fast-mlsirm

fast-mlsirm is an early high-performance toolkit for Multidimensional Latent Space Item Response Models, focused on MLS2PLM simulation, regularized point estimation, and true-parameter recovery checks.

The first implementation keeps the public API small:

import numpy as np

from fast_mlsirm import MLS2PLMConfig, FitConfig, fixed_item_calibration_diagnostics, simulate, dimensionality_diagnostics, fit, fit_diagnostics, recovery_report, render_diagnostics_report, response_process_dimensionality_diagnostics, response_process_fit_diagnostics

data = simulate(MLS2PLMConfig(seed=20260101))
result = fit(
    responses=data.Y,
    factor_id=data.factor_id,
    config=FitConfig(model="MLS2PLM", optimizer="adam_lbfgs", max_iter=100, backend="auto"),
)
report = recovery_report(data.truth, result.params)
diagnostics = fit_diagnostics(data.Y, result.params, data.factor_id, model=result.model)
dimensions = dimensionality_diagnostics(
    data.Y,
    data.factor_id,
    latent_dims=[1, 2, 3],
    config=FitConfig(model="MLS2PLM", optimizer="adam", max_iter=10, n_restarts=1),
)

print(report.summary)
print(diagnostics.model_fit)
print(dimensions.best)

category_probs = np.stack([1.0 - data.probabilities, data.probabilities], axis=2)
process_fit = response_process_fit_diagnostics(
    data.Y,
    category_probs,
    item_type="dichotomous",
    response_process="cumulative",
    group_id=np.arange(data.Y.shape[0]) % 2,
)
print(process_fit.itemfit["outfit_mnsq"])

process_dimensions = response_process_dimensionality_diagnostics(
    data.Y,
    {"dim2": category_probs},
    item_type="dichotomous",
    response_process="cumulative",
)
print(process_dimensions.best)

fixed_item_calibration = fixed_item_calibration_diagnostics(
    data.Y,
    {"dim2": category_probs},
    fixed_items=np.arange(min(4, data.Y.shape[1])),
    item_type="dichotomous",
    response_process="cumulative",
)
print(fixed_item_calibration.best)

What Works Now

• Canonical MLS2PLM binary response simulation.
• gamma=0 no-CD simulation.
• Regularized JML/MAP-style fitting for MIRT, MLSRM, MLS2PLM, ULSRM, and ULS2PLM constraints.
• Missing response exclusion via NaN, -1, or an explicit mask, including missing-by-design rows or items when at least one response is observed.
• Adam and small L-BFGS-style optimizers without SciPy.
• Procrustes alignment and distance-based recovery metrics.
• Point-estimate item, person, and model fit diagnostics for fitted models.
• K-fold held-out likelihood diagnostics for latent-space dimensionality.
• Shared dichotomous/polytomous response-process diagnostics from category probabilities.
• Multigroup and multilevel-context fit summaries from person-level group or cluster IDs.
• Response-process probability candidate comparisons for external dimensionality checks.
• True-parameter reproduction, observed-information Hessian, vcov, standard error, and second-order stability helpers.
• Fixed item parameter linking, CAT item-information selection, and greedy ATA form assembly with content min/max constraints.
• aFIPC-style fixed-item calibration diagnostics that select candidate probability tensors using fixed evaluation-item likelihood and kaefa-style item-fit penalty.
• Standalone HTML reports for saved fit or dimensionality diagnostics.
• Automated benchmark evidence reports from release-acceptance timing.
• Release evidence index reports that tie dist artifact hashes, acceptance, benchmark, sales-readiness, and buyer-packet evidence to one commit.
• Single-command commercial release evidence builder for dist, acceptance, benchmark, sales-readiness, buyer packet, release index, and final gate output.
• Procurement due-diligence evidence reports for distribution metadata, policy files, commercial-release integrity, GitHub snapshot state, and SHA256-verified HTML review output.
• PR queue governance evidence reports for open PR review state, stale and changes-requested risk counts, release-scope conflict classification, and SHA256-verified HTML review output.
• Figma evidence sync reports that verify the static buyer-review design packet still references buyer packet, release evidence index, procurement due diligence, and PR queue governance evidence while Code Connect stays disabled.
• CLI commands for simulation and fitting.
• Rust-backed fitting objective (neg-loglik, gradients, and distance kernels) via PyO3/maturin as the primary numeric path, with a numerically-identical NumPy reference backend kept for parity testing and fallback.

