampler.set_nominal_stepsize(epsilon); sampler.set_stepsize_jitter(0); @@ -311,7 +309,7 @@ TEST(McmcSoftAbsNuts, tree_boundary_test) { } TEST(McmcSoftAbsNuts, transition_test) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4839294, 0); stan::mcmc::softabs_point z_init(3); z_init.q(0) = 1; @@ -327,7 +325,7 @@ TEST(McmcSoftAbsNuts, transition_test) { stan::io::empty_var_context data_var_context; gauss3D_model_namespace::gauss3D_model model(data_var_context); - stan::mcmc::softabs_nuts + stan::mcmc::softabs_nuts sampler(model, base_rng); sampler.z() = z_init; @@ -340,15 +338,15 @@ TEST(McmcSoftAbsNuts, transition_test) { stan::mcmc::sample s = sampler.transition(init_sample, logger); - EXPECT_EQ(4, sampler.depth_); + EXPECT_EQ(3, sampler.depth_); EXPECT_EQ((2 << 3) - 1, sampler.n_leapfrog_); EXPECT_FALSE(sampler.divergent_); - EXPECT_FLOAT_EQ(1.9313564, s.cont_params()(0)); - EXPECT_FLOAT_EQ(-0.86902142, s.cont_params()(1)); - EXPECT_FLOAT_EQ(1.6008, s.cont_params()(2)); - EXPECT_FLOAT_EQ(-3.5239484, s.log_prob()); - EXPECT_FLOAT_EQ(0.99690288, s.accept_stat()); + EXPECT_FLOAT_EQ(0.74693149, s.cont_params()(0)); + EXPECT_FLOAT_EQ(-0.74414188, s.cont_params()(1)); + EXPECT_FLOAT_EQ(0.60859376, s.cont_params()(2)); + EXPECT_FLOAT_EQ(-0.74102008, s.log_prob()); + EXPECT_FLOAT_EQ(0.99934167, s.accept_stat()); EXPECT_EQ("", debug.str()); EXPECT_EQ("", info.str()); EXPECT_EQ("", warn.str()); diff --git a/src/test/unit/mcmc/hmc/nuts/unit_e_nuts_test.cpp b/src/test/unit/mcmc/hmc/nuts/unit_e_nuts_test.cpp index bd77c25b9a1..6e5bade65af 100644 --- a/src/test/unit/mcmc/hmc/nuts/unit_e_nuts_test.cpp +++ b/src/test/unit/mcmc/hmc/nuts/unit_e_nuts_test.cpp @@ -1,16 +1,14 @@ #include #include #include -#include +#include #include #include #include -typedef boost::ecuyer1988 rng_t; - TEST(McmcUnitENuts, build_tree_test) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4839294, 0); stan::mcmc::unit_e_point z_init(3); z_init.q(0) = 1; @@ -26,7 +24,7 @@ TEST(McmcUnitENuts, build_tree_test) { stan::io::empty_var_context data_var_context; gauss3D_model_namespace::gauss3D_model model(data_var_context); - stan::mcmc::unit_e_nuts + stan::mcmc::unit_e_nuts sampler(model, base_rng); sampler.z() = z_init; @@ -97,7 +95,7 @@ TEST(McmcUnitENuts, build_tree_test) { } TEST(McmcUnitENuts, tree_boundary_test) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4839294, 0); stan::mcmc::unit_e_point z_init(3); z_init.q(0) = 1; @@ -116,7 +114,7 @@ TEST(McmcUnitENuts, tree_boundary_test) { model_t model(data_var_context); // Compute expected tree boundaries - typedef stan::mcmc::unit_e_metric metric_t; + typedef stan::mcmc::unit_e_metric metric_t; metric_t metric(model); stan::mcmc::expl_leapfrog unit_e_integrator; @@ -169,7 +167,7 @@ TEST(McmcUnitENuts, tree_boundary_test) { Eigen::VectorXd p_sharp_backward_4 = metric.dtau_dp(z_test); // Check expected tree boundaries to those dynamically generated by NUTS - stan::mcmc::unit_e_nuts sampler(model, base_rng); + stan::mcmc::unit_e_nuts sampler(model, base_rng); sampler.set_nominal_stepsize(epsilon); sampler.set_stepsize_jitter(0); @@ -311,7 +309,7 @@ TEST(McmcUnitENuts, tree_boundary_test) { } TEST(McmcUnitENuts, transition_test) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4839294, 0); stan::mcmc::unit_e_point z_init(3); z_init.q(0) = 1; @@ -327,7 +325,7 @@ TEST(McmcUnitENuts, transition_test) { stan::io::empty_var_context data_var_context; gauss3D_model_namespace::gauss3D_model model(data_var_context); - stan::mcmc::unit_e_nuts + stan::mcmc::unit_e_nuts sampler(model, base_rng); sampler.z() = z_init; @@ -340,15 +338,15 @@ TEST(McmcUnitENuts, transition_test) { stan::mcmc::sample s = sampler.transition(init_sample, logger); - EXPECT_EQ(4, sampler.depth_); + EXPECT_EQ(3, sampler.depth_); EXPECT_EQ((2 << 3) - 1, sampler.n_leapfrog_); EXPECT_FALSE(sampler.divergent_); - EXPECT_FLOAT_EQ(1.8718261, s.cont_params()(0)); - EXPECT_FLOAT_EQ(-0.74208695, s.cont_params()(1)); - EXPECT_FLOAT_EQ(1.5202962, s.cont_params()(2)); - EXPECT_FLOAT_EQ(-3.1828632, s.log_prob()); - EXPECT_FLOAT_EQ(0.99604273, s.accept_stat()); + EXPECT_FLOAT_EQ(0.70149082, s.cont_params()(0)); + EXPECT_FLOAT_EQ(-0.69831347, s.cont_params()(1)); + EXPECT_FLOAT_EQ(0.54392564, s.cont_params()(2)); + EXPECT_FLOAT_EQ(-0.63779306, s.log_prob()); + EXPECT_FLOAT_EQ(0.99912512, s.accept_stat()); EXPECT_EQ("", debug.str()); EXPECT_EQ("", info.str()); EXPECT_EQ("", warn.str()); diff --git a/src/test/unit/mcmc/hmc/nuts_classic/base_nuts_classic_test.cpp b/src/test/unit/mcmc/hmc/nuts_classic/base_nuts_classic_test.cpp index 7fbd6304cf1..c3862c2c889 100644 --- a/src/test/unit/mcmc/hmc/nuts_classic/base_nuts_classic_test.cpp +++ b/src/test/unit/mcmc/hmc/nuts_classic/base_nuts_classic_test.cpp @@ -2,20 +2,19 @@ #include #include #include -#include +#include #include -typedef boost::ecuyer1988 rng_t; - namespace stan { namespace mcmc { -class mock_nuts_classic : public base_nuts_classic { +class mock_nuts_classic + : public base_nuts_classic { public: - mock_nuts_classic(const mock_model& m, rng_t& rng) - : base_nuts_classic( - m, rng) {} + mock_nuts_classic(const mock_model& m, stan::rng_t& rng) + : base_nuts_classic(m, rng) {} private: bool compute_criterion(ps_point& start, ps_point& finish, @@ -61,11 +60,11 @@ class divergent_hamiltonian : public base_hamiltonian { class divergent_nuts_classic : public base_nuts_classic { + stan::rng_t> { public: - divergent_nuts_classic(const mock_model& m, rng_t& rng) + divergent_nuts_classic(const mock_model& m, stan::rng_t& rng) : base_nuts_classic(m, rng) {} + stan::rng_t>(m, rng) {} private: bool compute_criterion(ps_point& start, ps_point& finish, @@ -78,7 +77,7 @@ class divergent_nuts_classic } // namespace stan TEST(McmcNutsBaseNutsClassic, set_max_depth) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); Eigen::VectorXd q(2); q(0) = 5; @@ -98,7 +97,7 @@ TEST(McmcNutsBaseNutsClassic, set_max_depth) { } TEST(McmcNutsBaseNuts, set_max_delta) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); Eigen::VectorXd q(2); q(0) = 5; @@ -113,7 +112,7 @@ TEST(McmcNutsBaseNuts, set_max_delta) { } TEST(McmcNutsBaseNutsClassic, build_tree) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); int model_size = 1; double init_momentum = 1.5; @@ -167,7 +166,7 @@ TEST(McmcNutsBaseNutsClassic, build_tree) { } TEST(McmcNutsBaseNutsClassic, slice_criterion) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); int model_size = 1; double init_momentum = 1.5; diff --git a/src/test/unit/mcmc/hmc/nuts_classic/derived_nuts_classic_test.cpp b/src/test/unit/mcmc/hmc/nuts_classic/derived_nuts_classic_test.cpp index e9f4637ab4e..593c1e56189 100644 --- a/src/test/unit/mcmc/hmc/nuts_classic/derived_nuts_classic_test.cpp +++ b/src/test/unit/mcmc/hmc/nuts_classic/derived_nuts_classic_test.cpp @@ -7,12 +7,10 @@ #include #include #include -#include - -typedef boost::ecuyer1988 rng_t; +#include TEST(McmcDerivedNutsClassic, compute_criterion_unit_e) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); int model_size = 1; @@ -21,7 +19,7 @@ TEST(McmcDerivedNutsClassic, compute_criterion_unit_e) { Eigen::VectorXd rho(model_size); stan::mcmc::mock_model model(model_size); - stan::mcmc::unit_e_nuts_classic sampler( + stan::mcmc::unit_e_nuts_classic sampler( model, base_rng); start.q(0) = 1; @@ -46,7 +44,7 @@ TEST(McmcDerivedNutsClassic, compute_criterion_unit_e) { } TEST(McmcDerivedNutsClassic, compute_criterion_diag_e) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); int model_size = 1; @@ -55,7 +53,7 @@ TEST(McmcDerivedNutsClassic, compute_criterion_diag_e) { Eigen::VectorXd rho(model_size); stan::mcmc::mock_model model(model_size); - stan::mcmc::diag_e_nuts_classic sampler( + stan::mcmc::diag_e_nuts_classic sampler( model, base_rng); start.q(0) = 1; @@ -80,7 +78,7 @@ TEST(McmcDerivedNutsClassic, compute_criterion_diag_e) { } TEST(McmcDerivedNutsClassic, compute_criterion_dense_e) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); int model_size = 1; @@ -89,7 +87,7 @@ TEST(McmcDerivedNutsClassic, compute_criterion_dense_e) { Eigen::VectorXd rho(model_size); stan::mcmc::mock_model model(model_size); - stan::mcmc::dense_e_nuts_classic sampler( + stan::mcmc::dense_e_nuts_classic sampler( model, base_rng); start.q(0) = 1; diff --git a/src/test/unit/mcmc/hmc/nuts_classic/instantiation_test.cpp b/src/test/unit/mcmc/hmc/nuts_classic/instantiation_test.cpp index 70f8f38977d..654f75079c1 100644 --- a/src/test/unit/mcmc/hmc/nuts_classic/instantiation_test.cpp +++ b/src/test/unit/mcmc/hmc/nuts_classic/instantiation_test.cpp @@ -6,16 +6,14 @@ #include #include #include -#include +#include #include #include #include -typedef boost::ecuyer1988 rng_t; - TEST(McmcNutsClassic, instantiaton_test) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4839294, 0); std::stringstream output; stan::callbacks::stream_writer writer(output); @@ -25,25 +23,27 @@ TEST(McmcNutsClassic, instantiaton_test) { stan::io::empty_var_context data_var_context; gauss3D_model_namespace::gauss3D_model model(data_var_context); - stan::mcmc::unit_e_nuts_classic + stan::mcmc::unit_e_nuts_classic unit_e_sampler(model, base_rng); - stan::mcmc::diag_e_nuts_classic + stan::mcmc::diag_e_nuts_classic diag_e_sampler(model, base_rng); stan::mcmc::dense_e_nuts_classic + stan::rng_t> dense_e_sampler(model, base_rng); stan::mcmc::adapt_unit_e_nuts_classic + stan::rng_t> adapt_unit_e_sampler(model, base_rng); stan::mcmc::adapt_diag_e_nuts_classic + stan::rng_t> adapt_diag_e_sampler(model, base_rng); stan::mcmc::adapt_dense_e_nuts_classic + stan::rng_t> adapt_dense_e_sampler(model, base_rng); } diff --git a/src/test/unit/mcmc/hmc/static/base_static_hmc_test.cpp b/src/test/unit/mcmc/hmc/static/base_static_hmc_test.cpp index 632426ff270..4d571f6a927 100644 --- a/src/test/unit/mcmc/hmc/static/base_static_hmc_test.cpp +++ b/src/test/unit/mcmc/hmc/static/base_static_hmc_test.cpp @@ -2,28 +2,26 @@ #include #include #include -#include +#include #include -typedef boost::ecuyer1988 rng_t; - namespace stan { namespace mcmc { // Mock Static HMC class mock_static_hmc : public base_static_hmc { + mock_integrator, stan::rng_t> { public: - mock_static_hmc(const mock_model& m, rng_t& rng) - : base_static_hmc( - m, rng) {} + mock_static_hmc(const mock_model& m, stan::rng_t& rng) + : base_static_hmc(m, rng) {} }; } // namespace mcmc } // namespace stan TEST(McmcStaticBaseStaticHMC, set_nominal_stepsize) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); std::vector q(5, 1.0); std::vector r(2, 2); @@ -43,7 +41,7 @@ TEST(McmcStaticBaseStaticHMC, set_nominal_stepsize) { } TEST(McmcStaticBaseStaticHMC, set_T) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); std::vector