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Python alone\nuses the `nirs4all-core` distribution name to avoid colliding with the full\nmodelling library.\n\nThis package is the runtime surface that `nirs4all-web` should consume. The web\napplication lives in `nirs4all-web`; this directory is for the reusable\nJavaScript/WASM binding and package metadata.\n\nThe portable execution API delegates Kennard-Stone, SNV, MSC, Savitzky-Golay, and\nPLS component sweeps to `@nirs4all/methods`:\n\nIt also accepts `n4m.Ridge`, `n4m.RidgePLS`, `n4m.RobustPLS`, `n4m.CPPLS`,\n`n4m.SparseSIMPLS`, `n4m.ECR`, `n4m.ContinuumRegression`, and `n4m.MIRPLS`\nas final model steps. These use Methods `fitModel` and `predictModel`; the\nserialized selected model can be replayed after JSON roundtrip. Recipe\nparameters map to Methods in positional order: `lambda`, `ridge_lambda`,\n`[huber_k, max_irls_iter]`, `gamma`, `sparsity_lambda`, `alpha`, `tau`, or no\nextra parameters, respectively. `n_components` is passed separately and may\nbe swept with `_range_`. Unknown model parameters are rejected.\n\nThe same `fitModel`/`predictModel` path also supports `n4m.FusedSparsePLS`,\n`n4m.BaggingPLS`, `n4m.BoostingPLS`, and `n4m.RandomSubspacePLS`. Their\npositional parameters are `[l1_lambda, fusion_lambda]` (defaults 0.05/0.05),\n`[n_estimators, seed]` (50/0), `[n_estimators, learning_rate]` (50/0.1), and\n`[n_estimators, features_per_subspace, seed]` (50/10/0). Subspace width is\nchecked against the fitted feature count. These recipes have native C and mock\ncontract checks, not numerical WASM parity without a fresh WASM artifact.\n\n`n4m.NPLS` accepts flattened tensor features with required positive integer\n`mode_j` and `mode_k`; their product must equal the fitted feature count.\n`n_components` defaults to 2. Methods fits the native N-PLS kernel and the\nserialized coefficient/mean state replays on later matrices. The Core recipe\ncurrently accepts a single target; a native C oracle additionally checks\ntwo-target held-out prediction. Real WASM parity requires a fresh artifact.\n\n`n4m.GroupSparsePLS` requires an explicit `group_assignment` array with one\nnon-negative 32-bit group ID per fitted feature. `group_lambda` defaults to\n0.05 and must be finite and non-negative. The recipe preserves the group IDs\nin JSON/YAML and fitted model metadata; fit and replay use the corrected native\nMethods post-SIMPLS group-wise coefficient shrinkage (not a refit of sparse\nlatent directions). The WASM parity gate checks held-out predictions\nat both zero and positive penalty against Python n4m. This is a bounded recipe\nand coefficient replay, not a claim that an R/Python trained archive can already\nbe imported into the browser.\n\n- `runPortablePipeline(source, dataset)` parses the shared nirs4all JSON/YAML\n  syntax, executes the portable subset, and returns parity-checkable split,\n  target, variant, and selected-result fields plus a serialized selected PLS\n  model.\n- `predictPortablePipeline(result, dataset)` replays the recorded preprocessing\n  chain and predicts with that serialized model through the same methods WASM\n  backend. Fitted preprocessing state is serialized as numeric arrays in each\n  step and restored before prediction. Older results with stateless steps remain\n  readable; a persisted MSC step without fitted state is rejected because its\n  training reference cannot be recovered from prediction data.\n\nMSC recipes accept `n4m.MSC` and the nirs4all Python `MSC` /\n`MultiplicativeScatterCorrection` class aliases. They call the Methods `MSC`\noperator with no numeric parameters. The Python `scale` and `copy` flags are\naccepted as booleans; they do not alter the Methods numerical operation.\n\nSPA recipes accept `n4m.SPA` (also `n4m.SPASelector` and\n`pls4all.sklearn.SPASelector`) before the final model. `top_k` is required;\n`n_components` defaults to 2. Methods fits the selector on training rows only.\nThe result stores validated zero-based selected indices in the native ranked\norder. Fit and prediction project a sorted copy onto ascending columns without\nre-fitting SPA. A missing, duplicate, or out-of-range index is rejected.