Structure change to make it easier for users to clone and use the repository.
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danielwilczak101
433
attributes.py
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433
attributes.py
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# Import signature tool to check if functions start with self or ga
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from inspect import signature
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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
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import sqlite3
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from copy import deepcopy
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# Import all the data structure prebuilt modules
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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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# Misc. Methods
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from examples import Fitness_Examples
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from termination import Termination
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# Parent/Survivor Selection Methods
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from parent import Parent
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from survivor import Survivor
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# Genetic Operator Methods
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from crossover import Crossover
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from mutation import Mutation
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# Database class
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from database import sql_database
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from sqlite3 import Error
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# Graphing package
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from database import matplotlib_graph
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import matplotlib.pyplot as plt
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class Attributes:
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"""Default GA attributes can be found here. If any attributes have not
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been set then they will fall back onto the default attribute. All
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attributes have been catigorized to explain sections in the ga process."""
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#=====================#
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# Default GA methods: #
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#=====================#
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# Default EasyGA implimentation structure
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fitness_function_impl = Fitness_Examples.is_it_5
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make_population = make_population
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make_chromosome = make_chromosome
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make_gene = make_gene
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# Methods for accomplishing Parent-Selection -> Crossover -> Survivor_Selection -> Mutation -> Termination
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parent_selection_impl = Parent.Rank.tournament
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crossover_individual_impl = Crossover.Individual.single_point
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crossover_population_impl = Crossover.Population.sequential
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survivor_selection_impl = Survivor.fill_in_best
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mutation_individual_impl = Mutation.Individual.individual_genes
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mutation_population_impl = Mutation.Population.random_avoid_best
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termination_impl = Termination.fitness_generation_tolerance
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def dist(self, chromosome_1, chromosome_2):
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"""Default distance lambda. Returns the square root of the difference in fitnesses."""
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return math.sqrt(abs(chromosome_1.fitness - chromosome_2.fitness))
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def weighted_random(self, weight):
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"""Returns a random value between 0 and 1. Returns values between the weight and the
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nearest of 0 and 1 less frequently than between weight and the farthest of 0 and 1."""
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rand_num = random.random()
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if rand_num < weight:
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return (1-weight) * rand_num / weight
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else:
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return 1 - weight * (1-rand_num) / (1-weight)
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def gene_impl(self, *args, **kwargs):
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"""Default gene implementation. Returns a random integer from 1 to 10."""
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return random.randint(1, 10)
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chromosome_impl = None
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#=====================================#
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# Special built-in class __methods__: #
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#=====================================#
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def __init__(
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self,
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*,
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# Attributes must be passed in using kwargs
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run = 0,
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chromosome_length = 10,
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population_size = 10,
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population = None,
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target_fitness_type = 'max',
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update_fitness = False,
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parent_ratio = 0.10,
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selection_probability = 0.50,
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tournament_size_ratio = 0.10,
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current_generation = 0,
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current_fitness = 0,
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generation_goal = 100,
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fitness_goal = None,
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tolerance_goal = None,
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percent_converged = 0.50,
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chromosome_mutation_rate = 0.15,
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gene_mutation_rate = 0.05,
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adapt_rate = 0.05,
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adapt_probability_rate = 0.05,
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adapt_population_flag = True,
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max_selection_probability = 0.75,
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min_selection_probability = 0.25,
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max_chromosome_mutation_rate = None,
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min_chromosome_mutation_rate = None,
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max_gene_mutation_rate = 0.15,
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min_gene_mutation_rate = 0.01,
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Database = sql_database.SQL_Database,
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database_name = 'database.db',
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sql_create_data_structure = f"""
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CREATE TABLE IF NOT EXISTS data (
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id INTEGER PRIMARY KEY,
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config_id INTEGER DEFAULT NULL,
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generation INTEGER NOT NULL,
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fitness REAL,
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chromosome TEXT
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); """,
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Graph = matplotlib_graph.Matplotlib_Graph,
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**kwargs
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):
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# Keep track of the current run
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self.run = run
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# Initilization variables
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self.chromosome_length = chromosome_length
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self.population_size = population_size
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self.population = population
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self.target_fitness_type = target_fitness_type
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self.update_fitness = update_fitness
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# Selection variables
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self.parent_ratio = parent_ratio
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self.selection_probability = selection_probability
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self.tournament_size_ratio = tournament_size_ratio
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# Termination variables
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self.current_generation = current_generation
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self.current_fitness = current_fitness
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self.generation_goal = generation_goal
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self.fitness_goal = fitness_goal
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self.tolerance_goal = tolerance_goal
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self.percent_converged = percent_converged
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# Mutation variables
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self.chromosome_mutation_rate = chromosome_mutation_rate
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self.gene_mutation_rate = gene_mutation_rate
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# Adapt variables
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self.adapt_rate = adapt_rate
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self.adapt_probability_rate = adapt_probability_rate
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self.adapt_population_flag = adapt_population_flag
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# Bounds on probabilities when adapting
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self.max_selection_probability = max_selection_probability
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self.min_selection_probability = min_selection_probability
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self.max_chromosome_mutation_rate = max_chromosome_mutation_rate
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self.min_chromosome_mutation_rate = min_chromosome_mutation_rate
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self.max_gene_mutation_rate = max_gene_mutation_rate
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self.min_gene_mutation_rate = min_gene_mutation_rate
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# Database varibles
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self.database = Database()
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self.database_name = database_name
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self.sql_create_data_structure = sql_create_data_structure
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# Graphing variables
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self.graph = Graph(self.database)
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# Any other custom kwargs?
