diff --git a/src/stan/io/stan_csv_reader.hpp b/src/stan/io/stan_csv_reader.hpp index 5ded3d49c9..a5dfc9f0d0 100644 --- a/src/stan/io/stan_csv_reader.hpp +++ b/src/stan/io/stan_csv_reader.hpp @@ -29,8 +29,6 @@ inline void prettify_stan_csv_name(std::string& variable) { } } -// FIXME: should consolidate with the options from -// the command line in stan::lang struct stan_csv_metadata { int stan_version_major; int stan_version_minor; @@ -47,6 +45,7 @@ struct stan_csv_metadata { bool save_warmup; size_t thin; bool append_samples; + std::string method; std::string algorithm; std::string engine; int max_depth; @@ -64,8 +63,9 @@ struct stan_csv_metadata { num_samples(0), num_warmup(0), save_warmup(false), - thin(0), + thin(1), append_samples(false), + method(""), algorithm(""), engine(""), max_depth(10) {} @@ -101,13 +101,12 @@ class stan_csv_reader { stan_csv_reader() {} ~stan_csv_reader() {} - static bool read_metadata(std::istream& in, stan_csv_metadata& metadata, - std::ostream* out) { + static void read_metadata(std::istream& in, stan_csv_metadata& metadata) { std::stringstream ss; std::string line; if (in.peek() != '#') - return false; + return; while (in.peek() == '#') { std::getline(in, line); ss << line << '\n'; @@ -161,9 +160,15 @@ class stan_csv_reader { metadata.model = value; } else if (name.compare("num_samples") == 0) { std::stringstream(value) >> metadata.num_samples; + } else if (name.compare("output_samples") == 0) { // ADVI config name + std::stringstream(value) >> metadata.num_samples; } else if (name.compare("num_warmup") == 0) { std::stringstream(value) >> metadata.num_warmup; } else if (name.compare("save_warmup") == 0) { + // cmdstan args can be "true" and "false", was "1", "0" + if (value.compare("true") == 0) { + value = "1"; + } std::stringstream(value) >> metadata.save_warmup; } else if (name.compare("thin") == 0) { std::stringstream(value) >> metadata.thin; @@ -177,6 +182,8 @@ class stan_csv_reader { metadata.random_seed = false; } else if (name.compare("append_samples") == 0) { std::stringstream(value) >> metadata.append_samples; + } else if (name.compare("method") == 0) { + metadata.method = value; } else if (name.compare("algorithm") == 0) { metadata.algorithm = value; } else if (name.compare("engine") == 0) { @@ -185,14 +192,10 @@ class stan_csv_reader { std::stringstream(value) >> metadata.max_depth; } } - if (ss.good() == true) - return false; - - return true; } // read_metadata static bool read_header(std::istream& in, std::vector& header, - std::ostream* out, bool prettify_name = true) { + bool prettify_name = true) { std::string line; if (!std::isalpha(in.peek())) @@ -216,66 +219,57 @@ class stan_csv_reader { return true; } - static bool read_adaptation(std::istream& in, stan_csv_adaptation& adaptation, - std::ostream* out) { + static void read_adaptation(std::istream& in, + stan_csv_adaptation& adaptation) { std::stringstream ss; std::string line; int lines = 0; - if (in.peek() != '#' || in.good() == false) - return false; - + return; while (in.peek() == '#') { std::getline(in, line); ss << line << std::endl; lines++; } ss.seekg(std::ios_base::beg); + if (lines < 2) + return; - if (lines < 4) - return false; - - char comment; // Buffer for comment indicator, # + std::getline(ss, line); // comment adaptation terminated - // Skip first two lines - std::getline(ss, line); - - // Stepsize - std::getline(ss, line, '='); + // parse stepsize + std::getline(ss, line, '='); // stepsize boost::trim(line); ss >> adaptation.step_size; + if (lines == 2) // ADVI reports stepsize, no metric + return; - // Metric parameters - std::getline(ss, line); - std::getline(ss, line); - std::getline(ss, line); + std::getline(ss, line); // consume