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Added decorators file

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SimpleArt
2021-01-01 16:09:27 -05:00
parent 423c9884a8
commit b2b1775e16
6 changed files with 292 additions and 258 deletions
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50
src/crossover/Crossover.py
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@ -1,47 +1,13 @@
from EasyGA import function_info
import random
# Import all crossover decorators
from decorators import _check_weight, _gene_by_gene
# Round to an integer near x with higher probability
# the closer it is to that integer.
randround = lambda x: int(x + random.random())
@function_info
def _check_weight(individual_method):
"""Checks if the weight is between 0 and 1 before running.
Exception may occur when using ga.adapt, which will catch
the error and try again with valid weight.
"""
def new_method(ga, parent_1, parent_2, *, weight = individual_method.__kwdefaults__.get('weight', None)):
if weight is None:
individual_method(ga, parent_1, parent_2)
elif 0 < weight < 1:
individual_method(ga, parent_1, parent_2, weight = weight)
else:
raise ValueError(f"Weight must be between 0 and 1 when using {individual_method.__name__}.")
return new_method
@function_info
def _gene_by_gene(individual_method):
"""Perform crossover by making a single new chromosome by combining each gene by gene."""
def new_method(ga, parent_1, parent_2, *, weight = individual_method.__kwdefaults__.get('weight', 'None')):
ga.population.add_child(
individual_method(ga, value_1, value_2)
if weight == 'None' else
individual_method(ga, value_1, value_2, weight = weight)
for value_1, value_2
in zip(parent_1.gene_value_iter, parent_2.gene_value_iter)
)
return new_method
class Population:
"""Methods for selecting chromosomes to crossover."""
@ -61,7 +27,7 @@ class Population:
)
def random_mate(ga):
def random(ga):
"""Select random pairs from the mating pool.
Every parent is paired with a random parent.
"""
@ -100,9 +66,9 @@ class Individual:
@_check_weight
@_gene_by_gene
def uniform(ga, *gene_values, *, weight = 0.5):
def uniform(ga, value_1, value_2, *, weight = 0.5):
"""Cross two parents by swapping all genes randomly."""
return random.choices(gene_values, cum_weights = [weight, 1])[0]
return random.choices(gene_pair, cum_weights = [weight, 1])[0]
class Arithmetic:
@ -136,7 +102,7 @@ class Individual:
@_check_weight
@_gene_by_gene
def random_value(ga, value_1, value_2, *, weight = 0.5):
def random(ga, value_1, value_2, *, weight = 0.5):
"""Cross two parents by taking a random integer or float value between each of the genes."""
value = value_1 + ga.weighted_random(weight) * (value_2-value_1)
@ -158,7 +124,7 @@ class Individual:
# Too small to cross
if len(parent_1) < 2:
raise ValueError("Parent lengths must be at least 2.")
return parent_1.gene_list
# Unequal parent lengths
if len(parent_1) != len(parent_2):

261
src/decorators.py Normal file
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@ -0,0 +1,261 @@
import random
from math import ceil
def function_info(decorator):
"""Recovers the name and doc-string for decorators throughout EasyGA for documentation purposes."""
def new_decorator(method):
# Apply old decorator
new_method = decorator(method)
# Recover name and doc-string
new_method.__name__ = method.__name__
new_method.__doc__ = method.__doc__
# Return new method with proper name and doc-string
return new_method
return new_decorator
#=======================#
# Crossover decorators: #
#=======================#
@function_info
def _check_weight(individual_method):
"""Checks if the weight is between 0 and 1 before running.
Exception may occur when using ga.adapt, which will catch
the error and try again with valid weight.