\n\nThe generic `n4m.Selector` step accepts all 25 Methods selector names with\n`{method, n_components, method_params}`. It calls the native selector once on\ntraining rows, stores zero-based indices in their native ranked order, and\nprojects a sorted copy onto validation and replay matrices. Methods requiring\ninternal validation receive deterministic folds over training rows only. Seeds,\nthreshold vectors, and other method parameters are passed through the native\nC ABI; this surface has mock-contract coverage but not numerical WASM parity\nuntil a current Methods WASM artifact is built and tested.\n- `replayMethodsArchiveV2(archiveBytes, dataset)` validates the bounded Archive\n  V2 stored-ZIP, manifest, inventory digests, DAG-ML package, execution bundle,\n  and N4MM binding in Rust, then imports and predicts the single multi-target\n  model through the public `@nirs4all/methods` C ABI. The native file reader and\n  WASM byte reader compile the same Core-owned `archive_v2.rs` validation source;\n  there is no second binding-owned archive parser. The JavaScript layer only\n  validates host arrays and handles marshalling/ownership; it contains no\n  numerical fallback and never fits a replacement model. Before import it also\n  compares the manifest's capability-derived `abi_min_minor` with the actual\n  Methods WASM `abiVersion()` and refuses an older runtime.\n\n`runPortablePipeline` also accepts one training-only native X augmentation\nbefore preprocessing and the model. It runs after splitting and applies only\nto the training matrix; `predictPortablePipeline` never replays it. The kind is\none of the 22 snake_case names in the closed Methods ABI 2.11 X→X subset, and\n`values` follows that kind's native positional parameter order. For example:\n\n```json\n{\"pipeline\":[\n  {\"train_augmentation\":{\"class\":\"n4m.NativeXAugmentation\",\"params\":{\"kind\":\"gaussian_noise\",\"values\":[0.03],\"seed\":42}}},\n  {\"model\":{\"class\":\"sklearn.cross_decomposition.PLSRegression\",\"params\":{\"n_components\":2}}}\n]}\n```\n\nThis Level 1 WASM runner requires a Methods package exposing `augmentNative`.\nIt records the declaration with the fitted result for provenance. The\naugmentation has no fitted state or prediction transform and is outside Core's\nArchive V2 and DAG training contracts.\n\nThe Archive V2 WASM replay intentionally covers the Phase 2 portable Methods\nPLS final-refit contract only. N4MM v2 preprocessing stays embedded in Methods\nand therefore receives the raw matrix without a JavaScript kernel. Archives with multiple predictor\nnodes, optimization checkpoints, conformal/robustness payloads, external or\nhost-only artifacts, undeclared inventory members, incompatible N4MM metadata,\nor non-Methods dispatch are refused rather than silently approximated.\n\nBuild the Rust validator with `npm run build:native`. A qualification archive\nand closed scenario can be replayed with:\n\n```sh\nnpm run qualify:archive-v2 -- /path/to/archive.n4a /path/to/scenario.json\n```\n\nThe qualification command asserts the ordered two-dimensional result from one\nmodel import and one multi-target prediction, checks that no Methods fit symbol\nwas called, and proves tampered-digest and inventory refusals. The isolated\ntarball gate separately proves refusal when the optional Methods peer is absent.\n\nSavitzky-Golay defaults to `mode: \"interp\"` for full nirs4all parity and\npreserves explicit methods-backed modes (`mirror`, `constant`, `nearest`,\n`wrap`, `interp`) plus `cval` in the serialized preprocessing chain.\n\n## Generic n4m role recipes and trained envelopes (v8)\n\nAny Methods estimator is a recipe step through the language-neutral token\n`\"n4m:<catalog method id>\"` (or `{ class: \"n4m:<id>\", params }`), shared with\nthe full Python `nirs4all`, the R package and the Rust binding.\n`loadPipelineDefinition(source, { methods })` accepts these tokens when they\nresolve in the loaded Methods manifest (`methodClass`), next to the legacy\nclass names above; `n4mRoleCapabilities()` lists the usable steps from the\nsame manifest instead of a hand-maintained list.\n\n`N4mRolePipeline` fits such a recipe (sample filters on training rows only,\ntransformers and selectors, then one regressor or classifier) in the native\nMethods `RolePipeline`, which validates the recipe, passes every target column\nto the steps that need `y`, checks the column names and refuses states that\ncontradict the recipe. It reads/writes the `nirs4all.n4m.trained_pipeline.v8`\nenvelope (`{schema, recipe, n_features, feature_names?, states: [{method_id,\nn4me_base64, sha256, contains_training_rows, class_names?