featuristic.selection.GeneticFeatureSelector

class featuristic.selection.GeneticFeatureSelector(objective_function: Callable, population_size: int = 50, max_generations: int = 100, tournament_size: int = 10, crossover_proba: float = 0.9, mutation_proba: float = 0.1, early_termination_iters: int = 15, n_jobs: int = -1, pbar: bool = True, verbose: bool = False)

The Genetic Feature Selector class uses genetic programming to select the best features to minimise a given objective function. This is done by initially building a population of naive random selection of the available features. The population is then evolved over a number of generations using genetic operators such as mutation and crossover to find the best combination of features to minimise the output of the objective function.

__init__(objective_function: Callable, population_size: int = 50, max_generations: int = 100, tournament_size: int = 10, crossover_proba: float = 0.9, mutation_proba: float = 0.1, early_termination_iters: int = 15, n_jobs: int = -1, pbar: bool = True, verbose: bool = False) → None

Initialize the genetic algorithm.

Parameters:

• objective_function (callable) – The cost function to minimize. Must take X and y as input and return a float. Note that the function should return a value to minimize so a smaller value is better. If you want to maximize a metric, you should multiply the output of your objective_function by -1.

• population_size (int) – The number of individuals in the population.

• max_generations (int) – The maximum number of iterations.

• crossover_proba (float) – The probability of crossover.

• mutation_proba (float) – The probability of mutation.

• early_termination_iters (int) – The number of iterations to wait for early termination.

• n_jobs (int) – The number of parallel jobs to run. If -1, use all available cores else uses the minimum of n_jobs and cpu_count.

• verbose (bool) – Whether to print progress.

Methods

__init__(objective_function[, ...])	Initialize the genetic algorithm.
fit(X, y)	Determine the optimal feature selection using a genetic algorithm.
fit_transform(X, y)	Fit the genetic algorithm and return the selected features.
get_metadata_routing()	Get metadata routing of this object.
get_params([deep])	Get parameters for this estimator.
plot_history([ax])	Plot the history of the fitness function.
set_output(*[, transform])	Set output container.
set_params(**params)	Set the parameters of this estimator.
transform(X[, y])	Transform the input features to the selected features.