q(5, 1.0); std::vector r(2, 2); @@ -63,7 +61,7 @@ TEST(McmcStaticBaseStaticHMC, set_T) { } TEST(McmcStaticBaseStaticHMC, set_nominal_stepsize_and_T) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); std::vector q(5, 1.0); std::vector r(2, 2); @@ -90,7 +88,7 @@ TEST(McmcStaticBaseStaticHMC, set_nominal_stepsize_and_T) { } TEST(McmcStaticBaseStaticHMC, set_nominal_stepsize_and_L) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); std::vector q(5, 1.0); std::vector r(2, 2); diff --git a/src/test/unit/mcmc/hmc/static_uniform/base_static_uniform_test.cpp b/src/test/unit/mcmc/hmc/static_uniform/base_static_uniform_test.cpp index 4b8892eb767..a9f81458d2c 100644 --- a/src/test/unit/mcmc/hmc/static_uniform/base_static_uniform_test.cpp +++ b/src/test/unit/mcmc/hmc/static_uniform/base_static_uniform_test.cpp @@ -5,27 +5,25 @@ #include #include -#include +#include #include -typedef boost::ecuyer1988 rng_t; - namespace stan { namespace mcmc { // Mock Static HMC class mock_static_uniform : public base_static_uniform { + stan::rng_t> { public: - mock_static_uniform(const mock_model& m, rng_t& rng) + mock_static_uniform(const mock_model& m, stan::rng_t& rng) : base_static_uniform(m, rng) {} + stan::rng_t>(m, rng) {} }; } // namespace mcmc } // namespace stan TEST(McmcBaseStaticUniform, set_nominal_stepsize) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); std::vector q(5, 1.0); std::vector r(2, 2); @@ -45,7 +43,7 @@ TEST(McmcBaseStaticUniform, set_nominal_stepsize) { } TEST(McmcBaseStaticUniform, set_T) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); std::vector q(5, 1.0); std::vector r(2, 2); @@ -65,7 +63,7 @@ TEST(McmcBaseStaticUniform, set_T) { } TEST(McmcBaseStaticUniform, set_nominal_stepsize_and_T) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); std::vector q(5, 1.0); std::vector r(2, 2); @@ -92,7 +90,7 @@ TEST(McmcBaseStaticUniform, set_nominal_stepsize_and_T) { } TEST(McmcBaseStaticUniform, set_nominal_stepsize_and_L) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); std::vector q(5, 1.0); std::vector r(2, 2); diff --git a/src/test/unit/mcmc/hmc/static_uniform/derived_static_uniform_test.cpp b/src/test/unit/mcmc/hmc/static_uniform/derived_static_uniform_test.cpp index 89f16df1edc..15527eaa8bd 100644 --- a/src/test/unit/mcmc/hmc/static_uniform/derived_static_uniform_test.cpp +++ b/src/test/unit/mcmc/hmc/static_uniform/derived_static_uniform_test.cpp @@ -8,17 +8,14 @@ #include #include #include +#include #include -#include - #include -typedef boost::ecuyer1988 rng_t; - TEST(McmcStaticUniform, unit_e_transition) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4839294, 0); stan::mcmc::unit_e_point z_init(1); z_init.q(0) = 1; @@ -30,7 +27,8 @@ TEST(McmcStaticUniform, unit_e_transition) { stan::io::empty_var_context data_var_context; gauss_model_namespace::gauss_model model(data_var_context); - stan::mcmc::unit_e_static_uniform + stan::mcmc::unit_e_static_uniform sampler(model, base_rng); sampler.z() = z_init; @@ -43,9 +41,9 @@ TEST(McmcStaticUniform, unit_e_transition) { stan::mcmc::sample s = sampler.transition(init_sample, logger); - EXPECT_FLOAT_EQ(0.27224374, s.cont_params()(0)); - EXPECT_FLOAT_EQ(-0.037058324, s.log_prob()); - EXPECT_FLOAT_EQ(0.9998666, s.accept_stat()); + EXPECT_FLOAT_EQ(1.0920367, s.cont_params()(0)); + EXPECT_FLOAT_EQ(-0.59627211, s.log_prob()); + EXPECT_FLOAT_EQ(0.99985325, s.accept_stat()); EXPECT_EQ("", debug.str()); EXPECT_EQ("", info.str()); EXPECT_EQ("", warn.str()); @@ -54,7 +52,7 @@ TEST(McmcStaticUniform, unit_e_transition) { } TEST(McmcStaticUniform, diag_e_transition) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4839294, 0); stan::mcmc::diag_e_point z_init(1); z_init.q(0) = 1; @@ -66,7 +64,8 @@ TEST(McmcStaticUniform, diag_e_transition) { stan::io::empty_var_context data_var_context; gauss_model_namespace::gauss_model model(data_var_context); - stan::mcmc::diag_e_static_uniform + stan::mcmc::diag_e_static_uniform sampler(model, base_rng); sampler.z() = z_init; @@ -79,9 +78,9 @@ TEST(McmcStaticUniform, diag_e_transition) { stan::mcmc::sample s = sampler.transition(init_sample, logger); - EXPECT_FLOAT_EQ(0.27224374, s.cont_params()(0)); - EXPECT_FLOAT_EQ(-0.037058324, s.log_prob()); - EXPECT_FLOAT_EQ(0.9998666, s.accept_stat()); + EXPECT_FLOAT_EQ(1.0920367, s.cont_params()(0)); + EXPECT_FLOAT_EQ(-0.59627211, s.log_prob()); + EXPECT_FLOAT_EQ(0.99985325, s.accept_stat()); EXPECT_EQ("", debug.str()); EXPECT_EQ("", info.str()); EXPECT_EQ("", warn.str()); @@ -90,7 +89,7 @@ TEST(McmcStaticUniform, diag_e_transition) { } TEST(McmcStaticUniform, dense_e_transition) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4839294, 0); stan::mcmc::dense_e_point z_init(1); z_init.q(0) = 1; @@ -102,7 +101,8 @@ TEST(McmcStaticUniform, dense_e_transition) { stan::io::empty_var_context data_var_context; gauss_model_namespace::gauss_model model(data_var_context); - stan::mcmc::dense_e_static_uniform + stan::mcmc::dense_e_static_uniform sampler(model, base_rng); sampler.z() = z_init; @@ -115,9 +115,9 @@ TEST(McmcStaticUniform, dense_e_transition) { stan::mcmc::sample s = sampler.transition(init_sample, logger); - EXPECT_FLOAT_EQ(0.27224374, s.cont_params()(0)); - EXPECT_FLOAT_EQ(-0.037058324, s.log_prob()); - EXPECT_FLOAT_EQ(0.9998666, s.accept_stat()); + EXPECT_FLOAT_EQ(1.0920367, s.cont_params()(0)); + EXPECT_FLOAT_EQ(-0.59627211, s.log_prob()); + EXPECT_FLOAT_EQ(0.99985325, s.accept_stat()); EXPECT_EQ("", debug.str()); EXPECT_EQ("", info.str()); EXPECT_EQ("", warn.str()); @@ -126,7 +126,7 @@ TEST(McmcStaticUniform, dense_e_transition) { } TEST(McmcStaticUniform, softabs_transition) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4839294, 0); stan::mcmc::softabs_point z_init(1); z_init.q(0) = 1; @@ -138,7 +138,8 @@ TEST(McmcStaticUniform, softabs_transition) { stan::io::empty_var_context data_var_context; gauss_model_namespace::gauss_model model(data_var_context); - stan::mcmc::softabs_static_uniform + stan::mcmc::softabs_static_uniform sampler(model, base_rng); sampler.z() = z_init; @@ -151,9 +152,9 @@ TEST(McmcStaticUniform, softabs_transition) { stan::mcmc::sample s = sampler.transition(init_sample, logger); - EXPECT_FLOAT_EQ(0.37006485, s.cont_params()(0)); - EXPECT_FLOAT_EQ(-0.068473995, s.log_prob()); - EXPECT_FLOAT_EQ(0.9999119, s.accept_stat()); + EXPECT_FLOAT_EQ(1.0826443, s.cont_params()(0)); + EXPECT_FLOAT_EQ(-0.58605933, s.log_prob()); + EXPECT_FLOAT_EQ(0.99989599, s.accept_stat()); EXPECT_EQ("", debug.str()); EXPECT_EQ("", info.str()); EXPECT_EQ("", warn.str()); @@ -162,7 +163,7 @@ TEST(McmcStaticUniform, softabs_transition) { } TEST(McmcStaticUniform, adapt_unit_e_transition) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4839294, 0); stan::mcmc::unit_e_point z_init(1); z_init.q(0) = 1; @@ -175,7 +176,7 @@ TEST(McmcStaticUniform, adapt_unit_e_transition) { gauss_model_namespace::gauss_model model(data_var_context); stan::mcmc::adapt_unit_e_static_uniform + stan::rng_t> sampler(model, base_rng); sampler.z() = z_init; @@ -188,9 +189,9 @@ TEST(McmcStaticUniform, adapt_unit_e_transition) { stan::mcmc::sample s = sampler.transition(init_sample, logger); - EXPECT_FLOAT_EQ(0.27224374, s.cont_params()(0)); - EXPECT_FLOAT_EQ(-0.037058324, s.log_prob()); - EXPECT_FLOAT_EQ(0.9998666, s.accept_stat()); + EXPECT_FLOAT_EQ(1.0920367, s.cont_params()(0)); + EXPECT_FLOAT_EQ(-0.59627211, s.log_prob()); + EXPECT_FLOAT_EQ(0.99985325, s.accept_stat()); EXPECT_EQ("", debug.str()); EXPECT_EQ("", info.str()); EXPECT_EQ("", warn.str()); @@ -199,7 +200,7 @@ TEST(McmcStaticUniform, adapt_unit_e_transition) { } TEST(McmcStaticUniform, adapt_diag_e_transition) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4839294, 0); stan::mcmc::diag_e_point z_init(1); z_init.q(0) = 1; @@ -212,7 +213,7 @@ TEST(McmcStaticUniform, adapt_diag_e_transition) { gauss_model_namespace::gauss_model model(data_var_context); stan::mcmc::adapt_diag_e_static_uniform + stan::rng_t> sampler(model, base_rng); sampler.z() = z_init; @@ -225,9 +226,9 @@ TEST(McmcStaticUniform, adapt_diag_e_transition) { stan::mcmc::sample s = sampler.transition(init_sample, logger); - EXPECT_FLOAT_EQ(0.27224374, s.cont_params()(0)); - EXPECT_FLOAT_EQ(-0.037058324, s.log_prob()); - EXPECT_FLOAT_EQ(0.9998666, s.accept_stat()); + EXPECT_FLOAT_EQ(1.0920367, s.cont_params()(0)); + EXPECT_FLOAT_EQ(-0.59627211, s.log_prob()); + EXPECT_FLOAT_EQ(0.99985325, s.accept_stat()); EXPECT_EQ("", debug.str()); EXPECT_EQ("", info.str()); EXPECT_EQ("", warn.str()); @@ -236,7 +237,7 @@ TEST(McmcStaticUniform, adapt_diag_e_transition) { } TEST(McmcStaticUniform, adapt_dense_e_transition) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4839294, 0); stan::mcmc::dense_e_point z_init(1); z_init.q(0) = 1; @@ -249,7 +250,7 @@ TEST(McmcStaticUniform, adapt_dense_e_transition) { gauss_model_namespace::gauss_model model(data_var_context); stan::mcmc::adapt_dense_e_static_uniform + stan::rng_t> sampler(model, base_rng); sampler.z() = z_init; @@ -262,9 +263,9 @@ TEST(McmcStaticUniform, adapt_dense_e_transition) { stan::mcmc::sample s = sampler.transition(init_sample, logger); - EXPECT_FLOAT_EQ(0.27224374, s.cont_params()(0)); - EXPECT_FLOAT_EQ(-0.037058324, s.log_prob()); - EXPECT_FLOAT_EQ(0.9998666, s.accept_stat()); + EXPECT_FLOAT_EQ(1.0920367, s.cont_params()(0)); + EXPECT_FLOAT_EQ(-0.59627211, s.log_prob()); + EXPECT_FLOAT_EQ(0.99985325, s.accept_stat()); EXPECT_EQ("", debug.str()); EXPECT_EQ("", info.str()); EXPECT_EQ("", warn.str()); @@ -273,7 +274,7 @@ TEST(McmcStaticUniform, adapt_dense_e_transition) { } TEST(McmcStaticUniform, adapt_softabs_e_transition) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4839294, 0); stan::mcmc::softabs_point z_init(1); z_init.q(0) = 1; @@ -286,7 +287,7 @@ TEST(McmcStaticUniform, adapt_softabs_e_transition) { gauss_model_namespace::gauss_model model(data_var_context); stan::mcmc::adapt_softabs_static_uniform + stan::rng_t> sampler(model, base_rng); sampler.z() = z_init; @@ -299,9 +300,9 @@ TEST(McmcStaticUniform, adapt_softabs_e_transition) { stan::mcmc::sample s = sampler.transition(init_sample, logger); - EXPECT_FLOAT_EQ(0.37006485, s.cont_params()(0)); - EXPECT_FLOAT_EQ(-0.068473995, s.log_prob()); - EXPECT_FLOAT_EQ(0.9999119, s.accept_stat()); + EXPECT_FLOAT_EQ(1.0826443, s.cont_params()(0)); + EXPECT_FLOAT_EQ(-0.58605933, s.log_prob()); + EXPECT_FLOAT_EQ(0.99989599, s.accept_stat()); EXPECT_EQ("", debug.str()); EXPECT_EQ("", info.str()); EXPECT_EQ("", warn.str()); diff --git a/src/test/unit/mcmc/hmc/xhmc/base_xhmc_test.cpp b/src/test/unit/mcmc/hmc/xhmc/base_xhmc_test.cpp