}]}`); envelopes\nwithout `feature_names` / `contains_training_rows` still load. A pipeline\ntrained in Python or R replays in the browser from its N4ME states (level L2):\n\n```js\nimport { N4mRolePipeline } from 'nirs4all';\n\nconst fitted = await N4mRolePipeline.fromJSON(envelopeText);\nconst { data } = fitted.predict({ X, rows, cols, featureNames }); // regressor\nconst { labels } = classifier.predict({ X, rows, cols });          // classifier\nconst trained = await N4mRolePipeline.fit(recipe, { X, rows, cols, y, featureNames });\nconst text = JSON.stringify(trained.toJSON({ allowTrainingRows: false }));\n```\n\nWith `featureNames`, renamed or reordered columns are refused. Nested `X` and\n`y` rows must match the declared `rows`/`cols` (every row is checked before\nflattening). The import refuses an `n_features` that is not a positive\ninteger equal to the native width, a column name holding NUL, and a\n`class_names` table that is not a non-empty list of unique strings or finite\nnumbers labelling every fitted class id (index = id). `recipe` is a copy of\nthe recipe the states attest, which is also the one exported. A state that\nembeds training rows (kernel PLS, LW-PLS, ...) is exported only with\n`toJSON({ allowTrainingRows: true })`. This path requires `@nirs4all/methods`\n1.2 (ABI 2.14 role pipelines).\n\nCustom app hosts can inspect `capabilityManifest()`, `controllerCapabilities`,\n`runtimeSurfaces`, and `runtimeContracts` before rendering graph nodes or\nselecting a runtime. The manifest schema is `nirs4all-core.capabilities.v1`; it\nexposes the stable V1 controller IDs for Kennard-Stone, SNV, Savitzky-Golay,\nPLS regression, and the portable methods pipeline, with parameter lists\nmatching the executable parser. `runtimeContracts` also makes explicit that\nstandalone serialized-model prediction is available on the WASM and Rust\nsurfaces.\n\n## JavaScript model controllers\n\n`createJsEstimatorController()` adapts a synchronous JavaScript estimator with\n`fit(X, y)` (or `train(X, y)`) and `predict(X)` to DAG-ML's native\n`HostControllerSpec`/`ControllerManifest` and `NodeTask`/`NodeResult` contracts.\nPass the initialized `dag-ml-wasm` module, a native DAG-ML `FoldSet`, and a\nsample-ID-aligned numeric dataset. DAG-ML assigns folds, variants, phases and\n64-bit seeds; the controller selects only each fold's training rows and emits\nidentity-keyed validation predictions. `REFIT` keeps the fitted JS object for\n`PREDICT`. The public `createDagMlNodeResult()` helper lets browser clients\nreturn the same wire result from an n4m-backed controller without duplicating\nlineage or prediction serialization.\n\n```js\nimport * as dagMl from 'dag-ml-wasm';\nimport { createRandomForestController } from 'nirs4all';\n\nconst controller = await createRandomForestController({\n  dagMl, // initialize the WASM module before this call\n  foldSet, // returned by dagMl.kfold_split_json / stratified_kfold_split_json\n  dataset: { sampleIds, X, y },\n});\nconst manifestsJson = JSON.stringify([controller.manifest]);\nconst resultsJson = dagMl.execute_execution_plan_phase_json(\n  planJson, manifestsJson, 'run:example', 42, 'FIT_CV', controller.invoke,\n);\n```\n\nInstall the optional `ml-random-forest` peer to use that adapter. It supports\nregression and numeric-class classification; `n_estimators` in pipeline params\nmaps to the library's `nEstimators` option. `fitFull()`/`predict()` and\n`exportModel()`/`importModel()` serve simple host workflows. Other synchronous\nclassic-ML libraries can use `createJsEstimatorController()` directly. The JS\nestimator's predictions are contract-compatible with DAG-ML, but numerical\nparity with Python is only claimed for the n4m-backed portable methods path.\nNo browser Python or deep-learning training runtime is required.\n\n`createN4mModelController()` uses the same fold/phase contract for any\ncoefficient-based `@nirs4all/methods` model supported by `fitModel()` and\n`predictModel()`. Supply `modelType` (for example, `\"PLSRegression\"` or\n`\"Ridge\"`) and an initialized Methods binding. DAG-ML params may contain\n`n_components` and the Methods positional `params` vector. The adapter has\nno numerical implementation; its fold predictions are tested against direct\nMethods WASM calls at a `1e-12` absolute threshold.