Improved documentation
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SimpleArt
236
EasyGA/EasyGA.py
236
EasyGA/EasyGA.py
@ -1,8 +1,5 @@
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# Import math for square root (ga.dist()) and ceil (crossover methods)
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import math
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# Import random for many methods
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import random
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from __future__ import annotations
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from typing import Optional, MutableSequence, Iterable
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# Import all decorators
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import decorators
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@ -11,6 +8,9 @@ import decorators
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from structure import Population as make_population
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from structure import Chromosome as make_chromosome
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from structure import Gene as make_gene
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from structure import Population
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from structure import Chromosome
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from structure import Gene
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# Misc. Methods
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from examples import Fitness
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@ -37,18 +37,27 @@ import matplotlib.pyplot as plt
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class GA(Attributes):
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"""GA is the main class in EasyGA. Everything is run through the ga
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class. The GA class inherites all the default ga attributes from the
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attributes class.
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"""
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GA is the main controller class for EasyGA. Everything is run
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through the GA class. The GA class inherits all default attributes
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from the Attributes dataclass.
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An extensive wiki going over all major functions can be found at
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An extensive wiki going over all major functionalities can be found at
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https://github.com/danielwilczak101/EasyGA/wiki
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"""
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def evolve(self, number_of_generations = float('inf'), consider_termination = True):
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"""Evolves the ga the specified number of generations
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or until the ga is no longer active if consider_termination is True."""
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def evolve(self: GA, number_of_generations: float = float('inf'), consider_termination: bool = True) -> None:
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"""
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Evolves the ga until the ga is no longer active.
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Parameters
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----------
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number_of_generations : float = inf
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The number of generations before the GA terminates. Runs forever by default.
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consider_termination : bool = True
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Whether GA.active() is checked for termination.
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"""
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# Create the initial population if necessary.
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if self.population is None:
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@ -84,7 +93,6 @@ class GA(Attributes):
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self.sort_by_best_fitness()
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# Save the population to the database
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if self.save_data == True:
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self.save_population()
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# Adapt the ga if the generation times the adapt rate
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@ -97,30 +105,25 @@ class GA(Attributes):
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self.current_generation += 1
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def update_population(self):
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"""Updates the population to the new population and resets
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the mating pool and new population."""
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def update_population(self: GA) -> None:
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"""
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Updates the population to the new population
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and resets the mating pool and new population.
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"""
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self.population.update()
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def reset_run(self):
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"""Resets a run by re-initializing the population
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and modifying counters."""
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def reset_run(self: GA) -> None:
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"""
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Resets a run by re-initializing the
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population and modifying counters.
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"""
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self.initialize_population()
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self.current_generation = 0
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self.run += 1
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def active(self):
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"""Returns if the ga should terminate based on the
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termination implimented."""
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return self.termination_impl()
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def adapt(self):
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def adapt(self: GA) -> None:
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"""Adapts the ga to hopefully get better results."""
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self.adapt_probabilities()
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@ -131,11 +134,11 @@ class GA(Attributes):
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self.sort_by_best_fitness()
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def adapt_probabilities(self):
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"""Modifies the parent ratio and mutation rates
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based on the adapt rate and percent converged.
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Attempts to balance out so that a portion of the
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population gradually approaches the solution.
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def adapt_probabilities(self: GA) -> None:
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"""
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Modifies the parent ratio and mutation rates based on the adapt
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rate and percent converged. Attempts to balance out so that a
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portion of the population gradually approaches the solution.
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"""
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# Determines how much to adapt by
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@ -173,7 +176,7 @@ class GA(Attributes):
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self.gene_mutation_rate = average(bounds[2], self.gene_mutation_rate)
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def adapt_population(self):
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def adapt_population(self: GA) -> None:
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"""
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Performs weighted crossover between the best chromosome and
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the rest of the chromosomes, using negative weights to push
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@ -221,39 +224,24 @@ class GA(Attributes):
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self.population.mating_pool = []
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def initialize_population(self):
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"""Initialize the population using
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the initialization implimentation
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that is currently set.
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def initialize_population(self: GA) -> None:
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"""
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if self.chromosome_impl is not None:
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self.population = self.make_population(
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self.chromosome_impl()
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for _
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in range(self.population_size)
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)
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elif self.gene_impl is not None:
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self.population = self.make_population(
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(
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self.gene_impl()
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for __
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in range(self.chromosome_length)
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)
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for _
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in range(self.population_size)
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)
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else:
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raise ValueError("No chromosome or gene impl specified.")
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Sets self.population using the chromosome implementation and population size.
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"""
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self.population = self.make_population(self.population_impl())
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def set_all_fitness(self):
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"""Will get and set the fitness of each chromosome in the population.
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If update_fitness is set then all fitness values are updated.
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Otherwise only fitness values set to None (i.e. uninitialized
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fitness values) are updated.
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def set_all_fitness(self: GA) -> None:
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"""
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Sets the fitness of each chromosome in the population.
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Attributes
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----------
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update_fitness : bool
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Whether fitnesses are recalculated even if they were previously calculated.
