Feature Selection

GeneticFeatureSelectionCV

• n_gen: int

  • Maximum number of generation (or loop) GenesSearchCV will run.

• initialization_fn

  • Class object to generate solution candidates.

• fitness_fn

  • Class object to evalute the fitness of solution candidates.

• selection_fn

  • Class object to evalute the fitness of solution candidates.

  • Can either be:

    • optimization.selection.RankSelection,

      • optimization.selection.RouletteWheelSelection,

      • optimization.selection.SteadyStateSelection,

      • optimization.selection.TournamentSelection,

      • optimization.selection.StochasticUniversalSampling,

      • optimization.selection.BoltzmannSelection

• mating_fn

  • Class object to pair the solution candidates for reproduction.

• reproduction_fn

  • Class object to reproduce child population.

  • Can either be

    • optimization.reproduction.KPointCrossover,

    • optimization.reproduction.LinearCombinationCrossover,

    • optimization.reproduction.FitnessProportionateAverage

• mutation_fn

  • Class object to mutate the child population.

  • Can either be

    • optimization.mutation.Boundary,

    • optimization.mutation.Shrink

• ``adaptive_population``=None

  • Class object to adaptively change the mating rate of the mating_fn.

• ``elitism``=None

  • Class object to perform elites selection, ace comparison and elites’ traits induction.

• ``adaptive_mutation``=None

  • Class object to adaptively change the mutation probaility of the mutation_fn.

Initialization

Genes

• search_space: dict

  • Defines the search range of the algorithm. Where keys are parameter names (strings) and values are int, float or str. Represents search spaceover parameters of the provided estimator.

• pop_size: int

  • Size of the initial population.

Evaluation

FitnessFunction

• estimator: BaseEstimator

  • A object of that type is instantiated for each search point. This object is assumed to implement the scikit-learn estimator api. Either estimator needs to provide a score function, or scoring must be passed.

• 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 in a (Stratified)KFold,

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

    • An iterable yielding train, test splits.

For integer/None inputs, if the estimator is a classifier and y is either binary or multiclass, StratifiedKFold is used. In all other cases, KFold is used.

• scoring: str

  • callable or None, default=None A string (see model evaluation documentation) or a scorer callable object / function with signature scorer(estimator, X, y). If None, the score method of the estimator is used.

Selection

RankSelection

• pct_survivors: int, float

  • Argument that controls the number of survivors.

RouletteWheelSelection

• pct_survivors: int, float

  • Argument that controls the number of survivors.

SteadyStateSelection

• elimination_ratio: float [default=.3]

  • Determine how many candidates are eliminated.

TournamentSelection

• k: int [default=2]

  • Argument that controls the number of participants in each tourament.

• preserve_remainders: bool [default=True]

  • If True, the remaining individuals not selected for tournament will survive the selection process.

StochasticUniversalSampling

• pct_survivors: int, float

  • Argument that controls the number of survivors.

BoltzmannSelection

• pct_survivors: float

  • Argument that controls the number of survivors.

• T0: int, float

  • Initial Temperature to calculate Boltzmann probability. A number between [5, 100].

• a: int, float

  • Alpha, a constant between [0, 1].

Mating

MatingFunction

• cr_proba: int, float [default=1]

  • Percentage of survived population. Determines how many couples are paired during mating.

• increst_prevention: bool [default=True]

  • If True, solution candidates sharing the same parents will be paired together.

Reproduction

KPointCrossover

• k: int

  • Number of times of the chromosomes being splitted.

• c_pt: int, str [default=’random’]

  • If int, c_pt will be the position index of the splitting points. If str, the splitting point location where be randomly determined. If ‘random’, the splitting point will be randomly picked.

Mutation

BitStringMutation

• epsilon: float [default=.15]

  • Mutation rate that determines if genes will mutate or not.

ExchangeMutation

• epsilon: float [default=.15]

  • Mutation rate that determines if genes will mutate or not.

ShiftMutation

• epsilon: float [default=.15]

  • Mutation rate that determines if genes will mutate or not.

Environment

AdaptiveReproduction

• pop_cap: int [default=None]

  • Maximum population size.

AdaptiveMutation

• a: int, float [default=.2]

  • Alpha, a constant to adjust the self-adaptive mutation rate.

Elitism

• pct: int, float [default=.05]

  • Percentage of population being selected as elites.