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Epic #324: first-class MLX and reference-faithful fitting - #364
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* Add shared pcakeep/pcadb validation policy validate_pca_reduction rejects non-integer or < 1 pcakeep and non-finite or <= 0 pcadb; pca_reduction_requested answers the upfront mir_step question; numerical_rank re-validates at fit time. Rank policy unit tests pass (44). * Validate pcakeep/pcadb in torch and NumPy Both constructors call the shared validate_pca_reduction. The torch mir_step gate delegates to pca_reduction_requested with the fitted data's channel count, so pcakeep >= n_channels (the bundled input.param's pcakeep 32) is no longer rejected. New regression test fails before the fix and passes after; wrapper suite green. * Persist torch pcakeep/pcadb as plain scalars The shared validator accepts numpy integers, which the weights_only torch.load in AMICA.load refuses to unpickle; state_dict now casts to int/float. None stays None, so default payloads are unchanged. * Add pcakeep/pcadb to the MLX backend AMICAMLXNG takes pcakeep/pcadb with AMICATorchNG's position, semantics and shared validation, passes them to numerical_rank, gains the same upfront mir_step gate and message, and persists both in state_dict config (additive; older payloads load with None). Docstrings and the mir() error no longer claim MLX lacks explicit reduction. MLX mir/persistence/backend suites pass (68). * Add cross-backend pcakeep/pcadb tests Real sample: torch/NumPy/MLX keep the same rank and sphere for pcakeep=20 and pcadb=18.5 (rank 10); pcakeep wins over pcadb; invalid values raise at construction; torch and MLX give the same mir_step gate verdict; torch vs MLX reduced fits match (min |corr| > 0.999); MLX and torch persistence round-trip the request. 61 passed; 53 of them fail on the pre-#323 sources. * Reword constructor comments flagged by typos Comment-only; full-repo typos check passes. * Document shared pcakeep/pcadb policy Differences guide: backend-table row, at-a-glance row 11 (pcadb is a pamica extension; pcakeep wins), a pcakeep/pcadb section, and the mir_step gate section rewritten for both backends. MLX API page and changelog entries. mkdocs build --strict passes. * Ignore pcakeep/pcadb without sphering No backend reduces when do_sphere=False (the reference sets numeigs = nx there, amica15.f90:527), yet the mir_step gate still fired. The shared predicate takes do_sphere and validates first; a new log_ignored_pca_request (called by all three constructors) warns once that the request is ignored, and carries the precedence INFO note, so validate_pca_reduction is now validation only. Rank policy and cross-backend tests pass (126). * Refit and restart from the input geometry _preprocess shrinks n_channels to the kept rank, and torch/MLX then validated the next fit's X against it: a refit, or the second of n_restarts under any rank reduction (explicit or automatic), raised 'X has 32 channels, model expects 20'. Both backends now keep the constructor's count in _n_input_channels, validate X against it, and reset n_channels/n_comps from it at the start of every _fit_once. _load_params derives it from the restored sphere's width, so a reloaded reduced model refits. 16 new cross-backend tests; the 12 torch/MLX cases fail before the fix, NumPy is the passing control. * Test reassignment, numpy scalars, torch payload A pcakeep/pcadb reassigned after construction raises the validator's ValueError at fit time on torch, NumPy and MLX (and at the mir_step gate on torch/MLX); the validator rejects numpy.bool_ and accepts numpy floats for pcadb; a torch state dict without the two config keys loads with None and keeps its reduced sphere. Rank policy and cross-backend suites: 160 passed. * Cite torch symbols, not lines, in MLX core Every torch_impl/core.py line citation in mlx_impl/core.py (about 50, many already stale and six shifted by this PR) now names the symbol (AMICATorchNG.<method> or pamica.torch_impl.core.<name>), which does not rot. Fortran line citations are unchanged. Comment-only. * Update docs for review findings Differences guide: backend-table intro describes the raw MLX backend accurately; PCA, MIR and PMI defined on first use; row 11 wording; the pcakeep/pcadb and mir_step sections cover do_sphere=False and fit-time checks. MLX API page and changelog use semantic line breaks and add the do_sphere, refit/restart and invalid-payload-load entries. mkdocs build --strict and typos pass.
* Add shared params-file reader with JSON aliases read_params_file (issue #304) is the single params-file entry point every backend should use: content-sniffs JSON vs Fortran input.param text and returns canonical pamica keys either way, aliasing a JSON file's schema spellings (min_grad_norm/max_decs/numrej/num_mix_comps/ share_int) via one table. A file with both an alias and its canonical key raises ValueError. writestep/do_history/histstep move from FORTRAN_UNSUPPORTED_KEYS to translated (identity) keys: the reader translates what any backend supports, not just the wrapper; the legacy NumPy backend already implements periodic checkpointing under these names. * Route AMICA.from_params_file through read_params_file Replaces the wrapper's own inline JSON/Fortran content sniffing with the shared reader (issue #304). Behavior change: sample_params.json's own max_decs/min_grad_norm/share_int settings are now applied (as maxdecs/min_nd/share_iter) instead of silently landing in the "not applied" warning. * Route NumPy backend through shared params-file reader AMICA_NumPy(params_file=...) and load_default_params now read through read_params_file (issue #304) instead of a bare json.load, so the legacy backend accepts the literal Fortran input.param text too, not just JSON. Canonical keys map to this backend's own spellings (min_nd -> min_grad_norm, maxdecs -> max_decs, share_iter -> share_int); files/data_dim/field_dim come off the same parsed dict instead of a second file read. A params-file setting this backend does not consume is named in one logger.warning. from_json_file is renamed to from_params_file (matches the PyTorch wrapper's name, now accepts both formats); no alias left behind. pdftype != 0 now raises NotImplementedError at construction: this backend implements only the generalized-Gaussian source density and previously silently ignored the setting. numpy_impl/params.json's bundled default pdftype changes 1 -> 0 to match; the value was never read by the fit path, so no previously-passing fit's numerics change. * Dedup validate_implementations.py's Fortran alias table _FORTRAN_ALIASES is now derived from fortran_params. PAMICA_KEY_TO_FORTRAN_KEY (the shared reader's single source of truth for pamica-key -> Fortran-keyword) plus the one JSON-only share_int addition, instead of a hand-maintained duplicate. load_sample_data reads sample_params.json through read_params_file, so min_nd/maxdecs/ share_iter land in run_pytorch_amica's ng_kwargs via the plain _NG_PARAMS filter; the max_decs special case is no longer needed. * Document the shared params-file reader (issue #304) docs/guides/validation.md: read_params_file, the JSON alias table, the writestep/do_history/histstep move, and the NumPy backend's own params-file/pdftype behavior. docs/guides/amica-differences.md: the backend-differences table's Fortran input.param reader row (NumPy now yes, MLX awaits epic #324 Phase 4) and the NumPy pdftype NotImplementedError. docs/api/numpy-backend.md: the renamed from_params_file classmethod and the pdftype guard. docs/changelog.md: Unreleased entry covering the shared reader, the wrapper's now-applied JSON aliases, NumPy's input.param support, the from_json_file rename, and the NumPy pdftype guard. * Route NumPy CLI through the shared params-file reader cli.py's load_params() did a bare json.load, so the CLI died with JSONDecodeError on a Fortran input.param even though AMICA(params_ file=...) already accepted it. It now calls the same _read_numpy_keyed_params helper the constructor uses (no duplicated key-mapping table), so both formats work; files stays unresolved relative to the working directory, matching Fortran-binary semantics. New test_numpy_cli.py exercises main() end to end on real bundled data for both formats; verified to fail with JSONDecodeError on the pre-fix load_params(). * Compose _FORTRAN_ALIASES from the shared alias tables Replaces the hand-added share_int entry with a composition of JSON_ALIAS_TO_CANONICAL (JSON alias -> canonical) and PAMICA_KEY_TO_FORTRAN_KEY (canonical -> Fortran keyword), so write_fortran_param_file accepts either raw JSON-schema keys or canonical keys with no hand-maintained entry left in this module. Adds coverage: canonical maxdecs/min_nd are written as Fortran max_decs/min_grad_norm, and an end-to-end test feeds load_sample_ data()'s canonical-keyed params straight into write_fortran_param_ file, asserting the written file carries Fortran's own spellings and none of the canonical ones. * Add precedence, warning and table-size regression tests NumPy: explicit kwargs win over params-file defaults (mirrors the PyTorch wrapper's precedence test). Wrapper: fitting from input.param names writestep/do_history/histstep in the "not applied" warning, since AMICATorchNG has no matching constructor keyword (issue #312). Pins FORTRAN_TO_PAMICA_KEY/FORTRAN_UNSUPPORTED_KEYS table sizes so a future move between them forces the docstring/doc counts to be revisited. * Fix stale docs from PR #327 review fortran_params.py module docstring and its mirror in validation.md still claimed share_int was "out of scope" and surfaced only via the "not applied" warning -- stale since read_params_file/ JSON_ALIAS_TO_CANONICAL landed; both now correctly say share_iter needs no Fortran-side rename and the JSON alias table handles share_int on the way in. Reworded four "silently landed in the not applied warning" claims (amica.py, changelog.md, validation.md, a test docstring): the warning already named max_decs/min_grad_norm/share_int before this epic, it just did not apply them -- "silently" overstated that. numpy_impl/core.py's __init__ docstring now says params_file accepts both formats. AGENTS.md's architecture-map line for fortran_params.py now describes it as the shared reader for the wrapper, AMICA_NumPy and the NumPy CLI. amica-differences.md spells out "generalized Gaussian (GG)" at first use. Applies semantic line breaks to the Markdown prose this epic added (changelog.md, validation.md, amica-differences.md, numpy-backend.md), without reflowing pre-existing text. * Fix unparsable spelling flagged by typos