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Allows chromosomes which exist in dynamic environments.
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fitness_function_impl(chromosome) -> float
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The fitness function which measures how well a chromosome is doing.
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"""
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# Check each chromosome
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@ -264,11 +252,40 @@ class GA(Attributes):
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chromosome.fitness = self.fitness_function_impl(chromosome)
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def sort_by_best_fitness(self, chromosome_list = None, in_place = True):
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"""Sorts the chromosome list by fitness based on fitness type.
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def sort_by_best_fitness(
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self: GA,
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chromosome_list: Optional[
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Union[MutableSequence[Chromosome],
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Iterable[Chromosome]]
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] = None,
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in_place: bool = True,
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) -> MutableSequence[Chromosome]:
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"""
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Sorts the chromosome list by fitness based on fitness type.
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1st element has best fitness.
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2nd element has second best fitness.
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etc.
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Parameters
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----------
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chromosome_list : MutableSequence[Chromosome] = self.population
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The list of chromosomes to be sorted. By default, the population is used.
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May be sorted in-place.
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chromosome_list : Iterable[Chromosome]
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The list of chromosomes to be sorted. By default, the population is used.
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May not be sorted in-place.
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in_place : bool = True
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Whether the sort is done in-place, modifying the original object, or not.
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Attributes
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----------
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target_fitness_type : str in ('max', 'min')
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The way the chromosomes should be sorted.
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Returns
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-------
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chromosome_list : MutableSequence[Chromosome]
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The sorted chromosomes.
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"""
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if self.target_fitness_type not in ('max', 'min'):
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@ -282,57 +299,100 @@ class GA(Attributes):
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reverse = (self.target_fitness_type == 'max')
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# Sort by fitness, assuming None should be moved to the end of the list
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key = lambda chromosome: (chromosome.fitness if (chromosome.fitness is not None) else (float('inf') * (+1, -1)[int(reverse)]))
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def key(chromosome):
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if chromosome.fitness is not None:
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return chromosome_fitness
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elif reverse:
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return float('-inf')
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else:
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return float('inf')
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if in_place:
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chromosome_list.sort(key = key, reverse = reverse)
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chromosome_list.sort(key=key, reverse=reverse)
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return chromosome_list
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else:
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return sorted(chromosome_list, key = key, reverse = reverse)
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return sorted(chromosome_list, key=key, reverse=reverse)
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def get_chromosome_fitness(self, index):
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"""Returns the fitness value of the chromosome
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at the specified index after conversion based
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on the target fitness type.
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def get_chromosome_fitness(self: GA, index: int) -> float:
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"""
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Computes the converted fitness of a chromosome at an index.
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The converted fitness remaps the fitness to sensible values
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for various methods.
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Parameters
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----------
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index : int
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The index of the chromosome in the population.
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Attributes
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----------
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convert_fitness(float) -> float
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A method for redefining the fitness value.
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Returns
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-------
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fitness : float
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The converted fitness value.
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"""
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return self.convert_fitness(self.population[index].fitness)
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def convert_fitness(self, fitness_value):
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"""Returns the fitness value if the type of problem
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is a maximization problem. Otherwise the fitness is
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inverted using max - value + min.
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def convert_fitness(self: GA, fitness: float) -> float:
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"""
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Calculates a modified version of the fitness for various
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methods, which assume the fitness should be maximized.
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Parameters
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----------
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fitness : float
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The fitness value to be changed.
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Attributes
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----------
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target_fitness_type : str in ('max', 'min')
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The way the chromosomes should be sorted.
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Returns
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-------
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fitness : float
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Unchanged if the fitness is already being maximized.
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max_fitness - fitness + min_fitness : float
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The fitness flipped if the fitness is being minimized.
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Requires
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--------
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The population must be sorted already, and the fitnesses can't be None.
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"""
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# No conversion needed
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if self.target_fitness_type == 'max': return fitness_value
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if self.target_fitness_type == 'max':
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return fitness
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max_fitness = self.population[-1].fitness
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min_fitness = self.population[0].fitness
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return max_fitness - fitness_value + min_fitness
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return max_fitness - fitness + min_fitness
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def print_generation(self):
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"""Prints the current generation"""
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def print_generation(self: GA) -> None:
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"""Prints the current generation."""
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print(f"Current Generation \t: {self.current_generation}")
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def print_population(self):
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"""Prints the entire population"""
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def print_population(self: GA) -> None:
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"""Prints the entire population."""
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print(self.population)
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def print_best_chromosome(self):
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"""Prints the best chromosome and its fitness"""
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def print_best_chromosome(self: GA) -> None:
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"""Prints the best chromosome and its fitness."""
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print(f"Best Chromosome \t: {self.population[0]}")
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print(f"Best Fitness \t: {self.population[0].fitness}")
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def print_worst_chromosome(self):
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"""Prints the worst chromosome and its fitness"""
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def print_worst_chromosome(self: GA) -> None:
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"""Prints the worst chromosome and its fitness."""
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print(f"Worst Chromosome \t: {self.population[-1]}")
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print(f"Worst Fitness \t: {self.population[-1].fitness}")
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