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for name, value in kwargs.items():
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self.__setattr__(name, value)
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def __setattr__(self, name, value):
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"""Custom setter for using
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self.name = value
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which follows the following guidelines:
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- if self.name is a property, the specific property setter is used
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- else if value is callable and the first parameter is either 'self' or 'ga', self is passed in as the first parameter
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- else if value is not None or self.name is not set, assign it like normal
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"""
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# Check for property
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if hasattr(type(self), name) and isinstance(getattr(type(self), name), property):
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getattr(type(self), name).fset(self, value)
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# Check for function
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elif callable(value) and next(iter(signature(value).parameters), None) in ('self', 'ga'):
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foo = lambda *args, **kwargs: value(self, *args, **kwargs)
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# Reassign name and doc-string for documentation
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foo.__name__ = value.__name__
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foo.__doc__ = value.__doc__
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self.__dict__[name] = foo
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# Assign like normal unless None or undefined self.name
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elif value is not None or not hasattr(self, name):
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self.__dict__[name] = value
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#============================#
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# Built-in database methods: #
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#============================#
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def save_population(self):
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"""Saves the current population to the database."""
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self.database.insert_current_population(self)
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def save_chromosome(self, chromosome):
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"""Saves the given chromosome to the database."""
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self.database.insert_current_chromosome(self.current_generation, chromosome)
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#===================#
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# Built-in options: #
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#===================#
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def numeric_chromosomes(self):
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"""Sets default numerical based methods"""
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# Adapt every 10th generation
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self.adapt_rate = 0.10
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# Use averaging for crossover
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self.crossover_individual_impl = Crossover.Individual.Arithmetic.average
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# Use averaging for mutation
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self.mutation_individual_impl = Mutation.Individual.individual_genes
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# Euclidean norm
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self.dist = lambda self, chromosome_1, chromosome_2:\
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math.sqrt(sum(
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(gene_1.value - gene_2.value) ** 2
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for gene_1, gene_2
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in zip(chromosome_1, chromosome_2)
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))
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def permutation_chromosomes(self, cycle = True):
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"""Sets default permutation based methods"""
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cycle = int(cycle)
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self.crossover_individual_impl = Crossover.Individual.Permutation.ox1
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self.mutation_individual_impl = Mutation.Individual.Permutation.swap_genes
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def dist(self, chromosome_1, chromosome_2):
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"""Count the number of gene pairs they don't have in common."""
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return sum(
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1
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for x, y
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in zip(chromosome_1, chromosome_2)
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if x != y
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)
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self.dist = dist
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#===========================#
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# Getter/setter properties: #
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#===========================#
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@property
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def run(self):
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"""Getter function for the run counter."""
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return self._run
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@run.setter
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def run(self, value):
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"""Setter function for the run counter."""
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if not(isinstance(value, int) and value >= 0):
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raise ValueError("ga.run counter must be an integer greater than or equal to 0.")
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self._run = value
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@property
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def current_generation(self):
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"""Getter function for the current generation."""
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return self._current_generation
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@current_generation.setter
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def current_generation(self, generation):
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"""Setter function for the current generation."""
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if not isinstance(generation, int) or generation < 0:
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raise ValueError("ga.current_generation must be an integer greater than or equal to 0")
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self._current_generation = generation
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@property
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def chromosome_length(self):
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"""Getter function for chromosome length"""
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return self._chromosome_length
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@chromosome_length.setter
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def chromosome_length(self, length):
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"""Setter function with error checking for chromosome length"""
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if(not isinstance(length, int) or length <= 0):
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raise ValueError("Chromosome length must be integer greater than 0")
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self._chromosome_length = length
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@property
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def population_size(self):
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"""Getter function for population size"""
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return self._population_size
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@population_size.setter
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def population_size(self, size):
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"""Setter function with error checking for population size"""
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if(not isinstance(size, int) or size <= 0):
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raise ValueError("Population size must be integer greater than 0")
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self._population_size = size
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@property
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def target_fitness_type(self):
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"""Getter function for target fitness type."""
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return self._target_fitness_type
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@target_fitness_type.setter
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def target_fitness_type(self, target_fitness_type):
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"""Setter function for target fitness type."""
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self._target_fitness_type = target_fitness_type
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@property
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def max_chromosome_mutation_rate(self):
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"""Getter function for max chromosome mutation rate"""
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return self._max_chromosome_mutation_rate
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@max_chromosome_mutation_rate.setter
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def max_chromosome_mutation_rate(self, rate):
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"""Setter function with error checking and default value for max chromosome mutation rate"""
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# Default value
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if rate is None:
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self._max_chromosome_mutation_rate = min(self.chromosome_mutation_rate*2, (1+self.chromosome_mutation_rate)/2)
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# Otherwise check value
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elif 0 <= rate <= 1:
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self._max_chromosome_mutation_rate = rate
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# Throw error
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else:
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raise ValueError("Max chromosome mutation rate must be between 0 and 1")
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@property
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def min_chromosome_mutation_rate(self):
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"""Getter function for min chromosome mutation rate"""
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return self._min_chromosome_mutation_rate
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@min_chromosome_mutation_rate.setter
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def min_chromosome_mutation_rate(self, rate):
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"""Setter function with error checking and default value for min chromosome mutation rate"""
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# Default value
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if rate is None:
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self._min_chromosome_mutation_rate = self.chromosome_mutation_rate/2
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# Otherwise check value
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elif 0 <= rate <= 1:
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self._min_chromosome_mutation_rate = rate
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# Throw error
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else:
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raise ValueError("Min chromosome mutation rate must be between 0 and 1")
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@property
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def database_name(self):
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"""Getter function for the database name"""
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return self._database_name
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@database_name.setter
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def database_name(self, value_input):
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"""Setter function with error checking for the database name"""
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# Update the database class of the name change
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self.database._database_name = value_input
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# Set the name in the ga attribute
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self._database_name = value_input
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