Install

For local development:

python -m pip install -e .

The default runtime backend is "auto", which uses the compiled Rust core (fast_mlsirm._core) as the primary numeric path and transparently falls back to the NumPy reference implementation when the extension is unavailable. Source and editable installs use maturin to build the extension, so they require a working Rust toolchain; installed wheels ship the compiled core. Pass backend="numpy" to force the pure-Python reference (used for parity testing). The core Rust workspace can be tested with:

cargo test --workspace

The PyO3 extension crate is built by maturin and exercised by the Python backend parity tests.

Commercial Readiness

The current release is supportable as a commercial beta for technical teams that need local MLS2PLM simulation, point-estimate fitting, diagnostics, and report generation. It is not a regulated decision product, hosted assessment platform, or Bayesian posterior inference engine. See:

• Commercial readiness gate
• Enterprise sales readiness gate
• KRW 2,000,000,000 product readiness gate
• Buyer demo storyboard
• Figma product design packet
• IRT stability product design and equation contract
• ROI evidence model
• Release acceptance guide
• Security policy
• Support policy
• Changelog

Sales readiness verification uses:

python scripts/build_commercial_release.py \
  --out commercial-release \
  --require-rust \
  --check-import

The commercial release builder writes commercial_release_manifest.json and commercial_release_report.html while keeping the underlying stage artifacts under the same output directory. The equivalent manual sequence is:

python scripts/release_acceptance.py --out acceptance_check --require-rust
python scripts/build_benchmark_report.py \
  --acceptance acceptance_check/acceptance_summary.json \
  --out acceptance_check/benchmark
python scripts/sales_readiness.py \
  --acceptance acceptance_check/acceptance_summary.json \
  --dist dist \
  --require-rust \
  --require-20b-product \
  --benchmark-report acceptance_check/benchmark/benchmark_report.json \
  --require-benchmark-report \
  --check-import \
  --out acceptance_check/sales_readiness_manifest.json
python scripts/build_buyer_packet.py \
  --acceptance acceptance_check/acceptance_summary.json \
  --sales-readiness acceptance_check/sales_readiness_manifest.json \
  --dist dist \
  --benchmark-report acceptance_check/benchmark/benchmark_report.json \
  --out buyer-evidence-packet
python scripts/build_release_evidence_index.py \
  --acceptance acceptance_check/acceptance_summary.json \
  --sales-readiness acceptance_check/sales_readiness_manifest.json \
  --dist dist \
  --benchmark-report acceptance_check/benchmark/benchmark_report.json \
  --buyer-packet-manifest buyer-evidence-packet/buyer_evidence_manifest.json \
  --out release-evidence-index
python scripts/sales_readiness.py \
  --acceptance acceptance_check/acceptance_summary.json \
  --dist dist \
  --require-rust \
  --require-20b-product \
  --benchmark-report acceptance_check/benchmark/benchmark_report.json \
  --require-benchmark-report \
  --buyer-packet-manifest buyer-evidence-packet/buyer_evidence_manifest.json \
  --require-buyer-packet \
  --release-evidence-index release-evidence-index/release_evidence_index.json \
  --require-release-evidence-index \
  --check-import \
  --out acceptance_check/final_sales_readiness_manifest.json
python scripts/build_procurement_due_diligence.py \
  --dist dist \
  --commercial-release-manifest commercial-release/commercial_release_manifest.json \
  --out procurement-due-diligence
python scripts/build_pr_queue_governance.py \
  --out pr-queue-governance
python scripts/build_figma_evidence_sync.py \
  --out figma-evidence-sync
python scripts/sales_readiness.py \
  --acceptance acceptance_check/acceptance_summary.json \
  --dist dist \
  --require-rust \
  --require-20b-product \
  --benchmark-report acceptance_check/benchmark/benchmark_report.json \
  --require-benchmark-report \
  --buyer-packet-manifest buyer-evidence-packet/buyer_evidence_manifest.json \
  --require-buyer-packet \
  --release-evidence-index release-evidence-index/release_evidence_index.json \
  --require-release-evidence-index \
  --procurement-due-diligence procurement-due-diligence/procurement_due_diligence_manifest.json \
  --require-procurement-due-diligence \
  --pr-queue-governance pr-queue-governance/pr_queue_governance_manifest.json \
  --require-pr-queue-governance \
  --figma-evidence-sync figma-evidence-sync/figma_evidence_sync_manifest.json \
  --require-figma-evidence-sync \
  --check-import \
  --out acceptance_check/final_procurement_sales_readiness_manifest.json

