auswahl.IPLS

class auswahl.IPLS(n_intervals_to_select: int = 1, interval_width: Optional[Union[int, float]] = None, n_cv_folds: int = 10, pls: Optional[PLSRegression] = None, n_jobs: int = 1, model_hyperparams: Optional[Union[Dict, List[Dict]]] = None, random_state: Optional[Union[int, RandomState]] = None)

Interval selection with Interval Partial Least Squares (iPLS).

The optimal interval of a specified width is calculated according to Norgaard et al. [1].

Read more in the User Guide.

Parameters

interval_widthint, default=None

Width of the interval to select.

n_cv_foldsint, default=10

Number of cross validation folds used to evaluate intervals

plsPLSRegression, default=None

Estimator instance of the PLSRegression class. Use this to adjust the hyperparameters of the PLS method.

Attributes

support_ndarray of shape (n_features,)

Mask of the selected interval.

score_float

Cross validation score of the interval selected.

References

1

L. Nogaard, A. Saudland, J. Wagner, J. P. Nielsen, L. Munck, S. B. Engelsen, ‘Interval Partial Least-Squares Regression (iPLS): A comparative chemometric study with an example from Near-Infrared Spectrocopy’ Applied Spectrosopy, Volume 54, Nr. 3, 413–419, 2000.

Examples

>>> import numpy as np
>>> from auswahl import IPLS
>>> X = np.random.randn(100, 10)
>>> y = 5 * X[:, 3] - 2 * X[:, 4]  # y only depends on two features
>>> selector = IPLS(interval_width=2)
>>> selector.fit(X, y).get_support()
array([False, False, False, True, True, False, False, False, False, False])

__init__(n_intervals_to_select: int = 1, interval_width: Optional[Union[int, float]] = None, n_cv_folds: int = 10, pls: Optional[PLSRegression] = None, n_jobs: int = 1, model_hyperparams: Optional[Union[Dict, List[Dict]]] = None, random_state: Optional[Union[int, RandomState]] = None)

evaluate(X, y, model, do_cv=True, *args)

Conduct a cross validationand hyperparameter optimization of the underlying estimator model.

Parameters

X: array-like, shape (n_samples, n_features)

Spectral data to be fitted

y: array-like, shape (n_samples,)

Regression targets

model: BaseEstimator

Regression model

do_cv: bool, default=True

If True, the model is fitted to the data and a cross validation score is provided

*args: arbitrary payload

Arbitrary payload returned with the evaluation result. Used for instance for identification of threads, if multiple models are evaluated in parallel

Returns

tuple: float, BaseEstimator

cross validation score if requested (otherwise None) and fitted estimator

fit(X, y, mask=None)

Run the feature selection process.

Parameters

Xarray-like of shape (n_samples, n_features)

The input samples.

yarray-like of shape (n_samples,)

The target values.

mask: array-like of shape (n_features,)

Mask indicating (values == 0), which features are not to be taken into account during the feature selection

Returns

SpectralSelectorself

Returns the instance itself.

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

Xarray-like of shape (n_samples, n_features)

Input samples.

yarray-like of shape (n_samples,) or (n_samples, n_outputs), default=None

Target values (None for unsupervised transformations).

**fit_paramsdict

Additional fit parameters.

Returns

X_newndarray array of shape (n_samples, n_features_new)

Transformed array.

get_best_estimator() → BaseEstimator

Retrieve the best estimator model fitted on the selected features

Returns

best model fitted on selected features: sklearn.base.BaseEstimator

get_feature_names_out(input_features=None)

Mask feature names according to selected features.

Parameters

input_featuresarray-like of str or None, default=None

Input features.

• If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: [“x0”, “x1”, …, “x(n_features_in_ - 1)”].

• If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns

feature_names_outndarray of str objects

Transformed feature names.

get_params(deep=True)

Get parameters for this estimator.

Parameters

deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns

paramsdict

Parameter names mapped to their values.

get_support(indices=False)

Get a mask, or integer index, of the features selected.

Parameters

indicesbool, default=False

If True, the return value will be an array of integers, rather than a boolean mask.

Returns

supportarray

An index that selects the retained features from a feature vector. If indices is False, this is a boolean array of shape [# input features], in which an element is True iff its corresponding feature is selected for retention. If indices is True, this is an integer array of shape [# output features] whose values are indices into the input feature vector.

inverse_transform(X)

Reverse the transformation operation.

Parameters

Xarray of shape [n_samples, n_selected_features]

The input samples.

Returns

X_rarray of shape [n_samples, n_original_features]

X with columns of zeros inserted where features would have been removed by transform().

reseed(seed: Union[int, RandomState])

Random state updating interface for benchmarking. Selector methods with more complex internal structure (such as methods wrapping other methods) are required to override this function accordingly.

rethread(n_jobs: int)

n_jobs updating interface for benchmarking. Selector methods with more complex internal structure (such as methods wrapping other methods) are required to override this function accordingly.

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

**paramsdict

Estimator parameters.

Returns

selfestimator instance

Estimator instance.

transform(X)

Reduce X to the selected features.

Parameters

Xarray of shape [n_samples, n_features]

The input samples.

Returns

X_rarray of shape [n_samples, n_selected_features]

The input samples with only the selected features.

Examples using auswahl.IPLS

IPLS - Basic example

IPLS - Basic example