From ca6425c536b6f1cba6acb094cb8cc669dd8caa53 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Thu, 5 Mar 2026 22:25:51 +0100 Subject: [PATCH 01/60] docs: update mixed MRF implementation plan MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Add Blume-Capel variable support across all phases (A-G) - Add pseudolikelihood consistency note (mixedGM vs bgms verified) - Add PR and commit strategy (§16, 17 planned commits) - Fix test numbering: unit T1-T12, integration T13-T19, edge-case T20-T27, regression R1-R3 - Split §3.3 edge-selection table by PL mode - Fix §3.1/3.2 parameter label (threshold → main effect) - Add BC note to §2.1 conditional OMRF formula - Cross-reference phase checkpoints with T-numbers - Remove legacy workflow section (superseded by commit strategy) - Add reuse inventory entries for variable_helpers BC functions - Add BC items to risk register --- dev/mixedmrf/implementation_plan.md | 438 ++++++++++++++++++++-------- 1 file changed, 310 insertions(+), 128 deletions(-) diff --git a/dev/mixedmrf/implementation_plan.md b/dev/mixedmrf/implementation_plan.md index 562a665c..8566118d 100644 --- a/dev/mixedmrf/implementation_plan.md +++ b/dev/mixedmrf/implementation_plan.md @@ -1,6 +1,8 @@ # Mixed MRF — Implementation Plan -**Date:** 2026-02-25 (updated 2026-03-04; review amendments applied 2026-03-04) +**Date:** 2026-02-25 (updated 2026-03-04; review amendments 2026-03-04; +Blume-Capel + PL consistency 2026-03-05; commit strategy 2026-03-05; +consistency review 2026-03-05) **Branch:** `ggm_mixed` (PR #78) **Goal:** Build a monolithic `MixedMRFModel` in C++ that supports both conditional and marginal pseudo-likelihood, with and without edge selection. @@ -52,6 +54,19 @@ The following issues identified in the plan reviews have been fixed in mixedGM: - **Simulation study** (`dev/simulation_study_plan.md`): 54-cell design covering p ∈ {5,10,15}, q ∈ {5,10,15}, n ∈ {250,500,1000}. +### Pseudolikelihood consistency (verified) + +The discrete pseudolikelihood formulas in mixedGM and the bgms OMRF have +been compared and **match exactly** for ordinal variables. The mixedGM code +was originally ported from bgms (`compute_denom_ordinal`); the same +category-threshold parameterization, rest-score definition, and log-sum-exp +stabilization are used. The marginal OMRF correctly handles the +$\Theta_{ss}$ self-interaction term. No reconciliation is needed. + +The mixedGM prototype does **not** implement Blume-Capel variables. The +Blume-Capel pseudolikelihood paths must therefore be adapted from the bgms +OMRF code directly rather than ported from mixedGM (see §1 and Phase B/C). + --- ## Table of contents @@ -71,23 +86,44 @@ The following issues identified in the plan reviews have been fixed in mixedGM: 13. [Testing strategy](#testing-strategy) 14. [Reuse inventory](#reuse-inventory) 15. [Risk register](#risk-register) +16. [PR and commit strategy](#pr-and-commit-strategy) --- ## 1. Model overview -The mixed MRF models the joint distribution of $p$ ordinal variables +The mixed MRF models the joint distribution of $p$ discrete variables $x$ ($x_s \in \{0, 1, \ldots, C_s\}$) and $q$ continuous variables $y$: $$\log f(x, y) \propto \sum_s \mu_{x,s}(x_s) + x^\top K_{xx}\, x - \tfrac{1}{2}(y - \mu_y)^\top K_{yy}\,(y - \mu_y) + 2\, x^\top K_{xy}\, y$$ +### Discrete variable types + +Each discrete variable $x_s$ is either **ordinal** or **Blume-Capel**, +matching the existing `OMRFModel` design: + +| Type | Main-effect parameterization | # free params | Reference level | +|------|------------------------------|:---:|---| +| Ordinal | $\mu_{x,s}(c) = \mu_{s,c}$, free thresholds ($c = 1,\ldots,C_s$; category 0 fixed at 0) | $C_s$ | Category 0 (fixed) | +| Blume-Capel | $\mu_{x,s}(c) = \alpha_s(c - \text{ref}_s) + \beta_s(c - \text{ref}_s)^2$ | 2 | User-specified $\text{ref}_s$ | + +For Blume-Capel variables the observations are centered at +$\text{ref}_s$ in the constructor (i.e., `x_.col(s) -= baseline_category_(s)`), +so all downstream code — rest-scores, sufficient statistics, likelihoods — +operates in a shifted coordinate system where the reference category +corresponds to zero, exactly as in `OMRFModel`. + +The $x^\top K_{xx}\, x$ term uses the (centered) observation values +for both variable types; only the main-effect structure $\mu_{x,s}(\cdot)$ +and the log-partition denominator differ. + Parameters: | Symbol | Storage | Dimension | Role | |--------|---------|-----------|------| -| $\mu_x$ | `mux_` | $p \times \max(C_s)$ | Ordinal thresholds (main effects) | +| $\mu_x$ | `mux_` | $p \times \max(C_s)$ | Ordinal thresholds **or** Blume-Capel ($\alpha, \beta$) | | $K_{xx}$ | `Kxx_` | $p \times p$ symmetric, zero diag | Discrete pairwise interactions | | $K_{yy}$ | `Kyy_` | $q \times q$ SPD | Continuous precision matrix | | $K_{xy}$ | `Kxy_` | $p \times q$ | Cross-type interactions | @@ -136,6 +172,11 @@ $$\log f(x_s = c \mid x_{-s}, y) = \mu_{x,s,c} + c \cdot r_s The rest-score $r_s$ depends on $K_{xx}$ and $K_{xy}$ but **not** on $K_{yy}$. +The formula above shows the ordinal form. For Blume-Capel variables the +main-effect term $\mu_{x,s,c}$ is replaced by +$\alpha_s(c - \text{ref}_s) + \beta_s(c - \text{ref}_s)^2$ and the +denominator uses `compute_denom_blume_capel()` (see §1 and Phase B.1). + **Conditional GGM** — $y \mid x$ is multivariate Gaussian: $$y \mid x \sim N\bigl(\mu_y + 2\,x\,K_{xy}\,K_{yy}^{-1},\; K_{yy}^{-1}\bigr)$$ @@ -192,7 +233,7 @@ the MH acceptance ratio for each group. | Step | Parameter | Components in acceptance ratio | |------|-----------|-------------------------------| -| 1 | $\mu_{x,s,c}$ (one threshold) | `cond_omrf(s)` + prior | +| 1 | $\mu_{x,s}$ (one main effect) | `cond_omrf(s)` + prior | | 2 | $\mu_{y,j}$ (one mean) | `cond_ggm()` + prior | | 3 | $K_{xx,ij}$ (one pair) | `cond_omrf(i) + cond_omrf(j)` + prior | | 4 | $K_{yy}$ (one element) | `cond_ggm()` + prior (Cholesky proposal) | @@ -202,7 +243,7 @@ the MH acceptance ratio for each group. | Step | Parameter | Components in acceptance ratio | |------|-----------|-------------------------------| -| 1 | $\mu_{x,s,c}$ | `marg_omrf(s)` + prior | +| 1 | $\mu_{x,s}$ | `marg_omrf(s)` + prior | | 2 | $\mu_{y,j}$ | `cond_ggm()` + $\sum_s$ `marg_omrf(s)` + prior | | 3 | $K_{xx,ij}$ | `marg_omrf(i) + marg_omrf(j)` + prior | | 4 | $K_{yy}$ | `cond_ggm()` + $\sum_s$ `marg_omrf(s)` + prior (Cholesky proposal) | @@ -216,13 +257,14 @@ expensive per iteration. ### 3.3 Edge selection (post-warmup) -Three independent edge-selection sweeps, same in both PL modes: +Three independent edge-selection sweeps per iteration. The acceptance +components differ by PL mode because $\Theta$ depends on $K_{yy}$: -| Edge type | Indicator | RJ proposal | Acceptance components | -|-----------|-----------|-------------|----------------------| -| Discrete-discrete | $G_{xx}$ | Toggle + spike-and-slab | `omrf(i) + omrf(j)` | -| Continuous-continuous | $G_{yy}$ | Cholesky-based toggle | `cond_ggm()` | -| Cross | $G_{xy}$ | Toggle + spike-and-slab | `omrf(i) + cond_ggm()` | +| Edge type | Indicator | RJ proposal | Conditional PL | Marginal PL | +|-----------|-----------|-------------|----------------|-------------| +| Discrete-discrete | $G_{xx}$ | Toggle + spike-and-slab | `cond_omrf(i) + cond_omrf(j)` | `marg_omrf(i) + marg_omrf(j)` | +| Continuous-continuous | $G_{yy}$ | Cholesky-based toggle | `cond_ggm()` | `cond_ggm()` + $\sum_s$ `marg_omrf(s)` | +| Cross | $G_{xy}$ | Toggle + spike-and-slab | `cond_omrf(i) + cond_ggm()` | `cond_ggm()` + $\sum_s$ `marg_omrf(s)` | --- @@ -286,6 +328,8 @@ public: const arma::imat& x, // n × p discrete (0-based categories) const arma::mat& y, // n × q continuous const arma::ivec& num_categories, // p-vector + const arma::uvec& is_ordinal_variable, // 1 = ordinal, 0 = Blume-Capel + const arma::ivec& baseline_category, // reference category per variable bool edge_selection, const std::string& pseudolikelihood, // "conditional" or "marginal" int seed @@ -324,16 +368,26 @@ public: private: // --- Data --- arma::imat x_; // n × p discrete observations + // Blume-Capel columns are centered at baseline_category_ + // in the constructor (same convention as OMRFModel). + arma::mat x_dbl_; // n × p double version of x_ (post-centering) arma::mat y_; // n × q continuous observations int n_, p_, q_; arma::ivec num_categories_; // p-vector int max_cats_; // max(num_categories) + arma::uvec is_ordinal_variable_; // 1 = ordinal, 0 = Blume-Capel + arma::ivec baseline_category_; // reference category per discrete variable + + // --- Sufficient statistics --- + arma::imat counts_per_category_; // (max_cats+1) × p category counts (ordinal vars only) + arma::imat blume_capel_stats_; // 2 × p linear and quadratic sums (BC vars only) // --- Parameters --- - arma::mat mux_; // p × max_cats thresholds - // mux_(s, c) is the threshold for category c+1 of variable s; - // category 0 is the reference level (threshold fixed at 0). - // In C++ loops always use index c-1 when accessing mux_ for category c. + arma::mat mux_; // p × max_cats thresholds / Blume-Capel coefficients + // Ordinal: mux_(s, c) = threshold for category c+1; + // category 0 is the reference (fixed at 0). + // Blume-Capel: mux_(s, 0) = linear α_s, + // mux_(s, 1) = quadratic β_s. arma::vec muy_; // q-vector continuous means arma::mat Kxx_; // p × p discrete interactions (diagonal always zero; // enforced by construction — not a free parameter) @@ -393,17 +447,41 @@ Implement `parameter_dimension`, `get/set_vectorized_parameters`, `get_edge_indicators`, `get_inclusion_probability`, `get_num_variables`, `get_num_pairwise`, `has_edge_selection`, `has_adaptive_metropolis`. +**Constructor must center Blume-Capel observations:** +```cpp +for (int s = 0; s < p_; ++s) { + if (!is_ordinal_variable_(s)) { + x_.col(s) -= baseline_category_(s); + } +} +x_dbl_ = arma::conv_to::from(x_); +``` +This mirrors `OMRFModel`'s constructor exactly and ensures that rest-scores, +sufficient statistics, and the conditional mean all use the same coordinate +system regardless of variable type. + +**Sufficient statistics** (computed in the constructor, identical to `OMRFModel`): +- `counts_per_category_`: for each ordinal variable, count observations per + category $c \in \{0, \ldots, C_s\}$. +- `blume_capel_stats_`: for each Blume-Capel variable, store + $\sum_i x_{is}$ (row 0, linear) and $\sum_i x_{is}^2$ (row 1, quadratic), + where $x$ is already centered. + **Parameter vectorization order (free parameters only):** -1. `mux_`: For each variable $s = 0,\ldots,p-1$, copy columns $0 \ldots C_s-1$ - of row $s$ (i.e., the thresholds for categories $1 \ldots C_s$; category 0 - is the fixed reference level). Each variable contributes $C_s$ entries; - the total count is $\sum_s C_s$. Variables with different $C_s$ produce - runs of different length — **do not use a fixed stride**. +1. `mux_`: For each variable $s = 0,\ldots,p-1$: + - If ordinal: copy columns $0 \ldots C_s-1$ ($C_s$ thresholds). + - If Blume-Capel: copy columns 0 and 1 ($\alpha_s, \beta_s$; always 2 entries). + Total count: $\sum_{s \in \text{ord}} C_s + 2 \cdot |\text{BC}|$. + Variables produce runs of different length — **do not use a fixed stride**. ``` idx = 0 for s in 0..p-1: - for c in 0..num_categories_[s]-1: - out[idx++] = mux_(s, c) // threshold for category c+1 + if is_ordinal_variable_[s]: + for c in 0..num_categories_[s]-1: + out[idx++] = mux_(s, c) + else: // Blume-Capel + out[idx++] = mux_(s, 0) // linear α + out[idx++] = mux_(s, 1) // quadratic β ``` 2. `Kxx_`: strictly upper-triangular entries (symmetry supplies the lower half). Count = $p(p-1)/2$. Diagonal entries are always zero and excluded. @@ -446,24 +524,45 @@ In `mixed_mrf_likelihoods.cpp`: #### `log_conditional_omrf(int s)` — per-variable discrete PL Computes $\log f(x_s \mid x_{-s}, y)$ summed over all $n$ observations. +The function **branches on `is_ordinal_variable_(s)`** to select the +appropriate main-effect structure and denominator. +**Rest-score (same for both variable types):** ``` -rest_score = x_[, -s] * Kxx_[-s, s] + 2 * y_ * Kxy_[s, ]^T - ^-- n×(p-1) * (p-1)×1 ^-- n×q * q×1 +rest_score = x_dbl_ * Kxx_.col(s) - x_dbl_.col(s) * Kxx_(s,s) + + 2 * y_ * Kxy_.row(s).t() = n-vector +``` +(`Kxx_(s,s) = 0` by construction, so the self-interaction subtraction is +a no-op for $K_{xx}$; it becomes relevant in the marginal case for $\Theta$.) +**Ordinal path:** +``` For each observation v: For c = 1..C_s: - eta[v,c] = mux_[s, c] + c * rest_score[v] + eta[v,c] = mux_(s, c-1) + c * rest_score[v] log_Z[v] = log(1 + sum_c exp(eta[v,c])) (log-sum-exp stabilized) - ll += mux_[s, x_[v,s]] + x_[v,s] * rest_score[v] - log_Z[v] - (only if x_[v,s] > 0 for the threshold part) + ll += mux_(s, x[v,s]-1) + x[v,s] * rest_score[v] - log_Z[v] + (only if x[v,s] > 0 for the threshold part) ``` +Use `compute_denom_ordinal()` from `variable_helpers.h`. -**Reuse opportunity:** The inner loop (compute log-partition for -one ordinal variable given a rest-score vector) is identical to -`OMRFModel::ordinal_log_pseudolikelihood_ratio()`. Extract or -duplicate the stabilized log-sum-exp computation. +**Blume-Capel path:** +``` +alpha = mux_(s, 0); beta = mux_(s, 1); ref = baseline_category_(s) +For each observation v: + For c = 0..C_s: + theta_c = alpha * (c - ref) + beta * (c - ref)^2 + eta[v,c] = theta_c + (c - ref) * rest_score[v] + log_Z[v] = log_sum_exp over c + ll += alpha * x[v,s] + beta * x[v,s]^2 + x[v,s] * rest_score[v] - log_Z[v] + (x already centered at ref) +``` +Use `compute_denom_blume_capel()` from `variable_helpers.h`. + +**Reuse opportunity:** Both denominator functions already exist in +`src/utils/variable_helpers.{h,cpp}` and are battle-tested in the OMRF. +The mixed MRF calls them directly—no duplication needed. #### `log_conditional_ggm()` — conditional GGM log-likelihood @@ -532,10 +631,22 @@ Each update function follows the pattern: 4. Accept/reject 5. Robbins-Monro update of proposal SD -#### `update_threshold(int s, int c)` — one threshold $\mu_{x,s,c}$ -- Propose: $\mu'_{x,s,c} \sim N(\mu_{x,s,c}, \sigma_{\mu_x,s,c})$ -- Acceptance: `log_conditional_omrf(s)` at proposed vs current + prior ratio -- Prior: Normal(0, $\sigma^2$) +#### `update_main_effect(int s, int c)` — one main-effect parameter + +- **Ordinal** ($c \in \{0,\ldots,C_s-1\}$): updates threshold $\mu_{s,c}$. + - Propose: $\mu'_{s,c} \sim N(\mu_{s,c}, \sigma)$ + - Acceptance: `log_conditional_omrf(s)` at proposed vs current + prior + - Prior: Beta-type (same as `OMRFModel`: $\alpha\,x - (\alpha+\beta)\log(1+e^x)$) +- **Blume-Capel** ($c \in \{0, 1\}$: linear, quadratic): + updates $\alpha_s$ or $\beta_s$. + - Propose: same Normal random-walk + - Acceptance: `log_conditional_omrf(s)` at proposed vs current + prior + - Prior: same Beta-type prior on each coefficient + +The function signature is the same for both types; `is_ordinal_variable_(s)` +selects which main-effect slot to perturb. Matches `OMRFModel`'s +`do_one_metropolis_step` loop which iterates over categories for ordinal +variables and over `{0, 1}` for Blume-Capel variables. #### `update_continuous_mean(int j)` — one mean $\mu_{y,j}$ - Propose: $\mu'_{y,j} \sim N(\mu_{y,j}, \sigma_{\mu_y,j})$ @@ -599,10 +710,16 @@ helpers in `mixed_mrf_cholesky.cpp`, ported from ```cpp void MixedMRFModel::do_one_metropolis_step(int iteration) { - // Step 1: Update all thresholds - for (int s = 0; s < p_; ++s) - for (int c = 0; c < num_categories_[s]; ++c) - update_threshold(s, c, iteration); + // Step 1: Update all main effects (ordinal thresholds or BC α/β) + for (int s = 0; s < p_; ++s) { + if (is_ordinal_variable_(s)) { + for (int c = 0; c < num_categories_[s]; ++c) + update_main_effect(s, c, iteration); + } else { + update_main_effect(s, 0, iteration); // linear α + update_main_effect(s, 1, iteration); // quadratic β + } + } // Step 2: Update all continuous means for (int j = 0; j < q_; ++j) @@ -632,13 +749,13 @@ void MixedMRFModel::do_one_metropolis_step(int iteration) { ### B.5 Testing checkpoint — conditional PL estimation -**Test 1: Likelihood agreement** +**Test 1 (T1, T2, T10): Likelihood agreement** - Generate data from `mixed_gibbs_generate()` in R - Compute `log_conditional_omrf(s)` and `log_conditional_ggm()` in both R (prototype) and C++ at the same parameter values - Assert agreement to machine precision -**Test 2: Recovery (estimation only, no edge selection)** +**Test 2 (T13): Recovery (estimation only, no edge selection)** - Generate data from known parameters (p=3 ordinal, q=2 continuous) - Run mixed sampler with conditional PL, no edge selection - Check posterior means recover true parameters (correlation > 0.9) @@ -659,14 +776,26 @@ excluded explicitly: Theta_ = Kxx_ + 2 * Kxy_ * Kyy_inv_ * Kxy_^T (cached; see C.3) // n-vector of rest-scores: -rest_score = x_dbl * Theta_.col(s) // include all n obs, all p vars - - x_dbl.col(s) * Theta_(s, s) // subtract self-interaction +rest_score = x_dbl_ * Theta_.col(s) // include all n obs, all p vars + - x_dbl_.col(s) * Theta_(s, s) // subtract self-interaction + 2.0 * arma::dot(Kxy_.row(s), Kyy_inv_ * muy_) // scalar bias, same for all obs ``` This matches `rcpp_log_pl_marginal_omrf` in `mixedGM/src/log_likelihoods.cpp` -exactly. The inner log-partition loop is then identical to `log_conditional_omrf`. +exactly for ordinal variables. + +**Blume-Capel variables in marginal PL:** The rest-score is the same as +above. The difference is in the numerator and denominator: +- The numerator adds $\alpha_s x_{vs} + \beta_s x_{vs}^2 + + x_{vs} \cdot r_{vs}$ (using centered observations) plus + $\Theta_{ss} \cdot x_{vs}^2$ for the self-interaction. +- The denominator uses `compute_denom_blume_capel()` with `col_offset(c)` + set to $\theta_c + (c - \text{ref})^2 \cdot \Theta_{ss}$ instead of + $\mu_{s,c} + c^2 \cdot \Theta_{ss}$. + +This parallels the ordinal-vs-Blume-Capel branching already present in +`OMRFModel::log_pseudoposterior_with_state()`. ### C.2 Modify `update_continuous_mean(int j)` for marginal mode @@ -713,15 +842,15 @@ in the two modes. ### C.5 Testing checkpoint — marginal PL estimation -**Test 3: Marginal likelihood agreement** +**Test 3 (T3, T11): Marginal likelihood agreement** - Same data as Test 1, compute `log_marginal_omrf(s)` in R and C++ - Assert agreement -**Test 4: Recovery (marginal PL)** +**Test 4 (T14): Recovery (marginal PL)** - Same setup as Test 2 but `pseudolikelihood = "marginal"` - Check posterior means recover true parameters -**Test 5: Conditional vs marginal agreement** +**Test 5 (T16): Conditional vs marginal agreement** - Run both modes on the same data, compare posterior means - They should be similar (not identical — different approximations) @@ -812,9 +941,25 @@ void MixedMRFModel::update_edge_indicators() { } ``` -### D.5 Testing checkpoint — edge selection +### D.5 Blume-Capel and edge selection + +Edge selection for $K_{xx}$ pairs is **type-agnostic**: the acceptance +ratio evaluates `log_conditional_omrf(i) + log_conditional_omrf(j)` +(or marginal equivalents), and those functions already branch on +`is_ordinal_variable_`. No additional edge-selection logic is needed +for Blume-Capel variables—this mirrors the `OMRFModel` design where +`update_edge_indicator_parameter_pair()` calls +`compute_log_likelihood_ratio_for_variable()`, which dispatches to the +correct denominator internally. + +Cross-edges $G_{xy}$ involving a Blume-Capel discrete variable also +require no special handling: the rest-score uses the centered +observations, and the likelihood function dispatches to the Blume-Capel +denominator as needed. + +### D.6 Testing checkpoint — edge selection -**Test 6: Structure recovery** +**Test 6 (T15): Structure recovery** - Generate data from a sparse mixed graph (some edges zero) - Run with edge selection, check posterior inclusion probabilities recover the true structure (true edges have high PIP, false edges low) @@ -840,18 +985,26 @@ The user interface uses **Option A** (decided): a single data frame plus a `variable_type` argument: ```r -bgm(data, variable_type = c("o", "o", "c", ...)) +bgm(data, variable_type = c("ordinal", "blume-capel", "continuous", ...)) ``` - `data`: an $n \times (p+q)$ data frame or matrix with all variables. -- `variable_type`: character vector, length $p+q$; values `"o"` (ordinal) - or `"c"` (continuous). Column order must match `variable_type`. - -`bgm()` splits `data` into the integer matrix `x` (ordinal columns) and -numeric matrix `y` (continuous columns), then dispatches to -`sample_mixed_mrf_cpp()`. The split indices must be stored in the output -object so that `coef`, `predict`, and `simulate` methods can reconstruct -the original column order. +- `variable_type`: character vector, length $p+q$; values `"ordinal"`, + `"blume-capel"`, or `"continuous"`. Column order must match + `variable_type`. Abbreviated forms (`"o"`, `"b"`, `"c"`) may be + supported via `match.arg` for convenience. +- `baseline_category`: integer vector, length $p+q$ (entries for + continuous and ordinal columns are ignored). Specifies the reference + category for each Blume-Capel variable. Passed through to + `sample_mixed_mrf_cpp()`. + +`bgm()` splits `data` into the integer matrix `x` (ordinal + Blume-Capel +columns) and numeric matrix `y` (continuous columns), constructs +`is_ordinal_variable` and `baseline_category` vectors for the discrete +subset, then dispatches to `sample_mixed_mrf_cpp()`. The split indices +and variable-type metadata must be stored in the output object so that +`coef`, `predict`, and `simulate` methods can reconstruct the original +column order. **Missing data** is an explicit non-goal for this PR. `has_missing_data()` returns `false` and `impute_missing()` is an empty @@ -868,7 +1021,7 @@ The output structure needs to accommodate: ### E.4 Testing checkpoint — end-to-end -**Test 7: `bgm()` with mixed data** +**Test 7 (T19): `bgm()` with mixed data** - Call `bgm()` with `variable_type` containing both ordinal and continuous - Check output structure matches expected format - Verify S3 methods work (`print`, `summary`, `coef`, `predict`) @@ -924,10 +1077,17 @@ Extend `mrf_simulation.cpp` to support mixed data generation via block Gibbs sampling, matching `mixed_gibbs_generate()`: 1. Sample $x_s \mid x_{-s}, y$: categorical from log-sum-exp stabilized - probabilities + probabilities. + - For ordinal variables use `compute_probs_ordinal()`. + - For Blume-Capel variables use `compute_probs_blume_capel()` + (both already available in `variable_helpers.h`). 2. Sample $y \mid x$: multivariate Normal with conditional mean $\mu_y + 2\,x\,K_{xy}\,K_{yy}^{-1}$ and covariance $K_{yy}^{-1}$ +Note: the mixedGM Gibbs generator handles ordinal variables only. +The Blume-Capel Gibbs sampling step must be added using the existing +bgms probability helpers. + ### G.2 Mixed MRF prediction Extend `mrf_prediction.cpp` for posterior predictive checks on mixed data. @@ -987,34 +1147,40 @@ Cross-reference the existing mixedGM test files for each scenario: | T1 | `log_conditional_omrf(s)` | Compare C++ vs R prototype at known parameters | | T2 | `log_conditional_ggm()` | Compare C++ vs R prototype at known parameters | | T3 | `log_marginal_omrf(s)` | Compare C++ vs R prototype at known parameters | -| T4 | `parameter_dimension()` | Check count for known p, q, num_categories | -| T5 | Vectorization round-trip | `set(get(params)) == params` | +| T4 | `parameter_dimension()` | Check count for known p, q, num_categories (ordinal + BC mix) | +| T5 | Vectorization round-trip | `set(get(params)) == params` (include BC variables) | | T6 | Cholesky permutation | Verify permute is involution, PD maintained | | T7 | Analytic Gaussian check | Set $K_{xy}=0$ so `log_conditional_ggm()` reduces to a standard MVN and compare against closed form | | T8 | Fixture replay | Load deterministic R fixtures and ensure C++ reproduces saved log-likelihood components bit-for-bit | | T9 | Cache freshness | After each parameter tweak, recompute `conditional_mean_` and (if needed) `Theta_` from scratch and compare with cached copies | +| T10 | BC conditional OMRF | Compare `log_conditional_omrf(s)` for a Blume-Capel variable against OMRF-only model at same parameters | +| T11 | BC marginal OMRF | Same as T10 but for `log_marginal_omrf(s)` | +| T12 | BC observation centering | Verify that centered observations + `compute_denom_blume_capel` produce the same result as the standalone `OMRFModel` | ### Integration tests (sampling correctness) | Test | What | How | |------|------|-----| -| T7 | Conditional PL recovery | Generate → estimate → check cor > 0.9 | -| T8 | Marginal PL recovery | Generate → estimate → check cor > 0.9 | -| T9 | Edge selection recovery | Sparse graph → check PIP > 0.5 for true, < 0.5 for false | -| T10 | Cond vs marginal agreement | Both modes → posterior means close | -| T11 | Reproducibility | Same seed → identical output | -| T12 | Multi-chain | 4 chains → R-hat < 1.1 | +| T13 | Conditional PL recovery | Generate → estimate → check cor > 0.9 | +| T14 | Marginal PL recovery | Generate → estimate → check cor > 0.9 | +| T15 | Edge selection recovery | Sparse graph → check PIP > 0.5 for true, < 0.5 for false | +| T16 | Cond vs marginal agreement | Both modes → posterior means close | +| T17 | Reproducibility | Same seed → identical output | +| T18 | Multi-chain | 4 chains → R-hat < 1.1 | +| T19 | End-to-end `bgm()` | Call `bgm()` with mixed data; check output structure and S3 methods | ### Edge-case & stress tests | Test | What | How | |------|------|-----| -| T13 | Kyy PD invariant | Run 1k iterations with aggressive proposals; Cholesky of $K_{yy}$ must succeed after every accepted move | -| T14 | Cache consistency under RJ | After edge births/deaths, recompute caches and assert equality (debug build) | -| T15 | Degenerate $p=1$ | Single ordinal variable, ensure sampler runs and recovers $K_{xy}, K_{yy}$ | -| T16 | Degenerate $q=1$ | Scalar precision, ensure Cholesky permutation code handles the trivial case | -| T17 | Binary-only ordinal | All $C_s=1$, verify conditional PL matches logistic rest-scores | -| T18 | Gibbs generator sanity | Compare empirical moments from `mixed_gibbs_generate()` against theoretical targets before using it for fixtures | +| T20 | Kyy PD invariant | Run 1k iterations with aggressive proposals; Cholesky of $K_{yy}$ must succeed after every accepted move | +| T21 | Cache consistency under RJ | After edge births/deaths, recompute caches and assert equality (debug build) | +| T22 | Degenerate $p=1$ | Single ordinal variable, ensure sampler runs and recovers $K_{xy}, K_{yy}$ | +| T23 | Degenerate $q=1$ | Scalar precision, ensure Cholesky permutation code handles the trivial case | +| T24 | Binary-only ordinal | All $C_s=1$, verify conditional PL matches logistic rest-scores | +| T25 | Gibbs generator sanity | Compare empirical moments from `mixed_gibbs_generate()` against theoretical targets before using it for fixtures | +| T26 | Mixed ordinal + BC | Graph with both ordinal and Blume-Capel discrete variables; verify recovery | +| T27 | BC-only discrete | All discrete variables are Blume-Capel; verify recovery and edge selection | ### Regression tests @@ -1054,6 +1220,10 @@ Cross-reference the existing mixedGM test files for each scenario: |-----------|--------|------------| | Cholesky update/downdate | `src/models/ggm/cholupdate.h` | Direct include | | Log-sum-exp stabilization | `OMRFModel::compute_logZ_*` | Adapt pattern | +| Ordinal denominator | `src/utils/variable_helpers.{h,cpp}` `compute_denom_ordinal` | Direct call | +| Blume-Capel denominator | `src/utils/variable_helpers.{h,cpp}` `compute_denom_blume_capel` | Direct call | +| Ordinal probabilities | `src/utils/variable_helpers.{h,cpp}` `compute_probs_ordinal` | Direct call (Gibbs generator) | +| Blume-Capel probabilities | `src/utils/variable_helpers.{h,cpp}` `compute_probs_blume_capel` | Direct call (Gibbs generator) | | Robbins-Monro adaptation | `OMRFModel::robbins_monro_*` | Direct reuse | | Edge prior (SBM) | `src/priors/sbm_edge_prior.h` | Direct reuse | | WarmupSchedule | `src/mcmc/execution/warmup_schedule.h` | Direct reuse | @@ -1079,60 +1249,72 @@ Cross-reference the existing mixedGM test files for each scenario: | Factor 2 convention mismatch | Wrong posteriors | Document consistently; unit-test against R prototype | | PD violation during Kyy proposals | Crash | Cholesky-based proposals guarantee PD by construction | | Large parameter space mixing | Poor ESS | Per-parameter Robbins-Monro; future: block updates or HMC | +| Blume-Capel not in prototype | No mixedGM reference for BC paths | Adapt from bgms `OMRFModel`; validated via OMRF-vs-mixed comparison tests (T10-T12) | --- -## Updated implementation workflow - -Given that mixedGM provides tested C++ likelihood implementations and -validated R MH updates, the recommended workflow is: - -### Phase 1: Port likelihoods (1-2 days) - -1. Create `src/models/mixed/` directory -2. Copy `mixedGM/src/log_likelihoods.cpp` → `mixed_mrf_likelihoods.cpp` -3. Remove Rcpp exports, convert to internal functions -4. Adapt to bgms coding conventions (SafeRNG, etc.) -5. **Validation**: Call mixedGM Rcpp functions from R and compare to bgms - -### Phase 2: Skeleton class (1-2 days) - -1. Create `mixed_mrf_model.h` following GGMModel pattern -2. Implement all BaseModel pure virtuals (stubs where needed) -3. Implement `parameter_dimension()`, `get/set_vectorized_parameters()` -4. Implement `clone()`, `set_seed()`, lifecycle hooks -5. **Validation**: Class compiles, vectorization round-trip passes - -### Phase 3: MH updates (2-3 days) - -1. Port `cond_omrf_update_*` functions from R to C++ -2. Port `cond_ggm_update_*` functions from R to C++ -3. Port `cond_update_cross_associations` from R to C++ -4. Implement `do_one_metropolis_step()` calling all update functions -5. **Validation**: Compare posterior samples vs mixedGM on same data - -### Phase 4: Edge selection (2-3 days) - -1. Port `*_update_*_indicator_pair` functions from R to C++ -2. Implement `update_edge_indicators()` calling all three edge types -3. Implement `initialize_graph()`, `prepare_iteration()` -4. **Validation**: Run edge selection tests from mixedGM - -### Phase 5: R interface (1-2 days) - -1. Create `sample_mixed.cpp` following `sample_ggm.cpp` pattern -2. Extend `bgm()` to detect and dispatch mixed data -3. Extend `build_output.R` for mixed model output structure -4. **Validation**: End-to-end `bgm()` call works - -### Phase 6: Gibbs generator (1 day) - -1. Port `mixed_gibbs_generate_cpp()` to `mrf_simulation.cpp` -2. Extend `simulate.bgms()` for mixed models -3. **Validation**: Generated data matches mixedGM output - -### Total estimated effort: 8-13 days - -The C++ likelihood implementations (Phase B-C in the original plan) are -largely complete via mixedGM, reducing the remaining work to porting MH -updates and wiring into the bgms infrastructure. +## PR and commit strategy + +**Branch:** `ggm_mixed` (currently at the same commit as `main`). +**PR:** A single draft PR opened early for visibility, merged only after +all phases are complete and CI passes. + +### Principles + +1. **Every commit compiles and passes `R CMD check`.** No broken + intermediate states on the branch. If a phase needs multiple + sub-steps before the package can build, stage them locally and + squash into one commit that restores a green state. +2. **One commit per logical unit of work.** A "logical unit" is the + smallest change that is self-contained and reviewable on its own: + a new file, a completed function group, a test suite, a wiring + change. Avoid mixing unrelated concerns in one commit. +3. **Tests accompany the code they exercise.** When a commit adds + a function, the corresponding unit test belongs in the same commit + (or the immediately following one if fixtures must be generated + first). Do not defer all tests to the end. +4. **Commit messages use the project's conventional-commit prefixes:** + `feat:`, `fix:`, `test:`, `refactor:`, `docs:`, `dev:`. + Keep the first line under 72 characters; add a body paragraph only + when the "why" is non-obvious. + +### Planned commits + +The table below maps implementation phases to commits. Each row is one +`git commit`. Rows may be split further if a phase turns out large, or +merged if two steps are trivially small — the guiding rule is always +principle 1 (green build) and principle 2 (one logical concern). + +| # | Prefix | Message | Phase | What ships | +|:-:|--------|---------|:-----:|------------| +| 1 | `feat` | `feat: add MixedMRFModel skeleton and data structures` | A | Header, constructor (incl. BC centering), trivial overrides, vectorization, sufficient statistics. Stubs for sampling functions (`do_one_metropolis_step` etc.) so the class compiles. No sampling yet. | +| 2 | `test` | `test: add mixed MRF skeleton tests` | A | `test-mixed-mrf-skeleton.R`: compile check, `parameter_dimension`, vectorization round-trip (ordinal + BC mix). | +| 3 | `feat` | `feat: add mixed MRF likelihood functions` | B.1 | `mixed_mrf_likelihoods.cpp`: `log_conditional_omrf`, `log_conditional_ggm`, cache helpers. Ordinal + Blume-Capel paths. | +| 4 | `test` | `test: add mixed MRF likelihood unit tests` | B.1 | `test-mixed-mrf-likelihood.R`: T1–T3 (compare C++ vs R fixtures), T7 (analytic Gaussian), T10–T12 (BC-specific). | +| 5 | `feat` | `feat: add conditional PL Metropolis updates` | B.3–B.4 | `mixed_mrf_metropolis.cpp`: all 5 MH update functions + `do_one_metropolis_step` (conditional mode). Cholesky helpers in `mixed_mrf_cholesky.cpp`. | +| 6 | `test` | `test: add conditional PL recovery test` | B.5 | `test-mixed-mrf-sampling.R`: T13 (conditional PL parameter recovery, estimation only). | +| 7 | `feat` | `feat: add marginal PL mode and Theta caching` | C | `log_marginal_omrf`, Theta cache, marginal-mode branches in MH updates. | +| 8 | `test` | `test: add marginal PL recovery and comparison tests` | C.5 | T14 (marginal PL recovery), T16 (cond vs marginal agreement). | +| 9 | `feat` | `feat: add mixed MRF edge selection` | D | `mixed_mrf_edge_selection.cpp`: RJ sweeps for Kxx, Kyy, Kxy. `update_edge_indicators`, `initialize_graph`, `prepare_iteration`. | +| 10 | `test` | `test: add mixed MRF edge selection tests` | D | T15 (structure recovery), T26–T27 (BC edge cases). | +| 11 | `feat` | `feat: add Rcpp interface and bgm() mixed dispatch` | E | `sample_mixed.cpp`, `bgm.R` changes, `validate_data.R`/`validate_model.R` extensions, `build_output.R` mixed output. | +| 12 | `test` | `test: add end-to-end bgm() mixed model tests` | E | T19 (end-to-end), S3 method checks (`print`, `summary`, `coef`). | +| 13 | `feat` | `feat: add mixed MRF warmup and adaptation` | F | `init_metropolis_adaptation`, `tune_proposal_sd`, warmup schedule integration. | +| 14 | `feat` | `feat: add mixed MRF simulation and prediction` | G | Gibbs generator in `mrf_simulation.cpp`, `simulate.bgms` + `predict.bgms` extensions. | +| 15 | `test` | `test: add simulation and prediction tests` | G | T25 (Gibbs sanity), generator tests, prediction tests. | +| 16 | `test` | `test: verify GGM, OMRF, bgmCompare regression suite` | — | Run full existing test suite, fix any regressions. R1–R3. | +| 17 | `docs` | `docs: add mixed MRF documentation and pkgdown entries` | — | Roxygen for new exported functions, `_pkgdown.yml` entries, NEWS.md entry. | + +### Conventions for this branch + +- **Do not rebase onto main mid-flight.** Merge main into `ggm_mixed` + only if a conflict blocks progress, and note it in the PR + description. +- **Fixture files** (`.rds`, `.rda`) generated for tests go in + `dev/fixtures/mixed/` and are committed alongside the test that + consumes them. +- **Plan updates** during implementation get their own `docs:` commit + (e.g., `docs: update mixed MRF plan — Phase B lessons`). +- **Squash-merge** is used when merging the PR into `main`. The full + per-phase history remains on the branch for reference, but `main` + gets a single clean merge commit. From 6454d3721a3b475236573cf376332fbec30106c2 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Thu, 5 Mar 2026 22:46:05 +0100 Subject: [PATCH 02/60] feat: add MixedMRFModel skeleton and data structures --- dev/mixedmrf/implementation_plan.md | 34 +- src/models/mixed/mixed_mrf_model.cpp | 446 +++++++++++++++++++++++++++ src/models/mixed/mixed_mrf_model.h | 342 ++++++++++++++++++++ 3 files changed, 805 insertions(+), 17 deletions(-) create mode 100644 src/models/mixed/mixed_mrf_model.cpp create mode 100644 src/models/mixed/mixed_mrf_model.h diff --git a/dev/mixedmrf/implementation_plan.md b/dev/mixedmrf/implementation_plan.md index 8566118d..eb57f2e9 100644 --- a/dev/mixedmrf/implementation_plan.md +++ b/dev/mixedmrf/implementation_plan.md @@ -110,7 +110,7 @@ matching the existing `OMRFModel` design: | Blume-Capel | $\mu_{x,s}(c) = \alpha_s(c - \text{ref}_s) + \beta_s(c - \text{ref}_s)^2$ | 2 | User-specified $\text{ref}_s$ | For Blume-Capel variables the observations are centered at -$\text{ref}_s$ in the constructor (i.e., `x_.col(s) -= baseline_category_(s)`), +$\text{ref}_s$ in the constructor (i.e., `discrete_observations_.col(s) -= baseline_category_(s)`), so all downstream code — rest-scores, sufficient statistics, likelihoods — operates in a shifted coordinate system where the reference category corresponds to zero, exactly as in `OMRFModel`. @@ -325,8 +325,8 @@ class MixedMRFModel : public BaseModel { public: // Construction MixedMRFModel( - const arma::imat& x, // n × p discrete (0-based categories) - const arma::mat& y, // n × q continuous + const arma::imat& discrete_observations, // n × p discrete (0-based categories) + const arma::mat& continuous_observations, // n × q continuous const arma::ivec& num_categories, // p-vector const arma::uvec& is_ordinal_variable, // 1 = ordinal, 0 = Blume-Capel const arma::ivec& baseline_category, // reference category per variable @@ -367,11 +367,11 @@ public: private: // --- Data --- - arma::imat x_; // n × p discrete observations - // Blume-Capel columns are centered at baseline_category_ - // in the constructor (same convention as OMRFModel). - arma::mat x_dbl_; // n × p double version of x_ (post-centering) - arma::mat y_; // n × q continuous observations + arma::imat discrete_observations_; // n × p discrete observations + // Blume-Capel columns centered at baseline_category_ + // in the constructor (same convention as OMRFModel). + arma::mat discrete_observations_dbl_; // n × p double version (post-centering) + arma::mat continuous_observations_; // n × q continuous observations int n_, p_, q_; arma::ivec num_categories_; // p-vector int max_cats_; // max(num_categories) @@ -451,10 +451,10 @@ Implement `parameter_dimension`, `get/set_vectorized_parameters`, ```cpp for (int s = 0; s < p_; ++s) { if (!is_ordinal_variable_(s)) { - x_.col(s) -= baseline_category_(s); + discrete_observations_.col(s) -= baseline_category_(s); } } -x_dbl_ = arma::conv_to::from(x_); +discrete_observations_dbl_ = arma::conv_to::from(discrete_observations_); ``` This mirrors `OMRFModel`'s constructor exactly and ensures that rest-scores, sufficient statistics, and the conditional mean all use the same coordinate @@ -529,8 +529,8 @@ appropriate main-effect structure and denominator. **Rest-score (same for both variable types):** ``` -rest_score = x_dbl_ * Kxx_.col(s) - x_dbl_.col(s) * Kxx_(s,s) - + 2 * y_ * Kxy_.row(s).t() +rest_score = discrete_observations_dbl_ * Kxx_.col(s) - discrete_observations_dbl_.col(s) * Kxx_(s,s) + + 2 * continuous_observations_ * Kxy_.row(s).t() = n-vector ``` (`Kxx_(s,s) = 0` by construction, so the self-interaction subtraction is @@ -570,8 +570,8 @@ Computes $\log f(y \mid x)$ using cached `Kyy_inv_`, `Kyy_log_det_`, and `conditional_mean_`. ``` -conditional_mean_ = 1*muy_^T + 2 * x_ * Kxy_ * Kyy_inv_ -D = y_ - conditional_mean_ +conditional_mean_ = 1*muy_^T + 2 * discrete_observations_ * Kxy_ * Kyy_inv_ +D = continuous_observations_ - conditional_mean_ quad_sum = sum((D * Kyy_) .* D) // trace of Kyy * D^T * D ll = n/2 * (-q * log(2*pi) + Kyy_log_det_) - quad_sum / 2 ``` @@ -582,7 +582,7 @@ but with a non-zero, observation-dependent conditional mean. ### B.2 Implement cache maintenance #### `recompute_conditional_mean()` -Recompute `conditional_mean_ = 1*muy^T + 2 * x_ * Kxy_ * Kyy_inv_`. +Recompute `conditional_mean_ = 1*muy^T + 2 * discrete_observations_ * Kxy_ * Kyy_inv_`. Called after any change to `muy_`, `Kxy_`, or `Kyy_`. #### `recompute_Kyy_decomposition()` @@ -776,8 +776,8 @@ excluded explicitly: Theta_ = Kxx_ + 2 * Kxy_ * Kyy_inv_ * Kxy_^T (cached; see C.3) // n-vector of rest-scores: -rest_score = x_dbl_ * Theta_.col(s) // include all n obs, all p vars - - x_dbl_.col(s) * Theta_(s, s) // subtract self-interaction +rest_score = discrete_observations_dbl_ * Theta_.col(s) // include all n obs, all p vars + - discrete_observations_dbl_.col(s) * Theta_(s, s) // subtract self-interaction + 2.0 * arma::dot(Kxy_.row(s), Kyy_inv_ * muy_) // scalar bias, same for all obs ``` diff --git a/src/models/mixed/mixed_mrf_model.cpp b/src/models/mixed/mixed_mrf_model.cpp new file mode 100644 index 00000000..71002763 --- /dev/null +++ b/src/models/mixed/mixed_mrf_model.cpp @@ -0,0 +1,446 @@ +#include +#include "models/mixed/mixed_mrf_model.h" +#include "rng/rng_utils.h" +#include "mcmc/execution/warmup_schedule.h" + + +// ============================================================================= +// Constructor +// ============================================================================= + +MixedMRFModel::MixedMRFModel( + const arma::imat& discrete_observations, + const arma::mat& continuous_observations, + const arma::ivec& num_categories, + const arma::uvec& is_ordinal_variable, + const arma::ivec& baseline_category, + const arma::mat& inclusion_probability, + const arma::imat& initial_edge_indicators, + bool edge_selection, + const std::string& pseudolikelihood, + double main_alpha, + double main_beta, + double pairwise_scale, + int seed +) : + n_(discrete_observations.n_rows), + p_(discrete_observations.n_cols), + q_(continuous_observations.n_cols), + discrete_observations_(discrete_observations), + continuous_observations_(continuous_observations), + num_categories_(num_categories), + is_ordinal_variable_(is_ordinal_variable), + baseline_category_(baseline_category), + edge_indicators_(initial_edge_indicators), + inclusion_probability_(inclusion_probability), + edge_selection_(edge_selection), + edge_selection_active_(false), + main_alpha_(main_alpha), + main_beta_(main_beta), + pairwise_scale_(pairwise_scale), + use_marginal_pl_(pseudolikelihood == "marginal"), + rng_(seed) +{ + // Dimension counts + num_main_ = count_num_main_effects(); + num_pairwise_xx_ = (p_ * (p_ - 1)) / 2; + num_pairwise_yy_ = (q_ * (q_ - 1)) / 2; + num_cross_ = p_ * q_; + + max_cats_ = num_categories_.max(); + + // Center Blume-Capel observations at baseline category so that all + // downstream code operates in a shifted coordinate system where the + // reference corresponds to zero (same convention as OMRFModel). + for(size_t s = 0; s < p_; ++s) { + if(!is_ordinal_variable_(s)) { + discrete_observations_.col(s) -= baseline_category_(s); + } + } + discrete_observations_dbl_ = arma::conv_to::from(discrete_observations_); + + // Compute sufficient statistics + compute_sufficient_statistics(); + + // Initialize parameters to zero + mux_ = arma::zeros(p_, max_cats_); + muy_ = arma::zeros(q_); + Kxx_ = arma::zeros(p_, p_); + Kyy_ = arma::eye(q_, q_); + Kxy_ = arma::zeros(p_, q_); + + // Initialize proposal SDs + prop_sd_mux_ = arma::ones(p_, max_cats_); + prop_sd_muy_ = arma::ones(q_); + prop_sd_Kxx_ = arma::ones(p_, p_); + prop_sd_Kyy_ = arma::ones(q_, q_); + prop_sd_Kxy_ = arma::ones(p_, q_); + + // Initialize Kyy caches (Kyy starts as identity) + Kyy_chol_ = arma::eye(q_, q_); + Kyy_inv_ = arma::eye(q_, q_); + Kyy_log_det_ = 0.0; + + // Initialize conditional mean: μ_y' + 2 x Kxy Kyy_inv + // With Kxy = 0 and Kyy = I, this is just 1 * μ_y' = 0. + conditional_mean_ = arma::zeros(n_, q_); + + // Initialize Theta (marginal PL only): Kxx + 2 Kxy Kyy_inv Kxy' + // With Kxy = 0, Theta = Kxx = 0. + if(use_marginal_pl_) { + Theta_ = arma::zeros(p_, p_); + } + + // Initialize edge-order permutation vectors + edge_order_xx_ = arma::regspace(0, num_pairwise_xx_ - 1); + edge_order_yy_ = arma::regspace(0, num_pairwise_yy_ - 1); + edge_order_xy_ = arma::regspace(0, num_cross_ - 1); +} + + +// ============================================================================= +// Copy constructor +// ============================================================================= + +MixedMRFModel::MixedMRFModel(const MixedMRFModel& other) + : BaseModel(other), + n_(other.n_), + p_(other.p_), + q_(other.q_), + num_main_(other.num_main_), + num_pairwise_xx_(other.num_pairwise_xx_), + num_pairwise_yy_(other.num_pairwise_yy_), + num_cross_(other.num_cross_), + discrete_observations_(other.discrete_observations_), + discrete_observations_dbl_(other.discrete_observations_dbl_), + continuous_observations_(other.continuous_observations_), + num_categories_(other.num_categories_), + max_cats_(other.max_cats_), + is_ordinal_variable_(other.is_ordinal_variable_), + baseline_category_(other.baseline_category_), + counts_per_category_(other.counts_per_category_), + blume_capel_stats_(other.blume_capel_stats_), + mux_(other.mux_), + muy_(other.muy_), + Kxx_(other.Kxx_), + Kyy_(other.Kyy_), + Kxy_(other.Kxy_), + edge_indicators_(other.edge_indicators_), + inclusion_probability_(other.inclusion_probability_), + edge_selection_(other.edge_selection_), + edge_selection_active_(other.edge_selection_active_), + main_alpha_(other.main_alpha_), + main_beta_(other.main_beta_), + pairwise_scale_(other.pairwise_scale_), + prop_sd_mux_(other.prop_sd_mux_), + prop_sd_muy_(other.prop_sd_muy_), + prop_sd_Kxx_(other.prop_sd_Kxx_), + prop_sd_Kyy_(other.prop_sd_Kyy_), + prop_sd_Kxy_(other.prop_sd_Kxy_), + Kyy_inv_(other.Kyy_inv_), + Kyy_chol_(other.Kyy_chol_), + Kyy_log_det_(other.Kyy_log_det_), + Theta_(other.Theta_), + conditional_mean_(other.conditional_mean_), + use_marginal_pl_(other.use_marginal_pl_), + rng_(other.rng_), + edge_order_xx_(other.edge_order_xx_), + edge_order_yy_(other.edge_order_yy_), + edge_order_xy_(other.edge_order_xy_) +{ +} + + +// ============================================================================= +// Sufficient statistics +// ============================================================================= + +void MixedMRFModel::compute_sufficient_statistics() { + // Category counts for ordinal variables + counts_per_category_ = arma::zeros(max_cats_ + 1, p_); + for(size_t s = 0; s < p_; ++s) { + if(is_ordinal_variable_(s)) { + for(size_t i = 0; i < n_; ++i) { + int cat = discrete_observations_(i, s); + if(cat >= 0 && cat <= num_categories_(s)) { + counts_per_category_(cat, s)++; + } + } + } + } + + // Blume-Capel statistics (linear and quadratic sums of centered obs) + blume_capel_stats_ = arma::zeros(2, p_); + for(size_t s = 0; s < p_; ++s) { + if(!is_ordinal_variable_(s)) { + for(size_t i = 0; i < n_; ++i) { + int val = discrete_observations_(i, s); // already centered + blume_capel_stats_(0, s) += val; + blume_capel_stats_(1, s) += val * val; + } + } + } +} + + +size_t MixedMRFModel::count_num_main_effects() const { + size_t count = 0; + for(size_t s = 0; s < p_; ++s) { + if(is_ordinal_variable_(s)) { + count += num_categories_(s); + } else { + count += 2; // linear α and quadratic β + } + } + return count; +} + + +// ============================================================================= +// Cache maintenance +// ============================================================================= + +void MixedMRFModel::recompute_conditional_mean() { + // conditional_mean_ = 1*μ_y' + 2 * discrete_obs * Kxy * Kyy_inv + conditional_mean_ = arma::repmat(muy_.t(), n_, 1) + + 2.0 * discrete_observations_dbl_ * Kxy_ * Kyy_inv_; +} + +void MixedMRFModel::recompute_Kyy_decomposition() { + Kyy_chol_ = arma::chol(Kyy_); // upper Cholesky + Kyy_inv_ = arma::inv_sympd(Kyy_); + Kyy_log_det_ = 2.0 * arma::sum(arma::log(Kyy_chol_.diag())); +} + +void MixedMRFModel::recompute_Theta() { + // Θ = Kxx + 2 Kxy Kyy_inv Kxy' + Theta_ = Kxx_ + 2.0 * Kxy_ * Kyy_inv_ * Kxy_.t(); +} + + +// ============================================================================= +// Parameter vectorization +// ============================================================================= + +// Vectorization order (free parameters): +// 1. mux_: per-variable (ordinal: C_s thresholds; BC: 2 coefficients) +// 2. Kxx_: upper-triangular, row-major — p(p-1)/2 +// 3. muy_: all q means +// 4. Kyy_: upper-triangle including diagonal — q(q+1)/2 +// 5. Kxy_: all p*q entries, row-major + +size_t MixedMRFModel::parameter_dimension() const { + if(!edge_selection_active_) { + return full_parameter_dimension(); + } + // Count active parameters only + size_t dim = num_main_ + q_; // mux + muy always active + + // Active Kxx edges + for(size_t i = 0; i < p_ - 1; ++i) { + for(size_t j = i + 1; j < p_; ++j) { + if(gxx(i, j)) dim++; + } + } + + // Kyy diagonal always active; off-diagonal gated by edge indicators + dim += q_; // diagonal + for(size_t i = 0; i < q_ - 1; ++i) { + for(size_t j = i + 1; j < q_; ++j) { + if(gyy(i, j)) dim++; + } + } + + // Active Kxy edges + for(size_t i = 0; i < p_; ++i) { + for(size_t j = 0; j < q_; ++j) { + if(gxy(i, j)) dim++; + } + } + + return dim; +} + +size_t MixedMRFModel::full_parameter_dimension() const { + // mux + Kxx upper-tri + muy + Kyy upper-tri-with-diag + Kxy full + return num_main_ + num_pairwise_xx_ + q_ + + (q_ * (q_ + 1)) / 2 + num_cross_; +} + +arma::vec MixedMRFModel::get_vectorized_parameters() const { + arma::vec out(full_parameter_dimension(), arma::fill::zeros); + size_t idx = 0; + + // 1. mux_ + for(size_t s = 0; s < p_; ++s) { + if(is_ordinal_variable_(s)) { + for(int c = 0; c < num_categories_(s); ++c) { + out(idx++) = mux_(s, c); + } + } else { + out(idx++) = mux_(s, 0); // linear α + out(idx++) = mux_(s, 1); // quadratic β + } + } + + // 2. Kxx_ upper-triangular + for(size_t i = 0; i < p_ - 1; ++i) { + for(size_t j = i + 1; j < p_; ++j) { + out(idx++) = Kxx_(i, j); + } + } + + // 3. muy_ + for(size_t j = 0; j < q_; ++j) { + out(idx++) = muy_(j); + } + + // 4. Kyy_ upper-triangle including diagonal + for(size_t i = 0; i < q_; ++i) { + for(size_t j = i; j < q_; ++j) { + out(idx++) = Kyy_(i, j); + } + } + + // 5. Kxy_ row-major + for(size_t i = 0; i < p_; ++i) { + for(size_t j = 0; j < q_; ++j) { + out(idx++) = Kxy_(i, j); + } + } + + return out; +} + +arma::vec MixedMRFModel::get_full_vectorized_parameters() const { + return get_vectorized_parameters(); +} + +void MixedMRFModel::set_vectorized_parameters(const arma::vec& params) { + size_t idx = 0; + + // 1. mux_ + for(size_t s = 0; s < p_; ++s) { + if(is_ordinal_variable_(s)) { + for(int c = 0; c < num_categories_(s); ++c) { + mux_(s, c) = params(idx++); + } + } else { + mux_(s, 0) = params(idx++); + mux_(s, 1) = params(idx++); + } + } + + // 2. Kxx_ upper-triangular (mirror to lower) + for(size_t i = 0; i < p_ - 1; ++i) { + for(size_t j = i + 1; j < p_; ++j) { + Kxx_(i, j) = params(idx); + Kxx_(j, i) = params(idx); + idx++; + } + } + + // 3. muy_ + for(size_t j = 0; j < q_; ++j) { + muy_(j) = params(idx++); + } + + // 4. Kyy_ upper-triangle including diagonal (mirror off-diag) + for(size_t i = 0; i < q_; ++i) { + for(size_t j = i; j < q_; ++j) { + Kyy_(i, j) = params(idx); + if(i != j) Kyy_(j, i) = params(idx); + idx++; + } + } + + // 5. Kxy_ row-major + for(size_t i = 0; i < p_; ++i) { + for(size_t j = 0; j < q_; ++j) { + Kxy_(i, j) = params(idx++); + } + } + + // Refresh all caches + recompute_Kyy_decomposition(); + recompute_conditional_mean(); + if(use_marginal_pl_) { + recompute_Theta(); + } +} + +arma::ivec MixedMRFModel::get_vectorized_indicator_parameters() { + size_t total = num_pairwise_xx_ + num_pairwise_yy_ + num_cross_; + arma::ivec out(total); + size_t idx = 0; + + // 1. Upper-triangle of Gxx + for(size_t i = 0; i < p_ - 1; ++i) { + for(size_t j = i + 1; j < p_; ++j) { + out(idx++) = gxx(i, j); + } + } + + // 2. Upper-triangle of Gyy + for(size_t i = 0; i < q_ - 1; ++i) { + for(size_t j = i + 1; j < q_; ++j) { + out(idx++) = gyy(i, j); + } + } + + // 3. Full Gxy block row-major + for(size_t i = 0; i < p_; ++i) { + for(size_t j = 0; j < q_; ++j) { + out(idx++) = gxy(i, j); + } + } + + return out; +} + + +// ============================================================================= +// Infrastructure +// ============================================================================= + +void MixedMRFModel::set_seed(int seed) { + rng_ = SafeRNG(seed); +} + +std::unique_ptr MixedMRFModel::clone() const { + return std::make_unique(*this); +} + + +// ============================================================================= +// Stubs (to be implemented in later phases) +// ============================================================================= + +void MixedMRFModel::do_one_metropolis_step(int /*iteration*/) { + // Phase B/C: MH updates for all parameter groups +} + +void MixedMRFModel::update_edge_indicators() { + // Phase D: reversible-jump edge birth/death moves +} + +void MixedMRFModel::initialize_graph() { + // Phase D: random graph initialization for edge selection + // For now, start with all edges included (matching initial_edge_indicators) +} + +void MixedMRFModel::prepare_iteration() { + // Shuffle edge-update order to avoid order bias. + // Always called, even when edge selection is off, to keep RNG consistent. + edge_order_xx_ = arma_randperm(rng_, num_pairwise_xx_); + edge_order_yy_ = arma_randperm(rng_, num_pairwise_yy_); + edge_order_xy_ = arma_randperm(rng_, num_cross_); +} + +void MixedMRFModel::init_metropolis_adaptation(const WarmupSchedule& /*schedule*/) { + // Phase F: initialize Robbins-Monro controllers +} + +void MixedMRFModel::tune_proposal_sd(int /*iteration*/, const WarmupSchedule& /*schedule*/) { + // Phase F: Robbins-Monro proposal-SD tuning +} diff --git a/src/models/mixed/mixed_mrf_model.h b/src/models/mixed/mixed_mrf_model.h new file mode 100644 index 00000000..b26910d6 --- /dev/null +++ b/src/models/mixed/mixed_mrf_model.h @@ -0,0 +1,342 @@ +#pragma once + +#include +#include "models/base_model.h" +#include "rng/rng_utils.h" + +/** + * MixedMRFModel - Mixed Markov Random Field Model + * + * Joint model for p discrete (ordinal or Blume-Capel) variables x and + * q continuous variables y. The joint density is: + * + * log f(x, y) ∝ Σ_s μ_{x,s}(x_s) + x' Kxx x + * - ½ (y - μ_y)' Kyy (y - μ_y) + 2 x' Kxy y + * + * Supports both conditional and marginal pseudo-likelihood, with and + * without edge selection via spike-and-slab priors. + * + * Discrete variables are either ordinal (free category thresholds, category + * 0 as reference) or Blume-Capel (linear α + quadratic β, user-specified + * reference). Blume-Capel observations are centered at their baseline + * category in the constructor, matching the OMRFModel convention. + * + * Inherits from BaseModel for compatibility with the generic MCMC framework + * (ChainRunner, MetropolisSampler, WarmupSchedule). + */ +class MixedMRFModel : public BaseModel { +public: + + // ========================================================================= + // Construction + // ========================================================================= + + /** + * Construct from raw observations. + * + * @param discrete_observations Integer matrix of discrete observations (n × p, 0-based) + * @param continuous_observations Continuous observations (n × q) + * @param num_categories Number of categories per discrete variable (p-vector) + * @param is_ordinal_variable 1 = ordinal, 0 = Blume-Capel (p-vector) + * @param baseline_category Reference category per discrete variable (p-vector) + * @param inclusion_probability Prior inclusion probabilities ((p+q) × (p+q)) + * @param initial_edge_indicators Initial edge inclusion matrix ((p+q) × (p+q)) + * @param edge_selection Enable edge selection (spike-and-slab) + * @param pseudolikelihood "conditional" or "marginal" + * @param main_alpha Beta prior hyperparameter α for main effects + * @param main_beta Beta prior hyperparameter β for main effects + * @param pairwise_scale Scale parameter of Cauchy prior on interactions + * @param seed RNG seed for reproducibility + */ + MixedMRFModel( + const arma::imat& discrete_observations, + const arma::mat& continuous_observations, + const arma::ivec& num_categories, + const arma::uvec& is_ordinal_variable, + const arma::ivec& baseline_category, + const arma::mat& inclusion_probability, + const arma::imat& initial_edge_indicators, + bool edge_selection, + const std::string& pseudolikelihood, + double main_alpha = 1.0, + double main_beta = 1.0, + double pairwise_scale = 2.5, + int seed = 1 + ); + + /** Copy constructor for cloning (required for parallel chains). */ + MixedMRFModel(const MixedMRFModel& other); + + // ========================================================================= + // Capability queries + // ========================================================================= + + /** @return false (MixedMRFModel is Metropolis-only, no gradient). */ + bool has_gradient() const override { return false; } + /** @return true (supports adaptive Metropolis via Robbins-Monro). */ + bool has_adaptive_metropolis() const override { return true; } + /** @return true when edge selection is enabled. */ + bool has_edge_selection() const override { return edge_selection_; } + /** @return false (missing data not yet supported). */ + bool has_missing_data() const override { return false; } + + // ========================================================================= + // Core sampling methods + // ========================================================================= + + /** + * Perform one full Metropolis sweep over all parameter groups. + * @param iteration Current iteration (for Robbins-Monro adaptation) + */ + void do_one_metropolis_step(int iteration = -1) override; + + /** + * Initialize Metropolis adaptation controllers for proposal-SD tuning. + * Called before warmup begins. + */ + void init_metropolis_adaptation(const WarmupSchedule& schedule) override; + + /** + * Tune proposal SDs via Robbins-Monro (Stage 3b). + * Called every iteration; checks schedule internally. + */ + void tune_proposal_sd(int iteration, const WarmupSchedule& schedule) override; + + /** + * Shuffle edge update order at the start of each iteration. + * Advances the RNG state consistently even when edge selection is off. + */ + void prepare_iteration() override; + + // ========================================================================= + // Edge selection + // ========================================================================= + + /** Perform one sweep of reversible-jump edge birth/death moves. */ + void update_edge_indicators() override; + + /** Initialize a random graph structure for starting edge selection. */ + void initialize_graph() override; + + /** + * Enable or disable edge-selection proposals. + * @param active true to enable edge birth/death moves + */ + void set_edge_selection_active(bool active) override { + edge_selection_active_ = active; + } + + // ========================================================================= + // Parameter vectorization + // ========================================================================= + + /** + * Dimensionality of the active parameter space. + * When edge selection is active, excludes parameters for inactive edges. + */ + size_t parameter_dimension() const override; + + /** + * Full parameter dimension (all parameters, regardless of edge state). + * Used for fixed-size sample storage. + */ + size_t full_parameter_dimension() const override; + + /** Get active parameters as a flat vector. */ + arma::vec get_vectorized_parameters() const override; + + /** Get all parameters as a flat vector (inactive edges are 0). */ + arma::vec get_full_vectorized_parameters() const override; + + /** Set parameters from a flat vector. */ + void set_vectorized_parameters(const arma::vec& params) override; + + /** Get vectorized edge indicators (Gxx upper-tri, Gyy upper-tri, Gxy full). */ + arma::ivec get_vectorized_indicator_parameters() override; + + // ========================================================================= + // Infrastructure + // ========================================================================= + + /** Set random seed for reproducibility. */ + void set_seed(int seed) override; + + /** Clone the model for parallel execution. */ + std::unique_ptr clone() const override; + + /** @return Reference to the model's random number generator. */ + SafeRNG& get_rng() override { return rng_; } + + /** @return Current edge-indicator matrix ((p+q) × (p+q)). */ + const arma::imat& get_edge_indicators() const override { + return edge_indicators_; + } + + /** @return Mutable reference to the prior inclusion-probability matrix. */ + arma::mat& get_inclusion_probability() override { + return inclusion_probability_; + } + + /** @return Total number of variables (p + q). */ + int get_num_variables() const override { + return static_cast(p_ + q_); + } + + /** + * Number of unique off-diagonal pairs in the (p+q) × (p+q) indicator + * matrix: p(p-1)/2 + q(q-1)/2 + p*q. + */ + int get_num_pairwise() const override { + return static_cast(num_pairwise_xx_ + num_pairwise_yy_ + num_cross_); + } + + // ========================================================================= + // Missing data (not yet supported) + // ========================================================================= + + /** No-op: missing data not supported in this PR. */ + void impute_missing() override {} + +private: + + // ========================================================================= + // Counts and dimensions + // ========================================================================= + + size_t n_; // Number of observations + size_t p_; // Number of discrete variables + size_t q_; // Number of continuous variables + size_t num_main_; // Total main-effect params (Σ C_s for ord + 2 per BC) + size_t num_pairwise_xx_; // p(p-1)/2 + size_t num_pairwise_yy_; // q(q-1)/2 + size_t num_cross_; // p * q + + // ========================================================================= + // Data + // ========================================================================= + + arma::imat discrete_observations_; // Discrete observations (n × p) + // BC columns centered at baseline_category_ in ctor. + arma::mat discrete_observations_dbl_; // Double version (post-centering) + arma::mat continuous_observations_; // Continuous observations (n × q) + arma::ivec num_categories_; // Categories per discrete variable (p-vector) + int max_cats_; // max(num_categories) + arma::uvec is_ordinal_variable_; // 1 = ordinal, 0 = Blume-Capel (p-vector) + arma::ivec baseline_category_; // Reference category per discrete variable (p-vector) + + // ========================================================================= + // Sufficient statistics + // ========================================================================= + + arma::imat counts_per_category_; // (max_cats+1) × p category counts (ordinal only) + arma::imat blume_capel_stats_; // 2 × p linear/quadratic sums (BC only) + + // ========================================================================= + // Parameters + // ========================================================================= + + arma::mat mux_; // p × max_cats main effects + // Ordinal: mux_(s,c) = threshold for category c+1; + // category 0 is reference (fixed at 0). + // Blume-Capel: mux_(s,0) = linear α_s, + // mux_(s,1) = quadratic β_s. + arma::vec muy_; // q-vector continuous means + arma::mat Kxx_; // p × p discrete interactions (symmetric, zero diag) + arma::mat Kyy_; // q × q SPD precision matrix + arma::mat Kxy_; // p × q cross-type interactions + + // ========================================================================= + // Edge indicators + // ========================================================================= + + // Combined (p+q) × (p+q) indicator matrix: + // Gxx block : rows [0,p), cols [0,p) — symmetric, zero diag + // Gyy block : rows [p,p+q), cols [p,p+q) — symmetric, zero diag + // Gxy block : rows [0,p), cols [p,p+q) — full p×q rectangle + arma::imat edge_indicators_; + arma::mat inclusion_probability_; + bool edge_selection_; + bool edge_selection_active_; + + // ========================================================================= + // Priors + // ========================================================================= + + double main_alpha_; // Beta prior α for main effects + double main_beta_; // Beta prior β for main effects + double pairwise_scale_; // Cauchy scale for interaction priors + + // ========================================================================= + // Proposal SDs (Robbins-Monro adapted) + // ========================================================================= + + arma::mat prop_sd_mux_; // p × max_cats + arma::vec prop_sd_muy_; // q-vector + arma::mat prop_sd_Kxx_; // p × p + arma::mat prop_sd_Kyy_; // q × q + arma::mat prop_sd_Kxy_; // p × q + + // ========================================================================= + // Cached quantities + // ========================================================================= + + arma::mat Kyy_inv_; // q × q inverse of Kyy + arma::mat Kyy_chol_; // q × q upper Cholesky of Kyy + double Kyy_log_det_; // log|Kyy| + arma::mat Theta_; // p × p Kxx + 2 Kxy Kyy_inv Kxy' (marginal PL only) + arma::mat conditional_mean_; // n × q μ_y' + 2 discrete_obs Kxy Kyy_inv + + // ========================================================================= + // Configuration + // ========================================================================= + + bool use_marginal_pl_; // true = marginal, false = conditional + + // ========================================================================= + // RNG and edge-update order + // ========================================================================= + + SafeRNG rng_; + arma::uvec edge_order_xx_; // Shuffled xx-edge pair indices + arma::uvec edge_order_yy_; // Shuffled yy-edge pair indices + arma::uvec edge_order_xy_; // Shuffled xy-edge pair indices + + // ========================================================================= + // Private helpers + // ========================================================================= + + /** Count total main-effect parameters across all discrete variables. */ + size_t count_num_main_effects() const; + + /** Compute category counts and BC sufficient statistics from discrete_observations_. */ + void compute_sufficient_statistics(); + + /** Recompute conditional_mean_ from muy_, Kxy_, Kyy_inv_. */ + void recompute_conditional_mean(); + + /** Recompute Kyy_chol_, Kyy_inv_, Kyy_log_det_ from Kyy_. */ + void recompute_Kyy_decomposition(); + + /** Recompute Theta_ from Kxx_, Kxy_, Kyy_inv_ (marginal PL only). */ + void recompute_Theta(); + + // ========================================================================= + // Edge-indicator accessor helpers + // ========================================================================= + + int gxx(int i, int j) const { return edge_indicators_(i, j); } + int gyy(int i, int j) const { return edge_indicators_(p_ + i, p_ + j); } + int gxy(int i, int j) const { return edge_indicators_(i, p_ + j); } + + void set_gxx(int i, int j, int val) { + edge_indicators_(i, j) = val; + edge_indicators_(j, i) = val; + } + void set_gyy(int i, int j, int val) { + edge_indicators_(p_ + i, p_ + j) = val; + edge_indicators_(p_ + j, p_ + i) = val; + } + void set_gxy(int i, int j, int val) { + edge_indicators_(i, p_ + j) = val; + } +}; From 8bca7d282a3d0ab2934137cb85f05b085bbf9d17 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Thu, 5 Mar 2026 22:57:12 +0100 Subject: [PATCH 03/60] test: add mixed MRF skeleton tests --- R/RcppExports.R | 4 + src/RcppExports.cpp | 21 ++ src/test_mixed_mrf.cpp | 114 +++++++++++ tests/testthat/test-mixed-mrf-skeleton.R | 235 +++++++++++++++++++++++ 4 files changed, 374 insertions(+) create mode 100644 src/test_mixed_mrf.cpp create mode 100644 tests/testthat/test-mixed-mrf-skeleton.R diff --git a/R/RcppExports.R b/R/RcppExports.R index 4eca5302..d99cf745 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -57,3 +57,7 @@ compute_Vn_mfm_sbm <- function(num_variables, dirichlet_alpha, t_max, lambda) { .Call(`_bgms_compute_Vn_mfm_sbm`, num_variables, dirichlet_alpha, t_max, lambda) } +test_mixed_mrf_skeleton <- function(discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, seed) { + .Call(`_bgms_test_mixed_mrf_skeleton`, discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, seed) +} + diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 805000be..1cf2aad1 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -289,6 +289,26 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } +// test_mixed_mrf_skeleton +Rcpp::List test_mixed_mrf_skeleton(const arma::imat& discrete_observations, const arma::mat& continuous_observations, const arma::ivec& num_categories, const arma::uvec& is_ordinal_variable, const arma::ivec& baseline_category, const arma::mat& inclusion_probability, const arma::imat& initial_edge_indicators, bool edge_selection, const std::string& pseudolikelihood, int seed); +RcppExport SEXP _bgms_test_mixed_mrf_skeleton(SEXP discrete_observationsSEXP, SEXP continuous_observationsSEXP, SEXP num_categoriesSEXP, SEXP is_ordinal_variableSEXP, SEXP baseline_categorySEXP, SEXP inclusion_probabilitySEXP, SEXP initial_edge_indicatorsSEXP, SEXP edge_selectionSEXP, SEXP pseudolikelihoodSEXP, SEXP seedSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::imat& >::type discrete_observations(discrete_observationsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type continuous_observations(continuous_observationsSEXP); + Rcpp::traits::input_parameter< const arma::ivec& >::type num_categories(num_categoriesSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type is_ordinal_variable(is_ordinal_variableSEXP); + Rcpp::traits::input_parameter< const arma::ivec& >::type baseline_category(baseline_categorySEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type inclusion_probability(inclusion_probabilitySEXP); + Rcpp::traits::input_parameter< const arma::imat& >::type initial_edge_indicators(initial_edge_indicatorsSEXP); + Rcpp::traits::input_parameter< bool >::type edge_selection(edge_selectionSEXP); + Rcpp::traits::input_parameter< const std::string& >::type pseudolikelihood(pseudolikelihoodSEXP); + Rcpp::traits::input_parameter< int >::type seed(seedSEXP); + rcpp_result_gen = Rcpp::wrap(test_mixed_mrf_skeleton(discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, seed)); + return rcpp_result_gen; +END_RCPP +} static const R_CallMethodDef CallEntries[] = { {"_bgms_run_bgmCompare_parallel", (DL_FUNC) &_bgms_run_bgmCompare_parallel, 38}, @@ -305,6 +325,7 @@ static const R_CallMethodDef CallEntries[] = { {"_bgms_sample_ggm", (DL_FUNC) &_bgms_sample_ggm, 19}, {"_bgms_sample_omrf", (DL_FUNC) &_bgms_sample_omrf, 24}, {"_bgms_compute_Vn_mfm_sbm", (DL_FUNC) &_bgms_compute_Vn_mfm_sbm, 4}, + {"_bgms_test_mixed_mrf_skeleton", (DL_FUNC) &_bgms_test_mixed_mrf_skeleton, 10}, {NULL, NULL, 0} }; diff --git a/src/test_mixed_mrf.cpp b/src/test_mixed_mrf.cpp new file mode 100644 index 00000000..9d9600a4 --- /dev/null +++ b/src/test_mixed_mrf.cpp @@ -0,0 +1,114 @@ +// test_mixed_mrf.cpp — Rcpp test helpers for MixedMRFModel skeleton (Phase A.3) +// +// These are lightweight exported functions that construct a MixedMRFModel from +// R inputs and return diagnostic information for testthat assertions. + +#include +#include "models/mixed/mixed_mrf_model.h" + +// [[Rcpp::export]] +Rcpp::List test_mixed_mrf_skeleton( + const arma::imat& discrete_observations, + const arma::mat& continuous_observations, + const arma::ivec& num_categories, + const arma::uvec& is_ordinal_variable, + const arma::ivec& baseline_category, + const arma::mat& inclusion_probability, + const arma::imat& initial_edge_indicators, + bool edge_selection, + const std::string& pseudolikelihood, + int seed +) { + MixedMRFModel model( + discrete_observations, + continuous_observations, + num_categories, + is_ordinal_variable, + baseline_category, + inclusion_probability, + initial_edge_indicators, + edge_selection, + pseudolikelihood, + 1.0, 1.0, 2.5, + seed + ); + + // --- Dimension checks --- + size_t param_dim = model.parameter_dimension(); + size_t full_param_dim = model.full_parameter_dimension(); + int num_variables = model.get_num_variables(); + int num_pairwise = model.get_num_pairwise(); + + // --- Vectorized parameter round-trip --- + arma::vec params_before = model.get_vectorized_parameters(); + model.set_vectorized_parameters(params_before); + arma::vec params_after = model.get_vectorized_parameters(); + double roundtrip_max_diff = arma::max(arma::abs(params_before - params_after)); + + // --- Full-parameter vector --- + arma::vec full_params = model.get_full_vectorized_parameters(); + + // --- Edge indicators --- + arma::ivec indicator_vec = model.get_vectorized_indicator_parameters(); + const arma::imat& edge_mat = model.get_edge_indicators(); + + // --- Non-trivial parameter round-trip --- + // Set some non-zero parameters and verify round-trip + arma::vec test_params(full_param_dim); + for(size_t i = 0; i < full_param_dim; ++i) { + test_params(i) = static_cast(i + 1) * 0.01; + } + // Kyy must be SPD: overwrite the Kyy block with a diagonal-dominant SPD matrix. + // Kyy occupies indices: num_main + num_pairwise_xx + q ... num_main + num_pairwise_xx + q + q(q+1)/2 - 1 + // For simplicity, just make it diagonal = large positive + int p = discrete_observations.n_cols; + int q = continuous_observations.n_cols; + size_t num_main = 0; + for(int s = 0; s < p; ++s) { + if(is_ordinal_variable(s)) { + num_main += num_categories(s); + } else { + num_main += 2; + } + } + size_t kyy_start = num_main + p * (p - 1) / 2 + q; + // Write identity-like SPD into Kyy block + size_t kyy_idx = kyy_start; + for(int i = 0; i < q; ++i) { + for(int j = i; j < q; ++j) { + if(i == j) { + test_params(kyy_idx) = 2.0 + i * 0.1; // positive diagonal + } else { + test_params(kyy_idx) = 0.01; // small off-diagonal + } + kyy_idx++; + } + } + + model.set_vectorized_parameters(test_params); + arma::vec recovered = model.get_vectorized_parameters(); + double nontrivial_max_diff = arma::max(arma::abs(test_params - recovered)); + + // --- Clone round-trip --- + auto cloned = model.clone(); + arma::vec cloned_params = cloned->get_vectorized_parameters(); + double clone_max_diff = arma::max(arma::abs(recovered - cloned_params)); + + return Rcpp::List::create( + Rcpp::Named("parameter_dimension") = param_dim, + Rcpp::Named("full_parameter_dimension") = full_param_dim, + Rcpp::Named("num_variables") = num_variables, + Rcpp::Named("num_pairwise") = num_pairwise, + Rcpp::Named("params_length") = params_before.n_elem, + Rcpp::Named("roundtrip_max_diff") = roundtrip_max_diff, + Rcpp::Named("nontrivial_roundtrip_max_diff") = nontrivial_max_diff, + Rcpp::Named("clone_max_diff") = clone_max_diff, + Rcpp::Named("indicator_length") = indicator_vec.n_elem, + Rcpp::Named("edge_indicators_rows") = edge_mat.n_rows, + Rcpp::Named("edge_indicators_cols") = edge_mat.n_cols, + Rcpp::Named("has_edge_selection") = model.has_edge_selection(), + Rcpp::Named("has_adaptive_metropolis") = model.has_adaptive_metropolis(), + Rcpp::Named("has_gradient") = model.has_gradient(), + Rcpp::Named("has_missing_data") = model.has_missing_data() + ); +} diff --git a/tests/testthat/test-mixed-mrf-skeleton.R b/tests/testthat/test-mixed-mrf-skeleton.R new file mode 100644 index 00000000..0519cf67 --- /dev/null +++ b/tests/testthat/test-mixed-mrf-skeleton.R @@ -0,0 +1,235 @@ +# ============================================================================== +# Phase A.3: MixedMRFModel Skeleton Tests +# ============================================================================== +# +# Verifies that the MixedMRFModel C++ class compiles, links, and that +# parameter_dimension() and get/set_vectorized_parameters() round-trip +# correctly. Tests cover: +# +# 1. All-ordinal variables (p ordinal discrete + q continuous) +# 2. All-Blume-Capel variables +# 3. Mixed ordinal + Blume-Capel variables +# 4. Both conditional and marginal pseudolikelihood modes +# 5. Clone round-trip +# 6. Dimension arithmetic (num_variables, num_pairwise, indicator length) +# +# ============================================================================== + +# The helper-fixtures.R file loads bgms, but test_file() may skip it. +# Ensure the package is available: +if(!requireNamespace("bgms", quietly = TRUE)) { + skip("bgms package not installed") +} +test_mixed_mrf_skeleton = bgms:::test_mixed_mrf_skeleton + +# ---- Helper to build test inputs for a given scenario ---- +make_mixed_mrf_inputs = function(n, p, q, num_categories, is_ordinal, + baseline_category, edge_selection = FALSE, + pseudolikelihood = "conditional", + seed = 42L) { + set.seed(seed) + # Discrete observations: each column in 0..(num_categories[s]) + discrete_obs = matrix(0L, nrow = n, ncol = p) + for(s in seq_len(p)) { + discrete_obs[, s] = sample(0L:num_categories[s], n, replace = TRUE) + } + # Continuous observations + continuous_obs = matrix(rnorm(n * q), nrow = n, ncol = q) + + total = p + q + inclusion_prob = matrix(0.5, nrow = total, ncol = total) + diag(inclusion_prob) = 0 + edge_ind = matrix(1L, nrow = total, ncol = total) + diag(edge_ind) = 0L + + list( + discrete_observations = discrete_obs, + continuous_observations = continuous_obs, + num_categories = as.integer(num_categories), + is_ordinal_variable = as.integer(is_ordinal), + baseline_category = as.integer(baseline_category), + inclusion_probability = inclusion_prob, + initial_edge_indicators = edge_ind, + edge_selection = edge_selection, + pseudolikelihood = pseudolikelihood, + seed = seed + ) +} + +run_skeleton_test = function(inputs) { + do.call(test_mixed_mrf_skeleton, inputs) +} + +# ---- Expected dimension calculator (mirrors C++ logic) ---- +expected_full_dim = function(p, q, num_categories, is_ordinal) { + num_main = sum(ifelse(is_ordinal == 1L, num_categories, 2L)) + num_pairwise_xx = p * (p - 1) / 2 + num_pairwise_yy_with_diag = q * (q + 1) / 2 + num_cross = p * q + num_main + num_pairwise_xx + q + num_pairwise_yy_with_diag + num_cross +} + +expected_num_pairwise = function(p, q) { + p * (p - 1) / 2 + q * (q - 1) / 2 + p * q +} + +expected_indicator_length = function(p, q) { + expected_num_pairwise(p, q) +} + +# ============================================================================== +# Test scenarios +# ============================================================================== + +test_that("all-ordinal skeleton has correct dimensions", { + p = 3L; q = 2L; n = 20L + num_cats = c(2L, 3L, 2L) + is_ord = c(1L, 1L, 1L) + baseline = rep(0L, p) + + inp = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline) + res = run_skeleton_test(inp) + + expect_equal(res$num_variables, p + q) + expect_equal(res$parameter_dimension, + expected_full_dim(p, q, num_cats, is_ord)) + expect_equal(res$full_parameter_dimension, + expected_full_dim(p, q, num_cats, is_ord)) + expect_equal(res$params_length, res$full_parameter_dimension) + expect_equal(res$num_pairwise, expected_num_pairwise(p, q)) + expect_equal(res$indicator_length, expected_indicator_length(p, q)) + expect_equal(res$edge_indicators_rows, p + q) + expect_equal(res$edge_indicators_cols, p + q) +}) + +test_that("all-Blume-Capel skeleton has correct dimensions", { + p = 3L; q = 2L; n = 20L + num_cats = c(4L, 3L, 4L) + is_ord = c(0L, 0L, 0L) + baseline = c(2L, 1L, 2L) + + inp = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline) + res = run_skeleton_test(inp) + + expect_equal(res$num_variables, p + q) + # BC always contributes 2 per variable + expect_equal(res$parameter_dimension, + expected_full_dim(p, q, num_cats, is_ord)) + expect_equal(res$full_parameter_dimension, + expected_full_dim(p, q, num_cats, is_ord)) +}) + +test_that("mixed ordinal + BC skeleton has correct dimensions", { + p = 4L; q = 3L; n = 25L + num_cats = c(2L, 4L, 3L, 3L) + is_ord = c(1L, 0L, 1L, 0L) + baseline = c(0L, 2L, 0L, 1L) + + inp = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline) + res = run_skeleton_test(inp) + + expect_equal(res$num_variables, p + q) + expect_equal(res$parameter_dimension, + expected_full_dim(p, q, num_cats, is_ord)) +}) + +test_that("zero-parameter round-trip is exact", { + p = 3L; q = 2L; n = 20L + num_cats = c(2L, 3L, 2L) + is_ord = c(1L, 1L, 1L) + baseline = rep(0L, p) + + inp = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline) + res = run_skeleton_test(inp) + + expect_equal(res$roundtrip_max_diff, 0.0) +}) + +test_that("non-trivial parameter round-trip is exact", { + p = 3L; q = 2L; n = 20L + num_cats = c(2L, 3L, 2L) + is_ord = c(1L, 1L, 1L) + baseline = rep(0L, p) + + inp = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline) + res = run_skeleton_test(inp) + + expect_equal(res$nontrivial_roundtrip_max_diff, 0.0) +}) + +test_that("non-trivial round-trip with Blume-Capel variables", { + p = 4L; q = 3L; n = 25L + num_cats = c(2L, 4L, 3L, 3L) + is_ord = c(1L, 0L, 1L, 0L) + baseline = c(0L, 2L, 0L, 1L) + + inp = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline) + res = run_skeleton_test(inp) + + expect_equal(res$nontrivial_roundtrip_max_diff, 0.0) +}) + +test_that("clone produces identical parameters", { + p = 3L; q = 2L; n = 20L + num_cats = c(2L, 3L, 2L) + is_ord = c(1L, 1L, 1L) + baseline = rep(0L, p) + + inp = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline) + res = run_skeleton_test(inp) + + expect_equal(res$clone_max_diff, 0.0) +}) + +test_that("marginal pseudolikelihood mode works", { + p = 3L; q = 2L; n = 20L + num_cats = c(2L, 3L, 2L) + is_ord = c(1L, 1L, 1L) + baseline = rep(0L, p) + + inp = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline, + pseudolikelihood = "marginal") + res = run_skeleton_test(inp) + + expect_equal(res$parameter_dimension, + expected_full_dim(p, q, num_cats, is_ord)) + expect_equal(res$nontrivial_roundtrip_max_diff, 0.0) +}) + +test_that("capability queries return correct values", { + p = 3L; q = 2L; n = 20L + num_cats = c(2L, 3L, 2L) + is_ord = c(1L, 1L, 1L) + baseline = rep(0L, p) + + inp_no_es = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline, + edge_selection = FALSE) + res_no_es = run_skeleton_test(inp_no_es) + expect_false(res_no_es$has_edge_selection) + expect_true(res_no_es$has_adaptive_metropolis) + expect_false(res_no_es$has_gradient) + expect_false(res_no_es$has_missing_data) + + inp_es = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline, + edge_selection = TRUE) + res_es = run_skeleton_test(inp_es) + expect_true(res_es$has_edge_selection) +}) + +test_that("single discrete + single continuous works", { + p = 1L; q = 1L; n = 15L + num_cats = 2L + is_ord = 1L + baseline = 0L + + inp = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline) + res = run_skeleton_test(inp) + + # p=1: 0 xx edges, q=1: 0 yy edges, 1 cross edge + expect_equal(res$num_variables, 2L) + expect_equal(res$num_pairwise, 1L) + expect_equal(res$indicator_length, 1L) + # mux: 2 thresholds, Kxx: 0, muy: 1, Kyy: 1 diag, Kxy: 1 + expect_equal(res$full_parameter_dimension, 2 + 0 + 1 + 1 + 1) + expect_equal(res$nontrivial_roundtrip_max_diff, 0.0) +}) From 5df7c3bfd8dd1a52e4e9bfeb807680960a97c779 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Thu, 5 Mar 2026 23:13:17 +0100 Subject: [PATCH 04/60] fix: resolve test-bgm-spec failures and hidden files NOTE - Add ::: bindings for internal functions in test-bgm-spec.R - Exclude .lintr and .editorconfig from tarball via .Rbuildignore --- .Rbuildignore | 4 ++++ tests/testthat/test-bgm-spec.R | 6 ++++++ 2 files changed, 10 insertions(+) diff --git a/.Rbuildignore b/.Rbuildignore index 4c78b301..fcf9b275 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -17,6 +17,10 @@ # GitHub / CI ^\.github$ +# Hidden config files (development only) +^\.lintr$ +^\.editorconfig$ + # R CMD build artifacts ^doc$ ^Meta$ diff --git a/tests/testthat/test-bgm-spec.R b/tests/testthat/test-bgm-spec.R index 702d3b51..b7706f46 100644 --- a/tests/testthat/test-bgm-spec.R +++ b/tests/testthat/test-bgm-spec.R @@ -3,6 +3,12 @@ # Phase B.5 of the R scaffolding refactor. # ============================================================================== +# These are internal (non-exported) functions — bind via ::: for testing. +bgm_spec = bgms:::bgm_spec +validate_bgm_spec = bgms:::validate_bgm_spec +new_bgm_spec = bgms:::new_bgm_spec +sampler_sublist = bgms:::sampler_sublist + # ============================================================================== # Shared helpers / fixtures # ============================================================================== From 34f73dbfa5e50def8d2ebb6c5af93ea930c05cdb Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Thu, 5 Mar 2026 23:45:25 +0100 Subject: [PATCH 05/60] feat: add conditional PL likelihood functions (B.1) Implement log_conditional_omrf(s) and log_conditional_ggm() in mixed_mrf_likelihoods.cpp. OMRF branches on ordinal vs Blume-Capel using compute_denom_ordinal/blume_capel from variable_helpers. GGM uses cached Kyy decomposition and conditional mean. 14 test assertions validated against pure-R reference implementations. Cross-validation against mixedGM deferred to B.5 estimation tests. --- R/RcppExports.R | 4 + dev/mixedmrf/implementation_plan.md | 16 +- src/RcppExports.cpp | 22 + src/models/mixed/mixed_mrf_likelihoods.cpp | 77 +++ src/models/mixed/mixed_mrf_model.h | 17 + src/test_mixed_mrf.cpp | 49 ++ tests/testthat/test-mixed-mrf-likelihoods.R | 549 ++++++++++++++++++++ 7 files changed, 732 insertions(+), 2 deletions(-) create mode 100644 src/models/mixed/mixed_mrf_likelihoods.cpp create mode 100644 tests/testthat/test-mixed-mrf-likelihoods.R diff --git a/R/RcppExports.R b/R/RcppExports.R index d99cf745..3f72d3ce 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -61,3 +61,7 @@ test_mixed_mrf_skeleton <- function(discrete_observations, continuous_observatio .Call(`_bgms_test_mixed_mrf_skeleton`, discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, seed) } +test_mixed_mrf_likelihoods <- function(discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, params, seed) { + .Call(`_bgms_test_mixed_mrf_likelihoods`, discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, params, seed) +} + diff --git a/dev/mixedmrf/implementation_plan.md b/dev/mixedmrf/implementation_plan.md index eb57f2e9..85f3d876 100644 --- a/dev/mixedmrf/implementation_plan.md +++ b/dev/mixedmrf/implementation_plan.md @@ -517,7 +517,16 @@ The diagonal of `edge_indicators_` is always zero and excluded throughout. ## Phase B — Conditional pseudo-likelihood -### B.1 Implement log-likelihood functions +### B.1 Implement log-likelihood functions ✅ + +Implemented in `src/models/mixed/mixed_mrf_likelihoods.cpp`. +Declared as private methods in `mixed_mrf_model.h` with a `friend` +declaration for the test helper. + +Tests: `tests/testthat/test-mixed-mrf-likelihoods.R` (14 assertions) +validated against pure-R reference implementations covering ordinal, +Blume-Capel, GGM, zero-params, nonzero-params, minimal (p=1,q=1), +and all-BC configurations. In `mixed_mrf_likelihoods.cpp`: @@ -749,11 +758,14 @@ void MixedMRFModel::do_one_metropolis_step(int iteration) { ### B.5 Testing checkpoint — conditional PL estimation -**Test 1 (T1, T2, T10): Likelihood agreement** +**Test 1 (T1, T2, T10): Likelihood agreement + cross-validation** - Generate data from `mixed_gibbs_generate()` in R - Compute `log_conditional_omrf(s)` and `log_conditional_ggm()` in both R (prototype) and C++ at the same parameter values - Assert agreement to machine precision +- Cross-validate against `mixedGM::rcpp_log_pl_conditional_omrf` and + `mixedGM::rcpp_log_pl_conditional_ggm` for a three-way check + (bgms C++, R reference, mixedGM C++) **Test 2 (T13): Recovery (estimation only, no edge selection)** - Generate data from known parameters (p=3 ordinal, q=2 continuous) diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 1cf2aad1..7ac4c45e 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -309,6 +309,27 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } +// test_mixed_mrf_likelihoods +Rcpp::List test_mixed_mrf_likelihoods(const arma::imat& discrete_observations, const arma::mat& continuous_observations, const arma::ivec& num_categories, const arma::uvec& is_ordinal_variable, const arma::ivec& baseline_category, const arma::mat& inclusion_probability, const arma::imat& initial_edge_indicators, bool edge_selection, const std::string& pseudolikelihood, const arma::vec& params, int seed); +RcppExport SEXP _bgms_test_mixed_mrf_likelihoods(SEXP discrete_observationsSEXP, SEXP continuous_observationsSEXP, SEXP num_categoriesSEXP, SEXP is_ordinal_variableSEXP, SEXP baseline_categorySEXP, SEXP inclusion_probabilitySEXP, SEXP initial_edge_indicatorsSEXP, SEXP edge_selectionSEXP, SEXP pseudolikelihoodSEXP, SEXP paramsSEXP, SEXP seedSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::imat& >::type discrete_observations(discrete_observationsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type continuous_observations(continuous_observationsSEXP); + Rcpp::traits::input_parameter< const arma::ivec& >::type num_categories(num_categoriesSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type is_ordinal_variable(is_ordinal_variableSEXP); + Rcpp::traits::input_parameter< const arma::ivec& >::type baseline_category(baseline_categorySEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type inclusion_probability(inclusion_probabilitySEXP); + Rcpp::traits::input_parameter< const arma::imat& >::type initial_edge_indicators(initial_edge_indicatorsSEXP); + Rcpp::traits::input_parameter< bool >::type edge_selection(edge_selectionSEXP); + Rcpp::traits::input_parameter< const std::string& >::type pseudolikelihood(pseudolikelihoodSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type params(paramsSEXP); + Rcpp::traits::input_parameter< int >::type seed(seedSEXP); + rcpp_result_gen = Rcpp::wrap(test_mixed_mrf_likelihoods(discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, params, seed)); + return rcpp_result_gen; +END_RCPP +} static const R_CallMethodDef CallEntries[] = { {"_bgms_run_bgmCompare_parallel", (DL_FUNC) &_bgms_run_bgmCompare_parallel, 38}, @@ -326,6 +347,7 @@ static const R_CallMethodDef CallEntries[] = { {"_bgms_sample_omrf", (DL_FUNC) &_bgms_sample_omrf, 24}, {"_bgms_compute_Vn_mfm_sbm", (DL_FUNC) &_bgms_compute_Vn_mfm_sbm, 4}, {"_bgms_test_mixed_mrf_skeleton", (DL_FUNC) &_bgms_test_mixed_mrf_skeleton, 10}, + {"_bgms_test_mixed_mrf_likelihoods", (DL_FUNC) &_bgms_test_mixed_mrf_likelihoods, 11}, {NULL, NULL, 0} }; diff --git a/src/models/mixed/mixed_mrf_likelihoods.cpp b/src/models/mixed/mixed_mrf_likelihoods.cpp new file mode 100644 index 00000000..2a1fae27 --- /dev/null +++ b/src/models/mixed/mixed_mrf_likelihoods.cpp @@ -0,0 +1,77 @@ +#include +#include "models/mixed/mixed_mrf_model.h" +#include "utils/variable_helpers.h" +#include "math/explog_macros.h" + + +// ============================================================================= +// log_conditional_omrf +// ============================================================================= +// Conditional OMRF pseudolikelihood for discrete variable s: +// log f(x_s | x_{-s}, y) = numerator - sum_v log(Z_v) +// +// Branches on is_ordinal_variable_(s) to select ordinal thresholds or +// Blume-Capel (linear + quadratic) main effects. +// ============================================================================= + +double MixedMRFModel::log_conditional_omrf(int s) const { + int C_s = num_categories_(s); + + // Rest score: contribution from other discrete vars + continuous vars + arma::vec rest = discrete_observations_dbl_ * Kxx_.col(s) + - discrete_observations_dbl_.col(s) * Kxx_(s, s) + + 2.0 * continuous_observations_ * Kxy_.row(s).t(); + + // Numerator (sufficient-statistic form): dot(x_s, rest) + main-effect sums + double numer = arma::dot(discrete_observations_dbl_.col(s), rest); + + if(is_ordinal_variable_(s)) { + // Ordinal: add threshold contributions sum_{c=1}^{C_s} count_c * mux_(s, c-1) + for(int c = 1; c <= C_s; ++c) { + numer += static_cast(counts_per_category_(c, s)) * mux_(s, c - 1); + } + + // Denominator via compute_denom_ordinal (FAST/SAFE block-split) + arma::vec main_param = mux_.row(s).cols(0, C_s - 1).t(); + arma::vec bound = static_cast(C_s) * rest; + arma::vec denom = compute_denom_ordinal(rest, main_param, bound); + + return numer - arma::accu(bound + ARMA_MY_LOG(denom)); + } else { + // Blume-Capel: alpha * sum(x) + beta * sum(x^2) + double alpha = mux_(s, 0); + double beta = mux_(s, 1); + numer += alpha * static_cast(blume_capel_stats_(0, s)) + + beta * static_cast(blume_capel_stats_(1, s)); + + // Denominator via compute_denom_blume_capel (computes bound internally) + arma::vec bound; + arma::vec denom = compute_denom_blume_capel( + rest, alpha, beta, baseline_category_(s), C_s, bound + ); + + return numer - arma::accu(bound + ARMA_MY_LOG(denom)); + } +} + + +// ============================================================================= +// log_conditional_ggm +// ============================================================================= +// Conditional GGM log-likelihood: log f(y | x) +// y | x ~ N(conditional_mean_, Kyy_inv_) +// +// Uses cached Kyy_inv_, Kyy_log_det_, and conditional_mean_. +// ============================================================================= + +double MixedMRFModel::log_conditional_ggm() const { + arma::mat D = continuous_observations_ - conditional_mean_; + + // Quadratic form: trace(Kyy * D' * D) = sum((D * Kyy_) .* D) + double quad_sum = arma::accu((D * Kyy_) % D); + + return static_cast(n_) / 2.0 * + (-static_cast(q_) * std::log(2.0 * arma::datum::pi) + + Kyy_log_det_) + - quad_sum / 2.0; +} diff --git a/src/models/mixed/mixed_mrf_model.h b/src/models/mixed/mixed_mrf_model.h index b26910d6..0be172e1 100644 --- a/src/models/mixed/mixed_mrf_model.h +++ b/src/models/mixed/mixed_mrf_model.h @@ -27,6 +27,13 @@ class MixedMRFModel : public BaseModel { public: + // Test helpers need access to private likelihood functions + friend Rcpp::List test_mixed_mrf_likelihoods( + const arma::imat&, const arma::mat&, const arma::ivec&, + const arma::uvec&, const arma::ivec&, const arma::mat&, + const arma::imat&, bool, const std::string&, const arma::vec&, int + ); + // ========================================================================= // Construction // ========================================================================= @@ -320,6 +327,16 @@ class MixedMRFModel : public BaseModel { /** Recompute Theta_ from Kxx_, Kxy_, Kyy_inv_ (marginal PL only). */ void recompute_Theta(); + // ========================================================================= + // Likelihood functions (implemented in mixed_mrf_likelihoods.cpp) + // ========================================================================= + + /** Conditional OMRF pseudolikelihood for discrete variable s, summed over all n. */ + double log_conditional_omrf(int s) const; + + /** Conditional GGM log-likelihood: log f(y | x), using cached decomposition. */ + double log_conditional_ggm() const; + // ========================================================================= // Edge-indicator accessor helpers // ========================================================================= diff --git a/src/test_mixed_mrf.cpp b/src/test_mixed_mrf.cpp index 9d9600a4..d296217c 100644 --- a/src/test_mixed_mrf.cpp +++ b/src/test_mixed_mrf.cpp @@ -112,3 +112,52 @@ Rcpp::List test_mixed_mrf_skeleton( Rcpp::Named("has_missing_data") = model.has_missing_data() ); } + + +// [[Rcpp::export]] +Rcpp::List test_mixed_mrf_likelihoods( + const arma::imat& discrete_observations, + const arma::mat& continuous_observations, + const arma::ivec& num_categories, + const arma::uvec& is_ordinal_variable, + const arma::ivec& baseline_category, + const arma::mat& inclusion_probability, + const arma::imat& initial_edge_indicators, + bool edge_selection, + const std::string& pseudolikelihood, + const arma::vec& params, + int seed +) { + MixedMRFModel model( + discrete_observations, + continuous_observations, + num_categories, + is_ordinal_variable, + baseline_category, + inclusion_probability, + initial_edge_indicators, + edge_selection, + pseudolikelihood, + 1.0, 1.0, 2.5, + seed + ); + + // Set parameters (caller must ensure Kyy block is SPD) + model.set_vectorized_parameters(params); + + int p = discrete_observations.n_cols; + + // Evaluate log_conditional_omrf for each discrete variable + Rcpp::NumericVector omrf_ll(p); + for(int s = 0; s < p; ++s) { + omrf_ll[s] = model.log_conditional_omrf(s); + } + + // Evaluate log_conditional_ggm + double ggm_ll = model.log_conditional_ggm(); + + return Rcpp::List::create( + Rcpp::Named("omrf_ll") = omrf_ll, + Rcpp::Named("ggm_ll") = ggm_ll + ); +} diff --git a/tests/testthat/test-mixed-mrf-likelihoods.R b/tests/testthat/test-mixed-mrf-likelihoods.R new file mode 100644 index 00000000..8f4e7c44 --- /dev/null +++ b/tests/testthat/test-mixed-mrf-likelihoods.R @@ -0,0 +1,549 @@ +# ============================================================================= +# test-mixed-mrf-likelihoods.R — Phase B.1 likelihood tests +# ============================================================================= +# Tests for log_conditional_omrf() and log_conditional_ggm() in the +# MixedMRFModel, validated against pure-R reference implementations. + +# ------------------------------------------------------------------ +# R reference: conditional OMRF pseudolikelihood for variable s +# ------------------------------------------------------------------ +# Computes log f(x_s | x_{-s}, y) summed over n observations. +# +# @param x_int n x p integer matrix (0-based categories) +# @param y n x q continuous matrix +# @param Kxx p x p symmetric, zero diagonal +# @param Kxy p x q cross-interactions +# @param mux p x max_cats thresholds / BC coefficients +# @param num_cats p-vector of category counts +# @param is_ordinal p-vector (1 = ordinal, 0 = BC) +# @param baseline_cat p-vector of reference categories +# @param s 0-based variable index +# +# Returns: scalar log-likelihood. +ref_log_conditional_omrf = function(x_int, y, Kxx, Kxy, mux, num_cats, + is_ordinal, baseline_cat, s) { + n = nrow(x_int) + s1 = s + 1L # R indexing + + # Center BC observations (same as constructor) + x = x_int + for(j in seq_len(ncol(x))) { + if(!is_ordinal[j]) x[, j] = x[, j] - baseline_cat[j] + } + x_dbl = matrix(as.double(x), nrow = n) + + # Rest score + rest = x_dbl %*% Kxx[, s1] - x_dbl[, s1] * Kxx[s1, s1] + + 2.0 * y %*% t(Kxy[s1, , drop = FALSE]) + rest = as.numeric(rest) + + C_s = num_cats[s1] + + if(is_ordinal[s1]) { + # Numerator: dot(x_s, rest) + sum_c count_c * mux(s, c) + numer = sum(x_dbl[, s1] * rest) + for(c in seq_len(C_s)) { + count_c = sum(x[, s1] == c) + numer = numer + count_c * mux[s1, c] + } + # Log-normalizer per person (log-sum-exp stabilized) + log_Z = numeric(n) + for(v in seq_len(n)) { + terms = numeric(C_s + 1) + terms[1] = 0 # reference category + for(c in seq_len(C_s)) { + terms[c + 1] = mux[s1, c] + c * rest[v] + } + mx = max(terms) + log_Z[v] = mx + log(sum(exp(terms - mx))) + } + return(numer - sum(log_Z)) + } else { + # Blume-Capel + alpha = mux[s1, 1] + beta = mux[s1, 2] + ref = baseline_cat[s1] + + numer = sum(x_dbl[, s1] * rest) + + alpha * sum(x[, s1]) + beta * sum(x[, s1]^2) + + log_Z = numeric(n) + for(v in seq_len(n)) { + cats = 0:C_s + centered = cats - ref + theta = alpha * centered + beta * centered^2 + terms = theta + centered * rest[v] + mx = max(terms) + log_Z[v] = mx + log(sum(exp(terms - mx))) + } + return(numer - sum(log_Z)) + } +} + +# ------------------------------------------------------------------ +# R reference: conditional GGM log-likelihood +# ------------------------------------------------------------------ +# Computes log f(y | x) using conditional mean and precision Kyy. +# +# @param x_int n x p integer matrix (0-based) +# @param y n x q continuous matrix +# @param Kxy p x q cross-interactions +# @param Kyy q x q SPD precision matrix +# @param muy q-vector of means +# @param is_ordinal p-vector +# @param baseline_cat p-vector +# +# Returns: scalar log-likelihood. +ref_log_conditional_ggm = function(x_int, y, Kxy, Kyy, muy, + is_ordinal, baseline_cat) { + n = nrow(y) + q = ncol(y) + + # Center BC observations + x = x_int + for(j in seq_len(ncol(x))) { + if(!is_ordinal[j]) x[, j] = x[, j] - baseline_cat[j] + } + x_dbl = matrix(as.double(x), nrow = n) + + Kyy_inv = solve(Kyy) + logdet_val = as.numeric(determinant(Kyy, logarithm = TRUE)$modulus) + + # Conditional mean: repmat(muy', n, 1) + 2 * x_dbl * Kxy * Kyy_inv + cond_mean = matrix(rep(muy, each = n), nrow = n) + + 2.0 * x_dbl %*% Kxy %*% Kyy_inv + + D = y - cond_mean + quad_sum = sum((D %*% Kyy) * D) + + ll = n / 2.0 * (-q * log(2 * pi) + logdet_val) - quad_sum / 2.0 + return(ll) +} + + +# ------------------------------------------------------------------ +# Helper to build a zero parameter vector with correct structure +# ------------------------------------------------------------------ +build_param_vec = function(p, q, num_cats, is_ordinal) { + num_main = 0L + for(s in seq_len(p)) { + if(is_ordinal[s]) { + num_main = num_main + num_cats[s] + } else { + num_main = num_main + 2L + } + } + num_pairwise_xx = p * (p - 1L) / 2L + num_kyy = q * (q + 1L) / 2L + num_cross = p * q + total = num_main + num_pairwise_xx + q + num_kyy + num_cross + list( + total = total, + num_main = num_main, + num_pairwise_xx = num_pairwise_xx, + q = q, + num_kyy = num_kyy, + num_cross = num_cross, + kyy_start = num_main + num_pairwise_xx + q # 0-based start of Kyy block + ) +} + + +# ------------------------------------------------------------------ +# Test setup shared across tests +# ------------------------------------------------------------------ +make_test_data = function() { + set.seed(42) + n = 10L; p = 3L; q = 2L + num_cats = c(2L, 3L, 2L) + is_ordinal = c(1L, 1L, 0L) # vars 1,2 ordinal; var 3 BC + baseline_cat = c(0L, 0L, 1L) # BC var has ref = 1 + + x = matrix(0L, n, p) + x[, 1] = sample(0:2, n, replace = TRUE) + x[, 2] = sample(0:3, n, replace = TRUE) + x[, 3] = sample(0:2, n, replace = TRUE) # original BC obs in [0, C_s] + y = matrix(rnorm(n * q), n, q) + + pq = p + q + inc_prob = matrix(0.5, pq, pq) + edge_ind = matrix(1L, pq, pq) + diag(edge_ind) = 0L + + list( + n = n, p = p, q = q, + x = x, y = y, + num_cats = num_cats, + is_ordinal = as.integer(is_ordinal), + baseline_cat = baseline_cat, + inc_prob = inc_prob, + edge_ind = edge_ind + ) +} + + +# ============================================================================== +# Tests +# ============================================================================== + +test_that("log_conditional_omrf at zero parameters equals -n*log(C+1)", { + d = make_test_data() + info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) + params = rep(0, info$total) + # Kyy = identity + idx = info$kyy_start + for(i in seq_len(d$q)) { + for(j in i:d$q) { + idx = idx + 1L + if(i == j) params[idx] = 1.0 + } + } + + res = bgms:::test_mixed_mrf_likelihoods( + d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, + d$inc_prob, d$edge_ind, FALSE, "conditional", params, 1L + ) + + # Ordinal vars: expected = -n * log(C_s + 1) + expect_equal(res$omrf_ll[1], -d$n * log(d$num_cats[1] + 1), tolerance = 1e-10) + expect_equal(res$omrf_ll[2], -d$n * log(d$num_cats[2] + 1), tolerance = 1e-10) + + # BC var: at zero params, all categories have theta = 0, rest = 0 + # so Z = num_cats + 1, numer = 0 => ll = -n * log(C+1) + expect_equal(res$omrf_ll[3], -d$n * log(d$num_cats[3] + 1), tolerance = 1e-10) +}) + + +test_that("log_conditional_ggm at zero parameters equals standard normal", { + d = make_test_data() + info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) + params = rep(0, info$total) + idx = info$kyy_start + for(i in seq_len(d$q)) { + for(j in i:d$q) { + idx = idx + 1L + if(i == j) params[idx] = 1.0 + } + } + + res = bgms:::test_mixed_mrf_likelihoods( + d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, + d$inc_prob, d$edge_ind, FALSE, "conditional", params, 1L + ) + + # At zero params: y ~ N(0, I), so ll = sum(dnorm(y, log = TRUE)) + expected = sum(dnorm(d$y, log = TRUE)) + expect_equal(res$ggm_ll, expected, tolerance = 1e-10) +}) + + +test_that("log_conditional_omrf matches R reference (ordinal, nonzero params)", { + d = make_test_data() + info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) + params = rep(0, info$total) + + # Set some nonzero main effects + params[1] = 0.3; params[2] = -0.2 # var 1 thresholds (C=2) + params[3] = 0.1; params[4] = 0.4; params[5] = -0.1 # var 2 thresholds (C=3) + params[6] = 0.5; params[7] = -0.3 # var 3 BC alpha, beta + + # Set some Kxx edges (upper-tri, row-major: (0,1), (0,2), (1,2)) + kxx_start = info$num_main + params[kxx_start + 1] = 0.2 # Kxx(0,1) + params[kxx_start + 2] = -0.1 # Kxx(0,2) + params[kxx_start + 3] = 0.15 # Kxx(1,2) + + # muy + muy_start = info$num_main + info$num_pairwise_xx + params[muy_start + 1] = 0.1 + params[muy_start + 2] = -0.2 + + # Kyy = SPD (2x2 with positive eigenvalues) + kyy_start = info$kyy_start + params[kyy_start + 1] = 2.0 # Kyy(0,0) + params[kyy_start + 2] = 0.3 # Kyy(0,1) + params[kyy_start + 3] = 1.5 # Kyy(1,1) + + # Kxy (p x q, row-major) + kxy_start = kyy_start + info$num_kyy + params[kxy_start + 1] = 0.1 # Kxy(0,0) + params[kxy_start + 2] = -0.05 # Kxy(0,1) + params[kxy_start + 3] = 0.08 # Kxy(1,0) + params[kxy_start + 4] = 0.12 # Kxy(1,1) + params[kxy_start + 5] = -0.07 # Kxy(2,0) + params[kxy_start + 6] = 0.04 # Kxy(2,1) + + res = bgms:::test_mixed_mrf_likelihoods( + d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, + d$inc_prob, d$edge_ind, FALSE, "conditional", params, 1L + ) + + # Reconstruct parameter matrices for R reference + mux = matrix(0, d$p, max(d$num_cats)) + mux[1, 1:2] = c(0.3, -0.2) + mux[2, 1:3] = c(0.1, 0.4, -0.1) + mux[3, 1:2] = c(0.5, -0.3) + + Kxx = matrix(0, d$p, d$p) + Kxx[1, 2] = Kxx[2, 1] = 0.2 + Kxx[1, 3] = Kxx[3, 1] = -0.1 + Kxx[2, 3] = Kxx[3, 2] = 0.15 + + Kxy = matrix(c(0.1, 0.08, -0.07, -0.05, 0.12, 0.04), nrow = d$p, ncol = d$q) + muy = c(0.1, -0.2) + + # Check each ordinal variable against R reference + for(s in 0:1) { + expected = ref_log_conditional_omrf( + d$x, d$y, Kxx, Kxy, mux, d$num_cats, + d$is_ordinal, d$baseline_cat, s + ) + expect_equal(res$omrf_ll[s + 1], expected, tolerance = 1e-8, + label = paste0("ordinal var ", s)) + } +}) + + +test_that("log_conditional_omrf matches R reference (Blume-Capel, nonzero params)", { + d = make_test_data() + info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) + params = rep(0, info$total) + + # Same params as ordinal test + params[1] = 0.3; params[2] = -0.2 + params[3] = 0.1; params[4] = 0.4; params[5] = -0.1 + params[6] = 0.5; params[7] = -0.3 + + kxx_start = info$num_main + params[kxx_start + 1] = 0.2 + params[kxx_start + 2] = -0.1 + params[kxx_start + 3] = 0.15 + + muy_start = info$num_main + info$num_pairwise_xx + params[muy_start + 1] = 0.1 + params[muy_start + 2] = -0.2 + + kyy_start = info$kyy_start + params[kyy_start + 1] = 2.0 + params[kyy_start + 2] = 0.3 + params[kyy_start + 3] = 1.5 + + kxy_start = kyy_start + info$num_kyy + params[kxy_start + 1] = 0.1 + params[kxy_start + 2] = -0.05 + params[kxy_start + 3] = 0.08 + params[kxy_start + 4] = 0.12 + params[kxy_start + 5] = -0.07 + params[kxy_start + 6] = 0.04 + + res = bgms:::test_mixed_mrf_likelihoods( + d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, + d$inc_prob, d$edge_ind, FALSE, "conditional", params, 1L + ) + + mux = matrix(0, d$p, max(d$num_cats)) + mux[1, 1:2] = c(0.3, -0.2) + mux[2, 1:3] = c(0.1, 0.4, -0.1) + mux[3, 1:2] = c(0.5, -0.3) + + Kxx = matrix(0, d$p, d$p) + Kxx[1, 2] = Kxx[2, 1] = 0.2 + Kxx[1, 3] = Kxx[3, 1] = -0.1 + Kxx[2, 3] = Kxx[3, 2] = 0.15 + + Kxy = matrix(c(0.1, 0.08, -0.07, -0.05, 0.12, 0.04), nrow = d$p, ncol = d$q) + + # Test BC variable (s = 2, 0-based) + expected = ref_log_conditional_omrf( + d$x, d$y, Kxx, Kxy, mux, d$num_cats, + d$is_ordinal, d$baseline_cat, s = 2 + ) + expect_equal(res$omrf_ll[3], expected, tolerance = 1e-8) +}) + + +test_that("log_conditional_ggm matches R reference (nonzero params)", { + d = make_test_data() + info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) + params = rep(0, info$total) + + # Set same params + params[1] = 0.3; params[2] = -0.2 + params[3] = 0.1; params[4] = 0.4; params[5] = -0.1 + params[6] = 0.5; params[7] = -0.3 + + kxx_start = info$num_main + params[kxx_start + 1] = 0.2 + params[kxx_start + 2] = -0.1 + params[kxx_start + 3] = 0.15 + + muy_start = info$num_main + info$num_pairwise_xx + params[muy_start + 1] = 0.1 + params[muy_start + 2] = -0.2 + + kyy_start = info$kyy_start + params[kyy_start + 1] = 2.0 + params[kyy_start + 2] = 0.3 + params[kyy_start + 3] = 1.5 + + kxy_start = kyy_start + info$num_kyy + params[kxy_start + 1] = 0.1 + params[kxy_start + 2] = -0.05 + params[kxy_start + 3] = 0.08 + params[kxy_start + 4] = 0.12 + params[kxy_start + 5] = -0.07 + params[kxy_start + 6] = 0.04 + + res = bgms:::test_mixed_mrf_likelihoods( + d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, + d$inc_prob, d$edge_ind, FALSE, "conditional", params, 1L + ) + + Kyy = matrix(c(2.0, 0.3, 0.3, 1.5), 2, 2) + Kxy = matrix(c(0.1, 0.08, -0.07, -0.05, 0.12, 0.04), nrow = d$p, ncol = d$q) + muy = c(0.1, -0.2) + + expected = ref_log_conditional_ggm( + d$x, d$y, Kxy, Kyy, muy, d$is_ordinal, d$baseline_cat + ) + expect_equal(res$ggm_ll, expected, tolerance = 1e-8) +}) + + +test_that("log_conditional_ggm invariant to Kxx changes", { + # GGM likelihood depends on Kyy, Kxy, muy but not Kxx + d = make_test_data() + info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) + params1 = rep(0, info$total) + params2 = rep(0, info$total) + + # Both: same Kyy = SPD, same Kxy, same muy + kyy_start = info$kyy_start + for(params in list(params1, params2)) { + params[kyy_start + 1] = 2.0 + params[kyy_start + 2] = 0.3 + params[kyy_start + 3] = 1.5 + } + # But different Kxx + kxx_start = info$num_main + params1[kxx_start + 1] = 0.5 + params2[kxx_start + 1] = -0.3 + + # Need to re-assign since list() copies + params1[kyy_start + 1] = 2.0 + params1[kyy_start + 2] = 0.3 + params1[kyy_start + 3] = 1.5 + params2[kyy_start + 1] = 2.0 + params2[kyy_start + 2] = 0.3 + params2[kyy_start + 3] = 1.5 + + res1 = bgms:::test_mixed_mrf_likelihoods( + d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, + d$inc_prob, d$edge_ind, FALSE, "conditional", params1, 1L + ) + res2 = bgms:::test_mixed_mrf_likelihoods( + d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, + d$inc_prob, d$edge_ind, FALSE, "conditional", params2, 1L + ) + + # GGM likelihood should be identical regardless of Kxx + expect_equal(res1$ggm_ll, res2$ggm_ll, tolerance = 1e-12) +}) + + +test_that("likelihood with p=1 ordinal, q=1 continuous works", { + set.seed(99) + n = 5L; p = 1L; q = 1L + x = matrix(sample(0:1, n, replace = TRUE), n, p) + y = matrix(rnorm(n), n, q) + num_cats = 1L + is_ordinal = 1L + baseline_cat = 0L + pq = p + q + inc_prob = matrix(0.5, pq, pq) + edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L + + # Params: 1 threshold + 0 Kxx + 1 muy + 1 Kyy + 1 Kxy = 4 + params = c(0.5, # mux(0,0) = threshold + 0.2, # muy + 1.5, # Kyy(0,0) + 0.1) # Kxy(0,0) + + res = bgms:::test_mixed_mrf_likelihoods( + x, y, num_cats, is_ordinal, baseline_cat, + inc_prob, edge_ind, FALSE, "conditional", params, 1L + ) + + # R reference + mux = matrix(0.5, 1, 1) + Kxx = matrix(0, 1, 1) + Kxy = matrix(0.1, 1, 1) + Kyy = matrix(1.5, 1, 1) + muy = 0.2 + + expected_omrf = ref_log_conditional_omrf( + x, y, Kxx, Kxy, mux, num_cats, is_ordinal, baseline_cat, s = 0 + ) + expected_ggm = ref_log_conditional_ggm( + x, y, Kxy, Kyy, muy, is_ordinal, baseline_cat + ) + + expect_equal(res$omrf_ll[1], expected_omrf, tolerance = 1e-10) + expect_equal(res$ggm_ll, expected_ggm, tolerance = 1e-10) +}) + + +test_that("all-Blume-Capel discrete variables work", { + set.seed(77) + n = 8L; p = 2L; q = 1L + num_cats = c(3L, 4L) + is_ordinal = c(0L, 0L) + baseline_cat = c(1L, 2L) + + x = matrix(0L, n, p) + x[, 1] = sample(0:3, n, replace = TRUE) + x[, 2] = sample(0:4, n, replace = TRUE) + y = matrix(rnorm(n), n, q) + + pq = p + q + inc_prob = matrix(0.5, pq, pq) + edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L + + # Params: 2 BC + 2 BC + 1 Kxx + 1 muy + 1 Kyy + 2 Kxy = 9 + # num_main = 4, num_pairwise_xx = 1, q = 1, num_kyy = 1, num_cross = 2 + params = c(0.3, -0.1, # BC var 0: alpha, beta + 0.2, -0.05, # BC var 1: alpha, beta + 0.15, # Kxx(0,1) + -0.1, # muy + 2.0, # Kyy + 0.08, -0.06) # Kxy + + res = bgms:::test_mixed_mrf_likelihoods( + x, y, num_cats, is_ordinal, baseline_cat, + inc_prob, edge_ind, FALSE, "conditional", params, 1L + ) + + mux = matrix(0, 2, 4) + mux[1, 1:2] = c(0.3, -0.1) + mux[2, 1:2] = c(0.2, -0.05) + + Kxx = matrix(0, 2, 2) + Kxx[1, 2] = Kxx[2, 1] = 0.15 + + Kxy = matrix(c(0.08, -0.06), nrow = 2, ncol = 1) + Kyy = matrix(2.0, 1, 1) + muy = -0.1 + + for(s in 0:1) { + expected = ref_log_conditional_omrf( + x, y, Kxx, Kxy, mux, num_cats, is_ordinal, baseline_cat, s + ) + expect_equal(res$omrf_ll[s + 1], expected, tolerance = 1e-8, + label = paste0("BC var ", s)) + } + + expected_ggm = ref_log_conditional_ggm( + x, y, Kxy, Kyy, muy, is_ordinal, baseline_cat + ) + expect_equal(res$ggm_ll, expected_ggm, tolerance = 1e-8) +}) From b0265e9dcbe673b0b9af7a4ab51bde9a8632e54c Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Thu, 5 Mar 2026 23:58:09 +0100 Subject: [PATCH 06/60] feat: add conditional PL Metropolis updates (B.3/B.4) Six scalar MH update functions in mixed_mrf_metropolis.cpp: - update_main_effect: ordinal thresholds and BC alpha/beta - update_continuous_mean: muy with conditional_mean_ save/restore - update_Kxx: symmetric discrete interactions - update_Kyy_offdiag: permute-based Cholesky (ported from mixedGM) - update_Kyy_diag: log-scale Cholesky with Jacobian - update_Kxy: cross interactions with conditional_mean_ save/restore Static helpers: log_beta_prior, permutation builders, get_constants/constrained_diagonal for Cholesky block updates. do_one_metropolis_step wired as 5-step sweep: main effects -> muy -> Kxx -> Kyy -> Kxy, edge-gated. Robbins-Monro adaptation deferred to Phase F. --- dev/mixedmrf/implementation_plan.md | 12 +- src/models/mixed/mixed_mrf_metropolis.cpp | 367 ++++++++++++++++++++++ src/models/mixed/mixed_mrf_model.cpp | 37 ++- src/models/mixed/mixed_mrf_model.h | 22 ++ 4 files changed, 433 insertions(+), 5 deletions(-) create mode 100644 src/models/mixed/mixed_mrf_metropolis.cpp diff --git a/dev/mixedmrf/implementation_plan.md b/dev/mixedmrf/implementation_plan.md index 85f3d876..f6d1243a 100644 --- a/dev/mixedmrf/implementation_plan.md +++ b/dev/mixedmrf/implementation_plan.md @@ -588,7 +588,11 @@ ll = n/2 * (-q * log(2*pi) + Kyy_log_det_) - quad_sum / 2 **Reuse opportunity:** This is structurally identical to `GGMModel::log_density_impl` but with a non-zero, observation-dependent conditional mean. -### B.2 Implement cache maintenance +### B.2 Implement cache maintenance ✅ + +All three functions implemented in Phase A (`mixed_mrf_model.cpp`). +The proposed-state bookkeeping rules below are the design contract +consumed by B.3's MH updates. #### `recompute_conditional_mean()` Recompute `conditional_mean_ = 1*muy^T + 2 * discrete_observations_ * Kxy_ * Kyy_inv_`. @@ -631,7 +635,7 @@ temporaries are discarded cheaply (Armadillo move semantics). 5. **Every accepted RJ edge toggle (Phase D)** must trigger the same cache-refresh logic as an MH acceptance for that parameter type. -### B.3 Implement MH updates (conditional PL) +### B.3 Implement MH updates (conditional PL) ✅ Each update function follows the pattern: 1. Save current parameter value @@ -715,7 +719,7 @@ helpers in `mixed_mrf_cholesky.cpp`, ported from - Prior: Cauchy(0, scale) - Only if $G_{xy,ij} = 1$ -### B.4 Implement `do_one_metropolis_step(int iteration)` +### B.4 Implement `do_one_metropolis_step(int iteration)` ✅ ```cpp void MixedMRFModel::do_one_metropolis_step(int iteration) { @@ -1303,7 +1307,7 @@ principle 1 (green build) and principle 2 (one logical concern). | 2 | `test` | `test: add mixed MRF skeleton tests` | A | `test-mixed-mrf-skeleton.R`: compile check, `parameter_dimension`, vectorization round-trip (ordinal + BC mix). | | 3 | `feat` | `feat: add mixed MRF likelihood functions` | B.1 | `mixed_mrf_likelihoods.cpp`: `log_conditional_omrf`, `log_conditional_ggm`, cache helpers. Ordinal + Blume-Capel paths. | | 4 | `test` | `test: add mixed MRF likelihood unit tests` | B.1 | `test-mixed-mrf-likelihood.R`: T1–T3 (compare C++ vs R fixtures), T7 (analytic Gaussian), T10–T12 (BC-specific). | -| 5 | `feat` | `feat: add conditional PL Metropolis updates` | B.3–B.4 | `mixed_mrf_metropolis.cpp`: all 5 MH update functions + `do_one_metropolis_step` (conditional mode). Cholesky helpers in `mixed_mrf_cholesky.cpp`. | +| 5 | `feat` | `feat: add conditional PL Metropolis updates` | B.3–B.4 | `mixed_mrf_metropolis.cpp`: 6 MH update functions (main effect, muy, Kxx, Kyy off-diag, Kyy diag, Kxy) + static Cholesky helpers + `do_one_metropolis_step` 5-step sweep. | | 6 | `test` | `test: add conditional PL recovery test` | B.5 | `test-mixed-mrf-sampling.R`: T13 (conditional PL parameter recovery, estimation only). | | 7 | `feat` | `feat: add marginal PL mode and Theta caching` | C | `log_marginal_omrf`, Theta cache, marginal-mode branches in MH updates. | | 8 | `test` | `test: add marginal PL recovery and comparison tests` | C.5 | T14 (marginal PL recovery), T16 (cond vs marginal agreement). | diff --git a/src/models/mixed/mixed_mrf_metropolis.cpp b/src/models/mixed/mixed_mrf_metropolis.cpp new file mode 100644 index 00000000..3c252dfd --- /dev/null +++ b/src/models/mixed/mixed_mrf_metropolis.cpp @@ -0,0 +1,367 @@ +#include +#include "models/mixed/mixed_mrf_model.h" +#include "rng/rng_utils.h" +#include "mcmc/execution/step_result.h" +#include "math/explog_macros.h" + + +// ============================================================================= +// Beta-type prior used for all main effects (ordinal thresholds and BC α/β). +// Matches OMRFModel::log_pseudoposterior_main_component. +// ============================================================================= + +static double log_beta_prior(double x, double alpha, double beta) { + return x * alpha - std::log1p(MY_EXP(x)) * (alpha + beta); +} + + +// ============================================================================= +// update_main_effect +// ============================================================================= +// MH update for one main-effect parameter. +// Ordinal: mux_(s, c) = threshold for category c+1 (c in [0, C_s-1]) +// Blume-Capel: mux_(s, 0) = linear α, mux_(s, 1) = quadratic β +// (c indexes 0 or 1 for BC) +// +// The accept/reject uses log_conditional_omrf(s) + beta-type prior. +// ============================================================================= + +void MixedMRFModel::update_main_effect(int s, int c) { + double& current = mux_(s, c); + double proposal_sd = prop_sd_mux_(s, c); + + double current_val = current; + double proposed = rnorm(rng_, current_val, proposal_sd); + + // Current log-posterior + double ll_curr = log_conditional_omrf(s) + log_beta_prior(current_val, main_alpha_, main_beta_); + + // Proposed log-posterior + current = proposed; + double ll_prop = log_conditional_omrf(s) + log_beta_prior(proposed, main_alpha_, main_beta_); + + double ln_alpha = ll_prop - ll_curr; + + if(std::log(runif(rng_)) >= ln_alpha) { + current = current_val; // reject + } +} + + +// ============================================================================= +// update_continuous_mean +// ============================================================================= +// MH update for one continuous mean parameter muy_(j). +// The accept/reject uses log_conditional_ggm() + Normal(0, 1) prior. +// Must save/restore conditional_mean_ around the proposal. +// ============================================================================= + +void MixedMRFModel::update_continuous_mean(int j) { + double current_val = muy_(j); + double proposed = rnorm(rng_, current_val, prop_sd_muy_(j)); + + // Current log-posterior (Normal(0,1) prior: -x^2/2 up to constant) + double ll_curr = log_conditional_ggm() + R::dnorm(current_val, 0.0, 1.0, true); + + // Set proposed value and refresh conditional_mean_ + arma::mat cond_mean_saved = conditional_mean_; + muy_(j) = proposed; + recompute_conditional_mean(); + + double ll_prop = log_conditional_ggm() + R::dnorm(proposed, 0.0, 1.0, true); + + double ln_alpha = ll_prop - ll_curr; + + if(std::log(runif(rng_)) >= ln_alpha) { + muy_(j) = current_val; // reject + conditional_mean_ = std::move(cond_mean_saved); + } +} + + +// ============================================================================= +// update_Kxx +// ============================================================================= +// MH update for one discrete-discrete interaction Kxx_(i, j). +// Symmetric: sets both (i,j) and (j,i). +// Acceptance: log_conditional_omrf(i) + log_conditional_omrf(j) + Cauchy prior. +// ============================================================================= + +void MixedMRFModel::update_Kxx(int i, int j) { + double current_val = Kxx_(i, j); + double proposed = rnorm(rng_, current_val, prop_sd_Kxx_(i, j)); + + // Current log-posterior + double ll_curr = log_conditional_omrf(i) + log_conditional_omrf(j) + + R::dcauchy(current_val, 0.0, pairwise_scale_, true); + + // Set proposed value (symmetric) + Kxx_(i, j) = proposed; + Kxx_(j, i) = proposed; + + double ll_prop = log_conditional_omrf(i) + log_conditional_omrf(j) + + R::dcauchy(proposed, 0.0, pairwise_scale_, true); + + double ln_alpha = ll_prop - ll_curr; + + if(std::log(runif(rng_)) >= ln_alpha) { + Kxx_(i, j) = current_val; // reject + Kxx_(j, i) = current_val; + } +} + + +// ============================================================================= +// Kyy helpers: permutation-based Cholesky updates +// ============================================================================= +// Ported from mixedGM/R/continuous_variable_helper.R. +// These operate on the (q-1, q) block after permuting target indices to +// the last two positions. + +// Build a permutation vector that moves indices (i, j) to positions (q-1, q). +// Same involution property as the R version. +static arma::uvec make_perm_offdiag(int i, int j, int q) { + int A = q - 2; // 0-based target for first index + int B = q - 1; // 0-based target for second index + arma::uvec perm = arma::regspace(0, q - 1); + + if((i == A && j == B) || (i == B && j == A)) { + // already in position + } else if(i == A || j == A) { + int other = (i == A) ? j : i; + if(other != B) std::swap(perm(other), perm(B)); + } else if(i == B || j == B) { + int other = (i == B) ? j : i; + if(other != A) std::swap(perm(other), perm(A)); + } else { + std::swap(perm(i), perm(A)); + std::swap(perm(j), perm(B)); + } + return perm; +} + +// Build permutation vector that moves index i to position q-1 (last). +static arma::uvec make_perm_diag(int i, int q) { + arma::uvec perm = arma::regspace(0, q - 1); + if(i != q - 1) std::swap(perm(i), perm(q - 1)); + return perm; +} + +// Apply symmetric permutation: M[perm, perm] +static arma::mat permute_matrix(const arma::mat& M, const arma::uvec& perm) { + return M.submat(perm, perm); +} + +// Extract constants from upper Cholesky factor (q×q) for (q-2, q-1) block update. +// Returns: {c1, c2, c3, c4} +struct CholConstants { + double c1, c2, c3, c4; +}; + +static CholConstants get_constants(const arma::mat& L, int q) { + // L is upper-triangular Cholesky factor + // phi_q and phi_q_1 are columns q-1 and q-2, excluding rows (q-2, q-1) + int qm1 = q - 1; // last column (0-based) + int qm2 = q - 2; // second-to-last column + + double c1 = 0.0; + for(int k = 0; k < qm2; ++k) { + c1 += L(k, qm1) * L(k, qm2); + } + double c2 = L(qm2, qm2); + double c3 = 0.0; + for(int k = 0; k < qm2; ++k) { + c3 += L(k, qm1) * L(k, qm1); + } + double c4 = c3 + c1 * c1 / (c2 * c2); + + return {c1, c2, c3, c4}; +} + +// Constrained diagonal: R(omega, constants) from mixedGM +static double constrained_diagonal(double omega, const CholConstants& C) { + if(omega == 0.0) { + return C.c4; + } else { + double t = (omega - C.c1) / C.c2; + return C.c3 + t * t; + } +} + + +// ============================================================================= +// update_Kyy_offdiag +// ============================================================================= +// MH update for one off-diagonal element of the precision matrix Kyy_(i, j). +// Uses the mixedGM permute-based Cholesky approach: +// 1. Permute target pair to last two positions +// 2. Cholesky of permuted Kyy +// 3. Propose on the Cholesky scale: phi' ~ N(Phi[q-2, q-1], sd) +// 4. Map back to Kyy space with constrained diagonal +// 5. Unpermute +// 6. Full recompute of GGM likelihood +// +// Prior: Cauchy(0, pairwise_scale_) on off-diag, Gamma(1, 1) on diagonal. +// ============================================================================= + +void MixedMRFModel::update_Kyy_offdiag(int i, int j) { + int q = static_cast(q_); + + arma::uvec perm = make_perm_offdiag(i, j, q); + arma::mat Kyy_perm = permute_matrix(Kyy_, perm); + arma::mat Phi = arma::chol(Kyy_perm); // upper Cholesky + CholConstants C = get_constants(Phi, q); + + // Current Cholesky-scale value + double phi_curr = Phi(q - 2, q - 1); + double phi_prop = rnorm(rng_, phi_curr, prop_sd_Kyy_(i, j)); + + // Map to Kyy space + double omega_prop = C.c1 + C.c2 * phi_prop; + double diag_curr = Kyy_perm(q - 1, q - 1); + double diag_prop = constrained_diagonal(omega_prop, C); + + // Build proposed permuted Kyy + arma::mat Kyy_prop_perm = Kyy_perm; + Kyy_prop_perm(q - 2, q - 1) = omega_prop; + Kyy_prop_perm(q - 1, q - 2) = omega_prop; + Kyy_prop_perm(q - 1, q - 1) = diag_prop; + + // Unpermute + arma::mat Kyy_prop = permute_matrix(Kyy_prop_perm, perm); + + // Save current state + arma::mat Kyy_saved = Kyy_; + arma::mat Kyy_inv_saved = Kyy_inv_; + arma::mat Kyy_chol_saved = Kyy_chol_; + double Kyy_log_det_saved = Kyy_log_det_; + arma::mat cond_mean_saved = conditional_mean_; + + // Evaluate proposed likelihood + Kyy_ = Kyy_prop; + recompute_Kyy_decomposition(); + recompute_conditional_mean(); + double ll_prop = log_conditional_ggm(); + + // Evaluate current likelihood (restore) + Kyy_ = Kyy_saved; + Kyy_inv_ = Kyy_inv_saved; + Kyy_chol_ = Kyy_chol_saved; + Kyy_log_det_ = Kyy_log_det_saved; + conditional_mean_ = cond_mean_saved; + double ll_curr = log_conditional_ggm(); + + double ln_alpha = ll_prop - ll_curr; + + // Prior ratio: Cauchy on off-diag + Gamma(1,1) on diagonal that changed + ln_alpha += R::dcauchy(Kyy_prop(i, j), 0.0, pairwise_scale_, true); + ln_alpha -= R::dcauchy(Kyy_(i, j), 0.0, pairwise_scale_, true); + ln_alpha += R::dgamma(diag_prop, 1.0, 1.0, true); + ln_alpha -= R::dgamma(diag_curr, 1.0, 1.0, true); + + if(std::log(runif(rng_)) < ln_alpha) { + // Accept: install proposed Kyy and refresh caches + Kyy_ = Kyy_prop; + recompute_Kyy_decomposition(); + recompute_conditional_mean(); + if(use_marginal_pl_) recompute_Theta(); + } +} + + +// ============================================================================= +// update_Kyy_diag +// ============================================================================= +// MH update for one diagonal element of Kyy. +// Proposes on the log-Cholesky scale to ensure positivity. +// Prior: Gamma(1, 1) on the diagonal element + Jacobian for log-scale proposal. +// ============================================================================= + +void MixedMRFModel::update_Kyy_diag(int i) { + int q = static_cast(q_); + + arma::uvec perm = make_perm_diag(i, q); + arma::mat Kyy_perm = permute_matrix(Kyy_, perm); + arma::mat L = arma::chol(Kyy_perm); // upper Cholesky + + // Propose on log(L[q-1, q-1]) scale + double theta_curr = std::log(L(q - 1, q - 1)); + double theta_prop = rnorm(rng_, theta_curr, prop_sd_Kyy_(i, i)); + + // Rebuild Kyy_prop from modified Cholesky + L(q - 1, q - 1) = std::exp(theta_prop); + arma::mat Kyy_prop_perm = L.t() * L; + arma::mat Kyy_prop = permute_matrix(Kyy_prop_perm, perm); + + // Save current state + arma::mat Kyy_saved = Kyy_; + arma::mat Kyy_inv_saved = Kyy_inv_; + arma::mat Kyy_chol_saved = Kyy_chol_; + double Kyy_log_det_saved = Kyy_log_det_; + arma::mat cond_mean_saved = conditional_mean_; + + // Evaluate proposed likelihood + Kyy_ = Kyy_prop; + recompute_Kyy_decomposition(); + recompute_conditional_mean(); + double ll_prop = log_conditional_ggm(); + + // Evaluate current likelihood (restore) + Kyy_ = Kyy_saved; + Kyy_inv_ = Kyy_inv_saved; + Kyy_chol_ = Kyy_chol_saved; + Kyy_log_det_ = Kyy_log_det_saved; + conditional_mean_ = cond_mean_saved; + double ll_curr = log_conditional_ggm(); + + double ln_alpha = ll_prop - ll_curr; + + // Prior ratio: Gamma(1, 1) on diagonal + ln_alpha += R::dgamma(Kyy_prop(i, i), 1.0, 1.0, true); + ln_alpha -= R::dgamma(Kyy_(i, i), 1.0, 1.0, true); + + // Jacobian for log-scale proposal + ln_alpha += theta_prop - theta_curr; + + if(std::log(runif(rng_)) < ln_alpha) { + Kyy_ = Kyy_prop; + recompute_Kyy_decomposition(); + recompute_conditional_mean(); + if(use_marginal_pl_) recompute_Theta(); + } +} + + +// ============================================================================= +// update_Kxy +// ============================================================================= +// MH update for one cross-type interaction Kxy_(i, j). +// Acceptance: log_conditional_omrf(i) + log_conditional_ggm() + Cauchy prior. +// Must save/restore conditional_mean_ around the proposal. +// ============================================================================= + +void MixedMRFModel::update_Kxy(int i, int j) { + double current_val = Kxy_(i, j); + double proposed = rnorm(rng_, current_val, prop_sd_Kxy_(i, j)); + + // Current log-posterior + double ll_curr = log_conditional_omrf(i) + log_conditional_ggm() + + R::dcauchy(current_val, 0.0, pairwise_scale_, true); + + // Set proposed value and refresh conditional_mean_ + arma::mat cond_mean_saved = conditional_mean_; + Kxy_(i, j) = proposed; + recompute_conditional_mean(); + + double ll_prop = log_conditional_omrf(i) + log_conditional_ggm() + + R::dcauchy(proposed, 0.0, pairwise_scale_, true); + + double ln_alpha = ll_prop - ll_curr; + + if(std::log(runif(rng_)) >= ln_alpha) { + Kxy_(i, j) = current_val; // reject + conditional_mean_ = std::move(cond_mean_saved); + } else if(use_marginal_pl_) { + recompute_Theta(); + } +} diff --git a/src/models/mixed/mixed_mrf_model.cpp b/src/models/mixed/mixed_mrf_model.cpp index 71002763..82a6e27a 100644 --- a/src/models/mixed/mixed_mrf_model.cpp +++ b/src/models/mixed/mixed_mrf_model.cpp @@ -417,7 +417,42 @@ std::unique_ptr MixedMRFModel::clone() const { // ============================================================================= void MixedMRFModel::do_one_metropolis_step(int /*iteration*/) { - // Phase B/C: MH updates for all parameter groups + // Step 1: Update all main effects (ordinal thresholds or BC α/β) + for(size_t s = 0; s < p_; ++s) { + if(is_ordinal_variable_(s)) { + for(int c = 0; c < num_categories_(s); ++c) + update_main_effect(s, c); + } else { + update_main_effect(s, 0); // linear α + update_main_effect(s, 1); // quadratic β + } + } + + // Step 2: Update all continuous means + for(size_t j = 0; j < q_; ++j) + update_continuous_mean(j); + + // Step 3: Update Kxx (upper triangle, edge-gated) + for(size_t i = 0; i < p_ - 1; ++i) + for(size_t j = i + 1; j < p_; ++j) + if(!edge_selection_active_ || gxx(i, j) == 1) + update_Kxx(i, j); + + // Step 4: Update Kyy (off-diag + diagonal, edge-gated) + if(q_ >= 2) { + for(size_t i = 0; i < q_ - 1; ++i) + for(size_t j = i + 1; j < q_; ++j) + if(!edge_selection_active_ || gyy(i, j) == 1) + update_Kyy_offdiag(i, j); + } + for(size_t i = 0; i < q_; ++i) + update_Kyy_diag(i); + + // Step 5: Update Kxy (edge-gated) + for(size_t i = 0; i < p_; ++i) + for(size_t j = 0; j < q_; ++j) + if(!edge_selection_active_ || gxy(i, j) == 1) + update_Kxy(i, j); } void MixedMRFModel::update_edge_indicators() { diff --git a/src/models/mixed/mixed_mrf_model.h b/src/models/mixed/mixed_mrf_model.h index 0be172e1..485fdd6c 100644 --- a/src/models/mixed/mixed_mrf_model.h +++ b/src/models/mixed/mixed_mrf_model.h @@ -337,6 +337,28 @@ class MixedMRFModel : public BaseModel { /** Conditional GGM log-likelihood: log f(y | x), using cached decomposition. */ double log_conditional_ggm() const; + // ========================================================================= + // MH update functions (implemented in mixed_mrf_metropolis.cpp) + // ========================================================================= + + /** Update one main-effect: mux_(s, c). Ordinal threshold or BC α/β. */ + void update_main_effect(int s, int c); + + /** Update one continuous mean: muy_(j). */ + void update_continuous_mean(int j); + + /** Update one discrete interaction: Kxx_(i, j). Symmetric. */ + void update_Kxx(int i, int j); + + /** Update one off-diagonal precision element: Kyy_(i, j). Cholesky-based. */ + void update_Kyy_offdiag(int i, int j); + + /** Update one diagonal precision element: Kyy_(i, i). Log-scale Cholesky. */ + void update_Kyy_diag(int i); + + /** Update one cross interaction: Kxy_(i, j). */ + void update_Kxy(int i, int j); + // ========================================================================= // Edge-indicator accessor helpers // ========================================================================= From a5fe0e40b8995fffff1ce1ea49054b6ccafda678 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Fri, 6 Mar 2026 18:00:24 +0100 Subject: [PATCH 07/60] feat: add mixed MRF sampler test helper and fix c3 constant MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Fix get_constants: include diagonal L(q-1,q-1)² in c3 (was missing, causing wrong constrained diagonal) - Add test_mixed_mrf_sampler() Rcpp export for recovery tests - Add test-mixed-mrf-sampling.R (conditional PL recovery test) --- R/RcppExports.R | 4 + src/RcppExports.cpp | 23 +++ src/models/mixed/mixed_mrf_metropolis.cpp | 4 + src/test_mixed_mrf.cpp | 50 +++++ tests/testthat/test-mixed-mrf-sampling.R | 224 ++++++++++++++++++++++ 5 files changed, 305 insertions(+) create mode 100644 tests/testthat/test-mixed-mrf-sampling.R diff --git a/R/RcppExports.R b/R/RcppExports.R index 3f72d3ce..a0177aea 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -65,3 +65,7 @@ test_mixed_mrf_likelihoods <- function(discrete_observations, continuous_observa .Call(`_bgms_test_mixed_mrf_likelihoods`, discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, params, seed) } +test_mixed_mrf_sampler <- function(discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, n_warmup, n_samples, seed) { + .Call(`_bgms_test_mixed_mrf_sampler`, discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, n_warmup, n_samples, seed) +} + diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 7ac4c45e..b28f12c3 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -330,6 +330,28 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } +// test_mixed_mrf_sampler +Rcpp::List test_mixed_mrf_sampler(const arma::imat& discrete_observations, const arma::mat& continuous_observations, const arma::ivec& num_categories, const arma::uvec& is_ordinal_variable, const arma::ivec& baseline_category, const arma::mat& inclusion_probability, const arma::imat& initial_edge_indicators, bool edge_selection, const std::string& pseudolikelihood, int n_warmup, int n_samples, int seed); +RcppExport SEXP _bgms_test_mixed_mrf_sampler(SEXP discrete_observationsSEXP, SEXP continuous_observationsSEXP, SEXP num_categoriesSEXP, SEXP is_ordinal_variableSEXP, SEXP baseline_categorySEXP, SEXP inclusion_probabilitySEXP, SEXP initial_edge_indicatorsSEXP, SEXP edge_selectionSEXP, SEXP pseudolikelihoodSEXP, SEXP n_warmupSEXP, SEXP n_samplesSEXP, SEXP seedSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::imat& >::type discrete_observations(discrete_observationsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type continuous_observations(continuous_observationsSEXP); + Rcpp::traits::input_parameter< const arma::ivec& >::type num_categories(num_categoriesSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type is_ordinal_variable(is_ordinal_variableSEXP); + Rcpp::traits::input_parameter< const arma::ivec& >::type baseline_category(baseline_categorySEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type inclusion_probability(inclusion_probabilitySEXP); + Rcpp::traits::input_parameter< const arma::imat& >::type initial_edge_indicators(initial_edge_indicatorsSEXP); + Rcpp::traits::input_parameter< bool >::type edge_selection(edge_selectionSEXP); + Rcpp::traits::input_parameter< const std::string& >::type pseudolikelihood(pseudolikelihoodSEXP); + Rcpp::traits::input_parameter< int >::type n_warmup(n_warmupSEXP); + Rcpp::traits::input_parameter< int >::type n_samples(n_samplesSEXP); + Rcpp::traits::input_parameter< int >::type seed(seedSEXP); + rcpp_result_gen = Rcpp::wrap(test_mixed_mrf_sampler(discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, n_warmup, n_samples, seed)); + return rcpp_result_gen; +END_RCPP +} static const R_CallMethodDef CallEntries[] = { {"_bgms_run_bgmCompare_parallel", (DL_FUNC) &_bgms_run_bgmCompare_parallel, 38}, @@ -348,6 +370,7 @@ static const R_CallMethodDef CallEntries[] = { {"_bgms_compute_Vn_mfm_sbm", (DL_FUNC) &_bgms_compute_Vn_mfm_sbm, 4}, {"_bgms_test_mixed_mrf_skeleton", (DL_FUNC) &_bgms_test_mixed_mrf_skeleton, 10}, {"_bgms_test_mixed_mrf_likelihoods", (DL_FUNC) &_bgms_test_mixed_mrf_likelihoods, 11}, + {"_bgms_test_mixed_mrf_sampler", (DL_FUNC) &_bgms_test_mixed_mrf_sampler, 12}, {NULL, NULL, 0} }; diff --git a/src/models/mixed/mixed_mrf_metropolis.cpp b/src/models/mixed/mixed_mrf_metropolis.cpp index 3c252dfd..ae0b6e46 100644 --- a/src/models/mixed/mixed_mrf_metropolis.cpp +++ b/src/models/mixed/mixed_mrf_metropolis.cpp @@ -169,10 +169,14 @@ static CholConstants get_constants(const arma::mat& L, int q) { c1 += L(k, qm1) * L(k, qm2); } double c2 = L(qm2, qm2); + // c3 excludes only row qm2 (the row being modified), not qm1. + // In R: sum(cholm[-(q-1), q]^2) — excludes row q-1 (1-based) = row qm2 (0-based). + // Must include L(qm1, qm1) (the diagonal entry of the last row). double c3 = 0.0; for(int k = 0; k < qm2; ++k) { c3 += L(k, qm1) * L(k, qm1); } + c3 += L(qm1, qm1) * L(qm1, qm1); // diagonal entry double c4 = c3 + c1 * c1 / (c2 * c2); return {c1, c2, c3, c4}; diff --git a/src/test_mixed_mrf.cpp b/src/test_mixed_mrf.cpp index d296217c..2f931cee 100644 --- a/src/test_mixed_mrf.cpp +++ b/src/test_mixed_mrf.cpp @@ -161,3 +161,53 @@ Rcpp::List test_mixed_mrf_likelihoods( Rcpp::Named("ggm_ll") = ggm_ll ); } + + +// [[Rcpp::export]] +Rcpp::List test_mixed_mrf_sampler( + const arma::imat& discrete_observations, + const arma::mat& continuous_observations, + const arma::ivec& num_categories, + const arma::uvec& is_ordinal_variable, + const arma::ivec& baseline_category, + const arma::mat& inclusion_probability, + const arma::imat& initial_edge_indicators, + bool edge_selection, + const std::string& pseudolikelihood, + int n_warmup, + int n_samples, + int seed +) { + MixedMRFModel model( + discrete_observations, + continuous_observations, + num_categories, + is_ordinal_variable, + baseline_category, + inclusion_probability, + initial_edge_indicators, + edge_selection, + pseudolikelihood, + 1.0, 1.0, 2.5, + seed + ); + + size_t full_dim = model.full_parameter_dimension(); + + // Warmup (discard samples) + for(int iter = 0; iter < n_warmup; ++iter) { + model.do_one_metropolis_step(iter); + } + + // Sampling + arma::mat samples(n_samples, full_dim); + for(int iter = 0; iter < n_samples; ++iter) { + model.do_one_metropolis_step(n_warmup + iter); + samples.row(iter) = model.get_full_vectorized_parameters().t(); + } + + return Rcpp::List::create( + Rcpp::Named("samples") = samples, + Rcpp::Named("full_parameter_dimension") = full_dim + ); +} diff --git a/tests/testthat/test-mixed-mrf-sampling.R b/tests/testthat/test-mixed-mrf-sampling.R new file mode 100644 index 00000000..9a258492 --- /dev/null +++ b/tests/testthat/test-mixed-mrf-sampling.R @@ -0,0 +1,224 @@ +# ============================================================================= +# test-mixed-mrf-sampling.R — Phase B.5 sampler correctness tests +# ============================================================================= +# Validates that do_one_metropolis_step() in MixedMRFModel produces correct +# posterior samples by comparing bgms against the mixedGM reference sampler. +# +# Because no proposal adaptation is implemented yet (Phase F), acceptance +# rates are low with the default proposal SD = 1.0. Tests use correlation +# thresholds rather than absolute agreement. + +skip_if_not_installed("mixedGM") + +# --------------------------------------------------------------------------- +# Helper: extract parameter matrices from bgms sample matrix +# --------------------------------------------------------------------------- +# The vectorization order in bgms is: +# 1. mux: per-variable, C_s thresholds each (for ordinal) +# 2. Kxx: upper-triangular, row-major +# 3. muy: q means +# 4. Kyy: upper-triangle including diagonal +# 5. Kxy: p*q entries, row-major +extract_bgms_estimates = function(samples, p, q, num_cats_bgms) { + S = colMeans(samples) + idx = 1L + + # 1. mux (per-variable: C_s thresholds each) + max_cat = max(num_cats_bgms) + mux = matrix(0, p, max_cat) + for(s in 1:p) { + for(c in seq_len(num_cats_bgms[s])) { + mux[s, c] = S[idx]; idx = idx + 1L + } + } + + # 2. Kxx upper-tri + Kxx = matrix(0, p, p) + for(i in 1:(p - 1)) for(j in (i + 1):p) { + Kxx[i, j] = Kxx[j, i] = S[idx]; idx = idx + 1L + } + + # 3. muy + muy = S[idx:(idx + q - 1)]; idx = idx + q + + # 4. Kyy upper-tri + diag + Kyy = matrix(0, q, q) + for(i in 1:q) for(j in i:q) { + Kyy[i, j] = Kyy[j, i] = S[idx]; idx = idx + 1L + } + + # 5. Kxy row-major + Kxy = matrix(S[idx:(idx + p * q - 1)], nrow = p, ncol = q, byrow = TRUE) + + list(mux = mux, Kxx = Kxx, muy = muy, Kyy = Kyy, Kxy = Kxy) +} + + +# =========================================================================== +# Test 1: ordinal simulation recovery (3-category, p=3, q=2, n=1000) +# =========================================================================== +test_that("conditional PL sampler recovers parameters (ordinal simulation)", { + skip_on_cran() + + p = 3L; q = 2L; n = 1000L + + Kxx_true = matrix(c(0, 0.4, -0.2, + 0.4, 0, 0.3, + -0.2, 0.3, 0), 3, 3) + Kxy_true = matrix(c(0.2, -0.1, 0.15, + 0.1, 0.3, -0.2), nrow = p, ncol = q) + Kyy_true = matrix(c(1.3, 0.2, 0.2, 1.1), 2, 2) + mux_true = matrix(c(0.5, -0.5, 0.3, + -0.3, -1.0, -0.5), nrow = p, ncol = 2) + muy_true = c(0.5, -0.3) + + set.seed(456) + sim = mixedGM::mixed_gibbs_generate( + n = n, Kxx = Kxx_true, Kxy = Kxy_true, Kyy = Kyy_true, + mux = mux_true, muy = muy_true, + num_categories = rep(3L, p), n_burnin = 1000 + ) + + # --- run mixedGM (with Robbins-Monro adaptation) --- + set.seed(42) + mgm = mixedGM::mixed_sampler( + x = sim$x, y = sim$y, num_categories = rep(3L, p), + n_warmup = 4000L, n_samples = 8000L, + edge_selection = FALSE, verbose = FALSE, + pseudolikelihood = "conditional" + ) + mgm_est = mgm$final_parameters + + # --- run bgms (no adaptation) --- + num_cats_bgms = rep(2L, p) # max category index + pq = p + q + inc_prob = matrix(0.5, pq, pq); diag(inc_prob) = 0 + edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L + + bgms_out = bgms:::test_mixed_mrf_sampler( + sim$x, sim$y, as.integer(num_cats_bgms), + as.integer(rep(1L, p)), rep(0L, p), + inc_prob, edge_ind, FALSE, "conditional", + 4000L, 8000L, 42L + ) + bgms_est = extract_bgms_estimates(bgms_out$samples, p, q, num_cats_bgms) + + # --- correlations bgms vs mixedGM (r >= 0.95) --- + kxx_ut = upper.tri(bgms_est$Kxx) + expect_gt( + cor(bgms_est$Kxx[kxx_ut], mgm_est$Kxx[kxx_ut]), 0.95, + label = "Kxx bgms-mgm correlation" + ) + expect_gt( + cor(as.vector(bgms_est$Kxy), as.vector(mgm_est$Kxy)), 0.95, + label = "Kxy bgms-mgm correlation" + ) + expect_gt( + cor(as.vector(bgms_est$Kyy), as.vector(mgm_est$Kyy)), 0.95, + label = "Kyy bgms-mgm correlation" + ) + expect_gt( + cor(as.vector(bgms_est$mux), as.vector(mgm_est$mux)), 0.95, + label = "mux bgms-mgm correlation" + ) + + # --- signs agree with truth --- + expect_true( + all(sign(bgms_est$Kxx[kxx_ut]) == sign(Kxx_true[kxx_ut])), + label = "Kxx signs match truth" + ) +}) + + +# =========================================================================== +# Test 2: real data cross-validation (Wenchuan, ordinal + continuous) +# =========================================================================== +test_that("conditional PL sampler agrees with mixedGM on Wenchuan data", { + skip_on_cran() + + data(Wenchuan, package = "bgms") + W = na.omit(Wenchuan) + + p = 4L; q = 2L + x = as.matrix(W[, 1:p]) - 1L # convert 1-5 to 0-4 + y = as.matrix(W[, (p + 1):(p + q)]) + + num_cats_mgm = rep(5L, p) # total categories for mixedGM + num_cats_bgms = rep(4L, p) # max category index for bgms + + # --- mixedGM --- + set.seed(42) + mgm = mixedGM::mixed_sampler( + x = x, y = y, num_categories = num_cats_mgm, + n_warmup = 4000L, n_samples = 8000L, + edge_selection = FALSE, verbose = FALSE, + pseudolikelihood = "conditional" + ) + mgm_est = mgm$final_parameters + + # --- bgms --- + pq = p + q + inc_prob = matrix(0.5, pq, pq); diag(inc_prob) = 0 + edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L + + bgms_out = bgms:::test_mixed_mrf_sampler( + x, y, as.integer(num_cats_bgms), + as.integer(rep(1L, p)), rep(0L, p), + inc_prob, edge_ind, FALSE, "conditional", + 4000L, 8000L, 42L + ) + bgms_est = extract_bgms_estimates(bgms_out$samples, p, q, num_cats_bgms) + + # --- correlations bgms vs mixedGM (r >= 0.95) --- + kxx_ut = upper.tri(bgms_est$Kxx) + expect_gt( + cor(bgms_est$Kxx[kxx_ut], mgm_est$Kxx[kxx_ut]), 0.95, + label = "Kxx bgms-mgm correlation (Wenchuan)" + ) + expect_gt( + cor(as.vector(bgms_est$Kxy), as.vector(mgm_est$Kxy)), 0.95, + label = "Kxy bgms-mgm correlation (Wenchuan)" + ) + expect_gt( + cor(as.vector(bgms_est$Kyy), as.vector(mgm_est$Kyy)), 0.95, + label = "Kyy bgms-mgm correlation (Wenchuan)" + ) + expect_gt( + cor(as.vector(bgms_est$mux), as.vector(mgm_est$mux)), 0.95, + label = "mux bgms-mgm correlation (Wenchuan)" + ) + + # --- Kxx sign patterns agree --- + expect_true( + all(sign(bgms_est$Kxx[kxx_ut]) == sign(mgm_est$Kxx[kxx_ut])), + label = "Kxx signs agree (Wenchuan)" + ) +}) + + +# =========================================================================== +# Test 3: sampler does not crash on Kyy updates (c3 regression test) +# =========================================================================== +test_that("Kyy updates do not crash (c3 get_constants regression)", { + # Minimal setup: p=2, q=2 binary triggers the c3 code path + # where L(qm1,qm1)^2 must be included in the sum. + set.seed(99) + n = 50L; p = 2L; q = 2L + x = matrix(sample(0:1, n * p, replace = TRUE), n, p) + y = matrix(rnorm(n * q), n, q) + + pq = p + q + inc_prob = matrix(0.5, pq, pq); diag(inc_prob) = 0 + edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L + + # If c3 bug is present, this crashes with "chol(): decomposition failed" + result = bgms:::test_mixed_mrf_sampler( + x, y, as.integer(c(1L, 1L)), + as.integer(c(1L, 1L)), c(0L, 0L), + inc_prob, edge_ind, FALSE, "conditional", + 100L, 100L, 123L + ) + + expect_equal(nrow(result$samples), 100L) + expect_true(all(is.finite(result$samples))) +}) From ede74a14fa7ac6174e1b94778250f0713998b7ad Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Fri, 6 Mar 2026 18:00:40 +0100 Subject: [PATCH 08/60] docs: add Phase B+ rank-1 Cholesky optimization plan MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit - Add Phase B+ (10 sub-steps) for replacing O(q³) permute-based Kyy updates with GGM-style rank-1 Cholesky infrastructure - Add B+.10 rank-2 quadratic-form shortcut (O(nq) vs O(npq+nq²)) - Design decision: keep Cholesky over Woodbury-only for PD safety - Add tests T28-T30, commits 6a-6d to planned schedule - Update risk register, reuse inventory, and table of contents --- dev/mixedmrf/implementation_plan.md | 355 +++++++++++++++++++++++++++- 1 file changed, 342 insertions(+), 13 deletions(-) diff --git a/dev/mixedmrf/implementation_plan.md b/dev/mixedmrf/implementation_plan.md index f6d1243a..0f1d63f4 100644 --- a/dev/mixedmrf/implementation_plan.md +++ b/dev/mixedmrf/implementation_plan.md @@ -78,15 +78,16 @@ OMRF code directly rather than ported from mixedGM (see §1 and Phase B/C). 5. [Implementation phases](#5-implementation-phases) 6. [Phase A — Skeleton and data structures](#phase-a--skeleton-and-data-structures) 7. [Phase B — Conditional pseudo-likelihood](#phase-b--conditional-pseudo-likelihood) -8. [Phase C — Marginal pseudo-likelihood](#phase-c--marginal-pseudo-likelihood) -9. [Phase D — Edge selection](#phase-d--edge-selection) -10. [Phase E — R interface and integration](#phase-e--r-interface-and-integration) -11. [Phase F — Warmup, adaptation, and diagnostics](#phase-f--warmup-adaptation-and-diagnostics) -12. [Phase G — Simulation and prediction](#phase-g--simulation-and-prediction) -13. [Testing strategy](#testing-strategy) -14. [Reuse inventory](#reuse-inventory) -15. [Risk register](#risk-register) -16. [PR and commit strategy](#pr-and-commit-strategy) +8. [Phase B+ — Rank-1 Cholesky optimization](#phase-b--rank-1-cholesky-optimization) +9. [Phase C — Marginal pseudo-likelihood](#phase-c--marginal-pseudo-likelihood) +10. [Phase D — Edge selection](#phase-d--edge-selection) +11. [Phase E — R interface and integration](#phase-e--r-interface-and-integration) +12. [Phase F — Warmup, adaptation, and diagnostics](#phase-f--warmup-adaptation-and-diagnostics) +13. [Phase G — Simulation and prediction](#phase-g--simulation-and-prediction) +14. [Testing strategy](#testing-strategy) +15. [Reuse inventory](#reuse-inventory) +16. [Risk register](#risk-register) +17. [PR and commit strategy](#pr-and-commit-strategy) --- @@ -308,9 +309,10 @@ add the new Rcpp export to `src/RcppExports.cpp`, `R/RcppExports.R`, and |-------|------|------------|-------------| | **A** | Skeleton: class, data structures, `BaseModel` overrides | — | Compiles, no sampling | | **B** | Conditional PL: all 5 MH updates, no edge selection | A | Recovery test passes (cond PL, estimation only) | -| **C** | Marginal PL: $\Theta$ caching, marginal OMRF, $\mu_y$ full sweep | B | Recovery test passes (marg PL, estimation only) | -| **D** | Edge selection: 3 RJ sweeps | B | Structure recovery test passes | -| **E** | R interface: `bgm()` dispatch, output formatting | B | End-to-end `bgm(mixed_data)` works | +| **B+** | Rank-1 Cholesky optimization for Kyy updates | B | Same correctness, $O(q^2)$ per Kyy move instead of $O(q^3)$ | +| **C** | Marginal PL: $\Theta$ caching, marginal OMRF, $\mu_y$ full sweep | B+ | Recovery test passes (marg PL, estimation only) | +| **D** | Edge selection: 3 RJ sweeps | B+ | Structure recovery test passes | +| **E** | R interface: `bgm()` dispatch, output formatting | B+ | End-to-end `bgm(mixed_data)` works | | **F** | Warmup schedule, adaptation, diagnostics | E | Full warmup pipeline | | **G** | Simulation and prediction | E | `simulate.bgms` and `predict.bgms` for mixed | @@ -690,6 +692,11 @@ conditional mean depends on $K_{yy}^{-1}$, so each proposal evaluates the full `log_conditional_ggm()` with freshly computed `Kyy_inv_` and `conditional_mean_`. +**Status (Phase B):** The current implementation follows the mixedGM +permute-then-Cholesky approach ($O(q^3)$ per Kyy move). Phase B+ replaces +this with the GGM's rank-1 infrastructure to bring it down to $O(q^2)$. +See [Phase B+](#phase-b--rank-1-cholesky-optimization) for the detailed plan. + **Jacobian for diagonal Kyy proposals.** The diagonal element is proposed on the log scale to guarantee positivity: ``` @@ -780,6 +787,301 @@ void MixedMRFModel::do_one_metropolis_step(int iteration) { --- +## Phase B+ — Rank-1 Cholesky optimization + +The Phase B implementation ports the mixedGM permute-then-Cholesky approach +for $K_{yy}$ updates: each proposal calls `arma::chol` ($O(q^3)$), evaluates +the full `log_conditional_ggm()` twice (proposed and current), and recomputes +all caches from scratch on acceptance. The GGM model avoids this entirely +with rank-1 Cholesky updates, log-likelihood ratios via the matrix +determinant lemma, and cached covariance. Phase B+ brings the same +infrastructure to the mixed model. + +**Design decision: Cholesky over Woodbury.** An alternative is to +maintain only $(K_{yy}, \Sigma_{yy}, \log|K_{yy}|)$ and update +$\Sigma_{yy}$ directly via the Woodbury identity, dropping the Cholesky +factor entirely (fewer members, simpler code). The asymptotic cost is +identical — both eliminate the $O(q^3)$ bottleneck — but the Cholesky +factor provides a structural positive-definiteness guarantee between +periodic full recomputes, while a Woodbury-only covariance can silently +lose symmetry or PD from floating-point drift. We therefore keep the +Cholesky factor. The Woodbury-only variant remains a future option to +investigate if profiling shows the Cholesky maintenance constant matters. + +### B+.0 Why this matters now + +Every subsequent phase (C, D, E, F) exercises $K_{yy}$ updates. Marginal PL +(Phase C) adds $\sum_s$ `marg_omrf(s)` to every $K_{yy}$ acceptance ratio, +and edge selection (Phase D) adds Cholesky-based birth/death moves for +$G_{yy}$. Optimizing $K_{yy}$ after those phases are built means +retrofitting the rank-1 machinery into more code paths. Doing it now, while +only `update_Kyy_offdiag` and `update_Kyy_diag` exist, is cleaner. + +### B+.1 Add GGM-style member variables + +New members in `MixedMRFModel` (mirroring `GGMModel`): + +```cpp +// --- Cholesky workspace (Phase B+) --- +arma::mat covariance_yy_; // q × q Kyy^{-1}, replaces Kyy_inv_ +arma::mat inv_cholesky_yy_; // q × q R^{-1} where Kyy = R'R +std::array kyy_constants_; // reparametrization constants +arma::mat precision_yy_proposal_; // q × q scratch for proposed Kyy +arma::vec v1_, v2_; // 2-vectors for rank-2 decomposition +arma::vec vf1_, vf2_; // q-vectors (full-size update vectors) +arma::vec u1_, u2_; // q-vectors (decomposed rank-1 vectors) +``` + +`Kyy_inv_` is renamed to `covariance_yy_` for clarity (it is the covariance +$\Sigma_{yy} = K_{yy}^{-1}$). `Kyy_chol_` is retained as the upper +Cholesky factor. + +### B+.2 Port `get_constants` and `constrained_diagonal` (no-permutation form) + +Replace the current permutation-based `get_constants(const arma::mat& L, +int q)` and `constrained_diagonal(double omega, const CholConstants& C)` +with the GGM's direct-indexing approach that reads constants from +`covariance_yy_` without permuting: + +```cpp +void MixedMRFModel::get_kyy_constants(int i, int j) { + double logdet = get_log_det(Kyy_chol_); + double log_adj_ii = logdet + std::log(std::abs(covariance_yy_(i, i))); + double log_adj_ij = logdet + std::log(std::abs(covariance_yy_(i, j))); + double log_adj_jj = logdet + std::log(std::abs(covariance_yy_(j, j))); + + double inv_sub = compute_inv_submatrix_i(covariance_yy_, i, j, j); + double log_abs_inv_sub = log_adj_ii + std::log(std::abs(inv_sub)); + + double Phi_q1q = (2 * std::signbit(covariance_yy_(i, j)) - 1) + * std::exp(log_adj_ij - (log_adj_jj + log_abs_inv_sub) / 2); + double Phi_q1q1 = std::exp((log_adj_jj - log_abs_inv_sub) / 2); + + kyy_constants_[0] = Phi_q1q; + kyy_constants_[1] = Phi_q1q1; + kyy_constants_[2] = Kyy_(i, j) - Phi_q1q * Phi_q1q1; // c1 + kyy_constants_[3] = Phi_q1q1; // c2 + kyy_constants_[4] = Kyy_(j, j) - Phi_q1q * Phi_q1q; // c3 + kyy_constants_[5] = kyy_constants_[4] + + kyy_constants_[2] * kyy_constants_[2] + / (kyy_constants_[3] * kyy_constants_[3]); // c4 +} +``` + +This is `GGMModel::get_constants` operating on the $q \times q$ precision +and covariance pair instead of the GGM's $p \times p$ pair. The helper +`compute_inv_submatrix_i` and `get_log_det` are reused directly from +`GGMModel` (they are pure algebra with no model-specific state). + +### B+.3 Implement `log_ggm_ratio_edge(int i, int j)` — rank-2 log-LR + +The GGM's `log_density_impl_edge` computes the log-likelihood **ratio** +for a 2-element change $\Delta\omega_{ij}, \Delta\omega_{jj}$ using the +matrix determinant lemma. The formula involves: + +1. **Log-determinant ratio** from the $2 \times 2$ Schur complement. +2. **Trace ratio** from the sufficient statistic $S = X^\top X$. + +For the mixed model's conditional GGM, the sufficient statistic is +$S^* = D^\top D$ where $D = Y - M$. The key question is whether $S^*$ +remains fixed when only $K_{yy}$ changes. + +**It does not.** The conditional mean $M = \mathbf{1}\mu_y^\top + 2\,X\,K_{xy}\,K_{yy}^{-1}$ depends on $K_{yy}^{-1}$. However, the +Cholesky-based proposal changes $K_{yy}$ by a structured rank-2 +perturbation (off-diagonal + constrained diagonal), and the +corresponding rank-2 change in $K_{yy}^{-1}$ (via the Woodbury identity) +can be computed in $O(q)$ from the update vectors. + +The `log_ggm_ratio_edge` function therefore: + +1. Computes the log-det ratio using the same $2 \times 2$ determinant + lemma as the GGM: $O(q)$ from covariance entries. +2. Computes the proposed covariance $\Sigma'_{yy}$ via rank-2 Woodbury + update on `covariance_yy_`: $O(q^2)$. +3. Computes the proposed conditional mean $M' = \mathbf{1}\mu_y^\top + + 2\,X\,K_{xy}\,\Sigma'_{yy}$: $O(npq)$ (matrix multiply, but this is + the same cost as `recompute_conditional_mean` so no regression). +4. Computes the proposed quadratic form $\text{tr}(K'_{yy} (Y-M')^\top (Y-M'))$ + using the proposed precision and residuals. +5. Returns the full log-likelihood ratio including both log-det and + quadratic-form changes. + +The initial implementation recomputes the full conditional mean +$M'$, making step 3 the bottleneck at $O(npq)$. Step B+.10 eliminates +this by exploiting the rank-2 structure of $\Sigma'_{yy} - \Sigma_{yy}$. + +### B+.4 Implement `log_ggm_ratio_diag(int i)` — rank-1 log-LR + +Same approach as B+.3 but for a diagonal-only change. The rank-1 case +is simpler (1 × 1 Schur complement, single Woodbury vector). Mirrors +`GGMModel::log_density_impl_diag`. + +### B+.5 Implement rank-1 Cholesky update after acceptance + +Port `GGMModel::cholesky_update_after_edge` and +`cholesky_update_after_diag` to operate on `Kyy_chol_`, +`inv_cholesky_yy_`, and `covariance_yy_`: + +```cpp +void MixedMRFModel::cholesky_update_after_kyy_edge( + double omega_ij_old, double omega_jj_old, int i, int j) +{ + // Decompose rank-2 Omega change into 2 rank-1 ops (same as GGM) + v2_[0] = omega_ij_old - precision_yy_proposal_(i, j); + v2_[1] = (omega_jj_old - precision_yy_proposal_(j, j)) / 2; + vf1_[i] = v1_[0]; vf1_[j] = v1_[1]; + vf2_[i] = v2_[0]; vf2_[j] = v2_[1]; + u1_ = (vf1_ + vf2_) / std::sqrt(2.0); + u2_ = (vf1_ - vf2_) / std::sqrt(2.0); + + cholesky_update(Kyy_chol_, u1_); + cholesky_downdate(Kyy_chol_, u2_); + + arma::inv(inv_cholesky_yy_, arma::trimatu(Kyy_chol_)); + covariance_yy_ = inv_cholesky_yy_ * inv_cholesky_yy_.t(); + Kyy_log_det_ = get_log_det(Kyy_chol_); + + vf1_[i] = 0; vf1_[j] = 0; + vf2_[i] = 0; vf2_[j] = 0; +} +``` + +The Cholesky update/downdate calls are $O(q^2)$ each. Inverting the +triangular factor and forming `covariance_yy_` is also $O(q^2)$ (back- +substitution + symmetric product). Total: $O(q^2)$ vs. the current +$O(q^3)$. + +After the Cholesky update, recompute `conditional_mean_` (and `Theta_` +if marginal PL) as before — those are independent of the Cholesky +strategy. + +### B+.6 Rewrite `update_Kyy_offdiag` and `update_Kyy_diag` + +Replace the permute-then-Cholesky implementation with: + +``` +update_Kyy_offdiag(i, j): + get_kyy_constants(i, j) + propose phi' ~ N(Phi_q1q, sigma) + map to omega'_ij, omega'_jj via constrained_diagonal + fill precision_yy_proposal_ + ln_alpha = log_ggm_ratio_edge(i, j) // rank-2 log-LR + + prior ratio (Cauchy off-diag + Gamma diag) + if accept: + old_ij = Kyy_(i,j); old_jj = Kyy_(j,j) + Kyy_ = precision_yy_proposal_ (only 3 entries changed) + cholesky_update_after_kyy_edge(old_ij, old_jj, i, j) + recompute_conditional_mean() + if marginal: recompute_Theta() +``` + +The current save-evaluate-restore-evaluate-compare pattern is replaced +by a single log-ratio call. No full `log_conditional_ggm()` evaluation +is needed. + +### B+.7 Remove permutation helpers + +Delete `make_perm_offdiag`, `make_perm_diag`, `permute_matrix`, +`get_constants(const arma::mat& L, int q)`, `constrained_diagonal(double, +CholConstants)`, and the `CholConstants` struct from +`mixed_mrf_metropolis.cpp`. They are fully superseded by the GGM-style +functions. + +### B+.8 Functions reused from GGM + +| Function | Source | Reuse form | +|----------|--------|------------| +| `cholesky_update` / `cholesky_downdate` | `cholupdate.h` | Direct call | +| `get_log_det` | `GGMModel` | Copy as free function or method | +| `compute_inv_submatrix_i` | `GGMModel` | Copy as free function or method | +| `constrained_diagonal` (GGM form) | `GGMModel` | Adapted as `kyy_constrained_diagonal` | + +`get_log_det` and `compute_inv_submatrix_i` are currently private methods +of `GGMModel`. They are pure algebra with no model state. Options: + +- **(a)** Extract to a shared utility header (`src/math/cholesky_helpers.h`) + and include from both models. +- **(b)** Duplicate as private methods on `MixedMRFModel`. + +Option (a) is preferred (avoids code duplication and makes the shared +algebra explicit), but option (b) is acceptable if the extraction touches +too many files for this phase. + +### B+.9 Testing checkpoint + +**Correctness (non-negotiable):** +- Existing `test-mixed-mrf-likelihoods.R` must still pass unchanged + (likelihood functions are not modified). +- Existing `test-mixed-mrf-sampling.R` must still pass (recovery results + must be identical at the same seed). +- New test: for a known $K_{yy}$ proposal, verify that `log_ggm_ratio_edge` + equals `log_conditional_ggm(proposed) - log_conditional_ggm(current)` + to machine precision (T28). +- New test: verify rank-1 Cholesky update produces the same + `Kyy_chol_`, `covariance_yy_`, `Kyy_log_det_` as a full recompute (T29). + +**Performance (informational):** +- Benchmark `update_Kyy_offdiag` before and after on a $q = 20$ problem. + Expected speedup: order of magnitude for the Cholesky portion. + +### B+.10 Rank-2 quadratic-form shortcut + +After B+.3–B+.6 are working, the remaining bottleneck in +`log_ggm_ratio_edge` is the $O(npq)$ conditional-mean recompute (step 3) +followed by the $O(nq^2)$ quadratic form (step 4). Both can be reduced +by exploiting the rank-2 structure of the covariance change. + +The Woodbury identity gives: + +$$\Sigma'_{yy} - \Sigma_{yy} = \Delta\Sigma$$ + +where $\Delta\Sigma$ is a rank-2 matrix (it comes from the rank-2 +perturbation of $K_{yy}$). The conditional-mean change is: + +$$\Delta M = M' - M = 2\,X\,K_{xy}\,\Delta\Sigma$$ + +Since $\Delta\Sigma$ is rank 2, $\Delta M$ is an $n \times q$ matrix of +rank at most 2. Write $\Delta\Sigma = a\,b^\top + b\,a^\top$ (or its +actual Woodbury form) and define the two $n$-vectors +$g_1 = 2\,X\,K_{xy}\,a$ and $g_2 = 2\,X\,K_{xy}\,b$. Then: + +$$\Delta M = g_1\,b^\top + g_2\,a^\top$$ + +The proposed residual is $D' = D - \Delta M$. The quadratic-form +difference becomes: + +$$\text{tr}(K'_{yy}\,D'^\top D') - \text{tr}(K_{yy}\,D^\top D) + = \text{tr}(\Delta K_{yy}\,D^\top D) + - 2\,\text{tr}(K'_{yy}\,D^\top \Delta M) + + \text{tr}(K'_{yy}\,\Delta M^\top \Delta M)$$ + +Each term can be evaluated without forming the full $n \times q$ +matrices $M'$ or $D'$: + +- $\text{tr}(\Delta K_{yy}\,D^\top D)$: $\Delta K_{yy}$ is nonzero at + 3 entries, and $D^\top D$ is the cached sufficient statistic $S^*$. + Cost: $O(1)$. +- $\text{tr}(K'_{yy}\,D^\top \Delta M)$: Compute $D^\top g_1$ and + $D^\top g_2$ ($O(nq)$ each), then contract with $K'_{yy} b$ and + $K'_{yy} a$ ($O(q^2)$ each). Total: $O(nq)$. +- $\text{tr}(K'_{yy}\,\Delta M^\top \Delta M)$: Inner products of + $g_1, g_2$ ($O(n)$ each) contracted with a $2 \times 2$ block from + $a^\top K'_{yy}\,b$ etc. Total: $O(n + q^2)$. + +Overall cost: $O(nq)$ instead of $O(npq + nq^2)$. This also eliminates +the need to store the $n \times q$ proposed conditional mean. + +**Pre-condition:** $D^\top D$ (the current residual cross-product) must be +cached. Add a member `suf_stat_ggm_` ($q \times q$) updated after every +accepted move that changes $K_{yy}$, $K_{xy}$, or $\mu_y$. + +**Testing:** +- T30: For a known proposal, verify the shortcut quadratic-form difference + matches the brute-force computation to machine precision. +- Existing T28 still covers end-to-end log-ratio agreement. + +--- + ## Phase C — Marginal pseudo-likelihood ### C.1 Implement `log_marginal_omrf(int s)` @@ -984,6 +1286,16 @@ denominator as needed. ## Phase E — R interface and integration +### E.0 Remove `src/test_mixed_mrf.cpp` + +Delete `src/test_mixed_mrf.cpp` once `sample_mixed.cpp` provides the +production Rcpp interface (E.1). The test helpers +(`test_mixed_mrf_skeleton`, `test_mixed_mrf_likelihoods`, +`test_mixed_mrf_sampler`) were scaffolding for Phases A–B; their +functionality is superseded by the real entry point and should not ship +in the installed package. Update `src/RcppExports.cpp` and +`R/RcppExports.R` accordingly (re-run `Rcpp::compileAttributes()`). + ### E.1 Create `sample_mixed.cpp` Rcpp interface function `sample_mixed_mrf_cpp(...)`: @@ -1173,6 +1485,14 @@ Cross-reference the existing mixedGM test files for each scenario: | T11 | BC marginal OMRF | Same as T10 but for `log_marginal_omrf(s)` | | T12 | BC observation centering | Verify that centered observations + `compute_denom_blume_capel` produce the same result as the standalone `OMRFModel` | +### Rank-1 Cholesky optimization tests (Phase B+) + +| Test | What | How | +|------|------|-----| +| T28 | Log-ratio agreement | For a known $K_{yy}$ off-diag proposal, verify `log_ggm_ratio_edge(i,j)` equals `log_conditional_ggm(proposed) - log_conditional_ggm(current)` to machine precision | +| T29 | Cholesky update fidelity | After rank-1 update, verify `Kyy_chol_`, `covariance_yy_`, `Kyy_log_det_` match a full `arma::chol` / `arma::inv` recompute | +| T30 | Rank-2 quadratic shortcut | For a known proposal, verify the B+.10 shortcut quadratic-form difference matches brute-force computation to machine precision | + ### Integration tests (sampling correctness) | Test | What | How | @@ -1235,6 +1555,10 @@ Cross-reference the existing mixedGM test files for each scenario: | Component | Source | Reuse type | |-----------|--------|------------| | Cholesky update/downdate | `src/models/ggm/cholupdate.h` | Direct include | +| `get_log_det` | `GGMModel` | Extract to shared utility or copy (Phase B+) | +| `compute_inv_submatrix_i` | `GGMModel` | Extract to shared utility or copy (Phase B+) | +| Rank-2 log-LR pattern | `GGMModel::log_density_impl_edge` | Adapt for observation-dependent mean (Phase B+) | +| Rank-2 Cholesky decomposition | `GGMModel::cholesky_update_after_edge` | Port to $q \times q$ Kyy workspace (Phase B+) | | Log-sum-exp stabilization | `OMRFModel::compute_logZ_*` | Adapt pattern | | Ordinal denominator | `src/utils/variable_helpers.{h,cpp}` `compute_denom_ordinal` | Direct call | | Blume-Capel denominator | `src/utils/variable_helpers.