index f27eb9f37ca..70f95981363 100644 --- a/src/test/unit/mcmc/hmc/xhmc/base_xhmc_test.cpp +++ b/src/test/unit/mcmc/hmc/xhmc/base_xhmc_test.cpp @@ -2,20 +2,18 @@ #include #include #include -#include +#include #include -typedef boost::ecuyer1988 rng_t; - namespace stan { namespace mcmc { -class mock_xhmc - : public base_xhmc { +class mock_xhmc : public base_xhmc { public: - mock_xhmc(const mock_model& m, rng_t& rng) - : base_xhmc(m, - rng) {} + mock_xhmc(const mock_model& m, stan::rng_t& rng) + : base_xhmc( + m, rng) {} }; // Mock Hamiltonian @@ -54,18 +52,18 @@ class divergent_hamiltonian : public base_hamiltonian { }; class divergent_xhmc : public base_xhmc { + expl_leapfrog, stan::rng_t> { public: - divergent_xhmc(const mock_model& m, rng_t& rng) - : base_xhmc( - m, rng) {} + divergent_xhmc(const mock_model& m, stan::rng_t& rng) + : base_xhmc(m, rng) {} }; } // namespace mcmc } // namespace stan TEST(McmcXHMCBaseXHMC, set_max_depth) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); Eigen::VectorXd q(2); q(0) = 5; @@ -83,7 +81,7 @@ TEST(McmcXHMCBaseXHMC, set_max_depth) { } TEST(McmcXHMCBaseXHMC, set_max_deltaH) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); Eigen::VectorXd q(2); q(0) = 5; @@ -98,7 +96,7 @@ TEST(McmcXHMCBaseXHMC, set_max_deltaH) { } TEST(McmcXHMCBaseXHMC, set_x_delta) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); Eigen::VectorXd q(2); q(0) = 5; @@ -113,7 +111,7 @@ TEST(McmcXHMCBaseXHMC, set_x_delta) { } TEST(McmcXHMCBaseXHMC, build_tree) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); int model_size = 1; double init_momentum = 1.5; @@ -164,7 +162,7 @@ TEST(McmcXHMCBaseXHMC, build_tree) { } TEST(McmcXHMCBaseXHMC, divergence_test) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); int model_size = 1; double init_momentum = 1.5; @@ -223,7 +221,7 @@ TEST(McmcXHMCBaseXHMC, divergence_test) { } TEST(McmcXHMCBaseXHMC, transition) { - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(1234, 0); int model_size = 1; double init_momentum = 1.5; @@ -247,7 +245,7 @@ TEST(McmcXHMCBaseXHMC, transition) { stan::mcmc::sample s = sampler.transition(init_sample, logger); - EXPECT_EQ(31.5, s.cont_params()(0)); + EXPECT_EQ(5 * init_momentum, s.cont_params()(0)); EXPECT_EQ(0, s.log_prob()); EXPECT_EQ(1, s.accept_stat()); EXPECT_EQ("", debug.str()); diff --git a/src/test/unit/mcmc/hmc/xhmc/softabs_xhmc_test.cpp b/src/test/unit/mcmc/hmc/xhmc/softabs_xhmc_test.cpp index 600785eaf76..faa96200538 100644 --- a/src/test/unit/mcmc/hmc/xhmc/softabs_xhmc_test.cpp +++ b/src/test/unit/mcmc/hmc/xhmc/softabs_xhmc_test.cpp @@ -1,16 +1,14 @@ #include #include -#include +#include #include #include #include #include -typedef boost::ecuyer1988 rng_t; - TEST(McmcUnitEXHMC, build_tree) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4839294, 0); stan::mcmc::softabs_point z_init(3); z_init.q(0) = 1; @@ -26,7 +24,7 @@ TEST(McmcUnitEXHMC, build_tree) { stan::io::empty_var_context data_var_context; gauss3D_model_namespace::gauss3D_model model(data_var_context); - stan::mcmc::softabs_xhmc + stan::mcmc::softabs_xhmc sampler(model, base_rng); sampler.z() = z_init; @@ -81,7 +79,7 @@ TEST(McmcUnitEXHMC, build_tree) { } TEST(McmcUnitEXHMC, transition) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4832942, 0); stan::mcmc::softabs_point z_init(3); z_init.q(0) = 1; @@ -97,7 +95,7 @@ TEST(McmcUnitEXHMC, transition) { stan::io::empty_var_context data_var_context; gauss3D_model_namespace::gauss3D_model model(data_var_context); - stan::mcmc::softabs_xhmc + stan::mcmc::softabs_xhmc sampler(model, base_rng); sampler.z() = z_init; @@ -114,11 +112,11 @@ TEST(McmcUnitEXHMC, transition) { EXPECT_FLOAT_EQ(-1, s.cont_params()(1)); EXPECT_FLOAT_EQ(1, s.cont_params()(2)); EXPECT_FLOAT_EQ(-1.5, s.log_prob()); - EXPECT_FLOAT_EQ(0.99829924, s.accept_stat()); + EXPECT_FLOAT_EQ(0.99870497, s.accept_stat()); EXPECT_EQ("", debug.str()); EXPECT_EQ("", info.str()); EXPECT_EQ("", warn.str()); EXPECT_EQ("", error.str()); EXPECT_EQ("", fatal.str()); -} \ No newline at end of file +} diff --git a/src/test/unit/mcmc/hmc/xhmc/unit_e_xhmc_test.cpp b/src/test/unit/mcmc/hmc/xhmc/unit_e_xhmc_test.cpp index a9c53459754..61639c32475 100644 --- a/src/test/unit/mcmc/hmc/xhmc/unit_e_xhmc_test.cpp +++ b/src/test/unit/mcmc/hmc/xhmc/unit_e_xhmc_test.cpp @@ -1,16 +1,14 @@ #include #include -#include +#include #include #include #include #include -typedef boost::ecuyer1988 rng_t; - TEST(McmcUnitEXHMC, build_tree) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(483294, 0); stan::mcmc::unit_e_point z_init(3); z_init.q(0) = 1; @@ -26,7 +24,7 @@ TEST(McmcUnitEXHMC, build_tree) { stan::io::empty_var_context data_var_context; gauss3D_model_namespace::gauss3D_model model(data_var_context); - stan::mcmc::unit_e_xhmc + stan::mcmc::unit_e_xhmc sampler(model, base_rng); sampler.z() = z_init; @@ -60,13 +58,13 @@ TEST(McmcUnitEXHMC, build_tree) { EXPECT_FLOAT_EQ(1.4131583, sampler.z().p(1)); EXPECT_FLOAT_EQ(-1.4131583, sampler.z().p(2)); - EXPECT_FLOAT_EQ(0.78105003, z_propose.q(0)); - EXPECT_FLOAT_EQ(-0.78105003, z_propose.q(1)); - EXPECT_FLOAT_EQ(0.78105003, z_propose.q(2)); + EXPECT_FLOAT_EQ(0.11940599, z_propose.q(0)); + EXPECT_FLOAT_EQ(-0.11940599, z_propose.q(1)); + EXPECT_FLOAT_EQ(0.11940599, z_propose.q(2)); - EXPECT_FLOAT_EQ(-1.1785525, z_propose.p(0)); - EXPECT_FLOAT_EQ(1.1785525, z_propose.p(1)); - EXPECT_FLOAT_EQ(-1.1785525, z_propose.p(2)); + EXPECT_FLOAT_EQ(-1.408289, z_propose.p(0)); + EXPECT_FLOAT_EQ(1.408289, z_propose.p(1)); + EXPECT_FLOAT_EQ(-1.408289, z_propose.p(2)); EXPECT_EQ(8, n_leapfrog); EXPECT_FLOAT_EQ(4.2207355, ave); @@ -81,7 +79,7 @@ TEST(McmcUnitEXHMC, build_tree) { } TEST(McmcUnitEXHMC, transition) { - rng_t base_rng(4839294); + stan::rng_t base_rng = stan::services::util::create_rng(4832942, 0); stan::mcmc::unit_e_point z_init(3); z_init.q(0) = 1; @@ -97,7 +95,7 @@ TEST(McmcUnitEXHMC, transition) { stan::io::empty_var_context data_var_context; gauss3D_model_namespace::gauss3D_model model(data_var_context); - stan::mcmc::unit_e_xhmc + stan::mcmc::unit_e_xhmc sampler(model, base_rng); sampler.z() = z_init; @@ -114,7 +112,7 @@ TEST(McmcUnitEXHMC, transition) { EXPECT_FLOAT_EQ(-1, s.cont_params()(1)); EXPECT_FLOAT_EQ(1, s.cont_params()(2)); EXPECT_FLOAT_EQ(-1.5, s.log_prob()); - EXPECT_FLOAT_EQ(0.99805242, s.accept_stat()); + EXPECT_FLOAT_EQ(0.99870926, s.accept_stat()); EXPECT_EQ("", debug.str()); EXPECT_EQ("", info.str()); EXPECT_EQ("", warn.str()); diff --git a/src/test/unit/model/array_functions_roundtrip_test.cpp b/src/test/unit/model/array_functions_roundtrip_test.cpp index 0f37719c42e..9c5cffa4a87 100644 --- a/src/test/unit/model/array_functions_roundtrip_test.cpp +++ b/src/test/unit/model/array_functions_roundtrip_test.cpp @@ -1,4 +1,5 @@ #include +#include #include #include #include @@ -20,7 +21,7 @@ class ModelArrayFunctionsRoundtripTest : public testing::Test { stan::io::empty_var_context context; stan_model model; - boost::ecuyer1988 rng; + stan::rng_t rng; std::unique_ptr inits; std::stringstream out; diff --git a/src/test/unit/model/model_base_test.cpp b/src/test/unit/model/model_base_test.cpp index a015588948e..c4a81f71323 100644 --- a/src/test/unit/model/model_base_test.cpp +++ b/src/test/unit/model/model_base_test.cpp @@ -79,7 +79,7 @@ struct mock_model : public stan::model::model_base { Eigen::VectorXd& params_r, std::ostream* msgs) const override {} - void write_array(boost::ecuyer1988& base_rng, Eigen::VectorXd& params_r, + void write_array(stan::rng_t& base_rng, Eigen::VectorXd& params_r, Eigen::VectorXd& params_constrained_r, bool include_tparams, bool include_gqs, std::ostream* msgs) const override {} @@ -139,7 +139,7 @@ struct mock_model : public stan::model::model_base { std::vector& params_r, std::ostream* msgs) const override {} - void write_array(boost::ecuyer1988& base_rng, std::vector& params_r, + void write_array(stan::rng_t& base_rng, std::vector& params_r, std::vector& params_i, std::vector& params_r_constrained, bool include_tparams, bool include_gqs, diff --git a/src/test/unit/services/optimize/laplace_jacobian_test.cpp b/src/test/unit/services/optimize/laplace_jacobian_test.cpp index 67a88eed59c..7abeadc9455 100644 --- a/src/test/unit/services/optimize/laplace_jacobian_test.cpp +++ b/src/test/unit/services/optimize/laplace_jacobian_test.cpp @@ -2,7 +2,6 @@ #include #include #include -#include #include #include #include diff --git a/src/test/unit/services/optimize/laplace_sample_test.cpp b/src/test/unit/services/optimize/laplace_sample_test.cpp index 82c6c131d70..e292305cb70 100644 --- a/src/test/unit/services/optimize/laplace_sample_test.cpp +++ b/src/test/unit/services/optimize/laplace_sample_test.cpp @@ -2,7 +2,7 @@ #include #include #include -#include +#include #include #include #include @@ -100,6 +100,60 @@ TEST_F(ServicesLaplaceSample, values) { EXPECT_NEAR(0.8, cov, 0.05); } +struct deleter_noop { + template + constexpr void operator()(T* arg) const {} +}; + +TEST_F(ServicesLaplaceSample, hessianOutput) { + Eigen::VectorXd theta_hat(2); + theta_hat << 2, 3; + int draws = 10; + bool calculate_lp = true; + unsigned int seed = 1234; + int refresh = 100; + std::stringstream sample_ss; + stan::callbacks::stream_writer sample_writer(sample_ss, ""); + + std::stringstream hessian_ss; + stan::callbacks::json_writer hessian_writer{ + std::unique_ptr(&hessian_ss)}; + + int return_code = stan::services::laplace_sample( + *model, theta_hat, draws, calculate_lp, seed, refresh, interrupt, logger, + sample_writer, hessian_writer); + EXPECT_EQ(stan::services::error_codes::OK, return_code); + + std::string hessian_str = hessian_ss.str(); + + ASSERT_TRUE(stan::test::is_valid_JSON(hessian_str)); + EXPECT_EQ(count_matches("lp_mode", hessian_str), 1); + EXPECT_EQ(count_matches("gradient", hessian_str), 1); + EXPECT_EQ(count_matches("Hessian", hessian_str), 1); +} + +TEST_F(ServicesLaplaceSample, noLP) { + Eigen::VectorXd theta_hat(2); + theta_hat << 2, 3; + unsigned int seed = 1234; + int refresh = 100; + std::stringstream sample_ss; + stan::callbacks::stream_writer sample_writer(sample_ss, ""); + stan::callbacks::structured_writer dummy_hessian_writer; + + int draws = 11; + bool calculate_lp = false; + + int return_code = stan::services::laplace_sample( + *model, theta_hat, draws, calculate_lp, seed, refresh, interrupt, logger, + sample_writer, dummy_hessian_writer); + EXPECT_EQ(stan::services::error_codes::OK, return_code); + + std::string samples_str = sample_ss.str(); + EXPECT_EQ(1, count_matches("log_p__", samples_str)); + EXPECT_EQ(draws, count_matches("nan", samples_str)); +} + TEST_F(ServicesLaplaceSample, wrongSizeModeError) { Eigen::VectorXd