* Guard AMICATorchNG accessors on degenerate fits (#306) transform/get_*matrix/get_rho/variance_order/model_loglik/ model_probability/mir/pmi now refuse a degenerate fit or a non-finite parameter, and validate X shape like fit() does. from_state_dict wraps a malformed config's TypeError as ValueError. * Guard AMICAMLXNG accessors on degenerate fits (#306) Same guard as AMICATorchNG (backend_parity.md): transform/ get_*matrix/get_rho/variance_order/model_loglik/model_probability/ mir/pmi refuse a degenerate fit or a non-finite parameter, and validate X shape like fit() does. from_state_dict (and load(), which delegates to it) wraps a malformed config's TypeError as ValueError. * Guard AMICA_NumPy accessors on degenerate fits (#306) Same guard, ported to the legacy backend's own converged/ stop_reason bookkeeping: transform/get_weights/ get_sensor_mixing_matrix refuse a degenerate fit or a non-finite parameter, and transform validates data shape like fit() does. * Add cross-backend backend-guard tests (#306) Parametrized over torch, MLX (importorskip) and NumPy: every guarded accessor raises on a degenerate stop_reason and on a non-finite parameter, and still works on a healthy fit; every data-taking accessor rejects a 1D or wrong-channel-count array, including after a pcakeep reduction; from_state_dict raises ValueError (chaining TypeError) on a malformed config. * Document raw-backend degenerate-fit guard (#306) amica-differences.md row 5: the raw torch/MLX/NumPy backends now refuse degenerate models themselves, not just the AMICA wrapper. changelog.md: note the behavior change under Unreleased. * Add shared model_probability_from_loglik helper (#306) Factors the NaN-vs--inf diagnosis and column-wise softmax normalization out of AMICATorchNG/AMICAMLXNG.model_probability so neither backend duplicates it; lives under pamica/metrics/, matching the existing mir/pairwise_mi split (backend computes the array, a shared pure function does the rest). * Guard get_pdftype/shared_components, dedupe sweep (torch) Reviewer follow-up on #306 (PR #329): - get_pdftype/shared_components now go through _check_usable too. - New _nonfinite_params() batches the isfinite sweep into one stacked reduction (one device sync instead of one per parameter, skipping None entries), used by _check_usable, state_dict and write_amica_output instead of each repeating the sweep. - model_probability no longer runs the guard twice (its own _check_usable plus model_loglik's): the core Lht computation moved to _model_loglik_unchecked, called by both public methods after their own single guard + shape check. - model_probability's NaN-vs--inf diagnosis now delegates to the shared pamica.metrics.model_probability_from_loglik. - mir()'s Raises docstring now covers the degenerate-fit RuntimeError and the bad-shape ValueError. * Guard get_pdftype/shared_components, dedupe sweep (MLX) Same follow-up as the torch commit, ported to MLX: - get_pdftype/shared_components now go through _check_usable too. - New _nonfinite_params() batches the isfinite sweep into one stacked reduction -- one MLX graph evaluation instead of one per parameter (measured ~2.2ms -> ~0.4ms for the sweep, transform() on 512 samples ~2.5ms -> ~0.6ms), skipping None entries, used by _check_usable, state_dict and write_amica_output. - model_probability no longer runs the guard twice: the core Lht computation moved to _model_loglik_unchecked, called by both public methods after their own single guard + shape check. - model_probability delegates its NaN-vs--inf diagnosis to the shared pamica.metrics.model_probability_from_loglik. - mir()'s Raises docstring now covers the degenerate-fit RuntimeError and the bad-shape ValueError. * Name what get_weights returns in its guard message (#306) Reviewer follow-up: the action string said 'get the unmixing matrix', not what the public method itself is called. * Extend backend-guard tests: pdftype, shared_components, pca (#306) get_pdftype/shared_components added to the torch/MLX accessor table, so the existing degenerate/non-finite/healthy parametrized tests now cover them. The pcakeep channel-count test now loops over every data-taking accessor (transform/model_loglik/ model_probability/pmi), not just transform; mir is deliberately excluded there since it always rejects a pcakeep-reduced model on other grounds (rank-deficient sphere). * Test model_probability_from_loglik directly (#306) Real Lht from a real 2-model fit's model_loglik() on the bundled sample: a healthy Lht's columns sum to 1 and match the backend's own model_probability() (behavior-preserving); a column poisoned entirely to -inf and a single entry poisoned to NaN raise distinct messages. * List get_pdftype/shared_components in the changelog entry (#306) The Unreleased entry for the backend-guard behavior change now names the two accessors added in the PR #329 review round.
* test: pin loading a version-1 AMICA save * feat: add float64 preprocessing accessors * feat: select the AMICA backend (torch or mlx) * feat: save the AMICA backend (format v2) * feat: select the AMICAICA backend * test: cover MLX export and torch-MLX agreement * docs: document backend selection in the wrappers * fix: name a malformed MLX save payload * docs: note the MLX wrapper in write_amica_output * fix: word the unexpected-keyword error for both * feat: show the backend in AMICA and AMICAICA repr * test: run MLX load errors without MLX * test: degenerate AMICAICA fit builds no basis * test: pin the weights-only save refusal * test: drive MLX from the JSON params file * test: cover fit_transform on both backends * test: fold the v1 backend check into the pin * docs: add Raises to the AMICA accessors * docs: add backend Raises to AMICAICA * test: AMICAICA propagates fit keyword errors * docs: name both backends in the API index * docs: point getting-started at backend=mlx * docs: keep MLX precision figures in one place * docs: mark the example's external input files * feat: guard the preprocessing accessors (#306)
* feat: validate every backend in the harness
validate_implementations.py gains --backend {torch,numpy,mlx}, a comma-separated list, or all. Each backend runs the bundled sample with the harness's canonical params (NumPy through its own key map, MLX through AMICAMLXNG) against one shared reference run; an explicit --backend adds a parity summary table with runtime. The default torch-only run prints the same report as before (diffed).
Tested: dispatch and argument-parsing tests (no binary), and the AMICA_RUN_FORTRAN-gated per-backend parity test against the v0.3.3 native binary (3 passed).
* refactor: run the harness MLX row via AMICA
With Phase 4 merged, the torch and MLX runs share one AMICA(backend=...) path, so the MLX row exercises the wrapper a user calls. Numbers unchanged (same summary table as the direct AMICAMLXNG run).
Tested: harness dispatch tests (29 passed); all-backend run against the v0.3.3 native binary.
* docs: use American English spellings
35 British spellings in comments, docstrings and Markdown (behaviour, colour, neighbour, cancelled, licence, optimiser, ...). Proper nouns (Centre de Recherche...) and 'analogue' left as is; no identifiers touched.
Tested: ruff, typos.
* docs: cite torch core by symbol, not line
Seven torch_impl/core.py:<line> citations outside the MLX module pointed at drifted lines; they now name the method (or are dropped where the sentence already names it). Also replaces a stale pamica.py:802-813 citation (a file renamed in #34) in AMICATorchNG._newton_direction. Fortran line citations unchanged.
Tested: ruff.
* docs: MLX getting-started path and harness rows
getting-started gains a complete Apple Silicon (MLX) section (install, fit/transform/save/load, params file, AMICAICA, precision). The validation guide documents running the harness per backend and records the per-backend parity rows with their expected bars. The differences page records do_sphere=False (#328) and NumPy refit warm-start (#312), both verified against the code. AGENTS.md, changelog and the native-backend and testing pages follow.
Tested: mkdocs build --strict (scratch env); anchors checked in the built site.
* test: keep suite output out of the repository
An audit of the non-slow suite found 63 tests (13 files) writing ./output via AMICA_NumPy's default outdir; the slow CLI, end-to-end and pdf-family tests wrote test_output, _ng_e2e_tmp and scratch_amica_parity into the repo. A session-scoped autouse fixture now runs each session (per xdist worker) from a temporary directory, the harness resolves its bundled inputs from its own location, and the slow tests write under tmp_path. Stale .gitignore entries removed.
Tested: full non-slow suite (1335 passed, nothing written to the repo); coverage reports still land in the repo root with and without xdist.
* test: end-to-end workflow on torch and MLX
New mne_tests/test_end_to_end_workflow.py mirrors our own pipeline on the bundled EEG: average reference, AMICAICA with pcakeep = n_channels - 1 on each backend, get_sources vs transform, a single-component exclusion (exact back-projection, residual kept, rank drops by one), EEGLAB export reloaded by loadmodout with the padded sphere, AMICA save/load, an input.param-driven fit, and torch vs MLX Hungarian-matched sources >= 0.999. Not marked slow (about 5 s); MLX tests skip without MLX or an Apple GPU.
Tested: 12 passed locally (torch and MLX); measured tolerances recorded in the module.