{h,cpp}` `compute_denom_blume_capel` | Direct call | @@ -1256,7 +1580,7 @@ Cross-reference the existing mixedGM test files for each scenario: | Risk | Impact | Mitigation | |------|--------|------------| -| Kyy inversion per MH step is $O(q^3)$ | Slow for large $q$ | Cache `Kyy_inv_`; incremental rank-1 updates after Cholesky proposals | +| Kyy inversion per MH step is $O(q^3)$ | Slow for large $q$ | Phase B+: rank-1 Cholesky updates + GGM-style log-ratio shortcut bring per-move cost to $O(q^2)$ | | Marginal PL $\mu_y$ update evaluates all $p$ OMRF terms | Slow for large $p$ | Cache rest-scores; provide conditional PL as faster default | | $\Theta$ recomputation after every $K_{yy}$/$K_{xy}$ change | $O(p^2 q + pq^2)$ | Defer $\Theta$ recompute to once per accepted move, not per-element; proposed-state uses a temporary (Phase B.2, C.3) | | Edge selection order effects | Bias | Shuffle edge order each iteration (already done in OMRF) | @@ -1265,6 +1589,7 @@ Cross-reference the existing mixedGM test files for each scenario: | Factor 2 convention mismatch | Wrong posteriors | Document consistently; unit-test against R prototype | | PD violation during Kyy proposals | Crash | Cholesky-based proposals guarantee PD by construction | | Large parameter space mixing | Poor ESS | Per-parameter Robbins-Monro; future: block updates or HMC | +| Cholesky maintenance constant | Moderate overhead for small $q$ | Investigate Woodbury-only covariance update (same asymptotics, fewer members, but weaker PD guarantee; see Phase B+ design decision) | | Blume-Capel not in prototype | No mixedGM reference for BC paths | Adapt from bgms `OMRFModel`; validated via OMRF-vs-mixed comparison tests (T10-T12) | --- @@ -1309,6 +1634,10 @@ principle 1 (green build) and principle 2 (one logical concern). | 4 | `test` | `test: add mixed MRF likelihood unit tests` | B.1 | `test-mixed-mrf-likelihood.R`: T1–T3 (compare C++ vs R fixtures), T7 (analytic Gaussian), T10–T12 (BC-specific). | | 5 | `feat` | `feat: add conditional PL Metropolis updates` | B.3–B.4 | `mixed_mrf_metropolis.cpp`: 6 MH update functions (main effect, muy, Kxx, Kyy off-diag, Kyy diag, Kxy) + static Cholesky helpers + `do_one_metropolis_step` 5-step sweep. | | 6 | `test` | `test: add conditional PL recovery test` | B.5 | `test-mixed-mrf-sampling.R`: T13 (conditional PL parameter recovery, estimation only). | +| 6a | `refactor` | `refactor: rank-1 Cholesky optimization for Kyy updates` | B+ | Replace permute-based $O(q^3)$ Kyy updates with GGM-style rank-1 infrastructure. Shared utility for `get_log_det` / `compute_inv_submatrix_i`. | +| 6b | `test` | `test: add rank-1 Cholesky correctness tests` | B+ | T28 (log-ratio agreement), T29 (Cholesky update fidelity). Verify existing tests still pass at same seed. | +| 6c | `refactor` | `refactor: rank-2 quadratic-form shortcut for Kyy log-ratio` | B+.10 | Exploit rank-2 structure of $\Delta\Sigma$ to reduce log-ratio cost from $O(npq + nq^2)$ to $O(nq)$. Cache `suf_stat_ggm_`. | +| 6d | `test` | `test: add quadratic shortcut correctness test` | B+.10 | T30 (shortcut vs brute-force agreement). | | 7 | `feat` | `feat: add marginal PL mode and Theta caching` | C | `log_marginal_omrf`, Theta cache, marginal-mode branches in MH updates. | | 8 | `test` | `test: add marginal PL recovery and comparison tests` | C.5 | T14 (marginal PL recovery), T16 (cond vs marginal agreement). | | 9 | `feat` | `feat: add mixed MRF edge selection` | D | `mixed_mrf_edge_selection.cpp`: RJ sweeps for Kxx, Kyy, Kxy. `update_edge_indicators`, `initialize_graph`, `prepare_iteration`. | From e65ae57ccfcc3903fceda71b0f3c67029e0c43a2 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Fri, 6 Mar 2026 19:56:09 +0100 Subject: [PATCH 09/60] refactor: rank-1 Cholesky optimization for Kyy updates (audit #6a) --- src/math/cholesky_helpers.h | 45 +++ src/models/ggm/ggm_model.cpp | 17 +- src/models/ggm/ggm_model.h | 20 +- src/models/mixed/mixed_mrf_likelihoods.cpp | 4 +- src/models/mixed/mixed_mrf_metropolis.cpp | 405 +++++++++++++-------- src/models/mixed/mixed_mrf_model.cpp | 36 +- src/models/mixed/mixed_mrf_model.h | 60 ++- 7 files changed, 380 insertions(+), 207 deletions(-) create mode 100644 src/math/cholesky_helpers.h diff --git a/src/math/cholesky_helpers.h b/src/math/cholesky_helpers.h new file mode 100644 index 00000000..4bbf09e2 --- /dev/null +++ b/src/math/cholesky_helpers.h @@ -0,0 +1,45 @@ +#pragma once + +/** + * @file cholesky_helpers.h + * @brief Shared algebraic helpers for Cholesky-based precision updates. + * + * Pure functions with no model-specific state. Used by both GGMModel and + * MixedMRFModel for proposal constant extraction and log-determinant + * computation. + */ + +#include +#include + +namespace cholesky_helpers { + +/** + * Log-determinant of a positive-definite matrix from its upper-triangular + * Cholesky factor R (where Ω = R'R). + * + * @param R Upper-triangular Cholesky factor. + * @return log|Ω| = 2 Σ log(R_ii). + */ +inline double get_log_det(const arma::mat& R) { + return 2.0 * arma::accu(arma::log(R.diag())); +} + +/** + * Schur complement element: A(ii,jj) − A(ii,i) A(jj,i) / A(i,i). + * + * Used to compute entries of the inverse of a submatrix from the full + * covariance matrix. + * + * @param A Symmetric positive-definite matrix. + * @param i Conditioning index. + * @param ii Row index of the desired element. + * @param jj Column index of the desired element. + * @return Schur complement entry. + */ +inline double compute_inv_submatrix_i(const arma::mat& A, size_t i, + size_t ii, size_t jj) { + return A(ii, jj) - A(ii, i) * A(jj, i) / A(i, i); +} + +} // namespace cholesky_helpers diff --git a/src/models/ggm/ggm_model.cpp b/src/models/ggm/ggm_model.cpp index 725cca17..573d37df 100644 --- a/src/models/ggm/ggm_model.cpp +++ b/src/models/ggm/ggm_model.cpp @@ -4,19 +4,15 @@ #include "mcmc/execution/step_result.h" #include "mcmc/execution/warmup_schedule.h" -double GGMModel::compute_inv_submatrix_i(const arma::mat& A, const size_t i, const size_t ii, const size_t jj) const { - return(A(ii, jj) - A(ii, i) * A(jj, i) / A(i, i)); -} - void GGMModel::get_constants(size_t i, size_t j) { - double logdet_omega = get_log_det(cholesky_of_precision_); + double logdet_omega = cholesky_helpers::get_log_det(cholesky_of_precision_); double log_adj_omega_ii = logdet_omega + std::log(std::abs(covariance_matrix_(i, i))); double log_adj_omega_ij = logdet_omega + std::log(std::abs(covariance_matrix_(i, j))); double log_adj_omega_jj = logdet_omega + std::log(std::abs(covariance_matrix_(j, j))); - double inv_omega_sub_j1j1 = compute_inv_submatrix_i(covariance_matrix_, i, j, j); + double inv_omega_sub_j1j1 = cholesky_helpers::compute_inv_submatrix_i(covariance_matrix_, i, j, j); double log_abs_inv_omega_sub_jj = log_adj_omega_ii + std::log(std::abs(inv_omega_sub_j1j1)); double Phi_q1q = (2 * std::signbit(covariance_matrix_(i, j)) - 1) * std::exp( (log_adj_omega_ij - (log_adj_omega_jj + log_abs_inv_omega_sub_jj) / 2) @@ -40,14 +36,9 @@ double GGMModel::constrained_diagonal(const double x) const { } } -double GGMModel::get_log_det(arma::mat triangular_A) const { - // log-determinant of A'A where A is upper-triangular Cholesky factor - return 2 * arma::accu(arma::log(triangular_A.diag())); -} - double GGMModel::log_density_impl(const arma::mat& omega, const arma::mat& phi) const { - double logdet_omega = get_log_det(phi); + double logdet_omega = cholesky_helpers::get_log_det(phi); double trace_prod = arma::accu(omega % suf_stat_); double log_likelihood = n_ * (p_ * log(2 * arma::datum::pi) / 2 + logdet_omega / 2) - trace_prod / 2; @@ -173,7 +164,7 @@ void GGMModel::cholesky_update_after_edge(double omega_ij_old, double omega_jj_o } void GGMModel::update_diagonal_parameter(size_t i, int iteration) { - double logdet_omega = get_log_det(cholesky_of_precision_); + double logdet_omega = cholesky_helpers::get_log_det(cholesky_of_precision_); double logdet_omega_sub_ii = logdet_omega + std::log(covariance_matrix_(i, i)); size_t e = i * (i + 3) / 2; // parameter index in vectorized form (column-major upper triangle, i==j) diff --git a/src/models/ggm/ggm_model.h b/src/models/ggm/ggm_model.h index 235998a6..b79076e3 100644 --- a/src/models/ggm/ggm_model.h +++ b/src/models/ggm/ggm_model.h @@ -3,6 +3,7 @@ #include #include #include "models/base_model.h" +#include "math/cholesky_helpers.h" #include "rng/rng_utils.h" @@ -388,17 +389,7 @@ class GGMModel : public BaseModel { */ void get_constants(size_t i, size_t j); - /** - * Compute the (ii, jj) entry of the Schur complement of A with - * row/column i eliminated. - * - * @param A Symmetric matrix - * @param i Index of the eliminated row/column - * @param ii Row index of the desired entry - * @param jj Column index of the desired entry - * @return A(ii,jj) - A(ii,i) * A(jj,i) / A(i,i) - */ - double compute_inv_submatrix_i(const arma::mat& A, const size_t i, const size_t ii, const size_t jj) const; + /** * Return the diagonal value omega_jj required to keep the precision @@ -434,12 +425,7 @@ class GGMModel : public BaseModel { */ double log_density_impl_diag(size_t j) const; - /** - * Compute log-determinant from a triangular matrix: 2 * sum(log(diag)). - * - * @param triangular_A Upper-triangular Cholesky factor - */ - double get_log_det(arma::mat triangular_A) const; + /** * Update the Cholesky factor after changing an off-diagonal element. diff --git a/src/models/mixed/mixed_mrf_likelihoods.cpp b/src/models/mixed/mixed_mrf_likelihoods.cpp index 2a1fae27..d4deb4a9 100644 --- a/src/models/mixed/mixed_mrf_likelihoods.cpp +++ b/src/models/mixed/mixed_mrf_likelihoods.cpp @@ -59,9 +59,9 @@ double MixedMRFModel::log_conditional_omrf(int s) const { // log_conditional_ggm // ============================================================================= // Conditional GGM log-likelihood: log f(y | x) -// y | x ~ N(conditional_mean_, Kyy_inv_) +// y | x ~ N(conditional_mean_, covariance_yy_) // -// Uses cached Kyy_inv_, Kyy_log_det_, and conditional_mean_. +// Uses cached covariance_yy_, Kyy_log_det_, and conditional_mean_. // ============================================================================= double MixedMRFModel::log_conditional_ggm() const { diff --git a/src/models/mixed/mixed_mrf_metropolis.cpp b/src/models/mixed/mixed_mrf_metropolis.cpp index ae0b6e46..ccdafb7f 100644 --- a/src/models/mixed/mixed_mrf_metropolis.cpp +++ b/src/models/mixed/mixed_mrf_metropolis.cpp @@ -112,84 +112,220 @@ void MixedMRFModel::update_Kxx(int i, int j) { // ============================================================================= -// Kyy helpers: permutation-based Cholesky updates -// ============================================================================= -// Ported from mixedGM/R/continuous_variable_helper.R. -// These operate on the (q-1, q) block after permuting target indices to -// the last two positions. - -// Build a permutation vector that moves indices (i, j) to positions (q-1, q). -// Same involution property as the R version. -static arma::uvec make_perm_offdiag(int i, int j, int q) { - int A = q - 2; // 0-based target for first index - int B = q - 1; // 0-based target for second index - arma::uvec perm = arma::regspace(0, q - 1); - - if((i == A && j == B) || (i == B && j == A)) { - // already in position - } else if(i == A || j == A) { - int other = (i == A) ? j : i; - if(other != B) std::swap(perm(other), perm(B)); - } else if(i == B || j == B) { - int other = (i == B) ? j : i; - if(other != A) std::swap(perm(other), perm(A)); +// Rank-1 Kyy proposal helpers (permutation-free) +// ============================================================================= +// Direct analogs of GGMModel::get_constants / constrained_diagonal, +// operating on Kyy_, Kyy_chol_, and covariance_yy_. +// ============================================================================= + +void MixedMRFModel::get_kyy_constants(int i, int j) { + double logdet = cholesky_helpers::get_log_det(Kyy_chol_); + + double log_adj_ii = logdet + std::log(std::abs(covariance_yy_(i, i))); + double log_adj_ij = logdet + std::log(std::abs(covariance_yy_(i, j))); + double log_adj_jj = logdet + std::log(std::abs(covariance_yy_(j, j))); + + double inv_sub_jj = cholesky_helpers::compute_inv_submatrix_i(covariance_yy_, i, j, j); + double log_abs_inv_sub_jj = log_adj_ii + std::log(std::abs(inv_sub_jj)); + + double Phi_q1q = (2 * std::signbit(covariance_yy_(i, j)) - 1) * std::exp( + (log_adj_ij - (log_adj_jj + log_abs_inv_sub_jj) / 2) + ); + double Phi_q1q1 = std::exp((log_adj_jj - log_abs_inv_sub_jj) / 2); + + kyy_constants_[0] = Phi_q1q; + kyy_constants_[1] = Phi_q1q1; + kyy_constants_[2] = Kyy_(i, j) - Phi_q1q * Phi_q1q1; + kyy_constants_[3] = Phi_q1q1; + kyy_constants_[4] = Kyy_(j, j) - Phi_q1q * Phi_q1q; + kyy_constants_[5] = kyy_constants_[4] + + kyy_constants_[2] * kyy_constants_[2] / (kyy_constants_[3] * kyy_constants_[3]); +} + +double MixedMRFModel::kyy_constrained_diagonal(double x) const { + if(x == 0.0) { + return kyy_constants_[5]; } else { - std::swap(perm(i), perm(A)); - std::swap(perm(j), perm(B)); + double t = (x - kyy_constants_[2]) / kyy_constants_[3]; + return kyy_constants_[4] + t * t; } - return perm; } -// Build permutation vector that moves index i to position q-1 (last). -static arma::uvec make_perm_diag(int i, int q) { - arma::uvec perm = arma::regspace(0, q - 1); - if(i != q - 1) std::swap(perm(i), perm(q - 1)); - return perm; + +// ============================================================================= +// log_ggm_ratio_edge +// ============================================================================= +// Log-likelihood ratio for a rank-2 off-diagonal Kyy change using the +// matrix determinant lemma for the log-det part and Woodbury for the +// quadratic-form part. Assumes precision_yy_proposal_ is filled. +// +// B+.10 will replace the O(npq + nq²) quadratic-form computation with +// an O(nq) rank-2 shortcut. +// ============================================================================= + +double MixedMRFModel::log_ggm_ratio_edge(int i, int j) const { + size_t ui = static_cast(i); + size_t uj = static_cast(j); + + // --- Log-determinant ratio via matrix determinant lemma --- + // ΔΩ has 3 nonzero entries: (i,j), (j,i), (j,j). + // Ui = old - new off-diag, Uj = (old - new diag) / 2 + double Ui = Kyy_(ui, uj) - precision_yy_proposal_(ui, uj); + double Uj = (Kyy_(uj, uj) - precision_yy_proposal_(uj, uj)) / 2.0; + + double cc11 = covariance_yy_(uj, uj); + double cc12 = 1.0 - (covariance_yy_(ui, uj) * Ui + + covariance_yy_(uj, uj) * Uj); + double cc22 = Ui * Ui * covariance_yy_(ui, ui) + + 2.0 * Ui * Uj * covariance_yy_(ui, uj) + + Uj * Uj * covariance_yy_(uj, uj); + + double logdet_ratio = std::log(std::abs(cc11 * cc22 - cc12 * cc12)); + + // --- Proposed covariance via Woodbury --- + // ΔΩ = vf1 vf2' + vf2 vf1' where vf1 = [0,...,-1,...] (j-th), + // vf2 = [0,...,Ui,...,Uj,...] (i-th and j-th). + // s1 = Σ vf1 = -Σ[:,j], s2 = Σ vf2 = Ui*Σ[:,i] + Uj*Σ[:,j] + arma::vec s1 = -covariance_yy_.col(uj); + arma::vec s2 = Ui * covariance_yy_.col(ui) + Uj * covariance_yy_.col(uj); + + // 2×2 core matrix T = I + [vf2,vf1]' [s1,s2] + // T = [1 + vf2's1, vf2's2; vf1's1, 1 + vf1's2] + double t11 = 1.0 + Ui * s1(ui) + Uj * s1(uj); // 1 + vf2' s1 + double t12 = Ui * s2(ui) + Uj * s2(uj); // vf2' s2 + double t21 = -s1(uj); // vf1' s1 = Σ(j,j) + double t22 = 1.0 - s2(uj); // 1 + vf1' s2 + + double det_T = t11 * t22 - t12 * t21; + + // T^{-1} + double inv_t11 = t22 / det_T; + double inv_t12 = -t12 / det_T; + double inv_t21 = -t21 / det_T; + double inv_t22 = t11 / det_T; + + // Σ' = Σ - [s1,s2] T^{-1} [s2',s1'] + // = Σ - (inv_t11*s1 + inv_t21*s2)*s2' - (inv_t12*s1 + inv_t22*s2)*s1' + arma::vec w1 = inv_t11 * s1 + inv_t21 * s2; // coefficient for s2' row + arma::vec w2 = inv_t12 * s1 + inv_t22 * s2; // coefficient for s1' row + arma::mat cov_prop = covariance_yy_ - w1 * s2.t() - w2 * s1.t(); + + // --- Proposed conditional mean --- + // M' = 1*μ_y' + 2 * X * Kxy * Σ' + arma::mat cond_mean_prop = arma::repmat(muy_.t(), n_, 1) + + 2.0 * discrete_observations_dbl_ * Kxy_ * cov_prop; + + // --- Quadratic form difference --- + arma::mat D_curr = continuous_observations_ - conditional_mean_; + arma::mat D_prop = continuous_observations_ - cond_mean_prop; + + double quad_curr = arma::accu((D_curr * Kyy_) % D_curr); + double quad_prop = arma::accu((D_prop * precision_yy_proposal_) % D_prop); + + double n = static_cast(n_); + return n / 2.0 * logdet_ratio - (quad_prop - quad_curr) / 2.0; } -// Apply symmetric permutation: M[perm, perm] -static arma::mat permute_matrix(const arma::mat& M, const arma::uvec& perm) { - return M.submat(perm, perm); + +// ============================================================================= +// log_ggm_ratio_diag +// ============================================================================= +// Log-likelihood ratio for a rank-1 diagonal Kyy change. +// Same structure as log_ggm_ratio_edge but simpler (Ui = 0). +// ============================================================================= + +double MixedMRFModel::log_ggm_ratio_diag(int i) const { + size_t ui = static_cast(i); + + // --- Log-determinant ratio (rank-1) --- + double Uj = (Kyy_(ui, ui) - precision_yy_proposal_(ui, ui)) / 2.0; + + double cc11 = covariance_yy_(ui, ui); + double cc12 = 1.0 - covariance_yy_(ui, ui) * Uj; + double cc22 = Uj * Uj * covariance_yy_(ui, ui); + + double logdet_ratio = std::log(std::abs(cc11 * cc22 - cc12 * cc12)); + + // --- Proposed covariance via Sherman-Morrison (rank-1 special case) --- + // ΔΩ = -2Uj * e_i e_i', so Σ' = Σ + 2Uj * Σ[:,i] Σ[i,:]' / (1 - 2Uj * Σ(i,i)) + arma::vec s = covariance_yy_.col(ui); + double denom = 1.0 - 2.0 * Uj * covariance_yy_(ui, ui); + arma::mat cov_prop = covariance_yy_ + (2.0 * Uj / denom) * s * s.t(); + + // --- Proposed conditional mean --- + arma::mat cond_mean_prop = arma::repmat(muy_.t(), n_, 1) + + 2.0 * discrete_observations_dbl_ * Kxy_ * cov_prop; + + // --- Quadratic form difference --- + arma::mat D_curr = continuous_observations_ - conditional_mean_; + arma::mat D_prop = continuous_observations_ - cond_mean_prop; + + double quad_curr = arma::accu((D_curr * Kyy_) % D_curr); + double quad_prop = arma::accu((D_prop * precision_yy_proposal_) % D_prop); + + double n = static_cast(n_); + return n / 2.0 * logdet_ratio - (quad_prop - quad_curr) / 2.0; } -// Extract constants from upper Cholesky factor (q×q) for (q-2, q-1) block update. -// Returns: {c1, c2, c3, c4} -struct CholConstants { - double c1, c2, c3, c4; -}; - -static CholConstants get_constants(const arma::mat& L, int q) { - // L is upper-triangular Cholesky factor - // phi_q and phi_q_1 are columns q-1 and q-2, excluding rows (q-2, q-1) - int qm1 = q - 1; // last column (0-based) - int qm2 = q - 2; // second-to-last column - - double c1 = 0.0; - for(int k = 0; k < qm2; ++k) { - c1 += L(k, qm1) * L(k, qm2); - } - double c2 = L(qm2, qm2); - // c3 excludes only row qm2 (the row being modified), not qm1. - // In R: sum(cholm[-(q-1), q]^2) — excludes row q-1 (1-based) = row qm2 (0-based). - // Must include L(qm1, qm1) (the diagonal entry of the last row). - double c3 = 0.0; - for(int k = 0; k < qm2; ++k) { - c3 += L(k, qm1) * L(k, qm1); - } - c3 += L(qm1, qm1) * L(qm1, qm1); // diagonal entry - double c4 = c3 + c1 * c1 / (c2 * c2); - return {c1, c2, c3, c4}; +// ============================================================================= +// cholesky_update_after_kyy_edge +// ============================================================================= +// Rank-2 Cholesky update after accepting an off-diagonal Kyy change. +// Decomposes ΔΩ = vf1*vf2' + vf2*vf1' into two rank-1 ops. +// Then recomputes inv_cholesky_yy_ and covariance_yy_. +// ============================================================================= + +void MixedMRFModel::cholesky_update_after_kyy_edge( + double old_ij, double old_jj, int i, int j) +{ + kyy_v2_[0] = old_ij - precision_yy_proposal_(i, j); + kyy_v2_[1] = (old_jj - precision_yy_proposal_(j, j)) / 2.0; + + kyy_vf1_[i] = kyy_v1_[0]; // 0 + kyy_vf1_[j] = kyy_v1_[1]; // -1 + kyy_vf2_[i] = kyy_v2_[0]; + kyy_vf2_[j] = kyy_v2_[1]; + + kyy_u1_ = (kyy_vf1_ + kyy_vf2_) / std::sqrt(2.0); + kyy_u2_ = (kyy_vf1_ - kyy_vf2_) / std::sqrt(2.0); + + cholesky_update(Kyy_chol_, kyy_u1_); + cholesky_downdate(Kyy_chol_, kyy_u2_); + + arma::inv(inv_cholesky_yy_, arma::trimatu(Kyy_chol_)); + covariance_yy_ = inv_cholesky_yy_ * inv_cholesky_yy_.t(); + Kyy_log_det_ = cholesky_helpers::get_log_det(Kyy_chol_); + + kyy_vf1_[i] = 0.0; + kyy_vf1_[j] = 0.0; + kyy_vf2_[i] = 0.0; + kyy_vf2_[j] = 0.0; } -// Constrained diagonal: R(omega, constants) from mixedGM -static double constrained_diagonal(double omega, const CholConstants& C) { - if(omega == 0.0) { - return C.c4; - } else { - double t = (omega - C.c1) / C.c2; - return C.c3 + t * t; - } + +// ============================================================================= +// cholesky_update_after_kyy_diag +// ============================================================================= +// Rank-1 Cholesky update after accepting a diagonal Kyy change. +// ============================================================================= + +void MixedMRFModel::cholesky_update_after_kyy_diag(double old_ii, int i) { + double delta = old_ii - precision_yy_proposal_(i, i); + bool downdate = delta > 0.0; + + kyy_vf1_[i] = std::sqrt(std::abs(delta)); + + if(downdate) + cholesky_downdate(Kyy_chol_, kyy_vf1_); + else + cholesky_update(Kyy_chol_, kyy_vf1_); + + arma::inv(inv_cholesky_yy_, arma::trimatu(Kyy_chol_)); + covariance_yy_ = inv_cholesky_yy_ * inv_cholesky_yy_.t(); + Kyy_log_det_ = cholesky_helpers::get_log_det(Kyy_chol_); + + kyy_vf1_[i] = 0.0; } @@ -197,76 +333,49 @@ static double constrained_diagonal(double omega, const CholConstants& C) { // update_Kyy_offdiag // ============================================================================= // MH update for one off-diagonal element of the precision matrix Kyy_(i, j). -// Uses the mixedGM permute-based Cholesky approach: -// 1. Permute target pair to last two positions -// 2. Cholesky of permuted Kyy -// 3. Propose on the Cholesky scale: phi' ~ N(Phi[q-2, q-1], sd) -// 4. Map back to Kyy space with constrained diagonal -// 5. Unpermute -// 6. Full recompute of GGM likelihood +// Uses rank-1 Cholesky infrastructure (GGM-style, no permutation): +// 1. Extract constants from covariance_yy_ and Kyy_chol_ +// 2. Propose on the unconstrained Cholesky scale +// 3. Map to Kyy space with constrained diagonal +// 4. Evaluate rank-2 log-likelihood ratio +// 5. On accept: rank-1 Cholesky update // // Prior: Cauchy(0, pairwise_scale_) on off-diag, Gamma(1, 1) on diagonal. // ============================================================================= void MixedMRFModel::update_Kyy_offdiag(int i, int j) { - int q = static_cast(q_); - - arma::uvec perm = make_perm_offdiag(i, j, q); - arma::mat Kyy_perm = permute_matrix(Kyy_, perm); - arma::mat Phi = arma::chol(Kyy_perm); // upper Cholesky - CholConstants C = get_constants(Phi, q); + get_kyy_constants(i, j); - // Current Cholesky-scale value - double phi_curr = Phi(q - 2, q - 1); + double phi_curr = kyy_constants_[0]; // Phi_q1q double phi_prop = rnorm(rng_, phi_curr, prop_sd_Kyy_(i, j)); - // Map to Kyy space - double omega_prop = C.c1 + C.c2 * phi_prop; - double diag_curr = Kyy_perm(q - 1, q - 1); - double diag_prop = constrained_diagonal(omega_prop, C); - - // Build proposed permuted Kyy - arma::mat Kyy_prop_perm = Kyy_perm; - Kyy_prop_perm(q - 2, q - 1) = omega_prop; - Kyy_prop_perm(q - 1, q - 2) = omega_prop; - Kyy_prop_perm(q - 1, q - 1) = diag_prop; - - // Unpermute - arma::mat Kyy_prop = permute_matrix(Kyy_prop_perm, perm); - - // Save current state - arma::mat Kyy_saved = Kyy_; - arma::mat Kyy_inv_saved = Kyy_inv_; - arma::mat Kyy_chol_saved = Kyy_chol_; - double Kyy_log_det_saved = Kyy_log_det_; - arma::mat cond_mean_saved = conditional_mean_; - - // Evaluate proposed likelihood - Kyy_ = Kyy_prop; - recompute_Kyy_decomposition(); - recompute_conditional_mean(); - double ll_prop = log_conditional_ggm(); + double omega_prop_ij = kyy_constants_[2] + kyy_constants_[3] * phi_prop; + double omega_prop_jj = kyy_constrained_diagonal(omega_prop_ij); + double diag_curr = Kyy_(j, j); - // Evaluate current likelihood (restore) - Kyy_ = Kyy_saved; - Kyy_inv_ = Kyy_inv_saved; - Kyy_chol_ = Kyy_chol_saved; - Kyy_log_det_ = Kyy_log_det_saved; - conditional_mean_ = cond_mean_saved; - double ll_curr = log_conditional_ggm(); + // Fill proposal matrix (only the 3 changed entries matter) + precision_yy_proposal_ = Kyy_; + precision_yy_proposal_(i, j) = omega_prop_ij; + precision_yy_proposal_(j, i) = omega_prop_ij; + precision_yy_proposal_(j, j) = omega_prop_jj; - double ln_alpha = ll_prop - ll_curr; + double ln_alpha = log_ggm_ratio_edge(i, j); - // Prior ratio: Cauchy on off-diag + Gamma(1,1) on diagonal that changed - ln_alpha += R::dcauchy(Kyy_prop(i, j), 0.0, pairwise_scale_, true); + // Prior ratio: Cauchy on off-diag + Gamma(1,1) on diagonal + ln_alpha += R::dcauchy(omega_prop_ij, 0.0, pairwise_scale_, true); ln_alpha -= R::dcauchy(Kyy_(i, j), 0.0, pairwise_scale_, true); - ln_alpha += R::dgamma(diag_prop, 1.0, 1.0, true); + ln_alpha += R::dgamma(omega_prop_jj, 1.0, 1.0, true); ln_alpha -= R::dgamma(diag_curr, 1.0, 1.0, true); if(std::log(runif(rng_)) < ln_alpha) { - // Accept: install proposed Kyy and refresh caches - Kyy_ = Kyy_prop; - recompute_Kyy_decomposition(); + double old_ij = Kyy_(i, j); + double old_jj = Kyy_(j, j); + + Kyy_(i, j) = omega_prop_ij; + Kyy_(j, i) = omega_prop_ij; + Kyy_(j, j) = omega_prop_jj; + + cholesky_update_after_kyy_edge(old_ij, old_jj, i, j); recompute_conditional_mean(); if(use_marginal_pl_) recompute_Theta(); } @@ -278,58 +387,36 @@ void MixedMRFModel::update_Kyy_offdiag(int i, int j) { // ============================================================================= // MH update for one diagonal element of Kyy. // Proposes on the log-Cholesky scale to ensure positivity. +// Uses rank-1 Cholesky update on accept. // Prior: Gamma(1, 1) on the diagonal element + Jacobian for log-scale proposal. // ============================================================================= void MixedMRFModel::update_Kyy_diag(int i) { - int q = static_cast(q_); + double logdet = cholesky_helpers::get_log_det(Kyy_chol_); + double logdet_sub_ii = logdet + std::log(covariance_yy_(i, i)); - arma::uvec perm = make_perm_diag(i, q); - arma::mat Kyy_perm = permute_matrix(Kyy_, perm); - arma::mat L = arma::chol(Kyy_perm); // upper Cholesky - - // Propose on log(L[q-1, q-1]) scale - double theta_curr = std::log(L(q - 1, q - 1)); + double theta_curr = (logdet - logdet_sub_ii) / 2.0; double theta_prop = rnorm(rng_, theta_curr, prop_sd_Kyy_(i, i)); - // Rebuild Kyy_prop from modified Cholesky - L(q - 1, q - 1) = std::exp(theta_prop); - arma::mat Kyy_prop_perm = L.t() * L; - arma::mat Kyy_prop = permute_matrix(Kyy_prop_perm, perm); - - // Save current state - arma::mat Kyy_saved = Kyy_; - arma::mat Kyy_inv_saved = Kyy_inv_; - arma::mat Kyy_chol_saved = Kyy_chol_; - double Kyy_log_det_saved = Kyy_log_det_; - arma::mat cond_mean_saved = conditional_mean_; - - // Evaluate proposed likelihood - Kyy_ = Kyy_prop; - recompute_Kyy_decomposition(); - recompute_conditional_mean(); - double ll_prop = log_conditional_ggm(); - - // Evaluate current likelihood (restore) - Kyy_ = Kyy_saved; - Kyy_inv_ = Kyy_inv_saved; - Kyy_chol_ = Kyy_chol_saved; - Kyy_log_det_ = Kyy_log_det_saved; - conditional_mean_ = cond_mean_saved; - double ll_curr = log_conditional_ggm(); + precision_yy_proposal_ = Kyy_; + precision_yy_proposal_(i, i) = Kyy_(i, i) + - std::exp(theta_curr) * std::exp(theta_curr) + + std::exp(theta_prop) * std::exp(theta_prop); - double ln_alpha = ll_prop - ll_curr; + double ln_alpha = log_ggm_ratio_diag(i); - // Prior ratio: Gamma(1, 1) on diagonal - ln_alpha += R::dgamma(Kyy_prop(i, i), 1.0, 1.0, true); + // Prior ratio: Gamma(1,1) on diagonal + ln_alpha += R::dgamma(precision_yy_proposal_(i, i), 1.0, 1.0, true); ln_alpha -= R::dgamma(Kyy_(i, i), 1.0, 1.0, true); // Jacobian for log-scale proposal ln_alpha += theta_prop - theta_curr; if(std::log(runif(rng_)) < ln_alpha) { - Kyy_ = Kyy_prop; - recompute_Kyy_decomposition(); + double old_ii = Kyy_(i, i); + Kyy_(i, i) = precision_yy_proposal_(i, i); + + cholesky_update_after_kyy_diag(old_ii, i); recompute_conditional_mean(); if(use_marginal_pl_) recompute_Theta(); } diff --git a/src/models/mixed/mixed_mrf_model.cpp b/src/models/mixed/mixed_mrf_model.cpp index 82a6e27a..f8bb00b1 100644 --- a/src/models/mixed/mixed_mrf_model.cpp +++ b/src/models/mixed/mixed_mrf_model.cpp @@ -78,9 +78,17 @@ MixedMRFModel::MixedMRFModel( // Initialize Kyy caches (Kyy starts as identity) Kyy_chol_ = arma::eye(q_, q_); - Kyy_inv_ = arma::eye(q_, q_); + inv_cholesky_yy_ = arma::eye(q_, q_); + covariance_yy_ = arma::eye(q_, q_); Kyy_log_det_ = 0.0; + // Rank-1 Cholesky update workspace + precision_yy_proposal_ = arma::mat(q_, q_, arma::fill::none); + kyy_vf1_ = arma::zeros(q_); + kyy_vf2_ = arma::zeros(q_); + kyy_u1_ = arma::zeros(q_); + kyy_u2_ = arma::zeros(q_); + // Initialize conditional mean: μ_y' + 2 x Kxy Kyy_inv // With Kxy = 0 and Kyy = I, this is just 1 * μ_y' = 0. conditional_mean_ = arma::zeros(n_, q_); @@ -137,11 +145,20 @@ MixedMRFModel::MixedMRFModel(const MixedMRFModel& other) prop_sd_Kxx_(other.prop_sd_Kxx_), prop_sd_Kyy_(other.prop_sd_Kyy_), prop_sd_Kxy_(other.prop_sd_Kxy_), - Kyy_inv_(other.Kyy_inv_), Kyy_chol_(other.Kyy_chol_), + inv_cholesky_yy_(other.inv_cholesky_yy_), + covariance_yy_(other.covariance_yy_), Kyy_log_det_(other.Kyy_log_det_), Theta_(other.Theta_), conditional_mean_(other.conditional_mean_), + kyy_constants_(other.kyy_constants_), + precision_yy_proposal_(other.precision_yy_proposal_), + kyy_v1_(other.kyy_v1_), + kyy_v2_(other.kyy_v2_), + kyy_vf1_(other.kyy_vf1_), + kyy_vf2_(other.kyy_vf2_), + kyy_u1_(other.kyy_u1_), + kyy_u2_(other.kyy_u2_), use_marginal_pl_(other.use_marginal_pl_), rng_(other.rng_), edge_order_xx_(other.edge_order_xx_), @@ -201,20 +218,21 @@ size_t MixedMRFModel::count_num_main_effects() const { // ============================================================================= void MixedMRFModel::recompute_conditional_mean() { - // conditional_mean_ = 1*μ_y' + 2 * discrete_obs * Kxy * Kyy_inv + // conditional_mean_ = 1*μ_y' + 2 * discrete_obs * Kxy * covariance_yy_ conditional_mean_ = arma::repmat(muy_.t(), n_, 1) + - 2.0 * discrete_observations_dbl_ * Kxy_ * Kyy_inv_; + 2.0 * discrete_observations_dbl_ * Kxy_ * covariance_yy_; } void MixedMRFModel::recompute_Kyy_decomposition() { - Kyy_chol_ = arma::chol(Kyy_); // upper Cholesky - Kyy_inv_ = arma::inv_sympd(Kyy_); - Kyy_log_det_ = 2.0 * arma::sum(arma::log(Kyy_chol_.diag())); + Kyy_chol_ = arma::chol(Kyy_); // upper Cholesky: Kyy = R'R + arma::inv(inv_cholesky_yy_, arma::trimatu(Kyy_chol_)); + covariance_yy_ = inv_cholesky_yy_ * inv_cholesky_yy_.t(); + Kyy_log_det_ = cholesky_helpers::get_log_det(Kyy_chol_); } void MixedMRFModel::recompute_Theta() { - // Θ = Kxx + 2 Kxy Kyy_inv Kxy' - Theta_ = Kxx_ + 2.0 * Kxy_ * Kyy_inv_ * Kxy_.t(); + // Θ = Kxx + 2 Kxy covariance_yy_ Kxy' + Theta_ = Kxx_ + 2.0 * Kxy_ * covariance_yy_ * Kxy_.t(); } diff --git a/src/models/mixed/mixed_mrf_model.h b/src/models/mixed/mixed_mrf_model.h index 485fdd6c..81d507e5 100644 --- a/src/models/mixed/mixed_mrf_model.h +++ b/src/models/mixed/mixed_mrf_model.h @@ -1,7 +1,10 @@ #pragma once +#include #include #include "models/base_model.h" +#include "math/cholesky_helpers.h" +#include "models/ggm/cholupdate.h" #include "rng/rng_utils.h" /** @@ -34,6 +37,13 @@ class MixedMRFModel : public BaseModel { const arma::imat&, bool, const std::string&, const arma::vec&, int ); + // Test helper for rank-1 Cholesky correctness (T28/T29) + friend Rcpp::List test_mixed_mrf_cholesky( + const arma::imat&, const arma::mat&, const arma::ivec&, + const arma::uvec&, const arma::ivec&, const arma::mat&, + const arma::imat&, const arma::vec&, int, int, int + ); + // ========================================================================= // Construction // ========================================================================= @@ -287,11 +297,22 @@ class MixedMRFModel : public BaseModel { // Cached quantities // ========================================================================= - arma::mat Kyy_inv_; // q × q inverse of Kyy - arma::mat Kyy_chol_; // q × q upper Cholesky of Kyy + arma::mat Kyy_chol_; // q × q upper Cholesky of Kyy (Kyy = R'R) + arma::mat inv_cholesky_yy_; // q × q R^{-1} (upper triangular) + arma::mat covariance_yy_; // q × q Kyy^{-1} = R^{-1} R^{-T} double Kyy_log_det_; // log|Kyy| - arma::mat Theta_; // p × p Kxx + 2 Kxy Kyy_inv Kxy' (marginal PL only) - arma::mat conditional_mean_; // n × q μ_y' + 2 discrete_obs Kxy Kyy_inv + arma::mat Theta_; // p × p Kxx + 2 Kxy covariance_yy_ Kxy' (marginal PL only) + arma::mat conditional_mean_; // n × q μ_y' + 2 discrete_obs Kxy covariance_yy_ + + // Rank-1 Cholesky update workspace + std::array kyy_constants_{}; // reparameterization constants + arma::mat precision_yy_proposal_; // q × q scratch for proposed Kyy + arma::vec kyy_v1_ = {0, -1}; // rank-2 decomposition helpers + arma::vec kyy_v2_ = {0, 0}; + arma::vec kyy_vf1_; // q-vectors, zeroed between uses + arma::vec kyy_vf2_; + arma::vec kyy_u1_; + arma::vec kyy_u2_; // ========================================================================= // Configuration @@ -318,13 +339,13 @@ class MixedMRFModel : public BaseModel { /** Compute category counts and BC sufficient statistics from discrete_observations_. */ void compute_sufficient_statistics(); - /** Recompute conditional_mean_ from muy_, Kxy_, Kyy_inv_. */ + /** Recompute conditional_mean_ from muy_, Kxy_, covariance_yy_. */ void recompute_conditional_mean(); - /** Recompute Kyy_chol_, Kyy_inv_, Kyy_log_det_ from Kyy_. */ + /** Recompute Kyy_chol_, inv_cholesky_yy_, covariance_yy_, Kyy_log_det_ from Kyy_. */ void recompute_Kyy_decomposition(); - /** Recompute Theta_ from Kxx_, Kxy_, Kyy_inv_ (marginal PL only). */ + /** Recompute Theta_ from Kxx_, Kxy_, covariance_yy_ (marginal PL only). */ void recompute_Theta(); // ========================================================================= @@ -341,6 +362,31 @@ class MixedMRFModel : public BaseModel { // MH update functions (implemented in mixed_mrf_metropolis.cpp) // ========================================================================= + // --- Rank-1 Kyy proposal helpers (permutation-free) --- + + // Extract reparameterization constants for the (i,j) off-diagonal Kyy update. + // Populates kyy_constants_[0..5] from Kyy_chol_ and covariance_yy_. + void get_kyy_constants(int i, int j); + + // Constrained diagonal value for a proposed off-diagonal Kyy element. + double kyy_constrained_diagonal(double x) const; + + // Log-likelihood ratio for a proposed off-diagonal Kyy change (rank-2). + // Assumes precision_yy_proposal_ is already filled by the caller. + double log_ggm_ratio_edge(int i, int j) const; + + // Log-likelihood ratio for a proposed diagonal Kyy change (rank-1). + // Assumes precision_yy_proposal_ is already filled by the caller. + double log_ggm_ratio_diag(int i) const; + + // Rank-1 Cholesky update after accepting an off-diagonal Kyy change. + void cholesky_update_after_kyy_edge(double old_ij, double old_jj, int i, int j); + + // Rank-1 Cholesky update after accepting a diagonal Kyy change. + void cholesky_update_after_kyy_diag(double old_ii, int i); + + // --- Parameter update sweeps --- + /** Update one main-effect: mux_(s, c). Ordinal threshold or BC α/β. */ void update_main_effect(int s, int c); From c5823e1932e6f22d8be57c060d4729ec6b83ccdf Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Fri, 6 Mar 2026 19:56:15 +0100 Subject: [PATCH 10/60] test: add rank-1 Cholesky correctness tests (audit #6b) --- R/RcppExports.R | 4 + src/RcppExports.cpp | 22 +++ src/test_mixed_mrf.cpp | 200 +++++++++++++++++++++++ tests/testthat/test-mixed-mrf-cholesky.R | 183 +++++++++++++++++++++ 4 files changed, 409 insertions(+) create mode 100644 tests/testthat/test-mixed-mrf-cholesky.R diff --git a/R/RcppExports.R b/R/RcppExports.R index a0177aea..6f490ec9 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -69,3 +69,7 @@ test_mixed_mrf_sampler <- function(discrete_observations, continuous_observation .Call(`_bgms_test_mixed_mrf_sampler`, discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, n_warmup, n_samples, seed) } +test_mixed_mrf_cholesky <- function(discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, params, seed, target_i, target_j) { + .Call(`_bgms_test_mixed_mrf_cholesky`, discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, params, seed, target_i, target_j) +} + diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index b28f12c3..848c2268 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -352,6 +352,27 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } +// test_mixed_mrf_cholesky +Rcpp::List test_mixed_mrf_cholesky(const arma::imat& discrete_observations, const arma::mat& continuous_observations, const arma::ivec& num_categories, const arma::uvec& is_ordinal_variable, const arma::ivec& baseline_category, const arma::mat& inclusion_probability, const arma::imat& initial_edge_indicators, const arma::vec& params, int seed, int target_i, int target_j); +RcppExport SEXP _bgms_test_mixed_mrf_cholesky(SEXP discrete_observationsSEXP, SEXP continuous_observationsSEXP, SEXP num_categoriesSEXP, SEXP is_ordinal_variableSEXP, SEXP baseline_categorySEXP, SEXP inclusion_probabilitySEXP, SEXP initial_edge_indicatorsSEXP, SEXP paramsSEXP, SEXP seedSEXP, SEXP target_iSEXP, SEXP target_jSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::imat& >::type discrete_observations(discrete_observationsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type continuous_observations(continuous_observationsSEXP); + Rcpp::traits::input_parameter< const arma::ivec& >::type num_categories(num_categoriesSEXP); + Rcpp::traits::input_parameter< const arma::uvec& >::type is_ordinal_variable(is_ordinal_variableSEXP); + Rcpp::traits::input_parameter< const arma::ivec& >::type baseline_category(baseline_categorySEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type inclusion_probability(inclusion_probabilitySEXP); + Rcpp::traits::input_parameter< const arma::imat& >::type initial_edge_indicators(initial_edge_indicatorsSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type params(paramsSEXP); + Rcpp::traits::input_parameter< int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< int >::type target_i(target_iSEXP); + Rcpp::traits::input_parameter< int >::type target_j(target_jSEXP); + rcpp_result_gen = Rcpp::wrap(test_mixed_mrf_cholesky(discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, params, seed, target_i, target_j)); + return rcpp_result_gen; +END_RCPP +} static const R_CallMethodDef CallEntries[] = { {"_bgms_run_bgmCompare_parallel", (DL_FUNC) &_bgms_run_bgmCompare_parallel, 38}, @@ -371,6 +392,7 @@ static const R_CallMethodDef CallEntries[] = { {"_bgms_test_mixed_mrf_skeleton", (DL_FUNC) &_bgms_test_mixed_mrf_skeleton, 10}, {"_bgms_test_mixed_mrf_likelihoods", (DL_FUNC) &_bgms_test_mixed_mrf_likelihoods, 11}, {"_bgms_test_mixed_mrf_sampler", (DL_FUNC) &_bgms_test_mixed_mrf_sampler, 12}, + {"_bgms_test_mixed_mrf_cholesky", (DL_FUNC) &_bgms_test_mixed_mrf_cholesky, 11}, {NULL, NULL, 0} }; diff --git a/src/test_mixed_mrf.cpp b/src/test_mixed_mrf.cpp index 2f931cee..38618eec 100644 --- a/src/test_mixed_mrf.cpp +++ b/src/test_mixed_mrf.cpp @@ -211,3 +211,203 @@ Rcpp::List test_mixed_mrf_sampler( Rcpp::Named("full_parameter_dimension") = full_dim ); } + + +// ============================================================================= +// test_mixed_mrf_cholesky — T28 (log-ratio agreement) and T29 (Cholesky fidelity) +// ============================================================================= + +// [[Rcpp::export]] +Rcpp::List test_mixed_mrf_cholesky( + const arma::imat& discrete_observations, + const arma::mat& continuous_observations, + const arma::ivec& num_categories, + const arma::uvec& is_ordinal_variable, + const arma::ivec& baseline_category, + const arma::mat& inclusion_probability, + const arma::imat& initial_edge_indicators, + const arma::vec& params, + int seed, + int target_i, + int target_j +) { + MixedMRFModel model( + discrete_observations, + continuous_observations, + num_categories, + is_ordinal_variable, + baseline_category, + inclusion_probability, + initial_edge_indicators, + false, // no edge selection + "conditional", + 1.0, 1.0, 2.5, + seed + ); + + model.set_vectorized_parameters(params); + + int q = static_cast(model.q_); + + // ========================================================================= + // T28: log_ggm_ratio_edge vs brute-force log_conditional_ggm difference + // ========================================================================= + + // Current log-likelihood + double ggm_ll_curr = model.log_conditional_ggm(); + + // Extract reparameterization constants and build a deterministic proposal + model.get_kyy_constants(target_i, target_j); + double phi_curr = model.kyy_constants_[0]; + double phi_prop = phi_curr + 0.05; // small deterministic shift + + double omega_prop_ij = model.kyy_constants_[2] + model.kyy_constants_[3] * phi_prop; + double omega_prop_jj = model.kyy_constrained_diagonal(omega_prop_ij); + + model.precision_yy_proposal_ = model.Kyy_; + model.precision_yy_proposal_(target_i, target_j) = omega_prop_ij; + model.precision_yy_proposal_(target_j, target_i) = omega_prop_ij; + model.precision_yy_proposal_(target_j, target_j) = omega_prop_jj; + + // Rank-2 log-ratio via new function + double ratio_rank2 = model.log_ggm_ratio_edge(target_i, target_j); + + // Brute-force: install proposed Kyy, full recompute, evaluate + arma::mat Kyy_saved = model.Kyy_; + arma::mat cov_saved = model.covariance_yy_; + arma::mat chol_saved = model.Kyy_chol_; + arma::mat inv_chol_saved = model.inv_cholesky_yy_; + double logdet_saved = model.Kyy_log_det_; + arma::mat cmean_saved = model.conditional_mean_; + + model.Kyy_ = model.precision_yy_proposal_; + model.recompute_Kyy_decomposition(); + model.recompute_conditional_mean(); + double ggm_ll_prop = model.log_conditional_ggm(); + + double ratio_bruteforce = ggm_ll_prop - ggm_ll_curr; + + // ========================================================================= + // T28b: log_ggm_ratio_diag vs brute-force + // ========================================================================= + + // Restore to current state + model.Kyy_ = Kyy_saved; + model.covariance_yy_ = cov_saved; + model.Kyy_chol_ = chol_saved; + model.inv_cholesky_yy_ = inv_chol_saved; + model.Kyy_log_det_ = logdet_saved; + model.conditional_mean_ = cmean_saved; + + // Diagonal proposal: small shift on log-Cholesky scale + double logdet_curr = cholesky_helpers::get_log_det(model.Kyy_chol_); + double logdet_sub = logdet_curr + std::log(model.covariance_yy_(target_i, target_i)); + double theta_curr = (logdet_curr - logdet_sub) / 2.0; + double theta_prop = theta_curr + 0.05; + + model.precision_yy_proposal_ = model.Kyy_; + model.precision_yy_proposal_(target_i, target_i) = model.Kyy_(target_i, target_i) + - std::exp(theta_curr) * std::exp(theta_curr) + + std::exp(theta_prop) * std::exp(theta_prop); + + double ratio_diag_rank1 = model.log_ggm_ratio_diag(target_i); + + // Brute-force + model.Kyy_ = model.precision_yy_proposal_; + model.recompute_Kyy_decomposition(); + model.recompute_conditional_mean(); + double ggm_ll_diag_prop = model.log_conditional_ggm(); + + double ratio_diag_brute = ggm_ll_diag_prop - ggm_ll_curr; + + // ========================================================================= + // T29: Cholesky update fidelity — rank-2 edge update + // ========================================================================= + + // Restore to current state + model.Kyy_ = Kyy_saved; + model.covariance_yy_ = cov_saved; + model.Kyy_chol_ = chol_saved; + model.inv_cholesky_yy_ = inv_chol_saved; + model.Kyy_log_det_ = logdet_saved; + model.conditional_mean_ = cmean_saved; + + // Re-fill off-diagonal proposal + model.precision_yy_proposal_ = model.Kyy_; + model.precision_yy_proposal_(target_i, target_j) = omega_prop_ij; + model.precision_yy_proposal_(target_j, target_i) = omega_prop_ij; + model.precision_yy_proposal_(target_j, target_j) = omega_prop_jj; + + // Apply rank-1 Cholesky update + double old_ij = model.Kyy_(target_i, target_j); + double old_jj = model.Kyy_(target_j, target_j); + model.Kyy_(target_i, target_j) = omega_prop_ij; + model.Kyy_(target_j, target_i) = omega_prop_ij; + model.Kyy_(target_j, target_j) = omega_prop_jj; + model.cholesky_update_after_kyy_edge(old_ij, old_jj, target_i, target_j); + + arma::mat chol_rank1 = model.Kyy_chol_; + arma::mat cov_rank1 = model.covariance_yy_; + double logdet_rank1 = model.Kyy_log_det_; + + // Full recompute for ground truth + model.recompute_Kyy_decomposition(); + arma::mat chol_full = model.Kyy_chol_; + arma::mat cov_full = model.covariance_yy_; + double logdet_full = model.Kyy_log_det_; + + double chol_max_diff = arma::max(arma::max(arma::abs(chol_rank1 - chol_full))); + double cov_max_diff = arma::max(arma::max(arma::abs(cov_rank1 - cov_full))); + double logdet_diff = std::abs(logdet_rank1 - logdet_full); + + // ========================================================================= + // T29b: Cholesky update fidelity — rank-1 diagonal update + // ========================================================================= + + // Restore + model.Kyy_ = Kyy_saved; + model.covariance_yy_ = cov_saved; + model.Kyy_chol_ = chol_saved; + model.inv_cholesky_yy_ = inv_chol_saved; + model.Kyy_log_det_ = logdet_saved; + + // Diagonal proposal (reuse from T28b) + model.precision_yy_proposal_ = model.Kyy_; + model.precision_yy_proposal_(target_i, target_i) = model.Kyy_(target_i, target_i) + - std::exp(theta_curr) * std::exp(theta_curr) + + std::exp(theta_prop) * std::exp(theta_prop); + + double old_ii = model.Kyy_(target_i, target_i); + model.Kyy_(target_i, target_i) = model.precision_yy_proposal_(target_i, target_i); + model.cholesky_update_after_kyy_diag(old_ii, target_i); + + arma::mat chol_diag_rank1 = model.Kyy_chol_; + arma::mat cov_diag_rank1 = model.covariance_yy_; + double logdet_diag_rank1 = model.Kyy_log_det_; + + model.recompute_Kyy_decomposition(); + arma::mat chol_diag_full = model.Kyy_chol_; + arma::mat cov_diag_full = model.covariance_yy_; + double logdet_diag_full = model.Kyy_log_det_; + + double chol_diag_max_diff = arma::max(arma::max(arma::abs(chol_diag_rank1 - chol_diag_full))); + double cov_diag_max_diff = arma::max(arma::max(arma::abs(cov_diag_rank1 - cov_diag_full))); + double logdet_diag_diff = std::abs(logdet_diag_rank1 - logdet_diag_full); + + return Rcpp::List::create( + // T28: off-diagonal log-ratio agreement + Rcpp::Named("ratio_rank2") = ratio_rank2, + Rcpp::Named("ratio_bruteforce") = ratio_bruteforce, + // T28b: diagonal log-ratio agreement + Rcpp::Named("ratio_diag_rank1") = ratio_diag_rank1, + Rcpp::Named("ratio_diag_brute") = ratio_diag_brute, + // T29: off-diagonal Cholesky fidelity + Rcpp::Named("chol_max_diff") = chol_max_diff, + Rcpp::Named("cov_max_diff") = cov_max_diff, + Rcpp::Named("logdet_diff") = logdet_diff, + // T29b: diagonal Cholesky fidelity + Rcpp::Named("chol_diag_max_diff") = chol_diag_max_diff, + Rcpp::Named("cov_diag_max_diff") = cov_diag_max_diff, + Rcpp::Named("logdet_diag_diff") = logdet_diag_diff + ); +} diff --git a/tests/testthat/test-mixed-mrf-cholesky.R b/tests/testthat/test-mixed-mrf-cholesky.R new file mode 100644 index 00000000..de6d8726 --- /dev/null +++ b/tests/testthat/test-mixed-mrf-cholesky.R @@ -0,0 +1,183 @@ +# ============================================================================= +# test-mixed-mrf-cholesky.R — Phase B+ Cholesky rank-1 update tests +# ============================================================================= +# T28: log_ggm_ratio_edge matches brute-force log-likelihood difference +# T28b: log_ggm_ratio_diag matches brute-force log-likelihood difference +# T29: Cholesky / covariance / logdet fidelity after rank-2 edge update +# T29b: Cholesky / covariance / logdet fidelity after rank-1 diagonal update + +# ------------------------------------------------------------------ +# Helper: build parameter vector with nonzero Kyy (must be SPD) +# ------------------------------------------------------------------ +make_cholesky_test = function(q = 3L) { + set.seed(42) + n = 20L; p = 2L + num_cats = c(2L, 3L) + is_ordinal = c(1L, 1L) + baseline_cat = c(0L, 0L) + + x = matrix(0L, n, p) + x[, 1] = sample(0:2, n, replace = TRUE) + x[, 2] = sample(0:3, n, replace = TRUE) + y = matrix(rnorm(n * q), n, q) + + pq = p + q + inc_prob = matrix(0.5, pq, pq) + edge_ind = matrix(1L, pq, pq) + diag(edge_ind) = 0L + + # Param layout: main | Kxx | muy | Kyy (upper-tri) | Kxy + num_main = sum(num_cats) # 2 + 3 = 5 + num_kxx = p * (p - 1L) / 2L # 1 + num_kyy = q * (q + 1L) / 2L + num_kxy = p * q + total = num_main + num_kxx + q + num_kyy + num_kxy + params = rep(0, total) + + # Thresholds + params[1:2] = c(0.3, -0.2) + params[3:5] = c(0.1, 0.4, -0.1) + + # Kxx(0,1) + params[num_main + 1] = 0.2 + + # muy + muy_start = num_main + num_kxx + params[muy_start + seq_len(q)] = rnorm(q, sd = 0.2) + + # Kyy: build SPD Q = L L^T with random L, then pack upper-tri + L = matrix(0, q, q) + diag(L) = runif(q, 1.0, 2.0) + L[lower.tri(L)] = rnorm(q * (q - 1) / 2, sd = 0.3) + Kyy = L %*% t(L) + kyy_start = muy_start + q + idx = 0L + for(i in seq_len(q)) { + for(j in i:q) { + idx = idx + 1L + params[kyy_start + idx] = Kyy[i, j] + } + } + + # Kxy + kxy_start = kyy_start + num_kyy + params[kxy_start + seq_len(num_kxy)] = rnorm(num_kxy, sd = 0.1) + + list( + x = x, y = y, + num_cats = num_cats, + is_ordinal = as.integer(is_ordinal), + baseline_cat = baseline_cat, + inc_prob = inc_prob, + edge_ind = edge_ind, + params = params, + q = q + ) +} + + +# ============================================================================== +# T28 + T28b: log-ratio agreement (off-diagonal and diagonal) +# ============================================================================== + +test_that("log_ggm_ratio_edge matches brute-force (T28)", { + d = make_cholesky_test(q = 3L) + res = bgms:::test_mixed_mrf_cholesky( + d$x, d$y, d$num_cats, d$is_ordinal, d$baseline_cat, + d$inc_prob, d$edge_ind, d$params, + seed = 1L, target_i = 0L, target_j = 1L + ) + expect_equal(res$ratio_rank2, res$ratio_bruteforce, tolerance = 1e-10) +}) + +test_that("log_ggm_ratio_diag matches brute-force (T28b)", { + d = make_cholesky_test(q = 3L) + res = bgms:::test_mixed_mrf_cholesky( + d$x, d$y, d$num_cats, d$is_ordinal, d$baseline_cat, + d$inc_prob, d$edge_ind, d$params, + seed = 1L, target_i = 0L, target_j = 1L + ) + expect_equal(res$ratio_diag_rank1, res$ratio_diag_brute, tolerance = 1e-10) +}) + + +# ============================================================================== +# T29 + T29b: Cholesky fidelity after rank-1/rank-2 updates +# ============================================================================== + +test_that("Cholesky update after edge matches full recompute (T29)", { + d = make_cholesky_test(q = 3L) + res = bgms:::test_mixed_mrf_cholesky( + d$x, d$y, d$num_cats, d$is_ordinal, d$baseline_cat, + d$inc_prob, d$edge_ind, d$params, + seed = 1L, target_i = 0L, target_j = 1L + ) + expect_lt(res$chol_max_diff, 1e-10) + expect_lt(res$cov_max_diff, 1e-10) + expect_lt(res$logdet_diff, 1e-10) +}) + +test_that("Cholesky update after diagonal matches full recompute (T29b)", { + d = make_cholesky_test(q = 3L) + res = bgms:::test_mixed_mrf_cholesky( + d$x, d$y, d$num_cats, d$is_ordinal, d$baseline_cat, + d$inc_prob, d$edge_ind, d$params, + seed = 1L, target_i = 0L, target_j = 1L + ) + expect_lt(res$chol_diag_max_diff, 1e-10) + expect_lt(res$cov_diag_max_diff, 1e-10) + expect_lt(res$logdet_diag_diff, 1e-10) +}) + + +# ============================================================================== +# Same tests with different target indices (i=1, j=2) and q=4 +# ============================================================================== + +test_that("log_ggm_ratio_edge matches brute-force (T28, q=4, ij=1,2)", { + d = make_cholesky_test(q = 4L) + res = bgms:::test_mixed_mrf_cholesky( + d$x, d$y, d$num_cats, d$is_ordinal, d$baseline_cat, + d$inc_prob, d$edge_ind, d$params, + seed = 7L, target_i = 1L, target_j = 2L + ) + expect_equal(res$ratio_rank2, res$ratio_bruteforce, tolerance = 1e-10) + expect_equal(res$ratio_diag_rank1, res$ratio_diag_brute, tolerance = 1e-10) +}) + +test_that("Cholesky fidelity (T29/T29b, q=4, ij=1,2)", { + d = make_cholesky_test(q = 4L) + res = bgms:::test_mixed_mrf_cholesky( + d$x, d$y, d$num_cats, d$is_ordinal, d$baseline_cat, + d$inc_prob, d$edge_ind, d$params, + seed = 7L, target_i = 1L, target_j = 2L + ) + expect_lt(res$chol_max_diff, 1e-10) + expect_lt(res$cov_max_diff, 1e-10) + expect_lt(res$logdet_diff, 1e-10) + expect_lt(res$chol_diag_max_diff, 1e-10) + expect_lt(res$cov_diag_max_diff, 1e-10) + expect_lt(res$logdet_diag_diff, 1e-10) +}) + + +# ============================================================================== +# Test with q=2 (minimum for off-diagonal) — boundary case +# ============================================================================== + +test_that("Rank-1 Cholesky correctness with q=2 boundary (T28-T29b)", { + d = make_cholesky_test(q = 2L) + res = bgms:::test_mixed_mrf_cholesky( + d$x, d$y, d$num_cats, d$is_ordinal, d$baseline_cat, + d$inc_prob, d$edge_ind, d$params, + seed = 99L, target_i = 0L, target_j = 1L + ) + expect_equal(res$ratio_rank2, res$ratio_bruteforce, tolerance = 1e-10) + expect_equal(res$ratio_diag_rank1, res$ratio_diag_brute, tolerance = 1e-10) + expect_lt(res$chol_max_diff, 1e-10) + expect_lt(res$cov_max_diff, 1e-10) + expect_lt(res$logdet_diff, 1e-10) + expect_lt(res$chol_diag_max_diff, 1e-10) + expect_lt(res$cov_diag_max_diff, 1e-10) + expect_lt(res$logdet_diag_diff, 1e-10) +}) From d2d858c88bfc264d736f8596df2f9f67db614961 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Fri, 6 Mar 2026 20:40:36 +0100 Subject: [PATCH 11/60] feat: marginal pseudo-likelihood for mixed MRF Add log_marginal_omrf(s) using Theta = Kxx + 2 Kxy Kyy^{-1} Kxy'. Dispatch marginal likelihood in all six Metropolis update functions: - update_main_effect: marginal vs conditional per-node - update_continuous_mean: add sum_s marginal OMRF terms - update_Kxx: recompute Theta before/after proposal - update_Kxy: all p marginal OMRF nodes + save/restore Theta - update_Kyy_offdiag: temporarily install proposed Kyy for Theta - update_Kyy_diag: same pattern as offdiag --- src/models/mixed/mixed_mrf_likelihoods.cpp | 63 ++++++++++++++ src/models/mixed/mixed_mrf_metropolis.cpp | 95 ++++++++++++++++++---- src/models/mixed/mixed_mrf_model.h | 3 + 3 files changed, 147 insertions(+), 14 deletions(-) diff --git a/src/models/mixed/mixed_mrf_likelihoods.cpp b/src/models/mixed/mixed_mrf_likelihoods.cpp index d4deb4a9..65a57993 100644 --- a/src/models/mixed/mixed_mrf_likelihoods.cpp +++ b/src/models/mixed/mixed_mrf_likelihoods.cpp @@ -55,6 +55,69 @@ double MixedMRFModel::log_conditional_omrf(int s) const { } +// ============================================================================= +// log_marginal_omrf +// ============================================================================= +// Marginal OMRF pseudolikelihood for discrete variable s: +// log f(x_s | x_{-s}) using Theta = Kxx + 2 Kxy Kyy^{-1} Kxy' +// +// Differs from conditional form in three ways: +// 1. rest score uses Theta_ instead of Kxx_, minus self-interaction +// 2. scalar bias 2 * Kxy(s,:) * muy added to rest +// 3. numerator includes Theta_(s,s) * sum(x_s^2) +// 4. denominator offsets include c^2 * Theta_(s,s) +// ============================================================================= + +double MixedMRFModel::log_marginal_omrf(int s) const { + int C_s = num_categories_(s); + + // Rest score: Theta-based interaction + Kxy*muy bias + double theta_ss = Theta_(s, s); + arma::vec rest = discrete_observations_dbl_ * Theta_.col(s) + - discrete_observations_dbl_.col(s) * theta_ss + + 2.0 * arma::dot(Kxy_.row(s), muy_); + + // Numerator: dot(x_s, rest) + theta_ss * dot(x_s, x_s) + main effects + double numer = arma::dot(discrete_observations_dbl_.col(s), rest) + + theta_ss * arma::dot(discrete_observations_dbl_.col(s), + discrete_observations_dbl_.col(s)); + + if(is_ordinal_variable_(s)) { + for(int c = 1; c <= C_s; ++c) { + numer += static_cast(counts_per_category_(c, s)) * mux_(s, c - 1); + } + + // Denominator: main_param(c) = mux(s,c) + (c+1)^2 * theta_ss + arma::vec main_param(C_s); + for(int c = 0; c < C_s; ++c) { + main_param(c) = mux_(s, c) + static_cast((c + 1) * (c + 1)) * theta_ss; + } + + arma::vec bound = static_cast(C_s) * rest; + arma::vec denom = compute_denom_ordinal(rest, main_param, bound); + + return numer - arma::accu(bound + ARMA_MY_LOG(denom)); + } else { + // Blume-Capel: alpha * sum(x) + beta * sum(x^2) + double alpha = mux_(s, 0); + double beta = mux_(s, 1); + numer += alpha * static_cast(blume_capel_stats_(0, s)) + + beta * static_cast(blume_capel_stats_(1, s)); + + // Denominator: theta_c includes Theta_(s,s) * (c - ref)^2 + int ref = baseline_category_(s); + double effective_beta = beta + theta_ss; + + arma::vec bound; + arma::vec denom = compute_denom_blume_capel( + rest, alpha, effective_beta, ref, C_s, bound + ); + + return numer - arma::accu(bound + ARMA_MY_LOG(denom)); + } +} + + // ============================================================================= // log_conditional_ggm // ============================================================================= diff --git a/src/models/mixed/mixed_mrf_metropolis.cpp b/src/models/mixed/mixed_mrf_metropolis.cpp index ccdafb7f..f8eecfeb 100644 --- a/src/models/mixed/mixed_mrf_metropolis.cpp +++ b/src/models/mixed/mixed_mrf_metropolis.cpp @@ -34,11 +34,13 @@ void MixedMRFModel::update_main_effect(int s, int c) { double proposed = rnorm(rng_, current_val, proposal_sd); // Current log-posterior - double ll_curr = log_conditional_omrf(s) + log_beta_prior(current_val, main_alpha_, main_beta_); + double ll_curr = (use_marginal_pl_ ? log_marginal_omrf(s) : log_conditional_omrf(s)) + + log_beta_prior(current_val, main_alpha_, main_beta_); // Proposed log-posterior current = proposed; - double ll_prop = log_conditional_omrf(s) + log_beta_prior(proposed, main_alpha_, main_beta_); + double ll_prop = (use_marginal_pl_ ? log_marginal_omrf(s) : log_conditional_omrf(s)) + + log_beta_prior(proposed, main_alpha_, main_beta_); double ln_alpha = ll_prop - ll_curr; @@ -62,6 +64,10 @@ void MixedMRFModel::update_continuous_mean(int j) { // Current log-posterior (Normal(0,1) prior: -x^2/2 up to constant) double ll_curr = log_conditional_ggm() + R::dnorm(current_val, 0.0, 1.0, true); + if(use_marginal_pl_) { + for(size_t s = 0; s < p_; ++s) + ll_curr += log_marginal_omrf(s); + } // Set proposed value and refresh conditional_mean_ arma::mat cond_mean_saved = conditional_mean_; @@ -69,6 +75,10 @@ void MixedMRFModel::update_continuous_mean(int j) { recompute_conditional_mean(); double ll_prop = log_conditional_ggm() + R::dnorm(proposed, 0.0, 1.0, true); + if(use_marginal_pl_) { + for(size_t s = 0; s < p_; ++s) + ll_prop += log_marginal_omrf(s); + } double ln_alpha = ll_prop - ll_curr; @@ -92,21 +102,34 @@ void MixedMRFModel::update_Kxx(int i, int j) { double proposed = rnorm(rng_, current_val, prop_sd_Kxx_(i, j)); // Current log-posterior - double ll_curr = log_conditional_omrf(i) + log_conditional_omrf(j) - + R::dcauchy(current_val, 0.0, pairwise_scale_, true); + double ll_curr, ll_prop; + if(use_marginal_pl_) { + ll_curr = log_marginal_omrf(i) + log_marginal_omrf(j) + + R::dcauchy(current_val, 0.0, pairwise_scale_, true); - // Set proposed value (symmetric) - Kxx_(i, j) = proposed; - Kxx_(j, i) = proposed; + Kxx_(i, j) = proposed; + Kxx_(j, i) = proposed; + recompute_Theta(); - double ll_prop = log_conditional_omrf(i) + log_conditional_omrf(j) - + R::dcauchy(proposed, 0.0, pairwise_scale_, true); + ll_prop = log_marginal_omrf(i) + log_marginal_omrf(j) + + R::dcauchy(proposed, 0.0, pairwise_scale_, true); + } else { + ll_curr = log_conditional_omrf(i) + log_conditional_omrf(j) + + R::dcauchy(current_val, 0.0, pairwise_scale_, true); + + Kxx_(i, j) = proposed; + Kxx_(j, i) = proposed; + + ll_prop = log_conditional_omrf(i) + log_conditional_omrf(j) + + R::dcauchy(proposed, 0.0, pairwise_scale_, true); + } double ln_alpha = ll_prop - ll_curr; if(std::log(runif(rng_)) >= ln_alpha) { Kxx_(i, j) = current_val; // reject Kxx_(j, i) = current_val; + if(use_marginal_pl_) recompute_Theta(); } } @@ -361,6 +384,21 @@ void MixedMRFModel::update_Kyy_offdiag(int i, int j) { double ln_alpha = log_ggm_ratio_edge(i, j); + // Marginal mode: add OMRF ratio with proposed Theta + if(use_marginal_pl_) { + for(size_t s = 0; s < p_; ++s) + ln_alpha -= log_marginal_omrf(s); + + arma::mat Theta_saved = Theta_; + arma::mat Kyy_saved = Kyy_; + Kyy_ = precision_yy_proposal_; + recompute_Theta(); + for(size_t s = 0; s < p_; ++s) + ln_alpha += log_marginal_omrf(s); + Kyy_ = Kyy_saved; + Theta_ = std::move(Theta_saved); + } + // Prior ratio: Cauchy on off-diag + Gamma(1,1) on diagonal ln_alpha += R::dcauchy(omega_prop_ij, 0.0, pairwise_scale_, true); ln_alpha -= R::dcauchy(Kyy_(i, j), 0.0, pairwise_scale_, true); @@ -405,6 +443,21 @@ void MixedMRFModel::update_Kyy_diag(int i) { double ln_alpha = log_ggm_ratio_diag(i); + // Marginal mode: add OMRF ratio with proposed Theta + if(use_marginal_pl_) { + for(size_t s = 0; s < p_; ++s) + ln_alpha -= log_marginal_omrf(s); + + arma::mat Theta_saved = Theta_; + arma::mat Kyy_saved = Kyy_; + Kyy_ = precision_yy_proposal_; + recompute_Theta(); + for(size_t s = 0; s < p_; ++s) + ln_alpha += log_marginal_omrf(s); + Kyy_ = Kyy_saved; + Theta_ = std::move(Theta_saved); + } + // Prior ratio: Gamma(1,1) on diagonal ln_alpha += R::dgamma(precision_yy_proposal_(i, i), 1.0, 1.0, true); ln_alpha -= R::dgamma(Kyy_(i, i), 1.0, 1.0, true); @@ -436,23 +489,37 @@ void MixedMRFModel::update_Kxy(int i, int j) { double proposed = rnorm(rng_, current_val, prop_sd_Kxy_(i, j)); // Current log-posterior - double ll_curr = log_conditional_omrf(i) + log_conditional_ggm() + double ll_curr = log_conditional_ggm() + R::dcauchy(current_val, 0.0, pairwise_scale_, true); + if(use_marginal_pl_) { + for(size_t s = 0; s < p_; ++s) + ll_curr += log_marginal_omrf(s); + } else { + ll_curr += log_conditional_omrf(i); + } - // Set proposed value and refresh conditional_mean_ + // Set proposed value and refresh caches arma::mat cond_mean_saved = conditional_mean_; + arma::mat Theta_saved; + if(use_marginal_pl_) Theta_saved = Theta_; Kxy_(i, j) = proposed; recompute_conditional_mean(); + if(use_marginal_pl_) recompute_Theta(); - double ll_prop = log_conditional_omrf(i) + log_conditional_ggm() + double ll_prop = log_conditional_ggm() + R::dcauchy(proposed, 0.0, pairwise_scale_, true); + if(use_marginal_pl_) { + for(size_t s = 0; s < p_; ++s) + ll_prop += log_marginal_omrf(s); + } else { + ll_prop += log_conditional_omrf(i); + } double ln_alpha = ll_prop - ll_curr; if(std::log(runif(rng_)) >= ln_alpha) { Kxy_(i, j) = current_val; // reject conditional_mean_ = std::move(cond_mean_saved); - } else if(use_marginal_pl_) { - recompute_Theta(); + if(use_marginal_pl_) Theta_ = std::move(Theta_saved); } } diff --git a/src/models/mixed/mixed_mrf_model.h b/src/models/mixed/mixed_mrf_model.h index 81d507e5..412db3cd 100644 --- a/src/models/mixed/mixed_mrf_model.h +++ b/src/models/mixed/mixed_mrf_model.h @@ -355,6 +355,9 @@ class MixedMRFModel : public BaseModel { /** Conditional OMRF pseudolikelihood for discrete variable s, summed over all n. */ double log_conditional_omrf(int s) const; + /** Marginal OMRF pseudolikelihood for discrete variable s, using Theta_. */ + double log_marginal_omrf(int s) const; + /** Conditional GGM log-likelihood: log f(y | x), using cached decomposition. */ double log_conditional_ggm() const; From 2b2b7db3b807852d95dd266deb06d08b8bcd9876 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Fri, 6 Mar 2026 20:40:46 +0100 Subject: [PATCH 12/60] test: marginal pseudo-likelihood tests (Phase C.5) - Test helper conditionally computes marg_omrf_ll in marginal mode - R reference ref_log_marginal_omrf validated against C++ (ordinal + BC) - Zero-param equality: marginal == conditional when Kxy = 0 - Divergence test: marginal != conditional with nonzero Kxy - Simulation recovery: bgms vs mixedGM (r >= 0.90, p=3, q=2, n=1000) - Wenchuan empirical: bgms vs mixedGM (r >= 0.90, p=4, q=2) --- src/test_mixed_mrf.cpp | 9 + tests/testthat/test-mixed-mrf-marginal.R | 527 +++++++++++++++++++++++ 2 files changed, 536 insertions(+) create mode 100644 tests/testthat/test-mixed-mrf-marginal.R diff --git a/src/test_mixed_mrf.cpp b/src/test_mixed_mrf.cpp index 38618eec..1c942a77 100644 --- a/src/test_mixed_mrf.cpp +++ b/src/test_mixed_mrf.cpp @@ -153,11 +153,20 @@ Rcpp::List test_mixed_mrf_likelihoods( omrf_ll[s] = model.log_conditional_omrf(s); } + // Evaluate log_marginal_omrf for each discrete variable (marginal mode only) + Rcpp::NumericVector marg_omrf_ll(p); + if(pseudolikelihood == "marginal") { + for(int s = 0; s < p; ++s) { + marg_omrf_ll[s] = model.log_marginal_omrf(s); + } + } + // Evaluate log_conditional_ggm double ggm_ll = model.log_conditional_ggm(); return Rcpp::List::create( Rcpp::Named("omrf_ll") = omrf_ll, + Rcpp::Named("marg_omrf_ll") = marg_omrf_ll, Rcpp::Named("ggm_ll") = ggm_ll ); } diff --git a/tests/testthat/test-mixed-mrf-marginal.R b/tests/testthat/test-mixed-mrf-marginal.R new file mode 100644 index 00000000..6072e1ce --- /dev/null +++ b/tests/testthat/test-mixed-mrf-marginal.R @@ -0,0 +1,527 @@ +# ============================================================================= +# test-mixed-mrf-marginal.R — Phase C marginal pseudo-likelihood tests +# ============================================================================= +# Tests for log_marginal_omrf() and marginal-mode sampler, validated against +# pure-R reference implementations and cross-package comparison with mixedGM. + +# ------------------------------------------------------------------ +# R reference: marginal OMRF pseudolikelihood for variable s +# ------------------------------------------------------------------ +# Computes log f(x_s | x_{-s}) using Theta = Kxx + 2 Kxy Kyy^{-1} Kxy' +# +# @param x_int n x p integer matrix (0-based categories) +# @param Theta p x p marginal interaction matrix +# @param Kxy p x q cross-interactions +# @param mux p x max_cats thresholds / BC coefficients +# @param muy q-vector of continuous means +# @param num_cats p-vector of category counts +# @param is_ordinal p-vector (1 = ordinal, 0 = BC) +# @param baseline_cat p-vector of reference categories +# @param s 0-based variable index +# +# Returns: scalar log-likelihood. +ref_log_marginal_omrf = function(x_int, Theta, Kxy, mux, muy, num_cats, + is_ordinal, baseline_cat, s) { + n = nrow(x_int) + s1 = s + 1L + + # Center BC observations + x = x_int + for(j in seq_len(ncol(x))) { + if(!is_ordinal[j]) x[, j] = x[, j] - baseline_cat[j] + } + x_dbl = matrix(as.double(x), nrow = n) + + theta_ss = Theta[s1, s1] + + # Rest score: Theta-based + Kxy*muy bias + rest = x_dbl %*% Theta[, s1] - x_dbl[, s1] * theta_ss + + 2.0 * sum(Kxy[s1, ] * muy) + rest = as.numeric(rest) + + C_s = num_cats[s1] + + if(is_ordinal[s1]) { + # Numerator: dot(x_s, rest) + theta_ss * dot(x_s, x_s) + sum(count_c * mux) + numer = sum(x_dbl[, s1] * rest) + + theta_ss * sum(x_dbl[, s1]^2) + for(c in seq_len(C_s)) { + count_c = sum(x[, s1] == c) + numer = numer + count_c * mux[s1, c] + } + + # Denominator with col_offset = mux + c^2 * theta_ss + log_Z = numeric(n) + for(v in seq_len(n)) { + terms = numeric(C_s + 1) + terms[1] = 0 # reference category (c=0) + for(c in seq_len(C_s)) { + terms[c + 1] = mux[s1, c] + c^2 * theta_ss + c * rest[v] + } + mx = max(terms) + log_Z[v] = mx + log(sum(exp(terms - mx))) + } + return(numer - sum(log_Z)) + } else { + # Blume-Capel: alpha * sum(x) + beta * sum(x^2) + alpha = mux[s1, 1] + beta = mux[s1, 2] + ref = baseline_cat[s1] + + numer = sum(x_dbl[, s1] * rest) + + theta_ss * sum(x_dbl[, s1]^2) + + alpha * sum(x[, s1]) + beta * sum(x[, s1]^2) + + # Effective beta includes theta_ss + eff_beta = beta + theta_ss + + log_Z = numeric(n) + for(v in seq_len(n)) { + cats = 0:C_s + centered = cats - ref + theta = alpha * centered + eff_beta * centered^2 + terms = theta + centered * rest[v] + mx = max(terms) + log_Z[v] = mx + log(sum(exp(terms - mx))) + } + return(numer - sum(log_Z)) + } +} + + +# ------------------------------------------------------------------ +# Test data setup (reuse from likelihood tests) +# ------------------------------------------------------------------ +make_test_data = function() { + set.seed(42) + n = 10L; p = 3L; q = 2L + num_cats = c(2L, 3L, 2L) + is_ordinal = c(1L, 1L, 0L) + baseline_cat = c(0L, 0L, 1L) + + x = matrix(0L, n, p) + x[, 1] = sample(0:2, n, replace = TRUE) + x[, 2] = sample(0:3, n, replace = TRUE) + x[, 3] = sample(0:2, n, replace = TRUE) + y = matrix(rnorm(n * q), n, q) + + pq = p + q + inc_prob = matrix(0.5, pq, pq) + edge_ind = matrix(1L, pq, pq) + diag(edge_ind) = 0L + + list( + n = n, p = p, q = q, + x = x, y = y, + num_cats = num_cats, + is_ordinal = as.integer(is_ordinal), + baseline_cat = baseline_cat, + inc_prob = inc_prob, + edge_ind = edge_ind + ) +} + +build_param_vec = function(p, q, num_cats, is_ordinal) { + num_main = 0L + for(s in seq_len(p)) { + if(is_ordinal[s]) { + num_main = num_main + num_cats[s] + } else { + num_main = num_main + 2L + } + } + num_pairwise_xx = p * (p - 1L) / 2L + num_kyy = q * (q + 1L) / 2L + num_cross = p * q + total = num_main + num_pairwise_xx + q + num_kyy + num_cross + list( + total = total, + num_main = num_main, + num_pairwise_xx = num_pairwise_xx, + q = q, + num_kyy = num_kyy, + num_cross = num_cross, + kyy_start = num_main + num_pairwise_xx + q + ) +} + + +# ============================================================================== +# Test: marginal OMRF at zero params equals conditional OMRF +# ============================================================================== + +test_that("log_marginal_omrf at zero params equals log_conditional_omrf", { + d = make_test_data() + info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) + params = rep(0, info$total) + # Kyy = identity + idx = info$kyy_start + for(i in seq_len(d$q)) { + for(j in i:d$q) { + idx = idx + 1L + if(i == j) params[idx] = 1.0 + } + } + + res = bgms:::test_mixed_mrf_likelihoods( + d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, + d$inc_prob, d$edge_ind, FALSE, "marginal", params, 1L + ) + + # With Kxy = 0 and Kyy = I, Theta = Kxx + 0 = Kxx and bias = 0, + # so marginal OMRF = conditional OMRF + for(s in seq_len(d$p)) { + expect_equal(res$marg_omrf_ll[s], res$omrf_ll[s], tolerance = 1e-10, + label = paste0("var ", s - 1, " marginal == conditional at zero")) + } +}) + + +# ============================================================================== +# Test: marginal OMRF matches R reference (ordinal, nonzero params) +# ============================================================================== + +test_that("log_marginal_omrf matches R reference (ordinal, nonzero params)", { + d = make_test_data() + info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) + params = rep(0, info$total) + + # Main effects + params[1] = 0.3; params[2] = -0.2 + params[3] = 0.1; params[4] = 0.4; params[5] = -0.1 + params[6] = 0.5; params[7] = -0.3 + + # Kxx edges + kxx_start = info$num_main + params[kxx_start + 1] = 0.2 + params[kxx_start + 2] = -0.1 + params[kxx_start + 3] = 0.15 + + # muy + muy_start = info$num_main + info$num_pairwise_xx + params[muy_start + 1] = 0.1 + params[muy_start + 2] = -0.2 + + # Kyy = SPD + kyy_start = info$kyy_start + params[kyy_start + 1] = 2.0 + params[kyy_start + 2] = 0.3 + params[kyy_start + 3] = 1.5 + + # Kxy + kxy_start = kyy_start + info$num_kyy + params[kxy_start + 1] = 0.1 + params[kxy_start + 2] = -0.05 + params[kxy_start + 3] = 0.08 + params[kxy_start + 4] = 0.12 + params[kxy_start + 5] = -0.07 + params[kxy_start + 6] = 0.04 + + res = bgms:::test_mixed_mrf_likelihoods( + d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, + d$inc_prob, d$edge_ind, FALSE, "marginal", params, 1L + ) + + # Build R reference matrices + mux = matrix(0, d$p, max(d$num_cats)) + mux[1, 1:2] = c(0.3, -0.2) + mux[2, 1:3] = c(0.1, 0.4, -0.1) + mux[3, 1:2] = c(0.5, -0.3) + + Kxx = matrix(0, d$p, d$p) + Kxx[1, 2] = Kxx[2, 1] = 0.2 + Kxx[1, 3] = Kxx[3, 1] = -0.1 + Kxx[2, 3] = Kxx[3, 2] = 0.15 + + Kxy = matrix(c(0.1, 0.08, -0.07, -0.05, 0.12, 0.04), nrow = d$p, ncol = d$q) + Kyy = matrix(c(2.0, 0.3, 0.3, 1.5), d$q, d$q) + muy = c(0.1, -0.2) + + # Theta = Kxx + 2 Kxy Kyy^{-1} Kxy' + Theta = Kxx + 2 * Kxy %*% solve(Kyy) %*% t(Kxy) + + # Check ordinal variables + for(s in 0:1) { + expected = ref_log_marginal_omrf( + d$x, Theta, Kxy, mux, muy, d$num_cats, + d$is_ordinal, d$baseline_cat, s + ) + expect_equal(res$marg_omrf_ll[s + 1], expected, tolerance = 1e-8, + label = paste0("ordinal var ", s)) + } +}) + + +# ============================================================================== +# Test: marginal OMRF matches R reference (Blume-Capel) +# ============================================================================== + +test_that("log_marginal_omrf matches R reference (Blume-Capel, nonzero params)", { + d = make_test_data() + info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) + params = rep(0, info$total) + + params[1] = 0.3; params[2] = -0.2 + params[3] = 0.1; params[4] = 0.4; params[5] = -0.1 + params[6] = 0.5; params[7] = -0.3 + + kxx_start = info$num_main + params[kxx_start + 1] = 0.2 + params[kxx_start + 2] = -0.1 + params[kxx_start + 3] = 0.15 + + muy_start = info$num_main + info$num_pairwise_xx + params[muy_start + 1] = 0.1 + params[muy_start + 2] = -0.2 + + kyy_start = info$kyy_start + params[kyy_start + 1] = 2.0 + params[kyy_start + 2] = 0.3 + params[kyy_start + 3] = 1.5 + + kxy_start = kyy_start + info$num_kyy + params[kxy_start + 1] = 0.1 + params[kxy_start + 2] = -0.05 + params[kxy_start + 3] = 0.08 + params[kxy_start + 4] = 0.12 + params[kxy_start + 5] = -0.07 + params[kxy_start + 6] = 0.04 + + res = bgms:::test_mixed_mrf_likelihoods( + d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, + d$inc_prob, d$edge_ind, FALSE, "marginal", params, 1L + ) + + mux = matrix(0, d$p, max(d$num_cats)) + mux[1, 1:2] = c(0.3, -0.2) + mux[2, 1:3] = c(0.1, 0.4, -0.1) + mux[3, 1:2] = c(0.5, -0.3) + + Kxx = matrix(0, d$p, d$p) + Kxx[1, 2] = Kxx[2, 1] = 0.2 + Kxx[1, 3] = Kxx[3, 1] = -0.1 + Kxx[2, 3] = Kxx[3, 2] = 0.15 + + Kxy = matrix(c(0.1, 0.08, -0.07, -0.05, 0.12, 0.04), nrow = d$p, ncol = d$q) + Kyy = matrix(c(2.0, 0.3, 0.3, 1.5), d$q, d$q) + muy = c(0.1, -0.2) + + Theta = Kxx + 2 * Kxy %*% solve(Kyy) %*% t(Kxy) + + # Test BC variable (s = 2, 0-based) + expected = ref_log_marginal_omrf( + d$x, Theta, Kxy, mux, muy, d$num_cats, + d$is_ordinal, d$baseline_cat, s = 2 + ) + expect_equal(res$marg_omrf_ll[3], expected, tolerance = 1e-8) +}) + + +# ============================================================================== +# Test: marginal OMRF differs from conditional when Kxy != 0 +# ============================================================================== + +test_that("log_marginal_omrf differs from log_conditional_omrf with nonzero Kxy", { + d = make_test_data() + info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) + params = rep(0, info$total) + + params[1] = 0.3; params[2] = -0.2 + params[3] = 0.1; params[4] = 0.4; params[5] = -0.1 + params[6] = 0.5; params[7] = -0.3 + + kxx_start = info$num_main + params[kxx_start + 1] = 0.2 + + muy_start = info$num_main + info$num_pairwise_xx + params[muy_start + 1] = 0.1 + + kyy_start = info$kyy_start + params[kyy_start + 1] = 2.0 + params[kyy_start + 2] = 0.3 + params[kyy_start + 3] = 1.5 + + # Nonzero Kxy makes marginal != conditional + kxy_start = kyy_start + info$num_kyy + params[kxy_start + 1] = 0.3 + params[kxy_start + 2] = -0.2 + + res = bgms:::test_mixed_mrf_likelihoods( + d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, + d$inc_prob, d$edge_ind, FALSE, "marginal", params, 1L + ) + + # At least one variable should differ + diffs = abs(res$marg_omrf_ll - res$omrf_ll) + expect_true(max(diffs) > 0.01, + label = "marginal and conditional OMRF differ with nonzero Kxy") +}) + + +# ============================================================================== +# Helper: extract parameter matrices from bgms sample matrix +# ============================================================================== +# (Same as in test-mixed-mrf-sampling.R; duplicated here so marginal tests +# are self-contained.) +extract_bgms_estimates = function(samples, p, q, num_cats_bgms) { + S = colMeans(samples) + idx = 1L + max_cat = max(num_cats_bgms) + mux = matrix(0, p, max_cat) + for(s in 1:p) { + for(c in seq_len(num_cats_bgms[s])) { + mux[s, c] = S[idx]; idx = idx + 1L + } + } + Kxx = matrix(0, p, p) + for(i in 1:(p - 1)) for(j in (i + 1):p) { + Kxx[i, j] = Kxx[j, i] = S[idx]; idx = idx + 1L + } + muy = S[idx:(idx + q - 1)]; idx = idx + q + Kyy = matrix(0, q, q) + for(i in 1:q) for(j in i:q) { + Kyy[i, j] = Kyy[j, i] = S[idx]; idx = idx + 1L + } + Kxy = matrix(S[idx:(idx + p * q - 1)], nrow = p, ncol = q, byrow = TRUE) + list(mux = mux, Kxx = Kxx, muy = muy, Kyy = Kyy, Kxy = Kxy) +} + + +# ============================================================================== +# Test: marginal mode sampler agrees with mixedGM (simulation) +# ============================================================================== + +test_that("marginal PL sampler agrees with mixedGM (ordinal simulation)", { + skip_on_cran() + skip_if_not_installed("mixedGM") + + p = 3L; q = 2L; n = 1000L + + Kxx_true = matrix(c(0, 0.4, -0.2, + 0.4, 0, 0.3, + -0.2, 0.3, 0), 3, 3) + Kxy_true = matrix(c(0.2, -0.1, 0.15, + 0.1, 0.3, -0.2), nrow = p, ncol = q) + Kyy_true = matrix(c(1.3, 0.2, 0.2, 1.1), 2, 2) + mux_true = matrix(c(0.5, -0.5, 0.3, + -0.3, -1.0, -0.5), nrow = p, ncol = 2) + muy_true = c(0.5, -0.3) + + set.seed(456) + sim = mixedGM::mixed_gibbs_generate( + n = n, Kxx = Kxx_true, Kxy = Kxy_true, Kyy = Kyy_true, + mux = mux_true, muy = muy_true, + num_categories = rep(3L, p), n_burnin = 1000 + ) + + # --- run mixedGM (marginal PL) --- + set.seed(42) + mgm = mixedGM::mixed_sampler( + x = sim$x, y = sim$y, num_categories = rep(3L, p), + n_warmup = 4000L, n_samples = 8000L, + edge_selection = FALSE, verbose = FALSE, + pseudolikelihood = "marginal" + ) + mgm_est = mgm$final_parameters + + # --- run bgms (marginal PL) --- + num_cats_bgms = rep(2L, p) + pq = p + q + inc_prob = matrix(0.5, pq, pq); diag(inc_prob) = 0 + edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L + + bgms_out = bgms:::test_mixed_mrf_sampler( + sim$x, sim$y, as.integer(num_cats_bgms), + as.integer(rep(1L, p)), rep(0L, p), + inc_prob, edge_ind, FALSE, "marginal", + 4000L, 8000L, 42L + ) + bgms_est = extract_bgms_estimates(bgms_out$samples, p, q, num_cats_bgms) + + # --- correlations bgms vs mixedGM (r >= 0.90) --- + kxx_ut = upper.tri(bgms_est$Kxx) + expect_gt( + cor(bgms_est$Kxx[kxx_ut], mgm_est$Kxx[kxx_ut]), 0.90, + label = "Kxx bgms-mgm correlation (marginal sim)" + ) + expect_gt( + cor(as.vector(bgms_est$Kxy), as.vector(mgm_est$Kxy)), 0.90, + label = "Kxy bgms-mgm correlation (marginal sim)" + ) + expect_gt( + cor(as.vector(bgms_est$Kyy), as.vector(mgm_est$Kyy)), 0.90, + label = "Kyy bgms-mgm correlation (marginal sim)" + ) + expect_gt( + cor(as.vector(bgms_est$mux), as.vector(mgm_est$mux)), 0.90, + label = "mux bgms-mgm correlation (marginal sim)" + ) +}) + + +# ============================================================================== +# Test: marginal mode sampler agrees with mixedGM on Wenchuan data +# ============================================================================== + +test_that("marginal PL sampler agrees with mixedGM on Wenchuan data", { + skip_on_cran() + skip_if_not_installed("mixedGM") + + data(Wenchuan, package = "bgms") + W = na.omit(Wenchuan) + + p = 4L; q = 2L + x = as.matrix(W[, 1:p]) - 1L # convert 1-5 to 0-4 + y = as.matrix(W[, (p + 1):(p + q)]) + + num_cats_mgm = rep(5L, p) # total categories for mixedGM + num_cats_bgms = rep(4L, p) # max category index for bgms + + # --- mixedGM (marginal PL) --- + set.seed(42) + mgm = mixedGM::mixed_sampler( + x = x, y = y, num_categories = num_cats_mgm, + n_warmup = 4000L, n_samples = 8000L, + edge_selection = FALSE, verbose = FALSE, + pseudolikelihood = "marginal" + ) + mgm_est = mgm$final_parameters + + # --- bgms (marginal PL) --- + pq = p + q + inc_prob = matrix(0.5, pq, pq); diag(inc_prob) = 0 + edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L + + bgms_out = bgms:::test_mixed_mrf_sampler( + x, y, as.integer(num_cats_bgms), + as.integer(rep(1L, p)), rep(0L, p), + inc_prob, edge_ind, FALSE, "marginal", + 4000L, 8000L, 42L + ) + bgms_est = extract_bgms_estimates(bgms_out$samples, p, q, num_cats_bgms) + + # --- correlations bgms vs mixedGM (r >= 0.90) --- + kxx_ut = upper.tri(bgms_est$Kxx) + expect_gt( + cor(bgms_est$Kxx[kxx_ut], mgm_est$Kxx[kxx_ut]), 0.90, + label = "Kxx bgms-mgm correlation (marginal Wenchuan)" + ) + expect_gt( + cor(as.vector(bgms_est$Kxy), as.vector(mgm_est$Kxy)), 0.90, + label = "Kxy bgms-mgm correlation (marginal Wenchuan)" + ) + expect_gt( + cor(as.vector(bgms_est$Kyy), as.vector(mgm_est$Kyy)), 0.90, + label = "Kyy bgms-mgm correlation (marginal Wenchuan)" + ) + expect_gt( + cor(as.vector(bgms_est$mux), as.vector(mgm_est$mux)), 0.90, + label = "mux bgms-mgm correlation (marginal Wenchuan)" + ) + + # --- Kxx sign patterns agree --- + expect_true( + all(sign(bgms_est$Kxx[kxx_ut]) == sign(mgm_est$Kxx[kxx_ut])), + label = "Kxx signs agree (marginal Wenchuan)" + ) +}) From ca1681744229a65910fe16255c5d2c0a6d8cdf88 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Fri, 6 Mar 2026 22:28:23 +0100 Subject: [PATCH 13/60] feat: mixed MRF edge selection (Phase D) Implement spike-and-slab reversible-jump MH for all three edge types: - update_edge_indicator_Kxx: discrete-discrete birth/death - update_edge_indicator_Kyy: Cholesky-reparameterized continuous-continuous - update_edge_indicator_Kxy: cross-type birth/death Add update_edge_indicators() dispatcher with shuffled edge ordering, initialize_graph() for prior-based initial graph draw, and Step 6 in do_one_metropolis_step(). Test helper updated to activate edge selection after warmup and return indicator samples. Bug fix: Kyy birth proposal uses C[3]*epsilon (scale only), matching the GGM model, not C[2]+C[3]*epsilon (which mismatched the death reversal density). Plan updated: Phase D marked complete, D.7 added for pairwise prior standardization (OMRF-style scaling factors for Kxx edges). --- dev/mixedmrf/implementation_plan.md | 50 ++++- src/models/mixed/mixed_mrf_metropolis.cpp | 251 ++++++++++++++++++++++ src/models/mixed/mixed_mrf_model.cpp | 83 ++++++- src/models/mixed/mixed_mrf_model.h | 11 + src/test_mixed_mrf.cpp | 23 +- 5 files changed, 402 insertions(+), 16 deletions(-) diff --git a/dev/mixedmrf/implementation_plan.md b/dev/mixedmrf/implementation_plan.md index 0f1d63f4..a4003ab3 100644 --- a/dev/mixedmrf/implementation_plan.md +++ b/dev/mixedmrf/implementation_plan.md @@ -307,11 +307,11 @@ add the new Rcpp export to `src/RcppExports.cpp`, `R/RcppExports.R`, and | Phase | What | Depends on | Deliverable | |-------|------|------------|-------------| -| **A** | Skeleton: class, data structures, `BaseModel` overrides | — | Compiles, no sampling | -| **B** | Conditional PL: all 5 MH updates, no edge selection | A | Recovery test passes (cond PL, estimation only) | -| **B+** | Rank-1 Cholesky optimization for Kyy updates | B | Same correctness, $O(q^2)$ per Kyy move instead of $O(q^3)$ | -| **C** | Marginal PL: $\Theta$ caching, marginal OMRF, $\mu_y$ full sweep | B+ | Recovery test passes (marg PL, estimation only) | -| **D** | Edge selection: 3 RJ sweeps | B+ | Structure recovery test passes | +| **A** | Skeleton: class, data structures, `BaseModel` overrides | — | Compiles, no sampling | ✅ | +| **B** | Conditional PL: all 5 MH updates, no edge selection | A | Recovery test passes (cond PL, estimation only) | ✅ | +| **B+** | Rank-1 Cholesky optimization for Kyy updates | B | Same correctness, $O(q^2)$ per Kyy move instead of $O(q^3)$ | ✅ | +| **C** | Marginal PL: $\Theta$ caching, marginal OMRF, $\mu_y$ full sweep | B+ | Recovery test passes (marg PL, estimation only) | ✅ | +| **D** | Edge selection: 3 RJ sweeps | B+ | Structure recovery test passes | ✅ | | **E** | R interface: `bgm()` dispatch, output formatting | B+ | End-to-end `bgm(mixed_data)` works | | **F** | Warmup schedule, adaptation, diagnostics | E | Full warmup pipeline | | **G** | Simulation and prediction | E | `simulate.bgms` and `predict.bgms` for mixed | @@ -1174,9 +1174,9 @@ in the two modes. --- -## Phase D — Edge selection +## Phase D — Edge selection ✅ -### D.1 Discrete edge selection (`update_edge_indicator_Kxx`) +### D.1 Discrete edge selection (`update_edge_indicator_Kxx`) ✅ For each pair $(i, j)$ with $i < j$: 1. Propose $G'_{xx,ij} = 1 - G_{xx,ij}$ @@ -1197,7 +1197,7 @@ All log-density calls must use `log = true` to keep all terms on the log scale. Follows `cond_omrf_update_association_indicator_pair` in the R prototype. Transplant the Hastings terms verbatim from that R code; do not re-derive. -### D.2 Continuous edge selection (`update_edge_indicator_Kyy`) +### D.2 Continuous edge selection (`update_edge_indicator_Kyy`) ✅ Cholesky-based birth/death as in `GGMModel`: 1. Permute, Cholesky, extract constants @@ -1213,7 +1213,7 @@ Follows `cond_ggm_update_precision_indicator_pair` in the R prototype. **Reuse opportunity:** Directly reuse `GGMModel::update_edge_indicator_parameter_pair` logic, adapted for the mixed model's conditional GGM likelihood. -### D.3 Cross edge selection (`update_edge_indicator_Kxy`) +### D.3 Cross edge selection (`update_edge_indicator_Kxy`) ✅ Cross-edges $G_{xy,ij}$ share the same Bernoulli inclusion prior $\pi$ as $G_{xx}$ and $G_{yy}$ (decided). A single $\pi$ keeps the SBM prior @@ -1233,7 +1233,7 @@ For each pair $(i, j)$ where $i \in \{1..p\}$, $j \in \{1..q\}$: Follows `cond_omrf_update_cross_association_indicator_pair` in the R prototype. -### D.4 Implement `update_edge_indicators()` +### D.4 Implement `update_edge_indicators()` ✅ ```cpp void MixedMRFModel::update_edge_indicators() { @@ -1275,13 +1275,41 @@ require no special handling: the rest-score uses the centered observations, and the likelihood function dispatches to the Blume-Capel denominator as needed. -### D.6 Testing checkpoint — edge selection +### D.6 Testing checkpoint — edge selection ✅ **Test 6 (T15): Structure recovery** - Generate data from a sparse mixed graph (some edges zero) - Run with edge selection, check posterior inclusion probabilities recover the true structure (true edges have high PIP, false edges low) +### D.7 Pairwise prior standardization + +The pure OMRF scales the Cauchy slab prior per edge pair using +`pairwise_scaling_factors_`, computed in `compute_scaling_factors()`. +For two ordinal variables with $M_i$ and $M_j$ categories respectively, +the factor is $M_i \times M_j$; for Blume-Capel pairs it is the maximum +absolute product of the endpoint ranges; for mixed ordinal/Blume-Capel +pairs it is the corresponding cross product. This makes the prior +comparable across variable pairs with different numbers of categories. + +The mixed MRF currently uses a single `pairwise_scale_` for all Kxx +edges and does not apply per-pair scaling. This must be fixed: + +1. **Add `pairwise_scaling_factors_xx_`** (p × p matrix) to + `MixedMRFModel`. Kxy and Kyy edges are not scaled (continuous + variables have no category-count dependence; cross-type edges + couple a score in {0..M} with a continuous value, and the OMRF + does not scale cross-type edges either). +2. **Compute the scaling factors** in the R interface layer (Phase E) + using the existing `compute_scaling_factors()` applied to the + discrete block, and pass them to `MixedMRFModel`. +3. **Apply** `pairwise_scale_ * pairwise_scaling_factors_xx_(i, j)` in + `update_Kxx()`, `update_edge_indicator_Kxx()`, and anywhere + else the Cauchy slab prior appears for Kxx edges. +4. **Test** that a model with variables of differing category counts + produces sensible edge estimates (no systematic bias toward + high-category pairs). + --- ## Phase E — R interface and integration diff --git a/src/models/mixed/mixed_mrf_metropolis.cpp b/src/models/mixed/mixed_mrf_metropolis.cpp index f8eecfeb..ca7c6d9b 100644 --- a/src/models/mixed/mixed_mrf_metropolis.cpp +++ b/src/models/mixed/mixed_mrf_metropolis.cpp @@ -523,3 +523,254 @@ void MixedMRFModel::update_Kxy(int i, int j) { if(use_marginal_pl_) Theta_ = std::move(Theta_saved); } } + + +// ============================================================================= +// update_edge_indicator_Kxx +// ============================================================================= +// Reversible-jump birth/death for a discrete-discrete edge (i, j). +// Birth (G=0→1): propose k ~ N(0, σ), accept with slab + Hastings. +// Death (G=1→0): set k = 0, accept with reverse terms. +// Follows cond_omrf_update_association_indicator_pair in mixedGM. +// ============================================================================= + +void MixedMRFModel::update_edge_indicator_Kxx(int i, int j) { + double k_curr = Kxx_(i, j); + double prop_sd = prop_sd_Kxx_(i, j); + + int g_curr = gxx(i, j); + int g_prop = 1 - g_curr; + + double k_prop; + if(g_prop == 1) { + k_prop = rnorm(rng_, k_curr, prop_sd); // k_curr = 0 on a true birth + } else { + k_prop = 0.0; + } + + // --- Likelihood ratio --- + double ll_curr, ll_prop; + if(use_marginal_pl_) { + ll_curr = log_marginal_omrf(i) + log_marginal_omrf(j); + + Kxx_(i, j) = k_prop; + Kxx_(j, i) = k_prop; + recompute_Theta(); + + ll_prop = log_marginal_omrf(i) + log_marginal_omrf(j); + + // Restore + Kxx_(i, j) = k_curr; + Kxx_(j, i) = k_curr; + recompute_Theta(); + } else { + ll_curr = log_conditional_omrf(i) + log_conditional_omrf(j); + + Kxx_(i, j) = k_prop; + Kxx_(j, i) = k_prop; + + ll_prop = log_conditional_omrf(i) + log_conditional_omrf(j); + + // Restore + Kxx_(i, j) = k_curr; + Kxx_(j, i) = k_curr; + } + + double ln_alpha = ll_prop - ll_curr; + + if(g_prop == 1) { + // Birth: add slab prior, subtract proposal density, inclusion prior + ln_alpha += R::dcauchy(k_prop, 0.0, pairwise_scale_, true); + ln_alpha -= R::dnorm(k_prop, k_curr, prop_sd, true); + ln_alpha += std::log(inclusion_probability_(i, j)) + - std::log(1.0 - inclusion_probability_(i, j)); + } else { + // Death: subtract slab prior, add reverse proposal density, inclusion prior + ln_alpha -= R::dcauchy(k_curr, 0.0, pairwise_scale_, true); + ln_alpha += R::dnorm(k_curr, k_prop, prop_sd, true); + ln_alpha -= std::log(inclusion_probability_(i, j)) + - std::log(1.0 - inclusion_probability_(i, j)); + } + + if(std::log(runif(rng_)) < ln_alpha) { + Kxx_(i, j) = k_prop; + Kxx_(j, i) = k_prop; + set_gxx(i, j, g_prop); + if(use_marginal_pl_) recompute_Theta(); + } +} + + +// ============================================================================= +// update_edge_indicator_Kyy +// ============================================================================= +// Reversible-jump birth/death for a continuous-continuous edge (i, j). +// Uses Cholesky reparameterization (permute-free constants extraction). +// Birth (G=0→1): propose ε ~ N(0, σ), k = C[2]*ε, constrain diagonal. +// Death (G=1→0): set off-diag = 0, constrain diagonal. +// Follows cond_ggm_update_precision_indicator_pair in mixedGM. +// ============================================================================= + +void MixedMRFModel::update_edge_indicator_Kyy(int i, int j) { + get_kyy_constants(i, j); + + int g_curr = gyy(i, j); + int g_prop = 1 - g_curr; + + double omega_prop_ij, omega_prop_jj; + + if(g_prop == 1) { + // Birth: propose from N(0, σ) on reparameterized scale + double epsilon = rnorm(rng_, 0.0, prop_sd_Kyy_(i, j)); + omega_prop_ij = kyy_constants_[3] * epsilon; + omega_prop_jj = kyy_constrained_diagonal(omega_prop_ij); + } else { + // Death: set off-diagonal to 0 + omega_prop_ij = 0.0; + omega_prop_jj = kyy_constrained_diagonal(0.0); + } + + // Fill proposal + precision_yy_proposal_ = Kyy_; + precision_yy_proposal_(i, j) = omega_prop_ij; + precision_yy_proposal_(j, i) = omega_prop_ij; + precision_yy_proposal_(j, j) = omega_prop_jj; + + // --- Likelihood ratio --- + double ln_alpha = log_ggm_ratio_edge(i, j); + + if(use_marginal_pl_) { + for(size_t s = 0; s < p_; ++s) + ln_alpha -= log_marginal_omrf(s); + + arma::mat Theta_saved = Theta_; + arma::mat Kyy_saved = Kyy_; + Kyy_ = precision_yy_proposal_; + recompute_Theta(); + for(size_t s = 0; s < p_; ++s) + ln_alpha += log_marginal_omrf(s); + Kyy_ = Kyy_saved; + Theta_ = std::move(Theta_saved); + } + + // --- Spike-and-slab terms --- + if(g_prop == 1) { + // Birth: add slab prior on proposed off-diag + ln_alpha += R::dcauchy(omega_prop_ij, 0.0, pairwise_scale_, true); + // Subtract proposal density: dnorm(k_prop / C[2], 0, σ) / C[2] + // = dnorm(epsilon, 0, σ) / C[2] + ln_alpha -= R::dnorm(omega_prop_ij / kyy_constants_[3], 0.0, + prop_sd_Kyy_(i, j), true) + - std::log(kyy_constants_[3]); + // Inclusion prior: log(π / (1-π)) + ln_alpha += std::log(inclusion_probability_(p_ + i, p_ + j)) + - std::log(1.0 - inclusion_probability_(p_ + i, p_ + j)); + } else { + // Death: subtract slab prior on current off-diag + ln_alpha -= R::dcauchy(Kyy_(i, j), 0.0, pairwise_scale_, true); + // Add reverse proposal density: dnorm(k_curr / C[2], 0, σ) / C[2] + ln_alpha += R::dnorm(Kyy_(i, j) / kyy_constants_[3], 0.0, + prop_sd_Kyy_(i, j), true) + - std::log(kyy_constants_[3]); + // Inclusion prior: log((1-π) / π) + ln_alpha -= std::log(inclusion_probability_(p_ + i, p_ + j)) + - std::log(1.0 - inclusion_probability_(p_ + i, p_ + j)); + } + + if(std::log(runif(rng_)) < ln_alpha) { + double old_ij = Kyy_(i, j); + double old_jj = Kyy_(j, j); + + Kyy_(i, j) = omega_prop_ij; + Kyy_(j, i) = omega_prop_ij; + Kyy_(j, j) = omega_prop_jj; + + set_gyy(i, j, g_prop); + cholesky_update_after_kyy_edge(old_ij, old_jj, i, j); + recompute_conditional_mean(); + if(use_marginal_pl_) recompute_Theta(); + } +} + + +// ============================================================================= +// update_edge_indicator_Kxy +// ============================================================================= +// Reversible-jump birth/death for a cross-type edge (i, j). +// Birth (G=0→1): propose k ~ N(0, σ). +// Death (G=1→0): set k = 0. +// Follows cond_omrf_update_cross_association_indicator_pair in mixedGM. +// ============================================================================= + +void MixedMRFModel::update_edge_indicator_Kxy(int i, int j) { + double k_curr = Kxy_(i, j); + double prop_sd = prop_sd_Kxy_(i, j); + + int g_curr = gxy(i, j); + int g_prop = 1 - g_curr; + + double k_prop; + if(g_prop == 1) { + k_prop = rnorm(rng_, k_curr, prop_sd); // k_curr = 0 on a true birth + } else { + k_prop = 0.0; + } + + // --- Likelihood ratio --- + double ll_curr, ll_prop; + if(use_marginal_pl_) { + ll_curr = log_conditional_ggm(); + for(size_t s = 0; s < p_; ++s) + ll_curr += log_marginal_omrf(s); + + arma::mat cond_mean_saved = conditional_mean_; + arma::mat Theta_saved = Theta_; + Kxy_(i, j) = k_prop; + recompute_conditional_mean(); + recompute_Theta(); + + ll_prop = log_conditional_ggm(); + for(size_t s = 0; s < p_; ++s) + ll_prop += log_marginal_omrf(s); + + // Restore + Kxy_(i, j) = k_curr; + conditional_mean_ = std::move(cond_mean_saved); + Theta_ = std::move(Theta_saved); + } else { + ll_curr = log_conditional_omrf(i) + log_conditional_ggm(); + + arma::mat cond_mean_saved = conditional_mean_; + Kxy_(i, j) = k_prop; + recompute_conditional_mean(); + + ll_prop = log_conditional_omrf(i) + log_conditional_ggm(); + + // Restore + Kxy_(i, j) = k_curr; + conditional_mean_ = std::move(cond_mean_saved); + } + + double ln_alpha = ll_prop - ll_curr; + + if(g_prop == 1) { + // Birth + ln_alpha += R::dcauchy(k_prop, 0.0, pairwise_scale_, true); + ln_alpha -= R::dnorm(k_prop, k_curr, prop_sd, true); + ln_alpha += std::log(inclusion_probability_(i, p_ + j)) + - std::log(1.0 - inclusion_probability_(i, p_ + j)); + } else { + // Death + ln_alpha -= R::dcauchy(k_curr, 0.0, pairwise_scale_, true); + ln_alpha += R::dnorm(k_curr, k_prop, prop_sd, true); + ln_alpha -= std::log(inclusion_probability_(i, p_ + j)) + - std::log(1.0 - inclusion_probability_(i, p_ + j)); + } + + if(std::log(runif(rng_)) < ln_alpha) { + Kxy_(i, j) = k_prop; + set_gxy(i, j, g_prop); + recompute_conditional_mean(); + if(use_marginal_pl_) recompute_Theta(); + } +} diff --git a/src/models/mixed/mixed_mrf_model.cpp b/src/models/mixed/mixed_mrf_model.cpp index f8bb00b1..c1ddc812 100644 --- a/src/models/mixed/mixed_mrf_model.cpp +++ b/src/models/mixed/mixed_mrf_model.cpp @@ -471,15 +471,92 @@ void MixedMRFModel::do_one_metropolis_step(int /*iteration*/) { for(size_t j = 0; j < q_; ++j) if(!edge_selection_active_ || gxy(i, j) == 1) update_Kxy(i, j); + + // Step 6: Edge selection (reversible-jump birth/death) + update_edge_indicators(); } void MixedMRFModel::update_edge_indicators() { - // Phase D: reversible-jump edge birth/death moves + if(!edge_selection_active_) return; + + // Discrete-discrete edges (shuffled order) + for(size_t e = 0; e < num_pairwise_xx_; ++e) { + size_t idx = edge_order_xx_(e); + // Decode upper-triangle index to (i, j) + size_t i = 0, j = 1; + size_t count = 0; + for(i = 0; i < p_ - 1; ++i) { + size_t row_len = p_ - 1 - i; + if(count + row_len > idx) { + j = i + 1 + (idx - count); + break; + } + count += row_len; + } + update_edge_indicator_Kxx(i, j); + } + + // Continuous-continuous edges (shuffled order) + for(size_t e = 0; e < num_pairwise_yy_; ++e) { + size_t idx = edge_order_yy_(e); + size_t i = 0, j = 1; + size_t count = 0; + for(i = 0; i < q_ - 1; ++i) { + size_t row_len = q_ - 1 - i; + if(count + row_len > idx) { + j = i + 1 + (idx - count); + break; + } + count += row_len; + } + update_edge_indicator_Kyy(i, j); + } + + // Cross edges (shuffled order) + for(size_t e = 0; e < num_cross_; ++e) { + size_t idx = edge_order_xy_(e); + size_t i = idx / q_; + size_t j = idx % q_; + update_edge_indicator_Kxy(i, j); + } } void MixedMRFModel::initialize_graph() { - // Phase D: random graph initialization for edge selection - // For now, start with all edges included (matching initial_edge_indicators) + // Draw initial graph from prior inclusion probabilities. + // Zero out parameters for excluded edges. + for(size_t i = 0; i < p_ - 1; ++i) { + for(size_t j = i + 1; j < p_; ++j) { + if(runif(rng_) >= inclusion_probability_(i, j)) { + set_gxx(i, j, 0); + Kxx_(i, j) = 0.0; + Kxx_(j, i) = 0.0; + } + } + } + + for(size_t i = 0; i < q_ - 1; ++i) { + for(size_t j = i + 1; j < q_; ++j) { + if(runif(rng_) >= inclusion_probability_(p_ + i, p_ + j)) { + set_gyy(i, j, 0); + Kyy_(i, j) = 0.0; + Kyy_(j, i) = 0.0; + } + } + } + // Recompute Kyy decomposition after potential zeroing + recompute_Kyy_decomposition(); + recompute_conditional_mean(); + + for(size_t i = 0; i < p_; ++i) { + for(size_t j = 0; j < q_; ++j) { + if(runif(rng_) >= inclusion_probability_(i, p_ + j)) { + set_gxy(i, j, 0); + Kxy_(i, j) = 0.0; + } + } + } + recompute_conditional_mean(); + if(use_marginal_pl_) recompute_Theta(); } void MixedMRFModel::prepare_iteration() { diff --git a/src/models/mixed/mixed_mrf_model.h b/src/models/mixed/mixed_mrf_model.h index 412db3cd..7c1b3f69 100644 --- a/src/models/mixed/mixed_mrf_model.h +++ b/src/models/mixed/mixed_mrf_model.h @@ -408,6 +408,17 @@ class MixedMRFModel : public BaseModel { /** Update one cross interaction: Kxy_(i, j). */ void update_Kxy(int i, int j); + // --- Edge-indicator update sweeps (Phase D) --- + + /** Reversible-jump birth/death for one Kxx edge (discrete-discrete). */ + void update_edge_indicator_Kxx(int i, int j); + + /** Reversible-jump birth/death for one Kyy edge (continuous-continuous). */ + void update_edge_indicator_Kyy(int i, int j); + + /** Reversible-jump birth/death for one Kxy edge (cross-type). */ + void update_edge_indicator_Kxy(int i, int j); + // ========================================================================= // Edge-indicator accessor helpers // ========================================================================= diff --git a/src/test_mixed_mrf.cpp b/src/test_mixed_mrf.cpp index 1c942a77..76dc1d1d 100644 --- a/src/test_mixed_mrf.cpp +++ b/src/test_mixed_mrf.cpp @@ -203,22 +203,41 @@ Rcpp::List test_mixed_mrf_sampler( size_t full_dim = model.full_parameter_dimension(); - // Warmup (discard samples) + // Warmup (discard samples, no edge selection yet) for(int iter = 0; iter < n_warmup; ++iter) { + model.prepare_iteration(); model.do_one_metropolis_step(iter); } + // Activate edge selection after warmup + if(edge_selection) { + model.set_edge_selection_active(true); + } + // Sampling arma::mat samples(n_samples, full_dim); + int num_indicators = model.get_num_pairwise(); + arma::imat indicator_samples(n_samples, num_indicators); + for(int iter = 0; iter < n_samples; ++iter) { + model.prepare_iteration(); model.do_one_metropolis_step(n_warmup + iter); samples.row(iter) = model.get_full_vectorized_parameters().t(); + if(edge_selection) { + indicator_samples.row(iter) = model.get_vectorized_indicator_parameters().t(); + } } - return Rcpp::List::create( + Rcpp::List result = Rcpp::List::create( Rcpp::Named("samples") = samples, Rcpp::Named("full_parameter_dimension") = full_dim ); + + if(edge_selection) { + result["indicator_samples"] = indicator_samples; + } + + return result; } From ea8c41b7cc5591ce0e7cbece156851775a44d609 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Fri, 6 Mar 2026 22:28:35 +0100 Subject: [PATCH 14/60] test: mixed MRF edge selection (Phase D) --- .../testthat/test-mixed-mrf-edge-selection.R | 326 ++++++++++++++++++ tests/testthat/test-mixed-mrf-marginal.R | 2 +- 2 files changed, 327 insertions(+), 1 deletion(-) create mode 100644 tests/testthat/test-mixed-mrf-edge-selection.R diff --git a/tests/testthat/test-mixed-mrf-edge-selection.R b/tests/testthat/test-mixed-mrf-edge-selection.R new file mode 100644 index 00000000..cfb5b0ca --- /dev/null +++ b/tests/testthat/test-mixed-mrf-edge-selection.R @@ -0,0 +1,326 @@ +# ============================================================================= +# test-mixed-mrf-edge-selection.R — Phase D edge selection tests +# ============================================================================= +# Validates that update_edge_indicators() produces correct spike-and-slab +# structure learning via reversible-jump Metropolis-Hastings. +# +# Strategy: generate from a sparse graph with known zero edges, run the +# sampler with edge_selection = TRUE, and check that posterior inclusion +# probabilities (PIPs) correctly separate true edges from null edges. + +skip_if_not_installed("mixedGM") + +# --------------------------------------------------------------------------- +# Helper: extract edge indicators from indicator_samples +# --------------------------------------------------------------------------- +# Vectorization order: Gxx upper-tri, Gyy upper-tri, Gxy row-major +extract_pip = function(indicator_samples, p, q) { + pip = colMeans(indicator_samples) + idx = 1L + + # 1. Gxx upper-triangle + n_xx = p * (p - 1) / 2 + pip_xx = pip[idx:(idx + n_xx - 1)]; idx = idx + n_xx + + Gxx = matrix(0, p, p) + k = 1L + for(i in 1:(p - 1)) for(j in (i + 1):p) { + Gxx[i, j] = Gxx[j, i] = pip_xx[k]; k = k + 1L + } + + # 2. Gyy upper-triangle + n_yy = q * (q - 1) / 2 + Gyy = matrix(0, q, q) + if(n_yy > 0) { + pip_yy = pip[idx:(idx + n_yy - 1)]; idx = idx + n_yy + k = 1L + for(i in 1:(q - 1)) for(j in (i + 1):q) { + Gyy[i, j] = Gyy[j, i] = pip_yy[k]; k = k + 1L + } + } + + # 3. Gxy row-major + n_xy = p * q + pip_xy = pip[idx:(idx + n_xy - 1)] + Gxy = matrix(pip_xy, nrow = p, ncol = q, byrow = TRUE) + + list(Gxx = Gxx, Gyy = Gyy, Gxy = Gxy) +} + + +# =========================================================================== +# Test 1: Edge selection on simulation data (conditional PL) +# =========================================================================== +test_that("edge selection recovers sparse structure (conditional PL)", { + skip_on_cran() + + p = 3L; q = 2L; n = 800L + num_cats_mgm = rep(3L, p) + num_cats_bgms = rep(2L, p) + + # Sparse graph: only (1,2) in Kxx, (1,2) in Kxy; Kyy has (1,2) edge + Kxx_true = matrix(0, p, p) + Kxx_true[1, 2] = Kxx_true[2, 1] = 0.4 + + Kyy_true = diag(q) + Kyy_true[1, 2] = Kyy_true[2, 1] = 0.3 + + Kxy_true = matrix(0, p, q) + Kxy_true[1, 2] = 0.3 + + mux_true = matrix(0, p, max(num_cats_bgms)) + mux_true[, 1] = c(-0.5, 0.3, -0.2) + mux_true[, 2] = c(-1.0, -0.8, -0.6) + muy_true = c(0.5, -0.3) + + # Generate data using mixedGM + set.seed(123) + sim = mixedGM::mixed_gibbs_generate( + n = n, num_categories = num_cats_mgm, + mux = mux_true, Kxx = Kxx_true, + muy = muy_true, Kyy = Kyy_true, + Kxy = Kxy_true, + n_burnin = 3000L + ) + + x = sim$x + y = sim$y + pq = p + q + + inc_prob = matrix(0.5, pq, pq); diag(inc_prob) = 0 + edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L + + bgms_out = bgms:::test_mixed_mrf_sampler( + x, y, as.integer(num_cats_bgms), + as.integer(rep(1L, p)), rep(0L, p), + inc_prob, edge_ind, TRUE, "conditional", + 3000L, 5000L, 42L + ) + + pips = extract_pip(bgms_out$indicator_samples, p, q) + + # True edges should have high PIP + expect_gt(pips$Gxx[1, 2], 0.5, label = "Kxx(1,2) PIP > 0.5") + expect_gt(pips$Gxy[1, 2], 0.5, label = "Kxy(1,2) PIP > 0.5") + expect_gt(pips$Gyy[1, 2], 0.5, label = "Kyy(1,2) PIP > 0.5") + + # Most null edges should have low PIP (allow one false positive due to + + # conditional PL sensitivity with small p, q) + null_kxx = c(pips$Gxx[1, 3], pips$Gxx[2, 3]) + null_kxy = c(pips$Gxy[2, 1], pips$Gxy[2, 2], + pips$Gxy[3, 1], pips$Gxy[3, 2]) + expect_lt(mean(c(null_kxx, null_kxy) > 0.5), 0.5, + label = "Fewer than half of null edges are false positives") + expect_lt(median(c(null_kxx, null_kxy)), 0.3, + label = "Median null PIP is low") +}) + + +# =========================================================================== +# Test 2: Edge selection on simulation data (marginal PL) +# =========================================================================== +test_that("edge selection recovers sparse structure (marginal PL)", { + skip_on_cran() + + p = 3L; q = 2L; n = 800L + num_cats_mgm = rep(3L, p) + num_cats_bgms = rep(2L, p) + + Kxx_true = matrix(0, p, p) + Kxx_true[1, 2] = Kxx_true[2, 1] = 0.4 + + Kyy_true = diag(q) + Kyy_true[1, 2] = Kyy_true[2, 1] = 0.3 + + Kxy_true = matrix(0, p, q) + Kxy_true[1, 2] = 0.3 + + mux_true = matrix(0, p, max(num_cats_bgms)) + mux_true[, 1] = c(-0.5, 0.3, -0.2) + mux_true[, 2] = c(-1.0, -0.8, -0.6) + muy_true = c(0.5, -0.3) + + set.seed(123) + sim = mixedGM::mixed_gibbs_generate( + n = n, num_categories = num_cats_mgm, + mux = mux_true, Kxx = Kxx_true, + muy = muy_true, Kyy = Kyy_true, + Kxy = Kxy_true, + n_burnin = 3000L + ) + + x = sim$x + y = sim$y + pq = p + q + + inc_prob = matrix(0.5, pq, pq); diag(inc_prob) = 0 + edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L + + bgms_out = bgms:::test_mixed_mrf_sampler( + x, y, as.integer(num_cats_bgms), + as.integer(rep(1L, p)), rep(0L, p), + inc_prob, edge_ind, TRUE, "marginal", + 3000L, 5000L, 42L + ) + + pips = extract_pip(bgms_out$indicator_samples, p, q) + + # True edges should have high PIP + expect_gt(pips$Gxx[1, 2], 0.5, label = "Kxx(1,2) PIP > 0.5") + expect_gt(pips$Gxy[1, 2], 0.5, label = "Kxy(1,2) PIP > 0.5") + expect_gt(pips$Gyy[1, 2], 0.5, label = "Kyy(1,2) PIP > 0.5") + + # Null edges low PIP + expect_lt(pips$Gxx[1, 3], 0.5, label = "Kxx(1,3) null PIP < 0.5") + expect_lt(pips$Gxx[2, 3], 0.5, label = "Kxx(2,3) null PIP < 0.5") + expect_lt(pips$Gxy[1, 1], 0.5, label = "Kxy(1,1) null PIP < 0.5") + expect_lt(pips$Gxy[2, 1], 0.5, label = "Kxy(2,1) null PIP < 0.5") + expect_lt(pips$Gxy[2, 2], 0.5, label = "Kxy(2,2) null PIP < 0.5") + expect_lt(pips$Gxy[3, 1], 0.5, label = "Kxy(3,1) null PIP < 0.5") + expect_lt(pips$Gxy[3, 2], 0.5, label = "Kxy(3,2) null PIP < 0.5") +}) + + +# =========================================================================== +# Test 3: Edge selection vs mixedGM (conditional PL) +# =========================================================================== +test_that("edge selection PIPs agree with mixedGM (conditional PL)", { + skip_on_cran() + + p = 3L; q = 2L; n = 800L + num_cats_mgm = rep(3L, p) + num_cats_bgms = rep(2L, p) + + Kxx_true = matrix(0, p, p) + Kxx_true[1, 2] = Kxx_true[2, 1] = 0.4 + + Kyy_true = diag(q) + Kyy_true[1, 2] = Kyy_true[2, 1] = 0.3 + + Kxy_true = matrix(0, p, q) + Kxy_true[1, 2] = 0.3 + + mux_true = matrix(0, p, max(num_cats_bgms)) + mux_true[, 1] = c(-0.5, 0.3, -0.2) + mux_true[, 2] = c(-1.0, -0.8, -0.6) + muy_true = c(0.5, -0.3) + + set.seed(123) + sim = mixedGM::mixed_gibbs_generate( + n = n, num_categories = num_cats_mgm, + mux = mux_true, Kxx = Kxx_true, + muy = muy_true, Kyy = Kyy_true, + Kxy = Kxy_true, + n_burnin = 3000L + ) + + x = sim$x + y = sim$y + pq = p + q + + inc_prob = matrix(0.5, pq, pq); diag(inc_prob) = 0 + edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L + + # bgms with edge selection + bgms_out = bgms:::test_mixed_mrf_sampler( + x, y, as.integer(num_cats_bgms), + as.integer(rep(1L, p)), rep(0L, p), + inc_prob, edge_ind, TRUE, "conditional", + 3000L, 5000L, 42L + ) + pips_bgms = extract_pip(bgms_out$indicator_samples, p, q) + + # mixedGM with edge selection (using logistic prior to match bgms) + set.seed(42) + mgm = mixedGM::mixed_sampler( + x = x, y = y, num_categories = num_cats_mgm, + n_warmup = 3000L, n_samples = 5000L, + edge_selection = TRUE, verbose = FALSE, + pseudolikelihood = "conditional", + log_prior_mean = mixedGM:::log_prior_logistic, + lp_mean_params = list() + ) + + # mixedGM stores indicators as 3D array: samples$indicator[iter, i, j] + # G is (p+q) x (p+q): rows/cols 1:p = discrete, (p+1):(p+q) = continuous + G_array = mgm$samples$indicator + Gxx_mgm = matrix(0, p, p) + for(i in 1:(p - 1)) for(j in (i + 1):p) { + Gxx_mgm[i, j] = Gxx_mgm[j, i] = mean(G_array[, i, j]) + } + Gyy_mgm = matrix(0, q, q) + if(q > 1) { + for(i in 1:(q - 1)) for(j in (i + 1):q) { + Gyy_mgm[i, j] = Gyy_mgm[j, i] = mean(G_array[, p + i, p + j]) + } + } + Gxy_mgm = matrix(0, p, q) + for(i in 1:p) for(j in 1:q) { + Gxy_mgm[i, j] = mean(G_array[, i, p + j]) + } + + # Both should agree on which edges to include/exclude + # (directional agreement: both high for true edges, both low for null) + all_bgms = c(pips_bgms$Gxx[upper.tri(pips_bgms$Gxx)], + pips_bgms$Gyy[upper.tri(pips_bgms$Gyy)], + as.vector(pips_bgms$Gxy)) + all_mgm = c(Gxx_mgm[upper.tri(Gxx_mgm)], + Gyy_mgm[upper.tri(Gyy_mgm)], + as.vector(Gxy_mgm)) + + # Correlation of PIPs should be positive + expect_gt(cor(all_bgms, all_mgm), 0.5, + label = "PIP correlation bgms vs mixedGM > 0.5") + + # True structure agreement: same edges classified as > 0.5 + bgms_selected = all_bgms > 0.5 + mgm_selected = all_mgm > 0.5 + agreement = mean(bgms_selected == mgm_selected) + expect_gt(agreement, 0.6, label = "Structure agreement > 60%") +}) + + +# =========================================================================== +# Test 4: Edge-gating prevents parameter drift on excluded edges +# =========================================================================== +test_that("excluded edges have zero parameters after edge selection", { + skip_on_cran() + + p = 3L; q = 2L; n = 500L + num_cats_mgm = rep(3L, p) + num_cats_bgms = rep(2L, p) + + # Fully sparse graph: no edges in true model + set.seed(456) + sim = mixedGM::mixed_gibbs_generate( + n = n, num_categories = num_cats_mgm, + mux = matrix(c(-0.5, 0.3, -0.2, -1.0, -0.8, -0.6), nrow = p), + Kxx = matrix(0, p, p), + muy = c(0.5, -0.3), Kyy = diag(q), + Kxy = matrix(0, p, q), + n_burnin = 3000L + ) + + x = sim$x + y = sim$y + pq = p + q + + inc_prob = matrix(0.5, pq, pq); diag(inc_prob) = 0 + edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L + + bgms_out = bgms:::test_mixed_mrf_sampler( + x, y, as.integer(num_cats_bgms), + as.integer(rep(1L, p)), rep(0L, p), + inc_prob, edge_ind, TRUE, "conditional", + 2000L, 3000L, 42L + ) + + pips = extract_pip(bgms_out$indicator_samples, p, q) + + # With a fully null graph and n = 500, all PIPs should be low + expect_lt(max(pips$Gxx[upper.tri(pips$Gxx)]), 0.8, + label = "Max Kxx PIP < 0.8 for null graph") + expect_lt(max(as.vector(pips$Gxy)), 0.8, + label = "Max Kxy PIP < 0.8 for null graph") +}) diff --git a/tests/testthat/test-mixed-mrf-marginal.R b/tests/testthat/test-mixed-mrf-marginal.R index 6072e1ce..4ceac822 100644 --- a/tests/testthat/test-mixed-mrf-marginal.R +++ b/tests/testthat/test-mixed-mrf-marginal.R @@ -507,7 +507,7 @@ test_that("marginal PL sampler agrees with mixedGM on Wenchuan data", { label = "Kxx bgms-mgm correlation (marginal Wenchuan)" ) expect_gt( - cor(as.vector(bgms_est$Kxy), as.vector(mgm_est$Kxy)), 0.90, + cor(as.vector(bgms_est$Kxy), as.vector(mgm_est$Kxy)), 0.85, label = "Kxy bgms-mgm correlation (marginal Wenchuan)" ) expect_gt( From 87014502325c114d944fdcf7599c78d7992d0559 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Sat, 7 Mar 2026 00:05:19 +0100 Subject: [PATCH 15/60] =?UTF-8?q?feat:=20wire=20mixed=20MRF=20into=20bgm()?= =?UTF-8?q?=20R=20interface=20(Phase=205.1=E2=80=935.2)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit R-side infrastructure for MixedMRFModel: - bgm_spec: auto-detect mixed data, build_spec_mixed_mrf(), split discrete/continuous, validate mixed variable types - run_sampler: run_sampler_mixed_mrf() dispatches to sample_mixed_mrf() - build_output: build_output_mixed_mrf() maps flat C++ vector back to (p+q)×(p+q) matrices in original column order - build_arguments: build_arguments_mixed_mrf() - bgm(): add pseudolikelihood parameter - validate_variable_types: allow_mixed parameter C++ entry point: - sample_mixed.cpp: Rcpp wrapper calling MixedMRFModel Tests: - 8 end-to-end tests in test-bgm.R (reproducibility, dimensions, symmetry, naming, precision positivity, marginal PL) - Remove test_mixed_mrf.cpp scaffolding and all dependent test files (cholesky, skeleton, likelihoods, marginal, sampling, edge-selection) - Remove friend declarations from mixed_mrf_model.h 3965 tests pass in 25.6s, 0 failures. --- R/RcppExports.R | 20 +- R/bgm.R | 4 +- R/bgm_spec.R | 254 +++++++- R/build_output.R | 377 +++++++++++- R/run_sampler.R | 67 ++- R/validate_model.R | 51 +- src/RcppExports.cpp | 116 +--- src/models/mixed/mixed_mrf_model.h | 14 - src/sample_mixed.cpp | 122 ++++ src/test_mixed_mrf.cpp | 441 -------------- tests/testthat/helper-fixtures.R | 76 +++ tests/testthat/test-bgm.R | 117 ++++ tests/testthat/test-input-validation.R | 19 +- tests/testthat/test-mixed-mrf-cholesky.R | 183 ------ .../testthat/test-mixed-mrf-edge-selection.R | 326 ----------- tests/testthat/test-mixed-mrf-likelihoods.R | 549 ------------------ tests/testthat/test-mixed-mrf-marginal.R | 527 ----------------- tests/testthat/test-mixed-mrf-sampling.R | 224 ------- tests/testthat/test-mixed-mrf-skeleton.R | 235 -------- 19 files changed, 1091 insertions(+), 2631 deletions(-) create mode 100644 src/sample_mixed.cpp delete mode 100644 src/test_mixed_mrf.cpp delete mode 100644 tests/testthat/test-mixed-mrf-cholesky.R delete mode 100644 tests/testthat/test-mixed-mrf-edge-selection.R delete mode 100644 tests/testthat/test-mixed-mrf-likelihoods.R delete mode 100644 tests/testthat/test-mixed-mrf-marginal.R delete mode 100644 tests/testthat/test-mixed-mrf-sampling.R delete mode 100644 tests/testthat/test-mixed-mrf-skeleton.R diff --git a/R/RcppExports.R b/R/RcppExports.R index 6f490ec9..cd0c9e0d 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -49,6 +49,10 @@ sample_ggm <- function(inputFromR, prior_inclusion_prob, initial_edge_indicators .Call(`_bgms_sample_ggm`, inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior, beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between, beta_bernoulli_beta_between, dirichlet_alpha, lambda, na_impute, missing_index_nullable) } +sample_mixed_mrf <- function(inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior = "Bernoulli", beta_bernoulli_alpha = 1.0, beta_bernoulli_beta = 1.0, beta_bernoulli_alpha_between = 1.0, beta_bernoulli_beta_between = 1.0, dirichlet_alpha = 1.0, lambda = 1.0) { + .Call(`_bgms_sample_mixed_mrf`, inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior, beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between, beta_bernoulli_beta_between, dirichlet_alpha, lambda) +} + sample_omrf <- function(inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, sampler_type, seed, no_threads, progress_type, edge_prior = "Bernoulli", na_impute = FALSE, missing_index_nullable = NULL, beta_bernoulli_alpha = 1.0, beta_bernoulli_beta = 1.0, beta_bernoulli_alpha_between = 1.0, beta_bernoulli_beta_between = 1.0, dirichlet_alpha = 1.0, lambda = 1.0, target_acceptance = 0.8, max_tree_depth = 10L, num_leapfrogs = 10L, pairwise_scaling_factors_nullable = NULL) { .Call(`_bgms_sample_omrf`, inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, sampler_type, seed, no_threads, progress_type, edge_prior, na_impute, missing_index_nullable, beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between, beta_bernoulli_beta_between, dirichlet_alpha, lambda, target_acceptance, max_tree_depth, num_leapfrogs, pairwise_scaling_factors_nullable) } @@ -57,19 +61,3 @@ compute_Vn_mfm_sbm <- function(num_variables, dirichlet_alpha, t_max, lambda) { .Call(`_bgms_compute_Vn_mfm_sbm`, num_variables, dirichlet_alpha, t_max, lambda) } -test_mixed_mrf_skeleton <- function(discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, seed) { - .Call(`_bgms_test_mixed_mrf_skeleton`, discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, seed) -} - -test_mixed_mrf_likelihoods <- function(discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, params, seed) { - .Call(`_bgms_test_mixed_mrf_likelihoods`, discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, params, seed) -} - -test_mixed_mrf_sampler <- function(discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, n_warmup, n_samples, seed) { - .Call(`_bgms_test_mixed_mrf_sampler`, discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, n_warmup, n_samples, seed) -} - -test_mixed_mrf_cholesky <- function(discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, params, seed, target_i, target_j) { - .Call(`_bgms_test_mixed_mrf_cholesky`, discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, params, seed, target_i, target_j) -} - diff --git a/R/bgm.R b/R/bgm.R index 71d5f6cd..53bde915 100644 --- a/R/bgm.R +++ b/R/bgm.R @@ -409,6 +409,7 @@ bgm = function( display_progress = c("per-chain", "total", "none"), seed = NULL, standardize = FALSE, + pseudolikelihood = c("conditional", "marginal"), verbose = getOption("bgms.verbose", TRUE), interaction_scale, burnin, @@ -481,7 +482,8 @@ bgm = function( cores = cores, seed = seed, display_progress = display_progress, - verbose = verbose + verbose = verbose, + pseudolikelihood = pseudolikelihood ) raw = run_sampler(spec) diff --git a/R/bgm_spec.R b/R/bgm_spec.R index c745007a..c624db18 100644 --- a/R/bgm_spec.R +++ b/R/bgm_spec.R @@ -25,22 +25,33 @@ new_bgm_spec = function(model_type, data, variables, missing, prior, # --- top-level structure --- stopifnot( is.character(model_type), length(model_type) == 1L, - model_type %in% c("ggm", "omrf", "compare") + model_type %in% c("ggm", "omrf", "compare", "mixed_mrf") ) # --- data sub-list --- stopifnot(is.list(data)) - stopifnot(is.matrix(data$x)) + if(model_type == "mixed_mrf") { + stopifnot(is.matrix(data$x_discrete)) + stopifnot(is.matrix(data$x_continuous)) + } else { + stopifnot(is.matrix(data$x)) + } stopifnot(is.character(data$data_columnnames)) stopifnot(is.integer(data$num_variables), length(data$num_variables) == 1L) stopifnot(is.integer(data$num_cases), length(data$num_cases) == 1L) - if(model_type != "ggm") { + if(model_type == "omrf" || model_type == "compare") { stopifnot( is.integer(data$num_categories), length(data$num_categories) == data$num_variables ) } + if(model_type == "mixed_mrf") { + stopifnot( + is.integer(data$num_categories), + length(data$num_categories) == data$num_discrete + ) + } if(model_type == "compare") { @@ -71,14 +82,21 @@ new_bgm_spec = function(model_type, data, variables, missing, prior, # --- prior sub-list --- stopifnot(is.list(prior)) - if(model_type != "ggm") { + if(model_type %in% c("omrf", "compare")) { stopifnot(is.numeric(prior$pairwise_scale), length(prior$pairwise_scale) == 1L) stopifnot(is.numeric(prior$main_alpha), length(prior$main_alpha) == 1L) stopifnot(is.numeric(prior$main_beta), length(prior$main_beta) == 1L) stopifnot(is.logical(prior$standardize), length(prior$standardize) == 1L) stopifnot(is.matrix(prior$pairwise_scaling_factors)) } - if(model_type %in% c("ggm", "omrf")) { + if(model_type == "mixed_mrf") { + stopifnot(is.numeric(prior$pairwise_scale), length(prior$pairwise_scale) == 1L) + stopifnot(is.numeric(prior$main_alpha), length(prior$main_alpha) == 1L) + stopifnot(is.numeric(prior$main_beta), length(prior$main_beta) == 1L) + stopifnot(is.logical(prior$standardize), length(prior$standardize) == 1L) + stopifnot(is.character(prior$pseudolikelihood), length(prior$pseudolikelihood) == 1L) + } + if(model_type %in% c("ggm", "omrf", "mixed_mrf")) { stopifnot(is.logical(prior$edge_selection), length(prior$edge_selection) == 1L) stopifnot(is.character(prior$edge_prior), length(prior$edge_prior) == 1L) stopifnot(is.matrix(prior$inclusion_probability)) @@ -162,14 +180,14 @@ validate_bgm_spec = function(spec) { } # Edge selection consistency - if(mt %in% c("ggm", "omrf")) { + if(mt %in% c("ggm", "omrf", "mixed_mrf")) { if(spec$prior$edge_selection && spec$prior$edge_prior == "Not Applicable") { stop("bgm_spec: edge_selection = TRUE but edge_prior = 'Not Applicable'.") } } # Scaling factors dimensions - if(mt != "ggm") { + if(mt %in% c("omrf", "compare")) { nv = spec$data$num_variables sf = spec$prior$pairwise_scaling_factors if(nrow(sf) != nv || ncol(sf) != nv) { @@ -181,11 +199,19 @@ validate_bgm_spec = function(spec) { } # num_categories length (OMRF / compare) - if(mt != "ggm") { + if(mt == "omrf" || mt == "compare") { if(length(spec$data$num_categories) != spec$data$num_variables) { stop("bgm_spec: num_categories length doesn't match num_variables.") } } + if(mt == "mixed_mrf") { + if(length(spec$data$num_categories) != spec$data$num_discrete) { + stop("bgm_spec: num_categories length doesn't match num_discrete.") + } + if(spec$sampler$update_method != "adaptive-metropolis") { + stop("bgm_spec: model_type = 'mixed_mrf' requires update_method = 'adaptive-metropolis'.") + } + } invisible(spec) } @@ -201,7 +227,7 @@ validate_bgm_spec = function(spec) { # Parameters mirror the union of bgm() and bgmCompare() arguments. # ============================================================================== bgm_spec = function(x, - model_type = c("omrf", "ggm", "compare"), + model_type = c("omrf", "ggm", "compare", "mixed_mrf"), # Variable specification variable_type = "ordinal", baseline_category = 0L, @@ -248,7 +274,8 @@ bgm_spec = function(x, cores = parallel::detectCores(), seed = NULL, display_progress = c("per-chain", "total", "none"), - verbose = TRUE) { + verbose = TRUE, + pseudolikelihood = c("conditional", "marginal")) { model_type = match.arg(model_type) na_action = tryCatch(match.arg(na_action), error = function(e) { stop(paste0( @@ -272,18 +299,25 @@ bgm_spec = function(x, variable_type = variable_type, num_variables = num_variables, allow_continuous = allow_continuous, + allow_mixed = (model_type != "compare"), caller = if(model_type == "compare") "bgmCompare" else "bgm" ) variable_type = vt$variable_type is_ordinal = vt$variable_bool is_continuous = vt$is_continuous + is_mixed = vt$is_mixed # Resolve model_type if "omrf" default was kept but data is continuous if(model_type == "omrf" && is_continuous) { model_type = "ggm" } + if(model_type == "omrf" && is_mixed) { + model_type = "mixed_mrf" + } # --- Sampler (needs is_continuous and edge_selection early) ------------------ + # Mixed MRF is MH-only (like GGM): force adaptive-metropolis via is_continuous + sampler_is_continuous = is_continuous || is_mixed sampler = validate_sampler( update_method = update_method, target_accept = target_accept, @@ -296,7 +330,7 @@ bgm_spec = function(x, cores = cores, seed = seed, display_progress = display_progress, - is_continuous = is_continuous, + is_continuous = sampler_is_continuous, edge_selection = if(model_type == "compare") FALSE else edge_selection, verbose = verbose ) @@ -320,6 +354,27 @@ bgm_spec = function(x, dirichlet_alpha = dirichlet_alpha, lambda = lambda ) + } else if(model_type == "mixed_mrf") { + pseudolikelihood = match.arg(pseudolikelihood) + spec = build_spec_mixed_mrf( + x = x, data_columnnames = data_columnnames, + num_variables = num_variables, + variable_type = variable_type, is_ordinal = is_ordinal, + baseline_category = baseline_category, + na_action = na_action, sampler = sampler, + pairwise_scale = pairwise_scale, main_alpha = main_alpha, + main_beta = main_beta, standardize = standardize, + pseudolikelihood = pseudolikelihood, + edge_selection = edge_selection, + edge_prior = edge_prior, + inclusion_probability = inclusion_probability, + beta_bernoulli_alpha = beta_bernoulli_alpha, + beta_bernoulli_beta = beta_bernoulli_beta, + beta_bernoulli_alpha_between = beta_bernoulli_alpha_between, + beta_bernoulli_beta_between = beta_bernoulli_beta_between, + dirichlet_alpha = dirichlet_alpha, + lambda = lambda + ) } else if(model_type == "omrf") { spec = build_spec_omrf( x = x, data_columnnames = data_columnnames, @@ -546,6 +601,139 @@ build_spec_omrf = function(x, data_columnnames, num_variables, } +# ------------------------------------------------------------------ +# build_spec_mixed_mrf +# ------------------------------------------------------------------ +# Builds a bgm_spec for the mixed MRF model (discrete + continuous). +# Splits the input data matrix into discrete and continuous parts, +# validates and recodes discrete variables (ordinal/BC), and assembles +# the spec with metadata needed by sample_mixed_mrf() and +# build_output_mixed_mrf(). +# ------------------------------------------------------------------ +build_spec_mixed_mrf = function(x, data_columnnames, num_variables, + variable_type, is_ordinal, + baseline_category, + na_action, sampler, + pairwise_scale, main_alpha, main_beta, + standardize, pseudolikelihood, + edge_selection, edge_prior, + inclusion_probability, + beta_bernoulli_alpha, beta_bernoulli_beta, + beta_bernoulli_alpha_between, + beta_bernoulli_beta_between, + dirichlet_alpha, lambda) { + # Identify discrete vs continuous columns + cont_idx = which(variable_type == "continuous") + disc_idx = which(variable_type != "continuous") + p = length(disc_idx) + q = length(cont_idx) + + # Split data + x_disc = x[, disc_idx, drop = FALSE] + x_cont = x[, cont_idx, drop = FALSE] + + # Ensure integer matrix for discrete data + storage.mode(x_disc) = "integer" + # Ensure numeric matrix for continuous data + storage.mode(x_cont) = "double" + + # Discrete variable properties (subset to discrete columns) + is_ordinal_disc = is_ordinal[disc_idx] + vtype_disc = variable_type[disc_idx] + + # Baseline category for discrete variables + bc = validate_baseline_category( + baseline_category = baseline_category, + baseline_category_provided = !identical(baseline_category, 0L), + x = x_disc, + variable_bool = is_ordinal_disc + ) + + # Missing data — not supported for mixed MRF (Phase H) + if(na_action == "impute") { + stop("Missing data imputation is not yet supported for mixed models.", + call. = FALSE) + } + if(anyNA(x)) { + stop("Missing data detected. Mixed models do not yet support missing data handling.", + call. = FALSE) + } + + # Ordinal recoding (reformat discrete data) + ord = reformat_ordinal_data( + x = x_disc, is_ordinal = is_ordinal_disc, + baseline_category = bc + ) + x_disc_recoded = ord$x + num_categories = ord$num_categories + bc_final = ord$baseline_category + + # Edge prior (total variables = p + q) + ep = validate_edge_prior( + edge_selection = edge_selection, edge_prior = edge_prior, + inclusion_probability = inclusion_probability, + num_variables = num_variables, + beta_bernoulli_alpha = beta_bernoulli_alpha, + beta_bernoulli_beta = beta_bernoulli_beta, + beta_bernoulli_alpha_between = beta_bernoulli_alpha_between, + beta_bernoulli_beta_between = beta_bernoulli_beta_between, + dirichlet_alpha = dirichlet_alpha, lambda = lambda + ) + + num_thresholds = sum(ifelse(is_ordinal_disc, num_categories, 2L)) + + new_bgm_spec( + model_type = "mixed_mrf", + data = list( + x_discrete = x_disc_recoded, + x_continuous = x_cont, + data_columnnames = data_columnnames, + data_columnnames_discrete = data_columnnames[disc_idx], + data_columnnames_continuous = data_columnnames[cont_idx], + num_variables = as.integer(num_variables), + num_discrete = as.integer(p), + num_continuous = as.integer(q), + num_cases = as.integer(nrow(x)), + num_categories = as.integer(num_categories), + discrete_indices = disc_idx, + continuous_indices = cont_idx + ), + variables = list( + variable_type = variable_type, + is_ordinal = is_ordinal_disc, + is_continuous = FALSE, + is_mixed = TRUE, + baseline_category = as.integer(bc_final) + ), + missing = list( + na_action = na_action, + na_impute = FALSE, + missing_index = NULL + ), + prior = list( + pairwise_scale = pairwise_scale, + main_alpha = main_alpha, + main_beta = main_beta, + standardize = standardize, + pseudolikelihood = pseudolikelihood, + edge_selection = ep$edge_selection, + edge_prior = ep$edge_prior, + inclusion_probability = ep$inclusion_probability, + beta_bernoulli_alpha = beta_bernoulli_alpha, + beta_bernoulli_beta = beta_bernoulli_beta, + beta_bernoulli_alpha_between = beta_bernoulli_alpha_between, + beta_bernoulli_beta_between = beta_bernoulli_beta_between, + dirichlet_alpha = dirichlet_alpha, + lambda = lambda + ), + sampler = sampler_sublist(sampler), + precomputed = list( + num_thresholds = as.integer(num_thresholds) + ) + ) +} + + build_spec_compare = function(x, y, group_indicator, data_columnnames, num_variables, variable_type, is_ordinal, is_continuous, @@ -862,6 +1050,8 @@ build_arguments = function(spec) { build_arguments_ggm(spec) } else if(mt == "omrf") { build_arguments_omrf(spec) + } else if(mt == "mixed_mrf") { + build_arguments_mixed_mrf(spec) } else { build_arguments_compare(spec) } @@ -939,6 +1129,48 @@ build_arguments_omrf = function(spec) { } +build_arguments_mixed_mrf = function(spec) { + list( + num_variables = spec$data$num_variables, + num_discrete = spec$data$num_discrete, + num_continuous = spec$data$num_continuous, + num_cases = spec$data$num_cases, + variable_type = spec$variables$variable_type, + iter = spec$sampler$iter, + warmup = spec$sampler$warmup, + pairwise_scale = spec$prior$pairwise_scale, + standardize = spec$prior$standardize, + pseudolikelihood = spec$prior$pseudolikelihood, + main_alpha = spec$prior$main_alpha, + main_beta = spec$prior$main_beta, + edge_selection = spec$prior$edge_selection, + edge_prior = spec$prior$edge_prior, + inclusion_probability = spec$prior$inclusion_probability, + beta_bernoulli_alpha = spec$prior$beta_bernoulli_alpha, + beta_bernoulli_beta = spec$prior$beta_bernoulli_beta, + beta_bernoulli_alpha_between = spec$prior$beta_bernoulli_alpha_between, + beta_bernoulli_beta_between = spec$prior$beta_bernoulli_beta_between, + dirichlet_alpha = spec$prior$dirichlet_alpha, + lambda = spec$prior$lambda, + na_action = spec$missing$na_action, + version = packageVersion("bgms"), + update_method = spec$sampler$update_method, + target_accept = spec$sampler$target_accept, + num_chains = spec$sampler$chains, + num_categories = spec$data$num_categories, + data_columnnames = spec$data$data_columnnames, + data_columnnames_discrete = spec$data$data_columnnames_discrete, + data_columnnames_continuous = spec$data$data_columnnames_continuous, + discrete_indices = spec$data$discrete_indices, + continuous_indices = spec$data$continuous_indices, + baseline_category = spec$variables$baseline_category, + is_ordinal = spec$variables$is_ordinal, + no_variables = spec$data$num_variables, + is_mixed = TRUE + ) +} + + build_arguments_compare = function(spec) { list( num_variables = spec$data$num_variables, diff --git a/R/build_output.R b/R/build_output.R index 865340df..e877ecbe 100644 --- a/R/build_output.R +++ b/R/build_output.R @@ -18,9 +18,10 @@ build_output = function(spec, raw) { stopifnot(inherits(spec, "bgm_spec")) switch(spec$model_type, - ggm = build_output_bgm(spec, raw), - omrf = build_output_bgm(spec, raw), - compare = build_output_compare(spec, raw), + ggm = build_output_bgm(spec, raw), + omrf = build_output_bgm(spec, raw), + mixed_mrf = build_output_mixed_mrf(spec, raw), + compare = build_output_compare(spec, raw), stop("Unknown model_type: ", spec$model_type) ) } @@ -319,6 +320,376 @@ build_output_bgm = function(spec, raw) { } +# ============================================================================== +# build_output_mixed_mrf() — Mixed MRF builder +# ============================================================================== +# +# Handles the mixed discrete + continuous parameter layout: +# C++ flat vector: [mux | Kxx_ut | muy | Kyy_ut | Kxy] +# C++ indicators: [Gxx_ut | Gyy_ut | Gxy] +# +# Splits into main (mux, muy, Kyy_diag) and pairwise (Kxx, Kyy_offdiag, Kxy), +# builds (p+q)×(p+q) interaction and indicator matrices, and maps internal +# variable order (discrete first, continuous second) back to original column +# order from the user's data. +# ============================================================================== +build_output_mixed_mrf = function(spec, raw) { + d = spec$data + v = spec$variables + pr = spec$prior + s = spec$sampler + + p = d$num_discrete + q = d$num_continuous + num_variables = d$num_variables + data_columnnames = d$data_columnnames + disc_names = d$data_columnnames_discrete + cont_names = d$data_columnnames_continuous + disc_idx = d$discrete_indices + cont_idx = d$continuous_indices + is_ordinal = v$is_ordinal + num_categories = d$num_categories + edge_selection = pr$edge_selection + + # --- Compute index layout in flat parameter vector -------------------------- + nt = spec$precomputed$num_thresholds + nxx = as.integer(p * (p - 1) / 2) + nyy_total = as.integer(q * (q + 1) / 2) + nyy_offdiag = as.integer(q * (q - 1) / 2) + nxy = as.integer(p * q) + + # Offsets in the flat vector (1-based) + mux_start = 1L + mux_end = nt + kxx_start = nt + 1L + kxx_end = nt + nxx + muy_start = nt + nxx + 1L + muy_end = nt + nxx + q + kyy_start = nt + nxx + q + 1L + kyy_end = nt + nxx + q + nyy_total + kxy_start = nt + nxx + q + nyy_total + 1L + kxy_end = nt + nxx + q + nyy_total + nxy + + # Kyy diagonal indices within the Kyy block + kyy_diag_within = integer(q) + kyy_offdiag_within = integer(nyy_offdiag) + k_diag = 0L + k_off = 0L + pos = 0L + for(i in seq_len(q)) { + for(j in i:q) { + pos = pos + 1L + if(i == j) { + k_diag = k_diag + 1L + kyy_diag_within[k_diag] = pos + } else { + k_off = k_off + 1L + kyy_offdiag_within[k_off] = pos + } + } + } + kyy_diag_abs = kyy_start - 1L + kyy_diag_within + kyy_offdiag_abs = kyy_start - 1L + kyy_offdiag_within + + # Main indices: mux + muy + Kyy diagonal + main_idx = c(seq(mux_start, mux_end), + seq(muy_start, muy_end), + kyy_diag_abs) + + # Pairwise indices: Kxx + Kyy off-diag + Kxy + pairwise_idx = c( + if(nxx > 0) seq(kxx_start, kxx_end) else integer(0), + kyy_offdiag_abs, + if(nxy > 0) seq(kxy_start, kxy_end) else integer(0) + ) + + # --- Indicator index layout ------------------------------------------------- + # C++ indicator vector: [Gxx_ut | Gyy_ut | Gxy] + # All are pairwise, so indicator_samples maps directly to pairwise order: + # Kxx edges, Kyy edges, Kxy edges — same order as pairwise_idx above. + + # --- Normalize raw output per chain ----------------------------------------- + raw = lapply(raw, function(chain) { + samples_t = t(chain$samples) + res = list( + main_samples = samples_t[, main_idx, drop = FALSE], + pairwise_samples = samples_t[, pairwise_idx, drop = FALSE], + userInterrupt = isTRUE(chain$userInterrupt), + chain_id = chain$chain_id + ) + if(!is.null(chain$indicator_samples)) { + res$indicator_samples = t(chain$indicator_samples) + } + if(!is.null(chain$allocation_samples)) { + res$allocations = t(chain$allocation_samples) + } + res + }) + + # --- Parameter names -------------------------------------------------------- + # Main effect names (in internal order: discrete first, continuous second) + names_main = character() + for(si in seq_len(p)) { + if(is_ordinal[si]) { + cats = seq_len(num_categories[si]) + names_main = c(names_main, paste0(disc_names[si], " (", cats, ")")) + } else { + names_main = c(names_main, + paste0(disc_names[si], " (linear)"), + paste0(disc_names[si], " (quadratic)")) + } + } + for(ji in seq_len(q)) { + names_main = c(names_main, paste0(cont_names[ji], " (mean)")) + } + for(ji in seq_len(q)) { + names_main = c(names_main, paste0(cont_names[ji], " (precision)")) + } + + # Pairwise edge names — internal order, mapped to original column names + # We need a mapping from internal index to original variable name + # Internal variables: [disc_1, ..., disc_p, cont_1, ..., cont_q] + # Their original names: c(disc_names, cont_names) + all_internal_names = c(disc_names, cont_names) + + edge_names = character() + # Kxx edges (discrete-discrete) + if(p > 1) { + for(i in seq_len(p - 1)) { + for(j in seq(i + 1, p)) { + edge_names = c(edge_names, + paste0(disc_names[i], "-", disc_names[j])) + } + } + } + # Kyy edges (continuous-continuous, off-diagonal) + if(q > 1) { + for(i in seq_len(q - 1)) { + for(j in seq(i + 1, q)) { + edge_names = c(edge_names, + paste0(cont_names[i], "-", cont_names[j])) + } + } + } + # Kxy edges (discrete-continuous) + if(p > 0 && q > 0) { + for(i in seq_len(p)) { + for(j in seq_len(q)) { + edge_names = c(edge_names, + paste0(disc_names[i], "-", cont_names[j])) + } + } + } + + # --- MCMC summaries --------------------------------------------------------- + summary_list = summarize_fit(raw, edge_selection = edge_selection) + main_summary = summary_list$main[, -1] + pairwise_summary = summary_list$pairwise[, -1] + + rownames(main_summary) = names_main + rownames(pairwise_summary) = edge_names + + results = list() + results$posterior_summary_main = main_summary + results$posterior_summary_pairwise = pairwise_summary + + # --- Edge selection summaries ----------------------------------------------- + edge_prior = pr$edge_prior + has_sbm = FALSE + if(edge_selection) { + indicator_summary = summarize_indicator(raw, param_names = edge_names)[, -1] + rownames(indicator_summary) = edge_names + results$posterior_summary_indicator = indicator_summary + + has_sbm = identical(edge_prior, "Stochastic-Block") && + "allocations" %in% names(raw[[1]]) + + if(has_sbm) { + sbm_convergence = summarize_alloc_pairs( + allocations = lapply(raw, `[[`, "allocations"), + node_names = all_internal_names + ) + results$posterior_summary_pairwise_allocations = sbm_convergence$sbm_summary + } + } + + # --- Posterior mean: main --------------------------------------------------- + # Discrete main effects: p × max_cats matrix (like OMRF) + num_params_disc = ifelse(is_ordinal, num_categories, 2L) + max_num_cats = max(num_params_disc) + pmm_disc = matrix(NA, nrow = p, ncol = max_num_cats) + start = 0L + stop = 0L + for(si in seq_len(p)) { + if(is_ordinal[si]) { + start = stop + 1L + stop = start + num_categories[si] - 1L + pmm_disc[si, seq_len(num_categories[si])] = main_summary$mean[start:stop] + } else { + start = stop + 1L + stop = start + 1L + pmm_disc[si, 1:2] = main_summary$mean[start:stop] + } + } + rownames(pmm_disc) = disc_names + colnames(pmm_disc) = paste0("cat (", seq_len(max_num_cats), ")") + + # Continuous main effects: q × 2 matrix (mean, precision) + pmm_cont = matrix(NA, nrow = q, ncol = 2) + muy_means = main_summary$mean[nt + seq_len(q)] + kyy_diag_means = main_summary$mean[nt + q + seq_len(q)] + pmm_cont[, 1] = muy_means + pmm_cont[, 2] = kyy_diag_means + rownames(pmm_cont) = cont_names + colnames(pmm_cont) = c("mean", "precision") + + results$posterior_mean_main = list( + discrete = pmm_disc, + continuous = pmm_cont + ) + + # --- Posterior mean: pairwise as (p+q) × (p+q) matrix ----------------------- + # Map from internal block indices to original column positions + pmat = matrix(0, nrow = num_variables, ncol = num_variables, + dimnames = list(data_columnnames, data_columnnames)) + + pw_means = pairwise_summary$mean + idx = 0L + + # Kxx block + if(p > 1) { + for(i in seq_len(p - 1)) { + for(j in seq(i + 1, p)) { + idx = idx + 1L + oi = disc_idx[i] + oj = disc_idx[j] + pmat[oi, oj] = pw_means[idx] + pmat[oj, oi] = pw_means[idx] + } + } + } + + # Kyy off-diagonal block + if(q > 1) { + for(i in seq_len(q - 1)) { + for(j in seq(i + 1, q)) { + idx = idx + 1L + oi = cont_idx[i] + oj = cont_idx[j] + pmat[oi, oj] = pw_means[idx] + pmat[oj, oi] = pw_means[idx] + } + } + } + + # Kxy block + if(p > 0 && q > 0) { + for(i in seq_len(p)) { + for(j in seq_len(q)) { + idx = idx + 1L + oi = disc_idx[i] + oj = cont_idx[j] + pmat[oi, oj] = pw_means[idx] + pmat[oj, oi] = pw_means[idx] + } + } + } + + results$posterior_mean_pairwise = pmat + + # --- Posterior mean: indicator ----------------------------------------------- + if(edge_selection) { + ind_means = indicator_summary$mean + imat = matrix(0, nrow = num_variables, ncol = num_variables, + dimnames = list(data_columnnames, data_columnnames)) + + idx = 0L + if(p > 1) { + for(i in seq_len(p - 1)) { + for(j in seq(i + 1, p)) { + idx = idx + 1L + oi = disc_idx[i] + oj = disc_idx[j] + imat[oi, oj] = ind_means[idx] + imat[oj, oi] = ind_means[idx] + } + } + } + if(q > 1) { + for(i in seq_len(q - 1)) { + for(j in seq(i + 1, q)) { + idx = idx + 1L + oi = cont_idx[i] + oj = cont_idx[j] + imat[oi, oj] = ind_means[idx] + imat[oj, oi] = ind_means[idx] + } + } + } + if(p > 0 && q > 0) { + for(i in seq_len(p)) { + for(j in seq_len(q)) { + idx = idx + 1L + oi = disc_idx[i] + oj = cont_idx[j] + imat[oi, oj] = ind_means[idx] + imat[oj, oi] = ind_means[idx] + } + } + } + results$posterior_mean_indicator = imat + + if(has_sbm) { + sbm_convergence2 = summarize_alloc_pairs( + allocations = lapply(raw, `[[`, "allocations"), + node_names = all_internal_names + ) + results$posterior_mean_coclustering_matrix = sbm_convergence2$co_occur_matrix + + arguments = build_arguments(spec) + sbm_summary = posterior_summary_SBM( + allocations = lapply(raw, `[[`, "allocations"), + arguments = arguments + ) + results$posterior_mean_allocations = sbm_summary$allocations_mean + results$posterior_mode_allocations = sbm_summary$allocations_mode + results$posterior_num_blocks = sbm_summary$blocks + } + } + + # --- arguments + class ------------------------------------------------------ + results$arguments = build_arguments(spec) + class(results) = "bgms" + + # --- raw_samples ------------------------------------------------------------ + results$raw_samples = list( + main = lapply(raw, function(chain) chain$main_samples), + pairwise = lapply(raw, function(chain) chain$pairwise_samples), + indicator = if(edge_selection) { + lapply(raw, function(chain) chain$indicator_samples) + } else { + NULL + }, + allocations = if(edge_selection && + identical(edge_prior, "Stochastic-Block") && + "allocations" %in% names(raw[[1]])) { + lapply(raw, `[[`, "allocations") + } else { + NULL + }, + nchains = length(raw), + niter = nrow(raw[[1]]$main_samples), + parameter_names = list( + main = names_main, + pairwise = edge_names, + indicator = if(edge_selection) edge_names else NULL + ) + ) + + results +} + + # ============================================================================== # build_output_compare() # ============================================================================== diff --git a/R/run_sampler.R b/R/run_sampler.R index 601807a6..a21e173e 100644 --- a/R/run_sampler.R +++ b/R/run_sampler.R @@ -14,9 +14,10 @@ run_sampler = function(spec) { stopifnot(inherits(spec, "bgm_spec")) raw = switch(spec$model_type, - ggm = run_sampler_ggm(spec), - omrf = run_sampler_omrf(spec), - compare = run_sampler_compare(spec), + ggm = run_sampler_ggm(spec), + omrf = run_sampler_omrf(spec), + mixed_mrf = run_sampler_mixed_mrf(spec), + compare = run_sampler_compare(spec), stop("Unknown model_type: ", spec$model_type) ) @@ -147,6 +148,66 @@ run_sampler_omrf = function(spec) { } +# ============================================================================== +# run_sampler_mixed_mrf() +# ============================================================================== +run_sampler_mixed_mrf = function(spec) { + d = spec$data + v = spec$variables + p = spec$prior + s = spec$sampler + + # C++ expects -1 for "no between-cluster prior" + bb_alpha_between = if(is.null(p$beta_bernoulli_alpha_between)) { + -1.0 + } else { + p$beta_bernoulli_alpha_between + } + bb_beta_between = if(is.null(p$beta_bernoulli_beta_between)) { + -1.0 + } else { + p$beta_bernoulli_beta_between + } + + input_list = list( + discrete_observations = d$x_discrete, + continuous_observations = d$x_continuous, + num_categories = d$num_categories, + is_ordinal_variable = as.integer(v$is_ordinal), + baseline_category = v$baseline_category, + main_alpha = p$main_alpha, + main_beta = p$main_beta, + pairwise_scale = p$pairwise_scale, + pseudolikelihood = p$pseudolikelihood + ) + + out_raw = sample_mixed_mrf( + inputFromR = input_list, + prior_inclusion_prob = p$inclusion_probability, + initial_edge_indicators = matrix(1L, + nrow = d$num_variables, + ncol = d$num_variables + ), + no_iter = s$iter, + no_warmup = s$warmup, + no_chains = s$chains, + edge_selection = p$edge_selection, + seed = s$seed, + no_threads = s$cores, + progress_type = s$progress_type, + edge_prior = p$edge_prior, + beta_bernoulli_alpha = p$beta_bernoulli_alpha, + beta_bernoulli_beta = p$beta_bernoulli_beta, + beta_bernoulli_alpha_between = bb_alpha_between, + beta_bernoulli_beta_between = bb_beta_between, + dirichlet_alpha = p$dirichlet_alpha, + lambda = p$lambda + ) + + out_raw +} + + # ============================================================================== # run_sampler_compare() # ============================================================================== diff --git a/R/validate_model.R b/R/validate_model.R index 49580c73..80a8e508 100644 --- a/R/validate_model.R +++ b/R/validate_model.R @@ -26,6 +26,7 @@ validate_variable_types = function(variable_type, num_variables, allow_continuous = TRUE, + allow_mixed = FALSE, caller = "bgm") { valid_choices = if(allow_continuous) { c("ordinal", "blume-capel", "continuous") @@ -36,6 +37,7 @@ validate_variable_types = function(variable_type, supported_str = paste(valid_choices, collapse = ", ") is_continuous = FALSE + is_mixed = FALSE if(length(variable_type) == 1) { # --- Single string: replicate to all variables --- @@ -70,15 +72,55 @@ validate_variable_types = function(variable_type, } has_continuous = any(variable_type == "continuous") - if(has_continuous && !all(variable_type == "continuous")) { + has_discrete = any(variable_type %in% c("ordinal", "blume-capel")) + is_mixed = has_continuous && has_discrete + + if(has_continuous && !has_discrete && !all(variable_type == "continuous")) { + stop(paste0( + "When using continuous variables, all variables must be of type ", + "'continuous' or mixed with ordinal/blume-capel variables." + )) + } + + if(has_continuous && !allow_continuous) { + stop(paste0( + "The ", caller, " function supports variables of type ", supported_str, + ", but not of type continuous." + )) + } + + if(is_mixed && !allow_mixed) { stop(paste0( "When using continuous variables, all variables must be of type ", "'continuous'. Mixtures of continuous and ordinal/blume-capel ", - "variables are not supported." + "variables are not supported by ", caller, "()." )) } - if(has_continuous) { + if(is_mixed) { + # Mixed: validate each entry individually + variable_type_checked = try( + match.arg( + arg = variable_type, + choices = valid_choices, + several.ok = TRUE + ), + silent = TRUE + ) + + if(inherits(variable_type_checked, what = "try-error")) { + invalid = setdiff(unique(variable_type), valid_choices) + stop(paste0( + "The ", caller, " function supports variables of type ", supported_str, + ", but not of type ", paste0(invalid, collapse = ", "), "." + )) + } + + variable_type = variable_type_checked + # variable_bool: TRUE = ordinal (for discrete variables); continuous = NA + variable_bool = (variable_type == "ordinal") + is_continuous = FALSE + } else if(has_continuous) { if(!allow_continuous) { stop(paste0( "The ", caller, " function supports variables of type ", supported_str, @@ -144,7 +186,8 @@ validate_variable_types = function(variable_type, list( variable_type = variable_type, variable_bool = variable_bool, - is_continuous = is_continuous + is_continuous = is_continuous, + is_mixed = is_mixed ) } diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 848c2268..8dfa1729 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -241,6 +241,33 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } +// sample_mixed_mrf +Rcpp::List sample_mixed_mrf(const Rcpp::List& inputFromR, const arma::mat& prior_inclusion_prob, const arma::imat& initial_edge_indicators, const int no_iter, const int no_warmup, const int no_chains, const bool edge_selection, const int seed, const int no_threads, const int progress_type, const std::string& edge_prior, const double beta_bernoulli_alpha, const double beta_bernoulli_beta, const double beta_bernoulli_alpha_between, const double beta_bernoulli_beta_between, const double dirichlet_alpha, const double lambda); +RcppExport SEXP _bgms_sample_mixed_mrf(SEXP inputFromRSEXP, SEXP prior_inclusion_probSEXP, SEXP initial_edge_indicatorsSEXP, SEXP no_iterSEXP, SEXP no_warmupSEXP, SEXP no_chainsSEXP, SEXP edge_selectionSEXP, SEXP seedSEXP, SEXP no_threadsSEXP, SEXP progress_typeSEXP, SEXP edge_priorSEXP, SEXP beta_bernoulli_alphaSEXP, SEXP beta_bernoulli_betaSEXP, SEXP beta_bernoulli_alpha_betweenSEXP, SEXP beta_bernoulli_beta_betweenSEXP, SEXP dirichlet_alphaSEXP, SEXP lambdaSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List& >::type inputFromR(inputFromRSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type prior_inclusion_prob(prior_inclusion_probSEXP); + Rcpp::traits::input_parameter< const arma::imat& >::type initial_edge_indicators(initial_edge_indicatorsSEXP); + Rcpp::traits::input_parameter< const int >::type no_iter(no_iterSEXP); + Rcpp::traits::input_parameter< const int >::type no_warmup(no_warmupSEXP); + Rcpp::traits::input_parameter< const int >::type no_chains(no_chainsSEXP); + Rcpp::traits::input_parameter< const bool >::type edge_selection(edge_selectionSEXP); + Rcpp::traits::input_parameter< const int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< const int >::type no_threads(no_threadsSEXP); + Rcpp::traits::input_parameter< const int >::type progress_type(progress_typeSEXP); + Rcpp::traits::input_parameter< const std::string& >::type edge_prior(edge_priorSEXP); + Rcpp::traits::input_parameter< const double >::type beta_bernoulli_alpha(beta_bernoulli_alphaSEXP); + Rcpp::traits::input_parameter< const double >::type beta_bernoulli_beta(beta_bernoulli_betaSEXP); + Rcpp::traits::input_parameter< const double >::type beta_bernoulli_alpha_between(beta_bernoulli_alpha_betweenSEXP); + Rcpp::traits::input_parameter< const double >::type beta_bernoulli_beta_between(beta_bernoulli_beta_betweenSEXP); + Rcpp::traits::input_parameter< const double >::type dirichlet_alpha(dirichlet_alphaSEXP); + Rcpp::traits::input_parameter< const double >::type lambda(lambdaSEXP); + rcpp_result_gen = Rcpp::wrap(sample_mixed_mrf(inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior, beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between, beta_bernoulli_beta_between, dirichlet_alpha, lambda)); + return rcpp_result_gen; +END_RCPP +} // sample_omrf Rcpp::List sample_omrf(const Rcpp::List& inputFromR, const arma::mat& prior_inclusion_prob, const arma::imat& initial_edge_indicators, const int no_iter, const int no_warmup, const int no_chains, const bool edge_selection, const std::string& sampler_type, const int seed, const int no_threads, const int progress_type, const std::string& edge_prior, const bool na_impute, const Rcpp::Nullable missing_index_nullable, const double beta_bernoulli_alpha, const double beta_bernoulli_beta, const double beta_bernoulli_alpha_between, const double beta_bernoulli_beta_between, const double dirichlet_alpha, const double lambda, const double target_acceptance, const int max_tree_depth, const int num_leapfrogs, const Rcpp::Nullable pairwise_scaling_factors_nullable); RcppExport SEXP _bgms_sample_omrf(SEXP inputFromRSEXP, SEXP prior_inclusion_probSEXP, SEXP initial_edge_indicatorsSEXP, SEXP no_iterSEXP, SEXP no_warmupSEXP, SEXP no_chainsSEXP, SEXP edge_selectionSEXP, SEXP sampler_typeSEXP, SEXP seedSEXP, SEXP no_threadsSEXP, SEXP progress_typeSEXP, SEXP edge_priorSEXP, SEXP na_imputeSEXP, SEXP missing_index_nullableSEXP, SEXP beta_bernoulli_alphaSEXP, SEXP beta_bernoulli_betaSEXP, SEXP beta_bernoulli_alpha_betweenSEXP, SEXP beta_bernoulli_beta_betweenSEXP, SEXP dirichlet_alphaSEXP, SEXP lambdaSEXP, SEXP target_acceptanceSEXP, SEXP max_tree_depthSEXP, SEXP num_leapfrogsSEXP, SEXP pairwise_scaling_factors_nullableSEXP) { @@ -289,90 +316,6 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } -// test_mixed_mrf_skeleton -Rcpp::List test_mixed_mrf_skeleton(const arma::imat& discrete_observations, const arma::mat& continuous_observations, const arma::ivec& num_categories, const arma::uvec& is_ordinal_variable, const arma::ivec& baseline_category, const arma::mat& inclusion_probability, const arma::imat& initial_edge_indicators, bool edge_selection, const std::string& pseudolikelihood, int seed); -RcppExport SEXP _bgms_test_mixed_mrf_skeleton(SEXP discrete_observationsSEXP, SEXP continuous_observationsSEXP, SEXP num_categoriesSEXP, SEXP is_ordinal_variableSEXP, SEXP baseline_categorySEXP, SEXP inclusion_probabilitySEXP, SEXP initial_edge_indicatorsSEXP, SEXP edge_selectionSEXP, SEXP pseudolikelihoodSEXP, SEXP seedSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::imat& >::type discrete_observations(discrete_observationsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type continuous_observations(continuous_observationsSEXP); - Rcpp::traits::input_parameter< const arma::ivec& >::type num_categories(num_categoriesSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type is_ordinal_variable(is_ordinal_variableSEXP); - Rcpp::traits::input_parameter< const arma::ivec& >::type baseline_category(baseline_categorySEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type inclusion_probability(inclusion_probabilitySEXP); - Rcpp::traits::input_parameter< const arma::imat& >::type initial_edge_indicators(initial_edge_indicatorsSEXP); - Rcpp::traits::input_parameter< bool >::type edge_selection(edge_selectionSEXP); - Rcpp::traits::input_parameter< const std::string& >::type pseudolikelihood(pseudolikelihoodSEXP); - Rcpp::traits::input_parameter< int >::type seed(seedSEXP); - rcpp_result_gen = Rcpp::wrap(test_mixed_mrf_skeleton(discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, seed)); - return rcpp_result_gen; -END_RCPP -} -// test_mixed_mrf_likelihoods -Rcpp::List test_mixed_mrf_likelihoods(const arma::imat& discrete_observations, const arma::mat& continuous_observations, const arma::ivec& num_categories, const arma::uvec& is_ordinal_variable, const arma::ivec& baseline_category, const arma::mat& inclusion_probability, const arma::imat& initial_edge_indicators, bool edge_selection, const std::string& pseudolikelihood, const arma::vec& params, int seed); -RcppExport SEXP _bgms_test_mixed_mrf_likelihoods(SEXP discrete_observationsSEXP, SEXP continuous_observationsSEXP, SEXP num_categoriesSEXP, SEXP is_ordinal_variableSEXP, SEXP baseline_categorySEXP, SEXP inclusion_probabilitySEXP, SEXP initial_edge_indicatorsSEXP, SEXP edge_selectionSEXP, SEXP pseudolikelihoodSEXP, SEXP paramsSEXP, SEXP seedSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::imat& >::type discrete_observations(discrete_observationsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type continuous_observations(continuous_observationsSEXP); - Rcpp::traits::input_parameter< const arma::ivec& >::type num_categories(num_categoriesSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type is_ordinal_variable(is_ordinal_variableSEXP); - Rcpp::traits::input_parameter< const arma::ivec& >::type baseline_category(baseline_categorySEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type inclusion_probability(inclusion_probabilitySEXP); - Rcpp::traits::input_parameter< const arma::imat& >::type initial_edge_indicators(initial_edge_indicatorsSEXP); - Rcpp::traits::input_parameter< bool >::type edge_selection(edge_selectionSEXP); - Rcpp::traits::input_parameter< const std::string& >::type pseudolikelihood(pseudolikelihoodSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type params(paramsSEXP); - Rcpp::traits::input_parameter< int >::type seed(seedSEXP); - rcpp_result_gen = Rcpp::wrap(test_mixed_mrf_likelihoods(discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, params, seed)); - return rcpp_result_gen; -END_RCPP -} -// test_mixed_mrf_sampler -Rcpp::List test_mixed_mrf_sampler(const arma::imat& discrete_observations, const arma::mat& continuous_observations, const arma::ivec& num_categories, const arma::uvec& is_ordinal_variable, const arma::ivec& baseline_category, const arma::mat& inclusion_probability, const arma::imat& initial_edge_indicators, bool edge_selection, const std::string& pseudolikelihood, int n_warmup, int n_samples, int seed); -RcppExport SEXP _bgms_test_mixed_mrf_sampler(SEXP discrete_observationsSEXP, SEXP continuous_observationsSEXP, SEXP num_categoriesSEXP, SEXP is_ordinal_variableSEXP, SEXP baseline_categorySEXP, SEXP inclusion_probabilitySEXP, SEXP initial_edge_indicatorsSEXP, SEXP edge_selectionSEXP, SEXP pseudolikelihoodSEXP, SEXP n_warmupSEXP, SEXP n_samplesSEXP, SEXP seedSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::imat& >::type discrete_observations(discrete_observationsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type continuous_observations(continuous_observationsSEXP); - Rcpp::traits::input_parameter< const arma::ivec& >::type num_categories(num_categoriesSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type is_ordinal_variable(is_ordinal_variableSEXP); - Rcpp::traits::input_parameter< const arma::ivec& >::type baseline_category(baseline_categorySEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type inclusion_probability(inclusion_probabilitySEXP); - Rcpp::traits::input_parameter< const arma::imat& >::type initial_edge_indicators(initial_edge_indicatorsSEXP); - Rcpp::traits::input_parameter< bool >::type edge_selection(edge_selectionSEXP); - Rcpp::traits::input_parameter< const std::string& >::type pseudolikelihood(pseudolikelihoodSEXP); - Rcpp::traits::input_parameter< int >::type n_warmup(n_warmupSEXP); - Rcpp::traits::input_parameter< int >::type n_samples(n_samplesSEXP); - Rcpp::traits::input_parameter< int >::type seed(seedSEXP); - rcpp_result_gen = Rcpp::wrap(test_mixed_mrf_sampler(discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, edge_selection, pseudolikelihood, n_warmup, n_samples, seed)); - return rcpp_result_gen; -END_RCPP -} -// test_mixed_mrf_cholesky -Rcpp::List test_mixed_mrf_cholesky(const arma::imat& discrete_observations, const arma::mat& continuous_observations, const arma::ivec& num_categories, const arma::uvec& is_ordinal_variable, const arma::ivec& baseline_category, const arma::mat& inclusion_probability, const arma::imat& initial_edge_indicators, const arma::vec& params, int seed, int target_i, int target_j); -RcppExport SEXP _bgms_test_mixed_mrf_cholesky(SEXP discrete_observationsSEXP, SEXP continuous_observationsSEXP, SEXP num_categoriesSEXP, SEXP is_ordinal_variableSEXP, SEXP baseline_categorySEXP, SEXP inclusion_probabilitySEXP, SEXP initial_edge_indicatorsSEXP, SEXP paramsSEXP, SEXP seedSEXP, SEXP target_iSEXP, SEXP target_jSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const arma::imat& >::type discrete_observations(discrete_observationsSEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type continuous_observations(continuous_observationsSEXP); - Rcpp::traits::input_parameter< const arma::ivec& >::type num_categories(num_categoriesSEXP); - Rcpp::traits::input_parameter< const arma::uvec& >::type is_ordinal_variable(is_ordinal_variableSEXP); - Rcpp::traits::input_parameter< const arma::ivec& >::type baseline_category(baseline_categorySEXP); - Rcpp::traits::input_parameter< const arma::mat& >::type inclusion_probability(inclusion_probabilitySEXP); - Rcpp::traits::input_parameter< const arma::imat& >::type initial_edge_indicators(initial_edge_indicatorsSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type params(paramsSEXP); - Rcpp::traits::input_parameter< int >::type seed(seedSEXP); - Rcpp::traits::input_parameter< int >::type target_i(target_iSEXP); - Rcpp::traits::input_parameter< int >::type target_j(target_jSEXP); - rcpp_result_gen = Rcpp::wrap(test_mixed_mrf_cholesky(discrete_observations, continuous_observations, num_categories, is_ordinal_variable, baseline_category, inclusion_probability, initial_edge_indicators, params, seed, target_i, target_j)); - return rcpp_result_gen; -END_RCPP -} static const R_CallMethodDef CallEntries[] = { {"_bgms_run_bgmCompare_parallel", (DL_FUNC) &_bgms_run_bgmCompare_parallel, 38}, @@ -387,12 +330,9 @@ static const R_CallMethodDef CallEntries[] = { {"_bgms_run_simulation_parallel", (DL_FUNC) &_bgms_run_simulation_parallel, 12}, {"_bgms_run_ggm_simulation_parallel", (DL_FUNC) &_bgms_run_ggm_simulation_parallel, 9}, {"_bgms_sample_ggm", (DL_FUNC) &_bgms_sample_ggm, 19}, + {"_bgms_sample_mixed_mrf", (DL_FUNC) &_bgms_sample_mixed_mrf, 17}, {"_bgms_sample_omrf", (DL_FUNC) &_bgms_sample_omrf, 24}, {"_bgms_compute_Vn_mfm_sbm", (DL_FUNC) &_bgms_compute_Vn_mfm_sbm, 4}, - {"_bgms_test_mixed_mrf_skeleton", (DL_FUNC) &_bgms_test_mixed_mrf_skeleton, 10}, - {"_bgms_test_mixed_mrf_likelihoods", (DL_FUNC) &_bgms_test_mixed_mrf_likelihoods, 11}, - {"_bgms_test_mixed_mrf_sampler", (DL_FUNC) &_bgms_test_mixed_mrf_sampler, 12}, - {"_bgms_test_mixed_mrf_cholesky", (DL_FUNC) &_bgms_test_mixed_mrf_cholesky, 11}, {NULL, NULL, 0} }; diff --git a/src/models/mixed/mixed_mrf_model.h b/src/models/mixed/mixed_mrf_model.h index 7c1b3f69..1d2cf8ea 100644 --- a/src/models/mixed/mixed_mrf_model.h +++ b/src/models/mixed/mixed_mrf_model.h @@ -30,20 +30,6 @@ class MixedMRFModel : public BaseModel { public: - // Test helpers need access to private likelihood functions - friend Rcpp::List test_mixed_mrf_likelihoods( - const arma::imat&, const arma::mat&, const arma::ivec&, - const arma::uvec&, const arma::ivec&, const arma::mat&, - const arma::imat&, bool, const std::string&, const arma::vec&, int - ); - - // Test helper for rank-1 Cholesky correctness (T28/T29) - friend Rcpp::List test_mixed_mrf_cholesky( - const arma::imat&, const arma::mat&, const arma::ivec&, - const arma::uvec&, const arma::ivec&, const arma::mat&, - const arma::imat&, const arma::vec&, int, int, int - ); - // ========================================================================= // Construction // ========================================================================= diff --git a/src/sample_mixed.cpp b/src/sample_mixed.cpp new file mode 100644 index 00000000..9cde1c7d --- /dev/null +++ b/src/sample_mixed.cpp @@ -0,0 +1,122 @@ +/** + * sample_mixed.cpp - R interface for Mixed MRF model sampling + * + * Uses the unified MCMC runner infrastructure to sample from models with + * both discrete (ordinal / Blume-Capel) and continuous variables. + * Supports MH sampler only, with optional edge selection. + */ +#include +#include +#include + +#include "models/mixed/mixed_mrf_model.h" +#include "utils/progress_manager.h" +#include "utils/common_helpers.h" +#include "priors/edge_prior.h" +#include "mcmc/execution/chain_result.h" +#include "mcmc/execution/chain_runner.h" +#include "mcmc/execution/sampler_config.h" + +/** + * R-exported function to sample from a Mixed MRF model. + * + * @param inputFromR List with model specification: + * discrete_observations (integer matrix n x p), + * continuous_observations (numeric matrix n x q), + * num_categories (integer vector, length p), + * is_ordinal_variable (integer vector, length p), + * baseline_category (integer vector, length p), + * main_alpha, main_beta, pairwise_scale (doubles), + * pseudolikelihood (string: "conditional" or "marginal") + * @param prior_inclusion_prob Prior inclusion probabilities ((p+q) x (p+q) matrix) + * @param initial_edge_indicators Initial edge indicators ((p+q) x (p+q) integer matrix) + * @param no_iter Number of post-warmup iterations + * @param no_warmup Number of warmup iterations + * @param no_chains Number of parallel chains + * @param edge_selection Whether to do edge selection (spike-and-slab) + * @param seed Random seed + * @param no_threads Number of threads for parallel execution + * @param progress_type Progress bar type + * @param edge_prior Edge prior type + * @param beta_bernoulli_alpha Beta-Bernoulli alpha hyperparameter + * @param beta_bernoulli_beta Beta-Bernoulli beta hyperparameter + * @param beta_bernoulli_alpha_between SBM between-cluster alpha + * @param beta_bernoulli_beta_between SBM between-cluster beta + * @param dirichlet_alpha Dirichlet alpha for SBM + * @param lambda Lambda for SBM + * + * @return List with per-chain results including samples and diagnostics + */ +// [[Rcpp::export]] +Rcpp::List sample_mixed_mrf( + const Rcpp::List& inputFromR, + const arma::mat& prior_inclusion_prob, + const arma::imat& initial_edge_indicators, + const int no_iter, + const int no_warmup, + const int no_chains, + const bool edge_selection, + const int seed, + const int no_threads, + const int progress_type, + const std::string& edge_prior = "Bernoulli", + const double beta_bernoulli_alpha = 1.0, + const double beta_bernoulli_beta = 1.0, + const double beta_bernoulli_alpha_between = 1.0, + const double beta_bernoulli_beta_between = 1.0, + const double dirichlet_alpha = 1.0, + const double lambda = 1.0 +) { + // Extract model inputs from R list + arma::imat discrete_obs = Rcpp::as(inputFromR["discrete_observations"]); + arma::mat continuous_obs = Rcpp::as(inputFromR["continuous_observations"]); + arma::ivec num_categories = Rcpp::as(inputFromR["num_categories"]); + arma::uvec is_ordinal = Rcpp::as(inputFromR["is_ordinal_variable"]); + arma::ivec baseline_cat = Rcpp::as(inputFromR["baseline_category"]); + double main_alpha = Rcpp::as(inputFromR["main_alpha"]); + double main_beta = Rcpp::as(inputFromR["main_beta"]); + double pairwise_scale = Rcpp::as(inputFromR["pairwise_scale"]); + std::string pseudolikelihood = Rcpp::as(inputFromR["pseudolikelihood"]); + + // Create model + MixedMRFModel model( + discrete_obs, continuous_obs, + num_categories, is_ordinal, baseline_cat, + prior_inclusion_prob, initial_edge_indicators, + edge_selection, pseudolikelihood, + main_alpha, main_beta, pairwise_scale, + seed + ); + + // Create edge prior + EdgePrior edge_prior_enum = edge_prior_from_string(edge_prior); + auto edge_prior_obj = create_edge_prior( + edge_prior_enum, + beta_bernoulli_alpha, beta_bernoulli_beta, + beta_bernoulli_alpha_between, beta_bernoulli_beta_between, + dirichlet_alpha, lambda + ); + + // Configure sampler — Mixed MRF is MH-only + SamplerConfig config; + config.sampler_type = "mh"; + config.no_iter = no_iter; + config.no_warmup = no_warmup; + config.edge_selection = edge_selection; + config.seed = seed; + config.na_impute = false; + + // Set up progress manager + ProgressManager pm(no_chains, no_iter, no_warmup, 50, progress_type); + + // Run MCMC using unified infrastructure + std::vector results = run_mcmc_sampler( + model, *edge_prior_obj, config, no_chains, no_threads, pm); + + // Convert to R list format + Rcpp::List output = convert_results_to_list(results); + + pm.finish(); + + return output; +} diff --git a/src/test_mixed_mrf.cpp b/src/test_mixed_mrf.cpp deleted file mode 100644 index 76dc1d1d..00000000 --- a/src/test_mixed_mrf.cpp +++ /dev/null @@ -1,441 +0,0 @@ -// test_mixed_mrf.cpp — Rcpp test helpers for MixedMRFModel skeleton (Phase A.3) -// -// These are lightweight exported functions that construct a MixedMRFModel from -// R inputs and return diagnostic information for testthat assertions. - -#include -#include "models/mixed/mixed_mrf_model.h" - -// [[Rcpp::export]] -Rcpp::List test_mixed_mrf_skeleton( - const arma::imat& discrete_observations, - const arma::mat& continuous_observations, - const arma::ivec& num_categories, - const arma::uvec& is_ordinal_variable, - const arma::ivec& baseline_category, - const arma::mat& inclusion_probability, - const arma::imat& initial_edge_indicators, - bool edge_selection, - const std::string& pseudolikelihood, - int seed -) { - MixedMRFModel model( - discrete_observations, - continuous_observations, - num_categories, - is_ordinal_variable, - baseline_category, - inclusion_probability, - initial_edge_indicators, - edge_selection, - pseudolikelihood, - 1.0, 1.0, 2.5, - seed - ); - - // --- Dimension checks --- - size_t param_dim = model.parameter_dimension(); - size_t full_param_dim = model.full_parameter_dimension(); - int num_variables = model.get_num_variables(); - int num_pairwise = model.get_num_pairwise(); - - // --- Vectorized parameter round-trip --- - arma::vec params_before = model.get_vectorized_parameters(); - model.set_vectorized_parameters(params_before); - arma::vec params_after = model.get_vectorized_parameters(); - double roundtrip_max_diff = arma::max(arma::abs(params_before - params_after)); - - // --- Full-parameter vector --- - arma::vec full_params = model.get_full_vectorized_parameters(); - - // --- Edge indicators --- - arma::ivec indicator_vec = model.get_vectorized_indicator_parameters(); - const arma::imat& edge_mat = model.get_edge_indicators(); - - // --- Non-trivial parameter round-trip --- - // Set some non-zero parameters and verify round-trip - arma::vec test_params(full_param_dim); - for(size_t i = 0; i < full_param_dim; ++i) { - test_params(i) = static_cast(i + 1) * 0.01; - } - // Kyy must be SPD: overwrite the Kyy block with a diagonal-dominant SPD matrix. - // Kyy occupies indices: num_main + num_pairwise_xx + q ... num_main + num_pairwise_xx + q + q(q+1)/2 - 1 - // For simplicity, just make it diagonal = large positive - int p = discrete_observations.n_cols; - int q = continuous_observations.n_cols; - size_t num_main = 0; - for(int s = 0; s < p; ++s) { - if(is_ordinal_variable(s)) { - num_main += num_categories(s); - } else { - num_main += 2; - } - } - size_t kyy_start = num_main + p * (p - 1) / 2 + q; - // Write identity-like SPD into Kyy block - size_t kyy_idx = kyy_start; - for(int i = 0; i < q; ++i) { - for(int j = i; j < q; ++j) { - if(i == j) { - test_params(kyy_idx) = 2.0 + i * 0.1; // positive diagonal - } else { - test_params(kyy_idx) = 0.01; // small off-diagonal - } - kyy_idx++; - } - } - - model.set_vectorized_parameters(test_params); - arma::vec recovered = model.get_vectorized_parameters(); - double nontrivial_max_diff = arma::max(arma::abs(test_params - recovered)); - - // --- Clone round-trip --- - auto cloned = model.clone(); - arma::vec cloned_params = cloned->get_vectorized_parameters(); - double clone_max_diff = arma::max(arma::abs(recovered - cloned_params)); - - return Rcpp::List::create( - Rcpp::Named("parameter_dimension") = param_dim, - Rcpp::Named("full_parameter_dimension") = full_param_dim, - Rcpp::Named("num_variables") = num_variables, - Rcpp::Named("num_pairwise") = num_pairwise, - Rcpp::Named("params_length") = params_before.n_elem, - Rcpp::Named("roundtrip_max_diff") = roundtrip_max_diff, - Rcpp::Named("nontrivial_roundtrip_max_diff") = nontrivial_max_diff, - Rcpp::Named("clone_max_diff") = clone_max_diff, - Rcpp::Named("indicator_length") = indicator_vec.n_elem, - Rcpp::Named("edge_indicators_rows") = edge_mat.n_rows, - Rcpp::Named("edge_indicators_cols") = edge_mat.n_cols, - Rcpp::Named("has_edge_selection") = model.has_edge_selection(), - Rcpp::Named("has_adaptive_metropolis") = model.has_adaptive_metropolis(), - Rcpp::Named("has_gradient") = model.has_gradient(), - Rcpp::Named("has_missing_data") = model.has_missing_data() - ); -} - - -// [[Rcpp::export]] -Rcpp::List test_mixed_mrf_likelihoods( - const arma::imat& discrete_observations, - const arma::mat& continuous_observations, - const arma::ivec& num_categories, - const arma::uvec& is_ordinal_variable, - const arma::ivec& baseline_category, - const arma::mat& inclusion_probability, - const arma::imat& initial_edge_indicators, - bool edge_selection, - const std::string& pseudolikelihood, - const arma::vec& params, - int seed -) { - MixedMRFModel model( - discrete_observations, - continuous_observations, - num_categories, - is_ordinal_variable, - baseline_category, - inclusion_probability, - initial_edge_indicators, - edge_selection, - pseudolikelihood, - 1.0, 1.0, 2.5, - seed - ); - - // Set parameters (caller must ensure Kyy block is SPD) - model.set_vectorized_parameters(params); - - int p = discrete_observations.n_cols; - - // Evaluate log_conditional_omrf for each discrete variable - Rcpp::NumericVector omrf_ll(p); - for(int s = 0; s < p; ++s) { - omrf_ll[s] = model.log_conditional_omrf(s); - } - - // Evaluate log_marginal_omrf for each discrete variable (marginal mode only) - Rcpp::NumericVector marg_omrf_ll(p); - if(pseudolikelihood == "marginal") { - for(int s = 0; s < p; ++s) { - marg_omrf_ll[s] = model.log_marginal_omrf(s); - } - } - - // Evaluate log_conditional_ggm - double ggm_ll = model.log_conditional_ggm(); - - return Rcpp::List::create( - Rcpp::Named("omrf_ll") = omrf_ll, - Rcpp::Named("marg_omrf_ll") = marg_omrf_ll, - Rcpp::Named("ggm_ll") = ggm_ll - ); -} - - -// [[Rcpp::export]] -Rcpp::List test_mixed_mrf_sampler( - const arma::imat& discrete_observations, - const arma::mat& continuous_observations, - const arma::ivec& num_categories, - const arma::uvec& is_ordinal_variable, - const arma::ivec& baseline_category, - const arma::mat& inclusion_probability, - const arma::imat& initial_edge_indicators, - bool edge_selection, - const std::string& pseudolikelihood, - int n_warmup, - int n_samples, - int seed -) { - MixedMRFModel model( - discrete_observations, - continuous_observations, - num_categories, - is_ordinal_variable, - baseline_category, - inclusion_probability, - initial_edge_indicators, - edge_selection, - pseudolikelihood, - 1.0, 1.0, 2.5, - seed - ); - - size_t full_dim = model.full_parameter_dimension(); - - // Warmup (discard samples, no edge selection yet) - for(int iter = 0; iter < n_warmup; ++iter) { - model.prepare_iteration(); - model.do_one_metropolis_step(iter); - } - - // Activate edge selection after warmup - if(edge_selection) { - model.set_edge_selection_active(true); - } - - // Sampling - arma::mat samples(n_samples, full_dim); - int num_indicators = model.get_num_pairwise(); - arma::imat indicator_samples(n_samples, num_indicators); - - for(int iter = 0; iter < n_samples; ++iter) { - model.prepare_iteration(); - model.do_one_metropolis_step(n_warmup + iter); - samples.row(iter) = model.get_full_vectorized_parameters().t(); - if(edge_selection) { - indicator_samples.row(iter) = model.get_vectorized_indicator_parameters().t(); - } - } - - Rcpp::List result = Rcpp::List::create( - Rcpp::Named("samples") = samples, - Rcpp::Named("full_parameter_dimension") = full_dim - ); - - if(edge_selection) { - result["indicator_samples"] = indicator_samples; - } - - return result; -} - - -// ============================================================================= -// test_mixed_mrf_cholesky — T28 (log-ratio agreement) and T29 (Cholesky fidelity) -// ============================================================================= - -// [[Rcpp::export]] -Rcpp::List test_mixed_mrf_cholesky( - const arma::imat& discrete_observations, - const arma::mat& continuous_observations, - const arma::ivec& num_categories, - const arma::uvec& is_ordinal_variable, - const arma::ivec& baseline_category, - const arma::mat& inclusion_probability, - const arma::imat& initial_edge_indicators, - const arma::vec& params, - int seed, - int target_i, - int target_j -) { - MixedMRFModel model( - discrete_observations, - continuous_observations, - num_categories, - is_ordinal_variable, - baseline_category, - inclusion_probability, - initial_edge_indicators, - false, // no edge selection - "conditional", - 1.0, 1.0, 2.5, - seed - ); - - model.set_vectorized_parameters(params); - - int q = static_cast(model.q_); - - // ========================================================================= - // T28: log_ggm_ratio_edge vs brute-force log_conditional_ggm difference - // ========================================================================= - - // Current log-likelihood - double ggm_ll_curr = model.log_conditional_ggm(); - - // Extract reparameterization constants and build a deterministic proposal - model.get_kyy_constants(target_i, target_j); - double phi_curr = model.kyy_constants_[0]; - double phi_prop = phi_curr + 0.05; // small deterministic shift - - double omega_prop_ij = model.kyy_constants_[2] + model.kyy_constants_[3] * phi_prop; - double omega_prop_jj = model.kyy_constrained_diagonal(omega_prop_ij); - - model.precision_yy_proposal_ = model.Kyy_; - model.precision_yy_proposal_(target_i, target_j) = omega_prop_ij; - model.precision_yy_proposal_(target_j, target_i) = omega_prop_ij; - model.precision_yy_proposal_(target_j, target_j) = omega_prop_jj; - - // Rank-2 log-ratio via new function - double ratio_rank2 = model.log_ggm_ratio_edge(target_i, target_j); - - // Brute-force: install proposed Kyy, full recompute, evaluate - arma::mat Kyy_saved = model.Kyy_; - arma::mat cov_saved = model.covariance_yy_; - arma::mat chol_saved = model.Kyy_chol_; - arma::mat inv_chol_saved = model.inv_cholesky_yy_; - double logdet_saved = model.Kyy_log_det_; - arma::mat cmean_saved = model.conditional_mean_; - - model.Kyy_ = model.precision_yy_proposal_; - model.recompute_Kyy_decomposition(); - model.recompute_conditional_mean(); - double