navicatGA.alphabet_solver

Attributes

logger

Classes

AlphabetGenAlgSolver

Shared base for solvers whose chromosome genes are drawn from a

Module Contents

Bases: navicatGA.base_solver.GenAlgSolver

Shared base for solvers whose chromosome genes are drawn from a per-gene alphabet (SMILES fragments, SELFIES tokens, XYZ fragments).

Factors out the alphabet/equivalences setup, population init/refill, crossover-point sampling, and mutation logic that SmilesGenAlgSolver/SelfiesGenAlgSolver/XYZGenAlgSolver used to each reimplement independently. Subclasses still own create_offspring (crossover behaviour genuinely differs between them) and write_population (different depiction backend per representation).

Base solver class for the GA. Has the core methods and attributes that any GA run will require. However, it lacks a way to create new offspring evaluate fitness, and generate mutations. Those must be defined in a child class.

Parameters: :param n_genes: number of genes (variables) to have in each chromosome :type n_genes: int :param fitness_function: a fitness function that takes assembler(chromosome) and returns one (or more) fitness scores :type fitness_function: object :param assembler: a function that takes a chromosome and returns an (ideally hashable) object to be evaluated :type assembler: object :param max_gen: maximum number of generations to perform the optimization :type max_gen: int :param max_conv: maximum number of generations with same max fitness until convergence is assumed :type max_conv : int :param pop_size: number of chromosomes in population :type pop_size: int :param mutation_rate: rate at which random mutations occur :type mutation_rate: float :param selection_rate: top percentage of the population to be selected for crossover :type selection_rate: float :param selection_strategy: strategy to use for selection, several available :type selection_strategy: string :param excluded_genes: indices of chromosomes that should not be changed during run :type excluded_genes: optional, array-like :param n_crossover_points: number of slices to make for the crossover :type n_crossover_points: int :param random_state: fixed the random seed for the run :type random_state: int, optional :param lru_cache: whether to use lru_cacheing, which is monkeypatched into the class. Requires that the fitness function is hashable. :type lru_cache: bool :param scalarizer: chimera scalarizer object initialized to work on the results of fitness function :type scalarizer: optional, object with a scalarize method that takes in a population fitness and rescales it :param prune_duplicates: whether to prune duplicates in each generation :type prune_duplicates: bool :param verbose: whether to print iterations status :type verbose: int :param show_stats: whether to print stats at the end :type show_stats: bool :param plot_results: whether to plot results of the run at the end :type plot_results: bool :param to_stdout: whether to write output to stdout :type to_stdout: bool :param to_file: whether to write output to file :type to_file: bool :param logger_file: name of the file where output will be written if to_file is True :type logger_file: string :param progess_bars: whether to monkeypatch progress bars for monitoring run :type progress_bars: bool :param problem_type: passing a simple flag from child class for some in built hashable fitness functions. :type problem_type: string

Pad or truncate a starting-population entry to conform to n_genes.

Retrieves random crossover points. :return: a numpy array with the crossover points

Initializes the population according to the population size and number of genes, seeding from self.starting_population and optionally randomizing per self.starting_random.

Returns: :return: a numpy array with initialized population

Mutates the population by randomizing specific positions of the population individuals, retrying until self.assembler accepts the result or self.max_counter attempts are exhausted.

Parameters:

  • population – the population at a given iteration

  • n_mutations – number of mutations to be performed.

Returns:

the mutated population