* fix: transpose NumPy sensor mixing matrix
AMICA_NumPy.get_sensor_mixing_matrix returned pinv(sphere) @ stored A without the transpose the torch and MLX backends apply (the stored W is the unmixing transposed, issue #24), so its columns were rows of the true mixing matrix: about 10% off the torch maps after five iterations and no inverse of get_weights() @ sphere. New cross-backend test pins torch/NumPy agreement (4.6e-12 full rank, 9.5e-11 at pcakeep=20), MLX agreement at float32 tolerance, and the identity on every backend.
Tested: new test fails without the fix (4 NumPy cases) and passes with it (10 passed); backend-guard and share-comps suites green.
* chore: remove stale 2025 debug output
pytorch_debug_test/out.txt and pytorch_output_test/out.txt were run logs committed in August 2025 by the long-removed basic PyTorch backend; nothing reads them. The only reference, a typos exclusion for the first directory, goes with them.
Tested: typos.
* docs: pin the harness rows to the v0.3.3 binary
States why the rows name an explicit release binary instead of the resolver default (its cached 'latest' is never refreshed) and how to fetch it.
Tested: mkdocs build --strict.
* Rebuild paper.pdf
* fix: NumPy writes files only with an outdir
AMICA_NumPy defaulted outdir to ./output, so every fit wrote out.txt at construction, writestep checkpoints, history and its final results into the caller's working directory. The default is now outdir=None, which writes nothing (as torch and MLX never do unless asked); an explicit outdir (keyword, params file, or the CLI's --outdir, whose own default stays output) writes exactly as before. Behavior change recorded in the changelog and the NumPy API page; the conftest chdir stays as defense in depth.
Tested: new test_numpy_outdir.py (default leaves the working directory empty with every write path on; explicit and params-file outdir write as before; fails on the old code) and a CLI test that a run without --outdir still writes ./output; NumPy suites (107 passed).
* fix: NumPy viz plots the true maps and sources
plot_components drew stored-A columns (rows of the true mixing matrix) as mixing vectors, and it and plot_pdf_fits built activations as stored W times raw data (no transpose, sphere or mean); plot_model_comparison skipped the sphere. One helper, _component_maps_and_sources, now computes exactly what get_sensor_mixing_matrix and transform return, and all three plots use it. load_results reads a rank-reduced fit's zero-padded sphere instead of raising.
Tested: new test_numpy_viz.py compares the helper and the plotted lines/histograms with the live accessors, full rank and pcakeep=20 (10 passed; 8 fail on the old code).
* test: pin the default harness run and summary
Pins the literal default run (no --backend: one validation_report.txt, no parity_summary.md, exit 0), renders format_parity_summary with populated rows from two real runs (torch as the stand-in reference, NumPy through compare_results) and checks every numeric column, and runs the no-MLX exit over both 'torch,mlx' and 'all'.
Tested: 32 passed, 3 skipped (the AMICA_RUN_FORTRAN-gated parity test).
* docs: fix two more British spellings
unlabelled and relabelled, missed by the first sweep's word-boundary match. Test names containing 'honour' (identifiers) are left as is.
Tested: typos, ruff.
* docs: trim the getting-started MLX route
The MLX section duplicated the backends guide's end-to-end and precision sections; it is now install, AMICA(backend="mlx") fit/transform, an AMICAICA one-liner and the float32 note, linking to the guide for the full workflow. The install heading no longer uses a bare GPU abbreviation.
Tested: mkdocs build --strict; linked anchors checked.
* docs: harness status and test working directory
progress_summary.md's harness line now matches AGENTS.md (every backend, #315), and the testing guide explains the conftest session chdir and how a test resolves repository paths.
Tested: mkdocs build --strict.
* docs: one description of reference resolution
default_reference_binary's docstring is now the single full description of the reference-binary resolution order; the --native-engine help summarizes it and points there. The native-backend page runs the harness with uv run.
Tested: --help renders; harness tests (32 passed).
* test: two-iteration fits for the map gate
The NumPy-vs-torch sensor-map gate (1e-9) failed on the macOS arm64 runner at 1.3e-9: the backends start within 1e-13 and EM amplifies the gap about sixfold per iteration, so five iterations was platform-sensitive. Two iterations keep the same gate with a 1e-12 gap while the pre-fix transposed maps still differ by 2.6-3%. Tested locally: module passes.
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* Write and read the EEGLAB sphere column-major (#336) * Fix S-order tests to expect column-major (#336) * Add cross-backend sphere export round-trip test (#336) * Document the sphere-order fix (#336) * Add native-binary sphere orientation test * Compare MLX sphere bytes bit-exactly * Fix sphere-order comments in write_amicaout * Warn about old asymmetric-sphere output * Correct scratch-history note on S order * Polish sphere-order wording in docs/tests
* Add seeded native-binary oracle helper Writes a pamica state in the reference's load_* formats, runs the pinned v0.3.3 binary and reads its raw output arrays back, for element-wise oracle tests (issue #333). Used by the tests that follow. * Normalize component rows in doscaling Each model's stored A block is the transpose of the reference's mixing matrix, so a component is a block row. doscaling normalized stored columns, which is not a change of scale; torch, NumPy and MLX now rescale each component row with the matching mu/beta, exactly as amica15.f90:1843-1851 does (issue #333). Tested: invariance, cross-backend and doscaling=False byte-identity tests; seeded native oracle matches A/mu/sbeta to round-off. Tests that encoded the column rule are updated. * Retune overshoot recipes for the new trajectories With components rescaled, the aggressive-Newton recipes run monotone, so keep_best restores never fired (failures or silent skips). Add newtrate=3.0 and longer budgets where needed, move the pdftype=1 switch to kurt_start=6, pick n_mix=1 for the default min_dll stop, and check the keep_best recompute only after a restore. Tested: every retuned test passes and no longer skips. * Retune two MLX cross-backend test configs The variance-order guard rejected the 30-iteration fit (gap 5e-4), so run 50 (gap 2.4e-3). One two-model sample sat on the rejsig=2.0 threshold between float32 and float64; use rejsig=2.5. Tested: both tests pass for one and two models. * Count scalestep from 1 and validate it The reference parses scalestep but rescales every iteration. Keep it as a pamica extension counted from 1 (iterations s, 2s, ...), so the default 1 is the reference, and reject a non-integer or < 1 value at construction on every backend instead of dividing by zero mid-fit. Record it as row 13 of the differences page. Tested: cadence and validation tests on torch, NumPy and MLX. * Document component orientation in ADR 0006 ADR 0006 states the storage convention (components are rows of each stored block), the doscaling fix and the Phase 8 layout change, and amends ADR 0001; the issue #24 notes point to it. The changelog records the behavior change with the oracle numbers. * Make keep_best-under-reject tests non-vacuous Both the MLX and torch tests ran monotone fits, so final_ll_ equal to the last LL proved nothing. Use the overshoot recipe, require that it restores without do_reject and that the do_reject trajectory also ends below an earlier peak, then assert no restore. Tested: both tests pass; the old configs were monotone. * Check reject agreement up to float32 rounding Exact agreement of the rejected set is config luck: float32 and float64 per-sample LLs differ by up to ~0.1 by the second pass. Record each pass, require every disagreement to lie within the band measured on that pass, cap the band and the disagreement count, and add controls where a threshold or trajectory divergence fails. Tested: seeds 7 and 8, one and two models, at rejsig 2.0. * Note ADR 0003 figures predate the row rule * Rebuild paper.pdf * Derive MLX pin tolerances from float32 study * Check variance order against a float32 band * Band-check the pdftype1 reject test * Use semantic line breaks in ADR 0006 * Note Phase 8 aligns the initial A norm * Word pre-fix norm ranges as history * Note parity rows predate the row rule * Document when scalestep is validated * Say oracle maxima span backends and models * Mirror the zero-norm guard in Newton test * Test the rescale on Newton-reached states * Rescale a permuted comp_list across backends * Test zero- and NaN-norm rows stay untouched * Fail, not skip, on recipe preconditions * Never read stale or partial oracle output * Check out full history in pytest CI jobs * Fail byte-identity pins in CI, not skip * Use the CI-proven overshoot recipe in torch --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
* Count schedule gates from 1 like the reference Route every iteration-schedule gate in the torch, NumPy and MLX backends through a shared pamica/schedule.py. The Newton switch (iter .ge. newt_start), the numdecs reset on the switch-on iteration, the iter > newt_start ceiling ratchets, the outlier-rejection schedule and NumPy's restart-on-NaN window compared the 0-based loop index with the reference's 1-based setting and fired one iteration late. Share, kurtosis, A-freeze and writestep/histstep gates were already 1-based and now use the same helpers. Seeded against the v0.3.3 binary (newt_start=3, doscaling off, 6 iterations) the LL deviation drops from 1.2e-3 to 5.4e-11 (torch) and 2.1e-12 (NumPy). * Update schedule-dependent tests to 1-based gates Tests whose configurations or replays encoded the old 0-based newt_start/rejstart: the aggressive-Newton keep_best recipe moves from newt_start=1 to 2 (the exact pre-change trajectory its documented measurements came from), the LLt reject tests fire on iteration 6 of a 6-iteration fit, and the MLX ratchet/reset replays spell out the reference's 1-based arithmetic. Tested: the 11 affected files pass (179 passed, 1 skipped). * Test schedule gates against 1-based iterations Cross-backend real-fit tests (torch, NumPy, MLX): the first Newton step is iteration newt_start, the rho-rate ceiling ratchet opens only after it, the switch-on counter reset follows the reference's replay, rejection first fires on iteration rejstart, and NumPy's restart-on-NaN window covers the first restartiter iterations. Tested: 15 passed; against the pre-fix backends 10 of them fail. * Add gated native oracle for the Newton start Seeds the pinned v0.3.3 binary with pamica's own