\n\n### Classical JavaScript ML and sklearn-style interfaces\n\nImport these optional adapters from `nirs4all/classic-ml`; the default entry\ndoes not load their ML dependencies.\nCall `loadMlJs()` or `loadScikitJs()` during setup, then pass the loaded module\nto the synchronous controller factory. Fit, predict and DAG-ML callbacks stay\nsynchronous for the supported controllers.\n\n`loadMlJs()` loads the optional [`ml`](https://github.com/mljs/ml) collection\non demand. `createMlJsEstimator()` wraps its random forests, decision trees and\nk-nearest-neighbor classifier with `getParams()`, `setParams()`, `fit()` and\n`predict()`. `createMlJsPca()` adds `fit()`, `transform()`, `fitTransform()` and\n`inverseTransform()` around ml.js PCA. `createMlJsController()` connects the\nsupported supervised models to the native DAG-ML fold and phase contract. The\ncollection also exposes its matrix, decomposition and statistics tools directly\nthrough `loadMlJs()`; this is a selected numerical toolbox, not the entire SciPy\nAPI. Model JSON belongs to ml.js and is not a sklearn pickle or a portable n4m\nartifact.\n\n`loadScikitJs()` loads optional `scikitjs` and a compatible TensorFlow.js 3.x\nbackend only when requested. `createScikitJsEstimator()` exposes the same\nstructural estimator/transformer interface for its classes, preserving whether\ntheir `fit()` returns immediately or returns a Promise. Its output tensors are\nconverted to ordinary arrays and disposed at the boundary. Sync decision-tree\nclassifiers and regressors can also use `createScikitJsController()` with the\nnative DAG-ML WASM scheduler. Other scikitjs estimators, including its linear\nmodels, train asynchronously and cannot run inside the current synchronous\nDAG-ML WASM callback. Their host API remains available; forcing a Promise into\nthe synchronous callback would break fold/seed validation. Scikitjs models use\n`exportModelAsync()` and `importModelAsync()` on the DAG controller because its\nserializer is asynchronous.\n\n`createScikitJsAsyncController()` is the awaiting host counterpart. It offers\n`invokeAsync()`, `fitFull()`, `predict()`, `exportModel()` and `importModel()` as\nPromises for scikitjs classes such as `LinearRegression`. A host can pass it\nnative DAG-ML `NodeTask` records and await each result. It cannot be passed to\nthe current synchronous `dag-ml-wasm` phase callback: blocking that browser\nworker would prevent the Promise from resolving. Native scheduler execution of\nthese estimators needs a DAG-ML async phase driver.\n\n| Classical ML capability | This JS package | Web pipeline catalogue |\n| --- | --- | --- |\n| ml.js random forests, CART trees, KNN classifier | Synchronous DAG-ML controllers | Five model nodes |\n| ml.js PCA | Host fit/transform/inverseTransform | No pipeline node |\n| scikitjs trees | Synchronous DAG-ML controllers | No node |\n| scikitjs async classes, e.g. LinearRegression | Awaiting host controller | No DAG-ML/Web node yet |\n| Kanaries ML | Evaluated, no binding | No node |\n| Torch, ONNX and neural-network training | No binding | No node |\n\n`docs/CLASSIC_ML_JS.md` in the Core repository records qualification evidence,\nthe portable n4m scope, and remaining gaps. ML library JSON artifacts are\nlibrary-specific; only the n4m portable subset has a cross-language binary\nand Python-oracle parity claim.\n\nBoth libraries are optional peer dependencies. They provide familiar classical\nML APIs in JavaScript, but no numerical equality with sklearn is claimed for\ntheir independently implemented algorithms. The Methods-backed portable subset\nretains its separate Python oracle gate.\n\nFor a browser-only custom host, pair this package with `nirs4all-ui`: keep\nruntime loading and portable execution in `nirs4all`, and consume shared React\ncomponents / view-model helpers / brand assets from `nirs4all-ui`. The\nreference composition lives in the `nirs4all-web` browser app, whose contract\ntests exercise `runPortablePipeline()` / `predictPortablePipeline()` together\nwith the shared UI package in a no-backend environment.\n","readmeFilename":"README.md"}