auswahl.SPA

class auswahl.SPA(n_features_to_select: Optional[int] = None, n_cv_folds: int = 5, pls: Optional[PLSRegression] = None, n_jobs: int = 1, model_hyperparams: Optional[Union[Dict, List[Dict]]] = None)[source]

Feature selection with the Successive Projection Algorithm (SPA).

The Successive Projections Algorithm conducts feature selection according to Araújo et al. [1]. The algorithm aims to find a set of features exhibiting minimal collinearity.

Read more in the User Guide.

Parameters
n_features_to_selectint, default=None

Upper bound of features to select.

n_cv_foldsint, default=5

Number of cross validation folds used in the evaluation of feature sets.

plsPLSRegression, default=None

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

n_jobsint, default=1

Number of jobs used for parallel calculation of SPA

Attributes
support_ndarray fo shape (n_features,)

Mask of selected features

References

1

Mário César Ugulino Araújo,Teresa Cristina Bezerra Saldanha, Roberto Kawakami Harrop Galvao, Takashi Yoneyama, Henrique Caldas Chame and Valeria Visani, The successive projections algorithm for variable selection in spectroscopic multicomponent analysis, Chemometrics and Intelligent Laboratory Systems, 57, 65-73, 2001

Examples

>>> import numpy as np
>>> from auswahl import SPA
>>> np.random.seed(1337)
>>> X = np.random.randn(1000, 10)
>>> y = 5 * X[:, 0] - 2 * X[:, 5]  # y only depends on two features
>>> selector = SPA(n_features_to_select=2)
>>> selector.fit(X, y).get_support()
array([ True, False, False, False, False, True, False, False, False, False])
__init__(n_features_to_select: Optional[int] = None, n_cv_folds: int = 5, pls: Optional[PLSRegression] = None, n_jobs: int = 1, model_hyperparams: Optional[Union[Dict, List[Dict]]] = None)[source]
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.SPA

SPA - Basic example

SPA - Basic example

SPA - Basic example