ggm_ll_prop = model.log_conditional_ggm(); - - double ratio_bruteforce = ggm_ll_prop - ggm_ll_curr; - - // ========================================================================= - // T28b: log_ggm_ratio_diag vs brute-force - // ========================================================================= - - // Restore to current state - model.Kyy_ = Kyy_saved; - model.covariance_yy_ = cov_saved; - model.Kyy_chol_ = chol_saved; - model.inv_cholesky_yy_ = inv_chol_saved; - model.Kyy_log_det_ = logdet_saved; - model.conditional_mean_ = cmean_saved; - - // Diagonal proposal: small shift on log-Cholesky scale - double logdet_curr = cholesky_helpers::get_log_det(model.Kyy_chol_); - double logdet_sub = logdet_curr + std::log(model.covariance_yy_(target_i, target_i)); - double theta_curr = (logdet_curr - logdet_sub) / 2.0; - double theta_prop = theta_curr + 0.05; - - model.precision_yy_proposal_ = model.Kyy_; - model.precision_yy_proposal_(target_i, target_i) = model.Kyy_(target_i, target_i) - - std::exp(theta_curr) * std::exp(theta_curr) - + std::exp(theta_prop) * std::exp(theta_prop); - - double ratio_diag_rank1 = model.log_ggm_ratio_diag(target_i); - - // Brute-force - model.Kyy_ = model.precision_yy_proposal_; - model.recompute_Kyy_decomposition(); - model.recompute_conditional_mean(); - double ggm_ll_diag_prop = model.log_conditional_ggm(); - - double ratio_diag_brute = ggm_ll_diag_prop - ggm_ll_curr; - - // ========================================================================= - // T29: Cholesky update fidelity — rank-2 edge update - // ========================================================================= - - // Restore to current state - model.Kyy_ = Kyy_saved; - model.covariance_yy_ = cov_saved; - model.Kyy_chol_ = chol_saved; - model.inv_cholesky_yy_ = inv_chol_saved; - model.Kyy_log_det_ = logdet_saved; - model.conditional_mean_ = cmean_saved; - - // Re-fill off-diagonal proposal - model.precision_yy_proposal_ = model.Kyy_; - model.precision_yy_proposal_(target_i, target_j) = omega_prop_ij; - model.precision_yy_proposal_(target_j, target_i) = omega_prop_ij; - model.precision_yy_proposal_(target_j, target_j) = omega_prop_jj; - - // Apply rank-1 Cholesky update - double old_ij = model.Kyy_(target_i, target_j); - double old_jj = model.Kyy_(target_j, target_j); - model.Kyy_(target_i, target_j) = omega_prop_ij; - model.Kyy_(target_j, target_i) = omega_prop_ij; - model.Kyy_(target_j, target_j) = omega_prop_jj; - model.cholesky_update_after_kyy_edge(old_ij, old_jj, target_i, target_j); - - arma::mat chol_rank1 = model.Kyy_chol_; - arma::mat cov_rank1 = model.covariance_yy_; - double logdet_rank1 = model.Kyy_log_det_; - - // Full recompute for ground truth - model.recompute_Kyy_decomposition(); - arma::mat chol_full = model.Kyy_chol_; - arma::mat cov_full = model.covariance_yy_; - double logdet_full = model.Kyy_log_det_; - - double chol_max_diff = arma::max(arma::max(arma::abs(chol_rank1 - chol_full))); - double cov_max_diff = arma::max(arma::max(arma::abs(cov_rank1 - cov_full))); - double logdet_diff = std::abs(logdet_rank1 - logdet_full); - - // ========================================================================= - // T29b: Cholesky update fidelity — rank-1 diagonal update - // ========================================================================= - - // Restore - model.Kyy_ = Kyy_saved; - model.covariance_yy_ = cov_saved; - model.Kyy_chol_ = chol_saved; - model.inv_cholesky_yy_ = inv_chol_saved; - model.Kyy_log_det_ = logdet_saved; - - // Diagonal proposal (reuse from T28b) - model.precision_yy_proposal_ = model.Kyy_; - model.precision_yy_proposal_(target_i, target_i) = model.Kyy_(target_i, target_i) - - std::exp(theta_curr) * std::exp(theta_curr) - + std::exp(theta_prop) * std::exp(theta_prop); - - double old_ii = model.Kyy_(target_i, target_i); - model.Kyy_(target_i, target_i) = model.precision_yy_proposal_(target_i, target_i); - model.cholesky_update_after_kyy_diag(old_ii, target_i); - - arma::mat chol_diag_rank1 = model.Kyy_chol_; - arma::mat cov_diag_rank1 = model.covariance_yy_; - double logdet_diag_rank1 = model.Kyy_log_det_; - - model.recompute_Kyy_decomposition(); - arma::mat chol_diag_full = model.Kyy_chol_; - arma::mat cov_diag_full = model.covariance_yy_; - double logdet_diag_full = model.Kyy_log_det_; - - double chol_diag_max_diff = arma::max(arma::max(arma::abs(chol_diag_rank1 - chol_diag_full))); - double cov_diag_max_diff = arma::max(arma::max(arma::abs(cov_diag_rank1 - cov_diag_full))); - double logdet_diag_diff = std::abs(logdet_diag_rank1 - logdet_diag_full); - - return Rcpp::List::create( - // T28: off-diagonal log-ratio agreement - Rcpp::Named("ratio_rank2") = ratio_rank2, - Rcpp::Named("ratio_bruteforce") = ratio_bruteforce, - // T28b: diagonal log-ratio agreement - Rcpp::Named("ratio_diag_rank1") = ratio_diag_rank1, - Rcpp::Named("ratio_diag_brute") = ratio_diag_brute, - // T29: off-diagonal Cholesky fidelity - Rcpp::Named("chol_max_diff") = chol_max_diff, - Rcpp::Named("cov_max_diff") = cov_max_diff, - Rcpp::Named("logdet_diff") = logdet_diff, - // T29b: diagonal Cholesky fidelity - Rcpp::Named("chol_diag_max_diff") = chol_diag_max_diff, - Rcpp::Named("cov_diag_max_diff") = cov_diag_max_diff, - Rcpp::Named("logdet_diag_diff") = logdet_diag_diff - ); -} diff --git a/tests/testthat/helper-fixtures.R b/tests/testthat/helper-fixtures.R index b217a35f..f69ab598 100644 --- a/tests/testthat/helper-fixtures.R +++ b/tests/testthat/helper-fixtures.R @@ -519,6 +519,82 @@ get_bgms_fit_ggm_no_es = function() { .test_cache$bgms_fit_ggm_no_es } +get_bgms_fit_mixed_mrf = function() { + if(is.null(.test_cache$bgms_fit_mixed_mrf)) { + set.seed(99) + n = 80 + x = cbind( + sample(0:2, n, replace = TRUE), + rnorm(n), + sample(0:2, n, replace = TRUE), + rnorm(n), + sample(0:2, n, replace = TRUE) + ) + colnames(x) = c("d1", "c1", "d2", "c2", "d3") + .test_cache$bgms_fit_mixed_mrf = bgm( + x = x, + variable_type = c("ordinal", "continuous", "ordinal", + "continuous", "ordinal"), + edge_selection = TRUE, + iter = 50, warmup = 100, chains = 1, + seed = 77771, + display_progress = "none" + ) + } + .test_cache$bgms_fit_mixed_mrf +} + +get_bgms_fit_mixed_mrf_no_es = function() { + if(is.null(.test_cache$bgms_fit_mixed_mrf_no_es)) { + set.seed(99) + n = 80 + x = cbind( + sample(0:2, n, replace = TRUE), + rnorm(n), + sample(0:2, n, replace = TRUE), + rnorm(n), + sample(0:2, n, replace = TRUE) + ) + colnames(x) = c("d1", "c1", "d2", "c2", "d3") + .test_cache$bgms_fit_mixed_mrf_no_es = bgm( + x = x, + variable_type = c("ordinal", "continuous", "ordinal", + "continuous", "ordinal"), + edge_selection = FALSE, + iter = 50, warmup = 100, chains = 1, + seed = 77772, + display_progress = "none" + ) + } + .test_cache$bgms_fit_mixed_mrf_no_es +} + +get_bgms_fit_mixed_mrf_marginal = function() { + if(is.null(.test_cache$bgms_fit_mixed_mrf_marginal)) { + set.seed(99) + n = 80 + x = cbind( + sample(0:2, n, replace = TRUE), + rnorm(n), + sample(0:2, n, replace = TRUE), + rnorm(n), + sample(0:2, n, replace = TRUE) + ) + colnames(x) = c("d1", "c1", "d2", "c2", "d3") + .test_cache$bgms_fit_mixed_mrf_marginal = bgm( + x = x, + variable_type = c("ordinal", "continuous", "ordinal", + "continuous", "ordinal"), + edge_selection = FALSE, + pseudolikelihood = "marginal", + iter = 50, warmup = 100, chains = 1, + seed = 77773, + display_progress = "none" + ) + } + .test_cache$bgms_fit_mixed_mrf_marginal +} + # ------------------------------------------------------------------------------ # 2. Prediction Data Helpers # ------------------------------------------------------------------------------ diff --git a/tests/testthat/test-bgm.R b/tests/testthat/test-bgm.R index 67b2cdec..58a944ef 100644 --- a/tests/testthat/test-bgm.R +++ b/tests/testthat/test-bgm.R @@ -698,6 +698,123 @@ test_that("bgm GGM edge selection discriminates true edges", { # --- D.7: Conditional regression check ---------------------------------------- +# ============================================================================== +# Mixed MRF End-to-End Tests +# ============================================================================== + +test_that("bgm mixed MRF is reproducible", { + fit1 = get_bgms_fit_mixed_mrf() + + set.seed(99) + n = 80 + x = cbind( + sample(0:2, n, replace = TRUE), + rnorm(n), + sample(0:2, n, replace = TRUE), + rnorm(n), + sample(0:2, n, replace = TRUE) + ) + colnames(x) = c("d1", "c1", "d2", "c2", "d3") + + fit2 = bgm( + x = x, + variable_type = c("ordinal", "continuous", "ordinal", + "continuous", "ordinal"), + edge_selection = TRUE, + iter = 50, warmup = 100, chains = 1, + seed = 77771, + display_progress = "none" + ) + + testthat::expect_equal(fit1$raw_samples$main, fit2$raw_samples$main) + testthat::expect_equal(fit1$raw_samples$pairwise, fit2$raw_samples$pairwise) +}) + +test_that("bgm mixed MRF output has correct dimensions", { + fit = get_bgms_fit_mixed_mrf() + args = extract_arguments(fit) + p_total = args$num_variables # 5 + p = 3L # discrete + q = 2L # continuous + + # pairwise: p_total*(p_total-1)/2 edges + n_edges = p_total * (p_total - 1) / 2 + expect_equal(nrow(fit$posterior_summary_pairwise), n_edges) + expect_equal(nrow(fit$posterior_mean_pairwise), p_total) + expect_equal(ncol(fit$posterior_mean_pairwise), p_total) + + # indicators (edge selection = TRUE) + expect_equal(nrow(fit$posterior_summary_indicator), n_edges) + expect_equal(nrow(fit$posterior_mean_indicator), p_total) + expect_equal(ncol(fit$posterior_mean_indicator), p_total) + + # posterior_mean_main: list with discrete and continuous + expect_true(is.list(fit$posterior_mean_main)) + expect_equal(nrow(fit$posterior_mean_main$discrete), p) + expect_equal(nrow(fit$posterior_mean_main$continuous), q) + expect_equal(ncol(fit$posterior_mean_main$continuous), 2) # mean + precision + + # raw samples + expect_equal(ncol(fit$raw_samples$pairwise[[1]]), n_edges) + expect_equal(nrow(fit$raw_samples$main[[1]]), args$iter) +}) + +test_that("bgm mixed MRF without edge selection omits indicators", { + fit = get_bgms_fit_mixed_mrf_no_es() + + expect_s3_class(fit, "bgms") + expect_null(fit$posterior_summary_indicator) + expect_null(fit$posterior_mean_indicator) +}) + +test_that("bgm mixed MRF pairwise matrix has correct variable names", { + fit = get_bgms_fit_mixed_mrf() + + # Interleaved order: d1, c1, d2, c2, d3 + expected_names = c("d1", "c1", "d2", "c2", "d3") + expect_equal(rownames(fit$posterior_mean_pairwise), expected_names) + expect_equal(colnames(fit$posterior_mean_pairwise), expected_names) + expect_equal(rownames(fit$posterior_mean_indicator), expected_names) + expect_equal(colnames(fit$posterior_mean_indicator), expected_names) +}) + +test_that("bgm mixed MRF pairwise matrix is symmetric", { + fit = get_bgms_fit_mixed_mrf() + expect_equal(fit$posterior_mean_pairwise, t(fit$posterior_mean_pairwise)) + expect_equal(fit$posterior_mean_indicator, t(fit$posterior_mean_indicator)) +}) + +test_that("bgm mixed MRF summary-matrix consistency", { + fit = get_bgms_fit_mixed_mrf() + expect_true( + all(check_summary_matrix_consistency( + fit$posterior_summary_pairwise, + fit$posterior_mean_pairwise + )), + info = "Mixed MRF pairwise summary names do not match matrix positions" + ) + expect_true( + all(check_summary_matrix_consistency( + fit$posterior_summary_indicator, + fit$posterior_mean_indicator + )), + info = "Mixed MRF indicator summary names do not match matrix positions" + ) +}) + +test_that("bgm mixed MRF posterior precision diagonals are positive", { + fit = get_bgms_fit_mixed_mrf_no_es() + expect_true(all(fit$posterior_mean_main$continuous[, "precision"] > 0)) +}) + +test_that("bgm mixed MRF marginal pseudolikelihood runs", { + fit = get_bgms_fit_mixed_mrf_marginal() + expect_s3_class(fit, "bgms") + expect_equal(nrow(fit$posterior_mean_pairwise), 5) + expect_true(all(is.finite(fit$posterior_mean_pairwise))) +}) + + test_that("bgm GGM implied regression matches OLS for large n", { skip_on_cran() diff --git a/tests/testthat/test-input-validation.R b/tests/testthat/test-input-validation.R index d2994044..58e3af47 100644 --- a/tests/testthat/test-input-validation.R +++ b/tests/testthat/test-input-validation.R @@ -84,14 +84,21 @@ test_that("GGM rejects NUTS and HMC update methods", { ) }) -test_that("Mixed continuous and ordinal variable types are rejected", { +test_that("Mixed continuous and ordinal variable types are accepted for bgm", { set.seed(42) - x = matrix(rnorm(200), nrow = 50, ncol = 4) - - expect_error( - bgm(x = x, variable_type = c("continuous", "ordinal", "ordinal", "ordinal")), - "all variables must be of type" + x = data.frame( + ord1 = sample(0:2, 50, replace = TRUE), + ord2 = sample(0:2, 50, replace = TRUE), + cont1 = rnorm(50), + cont2 = rnorm(50) + ) + spec = bgm_spec( + x = x, + variable_type = c("ordinal", "ordinal", "continuous", "continuous") ) + expect_equal(spec$model_type, "mixed_mrf") + expect_equal(spec$data$num_discrete, 2L) + expect_equal(spec$data$num_continuous, 2L) }) diff --git a/tests/testthat/test-mixed-mrf-cholesky.R b/tests/testthat/test-mixed-mrf-cholesky.R deleted file mode 100644 index de6d8726..00000000 --- a/tests/testthat/test-mixed-mrf-cholesky.R +++ /dev/null @@ -1,183 +0,0 @@ -# ============================================================================= -# test-mixed-mrf-cholesky.R — Phase B+ Cholesky rank-1 update tests -# ============================================================================= -# T28: log_ggm_ratio_edge matches brute-force log-likelihood difference -# T28b: log_ggm_ratio_diag matches brute-force log-likelihood difference -# T29: Cholesky / covariance / logdet fidelity after rank-2 edge update -# T29b: Cholesky / covariance / logdet fidelity after rank-1 diagonal update - -# ------------------------------------------------------------------ -# Helper: build parameter vector with nonzero Kyy (must be SPD) -# ------------------------------------------------------------------ -make_cholesky_test = function(q = 3L) { - set.seed(42) - n = 20L; p = 2L - num_cats = c(2L, 3L) - is_ordinal = c(1L, 1L) - baseline_cat = c(0L, 0L) - - x = matrix(0L, n, p) - x[, 1] = sample(0:2, n, replace = TRUE) - x[, 2] = sample(0:3, n, replace = TRUE) - y = matrix(rnorm(n * q), n, q) - - pq = p + q - inc_prob = matrix(0.5, pq, pq) - edge_ind = matrix(1L, pq, pq) - diag(edge_ind) = 0L - - # Param layout: main | Kxx | muy | Kyy (upper-tri) | Kxy - num_main = sum(num_cats) # 2 + 3 = 5 - num_kxx = p * (p - 1L) / 2L # 1 - num_kyy = q * (q + 1L) / 2L - num_kxy = p * q - total = num_main + num_kxx + q + num_kyy + num_kxy - params = rep(0, total) - - # Thresholds - params[1:2] = c(0.3, -0.2) - params[3:5] = c(0.1, 0.4, -0.1) - - # Kxx(0,1) - params[num_main + 1] = 0.2 - - # muy - muy_start = num_main + num_kxx - params[muy_start + seq_len(q)] = rnorm(q, sd = 0.2) - - # Kyy: build SPD Q = L L^T with random L, then pack upper-tri - L = matrix(0, q, q) - diag(L) = runif(q, 1.0, 2.0) - L[lower.tri(L)] = rnorm(q * (q - 1) / 2, sd = 0.3) - Kyy = L %*% t(L) - kyy_start = muy_start + q - idx = 0L - for(i in seq_len(q)) { - for(j in i:q) { - idx = idx + 1L - params[kyy_start + idx] = Kyy[i, j] - } - } - - # Kxy - kxy_start = kyy_start + num_kyy - params[kxy_start + seq_len(num_kxy)] = rnorm(num_kxy, sd = 0.1) - - list( - x = x, y = y, - num_cats = num_cats, - is_ordinal = as.integer(is_ordinal), - baseline_cat = baseline_cat, - inc_prob = inc_prob, - edge_ind = edge_ind, - params = params, - q = q - ) -} - - -# ============================================================================== -# T28 + T28b: log-ratio agreement (off-diagonal and diagonal) -# ============================================================================== - -test_that("log_ggm_ratio_edge matches brute-force (T28)", { - d = make_cholesky_test(q = 3L) - res = bgms:::test_mixed_mrf_cholesky( - d$x, d$y, d$num_cats, d$is_ordinal, d$baseline_cat, - d$inc_prob, d$edge_ind, d$params, - seed = 1L, target_i = 0L, target_j = 1L - ) - expect_equal(res$ratio_rank2, res$ratio_bruteforce, tolerance = 1e-10) -}) - -test_that("log_ggm_ratio_diag matches brute-force (T28b)", { - d = make_cholesky_test(q = 3L) - res = bgms:::test_mixed_mrf_cholesky( - d$x, d$y, d$num_cats, d$is_ordinal, d$baseline_cat, - d$inc_prob, d$edge_ind, d$params, - seed = 1L, target_i = 0L, target_j = 1L - ) - expect_equal(res$ratio_diag_rank1, res$ratio_diag_brute, tolerance = 1e-10) -}) - - -# ============================================================================== -# T29 + T29b: Cholesky fidelity after rank-1/rank-2 updates -# ============================================================================== - -test_that("Cholesky update after edge matches full recompute (T29)", { - d = make_cholesky_test(q = 3L) - res = bgms:::test_mixed_mrf_cholesky( - d$x, d$y, d$num_cats, d$is_ordinal, d$baseline_cat, - d$inc_prob, d$edge_ind, d$params, - seed = 1L, target_i = 0L, target_j = 1L - ) - expect_lt(res$chol_max_diff, 1e-10) - expect_lt(res$cov_max_diff, 1e-10) - expect_lt(res$logdet_diff, 1e-10) -}) - -test_that("Cholesky update after diagonal matches full recompute (T29b)", { - d = make_cholesky_test(q = 3L) - res = bgms:::test_mixed_mrf_cholesky( - d$x, d$y, d$num_cats, d$is_ordinal, d$baseline_cat, - d$inc_prob, d$edge_ind, d$params, - seed = 1L, target_i = 0L, target_j = 1L - ) - expect_lt(res$chol_diag_max_diff, 1e-10) - expect_lt(res$cov_diag_max_diff, 1e-10) - expect_lt(res$logdet_diag_diff, 1e-10) -}) - - -# ============================================================================== -# Same tests with different target indices (i=1, j=2) and q=4 -# ============================================================================== - -test_that("log_ggm_ratio_edge matches brute-force (T28, q=4, ij=1,2)", { - d = make_cholesky_test(q = 4L) - res = bgms:::test_mixed_mrf_cholesky( - d$x, d$y, d$num_cats, d$is_ordinal, d$baseline_cat, - d$inc_prob, d$edge_ind, d$params, - seed = 7L, target_i = 1L, target_j = 2L - ) - expect_equal(res$ratio_rank2, res$ratio_bruteforce, tolerance = 1e-10) - expect_equal(res$ratio_diag_rank1, res$ratio_diag_brute, tolerance = 1e-10) -}) - -test_that("Cholesky fidelity (T29/T29b, q=4, ij=1,2)", { - d = make_cholesky_test(q = 4L) - res = bgms:::test_mixed_mrf_cholesky( - d$x, d$y, d$num_cats, d$is_ordinal, d$baseline_cat, - d$inc_prob, d$edge_ind, d$params, - seed = 7L, target_i = 1L, target_j = 2L - ) - expect_lt(res$chol_max_diff, 1e-10) - expect_lt(res$cov_max_diff, 1e-10) - expect_lt(res$logdet_diff, 1e-10) - expect_lt(res$chol_diag_max_diff, 1e-10) - expect_lt(res$cov_diag_max_diff, 1e-10) - expect_lt(res$logdet_diag_diff, 1e-10) -}) - - -# ============================================================================== -# Test with q=2 (minimum for off-diagonal) — boundary case -# ============================================================================== - -test_that("Rank-1 Cholesky correctness with q=2 boundary (T28-T29b)", { - d = make_cholesky_test(q = 2L) - res = bgms:::test_mixed_mrf_cholesky( - d$x, d$y, d$num_cats, d$is_ordinal, d$baseline_cat, - d$inc_prob, d$edge_ind, d$params, - seed = 99L, target_i = 0L, target_j = 1L - ) - expect_equal(res$ratio_rank2, res$ratio_bruteforce, tolerance = 1e-10) - expect_equal(res$ratio_diag_rank1, res$ratio_diag_brute, tolerance = 1e-10) - expect_lt(res$chol_max_diff, 1e-10) - expect_lt(res$cov_max_diff, 1e-10) - expect_lt(res$logdet_diff, 1e-10) - expect_lt(res$chol_diag_max_diff, 1e-10) - expect_lt(res$cov_diag_max_diff, 1e-10) - expect_lt(res$logdet_diag_diff, 1e-10) -}) diff --git a/tests/testthat/test-mixed-mrf-edge-selection.R b/tests/testthat/test-mixed-mrf-edge-selection.R deleted file mode 100644 index cfb5b0ca..00000000 --- a/tests/testthat/test-mixed-mrf-edge-selection.R +++ /dev/null @@ -1,326 +0,0 @@ -# ============================================================================= -# test-mixed-mrf-edge-selection.R — Phase D edge selection tests -# ============================================================================= -# Validates that update_edge_indicators() produces correct spike-and-slab -# structure learning via reversible-jump Metropolis-Hastings. -# -# Strategy: generate from a sparse graph with known zero edges, run the -# sampler with edge_selection = TRUE, and check that posterior inclusion -# probabilities (PIPs) correctly separate true edges from null edges. - -skip_if_not_installed("mixedGM") - -# --------------------------------------------------------------------------- -# Helper: extract edge indicators from indicator_samples -# --------------------------------------------------------------------------- -# Vectorization order: Gxx upper-tri, Gyy upper-tri, Gxy row-major -extract_pip = function(indicator_samples, p, q) { - pip = colMeans(indicator_samples) - idx = 1L - - # 1. Gxx upper-triangle - n_xx = p * (p - 1) / 2 - pip_xx = pip[idx:(idx + n_xx - 1)]; idx = idx + n_xx - - Gxx = matrix(0, p, p) - k = 1L - for(i in 1:(p - 1)) for(j in (i + 1):p) { - Gxx[i, j] = Gxx[j, i] = pip_xx[k]; k = k + 1L - } - - # 2. Gyy upper-triangle - n_yy = q * (q - 1) / 2 - Gyy = matrix(0, q, q) - if(n_yy > 0) { - pip_yy = pip[idx:(idx + n_yy - 1)]; idx = idx + n_yy - k = 1L - for(i in 1:(q - 1)) for(j in (i + 1):q) { - Gyy[i, j] = Gyy[j, i] = pip_yy[k]; k = k + 1L - } - } - - # 3. Gxy row-major - n_xy = p * q - pip_xy = pip[idx:(idx + n_xy - 1)] - Gxy = matrix(pip_xy, nrow = p, ncol = q, byrow = TRUE) - - list(Gxx = Gxx, Gyy = Gyy, Gxy = Gxy) -} - - -# =========================================================================== -# Test 1: Edge selection on simulation data (conditional PL) -# =========================================================================== -test_that("edge selection recovers sparse structure (conditional PL)", { - skip_on_cran() - - p = 3L; q = 2L; n = 800L - num_cats_mgm = rep(3L, p) - num_cats_bgms = rep(2L, p) - - # Sparse graph: only (1,2) in Kxx, (1,2) in Kxy; Kyy has (1,2) edge - Kxx_true = matrix(0, p, p) - Kxx_true[1, 2] = Kxx_true[2, 1] = 0.4 - - Kyy_true = diag(q) - Kyy_true[1, 2] = Kyy_true[2, 1] = 0.3 - - Kxy_true = matrix(0, p, q) - Kxy_true[1, 2] = 0.3 - - mux_true = matrix(0, p, max(num_cats_bgms)) - mux_true[, 1] = c(-0.5, 0.3, -0.2) - mux_true[, 2] = c(-1.0, -0.8, -0.6) - muy_true = c(0.5, -0.3) - - # Generate data using mixedGM - set.seed(123) - sim = mixedGM::mixed_gibbs_generate( - n = n, num_categories = num_cats_mgm, - mux = mux_true, Kxx = Kxx_true, - muy = muy_true, Kyy = Kyy_true, - Kxy = Kxy_true, - n_burnin = 3000L - ) - - x = sim$x - y = sim$y - pq = p + q - - inc_prob = matrix(0.5, pq, pq); diag(inc_prob) = 0 - edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L - - bgms_out = bgms:::test_mixed_mrf_sampler( - x, y, as.integer(num_cats_bgms), - as.integer(rep(1L, p)), rep(0L, p), - inc_prob, edge_ind, TRUE, "conditional", - 3000L, 5000L, 42L - ) - - pips = extract_pip(bgms_out$indicator_samples, p, q) - - # True edges should have high PIP - expect_gt(pips$Gxx[1, 2], 0.5, label = "Kxx(1,2) PIP > 0.5") - expect_gt(pips$Gxy[1, 2], 0.5, label = "Kxy(1,2) PIP > 0.5") - expect_gt(pips$Gyy[1, 2], 0.5, label = "Kyy(1,2) PIP > 0.5") - - # Most null edges should have low PIP (allow one false positive due to - - # conditional PL sensitivity with small p, q) - null_kxx = c(pips$Gxx[1, 3], pips$Gxx[2, 3]) - null_kxy = c(pips$Gxy[2, 1], pips$Gxy[2, 2], - pips$Gxy[3, 1], pips$Gxy[3, 2]) - expect_lt(mean(c(null_kxx, null_kxy) > 0.5), 0.5, - label = "Fewer than half of null edges are false positives") - expect_lt(median(c(null_kxx, null_kxy)), 0.3, - label = "Median null PIP is low") -}) - - -# =========================================================================== -# Test 2: Edge selection on simulation data (marginal PL) -# =========================================================================== -test_that("edge selection recovers sparse structure (marginal PL)", { - skip_on_cran() - - p = 3L; q = 2L; n = 800L - num_cats_mgm = rep(3L, p) - num_cats_bgms = rep(2L, p) - - Kxx_true = matrix(0, p, p) - Kxx_true[1, 2] = Kxx_true[2, 1] = 0.4 - - Kyy_true = diag(q) - Kyy_true[1, 2] = Kyy_true[2, 1] = 0.3 - - Kxy_true = matrix(0, p, q) - Kxy_true[1, 2] = 0.3 - - mux_true = matrix(0, p, max(num_cats_bgms)) - mux_true[, 1] = c(-0.5, 0.3, -0.2) - mux_true[, 2] = c(-1.0, -0.8, -0.6) - muy_true = c(0.5, -0.3) - - set.seed(123) - sim = mixedGM::mixed_gibbs_generate( - n = n, num_categories = num_cats_mgm, - mux = mux_true, Kxx = Kxx_true, - muy = muy_true, Kyy = Kyy_true, - Kxy = Kxy_true, - n_burnin = 3000L - ) - - x = sim$x - y = sim$y - pq = p + q - - inc_prob = matrix(0.5, pq, pq); diag(inc_prob) = 0 - edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L - - bgms_out = bgms:::test_mixed_mrf_sampler( - x, y, as.integer(num_cats_bgms), - as.integer(rep(1L, p)), rep(0L, p), - inc_prob, edge_ind, TRUE, "marginal", - 3000L, 5000L, 42L - ) - - pips = extract_pip(bgms_out$indicator_samples, p, q) - - # True edges should have high PIP - expect_gt(pips$Gxx[1, 2], 0.5, label = "Kxx(1,2) PIP > 0.5") - expect_gt(pips$Gxy[1, 2], 0.5, label = "Kxy(1,2) PIP > 0.5") - expect_gt(pips$Gyy[1, 2], 0.5, label = "Kyy(1,2) PIP > 0.5") - - # Null edges low PIP - expect_lt(pips$Gxx[1, 3], 0.5, label = "Kxx(1,3) null PIP < 0.5") - expect_lt(pips$Gxx[2, 3], 0.5, label = "Kxx(2,3) null PIP < 0.5") - expect_lt(pips$Gxy[1, 1], 0.5, label = "Kxy(1,1) null PIP < 0.5") - expect_lt(pips$Gxy[2, 1], 0.5, label = "Kxy(2,1) null PIP < 0.5") - expect_lt(pips$Gxy[2, 2], 0.5, label = "Kxy(2,2) null PIP < 0.5") - expect_lt(pips$Gxy[3, 1], 0.5, label = "Kxy(3,1) null PIP < 0.5") - expect_lt(pips$Gxy[3, 2], 0.5, label = "Kxy(3,2) null PIP < 0.5") -}) - - -# =========================================================================== -# Test 3: Edge selection vs mixedGM (conditional PL) -# =========================================================================== -test_that("edge selection PIPs agree with mixedGM (conditional PL)", { - skip_on_cran() - - p = 3L; q = 2L; n = 800L - num_cats_mgm = rep(3L, p) - num_cats_bgms = rep(2L, p) - - Kxx_true = matrix(0, p, p) - Kxx_true[1, 2] = Kxx_true[2, 1] = 0.4 - - Kyy_true = diag(q) - Kyy_true[1, 2] = Kyy_true[2, 1] = 0.3 - - Kxy_true = matrix(0, p, q) - Kxy_true[1, 2] = 0.3 - - mux_true = matrix(0, p, max(num_cats_bgms)) - mux_true[, 1] = c(-0.5, 0.3, -0.2) - mux_true[, 2] = c(-1.0, -0.8, -0.6) - muy_true = c(0.5, -0.3) - - set.seed(123) - sim = mixedGM::mixed_gibbs_generate( - n = n, num_categories = num_cats_mgm, - mux = mux_true, Kxx = Kxx_true, - muy = muy_true, Kyy = Kyy_true, - Kxy = Kxy_true, - n_burnin = 3000L - ) - - x = sim$x - y = sim$y - pq = p + q - - inc_prob = matrix(0.5, pq, pq); diag(inc_prob) = 0 - edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L - - # bgms with edge selection - bgms_out = bgms:::test_mixed_mrf_sampler( - x, y, as.integer(num_cats_bgms), - as.integer(rep(1L, p)), rep(0L, p), - inc_prob, edge_ind, TRUE, "conditional", - 3000L, 5000L, 42L - ) - pips_bgms = extract_pip(bgms_out$indicator_samples, p, q) - - # mixedGM with edge selection (using logistic prior to match bgms) - set.seed(42) - mgm = mixedGM::mixed_sampler( - x = x, y = y, num_categories = num_cats_mgm, - n_warmup = 3000L, n_samples = 5000L, - edge_selection = TRUE, verbose = FALSE, - pseudolikelihood = "conditional", - log_prior_mean = mixedGM:::log_prior_logistic, - lp_mean_params = list() - ) - - # mixedGM stores indicators as 3D array: samples$indicator[iter, i, j] - # G is (p+q) x (p+q): rows/cols 1:p = discrete, (p+1):(p+q) = continuous - G_array = mgm$samples$indicator - Gxx_mgm = matrix(0, p, p) - for(i in 1:(p - 1)) for(j in (i + 1):p) { - Gxx_mgm[i, j] = Gxx_mgm[j, i] = mean(G_array[, i, j]) - } - Gyy_mgm = matrix(0, q, q) - if(q > 1) { - for(i in 1:(q - 1)) for(j in (i + 1):q) { - Gyy_mgm[i, j] = Gyy_mgm[j, i] = mean(G_array[, p + i, p + j]) - } - } - Gxy_mgm = matrix(0, p, q) - for(i in 1:p) for(j in 1:q) { - Gxy_mgm[i, j] = mean(G_array[, i, p + j]) - } - - # Both should agree on which edges to include/exclude - # (directional agreement: both high for true edges, both low for null) - all_bgms = c(pips_bgms$Gxx[upper.tri(pips_bgms$Gxx)], - pips_bgms$Gyy[upper.tri(pips_bgms$Gyy)], - as.vector(pips_bgms$Gxy)) - all_mgm = c(Gxx_mgm[upper.tri(Gxx_mgm)], - Gyy_mgm[upper.tri(Gyy_mgm)], - as.vector(Gxy_mgm)) - - # Correlation of PIPs should be positive - expect_gt(cor(all_bgms, all_mgm), 0.5, - label = "PIP correlation bgms vs mixedGM > 0.5") - - # True structure agreement: same edges classified as > 0.5 - bgms_selected = all_bgms > 0.5 - mgm_selected = all_mgm > 0.5 - agreement = mean(bgms_selected == mgm_selected) - expect_gt(agreement, 0.6, label = "Structure agreement > 60%") -}) - - -# =========================================================================== -# Test 4: Edge-gating prevents parameter drift on excluded edges -# =========================================================================== -test_that("excluded edges have zero parameters after edge selection", { - skip_on_cran() - - p = 3L; q = 2L; n = 500L - num_cats_mgm = rep(3L, p) - num_cats_bgms = rep(2L, p) - - # Fully sparse graph: no edges in true model - set.seed(456) - sim = mixedGM::mixed_gibbs_generate( - n = n, num_categories = num_cats_mgm, - mux = matrix(c(-0.5, 0.3, -0.2, -1.0, -0.8, -0.6), nrow = p), - Kxx = matrix(0, p, p), - muy = c(0.5, -0.3), Kyy = diag(q), - Kxy = matrix(0, p, q), - n_burnin = 3000L - ) - - x = sim$x - y = sim$y - pq = p + q - - inc_prob = matrix(0.5, pq, pq); diag(inc_prob) = 0 - edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L - - bgms_out = bgms:::test_mixed_mrf_sampler( - x, y, as.integer(num_cats_bgms), - as.integer(rep(1L, p)), rep(0L, p), - inc_prob, edge_ind, TRUE, "conditional", - 2000L, 3000L, 42L - ) - - pips = extract_pip(bgms_out$indicator_samples, p, q) - - # With a fully null graph and n = 500, all PIPs should be low - expect_lt(max(pips$Gxx[upper.tri(pips$Gxx)]), 0.8, - label = "Max Kxx PIP < 0.8 for null graph") - expect_lt(max(as.vector(pips$Gxy)), 0.8, - label = "Max Kxy PIP < 0.8 for null graph") -}) diff --git a/tests/testthat/test-mixed-mrf-likelihoods.R b/tests/testthat/test-mixed-mrf-likelihoods.R deleted file mode 100644 index 8f4e7c44..00000000 --- a/tests/testthat/test-mixed-mrf-likelihoods.R +++ /dev/null @@ -1,549 +0,0 @@ -# ============================================================================= -# test-mixed-mrf-likelihoods.R — Phase B.1 likelihood tests -# ============================================================================= -# Tests for log_conditional_omrf() and log_conditional_ggm() in the -# MixedMRFModel, validated against pure-R reference implementations. - -# ------------------------------------------------------------------ -# R reference: conditional OMRF pseudolikelihood for variable s -# ------------------------------------------------------------------ -# Computes log f(x_s | x_{-s}, y) summed over n observations. -# -# @param x_int n x p integer matrix (0-based categories) -# @param y n x q continuous matrix -# @param Kxx p x p symmetric, zero diagonal -# @param Kxy p x q cross-interactions -# @param mux p x max_cats thresholds / BC coefficients -# @param num_cats p-vector of category counts -# @param is_ordinal p-vector (1 = ordinal, 0 = BC) -# @param baseline_cat p-vector of reference categories -# @param s 0-based variable index -# -# Returns: scalar log-likelihood. -ref_log_conditional_omrf = function(x_int, y, Kxx, Kxy, mux, num_cats, - is_ordinal, baseline_cat, s) { - n = nrow(x_int) - s1 = s + 1L # R indexing - - # Center BC observations (same as constructor) - x = x_int - for(j in seq_len(ncol(x))) { - if(!is_ordinal[j]) x[, j] = x[, j] - baseline_cat[j] - } - x_dbl = matrix(as.double(x), nrow = n) - - # Rest score - rest = x_dbl %*% Kxx[, s1] - x_dbl[, s1] * Kxx[s1, s1] + - 2.0 * y %*% t(Kxy[s1, , drop = FALSE]) - rest = as.numeric(rest) - - C_s = num_cats[s1] - - if(is_ordinal[s1]) { - # Numerator: dot(x_s, rest) + sum_c count_c * mux(s, c) - numer = sum(x_dbl[, s1] * rest) - for(c in seq_len(C_s)) { - count_c = sum(x[, s1] == c) - numer = numer + count_c * mux[s1, c] - } - # Log-normalizer per person (log-sum-exp stabilized) - log_Z = numeric(n) - for(v in seq_len(n)) { - terms = numeric(C_s + 1) - terms[1] = 0 # reference category - for(c in seq_len(C_s)) { - terms[c + 1] = mux[s1, c] + c * rest[v] - } - mx = max(terms) - log_Z[v] = mx + log(sum(exp(terms - mx))) - } - return(numer - sum(log_Z)) - } else { - # Blume-Capel - alpha = mux[s1, 1] - beta = mux[s1, 2] - ref = baseline_cat[s1] - - numer = sum(x_dbl[, s1] * rest) + - alpha * sum(x[, s1]) + beta * sum(x[, s1]^2) - - log_Z = numeric(n) - for(v in seq_len(n)) { - cats = 0:C_s - centered = cats - ref - theta = alpha * centered + beta * centered^2 - terms = theta + centered * rest[v] - mx = max(terms) - log_Z[v] = mx + log(sum(exp(terms - mx))) - } - return(numer - sum(log_Z)) - } -} - -# ------------------------------------------------------------------ -# R reference: conditional GGM log-likelihood -# ------------------------------------------------------------------ -# Computes log f(y | x) using conditional mean and precision Kyy. -# -# @param x_int n x p integer matrix (0-based) -# @param y n x q continuous matrix -# @param Kxy p x q cross-interactions -# @param Kyy q x q SPD precision matrix -# @param muy q-vector of means -# @param is_ordinal p-vector -# @param baseline_cat p-vector -# -# Returns: scalar log-likelihood. -ref_log_conditional_ggm = function(x_int, y, Kxy, Kyy, muy, - is_ordinal, baseline_cat) { - n = nrow(y) - q = ncol(y) - - # Center BC observations - x = x_int - for(j in seq_len(ncol(x))) { - if(!is_ordinal[j]) x[, j] = x[, j] - baseline_cat[j] - } - x_dbl = matrix(as.double(x), nrow = n) - - Kyy_inv = solve(Kyy) - logdet_val = as.numeric(determinant(Kyy, logarithm = TRUE)$modulus) - - # Conditional mean: repmat(muy', n, 1) + 2 * x_dbl * Kxy * Kyy_inv - cond_mean = matrix(rep(muy, each = n), nrow = n) + - 2.0 * x_dbl %*% Kxy %*% Kyy_inv - - D = y - cond_mean - quad_sum = sum((D %*% Kyy) * D) - - ll = n / 2.0 * (-q * log(2 * pi) + logdet_val) - quad_sum / 2.0 - return(ll) -} - - -# ------------------------------------------------------------------ -# Helper to build a zero parameter vector with correct structure -# ------------------------------------------------------------------ -build_param_vec = function(p, q, num_cats, is_ordinal) { - num_main = 0L - for(s in seq_len(p)) { - if(is_ordinal[s]) { - num_main = num_main + num_cats[s] - } else { - num_main = num_main + 2L - } - } - num_pairwise_xx = p * (p - 1L) / 2L - num_kyy = q * (q + 1L) / 2L - num_cross = p * q - total = num_main + num_pairwise_xx + q + num_kyy + num_cross - list( - total = total, - num_main = num_main, - num_pairwise_xx = num_pairwise_xx, - q = q, - num_kyy = num_kyy, - num_cross = num_cross, - kyy_start = num_main + num_pairwise_xx + q # 0-based start of Kyy block - ) -} - - -# ------------------------------------------------------------------ -# Test setup shared across tests -# ------------------------------------------------------------------ -make_test_data = function() { - set.seed(42) - n = 10L; p = 3L; q = 2L - num_cats = c(2L, 3L, 2L) - is_ordinal = c(1L, 1L, 0L) # vars 1,2 ordinal; var 3 BC - baseline_cat = c(0L, 0L, 1L) # BC var has ref = 1 - - x = matrix(0L, n, p) - x[, 1] = sample(0:2, n, replace = TRUE) - x[, 2] = sample(0:3, n, replace = TRUE) - x[, 3] = sample(0:2, n, replace = TRUE) # original BC obs in [0, C_s] - y = matrix(rnorm(n * q), n, q) - - pq = p + q - inc_prob = matrix(0.5, pq, pq) - edge_ind = matrix(1L, pq, pq) - diag(edge_ind) = 0L - - list( - n = n, p = p, q = q, - x = x, y = y, - num_cats = num_cats, - is_ordinal = as.integer(is_ordinal), - baseline_cat = baseline_cat, - inc_prob = inc_prob, - edge_ind = edge_ind - ) -} - - -# ============================================================================== -# Tests -# ============================================================================== - -test_that("log_conditional_omrf at zero parameters equals -n*log(C+1)", { - d = make_test_data() - info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) - params = rep(0, info$total) - # Kyy = identity - idx = info$kyy_start - for(i in seq_len(d$q)) { - for(j in i:d$q) { - idx = idx + 1L - if(i == j) params[idx] = 1.0 - } - } - - res = bgms:::test_mixed_mrf_likelihoods( - d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, - d$inc_prob, d$edge_ind, FALSE, "conditional", params, 1L - ) - - # Ordinal vars: expected = -n * log(C_s + 1) - expect_equal(res$omrf_ll[1], -d$n * log(d$num_cats[1] + 1), tolerance = 1e-10) - expect_equal(res$omrf_ll[2], -d$n * log(d$num_cats[2] + 1), tolerance = 1e-10) - - # BC var: at zero params, all categories have theta = 0, rest = 0 - # so Z = num_cats + 1, numer = 0 => ll = -n * log(C+1) - expect_equal(res$omrf_ll[3], -d$n * log(d$num_cats[3] + 1), tolerance = 1e-10) -}) - - -test_that("log_conditional_ggm at zero parameters equals standard normal", { - d = make_test_data() - info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) - params = rep(0, info$total) - idx = info$kyy_start - for(i in seq_len(d$q)) { - for(j in i:d$q) { - idx = idx + 1L - if(i == j) params[idx] = 1.0 - } - } - - res = bgms:::test_mixed_mrf_likelihoods( - d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, - d$inc_prob, d$edge_ind, FALSE, "conditional", params, 1L - ) - - # At zero params: y ~ N(0, I), so ll = sum(dnorm(y, log = TRUE)) - expected = sum(dnorm(d$y, log = TRUE)) - expect_equal(res$ggm_ll, expected, tolerance = 1e-10) -}) - - -test_that("log_conditional_omrf matches R reference (ordinal, nonzero params)", { - d = make_test_data() - info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) - params = rep(0, info$total) - - # Set some nonzero main effects - params[1] = 0.3; params[2] = -0.2 # var 1 thresholds (C=2) - params[3] = 0.1; params[4] = 0.4; params[5] = -0.1 # var 2 thresholds (C=3) - params[6] = 0.5; params[7] = -0.3 # var 3 BC alpha, beta - - # Set some Kxx edges (upper-tri, row-major: (0,1), (0,2), (1,2)) - kxx_start = info$num_main - params[kxx_start + 1] = 0.2 # Kxx(0,1) - params[kxx_start + 2] = -0.1 # Kxx(0,2) - params[kxx_start + 3] = 0.15 # Kxx(1,2) - - # muy - muy_start = info$num_main + info$num_pairwise_xx - params[muy_start + 1] = 0.1 - params[muy_start + 2] = -0.2 - - # Kyy = SPD (2x2 with positive eigenvalues) - kyy_start = info$kyy_start - params[kyy_start + 1] = 2.0 # Kyy(0,0) - params[kyy_start + 2] = 0.3 # Kyy(0,1) - params[kyy_start + 3] = 1.5 # Kyy(1,1) - - # Kxy (p x q, row-major) - kxy_start = kyy_start + info$num_kyy - params[kxy_start + 1] = 0.1 # Kxy(0,0) - params[kxy_start + 2] = -0.05 # Kxy(0,1) - params[kxy_start + 3] = 0.08 # Kxy(1,0) - params[kxy_start + 4] = 0.12 # Kxy(1,1) - params[kxy_start + 5] = -0.07 # Kxy(2,0) - params[kxy_start + 6] = 0.04 # Kxy(2,1) - - res = bgms:::test_mixed_mrf_likelihoods( - d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, - d$inc_prob, d$edge_ind, FALSE, "conditional", params, 1L - ) - - # Reconstruct parameter matrices for R reference - mux = matrix(0, d$p, max(d$num_cats)) - mux[1, 1:2] = c(0.3, -0.2) - mux[2, 1:3] = c(0.1, 0.4, -0.1) - mux[3, 1:2] = c(0.5, -0.3) - - Kxx = matrix(0, d$p, d$p) - Kxx[1, 2] = Kxx[2, 1] = 0.2 - Kxx[1, 3] = Kxx[3, 1] = -0.1 - Kxx[2, 3] = Kxx[3, 2] = 0.15 - - Kxy = matrix(c(0.1, 0.08, -0.07, -0.05, 0.12, 0.04), nrow = d$p, ncol = d$q) - muy = c(0.1, -0.2) - - # Check each ordinal variable against R reference - for(s in 0:1) { - expected = ref_log_conditional_omrf( - d$x, d$y, Kxx, Kxy, mux, d$num_cats, - d$is_ordinal, d$baseline_cat, s - ) - expect_equal(res$omrf_ll[s + 1], expected, tolerance = 1e-8, - label = paste0("ordinal var ", s)) - } -}) - - -test_that("log_conditional_omrf matches R reference (Blume-Capel, nonzero params)", { - d = make_test_data() - info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) - params = rep(0, info$total) - - # Same params as ordinal test - params[1] = 0.3; params[2] = -0.2 - params[3] = 0.1; params[4] = 0.4; params[5] = -0.1 - params[6] = 0.5; params[7] = -0.3 - - kxx_start = info$num_main - params[kxx_start + 1] = 0.2 - params[kxx_start + 2] = -0.1 - params[kxx_start + 3] = 0.15 - - muy_start = info$num_main + info$num_pairwise_xx - params[muy_start + 1] = 0.1 - params[muy_start + 2] = -0.2 - - kyy_start = info$kyy_start - params[kyy_start + 1] = 2.0 - params[kyy_start + 2] = 0.3 - params[kyy_start + 3] = 1.5 - - kxy_start = kyy_start + info$num_kyy - params[kxy_start + 1] = 0.1 - params[kxy_start + 2] = -0.05 - params[kxy_start + 3] = 0.08 - params[kxy_start + 4] = 0.12 - params[kxy_start + 5] = -0.07 - params[kxy_start + 6] = 0.04 - - res = bgms:::test_mixed_mrf_likelihoods( - d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, - d$inc_prob, d$edge_ind, FALSE, "conditional", params, 1L - ) - - mux = matrix(0, d$p, max(d$num_cats)) - mux[1, 1:2] = c(0.3, -0.2) - mux[2, 1:3] = c(0.1, 0.4, -0.1) - mux[3, 1:2] = c(0.5, -0.3) - - Kxx = matrix(0, d$p, d$p) - Kxx[1, 2] = Kxx[2, 1] = 0.2 - Kxx[1, 3] = Kxx[3, 1] = -0.1 - Kxx[2, 3] = Kxx[3, 2] = 0.15 - - Kxy = matrix(c(0.1, 0.08, -0.07, -0.05, 0.12, 0.04), nrow = d$p, ncol = d$q) - - # Test BC variable (s = 2, 0-based) - expected = ref_log_conditional_omrf( - d$x, d$y, Kxx, Kxy, mux, d$num_cats, - d$is_ordinal, d$baseline_cat, s = 2 - ) - expect_equal(res$omrf_ll[3], expected, tolerance = 1e-8) -}) - - -test_that("log_conditional_ggm matches R reference (nonzero params)", { - d = make_test_data() - info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) - params = rep(0, info$total) - - # Set same params - params[1] = 0.3; params[2] = -0.2 - params[3] = 0.1; params[4] = 0.4; params[5] = -0.1 - params[6] = 0.5; params[7] = -0.3 - - kxx_start = info$num_main - params[kxx_start + 1] = 0.2 - params[kxx_start + 2] = -0.1 - params[kxx_start + 3] = 0.15 - - muy_start = info$num_main + info$num_pairwise_xx - params[muy_start + 1] = 0.1 - params[muy_start + 2] = -0.2 - - kyy_start = info$kyy_start - params[kyy_start + 1] = 2.0 - params[kyy_start + 2] = 0.3 - params[kyy_start + 3] = 1.5 - - kxy_start = kyy_start + info$num_kyy - params[kxy_start + 1] = 0.1 - params[kxy_start + 2] = -0.05 - params[kxy_start + 3] = 0.08 - params[kxy_start + 4] = 0.12 - params[kxy_start + 5] = -0.07 - params[kxy_start + 6] = 0.04 - - res = bgms:::test_mixed_mrf_likelihoods( - d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, - d$inc_prob, d$edge_ind, FALSE, "conditional", params, 1L - ) - - Kyy = matrix(c(2.0, 0.3, 0.3, 1.5), 2, 2) - Kxy = matrix(c(0.1, 0.08, -0.07, -0.05, 0.12, 0.04), nrow = d$p, ncol = d$q) - muy = c(0.1, -0.2) - - expected = ref_log_conditional_ggm( - d$x, d$y, Kxy, Kyy, muy, d$is_ordinal, d$baseline_cat - ) - expect_equal(res$ggm_ll, expected, tolerance = 1e-8) -}) - - -test_that("log_conditional_ggm invariant to Kxx changes", { - # GGM likelihood depends on Kyy, Kxy, muy but not Kxx - d = make_test_data() - info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) - params1 = rep(0, info$total) - params2 = rep(0, info$total) - - # Both: same Kyy = SPD, same Kxy, same muy - kyy_start = info$kyy_start - for(params in list(params1, params2)) { - params[kyy_start + 1] = 2.0 - params[kyy_start + 2] = 0.3 - params[kyy_start + 3] = 1.5 - } - # But different Kxx - kxx_start = info$num_main - params1[kxx_start + 1] = 0.5 - params2[kxx_start + 1] = -0.3 - - # Need to re-assign since list() copies - params1[kyy_start + 1] = 2.0 - params1[kyy_start + 2] = 0.3 - params1[kyy_start + 3] = 1.5 - params2[kyy_start + 1] = 2.0 - params2[kyy_start + 2] = 0.3 - params2[kyy_start + 3] = 1.5 - - res1 = bgms:::test_mixed_mrf_likelihoods( - d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, - d$inc_prob, d$edge_ind, FALSE, "conditional", params1, 1L - ) - res2 = bgms:::test_mixed_mrf_likelihoods( - d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, - d$inc_prob, d$edge_ind, FALSE, "conditional", params2, 1L - ) - - # GGM likelihood should be identical regardless of Kxx - expect_equal(res1$ggm_ll, res2$ggm_ll, tolerance = 1e-12) -}) - - -test_that("likelihood with p=1 ordinal, q=1 continuous works", { - set.seed(99) - n = 5L; p = 1L; q = 1L - x = matrix(sample(0:1, n, replace = TRUE), n, p) - y = matrix(rnorm(n), n, q) - num_cats = 1L - is_ordinal = 1L - baseline_cat = 0L - pq = p + q - inc_prob = matrix(0.5, pq, pq) - edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L - - # Params: 1 threshold + 0 Kxx + 1 muy + 1 Kyy + 1 Kxy = 4 - params = c(0.5, # mux(0,0) = threshold - 0.2, # muy - 1.5, # Kyy(0,0) - 0.1) # Kxy(0,0) - - res = bgms:::test_mixed_mrf_likelihoods( - x, y, num_cats, is_ordinal, baseline_cat, - inc_prob, edge_ind, FALSE, "conditional", params, 1L - ) - - # R reference - mux = matrix(0.5, 1, 1) - Kxx = matrix(0, 1, 1) - Kxy = matrix(0.1, 1, 1) - Kyy = matrix(1.5, 1, 1) - muy = 0.2 - - expected_omrf = ref_log_conditional_omrf( - x, y, Kxx, Kxy, mux, num_cats, is_ordinal, baseline_cat, s = 0 - ) - expected_ggm = ref_log_conditional_ggm( - x, y, Kxy, Kyy, muy, is_ordinal, baseline_cat - ) - - expect_equal(res$omrf_ll[1], expected_omrf, tolerance = 1e-10) - expect_equal(res$ggm_ll, expected_ggm, tolerance = 1e-10) -}) - - -test_that("all-Blume-Capel discrete variables work", { - set.seed(77) - n = 8L; p = 2L; q = 1L - num_cats = c(3L, 4L) - is_ordinal = c(0L, 0L) - baseline_cat = c(1L, 2L) - - x = matrix(0L, n, p) - x[, 1] = sample(0:3, n, replace = TRUE) - x[, 2] = sample(0:4, n, replace = TRUE) - y = matrix(rnorm(n), n, q) - - pq = p + q - inc_prob = matrix(0.5, pq, pq) - edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L - - # Params: 2 BC + 2 BC + 1 Kxx + 1 muy + 1 Kyy + 2 Kxy = 9 - # num_main = 4, num_pairwise_xx = 1, q = 1, num_kyy = 1, num_cross = 2 - params = c(0.3, -0.1, # BC var 0: alpha, beta - 0.2, -0.05, # BC var 1: alpha, beta - 0.15, # Kxx(0,1) - -0.1, # muy - 2.0, # Kyy - 0.08, -0.06) # Kxy - - res = bgms:::test_mixed_mrf_likelihoods( - x, y, num_cats, is_ordinal, baseline_cat, - inc_prob, edge_ind, FALSE, "conditional", params, 1L - ) - - mux = matrix(0, 2, 4) - mux[1, 1:2] = c(0.3, -0.1) - mux[2, 1:2] = c(0.2, -0.05) - - Kxx = matrix(0, 2, 2) - Kxx[1, 2] = Kxx[2, 1] = 0.15 - - Kxy = matrix(c(0.08, -0.06), nrow = 2, ncol = 1) - Kyy = matrix(2.0, 1, 1) - muy = -0.1 - - for(s in 0:1) { - expected = ref_log_conditional_omrf( - x, y, Kxx, Kxy, mux, num_cats, is_ordinal, baseline_cat, s - ) - expect_equal(res$omrf_ll[s + 1], expected, tolerance = 1e-8, - label = paste0("BC var ", s)) - } - - expected_ggm = ref_log_conditional_ggm( - x, y, Kxy, Kyy, muy, is_ordinal, baseline_cat - ) - expect_equal(res$ggm_ll, expected_ggm, tolerance = 1e-8) -}) diff --git a/tests/testthat/test-mixed-mrf-marginal.R b/tests/testthat/test-mixed-mrf-marginal.R deleted file mode 100644 index 4ceac822..00000000 --- a/tests/testthat/test-mixed-mrf-marginal.R +++ /dev/null @@ -1,527 +0,0 @@ -# ============================================================================= -# test-mixed-mrf-marginal.R — Phase C marginal pseudo-likelihood tests -# ============================================================================= -# Tests for log_marginal_omrf() and marginal-mode sampler, validated against -# pure-R reference implementations and cross-package comparison with mixedGM. - -# ------------------------------------------------------------------ -# R reference: marginal OMRF pseudolikelihood for variable s -# ------------------------------------------------------------------ -# Computes log f(x_s | x_{-s}) using Theta = Kxx + 2 Kxy Kyy^{-1} Kxy' -# -# @param x_int n x p integer matrix (0-based categories) -# @param Theta p x p marginal interaction matrix -# @param Kxy p x q cross-interactions -# @param mux p x max_cats thresholds / BC coefficients -# @param muy q-vector of continuous means -# @param num_cats p-vector of category counts -# @param is_ordinal p-vector (1 = ordinal, 0 = BC) -# @param baseline_cat p-vector of reference categories -# @param s 0-based variable index -# -# Returns: scalar log-likelihood. -ref_log_marginal_omrf = function(x_int, Theta, Kxy, mux, muy, num_cats, - is_ordinal, baseline_cat, s) { - n = nrow(x_int) - s1 = s + 1L - - # Center BC observations - x = x_int - for(j in seq_len(ncol(x))) { - if(!is_ordinal[j]) x[, j] = x[, j] - baseline_cat[j] - } - x_dbl = matrix(as.double(x), nrow = n) - - theta_ss = Theta[s1, s1] - - # Rest score: Theta-based + Kxy*muy bias - rest = x_dbl %*% Theta[, s1] - x_dbl[, s1] * theta_ss + - 2.0 * sum(Kxy[s1, ] * muy) - rest = as.numeric(rest) - - C_s = num_cats[s1] - - if(is_ordinal[s1]) { - # Numerator: dot(x_s, rest) + theta_ss * dot(x_s, x_s) + sum(count_c * mux) - numer = sum(x_dbl[, s1] * rest) + - theta_ss * sum(x_dbl[, s1]^2) - for(c in seq_len(C_s)) { - count_c = sum(x[, s1] == c) - numer = numer + count_c * mux[s1, c] - } - - # Denominator with col_offset = mux + c^2 * theta_ss - log_Z = numeric(n) - for(v in seq_len(n)) { - terms = numeric(C_s + 1) - terms[1] = 0 # reference category (c=0) - for(c in seq_len(C_s)) { - terms[c + 1] = mux[s1, c] + c^2 * theta_ss + c * rest[v] - } - mx = max(terms) - log_Z[v] = mx + log(sum(exp(terms - mx))) - } - return(numer - sum(log_Z)) - } else { - # Blume-Capel: alpha * sum(x) + beta * sum(x^2) - alpha = mux[s1, 1] - beta = mux[s1, 2] - ref = baseline_cat[s1] - - numer = sum(x_dbl[, s1] * rest) + - theta_ss * sum(x_dbl[, s1]^2) + - alpha * sum(x[, s1]) + beta * sum(x[, s1]^2) - - # Effective beta includes theta_ss - eff_beta = beta + theta_ss - - log_Z = numeric(n) - for(v in seq_len(n)) { - cats = 0:C_s - centered = cats - ref - theta = alpha * centered + eff_beta * centered^2 - terms = theta + centered * rest[v] - mx = max(terms) - log_Z[v] = mx + log(sum(exp(terms - mx))) - } - return(numer - sum(log_Z)) - } -} - - -# ------------------------------------------------------------------ -# Test data setup (reuse from likelihood tests) -# ------------------------------------------------------------------ -make_test_data = function() { - set.seed(42) - n = 10L; p = 3L; q = 2L - num_cats = c(2L, 3L, 2L) - is_ordinal = c(1L, 1L, 0L) - baseline_cat = c(0L, 0L, 1L) - - x = matrix(0L, n, p) - x[, 1] = sample(0:2, n, replace = TRUE) - x[, 2] = sample(0:3, n, replace = TRUE) - x[, 3] = sample(0:2, n, replace = TRUE) - y = matrix(rnorm(n * q), n, q) - - pq = p + q - inc_prob = matrix(0.5, pq, pq) - edge_ind = matrix(1L, pq, pq) - diag(edge_ind) = 0L - - list( - n = n, p = p, q = q, - x = x, y = y, - num_cats = num_cats, - is_ordinal = as.integer(is_ordinal), - baseline_cat = baseline_cat, - inc_prob = inc_prob, - edge_ind = edge_ind - ) -} - -build_param_vec = function(p, q, num_cats, is_ordinal) { - num_main = 0L - for(s in seq_len(p)) { - if(is_ordinal[s]) { - num_main = num_main + num_cats[s] - } else { - num_main = num_main + 2L - } - } - num_pairwise_xx = p * (p - 1L) / 2L - num_kyy = q * (q + 1L) / 2L - num_cross = p * q - total = num_main + num_pairwise_xx + q + num_kyy + num_cross - list( - total = total, - num_main = num_main, - num_pairwise_xx = num_pairwise_xx, - q = q, - num_kyy = num_kyy, - num_cross = num_cross, - kyy_start = num_main + num_pairwise_xx + q - ) -} - - -# ============================================================================== -# Test: marginal OMRF at zero params equals conditional OMRF -# ============================================================================== - -test_that("log_marginal_omrf at zero params equals log_conditional_omrf", { - d = make_test_data() - info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) - params = rep(0, info$total) - # Kyy = identity - idx = info$kyy_start - for(i in seq_len(d$q)) { - for(j in i:d$q) { - idx = idx + 1L - if(i == j) params[idx] = 1.0 - } - } - - res = bgms:::test_mixed_mrf_likelihoods( - d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, - d$inc_prob, d$edge_ind, FALSE, "marginal", params, 1L - ) - - # With Kxy = 0 and Kyy = I, Theta = Kxx + 0 = Kxx and bias = 0, - # so marginal OMRF = conditional OMRF - for(s in seq_len(d$p)) { - expect_equal(res$marg_omrf_ll[s], res$omrf_ll[s], tolerance = 1e-10, - label = paste0("var ", s - 1, " marginal == conditional at zero")) - } -}) - - -# ============================================================================== -# Test: marginal OMRF matches R reference (ordinal, nonzero params) -# ============================================================================== - -test_that("log_marginal_omrf matches R reference (ordinal, nonzero params)", { - d = make_test_data() - info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) - params = rep(0, info$total) - - # Main effects - params[1] = 0.3; params[2] = -0.2 - params[3] = 0.1; params[4] = 0.4; params[5] = -0.1 - params[6] = 0.5; params[7] = -0.3 - - # Kxx edges - kxx_start = info$num_main - params[kxx_start + 1] = 0.2 - params[kxx_start + 2] = -0.1 - params[kxx_start + 3] = 0.15 - - # muy - muy_start = info$num_main + info$num_pairwise_xx - params[muy_start + 1] = 0.1 - params[muy_start + 2] = -0.2 - - # Kyy = SPD - kyy_start = info$kyy_start - params[kyy_start + 1] = 2.0 - params[kyy_start + 2] = 0.3 - params[kyy_start + 3] = 1.5 - - # Kxy - kxy_start = kyy_start + info$num_kyy - params[kxy_start + 1] = 0.1 - params[kxy_start + 2] = -0.05 - params[kxy_start + 3] = 0.08 - params[kxy_start + 4] = 0.12 - params[kxy_start + 5] = -0.07 - params[kxy_start + 6] = 0.04 - - res = bgms:::test_mixed_mrf_likelihoods( - d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, - d$inc_prob, d$edge_ind, FALSE, "marginal", params, 1L - ) - - # Build R reference matrices - mux = matrix(0, d$p, max(d$num_cats)) - mux[1, 1:2] = c(0.3, -0.2) - mux[2, 1:3] = c(0.1, 0.4, -0.1) - mux[3, 1:2] = c(0.5, -0.3) - - Kxx = matrix(0, d$p, d$p) - Kxx[1, 2] = Kxx[2, 1] = 0.2 - Kxx[1, 3] = Kxx[3, 1] = -0.1 - Kxx[2, 3] = Kxx[3, 2] = 0.15 - - Kxy = matrix(c(0.1, 0.08, -0.07, -0.05, 0.12, 0.04), nrow = d$p, ncol = d$q) - Kyy = matrix(c(2.0, 0.3, 0.3, 1.5), d$q, d$q) - muy = c(0.1, -0.2) - - # Theta = Kxx + 2 Kxy Kyy^{-1} Kxy' - Theta = Kxx + 2 * Kxy %*% solve(Kyy) %*% t(Kxy) - - # Check ordinal variables - for(s in 0:1) { - expected = ref_log_marginal_omrf( - d$x, Theta, Kxy, mux, muy, d$num_cats, - d$is_ordinal, d$baseline_cat, s - ) - expect_equal(res$marg_omrf_ll[s + 1], expected, tolerance = 1e-8, - label = paste0("ordinal var ", s)) - } -}) - - -# ============================================================================== -# Test: marginal OMRF matches R reference (Blume-Capel) -# ============================================================================== - -test_that("log_marginal_omrf matches R reference (Blume-Capel, nonzero params)", { - d = make_test_data() - info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) - params = rep(0, info$total) - - params[1] = 0.3; params[2] = -0.2 - params[3] = 0.1; params[4] = 0.4; params[5] = -0.1 - params[6] = 0.5; params[7] = -0.3 - - kxx_start = info$num_main - params[kxx_start + 1] = 0.2 - params[kxx_start + 2] = -0.1 - params[kxx_start + 3] = 0.15 - - muy_start = info$num_main + info$num_pairwise_xx - params[muy_start + 1] = 0.1 - params[muy_start + 2] = -0.2 - - kyy_start = info$kyy_start - params[kyy_start + 1] = 2.0 - params[kyy_start + 2] = 0.3 - params[kyy_start + 3] = 1.5 - - kxy_start = kyy_start + info$num_kyy - params[kxy_start + 1] = 0.1 - params[kxy_start + 2] = -0.05 - params[kxy_start + 3] = 0.08 - params[kxy_start + 4] = 0.12 - params[kxy_start + 5] = -0.07 - params[kxy_start + 6] = 0.04 - - res = bgms:::test_mixed_mrf_likelihoods( - d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, - d$inc_prob, d$edge_ind, FALSE, "marginal", params, 1L - ) - - mux = matrix(0, d$p, max(d$num_cats)) - mux[1, 1:2] = c(0.3, -0.2) - mux[2, 1:3] = c(0.1, 0.4, -0.1) - mux[3, 1:2] = c(0.5, -0.3) - - Kxx = matrix(0, d$p, d$p) - Kxx[1, 2] = Kxx[2, 1] = 0.2 - Kxx[1, 3] = Kxx[3, 1] = -0.1 - Kxx[2, 3] = Kxx[3, 2] = 0.15 - - Kxy = matrix(c(0.1, 0.08, -0.07, -0.05, 0.12, 0.04), nrow = d$p, ncol = d$q) - Kyy = matrix(c(2.0, 0.3, 0.3, 1.5), d$q, d$q) - muy = c(0.1, -0.2) - - Theta = Kxx + 2 * Kxy %*% solve(Kyy) %*% t(Kxy) - - # Test BC variable (s = 2, 0-based) - expected = ref_log_marginal_omrf( - d$x, Theta, Kxy, mux, muy, d$num_cats, - d$is_ordinal, d$baseline_cat, s = 2 - ) - expect_equal(res$marg_omrf_ll[3], expected, tolerance = 1e-8) -}) - - -# ============================================================================== -# Test: marginal OMRF differs from conditional when Kxy != 0 -# ============================================================================== - -test_that("log_marginal_omrf differs from log_conditional_omrf with nonzero Kxy", { - d = make_test_data() - info = build_param_vec(d$p, d$q, d$num_cats, d$is_ordinal) - params = rep(0, info$total) - - params[1] = 0.3; params[2] = -0.2 - params[3] = 0.1; params[4] = 0.4; params[5] = -0.1 - params[6] = 0.5; params[7] = -0.3 - - kxx_start = info$num_main - params[kxx_start + 1] = 0.2 - - muy_start = info$num_main + info$num_pairwise_xx - params[muy_start + 1] = 0.1 - - kyy_start = info$kyy_start - params[kyy_start + 1] = 2.0 - params[kyy_start + 2] = 0.3 - params[kyy_start + 3] = 1.5 - - # Nonzero Kxy makes marginal != conditional - kxy_start = kyy_start + info$num_kyy - params[kxy_start + 1] = 0.3 - params[kxy_start + 2] = -0.2 - - res = bgms:::test_mixed_mrf_likelihoods( - d$x, d$y, d$num_cats, as.integer(d$is_ordinal), d$baseline_cat, - d$inc_prob, d$edge_ind, FALSE, "marginal", params, 1L - ) - - # At least one variable should differ - diffs = abs(res$marg_omrf_ll - res$omrf_ll) - expect_true(max(diffs) > 0.01, - label = "marginal and conditional OMRF differ with nonzero Kxy") -}) - - -# ============================================================================== -# Helper: extract parameter matrices from bgms sample matrix -# ============================================================================== -# (Same as in test-mixed-mrf-sampling.R; duplicated here so marginal tests -# are self-contained.) -extract_bgms_estimates = function(samples, p, q, num_cats_bgms) { - S = colMeans(samples) - idx = 1L - max_cat = max(num_cats_bgms) - mux = matrix(0, p, max_cat) - for(s in 1:p) { - for(c in seq_len(num_cats_bgms[s])) { - mux[s, c] = S[idx]; idx = idx + 1L - } - } - Kxx = matrix(0, p, p) - for(i in 1:(p - 1)) for(j in (i + 1):p) { - Kxx[i, j] = Kxx[j, i] = S[idx]; idx = idx + 1L - } - muy = S[idx:(idx + q - 1)]; idx = idx + q - Kyy = matrix(0, q, q) - for(i in 1:q) for(j in i:q) { - Kyy[i, j] = Kyy[j, i] = S[idx]; idx = idx + 1L - } - Kxy = matrix(S[idx:(idx + p * q - 1)], nrow = p, ncol = q, byrow = TRUE) - list(mux = mux, Kxx = Kxx, muy = muy, Kyy = Kyy, Kxy = Kxy) -} - - -# ============================================================================== -# Test: marginal mode sampler agrees with mixedGM (simulation) -# ============================================================================== - -test_that("marginal PL sampler agrees with mixedGM (ordinal simulation)", { - skip_on_cran() - skip_if_not_installed("mixedGM") - - p = 3L; q = 2L; n = 1000L - - Kxx_true = matrix(c(0, 0.4, -0.2, - 0.4, 0, 0.3, - -0.2, 0.3, 0), 3, 3) - Kxy_true = matrix(c(0.2, -0.1, 0.15, - 0.1, 0.3, -0.2), nrow = p, ncol = q) - Kyy_true = matrix(c(1.3, 0.2, 0.2, 1.1), 2, 2) - mux_true = matrix(c(0.5, -0.5, 0.3, - -0.3, -1.0, -0.5), nrow = p, ncol = 2) - muy_true = c(0.5, -0.3) - - set.seed(456) - sim = mixedGM::mixed_gibbs_generate( - n = n, Kxx = Kxx_true, Kxy = Kxy_true, Kyy = Kyy_true, - mux = mux_true, muy = muy_true, - num_categories = rep(3L, p), n_burnin = 1000 - ) - - # --- run mixedGM (marginal PL) --- - set.seed(42) - mgm = mixedGM::mixed_sampler( - x = sim$x, y = sim$y, num_categories = rep(3L, p), - n_warmup = 4000L, n_samples = 8000L, - edge_selection = FALSE, verbose = FALSE, - pseudolikelihood = "marginal" - ) - mgm_est = mgm$final_parameters - - # --- run bgms (marginal PL) --- - num_cats_bgms = rep(2L, p) - pq = p + q - inc_prob = matrix(0.5, pq, pq); diag(inc_prob) = 0 - edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L - - bgms_out = bgms:::test_mixed_mrf_sampler( - sim$x, sim$y, as.integer(num_cats_bgms), - as.integer(rep(1L, p)), rep(0L, p), - inc_prob, edge_ind, FALSE, "marginal", - 4000L, 8000L, 42L - ) - bgms_est = extract_bgms_estimates(bgms_out$samples, p, q, num_cats_bgms) - - # --- correlations bgms vs mixedGM (r >= 0.90) --- - kxx_ut = upper.tri(bgms_est$Kxx) - expect_gt( - cor(bgms_est$Kxx[kxx_ut], mgm_est$Kxx[kxx_ut]), 0.90, - label = "Kxx bgms-mgm correlation (marginal sim)" - ) - expect_gt( - cor(as.vector(bgms_est$Kxy), as.vector(mgm_est$Kxy)), 0.90, - label = "Kxy bgms-mgm correlation (marginal sim)" - ) - expect_gt( - cor(as.vector(bgms_est$Kyy), as.vector(mgm_est$Kyy)), 0.90, - label = "Kyy bgms-mgm correlation (marginal sim)" - ) - expect_gt( - cor(as.vector(bgms_est$mux), as.vector(mgm_est$mux)), 0.90, - label = "mux bgms-mgm correlation (marginal sim)" - ) -}) - - -# ============================================================================== -# Test: marginal mode sampler agrees with mixedGM on Wenchuan data -# ============================================================================== - -test_that("marginal PL sampler agrees with mixedGM on Wenchuan data", { - skip_on_cran() - skip_if_not_installed("mixedGM") - - data(Wenchuan, package = "bgms") - W = na.omit(Wenchuan) - - p = 4L; q = 2L - x = as.matrix(W[, 1:p]) - 1L # convert 1-5 to 0-4 - y = as.matrix(W[, (p + 1):(p + q)]) - - num_cats_mgm = rep(5L, p) # total categories for mixedGM - num_cats_bgms = rep(4L, p) # max category index for bgms - - # --- mixedGM (marginal PL) --- - set.seed(42) - mgm = mixedGM::mixed_sampler( - x = x, y = y, num_categories = num_cats_mgm, - n_warmup = 4000L, n_samples = 8000L, - edge_selection = FALSE, verbose = FALSE, - pseudolikelihood = "marginal" - ) - mgm_est = mgm$final_parameters - - # --- bgms (marginal PL) --- - pq = p + q - inc_prob = matrix(0.5, pq, pq); diag(inc_prob) = 0 - edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L - - bgms_out = bgms:::test_mixed_mrf_sampler( - x, y, as.integer(num_cats_bgms), - as.integer(rep(1L, p)), rep(0L, p), - inc_prob, edge_ind, FALSE, "marginal", - 4000L, 8000L, 42L - ) - bgms_est = extract_bgms_estimates(bgms_out$samples, p, q, num_cats_bgms) - - # --- correlations bgms vs mixedGM (r >= 0.90) --- - kxx_ut = upper.tri(bgms_est$Kxx) - expect_gt( - cor(bgms_est$Kxx[kxx_ut], mgm_est$Kxx[kxx_ut]), 0.90, - label = "Kxx bgms-mgm correlation (marginal Wenchuan)" - ) - expect_gt( - cor(as.vector(bgms_est$Kxy), as.vector(mgm_est$Kxy)), 0.85, - label = "Kxy bgms-mgm correlation (marginal Wenchuan)" - ) - expect_gt( - cor(as.vector(bgms_est$Kyy), as.vector(mgm_est$Kyy)), 0.90, - label = "Kyy bgms-mgm correlation (marginal Wenchuan)" - ) - expect_gt( - cor(as.vector(bgms_est$mux), as.vector(mgm_est$mux)), 0.90, - label = "mux bgms-mgm correlation (marginal Wenchuan)" - ) - - # --- Kxx sign patterns agree --- - expect_true( - all(sign(bgms_est$Kxx[kxx_ut]) == sign(mgm_est$Kxx[kxx_ut])), - label = "Kxx signs agree (marginal Wenchuan)" - ) -}) diff --git a/tests/testthat/test-mixed-mrf-sampling.R b/tests/testthat/test-mixed-mrf-sampling.R deleted file mode 100644 index 9a258492..00000000 --- a/tests/testthat/test-mixed-mrf-sampling.R +++ /dev/null @@ -1,224 +0,0 @@ -# ============================================================================= -# test-mixed-mrf-sampling.R — Phase B.5 sampler correctness tests -# ============================================================================= -# Validates that do_one_metropolis_step() in MixedMRFModel produces correct -# posterior samples by comparing bgms against the mixedGM reference sampler. -# -# Because no proposal adaptation is implemented yet (Phase F), acceptance -# rates are low with the default proposal SD = 1.0. Tests use correlation -# thresholds rather than absolute agreement. - -skip_if_not_installed("mixedGM") - -# --------------------------------------------------------------------------- -# Helper: extract parameter matrices from bgms sample matrix -# --------------------------------------------------------------------------- -# The vectorization order in bgms is: -# 1. mux: per-variable, C_s thresholds each (for ordinal) -# 2. Kxx: upper-triangular, row-major -# 3. muy: q means -# 4. Kyy: upper-triangle including diagonal -# 5. Kxy: p*q entries, row-major -extract_bgms_estimates = function(samples, p, q, num_cats_bgms) { - S = colMeans(samples) - idx = 1L - - # 1. mux (per-variable: C_s thresholds each) - max_cat = max(num_cats_bgms) - mux = matrix(0, p, max_cat) - for(s in 1:p) { - for(c in seq_len(num_cats_bgms[s])) { - mux[s, c] = S[idx]; idx = idx + 1L - } - } - - # 2. Kxx upper-tri - Kxx = matrix(0, p, p) - for(i in 1:(p - 1)) for(j in (i + 1):p) { - Kxx[i, j] = Kxx[j, i] = S[idx]; idx = idx + 1L - } - - # 3. muy - muy = S[idx:(idx + q - 1)]; idx = idx + q - - # 4. Kyy upper-tri + diag - Kyy = matrix(0, q, q) - for(i in 1:q) for(j in i:q) { - Kyy[i, j] = Kyy[j, i] = S[idx]; idx = idx + 1L - } - - # 5. Kxy row-major - Kxy = matrix(S[idx:(idx + p * q - 1)], nrow = p, ncol = q, byrow = TRUE) - - list(mux = mux, Kxx = Kxx, muy = muy, Kyy = Kyy, Kxy = Kxy) -} - - -# =========================================================================== -# Test 1: ordinal simulation recovery (3-category, p=3, q=2, n=1000) -# =========================================================================== -test_that("conditional PL sampler recovers parameters (ordinal simulation)", { - skip_on_cran() - - p = 3L; q = 2L; n = 1000L - - Kxx_true = matrix(c(0, 0.4, -0.2, - 0.4, 0, 0.3, - -0.2, 0.3, 0), 3, 3) - Kxy_true = matrix(c(0.2, -0.1, 0.15, - 0.1, 0.3, -0.2), nrow = p, ncol = q) - Kyy_true = matrix(c(1.3, 0.2, 0.2, 1.1), 2, 2) - mux_true = matrix(c(0.5, -0.5, 0.3, - -0.3, -1.0, -0.5), nrow = p, ncol = 2) - muy_true = c(0.5, -0.3) - - set.seed(456) - sim = mixedGM::mixed_gibbs_generate( - n = n, Kxx = Kxx_true, Kxy = Kxy_true, Kyy = Kyy_true, - mux = mux_true, muy = muy_true, - num_categories = rep(3L, p), n_burnin = 1000 - ) - - # --- run mixedGM (with Robbins-Monro adaptation) --- - set.seed(42) - mgm = mixedGM::mixed_sampler( - x = sim$x, y = sim$y, num_categories = rep(3L, p), - n_warmup = 4000L, n_samples = 8000L, - edge_selection = FALSE, verbose = FALSE, - pseudolikelihood = "conditional" - ) - mgm_est = mgm$final_parameters - - # --- run bgms (no adaptation) --- - num_cats_bgms = rep(2L, p) # max category index - pq = p + q - inc_prob = matrix(0.5, pq, pq); diag(inc_prob) = 0 - edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L - - bgms_out = bgms:::test_mixed_mrf_sampler( - sim$x, sim$y, as.integer(num_cats_bgms), - as.integer(rep(1L, p)), rep(0L, p), - inc_prob, edge_ind, FALSE, "conditional", - 4000L, 8000L, 42L - ) - bgms_est = extract_bgms_estimates(bgms_out$samples, p, q, num_cats_bgms) - - # --- correlations bgms vs mixedGM (r >= 0.95) --- - kxx_ut = upper.tri(bgms_est$Kxx) - expect_gt( - cor(bgms_est$Kxx[kxx_ut], mgm_est$Kxx[kxx_ut]), 0.95, - label = "Kxx bgms-mgm correlation" - ) - expect_gt( - cor(as.vector(bgms_est$Kxy), as.vector(mgm_est$Kxy)), 0.95, - label = "Kxy bgms-mgm correlation" - ) - expect_gt( - cor(as.vector(bgms_est$Kyy), as.vector(mgm_est$Kyy)), 0.95, - label = "Kyy bgms-mgm correlation" - ) - expect_gt( - cor(as.vector(bgms_est$mux), as.vector(mgm_est$mux)), 0.95, - label = "mux bgms-mgm correlation" - ) - - # --- signs agree with truth --- - expect_true( - all(sign(bgms_est$Kxx[kxx_ut]) == sign(Kxx_true[kxx_ut])), - label = "Kxx signs match truth" - ) -}) - - -# =========================================================================== -# Test 2: real data cross-validation (Wenchuan, ordinal + continuous) -# =========================================================================== -test_that("conditional PL sampler agrees with mixedGM on Wenchuan data", { - skip_on_cran() - - data(Wenchuan, package = "bgms") - W = na.omit(Wenchuan) - - p = 4L; q = 2L - x = as.matrix(W[, 1:p]) - 1L # convert 1-5 to 0-4 - y = as.matrix(W[, (p + 1):(p + q)]) - - num_cats_mgm = rep(5L, p) # total categories for mixedGM - num_cats_bgms = rep(4L, p) # max category index for bgms - - # --- mixedGM --- - set.seed(42) - mgm = mixedGM::mixed_sampler( - x = x, y = y, num_categories = num_cats_mgm, - n_warmup = 4000L, n_samples = 8000L, - edge_selection = FALSE, verbose = FALSE, - pseudolikelihood = "conditional" - ) - mgm_est = mgm$final_parameters - - # --- bgms --- - pq = p + q - inc_prob = matrix(0.5, pq, pq); diag(inc_prob) = 0 - edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L - - bgms_out = bgms:::test_mixed_mrf_sampler( - x, y, as.integer(num_cats_bgms), - as.integer(rep(1L, p)), rep(0L, p), - inc_prob, edge_ind, FALSE, "conditional", - 4000L, 8000L, 42L - ) - bgms_est = extract_bgms_estimates(bgms_out$samples, p, q, num_cats_bgms) - - # --- correlations bgms vs mixedGM (r >= 0.95) --- - kxx_ut = upper.tri(bgms_est$Kxx) - expect_gt( - cor(bgms_est$Kxx[kxx_ut], mgm_est$Kxx[kxx_ut]), 0.95, - label = "Kxx bgms-mgm correlation (Wenchuan)" - ) - expect_gt( - cor(as.vector(bgms_est$Kxy), as.vector(mgm_est$Kxy)), 0.95, - label = "Kxy bgms-mgm correlation (Wenchuan)" - ) - expect_gt( - cor(as.vector(bgms_est$Kyy), as.vector(mgm_est$Kyy)), 0.95, - label = "Kyy bgms-mgm correlation (Wenchuan)" - ) - expect_gt( - cor(as.vector(bgms_est$mux), as.vector(mgm_est$mux)), 0.95, - label = "mux bgms-mgm correlation (Wenchuan)" - ) - - # --- Kxx sign patterns agree --- - expect_true( - all(sign(bgms_est$Kxx[kxx_ut]) == sign(mgm_est$Kxx[kxx_ut])), - label = "Kxx signs agree (Wenchuan)" - ) -}) - - -# =========================================================================== -# Test 3: sampler does not crash on Kyy updates (c3 regression test) -# =========================================================================== -test_that("Kyy updates do not crash (c3 get_constants regression)", { - # Minimal setup: p=2, q=2 binary triggers the c3 code path - # where L(qm1,qm1)^2 must be included in the sum. - set.seed(99) - n = 50L; p = 2L; q = 2L - x = matrix(sample(0:1, n * p, replace = TRUE), n, p) - y = matrix(rnorm(n * q), n, q) - - pq = p + q - inc_prob = matrix(0.5, pq, pq); diag(inc_prob) = 0 - edge_ind = matrix(1L, pq, pq); diag(edge_ind) = 0L - - # If c3 bug is present, this crashes with "chol(): decomposition failed" - result = bgms:::test_mixed_mrf_sampler( - x, y, as.integer(c(1L, 1L)), - as.integer(c(1L, 1L)), c(0L, 0L), - inc_prob, edge_ind, FALSE, "conditional", - 100L, 100L, 123L - ) - - expect_equal(nrow(result$samples), 100L) - expect_true(all(is.finite(result$samples))) -}) diff --git a/tests/testthat/test-mixed-mrf-skeleton.R b/tests/testthat/test-mixed-mrf-skeleton.R deleted file mode 100644 index 0519cf67..00000000 --- a/tests/testthat/test-mixed-mrf-skeleton.R +++ /dev/null @@ -1,235 +0,0 @@ -# ============================================================================== -# Phase A.3: MixedMRFModel Skeleton Tests -# ============================================================================== -# -# Verifies that the MixedMRFModel C++ class compiles, links, and that -# parameter_dimension() and get/set_vectorized_parameters() round-trip -# correctly. Tests cover: -# -# 1. All-ordinal variables (p ordinal discrete + q continuous) -# 2. All-Blume-Capel variables -# 3. Mixed ordinal + Blume-Capel variables -# 4. Both conditional and marginal pseudolikelihood modes -# 5. Clone round-trip -# 6. Dimension arithmetic (num_variables, num_pairwise, indicator length) -# -# ============================================================================== - -# The helper-fixtures.R file loads bgms, but test_file() may skip it. -# Ensure the package is available: -if(!requireNamespace("bgms", quietly = TRUE)) { - skip("bgms package not installed") -} -test_mixed_mrf_skeleton = bgms:::test_mixed_mrf_skeleton - -# ---- Helper to build test inputs for a given scenario ---- -make_mixed_mrf_inputs = function(n, p, q, num_categories, is_ordinal, - baseline_category, edge_selection = FALSE, - pseudolikelihood = "conditional", - seed = 42L) { - set.seed(seed) - # Discrete observations: each column in 0..(num_categories[s]) - discrete_obs = matrix(0L, nrow = n, ncol = p) - for(s in seq_len(p)) { - discrete_obs[, s] = sample(0L:num_categories[s], n, replace = TRUE) - } - # Continuous observations - continuous_obs = matrix(rnorm(n * q), nrow = n, ncol = q) - - total = p + q - inclusion_prob = matrix(0.5, nrow = total, ncol = total) - diag(inclusion_prob) = 0 - edge_ind = matrix(1L, nrow = total, ncol = total) - diag(edge_ind) = 0L - - list( - discrete_observations = discrete_obs, - continuous_observations = continuous_obs, - num_categories = as.integer(num_categories), - is_ordinal_variable = as.integer(is_ordinal), - baseline_category = as.integer(baseline_category), - inclusion_probability = inclusion_prob, - initial_edge_indicators = edge_ind, - edge_selection = edge_selection, - pseudolikelihood = pseudolikelihood, - seed = seed - ) -} - -run_skeleton_test = function(inputs) { - do.call(test_mixed_mrf_skeleton, inputs) -} - -# ---- Expected dimension calculator (mirrors C++ logic) ---- -expected_full_dim = function(p, q, num_categories, is_ordinal) { - num_main = sum(ifelse(is_ordinal == 1L, num_categories, 2L)) - num_pairwise_xx = p * (p - 1) / 2 - num_pairwise_yy_with_diag = q * (q + 1) / 2 - num_cross = p * q - num_main + num_pairwise_xx + q + num_pairwise_yy_with_diag + num_cross -} - -expected_num_pairwise = function(p, q) { - p * (p - 1) / 2 + q * (q - 1) / 2 + p * q -} - -expected_indicator_length = function(p, q) { - expected_num_pairwise(p, q) -} - -# ============================================================================== -# Test scenarios -# ============================================================================== - -test_that("all-ordinal skeleton has correct dimensions", { - p = 3L; q = 2L; n = 20L - num_cats = c(2L, 3L, 2L) - is_ord = c(1L, 1L, 1L) - baseline = rep(0L, p) - - inp = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline) - res = run_skeleton_test(inp) - - expect_equal(res$num_variables, p + q) - expect_equal(res$parameter_dimension, - expected_full_dim(p, q, num_cats, is_ord)) - expect_equal(res$full_parameter_dimension, - expected_full_dim(p, q, num_cats, is_ord)) - expect_equal(res$params_length, res$full_parameter_dimension) - expect_equal(res$num_pairwise, expected_num_pairwise(p, q)) - expect_equal(res$indicator_length, expected_indicator_length(p, q)) - expect_equal(res$edge_indicators_rows, p + q) - expect_equal(res$edge_indicators_cols, p + q) -}) - -test_that("all-Blume-Capel skeleton has correct dimensions", { - p = 3L; q = 2L; n = 20L - num_cats = c(4L, 3L, 4L) - is_ord = c(0L, 0L, 0L) - baseline = c(2L, 1L, 2L) - - inp = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline) - res = run_skeleton_test(inp) - - expect_equal(res$num_variables, p + q) - # BC always contributes 2 per variable - expect_equal(res$parameter_dimension, - expected_full_dim(p, q, num_cats, is_ord)) - expect_equal(res$full_parameter_dimension, - expected_full_dim(p, q, num_cats, is_ord)) -}) - -test_that("mixed ordinal + BC skeleton has correct dimensions", { - p = 4L; q = 3L; n = 25L - num_cats = c(2L, 4L, 3L, 3L) - is_ord = c(1L, 0L, 1L, 0L) - baseline = c(0L, 2L, 0L, 1L) - - inp = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline) - res = run_skeleton_test(inp) - - expect_equal(res$num_variables, p + q) - expect_equal(res$parameter_dimension, - expected_full_dim(p, q, num_cats, is_ord)) -}) - -test_that("zero-parameter round-trip is exact", { - p = 3L; q = 2L; n = 20L - num_cats = c(2L, 3L, 2L) - is_ord = c(1L, 1L, 1L) - baseline = rep(0L, p) - - inp = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline) - res = run_skeleton_test(inp) - - expect_equal(res$roundtrip_max_diff, 0.0) -}) - -test_that("non-trivial parameter round-trip is exact", { - p = 3L; q = 2L; n = 20L - num_cats = c(2L, 3L, 2L) - is_ord = c(1L, 1L, 1L) - baseline = rep(0L, p) - - inp = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline) - res = run_skeleton_test(inp) - - expect_equal(res$nontrivial_roundtrip_max_diff, 0.0) -}) - -test_that("non-trivial round-trip with Blume-Capel variables", { - p = 4L; q = 3L; n = 25L - num_cats = c(2L, 4L, 3L, 3L) - is_ord = c(1L, 0L, 1L, 0L) - baseline = c(0L, 2L, 0L, 1L) - - inp = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline) - res = run_skeleton_test(inp) - - expect_equal(res$nontrivial_roundtrip_max_diff, 0.0) -}) - -test_that("clone produces identical parameters", { - p = 3L; q = 2L; n = 20L - num_cats = c(2L, 3L, 2L) - is_ord = c(1L, 1L, 1L) - baseline = rep(0L, p) - - inp = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline) - res = run_skeleton_test(inp) - - expect_equal(res$clone_max_diff, 0.0) -}) - -test_that("marginal pseudolikelihood mode works", { - p = 3L; q = 2L; n = 20L - num_cats = c(2L, 3L, 2L) - is_ord = c(1L, 1L, 1L) - baseline = rep(0L, p) - - inp = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline, - pseudolikelihood = "marginal") - res = run_skeleton_test(inp) - - expect_equal(res$parameter_dimension, - expected_full_dim(p, q, num_cats, is_ord)) - expect_equal(res$nontrivial_roundtrip_max_diff, 0.0) -}) - -test_that("capability queries return correct values", { - p = 3L; q = 2L; n = 20L - num_cats = c(2L, 3L, 2L) - is_ord = c(1L, 1L, 1L) - baseline = rep(0L, p) - - inp_no_es = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline, - edge_selection = FALSE) - res_no_es = run_skeleton_test(inp_no_es) - expect_false(res_no_es$has_edge_selection) - expect_true(res_no_es$has_adaptive_metropolis) - expect_false(res_no_es$has_gradient) - expect_false(res_no_es$has_missing_data) - - inp_es = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline, - edge_selection = TRUE) - res_es = run_skeleton_test(inp_es) - expect_true(res_es$has_edge_selection) -}) - -test_that("single discrete + single continuous works", { - p = 1L; q = 1L; n = 15L - num_cats = 2L - is_ord = 1L - baseline = 0L - - inp = make_mixed_mrf_inputs(n, p, q, num_cats, is_ord, baseline) - res = run_skeleton_test(inp) - - # p=1: 0 xx edges, q=1: 0 yy edges, 1 cross edge - expect_equal(res$num_variables, 2L) - expect_equal(res$num_pairwise, 1L) - expect_equal(res$indicator_length, 1L) - # mux: 2 thresholds, Kxx: 0, muy: 1, Kyy: 1 diag, Kxy: 1 - expect_equal(res$full_parameter_dimension, 2 + 0 + 1 + 1 + 1) - expect_equal(res$nontrivial_roundtrip_max_diff, 0.0) -}) From 7a3910fdb32c0be5b0b33e60435b626007383d45 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Sat, 7 Mar 2026 00:27:52 +0100 Subject: [PATCH 16/60] feat: Robbins-Monro proposal-SD adaptation for MixedMRFModel (Phase F) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Wire iteration index through all 6 MH update functions and add per-parameter Robbins-Monro adaptation (weight = iter^{-0.75}, target = 0.44, clamp [0.001, 2.0]) during warmup: - update_main_effect, update_continuous_mean, update_Kxx - update_Kyy_offdiag, update_Kyy_diag, update_Kxy init_metropolis_adaptation stores total_warmup from WarmupSchedule. No changes needed to WarmupSchedule or ChainRunner — the existing Metropolis-only pipeline already handles MH models correctly. Also: add @param pseudolikelihood to bgm() roxygen (fixes codoc mismatch from Phase E). 3965 tests pass, 0 failures. --- R/bgm.R | 16 ++++++++ man/bgm.Rd | 17 ++++++++ src/models/mixed/mixed_mrf_metropolis.cpp | 48 ++++++++++++++++++++--- src/models/mixed/mixed_mrf_model.cpp | 26 ++++++------ src/models/mixed/mixed_mrf_model.h | 13 +++--- 5 files changed, 96 insertions(+), 24 deletions(-) diff --git a/R/bgm.R b/R/bgm.R index 53bde915..ebbd5f1f 100644 --- a/R/bgm.R +++ b/R/bgm.R @@ -212,6 +212,22 @@ #' score endpoints \eqn{(-b, m-b)}. #' Default: \code{FALSE}. #' +#' @param pseudolikelihood Character. Specifies the pseudo-likelihood +#' approximation used for mixed MRF models (ignored for pure ordinal or +#' pure continuous data). Options: +#' \describe{ +#' \item{\code{"conditional"}}{Conditions on the observed continuous +#' variables when computing the discrete full conditionals. Faster +#' because the discrete pseudo-likelihood does not depend on the +#' continuous precision matrix.} +#' \item{\code{"marginal"}}{Integrates out the continuous variables, +#' giving discrete full conditionals that account for induced +#' interactions through the continuous block. More expensive per +#' iteration but can improve estimation when discrete--continuous +#' coupling is strong.} +#' } +#' Default: \code{"conditional"}. +#' #' @param main_alpha,main_beta Double. Shape parameters of the #' beta-prime prior for threshold parameters. Must be positive. If equal, #' the prior is symmetric. Defaults: \code{main_alpha = 0.5} and diff --git a/man/bgm.Rd b/man/bgm.Rd index 92826f3e..3dbe3dc6 100644 --- a/man/bgm.Rd +++ b/man/bgm.Rd @@ -33,6 +33,7 @@ bgm( display_progress = c("per-chain", "total", "none"), seed = NULL, standardize = FALSE, + pseudolikelihood = c("conditional", "marginal"), verbose = getOption("bgms.verbose", TRUE), interaction_scale, burnin, @@ -166,6 +167,22 @@ raw score endpoints \eqn{(0, m)} and Blume-Capel variables use centered score endpoints \eqn{(-b, m-b)}. Default: \code{FALSE}.} +\item{pseudolikelihood}{Character. Specifies the pseudo-likelihood +approximation used for mixed MRF models (ignored for pure ordinal or +pure continuous data). Options: +\describe{ + \item{\code{"conditional"}}{Conditions on the observed continuous + variables when computing the discrete full conditionals. Faster + because the discrete pseudo-likelihood does not depend on the + continuous precision matrix.} + \item{\code{"marginal"}}{Integrates out the continuous variables, + giving discrete full conditionals that account for induced + interactions through the continuous block. More expensive per + iteration but can improve estimation when discrete--continuous + coupling is strong.} +} +Default: \code{"conditional"}.} + \item{verbose}{Logical. If \code{TRUE}, prints informational messages during data processing (e.g., missing data handling, variable recoding). Defaults to \code{getOption("bgms.verbose", TRUE)}. Set diff --git a/src/models/mixed/mixed_mrf_metropolis.cpp b/src/models/mixed/mixed_mrf_metropolis.cpp index ca7c6d9b..d60c0288 100644 --- a/src/models/mixed/mixed_mrf_metropolis.cpp +++ b/src/models/mixed/mixed_mrf_metropolis.cpp @@ -26,7 +26,7 @@ static double log_beta_prior(double x, double alpha, double beta) { // The accept/reject uses log_conditional_omrf(s) + beta-type prior. // ============================================================================= -void MixedMRFModel::update_main_effect(int s, int c) { +void MixedMRFModel::update_main_effect(int s, int c, int iteration) { double& current = mux_(s, c); double proposal_sd = prop_sd_mux_(s, c); @@ -47,6 +47,12 @@ void MixedMRFModel::update_main_effect(int s, int c) { if(std::log(runif(rng_)) >= ln_alpha) { current = current_val; // reject } + + if(iteration >= 1 && iteration < total_warmup_) { + double rm_weight = std::pow(iteration, -0.75); + prop_sd_mux_(s, c) = update_proposal_sd_with_robbins_monro( + prop_sd_mux_(s, c), ln_alpha, rm_weight, 0.44); + } } @@ -58,7 +64,7 @@ void MixedMRFModel::update_main_effect(int s, int c) { // Must save/restore conditional_mean_ around the proposal. // ============================================================================= -void MixedMRFModel::update_continuous_mean(int j) { +void MixedMRFModel::update_continuous_mean(int j, int iteration) { double current_val = muy_(j); double proposed = rnorm(rng_, current_val, prop_sd_muy_(j)); @@ -86,6 +92,12 @@ void MixedMRFModel::update_continuous_mean(int j) { muy_(j) = current_val; // reject conditional_mean_ = std::move(cond_mean_saved); } + + if(iteration >= 1 && iteration < total_warmup_) { + double rm_weight = std::pow(iteration, -0.75); + prop_sd_muy_(j) = update_proposal_sd_with_robbins_monro( + prop_sd_muy_(j), ln_alpha, rm_weight, 0.44); + } } @@ -97,7 +109,7 @@ void MixedMRFModel::update_continuous_mean(int j) { // Acceptance: log_conditional_omrf(i) + log_conditional_omrf(j) + Cauchy prior. // ============================================================================= -void MixedMRFModel::update_Kxx(int i, int j) { +void MixedMRFModel::update_Kxx(int i, int j, int iteration) { double current_val = Kxx_(i, j); double proposed = rnorm(rng_, current_val, prop_sd_Kxx_(i, j)); @@ -131,6 +143,12 @@ void MixedMRFModel::update_Kxx(int i, int j) { Kxx_(j, i) = current_val; if(use_marginal_pl_) recompute_Theta(); } + + if(iteration >= 1 && iteration < total_warmup_) { + double rm_weight = std::pow(iteration, -0.75); + prop_sd_Kxx_(i, j) = update_proposal_sd_with_robbins_monro( + prop_sd_Kxx_(i, j), ln_alpha, rm_weight, 0.44); + } } @@ -366,7 +384,7 @@ void MixedMRFModel::cholesky_update_after_kyy_diag(double old_ii, int i) { // Prior: Cauchy(0, pairwise_scale_) on off-diag, Gamma(1, 1) on diagonal. // ============================================================================= -void MixedMRFModel::update_Kyy_offdiag(int i, int j) { +void MixedMRFModel::update_Kyy_offdiag(int i, int j, int iteration) { get_kyy_constants(i, j); double phi_curr = kyy_constants_[0]; // Phi_q1q @@ -417,6 +435,12 @@ void MixedMRFModel::update_Kyy_offdiag(int i, int j) { recompute_conditional_mean(); if(use_marginal_pl_) recompute_Theta(); } + + if(iteration >= 1 && iteration < total_warmup_) { + double rm_weight = std::pow(iteration, -0.75); + prop_sd_Kyy_(i, j) = update_proposal_sd_with_robbins_monro( + prop_sd_Kyy_(i, j), ln_alpha, rm_weight, 0.44); + } } @@ -429,7 +453,7 @@ void MixedMRFModel::update_Kyy_offdiag(int i, int j) { // Prior: Gamma(1, 1) on the diagonal element + Jacobian for log-scale proposal. // ============================================================================= -void MixedMRFModel::update_Kyy_diag(int i) { +void MixedMRFModel::update_Kyy_diag(int i, int iteration) { double logdet = cholesky_helpers::get_log_det(Kyy_chol_); double logdet_sub_ii = logdet + std::log(covariance_yy_(i, i)); @@ -473,6 +497,12 @@ void MixedMRFModel::update_Kyy_diag(int i) { recompute_conditional_mean(); if(use_marginal_pl_) recompute_Theta(); } + + if(iteration >= 1 && iteration < total_warmup_) { + double rm_weight = std::pow(iteration, -0.75); + prop_sd_Kyy_(i, i) = update_proposal_sd_with_robbins_monro( + prop_sd_Kyy_(i, i), ln_alpha, rm_weight, 0.44); + } } @@ -484,7 +514,7 @@ void MixedMRFModel::update_Kyy_diag(int i) { // Must save/restore conditional_mean_ around the proposal. // ============================================================================= -void MixedMRFModel::update_Kxy(int i, int j) { +void MixedMRFModel::update_Kxy(int i, int j, int iteration) { double current_val = Kxy_(i, j); double proposed = rnorm(rng_, current_val, prop_sd_Kxy_(i, j)); @@ -522,6 +552,12 @@ void MixedMRFModel::update_Kxy(int i, int j) { conditional_mean_ = std::move(cond_mean_saved); if(use_marginal_pl_) Theta_ = std::move(Theta_saved); } + + if(iteration >= 1 && iteration < total_warmup_) { + double rm_weight = std::pow(iteration, -0.75); + prop_sd_Kxy_(i, j) = update_proposal_sd_with_robbins_monro( + prop_sd_Kxy_(i, j), ln_alpha, rm_weight, 0.44); + } } diff --git a/src/models/mixed/mixed_mrf_model.cpp b/src/models/mixed/mixed_mrf_model.cpp index c1ddc812..38ef87e5 100644 --- a/src/models/mixed/mixed_mrf_model.cpp +++ b/src/models/mixed/mixed_mrf_model.cpp @@ -145,6 +145,7 @@ MixedMRFModel::MixedMRFModel(const MixedMRFModel& other) prop_sd_Kxx_(other.prop_sd_Kxx_), prop_sd_Kyy_(other.prop_sd_Kyy_), prop_sd_Kxy_(other.prop_sd_Kxy_), + total_warmup_(other.total_warmup_), Kyy_chol_(other.Kyy_chol_), inv_cholesky_yy_(other.inv_cholesky_yy_), covariance_yy_(other.covariance_yy_), @@ -434,43 +435,43 @@ std::unique_ptr MixedMRFModel::clone() const { // Stubs (to be implemented in later phases) // ============================================================================= -void MixedMRFModel::do_one_metropolis_step(int /*iteration*/) { +void MixedMRFModel::do_one_metropolis_step(int iteration) { // Step 1: Update all main effects (ordinal thresholds or BC α/β) for(size_t s = 0; s < p_; ++s) { if(is_ordinal_variable_(s)) { for(int c = 0; c < num_categories_(s); ++c) - update_main_effect(s, c); + update_main_effect(s, c, iteration); } else { - update_main_effect(s, 0); // linear α - update_main_effect(s, 1); // quadratic β + update_main_effect(s, 0, iteration); // linear α + update_main_effect(s, 1, iteration); // quadratic β } } // Step 2: Update all continuous means for(size_t j = 0; j < q_; ++j) - update_continuous_mean(j); + update_continuous_mean(j, iteration); // Step 3: Update Kxx (upper triangle, edge-gated) for(size_t i = 0; i < p_ - 1; ++i) for(size_t j = i + 1; j < p_; ++j) if(!edge_selection_active_ || gxx(i, j) == 1) - update_Kxx(i, j); + update_Kxx(i, j, iteration); // Step 4: Update Kyy (off-diag + diagonal, edge-gated) if(q_ >= 2) { for(size_t i = 0; i < q_ - 1; ++i) for(size_t j = i + 1; j < q_; ++j) if(!edge_selection_active_ || gyy(i, j) == 1) - update_Kyy_offdiag(i, j); + update_Kyy_offdiag(i, j, iteration); } for(size_t i = 0; i < q_; ++i) - update_Kyy_diag(i); + update_Kyy_diag(i, iteration); // Step 5: Update Kxy (edge-gated) for(size_t i = 0; i < p_; ++i) for(size_t j = 0; j < q_; ++j) if(!edge_selection_active_ || gxy(i, j) == 1) - update_Kxy(i, j); + update_Kxy(i, j, iteration); // Step 6: Edge selection (reversible-jump birth/death) update_edge_indicators(); @@ -567,10 +568,11 @@ void MixedMRFModel::prepare_iteration() { edge_order_xy_ = arma_randperm(rng_, num_cross_); } -void MixedMRFModel::init_metropolis_adaptation(const WarmupSchedule& /*schedule*/) { - // Phase F: initialize Robbins-Monro controllers +void MixedMRFModel::init_metropolis_adaptation(const WarmupSchedule& schedule) { + total_warmup_ = schedule.total_warmup; } void MixedMRFModel::tune_proposal_sd(int /*iteration*/, const WarmupSchedule& /*schedule*/) { - // Phase F: Robbins-Monro proposal-SD tuning + // Robbins-Monro adaptation is embedded in each MH update function, + // gated by iteration < total_warmup_. No separate tuning pass needed. } diff --git a/src/models/mixed/mixed_mrf_model.h b/src/models/mixed/mixed_mrf_model.h index 1d2cf8ea..bcfa607e 100644 --- a/src/models/mixed/mixed_mrf_model.h +++ b/src/models/mixed/mixed_mrf_model.h @@ -278,6 +278,7 @@ class MixedMRFModel : public BaseModel { arma::mat prop_sd_Kxx_; // p × p arma::mat prop_sd_Kyy_; // q × q arma::mat prop_sd_Kxy_; // p × q + int total_warmup_ = 0; // stored by init_metropolis_adaptation // ========================================================================= // Cached quantities @@ -377,22 +378,22 @@ class MixedMRFModel : public BaseModel { // --- Parameter update sweeps --- /** Update one main-effect: mux_(s, c). Ordinal threshold or BC α/β. */ - void update_main_effect(int s, int c); + void update_main_effect(int s, int c, int iteration); /** Update one continuous mean: muy_(j). */ - void update_continuous_mean(int j); + void update_continuous_mean(int j, int iteration); /** Update one discrete interaction: Kxx_(i, j). Symmetric. */ - void update_Kxx(int i, int j); + void update_Kxx(int i, int j, int iteration); /** Update one off-diagonal precision element: Kyy_(i, j). Cholesky-based. */ - void update_Kyy_offdiag(int i, int j); + void update_Kyy_offdiag(int i, int j, int iteration); /** Update one diagonal precision element: Kyy_(i, i). Log-scale Cholesky. */ - void update_Kyy_diag(int i); + void update_Kyy_diag(int i, int iteration); /** Update one cross interaction: Kxy_(i, j). */ - void update_Kxy(int i, int j); + void update_Kxy(int i, int j, int iteration); // --- Edge-indicator update sweeps (Phase D) --- From e44e131315998f02392f9faa1ac6bfec203622b7 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Sat, 7 Mar 2026 11:40:39 +0100 Subject: [PATCH 17/60] feat: mixed MRF simulation and prediction (Phase G) --- R/RcppExports.R | 12 + R/simulate_predict.R | 589 ++++++++++++++++++ dev/mixedmrf/implementation_plan.md | 76 ++- dev/tests/test-simulation-recovery.R | 109 ++++ src/RcppExports.cpp | 71 +++ src/mrf_prediction.cpp | 147 +++++ src/mrf_simulation.cpp | 491 +++++++++++++++ tests/testthat/helper-fixtures.R | 16 + .../test-mixed-mrf-simulate-predict.R | 397 ++++++++++++ .../test-simulate-predict-regression.R | 101 ++- 10 files changed, 1991 insertions(+), 18 deletions(-) create mode 100644 tests/testthat/test-mixed-mrf-simulate-predict.R diff --git a/R/RcppExports.R b/R/RcppExports.R index cd0c9e0d..39d81707 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -25,6 +25,10 @@ compute_conditional_probs <- function(observations, predict_vars, pairwise, main .Call(`_bgms_compute_conditional_probs`, observations, predict_vars, pairwise, main, num_categories, variable_type, baseline_category) } +compute_conditional_mixed <- function(x_observations, y_observations, predict_vars, Kxx, Kxy, Kyy, mux, muy, num_categories, variable_type, baseline_category) { + .Call(`_bgms_compute_conditional_mixed`, x_observations, y_observations, predict_vars, Kxx, Kxy, Kyy, mux, muy, num_categories, variable_type, baseline_category) +} + sample_omrf_gibbs <- function(num_states, num_variables, num_categories, pairwise, main, iter, seed) { .Call(`_bgms_sample_omrf_gibbs`, num_states, num_variables, num_categories, pairwise, main, iter, seed) } @@ -45,6 +49,14 @@ run_ggm_simulation_parallel <- function(pairwise_samples, main_samples, draw_ind .Call(`_bgms_run_ggm_simulation_parallel`, pairwise_samples, main_samples, draw_indices, num_states, num_variables, means, nThreads, seed, progress_type) } +sample_mixed_mrf_gibbs <- function(num_states, Kxx_r, Kxy_r, Kyy_r, mux_r, muy_r, num_categories_r, variable_type_r, baseline_category_r, iter, seed) { + .Call(`_bgms_sample_mixed_mrf_gibbs`, num_states, Kxx_r, Kxy_r, Kyy_r, mux_r, muy_r, num_categories_r, variable_type_r, baseline_category_r, iter, seed) +} + +run_mixed_simulation_parallel <- function(mux_samples, kxx_samples, muy_samples, kyy_samples, kxy_samples, draw_indices, num_states, p, q, num_categories, variable_type_r, baseline_category, iter, nThreads, seed, progress_type) { + .Call(`_bgms_run_mixed_simulation_parallel`, mux_samples, kxx_samples, muy_samples, kyy_samples, kxy_samples, draw_indices, num_states, p, q, num_categories, variable_type_r, baseline_category, iter, nThreads, seed, progress_type) +} + sample_ggm <- function(inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior = "Bernoulli", beta_bernoulli_alpha = 1.0, beta_bernoulli_beta = 1.0, beta_bernoulli_alpha_between = 1.0, beta_bernoulli_beta_between = 1.0, dirichlet_alpha = 1.0, lambda = 1.0, na_impute = FALSE, missing_index_nullable = NULL) { .Call(`_bgms_sample_ggm`, inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior, beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between, beta_bernoulli_beta_between, dirichlet_alpha, lambda, na_impute, missing_index_nullable) } diff --git a/R/simulate_predict.R b/R/simulate_predict.R index b1b291b2..1ab3f735 100644 --- a/R/simulate_predict.R +++ b/R/simulate_predict.R @@ -629,6 +629,23 @@ simulate.bgms = function(object, )) } + # ============================================================================ + # Mixed MRF (discrete + continuous) path + # ============================================================================ + if(isTRUE(arguments$is_mixed)) { + return(simulate_bgms_mixed( + object = object, + nsim = nsim, + seed = seed, + method = method, + ndraws = ndraws, + arguments = arguments, + iter = iter, + cores = cores, + progress_type = progress_type + )) + } + # ============================================================================ # OMRF (ordinal / Blume-Capel) path # ============================================================================ @@ -1033,6 +1050,21 @@ predict.bgms = function(object, )) } + # ============================================================================ + # Mixed MRF (discrete + continuous) path + # ============================================================================ + if(isTRUE(arguments$is_mixed)) { + return(predict_bgms_mixed( + object = object, + newdata = newdata, + predict_vars = predict_vars, + arguments = arguments, + type = type, + method = method, + ndraws = ndraws + )) + } + # ============================================================================ # OMRF (ordinal) path # ============================================================================ @@ -1635,3 +1667,560 @@ simulate_bgms_ggm = function(object, nsim, seed, method, ndraws, return(results) } } + + +# ============================================================================== +# Mixed MRF Simulation Helper +# ============================================================================== + +# ------------------------------------------------------------------ +# simulate_bgms_mixed +# ------------------------------------------------------------------ +# Simulation implementation for mixed MRF models (called from simulate.bgms). +# +# @param object Fitted bgms object (mixed MRF). +# @param nsim Number of observations to simulate. +# @param seed Random seed. +# @param method "posterior-mean" or "posterior-sample". +# @param ndraws Number of posterior draws (for posterior-sample). +# @param arguments Output of extract_arguments(). +# @param iter Gibbs burn-in iterations. +# @param cores Number of threads. +# @param progress_type Progress bar type. +# +# Returns: matrix (posterior-mean) or list of matrices (posterior-sample). +# ------------------------------------------------------------------ +simulate_bgms_mixed = function(object, nsim, seed, method, ndraws, + arguments, iter, cores, progress_type) { + p = arguments$num_discrete + q = arguments$num_continuous + data_columnnames = arguments$data_columnnames + disc_idx = arguments$discrete_indices + cont_idx = arguments$continuous_indices + num_categories = arguments$num_categories + is_ordinal = arguments$is_ordinal + baseline_category_disc = arguments$baseline_category + + disc_variable_type = ifelse(is_ordinal, "ordinal", "blume-capel") + + bc = integer(p) + for(s in seq_len(p)) { + if(is_ordinal[s]) { + bc[s] = 0L + } else { + bc[s] = as.integer(baseline_category_disc[s]) + } + } + + if(method == "posterior-mean") { + params = reconstruct_mixed_params_from_means(object, arguments) + + seed = check_seed(seed) + + result = sample_mixed_mrf_gibbs( + num_states = as.integer(nsim), + Kxx_r = params$Kxx, + Kxy_r = params$Kxy, + Kyy_r = params$Kyy, + mux_r = params$mux, + muy_r = params$muy, + num_categories_r = as.integer(num_categories), + variable_type_r = disc_variable_type, + baseline_category_r = as.integer(bc), + iter = as.integer(iter), + seed = seed + ) + + out = combine_mixed_result(result, disc_idx, cont_idx, data_columnnames) + return(out) + + } else { + sample_info = split_mixed_raw_samples(object, arguments) + + total_draws = sample_info$total_draws + if(is.null(ndraws)) ndraws = total_draws + ndraws = min(ndraws, total_draws) + + if(!is.null(seed)) set.seed(seed) + draw_indices = sample.int(total_draws, ndraws) + + results = run_mixed_simulation_parallel( + mux_samples = sample_info$mux_samples, + kxx_samples = sample_info$kxx_samples, + muy_samples = sample_info$muy_samples, + kyy_samples = sample_info$kyy_samples, + kxy_samples = sample_info$kxy_samples, + draw_indices = as.integer(draw_indices), + num_states = as.integer(nsim), + p = as.integer(p), + q = as.integer(q), + num_categories = as.integer(num_categories), + variable_type_r = disc_variable_type, + baseline_category = as.integer(bc), + iter = as.integer(iter), + nThreads = cores, + seed = check_seed(seed), + progress_type = progress_type + ) + + for(i in seq_along(results)) { + results[[i]] = combine_mixed_result( + results[[i]], disc_idx, cont_idx, data_columnnames + ) + } + + return(results) + } +} + + +# ============================================================================== +# Mixed MRF Prediction Helper +# ============================================================================== + +# ------------------------------------------------------------------ +# predict_bgms_mixed +# ------------------------------------------------------------------ +# Prediction implementation for mixed MRF models (called from predict.bgms). +# +# @param object Fitted bgms object (mixed MRF). +# @param newdata n x (p+q) matrix of observed data. +# @param predict_vars 1-based indices into the combined variable list. +# @param arguments Output of extract_arguments(). +# @param type "probabilities" or "response". +# @param method "posterior-mean" or "posterior-sample". +# @param ndraws Number of posterior draws (for posterior-sample). +# +# Returns: Named list of prediction matrices. +# ------------------------------------------------------------------ +predict_bgms_mixed = function(object, newdata, predict_vars, arguments, + type, method, ndraws) { + p = arguments$num_discrete + q = arguments$num_continuous + data_columnnames = arguments$data_columnnames + disc_idx = arguments$discrete_indices + cont_idx = arguments$continuous_indices + num_categories = arguments$num_categories + is_ordinal = arguments$is_ordinal + baseline_category_disc = arguments$baseline_category + + disc_variable_type = ifelse(is_ordinal, "ordinal", "blume-capel") + + bc = integer(p) + for(s in seq_len(p)) { + if(is_ordinal[s]) { + bc[s] = 0L + } else { + bc[s] = as.integer(baseline_category_disc[s]) + } + } + + # Split newdata into discrete and continuous parts + x_data = as.matrix(newdata[, disc_idx, drop = FALSE]) + storage.mode(x_data) = "integer" + y_data = as.matrix(newdata[, cont_idx, drop = FALSE]) + storage.mode(y_data) = "double" + + # Map user predict_vars (1-based, original order) to internal 0-based indices + # Internal layout: [discrete_0..p-1, continuous_p..p+q-1] + internal_predict_vars = integer(length(predict_vars)) + for(k in seq_along(predict_vars)) { + orig_col = predict_vars[k] + disc_match = match(orig_col, disc_idx) + cont_match = match(orig_col, cont_idx) + if(!is.na(disc_match)) { + internal_predict_vars[k] = disc_match - 1L + } else if(!is.na(cont_match)) { + internal_predict_vars[k] = p + cont_match - 1L + } else { + stop("Variable index ", orig_col, + " not found in discrete or continuous indices.") + } + } + + compute_one_draw = function(Kxx, Kxy, Kyy, mux, muy) { + compute_conditional_mixed( + x_observations = x_data, + y_observations = y_data, + predict_vars = as.integer(internal_predict_vars), + Kxx = Kxx, + Kxy = Kxy, + Kyy = Kyy, + mux = mux, + muy = muy, + num_categories = as.integer(num_categories), + variable_type = disc_variable_type, + baseline_category = as.integer(bc) + ) + } + + if(method == "posterior-mean") { + params = reconstruct_mixed_params_from_means(object, arguments) + raw_result = compute_one_draw( + params$Kxx, params$Kxy, params$Kyy, params$mux, params$muy + ) + + probs = format_mixed_predictions( + raw_result, predict_vars, internal_predict_vars, + p, num_categories, data_columnnames + ) + } else { + sample_info = split_mixed_raw_samples(object, arguments) + total_draws = sample_info$total_draws + if(is.null(ndraws)) ndraws = total_draws + ndraws = min(ndraws, total_draws) + + draw_indices = sample.int(total_draws, ndraws) + + all_results = vector("list", ndraws) + for(i in seq_len(ndraws)) { + params_i = reconstruct_mixed_params_from_row( + sample_info, draw_indices[i], p, q, num_categories, is_ordinal + ) + all_results[[i]] = compute_one_draw( + params_i$Kxx, params_i$Kxy, params_i$Kyy, params_i$mux, params_i$muy + ) + } + + # Average predictions across draws + num_pv = length(predict_vars) + probs = vector("list", num_pv) + probs_sd = vector("list", num_pv) + names(probs) = data_columnnames[predict_vars] + names(probs_sd) = data_columnnames[predict_vars] + + for(k in seq_len(num_pv)) { + # Stack all draws into an array: n x ncol x ndraws + var_preds = lapply(all_results, `[[`, k) + pred_array = array(unlist(var_preds), + dim = c(nrow(var_preds[[1]]), ncol(var_preds[[1]]), ndraws) + ) + + probs[[k]] = apply(pred_array, c(1, 2), mean) + probs_sd[[k]] = apply(pred_array, c(1, 2), sd) + } + + probs = format_mixed_predictions( + probs, predict_vars, internal_predict_vars, + p, num_categories, data_columnnames + ) + names(probs_sd) = names(probs) + attr(probs, "sd") = probs_sd + } + + if(type == "response") { + return(format_mixed_response(probs, predict_vars, internal_predict_vars, + p, data_columnnames)) + } + + return(probs) +} + + +# ============================================================================== +# Mixed MRF Internal Helpers +# ============================================================================== + +# ------------------------------------------------------------------ +# reconstruct_mixed_params_from_means +# ------------------------------------------------------------------ +# Reconstruct Kxx, Kxy, Kyy, mux, muy from posterior mean summaries. +# +# @param object Fitted bgms object (mixed MRF). +# @param arguments Output of extract_arguments(). +# +# Returns: list with Kxx, Kxy, Kyy, mux, muy. +# ------------------------------------------------------------------ +reconstruct_mixed_params_from_means = function(object, arguments) { + p = arguments$num_discrete + q = arguments$num_continuous + disc_idx = arguments$discrete_indices + cont_idx = arguments$continuous_indices + num_categories = arguments$num_categories + is_ordinal = arguments$is_ordinal + + pmat = object$posterior_mean_pairwise + + Kxx = matrix(0, p, p) + for(i in seq_len(p)) { + for(j in seq_len(p)) { + if(i != j) Kxx[i, j] = pmat[disc_idx[i], disc_idx[j]] + } + } + + Kxy = matrix(0, p, q) + for(i in seq_len(p)) { + for(j in seq_len(q)) { + Kxy[i, j] = pmat[disc_idx[i], cont_idx[j]] + } + } + + Kyy = matrix(0, q, q) + for(i in seq_len(q)) { + for(j in seq_len(q)) { + if(i != j) Kyy[i, j] = pmat[cont_idx[i], cont_idx[j]] + } + } + for(j in seq_len(q)) { + Kyy[j, j] = object$posterior_mean_main$continuous[j, "precision"] + } + + mux = object$posterior_mean_main$discrete + mux[is.na(mux)] = 0 + + muy = object$posterior_mean_main$continuous[, "mean"] + + list(Kxx = Kxx, Kxy = Kxy, Kyy = Kyy, mux = mux, muy = muy) +} + + +# ------------------------------------------------------------------ +# split_mixed_raw_samples +# ------------------------------------------------------------------ +# Split raw main and pairwise sample matrices into separate component +# matrices for the C++ parallel simulation worker. +# +# @param object Fitted bgms object (mixed MRF). +# @param arguments Output of extract_arguments(). +# +# Returns: list with mux_samples, kxx_samples, muy_samples, +# kyy_samples, kxy_samples, total_draws. +# ------------------------------------------------------------------ +split_mixed_raw_samples = function(object, arguments) { + p = arguments$num_discrete + q = arguments$num_continuous + num_categories = arguments$num_categories + is_ordinal = arguments$is_ordinal + + main_all = do.call(rbind, object$raw_samples$main) + pairwise_all = do.call(rbind, object$raw_samples$pairwise) + total_draws = nrow(main_all) + + # Main layout: [mux_flat | muy | kyy_diag] + num_mux = sum(ifelse(is_ordinal, num_categories, 2L)) + mux_cols = seq_len(num_mux) + muy_cols = num_mux + seq_len(q) + kyy_diag_cols = num_mux + q + seq_len(q) + + mux_samples = main_all[, mux_cols, drop = FALSE] + muy_samples = main_all[, muy_cols, drop = FALSE] + kyy_diag_values = main_all[, kyy_diag_cols, drop = FALSE] + + # Pairwise layout: [kxx_ut | kyy_offdiag | kxy] + nxx = as.integer(p * (p - 1) / 2) + nyy_offdiag = as.integer(q * (q - 1) / 2) + nxy = as.integer(p * q) + + kyy_off_end = nxx + nyy_offdiag + kxy_end = kyy_off_end + nxy + + kxx_samples = if(nxx > 0) { + pairwise_all[, seq_len(nxx), drop = FALSE] + } else { + matrix(0, nrow = total_draws, ncol = 0) + } + + kyy_offdiag_values = if(nyy_offdiag > 0) { + pairwise_all[, (nxx + 1):kyy_off_end, drop = FALSE] + } else { + matrix(0, nrow = total_draws, ncol = 0) + } + + kxy_samples = if(nxy > 0) { + pairwise_all[, (kyy_off_end + 1):kxy_end, drop = FALSE] + } else { + matrix(0, nrow = total_draws, ncol = 0) + } + + # Combine Kyy diagonal and off-diagonal into upper-triangle format + # C++ expects column-major upper-triangle including diagonal + nyy_total = as.integer(q * (q + 1) / 2) + kyy_samples = matrix(0, nrow = total_draws, ncol = nyy_total) + diag_pos = 0L + offdiag_pos = 0L + out_pos = 0L + for(col in seq_len(q)) { + for(row in col:q) { + out_pos = out_pos + 1L + if(row == col) { + diag_pos = diag_pos + 1L + kyy_samples[, out_pos] = kyy_diag_values[, diag_pos] + } else { + offdiag_pos = offdiag_pos + 1L + kyy_samples[, out_pos] = kyy_offdiag_values[, offdiag_pos] + } + } + } + + list( + mux_samples = mux_samples, + kxx_samples = kxx_samples, + muy_samples = muy_samples, + kyy_samples = kyy_samples, + kxy_samples = kxy_samples, + total_draws = total_draws + ) +} + + +# ------------------------------------------------------------------ +# reconstruct_mixed_params_from_row +# ------------------------------------------------------------------ +# Reconstruct Kxx, Kxy, Kyy, mux, muy from a single row of split +# sample matrices (used by predict posterior-sample). +# +# @param sample_info Output of split_mixed_raw_samples(). +# @param row_idx 1-based row index. +# @param p Number of discrete variables. +# @param q Number of continuous variables. +# @param num_categories Categories per discrete variable. +# @param is_ordinal Logical vector. +# +# Returns: list with Kxx, Kxy, Kyy, mux, muy. +# ------------------------------------------------------------------ +reconstruct_mixed_params_from_row = function(sample_info, row_idx, + p, q, num_categories, + is_ordinal) { + mux_vec = sample_info$mux_samples[row_idx, ] + num_params_disc = ifelse(is_ordinal, num_categories, 2L) + max_cats = max(num_params_disc) + mux = matrix(0, nrow = p, ncol = max_cats) + pos = 1L + for(s in seq_len(p)) { + nc = num_params_disc[s] + mux[s, seq_len(nc)] = mux_vec[pos:(pos + nc - 1L)] + pos = pos + nc + } + + Kxx = matrix(0, p, p) + if(p > 1) { + kxx_vec = sample_info$kxx_samples[row_idx, ] + idx = 0L + for(col in seq_len(p - 1)) { + for(row in (col + 1):p) { + idx = idx + 1L + Kxx[row, col] = kxx_vec[idx] + Kxx[col, row] = kxx_vec[idx] + } + } + } + + muy = sample_info$muy_samples[row_idx, ] + + kyy_vec = sample_info$kyy_samples[row_idx, ] + Kyy = matrix(0, q, q) + idx = 0L + for(col in seq_len(q)) { + for(row in col:q) { + idx = idx + 1L + Kyy[row, col] = kyy_vec[idx] + if(row != col) Kyy[col, row] = kyy_vec[idx] + } + } + + Kxy = matrix(0, p, q) + if(p > 0 && q > 0) { + kxy_vec = sample_info$kxy_samples[row_idx, ] + idx = 0L + for(s in seq_len(p)) { + for(j in seq_len(q)) { + idx = idx + 1L + Kxy[s, j] = kxy_vec[idx] + } + } + } + + list(Kxx = Kxx, Kxy = Kxy, Kyy = Kyy, mux = mux, muy = muy) +} + + +# ------------------------------------------------------------------ +# combine_mixed_result +# ------------------------------------------------------------------ +# Combine $x (n x p integer) and $y (n x q double) into a single +# n x (p+q) matrix in the original column order. +# +# @param result List with $x and $y matrices. +# @param disc_idx Original column indices for discrete variables. +# @param cont_idx Original column indices for continuous variables. +# @param colnames Original data column names. +# +# Returns: n x (p+q) numeric matrix. +# ------------------------------------------------------------------ +combine_mixed_result = function(result, disc_idx, cont_idx, colnames) { + n = nrow(result$x) + num_vars = length(disc_idx) + length(cont_idx) + out = matrix(NA_real_, nrow = n, ncol = num_vars) + out[, disc_idx] = result$x + out[, cont_idx] = result$y + colnames(out) = colnames + out +} + + +# ------------------------------------------------------------------ +# format_mixed_predictions +# ------------------------------------------------------------------ +# Add names and column labels to C++ prediction output. +# +# @param raw_result List from C++ compute_conditional_mixed. +# @param predict_vars 1-based user-facing variable indices. +# @param internal_predict_vars 0-based internal indices. +# @param p Number of discrete variables. +# @param num_categories Categories per discrete variable. +# @param data_columnnames Original data column names. +# +# Returns: Named list of prediction matrices. +# ------------------------------------------------------------------ +format_mixed_predictions = function(raw_result, predict_vars, + internal_predict_vars, p, + num_categories, data_columnnames) { + probs = raw_result + names(probs) = data_columnnames[predict_vars] + + for(k in seq_along(predict_vars)) { + int_idx = internal_predict_vars[k] + if(int_idx < p) { + s = int_idx + 1L + n_cats = num_categories[s] + 1 + colnames(probs[[k]]) = paste0("cat_", 0:(n_cats - 1)) + } else { + colnames(probs[[k]]) = c("mean", "sd") + } + } + + probs +} + + +# ------------------------------------------------------------------ +# format_mixed_response +# ------------------------------------------------------------------ +# Convert probability predictions to point predictions for mixed models. +# +# @param probs Named list of prediction matrices. +# @param predict_vars 1-based user-facing variable indices. +# @param internal_predict_vars 0-based internal indices. +# @param p Number of discrete variables. +# @param data_columnnames Original data column names. +# +# Returns: n x length(predict_vars) matrix of predicted values. +# ------------------------------------------------------------------ +format_mixed_response = function(probs, predict_vars, + internal_predict_vars, p, + data_columnnames) { + n = nrow(probs[[1]]) + out = matrix(NA_real_, nrow = n, ncol = length(predict_vars)) + colnames(out) = data_columnnames[predict_vars] + + for(k in seq_along(predict_vars)) { + int_idx = internal_predict_vars[k] + if(int_idx < p) { + out[, k] = apply(probs[[k]], 1, which.max) - 1L + } else { + out[, k] = probs[[k]][, 1] + } + } + + out +} diff --git a/dev/mixedmrf/implementation_plan.md b/dev/mixedmrf/implementation_plan.md index a4003ab3..71ff9947 100644 --- a/dev/mixedmrf/implementation_plan.md +++ b/dev/mixedmrf/implementation_plan.md @@ -312,13 +312,13 @@ add the new Rcpp export to `src/RcppExports.cpp`, `R/RcppExports.R`, and | **B+** | Rank-1 Cholesky optimization for Kyy updates | B | Same correctness, $O(q^2)$ per Kyy move instead of $O(q^3)$ | ✅ | | **C** | Marginal PL: $\Theta$ caching, marginal OMRF, $\mu_y$ full sweep | B+ | Recovery test passes (marg PL, estimation only) | ✅ | | **D** | Edge selection: 3 RJ sweeps | B+ | Structure recovery test passes | ✅ | -| **E** | R interface: `bgm()` dispatch, output formatting | B+ | End-to-end `bgm(mixed_data)` works | -| **F** | Warmup schedule, adaptation, diagnostics | E | Full warmup pipeline | +| **E** | R interface: `bgm()` dispatch, output formatting | B+ | End-to-end `bgm(mixed_data)` works | ✅ | +| **F** | Warmup schedule, adaptation, diagnostics | E | Full warmup pipeline | ✅ | | **G** | Simulation and prediction | E | `simulate.bgms` and `predict.bgms` for mixed | --- -## Phase A — Skeleton and data structures +## Phase A — Skeleton and data structures ✅ ### A.1 Create `mixed_mrf_model.h` @@ -517,7 +517,7 @@ The diagonal of `edge_indicators_` is always zero and excluded throughout. --- -## Phase B — Conditional pseudo-likelihood +## Phase B — Conditional pseudo-likelihood ✅ ### B.1 Implement log-likelihood functions ✅ @@ -787,7 +787,7 @@ void MixedMRFModel::do_one_metropolis_step(int iteration) { --- -## Phase B+ — Rank-1 Cholesky optimization +## Phase B+ — Rank-1 Cholesky optimization ✅ The Phase B implementation ports the mixedGM permute-then-Cholesky approach for $K_{yy}$ updates: each proposal calls `arma::chol` ($O(q^3)$), evaluates @@ -1082,7 +1082,7 @@ accepted move that changes $K_{yy}$, $K_{xy}$, or $\mu_y$. --- -## Phase C — Marginal pseudo-likelihood +## Phase C — Marginal pseudo-likelihood ✅ ### C.1 Implement `log_marginal_omrf(int s)` @@ -1312,9 +1312,9 @@ edges and does not apply per-pair scaling. This must be fixed: --- -## Phase E — R interface and integration +## Phase E — R interface and integration ✅ -### E.0 Remove `src/test_mixed_mrf.cpp` +### E.0 Remove `src/test_mixed_mrf.cpp` ✅ Delete `src/test_mixed_mrf.cpp` once `sample_mixed.cpp` provides the production Rcpp interface (E.1). The test helpers @@ -1324,7 +1324,7 @@ functionality is superseded by the real entry point and should not ship in the installed package. Update `src/RcppExports.cpp` and `R/RcppExports.R` accordingly (re-run `Rcpp::compileAttributes()`). -### E.1 Create `sample_mixed.cpp` +### E.1 Create `sample_mixed.cpp` ✅ Rcpp interface function `sample_mixed_mrf_cpp(...)`: - Takes R data (integer matrix `x`, numeric matrix `y`) @@ -1335,7 +1335,7 @@ Rcpp interface function `sample_mixed_mrf_cpp(...)`: Follow the pattern of `sample_ggm.cpp` and `sample_omrf.cpp`. -### E.2 Extend `bgm()` in R +### E.2 Extend `bgm()` in R ✅ The user interface uses **Option A** (decided): a single data frame plus a `variable_type` argument: @@ -1367,7 +1367,7 @@ column order. override so the shared sampler pipeline compiles. Future imputation support for mixed data should be a separate phase (Phase H). -### E.3 Extend `build_output.R` +### E.3 Extend `build_output.R` ✅ The output structure needs to accommodate: - Separate interaction matrices: `Kxx`, `Kyy`, `Kxy` (or a combined one) @@ -1375,7 +1375,7 @@ The output structure needs to accommodate: - Mean samples: `muy` matrix - Edge indicators decomposed by type -### E.4 Testing checkpoint — end-to-end +### E.4 Testing checkpoint — end-to-end ✅ **Test 7 (T19): `bgm()` with mixed data** - Call `bgm()` with `variable_type` containing both ordinal and continuous @@ -1384,9 +1384,9 @@ The output structure needs to accommodate: --- -## Phase F — Warmup, adaptation, and diagnostics +## Phase F — Warmup, adaptation, and diagnostics ✅ -### F.1 Warmup schedule +### F.1 Warmup schedule ✅ **Pre-condition check (before Phase F begins):** Verify that `WarmupSchedule` and `ChainRunner` support a Metropolis-only model without NUTS mass-matrix @@ -1404,7 +1404,7 @@ Use the same warmup schedule: - Stage 3b: Proposal SD tuning with edge selection (if enabled) - Stage 3c: Step-size re-adaptation with edge selection active -### F.2 Robbins-Monro adaptation +### F.2 Robbins-Monro adaptation ✅ Per-parameter proposal SD adaptation, matching the R prototype: ``` @@ -1415,7 +1415,7 @@ target = 0.44 Clamp to [0.001, 2.0]. -### F.3 Init metropolis adaptation +### F.3 Init metropolis adaptation ✅ Override `init_metropolis_adaptation(const WarmupSchedule&)` to store the schedule for use in `tune_proposal_sd()`. @@ -1425,7 +1425,7 @@ Robbins-Monro is already embedded in each update function. --- -## Phase G — Simulation and prediction +## Phase G — Simulation and prediction ✅ ### G.1 Mixed MRF simulation @@ -1444,10 +1444,52 @@ Note: the mixedGM Gibbs generator handles ordinal variables only. The Blume-Capel Gibbs sampling step must be added using the existing bgms probability helpers. +#### Implementation + +| File | Component | +|------|-----------| +| `src/mrf_simulation.cpp` | `simulate_mixed_mrf()` (core Gibbs), `sample_mixed_mrf_gibbs()` (single-draw Rcpp export), `MixedSimulationWorker` (RcppParallel worker with SafeRNG), `run_mixed_simulation_parallel()` (parallel Rcpp export) | +| `R/simulate_predict.R` | `simulate_bgms_mixed()` dispatched from `simulate.bgms()`, `reconstruct_mixed_params_from_means()`, `split_mixed_raw_samples()`, `combine_mixed_result()` | + ### G.2 Mixed MRF prediction Extend `mrf_prediction.cpp` for posterior predictive checks on mixed data. +#### Implementation + +| File | Component | +|------|-----------| +| `src/mrf_prediction.cpp` | `compute_conditional_mixed()` (Rcpp export): discrete probs via rest-score + ordinal/BC helpers, continuous conditional mean and SD from precision matrix | +| `R/simulate_predict.R` | `predict_bgms_mixed()` dispatched from `predict.bgms()`, `reconstruct_mixed_params_from_row()`, `format_mixed_predictions()`, `format_mixed_response()` | + +### G.3 Cycle-test validation + +Simulation-recovery cycle tests (fit → simulate → refit → correlate) +confirm that estimation and simulation code are consistent. + +| PL mode | Pairwise r | Main r | Max |diff| (main) | +|---------|-----------|--------|---------------------| +| Conditional | 1.000 | 1.000 | 0.50 | +| Marginal | 0.994 | 0.994 | 1.02 | + +The marginal PL main-effect offsets are a property of the parameterization, +not a code bug. The marginal PL estimates $\mu_x$ under $\Theta = K_{xx} - +K_{xy} K_{yy}^{-1} K_{xy}^\top$, but the Gibbs generator uses conditional +parameters ($K_{xx}$ directly). Both bgms and the mixedGM prototype show the +same pattern (mixedGM max |diff| = 2.4 on the same data). The pairwise +interactions are unaffected because $K_{xx}$, $K_{yy}$, and $K_{xy}$ are +shared across both parameterizations. + +Test files: + +| File | Content | +|------|---------| +| `tests/testthat/test-mixed-mrf-simulate-predict.R` | 77 tests: Gibbs sanity, parallel simulation, conditional prediction, S3 methods, edge cases (p=1, q=1) | +| `tests/testthat/test-simulate-predict-regression.R` | Mixed MRF fixtures added to existing regression test infrastructure | +| `dev/tests/test-simulation-recovery.R` | `run_simrec_test_mixed()` + Boredom cycle test | +| `dev/tests/plot_cycle_scatterplots.R` | Scatterplots for conditional and marginal PL cycle tests | +| `dev/tests/compare_bgms_mixedgm_cycle.R` | Cross-package comparison (bgms vs mixedGM) | + --- ## Testing strategy diff --git a/dev/tests/test-simulation-recovery.R b/dev/tests/test-simulation-recovery.R index 3a168607..5c0262f8 100644 --- a/dev/tests/test-simulation-recovery.R +++ b/dev/tests/test-simulation-recovery.R @@ -683,3 +683,112 @@ test_that("GGM posterior recovers parameters from simulated data", { expect_false(is.null(fit_vs_sbm$posterior_mode_allocations)) expect_false(is.null(fit_vs_sbm$raw_samples$allocations)) }) + + +# ============================================================================== +# Mixed MRF Simulation-Recovery +# ============================================================================== + +#' Run simulation-recovery test for a mixed MRF bgms fit +#' +#' @param fit A fitted bgms object (mixed MRF) +#' @param n_sim Number of observations to simulate +#' @param mcmc_args List of MCMC arguments for refitting +#' @param min_correlation Minimum acceptable correlation between estimates +#' @param seed Random seed for reproducibility +#' +#' @return List with correlation values and pass/fail status +run_simrec_test_mixed <- function(fit, n_sim = 500, mcmc_args = NULL, + min_correlation = 0.80, seed = 12345) { + + if(is.null(mcmc_args)) { + mcmc_args <- list(iter = 1000, warmup = 1000, chains = 1, + display_progress = "none") + } + + args <- extract_arguments(fit) + + # Extract pairwise estimates from original fit + original_pairwise <- colMeans(extract_pairwise_interactions(fit)) + + # Simulate data from the fitted model + set.seed(seed) + simulated_data <- simulate(fit, nsim = n_sim, method = "posterior-mean", + seed = seed) + + # Validate: check for constant discrete columns + disc_idx <- args$discrete_indices + col_vars <- apply(simulated_data[, disc_idx, drop = FALSE], 2, + function(x) length(unique(x))) + if(any(col_vars < 2)) { + return(list( + cor_pairwise = NA_real_, + passed = NA, + skipped = TRUE, + reason = sprintf("Degenerate discrete predictions for variable(s): %s", + paste(which(col_vars < 2), collapse = ", ")) + )) + } + + # Refit on simulated data + refit_args <- c( + list(x = simulated_data, + variable_type = args$variable_type, + edge_selection = FALSE), + mcmc_args + ) + + refit <- do.call(bgm, refit_args) + + # Extract estimates from refit + refit_pairwise <- colMeans(extract_pairwise_interactions(refit)) + + # Calculate correlation + cor_pairwise <- cor(original_pairwise, refit_pairwise) + if(is.na(cor_pairwise)) cor_pairwise <- 0 + + list( + cor_pairwise = cor_pairwise, + passed = cor_pairwise >= min_correlation + ) +} + + +test_that("bgm simulation-recovery: mixed MRF (Boredom, ordinal + continuous)", { + skip_on_cran() + + # Use Boredom data: 8 ordinal variables (1-7 scale), treat 3 as continuous. + # Shift ordinal columns to 0-based to match bgm() expectations. + data("Boredom", package = "bgms") + x <- as.matrix(Boredom[, 2:6]) + x <- x - 1L # shift from 1-7 to 0-6 + colnames(x) <- names(Boredom)[2:6] + n_obs <- nrow(x) + + # Treat columns 2 and 4 as continuous, rest as ordinal + vtype <- c("ordinal", "continuous", "ordinal", "continuous", "ordinal") + + fit <- bgm(x, variable_type = vtype, + edge_selection = FALSE, + iter = 1000, warmup = 1000, chains = 1, + seed = 44321, display_progress = "none") + + result <- run_simrec_test_mixed( + fit, + n_sim = n_obs, + mcmc_args = list(iter = 1000, warmup = 1000, chains = 1, + display_progress = "none"), + min_correlation = 0.80, + seed = 44321 + ) + + if(isTRUE(result$skipped)) { + skip(result$reason) + } + + expect_true( + result$cor_pairwise >= 0.80, + info = sprintf("Mixed MRF pairwise correlation = %.3f (expected >= 0.80)", + result$cor_pairwise) + ) +}) diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 8dfa1729..3fdb2d88 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -121,6 +121,27 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } +// compute_conditional_mixed +Rcpp::List compute_conditional_mixed(const arma::imat& x_observations, const arma::mat& y_observations, const arma::ivec& predict_vars, const arma::mat& Kxx, const arma::mat& Kxy, const arma::mat& Kyy, const arma::mat& mux, const arma::vec& muy, const arma::ivec& num_categories, const Rcpp::StringVector& variable_type, const arma::ivec& baseline_category); +RcppExport SEXP _bgms_compute_conditional_mixed(SEXP x_observationsSEXP, SEXP y_observationsSEXP, SEXP predict_varsSEXP, SEXP KxxSEXP, SEXP KxySEXP, SEXP KyySEXP, SEXP muxSEXP, SEXP muySEXP, SEXP num_categoriesSEXP, SEXP variable_typeSEXP, SEXP baseline_categorySEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::imat& >::type x_observations(x_observationsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type y_observations(y_observationsSEXP); + Rcpp::traits::input_parameter< const arma::ivec& >::type predict_vars(predict_varsSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type Kxx(KxxSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type Kxy(KxySEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type Kyy(KyySEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type mux(muxSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type muy(muySEXP); + Rcpp::traits::input_parameter< const arma::ivec& >::type num_categories(num_categoriesSEXP); + Rcpp::traits::input_parameter< const Rcpp::StringVector& >::type variable_type(variable_typeSEXP); + Rcpp::traits::input_parameter< const arma::ivec& >::type baseline_category(baseline_categorySEXP); + rcpp_result_gen = Rcpp::wrap(compute_conditional_mixed(x_observations, y_observations, predict_vars, Kxx, Kxy, Kyy, mux, muy, num_categories, variable_type, baseline_category)); + return rcpp_result_gen; +END_RCPP +} // sample_omrf_gibbs IntegerMatrix sample_omrf_gibbs(int num_states, int num_variables, IntegerVector num_categories, NumericMatrix pairwise, NumericMatrix main, int iter, int seed); RcppExport SEXP _bgms_sample_omrf_gibbs(SEXP num_statesSEXP, SEXP num_variablesSEXP, SEXP num_categoriesSEXP, SEXP pairwiseSEXP, SEXP mainSEXP, SEXP iterSEXP, SEXP seedSEXP) { @@ -212,6 +233,53 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } +// sample_mixed_mrf_gibbs +Rcpp::List sample_mixed_mrf_gibbs(int num_states, NumericMatrix Kxx_r, NumericMatrix Kxy_r, NumericMatrix Kyy_r, NumericMatrix mux_r, NumericVector muy_r, IntegerVector num_categories_r, Rcpp::StringVector variable_type_r, IntegerVector baseline_category_r, int iter, int seed); +RcppExport SEXP _bgms_sample_mixed_mrf_gibbs(SEXP num_statesSEXP, SEXP Kxx_rSEXP, SEXP Kxy_rSEXP, SEXP Kyy_rSEXP, SEXP mux_rSEXP, SEXP muy_rSEXP, SEXP num_categories_rSEXP, SEXP variable_type_rSEXP, SEXP baseline_category_rSEXP, SEXP iterSEXP, SEXP seedSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< int >::type num_states(num_statesSEXP); + Rcpp::traits::input_parameter< NumericMatrix >::type Kxx_r(Kxx_rSEXP); + Rcpp::traits::input_parameter< NumericMatrix >::type Kxy_r(Kxy_rSEXP); + Rcpp::traits::input_parameter< NumericMatrix >::type Kyy_r(Kyy_rSEXP); + Rcpp::traits::input_parameter< NumericMatrix >::type mux_r(mux_rSEXP); + Rcpp::traits::input_parameter< NumericVector >::type muy_r(muy_rSEXP); + Rcpp::traits::input_parameter< IntegerVector >::type num_categories_r(num_categories_rSEXP); + Rcpp::traits::input_parameter< Rcpp::StringVector >::type variable_type_r(variable_type_rSEXP); + Rcpp::traits::input_parameter< IntegerVector >::type baseline_category_r(baseline_category_rSEXP); + Rcpp::traits::input_parameter< int >::type iter(iterSEXP); + Rcpp::traits::input_parameter< int >::type seed(seedSEXP); + rcpp_result_gen = Rcpp::wrap(sample_mixed_mrf_gibbs(num_states, Kxx_r, Kxy_r, Kyy_r, mux_r, muy_r, num_categories_r, variable_type_r, baseline_category_r, iter, seed)); + return rcpp_result_gen; +END_RCPP +} +// run_mixed_simulation_parallel +Rcpp::List run_mixed_simulation_parallel(const arma::mat& mux_samples, const arma::mat& kxx_samples, const arma::mat& muy_samples, const arma::mat& kyy_samples, const arma::mat& kxy_samples, const arma::ivec& draw_indices, int num_states, int p, int q, const arma::ivec& num_categories, const Rcpp::StringVector& variable_type_r, const arma::ivec& baseline_category, int iter, int nThreads, int seed, int progress_type); +RcppExport SEXP _bgms_run_mixed_simulation_parallel(SEXP mux_samplesSEXP, SEXP kxx_samplesSEXP, SEXP muy_samplesSEXP, SEXP kyy_samplesSEXP, SEXP kxy_samplesSEXP, SEXP draw_indicesSEXP, SEXP num_statesSEXP, SEXP pSEXP, SEXP qSEXP, SEXP num_categoriesSEXP, SEXP variable_type_rSEXP, SEXP baseline_categorySEXP, SEXP iterSEXP, SEXP nThreadsSEXP, SEXP seedSEXP, SEXP progress_typeSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const arma::mat& >::type mux_samples(mux_samplesSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type kxx_samples(kxx_samplesSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type muy_samples(muy_samplesSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type kyy_samples(kyy_samplesSEXP); + Rcpp::traits::input_parameter< const arma::mat& >::type kxy_samples(kxy_samplesSEXP); + Rcpp::traits::input_parameter< const arma::ivec& >::type draw_indices(draw_indicesSEXP); + Rcpp::traits::input_parameter< int >::type num_states(num_statesSEXP); + Rcpp::traits::input_parameter< int >::type p(pSEXP); + Rcpp::traits::input_parameter< int >::type q(qSEXP); + Rcpp::traits::input_parameter< const arma::ivec& >::type num_categories(num_categoriesSEXP); + Rcpp::traits::input_parameter< const Rcpp::StringVector& >::type variable_type_r(variable_type_rSEXP); + Rcpp::traits::input_parameter< const arma::ivec& >::type baseline_category(baseline_categorySEXP); + Rcpp::traits::input_parameter< int >::type iter(iterSEXP); + Rcpp::traits::input_parameter< int >::type nThreads(nThreadsSEXP); + Rcpp::traits::input_parameter< int >::type seed(seedSEXP); + Rcpp::traits::input_parameter< int >::type progress_type(progress_typeSEXP); + rcpp_result_gen = Rcpp::wrap(run_mixed_simulation_parallel(mux_samples, kxx_samples, muy_samples, kyy_samples, kxy_samples, draw_indices, num_states, p, q, num_categories, variable_type_r, baseline_category, iter, nThreads, seed, progress_type)); + return rcpp_result_gen; +END_RCPP +} // sample_ggm Rcpp::List sample_ggm(const Rcpp::List& inputFromR, const arma::mat& prior_inclusion_prob, const arma::imat& initial_edge_indicators, const int no_iter, const int no_warmup, const int no_chains, const bool edge_selection, const int seed, const int no_threads, const int progress_type, const std::string& edge_prior, const double beta_bernoulli_alpha, const double beta_bernoulli_beta, const double beta_bernoulli_alpha_between, const double beta_bernoulli_beta_between, const double dirichlet_alpha, const double lambda, const bool na_impute, const Rcpp::Nullable missing_index_nullable); RcppExport SEXP _bgms_sample_ggm(SEXP inputFromRSEXP, SEXP prior_inclusion_probSEXP, SEXP initial_edge_indicatorsSEXP, SEXP no_iterSEXP, SEXP no_warmupSEXP, SEXP no_chainsSEXP, SEXP edge_selectionSEXP, SEXP seedSEXP, SEXP no_threadsSEXP, SEXP progress_typeSEXP, SEXP edge_priorSEXP, SEXP beta_bernoulli_alphaSEXP, SEXP beta_bernoulli_betaSEXP, SEXP beta_bernoulli_alpha_betweenSEXP, SEXP beta_bernoulli_beta_betweenSEXP, SEXP dirichlet_alphaSEXP, SEXP lambdaSEXP, SEXP na_imputeSEXP, SEXP missing_index_nullableSEXP) { @@ -324,11 +392,14 @@ static const R_CallMethodDef CallEntries[] = { {"_bgms_rcpp_ieee754_log", (DL_FUNC) &_bgms_rcpp_ieee754_log, 1}, {"_bgms_compute_conditional_ggm", (DL_FUNC) &_bgms_compute_conditional_ggm, 3}, {"_bgms_compute_conditional_probs", (DL_FUNC) &_bgms_compute_conditional_probs, 7}, + {"_bgms_compute_conditional_mixed", (DL_FUNC) &_bgms_compute_conditional_mixed, 11}, {"_bgms_sample_omrf_gibbs", (DL_FUNC) &_bgms_sample_omrf_gibbs, 7}, {"_bgms_sample_bcomrf_gibbs", (DL_FUNC) &_bgms_sample_bcomrf_gibbs, 9}, {"_bgms_sample_ggm_direct", (DL_FUNC) &_bgms_sample_ggm_direct, 4}, {"_bgms_run_simulation_parallel", (DL_FUNC) &_bgms_run_simulation_parallel, 12}, {"_bgms_run_ggm_simulation_parallel", (DL_FUNC) &_bgms_run_ggm_simulation_parallel, 9}, + {"_bgms_sample_mixed_mrf_gibbs", (DL_FUNC) &_bgms_sample_mixed_mrf_gibbs, 11}, + {"_bgms_run_mixed_simulation_parallel", (DL_FUNC) &_bgms_run_mixed_simulation_parallel, 16}, {"_bgms_sample_ggm", (DL_FUNC) &_bgms_sample_ggm, 19}, {"_bgms_sample_mixed_mrf", (DL_FUNC) &_bgms_sample_mixed_mrf, 17}, {"_bgms_sample_omrf", (DL_FUNC) &_bgms_sample_omrf, 24}, diff --git a/src/mrf_prediction.cpp b/src/mrf_prediction.cpp index 5b890d83..a4315194 100644 --- a/src/mrf_prediction.cpp +++ b/src/mrf_prediction.cpp @@ -159,3 +159,150 @@ Rcpp::List compute_conditional_probs( return result; } + + +// ============================================================================ +// Mixed MRF Conditional Prediction +// ============================================================================ + +/** + * Compute conditional distributions for a mixed MRF. + * + * For discrete variables: P(x_s = c | x_{-s}, y) using the conditional OMRF. + * For continuous variables: E(y_j | y_{-j}, x) and SD(y_j | y_{-j}, x) + * using the conditional GGM. + * + * @param x_observations n x p integer matrix of discrete data. + * @param y_observations n x q numeric matrix of continuous data. + * @param predict_vars 0-based indices into the combined (p+q) variable list. + * Indices 0..p-1 refer to discrete variables, + * p..p+q-1 refer to continuous variables. + * @param Kxx p x p pairwise interactions (diagonal zero). + * @param Kxy p x q cross interactions. + * @param Kyy q x q precision matrix. + * @param mux p x max_cats threshold / Blume-Capel parameters. + * @param muy q-vector of continuous means. + * @param num_categories p-vector: categories per discrete variable. + * @param variable_type p-vector: "ordinal" or "blume-capel". + * @param baseline_category p-vector. + * + * @return List of prediction matrices (one per predicted variable). + * For discrete: n x (num_cats+1) probability matrix. + * For continuous: n x 2 matrix (mean, sd). + */ +// [[Rcpp::export]] +Rcpp::List compute_conditional_mixed( + const arma::imat& x_observations, + const arma::mat& y_observations, + const arma::ivec& predict_vars, + const arma::mat& Kxx, + const arma::mat& Kxy, + const arma::mat& Kyy, + const arma::mat& mux, + const arma::vec& muy, + const arma::ivec& num_categories, + const Rcpp::StringVector& variable_type, + const arma::ivec& baseline_category +) { + int n = x_observations.n_rows; + int p = x_observations.n_cols; + int q = y_observations.n_cols; + int num_predict_vars = predict_vars.n_elem; + + // Precompute Kyy inverse for continuous conditionals + arma::mat Kyy_inv = arma::inv_sympd(Kyy); + + // Convert discrete to double (centered for rest-score computation) + arma::mat x_dbl = arma::conv_to::from(x_observations); + + Rcpp::List result(num_predict_vars); + + for (int pv = 0; pv < num_predict_vars; pv++) { + int var_idx = predict_vars[pv]; + + if (var_idx < p) { + // --- Discrete variable: P(x_s = c | x_{-s}, y) --- + int s = var_idx; + int Cs = num_categories[s]; + + // Rest score from discrete neighbours (centered by baseline) + arma::vec rest_discrete(n, arma::fill::zeros); + for (int k = 0; k < p; k++) { + if (k == s) continue; + arma::vec obs_k = x_dbl.col(k); + double ref_k = static_cast(baseline_category[k]); + rest_discrete += (obs_k - ref_k) * Kxx(k, s); + } + + // Rest score from continuous (factor of 2) + arma::vec rest_continuous(n, arma::fill::zeros); + for (int j = 0; j < q; j++) { + rest_continuous += 2.0 * Kxy(s, j) * y_observations.col(j); + } + + arma::vec rest_scores = rest_discrete + rest_continuous; + + arma::mat probs; + if (std::string(variable_type[s]) == "blume-capel") { + int ref = baseline_category[s]; + double lin_eff = mux(s, 0); + double quad_eff = mux(s, 1); + arma::vec bound; + probs = compute_probs_blume_capel( + rest_scores, lin_eff, quad_eff, ref, Cs, bound + ); + } else { + arma::vec main_param = mux.row(s).head(Cs).t(); + arma::vec bound(n, arma::fill::zeros); + for (int c = 0; c < Cs; c++) { + arma::vec exps = main_param[c] + (c + 1) * rest_scores; + bound = arma::max(bound, exps); + } + probs = compute_probs_ordinal(main_param, rest_scores, bound, Cs); + } + + Rcpp::NumericMatrix prob_mat(n, Cs + 1); + for (int i = 0; i < n; i++) { + for (int c = 0; c <= Cs; c++) { + prob_mat(i, c) = probs(i, c); + } + } + result[pv] = prob_mat; + + } else { + // --- Continuous variable: y_j | y_{-j}, x --- + int j = var_idx - p; + + double omega_jj = Kyy(j, j); + double cond_var = 1.0 / omega_jj; + double cond_sd = std::sqrt(cond_var); + + // Contribution from other continuous variables: + // -sum_{k != j} Kyy[j,k] * (y_k - muy_k) + arma::vec lp_continuous(n, arma::fill::zeros); + for (int k = 0; k < q; k++) { + if (k == j) continue; + lp_continuous -= Kyy(j, k) * (y_observations.col(k) - muy(k)); + } + + // Contribution from discrete variables (factor of 2): + // sum_s 2 * Kxy(s, j) * x_s_centered + arma::vec lp_discrete(n, arma::fill::zeros); + for (int s = 0; s < p; s++) { + double ref_s = static_cast(baseline_category[s]); + lp_discrete += 2.0 * Kxy(s, j) * (x_dbl.col(s) - ref_s); + } + + arma::vec cond_means = muy(j) + cond_var * (lp_continuous + lp_discrete); + + Rcpp::NumericMatrix out(n, 2); + for (int i = 0; i < n; i++) { + out(i, 0) = cond_means[i]; + out(i, 1) = cond_sd; + } + result[pv] = out; + } + } + + return result; +} \ No newline at end of file diff --git a/src/mrf_simulation.cpp b/src/mrf_simulation.cpp index 059b6145..3a85f9ad 100644 --- a/src/mrf_simulation.cpp +++ b/src/mrf_simulation.cpp @@ -721,5 +721,496 @@ Rcpp::List run_ggm_simulation_parallel( output[i] = Rcpp::wrap(results[i].observations); } + return output; +} + + +// ============================================================================ +// Mixed MRF Simulation (Block Gibbs: Discrete + Continuous) +// ============================================================================ + +/** + * Simulate observations from a mixed MRF using block Gibbs sampling. + * + * Each iteration updates all discrete variables from their full conditional + * given (x_{-s}, y), then updates all continuous variables from + * y | x ~ N(mu_y + 2 * x * Kxy * Kyy^{-1}, Kyy^{-1}). + * + * @param num_states Number of observations to simulate. + * @param Kxx p x p discrete pairwise interactions (diagonal zero). + * @param Kxy p x q cross interactions. + * @param Kyy q x q SPD continuous precision. + * @param mux p x max_cats threshold / Blume-Capel parameters. + * @param muy q-vector of continuous means. + * @param num_categories p-vector: number of categories per discrete variable + * (on top of baseline 0). + * @param variable_type p-vector: "ordinal" or "blume-capel". + * @param baseline_category p-vector: reference category per variable. + * @param iter Number of Gibbs iterations for burn-in. + * @param rng Thread-safe RNG. + * @param x_out Output: n x p integer matrix of discrete observations. + * @param y_out Output: n x q numeric matrix of continuous observations. + */ +void simulate_mixed_mrf( + int num_states, + const arma::mat& Kxx, + const arma::mat& Kxy, + const arma::mat& Kyy, + const arma::mat& mux, + const arma::vec& muy, + const arma::ivec& num_categories, + const std::vector& variable_type, + const arma::ivec& baseline_category, + int iter, + SafeRNG& rng, + arma::imat& x_out, + arma::mat& y_out) { + + int p = Kxx.n_rows; + int q = Kyy.n_rows; + + // Precompute Kyy decomposition + arma::mat Sigma_y = arma::inv_sympd(Kyy); + arma::mat L_Sigma = arma::chol(Sigma_y, "lower"); + + // Precompute Kxy * Kyy^{-1} for conditional mean + arma::mat Kxy_Sigma = Kxy * Sigma_y; // p x q + + // Copy Kxx with zeroed diagonal for safety + arma::mat Kxx_safe = Kxx; + Kxx_safe.diag().zeros(); + + // Generate each observation independently + for (int obs = 0; obs < num_states; obs++) { + // Initialize discrete variables uniformly + arma::ivec x_current(p); + for (int s = 0; s < p; s++) { + int max_cat = num_categories(s); + x_current(s) = static_cast(runif(rng) * (max_cat + 1)); + if (x_current(s) > max_cat) x_current(s) = max_cat; + } + + // Initialize continuous from marginal N(muy, Sigma_y) + arma::vec z = arma_rnorm_vec(rng, q); + arma::vec y_current = muy + L_Sigma * z; + + // Gibbs iterations + for (int it = 0; it < iter; it++) { + + // --- Update discrete variables from f(x_s | x_{-s}, y) --- + for (int s = 0; s < p; s++) { + int Cs = num_categories(s); + + // Rest score from discrete neighbours + double rest_discrete = 0.0; + for (int k = 0; k < p; k++) { + if (k != s) { + int obs_k = x_current(k); + int ref_k = baseline_category(k); + rest_discrete += (obs_k - ref_k) * Kxx_safe(k, s); + } + } + + // Rest score from continuous (factor of 2) + double rest_continuous = 0.0; + for (int j = 0; j < q; j++) { + rest_continuous += 2.0 * Kxy(s, j) * y_current(j); + } + + double rest = rest_discrete + rest_continuous; + + if (variable_type[s] == "blume-capel") { + int ref = baseline_category(s); + double alpha = mux(s, 0); + double beta = mux(s, 1); + + // Compute log-probabilities for categories 0..Cs + arma::vec log_probs(Cs + 1); + double max_lp = -std::numeric_limits::infinity(); + for (int c = 0; c <= Cs; c++) { + int d = c - ref; + log_probs(c) = alpha * d + beta * d * d + d * rest; + if (log_probs(c) > max_lp) max_lp = log_probs(c); + } + + // Stabilize and convert to cumulative probabilities + double cumsum = 0.0; + arma::vec cum_probs(Cs + 1); + for (int c = 0; c <= Cs; c++) { + cumsum += std::exp(log_probs(c) - max_lp); + cum_probs(c) = cumsum; + } + + // Sample + double u = runif(rng) * cumsum; + int sampled = 0; + while (sampled < Cs && u > cum_probs(sampled)) sampled++; + x_current(s) = sampled; + + } else { + // Ordinal: category 0 is reference with log-prob = 0 + arma::vec log_probs(Cs + 1); + log_probs(0) = 0.0; + double max_lp = 0.0; + for (int c = 1; c <= Cs; c++) { + log_probs(c) = mux(s, c - 1) + c * rest; + if (log_probs(c) > max_lp) max_lp = log_probs(c); + } + + double cumsum = 0.0; + arma::vec cum_probs(Cs + 1); + for (int c = 0; c <= Cs; c++) { + cumsum += std::exp(log_probs(c) - max_lp); + cum_probs(c) = cumsum; + } + + double u = runif(rng) * cumsum; + int sampled = 0; + while (sampled < Cs && u > cum_probs(sampled)) sampled++; + x_current(s) = sampled; + } + } + + // --- Update continuous variables from y | x --- + // y | x ~ N(muy + 2 * Kxy_Sigma^T * x_centered, Sigma_y) + // Compute centered discrete observations + arma::vec x_centered(p); + for (int s = 0; s < p; s++) { + x_centered(s) = static_cast(x_current(s) - baseline_category(s)); + } + + // Conditional mean: muy + 2 * Sigma_y * Kxy^T * x_centered + // = muy + 2 * Kxy_Sigma^T * x_centered + arma::vec cond_mean = muy + 2.0 * Kxy_Sigma.t() * x_centered; + + // Sample y ~ N(cond_mean, Sigma_y) + arma::vec z2 = arma_rnorm_vec(rng, q); + y_current = cond_mean + L_Sigma * z2; + } + + // Store final state + x_out.row(obs) = x_current.t(); + y_out.row(obs) = y_current.t(); + } +} + + +// ============================================================================ +// R Interface for Mixed MRF Simulation (standalone) +// ============================================================================ + +// [[Rcpp::export]] +Rcpp::List sample_mixed_mrf_gibbs( + int num_states, + NumericMatrix Kxx_r, + NumericMatrix Kxy_r, + NumericMatrix Kyy_r, + NumericMatrix mux_r, + NumericVector muy_r, + IntegerVector num_categories_r, + Rcpp::StringVector variable_type_r, + IntegerVector baseline_category_r, + int iter, + int seed) { + + SafeRNG rng(seed); + + int p = Kxx_r.nrow(); + int q = Kyy_r.nrow(); + + arma::mat Kxx = Rcpp::as(Kxx_r); + arma::mat Kxy = Rcpp::as(Kxy_r); + arma::mat Kyy = Rcpp::as(Kyy_r); + arma::mat mux = Rcpp::as(mux_r); + arma::vec muy = Rcpp::as(muy_r); + arma::ivec num_categories = Rcpp::as(num_categories_r); + arma::ivec baseline_category = Rcpp::as(baseline_category_r); + + // Convert variable_type + std::vector variable_type(p); + for (int i = 0; i < p; i++) { + variable_type[i] = Rcpp::as(variable_type_r[i]); + if (variable_type[i] != "blume-capel") { + baseline_category[i] = 0; + } + } + + arma::imat x_out(num_states, p); + arma::mat y_out(num_states, q); + + simulate_mixed_mrf( + num_states, Kxx, Kxy, Kyy, mux, muy, + num_categories, variable_type, baseline_category, + iter, rng, x_out, y_out + ); + + Rcpp::checkUserInterrupt(); + + return Rcpp::List::create( + Rcpp::Named("x") = Rcpp::wrap(x_out), + Rcpp::Named("y") = Rcpp::wrap(y_out) + ); +} + + +// ============================================================================ +// Parallel Mixed MRF Simulation for simulate.bgms() with Posterior Draws +// ============================================================================ + +struct MixedSimulationResult { + arma::imat x_observations; + arma::mat y_observations; + int draw_index; + bool error; + std::string error_msg; +}; + + +class MixedSimulationWorker : public RcppParallel::Worker { +public: + // Input: posterior samples as flat vectors (one row per draw) + const arma::mat& mux_samples; // ndraws x n_mux + const arma::mat& kxx_samples; // ndraws x p*(p-1)/2 + const arma::mat& muy_samples; // ndraws x q + const arma::mat& kyy_samples; // ndraws x q*(q+1)/2 + const arma::mat& kxy_samples; // ndraws x p*q + + const arma::ivec& draw_indices; + const int num_states; + const int p, q; + const arma::ivec& num_categories; + const std::vector& variable_type; + const arma::ivec& baseline_category; + const int iter; + const arma::ivec& mux_param_counts; + + const std::vector& draw_rngs; + ProgressManager& pm; + std::vector& results; + + MixedSimulationWorker( + const arma::mat& mux_samples, + const arma::mat& kxx_samples, + const arma::mat& muy_samples, + const arma::mat& kyy_samples, + const arma::mat& kxy_samples, + const arma::ivec& draw_indices, + int num_states, + int p, int q, + const arma::ivec& num_categories, + const std::vector& variable_type, + const arma::ivec& baseline_category, + int iter, + const arma::ivec& mux_param_counts, + const std::vector& draw_rngs, + ProgressManager& pm, + std::vector& results + ) : + mux_samples(mux_samples), + kxx_samples(kxx_samples), + muy_samples(muy_samples), + kyy_samples(kyy_samples), + kxy_samples(kxy_samples), + draw_indices(draw_indices), + num_states(num_states), + p(p), q(q), + num_categories(num_categories), + variable_type(variable_type), + baseline_category(baseline_category), + iter(iter), + mux_param_counts(mux_param_counts), + draw_rngs(draw_rngs), + pm(pm), + results(results) + {} + + void operator()(std::size_t begin, std::size_t end) { + for (std::size_t i = begin; i < end; i++) { + MixedSimulationResult result; + result.draw_index = draw_indices[i]; + result.error = false; + + try { + SafeRNG rng = draw_rngs[i]; + int draw_row = draw_indices[i] - 1; // 1-based to 0-based + + // Reconstruct mux (p x max_cats) + int max_cats = arma::max(mux_param_counts); + arma::mat mux(p, max_cats, arma::fill::zeros); + int idx = 0; + for (int s = 0; s < p; s++) { + for (int c = 0; c < mux_param_counts[s]; c++) { + mux(s, c) = mux_samples(draw_row, idx); + idx++; + } + } + + // Reconstruct Kxx (p x p symmetric, zero diagonal) + arma::mat Kxx(p, p, arma::fill::zeros); + idx = 0; + for (int col = 0; col < p; col++) { + for (int row = col + 1; row < p; row++) { + Kxx(row, col) = kxx_samples(draw_row, idx); + Kxx(col, row) = kxx_samples(draw_row, idx); + idx++; + } + } + + // Reconstruct muy (q-vector) + arma::vec muy(q); + for (int j = 0; j < q; j++) { + muy(j) = muy_samples(draw_row, j); + } + + // Reconstruct Kyy (q x q symmetric, upper triangle including diagonal) + arma::mat Kyy(q, q, arma::fill::zeros); + idx = 0; + for (int col = 0; col < q; col++) { + for (int row = col; row < q; row++) { + if (row == col) { + Kyy(row, col) = kyy_samples(draw_row, idx); + } else { + Kyy(row, col) = kyy_samples(draw_row, idx); + Kyy(col, row) = kyy_samples(draw_row, idx); + } + idx++; + } + } + + // Reconstruct Kxy (p x q, row-major) + arma::mat Kxy(p, q, arma::fill::zeros); + idx = 0; + for (int s = 0; s < p; s++) { + for (int j = 0; j < q; j++) { + Kxy(s, j) = kxy_samples(draw_row, idx); + idx++; + } + } + + // Simulate + result.x_observations.set_size(num_states, p); + result.y_observations.set_size(num_states, q); + + simulate_mixed_mrf( + num_states, Kxx, Kxy, Kyy, mux, muy, + num_categories, variable_type, baseline_category, + iter, rng, + result.x_observations, result.y_observations + ); + + } catch (const std::exception& e) { + result.error = true; + result.error_msg = e.what(); + } catch (...) { + result.error = true; + result.error_msg = "Unknown error"; + } + + results[i] = result; + pm.update(0); + } + } +}; + + +/** + * Run parallel mixed MRF simulations across posterior draws. + * + * The R layer splits the flat parameter vector into the 5 component matrices + * and passes them as separate sample matrices. + * + * @param mux_samples ndraws x n_mux + * @param kxx_samples ndraws x p*(p-1)/2 + * @param muy_samples ndraws x q + * @param kyy_samples ndraws x q*(q+1)/2 + * @param kxy_samples ndraws x p*q + * @param draw_indices 1-based indices of which draws to use + * @param num_states Number of observations per draw + * @param p Number of discrete variables + * @param q Number of continuous variables + * @param num_categories p-vector: categories per discrete variable + * @param variable_type_r p-vector: "ordinal" or "blume-capel" + * @param baseline_category p-vector + * @param iter Gibbs burn-in iterations + * @param nThreads Number of threads + * @param seed Random seed + * @param progress_type Progress bar type + * + * @return List of lists, each containing "x" (integer matrix) and "y" (numeric matrix). + */ +// [[Rcpp::export]] +Rcpp::List run_mixed_simulation_parallel( + const arma::mat& mux_samples, + const arma::mat& kxx_samples, + const arma::mat& muy_samples, + const arma::mat& kyy_samples, + const arma::mat& kxy_samples, + const arma::ivec& draw_indices, + int num_states, + int p, + int q, + const arma::ivec& num_categories, + const Rcpp::StringVector& variable_type_r, + const arma::ivec& baseline_category, + int iter, + int nThreads, + int seed, + int progress_type) { + + int ndraws = draw_indices.n_elem; + + std::vector variable_type(p); + arma::ivec baseline_category_safe = baseline_category; + for (int i = 0; i < p; i++) { + variable_type[i] = Rcpp::as(variable_type_r[i]); + if (variable_type[i] != "blume-capel") { + baseline_category_safe[i] = 0; + } + } + + // Compute mux param counts per discrete variable + arma::ivec mux_param_counts(p); + for (int s = 0; s < p; s++) { + if (variable_type[s] == "blume-capel") { + mux_param_counts[s] = 2; + } else { + mux_param_counts[s] = num_categories[s]; + } + } + + std::vector draw_rngs(ndraws); + for (int d = 0; d < ndraws; d++) { + draw_rngs[d] = SafeRNG(seed + d); + } + + std::vector results(ndraws); + ProgressManager pm(1, ndraws, 0, 50, progress_type); + + MixedSimulationWorker worker( + mux_samples, kxx_samples, muy_samples, kyy_samples, kxy_samples, + draw_indices, num_states, p, q, + num_categories, variable_type, baseline_category_safe, + iter, mux_param_counts, draw_rngs, pm, results + ); + + { + tbb::global_control control(tbb::global_control::max_allowed_parallelism, nThreads); + parallelFor(0, ndraws, worker); + } + + pm.finish(); + + Rcpp::List output(ndraws); + for (int i = 0; i < ndraws; i++) { + if (results[i].error) { + Rcpp::stop("Error in mixed MRF simulation draw %d: %s", + results[i].draw_index, results[i].error_msg.c_str()); + } + output[i] = Rcpp::List::create( + Rcpp::Named("x") = Rcpp::wrap(results[i].x_observations), + Rcpp::Named("y") = Rcpp::wrap(results[i].y_observations) + ); + } + return output; } \ No newline at end of file diff --git a/tests/testthat/helper-fixtures.R b/tests/testthat/helper-fixtures.R index f69ab598..0dbf6a21 100644 --- a/tests/testthat/helper-fixtures.R +++ b/tests/testthat/helper-fixtures.R @@ -637,6 +637,22 @@ get_prediction_data_ggm = function(n = 10) { x } +#' Get prediction data matching the mixed MRF bgms fixture +#' Columns: d1 (ordinal 0-2), c1 (continuous), d2 (ordinal 0-2), +#' c2 (continuous), d3 (ordinal 0-2) +get_prediction_data_mixed = function(n = 10) { + set.seed(199) + x = cbind( + sample(0:2, n, replace = TRUE), + rnorm(n), + sample(0:2, n, replace = TRUE), + rnorm(n), + sample(0:2, n, replace = TRUE) + ) + colnames(x) = c("d1", "c1", "d2", "c2", "d3") + x +} + # ------------------------------------------------------------------------------ # 3. Test Data Generators # ------------------------------------------------------------------------------ diff --git a/tests/testthat/test-mixed-mrf-simulate-predict.R b/tests/testthat/test-mixed-mrf-simulate-predict.R new file mode 100644 index 00000000..6f99ca8d --- /dev/null +++ b/tests/testthat/test-mixed-mrf-simulate-predict.R @@ -0,0 +1,397 @@ +# ============================================================================== +# tests/testthat/test-mixed-mrf-simulate-predict.R +# ============================================================================== +# Phase G tests for mixed MRF simulation and prediction. +# +# EXTENDS: test-simulate-predict-regression.R (which handles parameterized +# roundtrip tests via get_bgms_fixtures). This file covers: +# T25: Gibbs generator sanity (sample_mixed_mrf_gibbs) +# Mixed-specific structural tests for simulate.bgms / predict.bgms +# Edge cases: p=1, q=1, binary-only ordinal +# +# PATTERN: Stochastic-robust testing — dimensions, ranges, invariants, +# coarse distributional checks. No exact moment matching. +# ============================================================================== + + +# ============================================================================== +# 1. Gibbs generator sanity (T25) +# ============================================================================== +# Verify that sample_mixed_mrf_gibbs produces structurally correct output +# and coarse statistical properties match known targets. +# ============================================================================== + +test_that("sample_mixed_mrf_gibbs returns correct dimensions", { + p = 2; q = 2; n = 100 + nc = c(2L, 2L) + mux = matrix(c(0, 0.5, -0.3, 0, -0.2, 0.1), nrow = p, ncol = 3, byrow = TRUE) + Kxx = matrix(c(0, 0.3, 0.3, 0), p, p) + muy = c(0.5, -0.2) + Kyy = matrix(c(1.5, 0.2, 0.2, 1.8), q, q) + Kxy = matrix(c(0.1, -0.15, 0.2, 0.05), p, q) + + result = sample_mixed_mrf_gibbs( + num_states = n, Kxx_r = Kxx, Kxy_r = Kxy, Kyy_r = Kyy, + mux_r = mux, muy_r = muy, num_categories_r = nc, + variable_type_r = c("ordinal", "ordinal"), + baseline_category_r = c(0L, 0L), iter = 200L, seed = 42L + ) + + expect_true(is.list(result)) + expect_equal(dim(result$x), c(n, p)) + expect_equal(dim(result$y), c(n, q)) +}) + +test_that("sample_mixed_mrf_gibbs: discrete values in valid range", { + p = 3; q = 1; n = 500 + nc = c(2L, 3L, 1L) # categories 0-2, 0-3, 0-1 (binary) + mux = matrix(0, p, 4) + Kxx = matrix(0, p, p) + muy = 0 + Kyy = matrix(2) + Kxy = matrix(0, p, q) + + result = sample_mixed_mrf_gibbs( + num_states = n, Kxx_r = Kxx, Kxy_r = Kxy, Kyy_r = Kyy, + mux_r = mux, muy_r = muy, num_categories_r = nc, + variable_type_r = c("ordinal", "ordinal", "ordinal"), + baseline_category_r = c(0L, 0L, 0L), iter = 100L, seed = 1L + ) + + for(s in 1:p) { + expect_true(all(result$x[, s] >= 0), + info = sprintf("var %d has negative values", s) + ) + expect_true(all(result$x[, s] <= nc[s]), + info = sprintf("var %d exceeds max category %d", s, nc[s]) + ) + } +}) + +test_that("sample_mixed_mrf_gibbs: continuous marginal SD matches precision", { + # Independent model: Kxx = 0, Kxy = 0, so y ~ N(muy, Kyy^{-1}) + p = 1; q = 2; n = 2000 + nc = c(2L) + mux = matrix(0, p, 3) + Kxx = matrix(0) + muy = c(1.0, -0.5) + precision = c(2.0, 0.5) + Kyy = diag(precision) + Kxy = matrix(0, p, q) + + result = sample_mixed_mrf_gibbs( + num_states = n, Kxx_r = Kxx, Kxy_r = Kxy, Kyy_r = Kyy, + mux_r = mux, muy_r = muy, num_categories_r = nc, + variable_type_r = "ordinal", baseline_category_r = 0L, + iter = 300L, seed = 99L + ) + + for(j in 1:q) { + expected_sd = 1 / sqrt(precision[j]) + empirical_sd = sd(result$y[, j]) + # Loose check: within 30% of expected + expect_true( + abs(empirical_sd - expected_sd) / expected_sd < 0.3, + info = sprintf("y%d SD: expected %.3f, got %.3f", j, expected_sd, empirical_sd) + ) + } +}) + +test_that("sample_mixed_mrf_gibbs: seed reproducibility", { + p = 2; q = 1; n = 50 + nc = c(2L, 2L) + mux = matrix(c(0, 0.5, -0.3, 0, -0.2, 0.1), nrow = p, ncol = 3, byrow = TRUE) + Kxx = matrix(c(0, 0.3, 0.3, 0), p, p) + muy = 0; Kyy = matrix(1); Kxy = matrix(c(0.1, 0.2), p, q) + + args = list( + num_states = n, Kxx_r = Kxx, Kxy_r = Kxy, Kyy_r = Kyy, + mux_r = mux, muy_r = muy, num_categories_r = nc, + variable_type_r = c("ordinal", "ordinal"), + baseline_category_r = c(0L, 0L), iter = 100L, seed = 42L + ) + + r1 = do.call(sample_mixed_mrf_gibbs, args) + r2 = do.call(sample_mixed_mrf_gibbs, args) + + expect_equal(r1$x, r2$x) + expect_equal(r1$y, r2$y) +}) + +test_that("sample_mixed_mrf_gibbs: Blume-Capel variable works", { + p = 2; q = 1; n = 200 + nc = c(2L, 4L) + # ordinal: mux has num_categories entries; BC: 2 entries (alpha, beta) + max_cols = max(nc[1], 2) + mux = matrix(0, p, max_cols) + mux[2, 1] = 0.5 # alpha + mux[2, 2] = -0.3 # beta (penalizes distance from reference) + + Kxx = matrix(c(0, 0.2, 0.2, 0), p, p) + muy = 0; Kyy = matrix(1.5); Kxy = matrix(c(0.1, -0.1), p, q) + + result = sample_mixed_mrf_gibbs( + num_states = n, Kxx_r = Kxx, Kxy_r = Kxy, Kyy_r = Kyy, + mux_r = mux, muy_r = muy, num_categories_r = nc, + variable_type_r = c("ordinal", "blume-capel"), + baseline_category_r = c(0L, 2L), iter = 200L, seed = 7L + ) + + expect_equal(dim(result$x), c(n, p)) + expect_true(all(result$x[, 1] >= 0 & result$x[, 1] <= nc[1])) + expect_true(all(result$x[, 2] >= 0 & result$x[, 2] <= nc[2])) +}) + + +# ============================================================================== +# 2. Parallel simulation (run_mixed_simulation_parallel) +# ============================================================================== + +test_that("run_mixed_simulation_parallel returns correct structure", { + p = 2L; q = 1L; ndraws_total = 3L + nc = c(2L, 2L) + mux_s = matrix(rep(c(0, 0.5, -0.3, 0, -0.2, 0.1), each = ndraws_total), + nrow = ndraws_total) + kxx_s = matrix(0.3, nrow = ndraws_total, ncol = 1) + muy_s = matrix(0, nrow = ndraws_total, ncol = 1) + kyy_s = matrix(1.5, nrow = ndraws_total, ncol = 1) + kxy_s = matrix(c(0.1, -0.1), nrow = ndraws_total, ncol = 2, byrow = TRUE) + + n_use = 2L + n_obs = 15L + res = run_mixed_simulation_parallel( + mux_samples = mux_s, kxx_samples = kxx_s, + muy_samples = muy_s, kyy_samples = kyy_s, kxy_samples = kxy_s, + draw_indices = 1:n_use, num_states = n_obs, + p = p, q = q, num_categories = nc, + variable_type_r = c("ordinal", "ordinal"), + baseline_category = c(0L, 0L), + iter = 100L, nThreads = 1L, seed = 1L, progress_type = 0L + ) + + expect_true(is.list(res)) + expect_equal(length(res), n_use) + + for(d in seq_len(n_use)) { + expect_equal(dim(res[[d]]$x), c(n_obs, p)) + expect_equal(dim(res[[d]]$y), c(n_obs, q)) + expect_true(all(res[[d]]$x >= 0 & res[[d]]$x <= 2)) + } +}) + + +# ============================================================================== +# 3. compute_conditional_mixed +# ============================================================================== + +test_that("compute_conditional_mixed: discrete probs sum to 1", { + p = 2; q = 1; n = 5 + nc = c(2L, 2L) + mux = matrix(c(0, 0.5, -0.3, 0, -0.2, 0.1), nrow = p, ncol = 3, byrow = TRUE) + Kxx = matrix(c(0, 0.3, 0.3, 0), p, p) + muy = 0; Kyy = matrix(1.5); Kxy = matrix(c(0.1, 0.2), p, q) + + x_obs = matrix(c(0L, 1L, 2L, 0L, 1L, 1L, 2L, 0L, 1L, 2L), + nrow = n, ncol = p) + y_obs = matrix(rnorm(n), nrow = n, ncol = q) + + # Predict first discrete variable (0-based index 0) + preds = compute_conditional_mixed( + x_observations = x_obs, y_observations = y_obs, + predict_vars = 0L, Kxx = Kxx, Kxy = Kxy, Kyy = Kyy, + mux = mux, muy = muy, num_categories = nc, + variable_type = c("ordinal", "ordinal"), + baseline_category = c(0L, 0L) + ) + + expect_equal(length(preds), 1) + expect_equal(nrow(preds[[1]]), n) + expect_equal(ncol(preds[[1]]), nc[1] + 1) # 3 categories + + row_sums = rowSums(preds[[1]]) + expect_true(all(abs(row_sums - 1) < 1e-8)) + expect_true(all(preds[[1]] >= 0)) +}) + +test_that("compute_conditional_mixed: continuous returns mean and sd", { + p = 1; q = 2; n = 5 + nc = c(2L) + mux = matrix(c(0, 0.5, -0.3), nrow = 1, ncol = 3) + Kxx = matrix(0); muy = c(1.0, -0.5) + Kyy = matrix(c(2, 0.3, 0.3, 1.5), q, q) + Kxy = matrix(c(0.1, -0.1), 1, q) + + x_obs = matrix(c(0L, 1L, 2L, 1L, 0L), nrow = n, ncol = 1) + y_obs = matrix(rnorm(n * q), nrow = n, ncol = q) + + # Predict continuous variable at index p=1 (0-based) + preds = compute_conditional_mixed( + x_observations = x_obs, y_observations = y_obs, + predict_vars = as.integer(p), Kxx = Kxx, Kxy = Kxy, Kyy = Kyy, + mux = mux, muy = muy, num_categories = nc, + variable_type = "ordinal", baseline_category = 0L + ) + + expect_equal(length(preds), 1) + expect_equal(nrow(preds[[1]]), n) + expect_equal(ncol(preds[[1]]), 2) # mean, sd + expect_true(all(preds[[1]][, 2] > 0)) # sd > 0 +}) + +test_that("compute_conditional_mixed: mixed prediction variables", { + p = 2; q = 2; n = 5 + nc = c(2L, 2L) + mux = matrix(0, p, 3) + Kxx = matrix(c(0, 0.3, 0.3, 0), p, p) + muy = c(0.5, -0.2); Kyy = diag(c(1.5, 1.8)) + Kxy = matrix(0.1, p, q) + + x_obs = matrix(sample(0:2, n * p, replace = TRUE), n, p) + storage.mode(x_obs) = "integer" + y_obs = matrix(rnorm(n * q), n, q) + + # Predict one discrete (0) and one continuous (p+0 = 2) + preds = compute_conditional_mixed( + x_observations = x_obs, y_observations = y_obs, + predict_vars = c(0L, 2L), Kxx = Kxx, Kxy = Kxy, Kyy = Kyy, + mux = mux, muy = muy, num_categories = nc, + variable_type = c("ordinal", "ordinal"), + baseline_category = c(0L, 0L) + ) + + expect_equal(length(preds), 2) + # First is discrete: n x 3 + expect_equal(ncol(preds[[1]]), nc[1] + 1) + expect_true(all(abs(rowSums(preds[[1]]) - 1) < 1e-8)) + # Second is continuous: n x 2 + expect_equal(ncol(preds[[2]]), 2) +}) + + +# ============================================================================== +# 4. simulate.bgms for mixed MRF (posterior-mean path) +# ============================================================================== + +test_that("simulate.bgms works for mixed MRF with posterior-mean", { + fit = get_bgms_fit_mixed_mrf_no_es() + args = extract_arguments(fit) + + n_sim = 30 + result = simulate(fit, nsim = n_sim, method = "posterior-mean", seed = 1) + + expect_true(is.matrix(result)) + expect_equal(nrow(result), n_sim) + expect_equal(ncol(result), args$num_variables) + expect_equal(colnames(result), args$data_columnnames) + + # Discrete columns should be non-negative integers + for(di in args$discrete_indices) { + vals = result[, di] + expect_true(all(vals >= 0), info = sprintf("col %d negative", di)) + expect_true(all(vals == round(vals)), info = sprintf("col %d not integer", di)) + } +}) + +test_that("simulate.bgms seed reproducibility for mixed MRF", { + fit = get_bgms_fit_mixed_mrf_no_es() + + r1 = simulate(fit, nsim = 10, method = "posterior-mean", seed = 42) + r2 = simulate(fit, nsim = 10, method = "posterior-mean", seed = 42) + + expect_equal(r1, r2) +}) + + +# ============================================================================== +# 5. predict.bgms for mixed MRF (posterior-mean path) +# ============================================================================== + +test_that("predict.bgms works for mixed MRF with posterior-mean", { + fit = get_bgms_fit_mixed_mrf_no_es() + args = extract_arguments(fit) + + newdata = get_prediction_data_mixed(n = 10) + result = predict(fit, newdata = newdata, type = "probabilities") + + expect_true(is.list(result)) + expect_equal(length(result), args$num_variables) + + for(j in seq_len(args$num_variables)) { + vname = args$data_columnnames[j] + expect_equal(nrow(result[[j]]), 10, info = sprintf("var %s nrow", vname)) + expect_false(anyNA(result[[j]]), info = sprintf("var %s has NAs", vname)) + + if(args$variable_type[j] %in% c("ordinal", "blume-capel")) { + row_sums = rowSums(result[[j]]) + expect_true( + all(abs(row_sums - 1) < 1e-6), + info = sprintf("var %s probs don't sum to 1", vname) + ) + } else { + expect_equal(ncol(result[[j]]), 2, info = sprintf("var %s ncol", vname)) + expect_true(all(result[[j]][, 2] > 0), + info = sprintf("var %s sd not positive", vname) + ) + } + } +}) + +test_that("predict.bgms response type works for mixed MRF", { + fit = get_bgms_fit_mixed_mrf_no_es() + args = extract_arguments(fit) + + newdata = get_prediction_data_mixed(n = 10) + result = predict(fit, newdata = newdata, type = "response") + + expect_true(is.matrix(result)) + expect_equal(dim(result), c(10, args$num_variables)) + + # Discrete columns should be integer-valued + for(di in args$discrete_indices) { + expect_true(all(result[, di] == round(result[, di])), + info = sprintf("response col %d not integer", di) + ) + } +}) + + +# ============================================================================== +# 6. Edge cases +# ============================================================================== + +test_that("sample_mixed_mrf_gibbs: single discrete variable (p=1, q=2)", { + result = sample_mixed_mrf_gibbs( + num_states = 50L, + Kxx_r = matrix(0), + Kxy_r = matrix(c(0.1, -0.1), 1, 2), + Kyy_r = diag(c(1.5, 2.0)), + mux_r = matrix(c(0, 0.5), 1, 2), + muy_r = c(0, 0), + num_categories_r = 1L, + variable_type_r = "ordinal", + baseline_category_r = 0L, + iter = 100L, seed = 3L + ) + + expect_equal(dim(result$x), c(50, 1)) + expect_equal(dim(result$y), c(50, 2)) + expect_true(all(result$x %in% 0:1)) +}) + +test_that("sample_mixed_mrf_gibbs: single continuous variable (p=2, q=1)", { + result = sample_mixed_mrf_gibbs( + num_states = 50L, + Kxx_r = matrix(c(0, 0.2, 0.2, 0), 2, 2), + Kxy_r = matrix(c(0.1, -0.1), 2, 1), + Kyy_r = matrix(2.0), + mux_r = matrix(c(0, 0.5, -0.3, 0, -0.2, 0.1), 2, 3, byrow = TRUE), + muy_r = 0.5, + num_categories_r = c(2L, 2L), + variable_type_r = c("ordinal", "ordinal"), + baseline_category_r = c(0L, 0L), + iter = 100L, seed = 5L + ) + + expect_equal(dim(result$x), c(50, 2)) + expect_equal(dim(result$y), c(50, 1)) +}) diff --git a/tests/testthat/test-simulate-predict-regression.R b/tests/testthat/test-simulate-predict-regression.R index de9f9932..f441bbd9 100644 --- a/tests/testthat/test-simulate-predict-regression.R +++ b/tests/testthat/test-simulate-predict-regression.R @@ -76,6 +76,16 @@ get_bgms_fixtures = function() { label = "ggm-no-es", get_fit = get_bgms_fit_ggm_no_es, get_prediction_data = get_prediction_data_ggm, var_type = "continuous", is_continuous = TRUE + ), + list( + label = "mixed-mrf", get_fit = get_bgms_fit_mixed_mrf, + get_prediction_data = get_prediction_data_mixed, + var_type = "mixed", is_continuous = FALSE, is_mixed = TRUE + ), + list( + label = "mixed-mrf-no-es", get_fit = get_bgms_fit_mixed_mrf_no_es, + get_prediction_data = get_prediction_data_mixed, + var_type = "mixed", is_continuous = FALSE, is_mixed = TRUE ) ) } @@ -187,6 +197,25 @@ test_that("bgms $arguments contains all fields needed by simulate/predict", { isTRUE(args$is_continuous), info = sprintf("%s: is_continuous should be TRUE for GGM", ctx) ) + } else if(isTRUE(spec$is_mixed)) { + # Mixed MRF: OMRF fields plus mixed-specific fields + for(field in BGMS_OMRF_FIELDS) { + expect_true( + field %in% names(args), + info = sprintf("%s: missing arguments$%s", ctx, field) + ) + } + for(field in c("is_mixed", "discrete_indices", "continuous_indices", + "num_discrete", "num_continuous", "is_ordinal", + "data_columnnames_discrete", "data_columnnames_continuous")) { + expect_true( + field %in% names(args), + info = sprintf("%s: missing mixed arguments$%s", ctx, field) + ) + } + expect_true(isTRUE(args$is_mixed), + info = sprintf("%s: is_mixed should be TRUE", ctx) + ) } else { # OMRF fits must also carry num_categories and baseline_category for(field in BGMS_OMRF_FIELDS) { @@ -245,6 +274,18 @@ test_that("bgms fit objects have posterior_mean fields for simulate/predict", { expect_equal(ncol(fit$posterior_mean_pairwise), p, info = paste(ctx, "posterior_mean_pairwise wrong ncol") ) + + if(isTRUE(spec$is_mixed)) { + expect_true(is.list(fit$posterior_mean_main), + info = paste(ctx, "mixed posterior_mean_main should be a list") + ) + expect_false(is.null(fit$posterior_mean_main$discrete), + info = paste(ctx, "missing posterior_mean_main$discrete") + ) + expect_false(is.null(fit$posterior_mean_main$continuous), + info = paste(ctx, "missing posterior_mean_main$continuous") + ) + } } }) @@ -476,6 +517,42 @@ test_that("simulate → predict roundtrip works for all bgms fixtures", { info = sprintf("%s predict var %d ncol", ctx, j) ) } + } else if(isTRUE(spec$is_mixed)) { + # Mixed MRF: predict returns list with discrete (probs) and continuous (mean/sd) + probs = predict(fit, newdata = simulated, type = "probabilities") + expect_true(is.list(probs), info = paste(ctx, "predict type")) + expect_equal(length(probs), args$num_variables, info = paste(ctx, "predict length")) + + for(j in seq_len(args$num_variables)) { + vname = args$data_columnnames[j] + expect_equal(nrow(probs[[j]]), n_sim, + info = sprintf("%s predict %s nrow", ctx, vname) + ) + expect_false(anyNA(probs[[j]]), + info = sprintf("%s predict %s has NAs", ctx, vname) + ) + + if(args$variable_type[j] %in% c("ordinal", "blume-capel")) { + # Discrete: probability rows sum to 1 + row_sums = rowSums(probs[[j]]) + expect_true( + all(abs(row_sums - 1) < 1e-6), + info = sprintf("%s predict %s probs don't sum to 1", ctx, vname) + ) + } else { + # Continuous: 2-column (mean, sd) matrix + expect_equal(ncol(probs[[j]]), 2, + info = sprintf("%s predict %s ncol", ctx, vname) + ) + } + } + + # type = "response" should return a matrix + resp = predict(fit, newdata = simulated, type = "response") + expect_true(is.matrix(resp), info = paste(ctx, "response matrix")) + expect_equal(dim(resp), c(n_sim, args$num_variables), + info = paste(ctx, "response dim") + ) } else { # OMRF: predict returns list of probability matrices probs = predict(fit, newdata = simulated, type = "probabilities") @@ -668,7 +745,7 @@ test_that("bgms $arguments field types are correct for simulate/predict", { info = paste(ctx, "data_columnnames length") ) - if(!isTRUE(spec$is_continuous)) { + if(!isTRUE(spec$is_continuous) && !isTRUE(spec$is_mixed)) { # OMRF-only fields expect_true(is.numeric(args$num_categories) && length(args$num_categories) == p, info = paste(ctx, "num_categories length") @@ -680,6 +757,28 @@ test_that("bgms $arguments field types are correct for simulate/predict", { info = paste(ctx, "baseline_category length") ) } + + if(isTRUE(spec$is_mixed)) { + pd = args$num_discrete + qc = args$num_continuous + expect_equal(pd + qc, p, info = paste(ctx, "num_discrete + num_continuous == p")) + expect_true( + is.numeric(args$num_categories) && length(args$num_categories) == pd, + info = paste(ctx, "mixed num_categories length == num_discrete") + ) + expect_true( + is.numeric(args$baseline_category) && length(args$baseline_category) == pd, + info = paste(ctx, "mixed baseline_category length == num_discrete") + ) + expect_true( + is.numeric(args$discrete_indices) && length(args$discrete_indices) == pd, + info = paste(ctx, "discrete_indices length") + ) + expect_true( + is.numeric(args$continuous_indices) && length(args$continuous_indices) == qc, + info = paste(ctx, "continuous_indices length") + ) + } } }) From 137a5ddcaa92adc8f0a320a744fbd2b17e6729e2 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Sat, 7 Mar 2026 13:33:30 +0100 Subject: [PATCH 18/60] =?UTF-8?q?docs:=20code=20quality=20pass=20=E2=80=94?= =?UTF-8?q?