Operator Catalog

This catalog enumerates every pipeline operator exposed across the nirs4all backend and the nirs4all_ui component library after the October 2025 restructuring. The component manifest is generated from scripts/generate_component_library.py, which populates public/component-library.json by introspecting the codebase and the installed scikit-learn distribution.

Category Overview

Category	Feather icon	Subcategories	Components	Description
Augmentation	GitBranch	1	8	Spectral augmentation operators to improve robustness
Spectral Preprocessing	Sliders	7	18	Baseline, scatter, smoothing, derivatives, and spectral transforms
Feature Engineering	Layers	7	45	scikit-learn TransformerMixin utilities and feature builders
Dimension Reduction	Minimize2	3	39	Operators that change feature dimensionality
Classical Models	BarChart2	16	99	scikit-learn estimators (regressors, classifiers, wrappers)
Deep Learning	Cpu	1	29	TensorFlow models bundled with nirs4all
Validation & Splitting	DivideSquare	1	17	Cross-validation and sampling strategies
Target Processing	Crosshair	1	2	Transformations applied to the target variable (y)
Prediction & Outputs	Target	1	3	Prediction helpers and probability calibration
Pipeline Utilities	Box	3	11	Containers, generators, and visualization helpers

Augmentation

• Spectral Augmentation – Rotate & Translate, Random X Operation, Spline Smoothing, Spline X Perturbations, Spline Y Perturbations, Spline X Simplification, Spline Curve Simplification, Identity Augmenter

Spectral Preprocessing

• Baseline Correction – Baseline Removal, Detrend

• Scatter & Normalization – MSC, Standard Normal Variate, Robust Normal Variate

• Smoothing – Savitzky-Golay, Gaussian Filter

• Derivatives – First Derivative, Second Derivative, Sample Derivative

• Spectral Transforms – Wavelet Transform, Haar Wavelet, Log Transform

• NIRS Scaling – Normalize Rows, Simple Scale

• Resampling & Alignment – Adaptive Resampler, Crop Transformer, Resample Transformer

Feature Engineering (scikit-learn TransformerMixin)

• scikit-learn Scalers – Binarizer, FunctionTransformer, KernelCenterer, MaxAbsScaler, MinMaxScaler, Normalizer, PolynomialFeatures, PowerTransformer, QuantileTransformer, RobustScaler, SplineTransformer, StandardScaler

• Encoding & Binning – KBinsDiscretizer, LabelBinarizer, LabelEncoder, MultiLabelBinarizer, OneHotEncoder, OrdinalEncoder, TargetEncoder

• Imputation – KNNImputer, MissingIndicator, SimpleImputer

• Dimensionality Reduction – CCA, DictionaryLearning, FactorAnalysis, FastICA, IncrementalPCA, Isomap, KernelPCA, LatentDirichletAllocation, LocallyLinearEmbedding, MiniBatchDictionaryLearning, MiniBatchNMF, MiniBatchSparsePCA, NMF, PCA, PLSCanonical, PLSRegression, PLSSVD, SparseCoder, SparsePCA, TSNE, TruncatedSVD

• Feature Selection – GenericUnivariateSelect, RFE, RFECV, SelectFdr, SelectFpr, SelectFromModel, SelectFwe, SelectKBest, SelectPercentile, SequentialFeatureSelector, VarianceThreshold

• Kernel & Projection – AdditiveChi2Sampler, GaussianRandomProjection, Nystroem, PolynomialCountSketch, RBFSampler, SkewedChi2Sampler, SparseRandomProjection

• Feature Extraction – DictVectorizer, FeatureHasher, HashingVectorizer, PatchExtractor, TfidfTransformer

• Cluster & Neighbors – Birch, BisectingKMeans, FeatureAgglomeration, KMeans, KNeighborsTransformer, MiniBatchKMeans, NeighborhoodComponentsAnalysis, RadiusNeighborsTransformer

• Meta Transformers – ColumnTransformer, FeatureUnion, RandomTreesEmbedding, StackingClassifier, StackingRegressor, VotingClassifier, VotingRegressor

• Miscellaneous Transformers – BernoulliRBM, IsotonicRegression, LinearDiscriminantAnalysis

Dimension Reduction

• Dimensionality Reduction - CCA, DictionaryLearning, FactorAnalysis, FastICA, IncrementalPCA, Isomap, KernelPCA, LatentDirichletAllocation, LocallyLinearEmbedding, MiniBatchDictionaryLearning, MiniBatchNMF, MiniBatchSparsePCA, NMF, PCA, PLSCanonical, PLSRegression, PLSSVD, SparseCoder, SparsePCA, TSNE, TruncatedSVD

• Feature Selection - GenericUnivariateSelect, RFE, RFECV, SelectFdr, SelectFpr, SelectFromModel, SelectFwe, SelectKBest, SelectPercentile, SequentialFeatureSelector, VarianceThreshold

• Kernel & Projection - AdditiveChi2Sampler, GaussianRandomProjection, Nystroem, PolynomialCountSketch, RBFSampler, SkewedChi2Sampler, SparseRandomProjection

Note: Transformer lists are derived automatically via sklearn.utils.all_estimators(type_filter="transformer"), ensuring parity with the installed scikit-learn version.

