Hyperparameter Tuning

GenesSearchCV

• 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:

    • hyperparameter_tuning.selection.RankSelection,

      • hyperparameter_tuning.selection.RouletteWheelSelection,

      • hyperparameter_tuning.selection.SteadyStateSelection,

      • hyperparameter_tuning.selection.TournamentSelection,

      • hyperparameter_tuning.selection.StochasticUniversalSampling,

      • hyperparameter_tuning.selection.BoltzmannSelection

• mating_fn

  • Class object to pair the solution candidates for reproduction.

• reproduction_fn

  • Class object to reproduce child population.

  • Can either be

    • hyperparameter_tuning.reproduction.KPointCrossover,

    • hyperparameter_tuning.reproduction.LinearCombinationCrossover,

    • hyperparameter_tuning.reproduction.FitnessProportionateAverage

• mutation_fn

  • Class object to mutate the child population.

  • Can either be

    • hyperparameter_tuning.mutation.Boundary,

    • hyperparameter_tuning.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.

LinearCombinationCrossover * a: float

• Alpha, a constant to determine the scale of combinations.

FitnessProportionateAverage No parameters required to instantiate.

Mutation

Boundary * epsilon: float [default=.15]

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

Shrink * epsilon: float [default=.15]

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

• prior: str [default=’normal’]

  • Determines the probability distribution of sampling.

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.