init (indir/load_*), newt_start=3, doscaling off, single-threaded, 6 iterations. Torch and NumPy match its LL trajectory and A to round-off (torch LL 5.4e-11, A 4.2e-10; NumPy LL 2.1e-12, A 1.1e-10) where the pre-fix code deviated by 1.25e-3 and 3.1e-2. A no-Newton control shows the compared window is Newton-sensitive. Tested with AMICA_RUN_FORTRAN=1: 3 passed; 2 fail on the pre-fix backends. * Document the 1-based schedule gates Changelog entry for issue #335: which gates moved, the native-oracle numbers before and after, the default-path cases that change, and how to reproduce an old trajectory. No row in docs/guides/amica-differences.md changes: the anchored share A-freeze is already recorded there. * Rebuild paper.pdf * Validate iteration-schedule settings One shared validator, schedule.validate_iteration_setting, called by all three constructors with identical messages: newt_start must be an integer >= 0 whether or not do_newton is on (it also gates the rho-rate ratchet), and rejstart an integer >= 1 when do_reject is on (with 1-based counting, rejstart <= 0 skipped the reference's unconditional first pass; NumPy accepted 0, torch and MLX any value). NumPy also validates restartiter >= 0, maxrestarts >= 0 and histstep >= 1 under do_history (histstep=0 was a bare ZeroDivisionError mid-fit), and documents that restartiter=0 disables restart-on-NaN. schedule.every and periodic_due raise ValueError for a non-positive interval. Docstrings: newton_active cites amica15.f90:1666, newt_start=0 and 1 are stated to fit identically, and the MLX newt_start/newtrate entries document the newtrate ratchet. Tested: the new validation, restart-window and interval tests pass (36 passed with the reject suites). * Reflow MLX schedule docstrings The share_start/share_iter and kurt_start entries no longer break mid-clause. No code change. * Record NumPy restart-on-NaN difference Row 14 of the differences guide: the reference allows maxrestarts+1 restarts and, because startover is never cleared (amica15.f90:1046), never calls update_params again after the first one (:1115-1122); the NumPy backend allows maxrestarts restarts and resumes fitting. PyTorch and MLX have no restart-on-NaN path and stop with nan_ll. * Correct the schedule changelog entry Use the measured pre-fix deviations (1.25e-3 over 6 iterations, 1.33e-3 over 100) in the changelog and the oracle test docstring, list restartiter among the settings that now count from 1, and add the new validation. * Test schedule gates at their boundaries The arithmetic test gains the =1 rows for every helper (newton_switches_on at 1 and 0, past_newton_start at newt_start and newt_start+1, rejection with rejstart=1/rejint=1 through maxrej exhaustion, every/periodic_due at 1, the restart window at 1 and 0). The first-Newton-step test runs at newt_start 1 and 5, and a new real-fit test on every backend shows newt_start=0 and 1 give bit-identical trajectories on an overshooting run whose ratchets fire. Tested: 12 passed. * Make the switch-on reset test discriminating The replay-based reset tests passed with the reset one iteration late too. The cross-backend test now uses a searched configuration (4096 frames, lrate=0.8, newt_ramp=1, maxdecs=3, newt_start=3) whose likelihood decreases on iterations 2, 3 and 4, where the reference rule predicts no ratchet and the one-late rule predicts one; the fitted lrate ceiling must match the reference. It fails on all three backends with only newton_switches_on reverted and on the pre-fix backends. The MLX twin, which could not discriminate, is removed in favor of it. Tested: 3 passed; 3 failed with the reset reverted. * Test the rho-rate gate at newt_start=20 A searched natural-gradient configuration (4096 frames, seed 4, lrate 0.5, maxdecs 2) completes a maxdecs cycle on iteration 21 on all three backends, so the rho-rate ratchet gate is now pinned at the shipped default: open for newt_start=20, closed for 21. This is the one default-path change of issue #335. Tested: 3 passed; 3 fail on the pre-fix backends. * Document the reset-test configuration search States the full search that selected the switch-on reset configuration, including the two-model hit found after the first commit. * Shift Phase 7 recipes to 1-based settings Phase 7 retuned its overshoot recipes under 0-based gating. With newt_start and rejstart counted from 1, n becomes n+1 so each documented trajectory reproduces bit for bit: the min_dll overshoot recipe (newt_start 2, newtrate 3.0) again peaks at 70 and stops at 71 on torch (1.9e-4 below the peak) and at 63/64 on MLX (4.2e-4); its do_reject variants move rejstart 5 to 6; the doscaling-rows NEWTON state uses newt_start=2; the min_dll-reachable test uses 6 and the slow rholrate ratchet test 51. Tested: the affected suites pass. * Route scalestep through the schedule module Phase 7's inline (iteration + 1) % scalestep rule becomes schedule.every in all three backends, and its scalestep check becomes schedule.validate_iteration_setting (same semantics and message; still only when doscaling is on), which drops the numbers imports it needed. Tested: test_doscaling_rows passes. * Re-search the newt_start=20 gate configuration Phase 7's component-row doscaling moved the old configuration's second ratchet off iteration 21, and the test's precondition guard failed as intended. The re-searched configuration (4096 frames, seed 1, lrate 0.6, maxdecs 3, newt_ramp 1) decreases at ll indices 4, 5, 8 and 9, 19, 20 on all three backends (smallest decrease 4.7e-4), ratcheting at 8 and 20. Tested: 3 passed. * Note the combined doscaling and Newton-start parity Changelog: with doscaling on (component rows, issue #333) the seeded 100-iteration Newton comparison stays within 3.9e-6 (torch) and 7.0e-6 (NumPy), where the #333 fix alone left 2.1e-4; scalestep now uses the shared schedule helper. --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
* Add a loader for pre-change pamica in tests pamica/tests/pre_change.py imports the whole package at a pinned commit from git under a private name, so byte-identity tests can compare the live backends with the pre-change ones in one process. It fetches only when the object is missing (a --depth 1 fetch in a full clone makes it shallow), and fails under CI but skips locally when the commit is unreachable. * Store mixing matrix components as rows All three backends now hold A as (n_comps, n_channels): row k is the reference's column A(:, k), and model h's block is A[comp_list[:, h], :], the same matrix as before, so W = inv(block) is unchanged (issue #334). comp_list now indexes components in A as it does in the densities, so: - the share metric compares pinv(sphere) @ A.T, the components' scalp maps, not stored columns of each block; - a merge ties two components' mixing vectors and densities, and the gm-weighted dAk/zeta step averages one component across models; - doscaling rescales each component row once, shared or not; - accessors, W, the EEGLAB export (reference layout), load_results and the viz helpers follow the new layout. Fits without a merge are byte-identical on every backend; ndtmpsum moves by float round-off (it sums per component row, like the reference). Existing sharing, doscaling and MLX tests are updated to the row layout; two short sharing recipes are retuned because early scans now merge most components. Tested: full non-slow suite and slow tests. * Version saved models for the row layout The PyTorch state_dict is now format_version 4 and the MLX save format 2. A version 3 (PyTorch) or 1 (MLX) payload is converted through the shared pamica.component_layout.rows_from_legacy_columns: without loss when its comp_list is unmerged, and refused with a request to refit when a component id is shared across models, since that merge was made under the old column semantics. AMICA.save files (still version 2) go through the same path. Tested with test_component_rows_persistence.py (24 passed): round trips in the new formats, and payloads written by the pre-change classes loaded from git, converted or refused. * Refuse a stale multi-model A in load_results load_results now checks that each model's block of A inverts the W written beside it (max |A_h W_h - I| <= 1e-3; measured at most 3.8e-7 for float32 MLX exports and 1.1e-15 for float64). A multi-model directory written before issue #334 stored A with components as columns and reads back scrambled (residual 1.5); it is refused with the remedy instead of handing the viz helpers wrong sensor maps. Single-model files are the same bytes in both layouts and still load. * Test component rows and sharing semantics test_component_rows.py pins, on the sample EEG: - byte identity of every unshared configuration against the pre-change package (3627a6e) on PyTorch, NumPy and MLX: 1-3 models, doscaling, Newton, pdftype 0/1 and do_reject; - the metric compares get_sensor_mixing_matrix columns, a planted duplicate merges, and a merged group shares one mixing vector; - the EEGLAB A file is the reference layout, single-model exports are byte-identical, and load_results refuses a pre-change multi-model A; - opt-in (AMICA_RUN_FORTRAN=1): updates from a merged load_comp_list state match amica15mac to float64 round-off on PyTorch and NumPy, with doscaling on and off. All pass locally, including the gated oracle. * Document the component-row layout ADR 0007 records the row layout, the sharing semantics that follow from it, the persistence and export policy, and the measured evidence; ADR 0006 and 0001 are amended, and ADR 0006's initial-A normalization line now points to issue #341. The changelog, the differences page (sharing rows, a new section for issue #334, and row 15: the reference's single-precision density normalizers), and the backends, EEGLAB, validation and API pages describe the new behavior. mkdocs build --strict passes. * Reflow two sharing test docstrings Docstring text only; no test logic changes. * Skip MLX wrapper routes without MLX The merged-save refusal test's wrapper route fitted an MLX model without first checking MLX was installed, so it failed on the Linux runner. _wrapper_fit now performs the check for every wrapper route. Tested with MLX hidden: the new test files pass or skip. * Pin byte identity to the Phase 9 epic head The pre-change package is now 0930c0e, the epic head with Phase 9's schedule gates and without this phase, so the two sides differ by the layout change alone. The full byte-identity matrix (Newton included) and the persistence tests pass against it: 92 passed, 2 gated oracle tests skipped. * Stop tests from fetching pinned commits Tests that load historical code ran `git fetch origin <sha> --depth 1`, which in a full clone records a shallow boundary that gc can prune behind (issue #343). pre_change.py now only reads: it checks the pin with `git