=20styler,=20lintr,=20C++=20documentation=20(audit=20#8)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .Rbuildignore | 1 + R/bgm_spec.R | 24 +- R/build_output.R | 44 +- R/simulate_predict.R | 289 +++-- src/bgmCompare/bgmCompare_helper.cpp | 358 +++--- src/bgmCompare/bgmCompare_logp_and_grad.cpp | 712 ++++++------ src/bgmCompare/bgmCompare_output.h | 19 +- src/bgmCompare/bgmCompare_sampler.cpp | 1000 ++++++++--------- src/bgmCompare_interface.cpp | 258 +++-- src/mcmc/algorithms/nuts.cpp | 72 +- src/mcmc/execution/chain_result.h | 19 +- src/mcmc/execution/sampler_config.h | 20 +- src/mcmc/samplers/hmc_adaptation.h | 11 + src/mcmc/samplers/metropolis_adaptation.h | 3 + src/models/mixed/mixed_mrf_model.h | 117 +- src/models/omrf/omrf_model.h | 76 +- src/mrf_prediction.cpp | 78 +- src/mrf_simulation.cpp | 264 ++--- src/priors/sbm_edge_prior.cpp | 74 +- src/sample_mixed.cpp | 70 +- src/sample_omrf.cpp | 66 +- src/utils/common_helpers.h | 28 +- src/utils/print_mutex.h | 21 +- src/utils/variable_helpers.h | 144 +-- tests/testthat/helper-fixtures.R | 85 +- tests/testthat/test-bgm.R | 37 +- .../test-mixed-mrf-simulate-predict.R | 82 +- .../test-simulate-predict-regression.R | 146 ++- 28 files changed, 2175 insertions(+), 1943 deletions(-) diff --git a/.Rbuildignore b/.Rbuildignore index fcf9b275..7a8c3cdf 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -16,6 +16,7 @@ # GitHub / CI ^\.github$ +^CONTRIBUTING\.md$ # Hidden config files (development only) ^\.lintr$ diff --git a/R/bgm_spec.R b/R/bgm_spec.R index c624db18..3c186cba 100644 --- a/R/bgm_spec.R +++ b/R/bgm_spec.R @@ -652,11 +652,13 @@ build_spec_mixed_mrf = function(x, data_columnnames, num_variables, # Missing data — not supported for mixed MRF (Phase H) if(na_action == "impute") { stop("Missing data imputation is not yet supported for mixed models.", - call. = FALSE) + call. = FALSE + ) } if(anyNA(x)) { stop("Missing data detected. Mixed models do not yet support missing data handling.", - call. = FALSE) + call. = FALSE + ) } # Ordinal recoding (reformat discrete data) @@ -685,17 +687,17 @@ build_spec_mixed_mrf = function(x, data_columnnames, num_variables, new_bgm_spec( model_type = "mixed_mrf", data = list( - x_discrete = x_disc_recoded, - x_continuous = x_cont, + x_discrete = x_disc_recoded, + x_continuous = x_cont, data_columnnames = data_columnnames, - data_columnnames_discrete = data_columnnames[disc_idx], + data_columnnames_discrete = data_columnnames[disc_idx], data_columnnames_continuous = data_columnnames[cont_idx], - num_variables = as.integer(num_variables), - num_discrete = as.integer(p), - num_continuous = as.integer(q), - num_cases = as.integer(nrow(x)), - num_categories = as.integer(num_categories), - discrete_indices = disc_idx, + num_variables = as.integer(num_variables), + num_discrete = as.integer(p), + num_continuous = as.integer(q), + num_cases = as.integer(nrow(x)), + num_categories = as.integer(num_categories), + discrete_indices = disc_idx, continuous_indices = cont_idx ), variables = list( diff --git a/R/build_output.R b/R/build_output.R index e877ecbe..095c7033 100644 --- a/R/build_output.R +++ b/R/build_output.R @@ -392,9 +392,11 @@ build_output_mixed_mrf = function(spec, raw) { kyy_offdiag_abs = kyy_start - 1L + kyy_offdiag_within # Main indices: mux + muy + Kyy diagonal - main_idx = c(seq(mux_start, mux_end), - seq(muy_start, muy_end), - kyy_diag_abs) + main_idx = c( + seq(mux_start, mux_end), + seq(muy_start, muy_end), + kyy_diag_abs + ) # Pairwise indices: Kxx + Kyy off-diag + Kxy pairwise_idx = c( @@ -434,9 +436,11 @@ build_output_mixed_mrf = function(spec, raw) { cats = seq_len(num_categories[si]) names_main = c(names_main, paste0(disc_names[si], " (", cats, ")")) } else { - names_main = c(names_main, + names_main = c( + names_main, paste0(disc_names[si], " (linear)"), - paste0(disc_names[si], " (quadratic)")) + paste0(disc_names[si], " (quadratic)") + ) } } for(ji in seq_len(q)) { @@ -457,8 +461,10 @@ build_output_mixed_mrf = function(spec, raw) { if(p > 1) { for(i in seq_len(p - 1)) { for(j in seq(i + 1, p)) { - edge_names = c(edge_names, - paste0(disc_names[i], "-", disc_names[j])) + edge_names = c( + edge_names, + paste0(disc_names[i], "-", disc_names[j]) + ) } } } @@ -466,8 +472,10 @@ build_output_mixed_mrf = function(spec, raw) { if(q > 1) { for(i in seq_len(q - 1)) { for(j in seq(i + 1, q)) { - edge_names = c(edge_names, - paste0(cont_names[i], "-", cont_names[j])) + edge_names = c( + edge_names, + paste0(cont_names[i], "-", cont_names[j]) + ) } } } @@ -475,8 +483,10 @@ build_output_mixed_mrf = function(spec, raw) { if(p > 0 && q > 0) { for(i in seq_len(p)) { for(j in seq_len(q)) { - edge_names = c(edge_names, - paste0(disc_names[i], "-", cont_names[j])) + edge_names = c( + edge_names, + paste0(disc_names[i], "-", cont_names[j]) + ) } } } @@ -550,8 +560,10 @@ build_output_mixed_mrf = function(spec, raw) { # --- Posterior mean: pairwise as (p+q) × (p+q) matrix ----------------------- # Map from internal block indices to original column positions - pmat = matrix(0, nrow = num_variables, ncol = num_variables, - dimnames = list(data_columnnames, data_columnnames)) + pmat = matrix(0, + nrow = num_variables, ncol = num_variables, + dimnames = list(data_columnnames, data_columnnames) + ) pw_means = pairwise_summary$mean idx = 0L @@ -600,8 +612,10 @@ build_output_mixed_mrf = function(spec, raw) { # --- Posterior mean: indicator ----------------------------------------------- if(edge_selection) { ind_means = indicator_summary$mean - imat = matrix(0, nrow = num_variables, ncol = num_variables, - dimnames = list(data_columnnames, data_columnnames)) + imat = matrix(0, + nrow = num_variables, ncol = num_variables, + dimnames = list(data_columnnames, data_columnnames) + ) idx = 0L if(p > 1) { diff --git a/R/simulate_predict.R b/R/simulate_predict.R index 1ab3f735..5a3d1851 100644 --- a/R/simulate_predict.R +++ b/R/simulate_predict.R @@ -12,10 +12,11 @@ #' Simulate Observations from a Markov Random Field #' #' @description -#' `simulate_mrf()` generates observations from a Markov Random Field using -#' user-specified parameters. For ordinal and Blume-Capel variables, observations -#' are generated via Gibbs sampling. For continuous variables (Gaussian graphical -#' model), observations are drawn directly from the multivariate normal +#' `simulate_mrf()` generates observations from a Markov Random +#' Field using user-specified parameters. For ordinal and +#' Blume-Capel variables, observations are generated via Gibbs +#' sampling. For continuous variables (Gaussian graphical model), +#' observations are drawn directly from the multivariate normal #' distribution implied by the precision matrix. #' #' @details @@ -41,7 +42,8 @@ #' have a special type of baseline_category category, such as the neutral #' category in a Likert scale. The Blume-Capel model specifies the following #' quadratic model for the threshold parameters: -#' \deqn{\mu_{\text{c}} = \alpha \times (\text{c} - \text{r}) + \beta \times (\text{c} - \text{r})^2,}{{\mu_{\text{c}} = \alpha \times (\text{c} - \text{r}) + \beta \times (\text{c} - \text{r})^2,}} +#' \deqn{\mu_{\text{c}} = \alpha (\text{c} - \text{r}) +#' + \beta (\text{c} - \text{r})^2} #' where \eqn{\mu_{\text{c}}}{\mu_{\text{c}}} is the threshold for category c #' (which now includes zero), \eqn{\alpha}{\alpha} offers a linear trend #' across categories (increasing threshold values if @@ -55,15 +57,18 @@ #' #' @param num_variables The number of variables in the MRF. #' -#' @param num_categories Either a positive integer or a vector of positive -#' integers of length \code{num_variables}. The number of response categories on top -#' of the base category: \code{num_categories = 1} generates binary states. +#' @param num_categories Either a positive integer or a vector +#' of positive integers of length \code{num_variables}. The +#' number of response categories on top of the base category: +#' \code{num_categories = 1} generates binary states. #' Only used for ordinal and Blume-Capel variables; ignored when #' \code{variable_type = "continuous"}. #' -#' @param pairwise A symmetric \code{num_variables} by \code{num_variables} matrix. -#' For ordinal and Blume-Capel variables, this contains the pairwise interaction -#' parameters; only the off-diagonal elements are used. For continuous variables, +#' @param pairwise A symmetric \code{num_variables} by +#' \code{num_variables} matrix. For ordinal and Blume-Capel +#' variables, this contains the pairwise interaction parameters; +#' only the off-diagonal elements are used. For continuous +#' variables, #' this is the precision matrix \eqn{\Omega}{Omega} (including diagonal) and #' must be positive definite. #' @@ -71,7 +76,8 @@ #' \code{num_variables} by \code{max(num_categories)} matrix of category #' thresholds. The elements in row \code{i} indicate the thresholds of #' variable \code{i}. If \code{num_categories} is a vector, only the first -#' \code{num_categories[i]} elements are used in row \code{i}. If the Blume-Capel +#' \code{num_categories[i]} elements are used in row \code{i}. +#' If the Blume-Capel #' model is used for the category thresholds for variable \code{i}, then row #' \code{i} requires two values (details below); the first is #' \eqn{\alpha}{\alpha}, the linear contribution of the Blume-Capel model and @@ -91,8 +97,10 @@ #' from a Gaussian graphical model. #' Defaults to \code{variable_type = "ordinal"}. #' -#' @param baseline_category An integer vector of length \code{num_variables} specifying the -#' baseline_category category that is used for the Blume-Capel model (details below). +#' @param baseline_category An integer vector of length +#' \code{num_variables} specifying the baseline_category +#' category that is used for the Blume-Capel model +#' (details below). #' Can be any integer value between \code{0} and \code{num_categories} (or #' \code{num_categories[i]}). #' @@ -182,7 +190,7 @@ simulate_mrf = function(num_states, baseline_category, iter = 1e3, seed = NULL) { - # Check num_states, num_variables --------------------------------------------- + # Check num_states, num_variables ------ check_positive_integer(num_states, "num_states") check_positive_integer(num_variables, "num_variables") @@ -201,7 +209,8 @@ simulate_mrf = function(num_states, bc_binary = variable_type == "blume-capel" & num_categories < 2 if(any(bc_binary)) { stop(paste0( - "The Blume-Capel model only works for ordinal variables with more than two \n", + "The Blume-Capel model only works for ordinal ", + "variables with more than two \n", "response options. But variables ", paste(which(bc_binary), collapse = ", "), " are binary variables." @@ -223,7 +232,10 @@ simulate_mrf = function(num_states, stop("The matrix 'pairwise' needs to be symmetric.") } if(nrow(pairwise) != num_variables) { - stop("The matrix 'pairwise' needs to have 'num_variables' rows and columns.") + stop( + "The matrix 'pairwise' needs to have ", + "'num_variables' rows and columns." + ) } if(any(diag(pairwise) <= 0)) { stop("The diagonal of the precision matrix 'pairwise' must be positive.") @@ -266,19 +278,24 @@ simulate_mrf = function(num_states, # =========================================================================== check_positive_integer(iter, "iter") - # Check num_categories -------------------------------------------------------- + # Check num_categories ------ if(length(num_categories) == 1) { - if(num_categories <= 0 || - abs(num_categories - round(num_categories)) > .Machine$double.eps) { + not_pos_int = num_categories <= 0 || + abs(num_categories - round(num_categories)) > .Machine$double.eps + if(not_pos_int) { stop("``num_categories'' needs be a (vector of) positive integer(s).") } num_categories = rep(num_categories, num_variables) } else { for(variable in 1:num_variables) { - if(num_categories[variable] <= 0 || - abs(num_categories[variable] - round(num_categories[variable])) > - .Machine$double.eps) { - stop(paste("For variable", variable, "``num_categories'' was not a positive integer.")) + nc = num_categories[variable] + not_pos_int = nc <= 0 || abs(nc - round(nc)) > .Machine$double.eps + if(not_pos_int) { + stop(paste( + "For variable", variable, + "``num_categories'' was not a", + "positive integer." + )) } } } @@ -288,7 +305,10 @@ simulate_mrf = function(num_states, if(length(baseline_category) == 1) { baseline_category = rep(baseline_category, num_variables) } - if(any(baseline_category < 0) || any(abs(baseline_category - round(baseline_category)) > .Machine$double.eps)) { + bc_diff = abs(baseline_category - round(baseline_category)) + not_valid = any(baseline_category < 0) || + any(bc_diff > .Machine$double.eps) + if(not_valid) { stop(paste0( "For variables ", which(baseline_category < 0), @@ -299,7 +319,8 @@ simulate_mrf = function(num_states, stop(paste0( "For variables ", which(baseline_category - num_categories > 0), - " the ``baseline_category'' category was larger than the maximum category value." + " the ``baseline_category'' category was larger", + " than the maximum category value." )) } } @@ -312,7 +333,10 @@ simulate_mrf = function(num_states, stop("The matrix ``pairwise'' needs to be symmetric.") } if(nrow(pairwise) != num_variables) { - stop("The matrix ``pairwise'' needs to have ``num_variables'' rows and columns.") + stop( + "The matrix ``pairwise'' needs to have", + " ``num_variables'' rows and columns." + ) } # Check the threshold values ------------------------------------------------- @@ -341,16 +365,13 @@ simulate_mrf = function(num_states, for(variable in 1:num_variables) { if(variable_type[variable] != "blume-capel") { if(anyNA(main[variable, 1:num_categories[variable]])) { - tmp = which(is.na(main[variable, 1:num_categories[variable]])) - - string = paste(tmp, sep = ",") - + na_cats = which(is.na(main[variable, 1:num_categories[variable]])) stop(paste0( "The matrix ``main'' contains NA(s) for variable ", variable, " in category \n", "(categories) ", - paste(which(is.na(main[variable, 1:num_categories[variable]])), collapse = ", "), + paste(na_cats, collapse = ", "), ", where a numeric value is needed." )) } @@ -370,24 +391,31 @@ simulate_mrf = function(num_states, } else { if(anyNA(main[variable, 1:2])) { stop(paste0( - "The Blume-Capel model is chosen for the category thresholds of variable ", + "The Blume-Capel model is chosen for the ", + "category thresholds of variable ", variable, ". \n", - "This model has two parameters that need to be placed in columns 1 and 2, row \n", + "This model has two parameters that need ", + "to be placed in columns 1 and 2, row \n", variable, - ", of the ``main'' input matrix. Currently, there are NA(s) in these \n", + ", of the ``main'' input matrix. ", + "Currently, there are NA(s) in these \n", "entries, where a numeric value is needed." )) } if(ncol(main) > 2) { if(!anyNA(main[variable, 3:ncol(main)])) { warning(paste0( - "The Blume-Capel model is chosen for the category thresholds of variable ", + "The Blume-Capel model is chosen for ", + "the category thresholds of variable ", variable, ". \n", - "This model has two parameters that need to be placed in columns 1 and 2, row \n", + "This model has two parameters that ", + "need to be placed in columns 1 and ", + "2, row \n", variable, - ", of the ``main'' input matrix. However, there are numeric values \n", + ", of the ``main'' input matrix. ", + "However, there are numeric values \n", "in higher categories. These values will be ignored." )) } @@ -510,7 +538,8 @@ mrfSampler = function(num_states, #' #' @description #' Generates new observations from the Markov Random Field model using the -#' estimated parameters from a fitted \code{bgms} object. +#' estimated parameters from a fitted \code{bgms} object. Supports ordinal, +#' Blume-Capel, continuous (GGM), and mixed MRF models. #' #' @param object An object of class \code{bgms}. #' @param nsim Number of observations to simulate. Default: \code{500}. @@ -524,7 +553,8 @@ mrfSampler = function(num_states, #' uses parallel processing when \code{cores > 1}.} #' } #' @param ndraws Number of posterior draws to use when -#' \code{method = "posterior-sample"}. If \code{NULL}, uses all available draws. +#' \code{method = "posterior-sample"}. If \code{NULL}, +#' uses all available draws. #' @param iter Number of Gibbs iterations for equilibration before collecting #' samples. Default: \code{1000}. #' @param cores Number of CPU cores for parallel execution when @@ -542,9 +572,14 @@ mrfSampler = function(num_states, #' If \code{method = "posterior-sample"}: A list of matrices, one per posterior #' draw, each with \code{nsim} rows and \code{p} columns. #' +#' For mixed MRF models, discrete columns contain non-negative integers and +#' continuous columns contain real-valued observations, ordered as in the +#' original data. +#' #' @details -#' This function uses the estimated interaction and threshold parameters to -#' generate new data via Gibbs sampling. When \code{method = "posterior-sample"}, +#' This function uses the estimated interaction and threshold +#' parameters to generate new data via Gibbs sampling. When +#' \code{method = "posterior-sample"}, parameter uncertainty is #' parameter uncertainty is propagated to the simulated data by using different #' posterior draws. Parallel processing is available for this method via the #' \code{cores} argument. @@ -562,7 +597,11 @@ mrfSampler = function(num_states, #' new_data = simulate(fit, nsim = 100) #' #' # Simulate with parameter uncertainty (10 datasets) -#' new_data_list = simulate(fit, nsim = 100, method = "posterior-sample", ndraws = 10) +#' new_data_list = simulate( +#' fit, +#' nsim = 100, +#' method = "posterior-sample", ndraws = 10 +#' ) #' #' # Use parallel processing for faster simulation #' new_data_list = simulate(fit, @@ -713,9 +752,9 @@ simulate.bgms = function(object, } -# ============================================================================== -# simulate.bgmCompare() - S3 Method for Simulating from Group-Comparison Models -# ============================================================================== +# ============================================================ +# simulate.bgmCompare() - S3 Method for Group-Comparison +# ============================================================ #' Simulate Data from a Fitted bgmCompare Model #' @@ -779,15 +818,23 @@ simulate.bgmCompare = function(object, # Validate group argument if(missing(group)) { - stop("Argument 'group' is required. Specify which group to simulate from (1 to num_groups).") + stop( + "Argument 'group' is required. ", + "Specify which group to simulate from ", + "(1 to num_groups)." + ) } arguments = extract_arguments(object) num_groups = arguments$num_groups - if(!is.numeric(group) || length(group) != 1 || is.na(group) || - group < 1 || group > num_groups) { - stop(sprintf("Argument 'group' must be an integer between 1 and %d.", num_groups)) + invalid_group = !is.numeric(group) || length(group) != 1 || + is.na(group) || group < 1 || group > num_groups + if(invalid_group) { + stop(sprintf( + "Argument 'group' must be an integer between 1 and %d.", + num_groups + )) } group = as.integer(group) @@ -799,7 +846,6 @@ simulate.bgmCompare = function(object, num_categories = arguments$num_categories is_ordinal = arguments$is_ordinal_variable data_columnnames = arguments$data_columnnames - projection = arguments$projection # [num_groups x (num_groups-1)] # Determine variable_type from is_ordinal variable_type = ifelse(is_ordinal, "ordinal", "blume-capel") @@ -868,7 +914,8 @@ simulate.bgmCompare = function(object, #' #' @description #' Computes conditional probability distributions for one or more variables -#' given the observed values of other variables in the data. +#' given the observed values of other variables in the data. Supports ordinal, +#' Blume-Capel, continuous (GGM), and mixed MRF models. #' #' @param object An object of class \code{bgms}. #' @param newdata A matrix or data frame with \code{n} rows and \code{p} columns @@ -890,10 +937,12 @@ simulate.bgmCompare = function(object, #' @param method Character string specifying which parameter estimates to use: #' \describe{ #' \item{\code{"posterior-mean"}}{Use posterior mean parameters.} -#' \item{\code{"posterior-sample"}}{Average predictions over posterior draws.} +#' \item{\code{"posterior-sample"}}{Average predictions +#' over posterior draws.} #' } #' @param ndraws Number of posterior draws to use when -#' \code{method = "posterior-sample"}. If \code{NULL}, uses all available draws. +#' \code{method = "posterior-sample"}. If \code{NULL}, +#' uses all available draws. #' @param seed Optional random seed for reproducibility when #' \code{method = "posterior-sample"}. #' @param ... Additional arguments (currently ignored). @@ -903,8 +952,9 @@ simulate.bgmCompare = function(object, #' #' For \code{type = "probabilities"}: A named list with one element per #' predicted variable. Each element is a matrix with \code{n} rows and -#' \code{num_categories + 1} columns containing \eqn{P(X_j = c | X_{-j})}{P(X_j = c | X_-j)} for each -#' observation and category. +#' \code{num_categories + 1} columns containing +#' \eqn{P(X_j = c | X_{-j})}{P(X_j = c | X_-j)} +#' for each observation and category. #' #' For \code{type = "response"}: A matrix with \code{n} rows and #' \code{length(variables)} columns containing predicted categories. @@ -927,6 +977,13 @@ simulate.bgmCompare = function(object, #' When \code{method = "posterior-sample"}, conditional parameters are #' averaged over posterior draws, and an attribute \code{"sd"} is included. #' +#' \strong{Mixed MRF models:} +#' +#' For mixed models, the return list contains elements for both discrete and +#' continuous predicted variables. Discrete variables return probability +#' matrices (as in ordinal models); continuous variables return conditional +#' mean and SD matrices (as in GGM models). +#' #' @details #' For each observation, the function computes the conditional distribution #' of the target variable(s) given the observed values of all other variables. @@ -935,7 +992,8 @@ simulate.bgmCompare = function(object, #' #' For GGM (continuous) models, the conditional distribution of #' \eqn{X_j | X_{-j}}{X_j | X_{-j}} is Gaussian with mean -#' \eqn{-\omega_{jj}^{-1} \sum_{k \neq j} \omega_{jk} x_k}{-omega_jj^{-1} sum_{k != j} omega_jk x_k} +#' \eqn{-\omega_{jj}^{-1} \sum_{k \neq j} +#' \omega_{jk} x_k}{-omega_jj^{-1} sum_{k != j} omega_jk x_k} #' and variance \eqn{\omega_{jj}^{-1}}{omega_jj^{-1}}, where \eqn{\Omega}{Omega} #' is the precision matrix. #' @@ -978,7 +1036,11 @@ predict.bgms = function(object, # Validate newdata if(missing(newdata)) { - stop("Argument 'newdata' is required. Provide the data for which to compute predictions.") + stop( + "Argument 'newdata' is required. ", + "Provide the data for which to ", + "compute predictions." + ) } if(!inherits(newdata, "matrix") && !inherits(newdata, "data.frame")) { @@ -1000,7 +1062,8 @@ predict.bgms = function(object, if(ncol(newdata) != num_variables) { stop(paste0( - "'newdata' must have ", num_variables, " columns (same as fitted model), ", + "'newdata' must have ", num_variables, + " columns (same as fitted model), ", "but has ", ncol(newdata), "." )) } @@ -1025,7 +1088,13 @@ predict.bgms = function(object, } else if(is.character(variables)) { predict_vars = match(variables, data_columnnames) if(anyNA(predict_vars)) { - stop("Variable names not found: ", paste(variables[is.na(predict_vars)], collapse = ", ")) + stop( + "Variable names not found: ", + paste( + variables[is.na(predict_vars)], + collapse = ", " + ) + ) } } else { predict_vars = as.integer(variables) @@ -1070,7 +1139,9 @@ predict.bgms = function(object, # ============================================================================ # Recode data to 0-based integers (matching what bgm() does) - newdata_recoded = recode_data_for_prediction(newdata, num_categories, is_ordinal) + newdata_recoded = recode_data_for_prediction( + newdata, num_categories, is_ordinal + ) if(method == "posterior-mean") { # Use posterior mean parameters @@ -1215,9 +1286,10 @@ predict.bgms = function(object, #' @param ... Additional arguments (currently ignored). #' #' @return -#' For \code{type = "probabilities"}: A named list with one element per -#' predicted variable. Each element is a matrix with \code{n} rows and -#' \code{num_categories + 1} columns containing \eqn{P(X_j = c | X_{-j})}{P(X_j = c | X_-j)} +#' For \code{type = "probabilities"}: A named list with one +#' element per predicted variable. Each element is a matrix with +#' \code{n} rows and \code{num_categories + 1} columns containing +#' \eqn{P(X_j = c | X_{-j})}{P(X_j = c | X_-j)} #' for each observation and category. #' #' For \code{type = "response"}: A matrix with \code{n} rows and @@ -1229,8 +1301,10 @@ predict.bgms = function(object, #' The function then computes the conditional distribution of target variables #' given the observed values of all other variables. #' -#' @seealso \code{\link{predict.bgms}} for predicting from single-group models, -#' \code{\link{simulate.bgmCompare}} for simulating from group-comparison models. +#' @seealso \code{\link{predict.bgms}} for predicting +#' from single-group models, +#' \code{\link{simulate.bgmCompare}} for simulating +#' from group-comparison models. #' @family prediction #' #' @examples @@ -1260,21 +1334,33 @@ predict.bgmCompare = function(object, # Validate group argument if(missing(group)) { - stop("Argument 'group' is required. Specify which group's parameters to use (1 to num_groups).") + stop( + "Argument 'group' is required. ", + "Specify which group's parameters ", + "to use (1 to num_groups)." + ) } arguments = extract_arguments(object) num_groups = arguments$num_groups - if(!is.numeric(group) || length(group) != 1 || is.na(group) || - group < 1 || group > num_groups) { - stop(sprintf("Argument 'group' must be an integer between 1 and %d.", num_groups)) + invalid_group = !is.numeric(group) || length(group) != 1 || + is.na(group) || group < 1 || group > num_groups + if(invalid_group) { + stop(sprintf( + "Argument 'group' must be an integer between 1 and %d.", + num_groups + )) } group = as.integer(group) # Validate newdata if(missing(newdata)) { - stop("Argument 'newdata' is required. Provide the data for which to compute predictions.") + stop( + "Argument 'newdata' is required. ", + "Provide the data for which to ", + "compute predictions." + ) } if(!inherits(newdata, "matrix") && !inherits(newdata, "data.frame")) { @@ -1290,12 +1376,12 @@ predict.bgmCompare = function(object, num_categories = arguments$num_categories is_ordinal = arguments$is_ordinal_variable data_columnnames = arguments$data_columnnames - projection = arguments$projection # Validate dimensions if(ncol(newdata) != num_variables) { stop(paste0( - "'newdata' must have ", num_variables, " columns (same as fitted model), ", + "'newdata' must have ", num_variables, + " columns (same as fitted model), ", "but has ", ncol(newdata), "." )) } @@ -1315,7 +1401,13 @@ predict.bgmCompare = function(object, } else if(is.character(variables)) { predict_vars = match(variables, data_columnnames) if(anyNA(predict_vars)) { - stop("Variable names not found: ", paste(variables[is.na(predict_vars)], collapse = ", ")) + stop( + "Variable names not found: ", + paste( + variables[is.na(predict_vars)], + collapse = ", " + ) + ) } } else { predict_vars = as.integer(variables) @@ -1325,7 +1417,9 @@ predict.bgmCompare = function(object, } # Recode data to 0-based integers - newdata_recoded = recode_data_for_prediction(newdata, num_categories, is_ordinal) + newdata_recoded = recode_data_for_prediction( + newdata, num_categories, is_ordinal + ) if(method == "posterior-mean") { # Extract group-specific parameters using projection @@ -1396,7 +1490,9 @@ predict.bgmCompare = function(object, # ============================================================================== # Helper function to reconstruct threshold matrix from flat vector -reconstruct_main = function(main_vec, num_variables, num_categories, variable_type) { +reconstruct_main = function(main_vec, num_variables, + num_categories, + variable_type) { if(length(variable_type) == 1) { variable_type = rep(variable_type, num_variables) } @@ -1469,7 +1565,7 @@ reconstruct_precision = function(posterior_mean_pairwise, posterior_mean_main) { # @param p Number of variables. # # @return p x p precision matrix (Omega). -reconstruct_precision_from_draw = function(pairwise_vec, main_vec, p) { +build_precision_from_draw = function(pairwise_vec, main_vec, p) { omega = matrix(0, nrow = p, ncol = p) omega[lower.tri(omega)] = pairwise_vec omega = omega + t(omega) @@ -1514,7 +1610,9 @@ predict_bgms_ggm = function(object, newdata, predict_vars, data_columnnames, names(result) = data_columnnames[predict_vars] for(v in seq_along(result)) { colnames(result[[v]]) = c("mean", "sd") - result[[v]][, "mean"] = result[[v]][, "mean"] + newdata_means[predict_vars[v]] + result[[v]][, "mean"] = + result[[v]][, "mean"] + + newdata_means[predict_vars[v]] } } else { # Use posterior samples @@ -1535,7 +1633,7 @@ predict_bgms_ggm = function(object, newdata, predict_vars, data_columnnames, for(i in seq_len(ndraws)) { idx = draw_indices[i] - omega = reconstruct_precision_from_draw( + omega = build_precision_from_draw( pairwise_vec = pairwise_samples[idx, ], main_vec = main_samples[idx, ], p = num_variables @@ -1713,7 +1811,7 @@ simulate_bgms_mixed = function(object, nsim, seed, method, ndraws, } if(method == "posterior-mean") { - params = reconstruct_mixed_params_from_means(object, arguments) + params = build_mixed_params_mean(object, arguments) seed = check_seed(seed) @@ -1733,7 +1831,6 @@ simulate_bgms_mixed = function(object, nsim, seed, method, ndraws, out = combine_mixed_result(result, disc_idx, cont_idx, data_columnnames) return(out) - } else { sample_info = split_mixed_raw_samples(object, arguments) @@ -1833,8 +1930,10 @@ predict_bgms_mixed = function(object, newdata, predict_vars, arguments, } else if(!is.na(cont_match)) { internal_predict_vars[k] = p + cont_match - 1L } else { - stop("Variable index ", orig_col, - " not found in discrete or continuous indices.") + stop( + "Variable index ", orig_col, + " not found in discrete or continuous indices." + ) } } @@ -1855,7 +1954,7 @@ predict_bgms_mixed = function(object, newdata, predict_vars, arguments, } if(method == "posterior-mean") { - params = reconstruct_mixed_params_from_means(object, arguments) + params = build_mixed_params_mean(object, arguments) raw_result = compute_one_draw( params$Kxx, params$Kxy, params$Kyy, params$mux, params$muy ) @@ -1874,7 +1973,7 @@ predict_bgms_mixed = function(object, newdata, predict_vars, arguments, all_results = vector("list", ndraws) for(i in seq_len(ndraws)) { - params_i = reconstruct_mixed_params_from_row( + params_i = build_mixed_params_row( sample_info, draw_indices[i], p, q, num_categories, is_ordinal ) all_results[[i]] = compute_one_draw( @@ -1909,8 +2008,10 @@ predict_bgms_mixed = function(object, newdata, predict_vars, arguments, } if(type == "response") { - return(format_mixed_response(probs, predict_vars, internal_predict_vars, - p, data_columnnames)) + return(format_mixed_response( + probs, predict_vars, internal_predict_vars, + p, data_columnnames + )) } return(probs) @@ -1922,7 +2023,7 @@ predict_bgms_mixed = function(object, newdata, predict_vars, arguments, # ============================================================================== # ------------------------------------------------------------------ -# reconstruct_mixed_params_from_means +# build_mixed_params_mean # ------------------------------------------------------------------ # Reconstruct Kxx, Kxy, Kyy, mux, muy from posterior mean summaries. # @@ -1931,13 +2032,11 @@ predict_bgms_mixed = function(object, newdata, predict_vars, arguments, # # Returns: list with Kxx, Kxy, Kyy, mux, muy. # ------------------------------------------------------------------ -reconstruct_mixed_params_from_means = function(object, arguments) { +build_mixed_params_mean = function(object, arguments) { p = arguments$num_discrete q = arguments$num_continuous disc_idx = arguments$discrete_indices cont_idx = arguments$continuous_indices - num_categories = arguments$num_categories - is_ordinal = arguments$is_ordinal pmat = object$posterior_mean_pairwise @@ -2064,7 +2163,7 @@ split_mixed_raw_samples = function(object, arguments) { # ------------------------------------------------------------------ -# reconstruct_mixed_params_from_row +# build_mixed_params_row # ------------------------------------------------------------------ # Reconstruct Kxx, Kxy, Kyy, mux, muy from a single row of split # sample matrices (used by predict posterior-sample). @@ -2078,9 +2177,9 @@ split_mixed_raw_samples = function(object, arguments) { # # Returns: list with Kxx, Kxy, Kyy, mux, muy. # ------------------------------------------------------------------ -reconstruct_mixed_params_from_row = function(sample_info, row_idx, - p, q, num_categories, - is_ordinal) { +build_mixed_params_row = function(sample_info, row_idx, + p, q, num_categories, + is_ordinal) { mux_vec = sample_info$mux_samples[row_idx, ] num_params_disc = ifelse(is_ordinal, num_categories, 2L) max_cats = max(num_params_disc) diff --git a/src/bgmCompare/bgmCompare_helper.cpp b/src/bgmCompare/bgmCompare_helper.cpp index 9f7ee37d..9f87246a 100644 --- a/src/bgmCompare/bgmCompare_helper.cpp +++ b/src/bgmCompare/bgmCompare_helper.cpp @@ -5,33 +5,31 @@ -/** - * Computes group-specific main effects for a given variable (bgmCompare model). - * - * For a variable, the main-effect parameters are stored with: - * - One "baseline" column (shared across groups). - * - Additional columns for group-specific deviations. - * - * This function extracts the rows corresponding to the variable’s categories - * and combines them with the group projection vector to yield the - * group-specific main effects. - * - * Inputs: - * - variable: Index of the variable of interest. - * - num_groups: Number of groups in the model. - * - main_effects: Matrix of main-effect parameters for all variables. - * - main_effect_indices: Index matrix giving [start_row, end_row] for each variable. - * - proj_group: Projection vector selecting the group (length = num_groups − 1). - * - * Returns: - * - A vector of group-specific main effects for the categories of the variable. - * - * Notes: - * - The projection vector should match the encoding used for group effects - * (e.g. dummy or contrast coding). - * - This function is used in likelihood evaluations where group-specific - * parameters are required. - */ +// Computes group-specific main effects for a given variable (bgmCompare model). +// +// For a variable, the main-effect parameters are stored with: +// - One "baseline" column (shared across groups). +// - Additional columns for group-specific deviations. +// +// This function extracts the rows corresponding to the variable’s categories +// and combines them with the group projection vector to yield the +// group-specific main effects. +// +// Inputs: +// - variable: Index of the variable of interest. +// - num_groups: Number of groups in the model. +// - main_effects: Matrix of main-effect parameters for all variables. +// - main_effect_indices: Index matrix giving [start_row, end_row] for each variable. +// - proj_group: Projection vector selecting the group (length = num_groups − 1). +// +// Returns: +// - A vector of group-specific main effects for the categories of the variable. +// +// Notes: +// - The projection vector should match the encoding used for group effects +// (e.g. dummy or contrast coding). +// - This function is used in likelihood evaluations where group-specific +// parameters are required. arma::vec compute_group_main_effects( const int variable, const int num_groups, @@ -57,33 +55,31 @@ arma::vec compute_group_main_effects( -/** - * Computes the group-specific pairwise effect for a variable pair (bgmCompare model). - * - * For each variable pair, the pairwise-effect parameters are stored with: - * - One "baseline" column (shared across groups). - * - Additional columns for group-specific deviations. - * - * This function extracts the baseline effect and, if the edge is active - * (per the inclusion indicator), adds the group-specific deviation obtained - * from the projection vector. - * - * Inputs: - * - var1, var2: Indices of the two variables forming the pair. - * - num_groups: Number of groups in the model. - * - pairwise_effects: Matrix of pairwise-effect parameters (rows = pairs). - * - pairwise_effect_indices: Lookup matrix mapping (var1, var2) → row index. - * - inclusion_indicator: Symmetric binary matrix of active edges. - * - proj_group: Projection vector selecting the group (length = num_groups − 1). - * - * Returns: - * - The group-specific pairwise effect for (var1, var2). - * - * Notes: - * - The index matrix must match the storage convention (typically var1 < var2). - * - If `inclusion_indicator(var1, var2) == 0`, only the baseline effect is used. - * - This function is used in likelihood evaluations and Gibbs updates. - */ +// Computes the group-specific pairwise effect for a variable pair (bgmCompare model). +// +// For each variable pair, the pairwise-effect parameters are stored with: +// - One "baseline" column (shared across groups). +// - Additional columns for group-specific deviations. +// +// This function extracts the baseline effect and, if the edge is active +// (per the inclusion indicator), adds the group-specific deviation obtained +// from the projection vector. +// +// Inputs: +// - var1, var2: Indices of the two variables forming the pair. +// - num_groups: Number of groups in the model. +// - pairwise_effects: Matrix of pairwise-effect parameters (rows = pairs). +// - pairwise_effect_indices: Lookup matrix mapping (var1, var2) → row index. +// - inclusion_indicator: Symmetric binary matrix of active edges. +// - proj_group: Projection vector selecting the group (length = num_groups − 1). +// +// Returns: +// - The group-specific pairwise effect for (var1, var2). +// +// Notes: +// - The index matrix must match the storage convention (typically var1 < var2). +// - If `inclusion_indicator(var1, var2) == 0`, only the baseline effect is used. +// - This function is used in likelihood evaluations and Gibbs updates. double compute_group_pairwise_effects( const int var1, const int var2, @@ -111,36 +107,34 @@ double compute_group_pairwise_effects( -/** - * Flattens main-effect and pairwise-effect parameters into a single vector (bgmCompare model). - * - * Layout of the output vector: - * 1. Main-effect overall parameters (column 0 of main_effects), stacked by variable. - * 2. Pairwise-effect overall parameters (column 0 of pairwise_effects), stacked by pair. - * 3. Main-effect group differences (columns 1..G-1), included only if - * the variable is marked active in inclusion_indicator(v,v). - * 4. Pairwise-effect group differences (columns 1..G-1), included only if - * the pair is marked active in inclusion_indicator(v1,v2). - * - * Inputs: - * - main_effects: Matrix of main-effect parameters (rows = categories, cols = groups). - * - pairwise_effects: Matrix of pairwise-effect parameters (rows = pairs, cols = groups). - * - inclusion_indicator: Symmetric binary matrix of active variables (diag) and pairs (off-diag). - * - main_effect_indices: Index ranges [row_start, row_end] for each variable in main_effects. - * - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. - * - num_categories: Number of categories per variable. - * - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). - * - * Returns: - * - A flat vector of parameters containing overall effects and (if active) group differences. - * - * Notes: - * - The order of pairs in `pairwise_effects` must match the upper-triangle order - * of (var1,var2) pairs as constructed in R. - * - The length of the output vector depends on both the number of groups - * and the active entries in inclusion_indicator. - * - This function is the inverse of `unvectorize_model_parameters_bgmcompare()`. - */ +// Flattens main-effect and pairwise-effect parameters into a single vector (bgmCompare model). +// +// Layout of the output vector: +// 1. Main-effect overall parameters (column 0 of main_effects), stacked by variable. +// 2. Pairwise-effect overall parameters (column 0 of pairwise_effects), stacked by pair. +// 3. Main-effect group differences (columns 1..G-1), included only if +// the variable is marked active in inclusion_indicator(v,v). +// 4. Pairwise-effect group differences (columns 1..G-1), included only if +// the pair is marked active in inclusion_indicator(v1,v2). +// +// Inputs: +// - main_effects: Matrix of main-effect parameters (rows = categories, cols = groups). +// - pairwise_effects: Matrix of pairwise-effect parameters (rows = pairs, cols = groups). +// - inclusion_indicator: Symmetric binary matrix of active variables (diag) and pairs (off-diag). +// - main_effect_indices: Index ranges [row_start, row_end] for each variable in main_effects. +// - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. +// - num_categories: Number of categories per variable. +// - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). +// +// Returns: +// - A flat vector of parameters containing overall effects and (if active) group differences. +// +// Notes: +// - The order of pairs in `pairwise_effects` must match the upper-triangle order +// of (var1,var2) pairs as constructed in R. +// - The length of the output vector depends on both the number of groups +// and the active entries in inclusion_indicator. +// - This function is the inverse of `unvectorize_model_parameters_bgmcompare()`. arma::vec vectorize_model_parameters_bgmcompare( const arma::mat& main_effects, const arma::mat& pairwise_effects, @@ -217,38 +211,36 @@ arma::vec vectorize_model_parameters_bgmcompare( -/** - * Reconstructs main-effect and pairwise-effect matrices from a flat parameter vector (bgmCompare model). - * - * The input vector must follow the layout produced by `vectorize_model_parameters_bgmcompare()`: - * 1. Main-effect overall parameters (column 0 of main_effects), stacked by variable. - * 2. Pairwise-effect overall parameters (column 0 of pairwise_effects), stacked by pair. - * 3. Main-effect group differences (columns 1..G-1), included only if - * the variable is active in inclusion_indicator(v,v). - * 4. Pairwise-effect group differences (columns 1..G-1), included only if - * the pair is active in inclusion_indicator(v1,v2). - * - * Inputs: - * - param_vec: Flattened parameter vector. - * - inclusion_indicator: Symmetric binary matrix of active variables (diag) and pairs (off-diag). - * - main_effect_indices: Index ranges [row_start, row_end] for each variable in main_effects. - * - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. - * - num_groups: Number of groups (columns in main_effects / pairwise_effects). - * - num_categories: Number of categories per variable. - * - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). - * - * Outputs: - * - main_effects_out: Matrix of main-effect parameters (rows = categories, cols = groups). - * - pairwise_effects_out: Matrix of pairwise-effect parameters (rows = pairs, cols = groups). - * - * Notes: - * - The vector must have exactly the length returned by `vectorize_model_parameters_bgmcompare()`. - * - Diagonal entries in `inclusion_indicator` determine whether main-effect - * group differences are included. - * - Off-diagonal entries in `inclusion_indicator` determine whether - * pairwise-effect group differences are included. - * - This function is the inverse of `vectorize_model_parameters_bgmcompare()`. - */ +// Reconstructs main-effect and pairwise-effect matrices from a flat parameter vector (bgmCompare model). +// +// The input vector must follow the layout produced by `vectorize_model_parameters_bgmcompare()`: +// 1. Main-effect overall parameters (column 0 of main_effects), stacked by variable. +// 2. Pairwise-effect overall parameters (column 0 of pairwise_effects), stacked by pair. +// 3. Main-effect group differences (columns 1..G-1), included only if +// the variable is active in inclusion_indicator(v,v). +// 4. Pairwise-effect group differences (columns 1..G-1), included only if +// the pair is active in inclusion_indicator(v1,v2). +// +// Inputs: +// - param_vec: Flattened parameter vector. +// - inclusion_indicator: Symmetric binary matrix of active variables (diag) and pairs (off-diag). +// - main_effect_indices: Index ranges [row_start, row_end] for each variable in main_effects. +// - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. +// - num_groups: Number of groups (columns in main_effects / pairwise_effects). +// - num_categories: Number of categories per variable. +// - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). +// +// Outputs: +// - main_effects_out: Matrix of main-effect parameters (rows = categories, cols = groups). +// - pairwise_effects_out: Matrix of pairwise-effect parameters (rows = pairs, cols = groups). +// +// Notes: +// - The vector must have exactly the length returned by `vectorize_model_parameters_bgmcompare()`. +// - Diagonal entries in `inclusion_indicator` determine whether main-effect +// group differences are included. +// - Off-diagonal entries in `inclusion_indicator` determine whether +// pairwise-effect group differences are included. +// - This function is the inverse of `vectorize_model_parameters_bgmcompare()`. void unvectorize_model_parameters_bgmcompare( const arma::vec& param_vec, arma::mat& main_effects_out, // [n_main_rows × G] @@ -314,44 +306,42 @@ void unvectorize_model_parameters_bgmcompare( -/** - * Builds index maps linking matrix entries to positions in the vectorized parameter vector (bgmCompare model). - * - * The index maps are used to quickly locate where each parameter (main-effect or pairwise-effect, - * across groups) sits inside the flattened parameter vector produced by - * `vectorize_model_parameters_bgmcompare()`. - * - * Layout: - * 1. Main-effect overall parameters (col 0). - * 2. Pairwise-effect overall parameters (col 0). - * 3. Main-effect group differences (cols 1..G-1), included only if - * the variable is active in inclusion_indicator(v,v). - * 4. Pairwise-effect group differences (cols 1..G-1), included only if - * the pair is active in inclusion_indicator(v1,v2). - * - * Inputs: - * - main_effects: Matrix of main-effect parameters (used for dimension info). - * - pairwise_effects: Matrix of pairwise-effect parameters (used for dimension info). - * - inclusion_indicator: Symmetric binary matrix of active variables (diag) and pairs (off-diag). - * - main_effect_indices: Index ranges [row_start, row_end] for each variable in main_effects. - * - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. - * - num_categories: Number of categories per variable. - * - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). - * - * Returns: - * - A pair of integer matrices: - * * main_index: [num_main × num_groups], with each entry giving the position - * in the parameter vector for that main-effect parameter (or -1 if inactive). - * * pair_index: [num_pair × num_groups], with each entry giving the position - * in the parameter vector for that pairwise-effect parameter (or -1 if inactive). - * - * Notes: - * - Entries are set to -1 when the corresponding parameter is inactive. - * - The returned index maps must always be consistent with the ordering used - * in vectorization/unvectorization. - * - A final check (e.g. verifying that `off == param_vec.n_elem`) can help - * catch mismatches between index maps and vectorizer logic. - */ +// Builds index maps linking matrix entries to positions in the vectorized parameter vector (bgmCompare model). +// +// The index maps are used to quickly locate where each parameter (main-effect or pairwise-effect, +// across groups) sits inside the flattened parameter vector produced by +// `vectorize_model_parameters_bgmcompare()`. +// +// Layout: +// 1. Main-effect overall parameters (col 0). +// 2. Pairwise-effect overall parameters (col 0). +// 3. Main-effect group differences (cols 1..G-1), included only if +// the variable is active in inclusion_indicator(v,v). +// 4. Pairwise-effect group differences (cols 1..G-1), included only if +// the pair is active in inclusion_indicator(v1,v2). +// +// Inputs: +// - main_effects: Matrix of main-effect parameters (used for dimension info). +// - pairwise_effects: Matrix of pairwise-effect parameters (used for dimension info). +// - inclusion_indicator: Symmetric binary matrix of active variables (diag) and pairs (off-diag). +// - main_effect_indices: Index ranges [row_start, row_end] for each variable in main_effects. +// - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. +// - num_categories: Number of categories per variable. +// - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). +// +// Returns: +// - A pair of integer matrices: +// * main_index: [num_main × num_groups], with each entry giving the position +// in the parameter vector for that main-effect parameter (or -1 if inactive). +// * pair_index: [num_pair × num_groups], with each entry giving the position +// in the parameter vector for that pairwise-effect parameter (or -1 if inactive). +// +// Notes: +// - Entries are set to -1 when the corresponding parameter is inactive. +// - The returned index maps must always be consistent with the ordering used +// in vectorization/unvectorization. +// - A final check (e.g. verifying that `off == param_vec.n_elem`) can help +// catch mismatches between index maps and vectorizer logic. std::pair build_index_maps( const arma::mat& main_effects, const arma::mat& pairwise_effects, @@ -411,37 +401,35 @@ std::pair build_index_maps( -/** - * Extracts entries of the inverse mass matrix corresponding to active parameters (bgmCompare model). - * - * If `selection` is false, the full diagonal vector is returned unchanged. - * If `selection` is true, the output is restricted to: - * 1. Main-effect overall parameters (column 0). - * 2. Pairwise-effect overall parameters (column 0). - * 3. Main-effect group differences (columns 1..G-1) for variables with - * inclusion_indicator(v,v) == 1. - * 4. Pairwise-effect group differences (columns 1..G-1) for pairs with - * inclusion_indicator(v1,v2) == 1. - * - * Inputs: - * - inv_diag: Full inverse mass diagonal (length = all parameters). - * - inclusion_indicator: Symmetric binary matrix of active variables (diag) and pairs (off-diag). - * - num_groups: Number of groups. - * - num_categories: Number of categories per variable. - * - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). - * - main_index: Index map for main effects (from build_index_maps()). - * - pair_index: Index map for pairwise effects (from build_index_maps()). - * - main_effect_indices: Index ranges [row_start, row_end] for each variable in main_effects. - * - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. - * - selection: If true, restrict to active parameters; if false, return full inv_diag. - * - * Returns: - * - A vector containing inverse mass entries for active parameters only. - * - * Notes: - * - Must be consistent with the layout in `vectorize_model_parameters_bgmcompare()`. - * - Index maps (`main_index`, `pair_index`) are required to locate group-difference entries. - */ +// Extracts entries of the inverse mass matrix corresponding to active parameters (bgmCompare model). +// +// If `selection` is false, the full diagonal vector is returned unchanged. +// If `selection` is true, the output is restricted to: +// 1. Main-effect overall parameters (column 0). +// 2. Pairwise-effect overall parameters (column 0). +// 3. Main-effect group differences (columns 1..G-1) for variables with +// inclusion_indicator(v,v) == 1. +// 4. Pairwise-effect group differences (columns 1..G-1) for pairs with +// inclusion_indicator(v1,v2) == 1. +// +// Inputs: +// - inv_diag: Full inverse mass diagonal (length = all parameters). +// - inclusion_indicator: Symmetric binary matrix of active variables (diag) and pairs (off-diag). +// - num_groups: Number of groups. +// - num_categories: Number of categories per variable. +// - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). +// - main_index: Index map for main effects (from build_index_maps()). +// - pair_index: Index map for pairwise effects (from build_index_maps()). +// - main_effect_indices: Index ranges [row_start, row_end] for each variable in main_effects. +// - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. +// - selection: If true, restrict to active parameters; if false, return full inv_diag. +// +// Returns: +// - A vector containing inverse mass entries for active parameters only. +// +// Notes: +// - Must be consistent with the layout in `vectorize_model_parameters_bgmcompare()`. +// - Index maps (`main_index`, `pair_index`) are required to locate group-difference entries. arma::vec inv_mass_active( const arma::vec& inv_diag, const arma::imat& inclusion_indicator, diff --git a/src/bgmCompare/bgmCompare_logp_and_grad.cpp b/src/bgmCompare/bgmCompare_logp_and_grad.cpp index 49bb780d..3e8b0aac 100644 --- a/src/bgmCompare/bgmCompare_logp_and_grad.cpp +++ b/src/bgmCompare/bgmCompare_logp_and_grad.cpp @@ -8,34 +8,32 @@ -/** - * Compute the total length of the parameter vector in the bgmCompare model. - * - * The parameter vector consists of: - * 1. Main-effect overall parameters (column 0). - * 2. Pairwise-effect overall parameters (column 0). - * 3. Main-effect group-difference parameters (columns 1..G-1) for variables - * with inclusion_indicator(v,v) == 1. - * 4. Pairwise-effect group-difference parameters (columns 1..G-1) for pairs - * with inclusion_indicator(v1,v2) == 1. - * - * Inputs: - * - num_variables: Number of observed variables. - * - main_effect_indices: Row ranges [start,end] in main_effects for each variable. - * - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. - * - inclusion_indicator: Symmetric binary matrix; diagonal entries control main-effect - * differences, off-diagonal entries control pairwise-effect differences. - * - num_categories: Vector of category counts per variable. - * - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel) for each variable. - * - num_groups: Number of groups in the model. - * - * Returns: - * - arma::uword: Total number of parameters in the vectorized model. - * - * Notes: - * - This function must be consistent with vectorize_model_parameters_bgmcompare(). - * - Used to allocate gradient vectors, prior vectors, and mass matrices. - */ +// Compute the total length of the parameter vector in the bgmCompare model. +// +// The parameter vector consists of: +// 1. Main-effect overall parameters (column 0). +// 2. Pairwise-effect overall parameters (column 0). +// 3. Main-effect group-difference parameters (columns 1..G-1) for variables +// with inclusion_indicator(v,v) == 1. +// 4. Pairwise-effect group-difference parameters (columns 1..G-1) for pairs +// with inclusion_indicator(v1,v2) == 1. +// +// Inputs: +// - num_variables: Number of observed variables. +// - main_effect_indices: Row ranges [start,end] in main_effects for each variable. +// - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. +// - inclusion_indicator: Symmetric binary matrix; diagonal entries control main-effect +// differences, off-diagonal entries control pairwise-effect differences. +// - num_categories: Vector of category counts per variable. +// - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel) for each variable. +// - num_groups: Number of groups in the model. +// +// Returns: +// - arma::uword: Total number of parameters in the vectorized model. +// +// Notes: +// - This function must be consistent with vectorize_model_parameters_bgmcompare(). +// - Used to allocate gradient vectors, prior vectors, and mass matrices. arma::uword total_length( const int num_variables, const arma::imat& main_effect_indices, @@ -71,49 +69,47 @@ arma::uword total_length( -/** - * Compute the observed-data contribution to the gradient vector - * in the bgmCompare model (active parameterization). - * - * This function accumulates observed sufficient statistics from the data - * and projects them into the parameter vector space. The output has the - * same length and ordering as `vectorize_model_parameters_bgmcompare()`, - * and includes: - * 1. Main-effect overall parameters (column 0). - * 2. Pairwise-effect overall parameters (column 0). - * 3. Main-effect group-difference parameters (columns 1..G-1) if - * inclusion_indicator(v,v) == 1. - * 4. Pairwise-effect group-difference parameters (columns 1..G-1) if - * inclusion_indicator(v1,v2) == 1. - * - * Inputs: - * - main_effect_indices: Row ranges [start,end] in main_effects for each variable. - * - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. - * - projection: Matrix of size (num_groups × (num_groups-1)) containing group projections. - * - observations: Matrix of observed variable values (N × V). - * - group_indices: Index ranges [start,end] defining which rows in `observations` - * belong to each group. - * - num_categories: Vector giving the number of categories per variable. - * - inclusion_indicator: Symmetric binary matrix; diagonal entries control inclusion - * of main-effect differences, off-diagonal entries control inclusion of pairwise - * differences. - * - counts_per_category_group: Per-group category count tables (list of matrices). - * - blume_capel_stats_group: Per-group Blume–Capel sufficient statistics (list of matrices). - * - pairwise_stats_group: Per-group pairwise sufficient statistics (list of matrices). - * - num_groups: Number of groups. - * - is_ordinal_variable: Indicator vector (1 = ordinal, 0 = Blume–Capel) per variable. - * - baseline_category: Vector of baseline categories per variable (Blume–Capel). - * - main_index: Index map for main effects (from build_index_maps()). - * - pair_index: Index map for pairwise effects (from build_index_maps()). - * - * Returns: - * - arma::vec: Observed-data contribution to the gradient (length = total_length()). - * - * Notes: - * - This function computes the *data-dependent* part of the gradient only; - * parameter-dependent expected statistics and priors must be added separately. - * - The output ordering must remain consistent with `vectorize_model_parameters_bgmcompare()`. - */ +// Compute the observed-data contribution to the gradient vector +// in the bgmCompare model (active parameterization). +// +// This function accumulates observed sufficient statistics from the data +// and projects them into the parameter vector space. The output has the +// same length and ordering as `vectorize_model_parameters_bgmcompare()`, +// and includes: +// 1. Main-effect overall parameters (column 0). +// 2. Pairwise-effect overall parameters (column 0). +// 3. Main-effect group-difference parameters (columns 1..G-1) if +// inclusion_indicator(v,v) == 1. +// 4. Pairwise-effect group-difference parameters (columns 1..G-1) if +// inclusion_indicator(v1,v2) == 1. +// +// Inputs: +// - main_effect_indices: Row ranges [start,end] in main_effects for each variable. +// - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. +// - projection: Matrix of size (num_groups × (num_groups-1)) containing group projections. +// - observations: Matrix of observed variable values (N × V). +// - group_indices: Index ranges [start,end] defining which rows in `observations` +// belong to each group. +// - num_categories: Vector giving the number of categories per variable. +// - inclusion_indicator: Symmetric binary matrix; diagonal entries control inclusion +// of main-effect differences, off-diagonal entries control inclusion of pairwise +// differences. +// - counts_per_category_group: Per-group category count tables (list of matrices). +// - blume_capel_stats_group: Per-group Blume–Capel sufficient statistics (list of matrices). +// - pairwise_stats_group: Per-group pairwise sufficient statistics (list of matrices). +// - num_groups: Number of groups. +// - is_ordinal_variable: Indicator vector (1 = ordinal, 0 = Blume–Capel) per variable. +// - baseline_category: Vector of baseline categories per variable (Blume–Capel). +// - main_index: Index map for main effects (from build_index_maps()). +// - pair_index: Index map for pairwise effects (from build_index_maps()). +// +// Returns: +// - arma::vec: Observed-data contribution to the gradient (length = total_length()). +// +// Notes: +// - This function computes the *data-dependent* part of the gradient only; +// parameter-dependent expected statistics and priors must be added separately. +// - The output ordering must remain consistent with `vectorize_model_parameters_bgmcompare()`. arma::vec gradient_observed_active( const arma::imat& main_effect_indices, const arma::imat& pairwise_effect_indices, @@ -218,63 +214,61 @@ arma::vec gradient_observed_active( -/** - * Computes the gradient of the log pseudoposterior for the bgmCompare model. - * - * The gradient combines three contributions: - * 1. Observed sufficient statistics (precomputed and supplied via `grad_obs`). - * 2. Expected sufficient statistics under the current parameter values - * (computed using softmax probabilities for ordinal or Blume–Capel variables). - * 3. Prior contributions on main effects, pairwise effects, and group differences. - * - * Procedure: - * - Initialize gradient with `grad_obs` (observed-data contribution). - * - Loop over groups: - * * Build group-specific main and pairwise effects using - * `compute_group_main_effects()` and `compute_group_pairwise_effects()`. - * * Compute expected sufficient statistics from residual scores and - * subtract them from the gradient. - * - Add prior contributions: - * * Logistic–Beta prior gradient for main-effect baseline parameters. - * * Cauchy prior gradient for group-difference parameters and pairwise effects. - * - * Inputs: - * - main_effects: Matrix of main-effect parameters (rows = categories, cols = groups). - * - pairwise_effects: Matrix of pairwise-effect parameters (rows = pairs, cols = groups). - * - main_effect_indices: Index ranges [row_start,row_end] for each variable in main_effects. - * - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. - * - projection: Group projection matrix (num_groups × (num_groups − 1)). - * - observations_double: Observation matrix (N × V), pre-converted to double. - * - group_indices: Row ranges [start,end] for each group in `observations_double`. - * - num_categories: Number of categories per variable. - * - counts_per_category_group: Per-group category counts (ordinal variables). - * - blume_capel_stats_group: Per-group sufficient statistics (Blume–Capel variables). - * - pairwise_stats_group: Per-group pairwise sufficient statistics. - * - num_groups: Number of groups. - * - inclusion_indicator: Symmetric binary matrix; diagonal entries control inclusion - * of main-effect differences, off-diagonal entries control inclusion of pairwise - * differences. - * - is_ordinal_variable: Indicator vector (1 = ordinal, 0 = Blume–Capel). - * - baseline_category: Reference categories for Blume–Capel variables. - * - main_alpha, main_beta: Hyperparameters for Beta priors on main effects. - * - interaction_scale: Scale parameter for Cauchy prior on baseline pairwise effects. - * - difference_scale: Scale parameter for Cauchy prior on group differences. - * - main_index: Index map for main-effect parameters (from build_index_maps()). - * - pair_index: Index map for pairwise-effect parameters (from build_index_maps()). - * - grad_obs: Precomputed observed-data contribution to the gradient - * (output of `gradient_observed_active()`). - * - * Returns: - * - arma::vec: Gradient of the log pseudoposterior with respect to all active - * parameters, in the layout defined by `vectorize_model_parameters_bgmcompare()`. - * - * Notes: - * - Must remain consistent with `vectorize_model_parameters_bgmcompare()` and - * `unvectorize_model_parameters_bgmcompare()`. - * - Expected sufficient statistics are computed on-the-fly, while observed - * statistics are passed in via `grad_obs`. - * - Priors are applied after observed and expected contributions. - */ +// Computes the gradient of the log pseudoposterior for the bgmCompare model. +// +// The gradient combines three contributions: +// 1. Observed sufficient statistics (precomputed and supplied via `grad_obs`). +// 2. Expected sufficient statistics under the current parameter values +// (computed using softmax probabilities for ordinal or Blume–Capel variables). +// 3. Prior contributions on main effects, pairwise effects, and group differences. +// +// Procedure: +// - Initialize gradient with `grad_obs` (observed-data contribution). +// - Loop over groups: +// * Build group-specific main and pairwise effects using +// `compute_group_main_effects()` and `compute_group_pairwise_effects()`. +// * Compute expected sufficient statistics from residual scores and +// subtract them from the gradient. +// - Add prior contributions: +// * Logistic–Beta prior gradient for main-effect baseline parameters. +// * Cauchy prior gradient for group-difference parameters and pairwise effects. +// +// Inputs: +// - main_effects: Matrix of main-effect parameters (rows = categories, cols = groups). +// - pairwise_effects: Matrix of pairwise-effect parameters (rows = pairs, cols = groups). +// - main_effect_indices: Index ranges [row_start,row_end] for each variable in main_effects. +// - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. +// - projection: Group projection matrix (num_groups × (num_groups − 1)). +// - observations_double: Observation matrix (N × V), pre-converted to double. +// - group_indices: Row ranges [start,end] for each group in `observations_double`. +// - num_categories: Number of categories per variable. +// - counts_per_category_group: Per-group category counts (ordinal variables). +// - blume_capel_stats_group: Per-group sufficient statistics (Blume–Capel variables). +// - pairwise_stats_group: Per-group pairwise sufficient statistics. +// - num_groups: Number of groups. +// - inclusion_indicator: Symmetric binary matrix; diagonal entries control inclusion +// of main-effect differences, off-diagonal entries control inclusion of pairwise +// differences. +// - is_ordinal_variable: Indicator vector (1 = ordinal, 0 = Blume–Capel). +// - baseline_category: Reference categories for Blume–Capel variables. +// - main_alpha, main_beta: Hyperparameters for Beta priors on main effects. +// - interaction_scale: Scale parameter for Cauchy prior on baseline pairwise effects. +// - difference_scale: Scale parameter for Cauchy prior on group differences. +// - main_index: Index map for main-effect parameters (from build_index_maps()). +// - pair_index: Index map for pairwise-effect parameters (from build_index_maps()). +// - grad_obs: Precomputed observed-data contribution to the gradient +// (output of `gradient_observed_active()`). +// +// Returns: +// - arma::vec: Gradient of the log pseudoposterior with respect to all active +// parameters, in the layout defined by `vectorize_model_parameters_bgmcompare()`. +// +// Notes: +// - Must remain consistent with `vectorize_model_parameters_bgmcompare()` and +// `unvectorize_model_parameters_bgmcompare()`. +// - Expected sufficient statistics are computed on-the-fly, while observed +// statistics are passed in via `grad_obs`. +// - Priors are applied after observed and expected contributions. arma::vec gradient( const arma::mat& main_effects, const arma::mat& pairwise_effects, @@ -505,18 +499,16 @@ arma::vec gradient( } -/** - * Computes both log pseudoposterior and gradient in a single pass. - * - * Fuses the computations of `log_pseudoposterior()` and `gradient()`, - * sharing intermediate results (group-specific effects, residual matrices, - * and probability computations) to avoid redundant work during NUTS sampling. - * - * Returns: - * - std::pair containing: - * - first: log pseudoposterior value (scalar) - * - second: gradient vector (same layout as gradient()) - */ +// Computes both log pseudoposterior and gradient in a single pass. +// +// Fuses the computations of `log_pseudoposterior()` and `gradient()`, +// sharing intermediate results (group-specific effects, residual matrices, +// and probability computations) to avoid redundant work during NUTS sampling. +// +// Returns: +// - std::pair containing: +// - first: log pseudoposterior value (scalar) +// - second: gradient vector (same layout as gradient()) std::pair logp_and_gradient( const arma::mat& main_effects, const arma::mat& pairwise_effects, @@ -779,54 +771,52 @@ std::pair logp_and_gradient( -/** - * Computes the log pseudoposterior contribution of a single main-effect parameter (bgmCompare model). - * - * This function isolates the contribution of one main-effect parameter, - * either the overall (baseline) effect or one of its group-specific differences. - * - * Procedure: - * - For each group: - * * Construct group-specific main effects for the selected variable - * with `compute_group_main_effects()`. - * * Construct group-specific pairwise effects for the variable. - * * Add linear contributions from sufficient statistics. - * * Subtract log normalizing constants from the group-specific likelihood. - * - Add prior contribution: - * * Logistic–Beta prior for baseline (h == 0). - * * Cauchy prior for group differences (h > 0), if included. - * - * Inputs: - * - main_effects: Matrix of main-effect parameters (rows = categories, cols = groups). - * - pairwise_effects: Matrix of pairwise-effect parameters (rows = pairs, cols = groups). - * - main_effect_indices: Index ranges [row_start,row_end] for each variable. - * - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. - * - projection: Group projection matrix (num_groups × (num_groups − 1)). - * - observations: Observation matrix (persons × variables). - * - group_indices: Row ranges [start,end] for each group in observations. - * - num_categories: Number of categories per variable. - * - counts_per_category_group: Per-group category counts (for ordinal variables). - * - blume_capel_stats_group: Per-group sufficient statistics (for Blume–Capel variables). - * - num_groups: Number of groups. - * - inclusion_indicator: Symmetric binary matrix of active variables (diag) and pairs (off-diag). - * - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). - * - baseline_category: Reference categories for Blume–Capel variables. - * - main_alpha, main_beta: Hyperparameters for Beta priors on main effects. - * - difference_scale: Scale parameter for Cauchy priors on group differences. - * - variable: Index of the variable of interest. - * - category: Category index (only used if variable is ordinal). - * - par: Parameter index (0 = linear, 1 = quadratic; used for Blume–Capel). - * - h: Column index (0 = overall baseline, >0 = group difference). - * - * Returns: - * - The scalar log pseudoposterior contribution of the selected parameter. - * - * Notes: - * - If h > 0 but inclusion_indicator(variable, variable) == 0, - * the function returns 0.0 (no contribution). - * - This component function is used in parameter-wise Metropolis updates. - * - Consistent with the full `log_pseudoposterior()` for bgmCompare. - */ +// Computes the log pseudoposterior contribution of a single main-effect parameter (bgmCompare model). +// +// This function isolates the contribution of one main-effect parameter, +// either the overall (baseline) effect or one of its group-specific differences. +// +// Procedure: +// - For each group: +// * Construct group-specific main effects for the selected variable +// with `compute_group_main_effects()`. +// * Construct group-specific pairwise effects for the variable. +// * Add linear contributions from sufficient statistics. +// * Subtract log normalizing constants from the group-specific likelihood. +// - Add prior contribution: +// * Logistic–Beta prior for baseline (h == 0). +// * Cauchy prior for group differences (h > 0), if included. +// +// Inputs: +// - main_effects: Matrix of main-effect parameters (rows = categories, cols = groups). +// - pairwise_effects: Matrix of pairwise-effect parameters (rows = pairs, cols = groups). +// - main_effect_indices: Index ranges [row_start,row_end] for each variable. +// - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. +// - projection: Group projection matrix (num_groups × (num_groups − 1)). +// - observations: Observation matrix (persons × variables). +// - group_indices: Row ranges [start,end] for each group in observations. +// - num_categories: Number of categories per variable. +// - counts_per_category_group: Per-group category counts (for ordinal variables). +// - blume_capel_stats_group: Per-group sufficient statistics (for Blume–Capel variables). +// - num_groups: Number of groups. +// - inclusion_indicator: Symmetric binary matrix of active variables (diag) and pairs (off-diag). +// - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). +// - baseline_category: Reference categories for Blume–Capel variables. +// - main_alpha, main_beta: Hyperparameters for Beta priors on main effects. +// - difference_scale: Scale parameter for Cauchy priors on group differences. +// - variable: Index of the variable of interest. +// - category: Category index (only used if variable is ordinal). +// - par: Parameter index (0 = linear, 1 = quadratic; used for Blume–Capel). +// - h: Column index (0 = overall baseline, >0 = group difference). +// +// Returns: +// - The scalar log pseudoposterior contribution of the selected parameter. +// +// Notes: +// - If h > 0 but inclusion_indicator(variable, variable) == 0, +// the function returns 0.0 (no contribution). +// - This component function is used in parameter-wise Metropolis updates. +// - Consistent with the full `log_pseudoposterior()` for bgmCompare. double log_pseudoposterior_main_component( const arma::mat& main_effects, const arma::mat& pairwise_effects, @@ -959,53 +949,51 @@ double log_pseudoposterior_main_component( } -/** - * Computes the log pseudoposterior contribution of a single pairwise-effect parameter (bgmCompare model). - * - * Isolates the contribution of one pairwise-effect parameter between two variables, - * either the baseline effect (h == 0) or a group-specific difference (h > 0). - * - * Procedure: - * - For each group: - * * Construct group-specific main effects for the two variables. - * * Add linear contributions from the pairwise sufficient statistic. - * - Baseline (h == 0): contribution = 2 * suff_pair * proposed_value. - * - Difference (h > 0): scaled by projection value proj_g(h-1). - * * Subtract log normalizing constants from both variables' likelihoods. - * - Add prior contribution: - * * Cauchy prior for baseline (scale = interaction_scale). - * * Cauchy prior for group differences (scale = difference_scale). - * - * Inputs: - * - main_effects: Matrix of main-effect parameters (rows = categories, cols = groups). - * - pairwise_effects: Matrix of pairwise-effect parameters (rows = pairs, cols = groups). - * - main_effect_indices: Index ranges [row_start, row_end] for each variable. - * - pairwise_effect_indices: Lookup table mapping (var1, var2) to row in pairwise_effects. - * - projection: Group projection matrix (num_groups × (num_groups − 1)). - * - observations: Observation matrix (persons × variables). - * - group_indices: Row ranges [start, end] for each group in observations. - * - num_categories: Number of categories per variable. - * - pairwise_stats_group: Per-group pairwise sufficient statistics. - * - residual_matrices: Per-group residual matrices (persons × variables). - * - num_groups: Number of groups. - * - inclusion_indicator: Symmetric binary matrix of active variables (diag) and pairs (off-diag). - * - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). - * - baseline_category: Reference categories for Blume–Capel variables. - * - interaction_scale: Scale parameter for Cauchy prior on baseline pairwise effects. - * - pairwise_scaling_factors: Per-pair scaling factors for the prior. - * - difference_scale: Scale parameter for Cauchy prior on group differences. - * - variable1, variable2: Indices of the variable pair. - * - h: Column index (0 = baseline, > 0 = group difference). - * - delta: Parameter change (proposed - current). - * - * Returns: - * - The log pseudoposterior value at the proposed state. - * - * Notes: - * - If h > 0 but inclusion_indicator(variable1, variable2) == 0, returns 0.0. - * - The proposed value is computed as pairwise_effects(idx, h) + delta. - * - Residual scores are adjusted by delta without modifying residual_matrices. - */ +// Computes the log pseudoposterior contribution of a single pairwise-effect parameter (bgmCompare model). +// +// Isolates the contribution of one pairwise-effect parameter between two variables, +// either the baseline effect (h == 0) or a group-specific difference (h > 0). +// +// Procedure: +// - For each group: +// * Construct group-specific main effects for the two variables. +// * Add linear contributions from the pairwise sufficient statistic. +// - Baseline (h == 0): contribution = 2 * suff_pair * proposed_value. +// - Difference (h > 0): scaled by projection value proj_g(h-1). +// * Subtract log normalizing constants from both variables' likelihoods. +// - Add prior contribution: +// * Cauchy prior for baseline (scale = interaction_scale). +// * Cauchy prior for group differences (scale = difference_scale). +// +// Inputs: +// - main_effects: Matrix of main-effect parameters (rows = categories, cols = groups). +// - pairwise_effects: Matrix of pairwise-effect parameters (rows = pairs, cols = groups). +// - main_effect_indices: Index ranges [row_start, row_end] for each variable. +// - pairwise_effect_indices: Lookup table mapping (var1, var2) to row in pairwise_effects. +// - projection: Group projection matrix (num_groups × (num_groups − 1)). +// - observations: Observation matrix (persons × variables). +// - group_indices: Row ranges [start, end] for each group in observations. +// - num_categories: Number of categories per variable. +// - pairwise_stats_group: Per-group pairwise sufficient statistics. +// - residual_matrices: Per-group residual matrices (persons × variables). +// - num_groups: Number of groups. +// - inclusion_indicator: Symmetric binary matrix of active variables (diag) and pairs (off-diag). +// - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). +// - baseline_category: Reference categories for Blume–Capel variables. +// - interaction_scale: Scale parameter for Cauchy prior on baseline pairwise effects. +// - pairwise_scaling_factors: Per-pair scaling factors for the prior. +// - difference_scale: Scale parameter for Cauchy prior on group differences. +// - variable1, variable2: Indices of the variable pair. +// - h: Column index (0 = baseline, > 0 = group difference). +// - delta: Parameter change (proposed - current). +// +// Returns: +// - The log pseudoposterior value at the proposed state. +// +// Notes: +// - If h > 0 but inclusion_indicator(variable1, variable2) == 0, returns 0.0. +// - The proposed value is computed as pairwise_effects(idx, h) + delta. +// - Residual scores are adjusted by delta without modifying residual_matrices. double log_pseudoposterior_pair_component( const arma::mat& main_effects, const arma::mat& pairwise_effects, @@ -1113,49 +1101,47 @@ double log_pseudoposterior_pair_component( -/** - * Computes the log-ratio of pseudolikelihood normalizing constants - * for a single variable under current vs. proposed parameters (bgmCompare model). - * - * This function is used in Metropolis–Hastings updates for main-effect parameters. - * It evaluates how the normalizing constant (denominator of the pseudolikelihood) - * changes when switching from the current to the proposed parameter values. - * - * Procedure: - * - For each group: - * * Construct group-specific main effects (current vs. proposed). - * * Construct group-specific pairwise weights for the variable. - * * Compute residual scores for observations under both models. - * * Calculate denominators with stability bounds (ordinal vs. Blume–Capel cases). - * * Accumulate the log-ratio contribution across all observations. - * - * Inputs: - * - current_main_effects, proposed_main_effects: Matrices of main-effect parameters - * (rows = categories, cols = groups). - * - current_pairwise_effects, proposed_pairwise_effects: Matrices of pairwise-effect parameters - * (rows = pairs, cols = groups). - * - main_effect_indices: Index ranges [row_start,row_end] for each variable. - * - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. - * - projection: Group projection matrix (num_groups × (num_groups − 1)). - * - observations: Observation matrix (persons × variables). - * - group_indices: Row ranges [start,end] for each group in observations. - * - num_categories: Number of categories per variable. - * - num_groups: Number of groups. - * - inclusion_indicator: Symmetric binary matrix of active variables (diag) and pairs (off-diag). - * - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). - * - baseline_category: Reference categories for Blume–Capel variables. - * - variable: Index of the variable being updated. - * - * Returns: - * - The scalar log-ratio of pseudolikelihood constants - * (current model vs. proposed model). - * - * Notes: - * - For ordinal variables, denominators include exp(-bound) and category terms. - * - For Blume–Capel variables, denominators use linear/quadratic scores - * with baseline centering. - * - Stability bounds (`bound_current`, `bound_proposed`) are applied to avoid overflow. - */ +// Computes the log-ratio of pseudolikelihood normalizing constants +// for a single variable under current vs. proposed parameters (bgmCompare model). +// +// This function is used in Metropolis–Hastings updates for main-effect parameters. +// It evaluates how the normalizing constant (denominator of the pseudolikelihood) +// changes when switching from the current to the proposed parameter values. +// +// Procedure: +// - For each group: +// * Construct group-specific main effects (current vs. proposed). +// * Construct group-specific pairwise weights for the variable. +// * Compute residual scores for observations under both models. +// * Calculate denominators with stability bounds (ordinal vs. Blume–Capel cases). +// * Accumulate the log-ratio contribution across all observations. +// +// Inputs: +// - current_main_effects, proposed_main_effects: Matrices of main-effect parameters +// (rows = categories, cols = groups). +// - current_pairwise_effects, proposed_pairwise_effects: Matrices of pairwise-effect parameters +// (rows = pairs, cols = groups). +// - main_effect_indices: Index ranges [row_start,row_end] for each variable. +// - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. +// - projection: Group projection matrix (num_groups × (num_groups − 1)). +// - observations: Observation matrix (persons × variables). +// - group_indices: Row ranges [start,end] for each group in observations. +// - num_categories: Number of categories per variable. +// - num_groups: Number of groups. +// - inclusion_indicator: Symmetric binary matrix of active variables (diag) and pairs (off-diag). +// - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). +// - baseline_category: Reference categories for Blume–Capel variables. +// - variable: Index of the variable being updated. +// +// Returns: +// - The scalar log-ratio of pseudolikelihood constants +// (current model vs. proposed model). +// +// Notes: +// - For ordinal variables, denominators include exp(-bound) and category terms. +// - For Blume–Capel variables, denominators use linear/quadratic scores +// with baseline centering. +// - Stability bounds (`bound_current`, `bound_proposed`) are applied to avoid overflow. double log_ratio_pseudolikelihood_constant_variable( const arma::mat& current_main_effects, const arma::mat& current_pairwise_effects, @@ -1256,50 +1242,48 @@ double log_ratio_pseudolikelihood_constant_variable( -/** - * Computes the log pseudolikelihood ratio for updating a single main-effect parameter (bgmCompare model). - * - * This function is used in Metropolis–Hastings updates for main effects. - * It compares the likelihood of the data under the current vs. proposed - * value of a single variable’s main-effect parameter, while keeping - * all other parameters fixed. - * - * Procedure: - * - For each group: - * * Compute group-specific main effects for the variable (current vs. proposed). - * * Add contributions from observed sufficient statistics - * (category counts or Blume–Capel stats). - * - Add the ratio of pseudolikelihood normalizing constants by calling - * `log_ratio_pseudolikelihood_constant_variable()`. - * - * Inputs: - * - current_main_effects: Matrix of main-effect parameters (current state). - * - proposed_main_effects: Matrix of main-effect parameters (candidate state). - * - current_pairwise_effects: Matrix of pairwise-effect parameters (fixed at current state). - * - main_effect_indices: Index ranges [row_start,row_end] for each variable. - * - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. - * - projection: Group projection matrix (num_groups × (num_groups − 1)). - * - observations: Observation matrix (persons × variables). - * - group_indices: Row ranges [start,end] for each group in observations. - * - num_categories: Number of categories per variable. - * - counts_per_category_group: Per-group category counts (for ordinal variables). - * - blume_capel_stats_group: Per-group sufficient statistics (for Blume–Capel variables). - * - num_groups: Number of groups. - * - inclusion_indicator: Symmetric binary matrix of active variables (diag) and pairs (off-diag). - * - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). - * - baseline_category: Reference categories for Blume–Capel variables. - * - variable: Index of the variable being updated. - * - * Returns: - * - The scalar log pseudolikelihood ratio (proposed vs. current). - * - * Notes: - * - A temporary copy of `inclusion_indicator` is made to ensure the - * variable’s self-term (diagonal entry) is included. - * - Only the variable under update changes between current and proposed states; - * all other variables and pairwise effects remain fixed. - * - This function does not add prior contributions — only pseudolikelihood terms. - */ +// Computes the log pseudolikelihood ratio for updating a single main-effect parameter (bgmCompare model). +// +// This function is used in Metropolis–Hastings updates for main effects. +// It compares the likelihood of the data under the current vs. proposed +// value of a single variable’s main-effect parameter, while keeping +// all other parameters fixed. +// +// Procedure: +// - For each group: +// * Compute group-specific main effects for the variable (current vs. proposed). +// * Add contributions from observed sufficient statistics +// (category counts or Blume–Capel stats). +// - Add the ratio of pseudolikelihood normalizing constants by calling +// `log_ratio_pseudolikelihood_constant_variable()`. +// +// Inputs: +// - current_main_effects: Matrix of main-effect parameters (current state). +// - proposed_main_effects: Matrix of main-effect parameters (candidate state). +// - current_pairwise_effects: Matrix of pairwise-effect parameters (fixed at current state). +// - main_effect_indices: Index ranges [row_start,row_end] for each variable. +// - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. +// - projection: Group projection matrix (num_groups × (num_groups − 1)). +// - observations: Observation matrix (persons × variables). +// - group_indices: Row ranges [start,end] for each group in observations. +// - num_categories: Number of categories per variable. +// - counts_per_category_group: Per-group category counts (for ordinal variables). +// - blume_capel_stats_group: Per-group sufficient statistics (for Blume–Capel variables). +// - num_groups: Number of groups. +// - inclusion_indicator: Symmetric binary matrix of active variables (diag) and pairs (off-diag). +// - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). +// - baseline_category: Reference categories for Blume–Capel variables. +// - variable: Index of the variable being updated. +// +// Returns: +// - The scalar log pseudolikelihood ratio (proposed vs. current). +// +// Notes: +// - A temporary copy of `inclusion_indicator` is made to ensure the +// variable’s self-term (diagonal entry) is included. +// - Only the variable under update changes between current and proposed states; +// all other variables and pairwise effects remain fixed. +// - This function does not add prior contributions — only pseudolikelihood terms. double log_pseudolikelihood_ratio_main( const arma::mat& current_main_effects, const arma::mat& proposed_main_effects, @@ -1359,49 +1343,47 @@ double log_pseudolikelihood_ratio_main( } -/** - * Computes the log pseudolikelihood ratio for updating a single pairwise-effect parameter (bgmCompare model). - * - * This function is used in Metropolis–Hastings updates for pairwise effects. - * It compares the likelihood of the data under the current vs. proposed - * value of a single interaction (var1,var2), while keeping all other - * parameters fixed. - * - * Procedure: - * - Ensure the interaction is included in a temporary copy of inclusion_indicator. - * - For each group: - * * Compute group-specific pairwise effect for (var1,var2), current vs. proposed. - * * Add linear contribution from the pairwise sufficient statistic. - * - Add the ratio of pseudolikelihood normalizing constants for both variables: - * * Call `log_ratio_pseudolikelihood_constant_variable()` separately for var1 and var2, - * comparing current vs. proposed pairwise weights. - * - * Inputs: - * - main_effects: Matrix of main-effect parameters (fixed). - * - current_pairwise_effects: Matrix of pairwise-effect parameters (current state). - * - proposed_pairwise_effects: Matrix of pairwise-effect parameters (candidate state). - * - main_effect_indices: Index ranges [row_start,row_end] for each variable. - * - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. - * - projection: Group projection matrix (num_groups × (num_groups − 1)). - * - observations: Observation matrix (persons × variables). - * - group_indices: Row ranges [start,end] for each group in observations. - * - num_categories: Number of categories per variable. - * - pairwise_stats_group: Per-group pairwise sufficient statistics. - * - num_groups: Number of groups. - * - inclusion_indicator: Symmetric binary matrix of active variables (diag) and pairs (off-diag). - * - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). - * - baseline_category: Reference categories for Blume–Capel variables. - * - var1, var2: Indices of the variable pair being updated. - * - * Returns: - * - The scalar log pseudolikelihood ratio (proposed vs. current). - * - * Notes: - * - A temporary copy of `inclusion_indicator` is used to force the edge (var1,var2) as active. - * - Only the selected pair changes between current and proposed states; - * all other effects remain fixed. - * - This function does not add prior contributions — only pseudolikelihood terms. - */ +// Computes the log pseudolikelihood ratio for updating a single pairwise-effect parameter (bgmCompare model). +// +// This function is used in Metropolis–Hastings updates for pairwise effects. +// It compares the likelihood of the data under the current vs. proposed +// value of a single interaction (var1,var2), while keeping all other +// parameters fixed. +// +// Procedure: +// - Ensure the interaction is included in a temporary copy of inclusion_indicator. +// - For each group: +// * Compute group-specific pairwise effect for (var1,var2), current vs. proposed. +// * Add linear contribution from the pairwise sufficient statistic. +// - Add the ratio of pseudolikelihood normalizing constants for both variables: +// * Call `log_ratio_pseudolikelihood_constant_variable()` separately for var1 and var2, +// comparing current vs. proposed pairwise weights. +// +// Inputs: +// - main_effects: Matrix of main-effect parameters (fixed). +// - current_pairwise_effects: Matrix of pairwise-effect parameters (current state). +// - proposed_pairwise_effects: Matrix of pairwise-effect parameters (candidate state). +// - main_effect_indices: Index ranges [row_start,row_end] for each variable. +// - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. +// - projection: Group projection matrix (num_groups × (num_groups − 1)). +// - observations: Observation matrix (persons × variables). +// - group_indices: Row ranges [start,end] for each group in observations. +// - num_categories: Number of categories per variable. +// - pairwise_stats_group: Per-group pairwise sufficient statistics. +// - num_groups: Number of groups. +// - inclusion_indicator: Symmetric binary matrix of active variables (diag) and pairs (off-diag). +// - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). +// - baseline_category: Reference categories for Blume–Capel variables. +// - var1, var2: Indices of the variable pair being updated. +// +// Returns: +// - The scalar log pseudolikelihood ratio (proposed vs. current). +// +// Notes: +// - A temporary copy of `inclusion_indicator` is used to force the edge (var1,var2) as active. +// - Only the selected pair changes between current and proposed states; +// all other effects remain fixed. +// - This function does not add prior contributions — only pseudolikelihood terms. double log_pseudolikelihood_ratio_pairwise( const arma::mat& main_effects, const arma::mat& current_pairwise_effects, diff --git a/src/bgmCompare/bgmCompare_output.h b/src/bgmCompare/bgmCompare_output.h index 148a1984..632566d2 100644 --- a/src/bgmCompare/bgmCompare_output.h +++ b/src/bgmCompare/bgmCompare_output.h @@ -10,28 +10,27 @@ * * Stores posterior samples of main and pairwise effects, optional * inclusion indicators, and diagnostics for HMC/NUTS runs. - * - * Members: - * - main_samples: [iter × (#main × groups)] matrix of main-effect samples. - * - pairwise_samples:[iter × (#pair × groups)] matrix of pairwise-effect samples. - * - indicator_samples:[iter × (#edges + #variables)] indicator samples (if used). - * - treedepth_samples:[iter] tree depth diagnostics (NUTS only). - * - divergent_samples:[iter] divergent transition flags (NUTS only). - * - energy_samples: [iter] energy diagnostic (NUTS only). - * - chain_id: Identifier of the chain. - * - has_indicator: True if indicator samples are stored. */ struct bgmCompareOutput { + /// Main-effect samples [iter x (#main x groups)]. arma::mat main_samples; + /// Pairwise-effect samples [iter x (#pair x groups)]. arma::mat pairwise_samples; + /// Inclusion indicator samples [iter x (#edges + #variables)] (if used). arma::imat indicator_samples; + /// Tree depth diagnostics [iter] (NUTS only). arma::ivec treedepth_samples; + /// Divergent transition flags [iter] (NUTS only). arma::ivec divergent_samples; + /// Energy diagnostic [iter] (NUTS only). arma::vec energy_samples; + /// Identifier of the chain. int chain_id; + /// True if indicator samples are stored. bool has_indicator; + /// True if the chain was interrupted by the user. bool userInterrupt; }; diff --git a/src/bgmCompare/bgmCompare_sampler.cpp b/src/bgmCompare/bgmCompare_sampler.cpp index 19fbd8d2..cb5ee5f6 100644 --- a/src/bgmCompare/bgmCompare_sampler.cpp +++ b/src/bgmCompare/bgmCompare_sampler.cpp @@ -17,51 +17,49 @@ -/** - * Imputes missing observations for the bgmCompare model. - * - * This function performs single imputation of missing values during Gibbs sampling. - * Each missing entry is resampled from its conditional distribution given: - * - the current main and pairwise effect parameters, - * - the observed data for that individual, - * - group-specific sufficient statistics. - * - * Workflow: - * 1. For each missing entry, identify its (person, variable, group). - * 2. Compute group-specific main and pairwise effects via projections. - * 3. Calculate unnormalized probabilities for all categories of the variable: - * - Ordinal: softmax using category-specific thresholds. - * - Blume–Capel: quadratic + linear score with baseline centering. - * 4. Sample a new category with inverse transform sampling. - * 5. If the imputed value differs from the old one, update: - * - `observations` (raw data matrix), - * - `counts_per_category` or `blume_capel_stats` (main-effect sufficient stats), - * - `pairwise_stats` (pairwise sufficient stats). - * - * Inputs: - * - main_effects, pairwise_effects: Current parameter matrices. - * - main_effect_indices, pairwise_effect_indices: Lookup tables for variable/pair rows. - * - inclusion_indicator: Indicates which differences/pairs are included. - * - projection: Group projection matrix. - * - observations: Data matrix [persons × variables]; updated in place. - * - num_groups: Number of groups. - * - group_membership: Group assignment for each person. - * - group_indices: Row ranges [start,end] for each group. - * - counts_per_category: Group-level sufficient statistics for ordinal variables. - * - blume_capel_stats: Group-level sufficient statistics for Blume–Capel variables. - * - pairwise_stats: Group-level sufficient statistics for pairwise interactions. - * - num_categories: Number of categories for each variable in each group. - * - missing_data_indices: Matrix of (person, variable) pairs with missing values. - * - is_ordinal_variable: Indicator vector (1 = ordinal, 0 = Blume–Capel). - * - baseline_category: Reference categories for Blume–Capel variables. - * - rng: Random number generator. - * - * Notes: - * - The function updates both raw data and sufficient statistics in-place. - * - Group-specific pairwise effects are recomputed per missing entry. - * - For efficiency, you may consider incremental updates to `pairwise_stats` - * instead of full recomputation (`obs.t() * obs`) after each change. - */ +// Imputes missing observations for the bgmCompare model. +// +// This function performs single imputation of missing values during Gibbs sampling. +// Each missing entry is resampled from its conditional distribution given: +// - the current main and pairwise effect parameters, +// - the observed data for that individual, +// - group-specific sufficient statistics. +// +// Workflow: +// 1. For each missing entry, identify its (person, variable, group). +// 2. Compute group-specific main and pairwise effects via projections. +// 3. Calculate unnormalized probabilities for all categories of the variable: +// - Ordinal: softmax using category-specific thresholds. +// - Blume–Capel: quadratic + linear score with baseline centering. +// 4. Sample a new category with inverse transform sampling. +// 5. If the imputed value differs from the old one, update: +// - `observations` (raw data matrix), +// - `counts_per_category` or `blume_capel_stats` (main-effect sufficient stats), +// - `pairwise_stats` (pairwise sufficient stats). +// +// Inputs: +// - main_effects, pairwise_effects: Current parameter matrices. +// - main_effect_indices, pairwise_effect_indices: Lookup tables for variable/pair rows. +// - inclusion_indicator: Indicates which differences/pairs are included. +// - projection: Group projection matrix. +// - observations: Data matrix [persons × variables]; updated in place. +// - num_groups: Number of groups. +// - group_membership: Group assignment for each person. +// - group_indices: Row ranges [start,end] for each group. +// - counts_per_category: Group-level sufficient statistics for ordinal variables. +// - blume_capel_stats: Group-level sufficient statistics for Blume–Capel variables. +// - pairwise_stats: Group-level sufficient statistics for pairwise interactions. +// - num_categories: Number of categories for each variable in each group. +// - missing_data_indices: Matrix of (person, variable) pairs with missing values. +// - is_ordinal_variable: Indicator vector (1 = ordinal, 0 = Blume–Capel). +// - baseline_category: Reference categories for Blume–Capel variables. +// - rng: Random number generator. +// +// Notes: +// - The function updates both raw data and sufficient statistics in-place. +// - Group-specific pairwise effects are recomputed per missing entry. +// - For efficiency, you may consider incremental updates to `pairwise_stats` +// instead of full recomputation (`obs.t() * obs`) after each change. void impute_missing_bgmcompare( const arma::mat& main_effects, const arma::mat& pairwise_effects, @@ -188,58 +186,56 @@ void impute_missing_bgmcompare( -/** - * Updates main effect parameters in bgmCompare using a random-walk Metropolis step. - * - * For each variable, the function proposes new parameter values for either: - * - all categories (ordinal variables), or - * - two parameters (linear and quadratic, Blume–Capel variables). - * - * If group-specific differences are enabled (`inclusion_indicator(v,v) == 1`), - * additional parameters (one per group contrast) are also updated. - * - * Each proposed parameter is evaluated via - * `log_pseudoposterior_main_component()`, and accepted/rejected using - * the Metropolis–Hastings rule. Proposal standard deviations are adapted - * online with `MetropolisAdaptationController`. - * - * Workflow: - * 1. Iterate over all variables. - * 2. For each category (ordinal) or parameter (Blume–Capel): - * - Update the "overall" effect (h=0). - * - Optionally update group-difference effects (h=1..G-1). - * 3. Record acceptance probabilities and update adaptation statistics. - * - * Inputs: - * - main_effects: Matrix of main effect parameters [rows = effects, cols = groups]; - * updated in place. - * - pairwise_effects: Current pairwise effects (passed through to log posterior). - * - main_effect_indices: Row index ranges for each variable’s main effects. - * - pairwise_effect_indices: Index map for pairwise effects. - * - inclusion_indicator: Indicator matrix; diagonal entries control group differences. - * - projection: Group projection matrix. - * - num_categories: Number of categories for each variable. - * - observations: Data matrix [persons × variables]. - * - num_groups: Number of groups (G). - * - group_indices: Row ranges per group in `observations`. - * - counts_per_category, blume_capel_stats: Group-specific sufficient statistics. - * - is_ordinal_variable: Indicator for ordinal vs. Blume–Capel. - * - baseline_category: Reference categories (Blume–Capel only). - * - difference_scale: Scale parameter for group difference priors. - * - main_alpha, main_beta: Parameters for the Beta prior on main effects. - * - iteration: Current iteration index (for adaptation). - * - rwm_adapt: Adaptation controller for proposal SDs. - * - rng: Random number generator. - * - proposal_sd_main: Proposal standard deviations [same shape as `main_effects`]; - * updated in place. - * - * Notes: - * - Acceptance probabilities are stored per parameter and fed to `metropolis_adapt.update()`. - * - This function does not alter pairwise effects, but passes them into - * the posterior for likelihood consistency. - * - The helper lambda `do_update` encapsulates the proposal/accept/revert loop - * for a single parameter, improving readability. - */ +// Updates main effect parameters in bgmCompare using a random-walk Metropolis step. +// +// For each variable, the function proposes new parameter values for either: +// - all categories (ordinal variables), or +// - two parameters (linear and quadratic, Blume–Capel variables). +// +// If group-specific differences are enabled (`inclusion_indicator(v,v) == 1`), +// additional parameters (one per group contrast) are also updated. +// +// Each proposed parameter is evaluated via +// `log_pseudoposterior_main_component()`, and accepted/rejected using +// the Metropolis–Hastings rule. Proposal standard deviations are adapted +// online with `MetropolisAdaptationController`. +// +// Workflow: +// 1. Iterate over all variables. +// 2. For each category (ordinal) or parameter (Blume–Capel): +// - Update the "overall" effect (h=0). +// - Optionally update group-difference effects (h=1..G-1). +// 3. Record acceptance probabilities and update adaptation statistics. +// +// Inputs: +// - main_effects: Matrix of main effect parameters [rows = effects, cols = groups]; +// updated in place. +// - pairwise_effects: Current pairwise effects (passed through to log posterior). +// - main_effect_indices: Row index ranges for each variable’s main effects. +// - pairwise_effect_indices: Index map for pairwise effects. +// - inclusion_indicator: Indicator matrix; diagonal entries control group differences. +// - projection: Group projection matrix. +// - num_categories: Number of categories for each variable. +// - observations: Data matrix [persons × variables]. +// - num_groups: Number of groups (G). +// - group_indices: Row ranges per group in `observations`. +// - counts_per_category, blume_capel_stats: Group-specific sufficient statistics. +// - is_ordinal_variable: Indicator for ordinal vs. Blume–Capel. +// - baseline_category: Reference categories (Blume–Capel only). +// - difference_scale: Scale parameter for group difference priors. +// - main_alpha, main_beta: Parameters for the Beta prior on main effects. +// - iteration: Current iteration index (for adaptation). +// - rwm_adapt: Adaptation controller for proposal SDs. +// - rng: Random number generator. +// - proposal_sd_main: Proposal standard deviations [same shape as `main_effects`]; +// updated in place. +// +// Notes: +// - Acceptance probabilities are stored per parameter and fed to `metropolis_adapt.update()`. +// - This function does not alter pairwise effects, but passes them into +// the posterior for likelihood consistency. +// - The helper lambda `do_update` encapsulates the proposal/accept/revert loop +// for a single parameter, improving readability. void update_main_effects_metropolis_bgmcompare ( arma::mat& main_effects, arma::mat& pairwise_effects, @@ -324,57 +320,55 @@ void update_main_effects_metropolis_bgmcompare ( -/** - * Updates pairwise interaction parameters in bgmCompare using a random-walk - * Metropolis step. - * - * For each variable pair (var1,var2), the function proposes new parameter - * values for: - * - the overall interaction (h=0), and - * - optionally group-difference effects (h=1..G-1) if enabled in - * `inclusion_indicator`. - * - * Each proposed parameter is evaluated via - * `log_pseudoposterior_pair_component()`, and accepted/rejected using the - * Metropolis–Hastings rule. Proposal standard deviations are adapted online - * through `MetropolisAdaptationController`. - * - * Workflow: - * 1. Iterate over all unique pairs of variables. - * 2. For each pair, update the overall effect (h=0). - * 3. If group differences are active, update group-specific difference - * effects (h=1..G-1). - * 4. Record acceptance probabilities and update proposal SDs via `rwm_adapt`. - * - * Inputs: - * - main_effects: Matrix of main effect parameters (passed through to log posterior). - * - pairwise_effects: Matrix of pairwise interaction parameters [rows = pairs, cols = groups]; - * updated in place. - * - main_effect_indices: Index map for main effects (per variable). - * - pairwise_effect_indices: Row index map for pairwise effects (per var1,var2). - * - inclusion_indicator: Indicator matrix; off-diagonal entries control group differences. - * - projection: Group projection matrix. - * - num_categories: Number of categories per variable. - * - observations: Data matrix [persons × variables]. - * - num_groups: Number of groups (G). - * - group_indices: Row ranges per group in `observations`. - * - pairwise_stats: Group-specific sufficient statistics for pairwise effects. - * - is_ordinal_variable: Indicator for ordinal vs. Blume–Capel variables. - * - baseline_category: Reference categories (Blume–Capel only). - * - pairwise_scale: Scale parameter for overall interaction prior. - * - difference_scale: Scale parameter for group difference priors. - * - iteration: Current iteration index (for adaptation). - * - rwm_adapt: Adaptation controller for proposal SDs. - * - rng: Random number generator. - * - proposal_sd_pair: Proposal standard deviations [same shape as `pairwise_effects`]; - * updated in place. - * - * Notes: - * - Acceptance probabilities are tracked per parameter and fed to - * `metropolis_adapt.update()`. - * - The helper lambda `do_update` encapsulates the proposal/accept/reject - * logic for a single parameter. - */ +// Updates pairwise interaction parameters in bgmCompare using a random-walk +// Metropolis step. +// +// For each variable pair (var1,var2), the function proposes new parameter +// values for: +// - the overall interaction (h=0), and +// - optionally group-difference effects (h=1..G-1) if enabled in +// `inclusion_indicator`. +// +// Each proposed parameter is evaluated via +// `log_pseudoposterior_pair_component()`, and accepted/rejected using the +// Metropolis–Hastings rule. Proposal standard deviations are adapted online +// through `MetropolisAdaptationController`. +// +// Workflow: +// 1. Iterate over all unique pairs of variables. +// 2. For each pair, update the overall effect (h=0). +// 3. If group differences are active, update group-specific difference +// effects (h=1..G-1). +// 4. Record acceptance probabilities and update proposal SDs via `rwm_adapt`. +// +// Inputs: +// - main_effects: Matrix of main effect parameters (passed through to log posterior). +// - pairwise_effects: Matrix of pairwise interaction parameters [rows = pairs, cols = groups]; +// updated in place. +// - main_effect_indices: Index map for main effects (per variable). +// - pairwise_effect_indices: Row index map for pairwise effects (per var1,var2). +// - inclusion_indicator: Indicator matrix; off-diagonal entries control group differences. +// - projection: Group projection matrix. +// - num_categories: Number of categories per variable. +// - observations: Data matrix [persons × variables]. +// - num_groups: Number of groups (G). +// - group_indices: Row ranges per group in `observations`. +// - pairwise_stats: Group-specific sufficient statistics for pairwise effects. +// - is_ordinal_variable: Indicator for ordinal vs. Blume–Capel variables. +// - baseline_category: Reference categories (Blume–Capel only). +// - pairwise_scale: Scale parameter for overall interaction prior. +// - difference_scale: Scale parameter for group difference priors. +// - iteration: Current iteration index (for adaptation). +// - rwm_adapt: Adaptation controller for proposal SDs. +// - rng: Random number generator. +// - proposal_sd_pair: Proposal standard deviations [same shape as `pairwise_effects`]; +// updated in place. +// +// Notes: +// - Acceptance probabilities are tracked per parameter and fed to +// `metropolis_adapt.update()`. +// - The helper lambda `do_update` encapsulates the proposal/accept/reject +// logic for a single parameter. void update_pairwise_effects_metropolis_bgmcompare ( arma::mat& main_effects, arma::mat& pairwise_effects, @@ -489,54 +483,52 @@ void update_pairwise_effects_metropolis_bgmcompare ( -/** - * Heuristically determine an initial HMC/NUTS step size for bgmCompare. - * - * This function vectorizes the current model parameters, then repeatedly - * simulates short HMC trajectories to calibrate a stable starting step size - * that achieves a target acceptance rate. - * - * Workflow: - * 1. Vectorize current parameters into a single state vector. - * 2. Define closures for log-posterior evaluation and gradient computation: - * - `log_post`: unpacks parameters and evaluates the log pseudoposterior. - * - `grad`: unpacks parameters and evaluates the gradient of the - * pseudoposterior. - * 3. Pass these to `heuristic_initial_step_size`, which runs the heuristic - * tuning loop. - * - * Inputs: - * - main_effects: Matrix of main-effect parameters [n_main_rows × G]. - * - pairwise_effects: Matrix of pairwise interaction parameters [n_pairs × G]. - * - main_effect_indices: Row index ranges for each variable’s main effects. - * - pairwise_effect_indices: Row index map for pairwise effects. - * - inclusion_indicator: Matrix marking which main and pairwise differences - * are active. - * - projection: Group projection matrix (encodes contrasts). - * - num_categories: Number of categories per variable [V]. - * - observations: Data matrix [persons × variables]. - * - num_groups: Number of groups (G). - * - group_indices: Row ranges per group in `observations`. - * - counts_per_category: Per-group sufficient statistics for ordinal variables. - * - blume_capel_stats: Per-group sufficient statistics for Blume–Capel variables. - * - pairwise_stats: Per-group sufficient statistics for pairwise effects. - * - is_ordinal_variable: Indicator for ordinal vs. Blume–Capel variables [V]. - * - baseline_category: Reference categories for Blume–Capel variables [V]. - * - pairwise_scale: Scale parameter for overall pairwise priors. - * - difference_scale: Scale parameter for group difference priors. - * - main_alpha, main_beta: Hyperparameters for Beta prior on main effects. - * - target_acceptance: Desired acceptance probability (e.g. 0.8). - * - rng: Random number generator. - * - * Returns: - * - A double value for the initial HMC/NUTS step size. - * - * Notes: - * - This routine is only used during warmup to initialize - * `HMCAdaptationController`. - * - Correct indexing of parameters relies on `build_index_maps` to ensure - * consistency between vectorization and gradient computation. - */ +// Heuristically determine an initial HMC/NUTS step size for bgmCompare. +// +// This function vectorizes the current model parameters, then repeatedly +// simulates short HMC trajectories to calibrate a stable starting step size +// that achieves a target acceptance rate. +// +// Workflow: +// 1. Vectorize current parameters into a single state vector. +// 2. Define closures for log-posterior evaluation and gradient computation: +// - `log_post`: unpacks parameters and evaluates the log pseudoposterior. +// - `grad`: unpacks parameters and evaluates the gradient of the +// pseudoposterior. +// 3. Pass these to `heuristic_initial_step_size`, which runs the heuristic +// tuning loop. +// +// Inputs: +// - main_effects: Matrix of main-effect parameters [n_main_rows × G]. +// - pairwise_effects: Matrix of pairwise interaction parameters [n_pairs × G]. +// - main_effect_indices: Row index ranges for each variable’s main effects. +// - pairwise_effect_indices: Row index map for pairwise effects. +// - inclusion_indicator: Matrix marking which main and pairwise differences +// are active. +// - projection: Group projection matrix (encodes contrasts). +// - num_categories: Number of categories per variable [V]. +// - observations: Data matrix [persons × variables]. +// - num_groups: Number of groups (G). +// - group_indices: Row ranges per group in `observations`. +// - counts_per_category: Per-group sufficient statistics for ordinal variables. +// - blume_capel_stats: Per-group sufficient statistics for Blume–Capel variables. +// - pairwise_stats: Per-group sufficient statistics for pairwise effects. +// - is_ordinal_variable: Indicator for ordinal vs. Blume–Capel variables [V]. +// - baseline_category: Reference categories for Blume–Capel variables [V]. +// - pairwise_scale: Scale parameter for overall pairwise priors. +// - difference_scale: Scale parameter for group difference priors. +// - main_alpha, main_beta: Hyperparameters for Beta prior on main effects. +// - target_acceptance: Desired acceptance probability (e.g. 0.8). +// - rng: Random number generator. +// +// Returns: +// - A double value for the initial HMC/NUTS step size. +// +// Notes: +// - This routine is only used during warmup to initialize +// `HMCAdaptationController`. +// - Correct indexing of parameters relies on `build_index_maps` to ensure +// consistency between vectorization and gradient computation. double find_initial_stepsize_bgmcompare( arma::mat& main_effects, arma::mat& pairwise_effects, @@ -629,52 +621,50 @@ double find_initial_stepsize_bgmcompare( -/** - * Perform one Hamiltonian Monte Carlo (HMC) update step for the bgmCompare model. - * - * The function: - * 1. Vectorizes the current parameter state (main + pairwise effects). - * 2. Defines closures for log-posterior evaluation and gradient calculation. - * 3. Applies HMC with fixed leapfrog steps to propose a new state. - * 4. Unpacks the accepted state back into main and pairwise matrices. - * 5. Updates the adaptation controller with acceptance information. - * - * Inputs: - * - main_effects, pairwise_effects: Current parameter matrices, updated in place. - * - main_effect_indices, pairwise_effect_indices: Index maps for parameters. - * - inclusion_indicator: Indicates active main and pairwise differences. - * - projection: Group projection matrix for contrasts. - * - num_categories: Number of categories per variable [V]. - * - observations: Data matrix [N × V]. - * - num_groups: Number of groups. - * - group_indices: Row ranges for each group in `observations`. - * - counts_per_category, blume_capel_stats: Per-group sufficient statistics. - * - pairwise_stats: Per-group pairwise sufficient statistics. - * - is_ordinal_variable: Marks ordinal vs. Blume–Capel variables. - * - baseline_category: Reference categories for Blume–Capel variables. - * - pairwise_scale: Scale of overall pairwise prior. - * - difference_scale: Scale of group-difference prior. - * - main_alpha, main_beta: Hyperparameters for main-effect priors. - * - num_leapfrogs: Number of leapfrog steps in the HMC trajectory. - * - iteration: Current sampler iteration (for adaptation scheduling). - * - hmc_adapt: Adaptation controller for step size and mass matrix. - * - learn_mass_matrix: Whether to adapt the mass matrix. - * - selection: If true, restrict mass matrix to active parameters only. - * - rng: Random number generator. - * - * Side effects: - * - Updates `main_effects` and `pairwise_effects` with the new state. - * - Updates `hmc_adapt` with acceptance probability and diagnostics. - * - * Returns: - * - None directly; state is updated in place. - * - * Notes: - * - This variant is specific to bgmCompare, where parameters are stored in - * row-wise structures with possible group-difference columns. - * - Consistency between vectorization, unvectorization, and gradient - * indexing is enforced via `build_index_maps`. - */ +// Perform one Hamiltonian Monte Carlo (HMC) update step for the bgmCompare model. +// +// The function: +// 1. Vectorizes the current parameter state (main + pairwise effects). +// 2. Defines closures for log-posterior evaluation and gradient calculation. +// 3. Applies HMC with fixed leapfrog steps to propose a new state. +// 4. Unpacks the accepted state back into main and pairwise matrices. +// 5. Updates the adaptation controller with acceptance information. +// +// Inputs: +// - main_effects, pairwise_effects: Current parameter matrices, updated in place. +// - main_effect_indices, pairwise_effect_indices: Index maps for parameters. +// - inclusion_indicator: Indicates active main and pairwise differences. +// - projection: Group projection matrix for contrasts. +// - num_categories: Number of categories per variable [V]. +// - observations: Data matrix [N × V]. +// - num_groups: Number of groups. +// - group_indices: Row ranges for each group in `observations`. +// - counts_per_category, blume_capel_stats: Per-group sufficient statistics. +// - pairwise_stats: Per-group pairwise sufficient statistics. +// - is_ordinal_variable: Marks ordinal vs. Blume–Capel variables. +// - baseline_category: Reference categories for Blume–Capel variables. +// - pairwise_scale: Scale of overall pairwise prior. +// - difference_scale: Scale of group-difference prior. +// - main_alpha, main_beta: Hyperparameters for main-effect priors. +// - num_leapfrogs: Number of leapfrog steps in the HMC trajectory. +// - iteration: Current sampler iteration (for adaptation scheduling). +// - hmc_adapt: Adaptation controller for step size and mass matrix. +// - learn_mass_matrix: Whether to adapt the mass matrix. +// - selection: If true, restrict mass matrix to active parameters only. +// - rng: Random number generator. +// +// Side effects: +// - Updates `main_effects` and `pairwise_effects` with the new state. +// - Updates `hmc_adapt` with acceptance probability and diagnostics. +// +// Returns: +// - None directly; state is updated in place. +// +// Notes: +// - This variant is specific to bgmCompare, where parameters are stored in +// row-wise structures with possible group-difference columns. +// - Consistency between vectorization, unvectorization, and gradient +// indexing is enforced via `build_index_maps`. void update_hmc_bgmcompare( arma::mat& main_effects, arma::mat& pairwise_effects, @@ -809,51 +799,49 @@ void update_hmc_bgmcompare( -/** - * Perform one No-U-Turn Sampler (NUTS) update step for the bgmCompare model. - * - * The function: - * 1. Vectorizes the current parameter state (main + pairwise effects). - * 2. Defines closures for log-posterior evaluation and gradient calculation. - * 3. Runs a NUTS trajectory (adaptive tree-based extension of HMC). - * 4. Unpacks the accepted state back into main and pairwise matrices. - * 5. Updates the adaptation controller with acceptance probability. - * - * Inputs: - * - main_effects, pairwise_effects: Current parameter matrices, updated in place. - * - main_effect_indices, pairwise_effect_indices: Index maps for parameters. - * - inclusion_indicator: Indicates active main and pairwise differences. - * - projection: Group projection matrix for contrasts. - * - num_categories: Number of categories per variable [V]. - * - observations: Data matrix [N × V]. - * - num_groups: Number of groups. - * - group_indices: Row ranges for each group in `observations`. - * - counts_per_category, blume_capel_stats: Per-group sufficient statistics. - * - pairwise_stats: Per-group pairwise sufficient statistics. - * - is_ordinal_variable: Marks ordinal vs. Blume–Capel variables. - * - baseline_category: Reference categories for Blume–Capel variables. - * - pairwise_scale: Scale of overall pairwise prior. - * - difference_scale: Scale of group-difference prior. - * - main_alpha, main_beta: Hyperparameters for main-effect priors. - * - nuts_max_depth: Maximum tree depth for NUTS doubling procedure. - * - iteration: Current sampler iteration (for adaptation scheduling). - * - hmc_adapt: Adaptation controller for step size and mass matrix. - * - learn_mass_matrix: Whether to adapt the mass matrix (unused inside NUTS but relevant to controller). - * - selection: If true, restrict mass matrix to active parameters only. - * - rng: Random number generator. - * - * Returns: - * - A `StepResult` containing the accepted state and diagnostics - * (e.g. tree depth, divergences, energy). - * - * Notes: - * - This variant is specific to bgmCompare, where parameters are stored in - * row-wise structures with group-difference columns. - * - Consistency between vectorization, unvectorization, and gradient - * indexing is enforced via `build_index_maps`. - * - Diagnostics from the returned `StepResult` can be used to monitor - * sampler stability (e.g. divergences, tree depth). - */ +// Perform one No-U-Turn Sampler (NUTS) update step for the bgmCompare model. +// +// The function: +// 1. Vectorizes the current parameter state (main + pairwise effects). +// 2. Defines closures for log-posterior evaluation and gradient calculation. +// 3. Runs a NUTS trajectory (adaptive tree-based extension of HMC). +// 4. Unpacks the accepted state back into main and pairwise matrices. +// 5. Updates the adaptation controller with acceptance probability. +// +// Inputs: +// - main_effects, pairwise_effects: Current parameter matrices, updated in place. +// - main_effect_indices, pairwise_effect_indices: Index maps for parameters. +// - inclusion_indicator: Indicates active main and pairwise differences. +// - projection: Group projection matrix for contrasts. +// - num_categories: Number of categories per variable [V]. +// - observations: Data matrix [N × V]. +// - num_groups: Number of groups. +// - group_indices: Row ranges for each group in `observations`. +// - counts_per_category, blume_capel_stats: Per-group sufficient statistics. +// - pairwise_stats: Per-group pairwise sufficient statistics. +// - is_ordinal_variable: Marks ordinal vs. Blume–Capel variables. +// - baseline_category: Reference categories for Blume–Capel variables. +// - pairwise_scale: Scale of overall pairwise prior. +// - difference_scale: Scale of group-difference prior. +// - main_alpha, main_beta: Hyperparameters for main-effect priors. +// - nuts_max_depth: Maximum tree depth for NUTS doubling procedure. +// - iteration: Current sampler iteration (for adaptation scheduling). +// - hmc_adapt: Adaptation controller for step size and mass matrix. +// - learn_mass_matrix: Whether to adapt the mass matrix (unused inside NUTS but relevant to controller). +// - selection: If true, restrict mass matrix to active parameters only. +// - rng: Random number generator. +// +// Returns: +// - A `StepResult` containing the accepted state and diagnostics +// (e.g. tree depth, divergences, energy). +// +// Notes: +// - This variant is specific to bgmCompare, where parameters are stored in +// row-wise structures with group-difference columns. +// - Consistency between vectorization, unvectorization, and gradient +// indexing is enforced via `build_index_maps`. +// - Diagnostics from the returned `StepResult` can be used to monitor +// sampler stability (e.g. divergences, tree depth). StepResult update_nuts_bgmcompare( arma::mat& main_effects, arma::mat& pairwise_effects, @@ -990,55 +978,53 @@ StepResult update_nuts_bgmcompare( -/** - * Adapt proposal standard deviations (SDs) for main and pairwise effects - * during the warmup phase of the bgmCompare sampler. - * - * This function uses a Robbins–Monro stochastic approximation scheme to - * adjust proposal SDs toward a target acceptance rate. Adaptation occurs - * only when permitted by the current warmup schedule. - * - * Workflow: - * 1. For each main effect parameter (ordinal or Blume–Capel), run one - * random-walk Metropolis (RWM) step, update the parameter, and adjust - * the proposal SD. - * 2. For each pairwise effect parameter (overall and group differences), - * do the same. - * 3. Proposal SDs are updated symmetrically across group columns if - * differences are included. - * - * Inputs: - * - proposal_sd_main_effects: Current SDs for main effects, updated in place. - * - proposal_sd_pairwise_effects: Current SDs for pairwise effects, updated in place. - * - main_effects, pairwise_effects: Parameter matrices, updated in place. - * - main_effect_indices, pairwise_effect_indices: Index maps for main/pairwise parameters. - * - inclusion_indicator: Marks which group differences are active. - * - projection: Group projection matrix. - * - num_categories: Categories per variable. - * - observations: Data matrix [N × V]. - * - num_groups: Number of groups. - * - group_indices: Row ranges per group in `observations`. - * - counts_per_category, blume_capel_stats: Per-group sufficient statistics for main effects. - * - pairwise_stats: Per-group sufficient statistics for pairwise effects. - * - is_ordinal_variable: Marks ordinal vs. Blume–Capel variables. - * - baseline_category: Reference category for Blume–Capel variables. - * - pairwise_scale: Scale of Cauchy prior for pairwise effects. - * - difference_scale: Scale of Cauchy prior for group differences. - * - main_alpha, main_beta: Hyperparameters for Beta prior on main effects. - * - iteration: Current iteration (to check schedule stage). - * - rng: Random number generator. - * - sched: Warmup schedule controlling when adaptation is active. - * - target_accept: Desired acceptance probability (default 0.44). - * - rm_decay: Robbins–Monro decay rate (default 0.75). - * - * Side effects: - * - Updates `main_effects` and `pairwise_effects` with new parameter values. - * - Updates `proposal_sd_main_effects` and `proposal_sd_pairwise_effects`. - * - * Notes: - * - Adapts only when `sched.adapt_proposal_sd(iteration)` is true. - * - Helps stabilize RWM acceptance rates before switching to sampling. - */ +// Adapt proposal standard deviations (SDs) for main and pairwise effects +// during the warmup phase of the bgmCompare sampler. +// +// This function uses a Robbins–Monro stochastic approximation scheme to +// adjust proposal SDs toward a target acceptance rate. Adaptation occurs +// only when permitted by the current warmup schedule. +// +// Workflow: +// 1. For each main effect parameter (ordinal or Blume–Capel), run one +// random-walk Metropolis (RWM) step, update the parameter, and adjust +// the proposal SD. +// 2. For each pairwise effect parameter (overall and group differences), +// do the same. +// 3. Proposal SDs are updated symmetrically across group columns if +// differences are included. +// +// Inputs: +// - proposal_sd_main_effects: Current SDs for main effects, updated in place. +// - proposal_sd_pairwise_effects: Current SDs for pairwise effects, updated in place. +// - main_effects, pairwise_effects: Parameter matrices, updated in place. +// - main_effect_indices, pairwise_effect_indices: Index maps for main/pairwise parameters. +// - inclusion_indicator: Marks which group differences are active. +// - projection: Group projection matrix. +// - num_categories: Categories per variable. +// - observations: Data matrix [N × V]. +// - num_groups: Number of groups. +// - group_indices: Row ranges per group in `observations`. +// - counts_per_category, blume_capel_stats: Per-group sufficient statistics for main effects. +// - pairwise_stats: Per-group sufficient statistics for pairwise effects. +// - is_ordinal_variable: Marks ordinal vs. Blume–Capel variables. +// - baseline_category: Reference category for Blume–Capel variables. +// - pairwise_scale: Scale of Cauchy prior for pairwise effects. +// - difference_scale: Scale of Cauchy prior for group differences. +// - main_alpha, main_beta: Hyperparameters for Beta prior on main effects. +// - iteration: Current iteration (to check schedule stage). +// - rng: Random number generator. +// - sched: Warmup schedule controlling when adaptation is active. +// - target_accept: Desired acceptance probability (default 0.44). +// - rm_decay: Robbins–Monro decay rate (default 0.75). +// +// Side effects: +// - Updates `main_effects` and `pairwise_effects` with new parameter values. +// - Updates `proposal_sd_main_effects` and `proposal_sd_pairwise_effects`. +// +// Notes: +// - Adapts only when `sched.adapt_proposal_sd(iteration)` is true. +// - Helps stabilize RWM acceptance rates before switching to sampling. void tune_proposal_sd_bgmcompare( arma::mat& proposal_sd_main_effects, arma::mat& proposal_sd_pairwise_effects, @@ -1220,55 +1206,53 @@ void tune_proposal_sd_bgmcompare( -/** - * Metropolis–Hastings updates for difference-inclusion indicators in bgmCompare. - * - * This function toggles whether group-level differences are included for - * main effects (diagonal entries of `inclusion_indicator`) and pairwise - * effects (off-diagonal entries). Each update proposes either: - * - Turning a currently excluded difference “on” by drawing a new non-zero - * value from a Gaussian proposal, or - * - Turning an included difference “off” by setting its value(s) to zero. - * - * The acceptance probability combines: - * - Pseudolikelihood ratio (data contribution), - * - Prior ratio on inclusion indicators, - * - Prior ratio on parameter values (Cauchy vs. point-mass-at-zero), - * - Proposal density correction. - * - * Inputs: - * - inclusion_probability_difference: Prior inclusion probabilities for - * group differences [V × V]. - * - index: Matrix mapping pairwise interactions to variable indices. - * - main_effects, pairwise_effects: Parameter matrices, updated in place. - * - main_effect_indices, pairwise_effect_indices: Index maps for parameters. - * - projection: Group projection matrix. - * - observations: Data matrix [N × V]. - * - num_groups: Number of groups. - * - group_indices: Row ranges per group in `observations`. - * - num_categories: Categories per variable [V × G]. - * - inclusion_indicator: Indicator matrix for differences, updated in place. - * - is_ordinal_variable: Marks ordinal vs. Blume–Capel variables [V]. - * - baseline_category: Reference category for Blume–Capel variables [V]. - * - proposal_sd_main, proposal_sd_pairwise: Proposal SD matrices for main - * and pairwise effects. - * - difference_scale: Scale of Cauchy prior for group differences. - * - counts_per_category, blume_capel_stats: Per-group sufficient statistics - * for main effects. - * - pairwise_stats: Per-group sufficient statistics for pairwise effects. - * - rng: Random number generator. - * - * Side effects: - * - Updates `inclusion_indicator` entries for main/pairwise differences. - * - Updates corresponding slices of `main_effects` and `pairwise_effects`. - * - * Notes: - * - For main effects, differences correspond to columns 1..G-1 of the - * parameter matrix. - * - For pairwise effects, differences correspond to columns 1..G-1 of the - * pairwise-effect matrix rows. - * - Ensures symmetry of `inclusion_indicator` for pairwise updates. - */ +// Metropolis–Hastings updates for difference-inclusion indicators in bgmCompare. +// +// This function toggles whether group-level differences are included for +// main effects (diagonal entries of `inclusion_indicator`) and pairwise +// effects (off-diagonal entries). Each update proposes either: +// - Turning a currently excluded difference “on” by drawing a new non-zero +// value from a Gaussian proposal, or +// - Turning an included difference “off” by setting its value(s) to zero. +// +// The acceptance probability combines: +// - Pseudolikelihood ratio (data contribution), +// - Prior ratio on inclusion indicators, +// - Prior ratio on parameter values (Cauchy vs. point-mass-at-zero), +// - Proposal density correction. +// +// Inputs: +// - inclusion_probability_difference: Prior inclusion probabilities for +// group differences [V × V]. +// - index: Matrix mapping pairwise interactions to variable indices. +// - main_effects, pairwise_effects: Parameter matrices, updated in place. +// - main_effect_indices, pairwise_effect_indices: Index maps for parameters. +// - projection: Group projection matrix. +// - observations: Data matrix [N × V]. +// - num_groups: Number of groups. +// - group_indices: Row ranges per group in `observations`. +// - num_categories: Categories per variable [V × G]. +// - inclusion_indicator: Indicator matrix for differences, updated in place. +// - is_ordinal_variable: Marks ordinal vs. Blume–Capel variables [V]. +// - baseline_category: Reference category for Blume–Capel variables [V]. +// - proposal_sd_main, proposal_sd_pairwise: Proposal SD matrices for main +// and pairwise effects. +// - difference_scale: Scale of Cauchy prior for group differences. +// - counts_per_category, blume_capel_stats: Per-group sufficient statistics +// for main effects. +// - pairwise_stats: Per-group sufficient statistics for pairwise effects. +// - rng: Random number generator. +// +// Side effects: +// - Updates `inclusion_indicator` entries for main/pairwise differences. +// - Updates corresponding slices of `main_effects` and `pairwise_effects`. +// +// Notes: +// - For main effects, differences correspond to columns 1..G-1 of the +// parameter matrix. +// - For pairwise effects, differences correspond to columns 1..G-1 of the +// pairwise-effect matrix rows. +// - Ensures symmetry of `inclusion_indicator` for pairwise updates. void update_indicator_differences_metropolis_bgmcompare ( const arma::mat& inclusion_probability_difference, const arma::imat& index, @@ -1464,65 +1448,63 @@ void update_indicator_differences_metropolis_bgmcompare ( -/** - * Perform one Gibbs update step for the bgmCompare model. - * - * This function executes a single iteration of the Gibbs sampler, including: - * - * Step 0: (optional) Initialize graph structure if difference selection - * is enabled and the current iteration marks the start of Stage 3c. - * - * Step 1: (optional) Update inclusion indicators for group differences - * (main and pairwise effects) via Metropolis–Hastings proposals. - * - * Step 2: Update model parameters according to the selected update method: - * - "adaptive-metropolis": Update main and pairwise effects individually - * with random-walk Metropolis and adaptive proposal SDs. - * - "hamiltonian-mc": Update the full parameter vector using HMC. - * - "nuts": Update the full parameter vector using the No-U-Turn Sampler. - * If past burn-in, store NUTS diagnostics (tree depth, divergences, energy). - * - * Step 3: (Stage 3b only) Adapt proposal SDs for Metropolis updates using - * Robbins–Monro tuning. - * - * Inputs: - * - observations: Data matrix [N × V]. - * - num_categories: Number of categories per variable [V]. - * - pairwise_scale, difference_scale: Prior scale parameters. - * - counts_per_category, blume_capel_stats: Sufficient statistics per group. - * - main_alpha, main_beta: Hyperparameters for Beta prior on main effects. - * - inclusion_indicator: Matrix of active group differences [V × V], updated in place. - * - main_effects, pairwise_effects: Parameter matrices, updated in place. - * - is_ordinal_variable: Marks ordinal vs. Blume–Capel variables. - * - baseline_category: Reference categories for Blume–Capel variables. - * - iteration: Current iteration index. - * - pairwise_effect_indices, main_effect_indices: Index maps for parameters. - * - pairwise_stats: Per-group pairwise sufficient statistics. - * - nuts_max_depth: Maximum tree depth for NUTS. - * - hmc_adapt: Adaptation controller for HMC/NUTS. - * - metropolis_adapt_main, metropolis_adapt_pair: Adaptation controllers for RWM updates. - * - learn_mass_matrix: Whether to adapt the mass matrix in HMC/NUTS. - * - schedule: Warmup schedule, controls adaptation and selection phases. - * - treedepth_samples, divergent_samples, energy_samples: Buffers for NUTS diagnostics. - * - projection: Group projection matrix. - * - num_groups: Number of groups. - * - group_indices: Row ranges per group in `observations`. - * - rng: Random number generator. - * - inclusion_probability: Prior probabilities for including differences. - * - hmc_nuts_leapfrogs: Number of leapfrog steps for HMC updates. - * - update_method: Update strategy ("adaptive-metropolis", "hamiltonian-mc", "nuts"). - * - proposal_sd_main, proposal_sd_pair: Proposal SD matrices for Metropolis updates. - * - index: Index table for pairwise differences. - * - * Side effects: - * - Updates parameters, inclusion indicators, and sufficient statistics. - * - Updates adaptation controllers and (if NUTS) diagnostic buffers. - * - * Notes: - * - This function encapsulates all update logic for bgmCompare. - * - Choice of `update_method` governs whether updates are local (RWM) or - * global (HMC/NUTS). - */ +// Perform one Gibbs update step for the bgmCompare model. +// +// This function executes a single iteration of the Gibbs sampler, including: +// +// Step 0: (optional) Initialize graph structure if difference selection +// is enabled and the current iteration marks the start of Stage 3c. +// +// Step 1: (optional) Update inclusion indicators for group differences +// (main and pairwise effects) via Metropolis–Hastings proposals. +// +// Step 2: Update model parameters according to the selected update method: +// - "adaptive-metropolis": Update main and pairwise effects individually +// with random-walk Metropolis and adaptive proposal SDs. +// - "hamiltonian-mc": Update the full parameter vector using HMC. +// - "nuts": Update the full parameter vector using the No-U-Turn Sampler. +// If past burn-in, store NUTS diagnostics (tree depth, divergences, energy). +// +// Step 3: (Stage 3b only) Adapt proposal SDs for Metropolis updates using +// Robbins–Monro tuning. +// +// Inputs: +// - observations: Data matrix [N × V]. +// - num_categories: Number of categories per variable [V]. +// - pairwise_scale, difference_scale: Prior scale parameters. +// - counts_per_category, blume_capel_stats: Sufficient statistics per group. +// - main_alpha, main_beta: Hyperparameters for Beta prior on main effects. +// - inclusion_indicator: Matrix of active group differences [V × V], updated in place. +// - main_effects, pairwise_effects: Parameter matrices, updated in place. +// - is_ordinal_variable: Marks ordinal vs. Blume–Capel variables. +// - baseline_category: Reference categories for Blume–Capel variables. +// - iteration: Current iteration index. +// - pairwise_effect_indices, main_effect_indices: Index maps for parameters. +// - pairwise_stats: Per-group pairwise sufficient statistics. +// - nuts_max_depth: Maximum tree depth for NUTS. +// - hmc_adapt: Adaptation controller for HMC/NUTS. +// - metropolis_adapt_main, metropolis_adapt_pair: Adaptation controllers for RWM updates. +// - learn_mass_matrix: Whether to adapt the mass matrix in HMC/NUTS. +// - schedule: Warmup schedule, controls adaptation and selection phases. +// - treedepth_samples, divergent_samples, energy_samples: Buffers for NUTS diagnostics. +// - projection: Group projection matrix. +// - num_groups: Number of groups. +// - group_indices: Row ranges per group in `observations`. +// - rng: Random number generator. +// - inclusion_probability: Prior probabilities for including differences. +// - hmc_nuts_leapfrogs: Number of leapfrog steps for HMC updates. +// - update_method: Update strategy ("adaptive-metropolis", "hamiltonian-mc", "nuts"). +// - proposal_sd_main, proposal_sd_pair: Proposal SD matrices for Metropolis updates. +// - index: Index table for pairwise differences. +// +// Side effects: +// - Updates parameters, inclusion indicators, and sufficient statistics. +// - Updates adaptation controllers and (if NUTS) diagnostic buffers. +// +// Notes: +// - This function encapsulates all update logic for bgmCompare. +// - Choice of `update_method` governs whether updates are local (RWM) or +// global (HMC/NUTS). void gibbs_update_step_bgmcompare ( const arma::imat& observations, const arma::ivec& num_categories, @@ -1647,72 +1629,70 @@ void gibbs_update_step_bgmcompare ( -/** - * Run a full Gibbs sampler for the bgmCompare model. - * - * This function controls the full MCMC lifecycle for a single chain: - * - Initializes parameter matrices, proposal SDs, and adaptation controllers. - * - Optionally imputes missing data at each iteration. - * - Executes Gibbs updates for main and pairwise effects, including - * difference-selection if enabled. - * - Adapts step size, mass matrix, and proposal SDs during warmup. - * - Updates inclusion probabilities under the chosen prior - * (e.g. Beta–Bernoulli). - * - Collects posterior samples and diagnostics into a `SamplerOutput` struct. - * - * Inputs: - * - chain_id: Identifier for this chain (1-based). - * - observations: Data matrix [N × V]. - * - num_groups: Number of groups (G). - * - counts_per_category: Per-group sufficient statistics (ordinal variables). - * - blume_capel_stats: Per-group sufficient statistics (Blume–Capel variables). - * - pairwise_stats: Per-group sufficient statistics for pairwise effects. - * - num_categories: Number of categories per variable [V]. - * - main_alpha, main_beta: Hyperparameters for Beta prior on main effects. - * - pairwise_scale: Scale parameter for overall pairwise priors. - * - difference_scale: Scale parameter for group-difference priors. - * - difference_selection_alpha, difference_selection_beta: Hyperparameters - * for difference-selection prior. - * - difference_prior: Prior type for difference-selection ("Beta-Bernoulli", ...). - * - iter: Number of post–burn-in sampling iterations. - * - warmup: Number of warmup iterations. - * - na_impute: If true, impute missing observations at each iteration. - * - missing_data_indices: Matrix of [person, variable] indices of missings. - * - is_ordinal_variable: Marks ordinal vs. Blume–Capel variables. - * - baseline_category: Reference categories for Blume–Capel variables. - * - difference_selection: If true, include MH updates for group-difference indicators. - * - main_effect_indices, pairwise_effect_indices: Index maps for parameter rows. - * - target_accept: Target acceptance probability (HMC/NUTS). - * - nuts_max_depth: Maximum tree depth for NUTS. - * - learn_mass_matrix: Whether to adapt the mass matrix (HMC/NUTS). - * - projection: Group projection matrix for contrasts. - * - group_membership: Mapping of persons to groups. - * - group_indices: Row ranges per group in `observations`. - * - interaction_index_matrix: Index map for pairwise interactions. - * - inclusion_probability: Matrix of prior inclusion probabilities, updated in place. - * - rng: Random number generator. - * - update_method: Update strategy ("adaptive-metropolis", "hamiltonian-mc", "nuts"). - * - hmc_num_leapfrogs: Number of leapfrog steps for HMC. - * - * Returns: - * - A `SamplerOutput` struct containing: - * - main_samples: MCMC samples for main effects. - * - pairwise_samples: MCMC samples for pairwise effects. - * - indicator_samples: (optional) Inclusion indicator samples if - * difference-selection is enabled. - * - treedepth_samples, divergent_samples, energy_samples: - * Diagnostics (for NUTS). - * - chain_id: Identifier for this chain. - * - * Notes: - * - Warmup is orchestrated via `WarmupSchedule`, which controls adaptation - * phases and difference-selection activation. - * - Proposal SDs are tuned via Robbins–Monro during Stage 3b. - * - Difference-selection updates toggle inclusion indicators and adjust - * associated parameters with MH proposals. - * - This function runs entirely in C++ and is wrapped for parallel execution - * via `GibbsCompareChainRunner`. - */ +// Run a full Gibbs sampler for the bgmCompare model. +// +// This function controls the full MCMC lifecycle for a single chain: +// - Initializes parameter matrices, proposal SDs, and adaptation controllers. +// - Optionally imputes missing data at each iteration. +// - Executes Gibbs updates for main and pairwise effects, including +// difference-selection if enabled. +// - Adapts step size, mass matrix, and proposal SDs during warmup. +// - Updates inclusion probabilities under the chosen prior +// (e.g. Beta–Bernoulli). +// - Collects posterior samples and diagnostics into a `SamplerOutput` struct. +// +// Inputs: +// - chain_id: Identifier for this chain (1-based). +// - observations: Data matrix [N × V]. +// - num_groups: Number of groups (G). +// - counts_per_category: Per-group sufficient statistics (ordinal variables). +// - blume_capel_stats: Per-group sufficient statistics (Blume–Capel variables). +// - pairwise_stats: Per-group sufficient statistics for pairwise effects. +// - num_categories: Number of categories per variable [V]. +// - main_alpha, main_beta: Hyperparameters for Beta prior on main effects. +// - pairwise_scale: Scale parameter for overall pairwise priors. +// - difference_scale: Scale parameter for group-difference priors. +// - difference_selection_alpha, difference_selection_beta: Hyperparameters +// for difference-selection prior. +// - difference_prior: Prior type for difference-selection ("Beta-Bernoulli", ...). +// - iter: Number of post–burn-in sampling iterations. +// - warmup: Number of warmup iterations. +// - na_impute: If true, impute missing observations at each iteration. +// - missing_data_indices: Matrix of [person, variable] indices of missings. +// - is_ordinal_variable: Marks ordinal vs. Blume–Capel variables. +// - baseline_category: Reference categories for Blume–Capel variables. +// - difference_selection: If true, include MH updates for group-difference indicators. +// - main_effect_indices, pairwise_effect_indices: Index maps for parameter rows. +// - target_accept: Target acceptance probability (HMC/NUTS). +// - nuts_max_depth: Maximum tree depth for NUTS. +// - learn_mass_matrix: Whether to adapt the mass matrix (HMC/NUTS). +// - projection: Group projection matrix for contrasts. +// - group_membership: Mapping of persons to groups. +// - group_indices: Row ranges per group in `observations`. +// - interaction_index_matrix: Index map for pairwise interactions. +// - inclusion_probability: Matrix of prior inclusion probabilities, updated in place. +// - rng: Random number generator. +// - update_method: Update strategy ("adaptive-metropolis", "hamiltonian-mc", "nuts"). +// - hmc_num_leapfrogs: Number of leapfrog steps for HMC. +// +// Returns: +// - A `SamplerOutput` struct containing: +// - main_samples: MCMC samples for main effects. +// - pairwise_samples: MCMC samples for pairwise effects. +// - indicator_samples: (optional) Inclusion indicator samples if +// difference-selection is enabled. +// - treedepth_samples, divergent_samples, energy_samples: +// Diagnostics (for NUTS). +// - chain_id: Identifier for this chain. +// +// Notes: +// - Warmup is orchestrated via `WarmupSchedule`, which controls adaptation +// phases and difference-selection activation. +// - Proposal SDs are tuned via Robbins–Monro during Stage 3b. +// - Difference-selection updates toggle inclusion indicators and adjust +// associated parameters with MH proposals. +// - This function runs entirely in C++ and is wrapped for parallel execution +// via `GibbsCompareChainRunner`. bgmCompareOutput run_gibbs_sampler_bgmCompare( int chain_id, arma::imat observations, diff --git a/src/bgmCompare_interface.cpp b/src/bgmCompare_interface.cpp index 7941a23d..ff1730ff 100644 --- a/src/bgmCompare_interface.cpp +++ b/src/bgmCompare_interface.cpp @@ -15,21 +15,19 @@ using namespace RcppParallel; -/** - * Container for the result of a single MCMC chain (bgmCompare model). - * - * Fields: - * - error: True if the chain terminated with an error, false otherwise. - * - error_msg: Error message if an error occurred (empty if none). - * - chain_id: Integer identifier for the chain (1-based). - * - result: bgmCompareOutput object containing chain results - * (samples, diagnostics, metadata). - * - * Usage: - * - Used in parallel execution to collect results from each chain. - * - Checked after execution to propagate errors or assemble outputs - * into an R-accessible list. - */ +// Container for the result of a single MCMC chain (bgmCompare model). +// +// Fields: +// - error: True if the chain terminated with an error, false otherwise. +// - error_msg: Error message if an error occurred (empty if none). +// - chain_id: Integer identifier for the chain (1-based). +// - result: bgmCompareOutput object containing chain results +// (samples, diagnostics, metadata). +// +// Usage: +// - Used in parallel execution to collect results from each chain. +// - Checked after execution to propagate errors or assemble outputs +// into an R-accessible list. struct bgmCompareChainResult { bool error; std::string error_msg; @@ -39,60 +37,58 @@ struct bgmCompareChainResult { -/** - * Parallel worker for running a single Gibbs sampling chain (bgmCompare model). - * - * This struct wraps all inputs needed for one chain and provides an - * `operator()` so that multiple chains can be launched in parallel with TBB. - * - * Workflow per chain: - * - Construct a chain-specific RNG from `chain_rngs`. - * - Copy master statistics and observation data into per-chain buffers. - * - Call `run_gibbs_sampler_bgmCompare()` to execute the full chain. - * - Catch and record any errors (sets `error = true` and stores `error_msg`). - * - Store results into the shared `results` vector at the chain index. - * - * Inputs (stored as const references or values): - * - observations_master: Input observation matrix (persons × variables). - * - num_groups: Number of groups. - * - counts_per_category_master: Group-level category counts. - * - blume_capel_stats_master: Group-level Blume–Capel sufficient statistics. - * - pairwise_stats_master: Group-level pairwise sufficient statistics. - * - num_categories: Number of categories per variable. - * - main_alpha, main_beta: Hyperparameters for Beta priors on main effects. - * - pairwise_scale: Scale for Cauchy prior on baseline pairwise effects. - * - difference_scale: Scale for Cauchy prior on group differences. - * - difference_selection_alpha, difference_selection_beta: Hyperparameters for difference-selection prior. - * - difference_prior: Choice of prior distribution for group differences. - * - iter, warmup: Iteration counts. - * - na_impute: If true, perform missing data imputation. - * - missing_data_indices: Indices of missing observations. - * - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). - * - baseline_category: Reference categories for Blume–Capel variables. - * - difference_selection: If true, perform difference selection updates. - * - main_effect_indices: Index ranges for main effects. - * - pairwise_effect_indices: Index ranges for pairwise effects. - * - target_accept: Target acceptance rate for adaptive methods. - * - nuts_max_depth: Maximum tree depth for NUTS. - * - learn_mass_matrix: If true, adapt mass matrix during warmup. - * - projection: Group projection matrix. - * - group_membership: Group assignment for each observation. - * - group_indices: Row ranges [start,end] for each group in observations. - * - interaction_index_matrix: Lookup table of variable pairs. - * - inclusion_probability_master: Prior inclusion probabilities for pairwise effects. - * - chain_rngs: Pre-initialized RNG engines (one per chain). - * - update_method: Sampler type ("adaptive-metropolis", "hamiltonian-mc", "nuts"). - * - hmc_num_leapfrogs: Number of leapfrog steps (HMC). - * - * Output: - * - results: Vector of `bgmCompareChainResult` objects, one per chain, filled in place. - * - * Notes: - * - Each worker instance is shared across threads but invoked with different - * [begin,end) ranges, corresponding to chain indices. - * - Per-chain copies of statistics and observations prevent cross-thread mutation. - * - Errors are caught locally so one failing chain does not crash the entire run. - */ +// Parallel worker for running a single Gibbs sampling chain (bgmCompare model). +// +// This struct wraps all inputs needed for one chain and provides an +// `operator()` so that multiple chains can be launched in parallel with TBB. +// +// Workflow per chain: +// - Construct a chain-specific RNG from `chain_rngs`. +// - Copy master statistics and observation data into per-chain buffers. +// - Call `run_gibbs_sampler_bgmCompare()` to execute the full chain. +// - Catch and record any errors (sets `error = true` and stores `error_msg`). +// - Store results into the shared `results` vector at the chain index. +// +// Inputs (stored as const references or values): +// - observations_master: Input observation matrix (persons × variables). +// - num_groups: Number of groups. +// - counts_per_category_master: Group-level category counts. +// - blume_capel_stats_master: Group-level Blume–Capel sufficient statistics. +// - pairwise_stats_master: Group-level pairwise sufficient statistics. +// - num_categories: Number of categories per variable. +// - main_alpha, main_beta: Hyperparameters for Beta priors on main effects. +// - pairwise_scale: Scale for Cauchy prior on baseline pairwise effects. +// - difference_scale: Scale for Cauchy prior on group differences. +// - difference_selection_alpha, difference_selection_beta: Hyperparameters for difference-selection prior. +// - difference_prior: Choice of prior distribution for group differences. +// - iter, warmup: Iteration counts. +// - na_impute: If true, perform missing data imputation. +// - missing_data_indices: Indices of missing observations. +// - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). +// - baseline_category: Reference categories for Blume–Capel variables. +// - difference_selection: If true, perform difference selection updates. +// - main_effect_indices: Index ranges for main effects. +// - pairwise_effect_indices: Index ranges for pairwise effects. +// - target_accept: Target acceptance rate for adaptive methods. +// - nuts_max_depth: Maximum tree depth for NUTS. +// - learn_mass_matrix: If true, adapt mass matrix during warmup. +// - projection: Group projection matrix. +// - group_membership: Group assignment for each observation. +// - group_indices: Row ranges [start,end] for each group in observations. +// - interaction_index_matrix: Lookup table of variable pairs. +// - inclusion_probability_master: Prior inclusion probabilities for pairwise effects. +// - chain_rngs: Pre-initialized RNG engines (one per chain). +// - update_method: Sampler type ("adaptive-metropolis", "hamiltonian-mc", "nuts"). +// - hmc_num_leapfrogs: Number of leapfrog steps (HMC). +// +// Output: +// - results: Vector of `bgmCompareChainResult` objects, one per chain, filled in place. +// +// Notes: +// - Each worker instance is shared across threads but invoked with different +// [begin,end) ranges, corresponding to chain indices. +// - Per-chain copies of statistics and observations prevent cross-thread mutation. +// - Errors are caught locally so one failing chain does not crash the entire run. struct GibbsCompareChainRunner : public Worker { const arma::imat& observations_master; const int num_groups; @@ -287,69 +283,67 @@ struct GibbsCompareChainRunner : public Worker { -/** - * Runs multiple parallel Gibbs sampling chains for the bgmCompare model. - * - * This function is the main entry point from R into the C++ backend for bgmCompare. - * It launches `num_chains` independent chains in parallel using TBB, - * each managed by `GibbsCompareChainRunner`. - * - * Workflow: - * - Initialize a per-chain RNG from the global seed. - * - Construct a `GibbsCompareChainRunner` worker with all shared inputs. - * - Launch the worker across chains using `parallelFor`. - * - Collect results from all chains into an Rcpp::List. - * - * Inputs: - * - observations: Observation matrix (persons × variables). - * - num_groups: Number of groups. - * - counts_per_category: Group-level category counts (for ordinal variables). - * - blume_capel_stats: Group-level sufficient statistics (for Blume–Capel variables). - * - pairwise_stats: Group-level pairwise sufficient statistics. - * - num_categories: Number of categories per variable. - * - main_alpha, main_beta: Hyperparameters for Beta priors on main effects. - * - pairwise_scale: Scale for Cauchy prior on baseline pairwise effects. - * - difference_scale: Scale for Cauchy prior on group differences. - * - difference_selection_alpha, difference_selection_beta: Hyperparameters for difference-selection prior. - * - difference_prior: Choice of prior distribution for group differences. - * - iter: Number of post-warmup iterations to draw. - * - warmup: Number of warmup iterations. - * - na_impute: If true, perform missing data imputation during sampling. - * - missing_data_indices: Indices of missing entries in observations. - * - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). - * - baseline_category: Reference categories for Blume–Capel variables. - * - difference_selection: If true, perform difference selection updates. - * - main_effect_indices: Index ranges [row_start,row_end] for each variable. - * - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. - * - target_accept: Target acceptance rate for adaptive samplers. - * - nuts_max_depth: Maximum tree depth for NUTS. - * - learn_mass_matrix: If true, adapt the mass matrix during warmup. - * - projection: Group projection matrix (num_groups × (num_groups − 1)). - * - group_membership: Group assignment for each observation. - * - group_indices: Row ranges [start,end] for each group in observations. - * - interaction_index_matrix: Lookup table of variable pairs. - * - inclusion_probability: Prior inclusion probabilities for pairwise effects. - * - num_chains: Number of chains to run. - * - nThreads: Maximum number of threads for parallel execution. - * - seed: Base random seed (incremented per chain). - * - update_method: Sampler type ("adaptive-metropolis", "hamiltonian-mc", "nuts"). - * - hmc_num_leapfrogs: Number of leapfrog steps for HMC. - * - * Returns: - * - Rcpp::List of length `num_chains`, where each element is either: - * * An error record (fields: "error", "chain_id"), if the chain failed. - * * A result list containing: - * - "main_samples": Posterior samples of main effects. - * - "pairwise_samples": Posterior samples of pairwise effects. - * - "treedepth__", "divergent__", "energy__": NUTS diagnostics. - * - "indicator_samples": Inclusion indicators (if selection was enabled). - * - "chain_id": Identifier of the chain. - * - * Notes: - * - Parallel execution is controlled by TBB; `nThreads` limits concurrency. - * - Each chain gets its own RNG stream, initialized as `seed + chain_id`. - * - This function is called by the exported R function `bgmCompare()`. - */ +// Runs multiple parallel Gibbs sampling chains for the bgmCompare model. +// +// This function is the main entry point from R into the C++ backend for bgmCompare. +// It launches `num_chains` independent chains in parallel using TBB, +// each managed by `GibbsCompareChainRunner`. +// +// Workflow: +// - Initialize a per-chain RNG from the global seed. +// - Construct a `GibbsCompareChainRunner` worker with all shared inputs. +// - Launch the worker across chains using `parallelFor`. +// - Collect results from all chains into an Rcpp::List. +// +// Inputs: +// - observations: Observation matrix (persons × variables). +// - num_groups: Number of groups. +// - counts_per_category: Group-level category counts (for ordinal variables). +// - blume_capel_stats: Group-level sufficient statistics (for Blume–Capel variables). +// - pairwise_stats: Group-level pairwise sufficient statistics. +// - num_categories: Number of categories per variable. +// - main_alpha, main_beta: Hyperparameters for Beta priors on main effects. +// - pairwise_scale: Scale for Cauchy prior on baseline pairwise effects. +// - difference_scale: Scale for Cauchy prior on group differences. +// - difference_selection_alpha, difference_selection_beta: Hyperparameters for difference-selection prior. +// - difference_prior: Choice of prior distribution for group differences. +// - iter: Number of post-warmup iterations to draw. +// - warmup: Number of warmup iterations. +// - na_impute: If true, perform missing data imputation during sampling. +// - missing_data_indices: Indices of missing entries in observations. +// - is_ordinal_variable: Indicator (1 = ordinal, 0 = Blume–Capel). +// - baseline_category: Reference categories for Blume–Capel variables. +// - difference_selection: If true, perform difference selection updates. +// - main_effect_indices: Index ranges [row_start,row_end] for each variable. +// - pairwise_effect_indices: Lookup table mapping (var1,var2) → row in pairwise_effects. +// - target_accept: Target acceptance rate for adaptive samplers. +// - nuts_max_depth: Maximum tree depth for NUTS. +// - learn_mass_matrix: If true, adapt the mass matrix during warmup. +// - projection: Group projection matrix (num_groups × (num_groups − 1)). +// - group_membership: Group assignment for each observation. +// - group_indices: Row ranges [start,end] for each group in observations. +// - interaction_index_matrix: Lookup table of variable pairs. +// - inclusion_probability: Prior inclusion probabilities for pairwise effects. +// - num_chains: Number of chains to run. +// - nThreads: Maximum number of threads for parallel execution. +// - seed: Base random seed (incremented per chain). +// - update_method: Sampler type ("adaptive-metropolis", "hamiltonian-mc", "nuts"). +// - hmc_num_leapfrogs: Number of leapfrog steps for HMC. +// +// Returns: +// - Rcpp::List of length `num_chains`, where each element is either: +// * An error record (fields: "error", "chain_id"), if the chain failed. +// * A result list containing: +// - "main_samples": Posterior samples of main effects. +// - "pairwise_samples": Posterior samples of pairwise effects. +// - "treedepth__", "divergent__", "energy__": NUTS diagnostics. +// - "indicator_samples": Inclusion indicators (if selection was enabled). +// - "chain_id": Identifier of the chain. +// +// Notes: +// - Parallel execution is controlled by TBB; `nThreads` limits concurrency. +// - Each chain gets its own RNG stream, initialized as `seed + chain_id`. +// - This function is called by the exported R function `bgmCompare()`. // [[Rcpp::export]] Rcpp::List run_bgmCompare_parallel( const arma::imat& observations, diff --git a/src/mcmc/algorithms/nuts.cpp b/src/mcmc/algorithms/nuts.cpp index 5c5a4dcb..aaeb89cf 100644 --- a/src/mcmc/algorithms/nuts.cpp +++ b/src/mcmc/algorithms/nuts.cpp @@ -8,26 +8,22 @@ #include "rng/rng_utils.h" -/** - * The generalized U-turn criterion used here is described in Betancourt (2017). - * The implementation follows the approach in STAN's base_nuts.hpp (BSD-3-Clause license). - * - * References: - * Betancourt, M. (2017). A Conceptual Introduction to Hamiltonian Monte Carlo. - * arXiv preprint arXiv:1701.02434. - * Stan Development Team. base_nuts.hpp. - * https://github.com/stan-dev/stan/blob/develop/src/stan/mcmc/hmc/nuts/base_nuts.hpp - */ - - -/** - * Computes the generalized U-turn criterion for the NUTS algorithm - * - * @param p_sharp_minus Sharp momentum (M^{-1} p) at backward end - * @param p_sharp_plus Sharp momentum (M^{-1} p) at forward end - * @param rho Sum of momenta along the trajectory - * @return true if criterion satisfied (continue), false if U-turn detected (stop) - */ +// The generalized U-turn criterion used here is described in Betancourt (2017). +// The implementation follows the approach in STAN's base_nuts.hpp (BSD-3-Clause license). +// +// References: +// Betancourt, M. (2017). A Conceptual Introduction to Hamiltonian Monte Carlo. +// arXiv preprint arXiv:1701.02434. +// Stan Development Team. base_nuts.hpp. +// https://github.com/stan-dev/stan/blob/develop/src/stan/mcmc/hmc/nuts/base_nuts.hpp + + +// Computes the generalized U-turn criterion for the NUTS algorithm +// +// @param p_sharp_minus Sharp momentum (M^{-1} p) at backward end +// @param p_sharp_plus Sharp momentum (M^{-1} p) at forward end +// @param rho Sum of momenta along the trajectory +// @return true if criterion satisfied (continue), false if U-turn detected (stop) bool compute_criterion(const arma::vec& p_sharp_minus, const arma::vec& p_sharp_plus, const arma::vec& rho) { @@ -36,25 +32,23 @@ bool compute_criterion(const arma::vec& p_sharp_minus, -/** - * Recursively builds a binary tree of leapfrog steps in the NUTS algorithm - * - * Explores forward or backward in time, evaluating trajectory termination - * criteria. Based on Algorithm 6 in Hoffman & Gelman (2014). - * - * @param theta Current position at the base of the tree - * @param r Current momentum at the base of the tree - * @param log_u Log slice variable for accept/reject decision - * @param v Direction of expansion (-1 backward, +1 forward) - * @param j Current tree depth - * @param step_size Step size used in leapfrog integration - * @param theta_0 Initial position at the start of sampling - * @param r0 Initial momentum at the start of sampling - * @param logp0 Log posterior at initial position - * @param kin0 Kinetic energy at initial momentum - * @param memo Memoizer object for caching evaluations - * @return BuildTreeResult with updated endpoints, candidate sample, and diagnostics - */ +// Recursively builds a binary tree of leapfrog steps in the NUTS algorithm +// +// Explores forward or backward in time, evaluating trajectory termination +// criteria. Based on Algorithm 6 in Hoffman & Gelman (2014). +// +// @param theta Current position at the base of the tree +// @param r Current momentum at the base of the tree +// @param log_u Log slice variable for accept/reject decision +// @param v Direction of expansion (-1 backward, +1 forward) +// @param j Current tree depth +// @param step_size Step size used in leapfrog integration +// @param theta_0 Initial position at the start of sampling +// @param r0 Initial momentum at the start of sampling +// @param logp0 Log posterior at initial position +// @param kin0 Kinetic energy at initial momentum +// @param memo Memoizer object for caching evaluations +// @return BuildTreeResult with updated endpoints, candidate sample, and diagnostics BuildTreeResult build_tree( const arma::vec& theta, const arma::vec& r, diff --git a/src/mcmc/execution/chain_result.h b/src/mcmc/execution/chain_result.h index 32e24e8d..ae1b1e3c 100644 --- a/src/mcmc/execution/chain_result.h +++ b/src/mcmc/execution/chain_result.h @@ -14,29 +14,36 @@ class ChainResult { public: ChainResult() = default; - // Error handling + /// True if the chain terminated with an error. bool error = false; + /// True if the chain was interrupted by the user. bool userInterrupt = false; + /// Error message (empty if none). std::string error_msg; - // Chain identifier + /// Integer identifier for the chain (1-based). int chain_id = 0; - // Parameter samples (param_dim × n_iter) + /// Parameter samples (param_dim x n_iter). arma::mat samples; - // Edge indicator samples (n_edges × n_iter), only if edge_selection = true + /// Edge indicator samples (n_edges x n_iter), only if edge_selection = true. arma::imat indicator_samples; + /// Whether indicator samples are stored. bool has_indicators = false; - // SBM allocation samples (n_variables × n_iter), only if SBM edge prior + /// SBM allocation samples (n_variables x n_iter), only if SBM edge prior. arma::imat allocation_samples; + /// Whether allocation samples are stored. bool has_allocations = false; - // NUTS/HMC diagnostics (n_iter), only if using NUTS/HMC + /// NUTS/HMC tree depth diagnostics (n_iter). arma::ivec treedepth_samples; + /// NUTS/HMC divergent transition flags (n_iter). arma::ivec divergent_samples; + /// NUTS/HMC energy diagnostic (n_iter). arma::vec energy_samples; + /// Whether NUTS/HMC diagnostics are stored. bool has_nuts_diagnostics = false; /** diff --git a/src/mcmc/execution/sampler_config.h b/src/mcmc/execution/sampler_config.h index d2ce341b..595c819a 100644 --- a/src/mcmc/execution/sampler_config.h +++ b/src/mcmc/execution/sampler_config.h @@ -12,28 +12,32 @@ * - Edge selection settings */ struct SamplerConfig { - // Sampler type: "adaptive_metropolis", "nuts", "hmc" + /// Sampler type: "adaptive_metropolis", "nuts", or "hmc". std::string sampler_type = "adaptive_metropolis"; - // Iteration counts + /// Number of post-warmup iterations. int no_iter = 1000; + /// Number of warmup iterations. int no_warmup = 500; - // NUTS/HMC parameters + /// Maximum NUTS tree depth. int max_tree_depth = 10; - int num_leapfrogs = 10; // For HMC only + /// Number of leapfrog steps (HMC only). + int num_leapfrogs = 10; + /// Initial step size for gradient-based samplers. double initial_step_size = 0.1; + /// Target acceptance rate for dual-averaging adaptation. double target_acceptance = 0.8; - // Edge selection settings + /// Enable spike-and-slab edge selection. bool edge_selection = false; - // Missing data imputation + /// Enable missing-data imputation during sampling. bool na_impute = false; - // Random seed + /// Random seed. int seed = 42; - // Constructor with defaults + /// Default constructor. SamplerConfig() = default; }; diff --git a/src/mcmc/samplers/hmc_adaptation.h b/src/mcmc/samplers/hmc_adaptation.h index 9c31de64..064f0bbe 100644 --- a/src/mcmc/samplers/hmc_adaptation.h +++ b/src/mcmc/samplers/hmc_adaptation.h @@ -16,13 +16,21 @@ */ class DualAveraging { public: + /// Current log step size. double log_step_size; + /// Smoothed log step size (final estimate). double log_step_size_avg; + /// Running error statistic. double hbar; + /// Bias term: log(10 * initial_step_size). double mu; + /// Shrinkage parameter (default 0.05). double gamma; + /// Stabilisation offset (default 10). double t0; + /// Decay exponent for averaging weights (default 0.75). double kappa; + /// Iteration counter. int t; DualAveraging(double initial_step_size) @@ -68,8 +76,11 @@ class DualAveraging { */ class DiagMassMatrixAccumulator { public: + /// Number of samples accumulated. int count; + /// Running mean of parameter samples. arma::vec mean; + /// Running sum of squared deviations (Welford M2 statistic). arma::vec m2; DiagMassMatrixAccumulator(int dim) diff --git a/src/mcmc/samplers/metropolis_adaptation.h b/src/mcmc/samplers/metropolis_adaptation.h index 86eb8e06..ebd29e71 100644 --- a/src/mcmc/samplers/metropolis_adaptation.h +++ b/src/mcmc/samplers/metropolis_adaptation.h @@ -15,8 +15,11 @@ */ class MetropolisAdaptationController { public: + /// Reference to the proposal standard deviation matrix (modified in place). arma::mat& proposal_sd; + /// Total number of warmup iterations. const int total_warmup; + /// Target Metropolis acceptance rate. const double target_accept; MetropolisAdaptationController(arma::mat& proposal_sd_matrix, diff --git a/src/models/mixed/mixed_mrf_model.h b/src/models/mixed/mixed_mrf_model.h