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Merge pull request #3312 from stan-dev/feature/3299-improved-ESS-Rhat
Feature/3299 improved ess rhat
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,43 @@ | ||
| #ifndef STAN_ANALYZE_MCMC_CHECK_CHAINS_HPP | ||
| #define STAN_ANALYZE_MCMC_CHECK_CHAINS_HPP | ||
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| #include <stan/math/prim.hpp> | ||
| #include <cmath> | ||
| #include <cstdlib> | ||
| #include <limits> | ||
| #include <utility> | ||
| #include <vector> | ||
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| namespace stan { | ||
| namespace analyze { | ||
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| /** | ||
| * Checks that values across all matrix columns finite and non-identical. | ||
| * | ||
| * @param chains matrix of draws, one column per chain | ||
| * @return bool true if OK, false otherwise | ||
| */ | ||
| inline bool is_finite_and_varies(const Eigen::MatrixXd chains) { | ||
| size_t num_chains = chains.cols(); | ||
| size_t num_samples = chains.rows(); | ||
| Eigen::VectorXd first_draws = Eigen::VectorXd::Zero(num_chains); | ||
| for (std::size_t i = 0; i < num_chains; ++i) { | ||
| first_draws(i) = chains.col(i)(0); | ||
| for (int j = 0; j < num_samples; ++j) { | ||
| if (!std::isfinite(chains.col(i)(j))) | ||
| return false; | ||
| } | ||
| if (chains.col(i).isApproxToConstant(first_draws(i))) { | ||
| return false; | ||
| } | ||
| } | ||
| if (num_chains > 1 && first_draws.isApproxToConstant(first_draws(0))) { | ||
| return false; | ||
| } | ||
| return true; | ||
| } | ||
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| } // namespace analyze | ||
| } // namespace stan | ||
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| #endif |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,61 @@ | ||
| #ifndef STAN_ANALYZE_MCMC_RANK_NORMALIZATION_HPP | ||
| #define STAN_ANALYZE_MCMC_RANK_NORMALIZATION_HPP | ||
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| #include <stan/math/prim.hpp> | ||
| #include <boost/math/distributions/normal.hpp> | ||
| #include <algorithm> | ||
| #include <cmath> | ||
| #include <vector> | ||
| #include <limits> | ||
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| namespace stan { | ||
| namespace analyze { | ||
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| /** | ||
| * Computes normalized average ranks for pooled draws. Normal scores computed | ||
| * using inverse normal transformation and a fractional offset. Based on paper | ||
| * https://arxiv.org/abs/1903.08008 | ||
| * | ||
| * @param chains matrix of draws, one column per chain | ||
| * @return normal scores for average ranks of draws | ||
| */ | ||
| inline Eigen::MatrixXd rank_transform(const Eigen::MatrixXd& chains) { | ||
| const Eigen::Index rows = chains.rows(); | ||
| const Eigen::Index cols = chains.cols(); | ||
| const Eigen::Index size = rows * cols; | ||
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| std::vector<std::pair<double, int>> value_with_index(size); | ||
| for (Eigen::Index i = 0; i < size; ++i) { | ||
| value_with_index[i] = {chains(i), i}; | ||
| } | ||
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| 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; | ||
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| 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<double> dist; | ||
| for (std::size_t k = i; k < j; ++k) { | ||
| double p = (avg_rank - 0.375) / (size + 0.25); | ||
| const Eigen::Index index = value_with_index[k].second; | ||
| rank_matrix(index) = boost::math::quantile(dist, p); | ||
| } | ||
| i = j - 1; // Skip over tied elements | ||
| } | ||
| return rank_matrix; | ||
| } | ||
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| } // namespace analyze | ||
| } // namespace stan | ||
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| #endif |
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