Augmentation Operators Reference

An augmentation operator perturbs spectra. The sample-augmentation node creates origin-linked training copies; the feature-augmentation node creates representations. Choose the physical change you expect at deployment before selecting noise, drift, wavelength warp or masking. Check that the target remains valid.

Choose a perturbation that resembles real acquisition variability

Read sample_augmentation: give training examples plausible variants for the worked result figure, expected dimensions and exercises. Choose a family below to compare the enumerated operators.

Same worked recipe in JSON, YAML and Python

JSON

{
  "pipeline": [
    {
      "split": {
        "class": "sklearn.model_selection.KFold",
        "params": {
          "n_splits": 3
        }
      }
    },
    {
      "sample_augmentation": {
        "transformers": [
          {
            "class": "nirs4all.operators.augmentation.GaussianAdditiveNoise",
            "params": {
              "sigma": 0.01
            }
          }
        ],
        "count": 2,
        "selection": "random",
        "random_state": 42
      }
    },
    {
      "model": {
        "class": "sklearn.cross_decomposition.PLSRegression",
        "params": {
          "n_components": 2
        }
      }
    }
  ]
}

YAML

pipeline:
- split:
    class: sklearn.model_selection.KFold
    params:
      n_splits: 3
- sample_augmentation:
    transformers:
    - class: nirs4all.operators.augmentation.GaussianAdditiveNoise
      params:
        sigma: 0.01
    count: 2
    selection: random
    random_state: 42
- model:
    class: sklearn.cross_decomposition.PLSRegression
    params:
      n_components: 2

Python

from sklearn.model_selection import KFold
from sklearn.cross_decomposition import PLSRegression
from nirs4all.operators.augmentation import GaussianAdditiveNoise

pipeline = [
    KFold(n_splits=3),
    {"sample_augmentation": {
        "transformers": [GaussianAdditiveNoise(sigma=0.01)],
        "count": 2, "selection": "random", "random_state": 42,
    }},
    {"model": PLSRegression(n_components=2)},
]

These are Python SDK operators. Native R/Octave/WASM recipes use their own method IDs and facade; see 10. Run the same recipe in your language.

Noise

Class	Key Parameters	Description
`GaussianAdditiveNoise`	`sigma=0.01`, `smoothing_kernel_width=1`, `variation_scope="sample"`	Adds per-sample Gaussian noise scaled by sample std
`MultiplicativeNoise`	`sigma_gain=0.05`, `per_wavelength=False`, `variation_scope="sample"`	Multiplies spectra by random gain factor: X * (1 + epsilon)
`SpikeNoise`	`random_state=None`	Adds spike artifacts to random spectral positions
`HeteroscedasticNoiseAugmenter`	`random_state=None`, `variation_scope="sample"`	Signal-dependent detector noise (noise scales with signal magnitude)

Baseline Drift

Class	Key Parameters	Description
`LinearBaselineDrift`	`offset_range=(-0.1, 0.1)`, `slope_range=(-0.001, 0.001)`	Adds a linear baseline drift: X + a + b * lambda
`PolynomialBaselineDrift`	`degree=3`, `coeff_ranges=None`	Adds a polynomial baseline drift of given degree

Wavelength Distortion

Class	Key Parameters	Description
`WavelengthShift`	`shift_range=(-2.0, 2.0)`	Shifts the wavelength axis by a random amount (simulates calibration drift)
`WavelengthStretch`	`stretch_range=(0.99, 1.01)`	Stretches or compresses the wavelength axis around its center
`LocalWavelengthWarp`	`n_control_points=5`, `max_shift=1.0`	Non-linear warp of the wavelength axis using spline control points

Spectral Distortion

Class	Key Parameters	Description
`SmoothMagnitudeWarp`	`n_control_points=5`, `gain_range=(0.9, 1.1)`	Multiplies the spectrum by a smooth spline-based gain curve
`BandPerturbation`	`n_bands=3`, `bandwidth_range=(5, 20)`, `gain_range=(0.9, 1.1)`, `offset_range=(-0.01, 0.01)`	Perturbs random wavelength bands with gain and offset
`GaussianSmoothingJitter`	`sigma_range=(0.5, 2.0)`, `kernel_width=11`	Applies Gaussian smoothing with random sigma per sample
`UnsharpSpectralMask`	`amount_range=(0.1, 0.5)`, `sigma=1.0`, `kernel_width=11`	Spectral sharpening via unsharp masking: X + k * (X - smooth(X))
`BandMasking`	`n_bands_range=(1, 3)`, `bandwidth_range=(5, 20)`, `mode="interp"`	Masks random spectral bands; mode: `"zero"` or `"interp"`
`ChannelDropout`	`random_state=None`	Randomly zeroes out individual spectral channels
`LocalClipping`	`random_state=None`	Clips local regions of the spectrum