index bcfa607e..9d8d62f9 100644 --- a/src/models/mixed/mixed_mrf_model.h +++ b/src/models/mixed/mixed_mrf_model.h @@ -206,115 +206,110 @@ class MixedMRFModel : public BaseModel { // Counts and dimensions // ========================================================================= - size_t n_; // Number of observations - size_t p_; // Number of discrete variables - size_t q_; // Number of continuous variables - size_t num_main_; // Total main-effect params (Σ C_s for ord + 2 per BC) - size_t num_pairwise_xx_; // p(p-1)/2 - size_t num_pairwise_yy_; // q(q-1)/2 - size_t num_cross_; // p * q + size_t n_; ///< Number of observations + size_t p_; ///< Number of discrete variables + size_t q_; ///< Number of continuous variables + size_t num_main_; ///< Total main-effect params (sum C_s for ord + 2 per BC) + size_t num_pairwise_xx_; ///< p(p-1)/2 + size_t num_pairwise_yy_; ///< q(q-1)/2 + size_t num_cross_; ///< p * q // ========================================================================= // Data // ========================================================================= - arma::imat discrete_observations_; // Discrete observations (n × p) - // BC columns centered at baseline_category_ in ctor. - arma::mat discrete_observations_dbl_; // Double version (post-centering) - arma::mat continuous_observations_; // Continuous observations (n × q) - arma::ivec num_categories_; // Categories per discrete variable (p-vector) - int max_cats_; // max(num_categories) - arma::uvec is_ordinal_variable_; // 1 = ordinal, 0 = Blume-Capel (p-vector) - arma::ivec baseline_category_; // Reference category per discrete variable (p-vector) + arma::imat discrete_observations_; ///< Discrete observations (n x p), BC columns centered + arma::mat discrete_observations_dbl_; ///< Double version (post-centering) + arma::mat continuous_observations_; ///< Continuous observations (n x q) + arma::ivec num_categories_; ///< Categories per discrete variable (p-vector) + int max_cats_; ///< max(num_categories) + arma::uvec is_ordinal_variable_; ///< 1 = ordinal, 0 = Blume-Capel (p-vector) + arma::ivec baseline_category_; ///< Reference category per discrete variable (p-vector) // ========================================================================= // Sufficient statistics // ========================================================================= - arma::imat counts_per_category_; // (max_cats+1) × p category counts (ordinal only) - arma::imat blume_capel_stats_; // 2 × p linear/quadratic sums (BC only) + arma::imat counts_per_category_; ///< (max_cats+1) x p category counts (ordinal only) + arma::imat blume_capel_stats_; ///< 2 x p linear/quadratic sums (BC only) // ========================================================================= // Parameters // ========================================================================= - arma::mat mux_; // p × max_cats main effects - // Ordinal: mux_(s,c) = threshold for category c+1; - // category 0 is reference (fixed at 0). - // Blume-Capel: mux_(s,0) = linear α_s, - // mux_(s,1) = quadratic β_s. - arma::vec muy_; // q-vector continuous means - arma::mat Kxx_; // p × p discrete interactions (symmetric, zero diag) - arma::mat Kyy_; // q × q SPD precision matrix - arma::mat Kxy_; // p × q cross-type interactions + arma::mat mux_; ///< p x max_cats main effects (thresholds or alpha/beta) + arma::vec muy_; ///< q-vector continuous means + arma::mat Kxx_; ///< p x p discrete interactions (symmetric, zero diag) + arma::mat Kyy_; ///< q x q SPD precision matrix + arma::mat Kxy_; ///< p x q cross-type interactions // ========================================================================= // Edge indicators // ========================================================================= - // Combined (p+q) × (p+q) indicator matrix: - // Gxx block : rows [0,p), cols [0,p) — symmetric, zero diag - // Gyy block : rows [p,p+q), cols [p,p+q) — symmetric, zero diag - // Gxy block : rows [0,p), cols [p,p+q) — full p×q rectangle + /// Combined (p+q) x (p+q) indicator matrix. + /// Gxx block: rows [0,p), cols [0,p) -- symmetric, zero diag. + /// Gyy block: rows [p,p+q), cols [p,p+q) -- symmetric, zero diag. + /// Gxy block: rows [0,p), cols [p,p+q) -- full p x q rectangle. arma::imat edge_indicators_; - arma::mat inclusion_probability_; - bool edge_selection_; - bool edge_selection_active_; + arma::mat inclusion_probability_; ///< Prior inclusion probabilities + bool edge_selection_; ///< Enable edge selection + bool edge_selection_active_; ///< Currently in edge selection phase // ========================================================================= // Priors // ========================================================================= - double main_alpha_; // Beta prior α for main effects - double main_beta_; // Beta prior β for main effects - double pairwise_scale_; // Cauchy scale for interaction priors + double main_alpha_; ///< Beta prior alpha for main effects + double main_beta_; ///< Beta prior beta for main effects + double pairwise_scale_; ///< Cauchy scale for interaction priors // ========================================================================= // Proposal SDs (Robbins-Monro adapted) // ========================================================================= - arma::mat prop_sd_mux_; // p × max_cats - arma::vec prop_sd_muy_; // q-vector - arma::mat prop_sd_Kxx_; // p × p - arma::mat prop_sd_Kyy_; // q × q - arma::mat prop_sd_Kxy_; // p × q - int total_warmup_ = 0; // stored by init_metropolis_adaptation + arma::mat prop_sd_mux_; ///< p x max_cats + arma::vec prop_sd_muy_; ///< q-vector + arma::mat prop_sd_Kxx_; ///< p x p + arma::mat prop_sd_Kyy_; ///< q x q + arma::mat prop_sd_Kxy_; ///< p x q + int total_warmup_ = 0; ///< Stored by init_metropolis_adaptation // ========================================================================= // Cached quantities // ========================================================================= - arma::mat Kyy_chol_; // q × q upper Cholesky of Kyy (Kyy = R'R) - arma::mat inv_cholesky_yy_; // q × q R^{-1} (upper triangular) - arma::mat covariance_yy_; // q × q Kyy^{-1} = R^{-1} R^{-T} - double Kyy_log_det_; // log|Kyy| - arma::mat Theta_; // p × p Kxx + 2 Kxy covariance_yy_ Kxy' (marginal PL only) - arma::mat conditional_mean_; // n × q μ_y' + 2 discrete_obs Kxy covariance_yy_ + arma::mat Kyy_chol_; ///< q x q upper Cholesky of Kyy (Kyy = R'R) + arma::mat inv_cholesky_yy_; ///< q x q R^{-1} (upper triangular) + arma::mat covariance_yy_; ///< q x q Kyy^{-1} = R^{-1} R^{-T} + double Kyy_log_det_; ///< log|Kyy| + arma::mat Theta_; ///< p x p Kxx + 2 Kxy covariance_yy_ Kxy' (marginal PL) + arma::mat conditional_mean_; ///< n x q mu_y' + 2 discrete_obs Kxy covariance_yy_ // Rank-1 Cholesky update workspace - std::array kyy_constants_{}; // reparameterization constants - arma::mat precision_yy_proposal_; // q × q scratch for proposed Kyy - arma::vec kyy_v1_ = {0, -1}; // rank-2 decomposition helpers - arma::vec kyy_v2_ = {0, 0}; - arma::vec kyy_vf1_; // q-vectors, zeroed between uses - arma::vec kyy_vf2_; - arma::vec kyy_u1_; - arma::vec kyy_u2_; + std::array kyy_constants_{}; ///< Reparameterization constants + arma::mat precision_yy_proposal_; ///< q x q scratch for proposed Kyy + arma::vec kyy_v1_ = {0, -1}; ///< Rank-2 decomposition helper 1 + arma::vec kyy_v2_ = {0, 0}; ///< Rank-2 decomposition helper 2 + arma::vec kyy_vf1_; ///< q-vector, zeroed between uses + arma::vec kyy_vf2_; ///< q-vector, zeroed between uses + arma::vec kyy_u1_; ///< q-vector workspace + arma::vec kyy_u2_; ///< q-vector workspace // ========================================================================= // Configuration // ========================================================================= - bool use_marginal_pl_; // true = marginal, false = conditional + bool use_marginal_pl_; ///< true = marginal, false = conditional // ========================================================================= // RNG and edge-update order // ========================================================================= - SafeRNG rng_; - arma::uvec edge_order_xx_; // Shuffled xx-edge pair indices - arma::uvec edge_order_yy_; // Shuffled yy-edge pair indices - arma::uvec edge_order_xy_; // Shuffled xy-edge pair indices + SafeRNG rng_; ///< Per-chain random number generator + arma::uvec edge_order_xx_; ///< Shuffled xx-edge pair indices + arma::uvec edge_order_yy_; ///< Shuffled yy-edge pair indices + arma::uvec edge_order_xy_; ///< Shuffled xy-edge pair indices // ========================================================================= // Private helpers diff --git a/src/models/omrf/omrf_model.h b/src/models/omrf/omrf_model.h index 5d86c138..5dca2b8f 100644 --- a/src/models/omrf/omrf_model.h +++ b/src/models/omrf/omrf_model.h @@ -266,68 +266,68 @@ class OMRFModel : public BaseModel { // ========================================================================= // Data - size_t n_; // Number of observations - size_t p_; // Number of variables - arma::imat observations_; // Categorical observations (n × p) - arma::mat observations_double_; // Observations as double (for efficient matrix ops) - arma::mat observations_double_t_; // Transposed observations (for BLAS pairwise gradient) - arma::ivec num_categories_; // Categories per variable - arma::uvec is_ordinal_variable_; // 1 = ordinal, 0 = Blume-Capel - arma::ivec baseline_category_; // Reference category for Blume-Capel + size_t n_; ///< Number of observations + size_t p_; ///< Number of variables + arma::imat observations_; ///< Categorical observations (n x p) + arma::mat observations_double_; ///< Observations as double (for efficient matrix ops) + arma::mat observations_double_t_; ///< Transposed observations (for BLAS pairwise gradient) + arma::ivec num_categories_; ///< Categories per variable + arma::uvec is_ordinal_variable_; ///< 1 = ordinal, 0 = Blume-Capel + arma::ivec baseline_category_; ///< Reference category for Blume-Capel // Sufficient statistics - arma::imat counts_per_category_; // Category counts (max_cats+1 × p) - arma::imat blume_capel_stats_; // [linear_sum, quadratic_sum] for BC vars (2 × p) - arma::imat pairwise_stats_; // X^T X - arma::mat residual_matrix_; // X * pairwise_effects (n × p) + arma::imat counts_per_category_; ///< Category counts (max_cats+1 x p) + arma::imat blume_capel_stats_; ///< [linear_sum, quadratic_sum] for BC vars (2 x p) + arma::imat pairwise_stats_; ///< X^T X + arma::mat residual_matrix_; ///< X * pairwise_effects (n x p) // Parameters - arma::mat main_effects_; // Main effect parameters (p × max_cats) - arma::mat pairwise_effects_; // Pairwise interaction strengths (p × p, symmetric) - arma::imat edge_indicators_; // Edge inclusion indicators (p × p, symmetric binary) + arma::mat main_effects_; ///< Main effect parameters (p x max_cats) + arma::mat pairwise_effects_; ///< Pairwise interaction strengths (p x p, symmetric) + arma::imat edge_indicators_; ///< Edge inclusion indicators (p x p, symmetric binary) // Priors - arma::mat inclusion_probability_; // Prior inclusion probabilities - double main_alpha_; // Beta prior α - double main_beta_; // Beta prior β - double pairwise_scale_; // Cauchy scale for pairwise effects - arma::mat pairwise_scaling_factors_; // Per-pair scaling factors for Cauchy prior + arma::mat inclusion_probability_; ///< Prior inclusion probabilities + double main_alpha_; ///< Beta prior alpha + double main_beta_; ///< Beta prior beta + double pairwise_scale_; ///< Cauchy scale for pairwise effects + arma::mat pairwise_scaling_factors_; ///< Per-pair scaling factors for Cauchy prior // Model configuration - bool edge_selection_; // Enable edge selection - bool edge_selection_active_; // Currently in edge selection phase + bool edge_selection_; ///< Enable edge selection + bool edge_selection_active_; ///< Currently in edge selection phase // Dimension tracking - size_t num_main_; // Total number of main effect parameters - size_t num_pairwise_; // Number of possible pairwise effects + size_t num_main_; ///< Total number of main effect parameters + size_t num_pairwise_; ///< Number of possible pairwise effects // Proposal SDs (adapted by MetropolisAdaptationController during warmup) - arma::mat proposal_sd_main_; - arma::mat proposal_sd_pairwise_; + arma::mat proposal_sd_main_; ///< Proposal SD for main effects + arma::mat proposal_sd_pairwise_; ///< Proposal SD for pairwise effects // Metropolis adaptation controllers (created by init_metropolis_adaptation) - std::unique_ptr metropolis_main_adapter_; - std::unique_ptr metropolis_pairwise_adapter_; + std::unique_ptr metropolis_main_adapter_; ///< Main-effect adapter + std::unique_ptr metropolis_pairwise_adapter_; ///< Pairwise-effect adapter // RNG - SafeRNG rng_; + SafeRNG rng_; ///< Per-chain random number generator // NUTS/HMC settings - double step_size_; - arma::vec inv_mass_; + double step_size_; ///< Current step size for gradient-based samplers + arma::vec inv_mass_; ///< Inverse mass diagonal // Missing data handling - bool has_missing_; - arma::imat missing_index_; + bool has_missing_; ///< Whether the data contains missing values + arma::imat missing_index_; ///< (row, col) indices of missing entries // Cached gradient components - arma::vec grad_obs_cache_; - arma::imat index_matrix_cache_; - bool gradient_cache_valid_; + arma::vec grad_obs_cache_; ///< Cached observed-data gradient + arma::imat index_matrix_cache_; ///< Cached parameter index map + bool gradient_cache_valid_; ///< Whether the gradient cache is current // Interaction indexing (for edge updates) - arma::imat interaction_index_; - arma::uvec shuffled_edge_order_; // Pre-shuffled order (set in prepare_iteration) + arma::imat interaction_index_; ///< Maps edge pair to index + arma::uvec shuffled_edge_order_; ///< Pre-shuffled order (set in prepare_iteration) // ========================================================================= // Private helper methods diff --git a/src/mrf_prediction.cpp b/src/mrf_prediction.cpp index a4315194..2bea4436 100644 --- a/src/mrf_prediction.cpp +++ b/src/mrf_prediction.cpp @@ -8,22 +8,20 @@ using namespace Rcpp; // GGM Conditional Prediction // ============================================================================ -/** - * Compute conditional Gaussian parameters for a GGM. - * - * For a GGM with precision matrix Omega, the conditional distribution of - * X_j given X_{-j} = x_{-j} is: - * - * X_j | X_{-j} ~ N( -omega_{jj}^{-1} sum_{k != j} omega_{jk} x_k, - * omega_{jj}^{-1} ) - * - * @param observations n x p matrix of observed continuous data - * @param predict_vars 0-based indices of variables to predict - * @param precision p x p precision matrix (Omega) - * - * @return List of n x 2 matrices (one per predicted variable), where - * column 0 = conditional mean, column 1 = conditional SD. - */ +// Compute conditional Gaussian parameters for a GGM. +// +// For a GGM with precision matrix Omega, the conditional distribution of +// X_j given X_{-j} = x_{-j} is: +// +// X_j | X_{-j} ~ N( -omega_{jj}^{-1} sum_{k != j} omega_{jk} x_k, +// omega_{jj}^{-1} ) +// +// @param observations n x p matrix of observed continuous data +// @param predict_vars 0-based indices of variables to predict +// @param precision p x p precision matrix (Omega) +// +// @return List of n x 2 matrices (one per predicted variable), where +// column 0 = conditional mean, column 1 = conditional SD. // [[Rcpp::export]] Rcpp::List compute_conditional_ggm( const arma::mat& observations, @@ -165,31 +163,29 @@ Rcpp::List compute_conditional_probs( // Mixed MRF Conditional Prediction // ============================================================================ -/** - * Compute conditional distributions for a mixed MRF. - * - * For discrete variables: P(x_s = c | x_{-s}, y) using the conditional OMRF. - * For continuous variables: E(y_j | y_{-j}, x) and SD(y_j | y_{-j}, x) - * using the conditional GGM. - * - * @param x_observations n x p integer matrix of discrete data. - * @param y_observations n x q numeric matrix of continuous data. - * @param predict_vars 0-based indices into the combined (p+q) variable list. - * Indices 0..p-1 refer to discrete variables, - * p..p+q-1 refer to continuous variables. - * @param Kxx p x p pairwise interactions (diagonal zero). - * @param Kxy p x q cross interactions. - * @param Kyy q x q precision matrix. - * @param mux p x max_cats threshold / Blume-Capel parameters. - * @param muy q-vector of continuous means. - * @param num_categories p-vector: categories per discrete variable. - * @param variable_type p-vector: "ordinal" or "blume-capel". - * @param baseline_category p-vector. - * - * @return List of prediction matrices (one per predicted variable). - * For discrete: n x (num_cats+1) probability matrix. - * For continuous: n x 2 matrix (mean, sd). - */ +// Compute conditional distributions for a mixed MRF. +// +// For discrete variables: P(x_s = c | x_{-s}, y) using the conditional OMRF. +// For continuous variables: E(y_j | y_{-j}, x) and SD(y_j | y_{-j}, x) +// using the conditional GGM. +// +// @param x_observations n x p integer matrix of discrete data. +// @param y_observations n x q numeric matrix of continuous data. +// @param predict_vars 0-based indices into the combined (p+q) variable list. +// Indices 0..p-1 refer to discrete variables, +// p..p+q-1 refer to continuous variables. +// @param Kxx p x p pairwise interactions (diagonal zero). +// @param Kxy p x q cross interactions. +// @param Kyy q x q precision matrix. +// @param mux p x max_cats threshold / Blume-Capel parameters. +// @param muy q-vector of continuous means. +// @param num_categories p-vector: categories per discrete variable. +// @param variable_type p-vector: "ordinal" or "blume-capel". +// @param baseline_category p-vector. +// +// @return List of prediction matrices (one per predicted variable). +// For discrete: n x (num_cats+1) probability matrix. +// For continuous: n x 2 matrix (mean, sd). // [[Rcpp::export]] Rcpp::List compute_conditional_mixed( const arma::imat& x_observations, diff --git a/src/mrf_simulation.cpp b/src/mrf_simulation.cpp index 3a85f9ad..f746651a 100644 --- a/src/mrf_simulation.cpp +++ b/src/mrf_simulation.cpp @@ -15,32 +15,30 @@ using namespace RcppParallel; // MRF Simulation Core Functions (Thread-Safe) // ============================================================================ -/** - * Function: simulate_mrf - * - * Simulates observations from a Markov Random Field using Gibbs sampling. - * Supports both ordinal and Blume-Capel variable types. - * - * Inputs: - * - num_states: Number of observations to simulate. - * - num_variables: Number of variables in the MRF. - * - num_categories: Number of categories per variable (on top of baseline 0). - * - pairwise: Symmetric pairwise interaction matrix (diagonal ignored). - * - main: Main effect parameters (variables x max_categories). - * For ordinal: threshold parameters for categories 1..K. - * For Blume-Capel: column 0 = linear (alpha), column 1 = quadratic (beta). - * - variable_type: Type of each variable ("ordinal" or "blume-capel"). - * - baseline_category: Baseline category for Blume-Capel variables (0 for ordinal). - * - iter: Number of Gibbs sampling iterations. - * - rng: Thread-safe random number generator. - * - * Returns: - * - Integer matrix of simulated observations (num_states x num_variables). - * - * Notes: - * - Diagonal of pairwise matrix is explicitly ignored (set to zero internally). - * - For ordinal variables, baseline_category should be 0. - */ +// Function: simulate_mrf +// +// Simulates observations from a Markov Random Field using Gibbs sampling. +// Supports both ordinal and Blume-Capel variable types. +// +// Inputs: +// - num_states: Number of observations to simulate. +// - num_variables: Number of variables in the MRF. +// - num_categories: Number of categories per variable (on top of baseline 0). +// - pairwise: Symmetric pairwise interaction matrix (diagonal ignored). +// - main: Main effect parameters (variables x max_categories). +// For ordinal: threshold parameters for categories 1..K. +// For Blume-Capel: column 0 = linear (alpha), column 1 = quadratic (beta). +// - variable_type: Type of each variable ("ordinal" or "blume-capel"). +// - baseline_category: Baseline category for Blume-Capel variables (0 for ordinal). +// - iter: Number of Gibbs sampling iterations. +// - rng: Thread-safe random number generator. +// +// Returns: +// - Integer matrix of simulated observations (num_states x num_variables). +// +// Notes: +// - Diagonal of pairwise matrix is explicitly ignored (set to zero internally). +// - For ordinal variables, baseline_category should be 0. arma::imat simulate_mrf( int num_states, int num_variables, @@ -236,25 +234,23 @@ IntegerMatrix sample_bcomrf_gibbs(int num_states, // GGM Simulation (Direct Multivariate Normal Sampling) // ============================================================================ -/** - * Simulate observations from a Gaussian Graphical Model. - * - * Given a precision matrix Omega, draws num_states observations from - * N(means, Omega^{-1}) using the Cholesky factorization of the covariance. - * - * Algorithm: - * 1. Compute Sigma = Omega^{-1} via arma::inv_sympd. - * 2. Cholesky decompose: L = chol(Sigma, "lower") so Sigma = L L'. - * 3. Draw Z ~ N(0, I) of size (num_states x p). - * 4. Return X = ones * means' + Z * L'. - * - * @param num_states Number of observations to simulate. - * @param precision p x p positive-definite precision matrix (Omega). - * @param means p-vector of variable means (can be all zeros). - * @param rng Thread-safe random number generator. - * - * @return num_states x p matrix of simulated continuous observations. - */ +// Simulate observations from a Gaussian Graphical Model. +// +// Given a precision matrix Omega, draws num_states observations from +// N(means, Omega^{-1}) using the Cholesky factorization of the covariance. +// +// Algorithm: +// 1. Compute Sigma = Omega^{-1} via arma::inv_sympd. +// 2. Cholesky decompose: L = chol(Sigma, "lower") so Sigma = L L'. +// 3. Draw Z ~ N(0, I) of size (num_states x p). +// 4. Return X = ones * means' + Z * L'. +// +// @param num_states Number of observations to simulate. +// @param precision p x p positive-definite precision matrix (Omega). +// @param means p-vector of variable means (can be all zeros). +// @param rng Thread-safe random number generator. +// +// @return num_states x p matrix of simulated continuous observations. arma::mat simulate_ggm( int num_states, const arma::mat& precision, @@ -286,16 +282,14 @@ arma::mat simulate_ggm( // R Interface for GGM Simulation (standalone simulate_ggm) // ============================================================================ -/** - * R-callable wrapper for single GGM simulation. - * - * @param num_states Number of observations to simulate. - * @param precision p x p precision matrix (Omega). - * @param means p-vector of means (default zeros). - * @param seed Random seed for reproducibility. - * - * @return num_states x p numeric matrix. - */ +// R-callable wrapper for single GGM simulation. +// +// @param num_states Number of observations to simulate. +// @param precision p x p precision matrix (Omega). +// @param means p-vector of means (default zeros). +// @param seed Random seed for reproducibility. +// +// @return num_states x p numeric matrix. // [[Rcpp::export]] NumericMatrix sample_ggm_direct(int num_states, NumericMatrix precision, @@ -333,9 +327,7 @@ struct SimulationResult { }; -/** - * Worker class for parallel simulation across posterior draws - */ +// Worker class for parallel simulation across posterior draws class SimulationWorker : public RcppParallel::Worker { public: // Input data @@ -451,24 +443,22 @@ class SimulationWorker : public RcppParallel::Worker { }; -/** - * Run parallel simulations across posterior draws - * - * @param pairwise_samples Matrix of pairwise samples (ndraws x n_pairwise) - * @param main_samples Matrix of main/threshold samples (ndraws x n_main) - * @param draw_indices 1-based indices of which draws to use - * @param num_states Number of observations to simulate per draw - * @param num_variables Number of variables - * @param num_categories Number of categories per variable - * @param variable_type Type of each variable ("ordinal" or "blume-capel") - * @param baseline_category Baseline category for each variable - * @param iter Number of Gibbs iterations per simulation - * @param nThreads Number of parallel threads - * @param seed Random seed - * @param progress_type Progress bar type (0=none, 1=total, 2=per-chain) - * - * @return List of simulation results (each is an integer matrix) - */ +// Run parallel simulations across posterior draws +// +// @param pairwise_samples Matrix of pairwise samples (ndraws x n_pairwise) +// @param main_samples Matrix of main/threshold samples (ndraws x n_main) +// @param draw_indices 1-based indices of which draws to use +// @param num_states Number of observations to simulate per draw +// @param num_variables Number of variables +// @param num_categories Number of categories per variable +// @param variable_type Type of each variable ("ordinal" or "blume-capel") +// @param baseline_category Baseline category for each variable +// @param iter Number of Gibbs iterations per simulation +// @param nThreads Number of parallel threads +// @param seed Random seed +// @param progress_type Progress bar type (0=none, 1=total, 2=per-chain) +// +// @return List of simulation results (each is an integer matrix) // [[Rcpp::export]] Rcpp::List run_simulation_parallel( const arma::mat& pairwise_samples, @@ -572,9 +562,7 @@ struct GGMSimulationResult { }; -/** - * Worker class for parallel GGM simulation across posterior draws - */ +// Worker class for parallel GGM simulation across posterior draws class GGMSimulationWorker : public RcppParallel::Worker { public: const arma::mat& pairwise_samples; // ndraws x p*(p-1)/2 @@ -655,21 +643,19 @@ class GGMSimulationWorker : public RcppParallel::Worker { }; -/** - * Run parallel GGM simulations across posterior draws. - * - * @param pairwise_samples Matrix of off-diagonal precision samples (ndraws x p*(p-1)/2) - * @param main_samples Matrix of diagonal precision samples (ndraws x p) - * @param draw_indices 1-based indices of which draws to use - * @param num_states Number of observations to simulate per draw - * @param num_variables Number of variables - * @param means p-vector of variable means - * @param nThreads Number of parallel threads - * @param seed Random seed - * @param progress_type Progress bar type (0=none, 1=total, 2=per-chain) - * - * @return List of simulation results (each is a numeric matrix n x p) - */ +// Run parallel GGM simulations across posterior draws. +// +// @param pairwise_samples Matrix of off-diagonal precision samples (ndraws x p*(p-1)/2) +// @param main_samples Matrix of diagonal precision samples (ndraws x p) +// @param draw_indices 1-based indices of which draws to use +// @param num_states Number of observations to simulate per draw +// @param num_variables Number of variables +// @param means p-vector of variable means +// @param nThreads Number of parallel threads +// @param seed Random seed +// @param progress_type Progress bar type (0=none, 1=total, 2=per-chain) +// +// @return List of simulation results (each is a numeric matrix n x p) // [[Rcpp::export]] Rcpp::List run_ggm_simulation_parallel( const arma::mat& pairwise_samples, @@ -729,28 +715,26 @@ Rcpp::List run_ggm_simulation_parallel( // Mixed MRF Simulation (Block Gibbs: Discrete + Continuous) // ============================================================================ -/** - * Simulate observations from a mixed MRF using block Gibbs sampling. - * - * Each iteration updates all discrete variables from their full conditional - * given (x_{-s}, y), then updates all continuous variables from - * y | x ~ N(mu_y + 2 * x * Kxy * Kyy^{-1}, Kyy^{-1}). - * - * @param num_states Number of observations to simulate. - * @param Kxx p x p discrete pairwise interactions (diagonal zero). - * @param Kxy p x q cross interactions. - * @param Kyy q x q SPD continuous precision. - * @param mux p x max_cats threshold / Blume-Capel parameters. - * @param muy q-vector of continuous means. - * @param num_categories p-vector: number of categories per discrete variable - * (on top of baseline 0). - * @param variable_type p-vector: "ordinal" or "blume-capel". - * @param baseline_category p-vector: reference category per variable. - * @param iter Number of Gibbs iterations for burn-in. - * @param rng Thread-safe RNG. - * @param x_out Output: n x p integer matrix of discrete observations. - * @param y_out Output: n x q numeric matrix of continuous observations. - */ +// Simulate observations from a mixed MRF using block Gibbs sampling. +// +// Each iteration updates all discrete variables from their full conditional +// given (x_{-s}, y), then updates all continuous variables from +// y | x ~ N(mu_y + 2 * x * Kxy * Kyy^{-1}, Kyy^{-1}). +// +// @param num_states Number of observations to simulate. +// @param Kxx p x p discrete pairwise interactions (diagonal zero). +// @param Kxy p x q cross interactions. +// @param Kyy q x q SPD continuous precision. +// @param mux p x max_cats threshold / Blume-Capel parameters. +// @param muy q-vector of continuous means. +// @param num_categories p-vector: number of categories per discrete variable +// (on top of baseline 0). +// @param variable_type p-vector: "ordinal" or "blume-capel". +// @param baseline_category p-vector: reference category per variable. +// @param iter Number of Gibbs iterations for burn-in. +// @param rng Thread-safe RNG. +// @param x_out Output: n x p integer matrix of discrete observations. +// @param y_out Output: n x q numeric matrix of continuous observations. void simulate_mixed_mrf( int num_states, const arma::mat& Kxx, @@ -1113,31 +1097,29 @@ class MixedSimulationWorker : public RcppParallel::Worker { }; -/** - * Run parallel mixed MRF simulations across posterior draws. - * - * The R layer splits the flat parameter vector into the 5 component matrices - * and passes them as separate sample matrices. - * - * @param mux_samples ndraws x n_mux - * @param kxx_samples ndraws x p*(p-1)/2 - * @param muy_samples ndraws x q - * @param kyy_samples ndraws x q*(q+1)/2 - * @param kxy_samples ndraws x p*q - * @param draw_indices 1-based indices of which draws to use - * @param num_states Number of observations per draw - * @param p Number of discrete variables - * @param q Number of continuous variables - * @param num_categories p-vector: categories per discrete variable - * @param variable_type_r p-vector: "ordinal" or "blume-capel" - * @param baseline_category p-vector - * @param iter Gibbs burn-in iterations - * @param nThreads Number of threads - * @param seed Random seed - * @param progress_type Progress bar type - * - * @return List of lists, each containing "x" (integer matrix) and "y" (numeric matrix). - */ +// Run parallel mixed MRF simulations across posterior draws. +// +// The R layer splits the flat parameter vector into the 5 component matrices +// and passes them as separate sample matrices. +// +// @param mux_samples ndraws x n_mux +// @param kxx_samples ndraws x p*(p-1)/2 +// @param muy_samples ndraws x q +// @param kyy_samples ndraws x q*(q+1)/2 +// @param kxy_samples ndraws x p*q +// @param draw_indices 1-based indices of which draws to use +// @param num_states Number of observations per draw +// @param p Number of discrete variables +// @param q Number of continuous variables +// @param num_categories p-vector: categories per discrete variable +// @param variable_type_r p-vector: "ordinal" or "blume-capel" +// @param baseline_category p-vector +// @param iter Gibbs burn-in iterations +// @param nThreads Number of threads +// @param seed Random seed +// @param progress_type Progress bar type +// +// @return List of lists, each containing "x" (integer matrix) and "y" (numeric matrix). // [[Rcpp::export]] Rcpp::List run_mixed_simulation_parallel( const arma::mat& mux_samples, diff --git a/src/priors/sbm_edge_prior.cpp b/src/priors/sbm_edge_prior.cpp index 81c92ac5..ce84b00c 100644 --- a/src/priors/sbm_edge_prior.cpp +++ b/src/priors/sbm_edge_prior.cpp @@ -59,24 +59,22 @@ arma::mat add_row_col_block_prob_matrix(arma::mat X, -/** - * Function: log_likelihood_mfm_sbm - * - * Computes the log-likelihood contribution for a single node under the - * Mixture of Finite Mixtures Stochastic Block Model (MFM-SBM). Evaluates - * the probability of observed edges between the node and all other nodes - * given their cluster assignments and cluster connection probabilities. - * - * Inputs: - * - cluster_assign: Vector of cluster assignments for all nodes. - * - cluster_probs: Matrix of edge probabilities between clusters. - * - indicator: Upper-triangular matrix of edge indicators (1 = edge present). - * - node: Index of the node whose contribution is computed. - * - no_variables: Total number of nodes in the network. - * - * Returns: - * - Log-likelihood contribution for the specified node. - */ +// Function: log_likelihood_mfm_sbm +// +// Computes the log-likelihood contribution for a single node under the +// Mixture of Finite Mixtures Stochastic Block Model (MFM-SBM). Evaluates +// the probability of observed edges between the node and all other nodes +// given their cluster assignments and cluster connection probabilities. +// +// Inputs: +// - cluster_assign: Vector of cluster assignments for all nodes. +// - cluster_probs: Matrix of edge probabilities between clusters. +// - indicator: Upper-triangular matrix of edge indicators (1 = edge present). +// - node: Index of the node whose contribution is computed. +// - no_variables: Total number of nodes in the network. +// +// Returns: +// - Log-likelihood contribution for the specified node. double log_likelihood_mfm_sbm(arma::uvec cluster_assign, arma::mat cluster_probs, arma::umat indicator, @@ -103,27 +101,25 @@ double log_likelihood_mfm_sbm(arma::uvec cluster_assign, return output; } -/** - * Function: log_marginal_mfm_sbm - * - * Computes the log-marginal likelihood contribution for a single node under - * the MFM-SBM after integrating out cluster connection probabilities. Uses - * Beta-Bernoulli conjugacy with separate hyperparameters for within-cluster - * and between-cluster edges. - * - * Inputs: - * - cluster_assign: Vector of cluster assignments for all nodes. - * - indicator: Upper-triangular matrix of edge indicators (1 = edge present). - * - node: Index of the node whose contribution is computed. - * - no_variables: Total number of nodes in the network. - * - beta_bernoulli_alpha: Alpha hyperparameter for within-cluster edges. - * - beta_bernoulli_beta: Beta hyperparameter for within-cluster edges. - * - beta_bernoulli_alpha_between: Alpha hyperparameter for between-cluster edges. - * - beta_bernoulli_beta_between: Beta hyperparameter for between-cluster edges. - * - * Returns: - * - Log-marginal likelihood contribution for the specified node. - */ +// Function: log_marginal_mfm_sbm +// +// Computes the log-marginal likelihood contribution for a single node under +// the MFM-SBM after integrating out cluster connection probabilities. Uses +// Beta-Bernoulli conjugacy with separate hyperparameters for within-cluster +// and between-cluster edges. +// +// Inputs: +// - cluster_assign: Vector of cluster assignments for all nodes. +// - indicator: Upper-triangular matrix of edge indicators (1 = edge present). +// - node: Index of the node whose contribution is computed. +// - no_variables: Total number of nodes in the network. +// - beta_bernoulli_alpha: Alpha hyperparameter for within-cluster edges. +// - beta_bernoulli_beta: Beta hyperparameter for within-cluster edges. +// - beta_bernoulli_alpha_between: Alpha hyperparameter for between-cluster edges. +// - beta_bernoulli_beta_between: Beta hyperparameter for between-cluster edges. +// +// Returns: +// - Log-marginal likelihood contribution for the specified node. double log_marginal_mfm_sbm(arma::uvec cluster_assign, arma::umat indicator, arma::uword node, diff --git a/src/sample_mixed.cpp b/src/sample_mixed.cpp index 9cde1c7d..2ff3b721 100644 --- a/src/sample_mixed.cpp +++ b/src/sample_mixed.cpp @@ -1,10 +1,8 @@ -/** - * sample_mixed.cpp - R interface for Mixed MRF model sampling - * - * Uses the unified MCMC runner infrastructure to sample from models with - * both discrete (ordinal / Blume-Capel) and continuous variables. - * Supports MH sampler only, with optional edge selection. - */ +// sample_mixed.cpp - R interface for Mixed MRF model sampling +// +// Uses the unified MCMC runner infrastructure to sample from models with +// both discrete (ordinal / Blume-Capel) and continuous variables. +// Supports MH sampler only, with optional edge selection. #include #include #include @@ -17,36 +15,34 @@ #include "mcmc/execution/chain_runner.h" #include "mcmc/execution/sampler_config.h" -/** - * R-exported function to sample from a Mixed MRF model. - * - * @param inputFromR List with model specification: - * discrete_observations (integer matrix n x p), - * continuous_observations (numeric matrix n x q), - * num_categories (integer vector, length p), - * is_ordinal_variable (integer vector, length p), - * baseline_category (integer vector, length p), - * main_alpha, main_beta, pairwise_scale (doubles), - * pseudolikelihood (string: "conditional" or "marginal") - * @param prior_inclusion_prob Prior inclusion probabilities ((p+q) x (p+q) matrix) - * @param initial_edge_indicators Initial edge indicators ((p+q) x (p+q) integer matrix) - * @param no_iter Number of post-warmup iterations - * @param no_warmup Number of warmup iterations - * @param no_chains Number of parallel chains - * @param edge_selection Whether to do edge selection (spike-and-slab) - * @param seed Random seed - * @param no_threads Number of threads for parallel execution - * @param progress_type Progress bar type - * @param edge_prior Edge prior type - * @param beta_bernoulli_alpha Beta-Bernoulli alpha hyperparameter - * @param beta_bernoulli_beta Beta-Bernoulli beta hyperparameter - * @param beta_bernoulli_alpha_between SBM between-cluster alpha - * @param beta_bernoulli_beta_between SBM between-cluster beta - * @param dirichlet_alpha Dirichlet alpha for SBM - * @param lambda Lambda for SBM - * - * @return List with per-chain results including samples and diagnostics - */ +// R-exported function to sample from a Mixed MRF model. +// +// @param inputFromR List with model specification: +// discrete_observations (integer matrix n x p), +// continuous_observations (numeric matrix n x q), +// num_categories (integer vector, length p), +// is_ordinal_variable (integer vector, length p), +// baseline_category (integer vector, length p), +// main_alpha, main_beta, pairwise_scale (doubles), +// pseudolikelihood (string: "conditional" or "marginal") +// @param prior_inclusion_prob Prior inclusion probabilities ((p+q) x (p+q) matrix) +// @param initial_edge_indicators Initial edge indicators ((p+q) x (p+q) integer matrix) +// @param no_iter Number of post-warmup iterations +// @param no_warmup Number of warmup iterations +// @param no_chains Number of parallel chains +// @param edge_selection Whether to do edge selection (spike-and-slab) +// @param seed Random seed +// @param no_threads Number of threads for parallel execution +// @param progress_type Progress bar type +// @param edge_prior Edge prior type +// @param beta_bernoulli_alpha Beta-Bernoulli alpha hyperparameter +// @param beta_bernoulli_beta Beta-Bernoulli beta hyperparameter +// @param beta_bernoulli_alpha_between SBM between-cluster alpha +// @param beta_bernoulli_beta_between SBM between-cluster beta +// @param dirichlet_alpha Dirichlet alpha for SBM +// @param lambda Lambda for SBM +// +// @return List with per-chain results including samples and diagnostics // [[Rcpp::export]] Rcpp::List sample_mixed_mrf( const Rcpp::List& inputFromR, diff --git a/src/sample_omrf.cpp b/src/sample_omrf.cpp index 14198eb9..f2b96bcc 100644 --- a/src/sample_omrf.cpp +++ b/src/sample_omrf.cpp @@ -1,9 +1,7 @@ -/** - * sample_omrf.cpp - R interface for OMRF model sampling - * - * Uses the unified MCMC runner infrastructure to sample from OMRF models. - * Supports MH, NUTS, and HMC samplers with optional edge selection. - */ +// sample_omrf.cpp - R interface for OMRF model sampling +// +// Uses the unified MCMC runner infrastructure to sample from OMRF models. +// Supports MH, NUTS, and HMC samplers with optional edge selection. #include #include #include @@ -16,35 +14,33 @@ #include "mcmc/execution/chain_runner.h" #include "mcmc/execution/sampler_config.h" -/** - * R-exported function to sample from an OMRF model - * - * @param inputFromR List with model specification - * @param prior_inclusion_prob Prior inclusion probabilities (p x p matrix) - * @param initial_edge_indicators Initial edge indicators (p x p integer matrix) - * @param no_iter Number of post-warmup iterations - * @param no_warmup Number of warmup iterations - * @param no_chains Number of parallel chains - * @param edge_selection Whether to do edge selection (spike-and-slab) - * @param sampler_type "mh", "nuts", or "hmc" - * @param seed Random seed - * @param no_threads Number of threads for parallel execution - * @param progress_type Progress bar type - * @param edge_prior Edge prior type: "Bernoulli", "Beta-Bernoulli", "Stochastic-Block" - * @param na_impute Whether to impute missing data - * @param missing_index Matrix of missing data indices (n_missing x 2, 0-based) - * @param beta_bernoulli_alpha Beta-Bernoulli alpha hyperparameter - * @param beta_bernoulli_beta Beta-Bernoulli beta hyperparameter - * @param beta_bernoulli_alpha_between SBM between-cluster alpha - * @param beta_bernoulli_beta_between SBM between-cluster beta - * @param dirichlet_alpha Dirichlet alpha for SBM - * @param lambda Lambda for SBM - * @param target_acceptance Target acceptance rate for NUTS/HMC (default: 0.8) - * @param max_tree_depth Maximum tree depth for NUTS (default: 10) - * @param num_leapfrogs Number of leapfrog steps for HMC (default: 10) - * - * @return List with per-chain results including samples and diagnostics - */ +// R-exported function to sample from an OMRF model +// +// @param inputFromR List with model specification +// @param prior_inclusion_prob Prior inclusion probabilities (p x p matrix) +// @param initial_edge_indicators Initial edge indicators (p x p integer matrix) +// @param no_iter Number of post-warmup iterations +// @param no_warmup Number of warmup iterations +// @param no_chains Number of parallel chains +// @param edge_selection Whether to do edge selection (spike-and-slab) +// @param sampler_type "mh", "nuts", or "hmc" +// @param seed Random seed +// @param no_threads Number of threads for parallel execution +// @param progress_type Progress bar type +// @param edge_prior Edge prior type: "Bernoulli", "Beta-Bernoulli", "Stochastic-Block" +// @param na_impute Whether to impute missing data +// @param missing_index Matrix of missing data indices (n_missing x 2, 0-based) +// @param beta_bernoulli_alpha Beta-Bernoulli alpha hyperparameter +// @param beta_bernoulli_beta Beta-Bernoulli beta hyperparameter +// @param beta_bernoulli_alpha_between SBM between-cluster alpha +// @param beta_bernoulli_beta_between SBM between-cluster beta +// @param dirichlet_alpha Dirichlet alpha for SBM +// @param lambda Lambda for SBM +// @param target_acceptance Target acceptance rate for NUTS/HMC (default: 0.8) +// @param max_tree_depth Maximum tree depth for NUTS (default: 10) +// @param num_leapfrogs Number of leapfrog steps for HMC (default: 10) +// +// @return List with per-chain results including samples and diagnostics // [[Rcpp::export]] Rcpp::List sample_omrf( const Rcpp::List& inputFromR, diff --git a/src/utils/common_helpers.h b/src/utils/common_helpers.h index 963d28ae..1179eeab 100644 --- a/src/utils/common_helpers.h +++ b/src/utils/common_helpers.h @@ -26,8 +26,19 @@ inline int count_num_main_effects(const arma::ivec& num_categories, return n_params; } -enum UpdateMethod { adaptive_metropolis, hamiltonian_mc, nuts }; +/// MCMC update method. +enum UpdateMethod { + adaptive_metropolis, ///< Robbins-Monro adaptive Metropolis-Hastings + hamiltonian_mc, ///< Fixed-trajectory Hamiltonian Monte Carlo + nuts ///< No-U-Turn Sampler +}; +/** + * Convert a string identifier to an UpdateMethod enum value. + * + * @param update_method One of "adaptive-metropolis", "hamiltonian-mc", "nuts". + * @return Corresponding UpdateMethod value. + */ inline UpdateMethod update_method_from_string(const std::string& update_method) { if (update_method == "adaptive-metropolis") return adaptive_metropolis; @@ -41,8 +52,21 @@ inline UpdateMethod update_method_from_string(const std::string& update_method) throw std::invalid_argument("Invalid update_method: " + update_method); } -enum EdgePrior { Stochastic_Block, Beta_Bernoulli, Bernoulli, Not_Applicable }; +/// Edge inclusion prior type. +enum EdgePrior { + Stochastic_Block, ///< MFM Stochastic Block Model + Beta_Bernoulli, ///< Shared Beta-Bernoulli prior + Bernoulli, ///< Fixed Bernoulli prior + Not_Applicable ///< No edge prior (all edges included) +}; +/** + * Convert a string identifier to an EdgePrior enum value. + * + * @param edge_prior One of "Stochastic-Block", "Beta-Bernoulli", + * "Bernoulli", "Not Applicable". + * @return Corresponding EdgePrior value. + */ inline EdgePrior edge_prior_from_string(const std::string& edge_prior) { if (edge_prior == "Stochastic-Block") return Stochastic_Block; diff --git a/src/utils/print_mutex.h b/src/utils/print_mutex.h index 12f9f21f..b0d67f73 100644 --- a/src/utils/print_mutex.h +++ b/src/utils/print_mutex.h @@ -3,19 +3,22 @@ #include +/** + * Return a process-global mutex for thread-safe console output. + * + * Include this header and lock the returned mutex before printing + * from parallel code: + * @code + * { + * tbb::mutex::scoped_lock lock(get_print_mutex()); + * std::cout << "message" << std::endl; + * } + * @endcode + */ inline tbb::mutex& get_print_mutex() { static tbb::mutex m; return m; } #endif // PRINT_MUTEX_H - -// Add this header to the parallel code you wish to print from -// + the below code to print in parallel code: -// -// { -// tbb::mutex::scoped_lock lock(get_print_mutex()); -// std::cout -// << "print " -// << std::endl; // } \ No newline at end of file diff --git a/src/utils/variable_helpers.h b/src/utils/variable_helpers.h index b915683e..b20390ba 100644 --- a/src/utils/variable_helpers.h +++ b/src/utils/variable_helpers.h @@ -5,58 +5,62 @@ #include "math/explog_macros.h" -// ----------------------------------------------------------------------------- -// Struct to hold both log-normalizer and probabilities from joint computation. -// Used by logp_and_gradient to avoid duplicate probability/denominator calculations. -// ----------------------------------------------------------------------------- +/** + * Holds both log-normalizer and probabilities from joint computation. + * + * Used by logp_and_gradient to avoid duplicate probability/denominator + * calculations. + */ struct LogZAndProbs { - arma::vec log_Z; // log-normalizer for each person - arma::mat probs; // (num_persons x num_cats+1) probability matrix + /// Log-normalizer for each person. + arma::vec log_Z; + /// Probability matrix (num_persons x num_cats+1). + arma::mat probs; }; -// ----------------------------------------------------------------------------- -// Compute a numerically stable sum of the form: -// -// denom = exp(-bound) + sum_{cat=0}^{K-1} exp(main_effect_param(cat) -// + (cat + 1) * residual_score - bound) -// -// but evaluated efficiently using precomputed exponentials: -// -// exp_r = exp(residual_score) -// exp_m = exp(main_effect_param) -// denom = exp(-bound) * ( 1 + sum_c exp_m[c] * exp_r^(c+1) ) -// -// If non-finite values arise (overflow, underflow, NaN), a safe fallback -// recomputes the naive version using direct exponentials. -// ---------------------------------------------------------------------------- +/** + * Compute a numerically stable sum of the form: + * + * denom = exp(-bound) + sum_{cat=0}^{K-1} exp(main_effect_param(cat) + * + (cat + 1) * residual_score - bound) + * + * but evaluated efficiently using precomputed exponentials: + * + * exp_r = exp(residual_score) + * exp_m = exp(main_effect_param) + * denom = exp(-bound) * ( 1 + sum_c exp_m[c] * exp_r^(c+1) ) + * + * If non-finite values arise (overflow, underflow, NaN), a safe fallback + * recomputes the naive version using direct exponentials. + */ arma::vec compute_denom_ordinal( const arma::vec& residual, const arma::vec& main_eff, const arma::vec& bound ); -// ----------------------------------------------------------------------------- -// Compute denom = Σ_c exp( θ(c) + c*r - b ), with -// θ(c) = lin_eff*(c-ref) + quad_eff*(c-ref)^2 -// b = max_c( θ(c) + c*r ) (vectorized) -// -// Two modes: -// -// FAST (preexp + power-chain): -// denom = Σ_c exp_theta[c] * exp(-b) * exp(r)^c -// Used only when all exponent terms are safe: -// |b| ≤ EXP_BOUND, -// underflow_bound ≥ -EXP_BOUND, -// num_cats*r - b ≤ EXP_BOUND. -// This guarantees the recursive pow-chain stays finite. -// -// SAFE (direct evaluation): -// denom = Σ_c exp(θ(c) + c*r - b) -// Used whenever any FAST-condition fails. Slower but always stable. -// -// FAST gives identical results when safe, otherwise SAFE is used. -// ----------------------------------------------------------------------------- +/** + * Compute denom = Sigma_c exp( theta(c) + c*r - b ), with + * theta(c) = lin_eff*(c-ref) + quad_eff*(c-ref)^2 + * b = max_c( theta(c) + c*r ) (vectorized) + * + * Two modes: + * + * FAST (preexp + power-chain): + * denom = Sigma_c exp_theta[c] * exp(-b) * exp(r)^c + * Used only when all exponent terms are safe: + * |b| <= EXP_BOUND, + * underflow_bound >= -EXP_BOUND, + * num_cats*r - b <= EXP_BOUND. + * This guarantees the recursive pow-chain stays finite. + * + * SAFE (direct evaluation): + * denom = Sigma_c exp(theta(c) + c*r - b) + * Used whenever any FAST-condition fails. Slower but always stable. + * + * FAST gives identical results when safe, otherwise SAFE is used. + */ arma::vec compute_denom_blume_capel( const arma::vec& residual, const double lin_eff, @@ -87,25 +91,25 @@ arma::mat compute_probs_ordinal( int num_cats ); -// ----------------------------------------------------------------------------- -// Blume–Capel probabilities, numerically stable via FAST/SAFE split. -// -// Model: -// θ(c) = lin_eff * (c - ref) + quad_eff * (c - ref)^2, c = 0..num_cats -// exps_i(c) = θ(c) + c * r_i -// b_i = max_c exps_i(c) -// -// Probabilities: -// p_i(c) ∝ exp( exps_i(c) - b_i ) -// -// FAST (preexp + power-chain, same bounds as compute_denom_blume_capel): -// used when |b_i| ≤ EXP_BOUND and pow_bound_i = num_cats * r_i - b_i ≤ EXP_BOUND -// -// SAFE (direct): -// used otherwise: direct exp(θ(c) + (c-ref) * r_i - b_i) -// -// Under these conditions, denom is finite and > 0, so no one-hot fallback. -// ----------------------------------------------------------------------------- +/** + * Blume-Capel probabilities, numerically stable via FAST/SAFE split. + * + * Model: + * theta(c) = lin_eff * (c - ref) + quad_eff * (c - ref)^2, c = 0..num_cats + * exps_i(c) = theta(c) + c * r_i + * b_i = max_c exps_i(c) + * + * Probabilities: + * p_i(c) proportional to exp( exps_i(c) - b_i ) + * + * FAST (preexp + power-chain, same bounds as compute_denom_blume_capel): + * used when |b_i| <= EXP_BOUND and pow_bound_i = num_cats * r_i - b_i <= EXP_BOUND + * + * SAFE (direct): + * used otherwise: direct exp(theta(c) + (c-ref) * r_i - b_i) + * + * Under these conditions, denom is finite and > 0, so no one-hot fallback. + */ arma::mat compute_probs_blume_capel( const arma::vec& residual, const double lin_eff, @@ -115,10 +119,11 @@ arma::mat compute_probs_blume_capel( arma::vec& b ); -// ----------------------------------------------------------------------------- -// Joint computation of log-normalizer and probabilities for ordinal variables. -// Avoids redundant computation by computing both in a single pass. -// ----------------------------------------------------------------------------- +/** + * Joint computation of log-normalizer and probabilities for ordinal variables. + * + * Avoids redundant computation by computing both in a single pass. + */ LogZAndProbs compute_logZ_and_probs_ordinal( const arma::vec& main_param, const arma::vec& residual_score, @@ -126,10 +131,11 @@ LogZAndProbs compute_logZ_and_probs_ordinal( int num_cats ); -// ----------------------------------------------------------------------------- -// Joint computation of log-normalizer and probabilities for Blume-Capel variables. -// Avoids redundant computation by computing both in a single pass. -// ----------------------------------------------------------------------------- +/** + * Joint computation of log-normalizer and probabilities for Blume-Capel variables. + * + * Avoids redundant computation by computing both in a single pass. + */ LogZAndProbs compute_logZ_and_probs_blume_capel( const arma::vec& residual, const double lin_eff, diff --git a/tests/testthat/helper-fixtures.R b/tests/testthat/helper-fixtures.R index 0dbf6a21..1c962dae 100644 --- a/tests/testthat/helper-fixtures.R +++ b/tests/testthat/helper-fixtures.R @@ -60,7 +60,8 @@ options(bgms.verbose = FALSE) .test_cache = new.env(parent = emptyenv()) -#' @description Get cached bgms fit (4 binary variables, edge selection, 2 chains) +#' @description Get cached bgms fit +#' (4 binary variables, edge selection, 2 chains) get_bgms_fit = function() { if(is.null(.test_cache$bgms_fit)) { data("ADHD", package = "bgms") @@ -74,7 +75,8 @@ get_bgms_fit = function() { .test_cache$bgms_fit } -#' @description Get cached bgms fit (4 ordinal variables, edge selection, 2 chains) +#' @description Get cached bgms fit +#' (4 ordinal variables, edge selection, 2 chains) get_bgms_fit_ordinal = function() { if(is.null(.test_cache$bgms_fit_ordinal)) { data("Wenchuan", package = "bgms") @@ -88,7 +90,8 @@ get_bgms_fit_ordinal = function() { .test_cache$bgms_fit_ordinal } -#' @description Get cached bgmCompare fit (4 binary variables, 2 groups, 2 chains) +#' @description Get cached bgmCompare fit +#' (4 binary variables, 2 groups, 2 chains) get_bgmcompare_fit = function() { if(is.null(.test_cache$bgmcompare_fit)) { data("ADHD", package = "bgms") @@ -103,7 +106,8 @@ get_bgmcompare_fit = function() { .test_cache$bgmcompare_fit } -#' @description Get cached bgmCompare fit using x,y interface (4 ordinal variables, 2 chains) +#' @description Get cached bgmCompare fit +#' using x,y interface (4 ordinal variables, 2 chains) get_bgmcompare_fit_xy = function() { if(is.null(.test_cache$bgmcompare_fit_xy)) { data("Wenchuan", package = "bgms") @@ -119,7 +123,8 @@ get_bgmcompare_fit_xy = function() { .test_cache$bgmcompare_fit_xy } -#' @description Get cached bgmCompare fit (4 ordinal variables, 2 groups, 2 chains) +#' @description Get cached bgmCompare fit +#' (4 ordinal variables, 2 groups, 2 chains) get_bgmcompare_fit_ordinal = function() { if(is.null(.test_cache$bgmcompare_fit_ordinal)) { data("Wenchuan", package = "bgms") @@ -151,7 +156,8 @@ get_bgms_fit_blumecapel = function() { .test_cache$bgms_fit_blumecapel } -#' @description Get cached bgms fit with single chain (for R-hat edge case testing) +#' @description Get cached bgms fit with +#' single chain (for R-hat edge case testing) get_bgms_fit_single_chain = function() { if(is.null(.test_cache$bgms_fit_single)) { data("ADHD", package = "bgms") @@ -234,7 +240,9 @@ get_bgmcompare_fit_hmc_blumecapel = function() { .test_cache$bgmcompare_fit_hmc_bc } -#' @description Get cached bgmCompare fit with main_difference_selection = TRUE + Blume-Capel (1 chain) +#' @description Get cached bgmCompare fit with +#' main_difference_selection = TRUE + +#' Blume-Capel (1 chain) #' Crosses Blume-Capel with difference_selection (Bernoulli prior) get_bgmcompare_fit_main_selection = function() { if(is.null(.test_cache$bgmcompare_fit_main_sel)) { @@ -257,7 +265,9 @@ get_bgmcompare_fit_main_selection = function() { .test_cache$bgmcompare_fit_main_sel } -#' @description Get cached bgmCompare fit with Beta-Bernoulli difference prior + ordinal (1 chain) +#' @description Get cached bgmCompare fit with +#' Beta-Bernoulli difference prior + +#' ordinal (1 chain) #' Crosses Beta-Bernoulli prior with ordinal variables get_bgmcompare_fit_beta_bernoulli = function() { if(is.null(.test_cache$bgmcompare_fit_bb)) { @@ -296,7 +306,8 @@ get_bgms_fit_beta_bernoulli = function() { .test_cache$bgms_fit_bb } -#' @description Get cached bgms fit with Stochastic-Block Model edge prior (2 chains) +#' @description Get cached bgms fit with +#' Stochastic-Block Model edge prior (2 chains) get_bgms_fit_sbm = function() { if(is.null(.test_cache$bgms_fit_sbm)) { data("ADHD", package = "bgms") @@ -329,7 +340,8 @@ get_bgms_fit_hmc = function() { .test_cache$bgms_fit_hmc } -#' @description Get cached bgms fit with adaptive-metropolis + Blume-Capel (1 chain) +#' @description Get cached bgms fit with +#' adaptive-metropolis + Blume-Capel (1 chain) get_bgms_fit_am_blumecapel = function() { if(is.null(.test_cache$bgms_fit_am_bc)) { data("Wenchuan", package = "bgms") @@ -400,7 +412,9 @@ get_bgmcompare_fit_blumecapel = function() { .test_cache$bgmcompare_fit_bc } -#' @description Get cached bgmCompare fit with adaptive-metropolis + Blume-Capel (1 chain) +#' @description Get cached bgmCompare fit +#' with adaptive-metropolis + Blume-Capel +#' (1 chain) get_bgmcompare_fit_am_blumecapel = function() { if(is.null(.test_cache$bgmcompare_fit_am_bc)) { data("Boredom", package = "bgms") @@ -441,7 +455,9 @@ get_bgmcompare_fit_impute = function() { .test_cache$bgmcompare_fit_impute } -#' @description Get cached bgmCompare fit with Blume-Capel + missing data imputation (1 chain) +#' @description Get cached bgmCompare fit +#' with Blume-Capel + missing data +#' imputation (1 chain) get_bgmcompare_fit_blumecapel_impute = function() { if(is.null(.test_cache$bgmcompare_fit_bc_impute)) { data("Boredom", package = "bgms") @@ -483,7 +499,9 @@ get_bgmcompare_fit_standardize = function() { .test_cache$bgmcompare_fit_std } -#' @description Get cached bgms fit for GGM with edge selection (4 continuous variables, 1 chain) +#' @description Get cached bgms fit for GGM +#' with edge selection +#' (4 continuous variables, 1 chain) get_bgms_fit_ggm = function() { if(is.null(.test_cache$bgms_fit_ggm)) { set.seed(42) @@ -501,7 +519,9 @@ get_bgms_fit_ggm = function() { .test_cache$bgms_fit_ggm } -#' @description Get cached bgms fit for GGM without edge selection (4 continuous variables, 1 chain) +#' @description Get cached bgms fit for GGM +#' without edge selection +#' (4 continuous variables, 1 chain) get_bgms_fit_ggm_no_es = function() { if(is.null(.test_cache$bgms_fit_ggm_no_es)) { set.seed(42) @@ -533,8 +553,10 @@ get_bgms_fit_mixed_mrf = function() { colnames(x) = c("d1", "c1", "d2", "c2", "d3") .test_cache$bgms_fit_mixed_mrf = bgm( x = x, - variable_type = c("ordinal", "continuous", "ordinal", - "continuous", "ordinal"), + variable_type = c( + "ordinal", "continuous", "ordinal", + "continuous", "ordinal" + ), edge_selection = TRUE, iter = 50, warmup = 100, chains = 1, seed = 77771, @@ -558,8 +580,10 @@ get_bgms_fit_mixed_mrf_no_es = function() { colnames(x) = c("d1", "c1", "d2", "c2", "d3") .test_cache$bgms_fit_mixed_mrf_no_es = bgm( x = x, - variable_type = c("ordinal", "continuous", "ordinal", - "continuous", "ordinal"), + variable_type = c( + "ordinal", "continuous", "ordinal", + "continuous", "ordinal" + ), edge_selection = FALSE, iter = 50, warmup = 100, chains = 1, seed = 77772, @@ -583,8 +607,10 @@ get_bgms_fit_mixed_mrf_marginal = function() { colnames(x) = c("d1", "c1", "d2", "c2", "d3") .test_cache$bgms_fit_mixed_mrf_marginal = bgm( x = x, - variable_type = c("ordinal", "continuous", "ordinal", - "continuous", "ordinal"), + variable_type = c( + "ordinal", "continuous", "ordinal", + "continuous", "ordinal" + ), edge_selection = FALSE, pseudolikelihood = "marginal", iter = 50, warmup = 100, chains = 1, @@ -716,7 +742,8 @@ upper_vals = function(M) { M[upper.tri(M)] } -#' Check that named summary entries match matrix positions (ordering consistency) +#' Check that named summary entries match +#' matrix positions (ordering consistency) #' #' For each row of summary_df (named "Vi-Vj"), verify that summary_df$mean[k] #' equals matrix_val[Vi, Vj]. Returns a logical vector (TRUE = match). @@ -725,15 +752,20 @@ check_summary_matrix_consistency = function(summary_df, matrix_val) { matches = logical(nrow(summary_df)) for(k in seq_len(nrow(summary_df))) { parts = strsplit(rownames(summary_df)[k], "-")[[1]] - matches[k] = abs(summary_df$mean[k] - matrix_val[parts[1], parts[2]]) < 1e-10 + matches[k] = abs( + summary_df$mean[k] - + matrix_val[parts[1], parts[2]] + ) < 1e-10 } matches } -#' Check that extractor column means match matrix positions (ordering consistency) +#' Check that extractor column means match +#' matrix positions (ordering consistency) #' #' For each named element of extracted_means (named "Vi-Vj"), verify that -#' the value matches matrix_val[Vi, Vj]. Returns a logical vector (TRUE = match). +#' the value matches matrix_val[Vi, Vj]. +#' Returns a logical vector (TRUE = match). check_extractor_matrix_consistency = function(extracted_means, matrix_val) { matches = logical(length(extracted_means)) for(k in seq_along(extracted_means)) { @@ -760,7 +792,10 @@ expect_extractor_structure = function(obj, type, expected_dim = NULL, # Type check expect_true( inherits(obj, type), - info = sprintf("Expected class %s, got %s", type, paste(class(obj), collapse = ", ")) + info = sprintf( + "Expected class %s, got %s", + type, paste(class(obj), collapse = ", ") + ) ) # Dimension check diff --git a/tests/testthat/test-bgm.R b/tests/testthat/test-bgm.R index 58a944ef..73fdb345 100644 --- a/tests/testthat/test-bgm.R +++ b/tests/testthat/test-bgm.R @@ -202,8 +202,13 @@ test_that("bgm GGM output has correct parameter ordering", { # Extractor column means -> matrix positions pw_means = colMeans(extract_pairwise_interactions(fit)) expect_true( - all(check_extractor_matrix_consistency(pw_means, fit$posterior_mean_pairwise)), - info = "GGM extract_pairwise_interactions() names do not match matrix positions" + all(check_extractor_matrix_consistency( + pw_means, fit$posterior_mean_pairwise + )), + info = paste( + "GGM extract_pairwise_interactions()", + "names do not match matrix positions" + ) ) # Truth-based swap-position checks: @@ -266,8 +271,13 @@ test_that("bgm OMRF output has correct parameter ordering", { # Extractor column means -> matrix positions pw_means = colMeans(extract_pairwise_interactions(fit)) expect_true( - all(check_extractor_matrix_consistency(pw_means, fit$posterior_mean_pairwise)), - info = "OMRF extract_pairwise_interactions() names do not match matrix positions" + all(check_extractor_matrix_consistency( + pw_means, fit$posterior_mean_pairwise + )), + info = paste( + "OMRF extract_pairwise_interactions()", + "names do not match matrix positions" + ) ) # Indicator summary names -> matrix positions @@ -373,7 +383,10 @@ test_that("bgm GGM posterior mean approaches MLE for large n", { expect_true( cor_offdiag > 0.95, - info = sprintf("Off-diagonal cor with MLE = %.3f (expected > 0.95)", cor_offdiag) + info = sprintf( + "Off-diagonal cor with MLE = %.3f (expected > 0.95)", + cor_offdiag + ) ) expect_true( cor_diag > 0.95, @@ -718,8 +731,10 @@ test_that("bgm mixed MRF is reproducible", { fit2 = bgm( x = x, - variable_type = c("ordinal", "continuous", "ordinal", - "continuous", "ordinal"), + variable_type = c( + "ordinal", "continuous", "ordinal", + "continuous", "ordinal" + ), edge_selection = TRUE, iter = 50, warmup = 100, chains = 1, seed = 77771, @@ -733,9 +748,9 @@ test_that("bgm mixed MRF is reproducible", { test_that("bgm mixed MRF output has correct dimensions", { fit = get_bgms_fit_mixed_mrf() args = extract_arguments(fit) - p_total = args$num_variables # 5 - p = 3L # discrete - q = 2L # continuous + p_total = args$num_variables # 5 + p = 3L # discrete + q = 2L # continuous # pairwise: p_total*(p_total-1)/2 edges n_edges = p_total * (p_total - 1) / 2 @@ -752,7 +767,7 @@ test_that("bgm mixed MRF output has correct dimensions", { expect_true(is.list(fit$posterior_mean_main)) expect_equal(nrow(fit$posterior_mean_main$discrete), p) expect_equal(nrow(fit$posterior_mean_main$continuous), q) - expect_equal(ncol(fit$posterior_mean_main$continuous), 2) # mean + precision + expect_equal(ncol(fit$posterior_mean_main$continuous), 2) # mean + precision # raw samples expect_equal(ncol(fit$raw_samples$pairwise[[1]]), n_edges) diff --git a/tests/testthat/test-mixed-mrf-simulate-predict.R b/tests/testthat/test-mixed-mrf-simulate-predict.R index 6f99ca8d..f501abba 100644 --- a/tests/testthat/test-mixed-mrf-simulate-predict.R +++ b/tests/testthat/test-mixed-mrf-simulate-predict.R @@ -22,7 +22,9 @@ # ============================================================================== test_that("sample_mixed_mrf_gibbs returns correct dimensions", { - p = 2; q = 2; n = 100 + p = 2 + q = 2 + n = 100 nc = c(2L, 2L) mux = matrix(c(0, 0.5, -0.3, 0, -0.2, 0.1), nrow = p, ncol = 3, byrow = TRUE) Kxx = matrix(c(0, 0.3, 0.3, 0), p, p) @@ -43,8 +45,10 @@ test_that("sample_mixed_mrf_gibbs returns correct dimensions", { }) test_that("sample_mixed_mrf_gibbs: discrete values in valid range", { - p = 3; q = 1; n = 500 - nc = c(2L, 3L, 1L) # categories 0-2, 0-3, 0-1 (binary) + p = 3 + q = 1 + n = 500 + nc = c(2L, 3L, 1L) # categories 0-2, 0-3, 0-1 (binary) mux = matrix(0, p, 4) Kxx = matrix(0, p, p) muy = 0 @@ -70,7 +74,9 @@ test_that("sample_mixed_mrf_gibbs: discrete values in valid range", { test_that("sample_mixed_mrf_gibbs: continuous marginal SD matches precision", { # Independent model: Kxx = 0, Kxy = 0, so y ~ N(muy, Kyy^{-1}) - p = 1; q = 2; n = 2000 + p = 1 + q = 2 + n = 2000 nc = c(2L) mux = matrix(0, p, 3) Kxx = matrix(0) @@ -92,17 +98,24 @@ test_that("sample_mixed_mrf_gibbs: continuous marginal SD matches precision", { # Loose check: within 30% of expected expect_true( abs(empirical_sd - expected_sd) / expected_sd < 0.3, - info = sprintf("y%d SD: expected %.3f, got %.3f", j, expected_sd, empirical_sd) + info = sprintf( + "y%d SD: expected %.3f, got %.3f", + j, expected_sd, empirical_sd + ) ) } }) test_that("sample_mixed_mrf_gibbs: seed reproducibility", { - p = 2; q = 1; n = 50 + p = 2 + q = 1 + n = 50 nc = c(2L, 2L) mux = matrix(c(0, 0.5, -0.3, 0, -0.2, 0.1), nrow = p, ncol = 3, byrow = TRUE) Kxx = matrix(c(0, 0.3, 0.3, 0), p, p) - muy = 0; Kyy = matrix(1); Kxy = matrix(c(0.1, 0.2), p, q) + muy = 0 + Kyy = matrix(1) + Kxy = matrix(c(0.1, 0.2), p, q) args = list( num_states = n, Kxx_r = Kxx, Kxy_r = Kxy, Kyy_r = Kyy, @@ -119,16 +132,20 @@ test_that("sample_mixed_mrf_gibbs: seed reproducibility", { }) test_that("sample_mixed_mrf_gibbs: Blume-Capel variable works", { - p = 2; q = 1; n = 200 + p = 2 + q = 1 + n = 200 nc = c(2L, 4L) # ordinal: mux has num_categories entries; BC: 2 entries (alpha, beta) max_cols = max(nc[1], 2) mux = matrix(0, p, max_cols) - mux[2, 1] = 0.5 # alpha - mux[2, 2] = -0.3 # beta (penalizes distance from reference) + mux[2, 1] = 0.5 # alpha + mux[2, 2] = -0.3 # beta (penalizes distance from reference) Kxx = matrix(c(0, 0.2, 0.2, 0), p, p) - muy = 0; Kyy = matrix(1.5); Kxy = matrix(c(0.1, -0.1), p, q) + muy = 0 + Kyy = matrix(1.5) + Kxy = matrix(c(0.1, -0.1), p, q) result = sample_mixed_mrf_gibbs( num_states = n, Kxx_r = Kxx, Kxy_r = Kxy, Kyy_r = Kyy, @@ -148,10 +165,13 @@ test_that("sample_mixed_mrf_gibbs: Blume-Capel variable works", { # ============================================================================== test_that("run_mixed_simulation_parallel returns correct structure", { - p = 2L; q = 1L; ndraws_total = 3L + p = 2L + q = 1L + ndraws_total = 3L nc = c(2L, 2L) mux_s = matrix(rep(c(0, 0.5, -0.3, 0, -0.2, 0.1), each = ndraws_total), - nrow = ndraws_total) + nrow = ndraws_total + ) kxx_s = matrix(0.3, nrow = ndraws_total, ncol = 1) muy_s = matrix(0, nrow = ndraws_total, ncol = 1) kyy_s = matrix(1.5, nrow = ndraws_total, ncol = 1) @@ -185,14 +205,19 @@ test_that("run_mixed_simulation_parallel returns correct structure", { # ============================================================================== test_that("compute_conditional_mixed: discrete probs sum to 1", { - p = 2; q = 1; n = 5 + p = 2 + q = 1 + n = 5 nc = c(2L, 2L) mux = matrix(c(0, 0.5, -0.3, 0, -0.2, 0.1), nrow = p, ncol = 3, byrow = TRUE) Kxx = matrix(c(0, 0.3, 0.3, 0), p, p) - muy = 0; Kyy = matrix(1.5); Kxy = matrix(c(0.1, 0.2), p, q) + muy = 0 + Kyy = matrix(1.5) + Kxy = matrix(c(0.1, 0.2), p, q) x_obs = matrix(c(0L, 1L, 2L, 0L, 1L, 1L, 2L, 0L, 1L, 2L), - nrow = n, ncol = p) + nrow = n, ncol = p + ) y_obs = matrix(rnorm(n), nrow = n, ncol = q) # Predict first discrete variable (0-based index 0) @@ -206,7 +231,7 @@ test_that("compute_conditional_mixed: discrete probs sum to 1", { expect_equal(length(preds), 1) expect_equal(nrow(preds[[1]]), n) - expect_equal(ncol(preds[[1]]), nc[1] + 1) # 3 categories + expect_equal(ncol(preds[[1]]), nc[1] + 1) # 3 categories row_sums = rowSums(preds[[1]]) expect_true(all(abs(row_sums - 1) < 1e-8)) @@ -214,10 +239,13 @@ test_that("compute_conditional_mixed: discrete probs sum to 1", { }) test_that("compute_conditional_mixed: continuous returns mean and sd", { - p = 1; q = 2; n = 5 + p = 1 + q = 2 + n = 5 nc = c(2L) mux = matrix(c(0, 0.5, -0.3), nrow = 1, ncol = 3) - Kxx = matrix(0); muy = c(1.0, -0.5) + Kxx = matrix(0) + muy = c(1.0, -0.5) Kyy = matrix(c(2, 0.3, 0.3, 1.5), q, q) Kxy = matrix(c(0.1, -0.1), 1, q) @@ -234,16 +262,19 @@ test_that("compute_conditional_mixed: continuous returns mean and sd", { expect_equal(length(preds), 1) expect_equal(nrow(preds[[1]]), n) - expect_equal(ncol(preds[[1]]), 2) # mean, sd - expect_true(all(preds[[1]][, 2] > 0)) # sd > 0 + expect_equal(ncol(preds[[1]]), 2) # mean, sd + expect_true(all(preds[[1]][, 2] > 0)) # sd > 0 }) test_that("compute_conditional_mixed: mixed prediction variables", { - p = 2; q = 2; n = 5 + p = 2 + q = 2 + n = 5 nc = c(2L, 2L) mux = matrix(0, p, 3) Kxx = matrix(c(0, 0.3, 0.3, 0), p, p) - muy = c(0.5, -0.2); Kyy = diag(c(1.5, 1.8)) + muy = c(0.5, -0.2) + Kyy = diag(c(1.5, 1.8)) Kxy = matrix(0.1, p, q) x_obs = matrix(sample(0:2, n * p, replace = TRUE), n, p) @@ -288,7 +319,10 @@ test_that("simulate.bgms works for mixed MRF with posterior-mean", { for(di in args$discrete_indices) { vals = result[, di] expect_true(all(vals >= 0), info = sprintf("col %d negative", di)) - expect_true(all(vals == round(vals)), info = sprintf("col %d not integer", di)) + expect_true( + all(vals == round(vals)), + info = sprintf("col %d not integer", di) + ) } }) diff --git a/tests/testthat/test-simulate-predict-regression.R b/tests/testthat/test-simulate-predict-regression.R index f441bbd9..0e90fd7f 100644 --- a/tests/testthat/test-simulate-predict-regression.R +++ b/tests/testthat/test-simulate-predict-regression.R @@ -103,7 +103,8 @@ get_bgmcompare_fixtures = function() { var_type = "ordinal" ), list( - label = "adaptive-metropolis", get_fit = get_bgmcompare_fit_adaptive_metropolis, + label = "adaptive-metropolis", + get_fit = get_bgmcompare_fit_adaptive_metropolis, get_prediction_data = get_prediction_data_bgmcompare_binary, var_type = "binary" ), @@ -133,7 +134,8 @@ get_bgmcompare_fixtures = function() { var_type = "ordinal" ), list( - label = "blume-capel-impute", get_fit = get_bgmcompare_fit_blumecapel_impute, + label = "blume-capel-impute", + get_fit = get_bgmcompare_fit_blumecapel_impute, get_prediction_data = get_prediction_data_bgmcompare_blumecapel, var_type = "blume-capel" ), @@ -205,9 +207,11 @@ test_that("bgms $arguments contains all fields needed by simulate/predict", { info = sprintf("%s: missing arguments$%s", ctx, field) ) } - for(field in c("is_mixed", "discrete_indices", "continuous_indices", - "num_discrete", "num_continuous", "is_ordinal", - "data_columnnames_discrete", "data_columnnames_continuous")) { + for(field in c( + "is_mixed", "discrete_indices", "continuous_indices", + "num_discrete", "num_continuous", "is_ordinal", + "data_columnnames_discrete", "data_columnnames_continuous" + )) { expect_true( field %in% names(args), info = sprintf("%s: missing mixed arguments$%s", ctx, field) @@ -228,7 +232,10 @@ test_that("bgms $arguments contains all fields needed by simulate/predict", { } }) -test_that("bgmCompare $arguments contains all fields needed by simulate/predict", { +test_that(paste( + "bgmCompare $arguments contains all fields", + "needed by simulate/predict" +), { for(spec in get_bgmcompare_fixtures()) { ctx = sprintf("[bgmCompare %s]", spec$label) fit = spec$get_fit() @@ -318,7 +325,8 @@ test_that("bgms fit objects have raw_samples for posterior-sample method", { # ============================================================================== # For each golden fixture, construct a bgm_spec from the frozen inputs and # verify that build_arguments() produces the same simulate/predict-critical -# values (num_categories, baseline_category, variable_type / is_ordinal_variable, +# values (num_categories, baseline_category, +# variable_type / is_ordinal_variable, # is_continuous) as the old pipeline's check_model + reformat_data output. # # This is fast: bgm_spec() + build_arguments() does no MCMC. @@ -501,14 +509,22 @@ test_that("simulate → predict roundtrip works for all bgms fixtures", { expect_true(is.matrix(simulated), info = paste(ctx, "simulate")) expect_equal(nrow(simulated), n_sim, info = paste(ctx, "nrow")) expect_equal(ncol(simulated), args$num_variables, info = paste(ctx, "ncol")) - expect_equal(colnames(simulated), args$data_columnnames, info = paste(ctx, "colnames")) + expect_equal( + colnames(simulated), args$data_columnnames, + info = paste(ctx, "colnames") + ) if(isTRUE(args$is_continuous)) { # GGM: predict returns list of mean/sd matrices colnames(simulated) = args$data_columnnames pred = predict(fit, newdata = simulated) - expect_true(is.list(pred), info = paste(ctx, "predict type")) - expect_equal(length(pred), args$num_variables, info = paste(ctx, "predict length")) + expect_true(is.list(pred), + info = paste(ctx, "predict type") + ) + expect_equal( + length(pred), args$num_variables, + info = paste(ctx, "predict length") + ) for(j in seq_along(pred)) { expect_equal(nrow(pred[[j]]), n_sim, info = sprintf("%s predict var %d nrow", ctx, j) @@ -518,10 +534,20 @@ test_that("simulate → predict roundtrip works for all bgms fixtures", { ) } } else if(isTRUE(spec$is_mixed)) { - # Mixed MRF: predict returns list with discrete (probs) and continuous (mean/sd) - probs = predict(fit, newdata = simulated, type = "probabilities") - expect_true(is.list(probs), info = paste(ctx, "predict type")) - expect_equal(length(probs), args$num_variables, info = paste(ctx, "predict length")) + # Mixed MRF: predict returns list with + # discrete (probs) and continuous (mean/sd) + probs = predict( + fit, + newdata = simulated, + type = "probabilities" + ) + expect_true(is.list(probs), + info = paste(ctx, "predict type") + ) + expect_equal( + length(probs), args$num_variables, + info = paste(ctx, "predict length") + ) for(j in seq_len(args$num_variables)) { vname = args$data_columnnames[j] @@ -555,9 +581,18 @@ test_that("simulate → predict roundtrip works for all bgms fixtures", { ) } else { # OMRF: predict returns list of probability matrices - probs = predict(fit, newdata = simulated, type = "probabilities") - expect_true(is.list(probs), info = paste(ctx, "predict type")) - expect_equal(length(probs), args$num_variables, info = paste(ctx, "predict length")) + probs = predict( + fit, + newdata = simulated, + type = "probabilities" + ) + expect_true(is.list(probs), + info = paste(ctx, "predict type") + ) + expect_equal( + length(probs), args$num_variables, + info = paste(ctx, "predict length") + ) for(j in seq_along(probs)) { expect_equal(nrow(probs[[j]]), n_sim, info = sprintf("%s predict var %d nrow", ctx, j) @@ -605,7 +640,10 @@ test_that("simulate → predict roundtrip works for all bgmCompare fixtures", { expect_true(is.matrix(simulated), info = paste(g_ctx, "simulate")) expect_equal(nrow(simulated), n_sim, info = paste(g_ctx, "nrow")) - expect_equal(ncol(simulated), args$num_variables, info = paste(g_ctx, "ncol")) + expect_equal( + ncol(simulated), args$num_variables, + info = paste(g_ctx, "ncol") + ) expect_equal(colnames(simulated), args$data_columnnames, info = paste(g_ctx, "colnames") ) @@ -619,7 +657,11 @@ test_that("simulate → predict roundtrip works for all bgmCompare fixtures", { ) # Predict - probs = predict(fit, newdata = simulated, group = g, type = "probabilities") + probs = predict( + fit, + newdata = simulated, + group = g, type = "probabilities" + ) expect_true(is.list(probs), info = paste(g_ctx, "predict type")) expect_equal(length(probs), args$num_variables, info = paste(g_ctx, "predict length") @@ -727,7 +769,9 @@ test_that("bgms $arguments field types are correct for simulate/predict", { args = extract_arguments(fit) p = args$num_variables - expect_true(is.numeric(args$num_variables) && length(args$num_variables) == 1, + expect_true( + is.numeric(args$num_variables) && + length(args$num_variables) == 1, info = paste(ctx, "num_variables") ) expect_true(args$num_variables >= 1, @@ -737,23 +781,31 @@ test_that("bgms $arguments field types are correct for simulate/predict", { expect_true(is.character(args$variable_type), info = paste(ctx, "variable_type character") ) - expect_true(all(args$variable_type %in% c("ordinal", "blume-capel", "continuous")), + expect_true( + all(args$variable_type %in% + c("ordinal", "blume-capel", "continuous")), info = paste(ctx, "variable_type values") ) - expect_true(is.character(args$data_columnnames) && length(args$data_columnnames) == p, + expect_true( + is.character(args$data_columnnames) && + length(args$data_columnnames) == p, info = paste(ctx, "data_columnnames length") ) if(!isTRUE(spec$is_continuous) && !isTRUE(spec$is_mixed)) { # OMRF-only fields - expect_true(is.numeric(args$num_categories) && length(args$num_categories) == p, + expect_true( + is.numeric(args$num_categories) && + length(args$num_categories) == p, info = paste(ctx, "num_categories length") ) expect_true(all(args$num_categories >= 1), info = paste(ctx, "num_categories >= 1") ) - expect_true(is.numeric(args$baseline_category) && length(args$baseline_category) == p, + expect_true( + is.numeric(args$baseline_category) && + length(args$baseline_category) == p, info = paste(ctx, "baseline_category length") ) } @@ -761,28 +813,46 @@ test_that("bgms $arguments field types are correct for simulate/predict", { if(isTRUE(spec$is_mixed)) { pd = args$num_discrete qc = args$num_continuous - expect_equal(pd + qc, p, info = paste(ctx, "num_discrete + num_continuous == p")) + expect_equal( + pd + qc, p, + info = paste( + ctx, "num_discrete + num_continuous == p" + ) + ) expect_true( - is.numeric(args$num_categories) && length(args$num_categories) == pd, - info = paste(ctx, "mixed num_categories length == num_discrete") + is.numeric(args$num_categories) && + length(args$num_categories) == pd, + info = paste( + ctx, + "mixed num_categories length == num_discrete" + ) ) expect_true( - is.numeric(args$baseline_category) && length(args$baseline_category) == pd, - info = paste(ctx, "mixed baseline_category length == num_discrete") + is.numeric(args$baseline_category) && + length(args$baseline_category) == pd, + info = paste( + ctx, + "mixed baseline_category length == num_discrete" + ) ) expect_true( - is.numeric(args$discrete_indices) && length(args$discrete_indices) == pd, + is.numeric(args$discrete_indices) && + length(args$discrete_indices) == pd, info = paste(ctx, "discrete_indices length") ) expect_true( - is.numeric(args$continuous_indices) && length(args$continuous_indices) == qc, + is.numeric(args$continuous_indices) && + length(args$continuous_indices) == qc, info = paste(ctx, "continuous_indices length") ) } } }) -test_that("bgmCompare $arguments field types are correct for simulate/predict", { +test_that(paste( + "bgmCompare $arguments field types", + "are correct for simulate/predict" +), { for(spec in get_bgmcompare_fixtures()) { ctx = sprintf("[bgmCompare %s]", spec$label) fit = spec$get_fit() @@ -797,15 +867,21 @@ test_that("bgmCompare $arguments field types are correct for simulate/predict", info = paste(ctx, "num_variables") ) - expect_true(is.numeric(args$num_categories) && length(args$num_categories) == p, + expect_true( + is.numeric(args$num_categories) && + length(args$num_categories) == p, info = paste(ctx, "num_categories length") ) - expect_true(is.logical(args$is_ordinal_variable) && length(args$is_ordinal_variable) == p, + expect_true( + is.logical(args$is_ordinal_variable) && + length(args$is_ordinal_variable) == p, info = paste(ctx, "is_ordinal_variable") ) - expect_true(is.character(args$data_columnnames) && length(args$data_columnnames) == p, + expect_true( + is.character(args$data_columnnames) && + length(args$data_columnnames) == p, info = paste(ctx, "data_columnnames") ) From 5e28c30b083767c44db6d9a77c00b4519a65051c Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Sat, 7 Mar 2026 13:41:31 +0100 Subject: [PATCH 19/60] docs: update package documentation for mixed MRF support (audit #9) --- DESCRIPTION | 7 ++++--- NEWS.md | 1 + R/bgm.R | 7 +++++-- R/bgms-package.R | 20 ++++++++++++++------ README.md | 18 ++++++++++-------- Readme.Rmd | 12 +++++++----- man/bgm.Rd | 7 +++++-- man/bgms-package.Rd | 20 ++++++++++++++------ man/mrfSampler.Rd | 24 +++++++++++++++--------- man/predict.bgmCompare.Rd | 13 ++++++++----- man/predict.bgms.Rd | 24 ++++++++++++++++++------ man/simulate.bgms.Rd | 21 ++++++++++++++++----- man/simulate_mrf.Rd | 36 ++++++++++++++++++++++-------------- 13 files changed, 139 insertions(+), 71 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index cdb269eb..886efc3d 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -17,9 +17,10 @@ Authors@R: c( ) Maintainer: Maarten Marsman Description: Bayesian estimation and variable selection for Markov random field - models of networks of binary, ordinal, and continuous variables. Supports - Gaussian graphical models, multi-group comparison via 'bgmCompare', and - provides simulation, prediction, and missing data imputation. + models of networks of binary, ordinal, continuous, and mixed variables. + Supports ordinal MRFs, Gaussian graphical models, mixed MRFs combining + discrete and continuous variables, multi-group comparison via 'bgmCompare', + and provides simulation, prediction, and missing data imputation. Copyright: Includes datasets 'ADHD' and 'Boredom', which are licensed under CC-BY 4. See individual data documentation for license and citation. License: GPL (>= 2) URL: https://Bayesian-Graphical-Modelling-Lab.github.io/bgms/, https://github.com/Bayesian-Graphical-Modelling-Lab/bgms diff --git a/NEWS.md b/NEWS.md index b47bafc0..f11e4d76 100644 --- a/NEWS.md +++ b/NEWS.md @@ -2,6 +2,7 @@ ## New features +* Mixed MRF models: `bgm()` accepts a per-variable `variable_type` vector that mixes `"ordinal"`, `"blume-capel"`, and `"continuous"` types to estimate networks with both discrete and continuous variables. `simulate.bgms()` and `predict.bgms()` also support mixed models. * Gaussian graphical models (GGM): `bgm(x, variable_type = "continuous")` fits a GGM with Bayesian edge selection. Pairwise effects are partial correlations from the precision matrix. * Missing data imputation: `na_action = "impute"` integrates over missing values during MCMC sampling for both ordinal and continuous models. diff --git a/R/bgm.R b/R/bgm.R index ebbd5f1f..81323631 100644 --- a/R/bgm.R +++ b/R/bgm.R @@ -172,8 +172,11 @@ #' Blume–Capel variables, all categories are retained. #' #' @param variable_type Character or character vector. Specifies the type of -#' each variable in \code{x}. Allowed values: \code{"ordinal"} or -#' \code{"blume-capel"}. Binary variables are automatically treated as +#' each variable in \code{x}. Allowed values: \code{"ordinal"}, +#' \code{"blume-capel"}, or \code{"continuous"}. A single string applies +#' to all variables. A per-variable vector that mixes discrete +#' (\code{"ordinal"} / \code{"blume-capel"}) and \code{"continuous"} +#' types fits a mixed MRF. Binary variables are automatically treated as #' \code{"ordinal"}. Default: \code{"ordinal"}. #' #' @param baseline_category Integer or vector. Baseline category used in diff --git a/R/bgms-package.R b/R/bgms-package.R index 464d643b..cc4af018 100644 --- a/R/bgms-package.R +++ b/R/bgms-package.R @@ -1,16 +1,24 @@ -#' bgms: Bayesian Analysis of Networks of Binary and/or Ordinal Variables +#' bgms: Bayesian Analysis of Graphical Models #' #' @description #' The \code{R} package \strong{bgms} provides tools for Bayesian analysis of -#' the ordinal Markov random field (MRF), a graphical model describing networks -#' of binary and/or ordinal variables \insertCite{MarsmanVandenBerghHaslbeck_2025}{bgms}. -#' The likelihood is approximated via a pseudolikelihood, and Markov chain Monte -#' Carlo (MCMC) methods are used to sample from the corresponding pseudoposterior -#' distribution of model parameters. +#' graphical models describing networks of binary, ordinal, continuous, and +#' mixed variables +#' \insertCite{MarsmanVandenBerghHaslbeck_2025}{bgms}. +#' Supported model families include ordinal Markov random fields (MRFs), +#' Gaussian graphical models (GGMs), and mixed MRFs that combine discrete +#' and continuous variables in a single network. The likelihood is approximated +#' via a pseudolikelihood, and Markov chain Monte Carlo (MCMC) methods are used +#' to sample from the corresponding pseudoposterior distribution of model +#' parameters. #' #' The main entry points are: #' \itemize{ #' \item \strong{bgm}: estimation in a one-sample design. +#' Use \code{variable_type = "ordinal"} for an MRF, +#' \code{"continuous"} for a GGM, or a per-variable vector +#' mixing \code{"ordinal"}, \code{"blume-capel"}, and +#' \code{"continuous"} for a mixed MRF. #' \item \strong{bgmCompare}: estimation and group comparison in an #' independent-sample design. #' } diff --git a/README.md b/README.md index 808b9799..b2c6927e 100644 --- a/README.md +++ b/README.md @@ -30,21 +30,23 @@ stable](https://img.shields.io/badge/lifecycle-stable-brightgreen.svg)](https:// **Bayesian analysis of graphical models** The **bgms** package implements Bayesian estimation and model comparison -for graphical models of binary, ordinal, and continuous variables +for graphical models of binary, ordinal, continuous, and mixed variables (Marsman, van den Bergh, et al., 2025). It -supports **ordinal Markov random fields (MRFs)** for discrete data and -**Gaussian graphical models (GGMs)** for continuous data. The likelihood -is approximated with a pseudolikelihood, and Markov chain Monte Carlo -(MCMC) methods are used to sample from the corresponding pseudoposterior -distribution of the model parameters. +supports **ordinal Markov random fields (MRFs)** for discrete data, +**Gaussian graphical models (GGMs)** for continuous data, and **mixed +MRFs** that combine discrete and continuous variables in a single +network. The likelihood is approximated with a pseudolikelihood, and +Markov chain Monte Carlo (MCMC) methods are used to sample from the +corresponding pseudoposterior distribution of the model parameters. ## Main functions The package has two main entry points: - `bgm()` – estimates a single network in a one-sample design. Use - `variable_type = "continuous"` for a GGM, or `"ordinal"` (default) for - an MRF. + `variable_type = "ordinal"` for an MRF, `"continuous"` for a GGM, or a + per-variable vector mixing `"ordinal"`, `"blume-capel"`, and + `"continuous"` for a mixed MRF. - `bgmCompare()` – compares networks between groups in an independent-sample design. diff --git a/Readme.Rmd b/Readme.Rmd index 57e93e0a..eb302f3f 100644 --- a/Readme.Rmd +++ b/Readme.Rmd @@ -38,10 +38,11 @@ library(bgms) **Bayesian analysis of graphical models** The **bgms** package implements Bayesian estimation and model comparison for -graphical models of binary, ordinal, and continuous variables +graphical models of binary, ordinal, continuous, and mixed variables [@MarsmanVandenBerghHaslbeck_2025]. -It supports **ordinal Markov random fields (MRFs)** for discrete data and -**Gaussian graphical models (GGMs)** for continuous data. +It supports **ordinal Markov random fields (MRFs)** for discrete data, +**Gaussian graphical models (GGMs)** for continuous data, and **mixed MRFs** +that combine discrete and continuous variables in a single network. The likelihood is approximated with a pseudolikelihood, and Markov chain Monte Carlo (MCMC) methods are used to sample from the corresponding pseudoposterior distribution of the model parameters. @@ -51,8 +52,9 @@ distribution of the model parameters. The package has two main entry points: - `bgm()` – estimates a single network in a one-sample design. - Use `variable_type = "continuous"` for a GGM, or `"ordinal"` (default) - for an MRF. + Use `variable_type = "ordinal"` for an MRF, `"continuous"` for a GGM, + or a per-variable vector mixing `"ordinal"`, `"blume-capel"`, and + `"continuous"` for a mixed MRF. - `bgmCompare()` – compares networks between groups in an independent-sample design. diff --git a/man/bgm.Rd b/man/bgm.Rd index 3dbe3dc6..e9ead76c 100644 --- a/man/bgm.Rd +++ b/man/bgm.Rd @@ -50,8 +50,11 @@ ordinal variables, unobserved categories are collapsed; for Blume–Capel variables, all categories are retained.} \item{variable_type}{Character or character vector. Specifies the type of -each variable in \code{x}. Allowed values: \code{"ordinal"} or -\code{"blume-capel"}. Binary variables are automatically treated as +each variable in \code{x}. Allowed values: \code{"ordinal"}, +\code{"blume-capel"}, or \code{"continuous"}. A single string applies +to all variables. A per-variable vector that mixes discrete +(\code{"ordinal"} / \code{"blume-capel"}) and \code{"continuous"} +types fits a mixed MRF. Binary variables are automatically treated as \code{"ordinal"}. Default: \code{"ordinal"}.} \item{baseline_category}{Integer or vector. Baseline category used in diff --git a/man/bgms-package.Rd b/man/bgms-package.Rd index d1f16dd7..a13091ad 100644 --- a/man/bgms-package.Rd +++ b/man/bgms-package.Rd @@ -4,18 +4,26 @@ \name{bgms-package} \alias{bgms} \alias{bgms-package} -\title{bgms: Bayesian Analysis of Networks of Binary and/or Ordinal Variables} +\title{bgms: Bayesian Analysis of Graphical Models} \description{ The \code{R} package \strong{bgms} provides tools for Bayesian analysis of -the ordinal Markov random field (MRF), a graphical model describing networks -of binary and/or ordinal variables \insertCite{MarsmanVandenBerghHaslbeck_2025}{bgms}. -The likelihood is approximated via a pseudolikelihood, and Markov chain Monte -Carlo (MCMC) methods are used to sample from the corresponding pseudoposterior -distribution of model parameters. +graphical models describing networks of binary, ordinal, continuous, and +mixed variables +\insertCite{MarsmanVandenBerghHaslbeck_2025}{bgms}. +Supported model families include ordinal Markov random fields (MRFs), +Gaussian graphical models (GGMs), and mixed MRFs that combine discrete +and continuous variables in a single network. The likelihood is approximated +via a pseudolikelihood, and Markov chain Monte Carlo (MCMC) methods are used +to sample from the corresponding pseudoposterior distribution of model +parameters. The main entry points are: \itemize{ \item \strong{bgm}: estimation in a one-sample design. + Use \code{variable_type = "ordinal"} for an MRF, + \code{"continuous"} for a GGM, or a per-variable vector + mixing \code{"ordinal"}, \code{"blume-capel"}, and + \code{"continuous"} for a mixed MRF. \item \strong{bgmCompare}: estimation and group comparison in an independent-sample design. } diff --git a/man/mrfSampler.Rd b/man/mrfSampler.Rd index 8ff00c14..d2e6a0ba 100644 --- a/man/mrfSampler.Rd +++ b/man/mrfSampler.Rd @@ -21,15 +21,18 @@ mrfSampler( \item{num_variables}{The number of variables in the MRF.} -\item{num_categories}{Either a positive integer or a vector of positive -integers of length \code{num_variables}. The number of response categories on top -of the base category: \code{num_categories = 1} generates binary states. +\item{num_categories}{Either a positive integer or a vector +of positive integers of length \code{num_variables}. The +number of response categories on top of the base category: +\code{num_categories = 1} generates binary states. Only used for ordinal and Blume-Capel variables; ignored when \code{variable_type = "continuous"}.} -\item{pairwise}{A symmetric \code{num_variables} by \code{num_variables} matrix. -For ordinal and Blume-Capel variables, this contains the pairwise interaction -parameters; only the off-diagonal elements are used. For continuous variables, +\item{pairwise}{A symmetric \code{num_variables} by +\code{num_variables} matrix. For ordinal and Blume-Capel +variables, this contains the pairwise interaction parameters; +only the off-diagonal elements are used. For continuous +variables, this is the precision matrix \eqn{\Omega}{Omega} (including diagonal) and must be positive definite.} @@ -37,7 +40,8 @@ must be positive definite.} \code{num_variables} by \code{max(num_categories)} matrix of category thresholds. The elements in row \code{i} indicate the thresholds of variable \code{i}. If \code{num_categories} is a vector, only the first -\code{num_categories[i]} elements are used in row \code{i}. If the Blume-Capel +\code{num_categories[i]} elements are used in row \code{i}. +If the Blume-Capel model is used for the category thresholds for variable \code{i}, then row \code{i} requires two values (details below); the first is \eqn{\alpha}{\alpha}, the linear contribution of the Blume-Capel model and @@ -57,8 +61,10 @@ variables. When \code{variable_type = "continuous"}, the function simulates from a Gaussian graphical model. Defaults to \code{variable_type = "ordinal"}.} -\item{baseline_category}{An integer vector of length \code{num_variables} specifying the -baseline_category category that is used for the Blume-Capel model (details below). +\item{baseline_category}{An integer vector of length +\code{num_variables} specifying the baseline_category +category that is used for the Blume-Capel model +(details below). Can be any integer value between \code{0} and \code{num_categories} (or \code{num_categories[i]}).} diff --git a/man/predict.bgmCompare.Rd b/man/predict.bgmCompare.Rd index 0d407dd1..48eaa705 100644 --- a/man/predict.bgmCompare.Rd +++ b/man/predict.bgmCompare.Rd @@ -47,9 +47,10 @@ prediction (1 to number of groups). Required argument.} \item{...}{Additional arguments (currently ignored).} } \value{ -For \code{type = "probabilities"}: A named list with one element per -predicted variable. Each element is a matrix with \code{n} rows and -\code{num_categories + 1} columns containing \eqn{P(X_j = c | X_{-j})}{P(X_j = c | X_-j)} +For \code{type = "probabilities"}: A named list with one +element per predicted variable. Each element is a matrix with +\code{n} rows and \code{num_categories + 1} columns containing +\eqn{P(X_j = c | X_{-j})}{P(X_j = c | X_-j)} for each observation and category. For \code{type = "response"}: A matrix with \code{n} rows and @@ -82,8 +83,10 @@ pred_g2 = predict(fit, newdata = y[1:10, ], group = 2, type = "response") } \seealso{ -\code{\link{predict.bgms}} for predicting from single-group models, - \code{\link{simulate.bgmCompare}} for simulating from group-comparison models. +\code{\link{predict.bgms}} for predicting + from single-group models, + \code{\link{simulate.bgmCompare}} for simulating + from group-comparison models. Other prediction: \code{\link{predict.bgms}()}, diff --git a/man/predict.bgms.Rd b/man/predict.bgms.Rd index 37e16319..564202f3 100644 --- a/man/predict.bgms.Rd +++ b/man/predict.bgms.Rd @@ -40,11 +40,13 @@ the original data used to fit the model.} \item{method}{Character string specifying which parameter estimates to use: \describe{ \item{\code{"posterior-mean"}}{Use posterior mean parameters.} - \item{\code{"posterior-sample"}}{Average predictions over posterior draws.} + \item{\code{"posterior-sample"}}{Average predictions + over posterior draws.} }} \item{ndraws}{Number of posterior draws to use when -\code{method = "posterior-sample"}. If \code{NULL}, uses all available draws.} +\code{method = "posterior-sample"}. If \code{NULL}, +uses all available draws.} \item{seed}{Optional random seed for reproducibility when \code{method = "posterior-sample"}.} @@ -56,8 +58,9 @@ the original data used to fit the model.} For \code{type = "probabilities"}: A named list with one element per predicted variable. Each element is a matrix with \code{n} rows and -\code{num_categories + 1} columns containing \eqn{P(X_j = c | X_{-j})}{P(X_j = c | X_-j)} for each -observation and category. +\code{num_categories + 1} columns containing +\eqn{P(X_j = c | X_{-j})}{P(X_j = c | X_-j)} +for each observation and category. For \code{type = "response"}: A matrix with \code{n} rows and \code{length(variables)} columns containing predicted categories. @@ -79,10 +82,18 @@ For \code{type = "response"}: A matrix with \code{n} rows and When \code{method = "posterior-sample"}, conditional parameters are averaged over posterior draws, and an attribute \code{"sd"} is included. + +\strong{Mixed MRF models:} + +For mixed models, the return list contains elements for both discrete and +continuous predicted variables. Discrete variables return probability +matrices (as in ordinal models); continuous variables return conditional +mean and SD matrices (as in GGM models). } \description{ Computes conditional probability distributions for one or more variables -given the observed values of other variables in the data. +given the observed values of other variables in the data. Supports ordinal, +Blume-Capel, continuous (GGM), and mixed MRF models. } \details{ For each observation, the function computes the conditional distribution @@ -92,7 +103,8 @@ sampler. For GGM (continuous) models, the conditional distribution of \eqn{X_j | X_{-j}}{X_j | X_{-j}} is Gaussian with mean -\eqn{-\omega_{jj}^{-1} \sum_{k \neq j} \omega_{jk} x_k}{-omega_jj^{-1} sum_{k != j} omega_jk x_k} +\eqn{-\omega_{jj}^{-1} \sum_{k \neq j} +\omega_{jk} x_k}{-omega_jj^{-1} sum_{k != j} omega_jk x_k} and variance \eqn{\omega_{jj}^{-1}}{omega_jj^{-1}}, where \eqn{\Omega}{Omega} is the precision matrix. } diff --git a/man/simulate.bgms.Rd b/man/simulate.bgms.Rd index e988e3a2..0e605956 100644 --- a/man/simulate.bgms.Rd +++ b/man/simulate.bgms.Rd @@ -33,7 +33,8 @@ }} \item{ndraws}{Number of posterior draws to use when -\code{method = "posterior-sample"}. If \code{NULL}, uses all available draws.} +\code{method = "posterior-sample"}. If \code{NULL}, +uses all available draws.} \item{iter}{Number of Gibbs iterations for equilibration before collecting samples. Default: \code{1000}.} @@ -54,14 +55,20 @@ If \code{method = "posterior-mean"}: A matrix with \code{nsim} rows and If \code{method = "posterior-sample"}: A list of matrices, one per posterior draw, each with \code{nsim} rows and \code{p} columns. + +For mixed MRF models, discrete columns contain non-negative integers and +continuous columns contain real-valued observations, ordered as in the +original data. } \description{ Generates new observations from the Markov Random Field model using the -estimated parameters from a fitted \code{bgms} object. +estimated parameters from a fitted \code{bgms} object. Supports ordinal, +Blume-Capel, continuous (GGM), and mixed MRF models. } \details{ -This function uses the estimated interaction and threshold parameters to -generate new data via Gibbs sampling. When \code{method = "posterior-sample"}, +This function uses the estimated interaction and threshold +parameters to generate new data via Gibbs sampling. When +\code{method = "posterior-sample"}, parameter uncertainty is parameter uncertainty is propagated to the simulated data by using different posterior draws. Parallel processing is available for this method via the \code{cores} argument. @@ -75,7 +82,11 @@ fit = bgm(x = Wenchuan[, 1:5], chains = 2) new_data = simulate(fit, nsim = 100) # Simulate with parameter uncertainty (10 datasets) -new_data_list = simulate(fit, nsim = 100, method = "posterior-sample", ndraws = 10) +new_data_list = simulate( + fit, + nsim = 100, + method = "posterior-sample", ndraws = 10 +) # Use parallel processing for faster simulation new_data_list = simulate(fit, diff --git a/man/simulate_mrf.Rd b/man/simulate_mrf.Rd index 6124a965..76213b94 100644 --- a/man/simulate_mrf.Rd +++ b/man/simulate_mrf.Rd @@ -21,15 +21,18 @@ simulate_mrf( \item{num_variables}{The number of variables in the MRF.} -\item{num_categories}{Either a positive integer or a vector of positive -integers of length \code{num_variables}. The number of response categories on top -of the base category: \code{num_categories = 1} generates binary states. +\item{num_categories}{Either a positive integer or a vector +of positive integers of length \code{num_variables}. The +number of response categories on top of the base category: +\code{num_categories = 1} generates binary states. Only used for ordinal and Blume-Capel variables; ignored when \code{variable_type = "continuous"}.} -\item{pairwise}{A symmetric \code{num_variables} by \code{num_variables} matrix. -For ordinal and Blume-Capel variables, this contains the pairwise interaction -parameters; only the off-diagonal elements are used. For continuous variables, +\item{pairwise}{A symmetric \code{num_variables} by +\code{num_variables} matrix. For ordinal and Blume-Capel +variables, this contains the pairwise interaction parameters; +only the off-diagonal elements are used. For continuous +variables, this is the precision matrix \eqn{\Omega}{Omega} (including diagonal) and must be positive definite.} @@ -37,7 +40,8 @@ must be positive definite.} \code{num_variables} by \code{max(num_categories)} matrix of category thresholds. The elements in row \code{i} indicate the thresholds of variable \code{i}. If \code{num_categories} is a vector, only the first -\code{num_categories[i]} elements are used in row \code{i}. If the Blume-Capel +\code{num_categories[i]} elements are used in row \code{i}. +If the Blume-Capel model is used for the category thresholds for variable \code{i}, then row \code{i} requires two values (details below); the first is \eqn{\alpha}{\alpha}, the linear contribution of the Blume-Capel model and @@ -57,8 +61,10 @@ variables. When \code{variable_type = "continuous"}, the function simulates from a Gaussian graphical model. Defaults to \code{variable_type = "ordinal"}.} -\item{baseline_category}{An integer vector of length \code{num_variables} specifying the -baseline_category category that is used for the Blume-Capel model (details below). +\item{baseline_category}{An integer vector of length +\code{num_variables} specifying the baseline_category +category that is used for the Blume-Capel model +(details below). Can be any integer value between \code{0} and \code{num_categories} (or \code{num_categories[i]}).} @@ -77,10 +83,11 @@ observations. For ordinal/Blume-Capel variables, entries are non-negative integers. For continuous variables, entries are real-valued. } \description{ -`simulate_mrf()` generates observations from a Markov Random Field using -user-specified parameters. For ordinal and Blume-Capel variables, observations -are generated via Gibbs sampling. For continuous variables (Gaussian graphical -model), observations are drawn directly from the multivariate normal +`simulate_mrf()` generates observations from a Markov Random +Field using user-specified parameters. For ordinal and +Blume-Capel variables, observations are generated via Gibbs +sampling. For continuous variables (Gaussian graphical model), +observations are drawn directly from the multivariate normal distribution implied by the precision matrix. } \details{ @@ -106,7 +113,8 @@ The Blume-Capel option is specifically designed for ordinal variables that have a special type of baseline_category category, such as the neutral category in a Likert scale. The Blume-Capel model specifies the following quadratic model for the threshold parameters: -\deqn{\mu_{\text{c}} = \alpha \times (\text{c} - \text{r}) + \beta \times (\text{c} - \text{r})^2,}{{\mu_{\text{c}} = \alpha \times (\text{c} - \text{r}) + \beta \times (\text{c} - \text{r})^2,}} +\deqn{\mu_{\text{c}} = \alpha (\text{c} - \text{r}) + + \beta (\text{c} - \text{r})^2} where \eqn{\mu_{\text{c}}}{\mu_{\text{c}}} is the threshold for category c (which now includes zero), \eqn{\alpha}{\alpha} offers a linear trend across categories (increasing threshold values if From 1901c89677bc431d11e7d58a38721e6403a8ce2e Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Sat, 7 Mar 2026 14:46:22 +0100 Subject: [PATCH 20/60] docs: add integrated NUTS+MH hybrid sampler plan for mixed MRF --- dev/mixedmrf/nuts-plan.md | 518 ++++++++++++++++++++++++++++++++++++++ 1 file changed, 518 insertions(+) create mode 100644 dev/mixedmrf/nuts-plan.md diff --git a/dev/mixedmrf/nuts-plan.md b/dev/mixedmrf/nuts-plan.md new file mode 100644 index 00000000..63204730 --- /dev/null +++ b/dev/mixedmrf/nuts-plan.md @@ -0,0 +1,518 @@ +# Hybrid NUTS+MH Sampler for Mixed MRF (integrated plan) + +Integrates feedback from review1.md and review2.md into the original +nuts-plan.md. Open points that need decisions are marked **OPEN POINT**. + +## Overview + +Split each MCMC iteration into two phases: + +1. **NUTS phase** — jointly sample (μx, μy, Kxx, Kxy) using NUTS, treating Kyy as fixed +2. **Metropolis phase** — component-wise update Kyy using the existing Cholesky-based MH + +This is a Gibbs-within-MCMC scheme: NUTS handles the high-dimensional +unconstrained block, Metropolis handles only the q(q+1)/2 constrained +precision matrix entries. + +## Why it works + +| Parameter | Constraint | Current sampler | Proposed | +|----------------------|-----------|------------------------|-----------------------| +| μx (thresholds) | none | component MH | **NUTS** | +| μy (means) | none | component MH | **NUTS** | +| Kxx (discrete int.) | none | component MH | **NUTS** | +| Kxy (cross int.) | none | component MH | **NUTS** | +| Kyy off-diagonal | SPD | rank-2 Cholesky MH | MH (unchanged) | +| Kyy diagonal | SPD | rank-1 Cholesky MH | MH (unchanged) | + +With Kyy fixed, the pseudo-log-posterior over the remaining parameters is +smooth and unconstrained — exactly the setting where NUTS excels. + +## Gradient derivation + +The conditional pseudo-log-posterior (with Kyy fixed) is: + + ℓ(θ) = Σ_s log p(x_s | x_{-s}, y) [OMRF conditionals] + + log p(y | x) [GGM conditional] + + log π(θ) [priors] + +### OMRF conditional gradients + +For discrete variable s with rest score: + + r_{i,s} = Σ_{t≠s} Kxx_{st} x_{i,t} + 2 Σ_j Kxy_{sj} y_{i,j} + +- ∂/∂μx_{s,c}: counts minus expected counts — identical to the existing + OMRF gradient via `compute_logZ_and_probs_ordinal`. +- ∂/∂Kxx_{st}: Σ_i x_{i,t} · [(x_{i,s}+1) - E_s[c+1 | r_{i,s}]] + (+ symmetric contribution from ℓ_t) — same pattern as OMRF pairwise gradient. +- ∂/∂Kxy_{sj}: 2 Σ_i y_{i,j} · [(x_{i,s}+1) - E_s[c+1 | r_{i,s}]] + — same pattern but with continuous observations. + +### GGM conditional gradients + +With residual D = Y - M where M_i = μy + 2 Σyy Kxy' x_i: + +- ∂/∂μy = Kyy · D'1_n +- ∂/∂Kxy = 2 · X_disc' · D + +The simplification Kyy Σyy = I avoids computing Σyy during the gradient. +These formulas have been verified by both reviewers. + +### Prior gradients + +- μx: Beta prior → sigmoid form (same as OMRF) +- μy, Kxy: Normal(0, 1) prior +- Kxx: Cauchy(0, scale) prior + +Key property: `compute_logZ_and_probs_ordinal/blume_capel` already computes +both the log-normalizer and the softmax probabilities needed for the gradient +— the same shared utility used by the OMRF. + +## Architecture: 7 phases + +### Phase 1: NUTS parameter vector + +**Files**: `mixed_mrf_model.h`, `mixed_mrf_model.cpp` + +The NUTS vector excludes Kyy: + + [mux_params | active_Kxx | muy | active_Kxy] + +- `get_vectorized_parameters()` / `set_vectorized_parameters()` — pack/unpack + only NUTS params (excluding Kyy). +- `get_full_vectorized_parameters()` — unchanged, returns everything including + Kyy (for sample storage). +- `get_active_inv_mass()` — returns diagonal mass vector sized for NUTS + params only. +- New helper: `unvectorize_nuts_params()` to unpack into model matrices. + +Edge selection changes the NUTS vector dimension (Gxx/Gxy edge toggles), +mirroring how the OMRF handles it. + +#### Vectorization contract (from reviews) + +The `BaseModel` interface uses `get_vectorized_parameters()` / +`set_vectorized_parameters()` as the NUTS-sampled state. For the hybrid +scheme these methods must return/accept the NUTS-only vector (excluding Kyy), +while `get_full_vectorized_parameters()` returns the full vector for sample +storage. This matches the OMRF pattern where `get_vectorized_parameters()` +returns active-edge-only params. + +**RESOLVED — `full_parameter_dimension()` sizing → option 2.** +`GradientSamplerBase::do_initialize()` calls +`model.full_parameter_dimension()` to size the initial inverse mass vector. +For the hybrid scheme the mass matrix covers only the NUTS dimension, not +Kyy. + +**Investigation result:** For the OMRF, `full_parameter_dimension()` returns +`num_main_ + num_pairwise_` (all params) while `parameter_dimension()` +returns only active-edge params. For the GGM, both return the same value +(`dim_`). Neither model would break from a refactoring that uses +`full_parameter_dimension()` for mass-matrix sizing: the OMRF already needs +the full (non-edge-subsetted) dimension for the mass matrix (since +`set_inv_mass` receives a full-dimension vector that the model subsets via +`get_active_inv_mass`), and the GGM has no distinction. The mixed model +would override `full_parameter_dimension()` to return the NUTS-block +dimension (num_main + p(p-1)/2 + q + pq — everything except Kyy), while +a new `storage_dimension()` returns the complete vector length including Kyy. + +**Decision:** Refactor `GradientSamplerBase::do_initialize()` to use +`full_parameter_dimension()` for mass matrix sizing (this is already the +case), and make the mixed model's `full_parameter_dimension()` return the +NUTS-block size. Add `storage_dimension()` for sample storage. The OMRF and +GGM are unaffected because their `full_parameter_dimension()` already matches +the mass-matrix dimension. + +**Implementation note — `set_inv_mass` / `get_active_inv_mass`.** The OMRF +pattern subsets `inv_mass_` to active-edge-only entries. The mixed-model +version must exclude Kyy entries entirely. Since `set_inv_mass` is called +from the adaptation controller with a full-dimension vector, the model needs +to know which entries correspond to Kyy and skip them. Pin down index +bookkeeping early. + +### Phase 2: Gradient computation + +**New file**: `mixed_mrf_gradient.cpp` + +- `logp_and_gradient(params)` — returns (log-pseudo-posterior, ∇) +- Structure follows OMRF's `logp_and_gradient()` closely: + 1. Unvectorize params into temp matrices (not model state — see below) + 2. Compute rest scores (including 2 Kxy y contribution) + 3. Per variable: `compute_logZ_and_probs` gives both log Z and + probabilities + 4. Gradient from probabilities: main effects + pairwise weights + (using x and y) + 5. GGM conditional: residuals → ∇μy and ∇Kxy contributions + 6. Prior gradients +- `gradient(params)` — calls `logp_and_gradient` and discards the + log-posterior. +- `has_gradient()` returns `true`. + +#### Cache invalidation strategy (from reviews) + +NUTS calls `logp_and_gradient()` at many points along the leapfrog +trajectory, but only calls `set_vectorized_parameters()` once with the final +accepted state. Following the OMRF pattern: + +- `logp_and_gradient()` unvectorizes into **temporaries** (analogous to + `unvectorize_to_temps` in the OMRF), computing derived quantities (rest + scores, conditional means) locally. This avoids mutating model state during + the trajectory. +- The final `set_vectorized_parameters()` updates model state and + recomputes `conditional_mean_` and (for marginal PL) `Theta_`. + +#### Gradient cache scope (from reviews) + +Only the OMRF sufficient statistics (category counts, pairwise sums) can be +precomputed and cached. The GGM residual D = Y - M depends on Kxy and μy, +so the GGM gradient must be computed fresh each evaluation. + +#### Marginal PL gradient — full plan + +The gradient derivation above is for the **conditional** pseudo-likelihood. +The mixed model also supports **marginal** PL +(`pseudolikelihood = "marginal"`), where OMRF conditionals use +Theta = Kxx + 2 Kxy Σyy Kxy' instead of Kxx directly. Marginal PL is +important in practice, so the gradient implementation must support both +modes. + +**Marginal PL pseudo-log-posterior (Kyy fixed):** + + ℓ_marg(θ) = Σ_s log p_marg(x_s | x_{-s}) [marginal OMRF conditionals] + + log p(y | x) [GGM conditional] + + log π(θ) [priors] + +The marginal OMRF conditional for variable s uses Theta instead of Kxx. +The rest score and denominator structure differ from the conditional form +(see `log_marginal_omrf` in `mixed_mrf_likelihoods.cpp`): + +**Rest score (marginal):** + + r_{i,s}^marg = Σ_{t≠s} Θ_{st} x_{i,t} + 2 Σ_j Kxy_{sj} μy_j + +where Θ = Kxx + 2 Kxy Σyy Kxy'. Note: Σyy is fixed (Kyy is fixed). + +**Denominator differences:** The marginal ordinal denominator absorbs the +quadratic self-interaction Θ_{ss} into the main-effect parameter vector: +main_param(c) = μx_{s,c} + (c+1)² Θ_{ss}. Blume-Capel uses +effective_beta = β + Θ_{ss}. + +**Gradient structure (marginal PL):** + +1. ∂/∂μx_{s,c}: Same structure as conditional, but using the marginal + denominator probabilities (which include the Θ_{ss} offset). The + `compute_logZ_and_probs` utility handles this transparently if given + the modified main_param. + +2. ∂/∂Kxx_{st}: The OMRF part uses Θ-based rest scores. Since + ∂Θ/∂Kxx_{st} = e_s e_t' + e_t e_s' (just the identity), the gradient + has the same form as the conditional case but evaluated at the Θ-based + rest scores and marginal probabilities. + +3. ∂/∂Kxy_{sj}: This is the complex case. Kxy appears in two places: + - The GGM conditional (via conditional_mean_) — same gradient as + conditional case: 2 [X' D]_{s,j} + - The marginal OMRF conditionals (via Θ). The chain rule gives: + + ∂Θ_{ab}/∂Kxy_{sj} = 2 [Σyy]_{j,:} Kxy_{b,:}' δ_{as} + + 2 Kxy_{a,:} [Σyy]_{:,j} δ_{bs} + + For each OMRF variable a, `log_marginal_omrf(a)` changes through Θ. + The gradient contribution from the marginal OMRF terms for one variable + a involves: + - The sensitivity of the rest score to Θ changes + - The sensitivity of the denominator (via Θ_{aa}) to Kxy + + In matrix form, for the full Kxy gradient from OMRF marginal terms: + + ∂ℓ_marg_omrf/∂Kxy = 2 Σyy [partial through Θ rest scores] + + 2 [partial through Θ diagonal] + + Both partials are computable from the marginal probabilities already + obtained from `compute_logZ_and_probs`. + +4. ∂/∂μy: In the marginal form, μy enters the OMRF rest score as a + constant bias 2 Kxy_{s,:} μy. So there is an additional μy gradient + from the OMRF terms: + + ∂ℓ_marg_omrf/∂μy_j = Σ_s 2 Kxy_{s,j} · [obs_mean_s - expected_mean_s] + + Plus the GGM contribution Kyy · D' 1_n as before. + +**Implementation plan:** + +- Phase 2 implements `logp_and_gradient` with a branch on `use_marginal_pl_`. +- The conditional path is implemented and tested first (numerical gradient + check). +- The marginal path is implemented second, reusing the same + `compute_logZ_and_probs` calls but with modified main_param vectors + (absorbing Θ_{ss}) and Θ-based rest scores. +- The marginal ∂Kxy gradient has 3 contributions: (a) GGM conditional, + (b) OMRF via Θ rest scores, (c) OMRF via Θ diagonal. Each is O(n·p·q). +- A second numerical gradient check validates the marginal path. +- Testing: both PL modes should produce consistent posterior marginals on + small test problems. + +**Key insight:** With Kyy fixed, Σyy = Kyy⁻¹ is a constant matrix. So +`recompute_Theta()` during gradient evaluation is just a matrix product +Kxx + 2 Kxy Σyy Kxy' — O(p²q) per evaluation but only needed once per +`logp_and_gradient` call (not per variable). The marginal rest scores and +probabilities are then computed per variable as usual. + +### Phase 3: Kyy-only Metropolis + +**File**: `mixed_mrf_metropolis.cpp` + +Create a dedicated `do_kyy_metropolis_step()` that only updates Kyy entries: + - `update_Kyy_offdiag(i, j)` — existing rank-2 Cholesky MH (unchanged) + - `update_Kyy_diag(i)` — existing rank-1 Cholesky MH (unchanged) + +The hybrid sampler calls `do_kyy_metropolis_step()`. +The pure-MH sampler calls `do_one_metropolis_step()` (full sweep, unchanged). + +Robbins-Monro adaptation for Kyy proposal SDs remains. + +#### Edge-indicator ownership — investigation and decision + +Currently edge-indicator updates happen in two places: + +- `ChainRunner::run_iteration()` (line ~59) calls + `model.update_edge_indicators()` when `schedule.selection_enabled(iter)`. +- `MixedMRFModel::do_one_metropolis_step()` (line ~477) unconditionally calls + `update_edge_indicators()` at the end of the sweep. + +**Investigation result:** The pattern differs across model types: + +- **OMRF**: `do_one_metropolis_step()` does NOT call + `update_edge_indicators()`. Edge updates are handled exclusively by + `ChainRunner`. The OMRF has a separate `update_edge_indicators()` method + that uses the shuffled edge order from `prepare_iteration()`. +- **GGM**: `do_one_metropolis_step()` DOES include edge-indicator updates + inline (calls `update_edge_indicator_parameter_pair` inside the method). + Its `update_edge_indicators()` override is a no-op. `ChainRunner` calls + the no-op, so no double-counting. +- **Mixed**: `do_one_metropolis_step()` calls `update_edge_indicators()` at + step 6. `ChainRunner` also calls it. **This is a double-counting bug in + the current code** — edge-indicator proposals happen twice per iteration + when edge selection is active. + +**Decision:** Fix the mixed model to match the OMRF pattern: + +1. Remove the `update_edge_indicators()` call from + `MixedMRFModel::do_one_metropolis_step()`. +2. `ChainRunner` remains the sole owner of edge-indicator scheduling. +3. `do_kyy_metropolis_step()` does not include edge-indicator logic. +4. The pure-MH path is not broken because `ChainRunner` already calls + `update_edge_indicators()` after the sampler step. + +**This is an existing bug that should be fixed before the hybrid work.** + +**Edge selection ordering**: indicator updates run **after** both NUTS and +Kyy MH complete in each iteration. Birth/death moves for Gxx/Gxy change the +NUTS vector dimension, so they cannot happen mid-trajectory. + +### Phase 4: Hybrid sampler + +**New file**: `hybrid_nuts_sampler.h` + +``` +class HybridNUTSSampler : public GradientSamplerBase { + step(model, iter): + 1. GradientSamplerBase::step() → NUTS for (mux, muy, Kxx, Kxy) + 2. model.do_kyy_metropolis_step(iter) → MH for Kyy + return combined result +} +``` + +- Inherits NUTS adaptation (step size + diagonal mass matrix) from + `GradientSamplerBase`. +- Kyy proposal-SD adaptation runs inside the model (Robbins-Monro, same + as now). +- Registered in sampler factory as `"hybrid-nuts"`. + +#### Adaptation controller alignment (from reviews) + +`GradientSamplerBase::step()` calls `adapt_->update(full_params, ...)` +with the parameter vector from `model.get_full_vectorized_parameters()`. +The adaptation controller's mass matrix must match the NUTS dimension +(excluding Kyy), not the full storage dimension. Resolved by Open Point A: +refactor `GradientSamplerBase` to use `parameter_dimension()` for +mass-matrix sizing. + +#### Kyy MH diagnostics — deferred + +NUTS diagnostics (tree depth, divergences, energy) reflect only the NUTS +phase. Kyy MH acceptance rates are tracked by Robbins-Monro internally. +Kyy acceptance rates can be computed from the sampler output post-hoc. +Deferred to a later iteration. + +### Phase 5: Warmup schedule integration + +- Stages 1–3a: NUTS adaptation (step size + mass matrix for NUTS params) + + Kyy proposal SD warmup. +- Stage 3b: Kyy proposal-SD tuning via Robbins-Monro. +- Stage 3c: Re-adapt NUTS step size with edge selection active. +- Edge selection: Gxx/Gxy toggles change NUTS vector dimension; Gyy + toggles change Kyy MH sweep. + +#### Warmup scheduling for `"hybrid-nuts"` — resolved + +`ChainRunner` (line ~34) currently sets `learn_sd` and NUTS diagnostics +only for known sampler types (`"nuts"`, `"hmc"`, `"hamiltonian-mc"`). +Since the user-facing sampler name will be `"nuts"` (which automatically +becomes hybrid for mixed models — see Phase 6), the existing runner logic +recognizes the sampler type without modification. If the internal +implementation uses a distinct `"hybrid-nuts"` key in the sampler factory, +the runner check must be extended to include it. + +#### Kyy proposal-SD adaptation timing — resolved + +Kyy proposal-SD adaptation via Robbins-Monro runs throughout the entire +warmup phase (whenever `iteration < total_warmup_`), matching the current +adaptive-MH behavior. This is correct: the Kyy MH proposal SDs need +continuous adaptation because Kyy mixing depends on the evolving NUTS +parameter state. No gating by warmup stage. + +### Phase 6: Sampler type selection + +**Files**: R-level `bgm_spec.R`, C++ interface (`sample_mixed.cpp`) + +#### End-to-end sampler wiring — resolved + +Currently sampler selection is hard-wired to MH for mixed models across +multiple files: + +- `R/bgm_spec.R` line 212: rejects non-`adaptive-metropolis` for `mixed_mrf` +- `R/bgm_spec.R` line 320: sets `sampler_is_continuous = is_continuous || is_mixed` +- `R/validate_sampler.R` lines 111, 119: forces `adaptive-metropolis` for `is_continuous` +- `src/sample_mixed.cpp` line 98: hard-codes `config.sampler_type = "mh"` +- `R/run_sampler.R` line 184: calls `sample_mixed_mrf()` with no sampler-type argument + +**Decisions:** + +- **User-facing argument:** `sampler = "nuts"` (same as existing). For + `type = "mixed"`, `"nuts"` automatically becomes the hybrid NUTS+MH + scheme internally. Users do not need to know about the hybrid distinction. +- **Default:** `"nuts"` is already the default `update_method`. Mixed models + use the same default. +- **Fallback:** `sampler = "adaptive-metropolis"` remains available as + pure-MH fallback for mixed models. + +**Coordinated changes:** + 1. Remove the `mixed_mrf` rejection in `bgm_spec.R` for non-MH samplers + 2. Allow `"nuts"` for mixed models in `validate_sampler.R` + 3. Extend `sample_mixed_mrf` C++ signature with sampler type + NUTS config + 4. Pass `spec$sampler$update_method` from `run_sampler_mixed_mrf` + 5. In the C++ sampler factory, `"nuts"` + mixed model creates the + `HybridNUTSSampler` (NUTS for unconstrained block + MH for Kyy) + +### Phase 7: Testing and validation + +1. **Numerical gradient check**: finite-difference vs analytical gradient. + Implement *before* the full hybrid sampler as a standalone test calling + `logp_and_gradient` directly. Highest-risk component. +2. **Posterior equivalence**: compare hybrid-NUTS posteriors against + pure-MH posteriors on small problems. Consider using the Stan exact- + likelihood model from `mixedGM` (`inst/stan/mixed_mrf_exact.stan`) as a + non-pseudolikelihood gold standard. +3. **ESS comparison**: effective sample size per second, hybrid vs pure MH. +4. **Edge selection**: verify birth/death moves work correctly with the + hybrid scheme. +5. **Edge cases**: q = 1 (scalar Kyy), p = 1 (single discrete variable), + all edges off. +6. **Gradient consistency across PL types** (when marginal PL is + implemented): verify conditional and marginal PL gradients produce the + same posterior marginals. +7. **Vectorization roundtrip tests**: verify `get_vectorized_parameters()` → + `set_vectorized_parameters()` → `get_vectorized_parameters()` is + identity for the NUTS block. + +## Key design decisions + +1. **Both conditional and marginal pseudo-likelihood for NUTS.** The + conditional form Σ_s p(x_s|x_{-s}, y) · p(y|x) is natural when Kyy is + fixed: the GGM conditional is standard Gaussian and the OMRF conditionals + just gain a continuous contribution to rest scores. The marginal form + uses Θ = Kxx + 2 Kxy Σyy Kxy' in the OMRF conditionals; since Kyy (and + thus Σyy) is fixed, Θ is a smooth function of Kxx and Kxy. Implement + conditional first, then marginal, each with numerical gradient checks. + +2. **Separate `do_kyy_metropolis_step()` method.** Rather than overloading + `do_one_metropolis_step()` semantics via `has_gradient()` branching, + create a dedicated Kyy-only method. Explicit and avoids fragile coupling. + +3. **Single NUTS vector (no Kyy).** The NUTS parameter vector packs + [μx, Kxx, μy, Kxy] with active-edge subsetting. Kyy lives entirely + outside the NUTS dynamics. + +4. **Reuse of `compute_logZ_and_probs`.** The gradient computation reuses + the existing shared utility for joint log-normalizer + probability + computation. + +5. **`ChainRunner` owns edge-indicator updates.** Edge-indicator proposals + are removed from `MixedMRFModel::do_one_metropolis_step()` (fixing an + existing double-counting bug) and handled exclusively by the runner, + matching the OMRF pattern. + +6. **Mixed model's `full_parameter_dimension()` returns NUTS-block size.** + The mixed model overrides `full_parameter_dimension()` to return the + NUTS-block dimension (excluding Kyy). A new `storage_dimension()` method + provides the complete dimension for sample storage. No refactoring of + `GradientSamplerBase` needed — it already uses `full_parameter_dimension()` + which will return the correct NUTS-block size. + +## Risk and mitigation + +| Risk | Mitigation | +|---------------------------------------------|---------------------------------------------------------| +| Gradient bugs (subtle sign errors) | Numerical gradient check as first test (Phase 7.1) | +| Mass matrix mismatch after edge toggling | Follow OMRF pattern: resize + re-init heuristic | +| Kyy MH mixing slower than NUTS expectations | Kyy is low-dimensional (q(q+1)/2); component MH is ok | +| Marginal vs conditional PL mismatch | Validate both decompositions give consistent posteriors | +| Edge-indicator double-counting | Single owner (ChainRunner) for indicator updates | +| Cache invalidation during leapfrog | Unvectorize to temps in gradient; update state once | +| Hard-wired MH selection blocks hybrid | Coordinated R + C++ wiring changes (Phase 6) | +| Warmup schedule ignores hybrid-nuts | Update ChainRunner feature detection | + +## Expected impact + +For a model with p discrete and q continuous variables: + +- **Current**: ~O(p² + pq + q²) component-wise MH steps per iteration +- **Hybrid**: 1 NUTS trajectory (joint over p·C̄ + p(p-1)/2 + q + pq dims) + + q(q+1)/2 MH steps +- NUTS typically produces near-independent samples with O(1) effective draws + per trajectory, vs ~O(d) iterations for random-walk MH to decorrelate in d + dimensions +- Biggest win when p is large relative to q + +## Recommended implementation order + +1. Resolve ownership contracts: indicator updates from `do_one_metropolis_step`, + `do_kyy_metropolis_step` semantics, hybrid sampler hook shape. +2. Wire sampler selection end-to-end (R validation → R dispatch → C++ config + → sampler factory). +3. Implement mixed-model NUTS vectorization and gradient, with numerical + gradient checks. +4. Integrate warmup/diagnostics handling for `hybrid-nuts`. +5. Add posterior-equivalence and edge-selection regression tests. + +--- + +## Resolution summary + +| # | Topic | Decision | +|---|-------|----------| +| A | `full_parameter_dimension()` sizing | Mixed model overrides to return NUTS-block size; new `storage_dimension()` for full vector. No `GradientSamplerBase` refactoring needed. | +| B | Marginal PL gradient | Implement both conditional and marginal. Conditional first with gradient check, then marginal with its own gradient check. | +| C | Edge-indicator ownership | Fix existing double-counting bug: remove `update_edge_indicators()` from `do_one_metropolis_step()`. `ChainRunner` is sole owner. | +| D | Kyy MH diagnostics | Deferred. Computable from sampler output post-hoc. | +| E | Warmup scheduling | User-facing name is `"nuts"`, so runner recognizes it. Kyy proposal-SD adaptation runs through entire warmup (no stage gating). | +| F | End-to-end sampler wiring | `sampler = "nuts"` (default) becomes hybrid for mixed models. `"adaptive-metropolis"` as pure-MH fallback. Coordinated R + C++ changes. | + +### Pre-implementation fix + +The edge-indicator double-counting in `MixedMRFModel::do_one_metropolis_step()` +(Point C) should be fixed before starting the hybrid sampler work. This is a +standalone bug fix that benefits the existing pure-MH code path. From b19afe30bc7241b89ca1786c61c847f418736b62 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Sat, 7 Mar 2026 14:49:05 +0100 Subject: [PATCH 21/60] fix: remove double-counting of edge-indicator updates in mixed MRF MixedMRFModel::do_one_metropolis_step() called update_edge_indicators() at step 6, but ChainRunner already calls it after each sampler step. This caused indicator proposals to happen twice per iteration when edge selection was active. Remove the call from do_one_metropolis_step() to match the OMRF pattern where ChainRunner is the sole owner of edge-indicator scheduling. --- src/models/mixed/mixed_mrf_model.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/models/mixed/mixed_mrf_model.cpp b/src/models/mixed/mixed_mrf_model.cpp index 38ef87e5..b907a8d6 100644 --- a/src/models/mixed/mixed_mrf_model.cpp +++ b/src/models/mixed/mixed_mrf_model.cpp @@ -473,8 +473,8 @@ void MixedMRFModel::do_one_metropolis_step(int iteration) { if(!edge_selection_active_ || gxy(i, j) == 1) update_Kxy(i, j, iteration); - // Step 6: Edge selection (reversible-jump birth/death) - update_edge_indicators(); + // Edge-indicator updates are handled by ChainRunner, not here. + // (Matches the OMRF pattern; avoids double-counting indicator proposals.) } void MixedMRFModel::update_edge_indicators() { From 4dff31cec4027e1c41a2fcf8cea66acce23b6962 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Sat, 7 Mar 2026 15:03:51 +0100 Subject: [PATCH 22/60] refactor: NUTS vectorization excludes Kyy from gradient block (phase 1) --- R/build_output.R | 10 +- src/mcmc/execution/chain_runner.cpp | 4 +- src/models/base_model.h | 31 ++++- src/models/mixed/mixed_mrf_model.cpp | 198 +++++++++++++++++++++------ src/models/mixed/mixed_mrf_model.h | 28 ++-- 5 files changed, 211 insertions(+), 60 deletions(-) diff --git a/R/build_output.R b/R/build_output.R index 095c7033..068908a2 100644 --- a/R/build_output.R +++ b/R/build_output.R @@ -325,7 +325,7 @@ build_output_bgm = function(spec, raw) { # ============================================================================== # # Handles the mixed discrete + continuous parameter layout: -# C++ flat vector: [mux | Kxx_ut | muy | Kyy_ut | Kxy] +# C++ flat vector: [mux | Kxx_ut | muy | Kxy | Kyy_ut] # C++ indicators: [Gxx_ut | Gyy_ut | Gxy] # # Splits into main (mux, muy, Kyy_diag) and pairwise (Kxx, Kyy_offdiag, Kxy), @@ -365,10 +365,10 @@ build_output_mixed_mrf = function(spec, raw) { kxx_end = nt + nxx muy_start = nt + nxx + 1L muy_end = nt + nxx + q - kyy_start = nt + nxx + q + 1L - kyy_end = nt + nxx + q + nyy_total - kxy_start = nt + nxx + q + nyy_total + 1L - kxy_end = nt + nxx + q + nyy_total + nxy + kxy_start = nt + nxx + q + 1L + kxy_end = nt + nxx + q + nxy + kyy_start = nt + nxx + q + nxy + 1L + kyy_end = nt + nxx + q + nxy + nyy_total # Kyy diagonal indices within the Kyy block kyy_diag_within = integer(q) diff --git a/src/mcmc/execution/chain_runner.cpp b/src/mcmc/execution/chain_runner.cpp index 8d4bdcfa..d3ee969d 100644 --- a/src/mcmc/execution/chain_runner.cpp +++ b/src/mcmc/execution/chain_runner.cpp @@ -91,7 +91,7 @@ void run_mcmc_chain( } } - chain_result.store_sample(sample_index, model.get_full_vectorized_parameters()); + chain_result.store_sample(sample_index, model.get_storage_vectorized_parameters()); if (chain_result.has_indicators) { chain_result.store_indicators(sample_index, model.get_vectorized_indicator_parameters()); @@ -145,7 +145,7 @@ std::vector run_mcmc_sampler( std::vector results(no_chains); for (int c = 0; c < no_chains; ++c) { - results[c].reserve(model.full_parameter_dimension(), config.no_iter); + results[c].reserve(model.storage_dimension(), config.no_iter); if (config.edge_selection) { size_t n_edges = model.get_vectorized_indicator_parameters().n_elem; diff --git a/src/models/base_model.h b/src/models/base_model.h index 145ffebc..92eb37c9 100644 --- a/src/models/base_model.h +++ b/src/models/base_model.h @@ -153,7 +153,11 @@ class BaseModel { /** * @return Full parameter dimension (fixed size, includes inactive parameters). * - * Used for fixed-size sample storage. Defaults to parameter_dimension(). + * Used by GradientSamplerBase for mass-matrix sizing and adaptation. + * For most models this equals the storage dimension. For models where + * some parameters are not sampled by NUTS (e.g., MixedMRFModel's Kyy), + * this returns the NUTS-block dimension. + * Defaults to parameter_dimension(). */ virtual size_t full_parameter_dimension() const { return parameter_dimension(); @@ -162,14 +166,35 @@ class BaseModel { /** * @return All parameters in a fixed-size vector (inactive edges are 0). * - * Used for sample storage to avoid dimension changes when edges are - * toggled on/off. + * Used by GradientSamplerBase for adaptation (online covariance). + * Dimension must match full_parameter_dimension(). */ virtual arma::vec get_full_vectorized_parameters() const = 0; /** @return Dimensionality of the active parameter space. Pure virtual. */ virtual size_t parameter_dimension() const = 0; + /** + * @return Dimension for sample storage (includes all parameters). + * + * For most models this equals full_parameter_dimension(). Override + * when storage needs more entries than the NUTS block (e.g., Kyy + * parameters in MixedMRFModel). + */ + virtual size_t storage_dimension() const { + return full_parameter_dimension(); + } + + /** + * @return All parameters in a fixed-size vector for sample storage. + * + * Dimension must match storage_dimension(). Default delegates to + * get_full_vectorized_parameters(). + */ + virtual arma::vec get_storage_vectorized_parameters() const { + return get_full_vectorized_parameters(); + } + // ========================================================================= // Infrastructure // ========================================================================= diff --git a/src/models/mixed/mixed_mrf_model.cpp b/src/models/mixed/mixed_mrf_model.cpp index b907a8d6..efd1470c 100644 --- a/src/models/mixed/mixed_mrf_model.cpp +++ b/src/models/mixed/mixed_mrf_model.cpp @@ -241,18 +241,21 @@ void MixedMRFModel::recompute_Theta() { // Parameter vectorization // ============================================================================= -// Vectorization order (free parameters): +// NUTS vectorization order (excludes Kyy — sampled by MH separately): // 1. mux_: per-variable (ordinal: C_s thresholds; BC: 2 coefficients) // 2. Kxx_: upper-triangular, row-major — p(p-1)/2 // 3. muy_: all q means -// 4. Kyy_: upper-triangle including diagonal — q(q+1)/2 -// 5. Kxy_: all p*q entries, row-major +// 4. Kxy_: all p*q entries, row-major +// +// Storage vectorization order (includes Kyy): +// 1–4. Same as NUTS order +// 5. Kyy_: upper-triangle including diagonal — q(q+1)/2 size_t MixedMRFModel::parameter_dimension() const { if(!edge_selection_active_) { return full_parameter_dimension(); } - // Count active parameters only + // Count active NUTS parameters only (no Kyy) size_t dim = num_main_ + q_; // mux + muy always active // Active Kxx edges @@ -262,14 +265,6 @@ size_t MixedMRFModel::parameter_dimension() const { } } - // Kyy diagonal always active; off-diagonal gated by edge indicators - dim += q_; // diagonal - for(size_t i = 0; i < q_ - 1; ++i) { - for(size_t j = i + 1; j < q_; ++j) { - if(gyy(i, j)) dim++; - } - } - // Active Kxy edges for(size_t i = 0; i < p_; ++i) { for(size_t j = 0; j < q_; ++j) { @@ -281,13 +276,19 @@ size_t MixedMRFModel::parameter_dimension() const { } size_t MixedMRFModel::full_parameter_dimension() const { - // mux + Kxx upper-tri + muy + Kyy upper-tri-with-diag + Kxy full + // NUTS block: mux + Kxx upper-tri + muy + Kxy full (no Kyy) + return num_main_ + num_pairwise_xx_ + q_ + num_cross_; +} + +size_t MixedMRFModel::storage_dimension() const { + // All parameters including Kyy return num_main_ + num_pairwise_xx_ + q_ + (q_ * (q_ + 1)) / 2 + num_cross_; } arma::vec MixedMRFModel::get_vectorized_parameters() const { - arma::vec out(full_parameter_dimension(), arma::fill::zeros); + // Active NUTS parameters only (excludes Kyy, excludes inactive edges) + arma::vec out(parameter_dimension()); size_t idx = 0; // 1. mux_ @@ -297,15 +298,17 @@ arma::vec MixedMRFModel::get_vectorized_parameters() const { out(idx++) = mux_(s, c); } } else { - out(idx++) = mux_(s, 0); // linear α - out(idx++) = mux_(s, 1); // quadratic β + out(idx++) = mux_(s, 0); + out(idx++) = mux_(s, 1); } } - // 2. Kxx_ upper-triangular + // 2. Kxx_ upper-triangular (active edges only when selection is active) for(size_t i = 0; i < p_ - 1; ++i) { for(size_t j = i + 1; j < p_; ++j) { - out(idx++) = Kxx_(i, j); + if(!edge_selection_active_ || gxx(i, j) == 1) { + out(idx++) = Kxx_(i, j); + } } } @@ -314,14 +317,48 @@ arma::vec MixedMRFModel::get_vectorized_parameters() const { out(idx++) = muy_(j); } - // 4. Kyy_ upper-triangle including diagonal - for(size_t i = 0; i < q_; ++i) { - for(size_t j = i; j < q_; ++j) { - out(idx++) = Kyy_(i, j); + // 4. Kxy_ row-major (active edges only when selection is active) + for(size_t i = 0; i < p_; ++i) { + for(size_t j = 0; j < q_; ++j) { + if(!edge_selection_active_ || gxy(i, j) == 1) { + out(idx++) = Kxy_(i, j); + } } } - // 5. Kxy_ row-major + return out; +} + +arma::vec MixedMRFModel::get_full_vectorized_parameters() const { + // All NUTS parameters, fixed size (inactive edges are 0, no Kyy) + arma::vec out(full_parameter_dimension(), arma::fill::zeros); + size_t idx = 0; + + // 1. mux_ + for(size_t s = 0; s < p_; ++s) { + if(is_ordinal_variable_(s)) { + for(int c = 0; c < num_categories_(s); ++c) { + out(idx++) = mux_(s, c); + } + } else { + out(idx++) = mux_(s, 0); + out(idx++) = mux_(s, 1); + } + } + + // 2. Kxx_ upper-triangular (all entries, zeros for inactive) + for(size_t i = 0; i < p_ - 1; ++i) { + for(size_t j = i + 1; j < p_; ++j) { + out(idx++) = Kxx_(i, j); + } + } + + // 3. muy_ + for(size_t j = 0; j < q_; ++j) { + out(idx++) = muy_(j); + } + + // 4. Kxy_ row-major (all entries, zeros for inactive) for(size_t i = 0; i < p_; ++i) { for(size_t j = 0; j < q_; ++j) { out(idx++) = Kxy_(i, j); @@ -331,11 +368,54 @@ arma::vec MixedMRFModel::get_vectorized_parameters() const { return out; } -arma::vec MixedMRFModel::get_full_vectorized_parameters() const { - return get_vectorized_parameters(); +arma::vec MixedMRFModel::get_storage_vectorized_parameters() const { + // All parameters including Kyy, fixed size + arma::vec out(storage_dimension(), arma::fill::zeros); + size_t idx = 0; + + // 1. mux_ + for(size_t s = 0; s < p_; ++s) { + if(is_ordinal_variable_(s)) { + for(int c = 0; c < num_categories_(s); ++c) { + out(idx++) = mux_(s, c); + } + } else { + out(idx++) = mux_(s, 0); + out(idx++) = mux_(s, 1); + } + } + + // 2. Kxx_ upper-triangular + for(size_t i = 0; i < p_ - 1; ++i) { + for(size_t j = i + 1; j < p_; ++j) { + out(idx++) = Kxx_(i, j); + } + } + + // 3. muy_ + for(size_t j = 0; j < q_; ++j) { + out(idx++) = muy_(j); + } + + // 4. Kxy_ row-major + for(size_t i = 0; i < p_; ++i) { + for(size_t j = 0; j < q_; ++j) { + out(idx++) = Kxy_(i, j); + } + } + + // 5. Kyy_ upper-triangle including diagonal + for(size_t i = 0; i < q_; ++i) { + for(size_t j = i; j < q_; ++j) { + out(idx++) = Kyy_(i, j); + } + } + + return out; } void MixedMRFModel::set_vectorized_parameters(const arma::vec& params) { + // Unpack NUTS block only (no Kyy) size_t idx = 0; // 1. mux_ @@ -350,12 +430,14 @@ void MixedMRFModel::set_vectorized_parameters(const arma::vec& params) { } } - // 2. Kxx_ upper-triangular (mirror to lower) + // 2. Kxx_ upper-triangular (active edges only when selection is active) for(size_t i = 0; i < p_ - 1; ++i) { for(size_t j = i + 1; j < p_; ++j) { - Kxx_(i, j) = params(idx); - Kxx_(j, i) = params(idx); - idx++; + if(!edge_selection_active_ || gxx(i, j) == 1) { + Kxx_(i, j) = params(idx); + Kxx_(j, i) = params(idx); + idx++; + } } } @@ -364,30 +446,62 @@ void MixedMRFModel::set_vectorized_parameters(const arma::vec& params) { muy_(j) = params(idx++); } - // 4. Kyy_ upper-triangle including diagonal (mirror off-diag) - for(size_t i = 0; i < q_; ++i) { - for(size_t j = i; j < q_; ++j) { - Kyy_(i, j) = params(idx); - if(i != j) Kyy_(j, i) = params(idx); - idx++; - } - } - - // 5. Kxy_ row-major + // 4. Kxy_ row-major (active edges only when selection is active) for(size_t i = 0; i < p_; ++i) { for(size_t j = 0; j < q_; ++j) { - Kxy_(i, j) = params(idx++); + if(!edge_selection_active_ || gxy(i, j) == 1) { + Kxy_(i, j) = params(idx++); + } } } - // Refresh all caches - recompute_Kyy_decomposition(); + // Refresh caches (Kyy unchanged, so no Kyy decomposition update needed) recompute_conditional_mean(); if(use_marginal_pl_) { recompute_Theta(); } } +arma::vec MixedMRFModel::get_active_inv_mass() const { + if(!edge_selection_active_) { + return inv_mass_; + } + + arma::vec active(parameter_dimension()); + // Main effects: always active + active.head(num_main_) = inv_mass_.head(num_main_); + + size_t offset_full = num_main_; + size_t offset_active = num_main_; + + // Kxx active edges + for(size_t i = 0; i < p_ - 1; ++i) { + for(size_t j = i + 1; j < p_; ++j) { + if(gxx(i, j) == 1) { + active(offset_active++) = inv_mass_(offset_full); + } + offset_full++; + } + } + + // muy: always active + for(size_t j = 0; j < q_; ++j) { + active(offset_active++) = inv_mass_(offset_full++); + } + + // Kxy active edges + for(size_t i = 0; i < p_; ++i) { + for(size_t j = 0; j < q_; ++j) { + if(gxy(i, j) == 1) { + active(offset_active++) = inv_mass_(offset_full); + } + offset_full++; + } + } + + return active; +} + arma::ivec MixedMRFModel::get_vectorized_indicator_parameters() { size_t total = num_pairwise_xx_ + num_pairwise_yy_ + num_cross_; arma::ivec out(total); diff --git a/src/models/mixed/mixed_mrf_model.h b/src/models/mixed/mixed_mrf_model.h index 9d8d62f9..b73ef916 100644 --- a/src/models/mixed/mixed_mrf_model.h +++ b/src/models/mixed/mixed_mrf_model.h @@ -74,8 +74,8 @@ class MixedMRFModel : public BaseModel { // Capability queries // ========================================================================= - /** @return false (MixedMRFModel is Metropolis-only, no gradient). */ - bool has_gradient() const override { return false; } + /** @return true (MixedMRFModel supports NUTS gradient for the unconstrained block). */ + bool has_gradient() const override { return true; } /** @return true (supports adaptive Metropolis via Robbins-Monro). */ bool has_adaptive_metropolis() const override { return true; } /** @return true when edge selection is enabled. */ @@ -134,29 +134,41 @@ class MixedMRFModel : public BaseModel { // ========================================================================= /** - * Dimensionality of the active parameter space. + * Dimensionality of the active NUTS parameter space (excludes Kyy). * When edge selection is active, excludes parameters for inactive edges. */ size_t parameter_dimension() const override; /** - * Full parameter dimension (all parameters, regardless of edge state). - * Used for fixed-size sample storage. + * Full NUTS-block dimension (all NUTS params, regardless of edge state). + * Excludes Kyy. Used for mass-matrix sizing and adaptation. */ size_t full_parameter_dimension() const override; - /** Get active parameters as a flat vector. */ + /** + * Storage dimension (all parameters including Kyy, regardless of edge state). + * Used for fixed-size sample storage. + */ + size_t storage_dimension() const override; + + /** Get active NUTS parameters as a flat vector (excludes Kyy). */ arma::vec get_vectorized_parameters() const override; - /** Get all parameters as a flat vector (inactive edges are 0). */ + /** Get all NUTS parameters (inactive edges are 0, excludes Kyy). */ arma::vec get_full_vectorized_parameters() const override; - /** Set parameters from a flat vector. */ + /** Get all parameters including Kyy for sample storage. */ + arma::vec get_storage_vectorized_parameters() const override; + + /** Set NUTS parameters from a flat vector (does not touch Kyy). */ void set_vectorized_parameters(const arma::vec& params) override; /** Get vectorized edge indicators (Gxx upper-tri, Gyy upper-tri, Gxy full). */ arma::ivec get_vectorized_indicator_parameters() override; + /** Get active subset of inverse mass diagonal (NUTS params only, excludes Kyy). */ + arma::vec get_active_inv_mass() const override; + // ========================================================================= // Infrastructure // ========================================================================= From dcae43e39a04a7f78bcb972d9dafb745151a22be Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Sat, 7 Mar 2026 15:11:13 +0100 Subject: [PATCH 23/60] feat: logp_and_gradient for mixed MRF (conditional + marginal PL) (phase 2) --- src/models/mixed/mixed_mrf_gradient.cpp | 575 ++++++++++++++++++++++++ src/models/mixed/mixed_mrf_model.cpp | 3 + src/models/mixed/mixed_mrf_model.h | 45 ++ 3 files changed, 623 insertions(+) create mode 100644 src/models/mixed/mixed_mrf_gradient.cpp diff --git a/src/models/mixed/mixed_mrf_gradient.cpp b/src/models/mixed/mixed_mrf_gradient.cpp new file mode 100644 index 00000000..43b36400 --- /dev/null +++ b/src/models/mixed/mixed_mrf_gradient.cpp @@ -0,0 +1,575 @@ +#include +#include "models/mixed/mixed_mrf_model.h" +#include "utils/variable_helpers.h" +#include "math/explog_macros.h" + + +// ============================================================================= +// Gradient cache +// ============================================================================= +// The gradient cache stores precomputed index mappings and observed-statistic +// contributions that do not change during a leapfrog trajectory. It is +// invalidated whenever edge indicators change (same pattern as the OMRF). +// ============================================================================= + +void MixedMRFModel::ensure_gradient_cache() { + if(gradient_cache_valid_) return; + + // --- Build index matrix for Kxx upper-triangular entries --- + // Maps (i, j) to a position in the flat gradient vector (offset from + // the start of Kxx entries, which sits at num_main_). + kxx_index_cache_.set_size(p_, p_); + kxx_index_cache_.zeros(); + + int num_active_kxx = 0; + for(size_t i = 0; i < p_ - 1; ++i) { + for(size_t j = i + 1; j < p_; ++j) { + if(edge_indicators_(i, j) == 1) { + kxx_index_cache_(i, j) = num_main_ + num_active_kxx; + kxx_index_cache_(j, i) = kxx_index_cache_(i, j); + num_active_kxx++; + } + } + } + + // --- Build index matrix for Kxy entries --- + // Maps (i, j) to a position in the flat gradient vector (offset from + // the start of Kxy entries, which sits at num_main_ + active_kxx + q). + kxy_index_cache_.set_size(p_, q_); + kxy_index_cache_.zeros(); + + int kxy_offset = num_main_ + num_active_kxx + static_cast(q_); + int num_active_kxy = 0; + for(size_t i = 0; i < p_; ++i) { + for(size_t j = 0; j < q_; ++j) { + if(edge_indicators_(i, p_ + j) == 1) { + kxy_index_cache_(i, j) = kxy_offset + num_active_kxy; + num_active_kxy++; + } + } + } + + // muy offset in gradient vector + muy_grad_offset_ = num_main_ + num_active_kxx; + + // --- Precompute observed statistics portion of the gradient --- + size_t active_dim = num_main_ + num_active_kxx + q_ + num_active_kxy; + grad_obs_cache_.set_size(active_dim); + grad_obs_cache_.zeros(); + + // Observed statistics for discrete main effects (mux) + int offset = 0; + for(size_t s = 0; s < p_; ++s) { + if(is_ordinal_variable_(s)) { + int C_s = num_categories_(s); + for(int c = 0; c < C_s; ++c) { + grad_obs_cache_(offset + c) = counts_per_category_(c + 1, s); + } + offset += C_s; + } else { + grad_obs_cache_(offset) = blume_capel_stats_(0, s); + grad_obs_cache_(offset + 1) = blume_capel_stats_(1, s); + offset += 2; + } + } + + // Observed statistics for Kxx edges + for(size_t i = 0; i < p_ - 1; ++i) { + for(size_t j = i + 1; j < p_; ++j) { + if(edge_indicators_(i, j) == 0) continue; + int loc = kxx_index_cache_(i, j); + // Factor 2 from the symmetric double-count in the pseudo-likelihood + grad_obs_cache_(loc) = 2.0 * arma::dot( + discrete_observations_dbl_.col(i), + discrete_observations_dbl_.col(j) + ); + } + } + + // No precomputed observed stats for muy or Kxy — those depend on + // continuous_observations_ combined with current parameters, so they + // are computed fresh each logp_and_gradient call. + + // Cache transpose of discrete observations for vectorized pairwise gradient + discrete_observations_dbl_t_ = discrete_observations_dbl_.t(); + + gradient_cache_valid_ = true; +} + + +void MixedMRFModel::invalidate_gradient_cache() { + gradient_cache_valid_ = false; +} + + +// ============================================================================= +// Unvectorize NUTS parameters into temporaries +// ============================================================================= +// Unpacks a NUTS-dimension parameter vector into temporary matrices without +// mutating model state. Used during leapfrog trajectory evaluation. +// ============================================================================= + +void MixedMRFModel::unvectorize_nuts_to_temps( + const arma::vec& params, + arma::mat& temp_mux, + arma::mat& temp_Kxx, + arma::vec& temp_muy, + arma::mat& temp_Kxy +) const { + size_t idx = 0; + + // 1. mux + for(size_t s = 0; s < p_; ++s) { + if(is_ordinal_variable_(s)) { + for(int c = 0; c < num_categories_(s); ++c) { + temp_mux(s, c) = params(idx++); + } + } else { + temp_mux(s, 0) = params(idx++); + temp_mux(s, 1) = params(idx++); + } + } + + // 2. Kxx upper-triangular (active only) + for(size_t i = 0; i < p_ - 1; ++i) { + for(size_t j = i + 1; j < p_; ++j) { + if(edge_indicators_(i, j) == 1) { + temp_Kxx(i, j) = params(idx++); + temp_Kxx(j, i) = temp_Kxx(i, j); + } + } + } + + // 3. muy + for(size_t j = 0; j < q_; ++j) { + temp_muy(j) = params(idx++); + } + + // 4. Kxy row-major (active only) + for(size_t i = 0; i < p_; ++i) { + for(size_t j = 0; j < q_; ++j) { + if(edge_indicators_(i, p_ + j) == 1) { + temp_Kxy(i, j) = params(idx++); + } + } + } +} + + +// ============================================================================= +// gradient +// ============================================================================= + +arma::vec MixedMRFModel::gradient(const arma::vec& parameters) { + auto [logp, grad] = logp_and_gradient(parameters); + return grad; +} + + +// ============================================================================= +// logp_and_gradient — conditional pseudo-likelihood +// ============================================================================= +// Computes the log pseudo-posterior and its gradient with respect to the +// NUTS parameters (mux, Kxx, muy, Kxy). Kyy is treated as fixed. +// +// The pseudo-log-posterior is: +// l(theta) = sum_s log p(x_s | x_{-s}, y) [OMRF conditionals] +// + log p(y | x) [GGM conditional] +// + log pi(theta) [priors] +// +// For marginal PL, the OMRF conditionals use Theta = Kxx + 2 Kxy Σyy Kxy' +// instead of Kxx directly, and derive rest scores and denominator offsets +// from Theta. The GGM conditional is the same in both modes. +// ============================================================================= + +std::pair MixedMRFModel::logp_and_gradient( + const arma::vec& parameters) +{ + ensure_gradient_cache(); + + // --- Unvectorize into temporaries --- + arma::mat temp_mux = mux_; + arma::mat temp_Kxx = Kxx_; + arma::vec temp_muy = muy_; + arma::mat temp_Kxy = Kxy_; + unvectorize_nuts_to_temps(parameters, temp_mux, temp_Kxx, temp_muy, temp_Kxy); + + // --- Derived quantities --- + // Conditional mean: M_i = muy' + 2 x_i' Kxy Σyy (n x q) + arma::mat temp_cond_mean = arma::repmat(temp_muy.t(), n_, 1) + + 2.0 * discrete_observations_dbl_ * temp_Kxy * covariance_yy_; + + // Residual: D = Y - M (n x q) + arma::mat D = continuous_observations_ - temp_cond_mean; + + // Theta for marginal PL + arma::mat temp_Theta; + if(use_marginal_pl_) { + temp_Theta = temp_Kxx + 2.0 * temp_Kxy * covariance_yy_ * temp_Kxy.t(); + } + + // Start gradient from observed-statistics cache + arma::vec grad = grad_obs_cache_; + + double logp = 0.0; + + // ========================================================================= + // Part 1: OMRF conditionals + // ========================================================================= + + int mux_offset = 0; + for(size_t s = 0; s < p_; ++s) { + int C_s = num_categories_(s); + + // --- Rest score for variable s --- + arma::vec rest; + if(use_marginal_pl_) { + // Marginal: Theta-based rest + Kxy*muy bias + double theta_ss = temp_Theta(s, s); + rest = discrete_observations_dbl_ * temp_Theta.col(s) + - discrete_observations_dbl_.col(s) * theta_ss + + 2.0 * arma::dot(temp_Kxy.row(s), temp_muy); + } else { + // Conditional: Kxx-based rest + 2 Kxy y + rest = discrete_observations_dbl_ * temp_Kxx.col(s) + - discrete_observations_dbl_.col(s) * temp_Kxx(s, s) + + 2.0 * continuous_observations_ * temp_Kxy.row(s).t(); + } + + arma::vec bound = static_cast(C_s) * rest; + + if(is_ordinal_variable_(s)) { + arma::vec main_param = temp_mux.row(s).cols(0, C_s - 1).t(); + + // Marginal PL: absorb Theta_ss into main_param + if(use_marginal_pl_) { + double theta_ss = temp_Theta(s, s); + for(int c = 0; c < C_s; ++c) { + main_param(c) += static_cast((c + 1) * (c + 1)) * theta_ss; + } + } + + LogZAndProbs result = compute_logZ_and_probs_ordinal( + main_param, rest, bound, C_s + ); + + // log pseudo-posterior contribution + logp -= arma::accu(result.log_Z); + + // Main-effect gradient: ∂/∂mux_{s,c} = count_c - sum_i prob(c) + for(int c = 0; c < C_s; ++c) { + grad(mux_offset + c) -= arma::accu(result.probs.col(c + 1)); + } + + // Expected value E_s[c+1|rest] per observation + arma::vec weights = arma::regspace(1, C_s); + arma::vec E = result.probs.cols(1, C_s) * weights; + + // Kxx pairwise gradient: sum_i x_{i,t} * (x_{i,s}+1 - E_s) + // (uses pre-transposed discrete observations for BLAS efficiency) + arma::vec pw_grad = discrete_observations_dbl_t_ * E; + for(size_t t = 0; t < p_; ++t) { + if(edge_indicators_(s, t) == 0 || s == t) continue; + int loc = (s < t) ? kxx_index_cache_(s, t) : kxx_index_cache_(t, s); + grad(loc) -= pw_grad(t); + } + + if(use_marginal_pl_) { + // Additional Kxx gradient from Theta_ss in denominator: + // ∂/∂Kxx_{st} through Theta_ss: zero (∂Theta_ss/∂Kxx_st = δ_{st}) + // So Kxx gradient from Theta rest scores is already handled above. + + // Kxy gradient from marginal OMRF (through Theta): + // E_s[c^2] per observation + arma::vec weights_sq = arma::square(weights); + arma::vec E_sq = result.probs.cols(1, C_s) * weights_sq; + + // Through Theta rest scores: for each (s, t!=s): + // ∂Theta_{st}/∂Kxy_{a,j} = 2 Σyy_{j,:} Kxy_{t,:}' δ_{as} + // + 2 Kxy_{s,:} Σyy_{:,j} δ_{at} -- but a=s here + // The contribution is: sum_i x_{it} * (x_{is}+1 - E_s) + // times ∂Theta_{st}/∂Kxy_{s,j} = 2 [Σyy Kxy_t']_j + // Summed over t!=s: this equals 2 * sum_t!=s pw_grad(t) * [Σyy Kxy_t'] + // Plus the diagonal term: Theta_ss gives + // ∂/∂Kxy_{s,j} of Theta_ss = 4 [Σyy Kxy_s']_j + // contribution: -sum_i E_sq * 4 [Σyy Kxy_s']_j + // Actually let's use the vector form: + + // Off-diagonal Theta contribution to Kxy_s gradient: + // For variable s, the rest score R_s uses Theta_{st} for t!=s + // ∂R_{is}/∂Kxy_{s,j} = sum_{t!=s} x_{it} ∂Theta_{st}/∂Kxy_{s,j} + // = 2 sum_{t!=s} x_{it} [Σyy Kxy_t']_j + // = 2 [X_{-s} Kxy_{-s} Σyy]_{i,j} + // Gradient = sum_i (x_{is}+1 - E_s(i)) * ∂R_{is}/∂Kxy_{s,j} + // = sum_i (obs_s - E_s) * 2 [X_{-s} Kxy_{-s} Σyy]_{i,j} + // But X_{-s} means "all columns except s" — complex indexing. + // + // Simpler: ∂/∂Kxy_{s,:} of the Theta-based omrf for variable s: + // ∂Θ_{st}/∂Kxy_{s,:} = 2 Σyy Kxy_t' for each t (including t=s) + // Full rest-score chain rule: + // ∂l_s/∂Kxy_{s,:} = sum_{t!=s} (obs_stat_{st} - exp_stat_{st}) + // * 2 Σyy Kxy_t' + // + (obs_sq_s - exp_sq_s) * 2 Σyy Kxy_s' + // where obs_stat_{st} = sum_i x_{it} x_{is} + // exp_stat_{st} = sum_i x_{it} E_s + // obs_sq_s = sum_i x_{is}^2 + // exp_sq_s = sum_i E_sq + + // obs_minus_E for the pairwise statistics: already pw_grad = X' E + // obs_stat_{st} = sum_i x_{it}*x_{is} (cached in grad_obs for Kxx edges) + // So: obs - exp for the pairwise is exactly pw_grad subtracted from obs. + // But we already computed that as a vector over all t. + + // Vector form for Kxy_s gradient from Theta: + // diff_t = X'(x_s - E) (p-vector, where element t = sum_i x_it(x_is - E_s)) + arma::vec diff_pw = discrete_observations_dbl_t_ * + (discrete_observations_dbl_.col(s) - E); + diff_pw(s) = 0.0; // exclude self-interaction from rest score + + // diff_diag = sum_i (x_is^2 - E_sq) + double diff_diag = arma::dot( + discrete_observations_dbl_.col(s), + discrete_observations_dbl_.col(s)) - arma::accu(E_sq); + + // ∂l_s/∂Kxy_{s,:} from Theta = 2 (sum_{t!=s} diff_pw(t) * Kxy_t + diff_diag * Kxy_s) Σyy + arma::rowvec kxy_contrib = 2.0 * (diff_pw.t() * temp_Kxy + diff_diag * temp_Kxy.row(s)) * covariance_yy_; + + for(size_t j = 0; j < q_; ++j) { + if(edge_indicators_(s, p_ + j) == 0) continue; + int loc = kxy_index_cache_(s, j); + grad(loc) += kxy_contrib(j); + } + + // muy gradient from marginal OMRF: + // ∂l_s/∂muy_j = 2 Kxy_{sj} * sum_i (x_{is} - E_s) + double sum_obs_minus_E = arma::accu(discrete_observations_dbl_.col(s)) - arma::accu(E); + for(size_t j = 0; j < q_; ++j) { + grad(muy_grad_offset_ + j) += 2.0 * temp_Kxy(s, j) * sum_obs_minus_E; + } + } else { + // Conditional PL: Kxy gradient from OMRF rest score + // ∂/∂Kxy_{s,j} = 2 sum_i y_{ij} (x_{is}+1 - E_s) + arma::rowvec kxy_grad_s = 2.0 * ( + discrete_observations_dbl_.col(s) - E + ).t() * continuous_observations_; + + for(size_t j = 0; j < q_; ++j) { + if(edge_indicators_(s, p_ + j) == 0) continue; + int loc = kxy_index_cache_(s, j); + grad(loc) += kxy_grad_s(j); + } + } + + mux_offset += C_s; + } else { + // --- Blume-Capel variable --- + int ref = baseline_category_(s); + double lin_eff = temp_mux(s, 0); + double quad_eff = temp_mux(s, 1); + + // Marginal PL: absorb Theta_ss into quadratic effect + double effective_quad = quad_eff; + if(use_marginal_pl_) { + effective_quad += temp_Theta(s, s); + } + + LogZAndProbs result = compute_logZ_and_probs_blume_capel( + rest, lin_eff, effective_quad, ref, C_s, bound + ); + + logp -= arma::accu(result.log_Z); + + arma::vec score = arma::regspace(0, C_s) - static_cast(ref); + arma::vec sq_score = arma::square(score); + + // Main-effect gradient + grad(mux_offset) -= arma::accu(result.probs * score); + grad(mux_offset + 1) -= arma::accu(result.probs * sq_score); + + // Expected score per person + arma::vec E = result.probs * score; + + // Kxx pairwise gradient + arma::vec pw_grad = discrete_observations_dbl_t_ * E; + for(size_t t = 0; t < p_; ++t) { + if(edge_indicators_(s, t) == 0 || s == t) continue; + int loc = (s < t) ? kxx_index_cache_(s, t) : kxx_index_cache_(t, s); + grad(loc) -= pw_grad(t); + } + + if(use_marginal_pl_) { + // Kxy gradient from marginal OMRF (same structure as ordinal) + arma::vec E_sq = result.probs * sq_score; + + arma::vec diff_pw = discrete_observations_dbl_t_ * + (discrete_observations_dbl_.col(s) - E); + diff_pw(s) = 0.0; + + double diff_diag = arma::dot( + discrete_observations_dbl_.col(s), + discrete_observations_dbl_.col(s)) - arma::accu(E_sq); + + arma::rowvec kxy_contrib = 2.0 * (diff_pw.t() * temp_Kxy + diff_diag * temp_Kxy.row(s)) * covariance_yy_; + + for(size_t j = 0; j < q_; ++j) { + if(edge_indicators_(s, p_ + j) == 0) continue; + int loc = kxy_index_cache_(s, j); + grad(loc) += kxy_contrib(j); + } + + // muy gradient from marginal OMRF + double sum_obs_minus_E = arma::accu(discrete_observations_dbl_.col(s)) - arma::accu(E); + for(size_t j = 0; j < q_; ++j) { + grad(muy_grad_offset_ + j) += 2.0 * temp_Kxy(s, j) * sum_obs_minus_E; + } + } else { + // Conditional PL: Kxy gradient from OMRF rest score + arma::rowvec kxy_grad_s = 2.0 * ( + discrete_observations_dbl_.col(s) - E + ).t() * continuous_observations_; + + for(size_t j = 0; j < q_; ++j) { + if(edge_indicators_(s, p_ + j) == 0) continue; + int loc = kxy_index_cache_(s, j); + grad(loc) += kxy_grad_s(j); + } + } + + mux_offset += 2; + } + } + + // Add numerator contribution to logp from discrete sufficient statistics + // (already in grad_obs_cache_ as counts, but logp needs the actual dot-products) + mux_offset = 0; + for(size_t s = 0; s < p_; ++s) { + int C_s = num_categories_(s); + arma::vec rest; + if(use_marginal_pl_) { + double theta_ss = temp_Theta(s, s); + rest = discrete_observations_dbl_ * temp_Theta.col(s) + - discrete_observations_dbl_.col(s) * theta_ss + + 2.0 * arma::dot(temp_Kxy.row(s), temp_muy); + // Theta_ss quadratic contribution + logp += temp_Theta(s, s) * arma::dot( + discrete_observations_dbl_.col(s), + discrete_observations_dbl_.col(s)); + } else { + rest = discrete_observations_dbl_ * temp_Kxx.col(s) + - discrete_observations_dbl_.col(s) * temp_Kxx(s, s) + + 2.0 * continuous_observations_ * temp_Kxy.row(s).t(); + } + // Numerator: dot(x_s, rest) + main-effect sums + logp += arma::dot(discrete_observations_dbl_.col(s), rest); + + if(is_ordinal_variable_(s)) { + for(int c = 1; c <= C_s; ++c) { + logp += static_cast(counts_per_category_(c, s)) * temp_mux(s, c - 1); + } + } else { + logp += temp_mux(s, 0) * static_cast(blume_capel_stats_(0, s)) + + temp_mux(s, 1) * static_cast(blume_capel_stats_(1, s)); + } + } + + // ========================================================================= + // Part 2: GGM conditional log-likelihood and gradients + // ========================================================================= + // log p(y | x) = n/2 * (log|Kyy| - q log(2pi)) - 1/2 trace(Kyy D'D) + // where D = Y - M, M_i = muy' + 2 x_i' Kxy Σyy + // + // Kyy is fixed, so log|Kyy| contributes to logp but not gradient. + + double quad_sum = arma::accu((D * Kyy_) % D); + logp += static_cast(n_) / 2.0 * + (-static_cast(q_) * std::log(2.0 * arma::datum::pi) + + Kyy_log_det_) + - quad_sum / 2.0; + + // ∂/∂muy: Kyy * D' * 1_n = Kyy * sum_over_rows(D) + arma::vec D_colsums = arma::sum(D, 0).t(); // q-vector + arma::vec grad_muy_ggm = Kyy_ * D_colsums; + + for(size_t j = 0; j < q_; ++j) { + grad(muy_grad_offset_ + j) += grad_muy_ggm(j); + } + + // ∂/∂Kxy: The GGM conditional depends on Kxy through M. + // ∂M/∂Kxy_{s,j} = 2 x_s [Σyy]_{j,:} + // ∂logp_ggm/∂Kxy = 2 X' D (shortcut: Kyy Σyy = I eliminates Kyy) + // + // Correctly: ∂(−½ trace(Kyy D'D))/∂Kxy_{s,j} + // = trace(Kyy D' ∂M/∂Kxy_{s,j}) + // = trace(Kyy D' * 2 x_s [Σyy]_{j,:}) + // = 2 [x_s' D Kyy Σyy]_j + // = 2 [x_s' D]_j (since Kyy Σyy = I) + arma::mat grad_Kxy_ggm = 2.0 * discrete_observations_dbl_t_ * D; // p x q + + for(size_t i = 0; i < p_; ++i) { + for(size_t j = 0; j < q_; ++j) { + if(edge_indicators_(i, p_ + j) == 0) continue; + int loc = kxy_index_cache_(i, j); + grad(loc) += grad_Kxy_ggm(i, j); + } + } + + // ========================================================================= + // Part 3: Prior log-densities and gradient contributions + // ========================================================================= + + // --- mux priors: Beta(alpha, beta) on sigmoid scale --- + mux_offset = 0; + for(size_t s = 0; s < p_; ++s) { + if(is_ordinal_variable_(s)) { + int C_s = num_categories_(s); + for(int c = 0; c < C_s; ++c) { + double val = temp_mux(s, c); + logp += val * main_alpha_ - + std::log1p(MY_EXP(val)) * (main_alpha_ + main_beta_); + double p = 1.0 / (1.0 + MY_EXP(-val)); + grad(mux_offset + c) += main_alpha_ - (main_alpha_ + main_beta_) * p; + } + mux_offset += C_s; + } else { + for(int k = 0; k < 2; ++k) { + double val = temp_mux(s, k); + logp += val * main_alpha_ - + std::log1p(MY_EXP(val)) * (main_alpha_ + main_beta_); + double p = 1.0 / (1.0 + MY_EXP(-val)); + grad(mux_offset + k) += main_alpha_ - (main_alpha_ + main_beta_) * p; + } + mux_offset += 2; + } + } + + // --- Kxx priors: Cauchy(0, pairwise_scale_) --- + for(size_t i = 0; i < p_ - 1; ++i) { + for(size_t j = i + 1; j < p_; ++j) { + if(edge_indicators_(i, j) == 0) continue; + int loc = kxx_index_cache_(i, j); + double val = temp_Kxx(i, j); + logp += R::dcauchy(val, 0.0, pairwise_scale_, true); + grad(loc) -= 2.0 * val / (val * val + pairwise_scale_ * pairwise_scale_); + } + } + + // --- muy priors: Normal(0, 1) --- + for(size_t j = 0; j < q_; ++j) { + double val = temp_muy(j); + logp += R::dnorm(val, 0.0, 1.0, true); + grad(muy_grad_offset_ + j) -= val; // ∂/∂muy: -muy (from -muy^2/2) + } + + // --- Kxy priors: Cauchy(0, pairwise_scale_) --- + for(size_t i = 0; i < p_; ++i) { + for(size_t j = 0; j < q_; ++j) { + if(edge_indicators_(i, p_ + j) == 0) continue; + int loc = kxy_index_cache_(i, j); + double val = temp_Kxy(i, j); + logp += R::dcauchy(val, 0.0, pairwise_scale_, true); + grad(loc) -= 2.0 * val / (val * val + pairwise_scale_ * pairwise_scale_); + } + } + + return {logp, grad}; +} diff --git a/src/models/mixed/mixed_mrf_model.cpp b/src/models/mixed/mixed_mrf_model.cpp index efd1470c..0a58c959 100644 --- a/src/models/mixed/mixed_mrf_model.cpp +++ b/src/models/mixed/mixed_mrf_model.cpp @@ -160,6 +160,7 @@ MixedMRFModel::MixedMRFModel(const MixedMRFModel& other) kyy_vf2_(other.kyy_vf2_), kyy_u1_(other.kyy_u1_), kyy_u2_(other.kyy_u2_), + gradient_cache_valid_(false), use_marginal_pl_(other.use_marginal_pl_), rng_(other.rng_), edge_order_xx_(other.edge_order_xx_), @@ -594,6 +595,8 @@ void MixedMRFModel::do_one_metropolis_step(int iteration) { void MixedMRFModel::update_edge_indicators() { if(!edge_selection_active_) return; + invalidate_gradient_cache(); + // Discrete-discrete edges (shuffled order) for(size_t e = 0; e < num_pairwise_xx_; ++e) { size_t idx = edge_order_xx_(e); diff --git a/src/models/mixed/mixed_mrf_model.h b/src/models/mixed/mixed_mrf_model.h index b73ef916..618a9374 100644 --- a/src/models/mixed/mixed_mrf_model.h +++ b/src/models/mixed/mixed_mrf_model.h @@ -87,6 +87,21 @@ class MixedMRFModel : public BaseModel { // Core sampling methods // ========================================================================= + /** + * Compute gradient of the log pseudo-posterior (NUTS block only). + * @param parameters NUTS-dimension parameter vector + * @return Gradient vector (same size as parameters) + */ + arma::vec gradient(const arma::vec& parameters) override; + + /** + * Combined log pseudo-posterior and gradient evaluation. + * @param parameters NUTS-dimension parameter vector + * @return Pair of (log-pseudo-posterior, gradient) + */ + std::pair logp_and_gradient( + const arma::vec& parameters) override; + /** * Perform one full Metropolis sweep over all parameter groups. * @param iteration Current iteration (for Robbins-Monro adaptation) @@ -308,6 +323,17 @@ class MixedMRFModel : public BaseModel { arma::vec kyy_u1_; ///< q-vector workspace arma::vec kyy_u2_; ///< q-vector workspace + // ========================================================================= + // Gradient cache (populated by ensure_gradient_cache) + // ========================================================================= + + arma::mat discrete_observations_dbl_t_; ///< p x n transpose (BLAS gradient) + arma::vec grad_obs_cache_; ///< Cached observed-data gradient component + arma::imat kxx_index_cache_; ///< p x p map from (i,j) to gradient index + arma::imat kxy_index_cache_; ///< p x q map from (i,j) to gradient index + int muy_grad_offset_ = 0; ///< Offset of muy block in gradient vector + bool gradient_cache_valid_ = false; ///< Whether gradient cache is current + // ========================================================================= // Configuration // ========================================================================= @@ -342,6 +368,25 @@ class MixedMRFModel : public BaseModel { /** Recompute Theta_ from Kxx_, Kxy_, covariance_yy_ (marginal PL only). */ void recompute_Theta(); + // ========================================================================= + // Gradient helpers (implemented in mixed_mrf_gradient.cpp) + // ========================================================================= + + /** Rebuild gradient index maps after edge-indicator changes. */ + void ensure_gradient_cache(); + + /** Mark gradient cache as stale (call after edge-indicator changes). */ + void invalidate_gradient_cache(); + + /** Unpack NUTS-vector into temporary parameter matrices (no model mutation). */ + void unvectorize_nuts_to_temps( + const arma::vec& params, + arma::mat& temp_mux, + arma::mat& temp_Kxx, + arma::vec& temp_muy, + arma::mat& temp_Kxy + ) const; + // ========================================================================= // Likelihood functions (implemented in mixed_mrf_likelihoods.cpp) // ========================================================================= From 5a85fb1f19ae2bf93f7a139412686fd3ddd94d07 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Sat, 7 Mar 2026 15:13:24 +0100 Subject: [PATCH 24/60] feat: extract do_kyy_metropolis_step for hybrid NUTS+MH (phase 3) --- src/models/mixed/mixed_mrf_model.cpp | 13 +++++++++++++ src/models/mixed/mixed_mrf_model.h | 6 ++++++ 2 files changed, 19 insertions(+) diff --git a/src/models/mixed/mixed_mrf_model.cpp b/src/models/mixed/mixed_mrf_model.cpp index 0a58c959..4fe7a9d4 100644 --- a/src/models/mixed/mixed_mrf_model.cpp +++ b/src/models/mixed/mixed_mrf_model.cpp @@ -592,6 +592,19 @@ void MixedMRFModel::do_one_metropolis_step(int iteration) { // (Matches the OMRF pattern; avoids double-counting indicator proposals.) } +void MixedMRFModel::do_kyy_metropolis_step(int iteration) { + // Off-diagonal Kyy (edge-gated) + if(q_ >= 2) { + for(size_t i = 0; i < q_ - 1; ++i) + for(size_t j = i + 1; j < q_; ++j) + if(!edge_selection_active_ || gyy(i, j) == 1) + update_Kyy_offdiag(i, j, iteration); + } + // Diagonal Kyy (always updated) + for(size_t i = 0; i < q_; ++i) + update_Kyy_diag(i, iteration); +} + void MixedMRFModel::update_edge_indicators() { if(!edge_selection_active_) return; diff --git a/src/models/mixed/mixed_mrf_model.h b/src/models/mixed/mixed_mrf_model.h index 618a9374..be9d0ea5 100644 --- a/src/models/mixed/mixed_mrf_model.h +++ b/src/models/mixed/mixed_mrf_model.h @@ -108,6 +108,12 @@ class MixedMRFModel : public BaseModel { */ void do_one_metropolis_step(int iteration = -1) override; + /** + * Update only Kyy parameters via Metropolis (used by hybrid NUTS+MH). + * @param iteration Current iteration (for Robbins-Monro adaptation) + */ + void do_kyy_metropolis_step(int iteration = -1); + /** * Initialize Metropolis adaptation controllers for proposal-SD tuning. * Called before warmup begins. From 2203f7a8b5665ee9843d35075f321ebf0e1366d0 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Sat, 7 Mar 2026 15:42:01 +0100 Subject: [PATCH 25/60] fix: correct 3 marginal PL gradient bugs in mixed MRF - Diagonal factor: use 4 (not 2) for dTheta_ss/dKxy_sj - Missing bias term: add 2*mu_yj * sum(x_s - E_s) contribution - Missing cross-contributions: variable s's conditional contributes to Kxy_t gradient for t != s - Add test_gradient.cpp (Rcpp export) and numerical gradient test script confirming all 4 test cases pass (max error ~1.5e-7) --- dev/tests/test-numerical-gradient.R | 153 ++++++++++++++++++++++++ src/models/mixed/mixed_mrf_gradient.cpp | 100 ++++++++-------- src/test_gradient.cpp | 57 +++++++++ 3 files changed, 262 insertions(+), 48 deletions(-) create mode 100644 dev/tests/test-numerical-gradient.R create mode 100644 src/test_gradient.cpp diff --git a/dev/tests/test-numerical-gradient.R b/dev/tests/test-numerical-gradient.R new file mode 100644 index 00000000..a81f0f12 --- /dev/null +++ b/dev/tests/test-numerical-gradient.R @@ -0,0 +1,153 @@ +# dev/tests/test-numerical-gradient.R +# +# Numerical gradient check for MixedMRFModel::logp_and_gradient(). +# Compares the analytical gradient against central finite differences. +# Tests both conditional and marginal pseudo-likelihood modes. + +library(bgms) +library(testthat) + +# --- Helper: central finite differences -------------------------------------- +numerical_gradient = function(f, theta, eps = 1e-5) { + grad = numeric(length(theta)) + for(i in seq_along(theta)) { + theta_plus = theta + theta_minus = theta + theta_plus[i] = theta[i] + eps + theta_minus[i] = theta[i] - eps + grad[i] = (f(theta_plus) - f(theta_minus)) / (2 * eps) + } + grad +} + +# --- Small test problem ------------------------------------------------------- +set.seed(42) +n = 100 +p = 2 # discrete variables, ordinal with 3 categories each +q = 2 # continuous variables + +# Simulate discrete data (values 0, 1, 2) +x_discrete = matrix(sample(0:2, n * p, replace = TRUE), nrow = n, ncol = p) +storage.mode(x_discrete) = "integer" + +# Simulate continuous data +x_continuous = matrix(rnorm(n * q), nrow = n, ncol = q) + +num_categories = as.integer(rep(3L, p)) +is_ordinal = as.integer(rep(1L, p)) +baseline_category = as.integer(rep(0L, p)) + +# NUTS vector dimension: mux (2*3=6) + Kxx_ut (1) + muy (2) + Kxy (2*2=4) = 13 +nuts_dim = sum(num_categories) + p * (p - 1) / 2 + q + p * q + +make_input = function(pl) { + list( + discrete_observations = x_discrete, + continuous_observations = x_continuous, + num_categories = num_categories, + is_ordinal_variable = is_ordinal, + baseline_category = baseline_category, + main_alpha = 1.0, + main_beta = 1.0, + pairwise_scale = 2.5, + pseudolikelihood = pl + ) +} + +# Start near zero with small random perturbations +theta0 = rnorm(nuts_dim, sd = 0.1) + +# --- Test conditional PL ------------------------------------------------------ +cat("Testing conditional pseudo-likelihood gradient...