theta_hat(3); theta_hat << 2, 3, 4; diff --git a/src/test/unit/services/pathfinder/eight_schools_test.cpp b/src/test/unit/services/pathfinder/eight_schools_test.cpp index 8955f5e9e49..5d5626b9016 100644 --- a/src/test/unit/services/pathfinder/eight_schools_test.cpp +++ b/src/test/unit/services/pathfinder/eight_schools_test.cpp @@ -192,7 +192,7 @@ TEST_F(ServicesPathfinderEightSchools, single) { Eigen::MatrixXd r_constrainted_draws_mat(20, 100); { - auto rng = stan::services::util::create_rng(0123, 0); + stan::rng_t rng = stan::services::util::create_rng(0123, 0); auto fn = [&model = ServicesPathfinderEightSchools::model](auto&& u) { return -model.log_prob_propto_jacobian(u, 0); }; diff --git a/src/test/unit/services/pathfinder/normal_glm_test.cpp b/src/test/unit/services/pathfinder/normal_glm_test.cpp index b391e0036f6..1fd4863291d 100644 --- a/src/test/unit/services/pathfinder/normal_glm_test.cpp +++ b/src/test/unit/services/pathfinder/normal_glm_test.cpp @@ -2,7 +2,6 @@ #include #include #include -#include #include #include #include @@ -84,17 +83,19 @@ TEST_F(ServicesPathfinderGLM, single) { constexpr int refresh = 1; stan::test::mock_callback callback; - stan::io::empty_var_context empty_context; // = init_init_context(); + stan::io::array_var_context init_context = init_init_context(); std::unique_ptr empty_ostream(nullptr); stan::test::test_logger logger(std::move(empty_ostream)); std::vector> input_iters; - stan::services::pathfinder::pathfinder_lbfgs_single( - model, empty_context, seed, chain, init_radius, history_size, init_alpha, + int rc = stan::services::pathfinder::pathfinder_lbfgs_single( + model, init_context, seed, chain, init_radius, history_size, init_alpha, tol_obj, tol_rel_obj, tol_grad, tol_rel_grad, tol_param, num_iterations, num_elbo_draws, num_draws, save_iterations, refresh, callback, logger, init, parameter, diagnostics); + ASSERT_EQ(rc, 0); + Eigen::MatrixXd param_vals = std::move(parameter.values_); Eigen::IOFormat CommaInitFmt(Eigen::StreamPrecision, 0, ", ", ", ", "\n", "", "", ""); @@ -155,17 +156,18 @@ TEST_F(ServicesPathfinderGLM, single_noreturnlp) { constexpr bool calculate_lp = false; stan::test::mock_callback callback; - stan::io::empty_var_context empty_context; // = init_init_context(); + stan::io::array_var_context init_context = init_init_context(); std::unique_ptr empty_ostream(nullptr); stan::test::test_logger logger(std::move(empty_ostream)); std::vector> input_iters; - stan::services::pathfinder::pathfinder_lbfgs_single( - model, empty_context, seed, chain, init_radius, history_size, init_alpha, + int rc = stan::services::pathfinder::pathfinder_lbfgs_single( + model, init_context, seed, chain, init_radius, history_size, init_alpha, tol_obj, tol_rel_obj, tol_grad, tol_rel_grad, tol_param, num_iterations, num_elbo_draws, num_draws, save_iterations, refresh, callback, logger, init, parameter, diagnostics, calculate_lp); + ASSERT_EQ(rc, 0); Eigen::MatrixXd param_vals = std::move(parameter.values_); for (Eigen::Index i = 0; i < num_elbo_draws; ++i) { EXPECT_FALSE(std::isnan(param_vals.coeff(1, num_draws + i))) @@ -207,7 +209,7 @@ TEST_F(ServicesPathfinderGLM, multi) { std::make_unique(init_init_context())); } stan::test::mock_callback callback; - stan::services::pathfinder::pathfinder_lbfgs_multi( + int rc = stan::services::pathfinder::pathfinder_lbfgs_multi( model, single_path_inits, seed, chain, init_radius, history_size, init_alpha, tol_obj, tol_rel_obj, tol_grad, tol_rel_grad, tol_param, num_iterations, num_elbo_draws, num_draws, num_multi_draws, num_paths, @@ -215,6 +217,7 @@ TEST_F(ServicesPathfinderGLM, multi) { std::vector(num_paths, init), single_path_parameter_writer, single_path_diagnostic_writer, parameter, diagnostics); + ASSERT_EQ(rc, 0); Eigen::MatrixXd param_vals(parameter.eigen_states_.size(), parameter.eigen_states_[0].size()); @@ -292,7 +295,7 @@ TEST_F(ServicesPathfinderGLM, multi_noresample) { std::make_unique(init_init_context())); } stan::test::mock_callback callback; - stan::services::pathfinder::pathfinder_lbfgs_multi( + int rc = stan::services::pathfinder::pathfinder_lbfgs_multi( model, single_path_inits, seed, chain, init_radius, history_size, init_alpha, tol_obj, tol_rel_obj, tol_grad, tol_rel_grad, tol_param, num_iterations, num_elbo_draws, num_draws, num_multi_draws, num_paths, @@ -300,6 +303,7 @@ TEST_F(ServicesPathfinderGLM, multi_noresample) { std::vector(num_paths, init), single_path_parameter_writer, single_path_diagnostic_writer, parameter, diagnostics, calculate_lp, resample); + ASSERT_EQ(rc, 0); Eigen::MatrixXd param_vals = parameter.values_; Eigen::IOFormat CommaInitFmt(Eigen::StreamPrecision, 0, ", ", ", ", "\n", "", @@ -341,7 +345,7 @@ TEST_F(ServicesPathfinderGLM, multi_noresample_noreturnlp) { std::make_unique(init_init_context())); } stan::test::mock_callback callback; - stan::services::pathfinder::pathfinder_lbfgs_multi( + int rc = stan::services::pathfinder::pathfinder_lbfgs_multi( model, single_path_inits, seed, chain, init_radius, history_size, init_alpha, tol_obj, tol_rel_obj, tol_grad, tol_rel_grad, tol_param, num_iterations, num_elbo_draws, num_draws, num_multi_draws, num_paths, @@ -349,6 +353,7 @@ TEST_F(ServicesPathfinderGLM, multi_noresample_noreturnlp) { std::vector(num_paths, init), single_path_parameter_writer, single_path_diagnostic_writer, parameter, diagnostics, calculate_lp, resample); + ASSERT_EQ(rc, 0); Eigen::MatrixXd param_vals = parameter.values_; Eigen::IOFormat CommaInitFmt(Eigen::StreamPrecision, 0, ", ", ", ", "\n", "", @@ -400,7 +405,7 @@ TEST_F(ServicesPathfinderGLM, multi_resample_noreturnlp) { std::make_unique(init_init_context())); } stan::test::mock_callback callback; - stan::services::pathfinder::pathfinder_lbfgs_multi( + int rc = stan::services::pathfinder::pathfinder_lbfgs_multi( model, single_path_inits, seed, chain, init_radius, history_size, init_alpha, tol_obj, tol_rel_obj, tol_grad, tol_rel_grad, tol_param, num_iterations, num_elbo_draws, num_draws, num_multi_draws, num_paths, @@ -408,6 +413,7 @@ TEST_F(ServicesPathfinderGLM, multi_resample_noreturnlp) { std::vector(num_paths, init), single_path_parameter_writer, single_path_diagnostic_writer, parameter, diagnostics, calculate_lp, resample); + ASSERT_EQ(rc, 0); Eigen::MatrixXd param_vals = parameter.values_; Eigen::IOFormat CommaInitFmt(Eigen::StreamPrecision, 0, ", ", ", ", "\n", "", @@ -459,7 +465,7 @@ TEST_F(ServicesPathfinderGLM, multi_noresample_returnlp) { std::make_unique(init_init_context())); } stan::test::mock_callback callback; - stan::services::pathfinder::pathfinder_lbfgs_multi( + int rc = stan::services::pathfinder::pathfinder_lbfgs_multi( model, single_path_inits, seed, chain, init_radius, history_size, init_alpha, tol_obj, tol_rel_obj, tol_grad, tol_rel_grad, tol_param, num_iterations, num_elbo_draws, num_draws, num_multi_draws, num_paths, @@ -467,6 +473,7 @@ TEST_F(ServicesPathfinderGLM, multi_noresample_returnlp) { std::vector(num_paths, init), single_path_parameter_writer, single_path_diagnostic_writer, parameter, diagnostics, calculate_lp, resample); + ASSERT_EQ(rc, 0); Eigen::MatrixXd param_vals = parameter.values_; Eigen::IOFormat CommaInitFmt(Eigen::StreamPrecision, 0, ", ", ", ", "\n", "", diff --git a/src/test/unit/services/pathfinder/util.hpp b/src/test/unit/services/pathfinder/util.hpp index 53dda377314..6320f648732 100644 --- a/src/test/unit/services/pathfinder/util.hpp +++ b/src/test/unit/services/pathfinder/util.hpp @@ -93,7 +93,11 @@ class test_logger : public stan::callbacks::logger { * * @param[in] message message */ - virtual void error(const std::string& message) { *log_ << message << "\n"; } + virtual void error(const std::string& message) { + if (log_ != nullptr) { + *log_ << message << "\n"; + } + } /** * Logs an error with error log level. diff --git a/src/test/unit/services/sample/hmc_nuts_dense_inv_metric_test.cpp b/src/test/unit/services/sample/hmc_nuts_dense_inv_metric_test.cpp index fa73c8ceb9c..0f7a3eb95f2 100644 --- a/src/test/unit/services/sample/hmc_nuts_dense_inv_metric_test.cpp +++ b/src/test/unit/services/sample/hmc_nuts_dense_inv_metric_test.cpp @@ -1,6 +1,7 @@ #include #include #include +#include #include #include #include @@ -46,8 +47,8 @@ TEST_F(ServicesSampleHmcNutsDenseEMassMatrix, ident_no_adapt) { interrupt, logger, init, parameter, diagnostic); EXPECT_EQ(0, return_code); - stan::io::dump dmp = stan::services::util::create_unit_e_dense_inv_metric(3); - stan::io::var_context& inv_metric = dmp; + auto default_metric = stan::services::util::create_unit_e_dense_inv_metric(3); + stan::io::var_context& inv_metric = default_metric; std::vector dense_vals = inv_metric.vals_r("inv_metric"); // check returned Euclidean metric stan::test::unit::check_adaptation(3, 3, dense_vals, parameter, 0.1); @@ -99,15 +100,18 @@ TEST_F(ServicesSampleHmcNutsDenseEMassMatrix, use_metric_no_adapt) { stan::test::unit::instrumented_interrupt interrupt; EXPECT_EQ(interrupt.call_count(), 0); - std::string txt - = "inv_metric <- structure(c(" - "0.640211, 0.156096, -0.374048, " - "0.156096, 1.41239, -0.0412753, " - "-0.374048, -0.0412753, 1.29567 " - "), .Dim = c(3,3))"; + std::string txt = R"json( +{ + "inv_metric": [ + [0.640211, 0.156096, -0.374048], + [0.156096, 1.41239, -0.0412753], + [-0.374048, -0.0412753, 1.29567] + ] +} +)json"; + std::stringstream in(txt); - stan::io::dump dump(in); - stan::io::var_context& inv_metric = dump; + stan::json::json_data inv_metric(in); int return_code = stan::services::sample::hmc_nuts_dense_e( model, context, inv_metric, random_seed, chain, init_radius, num_warmup, @@ -142,15 +146,17 @@ TEST_F(ServicesSampleHmcNutsDenseEMassMatrix, use_metric_skip_adapt) { stan::test::unit::instrumented_interrupt interrupt; EXPECT_EQ(interrupt.call_count(), 0); - std::string txt - = "inv_metric <- structure(c(" - "0.640211, 0.156096, -0.374048, " - "0.156096, 1.41239, -0.0412753, " - "-0.374048, -0.0412753, 1.29567 " - "), .Dim = c(3,3))"; + std::string txt = R"json( +{ + "inv_metric": [ + [0.640211, 0.156096, -0.374048], + [0.156096, 1.41239, -0.0412753], + [-0.374048, -0.0412753, 1.29567] + ] +} +)json"; std::stringstream in(txt); - stan::io::dump dump(in); - stan::io::var_context& inv_metric = dump; + stan::json::json_data inv_metric(in); int return_code = stan::services::sample::hmc_nuts_dense_e_adapt( model, context, inv_metric, random_seed, chain, init_radius, num_warmup, diff --git a/src/test/unit/services/sample/hmc_nuts_diag_inv_metric_test.cpp b/src/test/unit/services/sample/hmc_nuts_diag_inv_metric_test.cpp index bc52864570e..2c0b175eb25 100644 --- a/src/test/unit/services/sample/hmc_nuts_diag_inv_metric_test.cpp +++ b/src/test/unit/services/sample/hmc_nuts_diag_inv_metric_test.cpp @@ -1,6 +1,7 @@ #include #include #include +#include #include #include #include @@ -46,8 +47,8 @@ TEST_F(ServicesSampleHmcNutsDiagEMassMatrix, unit_e_no_adapt) { interrupt, logger, init, parameter, diagnostic); EXPECT_EQ(0, return_code); - stan::io::dump dmp = stan::services::util::create_unit_e_diag_inv_metric(3); - stan::io::var_context& inv_metric = dmp; + auto default_metric = stan::services::util::create_unit_e_diag_inv_metric(3); + stan::io::var_context& inv_metric = default_metric; std::vector diag_vals = inv_metric.vals_r("inv_metric"); stan::test::unit::check_adaptation(3, diag_vals, parameter, 0.2); } @@ -98,11 +99,9 @@ TEST_F(ServicesSampleHmcNutsDiagEMassMatrix, use_metric_no_adapt) { stan::test::unit::instrumented_interrupt interrupt; EXPECT_EQ(interrupt.call_count(), 0); - std::string txt - = "inv_metric <- structure(c(0.787405, 0.884987, 1.19869),.Dim=c(3))"; + std::string txt = R"json({"inv_metric" : [0.787405, 0.884987, 1.19869]})json"; std::stringstream in(txt); - stan::io::dump dump(in); - stan::io::var_context& inv_metric = dump; + stan::json::json_data inv_metric(in); int return_code = stan::services::sample::hmc_nuts_diag_e( model, context, inv_metric, random_seed, chain, init_radius, num_warmup, @@ -140,11 +139,9 @@ TEST_F(ServicesSampleHmcNutsDiagEMassMatrix, use_metric_skip_adapt) { stan::test::unit::instrumented_interrupt interrupt; EXPECT_EQ(interrupt.call_count(), 0); - std::string txt - = "inv_metric <- structure(c(0.787405, 0.884987, 1.19869),.Dim=c(3))"; + std::string txt = R"json({"inv_metric":[0.787405, 0.884987, 1.19869]})json"; std::stringstream in(txt); - stan::io::dump dump(in); - stan::io::var_context& inv_metric = dump; + stan::json::json_data inv_metric(in); int return_code = stan::services::sample::hmc_nuts_diag_e_adapt( model, context, inv_metric, random_seed, chain, init_radius, num_warmup, diff --git a/src/test/unit/services/sample/hmc_nuts_unit_e_adapt_parallel_test.cpp b/src/test/unit/services/sample/hmc_nuts_unit_e_adapt_parallel_test.cpp index 2889c6b2295..ed1525263b7 100644 --- a/src/test/unit/services/sample/hmc_nuts_unit_e_adapt_parallel_test.cpp +++ b/src/test/unit/services/sample/hmc_nuts_unit_e_adapt_parallel_test.cpp @@ -21,7 +21,7 @@ struct deleter_noop { class ServicesSampleHmcNutsUnitEAdaptPar : public testing::Test { public: ServicesSampleHmcNutsUnitEAdaptPar() - : ss_metric(num_chains), model(data_context, 0, &model_log) { + : model(data_context, 0, &model_log), ss_metric(num_chains) { for (int i = 0; i < num_chains; ++i) { init.push_back(stan::test::unit::instrumented_writer{}); parameter.push_back(stan::test::unit::instrumented_writer{}); diff --git a/src/test/unit/services/sample/hmc_static_dense_inv_metric_test.cpp b/src/test/unit/services/sample/hmc_static_dense_inv_metric_test.cpp index 76bcc6ad3a3..74d8b4f8aad 100644 --- a/src/test/unit/services/sample/hmc_static_dense_inv_metric_test.cpp +++ b/src/test/unit/services/sample/hmc_static_dense_inv_metric_test.cpp @@ -1,6 +1,7 @@ #include #include #include +#include #include #include #include @@ -47,8 +48,8 @@ TEST_F(ServicesSampleHmcStaticDenseEMassMatrix, unit_e_no_adapt) { interrupt, logger, init, parameter, diagnostic); EXPECT_EQ(0, return_code); - stan::io::dump dmp = stan::services::util::create_unit_e_dense_inv_metric(3); - stan::io::var_context& inv_metric = dmp; + auto default_metric = stan::services::util::create_unit_e_dense_inv_metric(3); + stan::io::var_context& inv_metric = default_metric; std::vector dense_vals = inv_metric.vals_r("inv_metric"); // check returned Euclidean metric stan::test::unit::check_adaptation(3, dense_vals, parameter, 0.2); @@ -99,15 +100,17 @@ TEST_F(ServicesSampleHmcStaticDenseEMassMatrix, use_metric_no_adapt) { stan::test::unit::instrumented_interrupt interrupt; EXPECT_EQ(interrupt.call_count(), 0); - std::string txt - = "inv_metric <- structure(c(" - " 0.926739, 0.0734898, -0.12395, " - " 0.0734898, 0.876038, -0.051543, " - " -0.12395, -0.051543, 0.8274 " - "), .Dim = c(3,3))"; + std::string txt = R"json( +{ + "inv_metric": [ + [0.926739, 0.0734898, -0.12395], + [0.0734898, 0.876038, -0.051543], + [-0.12395, -0.051543, 0.8274] + ] +} +)json"; std::stringstream in(txt); - stan::io::dump dump(in); - stan::io::var_context& inv_metric = dump; + stan::json::json_data inv_metric(in); int return_code = stan::services::sample::hmc_static_dense_e( model, context, inv_metric, random_seed, chain, init_radius, num_warmup, @@ -143,15 +146,17 @@ TEST_F(ServicesSampleHmcStaticDenseEMassMatrix, use_metric_skip_adapt) { stan::test::unit::instrumented_interrupt interrupt; EXPECT_EQ(interrupt.call_count(), 0); - std::string txt - = "inv_metric <- structure(c(" - " 0.926739, 0.0734898, -0.12395, " - " 0.0734898, 0.876038, -0.051543, " - " -0.12395, -0.051543, 0.8274 " - "), .Dim = c(3,3))"; + std::string txt = R"json( +{ + "inv_metric": [ + [0.926739, 0.0734898, -0.12395], + [0.0734898, 0.876038, -0.051543], + [-0.12395, -0.051543, 0.8274] + ] +} +)json"; std::stringstream in(txt); - stan::io::dump dump(in); - stan::io::var_context& inv_metric = dump; + stan::json::json_data inv_metric(in); int return_code = stan::services::sample::hmc_static_dense_e_adapt( model, context, inv_metric, random_seed, chain, init_radius, num_warmup, diff --git a/src/test/unit/services/sample/hmc_static_diag_inv_metric_test.cpp b/src/test/unit/services/sample/hmc_static_diag_inv_metric_test.cpp index e2aa6168608..9a913940ebb 100644 --- a/src/test/unit/services/sample/hmc_static_diag_inv_metric_test.cpp +++ b/src/test/unit/services/sample/hmc_static_diag_inv_metric_test.cpp @@ -1,6 +1,7 @@ #include #include #include +#include #include #include #include @@ -46,8 +47,8 @@ TEST_F(ServicesSampleHmcStaticDiagEMassMatrix, unit_e_no_adapt) { interrupt, logger, init, parameter, diagnostic); EXPECT_EQ(0, return_code); - stan::io::dump dmp = stan::services::util::create_unit_e_diag_inv_metric(3); - stan::io::var_context& inv_metric = dmp; + auto default_metric = stan::services::util::create_unit_e_diag_inv_metric(3); + stan::io::var_context& inv_metric = default_metric; std::vector diag_vals = inv_metric.vals_r("inv_metric"); // check returned Euclidean metric stan::test::unit::check_adaptation(3, diag_vals, parameter, 0.2); @@ -98,11 +99,9 @@ TEST_F(ServicesSampleHmcStaticDiagEMassMatrix, use_metric_no_adapt) { stan::test::unit::instrumented_interrupt interrupt; EXPECT_EQ(interrupt.call_count(), 0); - std::string txt - = "inv_metric <- structure(c(0.787405, 0.884987, 1.19869),.Dim=c(3))"; + std::string txt = R"json({"inv_metric":[0.787405, 0.884987, 1.19869]})json"; std::stringstream in(txt); - stan::io::dump dump(in); - stan::io::var_context& inv_metric = dump; + stan::json::json_data inv_metric(in); int return_code = stan::services::sample::hmc_static_diag_e( model, context, inv_metric, random_seed, chain, init_radius, num_warmup, @@ -138,11 +137,9 @@ TEST_F(ServicesSampleHmcStaticDiagEMassMatrix, use_metric_skip_adapt) { stan::test::unit::instrumented_interrupt interrupt; EXPECT_EQ(interrupt.call_count(), 0); - std::string txt - = "inv_metric <- structure(c(0.787405, 0.884987, 1.19869),.Dim=c(3))"; + std::string txt = R"json({"inv_metric":[0.787405, 0.884987, 1.19869]})json"; std::stringstream in(txt); - stan::io::dump dump(in); - stan::io::var_context& inv_metric = dump; + stan::json::json_data inv_metric(in); int return_code = stan::services::sample::hmc_static_diag_e_adapt( model, context, inv_metric, random_seed, chain, init_radius, num_warmup, diff --git a/src/test/unit/services/sample/standalone_gqs_parallel_test.cpp b/src/test/unit/services/sample/standalone_gqs_parallel_test.cpp index b07cf79569b..3dbe943bb8e 100644 --- a/src/test/unit/services/sample/standalone_gqs_parallel_test.cpp +++ b/src/test/unit/services/sample/standalone_gqs_parallel_test.cpp @@ -3,7 +3,7 @@ #include #include #include -#include +#include #include #include #include @@ -26,9 +26,9 @@ class ServicesStandaloneGQ : public ::testing::Test { ServicesStandaloneGQ() : data_var_context([]() { std::fstream data_stream( - "src/test/test-models/good/services/bernoulli.data.R", + "src/test/test-models/good/services/bernoulli.data.json", std::fstream::in); - stan::io::dump data_context(data_stream); + stan::json::json_data data_context(data_stream); data_stream.close(); return data_context; }()), @@ -36,7 +36,7 @@ class ServicesStandaloneGQ : public ::testing::Test { logger_ss(), logger(logger_ss, logger_ss, logger_ss, logger_ss, logger_ss), model(data_var_context) {} - stan::io::dump data_var_context; + stan::json::json_data data_var_context; stan::test::unit::instrumented_interrupt interrupt; std::stringstream logger_ss; stan::callbacks::stream_logger logger; diff --git a/src/test/unit/services/sample/standalone_gqs_test.cpp b/src/test/unit/services/sample/standalone_gqs_test.cpp index e730e298b38..da4bda0ca86 100644 --- a/src/test/unit/services/sample/standalone_gqs_test.cpp +++ b/src/test/unit/services/sample/standalone_gqs_test.cpp @@ -3,7 +3,7 @@ #include #include #include -#include +#include #include #include #include @@ -19,9 +19,9 @@ class ServicesStandaloneGQ : public ::testing::Test { void SetUp() { std::fstream data_stream( - "src/test/test-models/good/services/bernoulli.data.R", + "src/test/test-models/good/services/bernoulli.data.json", std::fstream::in); - stan::io::dump data_var_context(data_stream); + stan::json::json_data data_var_context(data_stream); data_stream.close(); model = new stan_model(data_var_context); } diff --git a/src/test/unit/services/util/create_rng_test.cpp b/src/test/unit/services/util/create_rng_test.cpp index adca8ecccd1..5dee9c8656a 100644 --- a/src/test/unit/services/util/create_rng_test.cpp +++ b/src/test/unit/services/util/create_rng_test.cpp @@ -2,8 +2,8 @@ #include TEST(rng, initialize_with_seed) { - boost::ecuyer1988 rng1 = stan::services::util::create_rng(0, 1); - boost::ecuyer1988 rng2 = stan::services::util::create_rng(0, 1); + stan::rng_t rng1 = stan::services::util::create_rng(0, 1); + stan::rng_t rng2 = stan::services::util::create_rng(0, 1); EXPECT_EQ(rng1, rng2); rng2(); // generate a random number @@ -11,9 +11,9 @@ TEST(rng, initialize_with_seed) { } TEST(rng, initialize_with_id) { - boost::ecuyer1988 rng1 = stan::services::util::create_rng(0, 1); + stan::rng_t rng1 = stan::services::util::create_rng(0, 1); for (unsigned int n = 2; n < 20; n++) { - boost::ecuyer1988 rng2 = stan::services::util::create_rng(0, n); + stan::rng_t rng2 = stan::services::util::create_rng(0, n); EXPECT_NE(rng1, rng2); } } @@ -21,8 +21,8 @@ TEST(rng, initialize_with_id) { // warning---this will reuse draws from transformed data // if we initialize with zero TEST(rng, initialize_with_zero) { - boost::ecuyer1988 rng1 = stan::services::util::create_rng(0, 0); - boost::ecuyer1988 rng2 = stan::services::util::create_rng(0, 0); + stan::rng_t rng1 = stan::services::util::create_rng(0, 0); + stan::rng_t rng2 = stan::services::util::create_rng(0, 0); EXPECT_EQ(rng1, rng2); rng2(); diff --git a/src/test/unit/services/util/generate_transitions_test.cpp b/src/test/unit/services/util/generate_transitions_test.cpp index e4cbb5d4bac..0de3c699867 100644 --- a/src/test/unit/services/util/generate_transitions_test.cpp +++ b/src/test/unit/services/util/generate_transitions_test.cpp @@ -30,7 +30,7 @@ TEST_F(ServicesSamplesGenerateTransitions, call_counting) { stan::test::unit::instrumented_interrupt interrupt; EXPECT_EQ(interrupt.call_count(), 0); - boost::ecuyer1988 rng = stan::services::util::create_rng(seed, chain); + stan::rng_t rng = stan::services::util::create_rng(seed, chain); std::vector disc_vector; std::vector cont_vector = stan::services::util::initialize( @@ -86,7 +86,7 @@ TEST_F(ServicesSamplesGenerateTransitions, output_sizes) { stan::test::unit::instrumented_interrupt interrupt; EXPECT_EQ(interrupt.call_count(), 0); - boost::ecuyer1988 rng = stan::services::util::create_rng(seed, chain); + stan::rng_t rng = stan::services::util::create_rng(seed, chain); std::vector disc_vector; std::vector cont_vector = stan::services::util::initialize( diff --git a/src/test/unit/services/util/gq_writer_test.cpp b/src/test/unit/services/util/gq_writer_test.cpp index 04b93d9ec38..7b3017887ad 100644 --- a/src/test/unit/services/util/gq_writer_test.cpp +++ b/src/test/unit/services/util/gq_writer_test.cpp @@ -35,7 +35,7 @@ TEST_F(ServicesUtilGQWriter, t2) { stan::callbacks::stream_writer sample_writer(sample_ss, ""); stan::callbacks::stream_logger logger(logger_ss, logger_ss, logger_ss, logger_ss, logger_ss); - boost::ecuyer1988 rng1 = stan::services::util::create_rng(0, 1); + stan::rng_t rng1 = stan::services::util::create_rng(0, 1); std::vector draw; draw.push_back(-2.345); draw.push_back(-6.789); @@ -50,7 +50,7 @@ TEST_F(ServicesUtilGQWriter, TestExceptions) { stan::callbacks::stream_writer sample_writer(sample_ss, ""); stan::callbacks::stream_logger logger(logger_ss, logger_ss, logger_ss, logger_ss, logger_ss); - boost::ecuyer1988 rng1 = stan::services::util::create_rng(0, 1); + stan::rng_t rng1 = stan::services::util::create_rng(0, 1); std::vector draw; draw.push_back(2.345); draw.push_back(6.789); diff --git a/src/test/unit/services/util/initialize_test.cpp b/src/test/unit/services/util/initialize_test.cpp index 768929da5c1..a0acf6db685 100644 --- a/src/test/unit/services/util/initialize_test.cpp +++ b/src/test/unit/services/util/initialize_test.cpp @@ -1,4 +1,5 @@ #include +#include #include #include #include @@ -24,7 +25,7 @@ class ServicesUtilInitialize : public testing::Test { stan::callbacks::stream_writer message; stan::test::unit::instrumented_logger logger; stan::test::unit::instrumented_writer init; - boost::ecuyer1988 rng; + stan::rng_t rng; }; TEST_F(ServicesUtilInitialize, radius_zero__print_false) { diff --git a/src/test/unit/services/util/inv_metric_test.cpp b/src/test/unit/services/util/inv_metric_test.cpp index 42affe333a1..0c2f3888e13 100644 --- a/src/test/unit/services/util/inv_metric_test.cpp +++ b/src/test/unit/services/util/inv_metric_test.cpp @@ -1,24 +1,26 @@ #include +#include #include TEST(inv_metric, create_diag_sz1) { - stan::io::dump dmp = stan::services::util::create_unit_e_diag_inv_metric(1); - stan::io::var_context& inv_inv_metric = dmp; + auto default_metric = stan::services::util::create_unit_e_diag_inv_metric(1); + stan::io::var_context& inv_inv_metric = default_metric; std::vector diag_vals = inv_inv_metric.vals_r("inv_metric"); EXPECT_EQ(1, diag_vals.size()); ASSERT_NEAR(1.0, diag_vals[0], 0.0001); } TEST(inv_metric, create_diag_sz0) { - stan::io::dump dmp = stan::services::util::create_unit_e_diag_inv_metric(0); - stan::io::var_context& inv_inv_metric = dmp; + auto default_metric = stan::services::util::create_unit_e_diag_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_diag_sz100) { - stan::io::dump dmp = stan::services::util::create_unit_e_diag_inv_metric(100); - stan::io::var_context& inv_inv_metric = dmp; + auto default_metric + = stan::services::util::create_unit_e_diag_inv_metric(100); + stan::io::var_context& inv_inv_metric = default_metric; std::vector diag_vals = inv_inv_metric.vals_r("inv_metric"); EXPECT_EQ(100, diag_vals.size()); ASSERT_NEAR(1.0, diag_vals[0], 0.0001); @@ -26,8 +28,8 @@ TEST(inv_metric, create_diag_sz100) { } TEST(inv_metric, create_dense_sz2) { - stan::io::dump dmp = stan::services::util::create_unit_e_dense_inv_metric(2); - stan::io::var_context& inv_inv_metric = dmp; + auto default_metric = stan::services::util::create_unit_e_dense_inv_metric(2); + stan::io::var_context& inv_inv_metric = default_metric; std::vector dense_vals = inv_inv_metric.vals_r("inv_metric"); EXPECT_EQ(4, dense_vals.size()); ASSERT_NEAR(1.0, dense_vals[0], 0.0001); @@ -35,8 +37,8 @@ TEST(inv_metric, create_dense_sz2) { } TEST(inv_metric, create_dense_sz3) { - stan::io::dump dmp = stan::services::util::create_unit_e_dense_inv_metric(3); - stan::io::var_context& inv_inv_metric = dmp; + auto default_metric = stan::services::util::create_unit_e_dense_inv_metric(3); + stan::io::var_context& inv_inv_metric = default_metric; std::vector dense_vals = inv_inv_metric.vals_r("inv_metric"); EXPECT_EQ(9, dense_vals.size()); ASSERT_NEAR(1.0, dense_vals[0], 0.0001); @@ -51,8 +53,9 @@ TEST(inv_metric, create_dense_sz3) { } TEST(inv_metric, create_dense_sz10) { - stan::io::dump dmp = stan::services::util::create_unit_e_dense_inv_metric(10); - stan::io::var_context& inv_inv_metric = dmp; + auto default_metric + = stan::services::util::create_unit_e_dense_inv_metric(10); + stan::io::var_context& inv_inv_metric = default_metric; std::vector dense_vals = inv_inv_metric.vals_r("inv_metric"); EXPECT_EQ(100, dense_vals.size()); ASSERT_NEAR(1.0, dense_vals[0], 0.0001); diff --git a/src/test/unit/services/util/mcmc_writer_test.cpp b/src/test/unit/services/util/mcmc_writer_test.cpp index 6c0cba15b06..c9840a68806 100644 --- a/src/test/unit/services/util/mcmc_writer_test.cpp +++ b/src/test/unit/services/util/mcmc_writer_test.cpp @@ -247,7 +247,7 @@ TEST_F(ServicesUtil, write_sample_names) { } TEST_F(ServicesUtil, write_sample_params) { - boost::ecuyer1988 rng = stan::services::util::create_rng(0, 1); + stan::rng_t rng = stan::services::util::create_rng(0, 1); Eigen::VectorXd x = Eigen::VectorXd::Zero(2); stan::mcmc::sample sample(x, 1, 2); mock_sampler sampler; @@ -314,7 +314,7 @@ TEST_F(ServicesUtil, write_timing) { } TEST_F(ServicesUtil, throwing_model__write_sample_parameters) { - boost::ecuyer1988 rng = stan::services::util::create_rng(0, 1); + stan::rng_t rng = stan::services::util::create_rng(0, 1); Eigen::VectorXd x = Eigen::VectorXd::Zero(2); stan::mcmc::sample sample(x, 1, 2); mock_sampler sampler; diff --git a/src/test/unit/services/util/run_adaptive_sampler_test.cpp b/src/test/unit/services/util/run_adaptive_sampler_test.cpp index 476d4cbad74..9c7d892707b 100644 --- a/src/test/unit/services/util/run_adaptive_sampler_test.cpp +++ b/src/test/unit/services/util/run_adaptive_sampler_test.cpp @@ -1,4 +1,5 @@ #include +#include #include #include #include @@ -27,12 +28,12 @@ class ServicesUtil : public testing::Test { stan::io::empty_var_context context; stan_model model; std::vector cont_vector; - boost::ecuyer1988 rng; + stan::rng_t rng; stan::test::unit::instrumented_interrupt interrupt; stan::test::unit::instrumented_writer sample_writer, diagnostic_writer; stan::callbacks::structured_writer dummy_metric_writer; stan::test::unit::instrumented_logger logger; - stan::mcmc::adapt_unit_e_nuts sampler; + stan::mcmc::adapt_unit_e_nuts sampler; int num_warmup, num_samples, num_thin, refresh; bool save_warmup; }; diff --git a/src/test/unit/services/util/run_sampler_test.cpp b/src/test/unit/services/util/run_sampler_test.cpp index 1b51781569c..4b63d61d686 100644 --- a/src/test/unit/services/util/run_sampler_test.cpp +++ b/src/test/unit/services/util/run_sampler_test.cpp @@ -1,4 +1,5 @@ #include +#include #include #include #include @@ -73,7 +74,7 @@ class ServicesUtil : public testing::Test { stan::io::empty_var_context context; stan_model model; std::vector cont_vector; - boost::ecuyer1988 rng; + stan::rng_t rng; stan::test::unit::instrumented_interrupt interrupt; stan::test::unit::instrumented_writer sample_writer, diagnostic_writer; stan::test::unit::instrumented_logger logger; diff --git a/src/test/unit/variational/advi_messages_test.cpp b/src/test/unit/variational/advi_messages_test.cpp index 486a8240c7e..67946fcc22b 100644 --- a/src/test/unit/variational/advi_messages_test.cpp +++ b/src/test/unit/variational/advi_messages_test.cpp @@ -1,14 +1,13 @@ #include #include #include +#include #include #include #include #include #include -#include // L'Ecuyer RNG - -typedef boost::ecuyer1988 rng_t; +#include class advi_test : public ::testing::Test { public: @@ -24,25 +23,21 @@ class advi_test : public ::testing::Test { = "Informational Message: The ELBO at a previous iteration is larger " "than the ELBO upon convergence!"; - // Create mock data_var_context - static const std::string DATA = ""; - std::stringstream data_stream(DATA); - stan::io::dump data_var_context(data_stream); - // data_stream.close(); + stan::io::empty_var_context data_var_context; model_ = new stan_model(data_var_context, 0, &model_stream_); cont_params_ = Eigen::VectorXd::Zero(model_->num_params_r()); - base_rng_.seed(3021828106u); + base_rng_.seed(3021828109u); model_stream_.str(""); log_stream_.str(""); parameter_stream_.str(""); diagnostic_stream_.str(""); advi_meanfield_ = new stan::variational::advi< - stan_model, stan::variational::normal_meanfield, rng_t>( + stan_model, stan::variational::normal_meanfield, stan::rng_t>( *model_, cont_params_, base_rng_, 1, 100, 1, 1); advi_fullrank_ = new stan::variational::advi< - stan_model, stan::variational::normal_fullrank, rng_t>( + stan_model, stan::variational::normal_fullrank, stan::rng_t>( *model_, cont_params_, base_rng_, 1, 100, 1, 1); } @@ -56,9 +51,9 @@ class advi_test : public ::testing::Test { std::string err_msg2; stan::variational::advi *advi_meanfield_; - stan::variational::advi - *advi_fullrank_; + stan::rng_t> *advi_meanfield_; + stan::variational::advi *advi_fullrank_; std::stringstream model_stream_; std::stringstream