Cherry-pick diagnose changes from #1290

src/cmdstan/diagnose.cpp

@@ -1,12 +1,15 @@
+#include <cmdstan/return_codes.hpp>
 #include <cmdstan/stansummary_helper.hpp>
-#include <stan/mcmc/chains.hpp>
+#include <stan/mcmc/chainset.hpp>
 #include <algorithm>
 #include <fstream>
 #include <iomanip>
 #include <ios>
 #include <iostream>
 
-double RHAT_MAX = 1.05;
+using cmdstan::return_codes;
+
+double RHAT_MAX = 1.01499;  // round to 1.01
 
 void diagnose_usage() {
   std::cout << "USAGE:  diagnose <filename 1> [<filename 2> ... <filename N>]"
@@ -26,7 +29,7 @@ void diagnose_usage() {
 int main(int argc, const char *argv[]) {
   if (argc == 1) {
     diagnose_usage();
-    return 0;
+    return return_codes::OK;
   }
 
   // Parse any arguments specifying filenames
@@ -45,49 +48,47 @@ int main(int argc, const char *argv[]) {
 
   if (!filenames.size()) {
     std::cout << "No valid input files, exiting." << std::endl;
-    return 0;
+    return return_codes::NOT_OK;
   }
 
   std::cout << std::fixed << std::setprecision(2);
 
-  // Parse specified files
-  std::cout << "Processing csv files: " << filenames[0];
-  ifstream.open(filenames[0].c_str());
-
-  stan::io::stan_csv stan_csv
-      = stan::io::stan_csv_reader::parse(ifstream, &std::cout);
-  stan::mcmc::chains<> chains(stan_csv);
-  ifstream.close();
-
-  if (filenames.size() > 1)
-    std::cout << ", ";
-  else
-    std::cout << std::endl << std::endl;
-
-  for (std::vector<std::string>::size_type chain = 1; chain < filenames.size();
-       ++chain) {
-    std::cout << filenames[chain];
-    ifstream.open(filenames[chain].c_str());
-    stan_csv = stan::io::stan_csv_reader::parse(ifstream, &std::cout);
-    chains.add(stan_csv);
-    ifstream.close();
-    if (chain < filenames.size() - 1)
-      std::cout << ", ";
-    else
-      std::cout << std::endl << std::endl;
+  std::vector<stan::io::stan_csv> csv_parsed;
+  for (int i = 0; i < filenames.size(); ++i) {
+    std::ifstream infile;
+    std::stringstream out;
+    stan::io::stan_csv sample;
+    infile.open(filenames[i].c_str());
+    try {
+      sample = stan::io::stan_csv_reader::parse(infile, &out);
+      // csv_reader warnings are errors - fail fast.
+      if (!out.str().empty()) {
+        throw std::invalid_argument(out.str());
+      }
+      csv_parsed.push_back(sample);
+    } catch (const std::invalid_argument &e) {
+      std::cout << "Cannot parse input csv file: " << filenames[i] << e.what()
+                << "." << std::endl;
+      return return_codes::NOT_OK;
+    }
   }
-
+  stan::mcmc::chainset chains(csv_parsed);
+  stan::io::stan_csv_metadata metadata = csv_parsed[0].metadata;
+  std::vector<std::string> param_names = csv_parsed[0].header;
+  size_t num_params = param_names.size();
   int num_samples = chains.num_samples();
   std::vector<std::string> bad_n_eff_names;
   std::vector<std::string> bad_rhat_names;
   bool has_errors = false;
 