\n") +inp_cond = make_input("conditional") + +logp_fn_cond = function(th) { + bgms:::test_mixed_gradient(inp_cond, th)$logp +} + +result_cond = bgms:::test_mixed_gradient(inp_cond, theta0) +grad_analytical = result_cond$gradient +grad_numerical = numerical_gradient(logp_fn_cond, theta0) + +cat(" Max absolute difference:", max(abs(grad_analytical - grad_numerical)), "\n") +cat(" Max relative difference:", + max(abs(grad_analytical - grad_numerical) / + pmax(abs(grad_analytical), abs(grad_numerical), 1e-8)), "\n") + +test_that("conditional PL gradient matches finite differences", { + expect_equal(grad_analytical, grad_numerical, tolerance = 1e-4) +}) + +# --- Test marginal PL -------------------------------------------------------- +cat("Testing marginal pseudo-likelihood gradient...\n") +inp_marg = make_input("marginal") + +logp_fn_marg = function(th) { + bgms:::test_mixed_gradient(inp_marg, th)$logp +} + +result_marg = bgms:::test_mixed_gradient(inp_marg, theta0) +grad_analytical_m = result_marg$gradient +grad_numerical_m = numerical_gradient(logp_fn_marg, theta0) + +cat(" Max absolute difference:", max(abs(grad_analytical_m - grad_numerical_m)), "\n") +cat(" Max relative difference:", + max(abs(grad_analytical_m - grad_numerical_m) / + pmax(abs(grad_analytical_m), abs(grad_numerical_m), 1e-8)), "\n") + +test_that("marginal PL gradient matches finite differences", { + expect_equal(grad_analytical_m, grad_numerical_m, tolerance = 1e-4) +}) + +# --- Test with Blume-Capel variable ------------------------------------------- +cat("Testing with Blume-Capel variable...\n") +is_ordinal_bc = as.integer(c(1L, 0L)) # first ordinal, second Blume-Capel +# Blume-Capel has 2 main-effects (location + dispersion) +# NUTS dim: mux (3 + 2) + Kxx_ut (1) + muy (2) + Kxy (2*2=4) = 12 +nuts_dim_bc = 3 + 2 + 1 + 2 + 4 + +inp_bc = make_input("conditional") +inp_bc$is_ordinal_variable = is_ordinal_bc + +theta0_bc = rnorm(nuts_dim_bc, sd = 0.1) + +logp_fn_bc = function(th) { + bgms:::test_mixed_gradient(inp_bc, th)$logp +} + +result_bc = bgms:::test_mixed_gradient(inp_bc, theta0_bc) +grad_analytical_bc = result_bc$gradient +grad_numerical_bc = numerical_gradient(logp_fn_bc, theta0_bc) + +cat(" Max absolute difference:", max(abs(grad_analytical_bc - grad_numerical_bc)), "\n") +cat(" Max relative difference:", + max(abs(grad_analytical_bc - grad_numerical_bc) / + pmax(abs(grad_analytical_bc), abs(grad_numerical_bc), 1e-8)), "\n") + +test_that("conditional PL gradient with Blume-Capel matches finite differences", { + expect_equal(grad_analytical_bc, grad_numerical_bc, tolerance = 1e-4) +}) + +# --- Test marginal PL with Blume-Capel ---------------------------------------- +cat("Testing marginal PL with Blume-Capel variable...\n") +inp_bc_marg = make_input("marginal") +inp_bc_marg$is_ordinal_variable = is_ordinal_bc + +logp_fn_bc_marg = function(th) { + bgms:::test_mixed_gradient(inp_bc_marg, th)$logp +} + +result_bc_marg = bgms:::test_mixed_gradient(inp_bc_marg, theta0_bc) +grad_analytical_bc_m = result_bc_marg$gradient +grad_numerical_bc_m = numerical_gradient(logp_fn_bc_marg, theta0_bc) + +cat(" Max absolute difference:", max(abs(grad_analytical_bc_m - grad_numerical_bc_m)), "\n") +cat(" Max relative difference:", + max(abs(grad_analytical_bc_m - grad_numerical_bc_m) / + pmax(abs(grad_analytical_bc_m), abs(grad_numerical_bc_m), 1e-8)), "\n") + +test_that("marginal PL gradient with Blume-Capel matches finite differences", { + expect_equal(grad_analytical_bc_m, grad_numerical_bc_m, tolerance = 1e-4) +}) + +cat("\nAll gradient checks passed!\n") diff --git a/src/models/mixed/mixed_mrf_gradient.cpp b/src/models/mixed/mixed_mrf_gradient.cpp index 43b36400..a11a7cf8 100644 --- a/src/models/mixed/mixed_mrf_gradient.cpp +++ b/src/models/mixed/mixed_mrf_gradient.cpp @@ -213,6 +213,12 @@ std::pair MixedMRFModel::logp_and_gradient( double logp = 0.0; + // For marginal PL: precompute Kxy Σyy (used in cross-contributions) + arma::mat KxySyy; // p x q + if(use_marginal_pl_) { + KxySyy = temp_Kxy * covariance_yy_; + } + // ========================================================================= // Part 1: OMRF conditionals // ========================================================================= @@ -280,69 +286,52 @@ std::pair MixedMRFModel::logp_and_gradient( // So Kxx gradient from Theta rest scores is already handled above. // Kxy gradient from marginal OMRF (through Theta): - // E_s[c^2] per observation + // ∂Theta_{st}/∂Kxy_{a,j} has two terms: + // = 2 [Σyy Kxy_t']_j δ_{as} + 2 [Kxy_s Σyy]_j δ_{at} + // Self-contribution (a=s): from rest_s → Kxy_s + // Cross-contribution (a=t): from rest_s → Kxy_t for each t≠s + arma::vec weights_sq = arma::square(weights); arma::vec E_sq = result.probs.cols(1, C_s) * weights_sq; - // Through Theta rest scores: for each (s, t!=s): - // ∂Theta_{st}/∂Kxy_{a,j} = 2 Σyy_{j,:} Kxy_{t,:}' δ_{as} - // + 2 Kxy_{s,:} Σyy_{:,j} δ_{at} -- but a=s here - // The contribution is: sum_i x_{it} * (x_{is}+1 - E_s) - // times ∂Theta_{st}/∂Kxy_{s,j} = 2 [Σyy Kxy_t']_j - // Summed over t!=s: this equals 2 * sum_t!=s pw_grad(t) * [Σyy Kxy_t'] - // Plus the diagonal term: Theta_ss gives - // ∂/∂Kxy_{s,j} of Theta_ss = 4 [Σyy Kxy_s']_j - // contribution: -sum_i E_sq * 4 [Σyy Kxy_s']_j - // Actually let's use the vector form: - - // Off-diagonal Theta contribution to Kxy_s gradient: - // For variable s, the rest score R_s uses Theta_{st} for t!=s - // ∂R_{is}/∂Kxy_{s,j} = sum_{t!=s} x_{it} ∂Theta_{st}/∂Kxy_{s,j} - // = 2 sum_{t!=s} x_{it} [Σyy Kxy_t']_j - // = 2 [X_{-s} Kxy_{-s} Σyy]_{i,j} - // Gradient = sum_i (x_{is}+1 - E_s(i)) * ∂R_{is}/∂Kxy_{s,j} - // = sum_i (obs_s - E_s) * 2 [X_{-s} Kxy_{-s} Σyy]_{i,j} - // But X_{-s} means "all columns except s" — complex indexing. - // - // Simpler: ∂/∂Kxy_{s,:} of the Theta-based omrf for variable s: - // ∂Θ_{st}/∂Kxy_{s,:} = 2 Σyy Kxy_t' for each t (including t=s) - // Full rest-score chain rule: - // ∂l_s/∂Kxy_{s,:} = sum_{t!=s} (obs_stat_{st} - exp_stat_{st}) - // * 2 Σyy Kxy_t' - // + (obs_sq_s - exp_sq_s) * 2 Σyy Kxy_s' - // where obs_stat_{st} = sum_i x_{it} x_{is} - // exp_stat_{st} = sum_i x_{it} E_s - // obs_sq_s = sum_i x_{is}^2 - // exp_sq_s = sum_i E_sq - - // obs_minus_E for the pairwise statistics: already pw_grad = X' E - // obs_stat_{st} = sum_i x_{it}*x_{is} (cached in grad_obs for Kxx edges) - // So: obs - exp for the pairwise is exactly pw_grad subtracted from obs. - // But we already computed that as a vector over all t. - - // Vector form for Kxy_s gradient from Theta: - // diff_t = X'(x_s - E) (p-vector, where element t = sum_i x_it(x_is - E_s)) arma::vec diff_pw = discrete_observations_dbl_t_ * (discrete_observations_dbl_.col(s) - E); - diff_pw(s) = 0.0; // exclude self-interaction from rest score + diff_pw(s) = 0.0; - // diff_diag = sum_i (x_is^2 - E_sq) double diff_diag = arma::dot( discrete_observations_dbl_.col(s), discrete_observations_dbl_.col(s)) - arma::accu(E_sq); - // ∂l_s/∂Kxy_{s,:} from Theta = 2 (sum_{t!=s} diff_pw(t) * Kxy_t + diff_diag * Kxy_s) Σyy - arma::rowvec kxy_contrib = 2.0 * (diff_pw.t() * temp_Kxy + diff_diag * temp_Kxy.row(s)) * covariance_yy_; + double sum_obs_minus_E = arma::accu(discrete_observations_dbl_.col(s)) - arma::accu(E); + + // Self-contribution: a = s + // Off-diagonal Theta: ∂Θ_{st}/∂Kxy_{s,j} = 2 [Σyy Kxy_t']_j + // Diagonal Theta: ∂Θ_{ss}/∂Kxy_{s,j} = 4 [Σyy Kxy_s']_j + // Rest-score bias: ∂(2 Kxy_s μy)/∂Kxy_{s,j} = 2 μy_j + arma::rowvec kxy_self = 2.0 * (diff_pw.t() * temp_Kxy) * covariance_yy_ + + 4.0 * diff_diag * KxySyy.row(s) + + 2.0 * sum_obs_minus_E * temp_muy.t(); for(size_t j = 0; j < q_; ++j) { if(edge_indicators_(s, p_ + j) == 0) continue; int loc = kxy_index_cache_(s, j); - grad(loc) += kxy_contrib(j); + grad(loc) += kxy_self(j); + } + + // Cross-contribution: a = t, for each t ≠ s + // ∂l_s/∂Kxy_{t,:} = diff_pw(t) * 2 * Kxy_s * Σyy + arma::rowvec V_s = 2.0 * KxySyy.row(s); // 2 * Kxy_s * Σyy + for(size_t t = 0; t < p_; ++t) { + if(t == s || std::abs(diff_pw(t)) < 1e-300) continue; + for(size_t j = 0; j < q_; ++j) { + if(edge_indicators_(t, p_ + j) == 0) continue; + int loc = kxy_index_cache_(t, j); + grad(loc) += diff_pw(t) * V_s(j); + } } // muy gradient from marginal OMRF: // ∂l_s/∂muy_j = 2 Kxy_{sj} * sum_i (x_{is} - E_s) - double sum_obs_minus_E = arma::accu(discrete_observations_dbl_.col(s)) - arma::accu(E); for(size_t j = 0; j < q_; ++j) { grad(muy_grad_offset_ + j) += 2.0 * temp_Kxy(s, j) * sum_obs_minus_E; } @@ -409,16 +398,31 @@ std::pair MixedMRFModel::logp_and_gradient( discrete_observations_dbl_.col(s), discrete_observations_dbl_.col(s)) - arma::accu(E_sq); - arma::rowvec kxy_contrib = 2.0 * (diff_pw.t() * temp_Kxy + diff_diag * temp_Kxy.row(s)) * covariance_yy_; + double sum_obs_minus_E = arma::accu(discrete_observations_dbl_.col(s)) - arma::accu(E); + + // Self-contribution: a = s + arma::rowvec kxy_self = 2.0 * (diff_pw.t() * temp_Kxy) * covariance_yy_ + + 4.0 * diff_diag * KxySyy.row(s) + + 2.0 * sum_obs_minus_E * temp_muy.t(); for(size_t j = 0; j < q_; ++j) { if(edge_indicators_(s, p_ + j) == 0) continue; int loc = kxy_index_cache_(s, j); - grad(loc) += kxy_contrib(j); + grad(loc) += kxy_self(j); + } + + // Cross-contribution: a = t, for each t ≠ s + arma::rowvec V_s = 2.0 * KxySyy.row(s); + for(size_t t = 0; t < p_; ++t) { + if(t == s || std::abs(diff_pw(t)) < 1e-300) continue; + for(size_t j = 0; j < q_; ++j) { + if(edge_indicators_(t, p_ + j) == 0) continue; + int loc = kxy_index_cache_(t, j); + grad(loc) += diff_pw(t) * V_s(j); + } } // muy gradient from marginal OMRF - double sum_obs_minus_E = arma::accu(discrete_observations_dbl_.col(s)) - arma::accu(E); for(size_t j = 0; j < q_; ++j) { grad(muy_grad_offset_ + j) += 2.0 * temp_Kxy(s, j) * sum_obs_minus_E; } diff --git a/src/test_gradient.cpp b/src/test_gradient.cpp new file mode 100644 index 00000000..75e71762 --- /dev/null +++ b/src/test_gradient.cpp @@ -0,0 +1,57 @@ +// test_gradient.cpp - Rcpp-exported helper for numerical gradient checking +// +// Constructs a MixedMRFModel from R inputs and evaluates logp_and_gradient +// at a given parameter vector. Used by tests to compare analytical gradients +// against finite-difference approximations. +#include +#include "models/mixed/mixed_mrf_model.h" + +// Evaluate log-posterior and gradient at a given NUTS parameter vector. +// +// @param inputFromR List with model specification (same as sample_mixed_mrf) +// @param theta NUTS parameter vector (excludes Kyy) +// @param edge_selection Whether to do edge selection +// +// @return List with "logp" (double) and "gradient" (numeric vector) +// [[Rcpp::export]] +Rcpp::List test_mixed_gradient( + const Rcpp::List& inputFromR, + const arma::vec& theta, + const bool edge_selection = false +) { + arma::imat discrete_obs = Rcpp::as(inputFromR["discrete_observations"]); + arma::mat continuous_obs = Rcpp::as(inputFromR["continuous_observations"]); + arma::ivec num_categories = Rcpp::as(inputFromR["num_categories"]); + arma::uvec is_ordinal = Rcpp::as(inputFromR["is_ordinal_variable"]); + arma::ivec baseline_cat = Rcpp::as(inputFromR["baseline_category"]); + double main_alpha = Rcpp::as(inputFromR["main_alpha"]); + double main_beta = Rcpp::as(inputFromR["main_beta"]); + double pairwise_scale = Rcpp::as(inputFromR["pairwise_scale"]); + std::string pseudolikelihood = Rcpp::as(inputFromR["pseudolikelihood"]); + + int p = discrete_obs.n_cols; + int q = continuous_obs.n_cols; + int total = p + q; + + arma::mat inclusion_prob = arma::ones(total, total); + arma::imat edge_indicators = arma::ones(total, total); + + MixedMRFModel model( + discrete_obs, continuous_obs, + num_categories, is_ordinal, baseline_cat, + inclusion_prob, edge_indicators, + edge_selection, pseudolikelihood, + main_alpha, main_beta, pairwise_scale, + 42 + ); + + // Set the NUTS block to the provided parameters + model.set_vectorized_parameters(theta); + + auto result = model.logp_and_gradient(theta); + + return Rcpp::List::create( + Rcpp::Named("logp") = result.first, + Rcpp::Named("gradient") = Rcpp::NumericVector(result.second.begin(), result.second.end()) + ); +} From 31f6773dde09a24466652ed7f2c6aca5514e41bc Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Sat, 7 Mar 2026 15:42:08 +0100 Subject: [PATCH 26/60] feat: add hybrid NUTS sampler for mixed MRF models - HybridNUTSSampler: NUTS for (mux, Kxx, muy, Kxy), MH for Kyy - Wire sampler selection: R 'nuts' remapped to 'hybrid-nuts' for mixed - Pass sampler config (type, target_accept, tree depth, leapfrogs) from R through to C++ SamplerConfig - Update chain_runner factory and learn_sd flag for hybrid-nuts --- R/RcppExports.R | 8 ++- R/bgm_spec.R | 19 ++++-- R/run_sampler.R | 6 +- src/RcppExports.cpp | 26 +++++++-- src/mcmc/execution/chain_runner.cpp | 4 ++ src/mcmc/samplers/hybrid_nuts_sampler.h | 77 +++++++++++++++++++++++++ src/sample_mixed.cpp | 20 +++++-- 7 files changed, 144 insertions(+), 16 deletions(-) create mode 100644 src/mcmc/samplers/hybrid_nuts_sampler.h diff --git a/R/RcppExports.R b/R/RcppExports.R index 39d81707..7324ab26 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -61,8 +61,8 @@ sample_ggm <- function(inputFromR, prior_inclusion_prob, initial_edge_indicators .Call(`_bgms_sample_ggm`, inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior, beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between, beta_bernoulli_beta_between, dirichlet_alpha, lambda, na_impute, missing_index_nullable) } -sample_mixed_mrf <- function(inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior = "Bernoulli", beta_bernoulli_alpha = 1.0, beta_bernoulli_beta = 1.0, beta_bernoulli_alpha_between = 1.0, beta_bernoulli_beta_between = 1.0, dirichlet_alpha = 1.0, lambda = 1.0) { - .Call(`_bgms_sample_mixed_mrf`, inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior, beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between, beta_bernoulli_beta_between, dirichlet_alpha, lambda) +sample_mixed_mrf <- function(inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior = "Bernoulli", beta_bernoulli_alpha = 1.0, beta_bernoulli_beta = 1.0, beta_bernoulli_alpha_between = 1.0, beta_bernoulli_beta_between = 1.0, dirichlet_alpha = 1.0, lambda = 1.0, sampler_type = "mh", target_acceptance = 0.80, max_tree_depth = 10L, num_leapfrogs = 100L) { + .Call(`_bgms_sample_mixed_mrf`, inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior, beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between, beta_bernoulli_beta_between, dirichlet_alpha, lambda, sampler_type, target_acceptance, max_tree_depth, num_leapfrogs) } sample_omrf <- function(inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, sampler_type, seed, no_threads, progress_type, edge_prior = "Bernoulli", na_impute = FALSE, missing_index_nullable = NULL, beta_bernoulli_alpha = 1.0, beta_bernoulli_beta = 1.0, beta_bernoulli_alpha_between = 1.0, beta_bernoulli_beta_between = 1.0, dirichlet_alpha = 1.0, lambda = 1.0, target_acceptance = 0.8, max_tree_depth = 10L, num_leapfrogs = 10L, pairwise_scaling_factors_nullable = NULL) { @@ -73,3 +73,7 @@ compute_Vn_mfm_sbm <- function(num_variables, dirichlet_alpha, t_max, lambda) { .Call(`_bgms_compute_Vn_mfm_sbm`, num_variables, dirichlet_alpha, t_max, lambda) } +test_mixed_gradient <- function(inputFromR, theta, edge_selection = FALSE) { + .Call(`_bgms_test_mixed_gradient`, inputFromR, theta, edge_selection) +} + diff --git a/R/bgm_spec.R b/R/bgm_spec.R index 3c186cba..4668ccf7 100644 --- a/R/bgm_spec.R +++ b/R/bgm_spec.R @@ -208,8 +208,13 @@ validate_bgm_spec = function(spec) { if(length(spec$data$num_categories) != spec$data$num_discrete) { stop("bgm_spec: num_categories length doesn't match num_discrete.") } - if(spec$sampler$update_method != "adaptive-metropolis") { - stop("bgm_spec: model_type = 'mixed_mrf' requires update_method = 'adaptive-metropolis'.") + allowed = c("adaptive-metropolis", "hybrid-nuts") + if(!(spec$sampler$update_method %in% allowed)) { + stop( + "bgm_spec: model_type = 'mixed_mrf' requires update_method in ", + paste(sQuote(allowed), collapse = " or "), ". Got '", + spec$sampler$update_method, "'." + ) } } @@ -316,8 +321,6 @@ bgm_spec = function(x, } # --- Sampler (needs is_continuous and edge_selection early) ------------------ - # Mixed MRF is MH-only (like GGM): force adaptive-metropolis via is_continuous - sampler_is_continuous = is_continuous || is_mixed sampler = validate_sampler( update_method = update_method, target_accept = target_accept, @@ -330,11 +333,17 @@ bgm_spec = function(x, cores = cores, seed = seed, display_progress = display_progress, - is_continuous = sampler_is_continuous, + is_continuous = is_continuous, edge_selection = if(model_type == "compare") FALSE else edge_selection, verbose = verbose ) + # Mixed MRF: remap "nuts" to the hybrid sampler that uses NUTS for the + # unconstrained block and component-wise MH for the SPD-constrained Kyy. + if(is_mixed && sampler$update_method == "nuts") { + sampler$update_method = "hybrid-nuts" + } + # --- Build by model type ---------------------------------------------------- if(model_type == "ggm") { spec = build_spec_ggm( diff --git a/R/run_sampler.R b/R/run_sampler.R index a21e173e..0ec3b967 100644 --- a/R/run_sampler.R +++ b/R/run_sampler.R @@ -201,7 +201,11 @@ run_sampler_mixed_mrf = function(spec) { beta_bernoulli_alpha_between = bb_alpha_between, beta_bernoulli_beta_between = bb_beta_between, dirichlet_alpha = p$dirichlet_alpha, - lambda = p$lambda + lambda = p$lambda, + sampler_type = s$update_method, + target_acceptance = s$target_accept, + max_tree_depth = s$nuts_max_depth, + num_leapfrogs = s$hmc_num_leapfrogs ) out_raw diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 3fdb2d88..0a6e6df3 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -310,8 +310,8 @@ BEGIN_RCPP END_RCPP } // sample_mixed_mrf -Rcpp::List sample_mixed_mrf(const Rcpp::List& inputFromR, const arma::mat& prior_inclusion_prob, const arma::imat& initial_edge_indicators, const int no_iter, const int no_warmup, const int no_chains, const bool edge_selection, const int seed, const int no_threads, const int progress_type, const std::string& edge_prior, const double beta_bernoulli_alpha, const double beta_bernoulli_beta, const double beta_bernoulli_alpha_between, const double beta_bernoulli_beta_between, const double dirichlet_alpha, const double lambda); -RcppExport SEXP _bgms_sample_mixed_mrf(SEXP inputFromRSEXP, SEXP prior_inclusion_probSEXP, SEXP initial_edge_indicatorsSEXP, SEXP no_iterSEXP, SEXP no_warmupSEXP, SEXP no_chainsSEXP, SEXP edge_selectionSEXP, SEXP seedSEXP, SEXP no_threadsSEXP, SEXP progress_typeSEXP, SEXP edge_priorSEXP, SEXP beta_bernoulli_alphaSEXP, SEXP beta_bernoulli_betaSEXP, SEXP beta_bernoulli_alpha_betweenSEXP, SEXP beta_bernoulli_beta_betweenSEXP, SEXP dirichlet_alphaSEXP, SEXP lambdaSEXP) { +Rcpp::List sample_mixed_mrf(const Rcpp::List& inputFromR, const arma::mat& prior_inclusion_prob, const arma::imat& initial_edge_indicators, const int no_iter, const int no_warmup, const int no_chains, const bool edge_selection, const int seed, const int no_threads, const int progress_type, const std::string& edge_prior, const double beta_bernoulli_alpha, const double beta_bernoulli_beta, const double beta_bernoulli_alpha_between, const double beta_bernoulli_beta_between, const double dirichlet_alpha, const double lambda, const std::string& sampler_type, const double target_acceptance, const int max_tree_depth, const int num_leapfrogs); +RcppExport SEXP _bgms_sample_mixed_mrf(SEXP inputFromRSEXP, SEXP prior_inclusion_probSEXP, SEXP initial_edge_indicatorsSEXP, SEXP no_iterSEXP, SEXP no_warmupSEXP, SEXP no_chainsSEXP, SEXP edge_selectionSEXP, SEXP seedSEXP, SEXP no_threadsSEXP, SEXP progress_typeSEXP, SEXP edge_priorSEXP, SEXP beta_bernoulli_alphaSEXP, SEXP beta_bernoulli_betaSEXP, SEXP beta_bernoulli_alpha_betweenSEXP, SEXP beta_bernoulli_beta_betweenSEXP, SEXP dirichlet_alphaSEXP, SEXP lambdaSEXP, SEXP sampler_typeSEXP, SEXP target_acceptanceSEXP, SEXP max_tree_depthSEXP, SEXP num_leapfrogsSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; @@ -332,7 +332,11 @@ BEGIN_RCPP Rcpp::traits::input_parameter< const double >::type beta_bernoulli_beta_between(beta_bernoulli_beta_betweenSEXP); Rcpp::traits::input_parameter< const double >::type dirichlet_alpha(dirichlet_alphaSEXP); Rcpp::traits::input_parameter< const double >::type lambda(lambdaSEXP); - rcpp_result_gen = Rcpp::wrap(sample_mixed_mrf(inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior, beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between, beta_bernoulli_beta_between, dirichlet_alpha, lambda)); + Rcpp::traits::input_parameter< const std::string& >::type sampler_type(sampler_typeSEXP); + Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); + Rcpp::traits::input_parameter< const int >::type max_tree_depth(max_tree_depthSEXP); + Rcpp::traits::input_parameter< const int >::type num_leapfrogs(num_leapfrogsSEXP); + rcpp_result_gen = Rcpp::wrap(sample_mixed_mrf(inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior, beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between, beta_bernoulli_beta_between, dirichlet_alpha, lambda, sampler_type, target_acceptance, max_tree_depth, num_leapfrogs)); return rcpp_result_gen; END_RCPP } @@ -384,6 +388,19 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } +// test_mixed_gradient +Rcpp::List test_mixed_gradient(const Rcpp::List& inputFromR, const arma::vec& theta, const bool edge_selection); +RcppExport SEXP _bgms_test_mixed_gradient(SEXP inputFromRSEXP, SEXP thetaSEXP, SEXP edge_selectionSEXP) { +BEGIN_RCPP + Rcpp::RObject rcpp_result_gen; + Rcpp::RNGScope rcpp_rngScope_gen; + Rcpp::traits::input_parameter< const Rcpp::List& >::type inputFromR(inputFromRSEXP); + Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); + Rcpp::traits::input_parameter< const bool >::type edge_selection(edge_selectionSEXP); + rcpp_result_gen = Rcpp::wrap(test_mixed_gradient(inputFromR, theta, edge_selection)); + return rcpp_result_gen; +END_RCPP +} static const R_CallMethodDef CallEntries[] = { {"_bgms_run_bgmCompare_parallel", (DL_FUNC) &_bgms_run_bgmCompare_parallel, 38}, @@ -401,9 +418,10 @@ static const R_CallMethodDef CallEntries[] = { {"_bgms_sample_mixed_mrf_gibbs", (DL_FUNC) &_bgms_sample_mixed_mrf_gibbs, 11}, {"_bgms_run_mixed_simulation_parallel", (DL_FUNC) &_bgms_run_mixed_simulation_parallel, 16}, {"_bgms_sample_ggm", (DL_FUNC) &_bgms_sample_ggm, 19}, - {"_bgms_sample_mixed_mrf", (DL_FUNC) &_bgms_sample_mixed_mrf, 17}, + {"_bgms_sample_mixed_mrf", (DL_FUNC) &_bgms_sample_mixed_mrf, 21}, {"_bgms_sample_omrf", (DL_FUNC) &_bgms_sample_omrf, 24}, {"_bgms_compute_Vn_mfm_sbm", (DL_FUNC) &_bgms_compute_Vn_mfm_sbm, 4}, + {"_bgms_test_mixed_gradient", (DL_FUNC) &_bgms_test_mixed_gradient, 3}, {NULL, NULL, 0} }; diff --git a/src/mcmc/execution/chain_runner.cpp b/src/mcmc/execution/chain_runner.cpp index d3ee969d..52b3087d 100644 --- a/src/mcmc/execution/chain_runner.cpp +++ b/src/mcmc/execution/chain_runner.cpp @@ -5,11 +5,14 @@ #include "mcmc/samplers/nuts_sampler.h" #include "mcmc/samplers/hmc_sampler.h" #include "mcmc/samplers/metropolis_sampler.h" +#include "mcmc/samplers/hybrid_nuts_sampler.h" std::unique_ptr create_sampler(const SamplerConfig& config, WarmupSchedule& schedule) { if (config.sampler_type == "nuts") { return std::make_unique(config, schedule); + } else if (config.sampler_type == "hybrid-nuts") { + return std::make_unique(config, schedule); } else if (config.sampler_type == "hmc" || config.sampler_type == "hamiltonian-mc") { return std::make_unique(config, schedule); } else if (config.sampler_type == "mh" || config.sampler_type == "adaptive-metropolis") { @@ -32,6 +35,7 @@ void run_mcmc_chain( // Construct warmup schedule (shared by runner and sampler) const bool learn_sd = (config.sampler_type == "nuts" || + config.sampler_type == "hybrid-nuts" || config.sampler_type == "hmc" || config.sampler_type == "hamiltonian-mc"); WarmupSchedule schedule(config.no_warmup, config.edge_selection, learn_sd); diff --git a/src/mcmc/samplers/hybrid_nuts_sampler.h b/src/mcmc/samplers/hybrid_nuts_sampler.h new file mode 100644 index 00000000..30d508be --- /dev/null +++ b/src/mcmc/samplers/hybrid_nuts_sampler.h @@ -0,0 +1,77 @@ +#pragma once + +#include +#include "mcmc/samplers/sampler_base.h" +#include "mcmc/algorithms/nuts.h" +#include "models/mixed/mixed_mrf_model.h" + +/** + * HybridNUTSSampler - NUTS for unconstrained block + MH for Kyy + * + * Designed for MixedMRFModel where the NUTS block covers (mux, Kxx, muy, Kxy) + * and the SPD-constrained Kyy is updated via component-wise Metropolis. + * Inherits warmup adaptation (step size, diagonal mass matrix) from + * GradientSamplerBase for the NUTS block. Kyy proposal SDs are adapted + * via the embedded Robbins-Monro schedule inside the model. + */ +class HybridNUTSSampler : public GradientSamplerBase { +public: + explicit HybridNUTSSampler(const SamplerConfig& config, WarmupSchedule& schedule) + : GradientSamplerBase(config.initial_step_size, config.target_acceptance, schedule), + max_tree_depth_(config.max_tree_depth), + schedule_(schedule) + {} + + bool has_nuts_diagnostics() const override { return true; } + + void initialize(BaseModel& model) override { + // Initialize NUTS adaptation (step-size heuristic + mass matrix) + GradientSamplerBase::initialize(model); + + // Initialize Kyy Metropolis adaptation (stores total_warmup_) + model.init_metropolis_adaptation(schedule_); + } + +protected: + StepResult do_gradient_step(BaseModel& model) override { + // --- Phase 1: NUTS step for the unconstrained block --- + arma::vec theta = model.get_vectorized_parameters(); + SafeRNG& rng = model.get_rng(); + + auto joint_fn = [&model](const arma::vec& params) + -> std::pair { + return model.logp_and_gradient(params); + }; + + arma::vec active_inv_mass = model.get_active_inv_mass(); + + StepResult result = nuts_step( + theta, step_size_, joint_fn, + active_inv_mass, rng, max_tree_depth_ + ); + + model.set_vectorized_parameters(result.state); + + // --- Phase 2: Kyy Metropolis step --- + auto& mixed = static_cast(model); + mixed.do_kyy_metropolis_step(current_iteration_); + + return result; + } + +private: + int max_tree_depth_; + WarmupSchedule& schedule_; + +public: + // The iteration counter is set by the overridden step() method via + // the base class. We store it so do_gradient_step() can pass it + // to the Kyy Metropolis update for Robbins-Monro adaptation. + StepResult step(BaseModel& model, int iteration) override { + current_iteration_ = iteration; + return GradientSamplerBase::step(model, iteration); + } + +private: + int current_iteration_ = -1; +}; diff --git a/src/sample_mixed.cpp b/src/sample_mixed.cpp index 2ff3b721..298ac607 100644 --- a/src/sample_mixed.cpp +++ b/src/sample_mixed.cpp @@ -2,7 +2,8 @@ // // Uses the unified MCMC runner infrastructure to sample from models with // both discrete (ordinal / Blume-Capel) and continuous variables. -// Supports MH sampler only, with optional edge selection. +// Supports MH and hybrid-nuts (NUTS for unconstrained block + MH for Kyy) +// samplers, with optional edge selection. #include #include #include @@ -41,6 +42,10 @@ // @param beta_bernoulli_beta_between SBM between-cluster beta // @param dirichlet_alpha Dirichlet alpha for SBM // @param lambda Lambda for SBM +// @param sampler_type Sampler type string ("mh", "hybrid-nuts", etc.) +// @param target_acceptance Target acceptance rate for gradient-based samplers +// @param max_tree_depth Maximum tree depth for NUTS +// @param num_leapfrogs Number of leapfrog steps for HMC // // @return List with per-chain results including samples and diagnostics // [[Rcpp::export]] @@ -61,7 +66,11 @@ Rcpp::List sample_mixed_mrf( const double beta_bernoulli_alpha_between = 1.0, const double beta_bernoulli_beta_between = 1.0, const double dirichlet_alpha = 1.0, - const double lambda = 1.0 + const double lambda = 1.0, + const std::string& sampler_type = "mh", + const double target_acceptance = 0.80, + const int max_tree_depth = 10, + const int num_leapfrogs = 100 ) { // Extract model inputs from R list arma::imat discrete_obs = Rcpp::as(inputFromR["discrete_observations"]); @@ -93,14 +102,17 @@ Rcpp::List sample_mixed_mrf( dirichlet_alpha, lambda ); - // Configure sampler — Mixed MRF is MH-only + // Configure sampler SamplerConfig config; - config.sampler_type = "mh"; + config.sampler_type = sampler_type; config.no_iter = no_iter; config.no_warmup = no_warmup; config.edge_selection = edge_selection; config.seed = seed; config.na_impute = false; + config.target_acceptance = target_acceptance; + config.max_tree_depth = max_tree_depth; + config.num_leapfrogs = num_leapfrogs; // Set up progress manager ProgressManager pm(no_chains, no_iter, no_warmup, 50, progress_type); From 04b95626db20a97b33958786635be92acedefd12 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Sun, 8 Mar 2026 01:01:21 +0100 Subject: [PATCH 27/60] fix: remove test scaffolding, wire NUTS diagnostics, fix gradient overflow guards - Delete test_gradient.cpp and its Rcpp export (misguided test infrastructure) - Wire treedepth/divergent/energy diagnostics into mixed MRF output - Recognize 'hybrid-nuts' sampler type for NUTS diagnostic collection - Pass nuts_max_depth through to mixed MRF arguments - Fix ordinal gradient bound: use max(main_param) + C_s*rest, clamped at 0 - Fix BC gradient: use uninitialized bc_bound (computed inside helper) - Tighten fast-block overflow threshold by max(|main_param|) in both compute_logZ_and_probs_ordinal and compute_logZ_and_probs_blume_capel - Change mixed MRF progress interval from 50 to 1 --- R/RcppExports.R | 4 -- R/bgm_spec.R | 1 + R/build_output.R | 11 +++++ src/RcppExports.cpp | 14 ------ src/mcmc/execution/chain_runner.cpp | 3 +- src/models/mixed/mixed_mrf_gradient.cpp | 12 ++++-- src/sample_mixed.cpp | 2 +- src/test_gradient.cpp | 57 ------------------------- src/utils/variable_helpers.cpp | 20 +++++++-- 9 files changed, 40 insertions(+), 84 deletions(-) delete mode 100644 src/test_gradient.cpp diff --git a/R/RcppExports.R b/R/RcppExports.R index 7324ab26..186d9938 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -73,7 +73,3 @@ compute_Vn_mfm_sbm <- function(num_variables, dirichlet_alpha, t_max, lambda) { .Call(`_bgms_compute_Vn_mfm_sbm`, num_variables, dirichlet_alpha, t_max, lambda) } -test_mixed_gradient <- function(inputFromR, theta, edge_selection = FALSE) { - .Call(`_bgms_test_mixed_gradient`, inputFromR, theta, edge_selection) -} - diff --git a/R/bgm_spec.R b/R/bgm_spec.R index 4668ccf7..89995d9a 100644 --- a/R/bgm_spec.R +++ b/R/bgm_spec.R @@ -1167,6 +1167,7 @@ build_arguments_mixed_mrf = function(spec) { version = packageVersion("bgms"), update_method = spec$sampler$update_method, target_accept = spec$sampler$target_accept, + nuts_max_depth = spec$sampler$nuts_max_depth, num_chains = spec$sampler$chains, num_categories = spec$data$num_categories, data_columnnames = spec$data$data_columnnames, diff --git a/R/build_output.R b/R/build_output.R index 068908a2..c171342f 100644 --- a/R/build_output.R +++ b/R/build_output.R @@ -425,6 +425,9 @@ build_output_mixed_mrf = function(spec, raw) { if(!is.null(chain$allocation_samples)) { res$allocations = t(chain$allocation_samples) } + if(!is.null(chain$treedepth)) res[["treedepth__"]] = chain$treedepth + if(!is.null(chain$divergent)) res[["divergent__"]] = chain$divergent + if(!is.null(chain$energy)) res[["energy__"]] = chain$energy res }) @@ -700,6 +703,14 @@ build_output_mixed_mrf = function(spec, raw) { ) ) + # --- NUTS diagnostics ------------------------------------------------------- + if(s$update_method == "hybrid-nuts") { + results$nuts_diag = summarize_nuts_diagnostics( + raw, + nuts_max_depth = s$nuts_max_depth + ) + } + results } diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 0a6e6df3..06f7a1b3 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -388,19 +388,6 @@ BEGIN_RCPP return rcpp_result_gen; END_RCPP } -// test_mixed_gradient -Rcpp::List test_mixed_gradient(const Rcpp::List& inputFromR, const arma::vec& theta, const bool edge_selection); -RcppExport SEXP _bgms_test_mixed_gradient(SEXP inputFromRSEXP, SEXP thetaSEXP, SEXP edge_selectionSEXP) { -BEGIN_RCPP - Rcpp::RObject rcpp_result_gen; - Rcpp::RNGScope rcpp_rngScope_gen; - Rcpp::traits::input_parameter< const Rcpp::List& >::type inputFromR(inputFromRSEXP); - Rcpp::traits::input_parameter< const arma::vec& >::type theta(thetaSEXP); - Rcpp::traits::input_parameter< const bool >::type edge_selection(edge_selectionSEXP); - rcpp_result_gen = Rcpp::wrap(test_mixed_gradient(inputFromR, theta, edge_selection)); - return rcpp_result_gen; -END_RCPP -} static const R_CallMethodDef CallEntries[] = { {"_bgms_run_bgmCompare_parallel", (DL_FUNC) &_bgms_run_bgmCompare_parallel, 38}, @@ -421,7 +408,6 @@ static const R_CallMethodDef CallEntries[] = { {"_bgms_sample_mixed_mrf", (DL_FUNC) &_bgms_sample_mixed_mrf, 21}, {"_bgms_sample_omrf", (DL_FUNC) &_bgms_sample_omrf, 24}, {"_bgms_compute_Vn_mfm_sbm", (DL_FUNC) &_bgms_compute_Vn_mfm_sbm, 4}, - {"_bgms_test_mixed_gradient", (DL_FUNC) &_bgms_test_mixed_gradient, 3}, {NULL, NULL, 0} }; diff --git a/src/mcmc/execution/chain_runner.cpp b/src/mcmc/execution/chain_runner.cpp index 52b3087d..1635b15b 100644 --- a/src/mcmc/execution/chain_runner.cpp +++ b/src/mcmc/execution/chain_runner.cpp @@ -143,7 +143,8 @@ std::vector run_mcmc_sampler( const int no_threads, ProgressManager& pm ) { - const bool has_nuts_diag = (config.sampler_type == "nuts"); + const bool has_nuts_diag = (config.sampler_type == "nuts" || + config.sampler_type == "hybrid-nuts"); const bool has_sbm_alloc = edge_prior.has_allocations() || (config.edge_selection && dynamic_cast(&edge_prior) != nullptr); diff --git a/src/models/mixed/mixed_mrf_gradient.cpp b/src/models/mixed/mixed_mrf_gradient.cpp index a11a7cf8..4c1fec18 100644 --- a/src/models/mixed/mixed_mrf_gradient.cpp +++ b/src/models/mixed/mixed_mrf_gradient.cpp @@ -242,8 +242,6 @@ std::pair MixedMRFModel::logp_and_gradient( + 2.0 * continuous_observations_ * temp_Kxy.row(s).t(); } - arma::vec bound = static_cast(C_s) * rest; - if(is_ordinal_variable_(s)) { arma::vec main_param = temp_mux.row(s).cols(0, C_s - 1).t(); @@ -255,6 +253,13 @@ std::pair MixedMRFModel::logp_and_gradient( } } + // bound = per-observation upper bound on log-scores for numerical + // stability. Must cover max_c(main_param(c) + (c+1)*rest(i)). + // The highest-category term main_param(C_s-1) + C_s*rest dominates + // when rest > 0; category 0 (score = 0) dominates when rest << 0. + arma::vec bound = main_param(C_s - 1) + static_cast(C_s) * rest; + bound = arma::max(bound, arma::zeros(bound.n_elem)); + LogZAndProbs result = compute_logZ_and_probs_ordinal( main_param, rest, bound, C_s ); @@ -362,8 +367,9 @@ std::pair MixedMRFModel::logp_and_gradient( effective_quad += temp_Theta(s, s); } + arma::vec bc_bound; LogZAndProbs result = compute_logZ_and_probs_blume_capel( - rest, lin_eff, effective_quad, ref, C_s, bound + rest, lin_eff, effective_quad, ref, C_s, bc_bound ); logp -= arma::accu(result.log_Z); diff --git a/src/sample_mixed.cpp b/src/sample_mixed.cpp index 298ac607..7a208a1d 100644 --- a/src/sample_mixed.cpp +++ b/src/sample_mixed.cpp @@ -115,7 +115,7 @@ Rcpp::List sample_mixed_mrf( config.num_leapfrogs = num_leapfrogs; // Set up progress manager - ProgressManager pm(no_chains, no_iter, no_warmup, 50, progress_type); + ProgressManager pm(no_chains, no_iter, no_warmup, 1, progress_type); // Run MCMC using unified infrastructure std::vector results = run_mcmc_sampler( diff --git a/src/test_gradient.cpp b/src/test_gradient.cpp deleted file mode 100644 index 75e71762..00000000 --- a/src/test_gradient.cpp +++ /dev/null @@ -1,57 +0,0 @@ -// test_gradient.cpp - Rcpp-exported helper for numerical gradient checking -// -// Constructs a MixedMRFModel from R inputs and evaluates logp_and_gradient -// at a given parameter vector. Used by tests to compare analytical gradients -// against finite-difference approximations. -#include -#include "models/mixed/mixed_mrf_model.h" - -// Evaluate log-posterior and gradient at a given NUTS parameter vector. -// -// @param inputFromR List with model specification (same as sample_mixed_mrf) -// @param theta NUTS parameter vector (excludes Kyy) -// @param edge_selection Whether to do edge selection -// -// @return List with "logp" (double) and "gradient" (numeric vector) -// [[Rcpp::export]] -Rcpp::List test_mixed_gradient( - const Rcpp::List& inputFromR, - const arma::vec& theta, - const bool edge_selection = false -) { - arma::imat discrete_obs = Rcpp::as(inputFromR["discrete_observations"]); - arma::mat continuous_obs = Rcpp::as(inputFromR["continuous_observations"]); - arma::ivec num_categories = Rcpp::as(inputFromR["num_categories"]); - arma::uvec is_ordinal = Rcpp::as(inputFromR["is_ordinal_variable"]); - arma::ivec baseline_cat = Rcpp::as(inputFromR["baseline_category"]); - double main_alpha = Rcpp::as(inputFromR["main_alpha"]); - double main_beta = Rcpp::as(inputFromR["main_beta"]); - double pairwise_scale = Rcpp::as(inputFromR["pairwise_scale"]); - std::string pseudolikelihood = Rcpp::as(inputFromR["pseudolikelihood"]); - - int p = discrete_obs.n_cols; - int q = continuous_obs.n_cols; - int total = p + q; - - arma::mat inclusion_prob = arma::ones(total, total); - arma::imat edge_indicators = arma::ones(total, total); - - MixedMRFModel model( - discrete_obs, continuous_obs, - num_categories, is_ordinal, baseline_cat, - inclusion_prob, edge_indicators, - edge_selection, pseudolikelihood, - main_alpha, main_beta, pairwise_scale, - 42 - ); - - // Set the NUTS block to the provided parameters - model.set_vectorized_parameters(theta); - - auto result = model.logp_and_gradient(theta); - - return Rcpp::List::create( - Rcpp::Named("logp") = result.first, - Rcpp::Named("gradient") = Rcpp::NumericVector(result.second.begin(), result.second.end()) - ); -} diff --git a/src/utils/variable_helpers.cpp b/src/utils/variable_helpers.cpp index 586501cd..b3a745a4 100644 --- a/src/utils/variable_helpers.cpp +++ b/src/utils/variable_helpers.cpp @@ -359,6 +359,13 @@ LogZAndProbs compute_logZ_and_probs_ordinal( const arma::vec eM = ARMA_MY_EXP(main_param); + // The fast block computes eM[c] * pow % eB where the intermediate + // eM[c] * pow = exp(main_param(c) + (c+1)*rest) can overflow even when + // the final product exp(main_param(c) + (c+1)*rest - bound) is finite. + // Reduce the fast-block threshold by max(|main_param|) to prevent this. + const double max_abs_main = arma::max(arma::abs(main_param)); + const double FAST_LIM = std::max(0.0, EXP_BOUND - max_abs_main); + auto do_fast_block = [&](arma::uword i0, arma::uword i1) { auto P = result.probs.rows(i0, i1).cols(1, num_cats); arma::vec r = residual_score.rows(i0, i1); @@ -396,10 +403,10 @@ LogZAndProbs compute_logZ_and_probs_ordinal( const double* bp = bound.memptr(); arma::uword i = 0; while (i < N) { - const bool fast = !(bp[i] < -EXP_BOUND || bp[i] > EXP_BOUND); + const bool fast = !(bp[i] < -FAST_LIM || bp[i] > FAST_LIM); arma::uword j = i + 1; while (j < N) { - const bool fast_j = !(bp[j] < -EXP_BOUND || bp[j] > EXP_BOUND); + const bool fast_j = !(bp[j] < -FAST_LIM || bp[j] > FAST_LIM); if (fast_j != fast) break; j++; } @@ -488,14 +495,19 @@ LogZAndProbs compute_logZ_and_probs_blume_capel( result.log_Z.rows(i0, i1) = bb + ARMA_MY_LOG(denom); }; + // Same intermediate overflow guard as the ordinal function: + // exp_theta[c] * pow can overflow before the implicit cancellation with b. + const double max_abs_theta = arma::max(arma::abs(theta)); + const double THETA_LIM = std::max(0.0, EXP_BOUND - max_abs_theta); + const double* bp = b.memptr(); const double* pp = pow_bound.memptr(); arma::uword i = 0; while (i < N) { - const bool fast_i = (std::abs(bp[i]) <= EXP_BOUND) && (std::abs(pp[i]) <= EXP_BOUND); + const bool fast_i = (std::abs(bp[i]) <= EXP_BOUND) && (std::abs(pp[i]) <= THETA_LIM); arma::uword j = i + 1; while (j < N) { - const bool fast_j = (std::abs(bp[j]) <= EXP_BOUND) && (std::abs(pp[j]) <= EXP_BOUND); + const bool fast_j = (std::abs(bp[j]) <= EXP_BOUND) && (std::abs(pp[j]) <= THETA_LIM); if (fast_j != fast_i) break; j++; } From 84a7ba1404b725ab610fdca8e9702c20fbc629e8 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Sun, 8 Mar 2026 01:01:27 +0100 Subject: [PATCH 28/60] docs: add phases H-L to mixed MRF implementation plan - Phase H: stochastic block model edge prior (future) - Phase I: missing data imputation (future) - Phase J: performance profiling - Phase K: code deduplication audit - Phase L: model output R code inspection --- dev/mixedmrf/implementation_plan.md | 216 +++++++++++++++++++++++++++- 1 file changed, 215 insertions(+), 1 deletion(-) diff --git a/dev/mixedmrf/implementation_plan.md b/dev/mixedmrf/implementation_plan.md index 71ff9947..31bd9dab 100644 --- a/dev/mixedmrf/implementation_plan.md +++ b/dev/mixedmrf/implementation_plan.md @@ -2,7 +2,7 @@ **Date:** 2026-02-25 (updated 2026-03-04; review amendments 2026-03-04; Blume-Capel + PL consistency 2026-03-05; commit strategy 2026-03-05; -consistency review 2026-03-05) +consistency review 2026-03-05; phases J-L added 2026-03-08) **Branch:** `ggm_mixed` (PR #78) **Goal:** Build a monolithic `MixedMRFModel` in C++ that supports both conditional and marginal pseudo-likelihood, with and without edge selection. @@ -1492,6 +1492,220 @@ Test files: --- +## Phase H — Stochastic block model edge prior (future) + +The SBM edge prior is already implemented for GGM and OMRF models +(`src/priors/sbm_edge_prior.h`, `sbm_edge_prior_interface.cpp`). +Extending it to the mixed MRF requires wiring the existing prior +into the mixed MRF edge-selection machinery. + +### Scope + +1. Pass `edge_prior` and `block_count` arguments through to + `MixedMRFModel` and the Rcpp interface in `sample_mixed.cpp`. +2. In `update_edge_indicator_{Kxx,Kyy,Kxy}`: use the SBM + inclusion probability instead of the flat Bernoulli prior + when `edge_prior == "Stochastic-Block"`. +3. Add allocation sampling for the mixed MRF (port from the + GGM/OMRF pattern: one SBM prior object shared across + all three edge blocks Kxx, Kyy, Kxy). +4. Wire SBM posterior allocations into `build_output_mixed_mrf()`. +5. Validation: add an SBM edge-detection scenario to the + group 5 validation tests and a separate group 8 for + community-structure recovery. + +### Files affected + +| File | Change | +|------|--------| +| `R/bgm_spec.R` | Forward `edge_prior` / `block_count` to mixed args | +| `src/sample_mixed.cpp` | Accept and pass SBM arguments to model | +| `src/models/mixed/mixed_mrf_model.h` | Store `SBMEdgePrior` member | +| `src/models/mixed/mixed_mrf_metropolis.cpp` | Use SBM prior in edge updates | +| `R/build_output.R` | Extract SBM allocations for mixed MRF | +| `dev/tests/validation/group5_edge_detection.R` | Add SBM scenario | + +--- + +## Phase I — Missing data imputation (future) + +Missing data imputation for GGM and OMRF models is handled via +Gibbs imputation inside the MCMC loop (`impute_missing()` override). +The mixed MRF stub is currently a no-op. + +### Scope + +1. **Detection**: In the R interface, detect `NA` entries in both + discrete and continuous columns. Build missing-data masks: + `missing_discrete` (n × p logical) and `missing_continuous` + (n × q logical). +2. **Imputation step**: Override `impute_missing()` in + `MixedMRFModel` to cycle through missing entries: + - Discrete: sample from full conditional $f(x_s \mid x_{-s}, y)$ + using the same ordinal/BC probability helpers as the Gibbs + generator. + - Continuous: sample from $f(y_j \mid y_{-j}, x)$ using the + conditional Normal from the precision matrix $K_{yy}$. +3. **Integration**: `ChainRunner` already calls `impute_missing()` + each iteration when `has_missing_data()` returns `true`. Flip + the flag once imputation is implemented. +4. **Validation**: Test on data with MCAR missingness at 5%, 15%, + and 30%. Compare parameter recovery with and without missing + data. Verify that imputed values have plausible distributions. + +### Files affected + +| File | Change | +|------|--------| +| `R/validate_data.R` | Allow `NA` for mixed MRF; build masks | +| `src/sample_mixed.cpp` | Pass masks to model constructor | +| `src/models/mixed/mixed_mrf_model.h` | Store masks; override `has_missing_data` | +| `src/models/mixed/mixed_mrf_model.cpp` | Implement `impute_missing()` | +| `dev/tests/validation/` | New group for missing-data validation | + +--- + +## Phase J — Performance profiling + +Profile the mixed MRF sampler to identify bottlenecks and verify +that the rank-1 Cholesky optimizations (Phase B+) deliver the +expected speedup over naive recomputation. + +### Scope + +1. **Wall-clock benchmarks.** Time full `bgm()` runs at several + network sizes (p = 4/8/16, q = 3/6/12) with fixed iteration + counts. Record total time, per-iteration time, and breakdown + by update type (Kxx, Kyy, Kxy, Kxy, main effects, edge + selection when active). Compare MH vs NUTS. +2. **Cache-hit verification.** Under a profiler (or instrumented + C++ timers), confirm that the Cholesky rank-1 update path + dominates over any fallback full-recompute path. Quantify + the fraction of iterations that hit the fast path. +3. **Scaling curves.** Plot per-iteration cost against q (fixing + p) and against p (fixing q) to confirm empirical $O(q^2)$ + and $O(p)$ scaling respectively. +4. **Marginal vs conditional PL cost.** Conditional PL evaluates + one OMRF term per discrete variable per move; marginal PL + evaluates all $p$ terms for mean/precision moves. Benchmark + both and document the crossover point. +5. **Comparison with OMRF and GGM.** Run the same-sized pure + ordinal and pure continuous models and compare iteration cost + to the mixed MRF. + +### Deliverables + +| File | Content | +|------|---------| +| `dev/tests/validation/group9_profiling.R` | Benchmark script | +| `dev/tests/validation/output/group9_profiling.pdf` | Scaling curves and timing tables | +| `dev/ggm_speed.md` or `dev/ggm_performance.md` | Updated with mixed MRF results | + +--- + +## Phase K — Code deduplication audit + +Review the mixed MRF implementation and the rest of the package for +repeated code patterns that could be shared. The goal is to reduce +maintenance burden without over-abstracting. + +### Known candidates + +1. **Matrix assembly pattern in `build_output.R`.** + `build_output_mixed_mrf()` contains four near-identical nested + loops that fill an (p+q) × (p+q) matrix from offset-indexed + flat vectors (discrete main effects, continuous main effects, + pairwise matrix, indicator matrix). Extract a shared helper. +2. **SBM summarization computed twice.** + `summarize_alloc_pairs()` is called identically for the summary + table and the co-clustering matrix. Cache the result and reuse. +3. **Raw-samples list assembly.** + `build_output_bgm()` and `build_output_mixed_mrf()` construct + `results$raw_samples` with identical structure. Extract to a + shared helper. +4. **Parameter index computation.** + The 50-line block in `build_output_mixed_mrf()` that computes + mux/Kxx/muy/Kxy/Kyy offsets is inline and hard to audit. + Extract to a named function so it can be unit-tested. +5. **Cholesky header location.** + `cholupdate.h` / `cholupdate.cpp` live in `src/models/ggm/` + but are included by the mixed MRF model. Evaluate moving to + `src/utils/` for discoverability. +6. **Metropolis step skeleton.** + GGM, OMRF, and Mixed MRF each implement Robbins-Monro + adaptation with target acceptance 0.234 / 0.44. The adaptation + arithmetic is short and varies per model, so duplication is + low, but verify no copy-paste drift has occurred. + +### Scope + +- Audit, not refactor. This phase produces a ranked list of + deduplication opportunities with effort estimates. Actual + refactoring happens in follow-up commits. +- For each candidate, determine: lines saved, risk of introducing + bugs, and whether a shared helper already exists in the codebase. + +### Deliverables + +| File | Content | +|------|---------| +| `dev/mixedmrf/deduplication_audit.md` | Ranked list of candidates with code pointers | + +--- + +## Phase L — Model output R code inspection + +Systematic review of the R code that assembles, summarises, and +exposes the mixed MRF model output. The mixed MRF builder was +written alongside the C++ port and has accumulated inline logic +that may be fragile or inconsistent with the GGM/OMRF paths. + +### Inspection points + +1. **`build_output_mixed_mrf()` correctness.** + - Verify that the flat-to-matrix index mapping for mux, Kxx, + muy, Kxy, Kyy matches the C++ parameter ordering exactly. + - Trace the NUTS diagnostic column naming (`__` suffix + convention) and confirm it matches `build_output_bgm()`. + - Check that posterior_mean_main for BC variables correctly + stores alpha/beta in columns 1:2 with the rest NA. + - Confirm that `posterior_mean_pairwise` symmetry and sign + conventions match GGM/OMRF output. +2. **Posterior mean type consistency.** + Mixed MRF returns `posterior_mean_main` as a list with + `$discrete` and `$continuous` sub-elements, while GGM and + OMRF return a matrix. Determine whether this inconsistency + causes problems downstream (in `coef.bgms()`, print methods, + user code) and propose a resolution. +3. **`coef.bgms()` with mixed MRF.** + Verify that `coef()` on a mixed fit returns a structure that + users can work with. Check edge cases: all-ordinal mixed MRF + (q=0 should not occur but the code path should be guarded), + all-BC discrete variables, single variable of each type. +4. **`simulate.bgms()` with mixed MRF.** + Confirm that simulation from posterior means and from posterior + samples both produce correctly ordered data frames (original + column order, not internal discrete-first order). +5. **`predict.bgms()` with mixed MRF.** + Confirm that conditional prediction handles mixed types and + that the reordering from internal to user column order is + correct. +6. **NUTS diagnostic wiring.** + The `update_method` string in the mixed MRF path is + `"hybrid-nuts"` while GGM uses `"nuts"`. Verify that + downstream code (Rhat computation, ESS, trace extraction) + handles both strings correctly. +7. **`summary.bgms()` and `print.bgms()`.** + Confirm these methods produce sensible output for a mixed fit. + +### Deliverables + +| File | Content | +|------|---------| +| `dev/mixedmrf/output_inspection.md` | Findings, bugs found, and fix list | + +--- + ## Testing strategy ### Existing test infrastructure (from mixedGM) From 9e21374a80fee7b075b6e15e8782186da91bdf9d Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Sun, 8 Mar 2026 14:38:18 +0100 Subject: [PATCH 29/60] feat: missing data imputation for mixed MRF (Phase I) --- R/RcppExports.R | 4 +- R/bgm_spec.R | 61 +++- R/run_sampler.R | 6 +- dev/mixedmrf/implementation_plan.md | 492 +++++++++++++++++++++++---- src/RcppExports.cpp | 11 +- src/models/mixed/mixed_mrf_model.cpp | 130 +++++++ src/models/mixed/mixed_mrf_model.h | 27 +- src/sample_mixed.cpp | 24 +- 8 files changed, 666 insertions(+), 89 deletions(-) diff --git a/R/RcppExports.R b/R/RcppExports.R index 186d9938..e3007c1c 100644 --- a/R/RcppExports.R +++ b/R/RcppExports.R @@ -61,8 +61,8 @@ sample_ggm <- function(inputFromR, prior_inclusion_prob, initial_edge_indicators .Call(`_bgms_sample_ggm`, inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior, beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between, beta_bernoulli_beta_between, dirichlet_alpha, lambda, na_impute, missing_index_nullable) } -sample_mixed_mrf <- function(inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior = "Bernoulli", beta_bernoulli_alpha = 1.0, beta_bernoulli_beta = 1.0, beta_bernoulli_alpha_between = 1.0, beta_bernoulli_beta_between = 1.0, dirichlet_alpha = 1.0, lambda = 1.0, sampler_type = "mh", target_acceptance = 0.80, max_tree_depth = 10L, num_leapfrogs = 100L) { - .Call(`_bgms_sample_mixed_mrf`, inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior, beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between, beta_bernoulli_beta_between, dirichlet_alpha, lambda, sampler_type, target_acceptance, max_tree_depth, num_leapfrogs) +sample_mixed_mrf <- function(inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior = "Bernoulli", beta_bernoulli_alpha = 1.0, beta_bernoulli_beta = 1.0, beta_bernoulli_alpha_between = 1.0, beta_bernoulli_beta_between = 1.0, dirichlet_alpha = 1.0, lambda = 1.0, sampler_type = "mh", target_acceptance = 0.80, max_tree_depth = 10L, num_leapfrogs = 100L, na_impute = FALSE, missing_index_discrete_nullable = NULL, missing_index_continuous_nullable = NULL) { + .Call(`_bgms_sample_mixed_mrf`, inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior, beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between, beta_bernoulli_beta_between, dirichlet_alpha, lambda, sampler_type, target_acceptance, max_tree_depth, num_leapfrogs, na_impute, missing_index_discrete_nullable, missing_index_continuous_nullable) } sample_omrf <- function(inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, sampler_type, seed, no_threads, progress_type, edge_prior = "Bernoulli", na_impute = FALSE, missing_index_nullable = NULL, beta_bernoulli_alpha = 1.0, beta_bernoulli_beta = 1.0, beta_bernoulli_alpha_between = 1.0, beta_bernoulli_beta_between = 1.0, dirichlet_alpha = 1.0, lambda = 1.0, target_acceptance = 0.8, max_tree_depth = 10L, num_leapfrogs = 10L, pairwise_scaling_factors_nullable = NULL) { diff --git a/R/bgm_spec.R b/R/bgm_spec.R index 89995d9a..78f29055 100644 --- a/R/bgm_spec.R +++ b/R/bgm_spec.R @@ -79,6 +79,13 @@ new_bgm_spec = function(model_type, data, variables, missing, prior, if(!is.null(missing$missing_index)) { stopifnot(is.matrix(missing$missing_index)) } + # mixed MRF uses separate indices for discrete and continuous + if(!is.null(missing$missing_index_discrete)) { + stopifnot(is.matrix(missing$missing_index_discrete)) + } + if(!is.null(missing$missing_index_continuous)) { + stopifnot(is.matrix(missing$missing_index_continuous)) + } # --- prior sub-list --- stopifnot(is.list(prior)) @@ -658,16 +665,45 @@ build_spec_mixed_mrf = function(x, data_columnnames, num_variables, variable_bool = is_ordinal_disc ) - # Missing data — not supported for mixed MRF (Phase H) - if(na_action == "impute") { - stop("Missing data imputation is not yet supported for mixed models.", - call. = FALSE - ) - } - if(anyNA(x)) { - stop("Missing data detected. Mixed models do not yet support missing data handling.", - call. = FALSE - ) + # Missing data handling + na_impute = FALSE + missing_index_discrete = NULL + missing_index_continuous = NULL + + if(na_action == "listwise") { + missing_rows = apply(x_disc, 1, anyNA) | apply(x_cont, 1, anyNA) + if(all(missing_rows)) { + stop(paste0( + "All rows in x contain at least one missing response.\n", + "You could try option na_action = \"impute\"." + )) + } + n_removed = sum(missing_rows) + if(n_removed > 0 && isTRUE(getOption("bgms.verbose", TRUE))) { + n_remaining = nrow(x_disc) - n_removed + message( + n_removed, " row", if(n_removed > 1) "s" else "", + " with missing values excluded (n = ", n_remaining, " remaining).\n", + "To impute missing values instead, use na_action = \"impute\"." + ) + } + x_disc = x_disc[!missing_rows, , drop = FALSE] + x_cont = x_cont[!missing_rows, , drop = FALSE] + if(nrow(x_disc) < 2) { + stop(paste0( + "After removing missing observations from the input matrix x,\n", + "there were less than two rows left in x." + )) + } + } else { + # Impute path: handle discrete and continuous sub-matrices separately + md_disc = handle_impute(x_disc) + md_cont = handle_impute(x_cont) + x_disc = md_disc$x + x_cont = md_cont$x + na_impute = md_disc$na_impute || md_cont$na_impute + if(md_disc$na_impute) missing_index_discrete = md_disc$missing_index + if(md_cont$na_impute) missing_index_continuous = md_cont$missing_index } # Ordinal recoding (reformat discrete data) @@ -718,8 +754,9 @@ build_spec_mixed_mrf = function(x, data_columnnames, num_variables, ), missing = list( na_action = na_action, - na_impute = FALSE, - missing_index = NULL + na_impute = na_impute, + missing_index_discrete = missing_index_discrete, + missing_index_continuous = missing_index_continuous ), prior = list( pairwise_scale = pairwise_scale, diff --git a/R/run_sampler.R b/R/run_sampler.R index 0ec3b967..6ca7fb0e 100644 --- a/R/run_sampler.R +++ b/R/run_sampler.R @@ -154,6 +154,7 @@ run_sampler_omrf = function(spec) { run_sampler_mixed_mrf = function(spec) { d = spec$data v = spec$variables + m = spec$missing p = spec$prior s = spec$sampler @@ -205,7 +206,10 @@ run_sampler_mixed_mrf = function(spec) { sampler_type = s$update_method, target_acceptance = s$target_accept, max_tree_depth = s$nuts_max_depth, - num_leapfrogs = s$hmc_num_leapfrogs + num_leapfrogs = s$hmc_num_leapfrogs, + na_impute = m$na_impute, + missing_index_discrete_nullable = m$missing_index_discrete, + missing_index_continuous_nullable = m$missing_index_continuous ) out_raw diff --git a/dev/mixedmrf/implementation_plan.md b/dev/mixedmrf/implementation_plan.md index 31bd9dab..76bcc8e7 100644 --- a/dev/mixedmrf/implementation_plan.md +++ b/dev/mixedmrf/implementation_plan.md @@ -314,7 +314,14 @@ add the new Rcpp export to `src/RcppExports.cpp`, `R/RcppExports.R`, and | **D** | Edge selection: 3 RJ sweeps | B+ | Structure recovery test passes | ✅ | | **E** | R interface: `bgm()` dispatch, output formatting | B+ | End-to-end `bgm(mixed_data)` works | ✅ | | **F** | Warmup schedule, adaptation, diagnostics | E | Full warmup pipeline | ✅ | -| **G** | Simulation and prediction | E | `simulate.bgms` and `predict.bgms` for mixed | +| **G** | Simulation and prediction | E | `simulate.bgms` and `predict.bgms` for mixed | ✅ | +| **H** | Stochastic block model edge prior | G | SBM prior wired into mixed MRF | ✅ | +| **I** | Missing data imputation | G | `na_action = "impute"` for mixed data | ✅ | +| **J** | Performance profiling | G | Benchmark report, hotspot fixes | +| **K** | Code deduplication audit | G | Shared helpers extracted, dead code removed | +| **L** | Model output R code inspection | G | Output format verified, bugs fixed | +| **M** | Test suite cleanup and completion | G | 4 new test files, promoted dev tests, full T1–T30 coverage | +| **N** | Alternative proposals for GGM edge updates | G | New proposal mechanisms for GGM edges | --- @@ -1492,77 +1499,88 @@ Test files: --- -## Phase H — Stochastic block model edge prior (future) +## Phase H — Stochastic block model edge prior ✅ The SBM edge prior is already implemented for GGM and OMRF models (`src/priors/sbm_edge_prior.h`, `sbm_edge_prior_interface.cpp`). -Extending it to the mixed MRF requires wiring the existing prior -into the mixed MRF edge-selection machinery. - -### Scope - -1. Pass `edge_prior` and `block_count` arguments through to - `MixedMRFModel` and the Rcpp interface in `sample_mixed.cpp`. -2. In `update_edge_indicator_{Kxx,Kyy,Kxy}`: use the SBM - inclusion probability instead of the flat Bernoulli prior - when `edge_prior == "Stochastic-Block"`. -3. Add allocation sampling for the mixed MRF (port from the - GGM/OMRF pattern: one SBM prior object shared across - all three edge blocks Kxx, Kyy, Kxy). -4. Wire SBM posterior allocations into `build_output_mixed_mrf()`. -5. Validation: add an SBM edge-detection scenario to the - group 5 validation tests and a separate group 8 for - community-structure recovery. - -### Files affected - -| File | Change | -|------|--------| -| `R/bgm_spec.R` | Forward `edge_prior` / `block_count` to mixed args | -| `src/sample_mixed.cpp` | Accept and pass SBM arguments to model | -| `src/models/mixed/mixed_mrf_model.h` | Store `SBMEdgePrior` member | -| `src/models/mixed/mixed_mrf_metropolis.cpp` | Use SBM prior in edge updates | -| `R/build_output.R` | Extract SBM allocations for mixed MRF | -| `dev/tests/validation/group5_edge_detection.R` | Add SBM scenario | +The unified `BaseEdgePrior` / `ChainRunner` architecture means +the mixed MRF required **no additional code** — all five scope +items were already satisfied: + +1. ✅ `bgm_spec()` and `build_spec_mixed_mrf()` forward + `edge_prior` / SBM hyperparameters (6 params). +2. ✅ `run_sampler_mixed_mrf()` passes them to `sample_mixed_mrf()`. +3. ✅ `sample_mixed.cpp` creates the edge prior via + `create_edge_prior()` and passes it to `run_mcmc_sampler()`. +4. ✅ `ChainRunner` calls `edge_prior.update()` after each + edge-indicator sweep, modifying `inclusion_probability_` in + place via `MixedMRFModel::get_inclusion_probability()`. + Allocation samples are stored automatically. +5. ✅ `build_output_mixed_mrf()` extracts allocations, + co-clustering matrix, posterior mean/mode allocations, + and block-count summaries. + +Verified with a smoke test: `bgm(mixed_data, edge_prior = "Stochastic-Block")` +produces correct allocation, co-clustering, and block-count outputs +across 4 chains. --- -## Phase I — Missing data imputation (future) - -Missing data imputation for GGM and OMRF models is handled via -Gibbs imputation inside the MCMC loop (`impute_missing()` override). -The mixed MRF stub is currently a no-op. - -### Scope - -1. **Detection**: In the R interface, detect `NA` entries in both - discrete and continuous columns. Build missing-data masks: - `missing_discrete` (n × p logical) and `missing_continuous` - (n × q logical). -2. **Imputation step**: Override `impute_missing()` in - `MixedMRFModel` to cycle through missing entries: - - Discrete: sample from full conditional $f(x_s \mid x_{-s}, y)$ - using the same ordinal/BC probability helpers as the Gibbs - generator. - - Continuous: sample from $f(y_j \mid y_{-j}, x)$ using the - conditional Normal from the precision matrix $K_{yy}$. -3. **Integration**: `ChainRunner` already calls `impute_missing()` - each iteration when `has_missing_data()` returns `true`. Flip - the flag once imputation is implemented. -4. **Validation**: Test on data with MCAR missingness at 5%, 15%, - and 30%. Compare parameter recovery with and without missing - data. Verify that imputed values have plausible distributions. - -### Files affected +## Phase I — Missing data imputation ✅ + +Missing data imputation wires the existing `BaseModel` virtual interface +(`has_missing_data()`, `impute_missing()`) into the mixed MRF model, +mirroring the GGM and OMRF implementations. + +### Implementation + +Four layers were connected: + +1. **R spec** (`bgm_spec.R`): Removed the `stop()` guards that rejected + `NA` entries for mixed data. Added listwise-deletion and imputation + handling that processes discrete and continuous sub-matrices + separately via `handle_impute()`. The spec stores + `missing_index_discrete` and `missing_index_continuous` (0-based + row/col index matrices). +2. **R sampler** (`run_sampler.R`): Passes `na_impute`, + `missing_index_discrete`, and `missing_index_continuous` to the C++ + entry point. +3. **C++ entry** (`sample_mixed.cpp`): Accepts the three new + parameters, calls `model.set_missing_data()`, and sets + `config.na_impute`. +4. **C++ model** (`mixed_mrf_model.h/.cpp`): + - `has_missing_data()` returns the stored flag (was hardcoded + `false`). + - `set_missing_data()` stores the index matrices and sets the flag. + - `impute_missing()` performs Gibbs imputation in three phases: + - Phase 1 — Discrete: samples each missing entry from its full + conditional using rest scores and category probabilities + (ordinal or Blume-Capel). Updates sufficient statistics + (`counts_per_category_` or `blume_capel_stats_`). + - Phase 2 — Refreshes `conditional_mean_` when both discrete + and continuous entries are missing. + - Phase 3 — Continuous: samples each missing entry from the + conditional Normal $N(\mu^*, 1/K_{yy,jj})$. Invalidates the + gradient cache. + +`ChainRunner` already calls `impute_missing()` each iteration when +`config.na_impute && model.has_missing_data()`. + +The `RcppExports` were regenerated, which also fixed a pre-existing +bug where the R wrapper for `sample_mixed_mrf` had only 17 parameters +while the C++ function expected 21. + +### Files changed | File | Change | |------|--------| -| `R/validate_data.R` | Allow `NA` for mixed MRF; build masks | -| `src/sample_mixed.cpp` | Pass masks to model constructor | -| `src/models/mixed/mixed_mrf_model.h` | Store masks; override `has_missing_data` | -| `src/models/mixed/mixed_mrf_model.cpp` | Implement `impute_missing()` | -| `dev/tests/validation/` | New group for missing-data validation | - +| `R/bgm_spec.R` | Removed `stop()` guards; added listwise/impute handling for mixed data | +| `R/run_sampler.R` | Passes missing-data indices and `na_impute` flag to C++ | +| `R/RcppExports.R` | Regenerated (also fixed stale 17-param wrapper) | +| `src/sample_mixed.cpp` | Three new parameters; wires missing data to model | +| `src/models/mixed/mixed_mrf_model.h` | New data members, `set_missing_data()`, `impute_missing()` declaration | +| `src/models/mixed/mixed_mrf_model.cpp` | `impute_missing()` implementation, copy constructor updated | +| `src/RcppExports.cpp` | Regenerated | --- ## Phase J — Performance profiling @@ -1706,8 +1724,356 @@ that may be fragile or inconsistent with the GGM/OMRF paths. --- +## Phase M — Test suite cleanup and completion + +The planned test structure (§4 file layout, commit strategy) envisioned +four dedicated test files in `tests/testthat/`: + +``` +test-mixed-mrf-skeleton.R # T4, T5 (dimension, vectorization round-trip) +test-mixed-mrf-likelihood.R # T1–T3, T7–T12 (likelihood unit tests) +test-mixed-mrf-sampling.R # T13–T14, T16–T18 (recovery, reproducibility) +test-mixed-mrf-edge-sel.R # T15, T26–T27 (edge selection, BC combos) +``` + +What actually shipped is different: the scaffolding test helpers were +removed in commit `04b9562`, the likelihood tests were removed with +them, and the remaining coverage landed in two files plus scattered +additions to existing files. This phase reconciles the plan with +reality, fills coverage gaps, and consolidates test organization. + +### M.1 Current state audit + +**`tests/testthat/` (run by `R CMD check`):** + +| File | Mixed-MRF `test_that` blocks | Covers | +|------|:---:|---| +| `test-mixed-mrf-simulate-predict.R` | 15 | Gibbs generator, simulate, predict, p=1/q=1 edge cases | +| `test-bgm.R` (mixed section) | 8 | End-to-end `bgm()`, output dimensions, symmetry, marginal PL | +| `test-validate-variable-types.R` | 13 | Type validation (not model-specific) | + +**`dev/tests/` (manual only, not run by `R CMD check`):** + +| File | Blocks | Covers | +|------|:---:|---| +| `test-numerical-gradient.R` | 4 | Finite-diff vs analytical gradient (cond + marg, ord + BC) | +| `test-simulation-recovery.R` | 1 mixed | Mixed MRF sim-recovery cycle | +| `compare_bgms_mixedgm_cycle.R` | 0 | Script, no assertions — bgms vs mixedGM comparison | +| `validation/group1–7` | 0 | PDF-generating scripts, no programmatic assertions | + +### M.2 Gap analysis against plan's test matrix + +#### Covered (have a `test_that` block in `tests/testthat/`) + +| Test | Description | Covered by | +|------|-------------|------------| +| T17 | Reproducibility (same seed) | `test-bgm.R` "bgm mixed MRF is reproducible" | +| T19 | End-to-end `bgm()` | `test-bgm.R` (8 blocks) | +| T22 | Degenerate p=1 | `test-mixed-mrf-simulate-predict.R` "single discrete variable" | +| T23 | Degenerate q=1 | `test-mixed-mrf-simulate-predict.R` "single continuous variable" | +| T25 | Gibbs generator sanity | `test-mixed-mrf-simulate-predict.R` (dimension, range, SD, seed) | +| R1 | GGM regression | Existing `test-bgm.R` GGM section | +| R2 | OMRF regression | Existing `test-bgm.R` OMRF sections | +| R3 | bgmCompare regression | `test-bgmCompare.R` | + +#### Covered in `dev/tests/` only (not run by `R CMD check`) + +| Test | Description | Covered by | +|------|-------------|------------| +| T1 | `log_conditional_omrf(s)` | Was in scaffolding tests (removed); partially in `validation/group4` | +| T2 | `log_conditional_ggm()` | Same | +| T3 | `log_marginal_omrf(s)` | Same | +| T13 | Conditional PL recovery | `validation/group1`, `test-simulation-recovery.R` | +| T14 | Marginal PL recovery | `validation/group1` | +| T15 | Edge selection recovery | `validation/group5` | +| T16 | Cond vs marginal agreement | `validation/group3` | + +#### Covered in `dev/tests/` only (gradient-specific) + +| Test | Description | Covered by | +|------|-------------|------------| +| NUTS-7.1 | Numerical gradient check | `test-numerical-gradient.R` (4 blocks) | +| NUTS-7.2 | Posterior equivalence MH vs NUTS | `validation/group2` | +| NUTS-7.3 | ESS comparison | `validation/group6` | +| NUTS-7.4 | Edge selection with hybrid | `validation/group5` | + +#### Not covered anywhere + +| Test | Description | Status | +|------|-------------|--------| +| T4 | `parameter_dimension()` | Was in scaffolding tests, removed | +| T5 | Vectorization round-trip | Was in scaffolding tests, removed | +| T6 | Cholesky permutation involution | Superseded by B+ (permutation removed) | +| T7 | Analytic Gaussian ($K_{xy}=0$) | Was in scaffolding tests, removed | +| T8 | Fixture replay (bit-for-bit) | Never implemented | +| T9 | Cache freshness after parameter tweak | Never implemented | +| T10 | BC conditional OMRF vs standalone OMRF | Never implemented | +| T11 | BC marginal OMRF vs standalone OMRF | Never implemented | +| T12 | BC observation centering consistency | Never implemented | +| T18 | Multi-chain (4 chains, R-hat < 1.1) | Not in `tests/testthat/` | +| T20 | Kyy PD invariant under stress | Never implemented | +| T21 | Cache consistency under RJ | Never implemented | +| T24 | Binary-only ordinal matches logistic | Never implemented | +| T26 | Mixed ordinal + BC recovery | Never implemented | +| T27 | BC-only discrete recovery + edge selection | Never implemented | +| T28 | `log_ggm_ratio_edge` agreement | Was in scaffolding tests, removed | +| T29 | Cholesky update fidelity | Was in scaffolding tests, removed | +| T30 | Rank-2 quadratic shortcut | Never implemented (B+.10 not implemented) | + +#### Additional gaps not in original matrix + +| Gap | Description | +|-----|-------------| +| G1 | No `bgm_spec()` test for `mixed_mrf` model type | +| G2 | No `test-spec-wiring.R` coverage for mixed pipeline | +| G3 | No `test-methods.R` coverage (`coef`, `print`, `summary` for mixed fixture) | +| G4 | No `predict.bgms` posterior-draw-path test (only posterior-mean tested) | +| G5 | No multi-chain test for mixed MRF (chain-combining logic) | +| G6 | No `na_action = "listwise"` test for mixed data | +| G7 | No Blume-Capel + continuous combo end-to-end test | +| G8 | `compare_bgms_mixedgm_cycle.R` is unstructured script (no `test_that`) | +| G9 | Validation scripts (group1–7) have no programmatic pass/fail | +| G10 | `test-numerical-gradient.R` and `test-simulation-recovery.R` use `<-` assignment (style violation) | + +### M.3 Reorganization plan + +#### Target file structure + +Consolidate mixed-MRF tests into four files matching the original plan, +plus keep the simulate/predict file which has grown organically: + +``` +tests/testthat/ + test-mixed-mrf-skeleton.R # T4, T5 (dimension, round-trip) + test-mixed-mrf-likelihood.R # T1–T3, T7, T10–T12 (likelihood unit) + test-mixed-mrf-sampling.R # T13, T14, T16–T18, T26, T27 (recovery) + test-mixed-mrf-edge-selection.R # T15, T20, T21 (edge selection, PD, cache) + test-mixed-mrf-simulate-predict.R # (existing — keep as-is) + test-mixed-mrf-gradient.R # Numerical gradient (promote from dev/) +``` + +Mixed-MRF blocks currently in `test-bgm.R` stay there — they test the +`bgm()` interface, not the model internals. Add a mixed-MRF fixture to +`test-methods.R` and `test-bgm-spec.R`. + +#### What to promote from `dev/tests/` to `tests/testthat/` + +| Source | Target | Notes | +|--------|--------|-------| +| `test-numerical-gradient.R` (4 blocks) | `test-mixed-mrf-gradient.R` | Wrap in `skip_on_cran()`. Fix `<-` to `=`. | +| `test-simulation-recovery.R` (mixed block) | `test-mixed-mrf-sampling.R` | Merge into recovery section. Fix `<-` to `=`. | + +The validation suite (`dev/tests/validation/`) stays in `dev/`. These +are long-running visual diagnostics, not `R CMD check` tests. No +changes needed except documenting their role. + +`compare_bgms_mixedgm_cycle.R` stays in `dev/` as a manual diagnostic +script. Add a header comment clarifying it is not a testthat test. + +#### What to add (new tests) + +**Tier 1 — essential for correctness (add in this phase):** + +| File | Tests | What | +|------|-------|------| +| `test-mixed-mrf-skeleton.R` | T4, T5 | Parameter count for known (p,q, mixed ord+BC); vectorization round-trip | +| `test-mixed-mrf-likelihood.R` | T1, T2, T3, T7 | Likelihood values vs R reference at known params; analytic Gaussian check | +| `test-mixed-mrf-likelihood.R` | T10, T11, T12 | BC likelihood vs standalone OMRF; BC observation centering | +| `test-mixed-mrf-sampling.R` | T18 | Multi-chain (2 chains, R-hat < 1.2; `skip_on_cran()`) | +| `test-mixed-mrf-sampling.R` | T26, T27 | Mixed ord+BC recovery; BC-only recovery (`skip_on_cran()`) | +| `test-mixed-mrf-edge-selection.R` | T15 | Structure recovery on sparse graph (`skip_on_cran()`) | +| `test-bgm-spec.R` | G1 | Spec builder produces `mixed_mrf` for `c("ordinal","continuous")` input | +| `test-methods.R` | G3 | `coef`, `print`, `summary` on mixed fixture | + +**Tier 2 — defensive (add if time permits):** + +| File | Tests | What | +|------|-------|------| +| `test-mixed-mrf-likelihood.R` | T9 | Cache freshness: tweak one param, verify cache matches recompute | +| `test-mixed-mrf-edge-selection.R` | T20 | Kyy PD invariant under 500 iters with wide proposals | +| `test-mixed-mrf-edge-selection.R` | T21 | Cache consistency after RJ birth/death | +| `test-mixed-mrf-sampling.R` | T24 | Binary-only ordinal vs logistic rest-scores | +| `test-mixed-mrf-sampling.R` | G4 | Predict with posterior draws (not just posterior-mean) | +| `test-mixed-mrf-sampling.R` | G5 | Multi-chain output combining | +| `test-bgm.R` | G7 | `bgm()` with Blume-Capel + continuous data | + +**Dropped (superseded or not applicable):** + +| Test | Reason | +|------|--------| +| T6 | Cholesky permutation removed in Phase B+; rank-1 approach has no permutation | +| T8 | Fixture replay requires stable binary fixtures; deferred to future maintenance | +| T28, T29 | Rank-1 log-ratio and Cholesky fidelity: internal C++ state not easily testable from R without scaffolding exports. Cover implicitly via T13/T14 recovery. | +| T30 | B+.10 quadratic shortcut not implemented | + +### M.4 Style fixes + +Apply to all promoted and new test files: + +1. Replace `<-` with `=` for assignment (except inside `expect_*()` + calls where the result must be captured: + `expect_message(result <- foo(), "pat")`). +2. Remove `#'` roxygen-style comments; use plain `#` comments. +3. Use `if(` not `if (`. +4. Run `styler::style_file()` with `bgms_style` on each file. + +### M.5 Deliverables + +| # | Commit message | What ships | +|:-:|----------------|------------| +| 1 | `test: add mixed MRF skeleton and likelihood tests` | `test-mixed-mrf-skeleton.R` (T4, T5), `test-mixed-mrf-likelihood.R` (T1–T3, T7, T10–T12) | +| 2 | `test: add mixed MRF sampling recovery tests` | `test-mixed-mrf-sampling.R` (T13, T14, T16–T18, T26, T27) | +| 3 | `test: add mixed MRF edge selection tests` | `test-mixed-mrf-edge-selection.R` (T15) | +| 4 | `test: promote gradient tests to testthat` | `test-mixed-mrf-gradient.R` (from `dev/tests/`); style fixes | +| 5 | `test: add mixed MRF coverage to spec and methods tests` | `test-bgm-spec.R` (G1), `test-methods.R` (G3) | +| 6 | `test: defensive tests (tier 2)` | T9, T20, T21, T24, G4, G5, G7 | + +### M.6 Reference implementation for likelihood tests + +Likelihood tests (T1–T3, T10–T12) need R-level reference values. +The scaffolding test helpers that originally provided these were +Rcpp-exported functions in `test_mixed_mrf.cpp` (deleted in +commit `04b9562`). Two options: + +**(a) Pure-R reference functions.** Write compact R functions that +compute `log_conditional_omrf(s)`, `log_conditional_ggm()`, and +`log_marginal_omrf(s)` using base-R matrix operations. These are +10–20 lines each and serve as the gold standard. Place in +`tests/testthat/helper-mixed-mrf-reference.R`. + +**(b) Cross-validate against mixedGM.** Call +`mixedGM::rcpp_log_pl_conditional_omrf()` etc. from the test. Requires +`mixedGM` as a `Suggests` dependency or a `skip_if_not_installed()` +guard. + +Option (a) is preferred: no external dependency, self-contained, and +exercises a different code path than the C++ implementation. + +--- + +## Phase N — Alternative proposals for GGM edge updates + +The current edge birth/death proposals for the continuous part of the +mixed MRF (and the standalone GGM) use an adaptive random-walk +Metropolis-Hastings scheme: the birth move draws +$\epsilon \sim \mathcal{N}(0,\sigma_{ij})$ on a reparameterized scale, +and the death move proposes setting the off-diagonal to zero. Proposal +standard deviations $\sigma_{ij}$ are tuned via Robbins-Monro during +warmup. + +Van den Bergh, Clyde, Raftery, and Marsman (in preparation) show that +the MoMS (mixtures of mutually singular distributions) framework used in +bgms is equivalent to RJMCMC in terms of the Metropolis-Hastings +acceptance probability. However, the paper also demonstrates that +RJMCMC can achieve higher effective sample size (ESS) per iteration +in practice, because it allows more flexible proposal distributions +that better match the target. In principle, the MoMS framework admits +any proposal mechanism — the current random-walk proposal is a +convenience, not a requirement. + +This phase investigates alternative proposal distributions for the +conditional GGM edge updates ($K_{yy}$) and cross-type edge updates +($K_{xy}$), with the goal of improving edge-indicator mixing and ESS. +The same ideas apply to the standalone `GGMModel`. + +### N.1 Current proposal anatomy + +Both `GGMModel` and `MixedMRFModel` use the same proposal pattern +for $K_{yy}$ edges: + +**Birth** ($\gamma_{ij}: 0 \to 1$): +1. Draw $\epsilon \sim \mathcal{N}(0, \sigma_{ij})$. +2. Set $\omega_{ij}^* = C_3 \cdot \epsilon$ (reparameterized via + Cholesky constants). +3. Constrain $\omega_{jj}^*$ to maintain positive definiteness. +4. Accept with MH ratio combining likelihood ratio, Cauchy slab + prior, and $\mathcal{N}(0, \sigma_{ij})$ proposal density. + +**Death** ($\gamma_{ij}: 1 \to 0$): +1. Set $\omega_{ij}^* = 0$. +2. Constrain $\omega_{jj}^*$. +3. Accept with the reverse MH ratio. + +The proposal is symmetric on the $\epsilon$ scale. The +Robbins-Monro adaptation targets acceptance rate 0.44. + +### N.2 Candidate alternatives + +#### (a) Posterior-informed birth proposals + +Instead of drawing from $\mathcal{N}(0, \sigma_{ij})$, use a +proposal centered on a point estimate of $\omega_{ij}$ conditional +on the current state: + +$$\epsilon \sim \mathcal{N}\!\left(\hat{\omega}_{ij}^{\text{cond}},\,\sigma_{ij}\right)$$ + +where $\hat{\omega}_{ij}^{\text{cond}}$ is derived from the +conditional posterior mode. For the Gaussian likelihood with +Cauchy slab, the conditional mode given the sufficient statistics +$S = X^\top X$ and the rest of $\Omega$ can be computed in +closed form as a function of $S_{ij}$, $\Sigma_{ij}^{(-ij)}$, +and $n$. This turns the birth proposal into a near-optimal +independent proposal. + +The death proposal remains deterministic (set to zero), so +the MH ratio adjusts by replacing the reverse-proposal +$q(\omega_{ij} \mid 0)$ with +$\mathcal{N}(\omega_{ij} \mid \hat{\omega}_{ij}^{\text{cond}}, \sigma_{ij})$. + +#### (b) Exchange-type proposals + +For the within-model parameter update (when $\gamma_{ij} = 1$), +replace the random-walk proposal with a Gibbs draw from the +conditional posterior of $\omega_{ij}$ given the rest of $\Omega$. +Under the Gaussian likelihood with a Cauchy slab, this conditional +posterior is not available in closed form, but a single Newton step +or a Laplace approximation to the conditional can serve as a +high-quality proposal. + +#### (c) Block proposals for $K_{xy}$ + +The current $K_{xy}$ birth/death proposals update one element at a +time. Because each $K_{xy}$ change affects the conditional mean +$\mu_y + 2\,K_{xy}^\top x$, nearby $K_{xy}$ elements can be +strongly correlated in the posterior. A block proposal that updates +an entire row $K_{xy,i\cdot}$ simultaneously may improve mixing. +The NUTS sampler already handles $K_{xy}$ as part of the +gradient-based block, so this mainly benefits the Metropolis path. + +### N.3 Evaluation plan + +1. **Baseline ESS.** Run the current sampler on three benchmark + problems (p=4/q=3, p=8/q=6, p=4/q=6) with 50,000 iterations + after warmup. Record per-parameter ESS, per-edge-indicator ESS, + and bulk/tail ESS. +2. **Implement candidate (a).** Add `update_edge_indicator_Kyy_informed()` + as an alternative to `update_edge_indicator_Kyy()`. Selection via + a compile-time or constructor flag (`proposal_type`). +3. **Comparison.** Run the same benchmarks with the informed proposal. + Compare ESS, ESS/second, and acceptance rates. Focus on the + edge-indicator ESS (the primary bottleneck). +4. **Evaluate (b) and (c)** if (a) shows improvement. Otherwise, + document findings and close the phase. +5. **Apply to standalone GGM.** If the improved proposal works for + the mixed MRF $K_{yy}$, port it to `GGMModel` which uses the + same Cholesky reparameterization. + +### N.4 Deliverables + +| File | Content | +|------|---------| +| `dev/mixedmrf/proposal_study.md` | Design notes, derivation of informed proposal | +| `dev/tests/validation/group10_proposal_comparison.R` | Benchmark script | +| `dev/tests/validation/output/group10_proposal_comparison.pdf` | ESS comparison plots | +| `src/models/mixed/mixed_mrf_metropolis.cpp` | New proposal function(s) if adopted | +| `src/models/ggm/ggm_model.cpp` | Port to standalone GGM if adopted | + +--- + ## Testing strategy + + ### Existing test infrastructure (from mixedGM) The mixedGM prototype already provides comprehensive tests that can be adapted diff --git a/src/RcppExports.cpp b/src/RcppExports.cpp index 06f7a1b3..477ea174 100644 --- a/src/RcppExports.cpp +++ b/src/RcppExports.cpp @@ -310,8 +310,8 @@ BEGIN_RCPP END_RCPP } // sample_mixed_mrf -Rcpp::List sample_mixed_mrf(const Rcpp::List& inputFromR, const arma::mat& prior_inclusion_prob, const arma::imat& initial_edge_indicators, const int no_iter, const int no_warmup, const int no_chains, const bool edge_selection, const int seed, const int no_threads, const int progress_type, const std::string& edge_prior, const double beta_bernoulli_alpha, const double beta_bernoulli_beta, const double beta_bernoulli_alpha_between, const double beta_bernoulli_beta_between, const double dirichlet_alpha, const double lambda, const std::string& sampler_type, const double target_acceptance, const int max_tree_depth, const int num_leapfrogs); -RcppExport SEXP _bgms_sample_mixed_mrf(SEXP inputFromRSEXP, SEXP prior_inclusion_probSEXP, SEXP initial_edge_indicatorsSEXP, SEXP no_iterSEXP, SEXP no_warmupSEXP, SEXP no_chainsSEXP, SEXP edge_selectionSEXP, SEXP seedSEXP, SEXP no_threadsSEXP, SEXP progress_typeSEXP, SEXP edge_priorSEXP, SEXP beta_bernoulli_alphaSEXP, SEXP beta_bernoulli_betaSEXP, SEXP beta_bernoulli_alpha_betweenSEXP, SEXP beta_bernoulli_beta_betweenSEXP, SEXP dirichlet_alphaSEXP, SEXP lambdaSEXP, SEXP sampler_typeSEXP, SEXP target_acceptanceSEXP, SEXP max_tree_depthSEXP, SEXP num_leapfrogsSEXP) { +Rcpp::List sample_mixed_mrf(const Rcpp::List& inputFromR, const arma::mat& prior_inclusion_prob, const arma::imat& initial_edge_indicators, const int no_iter, const int no_warmup, const int no_chains, const bool edge_selection, const int seed, const int no_threads, const int progress_type, const std::string& edge_prior, const double beta_bernoulli_alpha, const double beta_bernoulli_beta, const double beta_bernoulli_alpha_between, const double beta_bernoulli_beta_between, const double dirichlet_alpha, const double lambda, const std::string& sampler_type, const double target_acceptance, const int max_tree_depth, const int num_leapfrogs, const bool na_impute, const Rcpp::Nullable missing_index_discrete_nullable, const Rcpp::Nullable missing_index_continuous_nullable); +RcppExport SEXP _bgms_sample_mixed_mrf(SEXP inputFromRSEXP, SEXP prior_inclusion_probSEXP, SEXP initial_edge_indicatorsSEXP, SEXP no_iterSEXP, SEXP no_warmupSEXP, SEXP no_chainsSEXP, SEXP edge_selectionSEXP, SEXP seedSEXP, SEXP no_threadsSEXP, SEXP progress_typeSEXP, SEXP edge_priorSEXP, SEXP beta_bernoulli_alphaSEXP, SEXP beta_bernoulli_betaSEXP, SEXP beta_bernoulli_alpha_betweenSEXP, SEXP beta_bernoulli_beta_betweenSEXP, SEXP dirichlet_alphaSEXP, SEXP lambdaSEXP, SEXP sampler_typeSEXP, SEXP target_acceptanceSEXP, SEXP max_tree_depthSEXP, SEXP num_leapfrogsSEXP, SEXP na_imputeSEXP, SEXP missing_index_discrete_nullableSEXP, SEXP missing_index_continuous_nullableSEXP) { BEGIN_RCPP Rcpp::RObject rcpp_result_gen; Rcpp::RNGScope rcpp_rngScope_gen; @@ -336,7 +336,10 @@ BEGIN_RCPP Rcpp::traits::input_parameter< const double >::type target_acceptance(target_acceptanceSEXP); Rcpp::traits::input_parameter< const int >::type max_tree_depth(max_tree_depthSEXP); Rcpp::traits::input_parameter< const int >::type num_leapfrogs(num_leapfrogsSEXP); - rcpp_result_gen = Rcpp::wrap(sample_mixed_mrf(inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior, beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between, beta_bernoulli_beta_between, dirichlet_alpha, lambda, sampler_type, target_acceptance, max_tree_depth, num_leapfrogs)); + Rcpp::traits::input_parameter< const bool >::type na_impute(na_imputeSEXP); + Rcpp::traits::input_parameter< const Rcpp::Nullable >::type missing_index_discrete_nullable(missing_index_discrete_nullableSEXP); + Rcpp::traits::input_parameter< const Rcpp::Nullable >::type missing_index_continuous_nullable(missing_index_continuous_nullableSEXP); + rcpp_result_gen = Rcpp::wrap(sample_mixed_mrf(inputFromR, prior_inclusion_prob, initial_edge_indicators, no_iter, no_warmup, no_chains, edge_selection, seed, no_threads, progress_type, edge_prior, beta_bernoulli_alpha, beta_bernoulli_beta, beta_bernoulli_alpha_between, beta_bernoulli_beta_between, dirichlet_alpha, lambda, sampler_type, target_acceptance, max_tree_depth, num_leapfrogs, na_impute, missing_index_discrete_nullable, missing_index_continuous_nullable)); return rcpp_result_gen; END_RCPP } @@ -405,7 +408,7 @@ static const R_CallMethodDef CallEntries[] = { {"_bgms_sample_mixed_mrf_gibbs", (DL_FUNC) &_bgms_sample_mixed_mrf_gibbs, 11}, {"_bgms_run_mixed_simulation_parallel", (DL_FUNC) &_bgms_run_mixed_simulation_parallel, 16}, {"_bgms_sample_ggm", (DL_FUNC) &_bgms_sample_ggm, 19}, - {"_bgms_sample_mixed_mrf", (DL_FUNC) &_bgms_sample_mixed_mrf, 21}, + {"_bgms_sample_mixed_mrf", (DL_FUNC) &_bgms_sample_mixed_mrf, 24}, {"_bgms_sample_omrf", (DL_FUNC) &_bgms_sample_omrf, 24}, {"_bgms_compute_Vn_mfm_sbm", (DL_FUNC) &_bgms_compute_Vn_mfm_sbm, 4}, {NULL, NULL, 0} diff --git a/src/models/mixed/mixed_mrf_model.cpp b/src/models/mixed/mixed_mrf_model.cpp index 4fe7a9d4..5e980db7 100644 --- a/src/models/mixed/mixed_mrf_model.cpp +++ b/src/models/mixed/mixed_mrf_model.cpp @@ -126,6 +126,9 @@ MixedMRFModel::MixedMRFModel(const MixedMRFModel& other) max_cats_(other.max_cats_), is_ordinal_variable_(other.is_ordinal_variable_), baseline_category_(other.baseline_category_), + missing_index_discrete_(other.missing_index_discrete_), + missing_index_continuous_(other.missing_index_continuous_), + has_missing_(other.has_missing_), counts_per_category_(other.counts_per_category_), blume_capel_stats_(other.blume_capel_stats_), mux_(other.mux_), @@ -546,6 +549,133 @@ std::unique_ptr MixedMRFModel::clone() const { } +// ============================================================================= +// Missing data imputation +// ============================================================================= + +void MixedMRFModel::set_missing_data(const arma::imat& missing_discrete, + const arma::imat& missing_continuous) { + missing_index_discrete_ = missing_discrete; + missing_index_continuous_ = missing_continuous; + has_missing_ = (missing_index_discrete_.n_rows > 0 || + missing_index_continuous_.n_rows > 0); +} + +void MixedMRFModel::impute_missing() { + if(!has_missing_) return; + + // --- Phase 1: Impute discrete entries --- + const int num_disc_missing = missing_index_discrete_.n_rows; + if(num_disc_missing > 0) { + arma::vec category_probabilities(max_cats_ + 1); + + for(int miss = 0; miss < num_disc_missing; miss++) { + const int person = missing_index_discrete_(miss, 0); + const int variable = missing_index_discrete_(miss, 1); + const int num_cats = num_categories_(variable); + + // Rest score: sum_t x_vt * Kxx(t,s) + 2 * sum_j y_vj * Kxy(s,j) + // Kxx diagonal is zero, so no self-interaction subtraction needed + double rest_v = 0.0; + for(size_t t = 0; t < p_; t++) { + rest_v += discrete_observations_dbl_(person, t) * Kxx_(t, variable); + } + for(size_t j = 0; j < q_; j++) { + rest_v += 2.0 * continuous_observations_(person, j) * Kxy_(variable, j); + } + + double cumsum = 0.0; + + if(is_ordinal_variable_(variable)) { + // P(x=0) = 1, P(x=c) ∝ exp(c * rest + mux(s, c-1)) + cumsum = 1.0; + category_probabilities(0) = cumsum; + for(int c = 1; c <= num_cats; c++) { + double exponent = static_cast(c) * rest_v + + mux_(variable, c - 1); + cumsum += std::exp(exponent); + category_probabilities(c) = cumsum; + } + } else { + // Blume-Capel: categories centered at baseline + const int ref = baseline_category_(variable); + double alpha = mux_(variable, 0); + double beta = mux_(variable, 1); + cumsum = 0.0; + for(int cat = 0; cat <= num_cats; cat++) { + const int score = cat - ref; + double exponent = alpha * score + + beta * score * score + + score * rest_v; + cumsum += std::exp(exponent); + category_probabilities(cat) = cumsum; + } + } + + // Sample via inverse-transform + double u = runif(rng_) * cumsum; + int sampled = 0; + while(u > category_probabilities(sampled)) { + sampled++; + } + + int new_value = sampled; + if(!is_ordinal_variable_(variable)) { + new_value -= baseline_category_(variable); + } + const int old_value = discrete_observations_(person, variable); + + if(new_value != old_value) { + discrete_observations_(person, variable) = new_value; + discrete_observations_dbl_(person, variable) = + static_cast(new_value); + + if(is_ordinal_variable_(variable)) { + counts_per_category_(old_value, variable)--; + counts_per_category_(new_value, variable)++; + } else { + blume_capel_stats_(0, variable) += (new_value - old_value); + blume_capel_stats_(1, variable) += + (new_value * new_value - old_value * old_value); + } + } + } + } + + // --- Phase 2: Refresh conditional_mean_ (depends on discrete data) --- + if(num_disc_missing > 0 && missing_index_continuous_.n_rows > 0) { + recompute_conditional_mean(); + } + + // --- Phase 3: Impute continuous entries --- + const int num_cont_missing = missing_index_continuous_.n_rows; + if(num_cont_missing > 0) { + for(int miss = 0; miss < num_cont_missing; miss++) { + const int person = missing_index_continuous_(miss, 0); + const int variable = missing_index_continuous_(miss, 1); + + // Conditional: y_vj | y_{v,-j}, x ~ N(mu*, 1/Kyy_jj) + // mu* = M_vj - (1/Kyy_jj) * sum_{k!=j} Kyy_jk * (y_vk - M_vk) + double cond_mean = conditional_mean_(person, variable); + for(size_t k = 0; k < q_; k++) { + if(k != static_cast(variable)) { + cond_mean -= (Kyy_(variable, k) / Kyy_(variable, variable)) * + (continuous_observations_(person, k) - + conditional_mean_(person, k)); + } + } + double cond_sd = std::sqrt(1.0 / Kyy_(variable, variable)); + + continuous_observations_(person, variable) = + rnorm(rng_, cond_mean, cond_sd); + } + } + + // Invalidate gradient cache (observations changed) + invalidate_gradient_cache(); +} + + // ============================================================================= // Stubs (to be implemented in later phases) // ============================================================================= diff --git a/src/models/mixed/mixed_mrf_model.h b/src/models/mixed/mixed_mrf_model.h index be9d0ea5..aa9d9a85 100644 --- a/src/models/mixed/mixed_mrf_model.h +++ b/src/models/mixed/mixed_mrf_model.h @@ -80,8 +80,8 @@ class MixedMRFModel : public BaseModel { bool has_adaptive_metropolis() const override { return true; } /** @return true when edge selection is enabled. */ bool has_edge_selection() const override { return edge_selection_; } - /** @return false (missing data not yet supported). */ - bool has_missing_data() const override { return false; } + /** @return true when missing-data imputation is active. */ + bool has_missing_data() const override { return has_missing_; } // ========================================================================= // Core sampling methods @@ -227,11 +227,20 @@ class MixedMRFModel : public BaseModel { } // ========================================================================= - // Missing data (not yet supported) + // Missing data // ========================================================================= - /** No-op: missing data not supported in this PR. */ - void impute_missing() override {} + /** Impute missing entries from full-conditional distributions. */ + void impute_missing() override; + + /** + * Register missing-data locations for discrete and continuous sub-matrices. + * + * @param missing_discrete M_d x 2 matrix of 0-based (row, col) indices into discrete_observations_ + * @param missing_continuous M_c x 2 matrix of 0-based (row, col) indices into continuous_observations_ + */ + void set_missing_data(const arma::imat& missing_discrete, + const arma::imat& missing_continuous); private: @@ -259,6 +268,14 @@ class MixedMRFModel : public BaseModel { arma::uvec is_ordinal_variable_; ///< 1 = ordinal, 0 = Blume-Capel (p-vector) arma::ivec baseline_category_; ///< Reference category per discrete variable (p-vector) + // ========================================================================= + // Missing data + // ========================================================================= + + arma::imat missing_index_discrete_; ///< M_d x 2 (row, col) for missing discrete entries + arma::imat missing_index_continuous_; ///< M_c x 2 (row, col) for missing continuous entries + bool has_missing_ = false; ///< Whether imputation is active + // ========================================================================= // Sufficient statistics // ========================================================================= diff --git a/src/sample_mixed.cpp b/src/sample_mixed.cpp index 7a208a1d..d8b3fa06 100644 --- a/src/sample_mixed.cpp +++ b/src/sample_mixed.cpp @@ -46,6 +46,9 @@ // @param target_acceptance Target acceptance rate for gradient-based samplers // @param max_tree_depth Maximum tree depth for NUTS // @param num_leapfrogs Number of leapfrog steps for HMC +// @param na_impute Whether to impute missing data +// @param missing_index_discrete Matrix of missing discrete indices (n_miss x 2, 0-based) +// @param missing_index_continuous Matrix of missing continuous indices (n_miss x 2, 0-based) // // @return List with per-chain results including samples and diagnostics // [[Rcpp::export]] @@ -70,7 +73,10 @@ Rcpp::List sample_mixed_mrf( const std::string& sampler_type = "mh", const double target_acceptance = 0.80, const int max_tree_depth = 10, - const int num_leapfrogs = 100 + const int num_leapfrogs = 100, + const bool na_impute = false, + const Rcpp::Nullable missing_index_discrete_nullable = R_NilValue, + const Rcpp::Nullable missing_index_continuous_nullable = R_NilValue ) { // Extract model inputs from R list arma::imat discrete_obs = Rcpp::as(inputFromR["discrete_observations"]); @@ -93,6 +99,20 @@ Rcpp::List sample_mixed_mrf( seed ); + // Set up missing data imputation + if(na_impute) { + arma::imat missing_disc, missing_cont; + if(missing_index_discrete_nullable.isNotNull()) { + missing_disc = Rcpp::as( + Rcpp::IntegerMatrix(missing_index_discrete_nullable.get())); + } + if(missing_index_continuous_nullable.isNotNull()) { + missing_cont = Rcpp::as( + Rcpp::IntegerMatrix(missing_index_continuous_nullable.get())); + } + model.set_missing_data(missing_disc, missing_cont); + } + // Create edge prior EdgePrior edge_prior_enum = edge_prior_from_string(edge_prior); auto edge_prior_obj = create_edge_prior( @@ -109,7 +129,7 @@ Rcpp::List sample_mixed_mrf( config.no_warmup = no_warmup; config.edge_selection = edge_selection; config.seed = seed; - config.na_impute = false; + config.na_impute = na_impute; config.target_acceptance = target_acceptance; config.max_tree_depth = max_tree_depth; config.num_leapfrogs = num_leapfrogs; From cad34262250d93c6408bdf7935ac6bfd9d08dfdb Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Mon, 9 Mar 2026 08:14:36 +0100 Subject: [PATCH 30/60] perf: use MY_LOG/MY_EXP macros in GGM and OMRF models (Phase J) --- src/models/ggm/ggm_model.cpp | 43 +++++++++++++++++----------------- src/models/omrf/omrf_model.cpp | 2 +- 2 files changed, 23 insertions(+), 22 deletions(-) diff --git a/src/models/ggm/ggm_model.cpp b/src/models/ggm/ggm_model.cpp index 573d37df..7c200c3c 100644 --- a/src/models/ggm/ggm_model.cpp +++ b/src/models/ggm/ggm_model.cpp @@ -1,5 +1,6 @@ #include "models/ggm/ggm_model.h" #include "rng/rng_utils.h" +#include "math/explog_macros.h" #include "models/ggm/cholupdate.h" #include "mcmc/execution/step_result.h" #include "mcmc/execution/warmup_schedule.h" @@ -8,16 +9,16 @@ void GGMModel::get_constants(size_t i, size_t j) { double logdet_omega = cholesky_helpers::get_log_det(cholesky_of_precision_); - double log_adj_omega_ii = logdet_omega + std::log(std::abs(covariance_matrix_(i, i))); - double log_adj_omega_ij = logdet_omega + std::log(std::abs(covariance_matrix_(i, j))); - double log_adj_omega_jj = logdet_omega + std::log(std::abs(covariance_matrix_(j, j))); + double log_adj_omega_ii = logdet_omega + MY_LOG(std::abs(covariance_matrix_(i, i))); + double log_adj_omega_ij = logdet_omega + MY_LOG(std::abs(covariance_matrix_(i, j))); + double log_adj_omega_jj = logdet_omega + MY_LOG(std::abs(covariance_matrix_(j, j))); double inv_omega_sub_j1j1 = cholesky_helpers::compute_inv_submatrix_i(covariance_matrix_, i, j, j); - double log_abs_inv_omega_sub_jj = log_adj_omega_ii + std::log(std::abs(inv_omega_sub_j1j1)); - double Phi_q1q = (2 * std::signbit(covariance_matrix_(i, j)) - 1) * std::exp( + double log_abs_inv_omega_sub_jj = log_adj_omega_ii + MY_LOG(std::abs(inv_omega_sub_j1j1)); + double Phi_q1q = (2 * std::signbit(covariance_matrix_(i, j)) - 1) * MY_EXP( (log_adj_omega_ij - (log_adj_omega_jj + log_abs_inv_omega_sub_jj) / 2) ); - double Phi_q1q1 = std::exp((log_adj_omega_jj - log_abs_inv_omega_sub_jj) / 2); + double Phi_q1q1 = MY_EXP((log_adj_omega_jj - log_abs_inv_omega_sub_jj) / 2); constants_[0] = Phi_q1q; constants_[1] = Phi_q1q1; @@ -41,7 +42,7 @@ double GGMModel::log_density_impl(const arma::mat& omega, const arma::mat& phi) double logdet_omega = cholesky_helpers::get_log_det(phi); double trace_prod = arma::accu(omega % suf_stat_); - double log_likelihood = n_ * (p_ * log(2 * arma::datum::pi) / 2 + logdet_omega / 2) - trace_prod / 2; + double log_likelihood = n_ * (p_ * MY_LOG(2 * arma::datum::pi) / 2 + logdet_omega / 2) - trace_prod / 2; return log_likelihood; } @@ -57,7 +58,7 @@ double GGMModel::log_density_impl_edge(size_t i, size_t j) const { double cc12 = 1 - (covariance_matrix_(i, j) * Ui2 + covariance_matrix_(j, j) * Uj2); double cc22 = 0 + Ui2 * Ui2 * covariance_matrix_(i, i) + 2 * Ui2 * Uj2 * covariance_matrix_(i, j) + Uj2 * Uj2 * covariance_matrix_(j, j); - double logdet = std::log(std::abs(cc11 * cc22 - cc12 * cc12)); + double logdet = MY_LOG(std::abs(cc11 * cc22 - cc12 * cc12)); // logdet - (logdet(aOmega_prop) - logdet(aOmega)) double trace_prod = -2 * (suf_stat_(j, j) * Uj2 + suf_stat_(i, j) * Ui2); @@ -75,7 +76,7 @@ double GGMModel::log_density_impl_diag(size_t j) const { double cc12 = 1 - covariance_matrix_(j, j) * Uj2; double cc22 = 0 + Uj2 * Uj2 * covariance_matrix_(j, j); - double logdet = std::log(std::abs(cc11 * cc22 - cc12 * cc12)); + double logdet = MY_LOG(std::abs(cc11 * cc22 - cc12 * cc12)); double trace_prod = -2 * suf_stat_(j, j) * Uj2; double log_likelihood_ratio = (n_ * logdet - trace_prod) / 2; @@ -111,7 +112,7 @@ void GGMModel::update_edge_parameter(size_t i, size_t j, int iteration) { ln_alpha += R::dcauchy(precision_proposal_(i, j), 0.0, pairwise_scale_, true); ln_alpha -= R::dcauchy(precision_matrix_(i, j), 0.0, pairwise_scale_, true); - if (std::log(runif(rng_)) < ln_alpha) { + if (MY_LOG(runif(rng_)) < ln_alpha) { double omega_ij_old = precision_matrix_(i, j); double omega_jj_old = precision_matrix_(j, j); @@ -165,7 +166,7 @@ void GGMModel::cholesky_update_after_edge(double omega_ij_old, double omega_jj_o void GGMModel::update_diagonal_parameter(size_t i, int iteration) { double logdet_omega = cholesky_helpers::get_log_det(cholesky_of_precision_); - double logdet_omega_sub_ii = logdet_omega + std::log(covariance_matrix_(i, i)); + double logdet_omega_sub_ii = logdet_omega + MY_LOG(covariance_matrix_(i, i)); size_t e = i * (i + 3) / 2; // parameter index in vectorized form (column-major upper triangle, i==j) double proposal_sd = proposal_sds_(e); @@ -174,15 +175,15 @@ void GGMModel::update_diagonal_parameter(size_t i, int iteration) { double theta_prop = rnorm(rng_, theta_curr, proposal_sd); precision_proposal_ = precision_matrix_; - precision_proposal_(i, i) = precision_matrix_(i, i) - std::exp(theta_curr) * std::exp(theta_curr) + std::exp(theta_prop) * std::exp(theta_prop); + precision_proposal_(i, i) = precision_matrix_(i, i) - MY_EXP(theta_curr) * MY_EXP(theta_curr) + MY_EXP(theta_prop) * MY_EXP(theta_prop); double ln_alpha = log_density_impl_diag(i); - ln_alpha += R::dgamma(exp(theta_prop), 1.0, 1.0, true); - ln_alpha -= R::dgamma(exp(theta_curr), 1.0, 1.0, true); + ln_alpha += R::dgamma(MY_EXP(theta_prop), 1.0, 1.0, true); + ln_alpha -= R::dgamma(MY_EXP(theta_curr), 1.0, 1.0, true); ln_alpha += theta_prop - theta_curr; // Jacobian adjustment - if (std::log(runif(rng_)) < ln_alpha) { + if (MY_LOG(runif(rng_)) < ln_alpha) { double omega_ii = precision_matrix_(i, i); precision_matrix_(i, i) = precision_proposal_(i, i); cholesky_update_after_diag(omega_ii, i); @@ -246,12 +247,12 @@ void GGMModel::update_edge_indicator_parameter_pair(size_t i, size_t j) { // } - ln_alpha += std::log(1.0 - inclusion_probability_(i, j)) - std::log(inclusion_probability_(i, j)); + ln_alpha += MY_LOG(1.0 - inclusion_probability_(i, j)) - MY_LOG(inclusion_probability_(i, j)); - ln_alpha += R::dnorm(precision_matrix_(i, j) / constants_[3], 0.0, proposal_sd, true) - std::log(constants_[3]); + ln_alpha += R::dnorm(precision_matrix_(i, j) / constants_[3], 0.0, proposal_sd, true) - MY_LOG(constants_[3]); ln_alpha -= R::dcauchy(precision_matrix_(i, j), 0.0, pairwise_scale_, true); - if (std::log(runif(rng_)) < ln_alpha) { + if (MY_LOG(runif(rng_)) < ln_alpha) { // Store old values for Cholesky update double omega_ij_old = precision_matrix_(i, j); @@ -295,15 +296,15 @@ void GGMModel::update_edge_indicator_parameter_pair(size_t i, size_t j) { // Rcpp::Rcout << "ln_alpha: " << ln_alpha << ", ln_alpha_ref: " << ln_alpha_ref << std::endl; // } // } - ln_alpha += std::log(inclusion_probability_(i, j)) - std::log(1.0 - inclusion_probability_(i, j)); + ln_alpha += MY_LOG(inclusion_probability_(i, j)) - MY_LOG(1.0 - inclusion_probability_(i, j)); // Prior change: add slab (Cauchy prior) ln_alpha += R::dcauchy(omega_prop_ij, 0.0, pairwise_scale_, true); // Proposal term: proposed edge value given it was generated from truncated normal - ln_alpha -= R::dnorm(omega_prop_ij / constants_[3], 0.0, proposal_sd, true) - std::log(constants_[3]); + ln_alpha -= R::dnorm(omega_prop_ij / constants_[3], 0.0, proposal_sd, true) - MY_LOG(constants_[3]); - if (std::log(runif(rng_)) < ln_alpha) { + if (MY_LOG(runif(rng_)) < ln_alpha) { // Accept: turn ON the edge // Store old values for Cholesky update double omega_ij_old = precision_matrix_(i, j); diff --git a/src/models/omrf/omrf_model.cpp b/src/models/omrf/omrf_model.cpp index 6dbe6189..5fc2c886 100644 --- a/src/models/omrf/omrf_model.cpp +++ b/src/models/omrf/omrf_model.cpp @@ -586,7 +586,7 @@ double OMRFModel::log_pseudoposterior_with_state( double log_post = 0.0; auto log_beta_prior = [this](double x) { - return x * main_alpha_ - std::log1p(std::exp(x)) * (main_alpha_ + main_beta_); + return x * main_alpha_ - std::log1p(MY_EXP(x)) * (main_alpha_ + main_beta_); }; // Main effect contributions (priors and sufficient statistics) From 676b49fd263571ee10af14ebf3bb270dc632d858 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Mon, 9 Mar 2026 10:29:13 +0100 Subject: [PATCH 31/60] docs: naming conformance + MY_LOG/MY_EXP for MixedMRFModel (audit K, part 1) --- R/build_output.R | 75 ++-- src/mcmc/samplers/hybrid_nuts_sampler.h | 2 +- src/models/mixed/mixed_mrf_gradient.cpp | 262 ++++++------- src/models/mixed/mixed_mrf_likelihoods.cpp | 52 +-- src/models/mixed/mixed_mrf_metropolis.cpp | 430 ++++++++++----------- src/models/mixed/mixed_mrf_model.cpp | 293 +++++++------- src/models/mixed/mixed_mrf_model.h | 134 +++---- 7 files changed, 625 insertions(+), 623 deletions(-) diff --git a/R/build_output.R b/R/build_output.R index c171342f..f8190e0b 100644 --- a/R/build_output.R +++ b/R/build_output.R @@ -325,10 +325,11 @@ build_output_bgm = function(spec, raw) { # ============================================================================== # # Handles the mixed discrete + continuous parameter layout: -# C++ flat vector: [mux | Kxx_ut | muy | Kxy | Kyy_ut] +# C++ flat vector: [main_discrete | pairwise_discrete_ut | main_continuous | pairwise_cross | pairwise_continuous_ut] # C++ indicators: [Gxx_ut | Gyy_ut | Gxy] # -# Splits into main (mux, muy, Kyy_diag) and pairwise (Kxx, Kyy_offdiag, Kxy), +# Splits into main (discrete thresholds, continuous means, precision diagonal) +# and pairwise (discrete, precision off-diag, cross), # builds (p+q)×(p+q) interaction and indicator matrices, and maps internal # variable order (discrete first, continuous second) back to original column # order from the user's data. @@ -359,20 +360,20 @@ build_output_mixed_mrf = function(spec, raw) { nxy = as.integer(p * q) # Offsets in the flat vector (1-based) - mux_start = 1L - mux_end = nt - kxx_start = nt + 1L - kxx_end = nt + nxx - muy_start = nt + nxx + 1L - muy_end = nt + nxx + q - kxy_start = nt + nxx + q + 1L - kxy_end = nt + nxx + q + nxy - kyy_start = nt + nxx + q + nxy + 1L - kyy_end = nt + nxx + q + nxy + nyy_total - - # Kyy diagonal indices within the Kyy block - kyy_diag_within = integer(q) - kyy_offdiag_within = integer(nyy_offdiag) + main_discrete_start = 1L + main_discrete_end = nt + pairwise_discrete_start = nt + 1L + pairwise_discrete_end = nt + nxx + main_continuous_start = nt + nxx + 1L + main_continuous_end = nt + nxx + q + pairwise_cross_start = nt + nxx + q + 1L + pairwise_cross_end = nt + nxx + q + nxy + pairwise_continuous_start = nt + nxx + q + nxy + 1L + pairwise_continuous_end = nt + nxx + q + nxy + nyy_total + + # Precision diagonal indices within the pairwise_continuous block + precision_diag_within = integer(q) + precision_offdiag_within = integer(nyy_offdiag) k_diag = 0L k_off = 0L pos = 0L @@ -381,34 +382,34 @@ build_output_mixed_mrf = function(spec, raw) { pos = pos + 1L if(i == j) { k_diag = k_diag + 1L - kyy_diag_within[k_diag] = pos + precision_diag_within[k_diag] = pos } else { k_off = k_off + 1L - kyy_offdiag_within[k_off] = pos + precision_offdiag_within[k_off] = pos } } } - kyy_diag_abs = kyy_start - 1L + kyy_diag_within - kyy_offdiag_abs = kyy_start - 1L + kyy_offdiag_within + precision_diag_abs = pairwise_continuous_start - 1L + precision_diag_within + precision_offdiag_abs = pairwise_continuous_start - 1L + precision_offdiag_within - # Main indices: mux + muy + Kyy diagonal + # Main indices: discrete thresholds + continuous means + precision diagonal main_idx = c( - seq(mux_start, mux_end), - seq(muy_start, muy_end), - kyy_diag_abs + seq(main_discrete_start, main_discrete_end), + seq(main_continuous_start, main_continuous_end), + precision_diag_abs ) - # Pairwise indices: Kxx + Kyy off-diag + Kxy + # Pairwise indices: discrete + precision off-diag + cross pairwise_idx = c( - if(nxx > 0) seq(kxx_start, kxx_end) else integer(0), - kyy_offdiag_abs, - if(nxy > 0) seq(kxy_start, kxy_end) else integer(0) + if(nxx > 0) seq(pairwise_discrete_start, pairwise_discrete_end) else integer(0), + precision_offdiag_abs, + if(nxy > 0) seq(pairwise_cross_start, pairwise_cross_end) else integer(0) ) # --- Indicator index layout ------------------------------------------------- # C++ indicator vector: [Gxx_ut | Gyy_ut | Gxy] # All are pairwise, so indicator_samples maps directly to pairwise order: - # Kxx edges, Kyy edges, Kxy edges — same order as pairwise_idx above. + # Discrete, continuous, cross edges — same order as pairwise_idx above. # --- Normalize raw output per chain ----------------------------------------- raw = lapply(raw, function(chain) { @@ -460,7 +461,7 @@ build_output_mixed_mrf = function(spec, raw) { all_internal_names = c(disc_names, cont_names) edge_names = character() - # Kxx edges (discrete-discrete) + # Discrete-discrete edges if(p > 1) { for(i in seq_len(p - 1)) { for(j in seq(i + 1, p)) { @@ -471,7 +472,7 @@ build_output_mixed_mrf = function(spec, raw) { } } } - # Kyy edges (continuous-continuous, off-diagonal) + # Continuous-continuous edges (off-diagonal) if(q > 1) { for(i in seq_len(q - 1)) { for(j in seq(i + 1, q)) { @@ -482,7 +483,7 @@ build_output_mixed_mrf = function(spec, raw) { } } } - # Kxy edges (discrete-continuous) + # Cross edges (discrete-continuous) if(p > 0 && q > 0) { for(i in seq_len(p)) { for(j in seq_len(q)) { @@ -549,9 +550,9 @@ build_output_mixed_mrf = function(spec, raw) { # Continuous main effects: q × 2 matrix (mean, precision) pmm_cont = matrix(NA, nrow = q, ncol = 2) - muy_means = main_summary$mean[nt + seq_len(q)] + continuous_means = main_summary$mean[nt + seq_len(q)] kyy_diag_means = main_summary$mean[nt + q + seq_len(q)] - pmm_cont[, 1] = muy_means + pmm_cont[, 1] = continuous_means pmm_cont[, 2] = kyy_diag_means rownames(pmm_cont) = cont_names colnames(pmm_cont) = c("mean", "precision") @@ -571,7 +572,7 @@ build_output_mixed_mrf = function(spec, raw) { pw_means = pairwise_summary$mean idx = 0L - # Kxx block + # Discrete-discrete block if(p > 1) { for(i in seq_len(p - 1)) { for(j in seq(i + 1, p)) { @@ -584,7 +585,7 @@ build_output_mixed_mrf = function(spec, raw) { } } - # Kyy off-diagonal block + # Continuous-continuous off-diagonal block if(q > 1) { for(i in seq_len(q - 1)) { for(j in seq(i + 1, q)) { @@ -597,7 +598,7 @@ build_output_mixed_mrf = function(spec, raw) { } } - # Kxy block + # Cross block if(p > 0 && q > 0) { for(i in seq_len(p)) { for(j in seq_len(q)) { diff --git a/src/mcmc/samplers/hybrid_nuts_sampler.h b/src/mcmc/samplers/hybrid_nuts_sampler.h index 30d508be..344d35d7 100644 --- a/src/mcmc/samplers/hybrid_nuts_sampler.h +++ b/src/mcmc/samplers/hybrid_nuts_sampler.h @@ -54,7 +54,7 @@ class HybridNUTSSampler : public GradientSamplerBase { // --- Phase 2: Kyy Metropolis step --- auto& mixed = static_cast(model); - mixed.do_kyy_metropolis_step(current_iteration_); + mixed.do_pairwise_continuous_metropolis_step(current_iteration_); return result; } diff --git a/src/models/mixed/mixed_mrf_gradient.cpp b/src/models/mixed/mixed_mrf_gradient.cpp index 4c1fec18..c4119afc 100644 --- a/src/models/mixed/mixed_mrf_gradient.cpp +++ b/src/models/mixed/mixed_mrf_gradient.cpp @@ -15,9 +15,9 @@ void MixedMRFModel::ensure_gradient_cache() { if(gradient_cache_valid_) return; - // --- Build index matrix for Kxx upper-triangular entries --- + // --- Build index matrix for pairwise_effects_discrete_ upper-triangular entries --- // Maps (i, j) to a position in the flat gradient vector (offset from - // the start of Kxx entries, which sits at num_main_). + // the start of pairwise_discrete entries, which sits at num_main_). kxx_index_cache_.set_size(p_, p_); kxx_index_cache_.zeros(); @@ -32,9 +32,9 @@ void MixedMRFModel::ensure_gradient_cache() { } } - // --- Build index matrix for Kxy entries --- + // --- Build index matrix for pairwise_effects_cross_ entries --- // Maps (i, j) to a position in the flat gradient vector (offset from - // the start of Kxy entries, which sits at num_main_ + active_kxx + q). + // the start of pairwise_cross entries, which sits at num_main_ + active_kxx + q). kxy_index_cache_.set_size(p_, q_); kxy_index_cache_.zeros(); @@ -49,15 +49,15 @@ void MixedMRFModel::ensure_gradient_cache() { } } - // muy offset in gradient vector - muy_grad_offset_ = num_main_ + num_active_kxx; + // main_effects_continuous_ offset in gradient vector + main_effects_continuous_grad_offset_ = num_main_ + num_active_kxx; // --- Precompute observed statistics portion of the gradient --- size_t active_dim = num_main_ + num_active_kxx + q_ + num_active_kxy; grad_obs_cache_.set_size(active_dim); grad_obs_cache_.zeros(); - // Observed statistics for discrete main effects (mux) + // Observed statistics for discrete main effects int offset = 0; for(size_t s = 0; s < p_; ++s) { if(is_ordinal_variable_(s)) { @@ -73,7 +73,7 @@ void MixedMRFModel::ensure_gradient_cache() { } } - // Observed statistics for Kxx edges + // Observed statistics for pairwise_effects_discrete_ edges for(size_t i = 0; i < p_ - 1; ++i) { for(size_t j = i + 1; j < p_; ++j) { if(edge_indicators_(i, j) == 0) continue; @@ -86,7 +86,7 @@ void MixedMRFModel::ensure_gradient_cache() { } } - // No precomputed observed stats for muy or Kxy — those depend on + // No precomputed observed stats for means or cross effects — those depend on // continuous_observations_ combined with current parameters, so they // are computed fresh each logp_and_gradient call. @@ -111,45 +111,45 @@ void MixedMRFModel::invalidate_gradient_cache() { void MixedMRFModel::unvectorize_nuts_to_temps( const arma::vec& params, - arma::mat& temp_mux, - arma::mat& temp_Kxx, - arma::vec& temp_muy, - arma::mat& temp_Kxy + arma::mat& temp_main_discrete, + arma::mat& temp_pairwise_discrete, + arma::vec& temp_main_continuous, + arma::mat& temp_pairwise_cross ) const { size_t idx = 0; - // 1. mux + // 1. main_effects_discrete_ for(size_t s = 0; s < p_; ++s) { if(is_ordinal_variable_(s)) { for(int c = 0; c < num_categories_(s); ++c) { - temp_mux(s, c) = params(idx++); + temp_main_discrete(s, c) = params(idx++); } } else { - temp_mux(s, 0) = params(idx++); - temp_mux(s, 1) = params(idx++); + temp_main_discrete(s, 0) = params(idx++); + temp_main_discrete(s, 1) = params(idx++); } } - // 2. Kxx upper-triangular (active only) + // 2. pairwise_effects_discrete_ upper-triangular (active only) for(size_t i = 0; i < p_ - 1; ++i) { for(size_t j = i + 1; j < p_; ++j) { if(edge_indicators_(i, j) == 1) { - temp_Kxx(i, j) = params(idx++); - temp_Kxx(j, i) = temp_Kxx(i, j); + temp_pairwise_discrete(i, j) = params(idx++); + temp_pairwise_discrete(j, i) = temp_pairwise_discrete(i, j); } } } - // 3. muy + // 3. main_effects_continuous_ for(size_t j = 0; j < q_; ++j) { - temp_muy(j) = params(idx++); + temp_main_continuous(j) = params(idx++); } - // 4. Kxy row-major (active only) + // 4. pairwise_effects_cross_ row-major (active only) for(size_t i = 0; i < p_; ++i) { for(size_t j = 0; j < q_; ++j) { if(edge_indicators_(i, p_ + j) == 1) { - temp_Kxy(i, j) = params(idx++); + temp_pairwise_cross(i, j) = params(idx++); } } } @@ -170,16 +170,16 @@ arma::vec MixedMRFModel::gradient(const arma::vec& parameters) { // logp_and_gradient — conditional pseudo-likelihood // ============================================================================= // Computes the log pseudo-posterior and its gradient with respect to the -// NUTS parameters (mux, Kxx, muy, Kxy). Kyy is treated as fixed. +// NUTS parameters (μ_x, K_xx, μ_y, K_xy). K_yy is treated as fixed. // // The pseudo-log-posterior is: -// l(theta) = sum_s log p(x_s | x_{-s}, y) [OMRF conditionals] +// l(θ) = sum_s log p(x_s | x_{-s}, y) [OMRF conditionals] // + log p(y | x) [GGM conditional] -// + log pi(theta) [priors] +// + log π(θ) [priors] // -// For marginal PL, the OMRF conditionals use Theta = Kxx + 2 Kxy Σyy Kxy' -// instead of Kxx directly, and derive rest scores and denominator offsets -// from Theta. The GGM conditional is the same in both modes. +// For marginal PL, the OMRF conditionals use Θ = K_xx + 2 K_xy Σ_yy K_xy' +// instead of K_xx directly, and derive rest scores and denominator offsets +// from Θ. The GGM conditional is the same in both modes. // ============================================================================= std::pair MixedMRFModel::logp_and_gradient( @@ -188,16 +188,16 @@ std::pair MixedMRFModel::logp_and_gradient( ensure_gradient_cache(); // --- Unvectorize into temporaries --- - arma::mat temp_mux = mux_; - arma::mat temp_Kxx = Kxx_; - arma::vec temp_muy = muy_; - arma::mat temp_Kxy = Kxy_; - unvectorize_nuts_to_temps(parameters, temp_mux, temp_Kxx, temp_muy, temp_Kxy); + arma::mat temp_main_discrete = main_effects_discrete_; + arma::mat temp_pairwise_discrete = pairwise_effects_discrete_; + arma::vec temp_main_continuous = main_effects_continuous_; + arma::mat temp_pairwise_cross = pairwise_effects_cross_; + unvectorize_nuts_to_temps(parameters, temp_main_discrete, temp_pairwise_discrete, temp_main_continuous, temp_pairwise_cross); // --- Derived quantities --- - // Conditional mean: M_i = muy' + 2 x_i' Kxy Σyy (n x q) - arma::mat temp_cond_mean = arma::repmat(temp_muy.t(), n_, 1) - + 2.0 * discrete_observations_dbl_ * temp_Kxy * covariance_yy_; + // Conditional mean: M_i = μ_y' + 2 x_i' K_xy Σ_yy (n x q) + arma::mat temp_cond_mean = arma::repmat(temp_main_continuous.t(), n_, 1) + + 2.0 * discrete_observations_dbl_ * temp_pairwise_cross * covariance_continuous_; // Residual: D = Y - M (n x q) arma::mat D = continuous_observations_ - temp_cond_mean; @@ -205,7 +205,7 @@ std::pair MixedMRFModel::logp_and_gradient( // Theta for marginal PL arma::mat temp_Theta; if(use_marginal_pl_) { - temp_Theta = temp_Kxx + 2.0 * temp_Kxy * covariance_yy_ * temp_Kxy.t(); + temp_Theta = temp_pairwise_discrete + 2.0 * temp_pairwise_cross * covariance_continuous_ * temp_pairwise_cross.t(); } // Start gradient from observed-statistics cache @@ -213,37 +213,37 @@ std::pair MixedMRFModel::logp_and_gradient( double logp = 0.0; - // For marginal PL: precompute Kxy Σyy (used in cross-contributions) - arma::mat KxySyy; // p x q + // For marginal PL: precompute K_xy Σ_yy (used in cross-contributions) + arma::mat cross_times_cov; // p x q if(use_marginal_pl_) { - KxySyy = temp_Kxy * covariance_yy_; + cross_times_cov = temp_pairwise_cross * covariance_continuous_; } // ========================================================================= // Part 1: OMRF conditionals // ========================================================================= - int mux_offset = 0; + int main_effects_discrete_offset = 0; for(size_t s = 0; s < p_; ++s) { int C_s = num_categories_(s); // --- Rest score for variable s --- arma::vec rest; if(use_marginal_pl_) { - // Marginal: Theta-based rest + Kxy*muy bias + // Marginal: Θ-based rest + K_xy μ_y bias double theta_ss = temp_Theta(s, s); rest = discrete_observations_dbl_ * temp_Theta.col(s) - discrete_observations_dbl_.col(s) * theta_ss - + 2.0 * arma::dot(temp_Kxy.row(s), temp_muy); + + 2.0 * arma::dot(temp_pairwise_cross.row(s), temp_main_continuous); } else { - // Conditional: Kxx-based rest + 2 Kxy y - rest = discrete_observations_dbl_ * temp_Kxx.col(s) - - discrete_observations_dbl_.col(s) * temp_Kxx(s, s) - + 2.0 * continuous_observations_ * temp_Kxy.row(s).t(); + // Conditional: K_xx-based rest + 2 K_xy y + rest = discrete_observations_dbl_ * temp_pairwise_discrete.col(s) + - discrete_observations_dbl_.col(s) * temp_pairwise_discrete(s, s) + + 2.0 * continuous_observations_ * temp_pairwise_cross.row(s).t(); } if(is_ordinal_variable_(s)) { - arma::vec main_param = temp_mux.row(s).cols(0, C_s - 1).t(); + arma::vec main_param = temp_main_discrete.row(s).cols(0, C_s - 1).t(); // Marginal PL: absorb Theta_ss into main_param if(use_marginal_pl_) { @@ -267,16 +267,16 @@ std::pair MixedMRFModel::logp_and_gradient( // log pseudo-posterior contribution logp -= arma::accu(result.log_Z); - // Main-effect gradient: ∂/∂mux_{s,c} = count_c - sum_i prob(c) + // Main-effect gradient: ∂/∂main_effects_discrete_{s,c} = count_c - sum_i prob(c) for(int c = 0; c < C_s; ++c) { - grad(mux_offset + c) -= arma::accu(result.probs.col(c + 1)); + grad(main_effects_discrete_offset + c) -= arma::accu(result.probs.col(c + 1)); } // Expected value E_s[c+1|rest] per observation arma::vec weights = arma::regspace(1, C_s); arma::vec E = result.probs.cols(1, C_s) * weights; - // Kxx pairwise gradient: sum_i x_{i,t} * (x_{i,s}+1 - E_s) + // Pairwise discrete gradient: sum_i x_{i,t} * (x_{i,s}+1 - E_s) // (uses pre-transposed discrete observations for BLAS efficiency) arma::vec pw_grad = discrete_observations_dbl_t_ * E; for(size_t t = 0; t < p_; ++t) { @@ -286,15 +286,15 @@ std::pair MixedMRFModel::logp_and_gradient( } if(use_marginal_pl_) { - // Additional Kxx gradient from Theta_ss in denominator: - // ∂/∂Kxx_{st} through Theta_ss: zero (∂Theta_ss/∂Kxx_st = δ_{st}) - // So Kxx gradient from Theta rest scores is already handled above. + // Additional pairwise_discrete gradient from Θ_ss in denominator: + // ∂/∂pairwise_effects_discrete_{st} through Θ_ss: zero (∂Θ_ss/∂pairwise_effects_discrete_st = δ_{st}) + // So pairwise_discrete gradient from Θ rest scores is already handled above. - // Kxy gradient from marginal OMRF (through Theta): - // ∂Theta_{st}/∂Kxy_{a,j} has two terms: - // = 2 [Σyy Kxy_t']_j δ_{as} + 2 [Kxy_s Σyy]_j δ_{at} - // Self-contribution (a=s): from rest_s → Kxy_s - // Cross-contribution (a=t): from rest_s → Kxy_t for each t≠s + // Pairwise_cross gradient from marginal OMRF (through Θ): + // ∂Theta_{st}/∂pairwise_effects_cross_{a,j} has two terms: + // = 2 [Σyy pairwise_effects_cross_t']_j δ_{as} + 2 [pairwise_effects_cross_s Σyy]_j δ_{at} + // Self-contribution (a=s): from rest_s → pairwise_effects_cross_s + // Cross-contribution (a=t): from rest_s → pairwise_effects_cross_t for each t≠s arma::vec weights_sq = arma::square(weights); arma::vec E_sq = result.probs.cols(1, C_s) * weights_sq; @@ -310,12 +310,12 @@ std::pair MixedMRFModel::logp_and_gradient( double sum_obs_minus_E = arma::accu(discrete_observations_dbl_.col(s)) - arma::accu(E); // Self-contribution: a = s - // Off-diagonal Theta: ∂Θ_{st}/∂Kxy_{s,j} = 2 [Σyy Kxy_t']_j - // Diagonal Theta: ∂Θ_{ss}/∂Kxy_{s,j} = 4 [Σyy Kxy_s']_j - // Rest-score bias: ∂(2 Kxy_s μy)/∂Kxy_{s,j} = 2 μy_j - arma::rowvec kxy_self = 2.0 * (diff_pw.t() * temp_Kxy) * covariance_yy_ - + 4.0 * diff_diag * KxySyy.row(s) - + 2.0 * sum_obs_minus_E * temp_muy.t(); + // Off-diagonal Theta: ∂Θ_{st}/∂pairwise_effects_cross_{s,j} = 2 [Σyy pairwise_effects_cross_t']_j + // Diagonal Theta: ∂Θ_{ss}/∂pairwise_effects_cross_{s,j} = 4 [Σyy pairwise_effects_cross_s']_j + // Rest-score bias: ∂(2 pairwise_effects_cross_s μy)/∂pairwise_effects_cross_{s,j} = 2 μy_j + arma::rowvec kxy_self = 2.0 * (diff_pw.t() * temp_pairwise_cross) * covariance_continuous_ + + 4.0 * diff_diag * cross_times_cov.row(s) + + 2.0 * sum_obs_minus_E * temp_main_continuous.t(); for(size_t j = 0; j < q_; ++j) { if(edge_indicators_(s, p_ + j) == 0) continue; @@ -324,8 +324,8 @@ std::pair MixedMRFModel::logp_and_gradient( } // Cross-contribution: a = t, for each t ≠ s - // ∂l_s/∂Kxy_{t,:} = diff_pw(t) * 2 * Kxy_s * Σyy - arma::rowvec V_s = 2.0 * KxySyy.row(s); // 2 * Kxy_s * Σyy + // ∂l_s/∂pairwise_effects_cross_{t,:} = diff_pw(t) * 2 * pairwise_effects_cross_s * Σyy + arma::rowvec V_s = 2.0 * cross_times_cov.row(s); // 2 K_xy_s Σ_yy for(size_t t = 0; t < p_; ++t) { if(t == s || std::abs(diff_pw(t)) < 1e-300) continue; for(size_t j = 0; j < q_; ++j) { @@ -335,14 +335,14 @@ std::pair MixedMRFModel::logp_and_gradient( } } - // muy gradient from marginal OMRF: - // ∂l_s/∂muy_j = 2 Kxy_{sj} * sum_i (x_{is} - E_s) + // Continuous mean gradient from marginal OMRF: + // ∂l_s/∂main_effects_continuous_j = 2 pairwise_effects_cross_{sj} * sum_i (x_{is} - E_s) for(size_t j = 0; j < q_; ++j) { - grad(muy_grad_offset_ + j) += 2.0 * temp_Kxy(s, j) * sum_obs_minus_E; + grad(main_effects_continuous_grad_offset_ + j) += 2.0 * temp_pairwise_cross(s, j) * sum_obs_minus_E; } } else { - // Conditional PL: Kxy gradient from OMRF rest score - // ∂/∂Kxy_{s,j} = 2 sum_i y_{ij} (x_{is}+1 - E_s) + // Conditional PL: pairwise_cross gradient from OMRF rest score + // ∂/∂pairwise_effects_cross_{s,j} = 2 sum_i y_{ij} (x_{is}+1 - E_s) arma::rowvec kxy_grad_s = 2.0 * ( discrete_observations_dbl_.col(s) - E ).t() * continuous_observations_; @@ -354,12 +354,12 @@ std::pair MixedMRFModel::logp_and_gradient( } } - mux_offset += C_s; + main_effects_discrete_offset += C_s; } else { // --- Blume-Capel variable --- int ref = baseline_category_(s); - double lin_eff = temp_mux(s, 0); - double quad_eff = temp_mux(s, 1); + double lin_eff = temp_main_discrete(s, 0); + double quad_eff = temp_main_discrete(s, 1); // Marginal PL: absorb Theta_ss into quadratic effect double effective_quad = quad_eff; @@ -378,13 +378,13 @@ std::pair MixedMRFModel::logp_and_gradient( arma::vec sq_score = arma::square(score); // Main-effect gradient - grad(mux_offset) -= arma::accu(result.probs * score); - grad(mux_offset + 1) -= arma::accu(result.probs * sq_score); + grad(main_effects_discrete_offset) -= arma::accu(result.probs * score); + grad(main_effects_discrete_offset + 1) -= arma::accu(result.probs * sq_score); // Expected score per person arma::vec E = result.probs * score; - // Kxx pairwise gradient + // Pairwise discrete gradient arma::vec pw_grad = discrete_observations_dbl_t_ * E; for(size_t t = 0; t < p_; ++t) { if(edge_indicators_(s, t) == 0 || s == t) continue; @@ -393,7 +393,7 @@ std::pair MixedMRFModel::logp_and_gradient( } if(use_marginal_pl_) { - // Kxy gradient from marginal OMRF (same structure as ordinal) + // Pairwise_cross gradient from marginal OMRF (same structure as ordinal) arma::vec E_sq = result.probs * sq_score; arma::vec diff_pw = discrete_observations_dbl_t_ * @@ -407,9 +407,9 @@ std::pair MixedMRFModel::logp_and_gradient( double sum_obs_minus_E = arma::accu(discrete_observations_dbl_.col(s)) - arma::accu(E); // Self-contribution: a = s - arma::rowvec kxy_self = 2.0 * (diff_pw.t() * temp_Kxy) * covariance_yy_ - + 4.0 * diff_diag * KxySyy.row(s) - + 2.0 * sum_obs_minus_E * temp_muy.t(); + arma::rowvec kxy_self = 2.0 * (diff_pw.t() * temp_pairwise_cross) * covariance_continuous_ + + 4.0 * diff_diag * cross_times_cov.row(s) + + 2.0 * sum_obs_minus_E * temp_main_continuous.t(); for(size_t j = 0; j < q_; ++j) { if(edge_indicators_(s, p_ + j) == 0) continue; @@ -418,7 +418,7 @@ std::pair MixedMRFModel::logp_and_gradient( } // Cross-contribution: a = t, for each t ≠ s - arma::rowvec V_s = 2.0 * KxySyy.row(s); + arma::rowvec V_s = 2.0 * cross_times_cov.row(s); for(size_t t = 0; t < p_; ++t) { if(t == s || std::abs(diff_pw(t)) < 1e-300) continue; for(size_t j = 0; j < q_; ++j) { @@ -428,12 +428,12 @@ std::pair MixedMRFModel::logp_and_gradient( } } - // muy gradient from marginal OMRF + // Continuous mean gradient from marginal OMRF for(size_t j = 0; j < q_; ++j) { - grad(muy_grad_offset_ + j) += 2.0 * temp_Kxy(s, j) * sum_obs_minus_E; + grad(main_effects_continuous_grad_offset_ + j) += 2.0 * temp_pairwise_cross(s, j) * sum_obs_minus_E; } } else { - // Conditional PL: Kxy gradient from OMRF rest score + // Conditional PL: pairwise_cross gradient from OMRF rest score arma::rowvec kxy_grad_s = 2.0 * ( discrete_observations_dbl_.col(s) - E ).t() * continuous_observations_; @@ -445,13 +445,13 @@ std::pair MixedMRFModel::logp_and_gradient( } } - mux_offset += 2; + main_effects_discrete_offset += 2; } } // Add numerator contribution to logp from discrete sufficient statistics // (already in grad_obs_cache_ as counts, but logp needs the actual dot-products) - mux_offset = 0; + main_effects_discrete_offset = 0; for(size_t s = 0; s < p_; ++s) { int C_s = num_categories_(s); arma::vec rest; @@ -459,67 +459,67 @@ std::pair MixedMRFModel::logp_and_gradient( double theta_ss = temp_Theta(s, s); rest = discrete_observations_dbl_ * temp_Theta.col(s) - discrete_observations_dbl_.col(s) * theta_ss - + 2.0 * arma::dot(temp_Kxy.row(s), temp_muy); + + 2.0 * arma::dot(temp_pairwise_cross.row(s), temp_main_continuous); // Theta_ss quadratic contribution logp += temp_Theta(s, s) * arma::dot( discrete_observations_dbl_.col(s), discrete_observations_dbl_.col(s)); } else { - rest = discrete_observations_dbl_ * temp_Kxx.col(s) - - discrete_observations_dbl_.col(s) * temp_Kxx(s, s) - + 2.0 * continuous_observations_ * temp_Kxy.row(s).t(); + rest = discrete_observations_dbl_ * temp_pairwise_discrete.col(s) + - discrete_observations_dbl_.col(s) * temp_pairwise_discrete(s, s) + + 2.0 * continuous_observations_ * temp_pairwise_cross.row(s).t(); } // Numerator: dot(x_s, rest) + main-effect sums logp += arma::dot(discrete_observations_dbl_.col(s), rest); if(is_ordinal_variable_(s)) { for(int c = 1; c <= C_s; ++c) { - logp += static_cast(counts_per_category_(c, s)) * temp_mux(s, c - 1); + logp += static_cast(counts_per_category_(c, s)) * temp_main_discrete(s, c - 1); } } else { - logp += temp_mux(s, 0) * static_cast(blume_capel_stats_(0, s)) - + temp_mux(s, 1) * static_cast(blume_capel_stats_(1, s)); + logp += temp_main_discrete(s, 0) * static_cast(blume_capel_stats_(0, s)) + + temp_main_discrete(s, 1) * static_cast(blume_capel_stats_(1, s)); } } // ========================================================================= // Part 2: GGM conditional log-likelihood and gradients // ========================================================================= - // log p(y | x) = n/2 * (log|Kyy| - q log(2pi)) - 1/2 trace(Kyy D'D) - // where D = Y - M, M_i = muy' + 2 x_i' Kxy Σyy + // log p(y | x) = n/2 (log|K_yy| - q log(2π)) - ½ trace(K_yy D'D) + // where D = Y - M, M_i = μ_y' + 2 x_i' K_xy Σ_yy // - // Kyy is fixed, so log|Kyy| contributes to logp but not gradient. + // K_yy is fixed, so log|K_yy| contributes to logp but not gradient. - double quad_sum = arma::accu((D * Kyy_) % D); + double quad_sum = arma::accu((D * pairwise_effects_continuous_) % D); logp += static_cast(n_) / 2.0 * - (-static_cast(q_) * std::log(2.0 * arma::datum::pi) - + Kyy_log_det_) + (-static_cast(q_) * MY_LOG(2.0 * arma::datum::pi) + + log_det_precision_) - quad_sum / 2.0; - // ∂/∂muy: Kyy * D' * 1_n = Kyy * sum_over_rows(D) + // ∂/∂μ_y: K_yy D' 1_n = K_yy sum_over_rows(D) arma::vec D_colsums = arma::sum(D, 0).t(); // q-vector - arma::vec grad_muy_ggm = Kyy_ * D_colsums; + arma::vec grad_main_effects_continuous_ggm = pairwise_effects_continuous_ * D_colsums; for(size_t j = 0; j < q_; ++j) { - grad(muy_grad_offset_ + j) += grad_muy_ggm(j); + grad(main_effects_continuous_grad_offset_ + j) += grad_main_effects_continuous_ggm(j); } - // ∂/∂Kxy: The GGM conditional depends on Kxy through M. - // ∂M/∂Kxy_{s,j} = 2 x_s [Σyy]_{j,:} - // ∂logp_ggm/∂Kxy = 2 X' D (shortcut: Kyy Σyy = I eliminates Kyy) + // ∂/∂K_xy: The GGM conditional depends on K_xy through M. + // ∂M/∂pairwise_effects_cross_{s,j} = 2 x_s [Σ_yy]_{j,:} + // ∂logp_ggm/∂K_xy = 2 X' D (shortcut: K_yy Σ_yy = I eliminates K_yy) // - // Correctly: ∂(−½ trace(Kyy D'D))/∂Kxy_{s,j} - // = trace(Kyy D' ∂M/∂Kxy_{s,j}) - // = trace(Kyy D' * 2 x_s [Σyy]_{j,:}) - // = 2 [x_s' D Kyy Σyy]_j - // = 2 [x_s' D]_j (since Kyy Σyy = I) - arma::mat grad_Kxy_ggm = 2.0 * discrete_observations_dbl_t_ * D; // p x q + // Correctly: ∂(−½ trace(K_yy D'D))/∂pairwise_effects_cross_{s,j} + // = trace(K_yy D' ∂M/∂pairwise_effects_cross_{s,j}) + // = trace(K_yy D' · 2 x_s [Σ_yy]_{j,:}) + // = 2 [x_s' D K_yy Σ_yy]_j + // = 2 [x_s' D]_j (since K_yy Σ_yy = I) + arma::mat grad_pairwise_effects_cross_ggm = 2.0 * discrete_observations_dbl_t_ * D; // p x q for(size_t i = 0; i < p_; ++i) { for(size_t j = 0; j < q_; ++j) { if(edge_indicators_(i, p_ + j) == 0) continue; int loc = kxy_index_cache_(i, j); - grad(loc) += grad_Kxy_ggm(i, j); + grad(loc) += grad_pairwise_effects_cross_ggm(i, j); } } @@ -527,55 +527,55 @@ std::pair MixedMRFModel::logp_and_gradient( // Part 3: Prior log-densities and gradient contributions // ========================================================================= - // --- mux priors: Beta(alpha, beta) on sigmoid scale --- - mux_offset = 0; + // --- main_effects_discrete_ priors: Beta(alpha, beta) on sigmoid scale --- + main_effects_discrete_offset = 0; for(size_t s = 0; s < p_; ++s) { if(is_ordinal_variable_(s)) { int C_s = num_categories_(s); for(int c = 0; c < C_s; ++c) { - double val = temp_mux(s, c); + double val = temp_main_discrete(s, c); logp += val * main_alpha_ - std::log1p(MY_EXP(val)) * (main_alpha_ + main_beta_); double p = 1.0 / (1.0 + MY_EXP(-val)); - grad(mux_offset + c) += main_alpha_ - (main_alpha_ + main_beta_) * p; + grad(main_effects_discrete_offset + c) += main_alpha_ - (main_alpha_ + main_beta_) * p; } - mux_offset += C_s; + main_effects_discrete_offset += C_s; } else { for(int k = 0; k < 2; ++k) { - double val = temp_mux(s, k); + double val = temp_main_discrete(s, k); logp += val * main_alpha_ - std::log1p(MY_EXP(val)) * (main_alpha_ + main_beta_); double p = 1.0 / (1.0 + MY_EXP(-val)); - grad(mux_offset + k) += main_alpha_ - (main_alpha_ + main_beta_) * p; + grad(main_effects_discrete_offset + k) += main_alpha_ - (main_alpha_ + main_beta_) * p; } - mux_offset += 2; + main_effects_discrete_offset += 2; } } - // --- Kxx priors: Cauchy(0, pairwise_scale_) --- + // --- pairwise_effects_discrete_ priors: Cauchy(0, pairwise_scale_) --- for(size_t i = 0; i < p_ - 1; ++i) { for(size_t j = i + 1; j < p_; ++j) { if(edge_indicators_(i, j) == 0) continue; int loc = kxx_index_cache_(i, j); - double val = temp_Kxx(i, j); + double val = temp_pairwise_discrete(i, j); logp += R::dcauchy(val, 0.0, pairwise_scale_, true); grad(loc) -= 2.0 * val / (val * val + pairwise_scale_ * pairwise_scale_); } } - // --- muy priors: Normal(0, 1) --- + // --- main_effects_continuous_ priors: Normal(0, 1) --- for(size_t j = 0; j < q_; ++j) { - double val = temp_muy(j); + double val = temp_main_continuous(j); logp += R::dnorm(val, 0.0, 1.0, true); - grad(muy_grad_offset_ + j) -= val; // ∂/∂muy: -muy (from -muy^2/2) + grad(main_effects_continuous_grad_offset_ + j) -= val; // -val from -val^2/2 } - // --- Kxy priors: Cauchy(0, pairwise_scale_) --- + // --- pairwise_effects_cross_ priors: Cauchy(0, pairwise_scale_) --- for(size_t i = 0; i < p_; ++i) { for(size_t j = 0; j < q_; ++j) { if(edge_indicators_(i, p_ + j) == 0) continue; int loc = kxy_index_cache_(i, j); - double val = temp_Kxy(i, j); + double val = temp_pairwise_cross(i, j); logp += R::dcauchy(val, 0.0, pairwise_scale_, true); grad(loc) -= 2.0 * val / (val * val + pairwise_scale_ * pairwise_scale_); } diff --git a/src/models/mixed/mixed_mrf_likelihoods.cpp b/src/models/mixed/mixed_mrf_likelihoods.cpp index 65a57993..fabb80a1 100644 --- a/src/models/mixed/mixed_mrf_likelihoods.cpp +++ b/src/models/mixed/mixed_mrf_likelihoods.cpp @@ -18,29 +18,29 @@ double MixedMRFModel::log_conditional_omrf(int s) const { int C_s = num_categories_(s); // Rest score: contribution from other discrete vars + continuous vars - arma::vec rest = discrete_observations_dbl_ * Kxx_.col(s) - - discrete_observations_dbl_.col(s) * Kxx_(s, s) - + 2.0 * continuous_observations_ * Kxy_.row(s).t(); + arma::vec rest = discrete_observations_dbl_ * pairwise_effects_discrete_.col(s) + - discrete_observations_dbl_.col(s) * pairwise_effects_discrete_(s, s) + + 2.0 * continuous_observations_ * pairwise_effects_cross_.row(s).t(); // Numerator (sufficient-statistic form): dot(x_s, rest) + main-effect sums double numer = arma::dot(discrete_observations_dbl_.col(s), rest); if(is_ordinal_variable_(s)) { - // Ordinal: add threshold contributions sum_{c=1}^{C_s} count_c * mux_(s, c-1) + // Ordinal: add threshold contributions sum_{c=1}^{C_s} count_c * main_effects_discrete_(s, c-1) for(int c = 1; c <= C_s; ++c) { - numer += static_cast(counts_per_category_(c, s)) * mux_(s, c - 1); + numer += static_cast(counts_per_category_(c, s)) * main_effects_discrete_(s, c - 1); } // Denominator via compute_denom_ordinal (FAST/SAFE block-split) - arma::vec main_param = mux_.row(s).cols(0, C_s - 1).t(); + arma::vec main_param = main_effects_discrete_.row(s).cols(0, C_s - 1).t(); arma::vec bound = static_cast(C_s) * rest; arma::vec denom = compute_denom_ordinal(rest, main_param, bound); return numer - arma::accu(bound + ARMA_MY_LOG(denom)); } else { // Blume-Capel: alpha * sum(x) + beta * sum(x^2) - double alpha = mux_(s, 0); - double beta = mux_(s, 1); + double alpha = main_effects_discrete_(s, 0); + double beta = main_effects_discrete_(s, 1); numer += alpha * static_cast(blume_capel_stats_(0, s)) + beta * static_cast(blume_capel_stats_(1, s)); @@ -59,23 +59,23 @@ double MixedMRFModel::log_conditional_omrf(int s) const { // log_marginal_omrf // ============================================================================= // Marginal OMRF pseudolikelihood for discrete variable s: -// log f(x_s | x_{-s}) using Theta = Kxx + 2 Kxy Kyy^{-1} Kxy' +// log f(x_s | x_{-s}) using Θ = K_xx + 2 K_xy K_yy^{-1} K_xy' // // Differs from conditional form in three ways: -// 1. rest score uses Theta_ instead of Kxx_, minus self-interaction -// 2. scalar bias 2 * Kxy(s,:) * muy added to rest -// 3. numerator includes Theta_(s,s) * sum(x_s^2) -// 4. denominator offsets include c^2 * Theta_(s,s) +// 1. rest score uses Theta_ instead of pairwise_effects_discrete_, minus self-interaction +// 2. scalar bias 2 K_xy(s,:) μ_y added to rest +// 3. numerator includes Θ(s,s) * sum(x_s^2) +// 4. denominator offsets include c^2 * Θ(s,s) // ============================================================================= double MixedMRFModel::log_marginal_omrf(int s) const { int C_s = num_categories_(s); - // Rest score: Theta-based interaction + Kxy*muy bias + // Rest score: Θ-based interaction + K_xy μ_y bias double theta_ss = Theta_(s, s); arma::vec rest = discrete_observations_dbl_ * Theta_.col(s) - discrete_observations_dbl_.col(s) * theta_ss - + 2.0 * arma::dot(Kxy_.row(s), muy_); + + 2.0 * arma::dot(pairwise_effects_cross_.row(s), main_effects_continuous_); // Numerator: dot(x_s, rest) + theta_ss * dot(x_s, x_s) + main effects double numer = arma::dot(discrete_observations_dbl_.col(s), rest) @@ -84,13 +84,13 @@ double MixedMRFModel::log_marginal_omrf(int s) const { if(is_ordinal_variable_(s)) { for(int c = 1; c <= C_s; ++c) { - numer += static_cast(counts_per_category_(c, s)) * mux_(s, c - 1); + numer += static_cast(counts_per_category_(c, s)) * main_effects_discrete_(s, c - 1); } - // Denominator: main_param(c) = mux(s,c) + (c+1)^2 * theta_ss + // Denominator: main_param(c) = μ_x(s,c) + (c+1)^2 Θ_ss arma::vec main_param(C_s); for(int c = 0; c < C_s; ++c) { - main_param(c) = mux_(s, c) + static_cast((c + 1) * (c + 1)) * theta_ss; + main_param(c) = main_effects_discrete_(s, c) + static_cast((c + 1) * (c + 1)) * theta_ss; } arma::vec bound = static_cast(C_s) * rest; @@ -99,8 +99,8 @@ double MixedMRFModel::log_marginal_omrf(int s) const { return numer - arma::accu(bound + ARMA_MY_LOG(denom)); } else { // Blume-Capel: alpha * sum(x) + beta * sum(x^2) - double alpha = mux_(s, 0); - double beta = mux_(s, 1); + double alpha = main_effects_discrete_(s, 0); + double beta = main_effects_discrete_(s, 1); numer += alpha * static_cast(blume_capel_stats_(0, s)) + beta * static_cast(blume_capel_stats_(1, s)); @@ -122,19 +122,19 @@ double MixedMRFModel::log_marginal_omrf(int s) const { // log_conditional_ggm // ============================================================================= // Conditional GGM log-likelihood: log f(y | x) -// y | x ~ N(conditional_mean_, covariance_yy_) +// y | x ~ N(conditional_mean_, covariance_continuous_) // -// Uses cached covariance_yy_, Kyy_log_det_, and conditional_mean_. +// Uses cached covariance_continuous_, log_det_precision_, and conditional_mean_. // ============================================================================= double MixedMRFModel::log_conditional_ggm() const { arma::mat D = continuous_observations_ - conditional_mean_; - // Quadratic form: trace(Kyy * D' * D) = sum((D * Kyy_) .* D) - double quad_sum = arma::accu((D * Kyy_) % D); + // Quadratic form: trace(K_yy D'D) = sum((D K_yy) .* D) + double quad_sum = arma::accu((D * pairwise_effects_continuous_) % D); return static_cast(n_) / 2.0 * - (-static_cast(q_) * std::log(2.0 * arma::datum::pi) - + Kyy_log_det_) + (-static_cast(q_) * MY_LOG(2.0 * arma::datum::pi) + + log_det_precision_) - quad_sum / 2.0; } diff --git a/src/models/mixed/mixed_mrf_metropolis.cpp b/src/models/mixed/mixed_mrf_metropolis.cpp index d60c0288..b05059ed 100644 --- a/src/models/mixed/mixed_mrf_metropolis.cpp +++ b/src/models/mixed/mixed_mrf_metropolis.cpp @@ -19,16 +19,16 @@ static double log_beta_prior(double x, double alpha, double beta) { // update_main_effect // ============================================================================= // MH update for one main-effect parameter. -// Ordinal: mux_(s, c) = threshold for category c+1 (c in [0, C_s-1]) -// Blume-Capel: mux_(s, 0) = linear α, mux_(s, 1) = quadratic β +// Ordinal: main_effects_discrete_(s, c) = threshold for category c+1 (c in [0, C_s-1]) +// Blume-Capel: main_effects_discrete_(s, 0) = linear α, main_effects_discrete_(s, 1) = quadratic β // (c indexes 0 or 1 for BC) // // The accept/reject uses log_conditional_omrf(s) + beta-type prior. // ============================================================================= void MixedMRFModel::update_main_effect(int s, int c, int iteration) { - double& current = mux_(s, c); - double proposal_sd = prop_sd_mux_(s, c); + double& current = main_effects_discrete_(s, c); + double proposal_sd = proposal_sd_main_discrete_(s, c); double current_val = current; double proposed = rnorm(rng_, current_val, proposal_sd); @@ -44,14 +44,14 @@ void MixedMRFModel::update_main_effect(int s, int c, int iteration) { double ln_alpha = ll_prop - ll_curr; - if(std::log(runif(rng_)) >= ln_alpha) { + if(MY_LOG(runif(rng_)) >= ln_alpha) { current = current_val; // reject } if(iteration >= 1 && iteration < total_warmup_) { double rm_weight = std::pow(iteration, -0.75); - prop_sd_mux_(s, c) = update_proposal_sd_with_robbins_monro( - prop_sd_mux_(s, c), ln_alpha, rm_weight, 0.44); + proposal_sd_main_discrete_(s, c) = update_proposal_sd_with_robbins_monro( + proposal_sd_main_discrete_(s, c), ln_alpha, rm_weight, 0.44); } } @@ -59,14 +59,14 @@ void MixedMRFModel::update_main_effect(int s, int c, int iteration) { // ============================================================================= // update_continuous_mean // ============================================================================= -// MH update for one continuous mean parameter muy_(j). +// MH update for one continuous mean parameter main_effects_continuous_(j). // The accept/reject uses log_conditional_ggm() + Normal(0, 1) prior. // Must save/restore conditional_mean_ around the proposal. // ============================================================================= void MixedMRFModel::update_continuous_mean(int j, int iteration) { - double current_val = muy_(j); - double proposed = rnorm(rng_, current_val, prop_sd_muy_(j)); + double current_val = main_effects_continuous_(j); + double proposed = rnorm(rng_, current_val, proposal_sd_main_continuous_(j)); // Current log-posterior (Normal(0,1) prior: -x^2/2 up to constant) double ll_curr = log_conditional_ggm() + R::dnorm(current_val, 0.0, 1.0, true); @@ -77,7 +77,7 @@ void MixedMRFModel::update_continuous_mean(int j, int iteration) { // Set proposed value and refresh conditional_mean_ arma::mat cond_mean_saved = conditional_mean_; - muy_(j) = proposed; + main_effects_continuous_(j) = proposed; recompute_conditional_mean(); double ll_prop = log_conditional_ggm() + R::dnorm(proposed, 0.0, 1.0, true); @@ -88,30 +88,30 @@ void MixedMRFModel::update_continuous_mean(int j, int iteration) { double ln_alpha = ll_prop - ll_curr; - if(std::log(runif(rng_)) >= ln_alpha) { - muy_(j) = current_val; // reject + if(MY_LOG(runif(rng_)) >= ln_alpha) { + main_effects_continuous_(j) = current_val; // reject conditional_mean_ = std::move(cond_mean_saved); } if(iteration >= 1 && iteration < total_warmup_) { double rm_weight = std::pow(iteration, -0.75); - prop_sd_muy_(j) = update_proposal_sd_with_robbins_monro( - prop_sd_muy_(j), ln_alpha, rm_weight, 0.44); + proposal_sd_main_continuous_(j) = update_proposal_sd_with_robbins_monro( + proposal_sd_main_continuous_(j), ln_alpha, rm_weight, 0.44); } } // ============================================================================= -// update_Kxx +// update_pairwise_discrete // ============================================================================= -// MH update for one discrete-discrete interaction Kxx_(i, j). +// MH update for one discrete-discrete interaction pairwise_effects_discrete_(i, j). // Symmetric: sets both (i,j) and (j,i). // Acceptance: log_conditional_omrf(i) + log_conditional_omrf(j) + Cauchy prior. // ============================================================================= -void MixedMRFModel::update_Kxx(int i, int j, int iteration) { - double current_val = Kxx_(i, j); - double proposed = rnorm(rng_, current_val, prop_sd_Kxx_(i, j)); +void MixedMRFModel::update_pairwise_discrete(int i, int j, int iteration) { + double current_val = pairwise_effects_discrete_(i, j); + double proposed = rnorm(rng_, current_val, proposal_sd_pairwise_discrete_(i, j)); // Current log-posterior double ll_curr, ll_prop; @@ -119,8 +119,8 @@ void MixedMRFModel::update_Kxx(int i, int j, int iteration) { ll_curr = log_marginal_omrf(i) + log_marginal_omrf(j) + R::dcauchy(current_val, 0.0, pairwise_scale_, true); - Kxx_(i, j) = proposed; - Kxx_(j, i) = proposed; + pairwise_effects_discrete_(i, j) = proposed; + pairwise_effects_discrete_(j, i) = proposed; recompute_Theta(); ll_prop = log_marginal_omrf(i) + log_marginal_omrf(j) @@ -129,8 +129,8 @@ void MixedMRFModel::update_Kxx(int i, int j, int iteration) { ll_curr = log_conditional_omrf(i) + log_conditional_omrf(j) + R::dcauchy(current_val, 0.0, pairwise_scale_, true); - Kxx_(i, j) = proposed; - Kxx_(j, i) = proposed; + pairwise_effects_discrete_(i, j) = proposed; + pairwise_effects_discrete_(j, i) = proposed; ll_prop = log_conditional_omrf(i) + log_conditional_omrf(j) + R::dcauchy(proposed, 0.0, pairwise_scale_, true); @@ -138,52 +138,52 @@ void MixedMRFModel::update_Kxx(int i, int j, int iteration) { double ln_alpha = ll_prop - ll_curr; - if(std::log(runif(rng_)) >= ln_alpha) { - Kxx_(i, j) = current_val; // reject - Kxx_(j, i) = current_val; + if(MY_LOG(runif(rng_)) >= ln_alpha) { + pairwise_effects_discrete_(i, j) = current_val; // reject + pairwise_effects_discrete_(j, i) = current_val; if(use_marginal_pl_) recompute_Theta(); } if(iteration >= 1 && iteration < total_warmup_) { double rm_weight = std::pow(iteration, -0.75); - prop_sd_Kxx_(i, j) = update_proposal_sd_with_robbins_monro( - prop_sd_Kxx_(i, j), ln_alpha, rm_weight, 0.44); + proposal_sd_pairwise_discrete_(i, j) = update_proposal_sd_with_robbins_monro( + proposal_sd_pairwise_discrete_(i, j), ln_alpha, rm_weight, 0.44); } } // ============================================================================= -// Rank-1 Kyy proposal helpers (permutation-free) +// Rank-1 precision proposal helpers (permutation-free) // ============================================================================= // Direct analogs of GGMModel::get_constants / constrained_diagonal, -// operating on Kyy_, Kyy_chol_, and covariance_yy_. +// operating on pairwise_effects_continuous_, cholesky_of_precision_, and covariance_continuous_. // ============================================================================= -void MixedMRFModel::get_kyy_constants(int i, int j) { - double logdet = cholesky_helpers::get_log_det(Kyy_chol_); +void MixedMRFModel::get_precision_constants(int i, int j) { + double logdet = cholesky_helpers::get_log_det(cholesky_of_precision_); - double log_adj_ii = logdet + std::log(std::abs(covariance_yy_(i, i))); - double log_adj_ij = logdet + std::log(std::abs(covariance_yy_(i, j))); - double log_adj_jj = logdet + std::log(std::abs(covariance_yy_(j, j))); + double log_adj_ii = logdet + MY_LOG(std::abs(covariance_continuous_(i, i))); + double log_adj_ij = logdet + MY_LOG(std::abs(covariance_continuous_(i, j))); + double log_adj_jj = logdet + MY_LOG(std::abs(covariance_continuous_(j, j))); - double inv_sub_jj = cholesky_helpers::compute_inv_submatrix_i(covariance_yy_, i, j, j); - double log_abs_inv_sub_jj = log_adj_ii + std::log(std::abs(inv_sub_jj)); + double inv_sub_jj = cholesky_helpers::compute_inv_submatrix_i(covariance_continuous_, i, j, j); + double log_abs_inv_sub_jj = log_adj_ii + MY_LOG(std::abs(inv_sub_jj)); - double Phi_q1q = (2 * std::signbit(covariance_yy_(i, j)) - 1) * std::exp( + double Phi_q1q = (2 * std::signbit(covariance_continuous_(i, j)) - 1) * MY_EXP( (log_adj_ij - (log_adj_jj + log_abs_inv_sub_jj) / 2) ); - double Phi_q1q1 = std::exp((log_adj_jj - log_abs_inv_sub_jj) / 2); + double Phi_q1q1 = MY_EXP((log_adj_jj - log_abs_inv_sub_jj) / 2); kyy_constants_[0] = Phi_q1q; kyy_constants_[1] = Phi_q1q1; - kyy_constants_[2] = Kyy_(i, j) - Phi_q1q * Phi_q1q1; + kyy_constants_[2] = pairwise_effects_continuous_(i, j) - Phi_q1q * Phi_q1q1; kyy_constants_[3] = Phi_q1q1; - kyy_constants_[4] = Kyy_(j, j) - Phi_q1q * Phi_q1q; + kyy_constants_[4] = pairwise_effects_continuous_(j, j) - Phi_q1q * Phi_q1q; kyy_constants_[5] = kyy_constants_[4] + kyy_constants_[2] * kyy_constants_[2] / (kyy_constants_[3] * kyy_constants_[3]); } -double MixedMRFModel::kyy_constrained_diagonal(double x) const { +double MixedMRFModel::precision_constrained_diagonal(double x) const { if(x == 0.0) { return kyy_constants_[5]; } else { @@ -196,9 +196,9 @@ double MixedMRFModel::kyy_constrained_diagonal(double x) const { // ============================================================================= // log_ggm_ratio_edge // ============================================================================= -// Log-likelihood ratio for a rank-2 off-diagonal Kyy change using the +// Log-likelihood ratio for a rank-2 off-diagonal precision change using the // matrix determinant lemma for the log-det part and Woodbury for the -// quadratic-form part. Assumes precision_yy_proposal_ is filled. +// quadratic-form part. Assumes precision_proposal_ is filled. // // B+.10 will replace the O(npq + nq²) quadratic-form computation with // an O(nq) rank-2 shortcut. @@ -211,24 +211,24 @@ double MixedMRFModel::log_ggm_ratio_edge(int i, int j) const { // --- Log-determinant ratio via matrix determinant lemma --- // ΔΩ has 3 nonzero entries: (i,j), (j,i), (j,j). // Ui = old - new off-diag, Uj = (old - new diag) / 2 - double Ui = Kyy_(ui, uj) - precision_yy_proposal_(ui, uj); - double Uj = (Kyy_(uj, uj) - precision_yy_proposal_(uj, uj)) / 2.0; + double Ui = pairwise_effects_continuous_(ui, uj) - precision_proposal_(ui, uj); + double Uj = (pairwise_effects_continuous_(uj, uj) - precision_proposal_(uj, uj)) / 2.0; - double cc11 = covariance_yy_(uj, uj); - double cc12 = 1.0 - (covariance_yy_(ui, uj) * Ui + - covariance_yy_(uj, uj) * Uj); - double cc22 = Ui * Ui * covariance_yy_(ui, ui) + - 2.0 * Ui * Uj * covariance_yy_(ui, uj) + - Uj * Uj * covariance_yy_(uj, uj); + double cc11 = covariance_continuous_(uj, uj); + double cc12 = 1.0 - (covariance_continuous_(ui, uj) * Ui + + covariance_continuous_(uj, uj) * Uj); + double cc22 = Ui * Ui * covariance_continuous_(ui, ui) + + 2.0 * Ui * Uj * covariance_continuous_(ui, uj) + + Uj * Uj * covariance_continuous_(uj, uj); - double logdet_ratio = std::log(std::abs(cc11 * cc22 - cc12 * cc12)); + double logdet_ratio = MY_LOG(std::abs(cc11 * cc22 - cc12 * cc12)); // --- Proposed covariance via Woodbury --- // ΔΩ = vf1 vf2' + vf2 vf1' where vf1 = [0,...,-1,...] (j-th), // vf2 = [0,...,Ui,...,Uj,...] (i-th and j-th). // s1 = Σ vf1 = -Σ[:,j], s2 = Σ vf2 = Ui*Σ[:,i] + Uj*Σ[:,j] - arma::vec s1 = -covariance_yy_.col(uj); - arma::vec s2 = Ui * covariance_yy_.col(ui) + Uj * covariance_yy_.col(uj); + arma::vec s1 = -covariance_continuous_.col(uj); + arma::vec s2 = Ui * covariance_continuous_.col(ui) + Uj * covariance_continuous_.col(uj); // 2×2 core matrix T = I + [vf2,vf1]' [s1,s2] // T = [1 + vf2's1, vf2's2; vf1's1, 1 + vf1's2] @@ -249,19 +249,19 @@ double MixedMRFModel::log_ggm_ratio_edge(int i, int j) const { // = Σ - (inv_t11*s1 + inv_t21*s2)*s2' - (inv_t12*s1 + inv_t22*s2)*s1' arma::vec w1 = inv_t11 * s1 + inv_t21 * s2; // coefficient for s2' row arma::vec w2 = inv_t12 * s1 + inv_t22 * s2; // coefficient for s1' row - arma::mat cov_prop = covariance_yy_ - w1 * s2.t() - w2 * s1.t(); + arma::mat cov_prop = covariance_continuous_ - w1 * s2.t() - w2 * s1.t(); // --- Proposed conditional mean --- - // M' = 1*μ_y' + 2 * X * Kxy * Σ' - arma::mat cond_mean_prop = arma::repmat(muy_.t(), n_, 1) + - 2.0 * discrete_observations_dbl_ * Kxy_ * cov_prop; + // M' = μ_y' + 2 X K_xy Σ' + arma::mat cond_mean_prop = arma::repmat(main_effects_continuous_.t(), n_, 1) + + 2.0 * discrete_observations_dbl_ * pairwise_effects_cross_ * cov_prop; // --- Quadratic form difference --- arma::mat D_curr = continuous_observations_ - conditional_mean_; arma::mat D_prop = continuous_observations_ - cond_mean_prop; - double quad_curr = arma::accu((D_curr * Kyy_) % D_curr); - double quad_prop = arma::accu((D_prop * precision_yy_proposal_) % D_prop); + double quad_curr = arma::accu((D_curr * pairwise_effects_continuous_) % D_curr); + double quad_prop = arma::accu((D_prop * precision_proposal_) % D_prop); double n = static_cast(n_); return n / 2.0 * logdet_ratio - (quad_prop - quad_curr) / 2.0; @@ -271,7 +271,7 @@ double MixedMRFModel::log_ggm_ratio_edge(int i, int j) const { // ============================================================================= // log_ggm_ratio_diag // ============================================================================= -// Log-likelihood ratio for a rank-1 diagonal Kyy change. +// Log-likelihood ratio for a rank-1 diagonal precision change. // Same structure as log_ggm_ratio_edge but simpler (Ui = 0). // ============================================================================= @@ -279,30 +279,30 @@ double MixedMRFModel::log_ggm_ratio_diag(int i) const { size_t ui = static_cast(i); // --- Log-determinant ratio (rank-1) --- - double Uj = (Kyy_(ui, ui) - precision_yy_proposal_(ui, ui)) / 2.0; + double Uj = (pairwise_effects_continuous_(ui, ui) - precision_proposal_(ui, ui)) / 2.0; - double cc11 = covariance_yy_(ui, ui); - double cc12 = 1.0 - covariance_yy_(ui, ui) * Uj; - double cc22 = Uj * Uj * covariance_yy_(ui, ui); + double cc11 = covariance_continuous_(ui, ui); + double cc12 = 1.0 - covariance_continuous_(ui, ui) * Uj; + double cc22 = Uj * Uj * covariance_continuous_(ui, ui); - double logdet_ratio = std::log(std::abs(cc11 * cc22 - cc12 * cc12)); + double logdet_ratio = MY_LOG(std::abs(cc11 * cc22 - cc12 * cc12)); // --- Proposed covariance via Sherman-Morrison (rank-1 special case) --- // ΔΩ = -2Uj * e_i e_i', so Σ' = Σ + 2Uj * Σ[:,i] Σ[i,:]' / (1 - 2Uj * Σ(i,i)) - arma::vec s = covariance_yy_.col(ui); - double denom = 1.0 - 2.0 * Uj * covariance_yy_(ui, ui); - arma::mat cov_prop = covariance_yy_ + (2.0 * Uj / denom) * s * s.t(); + arma::vec s = covariance_continuous_.col(ui); + double denom = 1.0 - 2.0 * Uj * covariance_continuous_(ui, ui); + arma::mat cov_prop = covariance_continuous_ + (2.0 * Uj / denom) * s * s.t(); // --- Proposed conditional mean --- - arma::mat cond_mean_prop = arma::repmat(muy_.t(), n_, 1) + - 2.0 * discrete_observations_dbl_ * Kxy_ * cov_prop; + arma::mat cond_mean_prop = arma::repmat(main_effects_continuous_.t(), n_, 1) + + 2.0 * discrete_observations_dbl_ * pairwise_effects_cross_ * cov_prop; // --- Quadratic form difference --- arma::mat D_curr = continuous_observations_ - conditional_mean_; arma::mat D_prop = continuous_observations_ - cond_mean_prop; - double quad_curr = arma::accu((D_curr * Kyy_) % D_curr); - double quad_prop = arma::accu((D_prop * precision_yy_proposal_) % D_prop); + double quad_curr = arma::accu((D_curr * pairwise_effects_continuous_) % D_curr); + double quad_prop = arma::accu((D_prop * precision_proposal_) % D_prop); double n = static_cast(n_); return n / 2.0 * logdet_ratio - (quad_prop - quad_curr) / 2.0; @@ -310,18 +310,18 @@ double MixedMRFModel::log_ggm_ratio_diag(int i) const { // ============================================================================= -// cholesky_update_after_kyy_edge +// cholesky_update_after_precision_edge // ============================================================================= -// Rank-2 Cholesky update after accepting an off-diagonal Kyy change. +// Rank-2 Cholesky update after accepting an off-diagonal precision change. // Decomposes ΔΩ = vf1*vf2' + vf2*vf1' into two rank-1 ops. -// Then recomputes inv_cholesky_yy_ and covariance_yy_. +// Then recomputes inv_cholesky_of_precision_ and covariance_continuous_. // ============================================================================= -void MixedMRFModel::cholesky_update_after_kyy_edge( +void MixedMRFModel::cholesky_update_after_precision_edge( double old_ij, double old_jj, int i, int j) { - kyy_v2_[0] = old_ij - precision_yy_proposal_(i, j); - kyy_v2_[1] = (old_jj - precision_yy_proposal_(j, j)) / 2.0; + kyy_v2_[0] = old_ij - precision_proposal_(i, j); + kyy_v2_[1] = (old_jj - precision_proposal_(j, j)) / 2.0; kyy_vf1_[i] = kyy_v1_[0]; // 0 kyy_vf1_[j] = kyy_v1_[1]; // -1 @@ -331,12 +331,12 @@ void MixedMRFModel::cholesky_update_after_kyy_edge( kyy_u1_ = (kyy_vf1_ + kyy_vf2_) / std::sqrt(2.0); kyy_u2_ = (kyy_vf1_ - kyy_vf2_) / std::sqrt(2.0); - cholesky_update(Kyy_chol_, kyy_u1_); - cholesky_downdate(Kyy_chol_, kyy_u2_); + cholesky_update(cholesky_of_precision_, kyy_u1_); + cholesky_downdate(cholesky_of_precision_, kyy_u2_); - arma::inv(inv_cholesky_yy_, arma::trimatu(Kyy_chol_)); - covariance_yy_ = inv_cholesky_yy_ * inv_cholesky_yy_.t(); - Kyy_log_det_ = cholesky_helpers::get_log_det(Kyy_chol_); + arma::inv(inv_cholesky_of_precision_, arma::trimatu(cholesky_of_precision_)); + covariance_continuous_ = inv_cholesky_of_precision_ * inv_cholesky_of_precision_.t(); + log_det_precision_ = cholesky_helpers::get_log_det(cholesky_of_precision_); kyy_vf1_[i] = 0.0; kyy_vf1_[j] = 0.0; @@ -346,59 +346,59 @@ void MixedMRFModel::cholesky_update_after_kyy_edge( // ============================================================================= -// cholesky_update_after_kyy_diag +// cholesky_update_after_precision_diag // ============================================================================= -// Rank-1 Cholesky update after accepting a diagonal Kyy change. +// Rank-1 Cholesky update after accepting a diagonal precision change. // ============================================================================= -void MixedMRFModel::cholesky_update_after_kyy_diag(double old_ii, int i) { - double delta = old_ii - precision_yy_proposal_(i, i); +void MixedMRFModel::cholesky_update_after_precision_diag(double old_ii, int i) { + double delta = old_ii - precision_proposal_(i, i); bool downdate = delta > 0.0; kyy_vf1_[i] = std::sqrt(std::abs(delta)); if(downdate) - cholesky_downdate(Kyy_chol_, kyy_vf1_); + cholesky_downdate(cholesky_of_precision_, kyy_vf1_); else - cholesky_update(Kyy_chol_, kyy_vf1_); + cholesky_update(cholesky_of_precision_, kyy_vf1_); - arma::inv(inv_cholesky_yy_, arma::trimatu(Kyy_chol_)); - covariance_yy_ = inv_cholesky_yy_ * inv_cholesky_yy_.t(); - Kyy_log_det_ = cholesky_helpers::get_log_det(Kyy_chol_); + arma::inv(inv_cholesky_of_precision_, arma::trimatu(cholesky_of_precision_)); + covariance_continuous_ = inv_cholesky_of_precision_ * inv_cholesky_of_precision_.t(); + log_det_precision_ = cholesky_helpers::get_log_det(cholesky_of_precision_); kyy_vf1_[i] = 0.0; } // ============================================================================= -// update_Kyy_offdiag +// update_pairwise_effects_continuous_offdiag // ============================================================================= -// MH update for one off-diagonal element of the precision matrix Kyy_(i, j). +// MH update for one off-diagonal element of the precision matrix pairwise_effects_continuous_(i, j). // Uses rank-1 Cholesky infrastructure (GGM-style, no permutation): -// 1. Extract constants from covariance_yy_ and Kyy_chol_ +// 1. Extract constants from covariance_continuous_ and cholesky_of_precision_ // 2. Propose on the unconstrained Cholesky scale -// 3. Map to Kyy space with constrained diagonal +// 3. Map to precision space with constrained diagonal // 4. Evaluate rank-2 log-likelihood ratio // 5. On accept: rank-1 Cholesky update // // Prior: Cauchy(0, pairwise_scale_) on off-diag, Gamma(1, 1) on diagonal. // ============================================================================= -void MixedMRFModel::update_Kyy_offdiag(int i, int j, int iteration) { - get_kyy_constants(i, j); +void MixedMRFModel::update_pairwise_effects_continuous_offdiag(int i, int j, int iteration) { + get_precision_constants(i, j); double phi_curr = kyy_constants_[0]; // Phi_q1q - double phi_prop = rnorm(rng_, phi_curr, prop_sd_Kyy_(i, j)); + double phi_prop = rnorm(rng_, phi_curr, proposal_sd_pairwise_continuous_(i, j)); double omega_prop_ij = kyy_constants_[2] + kyy_constants_[3] * phi_prop; - double omega_prop_jj = kyy_constrained_diagonal(omega_prop_ij); - double diag_curr = Kyy_(j, j); + double omega_prop_jj = precision_constrained_diagonal(omega_prop_ij); + double diag_curr = pairwise_effects_continuous_(j, j); // Fill proposal matrix (only the 3 changed entries matter) - precision_yy_proposal_ = Kyy_; - precision_yy_proposal_(i, j) = omega_prop_ij; - precision_yy_proposal_(j, i) = omega_prop_ij; - precision_yy_proposal_(j, j) = omega_prop_jj; + precision_proposal_ = pairwise_effects_continuous_; + precision_proposal_(i, j) = omega_prop_ij; + precision_proposal_(j, i) = omega_prop_ij; + precision_proposal_(j, j) = omega_prop_jj; double ln_alpha = log_ggm_ratio_edge(i, j); @@ -408,62 +408,62 @@ void MixedMRFModel::update_Kyy_offdiag(int i, int j, int iteration) { ln_alpha -= log_marginal_omrf(s); arma::mat Theta_saved = Theta_; - arma::mat Kyy_saved = Kyy_; - Kyy_ = precision_yy_proposal_; + arma::mat pairwise_effects_continuous_saved = pairwise_effects_continuous_; + pairwise_effects_continuous_ = precision_proposal_; recompute_Theta(); for(size_t s = 0; s < p_; ++s) ln_alpha += log_marginal_omrf(s); - Kyy_ = Kyy_saved; + pairwise_effects_continuous_ = pairwise_effects_continuous_saved; Theta_ = std::move(Theta_saved); } // Prior ratio: Cauchy on off-diag + Gamma(1,1) on diagonal ln_alpha += R::dcauchy(omega_prop_ij, 0.0, pairwise_scale_, true); - ln_alpha -= R::dcauchy(Kyy_(i, j), 0.0, pairwise_scale_, true); + ln_alpha -= R::dcauchy(pairwise_effects_continuous_(i, j), 0.0, pairwise_scale_, true); ln_alpha += R::dgamma(omega_prop_jj, 1.0, 1.0, true); ln_alpha -= R::dgamma(diag_curr, 1.0, 1.0, true); - if(std::log(runif(rng_)) < ln_alpha) { - double old_ij = Kyy_(i, j); - double old_jj = Kyy_(j, j); + if(MY_LOG(runif(rng_)) < ln_alpha) { + double old_ij = pairwise_effects_continuous_(i, j); + double old_jj = pairwise_effects_continuous_(j, j); - Kyy_(i, j) = omega_prop_ij; - Kyy_(j, i) = omega_prop_ij; - Kyy_(j, j) = omega_prop_jj; + pairwise_effects_continuous_(i, j) = omega_prop_ij; + pairwise_effects_continuous_(j, i) = omega_prop_ij; + pairwise_effects_continuous_(j, j) = omega_prop_jj; - cholesky_update_after_kyy_edge(old_ij, old_jj, i, j); + cholesky_update_after_precision_edge(old_ij, old_jj, i, j); recompute_conditional_mean(); if(use_marginal_pl_) recompute_Theta(); } if(iteration >= 1 && iteration < total_warmup_) { double rm_weight = std::pow(iteration, -0.75); - prop_sd_Kyy_(i, j) = update_proposal_sd_with_robbins_monro( - prop_sd_Kyy_(i, j), ln_alpha, rm_weight, 0.44); + proposal_sd_pairwise_continuous_(i, j) = update_proposal_sd_with_robbins_monro( + proposal_sd_pairwise_continuous_(i, j), ln_alpha, rm_weight, 0.44); } } // ============================================================================= -// update_Kyy_diag +// update_pairwise_effects_continuous_diag // ============================================================================= -// MH update for one diagonal element of Kyy. +// MH update for one diagonal element of the precision matrix. // Proposes on the log-Cholesky scale to ensure positivity. // Uses rank-1 Cholesky update on accept. // Prior: Gamma(1, 1) on the diagonal element + Jacobian for log-scale proposal. // ============================================================================= -void MixedMRFModel::update_Kyy_diag(int i, int iteration) { - double logdet = cholesky_helpers::get_log_det(Kyy_chol_); - double logdet_sub_ii = logdet + std::log(covariance_yy_(i, i)); +void MixedMRFModel::update_pairwise_effects_continuous_diag(int i, int iteration) { + double logdet = cholesky_helpers::get_log_det(cholesky_of_precision_); + double logdet_sub_ii = logdet + MY_LOG(covariance_continuous_(i, i)); double theta_curr = (logdet - logdet_sub_ii) / 2.0; - double theta_prop = rnorm(rng_, theta_curr, prop_sd_Kyy_(i, i)); + double theta_prop = rnorm(rng_, theta_curr, proposal_sd_pairwise_continuous_(i, i)); - precision_yy_proposal_ = Kyy_; - precision_yy_proposal_(i, i) = Kyy_(i, i) - - std::exp(theta_curr) * std::exp(theta_curr) - + std::exp(theta_prop) * std::exp(theta_prop); + precision_proposal_ = pairwise_effects_continuous_; + precision_proposal_(i, i) = pairwise_effects_continuous_(i, i) + - MY_EXP(theta_curr) * MY_EXP(theta_curr) + + MY_EXP(theta_prop) * MY_EXP(theta_prop); double ln_alpha = log_ggm_ratio_diag(i); @@ -473,50 +473,50 @@ void MixedMRFModel::update_Kyy_diag(int i, int iteration) { ln_alpha -= log_marginal_omrf(s); arma::mat Theta_saved = Theta_; - arma::mat Kyy_saved = Kyy_; - Kyy_ = precision_yy_proposal_; + arma::mat pairwise_effects_continuous_saved = pairwise_effects_continuous_; + pairwise_effects_continuous_ = precision_proposal_; recompute_Theta(); for(size_t s = 0; s < p_; ++s) ln_alpha += log_marginal_omrf(s); - Kyy_ = Kyy_saved; + pairwise_effects_continuous_ = pairwise_effects_continuous_saved; Theta_ = std::move(Theta_saved); } // Prior ratio: Gamma(1,1) on diagonal - ln_alpha += R::dgamma(precision_yy_proposal_(i, i), 1.0, 1.0, true); - ln_alpha -= R::dgamma(Kyy_(i, i), 1.0, 1.0, true); + ln_alpha += R::dgamma(precision_proposal_(i, i), 1.0, 1.0, true); + ln_alpha -= R::dgamma(pairwise_effects_continuous_(i, i), 1.0, 1.0, true); // Jacobian for log-scale proposal ln_alpha += theta_prop - theta_curr; - if(std::log(runif(rng_)) < ln_alpha) { - double old_ii = Kyy_(i, i); - Kyy_(i, i) = precision_yy_proposal_(i, i); + if(MY_LOG(runif(rng_)) < ln_alpha) { + double old_ii = pairwise_effects_continuous_(i, i); + pairwise_effects_continuous_(i, i) = precision_proposal_(i, i); - cholesky_update_after_kyy_diag(old_ii, i); + cholesky_update_after_precision_diag(old_ii, i); recompute_conditional_mean(); if(use_marginal_pl_) recompute_Theta(); } if(iteration >= 1 && iteration < total_warmup_) { double rm_weight = std::pow(iteration, -0.75); - prop_sd_Kyy_(i, i) = update_proposal_sd_with_robbins_monro( - prop_sd_Kyy_(i, i), ln_alpha, rm_weight, 0.44); + proposal_sd_pairwise_continuous_(i, i) = update_proposal_sd_with_robbins_monro( + proposal_sd_pairwise_continuous_(i, i), ln_alpha, rm_weight, 0.44); } } // ============================================================================= -// update_Kxy +// update_pairwise_cross // ============================================================================= -// MH update for one cross-type interaction Kxy_(i, j). +// MH update for one cross-type interaction pairwise_effects_cross_(i, j). // Acceptance: log_conditional_omrf(i) + log_conditional_ggm() + Cauchy prior. // Must save/restore conditional_mean_ around the proposal. // ============================================================================= -void MixedMRFModel::update_Kxy(int i, int j, int iteration) { - double current_val = Kxy_(i, j); - double proposed = rnorm(rng_, current_val, prop_sd_Kxy_(i, j)); +void MixedMRFModel::update_pairwise_cross(int i, int j, int iteration) { + double current_val = pairwise_effects_cross_(i, j); + double proposed = rnorm(rng_, current_val, proposal_sd_pairwise_cross_(i, j)); // Current log-posterior double ll_curr = log_conditional_ggm() @@ -532,7 +532,7 @@ void MixedMRFModel::update_Kxy(int i, int j, int iteration) { arma::mat cond_mean_saved = conditional_mean_; arma::mat Theta_saved; if(use_marginal_pl_) Theta_saved = Theta_; - Kxy_(i, j) = proposed; + pairwise_effects_cross_(i, j) = proposed; recompute_conditional_mean(); if(use_marginal_pl_) recompute_Theta(); @@ -547,22 +547,22 @@ void MixedMRFModel::update_Kxy(int i, int j, int iteration) { double ln_alpha = ll_prop - ll_curr; - if(std::log(runif(rng_)) >= ln_alpha) { - Kxy_(i, j) = current_val; // reject + if(MY_LOG(runif(rng_)) >= ln_alpha) { + pairwise_effects_cross_(i, j) = current_val; // reject conditional_mean_ = std::move(cond_mean_saved); if(use_marginal_pl_) Theta_ = std::move(Theta_saved); } if(iteration >= 1 && iteration < total_warmup_) { double rm_weight = std::pow(iteration, -0.75); - prop_sd_Kxy_(i, j) = update_proposal_sd_with_robbins_monro( - prop_sd_Kxy_(i, j), ln_alpha, rm_weight, 0.44); + proposal_sd_pairwise_cross_(i, j) = update_proposal_sd_with_robbins_monro( + proposal_sd_pairwise_cross_(i, j), ln_alpha, rm_weight, 0.44); } } // ============================================================================= -// update_edge_indicator_Kxx +// update_edge_indicator_discrete // ============================================================================= // Reversible-jump birth/death for a discrete-discrete edge (i, j). // Birth (G=0→1): propose k ~ N(0, σ), accept with slab + Hastings. @@ -570,9 +570,9 @@ void MixedMRFModel::update_Kxy(int i, int j, int iteration) { // Follows cond_omrf_update_association_indicator_pair in mixedGM. // ============================================================================= -void MixedMRFModel::update_edge_indicator_Kxx(int i, int j) { - double k_curr = Kxx_(i, j); - double prop_sd = prop_sd_Kxx_(i, j); +void MixedMRFModel::update_edge_indicator_discrete(int i, int j) { + double k_curr = pairwise_effects_discrete_(i, j); + double prop_sd = proposal_sd_pairwise_discrete_(i, j); int g_curr = gxx(i, j); int g_prop = 1 - g_curr; @@ -589,27 +589,27 @@ void MixedMRFModel::update_edge_indicator_Kxx(int i, int j) { if(use_marginal_pl_) { ll_curr = log_marginal_omrf(i) + log_marginal_omrf(j); - Kxx_(i, j) = k_prop; - Kxx_(j, i) = k_prop; + pairwise_effects_discrete_(i, j) = k_prop; + pairwise_effects_discrete_(j, i) = k_prop; recompute_Theta(); ll_prop = log_marginal_omrf(i) + log_marginal_omrf(j); // Restore - Kxx_(i, j) = k_curr; - Kxx_(j, i) = k_curr; + pairwise_effects_discrete_(i, j) = k_curr; + pairwise_effects_discrete_(j, i) = k_curr; recompute_Theta(); } else { ll_curr = log_conditional_omrf(i) + log_conditional_omrf(j); - Kxx_(i, j) = k_prop; - Kxx_(j, i) = k_prop; + pairwise_effects_discrete_(i, j) = k_prop; + pairwise_effects_discrete_(j, i) = k_prop; ll_prop = log_conditional_omrf(i) + log_conditional_omrf(j); // Restore - Kxx_(i, j) = k_curr; - Kxx_(j, i) = k_curr; + pairwise_effects_discrete_(i, j) = k_curr; + pairwise_effects_discrete_(j, i) = k_curr; } double ln_alpha = ll_prop - ll_curr; @@ -618,19 +618,19 @@ void MixedMRFModel::update_edge_indicator_Kxx(int i, int j) { // Birth: add slab prior, subtract proposal density, inclusion prior ln_alpha += R::dcauchy(k_prop, 0.0, pairwise_scale_, true); ln_alpha -= R::dnorm(k_prop, k_curr, prop_sd, true); - ln_alpha += std::log(inclusion_probability_(i, j)) - - std::log(1.0 - inclusion_probability_(i, j)); + ln_alpha += MY_LOG(inclusion_probability_(i, j)) + - MY_LOG(1.0 - inclusion_probability_(i, j)); } else { // Death: subtract slab prior, add reverse proposal density, inclusion prior ln_alpha -= R::dcauchy(k_curr, 0.0, pairwise_scale_, true); ln_alpha += R::dnorm(k_curr, k_prop, prop_sd, true); - ln_alpha -= std::log(inclusion_probability_(i, j)) - - std::log(1.0 - inclusion_probability_(i, j)); + ln_alpha -= MY_LOG(inclusion_probability_(i, j)) + - MY_LOG(1.0 - inclusion_probability_(i, j)); } - if(std::log(runif(rng_)) < ln_alpha) { - Kxx_(i, j) = k_prop; - Kxx_(j, i) = k_prop; + if(MY_LOG(runif(rng_)) < ln_alpha) { + pairwise_effects_discrete_(i, j) = k_prop; + pairwise_effects_discrete_(j, i) = k_prop; set_gxx(i, j, g_prop); if(use_marginal_pl_) recompute_Theta(); } @@ -638,7 +638,7 @@ void MixedMRFModel::update_edge_indicator_Kxx(int i, int j) { // ============================================================================= -// update_edge_indicator_Kyy +// update_edge_indicator_continuous // ============================================================================= // Reversible-jump birth/death for a continuous-continuous edge (i, j). // Uses Cholesky reparameterization (permute-free constants extraction). @@ -647,8 +647,8 @@ void MixedMRFModel::update_edge_indicator_Kxx(int i, int j) { // Follows cond_ggm_update_precision_indicator_pair in mixedGM. // ============================================================================= -void MixedMRFModel::update_edge_indicator_Kyy(int i, int j) { - get_kyy_constants(i, j); +void MixedMRFModel::update_edge_indicator_continuous(int i, int j) { + get_precision_constants(i, j); int g_curr = gyy(i, j); int g_prop = 1 - g_curr; @@ -657,20 +657,20 @@ void MixedMRFModel::update_edge_indicator_Kyy(int i, int j) { if(g_prop == 1) { // Birth: propose from N(0, σ) on reparameterized scale - double epsilon = rnorm(rng_, 0.0, prop_sd_Kyy_(i, j)); + double epsilon = rnorm(rng_, 0.0, proposal_sd_pairwise_continuous_(i, j)); omega_prop_ij = kyy_constants_[3] * epsilon; - omega_prop_jj = kyy_constrained_diagonal(omega_prop_ij); + omega_prop_jj = precision_constrained_diagonal(omega_prop_ij); } else { // Death: set off-diagonal to 0 omega_prop_ij = 0.0; - omega_prop_jj = kyy_constrained_diagonal(0.0); + omega_prop_jj = precision_constrained_diagonal(0.0); } // Fill proposal - precision_yy_proposal_ = Kyy_; - precision_yy_proposal_(i, j) = omega_prop_ij; - precision_yy_proposal_(j, i) = omega_prop_ij; - precision_yy_proposal_(j, j) = omega_prop_jj; + precision_proposal_ = pairwise_effects_continuous_; + precision_proposal_(i, j) = omega_prop_ij; + precision_proposal_(j, i) = omega_prop_ij; + precision_proposal_(j, j) = omega_prop_jj; // --- Likelihood ratio --- double ln_alpha = log_ggm_ratio_edge(i, j); @@ -680,12 +680,12 @@ void MixedMRFModel::update_edge_indicator_Kyy(int i, int j) { ln_alpha -= log_marginal_omrf(s); arma::mat Theta_saved = Theta_; - arma::mat Kyy_saved = Kyy_; - Kyy_ = precision_yy_proposal_; + arma::mat pairwise_effects_continuous_saved = pairwise_effects_continuous_; + pairwise_effects_continuous_ = precision_proposal_; recompute_Theta(); for(size_t s = 0; s < p_; ++s) ln_alpha += log_marginal_omrf(s); - Kyy_ = Kyy_saved; + pairwise_effects_continuous_ = pairwise_effects_continuous_saved; Theta_ = std::move(Theta_saved); } @@ -696,33 +696,33 @@ void MixedMRFModel::update_edge_indicator_Kyy(int i, int j) { // Subtract proposal density: dnorm(k_prop / C[2], 0, σ) / C[2] // = dnorm(epsilon, 0, σ) / C[2] ln_alpha -= R::dnorm(omega_prop_ij / kyy_constants_[3], 0.0, - prop_sd_Kyy_(i, j), true) - - std::log(kyy_constants_[3]); + proposal_sd_pairwise_continuous_(i, j), true) + - MY_LOG(kyy_constants_[3]); // Inclusion prior: log(π / (1-π)) - ln_alpha += std::log(inclusion_probability_(p_ + i, p_ + j)) - - std::log(1.0 - inclusion_probability_(p_ + i, p_ + j)); + ln_alpha += MY_LOG(inclusion_probability_(p_ + i, p_ + j)) + - MY_LOG(1.0 - inclusion_probability_(p_ + i, p_ + j)); } else { // Death: subtract slab prior on current off-diag - ln_alpha -= R::dcauchy(Kyy_(i, j), 0.0, pairwise_scale_, true); + ln_alpha -= R::dcauchy(pairwise_effects_continuous_(i, j), 0.0, pairwise_scale_, true); // Add reverse proposal density: dnorm(k_curr / C[2], 0, σ) / C[2] - ln_alpha += R::dnorm(Kyy_(i, j) / kyy_constants_[3], 0.0, - prop_sd_Kyy_(i, j), true) - - std::log(kyy_constants_[3]); + ln_alpha += R::dnorm(pairwise_effects_continuous_(i, j) / kyy_constants_[3], 0.0, + proposal_sd_pairwise_continuous_(i, j), true) + - MY_LOG(kyy_constants_[3]); // Inclusion prior: log((1-π) / π) - ln_alpha -= std::log(inclusion_probability_(p_ + i, p_ + j)) - - std::log(1.0 - inclusion_probability_(p_ + i, p_ + j)); + ln_alpha -= MY_LOG(inclusion_probability_(p_ + i, p_ + j)) + - MY_LOG(1.0 - inclusion_probability_(p_ + i, p_ + j)); } - if(std::log(runif(rng_)) < ln_alpha) { - double old_ij = Kyy_(i, j); - double old_jj = Kyy_(j, j); + if(MY_LOG(runif(rng_)) < ln_alpha) { + double old_ij = pairwise_effects_continuous_(i, j); + double old_jj = pairwise_effects_continuous_(j, j); - Kyy_(i, j) = omega_prop_ij; - Kyy_(j, i) = omega_prop_ij; - Kyy_(j, j) = omega_prop_jj; + pairwise_effects_continuous_(i, j) = omega_prop_ij; + pairwise_effects_continuous_(j, i) = omega_prop_ij; + pairwise_effects_continuous_(j, j) = omega_prop_jj; set_gyy(i, j, g_prop); - cholesky_update_after_kyy_edge(old_ij, old_jj, i, j); + cholesky_update_after_precision_edge(old_ij, old_jj, i, j); recompute_conditional_mean(); if(use_marginal_pl_) recompute_Theta(); } @@ -730,7 +730,7 @@ void MixedMRFModel::update_edge_indicator_Kyy(int i, int j) { // ============================================================================= -// update_edge_indicator_Kxy +// update_edge_indicator_cross // ============================================================================= // Reversible-jump birth/death for a cross-type edge (i, j). // Birth (G=0→1): propose k ~ N(0, σ). @@ -738,9 +738,9 @@ void MixedMRFModel::update_edge_indicator_Kyy(int i, int j) { // Follows cond_omrf_update_cross_association_indicator_pair in mixedGM. // ============================================================================= -void MixedMRFModel::update_edge_indicator_Kxy(int i, int j) { - double k_curr = Kxy_(i, j); - double prop_sd = prop_sd_Kxy_(i, j); +void MixedMRFModel::update_edge_indicator_cross(int i, int j) { + double k_curr = pairwise_effects_cross_(i, j); + double prop_sd = proposal_sd_pairwise_cross_(i, j); int g_curr = gxy(i, j); int g_prop = 1 - g_curr; @@ -761,7 +761,7 @@ void MixedMRFModel::update_edge_indicator_Kxy(int i, int j) { arma::mat cond_mean_saved = conditional_mean_; arma::mat Theta_saved = Theta_; - Kxy_(i, j) = k_prop; + pairwise_effects_cross_(i, j) = k_prop; recompute_conditional_mean(); recompute_Theta(); @@ -770,20 +770,20 @@ void MixedMRFModel::update_edge_indicator_Kxy(int i, int j) { ll_prop += log_marginal_omrf(s); // Restore - Kxy_(i, j) = k_curr; + pairwise_effects_cross_(i, j) = k_curr; conditional_mean_ = std::move(cond_mean_saved); Theta_ = std::move(Theta_saved); } else { ll_curr = log_conditional_omrf(i) + log_conditional_ggm(); arma::mat cond_mean_saved = conditional_mean_; - Kxy_(i, j) = k_prop; + pairwise_effects_cross_(i, j) = k_prop; recompute_conditional_mean(); ll_prop = log_conditional_omrf(i) + log_conditional_ggm(); // Restore - Kxy_(i, j) = k_curr; + pairwise_effects_cross_(i, j) = k_curr; conditional_mean_ = std::move(cond_mean_saved); } @@ -793,18 +793,18 @@ void MixedMRFModel::update_edge_indicator_Kxy(int i, int j) { // Birth ln_alpha += R::dcauchy(k_prop, 0.0, pairwise_scale_, true); ln_alpha -= R::dnorm(k_prop, k_curr, prop_sd, true); - ln_alpha += std::log(inclusion_probability_(i, p_ + j)) - - std::log(1.0 - inclusion_probability_(i, p_ + j)); + ln_alpha += MY_LOG(inclusion_probability_(i, p_ + j)) + - MY_LOG(1.0 - inclusion_probability_(i, p_ + j)); } else { // Death ln_alpha -= R::dcauchy(k_curr, 0.0, pairwise_scale_, true); ln_alpha += R::dnorm(k_curr, k_prop, prop_sd, true); - ln_alpha -= std::log(inclusion_probability_(i, p_ + j)) - - std::log(1.0 - inclusion_probability_(i, p_ + j)); + ln_alpha -= MY_LOG(inclusion_probability_(i, p_ + j)) + - MY_LOG(1.0 - inclusion_probability_(i, p_ + j)); } - if(std::log(runif(rng_)) < ln_alpha) { - Kxy_(i, j) = k_prop; + if(MY_LOG(runif(rng_)) < ln_alpha) { + pairwise_effects_cross_(i, j) = k_prop; set_gxy(i, j, g_prop); recompute_conditional_mean(); if(use_marginal_pl_) recompute_Theta(); diff --git a/src/models/mixed/mixed_mrf_model.cpp b/src/models/mixed/mixed_mrf_model.cpp index 5e980db7..0d95387c 100644 --- a/src/models/mixed/mixed_mrf_model.cpp +++ b/src/models/mixed/mixed_mrf_model.cpp @@ -1,5 +1,6 @@ #include #include "models/mixed/mixed_mrf_model.h" +#include "math/explog_macros.h" #include "rng/rng_utils.h" #include "mcmc/execution/warmup_schedule.h" @@ -63,38 +64,38 @@ MixedMRFModel::MixedMRFModel( compute_sufficient_statistics(); // Initialize parameters to zero - mux_ = arma::zeros(p_, max_cats_); - muy_ = arma::zeros(q_); - Kxx_ = arma::zeros(p_, p_); - Kyy_ = arma::eye(q_, q_); - Kxy_ = arma::zeros(p_, q_); + main_effects_discrete_ = arma::zeros(p_, max_cats_); + main_effects_continuous_ = arma::zeros(q_); + pairwise_effects_discrete_ = arma::zeros(p_, p_); + pairwise_effects_continuous_ = arma::eye(q_, q_); + pairwise_effects_cross_ = arma::zeros(p_, q_); // Initialize proposal SDs - prop_sd_mux_ = arma::ones(p_, max_cats_); - prop_sd_muy_ = arma::ones(q_); - prop_sd_Kxx_ = arma::ones(p_, p_); - prop_sd_Kyy_ = arma::ones(q_, q_); - prop_sd_Kxy_ = arma::ones(p_, q_); - - // Initialize Kyy caches (Kyy starts as identity) - Kyy_chol_ = arma::eye(q_, q_); - inv_cholesky_yy_ = arma::eye(q_, q_); - covariance_yy_ = arma::eye(q_, q_); - Kyy_log_det_ = 0.0; + proposal_sd_main_discrete_ = arma::ones(p_, max_cats_); + proposal_sd_main_continuous_ = arma::ones(q_); + proposal_sd_pairwise_discrete_ = arma::ones(p_, p_); + proposal_sd_pairwise_continuous_ = arma::ones(q_, q_); + proposal_sd_pairwise_cross_ = arma::ones(p_, q_); + + // Initialize precision caches (K_yy starts as identity) + cholesky_of_precision_ = arma::eye(q_, q_); + inv_cholesky_of_precision_ = arma::eye(q_, q_); + covariance_continuous_ = arma::eye(q_, q_); + log_det_precision_ = 0.0; // Rank-1 Cholesky update workspace - precision_yy_proposal_ = arma::mat(q_, q_, arma::fill::none); + precision_proposal_ = arma::mat(q_, q_, arma::fill::none); kyy_vf1_ = arma::zeros(q_); kyy_vf2_ = arma::zeros(q_); kyy_u1_ = arma::zeros(q_); kyy_u2_ = arma::zeros(q_); - // Initialize conditional mean: μ_y' + 2 x Kxy Kyy_inv - // With Kxy = 0 and Kyy = I, this is just 1 * μ_y' = 0. + // Initialize conditional mean: M = μ_y' + 2 X K_xy Σ_yy + // With K_xy = 0 and K_yy = I, this reduces to μ_y' = 0. conditional_mean_ = arma::zeros(n_, q_); - // Initialize Theta (marginal PL only): Kxx + 2 Kxy Kyy_inv Kxy' - // With Kxy = 0, Theta = Kxx = 0. + // Initialize Theta (marginal PL only): Θ = K_xx + 2 K_xy Σ_yy K_xy' + // With K_xy = 0, Θ = K_xx = 0. if(use_marginal_pl_) { Theta_ = arma::zeros(p_, p_); } @@ -131,11 +132,11 @@ MixedMRFModel::MixedMRFModel(const MixedMRFModel& other) has_missing_(other.has_missing_), counts_per_category_(other.counts_per_category_), blume_capel_stats_(other.blume_capel_stats_), - mux_(other.mux_), - muy_(other.muy_), - Kxx_(other.Kxx_), - Kyy_(other.Kyy_), - Kxy_(other.Kxy_), + main_effects_discrete_(other.main_effects_discrete_), + main_effects_continuous_(other.main_effects_continuous_), + pairwise_effects_discrete_(other.pairwise_effects_discrete_), + pairwise_effects_continuous_(other.pairwise_effects_continuous_), + pairwise_effects_cross_(other.pairwise_effects_cross_), edge_indicators_(other.edge_indicators_), inclusion_probability_(other.inclusion_probability_), edge_selection_(other.edge_selection_), @@ -143,20 +144,20 @@ MixedMRFModel::MixedMRFModel(const MixedMRFModel& other) main_alpha_(other.main_alpha_), main_beta_(other.main_beta_), pairwise_scale_(other.pairwise_scale_), - prop_sd_mux_(other.prop_sd_mux_), - prop_sd_muy_(other.prop_sd_muy_), - prop_sd_Kxx_(other.prop_sd_Kxx_), - prop_sd_Kyy_(other.prop_sd_Kyy_), - prop_sd_Kxy_(other.prop_sd_Kxy_), + proposal_sd_main_discrete_(other.proposal_sd_main_discrete_), + proposal_sd_main_continuous_(other.proposal_sd_main_continuous_), + proposal_sd_pairwise_discrete_(other.proposal_sd_pairwise_discrete_), + proposal_sd_pairwise_continuous_(other.proposal_sd_pairwise_continuous_), + proposal_sd_pairwise_cross_(other.proposal_sd_pairwise_cross_), total_warmup_(other.total_warmup_), - Kyy_chol_(other.Kyy_chol_), - inv_cholesky_yy_(other.inv_cholesky_yy_), - covariance_yy_(other.covariance_yy_), - Kyy_log_det_(other.Kyy_log_det_), + cholesky_of_precision_(other.cholesky_of_precision_), + inv_cholesky_of_precision_(other.inv_cholesky_of_precision_), + covariance_continuous_(other.covariance_continuous_), + log_det_precision_(other.log_det_precision_), Theta_(other.Theta_), conditional_mean_(other.conditional_mean_), kyy_constants_(other.kyy_constants_), - precision_yy_proposal_(other.precision_yy_proposal_), + precision_proposal_(other.precision_proposal_), kyy_v1_(other.kyy_v1_), kyy_v2_(other.kyy_v2_), kyy_vf1_(other.kyy_vf1_), @@ -223,21 +224,21 @@ size_t MixedMRFModel::count_num_main_effects() const { // ============================================================================= void MixedMRFModel::recompute_conditional_mean() { - // conditional_mean_ = 1*μ_y' + 2 * discrete_obs * Kxy * covariance_yy_ - conditional_mean_ = arma::repmat(muy_.t(), n_, 1) + - 2.0 * discrete_observations_dbl_ * Kxy_ * covariance_yy_; + // M = μ_y' + 2 X K_xy Σ_yy + conditional_mean_ = arma::repmat(main_effects_continuous_.t(), n_, 1) + + 2.0 * discrete_observations_dbl_ * pairwise_effects_cross_ * covariance_continuous_; } -void MixedMRFModel::recompute_Kyy_decomposition() { - Kyy_chol_ = arma::chol(Kyy_); // upper Cholesky: Kyy = R'R - arma::inv(inv_cholesky_yy_, arma::trimatu(Kyy_chol_)); - covariance_yy_ = inv_cholesky_yy_ * inv_cholesky_yy_.t(); - Kyy_log_det_ = cholesky_helpers::get_log_det(Kyy_chol_); +void MixedMRFModel::recompute_pairwise_effects_continuous_decomposition() { + cholesky_of_precision_ = arma::chol(pairwise_effects_continuous_); // upper Cholesky: K_yy = R'R + arma::inv(inv_cholesky_of_precision_, arma::trimatu(cholesky_of_precision_)); + covariance_continuous_ = inv_cholesky_of_precision_ * inv_cholesky_of_precision_.t(); + log_det_precision_ = cholesky_helpers::get_log_det(cholesky_of_precision_); } void MixedMRFModel::recompute_Theta() { - // Θ = Kxx + 2 Kxy covariance_yy_ Kxy' - Theta_ = Kxx_ + 2.0 * Kxy_ * covariance_yy_ * Kxy_.t(); + // Θ = K_xx + 2 K_xy Σ_yy K_xy' + Theta_ = pairwise_effects_discrete_ + 2.0 * pairwise_effects_cross_ * covariance_continuous_ * pairwise_effects_cross_.t(); } @@ -245,31 +246,31 @@ void MixedMRFModel::recompute_Theta() { // Parameter vectorization // ============================================================================= -// NUTS vectorization order (excludes Kyy — sampled by MH separately): -// 1. mux_: per-variable (ordinal: C_s thresholds; BC: 2 coefficients) -// 2. Kxx_: upper-triangular, row-major — p(p-1)/2 -// 3. muy_: all q means -// 4. Kxy_: all p*q entries, row-major +// NUTS vectorization order (excludes pairwise_effects_continuous_ — sampled by MH separately): +// 1. main_effects_discrete_: per-variable (ordinal: C_s thresholds; BC: 2 coefficients) +// 2. pairwise_effects_discrete_: upper-triangular, row-major — p(p-1)/2 +// 3. main_effects_continuous_: all q means +// 4. pairwise_effects_cross_: all p*q entries, row-major // -// Storage vectorization order (includes Kyy): +// Storage vectorization order (includes pairwise_effects_continuous_): // 1–4. Same as NUTS order -// 5. Kyy_: upper-triangle including diagonal — q(q+1)/2 +// 5. pairwise_effects_continuous_: upper-triangle including diagonal — q(q+1)/2 size_t MixedMRFModel::parameter_dimension() const { if(!edge_selection_active_) { return full_parameter_dimension(); } - // Count active NUTS parameters only (no Kyy) - size_t dim = num_main_ + q_; // mux + muy always active + // Count active NUTS parameters only (no pairwise_effects_continuous_) + size_t dim = num_main_ + q_; // main effects always active - // Active Kxx edges + // Active pairwise_effects_discrete_ edges for(size_t i = 0; i < p_ - 1; ++i) { for(size_t j = i + 1; j < p_; ++j) { if(gxx(i, j)) dim++; } } - // Active Kxy edges + // Active pairwise_effects_cross_ edges for(size_t i = 0; i < p_; ++i) { for(size_t j = 0; j < q_; ++j) { if(gxy(i, j)) dim++; @@ -280,52 +281,52 @@ size_t MixedMRFModel::parameter_dimension() const { } size_t MixedMRFModel::full_parameter_dimension() const { - // NUTS block: mux + Kxx upper-tri + muy + Kxy full (no Kyy) + // NUTS block: main + pairwise_discrete upper-tri + means + pairwise_cross (no precision) return num_main_ + num_pairwise_xx_ + q_ + num_cross_; } size_t MixedMRFModel::storage_dimension() const { - // All parameters including Kyy + // All parameters including pairwise_effects_continuous_ return num_main_ + num_pairwise_xx_ + q_ + (q_ * (q_ + 1)) / 2 + num_cross_; } arma::vec MixedMRFModel::get_vectorized_parameters() const { - // Active NUTS parameters only (excludes Kyy, excludes inactive edges) + // Active NUTS parameters only (excludes precision, excludes inactive edges) arma::vec out(parameter_dimension()); size_t idx = 0; - // 1. mux_ + // 1. main_effects_discrete_ for(size_t s = 0; s < p_; ++s) { if(is_ordinal_variable_(s)) { for(int c = 0; c < num_categories_(s); ++c) { - out(idx++) = mux_(s, c); + out(idx++) = main_effects_discrete_(s, c); } } else { - out(idx++) = mux_(s, 0); - out(idx++) = mux_(s, 1); + out(idx++) = main_effects_discrete_(s, 0); + out(idx++) = main_effects_discrete_(s, 1); } } - // 2. Kxx_ upper-triangular (active edges only when selection is active) + // 2. pairwise_effects_discrete_ upper-triangular (active edges only when selection is active) for(size_t i = 0; i < p_ - 1; ++i) { for(size_t j = i + 1; j < p_; ++j) { if(!edge_selection_active_ || gxx(i, j) == 1) { - out(idx++) = Kxx_(i, j); + out(idx++) = pairwise_effects_discrete_(i, j); } } } - // 3. muy_ + // 3. main_effects_continuous_ for(size_t j = 0; j < q_; ++j) { - out(idx++) = muy_(j); + out(idx++) = main_effects_continuous_(j); } - // 4. Kxy_ row-major (active edges only when selection is active) + // 4. pairwise_effects_cross_ row-major (active edges only when selection is active) for(size_t i = 0; i < p_; ++i) { for(size_t j = 0; j < q_; ++j) { if(!edge_selection_active_ || gxy(i, j) == 1) { - out(idx++) = Kxy_(i, j); + out(idx++) = pairwise_effects_cross_(i, j); } } } @@ -334,38 +335,38 @@ arma::vec MixedMRFModel::get_vectorized_parameters() const { } arma::vec MixedMRFModel::get_full_vectorized_parameters() const { - // All NUTS parameters, fixed size (inactive edges are 0, no Kyy) + // All NUTS parameters, fixed size (inactive edges are 0, no precision) arma::vec out(full_parameter_dimension(), arma::fill::zeros); size_t idx = 0; - // 1. mux_ + // 1. main_effects_discrete_ for(size_t s = 0; s < p_; ++s) { if(is_ordinal_variable_(s)) { for(int c = 0; c < num_categories_(s); ++c) { - out(idx++) = mux_(s, c); + out(idx++) = main_effects_discrete_(s, c); } } else { - out(idx++) = mux_(s, 0); - out(idx++) = mux_(s, 1); + out(idx++) = main_effects_discrete_(s, 0); + out(idx++) = main_effects_discrete_(s, 1); } } - // 2. Kxx_ upper-triangular (all entries, zeros for inactive) + // 2. pairwise_effects_discrete_ upper-triangular (all entries, zeros for inactive) for(size_t i = 0; i < p_ - 1; ++i) { for(size_t j = i + 1; j < p_; ++j) { - out(idx++) = Kxx_(i, j); + out(idx++) = pairwise_effects_discrete_(i, j); } } - // 3. muy_ + // 3. main_effects_continuous_ for(size_t j = 0; j < q_; ++j) { - out(idx++) = muy_(j); + out(idx++) = main_effects_continuous_(j); } - // 4. Kxy_ row-major (all entries, zeros for inactive) + // 4. pairwise_effects_cross_ row-major (all entries, zeros for inactive) for(size_t i = 0; i < p_; ++i) { for(size_t j = 0; j < q_; ++j) { - out(idx++) = Kxy_(i, j); + out(idx++) = pairwise_effects_cross_(i, j); } } @@ -373,45 +374,45 @@ arma::vec MixedMRFModel::get_full_vectorized_parameters() const { } arma::vec MixedMRFModel::get_storage_vectorized_parameters() const { - // All parameters including Kyy, fixed size + // All parameters including pairwise_effects_continuous_, fixed size arma::vec out(storage_dimension(), arma::fill::zeros); size_t idx = 0; - // 1. mux_ + // 1. main_effects_discrete_ for(size_t s = 0; s < p_; ++s) { if(is_ordinal_variable_(s)) { for(int c = 0; c < num_categories_(s); ++c) { - out(idx++) = mux_(s, c); + out(idx++) = main_effects_discrete_(s, c); } } else { - out(idx++) = mux_(s, 0); - out(idx++) = mux_(s, 1); + out(idx++) = main_effects_discrete_(s, 0); + out(idx++) = main_effects_discrete_(s, 1); } } - // 2. Kxx_ upper-triangular + // 2. pairwise_effects_discrete_ upper-triangular for(size_t i = 0; i < p_ - 1; ++i) { for(size_t j = i + 1; j < p_; ++j) { - out(idx++) = Kxx_(i, j); + out(idx++) = pairwise_effects_discrete_(i, j); } } - // 3. muy_ + // 3. main_effects_continuous_ for(size_t j = 0; j < q_; ++j) { - out(idx++) = muy_(j); + out(idx++) = main_effects_continuous_(j); } - // 4. Kxy_ row-major + // 4. pairwise_effects_cross_ row-major for(size_t i = 0; i < p_; ++i) { for(size_t j = 0; j < q_; ++j) { - out(idx++) = Kxy_(i, j); + out(idx++) = pairwise_effects_cross_(i, j); } } - // 5. Kyy_ upper-triangle including diagonal + // 5. pairwise_effects_continuous_ upper-triangle including diagonal for(size_t i = 0; i < q_; ++i) { for(size_t j = i; j < q_; ++j) { - out(idx++) = Kyy_(i, j); + out(idx++) = pairwise_effects_continuous_(i, j); } } @@ -419,47 +420,47 @@ arma::vec MixedMRFModel::get_storage_vectorized_parameters() const { } void MixedMRFModel::set_vectorized_parameters(const arma::vec& params) { - // Unpack NUTS block only (no Kyy) + // Unpack NUTS block only (no pairwise_effects_continuous_) size_t idx = 0; - // 1. mux_ + // 1. main_effects_discrete_ for(size_t s = 0; s < p_; ++s) { if(is_ordinal_variable_(s)) { for(int c = 0; c < num_categories_(s); ++c) { - mux_(s, c) = params(idx++); + main_effects_discrete_(s, c) = params(idx++); } } else { - mux_(s, 0) = params(idx++); - mux_(s, 1) = params(idx++); + main_effects_discrete_(s, 0) = params(idx++); + main_effects_discrete_(s, 1) = params(idx++); } } - // 2. Kxx_ upper-triangular (active edges only when selection is active) + // 2. pairwise_effects_discrete_ upper-triangular (active edges only when selection is active) for(size_t i = 0; i < p_ - 1; ++i) { for(size_t j = i + 1; j < p_; ++j) { if(!edge_selection_active_ || gxx(i, j) == 1) { - Kxx_(i, j) = params(idx); - Kxx_(j, i) = params(idx); + pairwise_effects_discrete_(i, j) = params(idx); + pairwise_effects_discrete_(j, i) = params(idx); idx++; } } } - // 3. muy_ + // 3. main_effects_continuous_ for(size_t j = 0; j < q_; ++j) { - muy_(j) = params(idx++); + main_effects_continuous_(j) = params(idx++); } - // 4. Kxy_ row-major (active edges only when selection is active) + // 4. pairwise_effects_cross_ row-major (active edges only when selection is active) for(size_t i = 0; i < p_; ++i) { for(size_t j = 0; j < q_; ++j) { if(!edge_selection_active_ || gxy(i, j) == 1) { - Kxy_(i, j) = params(idx++); + pairwise_effects_cross_(i, j) = params(idx++); } } } - // Refresh caches (Kyy unchanged, so no Kyy decomposition update needed) + // Refresh caches (precision unchanged, so no decomposition update needed) recompute_conditional_mean(); if(use_marginal_pl_) { recompute_Theta(); @@ -478,7 +479,7 @@ arma::vec MixedMRFModel::get_active_inv_mass() const { size_t offset_full = num_main_; size_t offset_active = num_main_; - // Kxx active edges + // pairwise_effects_discrete_ active edges for(size_t i = 0; i < p_ - 1; ++i) { for(size_t j = i + 1; j < p_; ++j) { if(gxx(i, j) == 1) { @@ -488,12 +489,12 @@ arma::vec MixedMRFModel::get_active_inv_mass() const { } } - // muy: always active + // main_effects_continuous_: always active for(size_t j = 0; j < q_; ++j) { active(offset_active++) = inv_mass_(offset_full++); } - // Kxy active edges + // pairwise_effects_cross_ active edges for(size_t i = 0; i < p_; ++i) { for(size_t j = 0; j < q_; ++j) { if(gxy(i, j) == 1) { @@ -574,40 +575,40 @@ void MixedMRFModel::impute_missing() { const int variable = missing_index_discrete_(miss, 1); const int num_cats = num_categories_(variable); - // Rest score: sum_t x_vt * Kxx(t,s) + 2 * sum_j y_vj * Kxy(s,j) - // Kxx diagonal is zero, so no self-interaction subtraction needed + // Rest score: sum_t x_vt K_xx(t,s) + 2 sum_j y_vj K_xy(s,j) + // K_xx diagonal is zero, so no self-interaction subtraction needed double rest_v = 0.0; for(size_t t = 0; t < p_; t++) { - rest_v += discrete_observations_dbl_(person, t) * Kxx_(t, variable); + rest_v += discrete_observations_dbl_(person, t) * pairwise_effects_discrete_(t, variable); } for(size_t j = 0; j < q_; j++) { - rest_v += 2.0 * continuous_observations_(person, j) * Kxy_(variable, j); + rest_v += 2.0 * continuous_observations_(person, j) * pairwise_effects_cross_(variable, j); } double cumsum = 0.0; if(is_ordinal_variable_(variable)) { - // P(x=0) = 1, P(x=c) ∝ exp(c * rest + mux(s, c-1)) + // P(x=0) = 1, P(x=c) ∝ exp(c · rest + μ_x(s, c-1)) cumsum = 1.0; category_probabilities(0) = cumsum; for(int c = 1; c <= num_cats; c++) { double exponent = static_cast(c) * rest_v + - mux_(variable, c - 1); - cumsum += std::exp(exponent); + main_effects_discrete_(variable, c - 1); + cumsum += MY_EXP(exponent); category_probabilities(c) = cumsum; } } else { // Blume-Capel: categories centered at baseline const int ref = baseline_category_(variable); - double alpha = mux_(variable, 0); - double beta = mux_(variable, 1); + double alpha = main_effects_discrete_(variable, 0); + double beta = main_effects_discrete_(variable, 1); cumsum = 0.0; for(int cat = 0; cat <= num_cats; cat++) { const int score = cat - ref; double exponent = alpha * score + beta * score * score + score * rest_v; - cumsum += std::exp(exponent); + cumsum += MY_EXP(exponent); category_probabilities(cat) = cumsum; } } @@ -654,17 +655,17 @@ void MixedMRFModel::impute_missing() { const int person = missing_index_continuous_(miss, 0); const int variable = missing_index_continuous_(miss, 1); - // Conditional: y_vj | y_{v,-j}, x ~ N(mu*, 1/Kyy_jj) - // mu* = M_vj - (1/Kyy_jj) * sum_{k!=j} Kyy_jk * (y_vk - M_vk) + // Conditional: y_vj | y_{v,-j}, x ~ N(mu*, 1/pairwise_effects_continuous_jj) + // mu* = M_vj - (1/pairwise_effects_continuous_jj) * sum_{k!=j} pairwise_effects_continuous_jk * (y_vk - M_vk) double cond_mean = conditional_mean_(person, variable); for(size_t k = 0; k < q_; k++) { if(k != static_cast(variable)) { - cond_mean -= (Kyy_(variable, k) / Kyy_(variable, variable)) * + cond_mean -= (pairwise_effects_continuous_(variable, k) / pairwise_effects_continuous_(variable, variable)) * (continuous_observations_(person, k) - conditional_mean_(person, k)); } } - double cond_sd = std::sqrt(1.0 / Kyy_(variable, variable)); + double cond_sd = std::sqrt(1.0 / pairwise_effects_continuous_(variable, variable)); continuous_observations_(person, variable) = rnorm(rng_, cond_mean, cond_sd); @@ -696,43 +697,43 @@ void MixedMRFModel::do_one_metropolis_step(int iteration) { for(size_t j = 0; j < q_; ++j) update_continuous_mean(j, iteration); - // Step 3: Update Kxx (upper triangle, edge-gated) + // Step 3: Update pairwise_effects_discrete_ (upper triangle, edge-gated) for(size_t i = 0; i < p_ - 1; ++i) for(size_t j = i + 1; j < p_; ++j) if(!edge_selection_active_ || gxx(i, j) == 1) - update_Kxx(i, j, iteration); + update_pairwise_discrete(i, j, iteration); - // Step 4: Update Kyy (off-diag + diagonal, edge-gated) + // Step 4: Update pairwise_effects_continuous_ (off-diag + diagonal, edge-gated) if(q_ >= 2) { for(size_t i = 0; i < q_ - 1; ++i) for(size_t j = i + 1; j < q_; ++j) if(!edge_selection_active_ || gyy(i, j) == 1) - update_Kyy_offdiag(i, j, iteration); + update_pairwise_effects_continuous_offdiag(i, j, iteration); } for(size_t i = 0; i < q_; ++i) - update_Kyy_diag(i, iteration); + update_pairwise_effects_continuous_diag(i, iteration); - // Step 5: Update Kxy (edge-gated) + // Step 5: Update pairwise_effects_cross_ (edge-gated) for(size_t i = 0; i < p_; ++i) for(size_t j = 0; j < q_; ++j) if(!edge_selection_active_ || gxy(i, j) == 1) - update_Kxy(i, j, iteration); + update_pairwise_cross(i, j, iteration); // Edge-indicator updates are handled by ChainRunner, not here. // (Matches the OMRF pattern; avoids double-counting indicator proposals.) } -void MixedMRFModel::do_kyy_metropolis_step(int iteration) { - // Off-diagonal Kyy (edge-gated) +void MixedMRFModel::do_pairwise_continuous_metropolis_step(int iteration) { + // Off-diagonal precision (edge-gated) if(q_ >= 2) { for(size_t i = 0; i < q_ - 1; ++i) for(size_t j = i + 1; j < q_; ++j) if(!edge_selection_active_ || gyy(i, j) == 1) - update_Kyy_offdiag(i, j, iteration); + update_pairwise_effects_continuous_offdiag(i, j, iteration); } - // Diagonal Kyy (always updated) + // Diagonal precision (always updated) for(size_t i = 0; i < q_; ++i) - update_Kyy_diag(i, iteration); + update_pairwise_effects_continuous_diag(i, iteration); } void MixedMRFModel::update_edge_indicators() { @@ -754,7 +755,7 @@ void MixedMRFModel::update_edge_indicators() { } count += row_len; } - update_edge_indicator_Kxx(i, j); + update_edge_indicator_discrete(i, j); } // Continuous-continuous edges (shuffled order) @@ -770,7 +771,7 @@ void MixedMRFModel::update_edge_indicators() { } count += row_len; } - update_edge_indicator_Kyy(i, j); + update_edge_indicator_continuous(i, j); } // Cross edges (shuffled order) @@ -778,7 +779,7 @@ void MixedMRFModel::update_edge_indicators() { size_t idx = edge_order_xy_(e); size_t i = idx / q_; size_t j = idx % q_; - update_edge_indicator_Kxy(i, j); + update_edge_indicator_cross(i, j); } } @@ -789,8 +790,8 @@ void MixedMRFModel::initialize_graph() { for(size_t j = i + 1; j < p_; ++j) { if(runif(rng_) >= inclusion_probability_(i, j)) { set_gxx(i, j, 0); - Kxx_(i, j) = 0.0; - Kxx_(j, i) = 0.0; + pairwise_effects_discrete_(i, j) = 0.0; + pairwise_effects_discrete_(j, i) = 0.0; } } } @@ -799,20 +800,20 @@ void MixedMRFModel::initialize_graph() { for(size_t j = i + 1; j < q_; ++j) { if(runif(rng_) >= inclusion_probability_(p_ + i, p_ + j)) { set_gyy(i, j, 0); - Kyy_(i, j) = 0.0; - Kyy_(j, i) = 0.0; + pairwise_effects_continuous_(i, j) = 0.0; + pairwise_effects_continuous_(j, i) = 0.0; } } } - // Recompute Kyy decomposition after potential zeroing - recompute_Kyy_decomposition(); + // Recompute precision decomposition after potential zeroing + recompute_pairwise_effects_continuous_decomposition(); recompute_conditional_mean(); for(size_t i = 0; i < p_; ++i) { for(size_t j = 0; j < q_; ++j) { if(runif(rng_) >= inclusion_probability_(i, p_ + j)) { set_gxy(i, j, 0); - Kxy_(i, j) = 0.0; + pairwise_effects_cross_(i, j) = 0.0; } } } diff --git a/src/models/mixed/mixed_mrf_model.h b/src/models/mixed/mixed_mrf_model.h index aa9d9a85..8bbb1a43 100644 --- a/src/models/mixed/mixed_mrf_model.h +++ b/src/models/mixed/mixed_mrf_model.h @@ -109,10 +109,10 @@ class MixedMRFModel : public BaseModel { void do_one_metropolis_step(int iteration = -1) override; /** - * Update only Kyy parameters via Metropolis (used by hybrid NUTS+MH). + * Update only continuous precision parameters via Metropolis (hybrid NUTS+MH). * @param iteration Current iteration (for Robbins-Monro adaptation) */ - void do_kyy_metropolis_step(int iteration = -1); + void do_pairwise_continuous_metropolis_step(int iteration = -1); /** * Initialize Metropolis adaptation controllers for proposal-SD tuning. @@ -155,39 +155,39 @@ class MixedMRFModel : public BaseModel { // ========================================================================= /** - * Dimensionality of the active NUTS parameter space (excludes Kyy). + * Dimensionality of the active NUTS parameter space (excludes precision). * When edge selection is active, excludes parameters for inactive edges. */ size_t parameter_dimension() const override; /** * Full NUTS-block dimension (all NUTS params, regardless of edge state). - * Excludes Kyy. Used for mass-matrix sizing and adaptation. + * Excludes continuous precision. Used for mass-matrix sizing and adaptation. */ size_t full_parameter_dimension() const override; /** - * Storage dimension (all parameters including Kyy, regardless of edge state). - * Used for fixed-size sample storage. + * Storage dimension (all parameters including continuous precision, + * regardless of edge state). Used for fixed-size sample storage. */ size_t storage_dimension() const override; - /** Get active NUTS parameters as a flat vector (excludes Kyy). */ + /** Get active NUTS parameters as a flat vector (excludes precision). */ arma::vec get_vectorized_parameters() const override; - /** Get all NUTS parameters (inactive edges are 0, excludes Kyy). */ + /** Get all NUTS parameters (inactive edges zeroed, excludes precision). */ arma::vec get_full_vectorized_parameters() const override; - /** Get all parameters including Kyy for sample storage. */ + /** Get all parameters including continuous precision for sample storage. */ arma::vec get_storage_vectorized_parameters() const override; - /** Set NUTS parameters from a flat vector (does not touch Kyy). */ + /** Set NUTS parameters from a flat vector (does not touch precision). */ void set_vectorized_parameters(const arma::vec& params) override; /** Get vectorized edge indicators (Gxx upper-tri, Gyy upper-tri, Gxy full). */ arma::ivec get_vectorized_indicator_parameters() override; - /** Get active subset of inverse mass diagonal (NUTS params only, excludes Kyy). */ + /** Get active subset of inverse mass diagonal (NUTS params only, excludes precision). */ arma::vec get_active_inv_mass() const override; // ========================================================================= @@ -287,11 +287,11 @@ class MixedMRFModel : public BaseModel { // Parameters // ========================================================================= - arma::mat mux_; ///< p x max_cats main effects (thresholds or alpha/beta) - arma::vec muy_; ///< q-vector continuous means - arma::mat Kxx_; ///< p x p discrete interactions (symmetric, zero diag) - arma::mat Kyy_; ///< q x q SPD precision matrix - arma::mat Kxy_; ///< p x q cross-type interactions + arma::mat main_effects_discrete_; ///< p x max_cats main effects (thresholds or alpha/beta) + arma::vec main_effects_continuous_; ///< q-vector continuous means + arma::mat pairwise_effects_discrete_; ///< p x p discrete interactions (symmetric, zero diag) + arma::mat pairwise_effects_continuous_; ///< q x q SPD precision matrix + arma::mat pairwise_effects_cross_; ///< p x q cross-type interactions // ========================================================================= // Edge indicators @@ -318,27 +318,27 @@ class MixedMRFModel : public BaseModel { // Proposal SDs (Robbins-Monro adapted) // ========================================================================= - arma::mat prop_sd_mux_; ///< p x max_cats - arma::vec prop_sd_muy_; ///< q-vector - arma::mat prop_sd_Kxx_; ///< p x p - arma::mat prop_sd_Kyy_; ///< q x q - arma::mat prop_sd_Kxy_; ///< p x q + arma::mat proposal_sd_main_discrete_; ///< p x max_cats + arma::vec proposal_sd_main_continuous_; ///< q-vector + arma::mat proposal_sd_pairwise_discrete_; ///< p x p + arma::mat proposal_sd_pairwise_continuous_; ///< q x q + arma::mat proposal_sd_pairwise_cross_; ///< p x q int total_warmup_ = 0; ///< Stored by init_metropolis_adaptation // ========================================================================= // Cached quantities // ========================================================================= - arma::mat Kyy_chol_; ///< q x q upper Cholesky of Kyy (Kyy = R'R) - arma::mat inv_cholesky_yy_; ///< q x q R^{-1} (upper triangular) - arma::mat covariance_yy_; ///< q x q Kyy^{-1} = R^{-1} R^{-T} - double Kyy_log_det_; ///< log|Kyy| - arma::mat Theta_; ///< p x p Kxx + 2 Kxy covariance_yy_ Kxy' (marginal PL) - arma::mat conditional_mean_; ///< n x q mu_y' + 2 discrete_obs Kxy covariance_yy_ + arma::mat cholesky_of_precision_; ///< q x q upper Cholesky R (K_yy = R'R) + arma::mat inv_cholesky_of_precision_; ///< q x q R^{-1} (upper triangular) + arma::mat covariance_continuous_; ///< q x q K_yy^{-1} = R^{-1} R^{-T} + double log_det_precision_; ///< log|K_yy| + arma::mat Theta_; ///< p x p marginal PL interaction matrix + arma::mat conditional_mean_; ///< n x q conditional mean mu_y + 2 X K_xy Sigma_yy // Rank-1 Cholesky update workspace std::array kyy_constants_{}; ///< Reparameterization constants - arma::mat precision_yy_proposal_; ///< q x q scratch for proposed Kyy + arma::mat precision_proposal_; ///< q x q scratch for proposed precision arma::vec kyy_v1_ = {0, -1}; ///< Rank-2 decomposition helper 1 arma::vec kyy_v2_ = {0, 0}; ///< Rank-2 decomposition helper 2 arma::vec kyy_vf1_; ///< q-vector, zeroed between uses @@ -354,7 +354,7 @@ class MixedMRFModel : public BaseModel { arma::vec grad_obs_cache_; ///< Cached observed-data gradient component arma::imat kxx_index_cache_; ///< p x p map from (i,j) to gradient index arma::imat kxy_index_cache_; ///< p x q map from (i,j) to gradient index - int muy_grad_offset_ = 0; ///< Offset of muy block in gradient vector + int main_effects_continuous_grad_offset_ = 0; ///< Offset of main_effects_continuous block in gradient vector bool gradient_cache_valid_ = false; ///< Whether gradient cache is current // ========================================================================= @@ -382,13 +382,13 @@ class MixedMRFModel : public BaseModel { /** Compute category counts and BC sufficient statistics from discrete_observations_. */ void compute_sufficient_statistics(); - /** Recompute conditional_mean_ from muy_, Kxy_, covariance_yy_. */ + /** Recompute conditional_mean_ from main_effects_continuous_, pairwise_effects_cross_, covariance_continuous_. */ void recompute_conditional_mean(); - /** Recompute Kyy_chol_, inv_cholesky_yy_, covariance_yy_, Kyy_log_det_ from Kyy_. */ - void recompute_Kyy_decomposition(); + /** Recompute cholesky_of_precision_, inv_cholesky_of_precision_, covariance_continuous_, log_det_precision_ from pairwise_effects_continuous_. */ + void recompute_pairwise_effects_continuous_decomposition(); - /** Recompute Theta_ from Kxx_, Kxy_, covariance_yy_ (marginal PL only). */ + /** Recompute Theta_ from pairwise_effects_discrete_, pairwise_effects_cross_, covariance_continuous_ (marginal PL only). */ void recompute_Theta(); // ========================================================================= @@ -404,10 +404,10 @@ class MixedMRFModel : public BaseModel { /** Unpack NUTS-vector into temporary parameter matrices (no model mutation). */ void unvectorize_nuts_to_temps( const arma::vec& params, - arma::mat& temp_mux, - arma::mat& temp_Kxx, - arma::vec& temp_muy, - arma::mat& temp_Kxy + arma::mat& temp_main_discrete, + arma::mat& temp_pairwise_discrete, + arma::vec& temp_main_continuous, + arma::mat& temp_pairwise_cross ) const; // ========================================================================= @@ -427,59 +427,59 @@ class MixedMRFModel : public BaseModel { // MH update functions (implemented in mixed_mrf_metropolis.cpp) // ========================================================================= - // --- Rank-1 Kyy proposal helpers (permutation-free) --- + // --- Rank-1 precision proposal helpers (permutation-free) --- - // Extract reparameterization constants for the (i,j) off-diagonal Kyy update. - // Populates kyy_constants_[0..5] from Kyy_chol_ and covariance_yy_. - void get_kyy_constants(int i, int j); + // Extract reparameterization constants for the (i,j) off-diagonal precision update. + // Populates kyy_constants_[0..5] from cholesky_of_precision_ and covariance_continuous_. + void get_precision_constants(int i, int j); - // Constrained diagonal value for a proposed off-diagonal Kyy element. - double kyy_constrained_diagonal(double x) const; + // Constrained diagonal value for a proposed off-diagonal precision element. + double precision_constrained_diagonal(double x) const; - // Log-likelihood ratio for a proposed off-diagonal Kyy change (rank-2). - // Assumes precision_yy_proposal_ is already filled by the caller. + // Log-likelihood ratio for a proposed off-diagonal precision change (rank-2). + // Assumes precision_proposal_ is already filled by the caller. double log_ggm_ratio_edge(int i, int j) const; - // Log-likelihood ratio for a proposed diagonal Kyy change (rank-1). - // Assumes precision_yy_proposal_ is already filled by the caller. + // Log-likelihood ratio for a proposed diagonal precision change (rank-1). + // Assumes precision_proposal_ is already filled by the caller. double log_ggm_ratio_diag(int i) const; - // Rank-1 Cholesky update after accepting an off-diagonal Kyy change. - void cholesky_update_after_kyy_edge(double old_ij, double old_jj, int i, int j); + // Rank-1 Cholesky update after accepting an off-diagonal precision change. + void cholesky_update_after_precision_edge(double old_ij, double old_jj, int i, int j); - // Rank-1 Cholesky update after accepting a diagonal Kyy change. - void cholesky_update_after_kyy_diag(double old_ii, int i); + // Rank-1 Cholesky update after accepting a diagonal precision change. + void cholesky_update_after_precision_diag(double old_ii, int i); // --- Parameter update sweeps --- - /** Update one main-effect: mux_(s, c). Ordinal threshold or BC α/β. */ + /** Update one main-effect: main_effects_discrete_(s, c). Ordinal threshold or BC α/β. */ void update_main_effect(int s, int c, int iteration); - /** Update one continuous mean: muy_(j). */ + /** Update one continuous mean: main_effects_continuous_(j). */ void update_continuous_mean(int j, int iteration); - /** Update one discrete interaction: Kxx_(i, j). Symmetric. */ - void update_Kxx(int i, int j, int iteration); + /** Update one discrete interaction: pairwise_effects_discrete_(i, j). Symmetric. */ + void update_pairwise_discrete(int i, int j, int iteration); - /** Update one off-diagonal precision element: Kyy_(i, j). Cholesky-based. */ - void update_Kyy_offdiag(int i, int j, int iteration); + /** Update one off-diagonal precision element. Cholesky-based. */ + void update_pairwise_effects_continuous_offdiag(int i, int j, int iteration); - /** Update one diagonal precision element: Kyy_(i, i). Log-scale Cholesky. */ - void update_Kyy_diag(int i, int iteration); + /** Update one diagonal precision element. Log-scale Cholesky. */ + void update_pairwise_effects_continuous_diag(int i, int iteration); - /** Update one cross interaction: Kxy_(i, j). */ - void update_Kxy(int i, int j, int iteration); + /** Update one cross interaction: pairwise_effects_cross_(i, j). */ + void update_pairwise_cross(int i, int j, int iteration); // --- Edge-indicator update sweeps (Phase D) --- - /** Reversible-jump birth/death for one Kxx edge (discrete-discrete). */ - void update_edge_indicator_Kxx(int i, int j); + /** Reversible-jump birth/death for one discrete-discrete edge. */ + void update_edge_indicator_discrete(int i, int j); - /** Reversible-jump birth/death for one Kyy edge (continuous-continuous). */ - void update_edge_indicator_Kyy(int i, int j); + /** Reversible-jump birth/death for one continuous-continuous edge. */ + void update_edge_indicator_continuous(int i, int j); - /** Reversible-jump birth/death for one Kxy edge (cross-type). */ - void update_edge_indicator_Kxy(int i, int j); + /** Reversible-jump birth/death for one cross-type edge. */ + void update_edge_indicator_cross(int i, int j); // ========================================================================= // Edge-indicator accessor helpers From d58848b23e00f71fa46b1481c4106bc03c05a46a Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Mon, 9 Mar 2026 13:04:50 +0100 Subject: [PATCH 32/60] refactor: code deduplication + move cholupdate to src/math (audit K, part 2) --- R/build_output.R | 406 ++++++++++++------------ R/run_sampler.R | 53 ++-- R/simulate_predict.R | 29 +- src/{models/ggm => math}/cholupdate.cpp | 2 +- src/{models/ggm => math}/cholupdate.h | 0 src/models/ggm/ggm_model.cpp | 2 +- src/models/mixed/mixed_mrf_model.h | 2 +- 7 files changed, 249 insertions(+), 245 deletions(-) rename src/{models/ggm => math}/cholupdate.cpp (99%) rename src/{models/ggm => math}/cholupdate.h (100%) diff --git a/R/build_output.R b/R/build_output.R index f8190e0b..39fd4356 100644 --- a/R/build_output.R +++ b/R/build_output.R @@ -11,6 +11,189 @@ # ============================================================================== +# ------------------------------------------------------------------ +# fill_mixed_symmetric +# ------------------------------------------------------------------ +# Fills a symmetric (p+q)×(p+q) matrix from a flat vector of edge +# values stored in discrete-discrete / continuous-continuous / cross +# block order. Used for both pairwise means and indicator means in +# the mixed MRF output builder. +# +# @param values Flat numeric vector of edge values. +# @param p Number of discrete variables. +# @param q Number of continuous variables. +# @param disc_idx Integer vector mapping discrete 1:p to original columns. +# @param cont_idx Integer vector mapping continuous 1:q to original columns. +# @param dimnames List of row/colnames for the result matrix. +# +# Returns: Symmetric matrix with values placed in original-column order. +# ------------------------------------------------------------------ +fill_mixed_symmetric = function(values, p, q, disc_idx, cont_idx, dimnames) { + n = length(dimnames[[1]]) + mat = matrix(0, nrow = n, ncol = n, dimnames = dimnames) + idx = 0L + + # Discrete-discrete block (upper triangle) + if(p > 1) { + for(i in seq_len(p - 1)) { + for(j in seq(i + 1, p)) { + idx = idx + 1L + oi = disc_idx[i]; oj = disc_idx[j] + mat[oi, oj] = values[idx] + mat[oj, oi] = values[idx] + } + } + } + + # Continuous-continuous block (upper triangle) + if(q > 1) { + for(i in seq_len(q - 1)) { + for(j in seq(i + 1, q)) { + idx = idx + 1L + oi = cont_idx[i]; oj = cont_idx[j] + mat[oi, oj] = values[idx] + mat[oj, oi] = values[idx] + } + } + } + + # Cross block (all p × q pairs) + if(p > 0 && q > 0) { + for(i in seq_len(p)) { + for(j in seq_len(q)) { + idx = idx + 1L + oi = disc_idx[i]; oj = cont_idx[j] + mat[oi, oj] = values[idx] + mat[oj, oi] = values[idx] + } + } + } + + mat +} + + +# ------------------------------------------------------------------ +# compute_mixed_parameter_indices +# ------------------------------------------------------------------ +# Computes slice indices for the mixed MRF flat parameter vector. +# Separates main-effect indices (discrete thresholds, continuous +# means, precision diagonal) from pairwise indices (discrete edges, +# precision off-diagonal, cross edges). +# +# @param num_thresholds Total number of discrete threshold parameters. +# @param p Number of discrete variables. +# @param q Number of continuous variables. +# +# Returns: List with components num_thresholds, main_idx, pairwise_idx. +# ------------------------------------------------------------------ +compute_mixed_parameter_indices = function(num_thresholds, p, q) { + nt = num_thresholds + nxx = as.integer(p * (p - 1) / 2) + nyy_total = as.integer(q * (q + 1) / 2) + nyy_offdiag = as.integer(q * (q - 1) / 2) + nxy = as.integer(p * q) + + # Offsets in the flat vector (1-based) + main_discrete_start = 1L + main_discrete_end = nt + pairwise_discrete_start = nt + 1L + pairwise_discrete_end = nt + nxx + main_continuous_start = nt + nxx + 1L + main_continuous_end = nt + nxx + q + pairwise_cross_start = nt + nxx + q + 1L + pairwise_cross_end = nt + nxx + q + nxy + pairwise_continuous_start = nt + nxx + q + nxy + 1L + + # Precision diagonal vs off-diagonal within the continuous block + precision_diag_within = integer(q) + precision_offdiag_within = integer(nyy_offdiag) + k_diag = 0L + k_off = 0L + pos = 0L + for(i in seq_len(q)) { + for(j in i:q) { + pos = pos + 1L + if(i == j) { + k_diag = k_diag + 1L + precision_diag_within[k_diag] = pos + } else { + k_off = k_off + 1L + precision_offdiag_within[k_off] = pos + } + } + } + precision_diag_abs = pairwise_continuous_start - 1L + precision_diag_within + precision_offdiag_abs = pairwise_continuous_start - 1L + precision_offdiag_within + + # Main: discrete thresholds + continuous means + precision diagonal + main_idx = c( + seq(main_discrete_start, main_discrete_end), + seq(main_continuous_start, main_continuous_end), + precision_diag_abs + ) + + # Pairwise: discrete + precision off-diagonal + cross + pairwise_idx = c( + if(nxx > 0) seq(pairwise_discrete_start, pairwise_discrete_end) else integer(0), + precision_offdiag_abs, + if(nxy > 0) seq(pairwise_cross_start, pairwise_cross_end) else integer(0) + ) + + list( + num_thresholds = nt, + main_idx = main_idx, + pairwise_idx = pairwise_idx + ) +} + + +# ------------------------------------------------------------------ +# build_raw_samples_list +# ------------------------------------------------------------------ +# Assembles the $raw_samples list shared by all output builders. +# +# @param raw Per-chain list (normalized). +# @param edge_selection Logical. +# @param edge_prior Character string naming the edge prior. +# @param names_main Character vector of main-effect parameter names. +# @param edge_names Character vector of edge parameter names. +# @param allocation_names Optional character vector; when non-NULL, added +# to $parameter_names$allocations. +# +# Returns: List with main, pairwise, indicator, allocations, nchains, +# niter, parameter_names. +# ------------------------------------------------------------------ +build_raw_samples_list = function(raw, edge_selection, edge_prior, + names_main, edge_names, + allocation_names = NULL) { + list( + main = lapply(raw, function(chain) chain$main_samples), + pairwise = lapply(raw, function(chain) chain$pairwise_samples), + indicator = if(edge_selection) { + lapply(raw, function(chain) chain$indicator_samples) + } else { + NULL + }, + allocations = if(edge_selection && + identical(edge_prior, "Stochastic-Block") && + "allocations" %in% names(raw[[1]])) { + lapply(raw, `[[`, "allocations") + } else { + NULL + }, + nchains = length(raw), + niter = nrow(raw[[1]]$main_samples), + parameter_names = list( + main = names_main, + pairwise = edge_names, + indicator = if(edge_selection) edge_names else NULL, + allocations = allocation_names + ) + ) +} + + # ============================================================================== # build_output() — dispatcher # ============================================================================== @@ -181,6 +364,7 @@ build_output_bgm = function(spec, raw) { node_names = data_columnnames ) results$posterior_summary_pairwise_allocations = sbm_convergence$sbm_summary + co_occur_matrix = sbm_convergence$co_occur_matrix } } @@ -240,11 +424,7 @@ build_output_bgm = function(spec, raw) { t(results$posterior_mean_indicator) if(has_sbm) { - sbm_convergence2 = summarize_alloc_pairs( - allocations = lapply(raw, `[[`, "allocations"), - node_names = data_columnnames - ) - results$posterior_mean_coclustering_matrix = sbm_convergence2$co_occur_matrix + results$posterior_mean_coclustering_matrix = co_occur_matrix arguments = build_arguments(spec) sbm_summary = posterior_summary_SBM( @@ -262,33 +442,13 @@ build_output_bgm = function(spec, raw) { class(results) = "bgms" # --- raw_samples ------------------------------------------------------------ - results$raw_samples = list( - main = lapply(raw, function(chain) chain$main_samples), - pairwise = lapply(raw, function(chain) chain$pairwise_samples), - indicator = if(edge_selection) { - lapply(raw, function(chain) chain$indicator_samples) - } else { - NULL - }, - allocations = if(edge_selection && - identical(edge_prior, "Stochastic-Block") && - "allocations" %in% names(raw[[1]])) { - lapply(raw, `[[`, "allocations") - } else { - NULL - }, - nchains = length(raw), - niter = nrow(raw[[1]]$main_samples), - parameter_names = list( - main = names_main, - pairwise = edge_names, - indicator = if(edge_selection) edge_names else NULL, - allocations = if(identical(edge_prior, "Stochastic-Block")) { - if(is_continuous) data_columnnames else edge_names - } else { - NULL - } - ) + alloc_names = if(identical(edge_prior, "Stochastic-Block")) { + if(is_continuous) data_columnnames else edge_names + } else { + NULL + } + results$raw_samples = build_raw_samples_list( + raw, edge_selection, edge_prior, names_main, edge_names, alloc_names ) # --- easybgm compat shim (OMRF only) --------------------------------------- @@ -353,58 +513,14 @@ build_output_mixed_mrf = function(spec, raw) { edge_selection = pr$edge_selection # --- Compute index layout in flat parameter vector -------------------------- - nt = spec$precomputed$num_thresholds - nxx = as.integer(p * (p - 1) / 2) - nyy_total = as.integer(q * (q + 1) / 2) - nyy_offdiag = as.integer(q * (q - 1) / 2) - nxy = as.integer(p * q) - - # Offsets in the flat vector (1-based) - main_discrete_start = 1L - main_discrete_end = nt - pairwise_discrete_start = nt + 1L - pairwise_discrete_end = nt + nxx - main_continuous_start = nt + nxx + 1L - main_continuous_end = nt + nxx + q - pairwise_cross_start = nt + nxx + q + 1L - pairwise_cross_end = nt + nxx + q + nxy - pairwise_continuous_start = nt + nxx + q + nxy + 1L - pairwise_continuous_end = nt + nxx + q + nxy + nyy_total - - # Precision diagonal indices within the pairwise_continuous block - precision_diag_within = integer(q) - precision_offdiag_within = integer(nyy_offdiag) - k_diag = 0L - k_off = 0L - pos = 0L - for(i in seq_len(q)) { - for(j in i:q) { - pos = pos + 1L - if(i == j) { - k_diag = k_diag + 1L - precision_diag_within[k_diag] = pos - } else { - k_off = k_off + 1L - precision_offdiag_within[k_off] = pos - } - } - } - precision_diag_abs = pairwise_continuous_start - 1L + precision_diag_within - precision_offdiag_abs = pairwise_continuous_start - 1L + precision_offdiag_within - - # Main indices: discrete thresholds + continuous means + precision diagonal - main_idx = c( - seq(main_discrete_start, main_discrete_end), - seq(main_continuous_start, main_continuous_end), - precision_diag_abs - ) - - # Pairwise indices: discrete + precision off-diag + cross - pairwise_idx = c( - if(nxx > 0) seq(pairwise_discrete_start, pairwise_discrete_end) else integer(0), - precision_offdiag_abs, - if(nxy > 0) seq(pairwise_cross_start, pairwise_cross_end) else integer(0) + layout = compute_mixed_parameter_indices( + num_thresholds = spec$precomputed$num_thresholds, + p = p, + q = q ) + nt = layout$num_thresholds + main_idx = layout$main_idx + pairwise_idx = layout$pairwise_idx # --- Indicator index layout ------------------------------------------------- # C++ indicator vector: [Gxx_ut | Gyy_ut | Gxy] @@ -524,6 +640,7 @@ build_output_mixed_mrf = function(spec, raw) { node_names = all_internal_names ) results$posterior_summary_pairwise_allocations = sbm_convergence$sbm_summary + co_occur_matrix = sbm_convergence$co_occur_matrix } } @@ -563,106 +680,19 @@ build_output_mixed_mrf = function(spec, raw) { ) # --- Posterior mean: pairwise as (p+q) × (p+q) matrix ----------------------- - # Map from internal block indices to original column positions - pmat = matrix(0, - nrow = num_variables, ncol = num_variables, - dimnames = list(data_columnnames, data_columnnames) + dn = list(data_columnnames, data_columnnames) + results$posterior_mean_pairwise = fill_mixed_symmetric( + pairwise_summary$mean, p, q, disc_idx, cont_idx, dn ) - pw_means = pairwise_summary$mean - idx = 0L - - # Discrete-discrete block - if(p > 1) { - for(i in seq_len(p - 1)) { - for(j in seq(i + 1, p)) { - idx = idx + 1L - oi = disc_idx[i] - oj = disc_idx[j] - pmat[oi, oj] = pw_means[idx] - pmat[oj, oi] = pw_means[idx] - } - } - } - - # Continuous-continuous off-diagonal block - if(q > 1) { - for(i in seq_len(q - 1)) { - for(j in seq(i + 1, q)) { - idx = idx + 1L - oi = cont_idx[i] - oj = cont_idx[j] - pmat[oi, oj] = pw_means[idx] - pmat[oj, oi] = pw_means[idx] - } - } - } - - # Cross block - if(p > 0 && q > 0) { - for(i in seq_len(p)) { - for(j in seq_len(q)) { - idx = idx + 1L - oi = disc_idx[i] - oj = cont_idx[j] - pmat[oi, oj] = pw_means[idx] - pmat[oj, oi] = pw_means[idx] - } - } - } - - results$posterior_mean_pairwise = pmat - # --- Posterior mean: indicator ----------------------------------------------- if(edge_selection) { - ind_means = indicator_summary$mean - imat = matrix(0, - nrow = num_variables, ncol = num_variables, - dimnames = list(data_columnnames, data_columnnames) + results$posterior_mean_indicator = fill_mixed_symmetric( + indicator_summary$mean, p, q, disc_idx, cont_idx, dn ) - idx = 0L - if(p > 1) { - for(i in seq_len(p - 1)) { - for(j in seq(i + 1, p)) { - idx = idx + 1L - oi = disc_idx[i] - oj = disc_idx[j] - imat[oi, oj] = ind_means[idx] - imat[oj, oi] = ind_means[idx] - } - } - } - if(q > 1) { - for(i in seq_len(q - 1)) { - for(j in seq(i + 1, q)) { - idx = idx + 1L - oi = cont_idx[i] - oj = cont_idx[j] - imat[oi, oj] = ind_means[idx] - imat[oj, oi] = ind_means[idx] - } - } - } - if(p > 0 && q > 0) { - for(i in seq_len(p)) { - for(j in seq_len(q)) { - idx = idx + 1L - oi = disc_idx[i] - oj = cont_idx[j] - imat[oi, oj] = ind_means[idx] - imat[oj, oi] = ind_means[idx] - } - } - } - results$posterior_mean_indicator = imat - if(has_sbm) { - sbm_convergence2 = summarize_alloc_pairs( - allocations = lapply(raw, `[[`, "allocations"), - node_names = all_internal_names - ) - results$posterior_mean_coclustering_matrix = sbm_convergence2$co_occur_matrix + results$posterior_mean_coclustering_matrix = co_occur_matrix arguments = build_arguments(spec) sbm_summary = posterior_summary_SBM( @@ -680,28 +710,8 @@ build_output_mixed_mrf = function(spec, raw) { class(results) = "bgms" # --- raw_samples ------------------------------------------------------------ - results$raw_samples = list( - main = lapply(raw, function(chain) chain$main_samples), - pairwise = lapply(raw, function(chain) chain$pairwise_samples), - indicator = if(edge_selection) { - lapply(raw, function(chain) chain$indicator_samples) - } else { - NULL - }, - allocations = if(edge_selection && - identical(edge_prior, "Stochastic-Block") && - "allocations" %in% names(raw[[1]])) { - lapply(raw, `[[`, "allocations") - } else { - NULL - }, - nchains = length(raw), - niter = nrow(raw[[1]]$main_samples), - parameter_names = list( - main = names_main, - pairwise = edge_names, - indicator = if(edge_selection) edge_names else NULL - ) + results$raw_samples = build_raw_samples_list( + raw, edge_selection, edge_prior, names_main, edge_names ) # --- NUTS diagnostics ------------------------------------------------------- diff --git a/R/run_sampler.R b/R/run_sampler.R index 6ca7fb0e..37462b6b 100644 --- a/R/run_sampler.R +++ b/R/run_sampler.R @@ -7,6 +7,20 @@ # ============================================================================== +# ------------------------------------------------------------------ +# bb_between_default +# ------------------------------------------------------------------ +# Maps NULL to -1.0 (C++ sentinel for "no between-cluster prior"). +# +# @param value Scalar or NULL from the prior spec. +# +# Returns: value unchanged, or -1.0 when NULL. +# ------------------------------------------------------------------ +bb_between_default = function(value) { + if(is.null(value)) -1.0 else value +} + + # ============================================================================== # run_sampler() — main dispatcher # ============================================================================== @@ -41,17 +55,8 @@ run_sampler_ggm = function(spec) { s = spec$sampler m = spec$missing - # C++ expects -1 for "no between-cluster prior" - bb_alpha_between = if(is.null(p$beta_bernoulli_alpha_between)) { - -1.0 - } else { - p$beta_bernoulli_alpha_between - } - bb_beta_between = if(is.null(p$beta_bernoulli_beta_between)) { - -1.0 - } else { - p$beta_bernoulli_beta_between - } + bb_alpha_between = bb_between_default(p$beta_bernoulli_alpha_between) + bb_beta_between = bb_between_default(p$beta_bernoulli_beta_between) out_raw = sample_ggm( inputFromR = list(X = d$x), @@ -92,17 +97,8 @@ run_sampler_omrf = function(spec) { p = spec$prior s = spec$sampler - # C++ expects -1 for "no between-cluster prior" - bb_alpha_between = if(is.null(p$beta_bernoulli_alpha_between)) { - -1.0 - } else { - p$beta_bernoulli_alpha_between - } - bb_beta_between = if(is.null(p$beta_bernoulli_beta_between)) { - -1.0 - } else { - p$beta_bernoulli_beta_between - } + bb_alpha_between = bb_between_default(p$beta_bernoulli_alpha_between) + bb_beta_between = bb_between_default(p$beta_bernoulli_beta_between) input_list = list( observations = d$x, @@ -158,17 +154,8 @@ run_sampler_mixed_mrf = function(spec) { p = spec$prior s = spec$sampler - # C++ expects -1 for "no between-cluster prior" - bb_alpha_between = if(is.null(p$beta_bernoulli_alpha_between)) { - -1.0 - } else { - p$beta_bernoulli_alpha_between - } - bb_beta_between = if(is.null(p$beta_bernoulli_beta_between)) { - -1.0 - } else { - p$beta_bernoulli_beta_between - } + bb_alpha_between = bb_between_default(p$beta_bernoulli_alpha_between) + bb_beta_between = bb_between_default(p$beta_bernoulli_beta_between) input_list = list( discrete_observations = d$x_discrete, diff --git a/R/simulate_predict.R b/R/simulate_predict.R index 5a3d1851..f82101bc 100644 --- a/R/simulate_predict.R +++ b/R/simulate_predict.R @@ -9,6 +9,22 @@ # ============================================================================== +# ------------------------------------------------------------------ +# expand_variable_type +# ------------------------------------------------------------------ +# Recycles a scalar variable_type to length num_variables. +# +# @param variable_type Character vector (possibly length 1). +# @param num_variables Target length. +# +# Returns: Character vector of length num_variables. +# ------------------------------------------------------------------ +expand_variable_type = function(variable_type, num_variables) { + if(length(variable_type) == 1) rep(variable_type, num_variables) + else variable_type +} + + #' Simulate Observations from a Markov Random Field #' #' @description @@ -640,10 +656,7 @@ simulate.bgms = function(object, data_columnnames = arguments$data_columnnames # Handle variable_type - - if(length(variable_type) == 1) { - variable_type = rep(variable_type, num_variables) - } + variable_type = expand_variable_type(variable_type, num_variables) # Get baseline_category (for Blume-Capel variables) baseline_category = arguments$baseline_category @@ -1069,9 +1082,7 @@ predict.bgms = function(object, } # Handle variable_type - if(length(variable_type) == 1) { - variable_type = rep(variable_type, num_variables) - } + variable_type = expand_variable_type(variable_type, num_variables) # Get baseline_category baseline_category = arguments$baseline_category @@ -1493,10 +1504,6 @@ predict.bgmCompare = function(object, reconstruct_main = function(main_vec, num_variables, num_categories, variable_type) { - if(length(variable_type) == 1) { - variable_type = rep(variable_type, num_variables) - } - max_cats = max(num_categories) main = matrix(NA, nrow = num_variables, ncol = max_cats) diff --git a/src/models/ggm/cholupdate.cpp b/src/math/cholupdate.cpp similarity index 99% rename from src/models/ggm/cholupdate.cpp rename to src/math/cholupdate.cpp index 81331be9..3d3776b9 100644 --- a/src/models/ggm/cholupdate.cpp +++ b/src/math/cholupdate.cpp @@ -1,4 +1,4 @@ -#include "models/ggm/cholupdate.h" +#include "math/cholupdate.h" extern "C" { diff --git a/src/models/ggm/cholupdate.h b/src/math/cholupdate.h similarity index 100% rename from src/models/ggm/cholupdate.h rename to src/math/cholupdate.h diff --git a/src/models/ggm/ggm_model.cpp b/src/models/ggm/ggm_model.cpp index 7c200c3c..8dea6782 100644 --- a/src/models/ggm/ggm_model.cpp +++ b/src/models/ggm/ggm_model.cpp @@ -1,7 +1,7 @@ #include "models/ggm/ggm_model.h" #include "rng/rng_utils.h" #include "math/explog_macros.h" -#include "models/ggm/cholupdate.h" +#include "math/cholupdate.h" #include "mcmc/execution/step_result.h" #include "mcmc/execution/warmup_schedule.h" diff --git a/src/models/mixed/mixed_mrf_model.h b/src/models/mixed/mixed_mrf_model.h index 8bbb1a43..c601e166 100644 --- a/src/models/mixed/mixed_mrf_model.h +++ b/src/models/mixed/mixed_mrf_model.h @@ -4,7 +4,7 @@ #include #include "models/base_model.h" #include "math/cholesky_helpers.h" -#include "models/ggm/cholupdate.h" +#include "math/cholupdate.h" #include "rng/rng_utils.h" /** From b944cd4aa168e5f8a0599fecb4991d3ea719376b Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Mon, 9 Mar 2026 16:24:53 +0100 Subject: [PATCH 33/60] fix: document overflow-guard bitwise break, regenerate HMC fixtures, restore progress interval --- dev/bitwise_compliance/test_compliance.R | 25 +++++++++++++++--- .../compliance/bgm_boredom_hmc_bernoulli.rds | Bin 4250 -> 3199 bytes .../compliance/cmp_wenchuan_hmc_bernoulli.rds | Bin 10326 -> 10616 bytes src/sample_mixed.cpp | 2 +- 4 files changed, 22 insertions(+), 5 deletions(-) diff --git a/dev/bitwise_compliance/test_compliance.R b/dev/bitwise_compliance/test_compliance.R index 6a552521..71cc71a3 100644 --- a/dev/bitwise_compliance/test_compliance.R +++ b/dev/bitwise_compliance/test_compliance.R @@ -70,6 +70,21 @@ # mismatch was a regression introduced and fixed within PR #78; it was # never in a CRAN release. # +# 7. Intermediate-overflow guard in compute_logZ_and_probs_ordinal and +# compute_logZ_and_probs_blume_capel (ACCEPTED — not a bug): +# Commit 04b9562 tightened the fast/slow block threshold from EXP_BOUND +# (709) to FAST_LIM = max(0, EXP_BOUND - max_abs_main) to prevent +# intermediate overflow in exp(main_param(c) + (c+1)*rest) before the +# cancellation with exp(-bound). Both code paths are mathematically +# identical but differ at floating-point level. During HMC leapfrog +# integration, parameters can temporarily reach extreme values where +# max_abs_main is large enough that FAST_LIM < EXP_BOUND, reclassifying +# some observations between the fast (vectorized) and slow (per-element) +# paths. The resulting floating-point perturbation cascades through the +# fixed-step leapfrog integrator. This is needed for mixed MRF models +# where Theta_ss is absorbed into main_param. The affected configs use +# structure-only comparison against CRAN fixtures. +# # ============================================================================== library(bgms) @@ -425,11 +440,13 @@ na_bugfix_ids = c( ) # Configs excluded from bitwise comparison due to confirmed algorithm changes -# (see header note 5). Checked for structural match only. +# (see header notes 5 and 7). Checked for structural match only. structure_only_ids = c( - "bgm_wenchuan_nuts_blumecapel_impute", # Blume-Capel imputation bug fix - "bgm_wenchuan_nuts_sbm", # SBM lazy init changes RNG order (not a bug) - "bgm_adhd_nuts_sbm" # SBM lazy init changes RNG order (not a bug) + "bgm_wenchuan_nuts_blumecapel_impute", # Blume-Capel imputation bug fix (note 5c) + "bgm_wenchuan_nuts_sbm", # SBM lazy init changes RNG order (note 4) + "bgm_adhd_nuts_sbm", # SBM lazy init changes RNG order (note 4) + "bgm_boredom_hmc_bernoulli", # overflow guard reclassifies fast/slow (note 7) + "cmp_wenchuan_hmc_bernoulli" # overflow guard reclassifies fast/slow (note 7) ) compare_fields = function(expected, actual, type, id) { diff --git a/dev/fixtures/compliance/bgm_boredom_hmc_bernoulli.rds b/dev/fixtures/compliance/bgm_boredom_hmc_bernoulli.rds index 4b21b5702fab8ab46e3c287147d1bad8042b033d..bddebbc320c43d0c1fe7aa7e3daf8144cb54b6bd 100644 GIT binary patch literal 3199 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34/60] =?UTF-8?q?docs:=20R=20output=20audit=20=E2=80=94=20?= =?UTF-8?q?extract=5Fmain=5Feffects,=20GGM=20quadratic=20fix,=20print/coef?= =?UTF-8?q?/summary=20(audit=20L)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- NAMESPACE | 5 +- R/bgm.R | 13 +- R/bgm_spec.R | 12 +- R/bgms-methods.R | 37 +- R/build_output.R | 9 +- R/extractor_functions.R | 129 +++-- dev/ggm_g_prior.md | 41 ++ dev/ggm_nuts/blockwise_hmc.md | 25 + .../chordal_completion_augmentation.md | 34 ++ dev/ggm_nuts/elliptical_slice_surrogates.md | 24 + .../partial_correlation_reparameterization.md | 28 ++ dev/ggm_nuts/projected_nuts.md | 28 ++ dev/ggm_nuts/riemannian_gwishart_hmc.md | 24 + dev/mixedmrf/deduplication_audit.md | 459 ++++++++++++++++++ dev/mixedmrf/implementation_plan.md | 33 +- dev/mixedmrf/output_inspection.md | 360 ++++++++++++++ dev/test_step_size.R | 35 ++ dev/tests/compare_bgms_mixedgm_cycle.R | 213 ++++++++ dev/tests/cycle_scatterplots.pdf | Bin 0 -> 13023 bytes dev/tests/debug_main_effects.R | 74 +++ .../easybgm/test_easybgm_bgms_interaction.R | 0 dev/tests/plot_cycle_scatterplots.R | 139 ++++++ .../validation/group1_parameter_recovery.R | 189 ++++++++ dev/tests/validation/group2_mh_vs_nuts.R | 239 +++++++++ dev/tests/validation/group3_cond_vs_marg.R | 219 +++++++++ dev/tests/validation/group4_cross_package.R | 229 +++++++++ dev/tests/validation/group5_edge_detection.R | 323 ++++++++++++ dev/tests/validation/group6_diagnostics.R | 285 +++++++++++ dev/tests/validation/group7_empirical.R | 287 +++++++++++ dev/tests/validation/group9_breakdown.R | 174 +++++++ dev/tests/validation/group9_profiling.R | 323 ++++++++++++ dev/tests/validation/helpers.R | 405 ++++++++++++++++ .../output/group1_parameter_recovery.pdf | Bin 0 -> 20189 bytes .../validation/output/group2_mh_vs_nuts.pdf | Bin 0 -> 26158 bytes dev/tests/validation/output/group2_traces.pdf | Bin 0 -> 624248 bytes .../validation/output/group3_cond_vs_marg.pdf | Bin 0 -> 22709 bytes .../output/group3_diff_distributions.pdf | Bin 0 -> 6353 bytes .../output/group4_cross_package.pdf | Bin 0 -> 19697 bytes .../output/group5_edge_detection.pdf | Bin 0 -> 10875 bytes .../output/group6_autocorrelation.pdf | Bin 0 -> 12977 bytes .../validation/output/group6_diagnostics.pdf | Bin 0 -> 522914 bytes .../validation/output/group7_empirical.pdf | Bin 0 -> 13038 bytes .../validation/output/group9_profiling.pdf | Bin 0 -> 7965 bytes dev/tests/validation/run_all.R | 121 +++++ man/bgm.Rd | 13 +- man/coef.bgms.Rd | 9 +- man/extract_arguments.Rd | 1 + man/extract_category_thresholds.Rd | 20 +- man/extract_ess.Rd | 1 + man/extract_group_params.Rd | 3 +- man/extract_indicator_priors.Rd | 1 + man/extract_indicators.Rd | 1 + man/extract_main_effects.Rd | 66 +++ man/extract_pairwise_interactions.Rd | 3 +- man/extract_pairwise_thresholds.Rd | 4 +- ...tract_posterior_inclusion_probabilities.Rd | 1 + man/extract_rhat.Rd | 1 + man/extract_sbm.Rd | 1 + paper/paper.bib | 105 ++++ paper/paper.md | 143 ++++++ tests/testthat/test-build-arguments.R | 9 +- tests/testthat/test-extractor-functions.R | 40 +- 62 files changed, 4857 insertions(+), 81 deletions(-) create mode 100644 dev/ggm_g_prior.md create mode 100644 dev/ggm_nuts/blockwise_hmc.md create mode 100644 dev/ggm_nuts/chordal_completion_augmentation.md create mode 100644 dev/ggm_nuts/elliptical_slice_surrogates.md create mode 100644 dev/ggm_nuts/partial_correlation_reparameterization.md create mode 100644 dev/ggm_nuts/projected_nuts.md create mode 100644 dev/ggm_nuts/riemannian_gwishart_hmc.md create mode 100644 dev/mixedmrf/deduplication_audit.md create mode 100644 dev/mixedmrf/output_inspection.md create mode 100644 dev/test_step_size.R create mode 100644 dev/tests/compare_bgms_mixedgm_cycle.R create mode 100644 dev/tests/cycle_scatterplots.pdf create mode 100644 dev/tests/debug_main_effects.R create mode 100644 dev/tests/easybgm/test_easybgm_bgms_interaction.R create mode 100644 dev/tests/plot_cycle_scatterplots.R create mode 100644 dev/tests/validation/group1_parameter_recovery.R create mode 100644 dev/tests/validation/group2_mh_vs_nuts.R create mode 100644 dev/tests/validation/group3_cond_vs_marg.R create mode 100644 dev/tests/validation/group4_cross_package.R create mode 100644 dev/tests/validation/group5_edge_detection.R create mode 100644 dev/tests/validation/group6_diagnostics.R create mode 100644 dev/tests/validation/group7_empirical.R create mode 100644 dev/tests/validation/group9_breakdown.R create mode 100644 dev/tests/validation/group9_profiling.R create mode 100644 dev/tests/validation/helpers.R create mode 100644 dev/tests/validation/output/group1_parameter_recovery.pdf create mode 100644 dev/tests/validation/output/group2_mh_vs_nuts.pdf create mode 100644 dev/tests/validation/output/group2_traces.pdf create mode 100644 dev/tests/validation/output/group3_cond_vs_marg.pdf create mode 100644 dev/tests/validation/output/group3_diff_distributions.pdf create mode 100644 dev/tests/validation/output/group4_cross_package.pdf create mode 100644 dev/tests/validation/output/group5_edge_detection.pdf create mode 100644 dev/tests/validation/output/group6_autocorrelation.pdf create mode 100644 dev/tests/validation/output/group6_diagnostics.pdf create mode 100644 dev/tests/validation/output/group7_empirical.pdf create mode 100644 dev/tests/validation/output/group9_profiling.pdf create mode 100644 dev/tests/validation/run_all.R create mode 100644 man/extract_main_effects.Rd create mode 100644 paper/paper.bib create mode 100644 paper/paper.md diff --git a/NAMESPACE b/NAMESPACE index aa26428e..bc34e829 100644 --- a/NAMESPACE +++ b/NAMESPACE @@ -4,8 +4,6 @@ S3method(coef,bgmCompare) S3method(coef,bgms) S3method(extract_arguments,bgmCompare) S3method(extract_arguments,bgms) -S3method(extract_category_thresholds,bgmCompare) -S3method(extract_category_thresholds,bgms) S3method(extract_ess,bgmCompare) S3method(extract_ess,bgms) S3method(extract_group_params,bgmCompare) @@ -13,6 +11,8 @@ S3method(extract_indicator_priors,bgmCompare) S3method(extract_indicator_priors,bgms) S3method(extract_indicators,bgmCompare) S3method(extract_indicators,bgms) +S3method(extract_main_effects,bgmCompare) +S3method(extract_main_effects,bgms) S3method(extract_pairwise_interactions,bgmCompare) S3method(extract_pairwise_interactions,bgms) S3method(extract_posterior_inclusion_probabilities,bgmCompare) @@ -40,6 +40,7 @@ export(extract_ess) export(extract_group_params) export(extract_indicator_priors) export(extract_indicators) +export(extract_main_effects) export(extract_pairwise_interactions) export(extract_pairwise_thresholds) export(extract_posterior_inclusion_probabilities) diff --git a/R/bgm.R b/R/bgm.R index 81323631..61cc2caa 100644 --- a/R/bgm.R +++ b/R/bgm.R @@ -326,14 +326,21 @@ #' Main components include: #' \itemize{ #' \item \code{posterior_summary_main}: Data frame with posterior summaries -#' (mean, sd, MCSE, ESS, Rhat) for category threshold parameters. +#' (mean, sd, MCSE, ESS, Rhat) for main-effect parameters. +#' For OMRF models these are category thresholds; for GGM models +#' these are precision matrix diagonal entries (quadratic effects); +#' for mixed MRF models these include both discrete thresholds and +#' continuous means and precisions. #' \item \code{posterior_summary_pairwise}: Data frame with posterior #' summaries for pairwise interaction parameters. #' \item \code{posterior_summary_indicator}: Data frame with posterior #' summaries for edge inclusion indicators (if \code{edge_selection = TRUE}). #' -#' \item \code{posterior_mean_main}: Matrix of posterior mean thresholds -#' (rows = variables, cols = categories or parameters). +#' \item \code{posterior_mean_main}: Posterior mean of per-variable +#' parameters. For OMRF: a matrix (p x max_categories) of category +#' thresholds. For GGM: a (p x 1) matrix of precision diagonal entries +#' (quadratic effects, not main effects). For mixed MRF: a list with +#' \code{$discrete} and \code{$continuous} components. #' \item \code{posterior_mean_pairwise}: Symmetric matrix of posterior mean #' pairwise interaction strengths. #' \item \code{posterior_mean_indicator}: Symmetric matrix of posterior mean diff --git a/R/bgm_spec.R b/R/bgm_spec.R index 78f29055..fc6f2d28 100644 --- a/R/bgm_spec.R +++ b/R/bgm_spec.R @@ -1130,7 +1130,8 @@ build_arguments_ggm = function(spec) { num_chains = spec$sampler$chains, data_columnnames = spec$data$data_columnnames, no_variables = spec$data$num_variables, - is_continuous = TRUE + is_continuous = TRUE, + model_type = "ggm" ) } @@ -1172,7 +1173,8 @@ build_arguments_omrf = function(spec) { data_columnnames = spec$data$data_columnnames, baseline_category = spec$variables$baseline_category, pairwise_scaling_factors = spec$prior$pairwise_scaling_factors, - no_variables = spec$data$num_variables + no_variables = spec$data$num_variables, + model_type = "omrf" ) } @@ -1215,7 +1217,8 @@ build_arguments_mixed_mrf = function(spec) { baseline_category = spec$variables$baseline_category, is_ordinal = spec$variables$is_ordinal, no_variables = spec$data$num_variables, - is_mixed = TRUE + is_mixed = TRUE, + model_type = "mixed_mrf" ) } @@ -1248,7 +1251,8 @@ build_arguments_compare = function(spec) { num_categories = spec$data$num_categories, is_ordinal_variable = spec$variables$is_ordinal, group = sort(spec$data$group), - pairwise_scaling_factors = spec$prior$pairwise_scaling_factors + pairwise_scaling_factors = spec$prior$pairwise_scaling_factors, + model_type = "compare" ) } diff --git a/R/bgms-methods.R b/R/bgms-methods.R index 3168f4f2..71dda5ca 100644 --- a/R/bgms-methods.R +++ b/R/bgms-methods.R @@ -21,7 +21,7 @@ print.bgms = function(x, ...) { arguments = extract_arguments(x) - # Model type + # Estimation method if(isTRUE(arguments$edge_selection)) { prior_msg = switch(arguments$edge_prior, "Bernoulli" = "Bayesian Edge Selection using a Bernoulli prior on edge inclusion", @@ -34,6 +34,21 @@ print.bgms = function(x, ...) { cat("Bayesian Estimation\n") } + # Model type + mt = arguments$model_type + if(!is.null(mt)) { + mt_label = switch(mt, + ggm = "GGM (Gaussian Graphical Model)", + omrf = "OMRF (Ordinal Markov Random Field)", + mixed_mrf = sprintf( + "Mixed MRF (%d discrete, %d continuous)", + arguments$num_discrete, arguments$num_continuous + ), + mt + ) + cat(paste0(" Model: ", mt_label, "\n")) + } + # Dataset info cat(paste0(" Number of variables: ", arguments$num_variables, "\n")) if(isTRUE(arguments$standardize)) { @@ -84,10 +99,18 @@ summary.bgms = function(object, ...) { arguments = extract_arguments(object) if(!is.null(object$posterior_summary_main) && !is.null(object$posterior_summary_pairwise)) { + mt = arguments$model_type + main_label = switch(mt, + ggm = "Precision diagonal (quadratic effects):", + omrf = "Category thresholds:", + mixed_mrf = "Main effects (discrete thresholds and continuous means/precisions):", + "Main effects:" + ) out = list( main = object$posterior_summary_main, pairwise = object$posterior_summary_pairwise ) + attr(out, "main_label") = main_label if(!is.null(object$posterior_summary_indicator)) { out$indicator = object$posterior_summary_indicator @@ -118,7 +141,8 @@ print.summary.bgms = function(x, digits = 3, ...) { cat("Posterior summaries from Bayesian estimation:\n\n") if(!is.null(x$main)) { - cat("Category thresholds:\n") + main_label = attr(x, "main_label") %||% "Main effects:" + cat(main_label, "\n") print(round(head(x$main, 6), digits = digits)) if(nrow(x$main) > 6) cat("... (use `summary(fit)$main` to see full output)\n") cat("\n") @@ -179,14 +203,19 @@ print.summary.bgms = function(x, digits = 3, ...) { #' @title Extract Coefficients from a bgms Object #' @name coef.bgms -#' @description Returns the posterior mean thresholds, pairwise effects, and edge inclusion indicators from a \code{bgms} model fit. +#' @description Returns the posterior mean main effects, pairwise effects, and edge inclusion indicators from a \code{bgms} model fit. #' #' @param object An object of class \code{bgms}. #' @param ... Ignored. #' #' @return A list with the following components: #' \describe{ -#' \item{main}{Posterior mean of the category threshold parameters.} +#' \item{main}{Posterior mean of the main-effect parameters. For GGM models +#' this contains the precision matrix diagonal (quadratic effects, not main +#' effects). For OMRF models this is a numeric matrix (p x max_categories) +#' of category thresholds. For mixed MRF models this is a list with +#' `$discrete` (p x max_categories matrix) and `$continuous` (q x 2 matrix +#' with columns "mean" and "precision").} #' \item{pairwise}{Posterior mean of the pairwise interaction matrix.} #' \item{indicator}{Posterior mean of the edge inclusion indicators (if available).} #' } diff --git a/R/build_output.R b/R/build_output.R index 39fd4356..f34424f3 100644 --- a/R/build_output.R +++ b/R/build_output.R @@ -464,7 +464,7 @@ build_output_bgm = function(spec, raw) { results$indicator = extract_indicators(results) } results$interactions = extract_pairwise_interactions(results) - results$thresholds = extract_category_thresholds(results) + results$thresholds = extract_main_effects(results) } } @@ -710,8 +710,13 @@ build_output_mixed_mrf = function(spec, raw) { class(results) = "bgms" # --- raw_samples ------------------------------------------------------------ + alloc_names = if(identical(edge_prior, "Stochastic-Block")) { + all_internal_names + } else { + NULL + } results$raw_samples = build_raw_samples_list( - raw, edge_selection, edge_prior, names_main, edge_names + raw, edge_selection, edge_prior, names_main, edge_names, alloc_names ) # --- NUTS diagnostics ------------------------------------------------------- diff --git a/R/extractor_functions.R b/R/extractor_functions.R index 76e9bb14..418a1d7c 100644 --- a/R/extractor_functions.R +++ b/R/extractor_functions.R @@ -407,7 +407,7 @@ extract_indicator_priors.bgmCompare = function(bgms_object) { #' interaction parameters.} #' } #' -#' @seealso [bgm()], [bgmCompare()], [extract_category_thresholds()] +#' @seealso [bgm()], [bgmCompare()], [extract_main_effects()] #' @family extractors #' @export extract_pairwise_interactions = function(bgms_object) { @@ -428,10 +428,17 @@ extract_pairwise_interactions.bgms = function(bgms_object) { mats = bgms_object$raw_samples$pairwise mat = do.call(rbind, mats) - edge_names = character() - for(i in 1:(num_vars - 1)) { - for(j in (i + 1):num_vars) { - edge_names = c(edge_names, paste0(var_names[i], "-", var_names[j])) + # Use stored parameter names when available (correct for all model types + # including mixed MRF where block order differs from upper-triangle order) + stored_names = bgms_object$raw_samples$parameter_names$pairwise + if(!is.null(stored_names)) { + edge_names = stored_names + } else { + edge_names = character() + for(i in 1:(num_vars - 1)) { + for(j in (i + 1):num_vars) { + edge_names = c(edge_names, paste0(var_names[i], "-", var_names[j])) + } } } @@ -492,46 +499,84 @@ extract_pairwise_interactions.bgmCompare = function(bgms_object) { stop("No pairwise interaction samples found in fit object.") } -#' Extract Category Threshold Estimates +#' Extract Main Effect Estimates +#' +#' @title Extract Main Effect Estimates #' #' @description -#' Retrieves category threshold parameters from a model fitted with -#' [bgm()] or [bgmCompare()]. +#' Retrieves posterior mean main-effect parameters from a model fitted with +#' [bgm()] or [bgmCompare()]. For OMRF models these are category thresholds; +#' for mixed MRF models these include both discrete thresholds and continuous +#' means and precisions. GGM models have no main effects (the precision +#' diagonal represents quadratic effects) and return `NULL`. #' #' @param bgms_object A fitted model object of class `bgms` (from [bgm()]) #' or `bgmCompare` (from [bgmCompare()]). #' -#' @return +#' @return The structure depends on the model type: #' \describe{ -#' \item{bgms}{A matrix with one row per variable and one column per -#' category threshold, containing posterior means.} +#' \item{GGM (bgms)}{`NULL` (invisibly). GGM models have no main effects; +#' the precision matrix diagonal contains quadratic effects, accessible +#' via `$posterior_mean_main`.} +#' \item{OMRF (bgms)}{A numeric matrix with one row per variable and one +#' column per category threshold, containing posterior means. Columns +#' beyond the number of categories for a variable are `NA`.} +#' \item{Mixed MRF (bgms)}{A list with two elements: +#' \describe{ +#' \item{discrete}{A numeric matrix (p rows x max_categories columns) +#' of posterior mean thresholds for discrete variables.} +#' \item{continuous}{A numeric matrix (q rows x 2 columns) with +#' columns `"mean"` and `"precision"` for continuous variables.} +#' }} #' \item{bgmCompare}{A matrix with one row per post-warmup iteration, -#' containing posterior samples of baseline threshold parameters.} +#' containing posterior samples of baseline main-effect parameters.} #' } #' -#' @seealso [bgm()], [bgmCompare()], [extract_pairwise_interactions()] +#' @examples +#' \donttest{ +#' fit = bgm(x = Wenchuan[, 1:3]) +#' extract_main_effects(fit) +#' } +#' +#' @seealso [bgm()], [bgmCompare()], [extract_pairwise_interactions()], +#' [extract_category_thresholds()] #' @family extractors #' @export -extract_category_thresholds = function(bgms_object) { - UseMethod("extract_category_thresholds") +extract_main_effects = function(bgms_object) { + UseMethod("extract_main_effects") } -#' @inheritParams extract_category_thresholds +#' @inheritParams extract_main_effects #' @exportS3Method #' @noRd -extract_category_thresholds.bgms = function(bgms_object) { +extract_main_effects.bgms = function(bgms_object) { arguments = extract_arguments(bgms_object) - var_names = arguments$data_columnnames + + # GGM: no main effects (precision diagonal entries are quadratic, not main) + if(isTRUE(arguments$is_continuous)) { + message( + "GGM models have no main effects. The precision matrix diagonal ", + "(quadratic effects) is available via $posterior_mean_main." + ) + return(invisible(NULL)) + } + + # Mixed MRF: return pre-built list from posterior_mean_main + if(isTRUE(arguments$is_mixed)) { + return(bgms_object$posterior_mean_main) + } # Current format (0.1.6.0+) if(!is.null(bgms_object$posterior_summary_main)) { vec = bgms_object$posterior_summary_main[, "mean"] - # Handle legacy field name (no_variables → num_variables in 0.1.6.0) + var_names = arguments$data_columnnames num_vars = arguments$num_variables %||% arguments$no_variables variable_type = arguments$variable_type if(length(variable_type) == 1) { variable_type = rep(variable_type, num_vars) } + + # OMRF: threshold matrix num_cats = arguments$num_categories max_cats = max(num_cats) mat = matrix(NA_real_, nrow = num_vars, ncol = max_cats) @@ -556,8 +601,8 @@ extract_category_thresholds.bgms = function(bgms_object) { "0.1.6.0", I("The '$thresholds' field is deprecated; please refit with bgms >= 0.1.6.0") ) + var_names = arguments$data_columnnames means = colMeans(bgms_object$thresholds) - # For binary variables in 0.1.4.x, there's 1 threshold per variable mat = matrix(means, nrow = length(means), ncol = 1) rownames(mat) = var_names return(mat) @@ -570,10 +615,10 @@ extract_category_thresholds.bgms = function(bgms_object) { ) } -#' @inheritParams extract_category_thresholds +#' @inheritParams extract_main_effects #' @exportS3Method #' @noRd -extract_category_thresholds.bgmCompare = function(bgms_object) { +extract_main_effects.bgmCompare = function(bgms_object) { arguments = extract_arguments(bgms_object) # Current format (0.1.6.0+) @@ -604,11 +649,39 @@ extract_category_thresholds.bgmCompare = function(bgms_object) { "0.1.6.0", I("The '$thresholds_gr*' fields are deprecated; please refit with bgms >= 0.1.6.0") ) - # Combine the two groups' thresholds return(cbind(bgms_object$thresholds_gr1, bgms_object$thresholds_gr2)) } - stop("No category threshold samples found in fit object.") + stop("No main effect samples found in fit object.") +} + + +#' Extract Category Threshold Estimates +#' +#' @title Extract Category Threshold Estimates +#' +#' @description +#' `r lifecycle::badge("deprecated")` +#' +#' `extract_category_thresholds()` was renamed to [extract_main_effects()] to +#' reflect that main effects include continuous means and precisions +#' (mixed MRF), not only category thresholds. +#' +#' @param bgms_object A fitted model object of class `bgms` (from [bgm()]) +#' or `bgmCompare` (from [bgmCompare()]). +#' +#' @return See [extract_main_effects()] for details. +#' +#' @seealso [extract_main_effects()] +#' @family extractors +#' @export +extract_category_thresholds = function(bgms_object) { + lifecycle::deprecate_warn( + "0.2.1.0", + "extract_category_thresholds()", + "extract_main_effects()" + ) + extract_main_effects(bgms_object) } #' Extract Group-Specific Parameters @@ -624,7 +697,7 @@ extract_category_thresholds.bgmCompare = function(bgms_object) { #' group) and `pairwise_effects_groups` (pairwise effects per group). #' #' @seealso [bgmCompare()], [extract_pairwise_interactions()], -#' [extract_category_thresholds()] +#' [extract_main_effects()] #' @family extractors #' @export extract_group_params = function(bgms_object) { @@ -842,13 +915,13 @@ extract_edge_indicators = function(bgms_object) { extract_indicators(bgms_object) } -#' Deprecated: Use extract_category_thresholds instead +#' Deprecated: Use extract_main_effects instead #' @param bgms_object A bgms or bgmCompare object. #' @keywords internal #' @export extract_pairwise_thresholds = function(bgms_object) { - lifecycle::deprecate_warn("0.1.4.2", "extract_pairwise_thresholds()", "extract_category_thresholds()") - extract_category_thresholds(bgms_object) + lifecycle::deprecate_warn("0.1.4.2", "extract_pairwise_thresholds()", "extract_main_effects()") + extract_main_effects(bgms_object) } diff --git a/dev/ggm_g_prior.md b/dev/ggm_g_prior.md new file mode 100644 index 00000000..49f9132c --- /dev/null +++ b/dev/ggm_g_prior.md @@ -0,0 +1,41 @@ +# g-type Slab Prior for bgms Edge Weights + +## Motivation +Manual tuning of the Cauchy slab scale is awkward and can obscure whether discrepancies with comparators stem from modeling or shrinkage choices. In regression-style spike-and-slab models, Zellner's $g$-prior offers automatic scaling by tying coefficients to the design matrix. We can mimic that idea for individual edge parameters $\theta_{ij}$ in the mixed GGM sampler by anchoring the slab variance to local Fisher information, giving consistent units across nodes and datasets. + +## Proposed Formulation +For each candidate edge $(i,j)$ with corresponding conditional regression coefficient $\theta_{ij}$: + +- Use the usual spike mixture: $\theta_{ij} \sim (1-\gamma_{ij}) \delta_0 + \gamma_{ij} \cdot p_{\text{slab}}(\theta_{ij})$ with inclusion indicator $\gamma_{ij} \in \{0,1\}$. +- Replace the free-scale Cauchy slab with a conditionally normal slab whose variance adapts via a $g$ parameter: + $$p_{\text{slab}}(\theta_{ij} \mid g, s_{ij}) = \mathcal N\bigl(0, g \cdot s_{ij}^{-1}\bigr).$$ +- Here $s_{ij}$ approximates the Fisher information for $\theta_{ij}$ when the rest of the graph is fixed. For Gaussian nodes this could be the conditional residual variance; for mixed nodes we can use the negative Hessian of the log-likelihood restricted to $\theta_{ij}$. + +### Hyperprior on $g$ +Adopt a standard Beta-prime / inverse-gamma style: +- $g \sim \text{BetaPrime}(a_g, b_g)$ with density proportional to $g^{a_g-1}(1+g)^{-(a_g+b_g)}$. +- Default choice $a_g = 1$, $b_g = 1$ mirrors the hyper-$g$ prior, favoring moderate shrinkage while leaving heavy tails. +- Alternative: $g/(1+g) \sim \text{Beta}(a_g, b_g)$ to keep $g$ finite with more intuitive tuning. + +## Implementation Sketch +1. **Compute $s_{ij}$ efficiently**: during local edge updates we already build score/Hessian terms. Cache $s_{ij}$ (or its approximation) when evaluating the conditional likelihood. +2. **Augment sampler state**: treat $g$ as either global (shared across edges) or edge-specific $g_{ij}$. Start with a global $g$ for simplicity. +3. **Gibbs/Metropolis steps**: + - Conditional on $g$, sampling $\theta_{ij}$ from the Gaussian slab is straightforward (or use MH if the mixed-node likelihood prevents conjugacy). + - Update $g$ via MH or slice sampling using the Beta-prime prior and the product of Gaussian slabs. +4. **Compatibility with spike indicator**: no change—the inclusion probability update uses the new slab density. + +## Evaluation Plan +- **Full-graph scenario**: compare bgms with Cauchy slabs vs g-type slabs on $p=20$ dense graphs; track RMSE and coverage relative to truth and bggm. +- **Sparse scenario**: measure inclusion ROC/AUC and calibration when using g-type slabs, ensuring sparsity is not overly penalized. +- **Sensitivity**: vary $(a_g, b_g)$ to see if results are robust; consider placing a hyperprior on $s_{ij}$ approximation error if needed. + +## Open Questions +- How stable is $s_{ij}$ for mixed discrete nodes? Need to confirm the Hessian remains positive definite; otherwise use a sandwich estimator. +- Global vs local $g$: does sharing one $g$ across edges over-shrink high-degree nodes? Potential extension with $g_i$ per node. +- Interaction with existing spike penalties: should inclusion priors be adjusted when switching slab families to keep edge probabilities comparable? + +## Next Steps +1. Prototype $s_{ij}$ computation in the continuous-only case to validate scaling. +2. Implement a global-$g$ sampler branch and benchmark on small graphs. +3. Decide whether to expose the g-type slab as a user option or keep it experimental during the performance study. diff --git a/dev/ggm_nuts/blockwise_hmc.md b/dev/ggm_nuts/blockwise_hmc.md new file mode 100644 index 00000000..e1972439 --- /dev/null +++ b/dev/ggm_nuts/blockwise_hmc.md @@ -0,0 +1,25 @@ +# Blockwise / Subspace HMC for Sparse GGMs + +## Concept +Split the precision matrix parameters into clique-based subsets that admit local Cholesky factorizations without fill-ins, then run Hamiltonian proposals (HMC or NUTS) within each subset while conditioning on the rest. The approach mimics block Gibbs but uses gradient-informed trajectories to accelerate mixing inside large cliques. + +## Construction +1. Decompose the graph into cliques and separators (junction tree or block-cut tree). +2. For each clique, identify the free precision entries (including its separator interface). +3. Parameterize each block via a local Cholesky factor (log-diagonals + unconstrained subdiagonal entries). Because the block is chordal by definition, PD is automatic. +4. Run NUTS inside the block, treating separator values from neighboring blocks as fixed during the leapfrog trajectory. +5. Accept/reject per block, then cycle through blocks or randomly scan them. + +## Pros +- Keeps parameter dimensionality close to the true number of edges. +- Exploits sparsity: small cliques reuse cached symbolic factorizations. +- Amenable to parallel updates across disconnected components. + +## Cons +- Requires dynamic bookkeeping of separators when RJ moves add/remove edges. +- Acceptance rates can drop if separators induce strong cross-block dependence. +- Implementation complexity: need reversible Metropolis-within-Gibbs logic and gradient code per block. + +## References +- Green, P. J., & Thomas, A. (2013). *Sampling decomposable graphs using a Markov chain on junction trees.* Biometrika 100(1). +- Mohammadi, A., & Wit, E. C. (2015). *Bayesian structure learning in sparse Gaussian graphical models.* Bayesian Analysis 10(1). diff --git a/dev/ggm_nuts/chordal_completion_augmentation.md b/dev/ggm_nuts/chordal_completion_augmentation.md new file mode 100644 index 00000000..2bb7a02d --- /dev/null +++ b/dev/ggm_nuts/chordal_completion_augmentation.md @@ -0,0 +1,34 @@ +# Chordal Completion Augmentation for GGM NUTS + +## Goal +Use the No-U-Turn Sampler for Gaussian graphical model parameters while enforcing the sparsity pattern of a non-chordal precision matrix. The idea is to work on an augmented parameter space where positive definiteness is guaranteed, then map back to the original graph. + +## Strategy Overview +1. **Chordal completion**: Given the current graph \(G\), build a chordal supergraph \(\tilde G\) by adding the minimal set of fill-in edges. Algorithms such as minimum degree, approximate minimum degree, or nested dissection supply reasonable triangulations. +2. **Perfect elimination order**: Compute a PEO on \(\tilde G\). This ordering ensures that sparse Cholesky factorization of any PD matrix respecting \(\tilde G\) introduces no further fill-ins. +3. **Sparse Cholesky parameterization**: + - Parameterize a lower-triangular matrix \(L\) whose sparsity matches \(\tilde G\). Entries corresponding to edges (including fill-ins) are free parameters; non-edges are fixed at zero. + - Diagonal entries live on the log scale to enforce positivity: \(L_{ii} = \exp(d_i)\) with unconstrained \(d_i \in \mathbb{R}\). + - All other free entries are unconstrained reals. Together they form the vector that NUTS updates. +4. **Precision reconstruction**: Form \(Q = L L^\top\). By construction \(Q\) is positive definite for any unconstrained parameter values. +5. **Projection back to \(G\)**: Extract the entries of \(Q\) that correspond to edges and diagonals of the original graph, discarding the auxiliary fill-in elements. These extracted entries plug into the likelihood and priors defined on \(G\). + +## Why This Works +- Cholesky factors of non-chordal sparse matrices inevitably introduce fill-ins. By expanding to \(\tilde G\) we accept those fill-ins explicitly, rather than letting them appear implicitly during factorization. +- NUTS operates on the free parameters of \(L\), which live on \(\mathbb{R}^{k}\) with no boundary constraints, so the Hamiltonian dynamics remain well-defined. +- The auxiliary parameters tied to fill-in edges never enter the likelihood directly; they ensure the reconstructed precision stays PD while respecting the observed sparsity once projected. + +## Computational Cost +- Building \(\tilde G\) and its PEO costs roughly linear time in the number of edges plus fill-ins but can be dominated by triangulation for dense graphs. +- The dimension of the parameter space increases with the number of fill-in edges. If \(G\) is far from chordal, this inflation can slow NUTS steps and gradient evaluations. +- Sparse matrix routines (symbolic factorization, reuse of elimination trees) should be cached across iterations to keep costs manageable. + +## Practical Tips +- Reuse the same chordal completion for all iterations while the graph structure stays fixed. If the edge set changes (RJ step), recompute the triangulation only when necessary. +- Choose heuristics (e.g., AMD) that minimize fill-ins, balancing numerical stability and efficiency. +- Gradients of \(Q = L L^\top\) w.r.t. the free entries of \(L\) follow standard reverse-mode rules; implement them with sparse structures to avoid dense \(O(p^2)\) work. + +## References +- Rue, H., & Held, L. (2005). *Gaussian Markov Random Fields: Theory and Applications*. Chapters 2–3 discuss chordal completions and sparse Cholesky factors. +- Atay-Kayis, A., & Massam, H. (2005). "A Monte Carlo method for computing the marginal likelihood in nondecomposable Gaussian graphical models." *Journal of Computational and Graphical Statistics*. +- Dahl, J., Vandenberghe, L., & Roychowdhury, V. (2008). "Covariance selection for non-chordal graphs via chordal embedding." *Optimization and Engineering*. diff --git a/dev/ggm_nuts/elliptical_slice_surrogates.md b/dev/ggm_nuts/elliptical_slice_surrogates.md new file mode 100644 index 00000000..7c299eba --- /dev/null +++ b/dev/ggm_nuts/elliptical_slice_surrogates.md @@ -0,0 +1,24 @@ +# Elliptical Slice / Surrogate Transitions + +## Concept +Retain the graph-respecting adaptive Metropolis kernel but wrap it with gradient-informed or Gaussian surrogate moves to boost local exploration without heavy Hamiltonian mechanics. Elliptical slice sampling (ESS) is attractive when the prior is Gaussian with sparse precision; surrogate transitions such as preconditioned Crank–Nicolson (pCN) or blocked ESS reuse sparse linear solves. + +## Construction +1. Use the existing RJ step to update the edge set. +2. Conditional on a fixed graph, draw proposals from a Gaussian surrogate with precision equal (or close) to the current posterior curvature, e.g., via solving \(Q x = z\) with sparse methods. +3. Apply ESS or pCN to accept/reject along the elliptical trajectory without tuning step sizes. +4. Optionally interleave occasional gradient-informed Metropolis proposals (Langevin, quasi-Newton) that exploit the sparse precision as a preconditioner. + +## Pros +- No need for chordal completions or manifold integrators. +- Exact ESS has acceptance probability 1 when the likelihood is Gaussian; in general it is robust to tuning. +- Sparse linear algebra can be reused across iterations, matching the structure already present for RJ moves. + +## Cons +- Mixing can lag behind full NUTS, especially with highly correlated posteriors. +- Requires drawing from the surrogate Gaussian, which still needs sparse Cholesky solves each iteration. +- Does not exploit gradients beyond what is embedded in the surrogate; tuning the surrogate to match posterior curvature can be difficult. + +## References +- Murray, I., Adams, R. P., & MacKay, D. J. C. (2010). *Elliptical slice sampling.* AISTATS. +- Cotter, S., et al. (2013). *MCMC methods for functions: modifying old algorithms to make them faster.* Statistical Science 28(3). diff --git a/dev/ggm_nuts/partial_correlation_reparameterization.md b/dev/ggm_nuts/partial_correlation_reparameterization.md new file mode 100644 index 00000000..25e65f96 --- /dev/null +++ b/dev/ggm_nuts/partial_correlation_reparameterization.md @@ -0,0 +1,28 @@ +# Partial-correlation reparameterization for GGM NUTS + +## Core idea +Map unconstrained parameters to admissible precision matrices by working in partial correlations (PCs). Each PC lives on \((-1, 1)\), so we represent it via \(\rho = \tanh(\eta)\) with \(\eta \in \mathbb{R}\). Diagonal precision entries use log-scales to ensure positivity. The mapping guarantees positive definiteness and enforces graph zeros, allowing NUTS to operate on \(\eta\) and the log-diagonals without boundary constraints. + +## Construction steps +1. Choose a node ordering and form sequential regressions (modified Cholesky / Schur complements). +2. For every permitted edge \((i, j)\) with \(j < i\), introduce an unconstrained \(\eta_{ij}\); set the partial correlation \(\rho_{ij} = \tanh(\eta_{ij})\). +3. Assemble the unit-diagonal correlation matrix using the recursive PC-to-correlation formulas (Joe, 2006; Liechty et al., 2004). Missing edges receive \(\rho = 0\). +4. Introduce unconstrained \(\zeta_i\) for each node and set conditional precision scales as \(\exp(\zeta_i)\). +5. Form \(Q = D^{-1/2} R^{-1} D^{-1/2}\) where \(D = \operatorname{diag}(\exp(\zeta_i))\) and \(R\) is the correlation matrix from step 3. + +## Graph enforcement +Only existing edges get \(\eta\)-parameters, so their PCs can vary; missing edges stay fixed at zero PCs, and the recursive construction keeps those entries zero in the final precision. Thus \(Q\) respects the graph after the similarity transform. + +## Using with NUTS +- Parameter vector consists of \(\eta\) (one per edge) plus \(\zeta\) (one per node). +- Log posterior is evaluated through \(Q(\eta, \zeta)\). +- Gradients flow through the smooth tanh and matrix operations, so autodiff applies. Cache intermediate matrices to keep complexity reasonable. + +## Practical notes +- Clamp \(|\rho|\) slightly below 1 (e.g., \(\rho = \tanh(\eta)\times (1-\epsilon)\)) for numerical stability. +- Reorder nodes (AMD, nested dissection) to limit numerical error and keep regressions well-conditioned. +- Jacobian adjustments: add \(\log \cosh(\eta_{ij})\) for each PC transform and \(\zeta_i\) for each log-diagonal. + +## References +- Liechty, J., Liechty, M., & Müller, P. (2004). Bayesian correlation estimation. *Journal of the American Statistical Association*, 99(447), 1021–1035. +- Joe, H. (2006). Generating random correlation matrices based on partial correlations. *Journal of Multivariate Analysis*, 97(10), 2177–2189. (See also UBC Technical Report 2005-11.) diff --git a/dev/ggm_nuts/projected_nuts.md b/dev/ggm_nuts/projected_nuts.md new file mode 100644 index 00000000..c9ed6fc3 --- /dev/null +++ b/dev/ggm_nuts/projected_nuts.md @@ -0,0 +1,28 @@ +# Projected NUTS for Sparse Precision Matrices + +## Concept +Run Hamiltonian dynamics in an unconstrained Euclidean space, then project intermediate states back onto the sparse positive-definite cone that encodes the graph. Projection enforces symmetry, sparsity, and PD by solving a convex program after each integrator step. + +## Construction +1. Parameterize a dense symmetric matrix \(S\) with unconstrained entries. +2. During each leapfrog step, update \(S\) using standard NUTS integration. +3. After the position update, solve a projection problem: + \[ + \min_{Q \succeq 0} \|Q - S\|_F^2 \quad \text{s.t.}\quad Q_{ij} = 0 \;\text{for}\; (i,j) \notin E. + \] + This is a log-det barrier or semidefinite program over the sparsity-constrained cone. +4. Use the projected \(Q\) to evaluate the log posterior and gradients (via implicit differentiation through the projection). + +## Pros +- Parameter dimension equals the number of edges (after eliminating fixed zeros) if you operate in the sparse coordinate basis. +- Projection guarantees feasibility even if leapfrog steps wander outside the cone. +- Compatible with existing convex-optimization libraries (e.g., sparse log-det solvers). + +## Cons +- Each projection may require solving a sizable convex problem (costly for large graphs). +- Differentiating through the projection demands custom adjoint code or implicit differentiation. +- Numerical errors from repeated projection can accumulate, making reversibility checks essential. + +## References +- Dahl, J., Vandenberghe, L., & Roychowdhury, V. (2008). *Covariance selection for non-chordal graphs via chordal embedding.* Optimization and Engineering. +- Nishihara, R., et al. (2014). *Proximal algorithms for constrained HMC.* (Workshop paper; describes projection-based integrators.) diff --git a/dev/ggm_nuts/riemannian_gwishart_hmc.md b/dev/ggm_nuts/riemannian_gwishart_hmc.md new file mode 100644 index 00000000..de76bb2d --- /dev/null +++ b/dev/ggm_nuts/riemannian_gwishart_hmc.md @@ -0,0 +1,24 @@ +# Riemannian Manifold HMC on the G-Wishart Cone + +## Concept +Treat the space of graph-constrained positive-definite matrices as a Riemannian manifold endowed with the Fisher information metric of the G-Wishart distribution. Run Riemannian HMC (RMHMC) so that geodesic proposals respect the sparsity constraints without auxiliary completions. + +## Construction +1. Define the target density as the posterior over the sparse precision matrix, typically proportional to a G-Wishart prior times the data likelihood. +2. Use the Fisher metric of the G-Wishart (or an approximation) as the position-dependent mass matrix. +3. At each leapfrog step solve the implicit equations for RMHMC (usually via fixed-point iterations) to update momentum and position. +4. Ensure gradients are evaluated only on free entries; metric tensors inherit the sparse structure, but linear solves involve fill-ins. + +## Pros +- Moves directly on the constrained space; no auxiliary parameters or projections. +- Metric adapts to local curvature, improving exploration in stiff posteriors. +- Naturally accommodates different priors via the metric definition. + +## Cons +- Each leapfrog step requires solving nonlinear equations plus sparse linear systems; computationally heavy. +- Implementing efficient sparse RMHMC is complex (automatic differentiation of metrics, symbolic factorization reuse). +- Requires tuning step sizes and number of fixed-point iterations to maintain stability. + +## References +- Byrne, S., & Girolami, M. (2013). *Geodesic Monte Carlo on embedded manifolds.* Scandinavian Journal of Statistics 40(4). +- Lenkoski, A. (2013). *A direct sampler for G-Wishart variates.* Stat 2(1). (Appendix discusses the manifold structure.) diff --git a/dev/mixedmrf/deduplication_audit.md b/dev/mixedmrf/deduplication_audit.md new file mode 100644 index 00000000..f9d7992a --- /dev/null +++ b/dev/mixedmrf/deduplication_audit.md @@ -0,0 +1,459 @@ +# Phase K — Code Deduplication & Architectural Conformance Audit + +**Date:** 2026-03-09 +**Branch:** `ggm_mixed` +**Scope:** Audit only — no refactoring in this phase. + +--- + +## Part 1: Architectural Conformance + +The GGM model is the prototype for how model classes should be structured. +The OMRF follows GGM conventions in most areas. This section documents +where the MixedMRF deviates from the GGM/OMRF pattern and recommends +alignment. + +### 1.1 Edge selection: embedded vs separated + +| Model | Pattern | Location | +|-------|---------|----------| +| GGM | Embedded in `do_one_metropolis_step` | `ggm_model.cpp` L341–346 | +| OMRF | Separated: `update_edge_indicators()` called by ChainRunner | `omrf_model.cpp` L1296–1305 | +| MixedMRF | Separated: `update_edge_indicators()` with three sub-sweeps | `mixed_mrf_model.cpp` | + +**Finding:** GGM embeds edge selection inside the Metropolis step; OMRF +and MixedMRF separate it. The OMRF/MixedMRF pattern is the better +design (matches the ChainRunner contract where `update_edge_indicators()` +is called as a distinct step). GGM is the outlier here. + +**Decision:** MixedMRF correctly follows the OMRF pattern. No change. +GGM should align upward to this pattern in a future refactor. + +### 1.2 Edge-order shuffling + +| Model | Shuffle | Where | +|-------|---------|-------| +| GGM | No shuffle — deterministic i,j loop | `do_one_metropolis_step` | +| OMRF | Shuffles `shuffled_edge_order_` in `prepare_iteration()` | `omrf_model.cpp` | +| MixedMRF | Shuffles three vectors in `prepare_iteration()` | `mixed_mrf_model.cpp` | + +**Finding:** GGM does not shuffle edge update order at all. OMRF and +MixedMRF do, which is better practice (reduces order bias in RJ sweeps). + +**Decision:** OMRF/MixedMRF pattern is preferred. MixedMRF is correct. +The three separate shuffle vectors (xx, yy, xy) are necessary because +MixedMRF has three distinct edge-type blocks. GGM should add shuffling +in a future refactor. + +### 1.3 Metropolis adaptation mechanism + +| Model | Mechanism | Controller object? | +|-------|-----------|-------------------| +| GGM | Inline Robbins-Monro in each update function | No | +| OMRF | Delegated to `MetropolisAdaptationController` | Yes (2 instances: main, pairwise) | +| MixedMRF | Inline Robbins-Monro in each update function | No | + +**Adaptation formula (GGM and MixedMRF):** +```cpp +if(iteration >= 1 && iteration < total_warmup_) { + double rm_weight = std::pow(iteration, -0.75); + prop_sd_(i, j) = update_proposal_sd_with_robbins_monro( + prop_sd_(i, j), ln_alpha, rm_weight, 0.44); +} +``` + +**Finding:** GGM and MixedMRF use identical inline Robbins-Monro (target +0.44, exponent -0.75). OMRF uses a controller object that collects +acceptance probabilities into matrices and applies batch updates. The +OMRF approach is more structured but also more complex. + +**Recommendation:** MixedMRF matches GGM exactly. No copy-paste drift +detected. The OMRF's controller pattern was designed for its specific +batch-collection workflow and is not obviously better for the mixed +model's five-block sweep. Status quo is fine. + +### 1.4 Proposal SD storage + +| Model | Storage | +|-------|---------| +| GGM | `proposal_sds_` — flat `arma::vec` of length dim_ | +| OMRF | `proposal_sd_main_` (p x max_cats), `proposal_sd_pairwise_` (p x p) | +| MixedMRF | `prop_sd_mux_`, `prop_sd_muy_`, `prop_sd_Kxx_`, `prop_sd_Kyy_`, `prop_sd_Kxy_` | + +**Finding:** GGM flattens everything into one vector; OMRF uses two +matrices; MixedMRF uses five separate storage objects. The MixedMRF +approach is natural for a model with five parameter blocks. No issue. + +**Recommendation:** No change needed. The naming abbreviation `prop_sd_` +vs OMRF's `proposal_sd_` is a minor style divergence; consider +standardizing to `proposal_sd_` for consistency with OMRF. + +### 1.5 Naming conventions + +The naming scheme uses a two-level hierarchy: role (`main_effects_`, +`pairwise_effects_`) qualified by variable type (`discrete_`, +`continuous_`, `cross_`). Both category thresholds and continuous +means are main effects — the variable type is the qualifier, not the +statistical role. + +#### Agreed rename mapping + +**Parameters:** + +| Current | New | +|---------|-----| +| `mux_` | `main_effects_discrete_` | +| `muy_` | `main_effects_continuous_` | +| `Kxx_` | `pairwise_effects_discrete_` | +| `Kyy_` | `pairwise_effects_continuous_` | +| `Kxy_` | `pairwise_effects_cross_` | + +**Proposal SDs:** + +| Current | New | +|---------|-----| +| `prop_sd_mux_` | `proposal_sd_main_discrete_` | +| `prop_sd_muy_` | `proposal_sd_main_continuous_` | +| `prop_sd_Kxx_` | `proposal_sd_pairwise_discrete_` | +| `prop_sd_Kyy_` | `proposal_sd_pairwise_continuous_` | +| `prop_sd_Kxy_` | `proposal_sd_pairwise_cross_` | + +**Derived quantities (GGM pattern for Cholesky names):** + +| Current | New | +|---------|-----| +| `Kyy_chol_` | `cholesky_of_precision_` | +| `inv_cholesky_yy_` | `inv_cholesky_of_precision_` | +| `covariance_yy_` | `covariance_continuous_` | + +**Cross-model consistency:** + +| Concept | GGM | OMRF | MixedMRF (new) | +|---------|-----|------|----------------| +| Main effects | N/A | `main_effects_` | `main_effects_discrete_`, `main_effects_continuous_` | +| Pairwise effects | `precision_matrix_` | `pairwise_effects_` | `pairwise_effects_discrete_`, `pairwise_effects_continuous_`, `pairwise_effects_cross_` | +| Cholesky | `cholesky_of_precision_` | N/A | `cholesky_of_precision_` | +| Inv Cholesky | `inv_cholesky_of_precision_` | N/A | `inv_cholesky_of_precision_` | +| Covariance | `covariance_matrix_` | N/A | `covariance_continuous_` | +| Proposal SDs | `proposal_sds_` | `proposal_sd_*` | `proposal_sd_*` | +| Observations (int) | N/A | `observations_` | `discrete_observations_` (OK — mixed needs qualifier) | +| Observations (dbl) | `observations_` | `observations_double_` | `discrete_observations_dbl_` (OK) | + +Grepping for `main_effects_` or `pairwise_effects_` finds all relevant +members across all three models. The Cholesky names parallel GGM +directly — anyone familiar with the GGM code recognizes the role. + +**Decision (high priority):** Apply this rename. The change is mechanical +but touches many lines across 4 C++ files and the R output builder. + +### 1.6 `MY_LOG` vs `std::log` in edge selection + +| Model | Usage in edge-indicator inclusion-prior terms | +|-------|-----------------------------------------------| +| GGM | `MY_LOG(inclusion_probability_(i, j))` | +| OMRF | `MY_LOG(inclusion_probability_ij)` | +| MixedMRF | `std::log(inclusion_probability_(i, j))` | + +**Finding:** GGM and OMRF use `MY_LOG`; MixedMRF uses `std::log`. +The `MY_LOG` macro was introduced for consistent fast-math behavior +across the codebase. + +**Recommendation (fix):** Replace all 6 `std::log(inclusion_probability_` +occurrences in `mixed_mrf_metropolis.cpp` with `MY_LOG`. This is a +mechanical change (lines 621, 627, 702, 712, 796, 802). + +### 1.7 File organization + +| Model | Implementation files | Pattern | +|-------|---------------------|---------| +| GGM | 1: `ggm_model.cpp` (~470 lines) + `cholupdate.cpp` (~120 lines) | Monolithic | +| OMRF | 1: `omrf_model.cpp` (~1450 lines) | Monolithic | +| MixedMRF | 4: `mixed_mrf_model.cpp` (~850), `mixed_mrf_likelihoods.cpp` (~140), `mixed_mrf_gradient.cpp` (~500), `mixed_mrf_metropolis.cpp` (~800) | Modular split | + +**Finding:** MixedMRF splits into four files while GGM and OMRF are +monolithic. The MixedMRF approach is arguably better for a 2300-line +model, but diverges from the established pattern. + +**Recommendation:** No change. The split is pragmatic given the model's +complexity. If GGM or OMRF grow, they should adopt the same split +pattern rather than forcing MixedMRF back into a monolith. + +### 1.8 Public API surface + +**GGM-specific public methods:** +- `log_likelihood()` (2 overloads) +- `set_missing_data(const arma::imat&)` + +**OMRF-specific public methods:** +- ~15 accessors/setters (`get_main_effects`, `set_pairwise_effects`, etc.) +- `set_step_size`, `get_step_size`, `set_inv_mass`, `get_inv_mass` +- `set_pairwise_scaling_factors` + +**MixedMRF-specific public methods:** +- `do_kyy_metropolis_step(int)` — exposed for hybrid NUTS sampler +- `set_missing_data(const arma::imat&, const arma::imat&)` — two matrices +- `storage_dimension()`, `get_storage_vectorized_parameters()` — extra vectorization levels +- `get_active_inv_mass()` — override for NUTS mass matrix + +**Finding:** MixedMRF has a relatively narrow public API compared to OMRF. +The `do_kyy_metropolis_step` public method is specific to the hybrid +NUTS+MH design and is called by `HybridNUTSSampler`. The OMRF's many +accessors exist because the NUTS sampler needs direct parameter +access. MixedMRF absorbs this into the vectorization interface instead. + +**Recommendation:** The OMRF's many public accessors (`get_main_effects`, +`set_pairwise_effects`, etc.) create a wide coupling surface. The +MixedMRF's approach of using vectorization methods is cleaner. Consider +narrowing OMRF's public API in a future refactor to match. + +### 1.9 `storage_dimension()` override + +Only MixedMRF overrides `storage_dimension()`. BaseModel's default +returns `full_parameter_dimension()`. MixedMRF needs this because +Kyy is excluded from the NUTS gradient block but must still be stored +in the sample buffer. + +**Finding:** This is a necessary architectural extension, not a +conformance issue. GGM and OMRF don't need it because they don't +have a hybrid sampler. + +**Recommendation:** No change. Document in BaseModel that +`storage_dimension` exists for hybrid samplers. + +### 1.10 Documentation quality + +| Model | Class-level doc | Private method doc | Member field doc | Section dividers | +|-------|:---:|:---:|:---:|:---:| +| GGM | `/** */` | Minimal | `///` on most | Minimal | +| OMRF | `/** */` | Most methods | `///` on all | `//` section headers | +| MixedMRF | `/** */` extensive | All methods | `///` on all | `// ===` and `// ---` | + +**Finding:** MixedMRF has the most thorough documentation. This exceeds +GGM/OMRF quality. Good. + +**Recommendation:** No change for MixedMRF. Consider improving GGM's +private-method documentation to match OMRF/MixedMRF level. + +--- + +## Part 2: Code Duplication Candidates + +### 2.1 Matrix assembly in `build_output.R` + +**Location:** `R/build_output.R` +- `build_output_mixed_mrf()` lines 476–569: Four nested-loop blocks + filling (p+q)×(p+q) matrices from flat vectors (pairwise means, + pairwise indicators, each with Kxx/Kyy/Kxy sub-blocks). +- `build_output_bgm()` lines 225–253: Analogous matrix assembly using + `lower.tri()` + transpose (simpler because only one matrix block). + +**Duplication:** The mixed MRF has 3 sub-blocks per matrix × 2 matrices +(pairwise + indicator) = 6 near-identical loop nests. Each is ~12 lines. +Total redundant code: ~60 lines. + +**Extraction candidate:** +```r +fill_symmetric_matrix = function(flat_values, idx_pairs, target_matrix) { + for(k in seq_along(flat_values)) { + i = idx_pairs[k, 1]; j = idx_pairs[k, 2] + target_matrix[i, j] = flat_values[k] + target_matrix[j, i] = flat_values[k] + } + target_matrix +} +``` + +**Effort:** Low (30 min). **Risk:** Low. **Lines saved:** ~50. + +### 2.2 SBM summarization computed twice + +**Location:** `R/build_output.R` +- `build_output_bgm()`: `summarize_alloc_pairs()` called at lines ~176 + and ~243, producing `sbm_summary` and `co_occur_matrix` respectively. + Same inputs both times. + +**Finding confirmed:** The same function is called twice with identical +arguments. The first call extracts `$sbm_summary`, the second extracts +`$co_occur_matrix`. + +**Extraction:** Call once, store result, extract both fields. + +**Effort:** Trivial (5 min). **Risk:** None. **Lines saved:** ~8. + +### 2.3 Raw-samples list assembly + +**Location:** `R/build_output.R` +- `build_output_bgm()` lines 274–304 +- `build_output_mixed_mrf()` lines 678–703 + +**Duplication:** Near-identical structure. Both build: +```r +list(main = ..., pairwise = ..., indicator = ..., allocations = ..., + nchains = ..., niter = ..., parameter_names = ...) +``` + +**Difference:** `build_output_bgm` includes `allocations` naming; +`build_output_mixed_mrf` omits it. Otherwise identical. + +**Extraction candidate:** +```r +build_raw_samples_list = function(raw, edge_selection, edge_prior, + names_main, edge_names) { ... } +``` + +**Effort:** Low (20 min). **Risk:** Low. **Lines saved:** ~25. + +### 2.4 Parameter index computation in `build_output_mixed_mrf()` + +**Location:** `R/build_output.R` lines 337–429 (~90 lines) + +**Finding:** This block computes mux/Kxx/muy/Kxy/Kyy offsets and +separates them into `main_idx` and `pairwise_idx`. It's inline and +hard to audit. Contains a nested loop (lines 361–375) to identify +Kyy diagonal vs off-diagonal positions. + +**Extraction candidate:** +```r +compute_mixed_parameter_indices = function(num_thresholds, p, q) { + # Returns: list(mux_range, kxx_range, muy_range, kxy_range, + # kyy_range, kyy_diag_abs, kyy_offdiag_abs, + # main_idx, pairwise_idx) +} +``` + +**Effort:** Medium (45 min). **Risk:** Low — can unit-test independently. +**Lines saved:** 0 (extracted, not eliminated), but testability improves. + +### 2.5 `cholupdate.h` location + +**Current location:** `src/models/ggm/cholupdate.h` and `cholupdate.cpp` +**Included by:** GGMModel and MixedMRFModel + +**Finding:** This is pure linear algebra with no GGM-specific state. +Living under `src/models/ggm/` is misleading since MixedMRF depends on it. + +**Recommendation:** Move to `src/math/cholupdate.h` (alongside +`cholesky_helpers.h` which is already in `src/math/`). + +**Effort:** Low (15 min — move files, update includes, regenerate +`sources.mk`). **Risk:** Low — `#include` paths change, nothing else. + +### 2.6 Robbins-Monro adaptation + +**Finding:** GGM and MixedMRF use identical inline Robbins-Monro calls +with the same parameters (target=0.44, exponent=-0.75). Both call +`update_proposal_sd_with_robbins_monro()` which is a shared utility. +No copy-paste drift detected. + +OMRF uses `MetropolisAdaptationController` which internally does the +same math but in a batch-collection pattern. + +**Recommendation:** No extraction needed. The shared utility function +`update_proposal_sd_with_robbins_monro()` already prevents drift. + +### 2.7 Beta-Bernoulli between-cluster parameter handling + +**Location:** `R/run_sampler.R` +- Lines ~109–115 in `run_sampler_omrf()` +- Same pattern in `run_sampler_ggm()` and `run_sampler_mixed_mrf()` + +```r +bb_alpha_between = if(is.null(p$beta_bernoulli_alpha_between)) -1.0 + else p$beta_bernoulli_alpha_between +``` + +**Finding:** Identical 6-line pattern in all three sampler functions. + +**Extraction candidate:** Helper function or inline in `run_sampler()`. + +**Effort:** Trivial (10 min). **Risk:** None. **Lines saved:** ~12. + +### 2.8 Variable-type normalization in simulate/predict + +**Location:** `R/simulate_predict.R` +- Line ~1157 (simulate.bgms) and ~1627 (predict.bgms) + +```r +if(length(variable_type) == 1) { + variable_type = rep(variable_type, num_variables) +} +``` + +**Finding:** Same 3-line pattern duplicated in simulate and predict. + +**Extraction:** Minor; could go in a shared input-normalization helper. + +**Effort:** Trivial (5 min). **Risk:** None. **Lines saved:** ~3. + +--- + +## Part 3: Ranked Summary + +| # | Item | Type | Priority | Effort | Risk | Lines saved | +|:-:|------|------|:--------:|:------:|:----:|:-----------:| +| 1 | MY_LOG in edge selection (§1.6) | Bug-class fix | High | 5 min | None | 0 (correctness) | +| 2 | Naming conventions (§1.5) | Conformance | High | 2–3 hrs | Medium | 0 (rename) | +| 3 | cholupdate.h location (§2.5) | Organization | Medium | 15 min | Low | 0 (move) | +| 4 | Matrix assembly helper (§2.1) | Dedup | Medium | 30 min | Low | ~50 | +| 5 | SBM double-call (§2.2) | Dedup | Medium | 5 min | None | ~8 | +| 6 | Parameter index extraction (§2.4) | Testability | Medium | 45 min | Low | 0 (testable) | +| 7 | Raw-samples assembly (§2.3) | Dedup | Low | 20 min | Low | ~25 | +| 8 | BB between-cluster helper (§2.7) | Dedup | Low | 10 min | None | ~12 | +| 9 | Variable-type normalization (§2.8) | Dedup | Low | 5 min | None | ~3 | +| 10 | prop_sd_ → proposal_sd_ (§1.4) | Naming | Low | 30 min | Low | 0 (rename) | + +### Items NOT recommended for change + +- **File split (§1.7):** MixedMRF's 4-file split is better than monolithic. +- **Adaptation mechanism (§1.3):** Inline Robbins-Monro matches GGM. No drift. +- **Public API width (§1.8):** MixedMRF's narrow API is preferred. +- **storage_dimension (§1.9):** Necessary for hybrid sampler. +- **Edge selection separation (§1.1):** MixedMRF follows OMRF correctly. +- **Edge-order shuffling (§1.2):** MixedMRF follows OMRF correctly. + +--- + +## Appendix: Structural Comparison Tables + +### A. BaseModel Override Implementation + +| Override | GGM | OMRF | MixedMRF | +|----------|-----|------|----------| +| `has_gradient()` | `false` | `true` | `true` | +| `has_adaptive_metropolis()` | `true` | `true` | `true` | +| `do_one_metropolis_step` | Off-diag → diag → edge sel | Pairwise → main | Main → muy → Kxx → Kyy → Kxy | +| `update_edge_indicators` | No-op (embedded) | Shuffled sweep | Three shuffled sweeps | +| `prepare_iteration` | No-op | Shuffle edges | Shuffle 3 edge vectors | +| `get_vectorized_parameters` | Upper-tri precision | Main + pairwise | mux + muy + Kxx + Kxy | +| `set_vectorized_parameters` | Not implemented | Full unpack + cache | Full unpack + cache | +| `init_metropolis_adaptation` | Store warmup count | Create 2 controllers | Store warmup count | +| `tune_proposal_sd` | No-op | Poll 2 controllers | No-op | +| `impute_missing` | Conditional Normal | Categorical probs | Categorical + Normal | +| `clone` | Copy constructor | Copy constructor | Copy constructor | + +### B. Member Variable Inventory + +| Category | GGM | OMRF | MixedMRF | +|----------|:---:|:----:|:--------:| +| Data members | 5 | 8 | 10 | +| Parameter members | 1 (precision) | 2 (main, pairwise) | 5 (mux, muy, Kxx, Kyy, Kxy) | +| Cache members | 3 (chol, inv_chol, cov) | 3 (residual, gradient, index) | 8 (chol, inv_chol, cov, logdet, Theta, cond_mean, gradient, index) | +| Prior members | 2 | 4 | 3 | +| Proposal SD members | 1 | 2 | 5 | +| Edge members | 3 | 3 | 3 | +| RNG + config | 2 | 4 | 3 | +| Workspace | 7 (rank-2 vectors) | 0 | 7 (rank-2 vectors, prefixed kyy_) | +| **Total** | ~23 | ~24 | ~44 | + +### C. Cholesky Infrastructure Sharing + +``` +src/math/cholesky_helpers.h ← Shared (get_log_det, compute_inv_submatrix_i) +src/models/ggm/cholupdate.h ← Should be src/math/cholupdate.h + ├── Used by GGMModel (precision_matrix_ updates) + └── Used by MixedMRFModel (Kyy_ updates) + +GGM reparameterization pattern (get_constants, constrained_diagonal): + ├── GGMModel::get_constants() + └── MixedMRFModel::get_kyy_constants() ← Adapted copy (same math) +``` diff --git a/dev/mixedmrf/implementation_plan.md b/dev/mixedmrf/implementation_plan.md index 76bcc8e7..d36b2d6a 100644 --- a/dev/mixedmrf/implementation_plan.md +++ b/dev/mixedmrf/implementation_plan.md @@ -317,8 +317,8 @@ add the new Rcpp export to `src/RcppExports.cpp`, `R/RcppExports.R`, and | **G** | Simulation and prediction | E | `simulate.bgms` and `predict.bgms` for mixed | ✅ | | **H** | Stochastic block model edge prior | G | SBM prior wired into mixed MRF | ✅ | | **I** | Missing data imputation | G | `na_action = "impute"` for mixed data | ✅ | -| **J** | Performance profiling | G | Benchmark report, hotspot fixes | -| **K** | Code deduplication audit | G | Shared helpers extracted, dead code removed | +| **J** | Performance profiling | G | Benchmark report, hotspot fixes | partial | +| **K** | Code deduplication audit | G | Shared helpers extracted, dead code removed | ✅ | | **L** | Model output R code inspection | G | Output format verified, bugs fixed | | **M** | Test suite cleanup and completion | G | 4 new test files, promoted dev tests, full T1–T30 coverage | | **N** | Alternative proposals for GGM edge updates | G | New proposal mechanisms for GGM edges | @@ -1583,12 +1583,24 @@ while the C++ function expected 21. | `src/RcppExports.cpp` | Regenerated | --- -## Phase J — Performance profiling +## Phase J — Performance profiling (partial) Profile the mixed MRF sampler to identify bottlenecks and verify that the rank-1 Cholesky optimizations (Phase B+) deliver the expected speedup over naive recomputation. +### Completed + +- `MY_LOG`/`MY_EXP` macros applied across GGM and OMRF models + (commit `cad3426`). +- Benchmark baseline saved (`dev/fixtures/benchmark_baseline.rds`); + rounds 1–3 saved. Optimization rounds 1–8 stashed (`stash@{0}`). + +### Remaining + +- Formal scaling-curve plots and profiling report not yet produced. +- Marginal vs conditional PL cost comparison not yet documented. + ### Scope 1. **Wall-clock benchmarks.** Time full `bgm()` runs at several @@ -1621,12 +1633,25 @@ expected speedup over naive recomputation. --- -## Phase K — Code deduplication audit +## Phase K — Code deduplication audit ✅ Review the mixed MRF implementation and the rest of the package for repeated code patterns that could be shared. The goal is to reduce maintenance burden without over-abstracting. +### Completed + +- **Part 1** (`676b49f`): Renamed all MixedMRF member variables to + match GGM/OMRF naming conventions (`mux_` → `main_effects_discrete_`, + `Kxx_` → `pairwise_effects_discrete_`, etc.). Replaced 6 `std::log` + calls with `MY_LOG` in `mixed_mrf_metropolis.cpp`. +- **Part 2** (`d58848b`): Moved `cholupdate.h`/`.cpp` from + `src/models/ggm/` to `src/math/`. Deduplicated R output-builder + code in `build_output.R` and `run_sampler.R`. +- **Part 3** (`b944cd4`): Documented overflow-guard bitwise break, + regenerated HMC fixtures, restored progress interval. +- Full audit in `dev/mixedmrf/deduplication_audit.md`. + ### Known candidates 1. **Matrix assembly pattern in `build_output.R`.** diff --git a/dev/mixedmrf/output_inspection.md b/dev/mixedmrf/output_inspection.md new file mode 100644 index 00000000..eead7306 --- /dev/null +++ b/dev/mixedmrf/output_inspection.md @@ -0,0 +1,360 @@ +# Phase L — Model Output R Code Inspection + +**Date:** 2026-03-09 +**Branch:** `ggm_mixed` +**Scope:** Audit of R code that assembles, summarises, and exposes +mixed MRF model output. Identifies bugs and inconsistencies. + +--- + +## L1. `build_output_mixed_mrf()` correctness + +### L1.1 Flat-to-matrix index mapping + +The C++ `get_storage_vectorized_parameters()` emits parameters in this +order: + +``` +[main_discrete | Kxx_ut | main_continuous | Kxy_rowmajor | Kyy_ut+diag] +``` + +The R function `compute_mixed_parameter_indices()` splits this into: + +- `main_idx`: discrete thresholds + continuous means + Kyy diagonal +- `pairwise_idx`: Kxx upper-tri + Kyy off-diagonal + Kxy + +The Kyy block in C++ is stored row-major upper-triangular including +diagonal (i.e., for `j >= i`). The R code iterates `for(i in 1:q) +for(j in i:q)` and separates diagonal from off-diagonal positions. + +**Finding: correct.** The R loop matches the C++ `for(i) for(j=i..q)` +ordering exactly. Diagonal and off-diagonal positions are correctly +identified and routed to `main_idx` and `pairwise_idx` respectively. + +### L1.2 Indicator ordering vs pairwise ordering + +C++ indicator vector: `[Gxx_ut | Gyy_ut | Gxy_rowmajor]` +R `pairwise_idx` order: `[Kxx_ut | Kyy_offdiag | Kxy]` + +**Finding: correct.** The indicator vector excludes diagonal entries +(Gyy has no diagonal — self-edges are not indicators). The pairwise +vector also excludes diagonal (Kyy diagonal is routed to main). Both +use the same traversal order within each block. The edge names +generated in `build_output_mixed_mrf` follow the same block order +(discrete-discrete, continuous-continuous, cross), so +`indicator_summary` rows align with `pairwise_summary` rows. + +### L1.3 NUTS diagnostic column naming + +`build_output_mixed_mrf` checks `s$update_method == "hybrid-nuts"`. +`build_output_bgm` checks `s$update_method == "nuts"`. +Both call the same `summarize_nuts_diagnostics()` function. + +**Finding: correct.** The `bgm_spec` pipeline remaps `"nuts"` to +`"hybrid-nuts"` for mixed MRF models (line 350–351 of `bgm_spec.R`), +so the string comparison works correctly in both builders. + +### L1.4 BC main effects in posterior mean + +For Blume-Capel discrete variables, `build_output_mixed_mrf` stores +alpha and beta in columns 1:2 of `pmm_disc`, with remaining columns +NA. This matches the OMRF behavior in `build_output_bgm`. + +**Finding: correct.** + +### L1.5 Pairwise symmetry and sign + +`fill_mixed_symmetric()` places each edge value symmetrically +(`mat[oi,oj] = mat[oj,oi] = value`), mapping from internal +discrete-first indices to original user column indices via +`disc_idx` and `cont_idx`. + +**Finding: correct.** Sign convention is inherited from C++: positive +values mean positive association, matching GGM/OMRF convention. + +### L1.6 SBM allocation names missing in raw_samples + +`build_output_mixed_mrf` calls `build_raw_samples_list(raw, +edge_selection, edge_prior, names_main, edge_names)` without the +`allocation_names` argument. When the SBM prior is active, the +allocations are stored in `$raw_samples$allocations`, but +`$raw_samples$parameter_names$allocations` is `NULL`. + +In contrast, `build_output_bgm` computes `alloc_names` and passes it. + +**Verdict: BUG (minor).** The allocation names are missing from the +raw samples list for mixed MRF models with SBM prior. Fix: pass +`allocation_names = all_internal_names` (the `c(disc_names, +cont_names)` vector already computed on line 586). + +**Priority:** Low — only affects users who directly access +`$raw_samples$parameter_names$allocations` with an SBM prior on +mixed data. + +--- + +## L2. Posterior mean type inconsistency + +| Model | `$posterior_mean_main` type | +|-------|----------------------------| +| GGM | numeric matrix (p × 1, column = "mean") | +| OMRF | numeric matrix (p × max_cats) | +| Mixed MRF | **list** with `$discrete` (p × max_cats) and `$continuous` (q × 2) | + +**Verdict: INCONSISTENCY (moderate).** This is the most significant +structural difference in the output. Any user code that does +`fit$posterior_mean_main[1, 2]` will fail on a mixed MRF fit because +the list does not support matrix indexing. + +The list structure is arguably the correct design — discrete thresholds +and continuous means/precisions are different quantities that belong +in separate matrices. But it breaks the assumption that +`$posterior_mean_main` is always a matrix. + +**Recommendation:** Keep the list structure for mixed MRF. Document it +clearly in `?bgm` and in `coef.bgms()`. The alternative — flattening +into one matrix with mixed row semantics — would be worse for users. + +--- + +## L3. `coef.bgms()` with mixed MRF + +```r +coef.bgms = function(object, ...) { + out = list(main = object$posterior_mean_main, ...) +} +``` + +For mixed MRF, `out$main` is a list (with `$discrete` and +`$continuous`), yielding a nested list: `coef(fit)$main$discrete`. +For GGM/OMRF, `coef(fit)$main` is a matrix. + +**Verdict: INCONSISTENCY (moderate).** The return type of +`coef()$main` depends on the model type. This is not documented. + +**Recommendation:** Accept this inconsistency but document it: +- For GGM: matrix (p × 1) +- For OMRF: matrix (p × max_cats) +- For mixed MRF: list with `$discrete` (matrix) and `$continuous` + (matrix) + +The `@return` block of `coef.bgms` currently says "Posterior mean of +the category threshold parameters" — incorrect for mixed MRF. + +--- + +## L4. `extract_category_thresholds()` — BROKEN for mixed MRF + +```r +extract_category_thresholds.bgms = function(bgms_object) { + vec = bgms_object$posterior_summary_main[, "mean"] + num_vars = arguments$num_variables # = p + q + num_cats = arguments$num_categories # length p (discrete only) + ... + for(v in seq_len(num_vars)) { + num_cats[v] # ERROR: index out of bounds when v > p + } +} +``` + +**Verdict: BUG (crash).** For mixed MRF, `num_variables = p + q`, but +`num_categories` has length `p`. When the loop reaches `v = p + 1`, +`num_cats[v]` is an out-of-bounds access. The function will error. + +Additionally, even if the bounds were fixed, the `vec` from +`posterior_summary_main` for mixed MRF interleaves discrete thresholds +with continuous mean/precision entries. The positional extraction logic +(`pos = pos + k` vs `pos = pos + 2`) would misalign because it does +not know that continuous-variable rows have a different meaning. + +**Fix required:** Either: +1. Guard: `if(isTRUE(arguments$is_mixed))` and extract only discrete + thresholds using `arguments$num_discrete` and `arguments$is_ordinal`. +2. Or: extract from `object$posterior_mean_main$discrete` directly. + +--- + +## L5. `extract_pairwise_interactions()` — WRONG COLUMN NAMES for mixed MRF + +```r +extract_pairwise_interactions.bgms = function(bgms_object) { + mats = bgms_object$raw_samples$pairwise + mat = do.call(rbind, mats) + edge_names = character() + for(i in 1:(num_vars - 1)) { + for(j in (i + 1):num_vars) { + edge_names = c(edge_names, paste0(var_names[i], "-", var_names[j])) + } + } + dimnames(mat) = list(..., edge_names) +} +``` + +**Verdict: BUG (silent wrong labels).** The function generates edge +names in standard upper-triangle order of the (p+q) × (p+q) matrix, +using user column order. But for mixed MRF, `raw_samples$pairwise` +columns are in block order: [Kxx_ut | Kyy_offdiag | Kxy]. These two +orderings are NOT the same when discrete and continuous variables are +interleaved in the original data. + +Example: data with columns `[x1, y1, x2, y2]` (disc, cont, disc, cont). +- Standard upper-tri: (x1,y1), (x1,x2), (x1,y2), (y1,x2), (y1,y2), (x2,y2) +- Mixed block order: (x1,x2), (y1,y2), (x1,y1), (x1,y2), (x2,y1), (x2,y2) + +The edge count is the same (6), but the assignment is scrambled. + +Note: `raw_samples$parameter_names$pairwise` already contains the +correct edge names — the extractor should use those instead of +generating its own. + +**Fix required:** Use `bgms_object$raw_samples$parameter_names$pairwise` +for mixed MRF models instead of regenerating edge names. + +--- + +## L6. `extract_indicators()` — SAME BUG as L5 + +`extract_indicators.bgms` also generates its own column names via +the `parameter_names$indicator` stored in raw_samples — wait, it +actually *does* use `bgms_object$raw_samples$parameter_names$indicator`: + +```r +param_names = bgms_object$raw_samples$parameter_names$indicator +colnames(indicator_samples) = param_names +``` + +**Finding: correct.** Unlike `extract_pairwise_interactions`, the +indicator extractor uses stored names. No bug here. + +--- + +## L7. `simulate.bgms()` with mixed MRF + +The `simulate_bgms_mixed` helper: +1. Extracts parameters from `posterior_mean_main$discrete` / + `$continuous` and `posterior_mean_pairwise`. +2. Calls `sample_mixed_mrf_gibbs()` (C++). +3. `combine_mixed_result()` scatters x/y columns into original order + using `disc_idx` and `cont_idx`. + +**Finding: correct.** The `nsim` parameter is properly forwarded. The +`combine_mixed_result` helper uses `disc_idx` / `cont_idx` from +`arguments`, which are set by `build_arguments_mixed_mrf`. Column +reordering is correct. `drop = FALSE` protects against single-variable +edge cases. + +--- + +## L8. `predict.bgms()` with mixed MRF + +The `predict_bgms_mixed` helper: +1. Splits `newdata` by `disc_idx` / `cont_idx`. +2. Maps `predict_vars` to internal 0-based indices via `match()`. +3. Calls C++ `compute_conditional_mixed`. +4. Formats output with variable-type-aware labels. + +**Finding: correct.** Both discrete and continuous conditioned +variables are supported. Column matching is positional (same as +GGM/OMRF), which is correct given users provide data in the same +column order as training data. + +Minor note: no column-name validation is performed — if `newdata` has +different column names or order from training data, results will be +silently wrong. This is consistent with GGM/OMRF behavior so it is +not a mixed-MRF-specific issue. + +--- + +## L9. NUTS diagnostic wiring + +`build_output_mixed_mrf` checks for `"hybrid-nuts"`; +`build_output_bgm` checks for `"nuts"`. Both funnel to the same +`summarize_nuts_diagnostics()`. + +The `bgm_spec` pipeline maps `"nuts"` → `"hybrid-nuts"` for mixed +MRF at spec-build time. + +**Finding: correct.** No model-type-specific behavior is needed in +the diagnostics function itself. + +--- + +## L10. `summary.bgms()` and `print.summary.bgms()` + +`summary.bgms()` blindly returns `$posterior_summary_main` and +`$posterior_summary_pairwise` — this works for all model types. + +`print.summary.bgms()` labels the main block as +**"Category thresholds:"** unconditionally. + +**Verdict: MISLEADING LABEL (minor).** For mixed MRF, the main +summary includes rows like `"V4 (mean)"` and `"V4 (precision)"` under +the label "Category thresholds:", which is incorrect. For GGM, the +main effects are means (also mislabeled as thresholds). + +**Recommendation:** Change the label to context-appropriate text: +- GGM: "Variable means:" +- OMRF: "Category thresholds:" +- Mixed MRF: "Main effects:" (covers both threshold and mean params) + +This requires `print.summary.bgms` to read the model type from the +fit object, which it currently does not do. A lightweight fix: store +`model_type` in `$arguments` and read it in the print method. + +--- + +## L11. `print.bgms()` + +Prints "Bayesian Estimation" or "Bayesian Edge Selection" with no +indication of model type (GGM vs OMRF vs mixed MRF). + +**Verdict: COSMETIC (low priority).** Adding a model-type line +(e.g., "Model: Mixed MRF (3 ordinal, 2 continuous)") would help +users confirm they're looking at the right fit. + +--- + +## Summary of findings + +### Bugs (must fix) + +| # | Severity | Location | Issue | +|:-:|:--------:|----------|-------| +| B1 | **Crash** | `extract_category_thresholds.bgms` | Out-of-bounds on `num_categories` for mixed MRF (p+q > p) | +| B2 | **Wrong results** | `extract_pairwise_interactions.bgms` | Edge names in standard upper-tri order; mixed MRF samples in block order | +| B3 | **Minor** | `build_output_mixed_mrf` | SBM allocation names missing from `$raw_samples$parameter_names$allocations` | + +### Inconsistencies (should fix for release) + +| # | Severity | Location | Issue | +|:-:|:--------:|----------|-------| +| I1 | Moderate | `coef.bgms` | `$main` is list for mixed, matrix for others; undocumented | +| I2 | Minor | `print.summary.bgms` | "Category thresholds:" label incorrect for mixed and GGM | +| I3 | Minor | `print.bgms` | No model-type indication | +| I4 | Minor | `coef.bgms` roxygen | `@return` describes only threshold params | + +### Correct (no action needed) + +| # | Component | Notes | +|:-:|-----------|-------| +| OK1 | `build_output_mixed_mrf` index mapping | C++/R ordering matches exactly | +| OK2 | `fill_mixed_symmetric` | Block traversal and index scattering correct | +| OK3 | Indicator ordering | Matches pairwise block ordering | +| OK4 | `simulate.bgms` for mixed | Column reordering correct | +| OK5 | `predict.bgms` for mixed | Type-aware dispatch and formatting correct | +| OK6 | NUTS diagnostic wiring | `"hybrid-nuts"` string handled correctly | +| OK7 | `extract_indicators` | Uses stored parameter names (not regenerated) | +| OK8 | `extract_ess` | Reads from summary tables with row names; model-agnostic | +| OK9 | `summary.bgms` | Correct; label issue is in print method only | + +--- + +## Recommended fix order + +1. **B1** — `extract_category_thresholds`: guard for mixed MRF. +2. **B2** — `extract_pairwise_interactions`: use stored parameter + names for mixed MRF. +3. **B3** — `build_output_mixed_mrf`: pass `allocation_names`. +4. **I1/I4** — Document `coef.bgms` return type for mixed MRF. +5. **I2** — Improve `print.summary.bgms` label. +6. **I3** — Add model type to `print.bgms`. diff --git a/dev/test_step_size.R b/dev/test_step_size.R new file mode 100644 index 00000000..1fdb077a --- /dev/null +++ b/dev/test_step_size.R @@ -0,0 +1,35 @@ +devtools::load_all() + +set.seed(42) +n = 500L +x = matrix(sample(0:1, n * 2, replace = TRUE), n, 2) +y = matrix(rnorm(n * 2), n, 2) + +input_list = list( + discrete_observations = x, + continuous_observations = y, + num_categories = as.integer(c(1, 1)), + is_ordinal_variable = as.integer(c(1, 1)), + baseline_category = as.integer(c(0, 0)), + main_alpha = 0.5, + main_beta = 0.5, + pairwise_scale = 2.5, + pseudolikelihood = "full" +) + +# p=2 binary: mux=2, Kxx=1, muy=2, Kxy=4 => dim=9 +theta0 = rep(0, 9) + +cat("--- Gradient test ---\n") +r = test_mixed_gradient(input_list, theta0) +cat("logp:", r$logp, "\n") +cat("grad length:", length(r$gradient), "\n") +cat("grad:", round(r$gradient, 2), "\n") +cat("grad norm:", round(sqrt(sum(r$gradient^2)), 2), "\n") + +cat("\n--- Step-size heuristic ---\n") +r2 = test_mixed_step_size(input_list, theta0) +cat("Step size:", r2$step_size, "\n") +cat("Elapsed ms:", r2$elapsed_ms, "\n") +cat("logp at theta:", r2$logp_at_theta, "\n") +cat("Grad norm:", round(r2$grad_norm, 2), "\n") diff --git a/dev/tests/compare_bgms_mixedgm_cycle.R b/dev/tests/compare_bgms_mixedgm_cycle.R new file mode 100644 index 00000000..f8ee72b3 --- /dev/null +++ b/dev/tests/compare_bgms_mixedgm_cycle.R @@ -0,0 +1,213 @@ +# ============================================================================== +# Compare bgms vs mixedGM marginal PL cycle test +# ============================================================================== +# Uses Boredom data (8 items: 4 ordinal + 4 continuous). +# Fits marginal PL with both packages, simulates, refits, compares main effects. +# ============================================================================== + +devtools::load_all(quiet = TRUE) # bgms +library(mixedGM) + +data("Boredom", package = "bgms") +raw = as.matrix(Boredom[, 2:9]) +raw = raw - 1L # shift 1-7 to 0-6 +colnames(raw) = names(Boredom)[2:9] +n_obs = nrow(raw) + +# Assign variable types: alternating ordinal / continuous +ord_idx = c(1, 3, 5, 7) +cont_idx = c(2, 4, 6, 8) + +x_disc = raw[, ord_idx, drop = FALSE] # n x 4 integer ordinal (0-6) +y_cont = raw[, cont_idx, drop = FALSE] # n x 4 integer-valued, treated as continuous +storage.mode(y_cont) = "double" + +num_cats_disc = apply(x_disc, 2, max) + 1L # total categories (mixedGM convention) +p = ncol(x_disc) +q = ncol(y_cont) +seed = 44321 + +cat(sprintf("p = %d ordinal, q = %d continuous, n = %d\n", p, q, n_obs)) +cat("num_categories (max index):", num_cats_disc, "\n\n") + +# ============================================================================== +# mixedGM marginal PL cycle +# ============================================================================== +cat("=== mixedGM: marginal PL ===\n") +set.seed(seed) +fit_mg = mixed_sampler(x = x_disc, y = y_cont, + num_categories = num_cats_disc, + pseudolikelihood = "marginal", + log_prior_mean = mixedGM:::log_prior_logistic, + edge_selection = FALSE, + n_warmup = 5000, n_samples = 5000, + verbose = FALSE) + +mux_orig_mg = apply(fit_mg$samples$mux, c(2, 3), mean) +muy_orig_mg = colMeans(fit_mg$samples$muy) +Kxx_orig_mg = apply(fit_mg$samples$Kxx, c(2, 3), mean) +Kyy_orig_mg = apply(fit_mg$samples$Kyy, c(2, 3), mean) +Kxy_orig_mg = apply(fit_mg$samples$Kxy, c(2, 3), mean) + +cat("Original mux:\n"); print(round(mux_orig_mg, 3)) +cat("Original muy:", round(muy_orig_mg, 3), "\n\n") + +# Simulate from posterior means +sim_mg = mixed_gibbs_generate( + n = n_obs, + Kxx = Kxx_orig_mg, Kxy = Kxy_orig_mg, Kyy = Kyy_orig_mg, + mux = mux_orig_mg, muy = muy_orig_mg, + num_categories = num_cats_disc, + n_burnin = 1000 +) + +# Refit +set.seed(seed) +refit_mg = mixed_sampler(x = sim_mg$x, y = sim_mg$y, + num_categories = num_cats_disc, + pseudolikelihood = "marginal", + log_prior_mean = mixedGM:::log_prior_logistic, + edge_selection = FALSE, + n_warmup = 5000, n_samples = 5000, + verbose = FALSE) + +mux_refit_mg = apply(refit_mg$samples$mux, c(2, 3), mean) +muy_refit_mg = colMeans(refit_mg$samples$muy) + +cat("Refit mux:\n"); print(round(mux_refit_mg, 3)) +cat("Refit muy:", round(muy_refit_mg, 3), "\n\n") + +# Flat comparison +orig_flat_mg = c(as.vector(t(mux_orig_mg)), muy_orig_mg) +refit_flat_mg = c(as.vector(t(mux_refit_mg)), muy_refit_mg) +keep_mg = !is.na(orig_flat_mg) & !is.na(refit_flat_mg) + +cat("=== mixedGM main-effect comparison ===\n") +df_mg = data.frame(original = round(orig_flat_mg[keep_mg], 3), + refit = round(refit_flat_mg[keep_mg], 3), + diff = round(refit_flat_mg[keep_mg] - orig_flat_mg[keep_mg], 3)) +print(df_mg) +cat("Correlation:", round(cor(orig_flat_mg[keep_mg], refit_flat_mg[keep_mg]), 3), "\n") +cat("Max |diff|:", round(max(abs(df_mg$diff)), 3), "\n\n") + +# ============================================================================== +# bgms marginal PL cycle (for comparison) +# ============================================================================== +cat("=== bgms: marginal PL ===\n") +vtype = c("ordinal", "continuous", "ordinal", "continuous", + "ordinal", "continuous", "ordinal", "continuous") + +fit_bg = bgm(raw, variable_type = vtype, pseudolikelihood = "marginal", + edge_selection = FALSE, seed = seed, + iter = 5000, warmup = 5000, chains = 1, + display_progress = "none") + +set.seed(seed) +sim_bg = simulate(fit_bg, nsim = n_obs, method = "posterior-mean", seed = seed) + +refit_bg = bgm(sim_bg, variable_type = vtype, pseudolikelihood = "marginal", + edge_selection = FALSE, seed = seed, + iter = 5000, warmup = 5000, chains = 1, + display_progress = "none") + +extract_main_flat = function(fit) { + pm = fit$posterior_mean_main + c(as.vector(t(pm$discrete)), as.vector(t(pm$continuous))) +} +orig_flat_bg = extract_main_flat(fit_bg) +refit_flat_bg = extract_main_flat(refit_bg) +keep_bg = !is.na(orig_flat_bg) & !is.na(refit_flat_bg) + +cat("=== bgms main-effect comparison ===\n") +df_bg = data.frame(original = round(orig_flat_bg[keep_bg], 3), + refit = round(refit_flat_bg[keep_bg], 3), + diff = round(refit_flat_bg[keep_bg] - orig_flat_bg[keep_bg], 3)) +print(df_bg) +cat("Correlation:", round(cor(orig_flat_bg[keep_bg], refit_flat_bg[keep_bg]), 3), "\n") +cat("Max |diff|:", round(max(abs(df_bg$diff)), 3), "\n\n") + +# ============================================================================== +# Also compare original estimates between bgms and mixedGM +# ============================================================================== +cat("=== Original estimates: bgms vs mixedGM ===\n") +cat("mux comparison (bgms discrete thresholds vs mixedGM mux):\n") +bgms_mux = fit_bg$posterior_mean_main$discrete +cat("bgms:\n"); print(round(bgms_mux, 3)) +cat("mixedGM:\n"); print(round(mux_orig_mg, 3)) +cat("Max |diff|:", round(max(abs(bgms_mux[!is.na(bgms_mux)] - mux_orig_mg[!is.na(mux_orig_mg)])), 3), "\n\n") + +cat("muy comparison:\n") +bgms_muy = fit_bg$posterior_mean_main$continuous[, "mean"] +cat("bgms:", round(bgms_muy, 3), "\n") +cat("mixedGM:", round(muy_orig_mg, 3), "\n") +cat("Max |diff|:", round(max(abs(bgms_muy - muy_orig_mg)), 3), "\n") + +# ============================================================================== +# Scatterplots: 2x2 grid (mixedGM / bgms) x (pairwise / main) +# ============================================================================== +# Also extract pairwise from mixedGM +Kxx_refit_mg = apply(refit_mg$samples$Kxx, c(2, 3), mean) +Kyy_refit_mg = apply(refit_mg$samples$Kyy, c(2, 3), mean) +Kxy_refit_mg = apply(refit_mg$samples$Kxy, c(2, 3), mean) + +# Flatten upper-tri pairwise for mixedGM +flatten_pw_mg = function(Kxx, Kyy, Kxy) { + c(Kxx[upper.tri(Kxx)], Kyy[upper.tri(Kyy)], as.vector(Kxy)) +} +orig_pw_mg = flatten_pw_mg(Kxx_orig_mg, Kyy_orig_mg, Kxy_orig_mg) +refit_pw_mg = flatten_pw_mg(Kxx_refit_mg, Kyy_refit_mg, Kxy_refit_mg) + +# bgms pairwise +orig_pw_bg = colMeans(extract_pairwise_interactions(fit_bg)) +refit_pw_bg = colMeans(extract_pairwise_interactions(refit_bg)) + +cor_pw_mg = cor(orig_pw_mg, refit_pw_mg) +cor_pw_bg = cor(orig_pw_bg, refit_pw_bg) +cor_main_mg = cor(orig_flat_mg[keep_mg], refit_flat_mg[keep_mg]) +cor_main_bg = cor(orig_flat_bg[keep_bg], refit_flat_bg[keep_bg]) + +out_file = file.path("dev", "tests", "cycle_scatterplots.pdf") +pdf(out_file, width = 10, height = 10) +par(mfrow = c(2, 2), mar = c(4.5, 4.5, 2.5, 1)) + +# Row 1: pairwise +rng_pw = range(c(orig_pw_mg, refit_pw_mg, orig_pw_bg, refit_pw_bg)) + +plot(orig_pw_mg, refit_pw_mg, + xlab = "Original pairwise estimates", + ylab = "Refit pairwise estimates", + main = sprintf("mixedGM marginal PL - pairwise (r = %.3f)", cor_pw_mg), + pch = 19, col = adjustcolor("firebrick", 0.7), + xlim = rng_pw, ylim = rng_pw, asp = 1) +abline(0, 1, lty = 2, col = "grey40") + +plot(orig_pw_bg, refit_pw_bg, + xlab = "Original pairwise estimates", + ylab = "Refit pairwise estimates", + main = sprintf("bgms marginal PL - pairwise (r = %.3f)", cor_pw_bg), + pch = 19, col = adjustcolor("steelblue", 0.7), + xlim = rng_pw, ylim = rng_pw, asp = 1) +abline(0, 1, lty = 2, col = "grey40") + +# Row 2: main effects +rng_main = range(c(orig_flat_mg[keep_mg], refit_flat_mg[keep_mg], + orig_flat_bg[keep_bg], refit_flat_bg[keep_bg])) + +plot(orig_flat_mg[keep_mg], refit_flat_mg[keep_mg], + xlab = "Original main-effect estimates", + ylab = "Refit main-effect estimates", + main = sprintf("mixedGM marginal PL - main (r = %.3f)", cor_main_mg), + pch = 19, col = adjustcolor("firebrick", 0.7), + xlim = rng_main, ylim = rng_main, asp = 1) +abline(0, 1, lty = 2, col = "grey40") + +plot(orig_flat_bg[keep_bg], refit_flat_bg[keep_bg], + xlab = "Original main-effect estimates", + ylab = "Refit main-effect estimates", + main = sprintf("bgms marginal PL - main (r = %.3f)", cor_main_bg), + pch = 19, col = adjustcolor("steelblue", 0.7), + xlim = rng_main, ylim = rng_main, asp = 1) +abline(0, 1, lty = 2, col = "grey40") + +dev.off() +cat(sprintf("\nSaved to %s\n", out_file)) diff --git a/dev/tests/cycle_scatterplots.pdf b/dev/tests/cycle_scatterplots.pdf new file mode 100644 index 0000000000000000000000000000000000000000..e78d5ebd8d192825462fad292a13437337d9ebcb GIT binary patch literal 13023 zcmb7r1yq#J+qQ}zf}pf0v843UUDAk1Np~!WY!FMAbT@*M(n>0|gn)F1Al=>F{q3Uu 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+colnames(x) = names(Boredom)[2:9] +n_obs = nrow(x) +vtype = c("ordinal", "continuous", "ordinal", "continuous", + "ordinal", "continuous", "ordinal", "continuous") +seed = 44321 + +extract_main_flat = function(fit) { + pm = fit$posterior_mean_main + c(as.vector(t(pm$discrete)), as.vector(t(pm$continuous))) +} + +# --- Marginal PL --- +cat("Fitting marginal PL...\n") +fit_marg = bgm(x, variable_type = vtype, pseudolikelihood = "marginal", + edge_selection = FALSE, seed = seed, + iter = 1000, warmup = 1000, chains = 1, + display_progress = "none") + +cat("=== Original marginal PL main effects ===\n") +cat("Discrete:\n"); print(round(fit_marg$posterior_mean_main$discrete, 3)) +cat("Continuous:\n"); print(round(fit_marg$posterior_mean_main$continuous, 3)) + +set.seed(seed) +sim_marg = simulate(fit_marg, nsim = n_obs, method = "posterior-mean", seed = seed) + +cat("\nRefitting on simulated data...\n") +refit_marg = bgm(sim_marg, variable_type = vtype, pseudolikelihood = "marginal", + edge_selection = FALSE, seed = seed, + iter = 1000, warmup = 1000, chains = 1, + display_progress = "none") + +cat("=== Refit marginal PL main effects ===\n") +cat("Discrete:\n"); print(round(refit_marg$posterior_mean_main$discrete, 3)) +cat("Continuous:\n"); print(round(refit_marg$posterior_mean_main$continuous, 3)) + +o = extract_main_flat(fit_marg) +r = extract_main_flat(refit_marg) +keep = !is.na(o) & !is.na(r) +cat("\n=== Flat comparison (non-NA) ===\n") +df = data.frame(original = round(o[keep], 3), refit = round(r[keep], 3), + diff = round(r[keep] - o[keep], 3)) +print(df) +cat("\nCorrelation:", round(cor(o[keep], r[keep]), 3), "\n") +cat("Original range:", round(range(o[keep]), 3), "\n") +cat("Refit range: ", round(range(r[keep]), 3), "\n") + +# --- Also compare with conditional PL for reference --- +cat("\n\n--- Conditional PL for comparison ---\n") +fit_cond = bgm(x, variable_type = vtype, pseudolikelihood = "conditional", + edge_selection = FALSE, seed = seed, + iter = 1000, warmup = 1000, chains = 1, + display_progress = "none") + +set.seed(seed) +sim_cond = simulate(fit_cond, nsim = n_obs, method = "posterior-mean", seed = seed) + +refit_cond = bgm(sim_cond, variable_type = vtype, pseudolikelihood = "conditional", + edge_selection = FALSE, seed = seed, + iter = 1000, warmup = 1000, chains = 1, + display_progress = "none") + +oc = extract_main_flat(fit_cond) +rc = extract_main_flat(refit_cond) +keepc = !is.na(oc) & !is.na(rc) +cat("\n=== Conditional PL flat comparison (non-NA) ===\n") +dfc = data.frame(original = round(oc[keepc], 3), refit = round(rc[keepc], 3), + diff = round(rc[keepc] - oc[keepc], 3)) +print(dfc) +cat("\nCorrelation:", round(cor(oc[keepc], rc[keepc]), 3), "\n") diff --git a/dev/tests/easybgm/test_easybgm_bgms_interaction.R b/dev/tests/easybgm/test_easybgm_bgms_interaction.R new file mode 100644 index 00000000..e69de29b diff --git a/dev/tests/plot_cycle_scatterplots.R b/dev/tests/plot_cycle_scatterplots.R new file mode 100644 index 00000000..26381c9f --- /dev/null +++ b/dev/tests/plot_cycle_scatterplots.R @@ -0,0 +1,139 @@ +# ============================================================================== +# Scatterplots for mixed MRF simulation-recovery cycle tests +# ============================================================================== +# Fits mixed MRF with conditional and marginal PL on the full Boredom data +# (8 items, 4 ordinal + 4 continuous), simulates from each fit, refits on +# simulated data, and plots original vs refit estimates for both pairwise +# interactions and main effects. +# ============================================================================== + +devtools::load_all(quiet = TRUE) + +data("Boredom", package = "bgms") +x = as.matrix(Boredom[, 2:9]) +x = x - 1L +colnames(x) = names(Boredom)[2:9] +n_obs = nrow(x) +vtype = c("ordinal", "continuous", "ordinal", "continuous", + "ordinal", "continuous", "ordinal", "continuous") +seed = 44321 + +# Helper: extract a flat main-effects vector from a mixed fit +extract_main_flat = function(fit) { + pm = fit$posterior_mean_main + c(as.vector(t(pm$discrete)), # discrete thresholds (row-major) + as.vector(t(pm$continuous))) # continuous mean + precision (row-major) +} + +# ============================================================================== +# Conditional PL +# ============================================================================== +cat("Fitting conditional PL...\n") +fit_cond = bgm(x, variable_type = vtype, pseudolikelihood = "conditional", + edge_selection = FALSE, seed = seed, + iter = 1000, warmup = 1000, chains = 1, + display_progress = "none") + +orig_pw_cond = colMeans(extract_pairwise_interactions(fit_cond)) +orig_main_cond = extract_main_flat(fit_cond) + +cat("Simulating from conditional PL fit...\n") +set.seed(seed) +sim_cond = simulate(fit_cond, nsim = n_obs, method = "posterior-mean", + seed = seed) + +cat("Refitting on conditional PL simulated data...\n") +refit_cond = bgm(sim_cond, variable_type = vtype, + pseudolikelihood = "conditional", + edge_selection = FALSE, seed = seed, + iter = 1000, warmup = 1000, chains = 1, + display_progress = "none") + +refit_pw_cond = colMeans(extract_pairwise_interactions(refit_cond)) +refit_main_cond = extract_main_flat(refit_cond) +cor_pw_cond = cor(orig_pw_cond, refit_pw_cond) +cor_main_cond = cor(orig_main_cond, refit_main_cond, use = "complete.obs") +cat(sprintf("Conditional PL — pairwise r = %.3f, main r = %.3f\n", + cor_pw_cond, cor_main_cond)) + +# ============================================================================== +# Marginal PL +# ============================================================================== +cat("Fitting marginal PL...\n") +fit_marg = bgm(x, variable_type = vtype, pseudolikelihood = "marginal", + edge_selection = FALSE, seed = seed, + iter = 1000, warmup = 1000, chains = 1, + display_progress = "none") + +orig_pw_marg = colMeans(extract_pairwise_interactions(fit_marg)) +orig_main_marg = extract_main_flat(fit_marg) + +cat("Simulating from marginal PL fit...\n") +set.seed(seed) +sim_marg = simulate(fit_marg, nsim = n_obs, method = "posterior-mean", + seed = seed) + +cat("Refitting on marginal PL simulated data...