Enterprise Sales Readiness for KRW 2,000,000,000 procurement review requires the release acceptance and sales-readiness commands to pass on the exact release artifact. The 20B product gate adds Product Design, Figma-without-Code-Connect, Data Analytics, ROI, benchmark, and synthetic demo evidence from examples/enterprise_demo/. The buyer packet command produces a portable zip, buyer_evidence_manifest.json, and buyer_evidence_report.html for procurement review. The benchmark command produces benchmark_report.json and benchmark_report.html from the same release-acceptance timing evidence. The release evidence index command produces release_evidence_index.json and release_evidence_index.html as a compact digest map over the candidate wheel, source distribution, release acceptance, benchmark report, sales-readiness manifest, and buyer packet. The commercial release builder produces the same evidence as a single buyer review entrypoint and records the failed stage when the gate does not pass. It now also invokes scripts/build_procurement_due_diligence.py by default and emits procurement_due_diligence_manifest.json plus procurement_due_diligence_report.html under the commercial release output. It also invokes scripts/build_pr_queue_governance.py by default and emits pr_queue_governance_manifest.json plus pr_queue_governance_report.html so open GitHub PRs are inventoried as managed queue evidence rather than treated as an unexamined release risk. It then invokes scripts/build_figma_evidence_sync.py by default and emits figma_evidence_sync_manifest.json plus figma_evidence_sync_report.html so the static Figma procurement frame is checked against the same repo-local buyer evidence packet without using Figma Code Connect.

CLI

fast-mlsirm simulate \
  --persons 500 \
  --dims 2 \
  --items-per-dim 8 \
  --latent-dim 2 \
  --phi 0.3 \
  --gamma 1.5 \
  --seed 20260101 \
  --out runs/sim_001

fast-mlsirm fit \
  --responses runs/sim_001/responses.npy \
  --factors runs/sim_001/item_factor.csv \
  --model MLS2PLM \
  --backend auto \
  --latent-dim 2 \
  --optimizer adam_lbfgs \
  --max-iter 100 \
  --out runs/fit_001

fast-mlsirm diagnose-fit \
  --responses runs/sim_001/responses.npy \
  --factors runs/sim_001/item_factor.csv \
  --params runs/fit_001/params.npz \
  --model MLS2PLM \
  --out runs/diagnostics_001

fast-mlsirm diagnose-dimensions \
  --responses runs/sim_001/responses.npy \
  --factors runs/sim_001/item_factor.csv \
  --latent-dims 1,2,3 \
  --folds 5 \
  --model MLS2PLM \
  --max-iter 100 \
  --out runs/dimensions_001

fast-mlsirm diagnose-response-process \
  --responses runs/sim_001/responses.npy \
  --probabilities runs/model_probabilities.npy \
  --item-type polytomous \
  --response-process cumulative \
  --group-id runs/group_id.npy \
  --cluster-id runs/school_id.npy \
  --out runs/process_fit_001

fast-mlsirm diagnose-response-candidates \
  --responses runs/sim_001/responses.npy \
  --candidate dim1=runs/prob_dim1.npy \
  --candidate dim2=runs/prob_dim2.npy \
  --item-type dichotomous \
  --response-process ideal_point \
  --out runs/process_dimensions_001

