PiecewiseDirectStandardization

class chemotools.adaptation.PiecewiseDirectStandardization(window_length: int = 25, n_components: int = 2, scale: bool = True)

Bases: DocLinkMixin, OneToOneFeatureMixin, TransformerMixin, BaseEstimator

Piecewise Direct Standardization (PDS) is a transformer used for domain adaptation (calibration) applications. The transformer uses least squares to find a linear map from the target instrument space to the source instrument space, following the implementation by [1] and [2].

Parámetros:

• window_length (int) – Half-width (w) of the local spectral window used in PDS

• n_components (int) – Number of components to keep for PLS model

• scale (bool, default = True) – Whether to scale X and Y in the PLS model

Variables:

• n_features_in (int) – Number of features seen during fit (set automatically by sklearn).

• x_mean (np.ndarray of shape (n_features, 2 * window_length + 1) or None) – Mean of the local X window for each feature. None if fitted with X_source=None (identity transformation).

• coef (np.ndarray of shape (n_features, 2 * window_length + 1) or None) – Regression coefficients for each local PLS model. None if fitted with X_source=None (identity transformation).

• intercept (np.ndarray of shape (n_features,) or None) – Intercept term for each local PLS model. None if fitted with X_source=None (identity transformation).

• x_source_provided (bool) – Boolean flag indicating if X_source was provided during fitting.

Muestra:

ValueError – If X and X_source do not have the same shape.

Ver también

DirectStandardization

Global linear transformation without local windows.

Referencias

[1]

Wang, Yongdong., Veltkamp, D. J., & Kowalski, B. R. (1991), Multivariate instrument standardization, Analytical Chemistry, 63(23), Pages 2750–2756, https://doi.org/10.1021/ac00023a016.

[2]

Bouveresse, E.; Massart, D. L. (1996), Improvement of the piecewise direct standardisation procedure for the transfer of NIR spectra for multivariate calibration, Chemometrics and Intelligent Laboratory Systems, 32(2), Pages 201–213, https://doi.org/10.1016/0169-7439(95)00074-7.

Ejemplos

>>> import numpy as np
>>> from chemotools.adaptation import PiecewiseDirectStandardization
>>> rng = np.random.default_rng(42)
>>> X = rng.normal(size=(50, 100))
>>> X_source = X * 1.2 + rng.normal(0, 0.1, size=(50, 100))
>>> pds = PiecewiseDirectStandardization(window_length=5, n_components=2)
>>> pds.fit(X, X_source=X_source)
PiecewiseDirectStandardization(n_components=2, window_length=5)
>>> X_transformed = pds.transform(X)
>>> X_transformed.shape
(50, 100)

Attributes

n_features_in_
x_mean_
coef_
intercept_
x_source_provided_

n_features_in_: int

x_mean_: ndarray | None

coef_: ndarray | None

intercept_: ndarray | None

x_source_provided_: bool

fit(X: ndarray, y=None, *, X_source: ndarray | None = None) → PiecewiseDirectStandardization

Fit the PiecewiseDirectStandardization to the input data.

Parámetros:

• X (np.ndarray of shape (n_samples, n_features)) – Data from the target instrument.

• y (None) – Ignored to align with API.

• X_source (np.ndarray of shape (n_samples, n_features), optional) – Data from the source instrument. If None, the transformer defaults to an identity transformation.

Devuelve:

self

Tipo del valor devuelto:

PiecewiseDirectStandardization

transform(X) → ndarray

Use the trained model to transform the source data

Parámetros:

X (np.ndarray of shape (n_samples, n_features)) – Input data to transform

Devuelve:

X_transformed – Data transformed

Tipo del valor devuelto:

np.ndarray of shape (n_samples, n_features)

set_fit_request(*, X_source: bool | None | str = '$UNCHANGED$') → PiecewiseDirectStandardization

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config()). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

• True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

• False: metadata is not requested and the meta-estimator will not pass it to fit.

• None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

• str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parámetros:

X_source (str, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED) – Metadata routing for X_source parameter in fit.

Devuelve:

self – The updated object.

Tipo del valor devuelto:

object