-  for (int i = 0; i < chains.num_params(); ++i) {
-    if (chains.param_name(i) == std::string("treedepth__")) {
+  for (int i = 0; i < num_params; ++i) {
+    if (param_names[i] == std::string("treedepth__")) {
       std::cout << "Checking sampler transitions treedepth." << std::endl;
-      int max_limit = stan_csv.metadata.max_depth;
+      int max_limit = metadata.max_depth;
       long n_max = 0;
-      Eigen::VectorXd t_samples = chains.samples(i);
+      Eigen::MatrixXd draws = chains.samples(i);
+      Eigen::VectorXd t_samples
+          = Eigen::Map<Eigen::VectorXd>(draws.data(), draws.size());
       for (long n = 0; n < t_samples.size(); ++n) {
         if (t_samples(n) >= max_limit) {
           ++n_max;
@@ -109,7 +110,7 @@ int main(int argc, const char *argv[]) {
         std::cout << "Treedepth satisfactory for all transitions." << std::endl
                   << std::endl;
       }
-    } else if (chains.param_name(i) == std::string("divergent__")) {
+    } else if (param_names[i] == std::string("divergent__")) {
       std::cout << "Checking sampler transitions for divergences." << std::endl;
       int n_divergent = chains.samples(i).sum();
       if (n_divergent > 0) {
@@ -129,26 +130,22 @@ int main(int argc, const char *argv[]) {
         std::cout << "No divergent transitions found." << std::endl
                   << std::endl;
       }
-    } else if (chains.param_name(i) == std::string("energy__")) {
+    } else if (param_names[i] == std::string("energy__")) {
       std::cout << "Checking E-BFMI - sampler transitions HMC potential energy."
                 << std::endl;
-      Eigen::VectorXd e_samples = chains.samples(i);
+      Eigen::MatrixXd draws = chains.samples(i);
+      Eigen::VectorXd e_samples
+          = Eigen::Map<Eigen::VectorXd>(draws.data(), draws.size());
       double delta_e_sq_mean = 0;
-      double e_mean = 0;
-      double e_var = 0;
-      e_mean += e_samples(0);
-      e_var += e_samples(0) * (e_samples(0) - e_mean);
+      double e_mean = chains.mean(i);
+      double e_var = chains.variance(i);
       for (long n = 1; n < e_samples.size(); ++n) {
         double e = e_samples(n);
         double delta_e_sq = (e - e_samples(n - 1)) * (e - e_samples(n - 1));
         double d = delta_e_sq - delta_e_sq_mean;
         delta_e_sq_mean += d / n;
         d = e - e_mean;
-        e_mean += d / (n + 1);
-        e_var += d * (e - e_mean);
       }
-
-      e_var /= static_cast<double>(e_samples.size() - 1);
       double e_bfmi = delta_e_sq_mean / e_var;
       double e_bfmi_threshold = 0.3;
       if (e_bfmi < e_bfmi_threshold) {
@@ -163,14 +160,16 @@ int main(int argc, const char *argv[]) {
       } else {
         std::cout << "E-BFMI satisfactory." << std::endl << std::endl;
       }
-    } else if (chains.param_name(i).find("__") == std::string::npos) {
-      double n_eff = chains.effective_sample_size(i);
+    } else if (param_names[i].find("__") == std::string::npos) {
+      auto [ess_bulk, ess_tail] = chains.split_rank_normalized_ess(i);
+      double n_eff = ess_bulk < ess_tail ? ess_bulk : ess_tail;
       if (n_eff / num_samples < 0.001)
-        bad_n_eff_names.push_back(chains.param_name(i));
+        bad_n_eff_names.push_back(param_names[i]);
 
-      double split_rhat = chains.split_potential_scale_reduction(i);
+      auto [rhat_bulk, rhat_tail] = chains.split_rank_normalized_rhat(i);
+      double split_rhat = rhat_bulk > rhat_tail ? rhat_bulk : rhat_tail;
       if (split_rhat > RHAT_MAX)
-        bad_rhat_names.push_back(chains.param_name(i));
+        bad_rhat_names.push_back(param_names[i]);
     }
   }
   if (bad_n_eff_names.size() > 0) {
@@ -187,13 +186,15 @@ int main(int argc, const char *argv[]) {
               << " may be substantially lower than quoted." << std::endl
               << std::endl;
   } else {
-    std::cout << "Effective sample size satisfactory." << std::endl
+    std::cout << "Rank-normalized split effective sample size satisfactory "
+              << "for all parameters." << std::endl
               << std::endl;
   }
 