Classical Models (scikit-learn estimators)

• Baseline Models – DummyClassifier, DummyRegressor

• Cross Decomposition – CCA, PLSCanonical, PLSRegression

• Decision Trees – DecisionTreeClassifier, DecisionTreeRegressor, ExtraTreeClassifier, ExtraTreeRegressor

• Discriminant Analysis – LinearDiscriminantAnalysis, QuadraticDiscriminantAnalysis

• Ensemble Methods – AdaBoostClassifier, AdaBoostRegressor, BaggingClassifier, BaggingRegressor, ExtraTreesClassifier, ExtraTreesRegressor, GradientBoostingClassifier, GradientBoostingRegressor, HistGradientBoostingClassifier, HistGradientBoostingRegressor, RandomForestClassifier, RandomForestRegressor, StackingClassifier, StackingRegressor, VotingClassifier, VotingRegressor

• Gaussian Process – GaussianProcessClassifier, GaussianProcessRegressor

• Kernel Ridge & Friends – KernelRidge

• Linear Models – ARDRegression, BayesianRidge, ElasticNet, ElasticNetCV, GammaRegressor, HuberRegressor, Lars, LarsCV, Lasso, LassoCV, LassoLars, LassoLarsCV, LassoLarsIC, LinearRegression, LogisticRegression, LogisticRegressionCV, MultiTaskElasticNet, MultiTaskElasticNetCV, MultiTaskLasso, MultiTaskLassoCV, OrthogonalMatchingPursuit, OrthogonalMatchingPursuitCV, PassiveAggressiveClassifier, PassiveAggressiveRegressor, Perceptron, PoissonRegressor, QuantileRegressor, RANSACRegressor, Ridge, RidgeCV, RidgeClassifier, RidgeClassifierCV, SGDClassifier, SGDRegressor, TheilSenRegressor, TweedieRegressor

• Meta Estimators – ClassifierChain, MultiOutputClassifier, MultiOutputRegressor, OneVsOneClassifier, OneVsRestClassifier, OutputCodeClassifier, RegressorChain, TransformedTargetRegressor

• Naive Bayes – BernoulliNB, CategoricalNB, ComplementNB, GaussianNB, MultinomialNB

• Nearest Neighbors – KNeighborsClassifier, KNeighborsRegressor, NearestCentroid, RadiusNeighborsClassifier, RadiusNeighborsRegressor

• Neural Networks (sklearn) – MLPClassifier, MLPRegressor

• Probabilistic & Calibration – CalibratedClassifierCV, FixedThresholdClassifier, IsotonicRegression, TunedThresholdClassifierCV

• Semi-supervised – LabelPropagation, LabelSpreading, SelfTrainingClassifier

• Support Vector Machines – LinearSVC, LinearSVR, NuSVC, NuSVR, SVC, SVR

• Miscellaneous Models – (currently empty; all estimators are classified above)

Note: Model listings are produced via sklearn.utils.all_estimators for both classifiers and regressors, preserving compatibility with the runtime environment.

Deep Learning (TensorFlow)

CONV_LSTM, Custom_Residuals, Custom_VG_Residuals, Custom_VG_Residuals2, FFT_Conv, MLP, ResNetV2_model, SEResNet_model, UNET, UNet_NIRS, VGG_1D, XCeption1D, bard, customizable_decon, customizable_nicon, customizable_nicon_classification, decon, decon_classification, decon_layer_classification, inception1D, nicon, nicon_VG, nicon_VG_classification, nicon_classification, senseen_origin, transformer, transformer_VG, transformer_VG_classification, transformer_classification

These factories are tagged via the framework("tensorflow") decorator and are surfaced under the TensorFlowModelController.

Validation & Splitting

• Splitting Strategies - Shuffle Split, K-Fold, Stratified K-Fold, Repeated K-Fold, Repeated Stratified K-Fold, Group K-Fold, Group Shuffle Split, Stratified Shuffle Split, Time Series Split, Leave-One-Out, Leave-P-Out, Kennard-Stone Splitter, SPXY Splitter, KMeans Splitter, SPlit Splitter, Systematic Circular, KBins Stratified

Target Processing

• Target Transforms – Integer KBins Discretizer, Range Discretizer

Prediction & Outputs

• Prediction Utilities – Batch Prediction, Real-time Prediction, Probability Calibration

Pipeline Utilities

• Containers - Feature Augmentation, Sample Augmentation, Sequential, Pipeline, Y Processing (augmentation/processing containers accept only preprocessing, augmentation, or feature-engineering nodes; Y Processing is limited to target transforms; Pipeline remains unrestricted)

• Generators – _OR_, _RANGE_ (parameter sweep and branching)

• Visualization – 2D Chart, Y Distribution Chart, Fold Chart

Maintenance Notes

• Run python scripts/generate_component_library.py from nirs4all_ui to regenerate the library after adding new operators. The script introspects nirs4all modules and calls sklearn.utils.all_estimators to keep the catalog aligned with the installed version.

• UI components consume public/component-library.json; the pipeline editor reads this file via libraryDataLoader.

• Container rules use category tokens (e.g. category:preprocessing) so augmentation and preprocessing containers reject models or incompatible nodes while Pipeline stays unrestricted.

• If new TensorFlow models are added under nirs4all.operators.models, ensure they carry the framework("tensorflow") decorator so they are picked up automatically.