log_stream_; std::stringstream parameter_stream_; @@ -69,7 +64,7 @@ class advi_test : public ::testing::Test { private: stan_model *model_; - rng_t base_rng_; + stan::rng_t base_rng_; Eigen::VectorXd cont_params_; }; diff --git a/src/test/unit/variational/advi_multivar_no_constraint_test.cpp b/src/test/unit/variational/advi_multivar_no_constraint_test.cpp index 4310938aec1..0bcdb1a6873 100644 --- a/src/test/unit/variational/advi_multivar_no_constraint_test.cpp +++ b/src/test/unit/variational/advi_multivar_no_constraint_test.cpp @@ -1,27 +1,24 @@ #include #include #include +#include #include #include #include #include -#include // L'Ecuyer RNG +#include -typedef boost::ecuyer1988 rng_t; typedef multivariate_no_constraint_model_namespace:: multivariate_no_constraint_model Model; TEST(advi_test, multivar_no_constraint_fullrank) { - // Create mock data_var_context - static const std::string DATA = ""; - std::stringstream data_stream(DATA); - stan::io::dump dummy_context(data_stream); + stan::io::empty_var_context dummy_context; // Instantiate model Model my_model(dummy_context); // RNG - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); // Other params int n_monte_carlo_grad = 10; @@ -36,7 +33,8 @@ TEST(advi_test, multivar_no_constraint_fullrank) { cont_params(1) = 0.75; // ADVI - stan::variational::advi + stan::variational::advi test_advi(my_model, cont_params, base_rng, n_monte_carlo_grad, n_grad_samples, 100, 1); @@ -130,16 +128,13 @@ TEST(advi_test, multivar_no_constraint_fullrank) { } TEST(advi_test, multivar_no_constraint_meanfield) { - // Create mock data_var_context - static const std::string DATA = ""; - std::stringstream data_stream(DATA); - stan::io::dump dummy_context(data_stream); + stan::io::empty_var_context dummy_context; // Instantiate model Model my_model(dummy_context); // RNG - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); // Other params int n_monte_carlo_grad = 10; @@ -153,7 +148,8 @@ TEST(advi_test, multivar_no_constraint_meanfield) { cont_params(1) = 0.75; // ADVI - stan::variational::advi + stan::variational::advi test_advi(my_model, cont_params, base_rng, n_monte_carlo_grad, 1e4, // absurdly high! 100, 1); diff --git a/src/test/unit/variational/advi_multivar_with_constraint_test.cpp b/src/test/unit/variational/advi_multivar_with_constraint_test.cpp index 8730b3b0672..622a8a0aeef 100644 --- a/src/test/unit/variational/advi_multivar_with_constraint_test.cpp +++ b/src/test/unit/variational/advi_multivar_with_constraint_test.cpp @@ -1,27 +1,24 @@ #include #include #include +#include #include #include #include #include -#include // L'Ecuyer RNG +#include -typedef boost::ecuyer1988 rng_t; typedef multivariate_with_constraint_model_namespace:: multivariate_with_constraint_model Model; TEST(advi_test, multivar_with_constraint_fullrank) { - // Create mock data_var_context - static const std::string DATA = ""; - std::stringstream data_stream(DATA); - stan::io::dump dummy_context(data_stream); + stan::io::empty_var_context dummy_context; // Instantiate model Model my_model(dummy_context); // RNG - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); // Other params int n_monte_carlo_grad = 10; @@ -36,7 +33,8 @@ TEST(advi_test, multivar_with_constraint_fullrank) { cont_params(1) = 0.75; // ADVI - stan::variational::advi + stan::variational::advi test_advi(my_model, cont_params, base_rng, n_monte_carlo_grad, n_monte_carlo_elbo, 100, 1); @@ -69,16 +67,13 @@ TEST(advi_test, multivar_with_constraint_fullrank) { } TEST(advi_test, multivar_with_constraint_meanfield) { - // Create mock data_var_context - static const std::string DATA = ""; - std::stringstream data_stream(DATA); - stan::io::dump dummy_context(data_stream); + stan::io::empty_var_context dummy_context; // Instantiate model Model my_model(dummy_context); // RNG - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); // Other params int n_monte_carlo_grad = 10; @@ -93,7 +88,8 @@ TEST(advi_test, multivar_with_constraint_meanfield) { cont_params(1) = 0.75; // ADVI - stan::variational::advi + stan::variational::advi test_advi(my_model, cont_params, base_rng, n_monte_carlo_grad, n_monte_carlo_elbo, 100, 1); diff --git a/src/test/unit/variational/advi_univar_no_constraint_test.cpp b/src/test/unit/variational/advi_univar_no_constraint_test.cpp index f1394948ff6..645944b9866 100644 --- a/src/test/unit/variational/advi_univar_no_constraint_test.cpp +++ b/src/test/unit/variational/advi_univar_no_constraint_test.cpp @@ -1,28 +1,25 @@ #include #include #include +#include #include -#include // L'Ecuyer RNG +#include #include #include #include #include -typedef boost::ecuyer1988 rng_t; typedef univariate_no_constraint_model_namespace::univariate_no_constraint_model Model; TEST(advi_test, univar_no_constraint_fullrank) { - // Create mock data_var_context - static const std::string DATA = ""; - std::stringstream data_stream(DATA); - stan::io::dump dummy_context(data_stream); + stan::io::empty_var_context dummy_context; // Instantiate model Model my_model(dummy_context); // RNG - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); // Other params int n_monte_carlo_grad = 10; @@ -35,7 +32,8 @@ TEST(advi_test, univar_no_constraint_fullrank) { cont_params(0) = -0.75; // ADVI - stan::variational::advi + stan::variational::advi test_advi(my_model, cont_params, base_rng, n_monte_carlo_grad, 1e4, // absurdly high! 100, 1); @@ -137,16 +135,13 @@ TEST(advi_test, univar_no_constraint_fullrank) { } TEST(advi_test, univar_no_constraint_meanfield) { - // Create mock data_var_context - static const std::string DATA = ""; - std::stringstream data_stream(DATA); - stan::io::dump dummy_context(data_stream); + stan::io::empty_var_context dummy_context; // Instantiate model Model my_model(dummy_context); // RNG - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); // Other params int n_monte_carlo_grad = 10; @@ -159,7 +154,8 @@ TEST(advi_test, univar_no_constraint_meanfield) { cont_params(0) = -0.75; // ADVI - stan::variational::advi + stan::variational::advi test_advi(my_model, cont_params, base_rng, n_monte_carlo_grad, 1e4, // absurdly high! 100, 1); diff --git a/src/test/unit/variational/advi_univar_with_constraint_test.cpp b/src/test/unit/variational/advi_univar_with_constraint_test.cpp index c12ac4f4b27..3812ff47628 100644 --- a/src/test/unit/variational/advi_univar_with_constraint_test.cpp +++ b/src/test/unit/variational/advi_univar_with_constraint_test.cpp @@ -1,28 +1,25 @@ #include #include #include +#include #include -#include // L'Ecuyer RNG +#include #include #include #include #include -typedef boost::ecuyer1988 rng_t; typedef univariate_with_constraint_model_namespace:: univariate_with_constraint_model Model; TEST(advi_test, univar_with_constraint_fullrank) { - // Create mock data_var_context - static const std::string DATA = ""; - std::stringstream data_stream(DATA); - stan::io::dump dummy_context(data_stream); + stan::io::empty_var_context dummy_context; // Instantiate model Model my_model(dummy_context); // RNG - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); // Other params int n_monte_carlo_grad = 10; @@ -35,7 +32,8 @@ TEST(advi_test, univar_with_constraint_fullrank) { cont_params(0) = -0.75; // ADVI - stan::variational::advi + stan::variational::advi test_advi(my_model, cont_params, base_rng, n_monte_carlo_grad, 5e5, // absurdly high! 100, 1); @@ -142,16 +140,13 @@ TEST(advi_test, univar_with_constraint_fullrank) { } TEST(advi_test, univar_with_constraint_meanfield) { - // Create mock data_var_context - static const std::string DATA = ""; - std::stringstream data_stream(DATA); - stan::io::dump dummy_context(data_stream); + stan::io::empty_var_context dummy_context; // Instantiate model Model my_model(dummy_context); // RNG - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); // Other params int n_monte_carlo_grad = 10; @@ -164,7 +159,8 @@ TEST(advi_test, univar_with_constraint_meanfield) { cont_params(0) = -0.75; // ADVI - stan::variational::advi + stan::variational::advi test_advi(my_model, cont_params, base_rng, n_monte_carlo_grad, 5e5, // absurdly high! 100, 1); diff --git a/src/test/unit/variational/eta_adapt_big_test.cpp b/src/test/unit/variational/eta_adapt_big_test.cpp index 080c666b328..170d5d7054e 100644 --- a/src/test/unit/variational/eta_adapt_big_test.cpp +++ b/src/test/unit/variational/eta_adapt_big_test.cpp @@ -1,14 +1,13 @@ #include #include #include +#include +#include #include #include #include #include #include -#include // L'Ecuyer RNG - -typedef boost::ecuyer1988 rng_t; class eta_adapt_big_test : public ::testing::Test { public: @@ -17,9 +16,7 @@ class eta_adapt_big_test : public ::testing::Test { log_stream_) {} void SetUp() { - static const std::string DATA = ""; - std::stringstream data_stream(DATA); - stan::io::dump data_var_context(data_stream); + stan::io::empty_var_context data_var_context; model_ = new stan_model(data_var_context, 0, &model_stream_); cont_params_ = Eigen::VectorXd::Zero(model_->num_params_r()); @@ -28,11 +25,11 @@ class eta_adapt_big_test : public ::testing::Test { log_stream_.str(""); advi_meanfield_ = new stan::variational::advi< - stan_model, stan::variational::normal_meanfield, rng_t>( + stan_model, stan::variational::normal_meanfield, stan::rng_t>( *model_, cont_params_, base_rng_, 1, 100, 100, 1); advi_fullrank_ = new stan::variational::advi< - stan_model, stan::variational::normal_fullrank, rng_t>( + stan_model, stan::variational::normal_fullrank, stan::rng_t>( *model_, cont_params_, base_rng_, 1, 100, 100, 1); } @@ -43,15 +40,15 @@ class eta_adapt_big_test : public ::testing::Test { } stan::variational::advi *advi_meanfield_; - stan::variational::advi - *advi_fullrank_; + stan::rng_t> *advi_meanfield_; + stan::variational::advi *advi_fullrank_; std::stringstream model_stream_; std::stringstream log_stream_; stan::callbacks::stream_logger logger; stan_model *model_; - rng_t base_rng_; + stan::rng_t base_rng_; Eigen::VectorXd cont_params_; }; diff --git a/src/test/unit/variational/eta_adapt_fail_test.cpp b/src/test/unit/variational/eta_adapt_fail_test.cpp index 1b98f930f6b..587a8acf1bd 100644 --- a/src/test/unit/variational/eta_adapt_fail_test.cpp +++ b/src/test/unit/variational/eta_adapt_fail_test.cpp @@ -1,14 +1,13 @@ #include #include #include +#include +#include #include #include #include #include #include -#include // L'Ecuyer RNG - -typedef boost::ecuyer1988 rng_t; class eta_should_fail_test : public ::testing::Test { public: @@ -17,9 +16,7 @@ class eta_should_fail_test : public ::testing::Test { log_stream_) {} void SetUp() { - static const std::string DATA = ""; - std::stringstream