   if (bad_rhat_names.size() > 0) {
     has_errors = true;
-    std::cout << "The following parameters had split R-hat greater than "
+    std::cout << "The following parameters had rank-normalized split R-hat "
+                 "greater than "
               << RHAT_MAX << ":" << std::endl;
     std::cout << "  ";
     for (size_t n = 0; n < bad_rhat_names.size() - 1; ++n)
@@ -207,13 +208,14 @@ int main(int argc, const char *argv[]) {
               << " effective parameterization." << std::endl
               << std::endl;
   } else {
-    std::cout << "Split R-hat values satisfactory all parameters." << std::endl
+    std::cout << "Rank-normalized split R-hat values satisfactory "
+              << "for all parameters." << std::endl
               << std::endl;
   }
   if (!has_errors)
     std::cout << "Processing complete, no problems detected." << std::endl;
   else
     std::cout << "Processing complete." << std::endl;
 
-  return 0;
+  return return_codes::OK;
 }

src/test/interface/example_output/corr_gauss.nom

@@ -9,8 +9,8 @@ No divergent transitions found.
 Checking E-BFMI - sampler transitions HMC potential energy.
 E-BFMI satisfactory.
 
-Effective sample size satisfactory.
+Rank-normalized split effective sample size satisfactory for all parameters.
 
-Split R-hat values satisfactory all parameters.
+Rank-normalized split R-hat values satisfactory for all parameters.
 
 Processing complete.

src/test/interface/example_output/corr_gauss_depth15.nom

@@ -7,8 +7,8 @@ No divergent transitions found.
 Checking E-BFMI - sampler transitions HMC potential energy.
 E-BFMI satisfactory.
 
-Effective sample size satisfactory.
+Rank-normalized split effective sample size satisfactory for all parameters.
 
-Split R-hat values satisfactory all parameters.
+Rank-normalized split R-hat values satisfactory for all parameters.
 
 Processing complete, no problems detected.

src/test/interface/example_output/corr_gauss_depth8.nom

@@ -9,8 +9,8 @@ No divergent transitions found.
 Checking E-BFMI - sampler transitions HMC potential energy.
 E-BFMI satisfactory.
 
-Effective sample size satisfactory.
+Rank-normalized split effective sample size satisfactory for all parameters.
 
-Split R-hat values satisfactory all parameters.
+Rank-normalized split R-hat values satisfactory for all parameters.
 
 Processing complete.

src/test/interface/example_output/eight_schools.nom

@@ -11,8 +11,8 @@ Checking E-BFMI - sampler transitions HMC potential energy.
 The E-BFMI, 0.26, is below the nominal threshold of 0.30 which suggests that HMC may have trouble exploring the target distribution.
 If possible, try to reparameterize the model.
 
-Effective sample size satisfactory.
+Rank-normalized split effective sample size satisfactory for all parameters.
 
-Split R-hat values satisfactory all parameters.
+Rank-normalized split R-hat values satisfactory for all parameters.
 
 Processing complete.

src/test/interface/example_output/mix.nom

@@ -7,12 +7,10 @@ No divergent transitions found.
 Checking E-BFMI - sampler transitions HMC potential energy.
 E-BFMI satisfactory.
 
-The following parameters had fewer than 0.001 effective draws per transition:
-  mu[1], mu[2], theta
-Such low values indicate that the effective sample size estimators may be biased high and actual performance may be substantially lower than quoted.
+Rank-normalized split effective sample size satisfactory for all parameters.
 
-The following parameters had split R-hat greater than 1.05:
-  mu[1], mu[2], theta
+The following parameters had rank-normalized split R-hat greater than 1.01:
+  mu[1], mu[2], sigma[1], theta
 Such high values indicate incomplete mixing and biased estimation.
 You should consider regularizating your model with additional prior information or a more effective parameterization.