cat-file -e` and, when the commit is missing, fails under CI and otherwise skips with the command that fetches it (`git fetch origin <sha>`, or `git fetch --unshallow origin` in a shallow clone). Pins must be full SHAs. test_mlx_fit_noop.py, the last loader that fetched, now imports the whole package at its pin through the same helper; its fits are still bit-identical. test_pre_change_loader.py runs the loader behind a git wrapper that logs its arguments and execs the real git, and asserts no fetch ran and the shallow state is unchanged (a negative control with a real fetch trips the assertion). Tested: loader and fit-noop tests pass, also with CI set. * Record the single-precision density constants The reference writes its density normalizers as dble(<literal>), which gfortran reads in single precision: log(dble(1.772453851)) in the rho == 2 branch is 3.0e-8 above log(sqrt(pi)) (measured in the seeded oracle), and the Gaussian and cosh families' normalizers differ from pamica's double values by 3.7e-10, 2.0e-8 and -2.1e-8 (computed). Row 16 of the differences page records this, citing issue #344, which decides whether to adopt the reference's values. The torch and MLX comments that called _LOG_SQRT_2PI and _LOG_NORM_COSH_* bit-for-bit with the binary are corrected, and the merged-state oracle's maxrho=1.99 cites the issue. Comments and docs only. * Show early mass merges happen in the reference With share_comps on, the pinned binary's scan runs but never merges: Spinv2 is never allocated, so every similarity is NaN, even at comp_thresh=0. Its similarity (Spinv2 = Spinv^T Spinv) applied to its own state after 8 iterations from pamica's initialization merges exactly the pairs the torch and NumPy scans merge (32, 32 and 30 at 0.9, 0.95 and 0.99). In the recipe that went non-finite, the reference's update from pamica's post-scan states collapses in step: the second model's gm falls to 5.04e-3 on both after the first scan, and after the second reaches zero in one iteration and NaN in the next. Two gated tests pin this (AMICA_RUN_FORTRAN=1, both pass). The differences page, ADR 0007, the changelog, the validation table and the torch/MLX comments that called the scan unrunnable now say what it does. * Harden the pre-change loader and its guard pre_change.py now fails under CI, and otherwise skips, with its own message when git is not installed (instead of a raw FileNotFoundError) and when the tests do not run from a git checkout (instead of advising a fetch that cannot help). The repository root is a parameter, and a cached alias must map to the same commit or loading raises. The test's PATH wrapper now refuses fetch, pull and every depth or shallow option without running git, so the committed negative control (a fetch sent through _git) is caught by _assert_no_fetch and leaves the clone untouched. New tests cover missing git, a non-checkout root and alias reuse. Tested: 10 passed, also with CI set; the clone is not shallow afterward. * Validate comp_list dtype in the layout conversion rows_from_legacy_columns now refuses a non-integer comp_list (float or NaN ids) with its own ValueError before the bounds check, instead of letting it reach the indexing. test_component_layout.py adds direct unit tests on small arrays for every ValueError branch: the comp_list rank, the dtype, the A shape, an out-of-range id, an id repeated within a model, and an id shared across models (the refit message), plus a block-for-block conversion of an unmerged, permuted comp_list. Tested: 11 new tests pass; persistence tests unchanged. * Refuse a truncated A in load_results load_results now checks that the A file holds num_comps * nw values before reshaping it to (num_comps, nw), and names the file and both counts when it does not. Before, a length no reshape accepts surfaced as a bare NumPy reshape error, and one short by a whole component reshaped to the wrong width and failed later as a matrix-product error. Tested on truncated copies of a real two-model export (both cases). * Raise instead of assert in NumPy _write_results The fitted-A and outdir preconditions of AMICA_NumPy._write_results are now explicit RuntimeErrors, since assert is stripped under python -O. The comment above them also said the reference omits A from its output; the binary does write it, in the layout pamica now writes. Tested: NumPy write and load tests pass. * Widen the byte-identity matrix The unshared byte-identity test now also covers the Gaussian, logistic and sub-Gaussian cosh families (pdftype 2, 3 and 4, single model, n_mix 1 for family 4), four blocks of 1024 samples, a keep_best fit whose log-likelihood falls so the safeguard restores an earlier snapshot (asserted), and best-of-two restarts, on every backend that supports each. The NumPy backend has only the generalized Gaussian and no keep_best, so those cases run on PyTorch and MLX; the test says so and test_numpy_rejects_other_pdftypes pins the first reason. The helper now lets a configuration override its defaults, and compares final_ll_. Tested with CI=1: 66 passed. * Check the MLX double conversion on save again test_old_mlx_save_converts_losslessly now saves the converted model again, checks the file is format 2 with a component-row A, reloads it, and compares A and every output with the saved model, as the torch test does: a second load must not convert the already-converted A again. Tested: both recipes pass. * Fix the Spinv citation and sharing comments The torch _identify_shared_comps docstring cited Spinv's allocation at :551; it is amica15.f90:569. A test_mlx_newton docstring said merged-away columns and curvature not indexed by mixing column; with components as rows the words are component and mixing vector. Comments only. * Label the merged-oracle figures consistently ADR 0007 and the changelog now say the 3-iteration oracle figures are the worst of doscaling on and off, and give the old column semantics' error as the matching worst value (A 0.21, LL 4.3e-4, not 0.20 and 4e-4); the test comment lists both measurements. Docs and comments only. * Describe component-row sharing in the notes AGENTS.md and .context/progress_summary.md described the old column metric and merge as working. They now describe the component-row semantics (ADR 0007, #334): the metric compares de-sphered component mixing vectors, the merge ties components, and the reference binary's own scan never merges because Spinv2 is never allocated. Docs only.
* Reject unknown AMICA_NumPy keyword arguments AMICA_NumPy(**kwargs) forwarded every keyword into a params dict read with params.get(...), so a typo (max_iters=50) or an option the legacy backend does not implement (keep_best=True) constructed silently and had no effect. __init__ now validates **kwargs before any other processing: an unrecognized name raises TypeError with a difflib-based suggestion, and a name implemented on the PyTorch backend (AMICATorchNG) but not this one raises TypeError naming it and pointing to AMICA(backend='torch'). The accepted-keyword set is derived from _CONSUMED_KEYS/_CANONICAL_TO_NUMPY_KEY (the same source the params-file routing uses) plus this constructor's own named parameters, so the two surfaces cannot drift apart. The canonical spelling of the three renamed settings (min_nd/maxdecs/share_iter) is now also accepted as a keyword argument directly, translated the same way a params file's setting already is; passing both spellings at once raises TypeError. * Add tests for AMICA_NumPy kwargs validation Real construction only, no mocks (no .fit() needed to exercise __init__'s validation): a typo raises TypeError with a suggestion; an unsupported cross-backend option raises the specific message; the three canonical-spelling aliases are accepted and their conflict is rejected; every documented NumPy option constructs, alone and all together; and a source scan proves _CONSUMED_KEYS matches every params.get(...)/raw_params.get(...) read site exactly, so the accepted-kwargs set cannot silently drift from what __init__ actually reads. * Document AMICA_NumPy kwargs rejection behavior change docs/changelog.md: Unreleased entry noting the legacy NumPy backend behavior change (unknown or unsupported options now raise instead of being ignored). docs/api/numpy-backend.md: a short paragraph mirroring the existing pdftype callout. * Improve AMICA_NumPy kwarg validation (review) PR #347 review follow-up, several items together since they touch the same functions: - files/data_dim/field_dim now work as constructor keywords, with the same meaning as in params_file, instead of passing the accepted- kwargs check but staying silently inert (fit() with no data still said "no 'files' configured" even after a caller set one via a kwarg). Conflicting values between params_file and a kwarg raise; the same value from both does not. - n_models/n_mix (AMICATorchNG's spelling of this backend's own num_models/num_mix) get a message naming the correct spelling instead of the generic (and here wrong) torch-only message. - n_channels gets its own message: it is inferred from the data passed to fit() on every backend, not a constructor keyword on any of them (the AMICA wrapper's own fit(**kwargs) excludes it too). - Every offending keyword in one call is named in a single error, however many different categories are mixed together. A first version raised on the first category found, so AMICA_NumPy(n_models=2, totally_bogus=1) named only n_models and dropped totally_bogus. - _torch_only_options() imports AMICATorchNG lazily, inside its own body, called only on the error path, so numpy_impl carries no module-level dependency on torch_impl. - Groups difflib/inspect with the other stdlib imports. - Hardens the source-scan test: fails loudly on a params[...] bracket read or a non-literal params.get(...) key, either of which would be invisible to the scan. - Adds typo-suggestion tests for params_file/use_tqdm alongside verbose. * Fix dropped keyword in AMICA.fit mlx check PR #347 review item 4: the same single-category drop AMICA_NumPy._reject_unknown_kwargs had (issue #346) existed here too -- backend='mlx' with fit(dtype=..., blocksize=...) named only 'dtype' (ValueError) and silently dropped 'blocksize' from the message, since the torch-only check raised before the unknown-keyword check ever ran. Both categories are computed before either is raised; a combination now names both in one TypeError. The two single-category messages are unchanged (still ValueError for torch-only alone, TypeError for unknown alone), so existing callers/tests keep working. * Document NumPy kwargs review follow-up docs/changelog.md: a Review follow-up sub-bullet under the Phase 14 entry. docs/api/numpy-backend.md: mentions files/data_dim/field_dim as keyword arguments and the n_models/n_mix/n_channels-specific messages.