fast-mlsirm diagnose-fixed-item-calibration \
  --responses runs/sim_001/responses.npy \
  --candidate dim1=runs/prob_dim1.npy \
  --candidate dim2=runs/prob_dim2.npy \
  --fixed-items runs/fixed_items.npy \
  --item-type dichotomous \
  --response-process ideal_point \
  --itemfit-penalty-weight 1.0 \
  --out runs/fixed_item_calibration_001

fast-mlsirm render-report \
  --diagnostics runs/diagnostics_001/fit_diagnostics.json \
  --out runs/diagnostics_001/report.html

For automation, every CLI command also accepts --json. In JSON mode, progress text is suppressed and stdout contains one status object with the output directory, key metrics, and generated file paths:

fast-mlsirm simulate \
  --persons 500 \
  --dims 2 \
  --items-per-dim 8 \
  --out runs/sim_001 \
  --json

fast-mlsirm fit \
  --responses runs/sim_001/responses.npy \
  --factors runs/sim_001/item_factor.csv \
  --out runs/fit_001 \
  --json

fit, diagnose-fit, and diagnose-dimensions validate that responses.npy is a 2D persons-by-items matrix and that item_factor.csv has exactly one factor id per item before running optimization or diagnostics. diagnose-fixed-item-calibration writes dimension_diagnostics.json with best_candidate, calibration_score, fixed-item coverage counts, and kaefa-style item-fit penalty metrics. --fixed-items accepts a .npy boolean mask or item-index vector; when omitted, all items are treated as the fixed calibration set.

fit --backend numpy uses the Python reference objective. fit --backend rust requires the installed fast_mlsirm._core extension and fails clearly if it is unavailable. fit --backend auto uses the Rust objective when available and falls back to NumPy otherwise.

The backend axis stays {numpy, rust, auto}. GPU acceleration is a device sub-option of the Rust backend rather than a separate backend, selected with fit --backend rust --rust-device {auto,cpu,gpu} (or FitConfig(backend="rust", rust_device=...)). The Rust core carries a wgpu (MIT/Apache-2.0) GPGPU implementation of the negative-log-likelihood and gradient hot path in crates/mlsirm-core/src/gpu.rs:

• auto (default) runs the GPGPU kernels when a compatible GPU adapter is present and otherwise falls back to the identical CPU path — no GPU required.
• gpu prefers the GPU and still falls back to CPU (with a warning) when none is available, so CI and GPU-less machines pass unchanged.
• cpu always uses the scalar CPU reference.

The GPU kernels run in single precision (WGSL has no f64); the CPU path is the f64 reference the numerical-parity tests assert against. The requested Rust device is recorded on FitResult.rust_device and in fit_summary.json; when gpu is explicitly requested on a machine without a compatible adapter, the runtime prints a warning and falls back to the CPU implementation.

render-report turns fit_diagnostics.json or dimension_diagnostics.json into a standalone HTML report with model summary cards, compact tables, and small bar views when chartable diagnostic metrics are present. Optional fit tables, dimensionality candidate comparisons, or metric summaries without values are summarized in a diagnostics coverage block instead of rendering as repeated blank-looking report sections or placeholder-only columns.

Repository Layout

python/fast_mlsirm/       Python public API and reference backend
crates/mlsirm-core/       Rust likelihood and gradient core
crates/fast-mlsirm-py/    PyO3 binding for the optional Rust backend
tests/                    Python smoke and numerical tests
docs/                     PRD/TRD summary and roadmap
examples/enterprise_demo/ Synthetic procurement evidence manifests

MVP Boundary

This is not a Bayesian sampler. The package intentionally starts with fast simulation, regularized point estimation, and recovery diagnostics. The current Rust backend keeps the same point-estimate formula contract as the NumPy reference path. Block-mode Rust execution, sparse response storage, benchmark automation, posterior predictive checking, and new ordinal response estimators remain future work.