data_stream(DATA); - stan::io::dump data_var_context(data_stream); + stan::io::empty_var_context data_var_context; model_ = new stan_model(data_var_context, 0, &model_stream_); cont_params_ = Eigen::VectorXd::Zero(model_->num_params_r()); @@ -28,11 +25,11 @@ class eta_should_fail_test : public ::testing::Test { log_stream_.str(""); advi_meanfield_ = new stan::variational::advi< - stan_model, stan::variational::normal_meanfield, rng_t>( + stan_model, stan::variational::normal_meanfield, stan::rng_t>( *model_, cont_params_, base_rng_, 1, 1, 100, 1); advi_fullrank_ = new stan::variational::advi< - stan_model, stan::variational::normal_fullrank, rng_t>( + stan_model, stan::variational::normal_fullrank, stan::rng_t>( *model_, cont_params_, base_rng_, 1, 1, 100, 1); } @@ -43,15 +40,15 @@ class eta_should_fail_test : public ::testing::Test { } stan::variational::advi *advi_meanfield_; - stan::variational::advi - *advi_fullrank_; + stan::rng_t> *advi_meanfield_; + stan::variational::advi *advi_fullrank_; std::stringstream model_stream_; std::stringstream log_stream_; stan::callbacks::stream_logger logger; stan_model *model_; - rng_t base_rng_; + stan::rng_t base_rng_; Eigen::VectorXd cont_params_; }; diff --git a/src/test/unit/variational/eta_adapt_mock_models_test.cpp b/src/test/unit/variational/eta_adapt_mock_models_test.cpp index 26d9ecb4b57..9a2a52714b2 100644 --- a/src/test/unit/variational/eta_adapt_mock_models_test.cpp +++ b/src/test/unit/variational/eta_adapt_mock_models_test.cpp @@ -1,15 +1,11 @@ #include #include -#include #include #include #include #include #include #include -#include // L'Ecuyer RNG - -typedef boost::ecuyer1988 rng_t; // Mock Model class mock_model : public stan::model::prob_grad { diff --git a/src/test/unit/variational/eta_adapt_small_test.cpp b/src/test/unit/variational/eta_adapt_small_test.cpp index 0b2fd28f79e..96e4bb16eeb 100644 --- a/src/test/unit/variational/eta_adapt_small_test.cpp +++ b/src/test/unit/variational/eta_adapt_small_test.cpp @@ -1,16 +1,15 @@ #include #include #include +#include #include #include #include #include #include -#include // L'Ecuyer RNG +#include #include -typedef boost::ecuyer1988 rng_t; - class eta_adapt_small_test : public ::testing::Test { public: eta_adapt_small_test() @@ -18,22 +17,20 @@ class eta_adapt_small_test : public ::testing::Test { log_stream_) {} void SetUp() { - static const std::string DATA = ""; - std::stringstream data_stream(DATA); - stan::io::dump data_var_context(data_stream); + stan::io::empty_var_context data_var_context; model_ = new stan_model(data_var_context, 0, &model_stream_); cont_params_ = Eigen::VectorXd::Zero(model_->num_params_r()); - base_rng_.seed(727802408); + base_rng_.seed(727802409); model_stream_.str(""); log_stream_.str(""); advi_meanfield_ = new stan::variational::advi< - stan_model, stan::variational::normal_meanfield, rng_t>( + stan_model, stan::variational::normal_meanfield, stan::rng_t>( *model_, cont_params_, base_rng_, 1, 100, 100, 1); advi_fullrank_ = new stan::variational::advi< - stan_model, stan::variational::normal_fullrank, rng_t>( + stan_model, stan::variational::normal_fullrank, stan::rng_t>( *model_, cont_params_, base_rng_, 1, 100, 100, 1); } @@ -44,15 +41,15 @@ class eta_adapt_small_test : public ::testing::Test { } stan::variational::advi *advi_meanfield_; - stan::variational::advi - *advi_fullrank_; + stan::rng_t> *advi_meanfield_; + stan::variational::advi *advi_fullrank_; std::stringstream model_stream_; std::stringstream log_stream_; stan::callbacks::stream_logger logger; stan_model *model_; - rng_t base_rng_; + stan::rng_t base_rng_; Eigen::VectorXd cont_params_; }; @@ -62,11 +59,6 @@ TEST_F(eta_adapt_small_test, eta_should_be_small) { stan::variational::normal_fullrank fullrank_init = stan::variational::normal_fullrank(cont_params_); -#if BOOST_VERSION >= 106400 EXPECT_EQ(0.1, advi_meanfield_->adapt_eta(meanfield_init, 1000, logger)); EXPECT_EQ(0.1, advi_fullrank_->adapt_eta(fullrank_init, 1000, logger)); -#else - EXPECT_EQ(0.1, advi_meanfield_->adapt_eta(meanfield_init, 50, logger)); - EXPECT_EQ(0.1, advi_fullrank_->adapt_eta(fullrank_init, 50, logger)); -#endif } diff --git a/src/test/unit/variational/gradient_warn_test.cpp b/src/test/unit/variational/gradient_warn_test.cpp index 2dab70c96dc..d44e1a4d682 100644 --- a/src/test/unit/variational/gradient_warn_test.cpp +++ b/src/test/unit/variational/gradient_warn_test.cpp @@ -2,14 +2,14 @@ #include #include #include +#include +#include + #include #include #include #include #include -#include // L'Ecuyer RNG - -typedef boost::ecuyer1988 rng_t; class advi_test : public ::testing::Test { public: @@ -21,9 +21,9 @@ class advi_test : public ::testing::Test { void SetUp() { std::fstream data_stream( - "src/test/test-models/good/variational/gradient_warn.data.R", + "src/test/test-models/good/variational/gradient_warn.data.json", std::fstream::in); - stan::io::dump data_var_context(data_stream); + stan::json::json_data data_var_context(data_stream); data_stream.close(); model_ = new stan_model(data_var_context, 0, &model_stream_); @@ -35,10 +35,10 @@ class advi_test : public ::testing::Test { diagnostic_stream_.str(""); advi_meanfield_ = new stan::variational::advi< - stan_model, stan::variational::normal_meanfield, rng_t>( + stan_model, stan::variational::normal_meanfield, stan::rng_t>( *model_, cont_params_, base_rng_, 1, 100, 100, 1); advi_fullrank_ = new stan::variational::advi< - stan_model, stan::variational::normal_fullrank, rng_t>( + stan_model, stan::variational::normal_fullrank, stan::rng_t>( *model_, cont_params_, base_rng_, 1, 100, 100, 1); } @@ -49,9 +49,9 @@ class advi_test : public ::testing::Test { } stan::variational::advi *advi_meanfield_; - stan::variational::advi - *advi_fullrank_; + stan::rng_t> *advi_meanfield_; + stan::variational::advi *advi_fullrank_; std::stringstream model_stream_; std::stringstream message_stream_; std::stringstream parameter_stream_; @@ -63,7 +63,7 @@ class advi_test : public ::testing::Test { private: stan_model *model_; - rng_t base_rng_; + stan::rng_t base_rng_; Eigen::VectorXd cont_params_; }; diff --git a/src/test/unit/variational/hier_logistic_cp_test.cpp b/src/test/unit/variational/hier_logistic_cp_test.cpp index 0905aeedc80..89f49ae18e3 100644 --- a/src/test/unit/variational/hier_logistic_cp_test.cpp +++ b/src/test/unit/variational/hier_logistic_cp_test.cpp @@ -2,22 +2,22 @@ #include #include #include +#include +#include #include #include #include #include #include -#include // L'Ecuyer RNG -typedef boost::ecuyer1988 rng_t; typedef hier_logistic_cp_model_namespace::hier_logistic_cp_model Model_cp; TEST(advi_test, hier_logistic_cp_constraint_meanfield) { // Create mock data_var_context std::fstream data_stream( - "src/test/test-models/good/variational/hier_logistic.data.R", + "src/test/test-models/good/variational/hier_logistic.data.json", std::fstream::in); - stan::io::dump data_var_context(data_stream); + stan::json::json_data data_var_context(data_stream); data_stream.close(); std::stringstream output; @@ -27,13 +27,14 @@ TEST(advi_test, hier_logistic_cp_constraint_meanfield) { Model_cp my_model(data_var_context); // RNG - rng_t base_rng(0); + stan::rng_t base_rng = stan::services::util::create_rng(0, 0); // Dummy input Eigen::VectorXd cont_params = Eigen::VectorXd::Zero(my_model.num_params_r()); // ADVI - stan::variational::advi + stan::variational::advi test_advi(my_model, cont_params, base_rng, 10, 100, 100, 1); stan::callbacks::writer writer; diff --git a/src/test/unit/variational/hier_logistic_test.cpp b/src/test/unit/variational/hier_logistic_test.cpp index df0092aa120..cd6291c494a 100644 --- a/src/test/unit/variational/hier_logistic_test.cpp +++ b/src/test/unit/variational/hier_logistic_test.cpp @@ -1,15 +1,14 @@ #include #include +#include #include #include #include #include #include -#include // L'Ecuyer RNG +#include #include -typedef boost::ecuyer1988 rng_t; - class advi_test : public ::testing::Test { public: advi_test() @@ -21,9 +20,9 @@ class advi_test : public ::testing::Test { void SetUp() { // Create mock data_var_context std::fstream data_stream( - "src/test/test-models/good/variational/hier_logistic.data.R", + "src/test/test-models/good/variational/hier_logistic.data.json", std::fstream::in); - stan::io::dump data_var_context(data_stream); + stan::json::json_data data_var_context(data_stream); data_stream.close(); model_ = new stan_model(data_var_context, 0, &model_stream_); @@ -38,10 +37,10 @@ class advi_test : public ::testing::Test { diagnostic_stream_.str(""); advi_ = new stan::variational::advi< - stan_model, stan::variational::normal_meanfield, rng_t>( + stan_model, stan::variational::normal_meanfield, stan::rng_t>( *model_, cont_params_, base_rng_, 10, 100, 100, 1); advi_fullrank_ = new stan::variational::advi< - stan_model, stan::variational::normal_fullrank, rng_t>( + stan_model, stan::variational::normal_fullrank, stan::rng_t>( *model_, cont_params_, base_rng_, 10, 100, 100, 1); } @@ -53,9 +52,9 @@ class advi_test : public ::testing::Test { } stan::variational::advi *advi_; - stan::variational::advi - *advi_fullrank_; + stan::rng_t> *advi_; + stan::variational::advi *advi_fullrank_; std::stringstream model_stream_; std::stringstream message_stream_; std::stringstream parameter_stream_; @@ -67,7 +66,7 @@ class advi_test : public ::testing::Test { private: stan_model *model_; stan_model *model_null_stream_; - rng_t base_rng_; + stan::rng_t base_rng_; Eigen::VectorXd cont_params_; }; diff --git a/src/test/unit/variational/stochastic_gradient_ascent_test.cpp b/src/test/unit/variational/stochastic_gradient_ascent_test.cpp index a9bf7e6273b..fb29c67ef4c 100644 --- a/src/test/unit/variational/stochastic_gradient_ascent_test.cpp +++ b/src/test/unit/variational/stochastic_gradient_ascent_test.cpp @@ -1,6 +1,5 @@ #include #include -#include #include #include #include @@ -8,9 +7,6 @@ #include #include #include -#include // L'Ecuyer RNG - -typedef boost::ecuyer1988 rng_t; // Mock Model class mock_model : public stan::model::prob_grad { diff --git a/src/test/unit/version_test.cpp b/src/test/unit/version_test.cpp index dbcf6e1bcaf..cc76d8f0c7f 100644 --- a/src/test/unit/version_test.cpp +++ b/src/test/unit/version_test.cpp @@ -3,12 +3,12 @@ TEST(Stan, macro) { EXPECT_EQ(2, STAN_MAJOR); - EXPECT_EQ(34, STAN_MINOR); - EXPECT_EQ(1, STAN_PATCH); + EXPECT_EQ(35, STAN_MINOR); + EXPECT_EQ(0, STAN_PATCH); } TEST(Stan, version) { EXPECT_EQ("2", stan::MAJOR_VERSION); - EXPECT_EQ("34", stan::MINOR_VERSION); - EXPECT_EQ("1", stan::PATCH_VERSION); + EXPECT_EQ("35", stan::MINOR_VERSION); + EXPECT_EQ("0", stan::PATCH_VERSION); }