PolynomialCorrection#

class chemotools.baseline.PolynomialCorrection(order: int = 1, indices: list | None = None)[source]

Bases: TransformerMixin, OneToOneFeatureMixin, BaseEstimator

A transformer that subtracts a polynomial baseline from the input data. The polynomial is fitted to the points in the spectrum specified by the indices parameter.

Parameters:

  • order (int, optional, default=1) – The order of the polynomial to fit to the baseline. Defaults to 1.

  • indices (list, optional, default=None) – The indices of the points in the spectrum to fit the polynomial to. Defaults to None, which fits the polynomial to all points in the spectrum (equivalent to detrend).

Variables:

  • n_features_in (int) – The number of features in the input data.

  • indices (list) – The indices of the points in the spectrum to fit the polynomial to. If indices is None, this will be a list of all indices in the spectrum.

Examples

>>> from chemotools.baseline import PolynomialCorrection
>>> from chemotools.datasets import load_fermentation_train
>>> # Load sample data
>>> X, _ = load_fermentation_train()
>>> # Instantiate the transformer
>>> transformer = PolynomialCorrection(order=2, indices=[0, 100, 200, 300, 400, 500])
PolynomialCorrection()
>>> transformer.fit(X)
>>> # Generate baseline-corrected data
>>> X_corrected = transformer.transform(X)
fit(X: ndarray, y=None) → PolynomialCorrection[source]

Fit the transformer to the input data.

Parameters:

  • X (np.ndarray of shape (n_samples, n_features)) – The input data to fit the transformer to.

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

Returns:

self – The fitted transformer.

Return type:

PolynomialCorrection

transform(X: ndarray, y=None) → ndarray[source]

Transform the input data by subtracting the polynomial baseline.

Parameters:

  • X (np.ndarray of shape (n_samples, n_features)) – The input data to transform.

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

Returns:

X_transformed – The transformed data.

Return type:

np.ndarray of shape (n_samples, n_features)

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