* Split the M-step into direction and update Compute the natural-gradient/Newton step and its norm (the reference's dAk and ndtmpsum) in _update_direction and apply it in _update_parameters, in all three backends, sharing one set of intermediates. The fit loops call both back to back, so every fit is byte-identical (checked on 8 configurations per backend). * Follow the reference's iteration order Run the likelihood-decrease response and the stopping checks before the parameter update, and exit on a stop before any parameter moves, as amica15.f90:1015-1122 does, in all three backends. The step taken right after a decrease now uses the halved rate, and a stopped fit returns the parameters whose likelihood is ll_history[-1]. Keep the reference's working rho rate and its ceiling apart (rholrate, rholrate_cap), reset inside the A-update branch. Fits without decreases or stops are byte-identical; seeded oracle deviations drop from 1.4e-2 to 2.7e-5. * Hold A on the reference's schedule without sharing Apply the reference's A-update guard (iter >= share_start and mod(iter, share_iter) <= 5, amica15.f90:1803) in every fit, share_comps on or off, through schedule.share_freeze, replacing the anchored post-merge window. Every backend now rejects share_iter < 7 (NumPy: share_int) whether or not sharing is on, since a shorter cycle would never update A again. Default fits of 100 or more iterations change. * Add iteration-order tests and native oracle Always-on cross-backend tests for the freeze iterations, the decrease timing, stop semantics, keep_best pairing and share_iter validation, plus an opt-in comparison against the native binary (AMICA_RUN_FORTRAN=1). All pass; the native comparison fails on the pre-change code. * Retune tests to the reference's iteration order Existing tests that pinned the old order (update before the checks, a freeze gated on share_comps and anchored on share_start, one rho rate) now pin the reference's, or use recipes that still exercise what they test. Full non-slow suite and the gated native oracles pass. * Document the reference's iteration order ADR 0008, changelog entry with oracle and parity numbers, and the differences guide: the A-freeze is now the reference's arithmetic, and convergence stops return the parameters final_ll_ describes. mkdocs build --strict passes. * Test the rho-rate ceiling as its own value Rate tests that read rholrate as the ceiling now read rholrate_cap, the cross-backend state copies carry it, and a new test round-trips it through state_dict on PyTorch and MLX, including a save without the key. Affected tests pass, including the slow torch ratchet test. * Stop overshoot recipes on the first decrease The keep_best overshoot recipes stopped via min_dll=1e-4/maxincs=2, which under the reference order ended at the peak or below it depending on round-off: at the peak on the macOS CI runner, and in 1 to 2 of 12 data perturbations locally. maxincs=0 with min_dll=1e-8 stops them on their first likelihood decrease instead, which held in all 12 perturbations on PyTorch and MLX, with and without rejection. Affected tests pass. * Stop identically on non-finite values Every backend now keeps a non-finite likelihood out of its history (NumPy recorded it), stops before applying a non-finite step or gradient norm (new degenerate stop_reason nan_direction), and stops on non-finite parameters right after an update (nan_params, now also in PyTorch and NumPy). NumPy defers only an A/W corruption inside its restart window to restart-on-NaN, which can repair it. Cross-backend injection tests pass. * Clear numincs on a NumPy restart The reference's min_dll comparison with the NaN likelihood of its restart iteration is false, so it zeroes numincs (numdecs is left as it was). NumPy's restart carried the small-gain count across; it now clears it. The new test stops at index 8 as expected and at 6 without the fix. * Validate saved learning rates on load from_state_dict (and the MLX file load) now refuses a missing or non-finite lrate, lrate_cap, newtrate, rholrate or rholrate_cap with a ValueError naming the field; a missing rholrate no longer surfaces as a bare KeyError on PyTorch. Tested on PyTorch and MLX. * Validate share_start on every fit The A-freeze reads share_start whether or not share_comps is on, so every backend now requires an integer share_start >= 1 always. The share_iter message now says why a value below 7 is refused (A held permanently from share_start), and NumPy's names both of its spellings. Both checks are reached through a Fortran params file on all backends; tests pass. * Test every convergence stop's parameters The stop test now runs min_dll, grad_norm, grad_norm_floor and lrate_floor on every backend, each with a precondition on its stop_reason and a twin that runs the same iterations to max_iter. All 12 cases pass. * Test that a NumPy restart skips the update A pass-through recorder on _update_parameters shows the restarting iteration applies no update; the same fit of the pre-change code, loaded through pre_change.py as a control, updated on it. Passes. * Test MNE n_iter_ after each kind of stop to_mne_ica().n_iter_ equals iteration + 1 and len(ll_history) after a min_dll stop and after a max_iter fit, on PyTorch and MLX. Passes. * Take the oracle floor over two thread pairs The reference noise floor is now the larger deviation of a 2- or 4-thread binary run from the 1-thread run. The multi-threaded runs are not reproducible, so the docstring gives each floor's range over three runs. All four gated oracle cases pass in each run. * Fix docstrings and Fortran citations mir_history_ records no waypoint on a stopping iteration; MLX documents its rholrate/rholrate_cap pair as PyTorch does; NumPy fit() gets the iteration-order notes. Rejection is cited as amica15.f90:1136-1140 and the check ranges start at 1056 and 1019. Docstring and comment changes only. * Document the non-finite stops and validation ADR 0008 states the final_ll_ invariant with its keep_best qualifier and the review's changes; the changelog and differences guide cover the non-finite stops, share_start validation and saved-rate checks, with the two-pair oracle floors. AGENTS.md and the context notes describe the unconditional A-freeze. mkdocs build --strict passes.
* Use the reference's single-precision constants The reference writes its density normalizers as default-kind literals widened with dble, so the binary uses their float32 roundings. All three backends now read those values, and epsdble's, from one shared module, pamica/reference_constants.py; MLX holds the rho == 2 normalizer in its lgamma table. The literal-formula tests and the benchmark's copy now use the reference's values. Tested: pdf-family and backend test files pass. * Compare pre-change fits at the old constants The byte-identity tests against code from before issue #344 (component rows, doscaling off, the MLX pre-Phase-3 tip) now give the live backend its old density constants through pre_change.use_pre_344_constants, so they still isolate the change each was written for: their two-model and non-GG fits reach the changed normalizers. Tested: the three files pass. * Pin the constants against the reference test_reference_constants.py checks each shared constant against the float32 rounding of its literal on the cited reference line (exact rational arithmetic), pins the sweep of amica15.f90 and its header, checks that no backend keeps a copy, checks every backend's rho == 2 log-density, and (opt-in) seeds the native binary for pdftype 2, 4 and 1. Tested: 23 passed with AMICA_RUN_FORTRAN=1. * Run the merged oracle at rho == 2 The merged-state oracle no longer holds maxrho at 1.99: its warm seed now has mixtures clamped at 2, it asserts that, and the code before issue #344 is replayed from the same state as a control (off by 2.8e-9 in log-likelihood after 1 iteration, against 2.2e-15 now). Tolerances unchanged. Tested with AMICA_RUN_FORTRAN=1. * Document the single-precision constants Changelog entry with the oracle numbers; the differences guide gains a section on the constants, and row 16 now records the one kind of single-precision literal pamica keeps at its decimal value (defaults of input.param keys); the validation guide notes the new family oracle. Tested: mkdocs build --strict, typos. * Note the reference's unset lrate0 in row 16 Without an lrate key the reference never sets its ceiling lrate0, so its lrate drops to 0 after the first iteration (checked against the pinned binary). Row 16 now says so and uses comp_thresh as the example. Tested: mkdocs build --strict, typos. * Skip MLX before patching its constants On a host without MLX the byte-identity tests imported the MLX module to patch it before the check that skips them. Resolve the classes first. Tested: the two files pass with MLX and skip its cases without. * Remove the unused compute_log_pdf numpy_impl.pdf.compute_log_pdf had no callers in the package, tests or docs. Tested: the pdf and viz tests pass. * Plot the fit's density in compute_pdf compute_pdf, which viz.plot_pdf_fits draws, now takes its normalizers from pamica.reference_constants, including the reference's single-precision sqrt(pi) at rho == 2, so it draws the density the fit uses. Its docstring says it is a plotting helper with the legacy pdftype numbering. Tested: the pdf, viz and constants tests pass. * Scan every backend module for copies The no-copy test now scans every .py file of torch_impl, numpy_impl and mlx_impl with comments and strings blanked, and flags the linear forms (sqrt(pi), sqrt(2*pi), ...) as well as the log forms and the literals. A new test checks that it flags every definition this change removed. Tested: passes; fails on the old pdf.py sqrt(pi) line (checked by hand). * Assert the family held in the oracle The family oracle now asserts that every source kept its family and no kurtosis switch ran (n_kurt_done == 0), for pdftype=1 with num_kurt=0 too, in the live and the pre-change runs. Tested with AMICA_RUN_FORTRAN=1. * Check the pre-#344 constant substitution A control test: every constant use_pre_344_constants patches differs from the live value, the patch takes effect, it covers every changed constant each backend module reads, and on MLX it restores the pre-change rho == 2 table entry. Tested: passes on all three backends. * Note the plotting helper in the changelog compute_pdf now draws the fit's density and compute_log_pdf is gone. Tested: mkdocs build --strict, typos.