end of stepsize line + std::getline(ss, line); // comment elements of mass matrix + std::getline(ss, line); // diagonal metric or row 1 of dense metric int rows = lines - 3; int cols = std::count(line.begin(), line.end(), ',') + 1; adaptation.metric.resize(rows, cols); + char comment; // Buffer for comment indicator, # + // parse metric, row by row, element by element for (int row = 0; row < rows; row++) { std::stringstream line_ss; line_ss.str(line); line_ss >> comment; - for (int col = 0; col < cols; col++) { std::string token; std::getline(line_ss, token, ','); boost::trim(token); std::stringstream(token) >> adaptation.metric(row, col); } - std::getline(ss, line); // Read in next line + std::getline(ss, line); } - - if (ss.good()) - return false; - else - return true; } static bool read_samples(std::istream& in, Eigen::MatrixXd& samples, - stan_csv_timing& timing, std::ostream* out) { + stan_csv_timing& timing) { std::stringstream ss; std::string line; @@ -283,14 +277,13 @@ class stan_csv_reader { int cols = -1; if (in.peek() == '#' || in.good() == false) - return false; + return false; // need at least one data row while (in.good()) { bool comment_line = (in.peek() == '#'); bool empty_line = (in.peek() == '\n'); std::getline(in, line); - if (empty_line) continue; if (!line.length()) @@ -316,11 +309,10 @@ class stan_csv_reader { if (cols == -1) { cols = current_cols; } else if (cols != current_cols) { - if (out) - *out << "Error: expected " << cols << " columns, but found " - << current_cols << " instead for row " << rows + 1 - << std::endl; - return false; + std::stringstream msg; + msg << "Error: expected " << cols << " columns, but found " + << current_cols << " instead for row " << rows + 1; + throw std::invalid_argument(msg.str()); } rows++; } @@ -348,36 +340,45 @@ class stan_csv_reader { /** * Parses the file. * + * Throws exception if contents can't be parsed into header + data rows. + * + * Emits warning message + * * @param[in] in input stream to parse * @param[out] out output stream to send messages */ static stan_csv parse(std::istream& in, std::ostream* out) { stan_csv data; + std::string line; - if (!read_metadata(in, data.metadata, out)) { - if (out) - *out << "Warning: non-fatal error reading metadata" << std::endl; + read_metadata(in, data.metadata); + if (!read_header(in, data.header)) { + throw std::invalid_argument("Error: no column names found in csv file"); } - if (!read_header(in, data.header, out)) { - if (out) - *out << "Error: error reading header" << std::endl; - throw std::invalid_argument("Error with header of input file in parse"); + // skip warmup draws, if any + if (data.metadata.algorithm != "fixed_param" && data.metadata.num_warmup > 0 + && data.metadata.save_warmup) { + while (in.peek() != '#') { + std::getline(in, line); + } } - if (!read_adaptation(in, data.adaptation, out)) { - if (out) - *out << "Warning: non-fatal error reading adaptation data" << std::endl; + if (data.metadata.algorithm != "fixed_param") { + read_adaptation(in, data.adaptation); } data.timing.warmup = 0; data.timing.sampling = 0; - if (!read_samples(in, data.samples, data.timing, out)) { - if (out) - *out << "Warning: non-fatal error reading samples" << std::endl; + if (data.metadata.method == "variational") { + std::getline(in, line); // discard variational estimate } + if (!read_samples(in, data.samples, data.timing)) { + if (out) + *out << "Unable to parse sample" << std::endl; + } return data; } }; diff --git a/src/test/unit/io/stan_csv_reader_test.cpp b/src/test/unit/io/stan_csv_reader_test.cpp index 8ee33547d2..ec91cf8684 100644 --- a/src/test/unit/io/stan_csv_reader_test.cpp +++ b/src/test/unit/io/stan_csv_reader_test.cpp @@ -1,5 +1,6 @@ #include #include +#include #include #include #include @@ -22,6 +23,13 @@ class StanIoStanCsvReader : public testing::Test { "src/test/unit/io/test_csv_files/blocker_nondiag.0.csv"); eight_schools_stream.open( "src/test/unit/io/test_csv_files/eight_schools.csv"); + + bernoulli_thin_stream.open( + "src/test/unit/io/test_csv_files/bernoulli_thin.csv"); + bernoulli_warmup_stream.open( + "src/test/unit/io/test_csv_files/bernoulli_warmup.csv"); + fixed_param_stream.open( + "src/test/unit/io/test_csv_files/fixed_param_output.csv"); } void TearDown() { @@ -33,10 +41,11 @@ class StanIoStanCsvReader : public testing::Test { header3_stream.close(); adaptation1_stream.close(); samples1_stream.close(); - epil0_stream.close(); - blocker_nondiag0_stream.close(); + bernoulli_thin_stream.close(); + bernoulli_warmup_stream.close(); + fixed_param_stream.close(); } std::ifstream blocker0_stream, epil0_stream; @@ -46,12 +55,14 @@ class StanIoStanCsvReader : public testing::Test { std::ifstream metadata3_stream, header2_stream; std::ifstream eight_schools_stream; std::ifstream header3_stream; + std::ifstream bernoulli_thin_stream; + std::ifstream bernoulli_warmup_stream; + std::ifstream fixed_param_stream; }; TEST_F(StanIoStanCsvReader, read_metadata1) { stan::io::stan_csv_metadata metadata; - EXPECT_TRUE( - stan::io::stan_csv_reader::read_metadata(metadata1_stream, metadata, 0)); + stan::io::stan_csv_reader::read_metadata(metadata1_stream, metadata); EXPECT_EQ(2, metadata.stan_version_major); EXPECT_EQ(9, metadata.stan_version_minor); @@ -77,8 +88,7 @@ TEST_F(StanIoStanCsvReader, read_metadata1) { TEST_F(StanIoStanCsvReader, read_metadata3) { stan::io::stan_csv_metadata metadata; - EXPECT_TRUE( - stan::io::stan_csv_reader::read_metadata(metadata3_stream, metadata, 0)); + stan::io::stan_csv_reader::read_metadata(metadata3_stream, metadata); EXPECT_EQ(2, metadata.stan_version_major); EXPECT_EQ(9, metadata.stan_version_minor); @@ -104,8 +114,7 @@ TEST_F(StanIoStanCsvReader, read_metadata3) { TEST_F(StanIoStanCsvReader, read_header1) { std::vector header; - EXPECT_TRUE( - stan::io::stan_csv_reader::read_header(header1_stream, header, 0)); + EXPECT_TRUE(stan::io::stan_csv_reader::read_header(header1_stream, header)); ASSERT_EQ(55, header.size()); EXPECT_EQ("lp__", header[0]); @@ -132,8 +141,7 @@ TEST_F(StanIoStanCsvReader, read_header1) { TEST_F(StanIoStanCsvReader, read_header2) { std::vector header; - EXPECT_TRUE( - stan::io::stan_csv_reader::read_header(header2_stream, header, 0)); + EXPECT_TRUE(stan::io::stan_csv_reader::read_header(header2_stream, header)); ASSERT_EQ(5, header.size()); EXPECT_EQ("d", header[0]); @@ -147,8 +155,7 @@ TEST_F(StanIoStanCsvReader, read_header2) { TEST_F(StanIoStanCsvReader, read_header_tuples) { std::vector header; - EXPECT_TRUE( - stan::io::stan_csv_reader::read_header(header3_stream, header, 0)); + EXPECT_TRUE(stan::io::stan_csv_reader::read_header(header3_stream, header)); ASSERT_EQ(46, header.size()); @@ -181,8 +188,7 @@ TEST_F(StanIoStanCsvReader, read_header_tuples) { TEST_F(StanIoStanCsvReader, read_adaptation1) { stan::io::stan_csv_adaptation adaptation; - EXPECT_TRUE(stan::io::stan_csv_reader::read_adaptation(adaptation1_stream, - adaptation, 0)); + stan::io::stan_csv_reader::read_adaptation(adaptation1_stream, adaptation); EXPECT_FLOAT_EQ(0.118745, adaptation.step_size); ASSERT_EQ(47, adaptation.metric.size()); @@ -240,7 +246,7 @@ TEST_F(StanIoStanCsvReader, read_samples1) { stan::io::stan_csv_timing timing; EXPECT_TRUE(stan::io::stan_csv_reader::read_samples(samples1_stream, samples, - timing, 0)); + timing)); ASSERT_EQ(5, samples.rows()); ASSERT_EQ(55, samples.cols()); @@ -537,3 +543,51 @@ TEST_F(StanIoStanCsvReader, ParseEightSchools) { EXPECT_EQ("", out.str()); } + +TEST_F(StanIoStanCsvReader, skip_warmup) { + stan::io::stan_csv bernoulli_warmup; + std::stringstream out; + bernoulli_warmup + = stan::io::stan_csv_reader::parse(bernoulli_warmup_stream, &out); + ASSERT_EQ(1000, bernoulli_warmup.samples.rows()); + ASSERT_EQ(1000, bernoulli_warmup.metadata.num_warmup); + ASSERT_EQ(1000, bernoulli_warmup.metadata.num_samples); + ASSERT_NE(0, bernoulli_warmup.adaptation.step_size); +} + +TEST_F(StanIoStanCsvReader, thinned_data) { + stan::io::stan_csv bernoulli_thin; + std::stringstream out; + bernoulli_thin + = stan::io::stan_csv_reader::parse(bernoulli_thin_stream, &out); + ASSERT_EQ(1000, bernoulli_thin.samples.rows()); +} + +TEST_F(StanIoStanCsvReader, fixed_param) { + stan::io::stan_csv fixed_param; + std::stringstream out; + fixed_param = stan::io::stan_csv_reader::parse(fixed_param_stream, &out); + ASSERT_EQ(10, fixed_param.samples.rows()); +} + +TEST_F(StanIoStanCsvReader, no_samples) { + std::ifstream no_samples_stream; + no_samples_stream.open( + "src/test/unit/io/test_csv_files/bernoulli_no_samples.csv"); + std::stringstream out; + stan::io::stan_csv no_samples + = stan::io::stan_csv_reader::parse(no_samples_stream, &out); + no_samples_stream.close(); + ASSERT_EQ(out.str(), "Unable to parse sample\n"); +} + +TEST_F(StanIoStanCsvReader, variational) { + std::ifstream variational_stream; + variational_stream.open( + "src/test/unit/io/test_csv_files/bernoulli_variational.csv"); + std::stringstream out; + stan::io::stan_csv variational + = stan::io::stan_csv_reader::parse(variational_stream, &out); + variational_stream.close(); + ASSERT_EQ(1000, variational.metadata.num_samples); +} diff --git a/src/test/unit/io/test_csv_files/bernoulli_no_samples.csv b/src/test/unit/io/test_csv_files/bernoulli_no_samples.csv new file mode 100644 index 0000000000..3244c03f7c --- /dev/null +++ b/src/test/unit/io/test_csv_files/bernoulli_no_samples.csv @@ -0,0 +1,57 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-10-03 16:54:16 UTC +# method = sample (Default) +# sample +# num_samples = 0 +# num_warmup = 1000 (Default) +# save_warmup = false (Default) +# thin = 1 (Default) +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 1 (Default) +# data +# file = examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 1517767693 (Default) +# output +# file = output.csv (Default) +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,theta +# Adaptation terminated +# Step size = 1.22726 +# Diagonal elements of inverse mass matrix: +# 0.489285 +# +# Elapsed Time: 0.005 seconds (Warm-up) +# 0 seconds (Sampling) +# 0.005 seconds (Total) +# diff --git a/src/test/unit/io/test_csv_files/bernoulli_thin.csv b/src/test/unit/io/test_csv_files/bernoulli_thin.csv new file mode 100644 index 0000000000..b0874d8ead --- /dev/null +++ b/src/test/unit/io/test_csv_files/bernoulli_thin.csv @@ -0,0 +1,1057 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-07-23 15:13:19 UTC +# method = sample (Default) +# sample +# num_samples = 2000 +# num_warmup = 1000 (Default) +# save_warmup = false (Default) +# thin = 2 +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 1 (Default) +# data +# file = examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 3880845880 (Default) +# output +# file = bernoulli_thin.csv +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,theta +# Adaptation terminated +# Step size = 0.893542 +# Diagonal elements of inverse mass matrix: +# 0.483503 +-8.91945,1,0.893542,1,1,0,9.73374,0.0619604 +-8.90814,1,0.893542,1,1,0,9.95846,0.545008 +-7.21915,1,0.893542,1,1,0,7.56642,0.382112 +-6.99262,0.974315,0.893542,1,1,0,6.99262,0.170032 +-7.177,0.949564,0.893542,2,7,0,7.19028,0.147604 +-7.02071,0.934256,0.893542,2,3,0,7.30094,0.348894 +-6.75447,0.998729,0.893542,2,3,0,6.76944,0.264366 +-6.99389,0.954672,0.893542,1,3,0,7.04803,0.169846 +-8.10147,0.867196,0.893542,1,3,0,8.27154,0.0887819 +-7.83949,1,0.893542,1,1,0,8.36851,0.100831 +-7.20348,0.899977,0.893542,1,3,0,7.65125,0.379758 +-7.12214,0.820496,0.893542,1,3,0,8.08646,0.153481 +-6.75619,1,0.893542,2,3,0,7.17582,0.266194 +-6.80721,0.968917,0.893542,1,3,0,7.36674,0.294557 +-7.03198,1,0.893542,1,1,0,7.76254,0.351031 +-6.85284,0.960446,0.893542,1,3,0,7.15062,0.195876 +-6.77208,0.987924,0.893542,1,3,0,7.03182,0.278062 +-6.91359,1,0.893542,2,3,0,8.13328,0.18305 +-6.81561,0.956574,0.893542,2,7,0,7.45532,0.297719 +-6.76032,1,0.893542,2,7,0,6.80503,0.230743 +-6.76597,0.998846,0.893542,1,1,0,6.76725,0.22683 +-6.7594,0.993657,0.893542,1,3,0,6.82523,0.231466 +-7.25866,0.839089,0.893542,2,3,0,8.4433,0.139735 +-7.1134,0.971276,0.893542,1,1,0,7.1556,0.365446 +-7.52094,0.727841,0.893542,1,3,0,8.73414,0.11935 +-7.67112,0.993503,0.893542,1,1,0,7.78017,0.109972 +-6.75103,0.998383,0.893542,2,3,0,6.83417,0.259776 +-8.62702,0.934538,0.893542,2,3,0,9.21545,0.0701174 +-6.85649,0.909432,0.893542,1,3,0,8.95293,0.310994 +-6.87955,0.968041,0.893542,1,3,0,6.94534,0.317443 +-6.84001,0.965601,0.893542,1,3,0,7.07766,0.199102 +-7.88997,1,0.893542,2,3,0,8.09487,0.0983237 +-7.06055,0.900192,0.893542,1,3,0,8.68948,0.356282 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+-6.75831,0.997242,0.893542,1,3,0,6.76497,0.268206 +-6.75111,0.998343,0.893542,1,3,0,6.76907,0.24027 +-10.3308,0.549825,0.893542,2,7,0,10.5088,0.629819 +-10.5954,0.978406,0.893542,1,1,0,10.7078,0.0322776 +-9.03458,1,0.893542,1,1,0,10.0862,0.0590819 +-7.58478,0.886552,0.893542,1,3,0,7.73728,0.115201 +-9.91973,1,0.893542,1,1,0,10.5231,0.0416263 +-7.48944,1,0.893542,2,3,0,9.43402,0.121496 +-7.71315,0.865118,0.893542,2,7,0,7.71389,0.107568 +# +# Elapsed Time: 0.005 seconds (Warm-up) +# 0.015 seconds (Sampling) +# 0.02 seconds (Total) +# diff --git a/src/test/unit/io/test_csv_files/bernoulli_variational.csv b/src/test/unit/io/test_csv_files/bernoulli_variational.csv new file mode 100644 index 0000000000..613de2e786 --- /dev/null +++ b/src/test/unit/io/test_csv_files/bernoulli_variational.csv @@ -0,0 +1,1039 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-10-03 17:01:50 UTC +# method = variational +# variational +# algorithm = meanfield (Default) +# meanfield +# iter = 10000 (Default) +# grad_samples = 1 (Default) +# elbo_samples = 100 (Default) +# eta = 1 (Default) +# adapt +# engaged = true (Default) +# iter = 50 (Default) +# tol_rel_obj = 0.01 (Default) +# eval_elbo = 100 (Default) +# output_samples = 1000 (Default) +# id = 1 (Default) +# data +# file = examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 1518221616 (Default) +# output +# file = output.csv (Default) +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0 +# stancflags = +lp__,log_p__,log_g__,theta +# Stepsize adaptation complete. +# eta = 1 +0,0,0,0.227641 +0,-7.53537,-0.958087,0.423587 +0,-6.78149,-0.0986588,0.283229 +0,-7.35647,-0.775269,0.401351 +0,-6.93635,-0.104432,0.178975 +0,-7.15947,-0.307329,0.149425 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+0,-6.95046,-0.327222,0.334533 +0,-6.7657,-0.0701092,0.273977 +0,-7.56697,-0.989831,0.427257 +0,-7.66416,-0.859248,0.110378 +0,-7.2843,-0.699713,0.391523 +0,-7.7543,-0.967393,0.105296 +0,-6.83115,-0.0277127,0.201482 diff --git a/src/test/unit/io/test_csv_files/bernoulli_warmup.csv b/src/test/unit/io/test_csv_files/bernoulli_warmup.csv new file mode 100644 index 0000000000..799f5c8260 --- /dev/null +++ b/src/test/unit/io/test_csv_files/bernoulli_warmup.csv @@ -0,0 +1,2057 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = bernoulli_model +# start_datetime = 2024-07-22 12:26:39 UTC +# method = sample (Default) +# sample +# num_samples = 1000 (Default) +# num_warmup = 1000 (Default) +# save_warmup = true +# thin = 1 (Default) +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = hmc (Default) +# hmc +# engine = nuts (Default) +# nuts +# max_depth = 10 (Default) +# metric = diag_e (Default) +# metric_file = (Default) +# stepsize = 1 (Default) +# stepsize_jitter = 0 (Default) +# num_chains = 1 (Default) +# id = 1 (Default) +# data +# file = examples/bernoulli/bernoulli.data.json +# init = 2 (Default) +# random +# seed = 3784445287 (Default) +# output +# file = output.csv (Default) +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0-25-gbb9ce42 +# stancflags = +lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,theta +-9.8506,1,0.5,1,1,0,11.6894,0.604056 +-9.8506,0,14.3855,0,1,1,9.9718,0.604056 +-9.8506,1.07054e-09,2.43117,1,1,0,10.1705,0.604056 +-8.24874,1,0.239791,2,3,0,9.78315,0.49418 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+-8.54523,0.363664,1.25927,1,1,0,8.64001,0.518278 +-6.92636,0.960794,0.437023,2,7,0,10.1493,0.180724 +-6.94618,0.924621,0.667654,1,3,0,7.99729,0.333591 +-6.98445,0.988443,0.928133,1,1,0,7.03448,0.341727 +-6.98445,0.179842,1.49883,1,1,0,7.94419,0.341727 +-6.9788,1,0.343737,1,1,0,6.99099,0.340566 +-7.21715,0.945161,0.570638,3,7,0,8.05234,0.381813 +-7.21715,0.677044,0.825931,1,1,0,8.31299,0.381813 +# Adaptation terminated +# Step size = 0.83124 +# Diagonal elements of inverse mass matrix: +# 0.525769 +-6.77384,0.964073,0.83124,1,3,0,7.46753,0.222338 +-6.863,0.968947,0.83124,1,3,0,7.01137,0.312874 +-6.83479,0.964185,0.83124,1,3,0,7.16339,0.200486 +-6.80509,0.978314,0.83124,1,3,0,7.03713,0.293726 +-6.79148,0.786013,0.83124,2,3,0,8.42285,0.214411 +-6.79943,0.998548,0.83124,1,1,0,6.80547,0.211417 +-6.85456,0.967137,0.83124,2,3,0,7.15794,0.310427 +-7.11014,0.963078,0.83124,2,7,0,7.14083,0.154842 +-6.75222,0.990723,0.83124,1,3,0,7.18664,0.238666 +-7.40454,0.858025,0.83124,1,3,0,7.7421,0.127645 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seconds (Total) +# diff --git a/src/test/unit/io/test_csv_files/fixed_param_output.csv b/src/test/unit/io/test_csv_files/fixed_param_output.csv new file mode 100644 index 0000000000..f18f70b822 --- /dev/null +++ b/src/test/unit/io/test_csv_files/fixed_param_output.csv @@ -0,0 +1,55 @@ +# stan_version_major = 2 +# stan_version_minor = 35 +# stan_version_patch = 0 +# model = linear_model_dgp_model +# start_datetime = 2024-07-23 18:24:49 UTC +# method = sample (Default) +# sample +# num_samples = 10 +# num_warmup = 1000 (Default) +# save_warmup = false (Default) +# thin = 1 (Default) +# adapt +# engaged = true (Default) +# gamma = 0.05 (Default) +# delta = 0.8 (Default) +# kappa = 0.75 (Default) +# t0 = 10 (Default) +# init_buffer = 75 (Default) +# term_buffer = 50 (Default) +# window = 25 (Default) +# save_metric = false (Default) +# algorithm = fixed_param +# num_chains = 1 (Default) +# id = 1 (Default) +# data +# file = linear_data.json +# init = 2 (Default) +# random +# seed = 3892335730 (Default) +# output +# file = output.csv (Default) +# diagnostic_file = (Default) +# refresh = 100 (Default) +# sig_figs = -1 (Default) +# profile_file = profile.csv (Default) +# save_cmdstan_config = false (Default) +# num_threads = 1 (Default) +# stanc_version = stanc3 v2.35.0 +# stancflags = --filename-in-msg=linear_model_dgp.stan 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+# +# Elapsed Time: 0 seconds (Warm-up) +# 0.001 seconds (Sampling) +# 0.001 seconds (Total) +#