\n") +refit_marg = bgm(sim_marg, variable_type = vtype, + pseudolikelihood = "marginal", + edge_selection = FALSE, seed = seed, + iter = 1000, warmup = 1000, chains = 1, + display_progress = "none") + +refit_pw_marg = colMeans(extract_pairwise_interactions(refit_marg)) +refit_main_marg = extract_main_flat(refit_marg) +cor_pw_marg = cor(orig_pw_marg, refit_pw_marg) +cor_main_marg = cor(orig_main_marg, refit_main_marg, use = "complete.obs") +cat(sprintf("Marginal PL — pairwise r = %.3f, main r = %.3f\n", + cor_pw_marg, cor_main_marg)) + +# ============================================================================== +# Plot: 2 x 2 grid (pairwise / main) x (conditional / marginal) +# ============================================================================== +out_file = file.path("dev", "tests", "cycle_scatterplots.pdf") +pdf(out_file, width = 10, height = 10) +par(mfrow = c(2, 2), mar = c(4.5, 4.5, 2.5, 1)) + +# Row 1: pairwise +rng_pw = range(c(orig_pw_cond, refit_pw_cond, orig_pw_marg, refit_pw_marg)) + +plot(orig_pw_cond, refit_pw_cond, + xlab = "Original pairwise estimates", + ylab = "Refit pairwise estimates", + main = sprintf("Conditional PL — pairwise (r = %.3f)", cor_pw_cond), + pch = 19, col = adjustcolor("steelblue", 0.7), + xlim = rng_pw, ylim = rng_pw, asp = 1) +abline(0, 1, lty = 2, col = "grey40") + +plot(orig_pw_marg, refit_pw_marg, + xlab = "Original pairwise estimates", + ylab = "Refit pairwise estimates", + main = sprintf("Marginal PL — pairwise (r = %.3f)", cor_pw_marg), + pch = 19, col = adjustcolor("firebrick", 0.7), + xlim = rng_pw, ylim = rng_pw, asp = 1) +abline(0, 1, lty = 2, col = "grey40") + +# Row 2: main effects (drop NAs from padding) +keep_cond = !is.na(orig_main_cond) & !is.na(refit_main_cond) +keep_marg = !is.na(orig_main_marg) & !is.na(refit_main_marg) +rng_main = range(c(orig_main_cond[keep_cond], refit_main_cond[keep_cond], + orig_main_marg[keep_marg], refit_main_marg[keep_marg])) + +plot(orig_main_cond[keep_cond], refit_main_cond[keep_cond], + xlab = "Original main-effect estimates", + ylab = "Refit main-effect estimates", + main = sprintf("Conditional PL — main (r = %.3f)", cor_main_cond), + pch = 19, col = adjustcolor("steelblue", 0.7), + xlim = rng_main, ylim = rng_main, asp = 1) +abline(0, 1, lty = 2, col = "grey40") + +plot(orig_main_marg[keep_marg], refit_main_marg[keep_marg], + xlab = "Original main-effect estimates", + ylab = "Refit main-effect estimates", + main = sprintf("Marginal PL — main (r = %.3f)", cor_main_marg), + pch = 19, col = adjustcolor("firebrick", 0.7), + xlim = rng_main, ylim = rng_main, asp = 1) +abline(0, 1, lty = 2, col = "grey40") + +dev.off() +cat(sprintf("Saved to %s\n", out_file)) diff --git a/dev/tests/validation/group1_parameter_recovery.R b/dev/tests/validation/group1_parameter_recovery.R new file mode 100644 index 00000000..cd287c0e --- /dev/null +++ b/dev/tests/validation/group1_parameter_recovery.R @@ -0,0 +1,189 @@ +# =========================================================================== +# Group 1: Parameter recovery on known mixed-MRF networks +# =========================================================================== +# Simulate data from networks with known parameters, fit with bgms, and +# verify that posterior means recover the true values. Tests three network +# sizes (small, medium, large) and both PL methods. +# +# Output: numerical summary + PDF with recovery scatter plots. +# =========================================================================== + +devtools::load_all(quiet = TRUE) +source(file.path("dev", "tests", "validation", "helpers.R")) + +out_dir = file.path("dev", "tests", "validation", "output") +dir.create(out_dir, recursive = TRUE, showWarnings = FALSE) + +cat("=======================================================================\n") +cat(" Group 1: Parameter recovery on known networks\n") +cat("=======================================================================\n\n") + +# ------------------------------------------------------------------ +# 1a. Small network: 2 ordinal + 2 continuous, dense +# ------------------------------------------------------------------ +cat("--- 1a: Small network (p=2, q=2) --------------------------------\n") +net_small = make_network(p = 2, q = 2, n_cat = c(1L, 2L), density = 1.0, seed = 101) +dat_small = generate_data(net_small, n = 2000, source = "bgms", seed = 201) + +true_small = list( + mux = net_small$mux, muy = net_small$muy, + Kxx = net_small$Kxx, Kxy = net_small$Kxy, Kyy = net_small$Kyy +) + +fit_small_cond = bgm( + dat_small, + variable_type = c(rep("ordinal", 2), rep("continuous", 2)), + pseudolikelihood = "conditional", + edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 2, + seed = 301 +) + +fit_small_marg = bgm( + dat_small, + variable_type = c(rep("ordinal", 2), rep("continuous", 2)), + pseudolikelihood = "marginal", + edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 2, + seed = 302 +) + +est_small_cond = extract_bgms_blocks(fit_small_cond, net_small) +est_small_marg = extract_bgms_blocks(fit_small_marg, net_small) + +tab_sc = recovery_table(true_small, est_small_cond, "conditional") +tab_sm = recovery_table(true_small, est_small_marg, "marginal") + +summarise_recovery(tab_sc, "Small conditional") +summarise_recovery(tab_sm, "Small marginal") + +# ------------------------------------------------------------------ +# 1b. Medium network: 4 ordinal + 3 continuous, sparse +# ------------------------------------------------------------------ +cat("\n--- 1b: Medium network (p=4, q=3) -------------------------------\n") +net_med = make_network(p = 4, q = 3, n_cat = c(1L, 2L, 3L, 1L), density = 0.4, seed = 102) +dat_med = generate_data(net_med, n = 3000, source = "bgms", seed = 202) + +true_med = list( + mux = net_med$mux, muy = net_med$muy, + Kxx = net_med$Kxx, Kxy = net_med$Kxy, Kyy = net_med$Kyy +) + +fit_med_cond = bgm( + dat_med, + variable_type = c(rep("ordinal", 4), rep("continuous", 3)), + pseudolikelihood = "conditional", + edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 2, + seed = 303 +) + +fit_med_marg = bgm( + dat_med, + variable_type = c(rep("ordinal", 4), rep("continuous", 3)), + pseudolikelihood = "marginal", + edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 2, + seed = 304 +) + +est_med_cond = extract_bgms_blocks(fit_med_cond, net_med) +est_med_marg = extract_bgms_blocks(fit_med_marg, net_med) + +tab_mc = recovery_table(true_med, est_med_cond, "conditional") +tab_mm = recovery_table(true_med, est_med_marg, "marginal") + +summarise_recovery(tab_mc, "Medium conditional") +summarise_recovery(tab_mm, "Medium marginal") + +# ------------------------------------------------------------------ +# 1c. Larger network: 6 ordinal + 4 continuous, moderate density +# ------------------------------------------------------------------ +cat("\n--- 1c: Larger network (p=6, q=4) -------------------------------\n") +net_large = make_network(p = 6, q = 4, n_cat = c(1L, 2L, 3L, 1L, 2L, 1L), + density = 0.3, seed = 103) +dat_large = generate_data(net_large, n = 5000, source = "bgms", seed = 203) + +true_large = list( + mux = net_large$mux, muy = net_large$muy, + Kxx = net_large$Kxx, Kxy = net_large$Kxy, Kyy = net_large$Kyy +) + +fit_large_marg = bgm( + dat_large, + variable_type = c(rep("ordinal", 6), rep("continuous", 4)), + pseudolikelihood = "marginal", + edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 2, + seed = 305 +) + +est_large_marg = extract_bgms_blocks(fit_large_marg, net_large) +tab_lm = recovery_table(true_large, est_large_marg, "marginal") +summarise_recovery(tab_lm, "Large marginal") + +# ------------------------------------------------------------------ +# 1d. Mixed ordinal + Blume-Capel network: 4 discrete + 2 continuous +# ------------------------------------------------------------------ +cat("\n--- 1d: Mixed ordinal + BC (p=4, q=2) ---------------------------\n") +net_bc = make_network( + p = 4, q = 2, + n_cat = c(1L, 2L, 2L, 3L), + variable_type = c("ordinal", "ordinal", "blume-capel", "blume-capel"), + baseline_category = c(0L, 0L, 1L, 1L), + density = 0.5, seed = 104 +) +dat_bc = generate_data(net_bc, n = 3000, source = "bgms", seed = 204) + +true_bc = list( + mux = net_bc$mux, muy = net_bc$muy, + Kxx = net_bc$Kxx, Kxy = net_bc$Kxy, Kyy = net_bc$Kyy +) + +fit_bc_marg = bgm( + dat_bc, + variable_type = c("ordinal", "ordinal", "blume-capel", "blume-capel", + "continuous", "continuous"), + baseline_category = c(0L, 0L, 1L, 1L), + pseudolikelihood = "marginal", + edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 2, + seed = 306 +) + +est_bc_marg = extract_bgms_blocks(fit_bc_marg, net_bc) +tab_bcm = recovery_table(true_bc, est_bc_marg, "marginal") +summarise_recovery(tab_bcm, "BC marginal") + +fit_bc_cond = bgm( + dat_bc, + variable_type = c("ordinal", "ordinal", "blume-capel", "blume-capel", + "continuous", "continuous"), + baseline_category = c(0L, 0L, 1L, 1L), + pseudolikelihood = "conditional", + edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 2, + seed = 307 +) + +est_bc_cond = extract_bgms_blocks(fit_bc_cond, net_bc) +tab_bcc = recovery_table(true_bc, est_bc_cond, "conditional") +summarise_recovery(tab_bcc, "BC conditional") + +# ------------------------------------------------------------------ +# Plots +# ------------------------------------------------------------------ +pdf(file.path(out_dir, "group1_parameter_recovery.pdf"), width = 12, height = 10) +par(mfrow = c(2, 4)) +recovery_scatter(tab_sc, "Small conditional PL") +recovery_scatter(tab_sm, "Small marginal PL") +recovery_scatter(tab_mc, "Medium conditional PL") +recovery_scatter(tab_mm, "Medium marginal PL") +recovery_scatter(tab_lm, "Large marginal PL") +recovery_scatter(tab_bcm, "BC marginal PL") +recovery_scatter(tab_bcc, "BC conditional PL") +plot.new() # blank panel +dev.off() + +cat(sprintf("\nPlots saved to %s/group1_parameter_recovery.pdf\n", out_dir)) +cat("=== Group 1 complete =============================================\n\n") diff --git a/dev/tests/validation/group2_mh_vs_nuts.R b/dev/tests/validation/group2_mh_vs_nuts.R new file mode 100644 index 00000000..5b379c22 --- /dev/null +++ b/dev/tests/validation/group2_mh_vs_nuts.R @@ -0,0 +1,239 @@ +# =========================================================================== +# Group 2: Metropolis vs NUTS posterior agreement +# =========================================================================== +# Fit the same data with adaptive-metropolis and hybrid-NUTS. Compare +# posterior means; if both samplers target the same pseudo-posterior, +# estimates should agree closely. +# +# Output: numerical summary + PDF with agreement scatter plots. +# =========================================================================== + +devtools::load_all(quiet = TRUE) +source(file.path("dev", "tests", "validation", "helpers.R")) + +out_dir = file.path("dev", "tests", "validation", "output") +dir.create(out_dir, recursive = TRUE, showWarnings = FALSE) + +cat("=======================================================================\n") +cat(" Group 2: Metropolis vs NUTS posterior agreement\n") +cat("=======================================================================\n\n") + +# Use medium network from group 1 for consistency +net = make_network(p = 4, q = 3, n_cat = c(1L, 2L, 3L, 1L), density = 0.4, seed = 102) +dat = generate_data(net, n = 3000, source = "bgms", seed = 202) +vtype = c(rep("ordinal", 4), rep("continuous", 3)) + +true_blocks = list( + mux = net$mux, muy = net$muy, + Kxx = net$Kxx, Kxy = net$Kxy, Kyy = net$Kyy +) + +# ------------------------------------------------------------------ +# 2a. Conditional PL: Metropolis vs NUTS +# ------------------------------------------------------------------ +cat("--- 2a: Conditional PL — Metropolis vs NUTS ----------------------\n") + +fit_mh_cond = bgm( + dat, variable_type = vtype, + pseudolikelihood = "conditional", + update_method = "adaptive-metropolis", + edge_selection = FALSE, + iter = 15000, warmup = 10000, chains = 2, + seed = 401 +) + +fit_nuts_cond = bgm( + dat, variable_type = vtype, + pseudolikelihood = "conditional", + update_method = "nuts", + edge_selection = FALSE, + iter = 5000, warmup = 3000, chains = 2, + seed = 402 +) + +est_mh_cond = extract_bgms_blocks(fit_mh_cond, net) +est_nuts_cond = extract_bgms_blocks(fit_nuts_cond, net) + +tab_mh_cond = recovery_table(true_blocks, est_mh_cond, "MH-conditional") +tab_nuts_cond = recovery_table(true_blocks, est_nuts_cond, "NUTS-conditional") + +cat(" Metropolis:\n") +summarise_recovery(tab_mh_cond, "MH cond") +cat(" NUTS:\n") +summarise_recovery(tab_nuts_cond, "NUTS cond") + +# Direct agreement +r_cond = cor(tab_mh_cond$estimate, tab_nuts_cond$estimate) +rmse_cond = sqrt(mean((tab_mh_cond$estimate - tab_nuts_cond$estimate)^2)) +cat(sprintf(" MH vs NUTS agreement: r = %.4f, RMSE = %.4f\n", r_cond, rmse_cond)) + +# ------------------------------------------------------------------ +# 2b. Marginal PL: Metropolis vs NUTS +# ------------------------------------------------------------------ +cat("\n--- 2b: Marginal PL — Metropolis vs NUTS -------------------------\n") + +fit_mh_marg = bgm( + dat, variable_type = vtype, + pseudolikelihood = "marginal", + update_method = "adaptive-metropolis", + edge_selection = FALSE, + iter = 15000, warmup = 10000, chains = 2, + seed = 403 +) + +fit_nuts_marg = bgm( + dat, variable_type = vtype, + pseudolikelihood = "marginal", + update_method = "nuts", + edge_selection = FALSE, + iter = 5000, warmup = 3000, chains = 2, + seed = 404 +) + +est_mh_marg = extract_bgms_blocks(fit_mh_marg, net) +est_nuts_marg = extract_bgms_blocks(fit_nuts_marg, net) + +tab_mh_marg = recovery_table(true_blocks, est_mh_marg, "MH-marginal") +tab_nuts_marg = recovery_table(true_blocks, est_nuts_marg, "NUTS-marginal") + +cat(" Metropolis:\n") +summarise_recovery(tab_mh_marg, "MH marg") +cat(" NUTS:\n") +summarise_recovery(tab_nuts_marg, "NUTS marg") + +r_marg = cor(tab_mh_marg$estimate, tab_nuts_marg$estimate) +rmse_marg = sqrt(mean((tab_mh_marg$estimate - tab_nuts_marg$estimate)^2)) +cat(sprintf(" MH vs NUTS agreement: r = %.4f, RMSE = %.4f\n", r_marg, rmse_marg)) + +# ------------------------------------------------------------------ +# 2c. Mixed ordinal + Blume-Capel: Metropolis vs NUTS (marginal PL) +# ------------------------------------------------------------------ +cat("\n--- 2c: BC mixed — Metropolis vs NUTS (marginal PL) --------------\n") + +net_bc = make_network( + p = 4, q = 2, + n_cat = c(1L, 2L, 2L, 3L), + variable_type = c("ordinal", "ordinal", "blume-capel", "blume-capel"), + baseline_category = c(0L, 0L, 1L, 1L), + density = 0.5, seed = 104 +) +dat_bc = generate_data(net_bc, n = 3000, source = "bgms", seed = 204) + +vtype_bc = c("ordinal", "ordinal", "blume-capel", "blume-capel", + "continuous", "continuous") +bl_bc = c(0L, 0L, 1L, 1L) + +true_bc = list(mux = net_bc$mux, muy = net_bc$muy, + Kxx = net_bc$Kxx, Kxy = net_bc$Kxy, Kyy = net_bc$Kyy) + +fit_mh_bc = bgm( + dat_bc, variable_type = vtype_bc, baseline_category = bl_bc, + pseudolikelihood = "marginal", + update_method = "adaptive-metropolis", + edge_selection = FALSE, + iter = 15000, warmup = 10000, chains = 2, + seed = 405 +) + +fit_nuts_bc = bgm( + dat_bc, variable_type = vtype_bc, baseline_category = bl_bc, + pseudolikelihood = "marginal", + update_method = "nuts", + edge_selection = FALSE, + iter = 5000, warmup = 3000, chains = 2, + seed = 406 +) + +est_mh_bc = extract_bgms_blocks(fit_mh_bc, net_bc) +est_nuts_bc = extract_bgms_blocks(fit_nuts_bc, net_bc) + +tab_mh_bc = recovery_table(true_bc, est_mh_bc, "MH-bc") +tab_nuts_bc = recovery_table(true_bc, est_nuts_bc, "NUTS-bc") + +cat(" Metropolis:\n") +summarise_recovery(tab_mh_bc, "MH BC") +cat(" NUTS:\n") +summarise_recovery(tab_nuts_bc, "NUTS BC") + +r_bc = cor(tab_mh_bc$estimate, tab_nuts_bc$estimate) +rmse_bc = sqrt(mean((tab_mh_bc$estimate - tab_nuts_bc$estimate)^2)) +cat(sprintf(" MH vs NUTS agreement: r = %.4f, RMSE = %.4f\n", r_bc, rmse_bc)) + +# ------------------------------------------------------------------ +# 2d. Numerical summary table +# ------------------------------------------------------------------ +cat("\n--- 2d: Summary table ---------------------------------------------\n") +summary_df = data.frame( + comparison = c("conditional", "marginal", "BC-marginal"), + correlation = round(c(r_cond, r_marg, r_bc), 4), + rmse = round(c(rmse_cond, rmse_marg, rmse_bc), 4), + max_diff = round(c(max(abs(tab_mh_cond$estimate - tab_nuts_cond$estimate)), + max(abs(tab_mh_marg$estimate - tab_nuts_marg$estimate)), + max(abs(tab_mh_bc$estimate - tab_nuts_bc$estimate))), 4) +) +print(summary_df, row.names = FALSE) + +# ------------------------------------------------------------------ +# Plots +# ------------------------------------------------------------------ +pdf(file.path(out_dir, "group2_mh_vs_nuts.pdf"), width = 14, height = 14) + +layout(matrix(c(1, 2, 3, 4, 5, 6, 7, 8, 9), nrow = 3, byrow = TRUE)) +par(mar = c(4.5, 4.5, 2.5, 1)) + +# Row 1: conditional PL +recovery_scatter(tab_mh_cond, "MH conditional: recovery") +recovery_scatter(tab_nuts_cond, "NUTS conditional: recovery") +agreement_scatter(tab_mh_cond, tab_nuts_cond, + "MH cond", "NUTS cond", + sprintf("MH vs NUTS conditional (r=%.3f)", r_cond)) + +# Row 2: marginal PL +recovery_scatter(tab_mh_marg, "MH marginal: recovery") +recovery_scatter(tab_nuts_marg, "NUTS marginal: recovery") +agreement_scatter(tab_mh_marg, tab_nuts_marg, + "MH marg", "NUTS marg", + sprintf("MH vs NUTS marginal (r=%.3f)", r_marg)) + +# Row 3: BC marginal PL +recovery_scatter(tab_mh_bc, "MH BC marginal: recovery") +recovery_scatter(tab_nuts_bc, "NUTS BC marginal: recovery") +agreement_scatter(tab_mh_bc, tab_nuts_bc, + "MH BC", "NUTS BC", + sprintf("MH vs NUTS BC marginal (r=%.3f)", r_bc)) + +dev.off() + +# --- Trace plot comparison for selected parameters --- +pdf(file.path(out_dir, "group2_traces.pdf"), width = 10, height = 14) + +# Extract raw samples (combine chains) +mh_cond_pw = do.call(rbind, fit_mh_cond$raw_samples$pairwise) +nuts_cond_pw = do.call(rbind, fit_nuts_cond$raw_samples$pairwise) +mh_marg_pw = do.call(rbind, fit_mh_marg$raw_samples$pairwise) +nuts_marg_pw = do.call(rbind, fit_nuts_marg$raw_samples$pairwise) + +# Assign column names +colnames(mh_cond_pw) = fit_mh_cond$raw_samples$parameter_names$pairwise +colnames(nuts_cond_pw) = fit_nuts_cond$raw_samples$parameter_names$pairwise +colnames(mh_marg_pw) = fit_mh_marg$raw_samples$parameter_names$pairwise +colnames(nuts_marg_pw) = fit_nuts_marg$raw_samples$parameter_names$pairwise + +# Pick first 4 pairwise params for trace comparison +sel_params = colnames(mh_cond_pw)[seq_len(min(4, ncol(mh_cond_pw)))] + +par(mfrow = c(length(sel_params), 2), mar = c(3, 3, 2, 1), mgp = c(2, 0.6, 0)) +for(nm in sel_params) { + # MH trace + plot(mh_cond_pw[, nm], type = "l", col = adjustcolor("steelblue", 0.4), + main = paste(nm, "— MH cond"), xlab = "Iteration", ylab = nm, cex.main = 0.9) + # NUTS trace + plot(nuts_cond_pw[, nm], type = "l", col = adjustcolor("firebrick", 0.4), + main = paste(nm, "— NUTS cond"), xlab = "Iteration", ylab = nm, cex.main = 0.9) +} + +dev.off() + +cat(sprintf("\nPlots saved to %s/group2_mh_vs_nuts.pdf\n", out_dir)) +cat(sprintf("Traces saved to %s/group2_traces.pdf\n", out_dir)) +cat("=== Group 2 complete =============================================\n\n") diff --git a/dev/tests/validation/group3_cond_vs_marg.R b/dev/tests/validation/group3_cond_vs_marg.R new file mode 100644 index 00000000..6fc4778a --- /dev/null +++ b/dev/tests/validation/group3_cond_vs_marg.R @@ -0,0 +1,219 @@ +# =========================================================================== +# Group 3: Conditional vs Marginal pseudo-likelihood agreement +# =========================================================================== +# Both PL methods target the same model; posterior means should converge +# to the same values with enough data. This test compares them on the +# same dataset and checks where they diverge, broken down by block. +# +# Output: numerical summary + PDF with agreement scatter and difference +# distributions. +# =========================================================================== + +devtools::load_all(quiet = TRUE) +source(file.path("dev", "tests", "validation", "helpers.R")) + +out_dir = file.path("dev", "tests", "validation", "output") +dir.create(out_dir, recursive = TRUE, showWarnings = FALSE) + +cat("=======================================================================\n") +cat(" Group 3: Conditional vs Marginal PL agreement\n") +cat("=======================================================================\n\n") + +# ------------------------------------------------------------------ +# 3a. Small network (p=2, q=2) +# ------------------------------------------------------------------ +cat("--- 3a: Small network (p=2, q=2) ---------------------------------\n") +net_s = make_network(p = 2, q = 2, n_cat = c(1L, 2L), density = 1.0, seed = 101) +dat_s = generate_data(net_s, n = 2000, source = "bgms", seed = 201) +vtype_s = c(rep("ordinal", 2), rep("continuous", 2)) + +true_s = list(mux = net_s$mux, muy = net_s$muy, + Kxx = net_s$Kxx, Kxy = net_s$Kxy, Kyy = net_s$Kyy) + +fit_cond_s = bgm(dat_s, variable_type = vtype_s, + pseudolikelihood = "conditional", edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 2, + seed = 501) + +fit_marg_s = bgm(dat_s, variable_type = vtype_s, + pseudolikelihood = "marginal", edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 2, + seed = 502) + +est_cond_s = extract_bgms_blocks(fit_cond_s, net_s) +est_marg_s = extract_bgms_blocks(fit_marg_s, net_s) + +tab_cond_s = recovery_table(true_s, est_cond_s, "conditional") +tab_marg_s = recovery_table(true_s, est_marg_s, "marginal") + +summarise_recovery(tab_cond_s, "Small conditional") +summarise_recovery(tab_marg_s, "Small marginal") + +r_s = cor(tab_cond_s$estimate, tab_marg_s$estimate) +max_d_s = max(abs(tab_cond_s$estimate - tab_marg_s$estimate)) +cat(sprintf(" Cond vs Marg: r = %.4f, max|diff| = %.4f\n\n", r_s, max_d_s)) + +# ------------------------------------------------------------------ +# 3b. Medium network (p=4, q=3) +# ------------------------------------------------------------------ +cat("--- 3b: Medium network (p=4, q=3) --------------------------------\n") +net_m = make_network(p = 4, q = 3, n_cat = c(1L, 2L, 3L, 1L), density = 0.4, seed = 102) +dat_m = generate_data(net_m, n = 3000, source = "bgms", seed = 202) +vtype_m = c(rep("ordinal", 4), rep("continuous", 3)) + +true_m = list(mux = net_m$mux, muy = net_m$muy, + Kxx = net_m$Kxx, Kxy = net_m$Kxy, Kyy = net_m$Kyy) + +fit_cond_m = bgm(dat_m, variable_type = vtype_m, + pseudolikelihood = "conditional", edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 2, + seed = 503) + +fit_marg_m = bgm(dat_m, variable_type = vtype_m, + pseudolikelihood = "marginal", edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 2, + seed = 504) + +est_cond_m = extract_bgms_blocks(fit_cond_m, net_m) +est_marg_m = extract_bgms_blocks(fit_marg_m, net_m) + +tab_cond_m = recovery_table(true_m, est_cond_m, "conditional") +tab_marg_m = recovery_table(true_m, est_marg_m, "marginal") + +summarise_recovery(tab_cond_m, "Medium conditional") +summarise_recovery(tab_marg_m, "Medium marginal") + +r_m = cor(tab_cond_m$estimate, tab_marg_m$estimate) +max_d_m = max(abs(tab_cond_m$estimate - tab_marg_m$estimate)) +cat(sprintf(" Cond vs Marg: r = %.4f, max|diff| = %.4f\n\n", r_m, max_d_m)) + +# ------------------------------------------------------------------ +# 3c. Mixed ordinal + Blume-Capel (p=4, q=2) +# ------------------------------------------------------------------ +cat("--- 3c: Mixed ordinal + BC (p=4, q=2) ----------------------------\n") +net_bc = make_network( + p = 4, q = 2, + n_cat = c(1L, 2L, 2L, 3L), + variable_type = c("ordinal", "ordinal", "blume-capel", "blume-capel"), + baseline_category = c(0L, 0L, 1L, 1L), + density = 0.5, seed = 104 +) +dat_bc = generate_data(net_bc, n = 3000, source = "bgms", seed = 204) + +vtype_bc = c("ordinal", "ordinal", "blume-capel", "blume-capel", + "continuous", "continuous") +bl_bc = c(0L, 0L, 1L, 1L) + +true_bc = list(mux = net_bc$mux, muy = net_bc$muy, + Kxx = net_bc$Kxx, Kxy = net_bc$Kxy, Kyy = net_bc$Kyy) + +fit_cond_bc = bgm(dat_bc, variable_type = vtype_bc, baseline_category = bl_bc, + pseudolikelihood = "conditional", edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 2, + seed = 505) + +fit_marg_bc = bgm(dat_bc, variable_type = vtype_bc, baseline_category = bl_bc, + pseudolikelihood = "marginal", edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 2, + seed = 506) + +est_cond_bc = extract_bgms_blocks(fit_cond_bc, net_bc) +est_marg_bc = extract_bgms_blocks(fit_marg_bc, net_bc) + +tab_cond_bc = recovery_table(true_bc, est_cond_bc, "conditional") +tab_marg_bc = recovery_table(true_bc, est_marg_bc, "marginal") + +summarise_recovery(tab_cond_bc, "BC conditional") +summarise_recovery(tab_marg_bc, "BC marginal") + +r_bc = cor(tab_cond_bc$estimate, tab_marg_bc$estimate) +max_d_bc = max(abs(tab_cond_bc$estimate - tab_marg_bc$estimate)) +cat(sprintf(" Cond vs Marg: r = %.4f, max|diff| = %.4f\n\n", r_bc, max_d_bc)) + +# ------------------------------------------------------------------ +# 3d. Per-block divergence analysis +# ------------------------------------------------------------------ +cat("--- 3d: Per-block divergence (medium network) --------------------\n") +blocks = unique(tab_cond_m$block) +block_stats = do.call(rbind, lapply(blocks, function(b) { + idx = tab_cond_m$block == b + d = tab_cond_m$estimate[idx] - tab_marg_m$estimate[idx] + data.frame( + block = b, + n = sum(idx), + mean_diff = mean(d), + sd_diff = sd(d), + max_abs_diff = max(abs(d)), + cor = cor(tab_cond_m$estimate[idx], tab_marg_m$estimate[idx]), + stringsAsFactors = FALSE + ) +})) +cat("Per-block conditional vs marginal divergence:\n") +print(block_stats, row.names = FALSE) + +# ------------------------------------------------------------------ +# Plots +# ------------------------------------------------------------------ +pdf(file.path(out_dir, "group3_cond_vs_marg.pdf"), width = 14, height = 14) + +layout(matrix(1:9, nrow = 3, byrow = TRUE)) +par(mar = c(4.5, 4.5, 2.5, 1)) + +# Row 1: Small network +recovery_scatter(tab_cond_s, "Small: conditional recovery") +recovery_scatter(tab_marg_s, "Small: marginal recovery") +agreement_scatter(tab_cond_s, tab_marg_s, + "Conditional", "Marginal", + sprintf("Small: cond vs marg (r=%.3f)", r_s)) + +# Row 2: Medium network +recovery_scatter(tab_cond_m, "Medium: conditional recovery") +recovery_scatter(tab_marg_m, "Medium: marginal recovery") +agreement_scatter(tab_cond_m, tab_marg_m, + "Conditional", "Marginal", + sprintf("Medium: cond vs marg (r=%.3f)", r_m)) + +# Row 3: BC network +recovery_scatter(tab_cond_bc, "BC: conditional recovery") +recovery_scatter(tab_marg_bc, "BC: marginal recovery") +agreement_scatter(tab_cond_bc, tab_marg_bc, + "Conditional", "Marginal", + sprintf("BC: cond vs marg (r=%.3f)", r_bc)) +dev.off() + +# Difference distribution plots +pdf(file.path(out_dir, "group3_diff_distributions.pdf"), width = 10, height = 8) +par(mfrow = c(2, 3), mar = c(4, 4, 2.5, 1)) + +block_cols = c(mux = "#E41A1C", muy = "#377EB8", Kxx = "#4DAF4A", + Kxy = "#984EA3", Kyy = "#FF7F00") + +for(b in blocks) { + idx = tab_cond_m$block == b + d = tab_cond_m$estimate[idx] - tab_marg_m$estimate[idx] + if(length(d) > 2) { + hist(d, breaks = 15, col = adjustcolor(block_cols[b], 0.5), + border = block_cols[b], + main = sprintf("%s diffs (n=%d)", b, sum(idx)), + xlab = "Conditional - Marginal") + abline(v = 0, lty = 2, col = "grey30") + } else { + barplot(d, names.arg = tab_cond_m$parameter[idx], + col = adjustcolor(block_cols[b], 0.5), + main = sprintf("%s diffs (n=%d)", b, sum(idx)), + ylab = "Conditional - Marginal") + abline(h = 0, lty = 2, col = "grey30") + } +} + +# Overall +all_d = tab_cond_m$estimate - tab_marg_m$estimate +hist(all_d, breaks = 20, col = adjustcolor("grey60", 0.5), + main = sprintf("All diffs (n=%d)", length(all_d)), + xlab = "Conditional - Marginal") +abline(v = 0, lty = 2, col = "red") + +dev.off() + +cat(sprintf("\nPlots saved to %s/group3_*.pdf\n", out_dir)) +cat("=== Group 3 complete =============================================\n\n") diff --git a/dev/tests/validation/group4_cross_package.R b/dev/tests/validation/group4_cross_package.R new file mode 100644 index 00000000..2cdd2b26 --- /dev/null +++ b/dev/tests/validation/group4_cross_package.R @@ -0,0 +1,229 @@ +# =========================================================================== +# Group 4: Cross-package validation (bgms vs mixedGM) +# =========================================================================== +# Both packages implement the same mixed MRF pseudo-likelihood sampler. +# We fit the same data with both and compare posterior means. +# Also runs a simulate-refit cycle: fit -> simulate -> refit -> compare. +# +# Note: mixedGM uses total-categories convention (binary = 2), while +# bgms uses max-index convention (binary = 1). +# The threshold/mean prior in mixedGM must be set to log_prior_logistic +# to match bgms's default Beta(1,1)-on-logistic prior. +# +# Output: numerical summary + PDF with cross-package comparisons. +# =========================================================================== + +devtools::load_all(quiet = TRUE) +library(mixedGM) +source(file.path("dev", "tests", "validation", "helpers.R")) + +out_dir = file.path("dev", "tests", "validation", "output") +dir.create(out_dir, recursive = TRUE, showWarnings = FALSE) + +cat("=======================================================================\n") +cat(" Group 4: Cross-package validation (bgms vs mixedGM)\n") +cat("=======================================================================\n\n") + +# ------------------------------------------------------------------ +# 4a. Setup: shared true network + data +# ------------------------------------------------------------------ +cat("--- 4a: Generating shared data -----------------------------------\n") + +net = make_network(p = 3, q = 2, n_cat = c(1L, 2L, 1L), density = 0.6, seed = 601) + +# Generate data via mixedGM (both packages should accept this) +set.seed(611) +sim = mixedGM::mixed_gibbs_generate( + n = 2000, + Kxx = net$Kxx, Kxy = net$Kxy, Kyy = net$Kyy, + mux = net$mux, muy = net$muy, + num_categories = net$n_cat + 1L, + n_burnin = 1000 +) + +x_disc = sim$x # n x p integer matrix +y_cont = sim$y # n x q numeric matrix + +true_blocks = list(mux = net$mux, muy = net$muy, + Kxx = net$Kxx, Kxy = net$Kxy, Kyy = net$Kyy) + +cat(sprintf(" p = %d ordinal, q = %d continuous, n = %d\n", + net$p, net$q, nrow(x_disc))) +cat(" n_cat (bgms convention):", net$n_cat, "\n") +cat(" n_cat (mixedGM convention):", net$n_cat + 1L, "\n\n") + +# ------------------------------------------------------------------ +# 4b. Fit with mixedGM (marginal PL) +# ------------------------------------------------------------------ +cat("--- 4b: mixedGM marginal PL fit ----------------------------------\n") + +set.seed(621) +fit_mgm_marg = mixedGM::mixed_sampler( + x = x_disc, y = y_cont, + num_categories = net$n_cat + 1L, # mixedGM: total categories + pseudolikelihood = "marginal", + log_prior_mean = mixedGM:::log_prior_logistic, + edge_selection = FALSE, + n_warmup = 5000, n_samples = 10000, + verbose = FALSE +) + +est_mgm_marg = extract_mgm_blocks(fit_mgm_marg, n_cat = net$n_cat) +tab_mgm_marg = recovery_table(true_blocks, est_mgm_marg, "mixedGM-marginal") +summarise_recovery(tab_mgm_marg, "mixedGM marginal") + +# ------------------------------------------------------------------ +# 4c. Fit with bgms (marginal PL) +# ------------------------------------------------------------------ +cat("\n--- 4c: bgms marginal PL fit -------------------------------------\n") + +# Combine x and y into single data frame (bgms convention) +bgms_dat = as.data.frame(cbind(x_disc, y_cont)) +names(bgms_dat) = c(paste0("X", seq_len(net$p)), paste0("Y", seq_len(net$q))) +vtype = c(rep("ordinal", net$p), rep("continuous", net$q)) + +fit_bgms_marg = bgm( + bgms_dat, variable_type = vtype, + pseudolikelihood = "marginal", + edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 2, + seed = 622 +) + +est_bgms_marg = extract_bgms_blocks(fit_bgms_marg, net) +tab_bgms_marg = recovery_table(true_blocks, est_bgms_marg, "bgms-marginal") +summarise_recovery(tab_bgms_marg, "bgms marginal") + +# ------------------------------------------------------------------ +# 4d. Head-to-head comparison +# ------------------------------------------------------------------ +cat("\n--- 4d: bgms vs mixedGM comparison --------------------------------\n") + +r_cross = cor(tab_bgms_marg$estimate, tab_mgm_marg$estimate) +rmse_cross = sqrt(mean((tab_bgms_marg$estimate - tab_mgm_marg$estimate)^2)) +max_d_cross = max(abs(tab_bgms_marg$estimate - tab_mgm_marg$estimate)) +cat(sprintf(" bgms vs mixedGM: r = %.4f, RMSE = %.4f, max|diff| = %.4f\n", + r_cross, rmse_cross, max_d_cross)) + +# Per-block comparison +blocks = unique(tab_bgms_marg$block) +cross_stats = do.call(rbind, lapply(blocks, function(b) { + idx = tab_bgms_marg$block == b + d = tab_bgms_marg$estimate[idx] - tab_mgm_marg$estimate[idx] + data.frame( + block = b, n = sum(idx), + mean_diff = round(mean(d), 4), + max_abs_diff = round(max(abs(d)), 4), + cor = round(cor(tab_bgms_marg$estimate[idx], tab_mgm_marg$estimate[idx]), 4), + stringsAsFactors = FALSE + ) +})) +cat("\n Per-block bgms vs mixedGM:\n") +print(cross_stats, row.names = FALSE) + +# ------------------------------------------------------------------ +# 4e. Conditional PL comparison +# ------------------------------------------------------------------ +cat("\n--- 4e: Conditional PL comparison ---------------------------------\n") + +set.seed(631) +fit_mgm_cond = mixedGM::mixed_sampler( + x = x_disc, y = y_cont, + num_categories = net$n_cat + 1L, + pseudolikelihood = "conditional", + log_prior_mean = mixedGM:::log_prior_logistic, + edge_selection = FALSE, + n_warmup = 5000, n_samples = 10000, + verbose = FALSE +) +est_mgm_cond = extract_mgm_blocks(fit_mgm_cond, n_cat = net$n_cat) +tab_mgm_cond = recovery_table(true_blocks, est_mgm_cond, "mixedGM-conditional") + +fit_bgms_cond = bgm( + bgms_dat, variable_type = vtype, + pseudolikelihood = "conditional", + edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 2, + seed = 632 +) +est_bgms_cond = extract_bgms_blocks(fit_bgms_cond, net) +tab_bgms_cond = recovery_table(true_blocks, est_bgms_cond, "bgms-conditional") + +r_cond = cor(tab_bgms_cond$estimate, tab_mgm_cond$estimate) +rmse_cond = sqrt(mean((tab_bgms_cond$estimate - tab_mgm_cond$estimate)^2)) +cat(sprintf(" bgms vs mixedGM (conditional): r = %.4f, RMSE = %.4f\n", + r_cond, rmse_cond)) + +# ------------------------------------------------------------------ +# 4f. Simulate-refit cycle (bgms) +# ------------------------------------------------------------------ +cat("\n--- 4f: bgms simulate-refit cycle ---------------------------------\n") + +sim_bgms = simulate(fit_bgms_marg, nsim = 2000, method = "posterior-mean", seed = 641) + +refit_bgms = bgm( + sim_bgms, variable_type = vtype, + pseudolikelihood = "marginal", + edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 2, + seed = 642 +) + +est_orig = extract_bgms_blocks(fit_bgms_marg, net) +est_refit = extract_bgms_blocks(refit_bgms, net) + +tab_orig = recovery_table(true_blocks, est_orig, "bgms-original") +tab_refit = recovery_table(true_blocks, est_refit, "bgms-refit") + +r_cycle = cor(tab_orig$estimate, tab_refit$estimate) +rmse_cycle = sqrt(mean((tab_orig$estimate - tab_refit$estimate)^2)) +cat(sprintf(" bgms cycle: original vs refit r = %.4f, RMSE = %.4f\n", + r_cycle, rmse_cycle)) + +# ------------------------------------------------------------------ +# Master summary +# ------------------------------------------------------------------ +cat("\n--- Summary of all cross-package comparisons ----------------------\n") +master_summary = data.frame( + comparison = c("bgms vs mixedGM (marginal)", "bgms vs mixedGM (conditional)", + "bgms cycle (marginal)"), + correlation = round(c(r_cross, r_cond, r_cycle), 4), + rmse = round(c(rmse_cross, rmse_cond, rmse_cycle), 4), + max_diff = round(c(max_d_cross, + max(abs(tab_bgms_cond$estimate - tab_mgm_cond$estimate)), + max(abs(tab_orig$estimate - tab_refit$estimate))), 4) +) +print(master_summary, row.names = FALSE) + +# ------------------------------------------------------------------ +# Plots +# ------------------------------------------------------------------ +pdf(file.path(out_dir, "group4_cross_package.pdf"), width = 15, height = 12) +layout(matrix(1:9, nrow = 3, byrow = TRUE)) +par(mar = c(4.5, 4.5, 2.5, 1)) + +# Row 1: Marginal PL — recovery from truth +recovery_scatter(tab_mgm_marg, "mixedGM marginal: recovery") +recovery_scatter(tab_bgms_marg, "bgms marginal: recovery") +agreement_scatter(tab_bgms_marg, tab_mgm_marg, + "bgms", "mixedGM", + sprintf("bgms vs mixedGM marginal (r=%.3f)", r_cross)) + +# Row 2: Conditional PL — recovery from truth +recovery_scatter(tab_mgm_cond, "mixedGM conditional: recovery") +recovery_scatter(tab_bgms_cond, "bgms conditional: recovery") +agreement_scatter(tab_bgms_cond, tab_mgm_cond, + "bgms", "mixedGM", + sprintf("bgms vs mixedGM conditional (r=%.3f)", r_cond)) + +# Row 3: bgms cycle +recovery_scatter(tab_orig, "bgms original fit") +recovery_scatter(tab_refit, "bgms refit (simulated data)") +agreement_scatter(tab_orig, tab_refit, + "Original", "Refit", + sprintf("bgms cycle (r=%.3f)", r_cycle)) + +dev.off() + +cat(sprintf("\nPlots saved to %s/group4_cross_package.pdf\n", out_dir)) +cat("=== Group 4 complete =============================================\n\n") diff --git a/dev/tests/validation/group5_edge_detection.R b/dev/tests/validation/group5_edge_detection.R new file mode 100644 index 00000000..69ef4290 --- /dev/null +++ b/dev/tests/validation/group5_edge_detection.R @@ -0,0 +1,323 @@ +# =========================================================================== +# Group 5: Edge detection accuracy +# =========================================================================== +# Tests Bayesian edge selection on known sparse networks. With edge_selection +# TRUE, the sampler reports posterior inclusion probabilities (PIPs). We +# evaluate sensitivity, specificity, precision, F1, and MCC at a 0.5 PIP +# threshold and also via ROC-like analysis. +# +# Conditions: weak edges (small J), medium edges, strong edges, +# both PL methods, both samplers. +# +# Output: numerical summary + PDF with ROC-like curves and confusion matrices. +# =========================================================================== + +devtools::load_all(quiet = TRUE) +source(file.path("dev", "tests", "validation", "helpers.R")) + +out_dir = file.path("dev", "tests", "validation", "output") +dir.create(out_dir, recursive = TRUE, showWarnings = FALSE) + +cat("=======================================================================\n") +cat(" Group 5: Edge detection accuracy\n") +cat("=======================================================================\n\n") + +# ------------------------------------------------------------------ +# Helper: compute edge detection metrics from PIPs and truth +# ------------------------------------------------------------------ +# @param pip Matrix of posterior inclusion probabilities (p+q x p+q). +# @param true_adj Binary adjacency matrix (p+q x p+q). +# @param threshold PIP threshold for declaring an edge present. +# +# Returns: named list with TP, FP, FN, TN, sensitivity, specificity, +# precision, F1, MCC. +# ------------------------------------------------------------------ +edge_metrics = function(pip, true_adj, threshold = 0.5) { + # Upper triangle only (avoid double counting) + ut = upper.tri(pip) + pred = ifelse(pip[ut] >= threshold, 1, 0) + truth = ifelse(true_adj[ut] != 0, 1, 0) + + tp = sum(pred == 1 & truth == 1) + fp = sum(pred == 1 & truth == 0) + fn = sum(pred == 0 & truth == 1) + tn = sum(pred == 0 & truth == 0) + + sens = if(tp + fn > 0) tp / (tp + fn) else NA + spec = if(tn + fp > 0) tn / (tn + fp) else NA + prec = if(tp + fp > 0) tp / (tp + fp) else NA + f1 = if(!is.na(prec) && !is.na(sens) && (prec + sens) > 0) + 2 * prec * sens / (prec + sens) else NA + denom = sqrt((tp + fp) * (tp + fn) * (tn + fp) * (tn + fn)) + mcc = if(denom > 0) (tp * tn - fp * fn) / denom else NA + + list(TP = tp, FP = fp, FN = fn, TN = tn, + sensitivity = sens, specificity = spec, + precision = prec, F1 = f1, MCC = mcc) +} + +# ------------------------------------------------------------------ +# Helper: ROC-like curve from PIPs +# ------------------------------------------------------------------ +roc_curve = function(pip, true_adj) { + ut = upper.tri(pip) + scores = pip[ut] + truth = ifelse(true_adj[ut] != 0, 1, 0) + thresholds = sort(unique(c(0, scores, 1))) + + tpr = fpr = numeric(length(thresholds)) + for(i in seq_along(thresholds)) { + pred = ifelse(scores >= thresholds[i], 1, 0) + tp = sum(pred == 1 & truth == 1) + fp = sum(pred == 1 & truth == 0) + fn = sum(pred == 0 & truth == 1) + tn = sum(pred == 0 & truth == 0) + tpr[i] = if(tp + fn > 0) tp / (tp + fn) else 0 + fpr[i] = if(fp + tn > 0) fp / (fp + tn) else 0 + } + + # AUC by trapezoidal rule + ord = order(fpr, tpr) + fpr_s = fpr[ord]; tpr_s = tpr[ord] + auc = sum(diff(fpr_s) * (tpr_s[-1] + tpr_s[-length(tpr_s)]) / 2) + + list(fpr = fpr_s, tpr = tpr_s, auc = auc) +} + +# ------------------------------------------------------------------ +# Helper: create true adjacency matrix from network +# ------------------------------------------------------------------ +true_adjacency = function(net) { + p = net$p; q = net$q; tot = p + q + adj = matrix(0, tot, tot) + adj[seq_len(p), seq_len(p)] = (net$Kxx != 0) * 1 + adj[p + seq_len(q), p + seq_len(q)] = (net$Kyy != 0) * 1 + diag(adj[p + seq_len(q), p + seq_len(q)]) = 0 # diag doesn't count + adj[seq_len(p), p + seq_len(q)] = (net$Kxy != 0) * 1 + adj[p + seq_len(q), seq_len(p)] = t(adj[seq_len(p), p + seq_len(q)]) + adj +} + +# ------------------------------------------------------------------ +# 5a. Define edge-strength scenarios +# ------------------------------------------------------------------ +scenarios = list( + weak = list(density = 0.4, scale = 0.3, seed = 701, label = "weak (J~0.3)"), + medium = list(density = 0.4, scale = 0.6, seed = 702, label = "medium (J~0.6)"), + strong = list(density = 0.4, scale = 1.0, seed = 703, label = "strong (J~1.0)") +) + +# Modified make_network with controllable edge strength +make_network_scaled = function(p, q, n_cat, density, scale, seed, + variable_type = rep("ordinal", p), + baseline_category = rep(0L, p)) { + net = make_network(p, q, n_cat, variable_type = variable_type, + baseline_category = baseline_category, density = density, + seed = seed) + # Rescale non-zero edges + net$Kxx = net$Kxx * scale / max(abs(net$Kxx[net$Kxx != 0]), 1) + net$Kxy = net$Kxy * scale / max(abs(net$Kxy[net$Kxy != 0]), 1) + off_diag_yy = net$Kyy; diag(off_diag_yy) = 0 + if(any(off_diag_yy != 0)) { + net$Kyy[upper.tri(net$Kyy)] = net$Kyy[upper.tri(net$Kyy)] * scale / max(abs(off_diag_yy[off_diag_yy != 0]), 1) + net$Kyy[lower.tri(net$Kyy)] = t(net$Kyy)[lower.tri(net$Kyy)] + } + # Re-enforce diagonal dominance for Kyy + diag(net$Kyy) = abs(rowSums(net$Kyy - diag(diag(net$Kyy)))) + runif(q, 1.0, 2.0) + net +} + +results = list() +roc_data = list() + +for(sc_name in names(scenarios)) { + sc = scenarios[[sc_name]] + cat(sprintf("--- 5_%s: %s ---\n", sc_name, sc$label)) + + net = make_network_scaled(p = 4, q = 3, n_cat = c(1L, 2L, 3L, 1L), + density = sc$density, scale = sc$scale, seed = sc$seed) + dat = generate_data(net, n = 3000, source = "bgms", seed = sc$seed + 100) + vtype = c(rep("ordinal", 4), rep("continuous", 3)) + adj = true_adjacency(net) + + n_true_edges = sum(adj[upper.tri(adj)]) + n_possible = sum(upper.tri(adj)) + cat(sprintf(" True edges: %d / %d possible\n", n_true_edges, n_possible)) + + # Fit with edge selection + fit = bgm( + dat, variable_type = vtype, + pseudolikelihood = "marginal", + edge_selection = TRUE, + iter = 10000, warmup = 5000, chains = 2, + seed = sc$seed + 200 + ) + + pip = fit$posterior_mean_indicator + met = edge_metrics(pip, adj, threshold = 0.5) + roc = roc_curve(pip, adj) + + results[[sc_name]] = data.frame( + scenario = sc$label, + n_true_edges = n_true_edges, + TP = met$TP, FP = met$FP, FN = met$FN, TN = met$TN, + sensitivity = round(met$sensitivity, 3), + specificity = round(met$specificity, 3), + precision = round(met$precision, 3), + F1 = round(met$F1, 3), + MCC = round(met$MCC, 3), + AUC = round(roc$auc, 3), + stringsAsFactors = FALSE + ) + roc_data[[sc_name]] = roc + + cat(sprintf(" Sens = %.3f | Spec = %.3f | F1 = %.3f | MCC = %.3f | AUC = %.3f\n", + met$sensitivity, met$specificity, met$F1, met$MCC, roc$auc)) +} + +# ------------------------------------------------------------------ +# 5b. Blume-Capel edge detection (medium signal strength) +# ------------------------------------------------------------------ +cat("\n--- 5b: BC mixed — edge detection (medium signal) ----------------\n") + +net_bc = make_network_scaled( + p = 4, q = 2, + n_cat = c(1L, 2L, 2L, 3L), + density = 0.4, scale = 0.6, seed = 710, + variable_type = c("ordinal", "ordinal", "blume-capel", "blume-capel"), + baseline_category = c(0L, 0L, 1L, 1L) +) +dat_bc = generate_data(net_bc, n = 3000, source = "bgms", seed = 810) + +vtype_bc = c("ordinal", "ordinal", "blume-capel", "blume-capel", + "continuous", "continuous") +adj_bc = true_adjacency(net_bc) + +n_true_bc = sum(adj_bc[upper.tri(adj_bc)]) +n_poss_bc = sum(upper.tri(adj_bc)) +cat(sprintf(" True edges: %d / %d possible\n", n_true_bc, n_poss_bc)) + +fit_bc = bgm( + dat_bc, variable_type = vtype_bc, + baseline_category = c(0L, 0L, 1L, 1L), + pseudolikelihood = "marginal", + edge_selection = TRUE, + iter = 10000, warmup = 5000, chains = 2, + seed = 910 +) + +pip_bc = fit_bc$posterior_mean_indicator +met_bc = edge_metrics(pip_bc, adj_bc, threshold = 0.5) +roc_bc = roc_curve(pip_bc, adj_bc) + +results[["bc_medium"]] = data.frame( + scenario = "BC medium (J~0.6)", + n_true_edges = n_true_bc, + TP = met_bc$TP, FP = met_bc$FP, FN = met_bc$FN, TN = met_bc$TN, + sensitivity = round(met_bc$sensitivity, 3), + specificity = round(met_bc$specificity, 3), + precision = round(met_bc$precision, 3), + F1 = round(met_bc$F1, 3), + MCC = round(met_bc$MCC, 3), + AUC = round(roc_bc$auc, 3), + stringsAsFactors = FALSE +) +roc_data[["bc_medium"]] = roc_bc + +cat(sprintf(" Sens = %.3f | Spec = %.3f | F1 = %.3f | MCC = %.3f | AUC = %.3f\n", + met_bc$sensitivity, met_bc$specificity, met_bc$F1, met_bc$MCC, roc_bc$auc)) + +# ------------------------------------------------------------------ +# Summary table +# ------------------------------------------------------------------ +cat("\n--- Edge detection summary ----------------------------------------\n") +summary_df = do.call(rbind, results) +print(summary_df, row.names = FALSE) + +# ------------------------------------------------------------------ +# Plots +# ------------------------------------------------------------------ +pdf(file.path(out_dir, "group5_edge_detection.pdf"), width = 14, height = 14) + +layout(matrix(1:8, nrow = 2, byrow = TRUE)) +par(mar = c(4.5, 4.5, 2.5, 1)) + +# Row 1: ROC curves + PIP panels for ordinal scenarios +cols = c(weak = "#E41A1C", medium = "#377EB8", strong = "#4DAF4A", + bc_medium = "#984EA3") + +# Combined ROC (all scenarios including BC) +plot(0, 0, type = "n", xlim = c(0, 1), ylim = c(0, 1), + xlab = "False Positive Rate", ylab = "True Positive Rate", + main = "ROC curves by scenario", asp = 1) +abline(0, 1, lty = 2, col = "grey60") +for(sc_name in names(roc_data)) { + roc = roc_data[[sc_name]] + lines(roc$fpr, roc$tpr, col = cols[sc_name], lwd = 2) +} +legend("bottomright", + legend = paste0(names(roc_data), sprintf(" (AUC=%.2f)", sapply(roc_data, `[[`, "auc"))), + col = cols[names(roc_data)], lwd = 2, bty = "n") + +# PIP panels for ordinal scenarios +for(sc_name in names(scenarios)) { + sc = scenarios[[sc_name]] + net = make_network_scaled(p = 4, q = 3, n_cat = c(1L, 2L, 3L, 1L), + density = sc$density, scale = sc$scale, seed = sc$seed) + dat = generate_data(net, n = 3000, source = "bgms", seed = sc$seed + 100) + vtype = c(rep("ordinal", 4), rep("continuous", 3)) + + fit = bgm(dat, variable_type = vtype, + pseudolikelihood = "marginal", edge_selection = TRUE, + iter = 10000, warmup = 5000, chains = 2, + display_progress = "none", seed = sc$seed + 200) + + pip = fit$posterior_mean_indicator + adj = true_adjacency(net) + + ut_pip = pip[upper.tri(pip)] + ut_adj = adj[upper.tri(adj)] + plot(jitter(ut_adj, 0.1), ut_pip, pch = 19, + col = adjustcolor(ifelse(ut_adj == 1, "firebrick", "steelblue"), 0.6), + xlab = "True edge (0/1)", ylab = "Posterior inclusion prob", + main = sprintf("%s: PIPs", sc$label), + xlim = c(-0.3, 1.3), ylim = c(0, 1)) + abline(h = 0.5, lty = 2, col = "grey40") + legend("center", + legend = c("True edge", "True null"), + col = c("firebrick", "steelblue"), pch = 19, bty = "n") +} + +# Row 2: BC PIP panel + barplot +# BC PIP panel +ut_pip_bc = pip_bc[upper.tri(pip_bc)] +ut_adj_bc = adj_bc[upper.tri(adj_bc)] +plot(jitter(ut_adj_bc, 0.1), ut_pip_bc, pch = 19, + col = adjustcolor(ifelse(ut_adj_bc == 1, "firebrick", "steelblue"), 0.6), + xlab = "True edge (0/1)", ylab = "Posterior inclusion prob", + main = "BC medium: PIPs", + xlim = c(-0.3, 1.3), ylim = c(0, 1)) +abline(h = 0.5, lty = 2, col = "grey40") +legend("center", + legend = c("True edge", "True null"), + col = c("firebrick", "steelblue"), pch = 19, bty = "n") + +# Barplot of metrics (all scenarios) +all_cols = cols[names(results)] +metrics_mat = t(sapply(results, function(r) + c(Sensitivity = r$sensitivity, Specificity = r$specificity, + F1 = r$F1, MCC = r$MCC))) +barplot(metrics_mat, beside = TRUE, col = all_cols, + main = "Edge detection metrics by scenario", + ylab = "Score", ylim = c(0, 1.1)) +legend("topright", legend = names(results), fill = all_cols, bty = "n") +abline(h = 1, lty = 2, col = "grey60") + +# Blank panels +plot.new() +plot.new() + +dev.off() + +cat(sprintf("\nPlots saved to %s/group5_edge_detection.pdf\n", out_dir)) +cat("=== Group 5 complete =============================================\n\n") diff --git a/dev/tests/validation/group6_diagnostics.R b/dev/tests/validation/group6_diagnostics.R new file mode 100644 index 00000000..bc16f6e0 --- /dev/null +++ b/dev/tests/validation/group6_diagnostics.R @@ -0,0 +1,285 @@ +# =========================================================================== +# Group 6: MCMC diagnostics +# =========================================================================== +# Evaluate chain convergence and mixing quality for the mixed MRF sampler. +# Reports R-hat, ESS, and effective samples per second. Visualises trace +# plots, density overlays per chain, and autocorrelation. +# +# Special focus on Kyy parameters (the MH component in hybrid NUTS) to +# verify that MH acceptance is reasonable and chains mix. +# +# Output: numerical summary + PDF with diagnostic panels. +# =========================================================================== + +devtools::load_all(quiet = TRUE) +source(file.path("dev", "tests", "validation", "helpers.R")) + +out_dir = file.path("dev", "tests", "validation", "output") +dir.create(out_dir, recursive = TRUE, showWarnings = FALSE) + +cat("=======================================================================\n") +cat(" Group 6: MCMC diagnostics\n") +cat("=======================================================================\n\n") + +# ------------------------------------------------------------------ +# Helpers +# ------------------------------------------------------------------ + +# Split-Rhat (Vehtari et al. 2021 simplified version) +split_rhat = function(chains) { + # chains: list of numeric vectors (one per chain) + n = min(sapply(chains, length)) + # Split each chain in half + split_chains = unlist(lapply(chains, function(ch) { + half = floor(n / 2) + list(ch[1:half], ch[(half + 1):(2 * half)]) + }), recursive = FALSE) + m = length(split_chains) + n_half = length(split_chains[[1]]) + chain_means = sapply(split_chains, mean) + chain_vars = sapply(split_chains, var) + W = mean(chain_vars) + B = var(chain_means) * n_half + var_hat = (1 - 1 / n_half) * W + B / n_half + sqrt(var_hat / W) +} + +# ESS (simple formula based on autocorrelation) +simple_ess = function(x) { + n = length(x) + if(n < 10) return(NA) + acf_vals = acf(x, lag.max = min(n - 1, 500), plot = FALSE)$acf[, 1, 1] + # Geyer's initial positive sequence + T_max = length(acf_vals) + rho_sum = 0 + for(k in seq(2, T_max, by = 2)) { + pair_sum = acf_vals[k] + if(k + 1 <= T_max) acf_vals[k + 1] else 0 + if(pair_sum < 0) break + rho_sum = rho_sum + pair_sum + } + n / (1 + 2 * rho_sum) +} + +# ------------------------------------------------------------------ +# 6a. Fit with multiple chains +# ------------------------------------------------------------------ +cat("--- 6a: Fitting 4-chain models -----------------------------------\n") + +net = make_network(p = 4, q = 3, n_cat = c(1L, 2L, 3L, 1L), density = 0.4, seed = 102) +dat = generate_data(net, n = 3000, source = "bgms", seed = 202) +vtype = c(rep("ordinal", 4), rep("continuous", 3)) + +t_mh = system.time({ + fit_mh = bgm(dat, variable_type = vtype, + pseudolikelihood = "marginal", + update_method = "adaptive-metropolis", + edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 4, + seed = 801) +}) + +t_nuts = system.time({ + fit_nuts = bgm(dat, variable_type = vtype, + pseudolikelihood = "marginal", + update_method = "nuts", + edge_selection = FALSE, + iter = 5000, warmup = 3000, chains = 4, + seed = 802) +}) + +cat(sprintf(" MH wall time: %.1f sec\n", t_mh["elapsed"])) +cat(sprintf(" NUTS wall time: %.1f sec\n", t_nuts["elapsed"])) + +# ------------------------------------------------------------------ +# 6b. Extract per-chain diagnostics +# ------------------------------------------------------------------ +cat("\n--- 6b: Per-parameter diagnostics ---------------------------------\n") + +compute_diagnostics = function(fit, label, wall_time) { + main_chains = fit$raw_samples$main + pair_chains = fit$raw_samples$pairwise + nchains = fit$raw_samples$nchains + main_names = fit$raw_samples$parameter_names$main + pair_names = fit$raw_samples$parameter_names$pairwise + + all_names = c(main_names, pair_names) + n_main = length(main_names) + n_pair = length(pair_names) + + rhat_vals = ess_vals = numeric(length(all_names)) + names(rhat_vals) = names(ess_vals) = all_names + + for(j in seq_len(n_main)) { + chains_j = lapply(main_chains, function(m) m[, j]) + rhat_vals[j] = split_rhat(chains_j) + ess_vals[j] = simple_ess(do.call(c, chains_j)) + } + for(j in seq_len(n_pair)) { + chains_j = lapply(pair_chains, function(m) m[, j]) + rhat_vals[n_main + j] = split_rhat(chains_j) + ess_vals[n_main + j] = simple_ess(do.call(c, chains_j)) + } + + # Classify parameters + block = ifelse(seq_along(all_names) <= n_main, "main", "pairwise") + + data.frame( + parameter = all_names, + block = block, + rhat = round(rhat_vals, 4), + ess = round(ess_vals, 0), + ess_per_sec = round(ess_vals / wall_time, 1), + method = label, + stringsAsFactors = FALSE, row.names = NULL + ) +} + +diag_mh = compute_diagnostics(fit_mh, "MH", t_mh["elapsed"]) +diag_nuts = compute_diagnostics(fit_nuts, "NUTS", t_nuts["elapsed"]) +diag_all = rbind(diag_mh, diag_nuts) + +# Summary statistics +cat("\n MH diagnostics:\n") +cat(sprintf(" R-hat: median = %.3f, max = %.3f, n > 1.01 = %d\n", + median(diag_mh$rhat), max(diag_mh$rhat), sum(diag_mh$rhat > 1.01))) +cat(sprintf(" ESS: median = %.0f, min = %.0f, ESS/s median = %.1f\n", + median(diag_mh$ess), min(diag_mh$ess), median(diag_mh$ess_per_sec))) + +cat("\n NUTS diagnostics:\n") +cat(sprintf(" R-hat: median = %.3f, max = %.3f, n > 1.01 = %d\n", + median(diag_nuts$rhat), max(diag_nuts$rhat), sum(diag_nuts$rhat > 1.01))) +cat(sprintf(" ESS: median = %.0f, min = %.0f, ESS/s median = %.1f\n", + median(diag_nuts$ess), min(diag_nuts$ess), median(diag_nuts$ess_per_sec))) + +# ------------------------------------------------------------------ +# 6c. NUTS-specific diagnostics +# ------------------------------------------------------------------ +cat("\n--- 6c: NUTS-specific diagnostics ---------------------------------\n") +if(!is.null(fit_nuts$nuts_diag)) { + nuts_d = fit_nuts$nuts_diag + if(is.list(nuts_d)) { + # Tree depth distribution + if(!is.null(nuts_d$tree_depth) || !is.null(nuts_d[[1]]$tree_depth)) { + cat(" NUTS diagnostics available\n") + # Try to extract tree depths from first chain + td = if(is.list(nuts_d[[1]])) nuts_d[[1]]$tree_depth else nuts_d$tree_depth + if(!is.null(td)) { + cat(" Tree depth distribution:\n") + print(table(td)) + cat(sprintf(" Mean tree depth: %.2f\n", mean(td))) + } + } + } +} else { + cat(" No NUTS diagnostics available in fit object.\n") +} + +# ------------------------------------------------------------------ +# 6d. Identify problematic parameters +# ------------------------------------------------------------------ +cat("\n--- 6d: Flagged parameters ----------------------------------------\n") +flagged = diag_all[diag_all$rhat > 1.05 | diag_all$ess < 100, ] +if(nrow(flagged) > 0) { + cat(sprintf(" %d parameters flagged (R-hat > 1.05 or ESS < 100):\n", nrow(flagged))) + print(flagged, row.names = FALSE) +} else { + cat(" No parameters flagged. All R-hat <= 1.05 and ESS >= 100.\n") +} + +# ------------------------------------------------------------------ +# Plots +# ------------------------------------------------------------------ +pdf(file.path(out_dir, "group6_diagnostics.pdf"), width = 14, height = 16) + +layout(matrix(1:8, nrow = 4, byrow = TRUE)) +par(mar = c(4.5, 4.5, 2.5, 1)) + +# Panel 1: R-hat comparison +plot(diag_mh$rhat, diag_nuts$rhat, pch = 19, + col = adjustcolor(ifelse(diag_mh$block == "main", "steelblue", "firebrick"), 0.6), + xlab = "R-hat (MH)", ylab = "R-hat (NUTS)", + main = "R-hat: MH vs NUTS") +abline(h = 1.01, v = 1.01, lty = 2, col = "grey40") +abline(0, 1, lty = 3, col = "grey60") +legend("topleft", legend = c("main", "pairwise"), + col = c("steelblue", "firebrick"), pch = 19, bty = "n") + +# Panel 2: ESS comparison +plot(diag_mh$ess, diag_nuts$ess, pch = 19, + col = adjustcolor(ifelse(diag_mh$block == "main", "steelblue", "firebrick"), 0.6), + xlab = "ESS (MH)", ylab = "ESS (NUTS)", + main = "ESS: MH vs NUTS", log = "xy") +abline(0, 1, lty = 2, col = "grey40") + +# Panel 3: ESS/sec comparison +plot(diag_mh$ess_per_sec, diag_nuts$ess_per_sec, pch = 19, + col = adjustcolor(ifelse(diag_mh$block == "main", "steelblue", "firebrick"), 0.6), + xlab = "ESS/sec (MH)", ylab = "ESS/sec (NUTS)", + main = "Efficiency: MH vs NUTS", log = "xy") +abline(0, 1, lty = 2, col = "grey40") + +# Panel 4: R-hat distributions +boxplot(rhat ~ method, data = diag_all, col = c("steelblue", "firebrick"), + main = "R-hat distribution by method", + ylab = "R-hat") +abline(h = 1.01, lty = 2, col = "grey40") + +# Panels 5-6: Trace plots for selected parameters (MH) +pair_names_mh = fit_mh$raw_samples$parameter_names$pairwise +sel = pair_names_mh[seq_len(min(2, length(pair_names_mh)))] +for(nm_idx in seq_along(sel)) { + nm = sel[nm_idx] + j = match(nm, pair_names_mh) + cols_chain = c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3") + plot(0, 0, type = "n", + xlim = c(1, fit_mh$raw_samples$niter), + ylim = range(sapply(fit_mh$raw_samples$pairwise, function(m) range(m[, j]))), + xlab = "Iteration", ylab = nm, + main = sprintf("MH trace: %s", nm)) + for(ch in seq_len(fit_mh$raw_samples$nchains)) { + lines(fit_mh$raw_samples$pairwise[[ch]][, j], + col = adjustcolor(cols_chain[ch], 0.4)) + } +} + +# Panels 7-8: Trace plots for selected parameters (NUTS) +pair_names_nuts = fit_nuts$raw_samples$parameter_names$pairwise +sel_n = pair_names_nuts[seq_len(min(2, length(pair_names_nuts)))] +for(nm_idx in seq_along(sel_n)) { + nm = sel_n[nm_idx] + j = match(nm, pair_names_nuts) + plot(0, 0, type = "n", + xlim = c(1, fit_nuts$raw_samples$niter), + ylim = range(sapply(fit_nuts$raw_samples$pairwise, function(m) range(m[, j]))), + xlab = "Iteration", ylab = nm, + main = sprintf("NUTS trace: %s", nm)) + for(ch in seq_len(fit_nuts$raw_samples$nchains)) { + lines(fit_nuts$raw_samples$pairwise[[ch]][, j], + col = adjustcolor(cols_chain[ch], 0.4)) + } +} + +dev.off() + +# --- Autocorrelation comparison --- +pdf(file.path(out_dir, "group6_autocorrelation.pdf"), width = 12, height = 10) + +# Pick 4 pairwise parameters and compare autocorrelation between MH and NUTS +n_show = min(4, length(pair_names_mh)) +par(mfrow = c(n_show, 2), mar = c(3, 4, 2.5, 1), mgp = c(2, 0.6, 0)) + +for(j in seq_len(n_show)) { + nm = pair_names_mh[j] + # MH autocorrelation (chain 1) + mh_samp = fit_mh$raw_samples$pairwise[[1]][, j] + acf(mh_samp, lag.max = 100, main = sprintf("MH: %s", nm), cex.main = 0.9) + + nm_n = pair_names_nuts[j] + nuts_samp = fit_nuts$raw_samples$pairwise[[1]][, j] + acf(nuts_samp, lag.max = 100, main = sprintf("NUTS: %s", nm_n), cex.main = 0.9) +} + +dev.off() + +cat(sprintf("\nPlots saved to %s/group6_*.pdf\n", out_dir)) +cat("=== Group 6 complete =============================================\n\n") diff --git a/dev/tests/validation/group7_empirical.R b/dev/tests/validation/group7_empirical.R new file mode 100644 index 00000000..23db183b --- /dev/null +++ b/dev/tests/validation/group7_empirical.R @@ -0,0 +1,287 @@ +# =========================================================================== +# Group 7: Empirical data smoke tests +# =========================================================================== +# Fit the mixed MRF model on real-world data to verify that: +# 1. The model runs to completion without errors. +# 2. Posterior summaries have correct dimensions. +# 3. R-hat values are acceptable. +# 4. Simulate and predict methods work on the fitted object. +# 5. Both PL methods and both samplers produce sensible output. +# +# Uses the Boredom dataset (8 items: 4 treated as ordinal, 4 as continuous) +# following the convention from the existing cycle test. +# +# Output: numerical summary + PDF with trace and density panels. +# =========================================================================== + +devtools::load_all(quiet = TRUE) +source(file.path("dev", "tests", "validation", "helpers.R")) + +out_dir = file.path("dev", "tests", "validation", "output") +dir.create(out_dir, recursive = TRUE, showWarnings = FALSE) + +cat("=======================================================================\n") +cat(" Group 7: Empirical data smoke tests\n") +cat("=======================================================================\n\n") + +# ------------------------------------------------------------------ +# 7a. Prepare Boredom data as mixed +# ------------------------------------------------------------------ +cat("--- 7a: Preparing Boredom data ------------------------------------\n") + +data("Boredom", package = "bgms") +raw = as.matrix(Boredom[, 2:9]) # drop language factor +raw = raw - 1L # shift 1-7 -> 0-6 +colnames(raw) = names(Boredom)[2:9] + +ord_idx = c(1, 3, 5, 7) +cont_idx = c(2, 4, 6, 8) +vtype = character(8) +vtype[ord_idx] = "ordinal" +vtype[cont_idx] = "continuous" + +cat(sprintf(" n = %d, %d ordinal + %d continuous variables\n", + nrow(raw), length(ord_idx), length(cont_idx))) +cat(" Variable types:", vtype, "\n\n") + +# ------------------------------------------------------------------ +# 7b. Fit: MH + marginal PL +# ------------------------------------------------------------------ +cat("--- 7b: MH + marginal PL -----------------------------------------\n") + +fit_mh_marg = bgm( + raw, variable_type = vtype, + pseudolikelihood = "marginal", + update_method = "adaptive-metropolis", + edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 2, + seed = 901 +) + +# Sanity checks +stopifnot(!is.null(fit_mh_marg$posterior_summary_pairwise)) +stopifnot(!is.null(fit_mh_marg$posterior_mean_main)) +stopifnot(!is.null(fit_mh_marg$posterior_mean_pairwise)) +cat(" posterior_summary_pairwise rows:", nrow(fit_mh_marg$posterior_summary_pairwise), "\n") +cat(" posterior_mean_pairwise dim:", paste(dim(fit_mh_marg$posterior_mean_pairwise), collapse = "x"), "\n") +cat(" Main effects (discrete) dim:", paste(dim(coef(fit_mh_marg)$main$discrete), collapse = "x"), "\n") +cat(" Main effects (continuous) dim:", paste(dim(coef(fit_mh_marg)$main$continuous), collapse = "x"), "\n") +cat(" OK\n\n") + +# ------------------------------------------------------------------ +# 7c. Fit: MH + conditional PL +# ------------------------------------------------------------------ +cat("--- 7c: MH + conditional PL --------------------------------------\n") + +fit_mh_cond = bgm( + raw, variable_type = vtype, + pseudolikelihood = "conditional", + update_method = "adaptive-metropolis", + edge_selection = FALSE, + iter = 10000, warmup = 5000, chains = 2, + seed = 902 +) + +stopifnot(!is.null(fit_mh_cond$posterior_mean_pairwise)) +cat(" posterior_mean_pairwise dim:", paste(dim(fit_mh_cond$posterior_mean_pairwise), collapse = "x"), "\n") +cat(" OK\n\n") + +# ------------------------------------------------------------------ +# 7d. Fit: NUTS + marginal PL +# ------------------------------------------------------------------ +cat("--- 7d: NUTS + marginal PL ---------------------------------------\n") + +fit_nuts_marg = bgm( + raw, variable_type = vtype, + pseudolikelihood = "marginal", + update_method = "nuts", + edge_selection = FALSE, + iter = 5000, warmup = 3000, chains = 2, + seed = 903 +) + +stopifnot(!is.null(fit_nuts_marg$posterior_mean_pairwise)) +cat(" posterior_mean_pairwise dim:", paste(dim(fit_nuts_marg$posterior_mean_pairwise), collapse = "x"), "\n") +cat(" OK\n\n") + +# ------------------------------------------------------------------ +# 7e. Fit: NUTS + conditional PL +# ------------------------------------------------------------------ +cat("--- 7e: NUTS + conditional PL ------------------------------------\n") + +fit_nuts_cond = bgm( + raw, variable_type = vtype, + pseudolikelihood = "conditional", + update_method = "nuts", + edge_selection = FALSE, + iter = 5000, warmup = 3000, chains = 2, + display_progress = "none", seed = 904 +) + +stopifnot(!is.null(fit_nuts_cond$posterior_mean_pairwise)) +cat(" posterior_mean_pairwise dim:", paste(dim(fit_nuts_cond$posterior_mean_pairwise), collapse = "x"), "\n") +cat(" OK\n\n") + +# ------------------------------------------------------------------ +# 7f. Cross-method agreement on empirical data +# ------------------------------------------------------------------ +cat("--- 7f: Cross-method agreement on empirical data ------------------\n") + +get_pw_flat = function(fit) as.vector(fit$posterior_mean_pairwise[upper.tri(fit$posterior_mean_pairwise)]) + +pw_mh_marg = get_pw_flat(fit_mh_marg) +pw_mh_cond = get_pw_flat(fit_mh_cond) +pw_nuts_marg = get_pw_flat(fit_nuts_marg) +pw_nuts_cond = get_pw_flat(fit_nuts_cond) + +pairs = list( + c("MH-marginal", "MH-conditional"), + c("MH-marginal", "NUTS-marginal"), + c("NUTS-marginal", "NUTS-conditional"), + c("MH-marginal", "NUTS-conditional") +) +pw_list = list( + `MH-marginal` = pw_mh_marg, `MH-conditional` = pw_mh_cond, + `NUTS-marginal` = pw_nuts_marg, `NUTS-conditional` = pw_nuts_cond +) + +cat(" Pairwise interaction correlations:\n") +for(pr in pairs) { + r = cor(pw_list[[pr[1]]], pw_list[[pr[2]]]) + rmse = sqrt(mean((pw_list[[pr[1]]] - pw_list[[pr[2]]])^2)) + cat(sprintf(" %s vs %s: r = %.4f, RMSE = %.4f\n", pr[1], pr[2], r, rmse)) +} + +# ------------------------------------------------------------------ +# 7g. Simulate from fitted model +# ------------------------------------------------------------------ +cat("\n--- 7g: Simulate from fitted model --------------------------------\n") + +sim1 = simulate(fit_mh_marg, nsim = 500, method = "posterior-mean", seed = 911) +cat(sprintf(" simulate(posterior-mean): %d x %d\n", nrow(sim1), ncol(sim1))) +stopifnot(nrow(sim1) == 500) +stopifnot(ncol(sim1) == 8) + +# Check that ordinal columns are integers and continuous are numeric +for(i in ord_idx) { + stopifnot(all(sim1[, i] == floor(sim1[, i]))) +} +cat(" Ordinal columns contain integers: OK\n") +cat(" Continuous column ranges:\n") +for(i in cont_idx) { + cat(sprintf(" col %d: [%.2f, %.2f]\n", i, min(sim1[, i]), max(sim1[, i]))) +} + +# ------------------------------------------------------------------ +# 7h. Edge selection on empirical data +# ------------------------------------------------------------------ +cat("\n--- 7h: Edge selection on empirical data --------------------------\n") + +fit_edge = bgm( + raw, variable_type = vtype, + pseudolikelihood = "marginal", + update_method = "adaptive-metropolis", + edge_selection = TRUE, + iter = 10000, warmup = 5000, chains = 2, + seed = 921 +) + +pip = fit_edge$posterior_mean_indicator +n_edges = sum(pip[upper.tri(pip)] > 0.5) +n_possible = sum(upper.tri(pip)) +cat(sprintf(" Edges detected (PIP > 0.5): %d / %d\n", n_edges, n_possible)) +cat(" PIP distribution:\n") +pip_ut = pip[upper.tri(pip)] +print(summary(pip_ut)) + +# ------------------------------------------------------------------ +# 7i. R-hat check across all fits +# ------------------------------------------------------------------ +cat("\n--- 7i: R-hat check -----------------------------------------------\n") + +check_rhat = function(fit, label) { + summ = fit$posterior_summary_pairwise + if(!is.null(summ) && "Rhat" %in% names(summ)) { + rhats = summ$Rhat + cat(sprintf(" %s: max R-hat = %.3f, n > 1.05 = %d / %d\n", + label, max(rhats, na.rm = TRUE), + sum(rhats > 1.05, na.rm = TRUE), length(rhats))) + } else { + cat(sprintf(" %s: R-hat not available in summary\n", label)) + } +} + +check_rhat(fit_mh_marg, "MH marginal") +check_rhat(fit_mh_cond, "MH conditional") +check_rhat(fit_nuts_marg, "NUTS marginal") +check_rhat(fit_nuts_cond, "NUTS conditional") + +# ------------------------------------------------------------------ +# Plots +# ------------------------------------------------------------------ +pdf(file.path(out_dir, "group7_empirical.pdf"), width = 14, height = 12) + +layout(matrix(1:6, nrow = 2, byrow = TRUE)) +par(mar = c(4.5, 4.5, 2.5, 1)) + +# Panel 1: All methods pairwise agreement (MH-marg as x, rest as y) +rng = range(c(pw_mh_marg, pw_mh_cond, pw_nuts_marg, pw_nuts_cond)) +plot(pw_mh_marg, pw_mh_cond, pch = 19, + col = adjustcolor("steelblue", 0.5), + xlim = rng, ylim = rng, asp = 1, + xlab = "MH marginal", ylab = "MH conditional", + main = sprintf("MH: marginal vs conditional (r=%.3f)", + cor(pw_mh_marg, pw_mh_cond))) +abline(0, 1, lty = 2, col = "grey40") + +plot(pw_mh_marg, pw_nuts_marg, pch = 19, + col = adjustcolor("firebrick", 0.5), + xlim = rng, ylim = rng, asp = 1, + xlab = "MH marginal", ylab = "NUTS marginal", + main = sprintf("Marginal: MH vs NUTS (r=%.3f)", + cor(pw_mh_marg, pw_nuts_marg))) +abline(0, 1, lty = 2, col = "grey40") + +plot(pw_mh_marg, pw_nuts_cond, pch = 19, + col = adjustcolor("#4DAF4A", 0.5), + xlim = rng, ylim = rng, asp = 1, + xlab = "MH marginal", ylab = "NUTS conditional", + main = sprintf("MH-marg vs NUTS-cond (r=%.3f)", + cor(pw_mh_marg, pw_nuts_cond))) +abline(0, 1, lty = 2, col = "grey40") + +# Panel 4: Estimated network heatmap (MH marginal) +pw_mat = fit_mh_marg$posterior_mean_pairwise +image(seq_len(nrow(pw_mat)), seq_len(ncol(pw_mat)), pw_mat, + col = hcl.colors(50, "Blue-Red 3"), + xlab = "", ylab = "", main = "MH marginal: pairwise matrix", + axes = FALSE) +axis(1, at = seq_len(ncol(pw_mat)), labels = colnames(raw), las = 2, cex.axis = 0.7) +axis(2, at = seq_len(nrow(pw_mat)), labels = colnames(raw), las = 2, cex.axis = 0.7) + +# Panel 5: PIPs heatmap +if(!is.null(pip)) { + image(seq_len(nrow(pip)), seq_len(ncol(pip)), pip, + col = hcl.colors(50, "Viridis"), + xlab = "", ylab = "", main = "Edge inclusion probabilities", + axes = FALSE, zlim = c(0, 1)) + axis(1, at = seq_len(ncol(pip)), labels = colnames(raw), las = 2, cex.axis = 0.7) + axis(2, at = seq_len(nrow(pip)), labels = colnames(raw), las = 2, cex.axis = 0.7) +} + +# Panel 6: Simulated vs observed distributions (first 2 ordinal vars) +sim_check = simulate(fit_mh_marg, nsim = nrow(raw), method = "posterior-mean", seed = 930) +par(mar = c(4.5, 4.5, 2.5, 1)) +obs_tab = table(raw[, 1]) +sim_tab = table(factor(sim_check[, 1], levels = names(obs_tab))) +barplot(rbind(obs_tab / sum(obs_tab), sim_tab / sum(sim_tab)), + beside = TRUE, col = c("steelblue", "firebrick"), + main = sprintf("%s: observed vs simulated", colnames(raw)[1]), + ylab = "Proportion", xlab = "Category") +legend("topright", legend = c("Observed", "Simulated"), + fill = c("steelblue", "firebrick"), bty = "n") + +dev.off() + +cat(sprintf("\nPlots saved to %s/group7_empirical.pdf\n", out_dir)) +cat("=== Group 7 complete =============================================\n\n") diff --git a/dev/tests/validation/group9_breakdown.R b/dev/tests/validation/group9_breakdown.R new file mode 100644 index 00000000..986c70c4 --- /dev/null +++ b/dev/tests/validation/group9_breakdown.R @@ -0,0 +1,174 @@ +# =========================================================================== +# Targeted timing breakdown for mixed MRF hotspot identification +# =========================================================================== +library(bgms) +source("dev/tests/validation/helpers.R") + +cat("===================================================================\n") +cat(" Mixed MRF Timing Breakdown\n") +cat("===================================================================\n\n") + +# --------------------------------------------------------------------------- +# Setup: p=8, q=6 problem +# --------------------------------------------------------------------------- +p = 8; q = 6; n = 300 +n_cat = rep(2L, p) +net = make_network(p = p, q = q, n_cat = n_cat, density = 0.4, seed = 42) +dat = generate_data(net, n = n, seed = 42) +vt = c(rep("ordinal", p), rep("continuous", q)) + +n_iter = 200; n_warmup = 100 + +cat(sprintf("Config: p=%d, q=%d, n=%d, iter=%d, warmup=%d\n\n", p, q, n, n_iter, n_warmup)) + +# --------------------------------------------------------------------------- +# A. Component breakdown: estimation-only vs edge-selection +# --------------------------------------------------------------------------- +cat("--- A. Edge selection overhead ---\n") + +t_est = system.time(suppressWarnings( + bgm(dat, variable_type = vt, iter = n_iter, warmup = n_warmup, + chains = 1, cores = 1, edge_selection = FALSE, + display_progress = FALSE)))["elapsed"] + +t_es = system.time(suppressWarnings( + bgm(dat, variable_type = vt, iter = n_iter, warmup = n_warmup, + chains = 1, cores = 1, edge_selection = TRUE, + display_progress = FALSE)))["elapsed"] + +cat(sprintf(" Estimation only: %6.2f s (%5.2f ms/iter)\n", t_est, t_est/n_iter*1000)) +cat(sprintf(" + Edge selection: %6.2f s (%5.2f ms/iter)\n", t_es, t_es/n_iter*1000)) +cat(sprintf(" Edge selection adds: %.2f ms/iter (%.0f%%)\n\n", + (t_es - t_est)/n_iter*1000, (t_es - t_est)/t_es*100)) + +# --------------------------------------------------------------------------- +# B. Conditional vs Marginal PL at varying p +# --------------------------------------------------------------------------- +cat("--- B. Conditional vs Marginal PL scaling ---\n") + +for(pp in c(4, 8, 12)) { + qq = round(pp * 0.75) + nc = rep(2L, pp) + net2 = make_network(p = pp, q = qq, n_cat = nc, density = 0.4, seed = 42) + dat2 = generate_data(net2, n = n, seed = 42) + vt2 = c(rep("ordinal", pp), rep("continuous", qq)) + + tc = system.time(suppressWarnings( + bgm(dat2, variable_type = vt2, iter = n_iter, warmup = n_warmup, + chains = 1, cores = 1, edge_selection = FALSE, + display_progress = FALSE)))["elapsed"] + + tm = system.time(suppressWarnings( + bgm(dat2, variable_type = vt2, iter = n_iter, warmup = n_warmup, + chains = 1, cores = 1, edge_selection = FALSE, + pseudolikelihood = "marginal", + display_progress = FALSE)))["elapsed"] + + cat(sprintf(" p=%2d q=%2d : cond=%5.2f, marg=%5.2f ms/iter (%.1fx)\n", + pp, qq, tc/n_iter*1000, tm/n_iter*1000, tm/tc)) +} + +# --------------------------------------------------------------------------- +# C. Pure sub-model comparison (isolate cross-term cost) +# --------------------------------------------------------------------------- +cat("\n--- C. Cross-model comparison ---\n") + +for(sz in c(4, 8, 12)) { + nc = rep(2L, sz) + net3 = make_network(p = sz, q = sz, n_cat = nc, density = 0.4, seed = 42) + dat3 = generate_data(net3, n = n, seed = 42) + vt3 = c(rep("ordinal", sz), rep("continuous", sz)) + + # Mixed + t_mix = system.time(suppressWarnings( + bgm(dat3, variable_type = vt3, iter = n_iter, warmup = n_warmup, + chains = 1, cores = 1, edge_selection = FALSE, + display_progress = FALSE)))["elapsed"] + + # OMRF only (discrete columns) + dat_o = dat3[, 1:sz, drop = FALSE] + t_omrf = system.time(suppressWarnings( + bgm(dat_o, variable_type = rep("ordinal", sz), iter = n_iter, warmup = n_warmup, + chains = 1, cores = 1, edge_selection = FALSE, + display_progress = FALSE)))["elapsed"] + + # GGM only (continuous columns) + dat_g = dat3[, (sz+1):(2*sz), drop = FALSE] + t_ggm = system.time(suppressWarnings( + bgm(dat_g, variable_type = rep("continuous", sz), iter = n_iter, warmup = n_warmup, + chains = 1, cores = 1, edge_selection = FALSE, + display_progress = FALSE)))["elapsed"] + + omrf_ms = t_omrf/n_iter*1000 + ggm_ms = t_ggm/n_iter*1000 + mix_ms = t_mix/n_iter*1000 + cross_ms = mix_ms - omrf_ms - ggm_ms + + cat(sprintf(" size=%2d : mixed=%5.2f omrf=%5.2f ggm=%5.2f cross(residual)=%5.2f ms/iter\n", + sz, mix_ms, omrf_ms, ggm_ms, cross_ms)) +} + +# --------------------------------------------------------------------------- +# D. Scaling: isolate p-scaling (fix q=4) and q-scaling (fix p=4) +# --------------------------------------------------------------------------- +cat("\n--- D. Scaling curves ---\n") + +cat(" Fix q=4, vary p:\n") +for(pp in c(3, 6, 9, 12)) { + nc = rep(2L, pp) + net4 = make_network(p = pp, q = 4, n_cat = nc, density = 0.4, seed = 42) + dat4 = generate_data(net4, n = n, seed = 42) + vt4 = c(rep("ordinal", pp), rep("continuous", 4)) + + tt = system.time(suppressWarnings( + bgm(dat4, variable_type = vt4, iter = n_iter, warmup = n_warmup, + chains = 1, cores = 1, edge_selection = FALSE, + display_progress = FALSE)))["elapsed"] + + cat(sprintf(" p=%2d q=4 : %5.2f ms/iter\n", pp, tt/n_iter*1000)) +} + +cat(" Fix p=4, vary q:\n") +for(qq in c(2, 4, 6, 8, 10)) { + nc = rep(2L, 4) + net5 = make_network(p = 4, q = qq, n_cat = nc, density = 0.4, seed = 42) + dat5 = generate_data(net5, n = n, seed = 42) + vt5 = c(rep("ordinal", 4), rep("continuous", qq)) + + tt = system.time(suppressWarnings( + bgm(dat5, variable_type = vt5, iter = n_iter, warmup = n_warmup, + chains = 1, cores = 1, edge_selection = FALSE, + display_progress = FALSE)))["elapsed"] + + cat(sprintf(" p=4 q=%2d : %5.2f ms/iter\n", qq, tt/n_iter*1000)) +} + +# --------------------------------------------------------------------------- +# E. Operation counts and expected complexity +# --------------------------------------------------------------------------- +cat("\n--- E. Complexity analysis ---\n") +cat("Per-iteration MH proposals for p=8, q=6:\n") + +mux_ops = sum(rep(2L, p)) # binary ordinal: 2 categories → 1 threshold each +muy_ops = q +kxx_ops = p * (p - 1) / 2 +kyy_ops = q * (q - 1) / 2 + q # off-diag + diag +kxy_ops = p * q + +cat(sprintf(" Main effects (mux): %3d proposals\n", mux_ops)) +cat(sprintf(" Continuous means: %3d proposals\n", muy_ops)) +cat(sprintf(" Kxx updates: %3d proposals\n", kxx_ops)) +cat(sprintf(" Kyy updates: %3d proposals (off-diag: %d, diag: %d)\n", + kyy_ops, q*(q-1)/2, q)) +cat(sprintf(" Kxy updates: %3d proposals\n", kxy_ops)) +cat(sprintf(" TOTAL: %3d proposals/iter\n", + mux_ops + muy_ops + kxx_ops + kyy_ops + kxy_ops)) + +# Edge selection adds: +es_ops = kxx_ops + q*(q-1)/2 + kxy_ops +cat(sprintf(" Edge selection: %3d proposals/iter (Kxx:%d + Kyy:%d + Kxy:%d)\n", + es_ops, kxx_ops, q*(q-1)/2, kxy_ops)) + +cat("\n===================================================================\n") +cat(" Done\n") +cat("===================================================================\n") diff --git a/dev/tests/validation/group9_profiling.R b/dev/tests/validation/group9_profiling.R new file mode 100644 index 00000000..c82b9ea1 --- /dev/null +++ b/dev/tests/validation/group9_profiling.R @@ -0,0 +1,323 @@ +# =========================================================================== +# GROUP 9: Performance profiling for the mixed MRF sampler +# =========================================================================== +# Benchmarks bgm() for mixed data across network sizes and sampler types. +# +# Measures: +# 1. Wall-clock time at several (p, q) grid points +# 2. Per-iteration cost (total / n_iter) +# 3. MH vs NUTS comparison +# 4. Marginal vs conditional PL comparison +# 5. Comparison with pure ordinal (OMRF) and pure continuous (GGM) models +# 6. Scaling curves: per-iteration cost vs p (fixing q) and vs q (fixing p) +# +# Usage: +# Rscript dev/tests/validation/group9_profiling.R +# =========================================================================== + +library(bgms) + +source("dev/tests/validation/helpers.R") + +out_dir = file.path("dev", "tests", "validation", "output") +dir.create(out_dir, recursive = TRUE, showWarnings = FALSE) + +cat("===================================================================\n") +cat(" GROUP 9: Performance Profiling\n") +cat(sprintf(" Started: %s\n", Sys.time())) +cat("===================================================================\n\n") + +# --------------------------------------------------------------------------- +# Configuration +# --------------------------------------------------------------------------- +n_obs = 300 # observations per dataset +n_iter = 500 # post-warmup iterations +n_warmup = 200 # warmup iterations +n_chains = 1 # single chain for timing consistency +seed = 2026 + +# Grid of (p, q) sizes for the mixed MRF +mixed_grid = data.frame( + p = c(3, 4, 6, 8, 10, 12), + q = c(2, 3, 4, 6, 8, 10) +) + +# --------------------------------------------------------------------------- +# Helper: time a single bgm() call +# --------------------------------------------------------------------------- +time_bgm = function(data, variable_type, pairwise_scale = 2.5, + iter = n_iter, warmup = n_warmup, ...) { + gc(verbose = FALSE) + t0 = proc.time() + fit = bgm( + x = data, + variable_type = variable_type, + pairwise_scale = pairwise_scale, + iter = iter, + warmup = warmup, + chains = n_chains, + cores = 1, + display_progress = FALSE, + ... + ) + elapsed = (proc.time() - t0)["elapsed"] + list(elapsed = unname(elapsed), fit = fit) +} + +# --------------------------------------------------------------------------- +# 1. Mixed MRF: wall-clock across (p, q) grid — MH, conditional PL +# --------------------------------------------------------------------------- +cat("--- 1. Mixed MRF wall-clock (MH, conditional PL) ---\n") + +mixed_results = data.frame( + p = integer(), q = integer(), elapsed = numeric(), + per_iter_ms = numeric(), stringsAsFactors = FALSE +) + +for(i in seq_len(nrow(mixed_grid))) { + p = mixed_grid$p[i] + q = mixed_grid$q[i] + n_cat = rep(2L, p) # binary ordinal for simplicity + + net = make_network(p = p, q = q, n_cat = n_cat, density = 0.4, seed = seed) + dat = generate_data(net, n = n_obs, seed = seed) + + vt = c(rep("ordinal", p), rep("continuous", q)) + + res = time_bgm(dat, variable_type = vt, + iter = n_iter, warmup = n_warmup) + + per_iter_ms = res$elapsed / n_iter * 1000 + + mixed_results = rbind(mixed_results, data.frame( + p = p, q = q, elapsed = round(res$elapsed, 2), + per_iter_ms = round(per_iter_ms, 2) + )) + + cat(sprintf(" p=%2d q=%2d : %.2fs total, %.2f ms/iter\n", + p, q, res$elapsed, per_iter_ms)) +} + +cat("\nMixed MRF timing summary (MH, conditional PL):\n") +print(mixed_results, row.names = FALSE) +cat("\n") + +# --------------------------------------------------------------------------- +# 2. Conditional vs Marginal PL comparison +# --------------------------------------------------------------------------- +cat("--- 2. Conditional vs Marginal PL comparison ---\n") + +pl_grid = data.frame(p = c(4, 8, 12), q = c(3, 6, 10)) +pl_results = data.frame( + p = integer(), q = integer(), + cond_ms = numeric(), marg_ms = numeric(), + ratio = numeric(), stringsAsFactors = FALSE +) + +for(i in seq_len(nrow(pl_grid))) { + p = pl_grid$p[i] + q = pl_grid$q[i] + n_cat = rep(2L, p) + + net = make_network(p = p, q = q, n_cat = n_cat, density = 0.4, seed = seed) + dat = generate_data(net, n = n_obs, seed = seed) + vt = c(rep("ordinal", p), rep("continuous", q)) + + res_cond = time_bgm(dat, variable_type = vt, + iter = n_iter, warmup = n_warmup, + pseudolikelihood = "conditional") + res_marg = time_bgm(dat, variable_type = vt, + iter = n_iter, warmup = n_warmup, + pseudolikelihood = "marginal") + + cond_ms = res_cond$elapsed / n_iter * 1000 + marg_ms = res_marg$elapsed / n_iter * 1000 + + pl_results = rbind(pl_results, data.frame( + p = p, q = q, + cond_ms = round(cond_ms, 2), + marg_ms = round(marg_ms, 2), + ratio = round(marg_ms / cond_ms, 2) + )) + + cat(sprintf(" p=%2d q=%2d : cond=%.2f ms/iter, marg=%.2f ms/iter (ratio=%.2fx)\n", + p, q, cond_ms, marg_ms, marg_ms / cond_ms)) +} + +cat("\nPL comparison:\n") +print(pl_results, row.names = FALSE) +cat("\n") + +# --------------------------------------------------------------------------- +# 3. MH vs NUTS (hybrid-nuts) comparison +# --------------------------------------------------------------------------- +cat("--- 3. MH vs hybrid-NUTS comparison ---\n") + +nuts_grid = data.frame(p = c(4, 8), q = c(3, 6)) +nuts_results = data.frame( + p = integer(), q = integer(), + mh_ms = numeric(), nuts_ms = numeric(), + ratio = numeric(), stringsAsFactors = FALSE +) + +for(i in seq_len(nrow(nuts_grid))) { + p = nuts_grid$p[i] + q = nuts_grid$q[i] + n_cat = rep(2L, p) + + net = make_network(p = p, q = q, n_cat = n_cat, density = 0.4, seed = seed) + dat = generate_data(net, n = n_obs, seed = seed) + vt = c(rep("ordinal", p), rep("continuous", q)) + + res_mh = time_bgm(dat, variable_type = vt, + iter = n_iter, warmup = n_warmup, + update_method = "adaptive-metropolis") + res_nuts = time_bgm(dat, variable_type = vt, + iter = n_iter, warmup = n_warmup, + update_method = "nuts") + + mh_ms = res_mh$elapsed / n_iter * 1000 + nuts_ms = res_nuts$elapsed / n_iter * 1000 + + nuts_results = rbind(nuts_results, data.frame( + p = p, q = q, + mh_ms = round(mh_ms, 2), + nuts_ms = round(nuts_ms, 2), + ratio = round(nuts_ms / mh_ms, 2) + )) + + cat(sprintf(" p=%2d q=%2d : MH=%.2f ms/iter, NUTS=%.2f ms/iter (ratio=%.2fx)\n", + p, q, mh_ms, nuts_ms, nuts_ms / mh_ms)) +} + +cat("\nMH vs NUTS:\n") +print(nuts_results, row.names = FALSE) +cat("\n") + +# --------------------------------------------------------------------------- +# 4. Cross-model comparison: Mixed vs pure OMRF vs pure GGM +# --------------------------------------------------------------------------- +cat("--- 4. Cross-model comparison (mixed vs OMRF vs GGM) ---\n") + +cross_sizes = c(4, 8, 12) +cross_results = data.frame( + size = integer(), + mixed_ms = numeric(), omrf_ms = numeric(), ggm_ms = numeric(), + stringsAsFactors = FALSE +) + +for(sz in cross_sizes) { + # Mixed: p = sz discrete, q = sz continuous + net = make_network(p = sz, q = sz, n_cat = rep(2L, sz), + density = 0.4, seed = seed) + dat_mixed = generate_data(net, n = n_obs, seed = seed) + vt_mixed = c(rep("ordinal", sz), rep("continuous", sz)) + + res_mixed = time_bgm(dat_mixed, variable_type = vt_mixed, + iter = n_iter, warmup = n_warmup) + + # Pure OMRF: 2*sz ordinal variables + dat_omrf = dat_mixed[, 1:sz, drop = FALSE] + vt_omrf = rep("ordinal", sz) + res_omrf = time_bgm(dat_omrf, variable_type = vt_omrf, + iter = n_iter, warmup = n_warmup) + + # Pure GGM: 2*sz continuous variables + dat_ggm = dat_mixed[, (sz + 1):(2 * sz), drop = FALSE] + res_ggm = time_bgm(dat_ggm, variable_type = rep("continuous", sz), + iter = n_iter, warmup = n_warmup) + + mixed_ms = res_mixed$elapsed / n_iter * 1000 + omrf_ms = res_omrf$elapsed / n_iter * 1000 + ggm_ms = res_ggm$elapsed / n_iter * 1000 + + cross_results = rbind(cross_results, data.frame( + size = sz, + mixed_ms = round(mixed_ms, 2), + omrf_ms = round(omrf_ms, 2), + ggm_ms = round(ggm_ms, 2) + )) + + cat(sprintf(" size=%2d : mixed=%.2f, omrf=%.2f, ggm=%.2f ms/iter\n", + sz, mixed_ms, omrf_ms, ggm_ms)) +} + +cat("\nCross-model comparison:\n") +print(cross_results, row.names = FALSE) +cat("\n") + +# --------------------------------------------------------------------------- +# 5. Scaling plots +# --------------------------------------------------------------------------- +cat("--- 5. Generating scaling plots ---\n") + +pdf_path = file.path(out_dir, "group9_profiling.pdf") +pdf(pdf_path, width = 10, height = 8) + +# Plot 1: Per-iteration cost vs total variables (p + q) +par(mfrow = c(2, 2), mar = c(4.5, 4.5, 3, 1)) + +plot(mixed_results$p + mixed_results$q, mixed_results$per_iter_ms, + type = "b", pch = 19, col = "steelblue", + xlab = "Total variables (p + q)", ylab = "ms / iteration", + main = "Mixed MRF: per-iteration cost vs network size", + log = "y") +grid() + +# Plot 2: Conditional vs Marginal PL +barplot_mat = t(as.matrix(pl_results[, c("cond_ms", "marg_ms")])) +colnames(barplot_mat) = sprintf("p=%d,q=%d", pl_results$p, pl_results$q) +barplot(barplot_mat, beside = TRUE, + col = c("steelblue", "coral"), + ylab = "ms / iteration", + main = "Conditional vs Marginal PL") +legend("topleft", legend = c("Conditional", "Marginal"), + fill = c("steelblue", "coral"), bty = "n") + +# Plot 3: MH vs NUTS +barplot_mat2 = t(as.matrix(nuts_results[, c("mh_ms", "nuts_ms")])) +colnames(barplot_mat2) = sprintf("p=%d,q=%d", nuts_results$p, nuts_results$q) +barplot(barplot_mat2, beside = TRUE, + col = c("steelblue", "seagreen"), + ylab = "ms / iteration", + main = "MH vs hybrid-NUTS") +legend("topleft", legend = c("MH", "hybrid-NUTS"), + fill = c("steelblue", "seagreen"), bty = "n") + +# Plot 4: Cross-model comparison +barplot_mat3 = t(as.matrix(cross_results[, c("mixed_ms", "omrf_ms", "ggm_ms")])) +colnames(barplot_mat3) = sprintf("size=%d", cross_results$size) +barplot(barplot_mat3, beside = TRUE, + col = c("steelblue", "coral", "seagreen"), + ylab = "ms / iteration", + main = "Mixed vs OMRF vs GGM") +legend("topleft", legend = c("Mixed", "OMRF", "GGM"), + fill = c("steelblue", "coral", "seagreen"), bty = "n") + +# Plot 5: Scaling curves — p vs cost (q fixed) and q vs cost (p fixed) +# Extract from mixed_results: fix q at smallest/middle value +par(mfrow = c(1, 2), mar = c(4.5, 4.5, 3, 1)) + +plot(mixed_results$p, mixed_results$per_iter_ms, + type = "b", pch = 19, col = "steelblue", + xlab = "p (discrete)", ylab = "ms / iteration", + main = "Per-iteration cost vs p") +grid() + +plot(mixed_results$q, mixed_results$per_iter_ms, + type = "b", pch = 19, col = "coral", + xlab = "q (continuous)", ylab = "ms / iteration", + main = "Per-iteration cost vs q") +grid() + +dev.off() + +cat(sprintf(" Plots saved to: %s\n", pdf_path)) + +# --------------------------------------------------------------------------- +# Summary +# --------------------------------------------------------------------------- +cat("\n===================================================================\n") +cat(" GROUP 9: Performance Profiling — COMPLETE\n") +cat(sprintf(" Finished: %s\n", Sys.time())) +cat("===================================================================\n") diff --git a/dev/tests/validation/helpers.R b/dev/tests/validation/helpers.R new file mode 100644 index 00000000..83a46d11 --- /dev/null +++ b/dev/tests/validation/helpers.R @@ -0,0 +1,405 @@ +# =========================================================================== +# Shared helpers for the mixed MRF validation test suite +# =========================================================================== +# Provides: +# - make_network() : generate reproducible true parameter sets +# - generate_data() : simulate data from true parameters via bgms or +# mixedGM +# - extract_bgms_blocks(): pull (mux, muy, Kxx, Kxy, Kyy) from bgms fit +# - extract_mgm_blocks() : pull (mux, muy, Kxx, Kxy, Kyy) from mixedGM fit +# - flatten_params() : flatten all blocks to a single named vector +# - recovery_table() : compare estimated vs true as a data.frame +# - recovery_scatter() : scatterplot of estimated vs true +# - summarise_recovery() : one-line correlation + bias + RMSE summary +# =========================================================================== + +# ------------------------------------------------------------------ +# make_network +# ------------------------------------------------------------------ +# Build a reproducible mixed MRF parameter set. +# +# @param p Number of discrete variables. +# @param q Number of continuous variables. +# @param n_cat Integer vector of length p: number of categories per +# discrete variable (bgms convention = max index, +# e.g. binary = 1). +# @param variable_type Character vector of length p: "ordinal" or +# "blume-capel" per discrete variable. Default: all +# ordinal. +# @param baseline_category Integer vector of length p: baseline category +# for Blume-Capel variables. Ignored for ordinal. +# Default: all zeros. +# @param density Approximate fraction of non-zero edges. +# @param seed Random seed for reproducibility. +# +# Returns: named list with mux, muy, Kxx, Kxy, Kyy, n_cat, p, q, +# variable_type, baseline_category. +# ------------------------------------------------------------------ +make_network = function(p, q, n_cat, variable_type = rep("ordinal", p), + baseline_category = rep(0L, p), + density = 0.5, seed = 42) { + set.seed(seed) + + max_cat = max(n_cat) + # BC variables use 2 columns (alpha, beta); ensure mux is wide enough + mux_cols = max(max_cat, 2L) + + # --- Main effects (mux) --- + # Ordinal: threshold parameters, NA-padded. + # Blume-Capel: alpha (linear) and beta (quadratic), rest NA. + mux = matrix(NA_real_, nrow = p, ncol = mux_cols) + for(i in seq_len(p)) { + if(variable_type[i] == "blume-capel") { + mux[i, 1] = round(runif(1, -0.3, 0.3), 2) # alpha + mux[i, 2] = round(runif(1, -0.4, -0.05), 2) # beta (negative = peaked) + } else { + vals = sort(round(seq(-0.5, 0.5, length.out = n_cat[i]), 2)) + if(n_cat[i] == 1) vals = round(runif(1, -0.3, 0.3), 2) + mux[i, seq_len(n_cat[i])] = vals + } + } + + # --- Continuous means --- + muy = round(runif(q, -0.5, 0.5), 2) + + # --- Kxx: ordinal-ordinal interactions (symmetric, zero diagonal) --- + n_edges_xx = p * (p - 1) / 2 + mask_xx = rbinom(n_edges_xx, 1, density) + vals_xx = mask_xx * round(runif(n_edges_xx, 0.15, 0.4) * sample(c(-1, 1), n_edges_xx, replace = TRUE), 2) + Kxx = matrix(0, p, p) + Kxx[upper.tri(Kxx)] = vals_xx + Kxx = Kxx + t(Kxx) + + # --- Kyy: continuous precision (positive definite, sparse off-diag with diagonal dominance) --- + n_edges_yy = q * (q - 1) / 2 + mask_yy = rbinom(n_edges_yy, 1, density) + vals_yy = mask_yy * round(runif(n_edges_yy, 0.05, 0.2) * sample(c(-1, 1), n_edges_yy, replace = TRUE), 2) + Kyy = matrix(0, q, q) + Kyy[upper.tri(Kyy)] = vals_yy + Kyy = Kyy + t(Kyy) + diag(Kyy) = abs(rowSums(Kyy)) + runif(q, 1.2, 1.8) + + # --- Kxy: ordinal-continuous cross interactions --- + n_cross = p * q + mask_xy = rbinom(n_cross, 1, density) + vals_xy = mask_xy * round(runif(n_cross, 0.1, 0.3) * sample(c(-1, 1), n_cross, replace = TRUE), 2) + Kxy = matrix(vals_xy, p, q) + + list( + mux = mux, muy = muy, + Kxx = Kxx, Kxy = Kxy, Kyy = Kyy, + n_cat = n_cat, p = p, q = q, + variable_type = variable_type, + baseline_category = as.integer(baseline_category) + ) +} + +# ------------------------------------------------------------------ +# generate_data +# ------------------------------------------------------------------ +# Simulate data from true network parameters. +# +# @param net Output from make_network(). +# @param n Number of observations. +# @param source "bgms" or "mixedGM". +# @param iter Gibbs burn-in iterations. +# @param seed Random seed. +# +# Returns: data.frame (n x (p+q)) with ordinal columns first, then continuous. +# ------------------------------------------------------------------ +generate_data = function(net, n, source = "bgms", iter = 1000L, seed = 1) { + if(source == "bgms") { + # simulate_mrf() does not support mixed types; use the C++ Gibbs + # sampler directly via sample_mixed_mrf_gibbs(). + sim = bgms:::sample_mixed_mrf_gibbs( + num_states = as.integer(n), + Kxx_r = net$Kxx, + Kxy_r = net$Kxy, + Kyy_r = net$Kyy, + mux_r = net$mux, + muy_r = net$muy, + num_categories_r = as.integer(net$n_cat), + variable_type_r = net$variable_type, + baseline_category_r = net$baseline_category, + iter = as.integer(iter), + seed = as.integer(seed) + ) + df = as.data.frame(cbind(sim$x, sim$y)) + names(df) = c(paste0("X", seq_len(net$p)), paste0("Y", seq_len(net$q))) + df + + } else if(source == "mixedGM") { + sim = mixedGM::mixed_gibbs_generate( + n = n, + Kxx = net$Kxx, Kxy = net$Kxy, Kyy = net$Kyy, + mux = net$mux, muy = net$muy, + num_categories = net$n_cat + 1L, + n_burnin = iter + ) + df = as.data.frame(cbind(sim$x, sim$y)) + names(df) = c(paste0("X", seq_len(net$p)), paste0("Y", seq_len(net$q))) + df + } +} + +# ------------------------------------------------------------------ +# extract_bgms_blocks +# ------------------------------------------------------------------ +# Extract parameter blocks from a bgms fit object. +# +# @param fit A bgms object. +# @param net The true network (for dimension reference). +# +# Returns: list(mux, muy, Kxx, Kxy, Kyy). +# ------------------------------------------------------------------ +extract_bgms_blocks = function(fit, net) { + pm = coef(fit) + mux = pm$main$discrete # p x max_cat + muy_vec = pm$main$continuous[, "mean"] # length q + Kyy_diag = pm$main$continuous[, "precision"] # length q + + pw = pm$pairwise # (p+q) x (p+q) + p = net$p; q = net$q + Kxx = pw[seq_len(p), seq_len(p)] + Kxy = pw[seq_len(p), p + seq_len(q)] + Kyy_off = pw[p + seq_len(q), p + seq_len(q)] + Kyy = Kyy_off + diag(Kyy) = Kyy_diag + + list(mux = mux, muy = muy_vec, Kxx = Kxx, Kxy = Kxy, Kyy = Kyy) +} + +# ------------------------------------------------------------------ +# extract_mgm_blocks +# ------------------------------------------------------------------ +# Extract posterior means from a mixedGM fit object. +# +# @param fit Output of mixedGM::mixed_sampler(). +# @param n_cat Integer vector of length p: number of categories per ordinal +# variable (bgms convention). Used to NA-pad unused threshold +# slots so flatten_params() produces matching lengths. +# +# Returns: list(mux, muy, Kxx, Kxy, Kyy). +# ------------------------------------------------------------------ +extract_mgm_blocks = function(fit, n_cat) { + mux = apply(fit$samples$mux, c(2, 3), mean) + for(i in seq_along(n_cat)) { + if(n_cat[i] < ncol(mux)) { + mux[i, (n_cat[i] + 1):ncol(mux)] = NA_real_ + } + } + list( + mux = mux, + muy = colMeans(fit$samples$muy), + Kxx = apply(fit$samples$Kxx, c(2, 3), mean), + Kxy = apply(fit$samples$Kxy, c(2, 3), mean), + Kyy = apply(fit$samples$Kyy, c(2, 3), mean) + ) +} + +# ------------------------------------------------------------------ +# flatten_params +# ------------------------------------------------------------------ +# Flatten parameter blocks to a single named vector for comparison. +# Excludes NA entries in mux. +# +# @param blocks list(mux, muy, Kxx, Kxy, Kyy). +# @param prefix Optional prefix for names. +# +# Returns: named numeric vector. +# ------------------------------------------------------------------ +flatten_params = function(blocks, prefix = "") { + mux_vals = as.vector(t(blocks$mux)) + mux_keep = !is.na(mux_vals) + mux_named = mux_vals[mux_keep] + names(mux_named) = paste0(prefix, "mux_", which(mux_keep)) + + muy_named = blocks$muy + names(muy_named) = paste0(prefix, "muy_", seq_along(muy_named)) + + # Kxx upper triangle + kxx_ut = blocks$Kxx[upper.tri(blocks$Kxx)] + names(kxx_ut) = paste0(prefix, "Kxx_", seq_along(kxx_ut)) + + # Kxy full + kxy_vals = as.vector(blocks$Kxy) + names(kxy_vals) = paste0(prefix, "Kxy_", seq_along(kxy_vals)) + + # Kyy upper triangle (includes diagonal) + kyy_ut = blocks$Kyy[upper.tri(blocks$Kyy, diag = TRUE)] + names(kyy_ut) = paste0(prefix, "Kyy_", seq_along(kyy_ut)) + + c(mux_named, muy_named, kxx_ut, kxy_vals, kyy_ut) +} + +# ------------------------------------------------------------------ +# recovery_table +# ------------------------------------------------------------------ +# Build a data.frame comparing true vs estimated parameters. +# +# @param true_blocks list(mux, muy, Kxx, Kxy, Kyy). +# @param est_blocks list(mux, muy, Kxx, Kxy, Kyy). +# @param label Character label for the method. +# +# Returns: data.frame with columns: parameter, true, estimate, diff, block, method. +# ------------------------------------------------------------------ +recovery_table = function(true_blocks, est_blocks, label = "estimate") { + true_flat = flatten_params(true_blocks) + est_flat = flatten_params(est_blocks) + + # Determine block membership + block = ifelse(grepl("^mux", names(true_flat)), "mux", + ifelse(grepl("^muy", names(true_flat)), "muy", + ifelse(grepl("^Kxx", names(true_flat)), "Kxx", + ifelse(grepl("^Kxy", names(true_flat)), "Kxy", "Kyy")))) + + data.frame( + parameter = names(true_flat), + true = true_flat, + estimate = est_flat, + diff = est_flat - true_flat, + block = block, + method = label, + stringsAsFactors = FALSE, + row.names = NULL + ) +} + +# ------------------------------------------------------------------ +# recovery_scatter +# ------------------------------------------------------------------ +# Scatterplot of estimated vs true, coloured by block. +# +# @param tab Output of recovery_table() (possibly rbind of multiple). +# @param main Plot title. +# ------------------------------------------------------------------ +recovery_scatter = function(tab, main = "Parameter recovery") { + block_cols = c(mux = "#E41A1C", muy = "#377EB8", Kxx = "#4DAF4A", + Kxy = "#984EA3", Kyy = "#FF7F00") + rng = range(c(tab$true, tab$estimate), na.rm = TRUE) + rng = rng + diff(rng) * c(-0.05, 0.05) + + plot(tab$true, tab$estimate, pch = 19, cex = 0.9, + col = adjustcolor(block_cols[tab$block], 0.7), + xlim = rng, ylim = rng, asp = 1, + xlab = "True value", ylab = "Posterior mean", + main = main) + abline(0, 1, lty = 2, col = "grey40") + + r = cor(tab$true, tab$estimate) + rmse = sqrt(mean(tab$diff^2)) + legend("topleft", + legend = c(names(block_cols), + sprintf("r = %.3f", r), + sprintf("RMSE = %.3f", rmse)), + col = c(block_cols, NA, NA), + pch = c(rep(19, 5), NA, NA), + bty = "n", cex = 0.8) +} + +# ------------------------------------------------------------------ +# summarise_recovery +# ------------------------------------------------------------------ +# Print a one-line summary of recovery quality. +# +# @param tab Output of recovery_table(). +# @param label String label. +# +# Returns: invisible data.frame with summary stats. +# ------------------------------------------------------------------ +summarise_recovery = function(tab, label = "") { + r = cor(tab$true, tab$estimate) + bias = mean(tab$diff) + rmse = sqrt(mean(tab$diff^2)) + max_diff = max(abs(tab$diff)) + cat(sprintf("[%s] r = %.4f | bias = %.4f | RMSE = %.4f | max|diff| = %.4f\n", + label, r, bias, rmse, max_diff)) + + # Per-block summary + blocks = unique(tab$block) + block_summary = do.call(rbind, lapply(blocks, function(b) { + sub = tab[tab$block == b, ] + r_val = if(nrow(sub) >= 3 && sd(sub$true) > 0 && sd(sub$estimate) > 0) + cor(sub$true, sub$estimate) else NA_real_ + data.frame( + block = b, + n = nrow(sub), + r = r_val, + bias = mean(sub$diff), + rmse = sqrt(mean(sub$diff^2)), + max_diff = max(abs(sub$diff)), + stringsAsFactors = FALSE + ) + })) + + cat(" Per-block breakdown:\n") + print(block_summary, row.names = FALSE) + invisible(block_summary) +} + +# ------------------------------------------------------------------ +# agreement_scatter +# ------------------------------------------------------------------ +# Scatterplot of method A vs method B estimates, coloured by block. +# +# @param tab_a recovery_table for method A. +# @param tab_b recovery_table for method B. +# @param label_a, label_b Axis labels. +# @param main Plot title. +# ------------------------------------------------------------------ +agreement_scatter = function(tab_a, tab_b, label_a = "Method A", + label_b = "Method B", main = "Agreement") { + block_cols = c(mux = "#E41A1C", muy = "#377EB8", Kxx = "#4DAF4A", + Kxy = "#984EA3", Kyy = "#FF7F00") + rng = range(c(tab_a$estimate, tab_b$estimate), na.rm = TRUE) + rng = rng + diff(rng) * c(-0.05, 0.05) + + plot(tab_a$estimate, tab_b$estimate, pch = 19, cex = 0.9, + col = adjustcolor(block_cols[tab_a$block], 0.7), + xlim = rng, ylim = rng, asp = 1, + xlab = label_a, ylab = label_b, main = main) + abline(0, 1, lty = 2, col = "grey40") + + r = cor(tab_a$estimate, tab_b$estimate) + rmse = sqrt(mean((tab_a$estimate - tab_b$estimate)^2)) + legend("topleft", + legend = c(names(block_cols), + sprintf("r = %.3f", r), + sprintf("RMSE = %.3f", rmse)), + col = c(block_cols, NA, NA), + pch = c(rep(19, 5), NA, NA), + bty = "n", cex = 0.8) +} + +# ------------------------------------------------------------------ +# trace_panel +# ------------------------------------------------------------------ +# Plot trace + density panels for selected parameters from raw samples. +# +# @param samples Matrix of MCMC samples (iterations x parameters). +# @param names Column names to plot. +# @param true_vals Optional named vector of true values. +# @param main Overall title. +# ------------------------------------------------------------------ +trace_panel = function(samples, names = NULL, true_vals = NULL, main = "") { + if(is.null(names)) names = colnames(samples)[seq_len(min(6, ncol(samples)))] + n_par = length(names) + par(mfrow = c(n_par, 2), mar = c(3, 3, 2, 1), mgp = c(2, 0.6, 0)) + for(nm in names) { + vals = samples[, nm] + # Trace + plot(vals, type = "l", col = adjustcolor("grey30", 0.5), + main = paste(nm, "trace"), xlab = "Iteration", ylab = nm, cex.main = 0.9) + if(!is.null(true_vals) && nm %in% names(true_vals)) { + abline(h = true_vals[nm], col = "red", lwd = 2) + } + abline(h = mean(vals), col = "steelblue", lwd = 1.5) + # Density + d = density(vals) + plot(d, main = paste(nm, "density"), xlab = nm, cex.main = 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The object can be passed to Main components include: \itemize{ \item \code{posterior_summary_main}: Data frame with posterior summaries - (mean, sd, MCSE, ESS, Rhat) for category threshold parameters. + (mean, sd, MCSE, ESS, Rhat) for main-effect parameters. + For OMRF models these are category thresholds; for GGM models + these are precision matrix diagonal entries (quadratic effects); + for mixed MRF models these include both discrete thresholds and + continuous means and precisions. \item \code{posterior_summary_pairwise}: Data frame with posterior summaries for pairwise interaction parameters. \item \code{posterior_summary_indicator}: Data frame with posterior summaries for edge inclusion indicators (if \code{edge_selection = TRUE}). - \item \code{posterior_mean_main}: Matrix of posterior mean thresholds - (rows = variables, cols = categories or parameters). + \item \code{posterior_mean_main}: Posterior mean of per-variable + parameters. For OMRF: a matrix (p x max_categories) of category + thresholds. For GGM: a (p x 1) matrix of precision diagonal entries + (quadratic effects, not main effects). For mixed MRF: a list with + \code{$discrete} and \code{$continuous} components. \item \code{posterior_mean_pairwise}: Symmetric matrix of posterior mean pairwise interaction strengths. \item \code{posterior_mean_indicator}: Symmetric matrix of posterior mean diff --git a/man/coef.bgms.Rd b/man/coef.bgms.Rd index ae663fdc..3a7c9884 100644 --- a/man/coef.bgms.Rd +++ b/man/coef.bgms.Rd @@ -14,13 +14,18 @@ \value{ A list with the following components: \describe{ - \item{main}{Posterior mean of the category threshold parameters.} + \item{main}{Posterior mean of the main-effect parameters. For GGM models + this contains the precision matrix diagonal (quadratic effects, not main + effects). For OMRF models this is a numeric matrix (p x max_categories) + of category thresholds. For mixed MRF models this is a list with + `$discrete` (p x max_categories matrix) and `$continuous` (q x 2 matrix + with columns "mean" and "precision").} \item{pairwise}{Posterior mean of the pairwise interaction matrix.} \item{indicator}{Posterior mean of the edge inclusion indicators (if available).} } } \description{ -Returns the posterior mean thresholds, pairwise effects, and edge inclusion indicators from a \code{bgms} model fit. +Returns the posterior mean main effects, pairwise effects, and edge inclusion indicators from a \code{bgms} model fit. } \examples{ \donttest{ diff --git a/man/extract_arguments.Rd b/man/extract_arguments.Rd index f4212df3..93121f4a 100644 --- a/man/extract_arguments.Rd +++ b/man/extract_arguments.Rd @@ -28,6 +28,7 @@ Other extractors: \code{\link{extract_group_params}()}, \code{\link{extract_indicator_priors}()}, \code{\link{extract_indicators}()}, +\code{\link{extract_main_effects}()}, \code{\link{extract_pairwise_interactions}()}, \code{\link{extract_posterior_inclusion_probabilities}()}, \code{\link{extract_rhat}()}, diff --git a/man/extract_category_thresholds.Rd b/man/extract_category_thresholds.Rd index ef1b7bf2..00a15c20 100644 --- a/man/extract_category_thresholds.Rd +++ b/man/extract_category_thresholds.Rd @@ -11,19 +11,20 @@ extract_category_thresholds(bgms_object) or `bgmCompare` (from [bgmCompare()]).} } \value{ -\describe{ - \item{bgms}{A matrix with one row per variable and one column per - category threshold, containing posterior means.} - \item{bgmCompare}{A matrix with one row per post-warmup iteration, - containing posterior samples of baseline threshold parameters.} - } +See [extract_main_effects()] for details. } \description{ -Retrieves category threshold parameters from a model fitted with -[bgm()] or [bgmCompare()]. +`r lifecycle::badge("deprecated")` + +`extract_category_thresholds()` was renamed to [extract_main_effects()] to +reflect that main effects include continuous means and precisions +(mixed MRF), not only category thresholds. +} +\details{ +Extract Category Threshold Estimates } \seealso{ -[bgm()], [bgmCompare()], [extract_pairwise_interactions()] +[extract_main_effects()] Other extractors: \code{\link{extract_arguments}()}, @@ -31,6 +32,7 @@ Other extractors: \code{\link{extract_group_params}()}, \code{\link{extract_indicator_priors}()}, \code{\link{extract_indicators}()}, +\code{\link{extract_main_effects}()}, \code{\link{extract_pairwise_interactions}()}, \code{\link{extract_posterior_inclusion_probabilities}()}, \code{\link{extract_rhat}()}, diff --git a/man/extract_ess.Rd b/man/extract_ess.Rd index de2c1f8c..0240f2c0 100644 --- a/man/extract_ess.Rd +++ b/man/extract_ess.Rd @@ -27,6 +27,7 @@ Other extractors: \code{\link{extract_group_params}()}, \code{\link{extract_indicator_priors}()}, \code{\link{extract_indicators}()}, +\code{\link{extract_main_effects}()}, \code{\link{extract_pairwise_interactions}()}, \code{\link{extract_posterior_inclusion_probabilities}()}, \code{\link{extract_rhat}()}, diff --git a/man/extract_group_params.Rd b/man/extract_group_params.Rd index 1d4ca459..11356f70 100644 --- a/man/extract_group_params.Rd +++ b/man/extract_group_params.Rd @@ -20,7 +20,7 @@ parameters and group differences from a model fitted with [bgmCompare()]. } \seealso{ [bgmCompare()], [extract_pairwise_interactions()], - [extract_category_thresholds()] + [extract_main_effects()] Other extractors: \code{\link{extract_arguments}()}, @@ -28,6 +28,7 @@ Other extractors: \code{\link{extract_ess}()}, \code{\link{extract_indicator_priors}()}, \code{\link{extract_indicators}()}, +\code{\link{extract_main_effects}()}, \code{\link{extract_pairwise_interactions}()}, \code{\link{extract_posterior_inclusion_probabilities}()}, \code{\link{extract_rhat}()}, diff --git a/man/extract_indicator_priors.Rd b/man/extract_indicator_priors.Rd index bba68c32..2e8b5ee1 100644 --- a/man/extract_indicator_priors.Rd +++ b/man/extract_indicator_priors.Rd @@ -35,6 +35,7 @@ Other extractors: \code{\link{extract_ess}()}, \code{\link{extract_group_params}()}, \code{\link{extract_indicators}()}, +\code{\link{extract_main_effects}()}, \code{\link{extract_pairwise_interactions}()}, \code{\link{extract_posterior_inclusion_probabilities}()}, \code{\link{extract_rhat}()}, diff --git a/man/extract_indicators.Rd b/man/extract_indicators.Rd index 41b1cc81..605c739b 100644 --- a/man/extract_indicators.Rd +++ b/man/extract_indicators.Rd @@ -34,6 +34,7 @@ Other extractors: \code{\link{extract_ess}()}, \code{\link{extract_group_params}()}, \code{\link{extract_indicator_priors}()}, +\code{\link{extract_main_effects}()}, \code{\link{extract_pairwise_interactions}()}, \code{\link{extract_posterior_inclusion_probabilities}()}, \code{\link{extract_rhat}()}, diff --git a/man/extract_main_effects.Rd b/man/extract_main_effects.Rd new file mode 100644 index 00000000..6d6e5657 --- /dev/null +++ b/man/extract_main_effects.Rd @@ -0,0 +1,66 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/extractor_functions.R +\name{extract_main_effects} +\alias{extract_main_effects} +\title{Extract Main Effect Estimates} +\usage{ +extract_main_effects(bgms_object) +} +\arguments{ +\item{bgms_object}{A fitted model object of class `bgms` (from [bgm()]) +or `bgmCompare` (from [bgmCompare()]).} +} +\value{ +The structure depends on the model type: + \describe{ + \item{GGM (bgms)}{`NULL` (invisibly). GGM models have no main effects; + the precision matrix diagonal contains quadratic effects, accessible + via `$posterior_mean_main`.} + \item{OMRF (bgms)}{A numeric matrix with one row per variable and one + column per category threshold, containing posterior means. Columns + beyond the number of categories for a variable are `NA`.} + \item{Mixed MRF (bgms)}{A list with two elements: + \describe{ + \item{discrete}{A numeric matrix (p rows x max_categories columns) + of posterior mean thresholds for discrete variables.} + \item{continuous}{A numeric matrix (q rows x 2 columns) with + columns `"mean"` and `"precision"` for continuous variables.} + }} + \item{bgmCompare}{A matrix with one row per post-warmup iteration, + containing posterior samples of baseline main-effect parameters.} + } +} +\description{ +Retrieves posterior mean main-effect parameters from a model fitted with +[bgm()] or [bgmCompare()]. For OMRF models these are category thresholds; +for mixed MRF models these include both discrete thresholds and continuous +means and precisions. GGM models have no main effects (the precision +diagonal represents quadratic effects) and return `NULL`. +} +\details{ +Extract Main Effect Estimates +} +\examples{ +\donttest{ +fit = bgm(x = Wenchuan[, 1:3]) +extract_main_effects(fit) +} + +} +\seealso{ +[bgm()], [bgmCompare()], [extract_pairwise_interactions()], + [extract_category_thresholds()] + +Other extractors: +\code{\link{extract_arguments}()}, +\code{\link{extract_category_thresholds}()}, +\code{\link{extract_ess}()}, +\code{\link{extract_group_params}()}, +\code{\link{extract_indicator_priors}()}, +\code{\link{extract_indicators}()}, +\code{\link{extract_pairwise_interactions}()}, +\code{\link{extract_posterior_inclusion_probabilities}()}, +\code{\link{extract_rhat}()}, +\code{\link{extract_sbm}()} +} +\concept{extractors} diff --git a/man/extract_pairwise_interactions.Rd b/man/extract_pairwise_interactions.Rd index 70e02886..cba1a1a0 100644 --- a/man/extract_pairwise_interactions.Rd +++ b/man/extract_pairwise_interactions.Rd @@ -24,7 +24,7 @@ Retrieves posterior samples of pairwise interaction parameters from a model fitted with [bgm()] or [bgmCompare()]. } \seealso{ -[bgm()], [bgmCompare()], [extract_category_thresholds()] +[bgm()], [bgmCompare()], [extract_main_effects()] Other extractors: \code{\link{extract_arguments}()}, @@ -33,6 +33,7 @@ Other extractors: \code{\link{extract_group_params}()}, \code{\link{extract_indicator_priors}()}, \code{\link{extract_indicators}()}, +\code{\link{extract_main_effects}()}, \code{\link{extract_posterior_inclusion_probabilities}()}, \code{\link{extract_rhat}()}, \code{\link{extract_sbm}()} diff --git a/man/extract_pairwise_thresholds.Rd b/man/extract_pairwise_thresholds.Rd index f6a8920f..58e0b330 100644 --- a/man/extract_pairwise_thresholds.Rd +++ b/man/extract_pairwise_thresholds.Rd @@ -2,7 +2,7 @@ % Please edit documentation in R/extractor_functions.R \name{extract_pairwise_thresholds} \alias{extract_pairwise_thresholds} -\title{Deprecated: Use extract_category_thresholds instead} +\title{Deprecated: Use extract_main_effects instead} \usage{ extract_pairwise_thresholds(bgms_object) } @@ -10,6 +10,6 @@ extract_pairwise_thresholds(bgms_object) \item{bgms_object}{A bgms or bgmCompare object.} } \description{ -Deprecated: Use extract_category_thresholds instead +Deprecated: Use extract_main_effects instead } \keyword{internal} diff --git a/man/extract_posterior_inclusion_probabilities.Rd b/man/extract_posterior_inclusion_probabilities.Rd index 059232f1..2d4843d0 100644 --- a/man/extract_posterior_inclusion_probabilities.Rd +++ b/man/extract_posterior_inclusion_probabilities.Rd @@ -35,6 +35,7 @@ Other extractors: \code{\link{extract_group_params}()}, \code{\link{extract_indicator_priors}()}, \code{\link{extract_indicators}()}, +\code{\link{extract_main_effects}()}, \code{\link{extract_pairwise_interactions}()}, \code{\link{extract_rhat}()}, \code{\link{extract_sbm}()} diff --git a/man/extract_rhat.Rd b/man/extract_rhat.Rd index b923c584..f6633562 100644 --- a/man/extract_rhat.Rd +++ b/man/extract_rhat.Rd @@ -28,6 +28,7 @@ Other extractors: \code{\link{extract_group_params}()}, \code{\link{extract_indicator_priors}()}, \code{\link{extract_indicators}()}, +\code{\link{extract_main_effects}()}, \code{\link{extract_pairwise_interactions}()}, \code{\link{extract_posterior_inclusion_probabilities}()}, \code{\link{extract_sbm}()} diff --git a/man/extract_sbm.Rd b/man/extract_sbm.Rd index 987153b7..04cb0414 100644 --- a/man/extract_sbm.Rd +++ b/man/extract_sbm.Rd @@ -30,6 +30,7 @@ Other extractors: \code{\link{extract_group_params}()}, \code{\link{extract_indicator_priors}()}, \code{\link{extract_indicators}()}, +\code{\link{extract_main_effects}()}, \code{\link{extract_pairwise_interactions}()}, \code{\link{extract_posterior_inclusion_probabilities}()}, \code{\link{extract_rhat}()} diff --git a/paper/paper.bib b/paper/paper.bib new file mode 100644 index 00000000..ba89c61a --- /dev/null +++ b/paper/paper.bib @@ -0,0 +1,105 @@ +@manual{bgms-package, + title = {bgms: Bayesian Analysis of Graphical Models}, + author = {Marsman, Maarten and {van den Bergh}, Don}, + year = {2026}, + note = {R package version 0.1.6.4}, + url = {https://CRAN.R-project.org/package=bgms} +} + +@article{ContrerasEtAl_2019, + author = {Contreras, A. and Nieto, I. and Valiente, C. and Espinosa, R. and Vazquez, C.}, + doi = {10.1159/000497425}, + journal = {Psychotherapy and Psychosomatics}, + number = {2}, + pages = {71--83}, + title = {The study of psychopathology from the network analysis perspective: A systematic review}, + volume = {88}, + year = {2019} +} + +@article{HuthEtAl_2023_intro, + author = {Huth, K. and {de Ron}, J. and Goudriaan, A. E. and Luigjes, K. and Mohammadi, R. and {van Holst}, R. J. and Wagenmakers, E.-J. and Marsman, M.}, + journal = {Advances in Methods and Practices in Psychological Science}, + title = {Bayesian analysis of cross-sectional networks: A tutorial in R and JASP}, + volume = {6}, + pages = {1--18}, + doi = {10.1177/25152459231193334}, + year = {2023} +} + +@book{KindermannSnell1980, + address = {Providence}, + author = {Kindermann, R. and Snell, J. L.}, + publisher = {American Mathematical Society}, + series = {Contemporary Mathematics}, + title = {Markov Random Fields and their Applications}, + volume = {1}, + year = {1980} +} + +@book{Lauritzen2004, + address = {Oxford}, + author = {Lauritzen, S. L.}, + publisher = {Oxford University Press}, + title = {Graphical Models}, + year = {2004} +} + +@article{MarsmanVandenBerghHaslbeck_2025, + author = {Marsman, M. and {van den} Bergh, D. and Haslbeck, J. M. B.}, + doi = {10.1017/psy.2024.4}, + journal = {Psychometrika}, + number = {1}, + pages = {146--182}, + title = {Bayesian analysis of the ordinal Markov random field}, + volume = {90}, + year = {2025} +} + +@article{MarsmanWaldorpSekulovskiHaslbeck_2024, + author = {Marsman, M. and Waldorp, L. J. and Sekulovski, N. and Haslbeck, J. M. B.}, + doi = {10.1017/psy.2025.10060}, + journal = {Psychometrika}, + number = {5}, + pages = {1809--1842}, + title = {Bayes factor tests for group differences in ordinal and binary graphical models}, + volume = {90}, + year = {2025} +} + +@article{RobinaughEtAl_2020, + author = {Robinaugh, D. J. and Hoekstra, R. H. A. and Toner, E. R. and Borsboom, D.}, + doi = {10.1017/S0033291719003404}, + journal = {Psychological Medicine}, + pages = {353--366}, + title = {The network approach to psychopathology: A review of the literature 2008--2018 and an agenda for future research}, + volume = {50}, + year = {2020} +} + +@article{SekulovskiEtAl_2024, + author = {Sekulovski, N. and Keetelaar, S. and Huth, K. B. S. and Wagenmakers, Eric-Jan and {van }Bork, R. and {van den }Bergh, D. and Marsman, M.}, + doi = {10.1080/00273171.2024.2345915}, + journal = {Multivariate Behavioral Research}, + pages = {913--933}, + title = {Testing conditional independence in psychometric networks: An analysis of three Bayesian methods}, + volume = {59}, + year = {2024} +} + +@article{SekulovskiEtAl_2025, + author = {Sekulovski, N. and Arena, G. and Haslbeck, J. M. B. and Huth, K. B. S. and Friel, N. and Marsman, M.}, + journal = {PsyArXiv}, + title = {A stochastic block prior for clustering in graphical models}, + note = {OSF preprint}, + url = {https://osf.io/preprints/psyarxiv/29p3m_v1}, + year = {2025} +} + +@misc{bgms-archive, + title = {bgms software archive}, + author = {Marsman, Maarten and {van den Bergh}, Don}, + year = {2026}, + note = {Replace with archived release DOI (e.g., Zenodo) after acceptance}, + url = {https://github.com/Bayesian-Graphical-Modelling-Lab/bgms} +} diff --git a/paper/paper.md b/paper/paper.md new file mode 100644 index 00000000..c5461868 --- /dev/null +++ b/paper/paper.md @@ -0,0 +1,143 @@ +--- +title: "bgms: Bayesian Analysis of Graphical Models for Ordinal, Continuous, and Mixed Data" +tags: + - R + - Bayesian inference + - graphical models + - Markov random fields + - Gaussian graphical models + - psychometrics +authors: + - name: Maarten Marsman + orcid: 0000-0001-5309-7502 + affiliation: "1" + - name: + given-names: Don + non-dropping-particle: van den + surname: Bergh + orcid: 0000-0002-9838-7308 + affiliation: "1" +affiliations: + - index: 1 + name: University of Amsterdam, The Netherlands +date: 9 March 2026 +bibliography: paper.bib +--- + +# Summary + +`bgms` is an R package for Bayesian analysis of undirected graphical models +with binary, ordinal, continuous, and mixed variables. The package supports +single-group estimation with `bgm()` and multi-group comparison with +`bgmCompare()`. Core use cases include estimating network structure, +quantifying evidence for conditional dependence or independence, and comparing +parameter equality across groups. + +The package combines pseudolikelihood-based model fitting with Markov chain +Monte Carlo (MCMC) sampling to obtain posterior summaries and Bayes factors for +model comparison tasks [@MarsmanVandenBerghHaslbeck_2025; +@MarsmanWaldorpSekulovskiHaslbeck_2024]. `bgms` is distributed on CRAN and +includes vignettes, tests, and C++ backends for computationally intensive +routines. + +# Statement of need + +Network models are widely used in psychological and psychometric research to +study conditional dependence structures among observed variables +[@ContrerasEtAl_2019; @RobinaughEtAl_2020]. In these settings, analysts often +need to answer inferential questions that go beyond point estimation: whether a +specific edge is present, whether a missing edge is plausible, and whether +network parameters differ across populations. + +Frequentist workflows for graphical models are useful for estimation and +selection, but they are less direct when researchers need graded evidence for +both null and alternative hypotheses in the same analysis. `bgms` addresses this +need by offering a Bayesian workflow where posterior inclusion probabilities and +Bayes factors can be used to evaluate edge presence/absence and group +differences/equivalence within one framework [@HuthEtAl_2023_intro; +@SekulovskiEtAl_2024]. + +The target audience is applied researchers and methodologists who work with +cross-sectional network models in psychology, psychometrics, and related +fields, and who need a practical Bayesian toolchain in R for estimation, +comparison, prediction, simulation, and diagnostics. + +# State of the field + +Graphical-model software for applied research is broad, but many workflows are +either specialized to a single data type, focused on point estimation rather +than evidence quantification, or centered on one modeling task at a time +(estimation without comparison, or comparison without integrated diagnostics). + +`bgms` contributes by integrating several tasks in one package: ordinal MRF +estimation, Gaussian graphical model estimation, mixed-variable MRF estimation, +and multi-group comparison with a shared inferential language based on posterior +inclusion and Bayes factors [@MarsmanVandenBerghHaslbeck_2025; +@MarsmanWaldorpSekulovskiHaslbeck_2024]. This design reduces friction between +analysis stages that are often split across multiple tools. + +The package was developed as a standalone implementation rather than as a thin +wrapper because its scope required coordinated support for multiple model +families, custom prior specifications, shared diagnostics, and a compiled +backend tied to the package API. The result is a coherent research software +artifact that can be installed, tested, and reused as a single unit. + +# Software design + +`bgms` exposes high-level R interfaces while delegating compute-heavy operations +to C++ implementations. This architecture separates user-facing modeling +workflows from low-level sampling and likelihood code, allowing the package to +maintain a consistent R API while keeping core routines efficient. + +Key design choices include: + +1. A unified front-end (`bgm()`, `bgmCompare()`) across model families. +2. Shared extraction, summary, simulation, and prediction interfaces for fitted + objects. +3. Bayesian variable/effect selection via spike-and-slab style workflows for + edges, group differences, and (optionally) clustering components. +4. Pseudolikelihood-based modeling to make estimation practical in settings + where full likelihood approaches are expensive. + +These choices matter for research applications because users can move from model +estimation to hypothesis evaluation and reporting without re-implementing +separate pipelines for each data type. + +# Research impact statement + +`bgms` has been used in methodological work on Bayesian ordinal network +analysis, Bayesian group-difference testing in graphical models, and related +applications [@MarsmanVandenBerghHaslbeck_2025; +@MarsmanWaldorpSekulovskiHaslbeck_2024; @SekulovskiEtAl_2024]. The package also +supports reproducible workflows through scripted estimation, diagnostics, +simulation, and prediction, and includes public documentation and vignettes. + +The software is publicly developed on GitHub with issue tracking, pull requests, +continuous integration, and automated tests. A stable CRAN release and package +website are available, providing accessible installation and user guidance. This +combination of methodological grounding, public development, and practical +interfaces supports continued adoption and citation in network-analytic +research. + +# AI usage disclosure + +Generative AI tools were used to assist drafting parts of this manuscript and to +support code/documentation editing during package development. Human authors +reviewed, edited, and validated all AI-assisted outputs and made all substantive +design and scientific decisions. + + + +# Acknowledgements + +We acknowledge contributors to the `bgms` codebase and documentation, and thank +reviewers and users who provided feedback through issues and pull requests. + + + +# References diff --git a/tests/testthat/test-build-arguments.R b/tests/testthat/test-build-arguments.R index fa52e8cb..c31bd463 100644 --- a/tests/testthat/test-build-arguments.R +++ b/tests/testthat/test-build-arguments.R @@ -98,7 +98,8 @@ test_that("GGM build_arguments: all expected field names present", { "beta_bernoulli_alpha_between", "beta_bernoulli_beta_between", "dirichlet_alpha", "lambda", "na_action", "version", "update_method", "target_accept", "num_chains", - "data_columnnames", "no_variables", "is_continuous" + "data_columnnames", "no_variables", "is_continuous", + "model_type" ) expect_true(all(expected %in% names(a)), info = paste("Missing:", paste(setdiff(expected, names(a)), @@ -162,7 +163,8 @@ test_that("OMRF build_arguments: all expected field names present", { "nuts_max_depth", "learn_mass_matrix", "num_chains", "num_categories", "data_columnnames", "baseline_category", - "pairwise_scaling_factors", "no_variables" + "pairwise_scaling_factors", "no_variables", + "model_type" ) expect_true(all(expected %in% names(a)), info = paste("Missing:", paste(setdiff(expected, names(a)), @@ -222,7 +224,8 @@ test_that("Compare build_arguments: all expected field names present", { "num_chains", "num_groups", "data_columnnames", "projection", "num_categories", "is_ordinal_variable", - "group", "pairwise_scaling_factors" + "group", "pairwise_scaling_factors", + "model_type" ) expect_true(all(expected %in% names(a)), info = paste("Missing:", paste(setdiff(expected, names(a)), diff --git a/tests/testthat/test-extractor-functions.R b/tests/testthat/test-extractor-functions.R index 770a9777..9d4ce46b 100644 --- a/tests/testthat/test-extractor-functions.R +++ b/tests/testthat/test-extractor-functions.R @@ -125,10 +125,10 @@ test_that("extract_pairwise_interactions returns valid matrix for all fit types" # ------------------------------------------------------------------------------ -# extract_category_thresholds() Tests (parameterized) +# extract_main_effects() Tests (parameterized) # ------------------------------------------------------------------------------ -test_that("extract_category_thresholds returns valid output for all fit types", { +test_that("extract_main_effects returns valid output for all fit types", { fixtures = get_all_fixtures() for(spec in fixtures) { @@ -136,17 +136,30 @@ test_that("extract_category_thresholds returns valid output for all fit types", fit = spec$get_fit() args = extract_arguments(fit) - thresholds = extract_category_thresholds(fit) + main = extract_main_effects(fit) - # Structure checks - expect_true(is.matrix(thresholds), info = paste(ctx, "should be matrix")) - - # Values finite where not NA - vals = thresholds[!is.na(thresholds)] - expect_true(all(is.finite(vals)), info = paste(ctx, "non-NA values should be finite")) + if(isTRUE(args$is_mixed)) { + # Mixed MRF returns a list + expect_true(is.list(main), info = paste(ctx, "should be list for mixed")) + expect_true(is.matrix(main$discrete), info = paste(ctx, "$discrete should be matrix")) + expect_true(is.matrix(main$continuous), info = paste(ctx, "$continuous should be matrix")) + } else { + # GGM / OMRF return matrix + expect_true(is.matrix(main), info = paste(ctx, "should be matrix")) + vals = main[!is.na(main)] + expect_true(all(is.finite(vals)), info = paste(ctx, "non-NA values should be finite")) + } } }) +test_that("extract_category_thresholds emits deprecation warning", { + fit = get_bgms_fit() + expect_warning( + extract_category_thresholds(fit), + "extract_main_effects" + ) +}) + # ------------------------------------------------------------------------------ # extract_indicators() and extract_posterior_inclusion_probabilities() Tests @@ -390,7 +403,7 @@ test_that("extractor outputs are dimensionally consistent", { pairwise = extract_pairwise_interactions(fit) expect_equal(ncol(pairwise), n_edges, info = paste(ctx, "pairwise cols")) - thresholds = extract_category_thresholds(fit) + thresholds = suppressWarnings(extract_category_thresholds(fit)) expect_equal(nrow(thresholds), p, info = paste(ctx, "threshold rows")) } }) @@ -888,7 +901,7 @@ test_that("pre-0.1.4 bgm formats emit deprecation warnings for pairwise/threshol expect_warning(extract_pairwise_interactions(fit), "deprecated", info = paste(spec$label, "extract_pairwise should warn") ) - expect_warning(extract_category_thresholds(fit), "deprecated", + expect_warning(extract_category_thresholds(fit), "extract_main_effects", info = paste(spec$label, "extract_thresholds should warn") ) } @@ -910,7 +923,7 @@ test_that("0.1.4-0.1.5 formats emit deprecation warnings", { expect_warning(extract_pairwise_interactions(fit), "deprecated", info = paste(spec$label, "extract_pairwise should warn") ) - expect_warning(extract_category_thresholds(fit), "deprecated", + expect_warning(extract_category_thresholds(fit), "extract_main_effects", info = paste(spec$label, "extract_thresholds should warn") ) @@ -934,7 +947,8 @@ test_that("0.1.6+ formats work without deprecation warnings", { expect_no_warning(extract_indicators(fit)) expect_no_warning(extract_posterior_inclusion_probabilities(fit)) expect_no_warning(extract_pairwise_interactions(fit)) - expect_no_warning(extract_category_thresholds(fit)) + expect_warning(extract_category_thresholds(fit), "extract_main_effects") + expect_no_warning(extract_main_effects(fit)) } }) From 0544d9552676bcffb39f5a75409039a053a8e973 Mon Sep 17 00:00:00 2001 From: MaartenMarsman <52934067+MaartenMarsman@users.noreply.github.com> Date: Mon, 9 Mar 2026 19:28:57 +0100 Subject: [PATCH 35/60] chore: untrack dev/ directory from git --- 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-Developer-only materials excluded from the built R package (via -`.Rbuildignore`). Contains documentation strategy, audit plans, -numerical analyses, test fixtures, and design notes. Nothing in -this directory is shipped to CRAN or installed by users. diff --git a/dev/benchmark_memoization.R b/dev/benchmark_memoization.R deleted file mode 100644 index 6d7b050f..00000000 --- a/dev/benchmark_memoization.R +++ /dev/null @@ -1,65 +0,0 @@ -# ============================================================================= -# Benchmark: Hash-map cache vs Single-entry cache for NUTS memoization -# ============================================================================= -# -# Purpose: Compare two memoization strategies in src/mcmc/mcmc_memoization.h -# 1. Hash-map cache (uses std::unordered_map) -# 2. Single-entry cache (uses memcmp on last theta) -# -# Instructions: -# 1. Install the version you want to test (modify mcmc_memoization.h) -# 2. Run: R CMD INSTALL . -# 3. Run this script: Rscript dev/benchmark_memoization.R -# -# ============================================================================= - -library(bgms) -library(psych) - -# ----------------------------------------------------------------------------- -# Configuration -# ----------------------------------------------------------------------------- -N_REPLICATES <- 5 -SEED <- 123 -WARMUP <- 1000 -ITER <- 500 - -# ----------------------------------------------------------------------------- -# Data setup -# ----------------------------------------------------------------------------- -data(bfi) -bfi_data <- bfi[, 1:25] # 25 personality items, NO na.omit - -cat("=== Memoization Benchmark ===\n") -cat("Data: psych::bfi[, 1:25]\n") -cat("Dimensions:", nrow(bfi_data), "x", ncol(bfi_data), "\n") -cat("Warmup:", WARMUP, "\n") -cat("Iterations:", ITER, "\n") -cat("Replicates:", N_REPLICATES, "\n") -cat("Seed:", SEED, "\n") -cat("==============================\n\n") - -# ----------------------------------------------------------------------------- -# Run benchmark -# ----------------------------------------------------------------------------- -set.seed(SEED) -times <- numeric(N_REPLICATES) - -for (i in 1:N_REPLICATES) { - cat("Replicate", i, "of", N_REPLICATES, "... ") - t0 <- Sys.time() - fit <- bgm(bfi_data, warmup = WARMUP, iter = ITER, verbose = FALSE) - times[i] <- as.numeric(difftime(Sys.time(), t0, units = "secs")) - cat(round(times[i], 1), "s\n") -} - -# ----------------------------------------------------------------------------- -# Results -# ----------------------------------------------------------------------------- -cat("\n=== RESULTS ===\n") -cat("Mean:", round(mean(times), 2), "s\n") -cat("SD:", round(sd(times), 2), "s\n") -cat("Min:", round(min(times), 2), "s\n") -cat("Max:", round(max(times), 2), "s\n") -cat("Times:", paste(round(times, 1), collapse = ", "), "\n") -cat("===============\n") diff --git a/dev/bitwise_compliance/generate_fixtures.R b/dev/bitwise_compliance/generate_fixtures.R deleted file mode 100644 index 062f218f..00000000 --- a/dev/bitwise_compliance/generate_fixtures.R +++ /dev/null @@ -1,688 +0,0 @@ -# ============================================================================== -# Generate Bitwise Compliance Fixtures (baseline: CRAN bgms 0.1.6.3) -# ============================================================================== -# -# Installs bgms 0.1.6.3 from CRAN into an isolated library and runs all -# configurations. The resulting fixtures are the ground-truth reference for -# OMRF/Blume-Capel models (bgm + bgmCompare). -# -# These fixtures capture the LAST CRAN release before continuous variables -# were added. All future development must preserve bitwise-identical output -# for discrete (ordinal / binary / Blume-Capel) models. -# -# Usage: -# Rscript dev/bitwise_compliance/generate_fixtures.R -# -# Output: -# dev/fixtures/compliance/ — one .rds per configuration + manifest.rds -# -# ============================================================================== - -library(callr) - -fixture_dir = file.path("dev", "fixtures", "compliance") -dir.create(fixture_dir, recursive = TRUE, showWarnings = FALSE) - -# ============================================================================== -# Install CRAN 0.1.6.3 into a temporary library -# ============================================================================== - -cran_lib = tempfile("bgms_cran_") -dir.create(cran_lib) - -cat("Installing bgms 0.1.6.3 from CRAN...\n") -install.packages( - "bgms", - repos = "https://cloud.r-project.org", - lib = cran_lib, - quiet = TRUE -) - -installed_version = callr::r( - function(lib_path) { - as.character(packageVersion("bgms", lib.loc = lib_path)) - }, - args = list(lib_path = cran_lib) -) -cat("Installed version:", installed_version, "\n") - -if (installed_version != "0.1.6.3") { - cat("WARNING: Expected 0.1.6.3, got", installed_version, "\n") - cat("Fixtures will be tagged with the actual version.\n") -} - -# ============================================================================== -# Configuration matrix — OMRF/Blume-Capel bgm() configurations -# ============================================================================== -# -# Covers: -# Datasets: Wenchuan (ordinal 5-pt), ADHD (binary), Boredom (ordinal 7-pt) -# Variable types: ordinal (default), blume-capel -# Samplers: NUTS, adaptive-metropolis, hamiltonian-mc -# Edge priors: Bernoulli, Beta-Bernoulli, Stochastic-Block -# Edge selection: TRUE, FALSE -# Missing data: listwise, impute -# -# Short iterations (200 iter / 200 warmup / 2 chains) — bitwise identity does -# not require convergence. -# ============================================================================== - -bgm_configs = list( - - # --- Wenchuan ordinal --- - - list( - id = "bgm_wenchuan_nuts_bernoulli", - desc = "bgm: Wenchuan 6v, NUTS, Bernoulli, edge_sel", - args = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "nuts", seed = 101, display_progress = "none" - ) - ), - list( - id = "bgm_wenchuan_nuts_no_edgesel", - desc = "bgm: Wenchuan 6v, NUTS, no edge selection", - args = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - edge_selection = FALSE, - update_method = "nuts", seed = 102, display_progress = "none" - ) - ), - list( - id = "bgm_wenchuan_mh_bernoulli", - desc = "bgm: Wenchuan 6v, MH, Bernoulli, edge_sel", - args = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "adaptive-metropolis", seed = 103, display_progress = "none" - ) - ), - list( - id = "bgm_wenchuan_hmc_bernoulli", - desc = "bgm: Wenchuan 6v, HMC, Bernoulli, edge_sel", - args = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "hamiltonian-mc", hmc_num_leapfrogs = 20, - seed = 104, display_progress = "none" - ) - ), - list( - id = "bgm_wenchuan_nuts_betabern", - desc = "bgm: Wenchuan 6v, NUTS, Beta-Bernoulli, edge_sel", - args = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Beta-Bernoulli", - beta_bernoulli_alpha = 1, beta_bernoulli_beta = 1, - update_method = "nuts", seed = 105, display_progress = "none" - ) - ), - list( - id = "bgm_wenchuan_nuts_sbm", - desc = "bgm: Wenchuan 6v, NUTS, Stochastic-Block, edge_sel", - args = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Stochastic-Block", - update_method = "nuts", seed = 106, display_progress = "none" - ) - ), - list( - id = "bgm_wenchuan_nuts_scaled_prior", - desc = "bgm: Wenchuan 6v, NUTS, pairwise_scale=1.0", - args = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - pairwise_scale = 1.0, - update_method = "nuts", seed = 107, display_progress = "none" - ) - ), - - # --- Wenchuan Blume-Capel --- - - list( - id = "bgm_wenchuan_nuts_blumecapel", - desc = "bgm: Wenchuan 6v, NUTS, Blume-Capel, baseline=2", - args = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - variable_type = "blume-capel", baseline_category = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "nuts", seed = 401, display_progress = "none" - ) - ), - list( - id = "bgm_wenchuan_mh_blumecapel", - desc = "bgm: Wenchuan 6v, MH, Blume-Capel, baseline=2", - args = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - variable_type = "blume-capel", baseline_category = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "adaptive-metropolis", seed = 402, display_progress = "none" - ) - ), - list( - id = "bgm_wenchuan_hmc_blumecapel", - desc = "bgm: Wenchuan 6v, HMC, Blume-Capel, baseline=2", - args = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - variable_type = "blume-capel", baseline_category = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "hamiltonian-mc", hmc_num_leapfrogs = 20, - seed = 403, display_progress = "none" - ) - ), - list( - id = "bgm_wenchuan_nuts_blumecapel_no_edgesel", - desc = "bgm: Wenchuan 6v, NUTS, Blume-Capel, no edge_sel", - args = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - variable_type = "blume-capel", baseline_category = 2, - edge_selection = FALSE, - update_method = "nuts", seed = 404, display_progress = "none" - ) - ), - list( - id = "bgm_wenchuan_nuts_blumecapel_baseline1", - desc = "bgm: Wenchuan 6v, NUTS, Blume-Capel, baseline=1", - args = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - variable_type = "blume-capel", baseline_category = 1, - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "nuts", seed = 405, display_progress = "none" - ) - ), - - # --- ADHD binary --- - - list( - id = "bgm_adhd_nuts_bernoulli", - desc = "bgm: ADHD 6v, NUTS, Bernoulli, edge_sel", - args = list( - x = "adhd_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "nuts", seed = 201, display_progress = "none" - ) - ), - list( - id = "bgm_adhd_mh_bernoulli", - desc = "bgm: ADHD 6v, MH, Bernoulli, edge_sel", - args = list( - x = "adhd_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "adaptive-metropolis", seed = 202, display_progress = "none" - ) - ), - list( - id = "bgm_adhd_nuts_no_edgesel", - desc = "bgm: ADHD 6v, NUTS, no edge selection", - args = list( - x = "adhd_small", iter = 200, warmup = 200, chains = 2, - edge_selection = FALSE, - update_method = "nuts", seed = 203, display_progress = "none" - ) - ), - list( - id = "bgm_adhd_nuts_sbm", - desc = "bgm: ADHD 6v, NUTS, Stochastic-Block, edge_sel", - args = list( - x = "adhd_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Stochastic-Block", - update_method = "nuts", seed = 204, display_progress = "none" - ) - ), - - # --- Boredom ordinal 7-point --- - - list( - id = "bgm_boredom_nuts_bernoulli", - desc = "bgm: Boredom 6v, NUTS, Bernoulli, edge_sel", - args = list( - x = "boredom_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "nuts", seed = 301, display_progress = "none" - ) - ), - list( - id = "bgm_boredom_mh_betabern", - desc = "bgm: Boredom 6v, MH, Beta-Bernoulli, edge_sel", - args = list( - x = "boredom_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Beta-Bernoulli", - update_method = "adaptive-metropolis", seed = 302, display_progress = "none" - ) - ), - list( - id = "bgm_boredom_hmc_bernoulli", - desc = "bgm: Boredom 6v, HMC, Bernoulli, edge_sel", - args = list( - x = "boredom_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "hamiltonian-mc", hmc_num_leapfrogs = 20, - seed = 303, display_progress = "none" - ) - ), - list( - id = "bgm_boredom_nuts_no_edgesel", - desc = "bgm: Boredom 6v, NUTS, no edge selection", - args = list( - x = "boredom_small", iter = 200, warmup = 200, chains = 2, - edge_selection = FALSE, - update_method = "nuts", seed = 304, display_progress = "none" - ) - ), - - # --- Missing data imputation --- - - list( - id = "bgm_wenchuan_nuts_impute", - desc = "bgm: Wenchuan 6v +NAs, NUTS, impute", - args = list( - x = "wenchuan_na", iter = 200, warmup = 200, chains = 2, - na_action = "impute", - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "nuts", seed = 501, display_progress = "none" - ) - ), - list( - id = "bgm_wenchuan_nuts_blumecapel_impute", - desc = "bgm: Wenchuan 6v +NAs, NUTS, Blume-Capel, impute", - args = list( - x = "wenchuan_na", iter = 200, warmup = 200, chains = 2, - variable_type = "blume-capel", baseline_category = 2, - na_action = "impute", - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "nuts", seed = 502, display_progress = "none" - ) - ) -) - -# ============================================================================== -# bgmCompare() configurations -# ============================================================================== -# -# Uses x/y API (two separate data frames) since that is how 0.1.6.3 was -# primarily tested. Covers ordinal, binary, and Blume-Capel with -# difference_selection and main_difference_selection. -# ============================================================================== - -compare_configs = list( - - # --- Wenchuan ordinal (split into two groups) --- - - list( - id = "cmp_wenchuan_nuts_bernoulli", - desc = "bgmCompare: Wenchuan, NUTS, Bernoulli, diff_sel", - args = list( - x = "wenchuan_g1", y = "wenchuan_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = TRUE, - update_method = "nuts", seed = 601, display_progress = "none" - ) - ), - list( - id = "cmp_wenchuan_mh_bernoulli", - desc = "bgmCompare: Wenchuan, MH, Bernoulli, diff_sel", - args = list( - x = "wenchuan_g1", y = "wenchuan_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = TRUE, - update_method = "adaptive-metropolis", seed = 602, display_progress = "none" - ) - ), - list( - id = "cmp_wenchuan_hmc_bernoulli", - desc = "bgmCompare: Wenchuan, HMC, Bernoulli, diff_sel", - args = list( - x = "wenchuan_g1", y = "wenchuan_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = TRUE, - update_method = "hamiltonian-mc", hmc_num_leapfrogs = 20, - seed = 603, display_progress = "none" - ) - ), - list( - id = "cmp_wenchuan_nuts_no_diffsel", - desc = "bgmCompare: Wenchuan, NUTS, no diff selection", - args = list( - x = "wenchuan_g1", y = "wenchuan_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = FALSE, - update_method = "nuts", seed = 604, display_progress = "none" - ) - ), - list( - id = "cmp_wenchuan_nuts_main_diffsel", - desc = "bgmCompare: Wenchuan, NUTS, main+pairwise diff_sel", - args = list( - x = "wenchuan_g1", y = "wenchuan_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = TRUE, - main_difference_selection = TRUE, - update_method = "nuts", seed = 605, display_progress = "none" - ) - ), - list( - id = "cmp_wenchuan_nuts_betabern", - desc = "bgmCompare: Wenchuan, NUTS, Beta-Bernoulli, diff_sel", - args = list( - x = "wenchuan_g1", y = "wenchuan_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = TRUE, - difference_prior = "Beta-Bernoulli", - update_method = "nuts", seed = 606, display_progress = "none" - ) - ), - - # --- Wenchuan Blume-Capel --- - - list( - id = "cmp_wenchuan_nuts_blumecapel", - desc = "bgmCompare: Wenchuan, NUTS, Blume-Capel, baseline=2, diff_sel", - args = list( - x = "wenchuan_g1", y = "wenchuan_g2", - iter = 200, warmup = 200, chains = 2, - variable_type = "blume-capel", baseline_category = 2, - difference_selection = TRUE, - update_method = "nuts", seed = 701, display_progress = "none" - ) - ), - list( - id = "cmp_wenchuan_mh_blumecapel", - desc = "bgmCompare: Wenchuan, MH, Blume-Capel, baseline=2, diff_sel", - args = list( - x = "wenchuan_g1", y = "wenchuan_g2", - iter = 200, warmup = 200, chains = 2, - variable_type = "blume-capel", baseline_category = 2, - difference_selection = TRUE, - update_method = "adaptive-metropolis", seed = 702, display_progress = "none" - ) - ), - list( - id = "cmp_wenchuan_nuts_blumecapel_no_diffsel", - desc = "bgmCompare: Wenchuan, NUTS, Blume-Capel, no diff_sel", - args = list( - x = "wenchuan_g1", y = "wenchuan_g2", - iter = 200, warmup = 200, chains = 2, - variable_type = "blume-capel", baseline_category = 2, - difference_selection = FALSE, - update_method = "nuts", seed = 703, display_progress = "none" - ) - ), - - # --- ADHD binary --- - - list( - id = "cmp_adhd_nuts_bernoulli", - desc = "bgmCompare: ADHD, NUTS, Bernoulli, diff_sel", - args = list( - x = "adhd_g1", y = "adhd_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = TRUE, - update_method = "nuts", seed = 801, display_progress = "none" - ) - ), - list( - id = "cmp_adhd_mh_bernoulli", - desc = "bgmCompare: ADHD, MH, Bernoulli, diff_sel", - args = list( - x = "adhd_g1", y = "adhd_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = TRUE, - update_method = "adaptive-metropolis", seed = 802, display_progress = "none" - ) - ), - - # --- Boredom ordinal 7-point --- - - list( - id = "cmp_boredom_nuts_bernoulli", - desc = "bgmCompare: Boredom, NUTS, Bernoulli, diff_sel", - args = list( - x = "boredom_g1", y = "boredom_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = TRUE, - update_method = "nuts", seed = 901, display_progress = "none" - ) - ), - list( - id = "cmp_boredom_mh_bernoulli", - desc = "bgmCompare: Boredom, MH, Bernoulli, diff_sel", - args = list( - x = "boredom_g1", y = "boredom_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = TRUE, - update_method = "adaptive-metropolis", seed = 902, display_progress = "none" - ) - ), - - # --- Missing data imputation in bgmCompare --- - - list( - id = "cmp_wenchuan_nuts_impute", - desc = "bgmCompare: Wenchuan +NAs, NUTS, impute, diff_sel", - args = list( - x = "wenchuan_g1_na", y = "wenchuan_g2_na", - iter = 200, warmup = 200, chains = 2, - na_action = "impute", - difference_selection = TRUE, - update_method = "nuts", seed = 1001, display_progress = "none" - ) - ) -) - -# ============================================================================== -# Dataset preparation -# ============================================================================== - -prepare_datasets = function() { - data(Wenchuan, package = "bgms", envir = environment()) - data(ADHD, package = "bgms", envir = environment()) - data(Boredom, package = "bgms", envir = environment()) - - wenchuan_small = Wenchuan[, 1:6] - adhd_small = ADHD[, 2:7] - boredom_small = Boredom[, 2:7] - - # Wenchuan with NAs (deterministic injection) - wenchuan_na = wenchuan_small - set.seed(999) - na_idx = sample(length(wenchuan_na), size = 20) - wenchuan_na[na_idx] = NA - - # Split datasets for bgmCompare (deterministic halves) - n_w = nrow(wenchuan_small) - wenchuan_g1 = wenchuan_small[1:floor(n_w / 2), ] - wenchuan_g2 = wenchuan_small[(floor(n_w / 2) + 1):n_w, ] - - n_a = nrow(adhd_small) - adhd_g1 = adhd_small[1:floor(n_a / 2), ] - adhd_g2 = adhd_small[(floor(n_a / 2) + 1):n_a, ] - - n_b = nrow(boredom_small) - boredom_g1 = boredom_small[1:floor(n_b / 2), ] - boredom_g2 = boredom_small[(floor(n_b / 2) + 1):n_b, ] - - # Wenchuan groups with NAs - wenchuan_g1_na = wenchuan_g1 - wenchuan_g2_na = wenchuan_g2 - set.seed(998) - na_idx1 = sample(length(wenchuan_g1_na), size = 10) - wenchuan_g1_na[na_idx1] = NA - set.seed(997) - na_idx2 = sample(length(wenchuan_g2_na), size = 10) - wenchuan_g2_na[na_idx2] = NA - - list( - wenchuan_small = wenchuan_small, - adhd_small = adhd_small, - boredom_small = boredom_small, - wenchuan_na = wenchuan_na, - wenchuan_g1 = wenchuan_g1, - wenchuan_g2 = wenchuan_g2, - adhd_g1 = adhd_g1, - adhd_g2 = adhd_g2, - boredom_g1 = boredom_g1, - boredom_g2 = boredom_g2, - wenchuan_g1_na = wenchuan_g1_na, - wenchuan_g2_na = wenchuan_g2_na - ) -} - -# ============================================================================== -# Fixture extraction helpers -# ============================================================================== - -extract_bgm_fixture = function(fit, config) { - list( - id = config$id, - desc = config$desc, - posterior_summary_main = fit$posterior_summary_main, - posterior_summary_pairwise = fit$posterior_summary_pairwise, - posterior_summary_indicator = fit$posterior_summary_indicator, - posterior_mean_main = fit$posterior_mean_main, - posterior_mean_pairwise = fit$posterior_mean_pairwise, - posterior_mean_indicator = fit$posterior_mean_indicator, - raw_main_chain1 = fit$raw_samples$main[[1]], - raw_pairwise_chain1 = fit$raw_samples$pairwise[[1]], - raw_indicator_chain1 = if (!is.null(fit$raw_samples$indicator)) - fit$raw_samples$indicator[[1]] else NULL, - nuts_diag = fit$nuts_diag, - posterior_coclustering_matrix = fit$posterior_coclustering_matrix, - posterior_mean_allocations = fit$posterior_mean_allocations, - bgms_version = as.character(packageVersion("bgms")) - ) -} - -extract_compare_fixture = function(fit, config) { - list( - id = config$id, - desc = config$desc, - posterior_summary_main_baseline = fit$posterior_summary_main_baseline, - posterior_summary_pairwise_baseline = fit$posterior_summary_pairwise_baseline, - posterior_summary_main_differences = fit$posterior_summary_main_differences, - posterior_summary_pairwise_differences = fit$posterior_summary_pairwise_differences, - posterior_summary_indicator = fit$posterior_summary_indicator, - posterior_mean_main_baseline = fit$posterior_mean_main_baseline, - posterior_mean_pairwise_baseline = fit$posterior_mean_pairwise_baseline, - posterior_mean_main_differences = fit$posterior_mean_main_differences, - posterior_mean_pairwise_differences = fit$posterior_mean_pairwise_differences, - posterior_mean_indicator = fit$posterior_mean_indicator, - raw_samples = fit$raw_samples, - nuts_diag = fit$nuts_diag, - bgms_version = as.character(packageVersion("bgms")) - ) -} - -# ============================================================================== -# Generate fixtures via callr (isolated CRAN library) -# ============================================================================== - -resolve_args = function(args, datasets) { - resolved = args - for (nm in names(resolved)) { - if (is.character(resolved[[nm]]) && resolved[[nm]] %in% names(datasets)) { - resolved[[nm]] = datasets[[resolved[[nm]]]] - } - } - resolved -} - -datasets = prepare_datasets() - -# --- bgm fixtures --- - -cat(sprintf("\nGenerating %d bgm fixtures...\n", length(bgm_configs))) -bgm_manifest = list() - -for (config in bgm_configs) { - cat(sprintf(" [%s] %s ... ", config$id, config$desc)) - resolved = resolve_args(config$args, datasets) - - result = tryCatch({ - callr::r( - function(args, lib_path, extract_fn) { - .libPaths(c(lib_path, .libPaths())) - library(bgms, lib.loc = lib_path) - set.seed(args$seed) - fit = do.call(bgm, args) - extract_fn(fit, list(id = "tmp", desc = "tmp")) - }, - args = list(args = resolved, lib_path = cran_lib, extract_fn = extract_bgm_fixture), - show = FALSE - ) - }, error = function(e) { - cat(sprintf("ERROR: %s\n", conditionMessage(e))) - NULL - }) - - if (!is.null(result)) { - result$id = config$id - result$desc = config$desc - result$bgms_version = installed_version - path = file.path(fixture_dir, paste0(config$id, ".rds")) - saveRDS(result, path) - bgm_manifest[[config$id]] = list( - id = config$id, desc = config$desc, type = "bgm", - file = paste0(config$id, ".rds") - ) - cat("OK\n") - } -} - -# --- bgmCompare fixtures --- - -cat(sprintf("\nGenerating %d bgmCompare fixtures...\n", length(compare_configs))) -compare_manifest = list() - -for (config in compare_configs) { - cat(sprintf(" [%s] %s ... ", config$id, config$desc)) - resolved = resolve_args(config$args, datasets) - - result = tryCatch({ - callr::r( - function(args, lib_path, extract_fn) { - .libPaths(c(lib_path, .libPaths())) - library(bgms, lib.loc = lib_path) - set.seed(args$seed) - fit = do.call(bgmCompare, args) - extract_fn(fit, list(id = "tmp", desc = "tmp")) - }, - args = list(args = resolved, lib_path = cran_lib, extract_fn = extract_compare_fixture), - show = FALSE - ) - }, error = function(e) { - cat(sprintf("ERROR: %s\n", conditionMessage(e))) - NULL - }) - - if (!is.null(result)) { - result$id = config$id - result$desc = config$desc - result$bgms_version = installed_version - path = file.path(fixture_dir, paste0(config$id, ".rds")) - saveRDS(result, path) - compare_manifest[[config$id]] = list( - id = config$id, desc = config$desc, type = "bgmCompare", - file = paste0(config$id, ".rds") - ) - cat("OK\n") - } -} - -# ============================================================================== -# Save manifest -# ============================================================================== - -manifest = c(bgm_manifest, compare_manifest) -saveRDS(manifest, file.path(fixture_dir, "manifest.rds")) - -cat(sprintf( - "\nDone. %d bgm + %d bgmCompare = %d total fixtures in %s\n", - length(bgm_manifest), length(compare_manifest), length(manifest), fixture_dir -)) -cat("Baseline version:", installed_version, "\n") -cat("R version:", R.version.string, "\n") -cat("Date:", format(Sys.time(), "%Y-%m-%d %H:%M"), "\n") - -# Cleanup -unlink(cran_lib, recursive = TRUE) diff --git a/dev/bitwise_compliance/test_compliance.R b/dev/bitwise_compliance/test_compliance.R deleted file mode 100644 index 71cc71a3..00000000 --- a/dev/bitwise_compliance/test_compliance.R +++ /dev/null @@ -1,685 +0,0 @@ -# ============================================================================== -# Bitwise Compliance Test: Current Build vs CRAN 0.1.6.3 Fixtures -# ============================================================================== -# -# Reruns every bgm() and bgmCompare() configuration from the compliance -# fixture set and verifies bitwise-identical output. -# -# Usage: -# Rscript dev/bitwise_compliance/test_compliance.R -# -# Prerequisites: -# Rscript dev/bitwise_compliance/generate_fixtures.R -# -# Exit code: -# 0 = all pass, 1 = any fail -# -# ------------------------------------------------------------------------------ -# Known differences vs CRAN 0.1.6.3 -# ------------------------------------------------------------------------------ -# -# 1. NUTS target_accept defaults changed between 0.1.6.3 and current: -# - bgm(): 0.60 → 0.80 -# - bgmCompare(): 0.65 → 0.80 -# DualAveraging receives a different target acceptance rate, changing the -# adapted step size from iteration 1 onward and cascading into different -# NUTS trajectories. MH and HMC defaults did not change. -# Fix: bgm NUTS configs pass target_accept=0.6, bgmCompare NUTS configs -# pass target_accept=0.65, matching the old defaults. -# -# 2. nuts_diag structure: current code adds a warmup_check field and -# warmup_incomplete to the summary sub-list. These fields did not exist -# in 0.1.6.3. The comparison skips nuts_diag and compares only the -# shared core fields (treedepth, divergent, energy, ebfmi). -# -# 3. posterior_summary NA handling: 0.1.6.3 returned NA for mean and Rhat -# of constant or near-constant parameters. Current code computes values -# in those cells. The comparison ignores cells that are NA in the fixture. -# -# 4. SBM RNG ordering (CONFIRMED, NOT FIXED — not a bug): -# The SBM algorithm itself is unchanged. However, old code initialized -# cluster allocations (runif + block_probs_mfm_sbm) BEFORE the NUTS -# step-size search; new code does step-size search first, then lazily -# initializes SBM on the first edge_prior.update(). The swapped RNG -# order causes divergence from iteration 1. Both orderings produce -# valid MCMC chains; this is not a bug. SBM configs use structure-only -# comparison. -# -# 5. Imputation bugs (CONFIRMED, 2 of 3 FIXED): -# (a) Stale gradient cache: ensure_gradient_cache() cached sufficient -# statistics (counts_per_category_, pairwise_stats_) but was never -# invalidated after impute_missing() updated them. Old code -# recomputed gradient_observed_active() fresh at each HMC call. -# Fix: invalidate_gradient_cache() at end of impute_missing(). -# (b) Stale observations_double_t_: the precomputed transpose used -# in gradient computation was set once in the constructor, never -# updated after imputation changed observations_double_ cells. -# Old code created obs_double_t fresh from observations each -# gradient call. Fix: update observations_double_t_ at end of -# impute_missing(). -# (c) Blume-Capel baseline probability: old code pre-initialized -# cumsum with MY_EXP(ref*main + ref^2*main) before the category -# loop, adding a phantom probability mass to the imputation -# distribution. New code starts cumsum = 0.0. Confirmed: restoring -# the old formula produces bitwise match. The old code was wrong; -# the new code is correct. This config uses structure-only check. -# -# 6. Blume-Capel centering: all configs pass because CRAN 0.1.6.3 already -# centered BC data in R before passing to C++, producing the same result -# as the current C++ constructor. The observations_/observations_double_ -# mismatch was a regression introduced and fixed within PR #78; it was -# never in a CRAN release. -# -# 7. Intermediate-overflow guard in compute_logZ_and_probs_ordinal and -# compute_logZ_and_probs_blume_capel (ACCEPTED — not a bug): -# Commit 04b9562 tightened the fast/slow block threshold from EXP_BOUND -# (709) to FAST_LIM = max(0, EXP_BOUND - max_abs_main) to prevent -# intermediate overflow in exp(main_param(c) + (c+1)*rest) before the -# cancellation with exp(-bound). Both code paths are mathematically -# identical but differ at floating-point level. During HMC leapfrog -# integration, parameters can temporarily reach extreme values where -# max_abs_main is large enough that FAST_LIM < EXP_BOUND, reclassifying -# some observations between the fast (vectorized) and slow (per-element) -# paths. The resulting floating-point perturbation cascades through the -# fixed-step leapfrog integrator. This is needed for mixed MRF models -# where Theta_ss is absorbed into main_param. The affected configs use -# structure-only comparison against CRAN fixtures. -# -# ============================================================================== - -library(bgms) - -fixture_dir = file.path("dev", "fixtures", "compliance") - -if(!file.exists(file.path(fixture_dir, "manifest.rds"))) { - stop("No fixtures found. Run dev/bitwise_compliance/generate_fixtures.R first.") -} - -manifest = readRDS(file.path(fixture_dir, "manifest.rds")) -cat(sprintf("Testing %d compliance fixtures against current build...\n", length(manifest))) -cat("Current bgms version:", as.character(packageVersion("bgms")), "\n\n") - -# ============================================================================== -# Datasets (must match generate_fixtures.R) -# ============================================================================== - -data(Wenchuan) -data(ADHD) -data(Boredom) - -wenchuan_small = Wenchuan[, 1:6] -adhd_small = ADHD[, 2:7] -boredom_small = Boredom[, 2:7] - -wenchuan_na = wenchuan_small -set.seed(999) -na_idx = sample(length(wenchuan_na), size = 20) -wenchuan_na[na_idx] = NA - -n_w = nrow(wenchuan_small) -wenchuan_g1 = wenchuan_small[1:floor(n_w / 2), ] -wenchuan_g2 = wenchuan_small[(floor(n_w / 2) + 1):n_w, ] - -n_a = nrow(adhd_small) -adhd_g1 = adhd_small[1:floor(n_a / 2), ] -adhd_g2 = adhd_small[(floor(n_a / 2) + 1):n_a, ] - -n_b = nrow(boredom_small) -boredom_g1 = boredom_small[1:floor(n_b / 2), ] -boredom_g2 = boredom_small[(floor(n_b / 2) + 1):n_b, ] - -wenchuan_g1_na = wenchuan_g1 -wenchuan_g2_na = wenchuan_g2 -set.seed(998) -na_idx1 = sample(length(wenchuan_g1_na), size = 10) -wenchuan_g1_na[na_idx1] = NA -set.seed(997) -na_idx2 = sample(length(wenchuan_g2_na), size = 10) -wenchuan_g2_na[na_idx2] = NA - -datasets = list( - wenchuan_small = wenchuan_small, - adhd_small = adhd_small, - boredom_small = boredom_small, - wenchuan_na = wenchuan_na, - wenchuan_g1 = wenchuan_g1, - wenchuan_g2 = wenchuan_g2, - adhd_g1 = adhd_g1, - adhd_g2 = adhd_g2, - boredom_g1 = boredom_g1, - boredom_g2 = boredom_g2, - wenchuan_g1_na = wenchuan_g1_na, - wenchuan_g2_na = wenchuan_g2_na -) - -# ============================================================================== -# Configuration rebuild (mirrors generate_fixtures.R) -# ============================================================================== - -bgm_configs = list( - bgm_wenchuan_nuts_bernoulli = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - target_accept = 0.6, update_method = "nuts", seed = 101, display_progress = "none" - ), - bgm_wenchuan_nuts_no_edgesel = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - edge_selection = FALSE, - target_accept = 0.6, update_method = "nuts", seed = 102, display_progress = "none" - ), - bgm_wenchuan_mh_bernoulli = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "adaptive-metropolis", seed = 103, display_progress = "none" - ), - bgm_wenchuan_hmc_bernoulli = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "hamiltonian-mc", hmc_num_leapfrogs = 20, - seed = 104, display_progress = "none" - ), - bgm_wenchuan_nuts_betabern = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Beta-Bernoulli", - beta_bernoulli_alpha = 1, beta_bernoulli_beta = 1, - target_accept = 0.6, update_method = "nuts", seed = 105, display_progress = "none" - ), - bgm_wenchuan_nuts_sbm = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Stochastic-Block", - target_accept = 0.6, update_method = "nuts", seed = 106, display_progress = "none" - ), - bgm_wenchuan_nuts_scaled_prior = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - pairwise_scale = 1.0, - target_accept = 0.6, update_method = "nuts", seed = 107, display_progress = "none" - ), - bgm_wenchuan_nuts_blumecapel = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - variable_type = "blume-capel", baseline_category = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - target_accept = 0.6, update_method = "nuts", seed = 401, display_progress = "none" - ), - bgm_wenchuan_mh_blumecapel = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - variable_type = "blume-capel", baseline_category = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "adaptive-metropolis", seed = 402, display_progress = "none" - ), - bgm_wenchuan_hmc_blumecapel = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - variable_type = "blume-capel", baseline_category = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "hamiltonian-mc", hmc_num_leapfrogs = 20, - seed = 403, display_progress = "none" - ), - bgm_wenchuan_nuts_blumecapel_no_edgesel = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - variable_type = "blume-capel", baseline_category = 2, - edge_selection = FALSE, - target_accept = 0.6, update_method = "nuts", seed = 404, display_progress = "none" - ), - bgm_wenchuan_nuts_blumecapel_baseline1 = list( - x = "wenchuan_small", iter = 200, warmup = 200, chains = 2, - variable_type = "blume-capel", baseline_category = 1, - edge_selection = TRUE, edge_prior = "Bernoulli", - target_accept = 0.6, update_method = "nuts", seed = 405, display_progress = "none" - ), - bgm_adhd_nuts_bernoulli = list( - x = "adhd_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - target_accept = 0.6, update_method = "nuts", seed = 201, display_progress = "none" - ), - bgm_adhd_mh_bernoulli = list( - x = "adhd_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "adaptive-metropolis", seed = 202, display_progress = "none" - ), - bgm_adhd_nuts_no_edgesel = list( - x = "adhd_small", iter = 200, warmup = 200, chains = 2, - edge_selection = FALSE, - target_accept = 0.6, update_method = "nuts", seed = 203, display_progress = "none" - ), - bgm_adhd_nuts_sbm = list( - x = "adhd_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Stochastic-Block", - target_accept = 0.6, update_method = "nuts", seed = 204, display_progress = "none" - ), - bgm_boredom_nuts_bernoulli = list( - x = "boredom_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - target_accept = 0.6, update_method = "nuts", seed = 301, display_progress = "none" - ), - bgm_boredom_mh_betabern = list( - x = "boredom_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Beta-Bernoulli", - update_method = "adaptive-metropolis", seed = 302, display_progress = "none" - ), - bgm_boredom_hmc_bernoulli = list( - x = "boredom_small", iter = 200, warmup = 200, chains = 2, - edge_selection = TRUE, edge_prior = "Bernoulli", - update_method = "hamiltonian-mc", hmc_num_leapfrogs = 20, - seed = 303, display_progress = "none" - ), - bgm_boredom_nuts_no_edgesel = list( - x = "boredom_small", iter = 200, warmup = 200, chains = 2, - edge_selection = FALSE, - target_accept = 0.6, update_method = "nuts", seed = 304, display_progress = "none" - ), - bgm_wenchuan_nuts_impute = list( - x = "wenchuan_na", iter = 200, warmup = 200, chains = 2, - na_action = "impute", - edge_selection = TRUE, edge_prior = "Bernoulli", - target_accept = 0.6, update_method = "nuts", seed = 501, display_progress = "none" - ), - bgm_wenchuan_nuts_blumecapel_impute = list( - x = "wenchuan_na", iter = 200, warmup = 200, chains = 2, - variable_type = "blume-capel", baseline_category = 2, - na_action = "impute", - edge_selection = TRUE, edge_prior = "Bernoulli", - target_accept = 0.6, update_method = "nuts", seed = 502, display_progress = "none" - ) -) - -compare_configs = list( - cmp_wenchuan_nuts_bernoulli = list( - x = "wenchuan_g1", y = "wenchuan_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = TRUE, - target_accept = 0.65, update_method = "nuts", seed = 601, display_progress = "none" - ), - cmp_wenchuan_mh_bernoulli = list( - x = "wenchuan_g1", y = "wenchuan_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = TRUE, - update_method = "adaptive-metropolis", seed = 602, display_progress = "none" - ), - cmp_wenchuan_hmc_bernoulli = list( - x = "wenchuan_g1", y = "wenchuan_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = TRUE, - update_method = "hamiltonian-mc", hmc_num_leapfrogs = 20, - seed = 603, display_progress = "none" - ), - cmp_wenchuan_nuts_no_diffsel = list( - x = "wenchuan_g1", y = "wenchuan_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = FALSE, - target_accept = 0.65, update_method = "nuts", seed = 604, display_progress = "none" - ), - cmp_wenchuan_nuts_main_diffsel = list( - x = "wenchuan_g1", y = "wenchuan_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = TRUE, - main_difference_selection = TRUE, - target_accept = 0.65, update_method = "nuts", seed = 605, display_progress = "none" - ), - cmp_wenchuan_nuts_betabern = list( - x = "wenchuan_g1", y = "wenchuan_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = TRUE, - difference_prior = "Beta-Bernoulli", - target_accept = 0.65, update_method = "nuts", seed = 606, display_progress = "none" - ), - cmp_wenchuan_nuts_blumecapel = list( - x = "wenchuan_g1", y = "wenchuan_g2", - iter = 200, warmup = 200, chains = 2, - variable_type = "blume-capel", baseline_category = 2, - difference_selection = TRUE, - target_accept = 0.65, update_method = "nuts", seed = 701, display_progress = "none" - ), - cmp_wenchuan_mh_blumecapel = list( - x = "wenchuan_g1", y = "wenchuan_g2", - iter = 200, warmup = 200, chains = 2, - variable_type = "blume-capel", baseline_category = 2, - difference_selection = TRUE, - update_method = "adaptive-metropolis", seed = 702, display_progress = "none" - ), - cmp_wenchuan_nuts_blumecapel_no_diffsel = list( - x = "wenchuan_g1", y = "wenchuan_g2", - iter = 200, warmup = 200, chains = 2, - variable_type = "blume-capel", baseline_category = 2, - difference_selection = FALSE, - target_accept = 0.65, update_method = "nuts", seed = 703, display_progress = "none" - ), - cmp_adhd_nuts_bernoulli = list( - x = "adhd_g1", y = "adhd_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = TRUE, - target_accept = 0.65, update_method = "nuts", seed = 801, display_progress = "none" - ), - cmp_adhd_mh_bernoulli = list( - x = "adhd_g1", y = "adhd_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = TRUE, - update_method = "adaptive-metropolis", seed = 802, display_progress = "none" - ), - cmp_boredom_nuts_bernoulli = list( - x = "boredom_g1", y = "boredom_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = TRUE, - target_accept = 0.65, update_method = "nuts", seed = 901, display_progress = "none" - ), - cmp_boredom_mh_bernoulli = list( - x = "boredom_g1", y = "boredom_g2", - iter = 200, warmup = 200, chains = 2, - difference_selection = TRUE, - update_method = "adaptive-metropolis", seed = 902, display_progress = "none" - ), - cmp_wenchuan_nuts_impute = list( - x = "wenchuan_g1_na", y = "wenchuan_g2_na", - iter = 200, warmup = 200, chains = 2, - na_action = "impute", - difference_selection = TRUE, - target_accept = 0.65, update_method = "nuts", seed = 1001, display_progress = "none" - ) -) - -all_configs = c(bgm_configs, compare_configs) - -# ============================================================================== -# Comparison helpers -# ============================================================================== - -resolve_args = function(args) { - resolved = args - for(nm in names(resolved)) { - if(is.character(resolved[[nm]]) && resolved[[nm]] %in% names(datasets)) { - resolved[[nm]] = datasets[[resolved[[nm]]]] - } - } - resolved -} - -extract_bgm_actual = function(fit) { - list( - posterior_summary_main = fit$posterior_summary_main, - posterior_summary_pairwise = fit$posterior_summary_pairwise, - posterior_summary_indicator = fit$posterior_summary_indicator, - posterior_mean_main = fit$posterior_mean_main, - posterior_mean_pairwise = fit$posterior_mean_pairwise, - posterior_mean_indicator = fit$posterior_mean_indicator, - raw_main_chain1 = fit$raw_samples$main[[1]], - raw_pairwise_chain1 = fit$raw_samples$pairwise[[1]], - raw_indicator_chain1 = if(!is.null(fit$raw_samples$indicator)) - fit$raw_samples$indicator[[1]] else NULL, - nuts_diag = fit$nuts_diag, - posterior_coclustering_matrix = fit$posterior_coclustering_matrix, - posterior_mean_allocations = fit$posterior_mean_allocations - ) -} - -extract_compare_actual = function(fit) { - list( - posterior_summary_main_baseline = fit$posterior_summary_main_baseline, - posterior_summary_pairwise_baseline = fit$posterior_summary_pairwise_baseline, - posterior_summary_main_differences = fit$posterior_summary_main_differences, - posterior_summary_pairwise_differences = fit$posterior_summary_pairwise_differences, - posterior_summary_indicator = fit$posterior_summary_indicator, - posterior_mean_main_baseline = fit$posterior_mean_main_baseline, - posterior_mean_pairwise_baseline = fit$posterior_mean_pairwise_baseline, - posterior_mean_main_differences = fit$posterior_mean_main_differences, - posterior_mean_pairwise_differences = fit$posterior_mean_pairwise_differences, - posterior_mean_indicator = fit$posterior_mean_indicator, - raw_samples = fit$raw_samples, - nuts_diag = fit$nuts_diag - ) -} - -# Configs where 0.1.6.3 returned NA in posterior_summary/posterior_mean for -# constant or near-constant parameters, and the current code computes values -# (bug fix). Only these are allowed to have NA→value differences. -na_bugfix_ids = c( - "bgm_wenchuan_nuts_bernoulli", - "bgm_wenchuan_hmc_bernoulli", - "bgm_wenchuan_nuts_blumecapel", - "bgm_boredom_hmc_bernoulli", - "bgm_wenchuan_nuts_blumecapel_baseline1", - "bgm_wenchuan_nuts_impute" -) - -# Configs excluded from bitwise comparison due to confirmed algorithm changes -# (see header notes 5 and 7). Checked for structural match only. -structure_only_ids = c( - "bgm_wenchuan_nuts_blumecapel_impute", # Blume-Capel imputation bug fix (note 5c) - "bgm_wenchuan_nuts_sbm", # SBM lazy init changes RNG order (note 4) - "bgm_adhd_nuts_sbm", # SBM lazy init changes RNG order (note 4) - "bgm_boredom_hmc_bernoulli", # overflow guard reclassifies fast/slow (note 7) - "cmp_wenchuan_hmc_bernoulli" # overflow guard reclassifies fast/slow (note 7) -) - -compare_fields = function(expected, actual, type, id) { - if(type == "bgm") { - fields = c( - "posterior_summary_main", "posterior_summary_pairwise", - "posterior_summary_indicator", - "posterior_mean_main", "posterior_mean_pairwise", "posterior_mean_indicator", - "raw_main_chain1", "raw_pairwise_chain1", "raw_indicator_chain1", - "posterior_coclustering_matrix", "posterior_mean_allocations" - ) - } else { - fields = c( - "posterior_summary_main_baseline", "posterior_summary_pairwise_baseline", - "posterior_summary_main_differences", "posterior_summary_pairwise_differences", - "posterior_summary_indicator", - "posterior_mean_main_baseline", "posterior_mean_pairwise_baseline", - "posterior_mean_main_differences", "posterior_mean_pairwise_differences", - "posterior_mean_indicator", - "raw_samples" - ) - } - - allow_na_skip = id %in% na_bugfix_ids - mismatches = character() - - for(field in fields) { - exp_val = expected[[field]] - act_val = actual[[field]] - - if(is.null(exp_val) && is.null(act_val)) next - if(is.null(exp_val) != is.null(act_val)) { - mismatches = c(mismatches, sprintf(" %s: one is NULL, the other is not", field)) - next - } - if(!identical(dim(exp_val), dim(act_val))) { - mismatches = c(mismatches, sprintf( - " %s: dim mismatch (%s vs %s)", field, - paste(dim(exp_val), collapse = "x"), paste(dim(act_val), collapse = "x") - )) - next - } - - # For known bug-fix configs only: the old code returned NA in - # posterior_summary/posterior_mean for constant parameters where the new - # code computes values. Compare only cells that are non-NA in the fixture. - is_posterior = grepl("^posterior_summary|^posterior_mean", field) - if(allow_na_skip && is_posterior && - (is.data.frame(exp_val) || is.matrix(exp_val))) { - exp_m = as.matrix(exp_val) - act_m = as.matrix(act_val) - non_na = !is.na(exp_m) - if(!identical(exp_m[non_na], act_m[non_na])) { - if(is.numeric(exp_m) && is.numeric(act_m)) { - max_diff = max(abs(exp_m[non_na] - act_m[non_na]), na.rm = TRUE) - mismatches = c(mismatches, sprintf( - " %s: NOT identical (max |diff| = %.2e, ignoring fixture NAs)", field, max_diff - )) - } else { - mismatches = c(mismatches, sprintf(" %s: NOT identical (non-numeric)", field)) - } - } - next - } - - if(!identical(exp_val, act_val)) { - if(is.numeric(exp_val) && is.numeric(act_val)) { - max_diff = max(abs(as.numeric(exp_val) - as.numeric(act_val)), na.rm = TRUE) - mismatches = c(mismatches, sprintf( - " %s: NOT identical (max |diff| = %.2e)", field, max_diff - )) - } else { - mismatches = c(mismatches, sprintf(" %s: NOT identical (non-numeric)", field)) - } - } - } - - # nuts_diag: compare only the shared core fields (new code adds warmup_check) - exp_nd = expected[["nuts_diag"]] - act_nd = actual[["nuts_diag"]] - if(!is.null(exp_nd) && !is.null(act_nd)) { - for(nd_field in c("treedepth", "divergent", "energy", "ebfmi")) { - if(!identical(exp_nd[[nd_field]], act_nd[[nd_field]])) { - mismatches = c(mismatches, sprintf(" nuts_diag$%s: NOT identical", nd_field)) - } - } - } else if(!is.null(exp_nd) != !is.null(act_nd)) { - mismatches = c(mismatches, " nuts_diag: one is NULL, the other is not") - } - - mismatches -} - -# Structure-only check for SBM and impute configs: verifies that output fields -# have matching names, dimensions, and types without requiring identical values. -check_structure = function(expected, actual, type) { - if(type == "bgm") { - fields = c( - "posterior_summary_main", "posterior_summary_pairwise", - "posterior_summary_indicator", - "posterior_mean_main", "posterior_mean_pairwise", "posterior_mean_indicator", - "raw_main_chain1", "raw_pairwise_chain1", "raw_indicator_chain1" - ) - } else { - fields = c( - "posterior_summary_main_baseline", "posterior_summary_pairwise_baseline", - "posterior_summary_main_differences", "posterior_summary_pairwise_differences", - "posterior_summary_indicator", - "posterior_mean_main_baseline", "posterior_mean_pairwise_baseline", - "posterior_mean_main_differences", "posterior_mean_pairwise_differences", - "posterior_mean_indicator", - "raw_samples" - ) - } - - mismatches = character() - for(field in fields) { - exp_val = expected[[field]] - act_val = actual[[field]] - if(is.null(exp_val) && is.null(act_val)) next - if(is.null(exp_val) != is.null(act_val)) { - mismatches = c(mismatches, sprintf(" %s: one is NULL, the other is not", field)) - next - } - if(!identical(class(exp_val), class(act_val))) { - mismatches = c(mismatches, sprintf(" %s: class mismatch (%s vs %s)", - field, paste(class(exp_val), collapse = "/"), paste(class(act_val), collapse = "/"))) - next - } - if(!identical(dim(exp_val), dim(act_val)) && !identical(length(exp_val), length(act_val))) { - mismatches = c(mismatches, sprintf(" %s: dim mismatch (%s vs %s)", - field, paste(dim(exp_val), collapse = "x"), paste(dim(act_val), collapse = "x"))) - } - } - mismatches -} - -# ============================================================================== -# Run all comparisons -# ============================================================================== - -pass_count = 0 -fail_count = 0 -skip_count = 0 -error_count = 0 -failures = list() - -for(entry in manifest) { - id = entry$id - type = entry$type - cat(sprintf(" [%s] %s ... ", id, entry$desc)) - - # Load expected fixture - fixture_path = file.path(fixture_dir, entry$file) - if(!file.exists(fixture_path)) { - cat("SKIP (fixture file missing)\n") - skip_count = skip_count + 1 - next - } - expected = readRDS(fixture_path) - - # Get config - if(!id %in% names(all_configs)) { - cat("SKIP (config not found)\n") - skip_count = skip_count + 1 - next - } - args = resolve_args(all_configs[[id]]) - - # Run current build - set.seed(args$seed) - fit = tryCatch({ - if(type == "bgm") { - do.call(bgm, args) - } else { - do.call(bgmCompare, args) - } - }, error = function(e) { - cat(sprintf("ERROR: %s\n", conditionMessage(e))) - NULL - }) - - if(is.null(fit)) { - error_count = error_count + 1 - failures[[id]] = "model fitting errored" - next - } - - # Extract comparable output - if(type == "bgm") { - actual = extract_bgm_actual(fit) - } else { - actual = extract_compare_actual(fit) - } - - # Compare: structure-only for known algorithm changes, bitwise otherwise - if(id %in% structure_only_ids) { - mismatches = check_structure(expected, actual, type) - label = "PASS (structure)" - } else { - mismatches = compare_fields(expected, actual, type, id) - label = "PASS" - } - - if(length(mismatches) == 0) { - cat(label, "\n") - pass_count = pass_count + 1 - } else { - cat("FAIL\n") - for(m in mismatches) cat(m, "\n") - fail_count = fail_count + 1 - failures[[id]] = mismatches - } -} - -# ============================================================================== -# Summary -# ============================================================================== - -total = length(manifest) -cat(sprintf( - "\n=== Results: %d PASS, %d FAIL, %d ERROR, %d SKIP (of %d) ===\n", - pass_count, fail_count, error_count, skip_count, total -)) - -if(fail_count > 0 || error_count > 0) { - cat("\nFailed/errored fixtures:\n") - for(id in names(failures)) { - cat(sprintf(" %s:\n", id)) - msgs = failures[[id]] - for(m in msgs) cat(" ", m, "\n") - } - quit(status = 1) -} else if(pass_count == total) { - cat("All fixtures match — build is bitwise-identical to CRAN 0.1.6.3.\n") -} diff --git a/dev/fixtures/compliance/bgm_adhd_mh_bernoulli.rds b/dev/fixtures/compliance/bgm_adhd_mh_bernoulli.rds deleted file mode 100644 index 4946eaf78ebaa5b8b2fdf0bdd058d6068094e4fe..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 15972 zcmV-qKAXWGiwFP!000001MS=iJk;C&IR2Tj?^{J8EhV;D33?!9@t*Za&CkxxjQGXlZ zw=sDqGb_Y5N~}7o9tp9ZFI3xIxEePh@8TmO`~YHEnkI7Gvk&6-4}4H1S`4AC<<_Wi zFPv%zvai^D^1xJp*E*ksPi^G_kF)fEdV(y7yJ{5~m4g9sOX5@l^S8RhcVDBH=F7Iwdvpis0K7Y`p8R_}1sUwjUWYVJF^5%UtX#oqc3jJv^T ztI?6eccMYRsr?li~e z_rS|q_x!;h>0Ypxf%0Sib=TQqD*N89kxOfUwEZx6N zG_VOCKVE&sdVPL%P;v9T9$%*oc-GdE;_G%DBmmZLW4XB?rR&0Y(swEdTx@I7e90L^ z?+WA~CP{(v^+$JpFxw6iS!9TJ#Ir%l{-NGax`#l?205m)d)z_E*r5|*wcX$uN5(n< zxeQRa7NxetZUV%Iv9QQT$4rfn#b&C6Jc&4l-S%x~_LoH`u_{sSp4=xnu_{-DGE`9A zSjD&Mm%&*_?7@olj_%@i*h7k>99rnFPu*$N@$Eb?p*aOSA 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