Mixup

Class	Key Parameters	Description
`MixupAugmenter`	`random_state=None`	Blends samples with KNN neighbors (global mixup)
`LocalMixupAugmenter`	`random_state=None`	Band-local mixup with KNN neighbors
`ScatterSimulationMSC`	`random_state=None`	Simple MSC-style scatter simulation between samples

Physical / Instrumental

Class	Key Parameters	Description
`PathLengthAugmenter`	`path_length_std=0.05`, `min_path_length=0.5`, `variation_scope="sample"`	Simulates optical path length variation via multiplicative scaling
`BatchEffectAugmenter`	`offset_std=0.02`, `slope_std=0.01`, `gain_std=0.03`, `variation_scope="sample"`	Simulates batch/session effects with wavelength-dependent offset and gain
`InstrumentalBroadeningAugmenter`	`random_state=None`, `variation_scope="sample"`	Simulates spectral resolution broadening via Gaussian convolution
`DeadBandAugmenter`	`random_state=None`	Simulates dead spectral bands from detector saturation or failure

Environmental

These operators require wavelength information and inherit from SpectraTransformerMixin.

Class	Key Parameters	Description
`TemperatureAugmenter`	`temperature_delta=5.0`, `temperature_range=None`, `reference_temperature=25.0`, `enable_shift=True`, `enable_intensity=True`, `enable_broadening=True`, `region_specific=True`	Simulates temperature-induced spectral changes (peak shifts, intensity, broadening)
`MoistureAugmenter`	`random_state=None`	Simulates moisture/water activity effects on NIR spectra

Scattering

These operators require wavelength information.

Class	Key Parameters	Description
`ParticleSizeAugmenter`	`mean_size_um=50.0`, `size_variation_um=15.0`, `size_range_um=None`, `wavelength_exponent=1.5`, `size_effect_strength=0.1`, `include_path_length=True`	Simulates particle size effects on scattering (wavelength-dependent baseline)
`EMSCDistortionAugmenter`	`random_state=None`	Applies EMSC-style polynomial scattering distortions

Edge Artifacts

These operators require wavelength information and simulate detector boundary effects.

Class	Key Parameters	Description
`DetectorRollOffAugmenter`	`detector_model="generic_nir"`, `effect_strength=1.0`, `noise_amplification=0.02`, `include_baseline_distortion=True`	Simulates detector sensitivity roll-off at spectral edges
`StrayLightAugmenter`	`random_state=None`	Simulates stray light effects (peak truncation at edges)
`EdgeCurvatureAugmenter`	`random_state=None`	Simulates edge curvature/baseline bending from optical aberrations
`TruncatedPeakAugmenter`	`random_state=None`	Adds truncated peaks at spectral boundaries
`EdgeArtifactsAugmenter`	`random_state=None`	Combined edge artifacts augmenter (applies multiple edge effects)

Available detector models for DetectorRollOffAugmenter:

"ingaas_standard" – Standard InGaAs (1000-1600 nm optimal)
"ingaas_extended" – Extended InGaAs (1100-2200 nm optimal)
"pbs" – PbS detector (1000-2800 nm optimal)
"silicon_ccd" – Silicon CCD (400-900 nm optimal)
"generic_nir" – Generic NIR detector (900-1700 nm optimal)

Spline-Based

Class	Key Parameters	Description
`Spline_Smoothing`	`random_state=None`	Applies a smoothing spline to each spectrum
`Spline_X_Perturbations`	`random_state=None`	Perturbs the wavelength (x) axis via B-spline control points
`Spline_Y_Perturbations`	`random_state=None`	Perturbs the intensity (y) axis via B-spline control points
`Spline_X_Simplification`	`random_state=None`	Simplifies the spectrum along the x-axis using spline resampling
`Spline_Curve_Simplification`	`random_state=None`	Simplifies the spectrum along the curve length using spline resampling

Random Geometric

Class	Key Parameters	Description
`Rotate_Translate`	`p_range=2`, `y_factor=3`	Rotation and translation augmentation: adds piecewise linear distortion
`Random_X_Operation`	`random_state=None`	Random multiplicative/additive operations on spectra

What the perturbations mean

The original synthetic spectrum shares peak positions with versions perturbed by additive noise and by a gain plus offset. — Illustrative acquisition changes. Copies share the original sample’s identity and target assumptions; they do not add independent validation evidence.