* Run the native binary from its own draw run_drawn_reference runs the binary with nothing loaded and a fixed seed; with max_iter=0 it writes its drawn initialization. The run and read-back are shared with run_seeded_reference. * Normalize the drawn initial mixing matrix Every backend now draws its initial A through the shared pamica.initialization.initial_mixing: per model, 0.01*(0.5-u) with the diagonal set to one and each component divided by its norm, as the reference does (amica15.f90:805-823), including the NumPy restart redraw. A supplied or loaded A is used as is. Tested in later commits. * Start pre-change classes from the new init The byte-identity pins (fb13d76, 0930c0e, 2e04006) and the #339 oracle control now fit the historical class from the live normalized initial A through pre_change.with_normalized_initial_mixing, so each still isolates its own change. Tested: all those byte-identity tests pass. * Test the normalized initial mixing matrix Always-on cross-backend start checks with a pre-change control, the supplied/loaded A paths, the NumPy restart redraw, and two gated native oracles. Tested: 34 passed with AMICA_RUN_FORTRAN=1. * Re-search the MLX pdftype=1 restore recipe The normalized initial A made seed 3 at lrate 0.5 climb monotonically to max_iter, so the restore was never exercised. Seed 6 at lrate 0.4 (kurt_start 7) overshoots by 0.17 after all five switch passes, on all 12 perturbed inputs. Tested: test_mlx_keepbest.py passes. * Re-record the MLX fit-path canary The normalized initial A moved the recorded trajectory (ll_history[0] by 1.55e-5). Pins re-recorded on the M4 Pro and tolerances re-derived from the same 48-draw float32 perturbation study, floored at one float32 unit: no draw moved ll_history[0]. Tested: the canary passes. * Seed the doscaling oracle from a conditioned state From seed 42's normalized init, one sample sits 2.3e-7 from a mixture center; the mu update amplifies the 1e-14 sphere round-off to 1.3e-9 (PyTorch and NumPy differ by 7.9e-10), over the 1e-9 bound. Seed 45 stays 7x under every bound; a new precondition asserts the backends' own disagreement sits 3x below each bound. Tested with the binary. * Re-search the early-merge collapse recipe From its normalized initial A, seed 20's second model keeps gm 1.2e-3 after the second scan instead of collapsing to zero in one iteration. Seed 23 (of 23, 32, 40, 41 in seeds 0-49) collapses as the test needs. Tested with the binary (AMICA_RUN_FORTRAN=1). * Document the normalized initial mixing matrix Changelog entry with the measured behavior change, ADR 0006's remaining difference marked resolved (and ADR 0007's pointer), the differences guide's init wording and the re-searched collapse recipe. mkdocs build --strict passes. * Re-seed the MLX MIR restore test From its normalized initial A, seed 0's discarded step moved the MIR by 2.1e-4 relative (3.4e-5 to 2.1e-3 over 8 perturbed inputs), inside the 1e-4 margin on the CI GPU. Seed 7 moves it by 2.3e-3 to 2.9e-3 on every input. Tested: test_mlx_mir.py passes. * List the re-seeded MLX MIR test in the changelog * Refuse a zero-norm component in normalization normalize_components now raises ValueError on a zero or non-finite row norm instead of returning NaN. The reference path cannot reach it (the unit diagonal keeps every norm >= 1). Tested: zero and NaN rows. * Check a supplied NumPy A at fit start A supplied A (or one left by a previous fit) now raises ValueError on a wrong shape, a non-finite entry or a singular model block, through the shared validate_supplied_mixing. Before, it raised LinAlgError deep in the fit, or (NaN) ended with no stop reason. Tested: all three. * Test NumPy fix_init and the wrapper round trip fix_init (an attribute) starts from the stacked identity and leaves the generator untouched; AMICA save/load restores the PyTorch A bit for bit. Tested: test_initial_mixing.py passes. * Separate the freeze from the collapse figures The 9.0e-4 gm drop is pamica's own fit, whose A is held on those iterations; the 4.2e-3 both sides reach comes from the freeze-free continuation. Docs and test comment now say so. Docs only. * Tighten three comments from the review ADR 0006 marks the init difference as former; the _FLOOR_MARGIN comment names the worse of doscaling on and off; pre_change notes a component is a block row in both layouts. Comments only. * Record the supplied-A check in the changelog * Sphere both oracle sides with the reference S The merged-state oracle now sphers pamica's data with the binary's own sphere, so both sides start from the same bits after the Phase 12 and 13 merge. Tested: gated component-row oracles pass. * Start the pre-344 control from the new init The pre-#344 control class predates #341, so it now starts from the normalized initial A the seeded reference uses. Tested: gated family oracles pass.
* Delete the dead legacy pdf code in numpy_impl/pdf.py choose_pdf_type had no callers, and compute_pdf's pdftype 2-4 branches were unreachable: every caller uses the generalized Gaussian. Tested with the pdf, reference-constant and viz suites. * Correct public docstrings against the epic code Rewrap docstring lines that began with an issue number, which the API pages rendered as headings; state the sphered-space shapes of the wrapper's mixing and unmixing accessors; name restart_error among the degenerate stops; drop the claim that natural gradient alone falls short of the reference; fix the NumPy n_channels and doscaling wording. Checked on the bundled sample (accessor shapes and composition). * Say that maxdecs counts decreases, not consecutive ones The reference resets its decrease count only at each ratchet and when Newton switches on (amica15.f90:1062-1076), as every backend does. * Describe the reference's iteration order in the concept pages How AMICA works now walks one iteration as the code runs it: the E-step, the likelihood-decrease response and stopping checks, the exit before any update, then the update with Newton, the A-freeze windows and the doscaling rescale. The false claim that every iteration raises the likelihood is gone, and the flow figure shows the checks before the update. What is AMICA covers the unit-norm scale convention. * Fix the EEGLAB guide's scalp-map example and multi-model note The variance-order example took scalp maps from the sphered-space get_mixing_matrix; it now uses get_sensor_mixing_matrix. The multi-model note still said the per-model layout differs from the reference's, which #159 and #334 fixed. The guide index lists all four guides. * Correct the backends guide on devices, CUDA float32 and scope Auto device selection picks MPS first and the wrapper redirects it to CPU for float64; the raw AMICATorchNG raises there instead (checked on this host). CUDA float32 is about as fast as float64, not faster, as the same page says above. The native backend is listed, the wrappers' two backends and their parameter-file route are stated, and a stale 'sweep is being finalized' note points to the published sweeps. * Document the raw PyTorch backend's automatic device choice With device=None it picks MPS first and, at the float64 default, raises on an Apple machine; the AMICA wrapper redirects to CPU. Checked on this host. * Update install, accessor and precision notes on the entry pages pamica is on PyPI (0.3.3), so getting started no longer says a release is planned, and says unreleased changes need a source install. The README's get_mixing_matrix comment called the sphered-space matrix scalp maps; the examples now use get_sensor_mixing_matrix for maps. The home page no longer calls float32 faster, the final_ll_ note covers the max_iter case, and the NumPy CLI takes either parameter-file format. * Correct the API overview and backend pages List AMICANative in the public import surface; install extras with uv from PyPI or a source checkout; correct the NumPy page's n_channels claim (the raw PyTorch and MLX constructors take it) and list what that backend lacks; drop the viz page's reference to the closed #159 as an open question; replace em-dashes in list items. * Say the reference compiles Newton off, not on amica15_header.f90 declares do_newton = .false.; only the bundled input.param turns it on. The Newton docstrings and the concept page said the reference runs Newton by default. * Correct the differences guide's Newton row and backend table The reference compiles do_newton off (only the bundled input.param turns it on); row 5 names singular_ll; the backend table gains the variance_order, scoring and wrapper rows and the wrapper .pt save; an obsolete pre-#334 comparison of two retired sharing metrics is replaced by the current single-kernel account; the LLt exception names the last iteration; table cells say none instead of an em-dash. * Bring the validation guide's non-numeric claims up to date The degenerate-fit row still said raw backends were ungated (#306 is done) and named only the likelihood stops; the EEGLAB section still said multi-model files are not in the reference layout. Parity figures are left to #351. * Make the Unreleased changelog one coherent account Group the phase entries by what changed (fitting, MLX and the wrappers, the MNE wrapper, persistence and exports, fixes, removals, docs) and open with a warning that default fits differ from 0.3.3 and why. Drop the entries a later phase superseded (Phase 8's pending #344 note, the polish round's harness and Phase 1's remaining-gap notes), merge the duplicate pdftype-default entries, correct the n_channels claim, and mark measurements taken before later phases. No figure is changed. * Map the shared modules and the epic's fitting changes in AGENTS.md Add rank, schedule, initialization, component_layout and reference_constants (with blocktune, restarts and the params reader) as the shared-decision modules, plus mne_compat, native and metrics; name amica15.f90 as the reference source; add a status line for the reference-fidelity changes and the #306/#339 degenerate-fit extensions; shorten the epic #278 history sentence. * Bring the context plan and progress summary up to date Record epic #324's phases and the reference-fidelity changes, the #306/#339 degenerate-fit extensions and the #334 sharing oracle; close the stale release items (repository transfer, docs deploy, PyPI and Zenodo