chemometrics.IHM

class chemometrics.IHM(features, peak_parameters, bl_order=2, spectra_generator=<function pseudo_voigt_spectra>, method='LG', gradient_truncation=20)

Bases: TransformerMixin, MultiOutputMixin, BaseEstimator

Indirect Hard Modeling (IHM) without linear regression

IHM models spectra based on a mechanistic model of multiple pure component spectra each consisting of flexible peaks. The spectra are described by the peak parameters. For new spectra, the mechanistic spectral model is adjusted by a parameter optimization. This allows to correct for a variety of effects such as instrument specific shifts or sensor variability. IHM returns the parameters of the fitted model.

Parameters

• features (ndarray of shape (n_features, 1), default=None) – feature-related x variable

• peak_parameters (list of ndarrays) – List of peak parameter arrays

• bl_order (int (default: 2)) – Order of background polynome

• spectra_generator (function, default=pseudo_voigt_spectra) – Reference to spectra-generating function

• method ({'LG'}) –

  Algorithm for spectral fit:

  • ’LG’ (default): largest gradient method as descirbed in

    [EKriesten].

• gradient_truncation (int (default: 20)) – For peak_parameter fitting, only step along the most important gradient directions up to the number of directions given by gradient_truncation.

n_components_

Number of components in model

Type

int

linearized_breakpoints_

Vector which indicates at what point different sections of the linarized parameter vector end. Structure: (backkground parameters, component weights, component shifts, spectra parameters)

Type

ndarray

Notes

The current optimization strategy follows the largest gradient approach described in [EKriesten] . To reduce the complexity of the optimization problem, first global parameters are optimized (background, spectral shift, spectral weights). Peak parameters are optimized one by one depending on the gradient size up to a certain number of parameters.

features:

vector of feature or spectral dimension (e.g. wavelength, wavenumber)

peak:

a feature-associated effect described by a scaled probablity function

component:

a chemical species described by a linear combination of peaks

baseline:

slowely varying effect not associated to a specific component

spectra:

a linear combination of multiple components and baseline effects

References

Implemented according to .. [EKriesten] Kriesten et al. Chemometrics and Intelligent Laboratory

Systems 91 (2008) 181-193.

__init__(features, peak_parameters, bl_order=2, spectra_generator=<function pseudo_voigt_spectra>, method='LG', gradient_truncation=20)

Methods

__init__(features, peak_parameters[, ...])
fit(X[, y])
fit_transform(X[, y])	Fit to data, then transform it.
get_params([deep])	Get parameters for this estimator.
set_params(**params)	Set the parameters of this estimator.
transform(X[, y])	Transform spectra in IHM parameter set

fit_transform(X, y=None, **fit_params)

Fit to data, then transform it.

Fits transformer to X and y with optional parameters fit_params and returns a transformed version of X.

Parameters

• X (array-like of shape (n_samples, n_features)) – Input samples.

• y (array-like of shape (n_samples,) or (n_samples, n_outputs), default=None) – Target values (None for unsupervised transformations).

• **fit_params (dict) – Additional fit parameters.

Returns

X_new – Transformed array.

Return type

ndarray array of shape (n_samples, n_features_new)

get_params(deep=True)

Get parameters for this estimator.

Parameters

deep (bool, default=True) – If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

params – Parameter names mapped to their values.

Return type

dict

set_params(**params)

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters

**params (dict) – Estimator parameters.

Returns

self – Estimator instance.

Return type

estimator instance

transform(X, y=None)

Transform spectra in IHM parameter set