API Reference

class genetic_selection.GeneticSelectionCV(estimator, cv=None, scoring=None, fit_params=None, max_features=None, min_features=None, verbose=0, n_jobs=1, n_population=300, crossover_proba=0.5, mutation_proba=0.2, n_generations=40, crossover_independent_proba=0.1, mutation_independent_proba=0.05, tournament_size=3, n_gen_no_change=None, caching=False)[source]

Feature selection with genetic algorithm.

Parameters

  • estimator (object) – A supervised learning estimator with a fit method.

  • cv (int, cross-validation generator or an iterable, optional) –

    Determines the cross-validation splitting strategy. Possible inputs for cv are:

    • None, to use the default 3-fold cross-validation,

    • integer, to specify the number of folds.

    • An object to be used as a cross-validation generator.

    • An iterable yielding train/test splits.

    For integer/None inputs, if y is binary or multiclass, StratifiedKFold used. If the estimator is a classifier or if y is neither binary nor multiclass, KFold is used.

  • scoring (string, callable or None, optional, default: None) – A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y).

  • fit_params (dict, optional) – Parameters to pass to the fit method.

  • max_features (int or None, optional) – The maximum number of features selected.

  • min_features (int or None, optional) – The minimum number of features selected.

  • verbose (int, default=0) – Controls verbosity of output.

  • n_jobs (int, default 1) – Number of cores to run in parallel. Defaults to 1 core. If n_jobs=-1, then number of jobs is set to number of cores.

  • n_population (int, default=300) – Number of population for the genetic algorithm.

  • crossover_proba (float, default=0.5) – Probability of crossover for the genetic algorithm.

  • mutation_proba (float, default=0.2) – Probability of mutation for the genetic algorithm.

  • n_generations (int, default=40) – Number of generations for the genetic algorithm.

  • crossover_independent_proba (float, default=0.1) – Independent probability for each attribute to be exchanged, for the genetic algorithm.

  • mutation_independent_proba (float, default=0.05) – Independent probability for each attribute to be mutated, for the genetic algorithm.

  • tournament_size (int, default=3) – Tournament size for the genetic algorithm.

  • n_gen_no_change (int, default None) – If set to a number, it will terminate optimization when best individual is not changing in all of the previous n_gen_no_change number of generations.

  • caching (boolean, default=False) – If True, scores of the genetic algorithm are cached.

n_features_

The number of selected features with cross-validation.

Type

int

support_

The mask of selected features.

Type

array of shape [n_features]

generation_scores_

The maximum cross-validation score for each generation.

Type

array of shape [n_generations]

estimator_

The external estimator fit on the reduced dataset.

Type

object

Examples

An example showing genetic feature selection.

>>> import numpy as np
>>> from sklearn import datasets, linear_model
>>> from genetic_selection import GeneticSelectionCV
>>> iris = datasets.load_iris()
>>> E = np.random.uniform(0, 0.1, size=(len(iris.data), 20))
>>> X = np.hstack((iris.data, E))
>>> y = iris.target
>>> estimator = linear_model.LogisticRegression(solver="liblinear", multi_class="ovr")
>>> selector = GeneticSelectionCV(estimator, cv=5)
>>> selector = selector.fit(X, y)
>>> selector.support_ 
array([ True  True  True  True False False False False False False False False
       False False False False False False False False False False False False], dtype=bool)
fit(X, y, groups=None)[source]

Fit the GeneticSelectionCV model and the underlying estimator on the selected features.

Parameters

  • X ({array-like, sparse matrix}, shape = [n_samples, n_features]) – The training input samples.

  • y (array-like, shape = [n_samples]) – The target values.

  • groups (array-like, shape = [n_samples], optional) – Group labels for the samples used while splitting the dataset into train/test set. Only used in conjunction with a “Group” cv instance (e.g., GroupKFold).

predict(X)[source]

Reduce X to the selected features and then predict using the underlying estimator.

Parameters

X (array of shape [n_samples, n_features]) – The input samples.

Returns

y – The predicted target values.

Return type

array of shape [n_samples]

score(X, y)[source]

Reduce X to the selected features and return the score of the underlying estimator.

Parameters

  • X (array of shape [n_samples, n_features]) – The input samples.

  • y (array of shape [n_samples]) – The target values.

set_fit_request(*, groups: Union[bool, None, str] = '$UNCHANGED$') → GeneticSelectionCV

Request metadata passed to the fit method.

Note that this method is only relevant if enable_metadata_routing=True (see sklearn.set_config()). Please see 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.

New in version 1.3.

Note

This method is only relevant if this estimator is used as a sub-estimator of a meta-estimator, e.g. used inside a Pipeline. Otherwise it has no effect.

Parameters

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

Returns

self – The updated object.

Return type

object