are done) and the #15 coverage item. * Document the opt-in native-binary tests and MNE's pre-whitener The testing page names AMICA_RUN_FORTRAN=1 and the mne_tests directory; the MNE page's source formula includes the per-channel-type scaling the export applies. * Say shared component, not shared column, in AMICAICA's docstring * Note that doscaling is on by default in What is AMICA * Avoid em-dashes in the plan's updated phase headings * Mention the post-update non-finite stop in How AMICA works * Reword the paper's framing in nuanced terms Drop negation-contrast and candor framing outside the Validation section, count releases as of today (nine, six on PyPI), and describe the MEG status as end-to-end use without measured parity. * Drop candor framing in the differences guide and ADR 0007 * Rebuild paper.pdf * State the complete transform in get_unmixing_matrix The docstring called W @ get_sphere() the full transform; transform also subtracts the mean before sphering and the model center after. The MIR docstrings and the metrics page now call W @ sphere the linear part. Checked on the bundled sample with two models: the full formula matches transform exactly, W @ sphere @ X is off by up to 4.2, and MIR is unchanged by a shift of the data. * Say which default fits #335 and #334 reach, in the changelog The opening warning said the schedule gates and component rows matter only with Newton, rejection, restarts or sharing; they also reach plain default fits in narrow cases (a maxdecs ratchet past newt_start, and the per-component ndtmpsum sum near a grad-norm stop). The stopping iteration's skipped steps are listed in code order (kurtosis switch, share scan, MIR waypoint, rejection), in ADR 0008 too, and the #304 entry says the fit applies the file's settings. * Replace the two em-dashes left in the validation guide Prose only; no figure on those lines changed. * Say that fit applies from_params_file's settings from_params_file stores the file's settings on the instance; fit passes them to the backend constructor as defaults and emits the warning. * State effects directly in the prose this PR added Reword contrast framings in the concept page, home page, viz page and module docstring, and the differences guide's opening, per the style rule: say what the code does and its known effect. --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
* Take the wrapper's fit defaults from the backend AMICA.fit restated max_iter/lrate/do_mean/do_sphere/do_newton, and its lrate (0.05, runamica15.m's) differed from every backend's 0.1. It now reads them from the selected backend's signatures, so AMICA and AMICAICA fits without lrate run at 0.1. Tests for torch and MLX: resolved defaults, a default wrapper fit equals the default backend fit, and one set of constructor defaults across the backends. * Move the MPS/float64 device fallback into AMICATorchNG AMICATorchNG(n_channels) with default arguments raised ValueError on Apple Silicon: device=None picked MPS, which has no float64. The constructor now uses the CPU for that case and logs a warning; the wrapper and AMICA.load pass device through and drop their copy. An explicit device='mps' at float64 still raises. Torch-only (MLX has no device choice). Tested on MPS: raw construction and fit, float32 auto, explicit MPS, load with device=None. * Remove the dead pamica/data package-data entry No pamica/data directory exists. Built with uv build: the wheel's file list is unchanged, it imports from a clean venv, and numpy_impl/params.json is inside. * Document pamica's defaults and the backend device choice Add a defaults table to the differences guide: pamica against the compiled amica15 binary and EEGLAB's runamica15.m, why pamica follows the binary, and how to reproduce an EEGLAB run. Update the pages that stated the wrapper's lrate or the raw backend's MPS raise. mkdocs build --strict passes. * Add the Phase 17 changelog entries The wrapper lrate change joins the 'Default fits differ from 0.3.3' warning; entries for the backend device fallback and the package-data cleanup. * Give AMICA_NumPy the shared default settings params.json set do_newton on and max_iter 2000, the only shared settings where NumPy differed from AMICATorchNG; it now sets them off and 100, as does the constructor's max_iter fallback (reached by the CLI). Fits shorter than newt_start are byte-identical. New test_default_settings.py holds NumPy and MLX to every shared AMICATorchNG default (the MLX check moves there from test_wrapper_backends.py). * Correct the degenerate-fit test's measured stop The comment said torch stops on nan_ll at iteration 3; both backends stop on nan_params with iteration 2 in all 16 measured runs, at either lrate. The test asserts only a degenerate stop. * Document the NumPy backend's shared defaults Defaults section, validation guide's stops table, NumPy API page, and a changelog entry (NumPy joins the opening warning's defaults line). * Give AMICANative pamica's shared defaults The engine wrote the bundled input.param's values (lrate 0.05, Newton on, max_iter 2000, block_size 512, ...). It now takes every setting the binary has a keyword for from AMICATorchNG's signature through FORTRAN_TO_PAMICA_KEY; binary-only knobs are unchanged. The default block is pamica's 8192 samples split over max_threads and capped at the data, since the binary's block_size is per thread and a block longer than the data gives an all-NaN fit (#292). The native oracles now start from the bundled input.param itself (byte-identical files). Tested with the v0.3.3 binary: native, oracle and default-settings tests pass. * Document AMICANative's shared defaults AMICANative joins the defaults table's pamica column; the section and the native API page cover the settings it cannot write, the per-thread block size, and running the binary as an input.param configures it. The changelog records the behavior change. * Say where AMICANative's default pcakeep comes from
* Add issue-351 Newton independent-seed study script * Add hallu driver for the Newton seed study * Record harness, same-start and fixture parity runs * Record share_comps counts and float32 agreement runs * Update share_comps merge counts in differences guide * Re-state the harness rows with same-start parity * Add an MLX float32 option to the Newton seed study * Mark the issue-27 and issue-51 records as pre-epic * Update the harness figures in AGENTS and progress summary * Add ensemble scripts and dimsweep LL records * Add the hallu CUDA dimsweep LL record * Re-state the cross-backend LL table with the chaos caveat * Reword the harness and cross-backend notes * Pin the ensemble lrate in reproduce_table1 * Add basin and pre-epic ensemble scripts * Record the bundled tier, basin, ensemble and MLX Newton runs * Add the equivalence-margin check on saved ensembles * Record the seeded keep_best ensemble * Add the re-measured keep_best figures to ADR 0003 * Name the pre-change code precisely in validation guide * Regenerate the multi-model ensemble figure * Rebuild paper.pdf * Re-state the bundled single-model and multi-model results * Update keep_best and multi-model figures in AGENTS and guides * Update the fixture parity figures in AGENTS and progress summary * Record the bundled tier wall-clock on the M4 Pro * Update the cross-backend LL claim in the backends guide * Record the Amari pairing-correction measurement * Update paper Table 1 bundled rows and the float32 paragraph * Clarify the benchmark excerpt in the paper * Rebuild paper.pdf * Record the gated oracle and full-suite runs * Add the Phase 15 re-measurement to the changelog * Record the Newton independent-seed fits * Record the same-session 70-channel timing check * Record the unseeded-start check of the issue-145 binary * Mark the sections of the validation guide that predate the epic * Pin every reference setting in the Table 1 and run scripts * Re-state the Newton-enabled basin results * Give both matching seeds in the paper * Rebuild paper.pdf * Document the ensemble's invsigmin difference * Smooth the ensemble protocol sentence * Address the docs-claims review findings * Address the paper review findings * Give each ensemble distribution its own line style * Label the external single-model figures as pre-epic * Reflow the README parity sentence * Record the external tier run * Fill the external-tier figures and fix rounding slips * Rebuild paper.pdf * Save the pinned parameter files before Phase 17 * Re-check the reference pins after Phase 17 * Pin do_approx_sphere in the reference settings * Record the test runs after the Phase 17 merge * Add the issue-351 findings record * Reword two remaining contrast phrasings in the paper * Rebuild paper.pdf --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
Filter CI on code paths (docs, paper, paper.pdf, metadata and non-Python .context records skip; .context Python files and the tested ensemble.npz still run), and commit the rebuilt paper.pdf back only on dev and main. Checked with actionlint.
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Epic #324 makes the MLX backend first-class end to end, and brings every backend's fitting closer to the Fortran reference.
Reviews and oracle tests along the way found several places where pamica's fit departed from
amica15.f90. The epic grew to fix them, so default fits differ from 0.3.3 on every backend; the changelog's Unreleased section opens with the list.Phases
Each phase was squash-merged into the epic after its review (pr-review-toolkit, Sonnet) and green CI.
pcakeep/pcadbpolicy; MLX supportAMICAICA.applyrestores the PCA residualbackend="mlx"inAMICAandAMICAICAvalidate_implementations.py --backend all; end-to-end workflow testdoscalingnormalizes component rowsshare_compsAMICA_NumPyrejects unknown optionsParity after the epic
Re-measured against the pinned v0.3.3 binary (
.context/issue-351/findings.md).Per-iteration cost is unchanged.
Also in this PR
.context/records.paper.pdfis committed back ondevandmainonly.Follow-ups
Filed as their own issues: #328, #331, #358, #359, #360, #361, #362 and #363.
Closes #323, closes #304, closes #313, closes #306, closes #315, closes #333, closes #334, closes #335, closes #336, closes #339, closes #341, closes #343, closes #344, closes #345, closes #346, closes #351, closes #352, closes #354, closes #324.