Mechanism	Operators	Interpretation and important limit
Detector noise	GaussianAdditiveNoise, HeteroscedasticNoiseAugmenter	Constant-relative versus signal-dependent uncertainty; too much noise erases weak peaks
Gain/scatter	MultiplicativeNoise, PathLengthAugmenter, ScatterSimulationMSC	Alters intensity without moving peaks; may conflict with a concentration target if used beyond plausible acquisition variability
Baseline	LinearBaselineDrift, PolynomialBaselineDrift, BatchEffectAugmenter	Adds nuisance offset/slope/curvature; batch-wide perturbations differ from independent sample noise
Calibration drift	WavelengthShift, WavelengthStretch, LocalWavelengthWarp	Moves spectral features; shift/scale/warp have different geometry and boundary behavior
Local amplitude	SmoothMagnitudeWarp, BandPerturbation	Alters broad envelopes or particular bands; can change the chemical meaning of a band
Resolution	GaussianSmoothingJitter, InstrumentalBroadeningAugmenter, UnsharpSpectralMask	Blur or sharpen local detail; broadening is a more direct resolution model than arbitrary sharpening
Missing/saturated channels	BandMasking, ChannelDropout, DeadBandAugmenter, LocalClipping, SpikeNoise	Simulates failures rather than an alternative physical sample; zero masking and interpolation produce different signatures
Inter-sample mixing	MixupAugmenter, LocalMixupAugmenter	Requires a meaningful mixed-target assumption; nearest-neighbor structure and source origins matter
Spline perturbations	Spline_Smoothing, Spline_X_Perturbations, Spline_Y_Perturbations, Spline_X_Simplification, Spline_Curve_Simplification	Changes curve smoothness, axis, amplitude or representation; distinguish displacement from simplification
Environmental changes	TemperatureAugmenter, MoistureAugmenter	Models effects associated with temperature/water; those effects may also affect the true target
Particle scattering	ParticleSizeAugmenter, EMSCDistortionAugmenter	Empirical wavelength-dependent scattering; not a complete physical scattering simulation
Detector edges	DetectorRollOffAugmenter, StrayLightAugmenter, EdgeCurvatureAugmenter, TruncatedPeakAugmenter, EdgeArtifactsAugmenter	Models edge sensitivity, flattening, curvature or truncated peaks; detector range and wavelength units are essential
General geometry	Rotate_Translate, Random_X_Operation	Broad numerical transformations; justify their relation to actual instrument variability

These mechanisms are hypotheses about deployment variability. Use an unchanged baseline and the same independent-unit folds to test each hypothesis. A visibly plausible curve does not establish target-label validity or improved accuracy.

Sample versus feature augmentation

Training copies keep origin identities while validation remains unchanged. — Educational workflow result; read the accompanying explanation for scope and interpretation.

Sample augmentation increases training observations; feature augmentation adds representations of each observation. Prediction must replay the feature construction needed by the fitted model, while training-only sample generation is skipped. The container, rather than the augmentation class name alone, determines workflow semantics. See sample_augmentation: give training examples plausible variants and feature_augmentation: retain several views of the same observations for layout, counts and replay rules.

Augmented rows must remain with their original in every train/validation split. If an operator combines several origins, its relation must be accounted for; arbitrary random splitting after augmentation creates leakage. The reported sample count should distinguish independent samples from generated rows.

Diagnose an augmentation experiment

Inspect before/after overlays, peak positions, signal ranges and target distribution. Check that held-out rows are unchanged and original-to-copy relations are recorded. Compare per-origin errors, not only errors averaged over generated rows. If augmentation helps one instrument but harms another, report the subgroup effect and revisit the assumed perturbation distribution. For parameter and method details use nirs4all.operators.augmentation under API Reference.