"""
def new_method(ga, parent_1, parent_2, *, weight = individual_method.__kwdefaults__.get('weight', None)):
if weight is None:
individual_method(ga, parent_1, parent_2)
elif 0 < weight < 1:
individual_method(ga, parent_1, parent_2, weight = weight)
else:
raise ValueError(f"Weight must be between 0 and 1 when using {individual_method.__name__}.")
return new_method
@function_info
def _gene_by_gene(individual_method):
"""Perform crossover by making a single new chromosome by combining each gene by gene."""
def new_method(ga, parent_1, parent_2, *, weight = individual_method.__kwdefaults__.get('weight', 'None')):
ga.population.add_child(
individual_method(ga, value_1, value_2)
if weight == 'None' else
individual_method(ga, value_1, value_2, weight = weight)
for value_1, value_2
in zip(parent_1.gene_value_iter, parent_2.gene_value_iter)
)
return new_method
#====================#
# Parent decorators: #
#====================#
@function_info
def _check_selection_probability(selection_method):
"""Raises a ValueError if the selection_probability is not between 0 and 1 inclusively.
Otherwise runs the selection method."""
def new_method(ga):
if 0 <= ga.selection_probability <= 1:
selection_method(ga)
else:
raise ValueError("Selection probability must be between 0 and 1 to select parents.")
return new_method
@function_info
def _check_positive_fitness(selection_method):
"""Raises a ValueError if the population contains a chromosome with negative fitness.
Otherwise runs the selection method."""
def new_method(ga):
if ga.get_chromosome_fitness(0) > 0 and ga.get_chromosome_fitness(-1) >= 0:
selection_method(ga)
else:
raise ValueError("Converted fitness values can't have negative values or be all 0."
+ " Consider using rank selection or stochastic selection instead.")
return new_method
@function_info
def _ensure_sorted(selection_method):
"""Sorts the population by fitness and then runs the selection method."""
def new_method(ga):
ga.sort_by_best_fitness()
selection_method(ga)
return new_method
@function_info
def _compute_parent_amount(selection_method):
"""Computes the amount of parents needed to be selected,
and passes it as another argument for the method."""
def new_method(ga):
parent_amount = max(2, round(len(ga.population)*ga.parent_ratio))
selection_method(ga, parent_amount)
return new_method
#======================#
# Mutation decorators: #
#======================#
@function_info
def _check_chromosome_mutation_rate(population_method):
"""Checks if the chromosome mutation rate is a float between 0 and 1 before running."""
def new_method(ga):
if not isinstance(ga.chromosome_mutation_rate, float):
raise TypeError("Chromosome mutation rate must be a float.")
elif 0 < ga.chromosome_mutation_rate < 1:
population_method(ga)
else:
raise ValueError("Chromosome mutation rate must be between 0 and 1.")
return new_method
@function_info
def _check_gene_mutation_rate(individual_method):
"""Checks if the gene mutation rate is a float between 0 and 1 before running."""
def new_method(ga, index):
if not isinstance(ga.gene_mutation_rate, float):
raise TypeError("Gene mutation rate must be a float.")
elif 0 < ga.gene_mutation_rate <= 1:
individual_method(ga, index)
else:
raise ValueError("Gene mutation rate must be between 0 and 1.")
return new_method
@function_info
def _reset_fitness(individual_method):
"""Resets the fitness value of the chromosome."""
def new_method(ga, chromosome):
chromosome.fitness = None
individual_method(ga, chromosome)
return new_method
@function_info
def _loop_random_mutations(individual_method):
"""Runs the individual method until enough
genes are mutated on the indexed chromosome."""
# Change input to include the gene index being mutated.
def new_method(ga, chromosome):
sample_space = range(len(chromosome))
sample_size = ceil(len(chromosome)*ga.gene_mutation_rate)
# Loop the individual method until enough genes are mutated.
for index in random.sample(sample_space, sample_size):
individual_method(ga, chromosome, index)
return new_method
#======================#
# Survivor decorators: #
#======================#
#=========================#
# Termination decorators: #
#=========================#
@function_info
def _add_by_fitness_goal(termination_impl):
"""Adds termination by fitness goal to the method."""
def new_method(ga):
# Try to check the fitness goal
try:
# If minimum fitness goal reached, stop ga.
if ga.target_fitness_type == 'min' and ga.population[0].fitness <= ga.fitness_goal:
return False
# If maximum fitness goal reached, stop ga.
elif ga.target_fitness_type == 'max' and ga.population[0].fitness >= ga.fitness_goal:
return False
# Fitness or fitness goals are None, or Population not initialized
except (TypeError, AttributeError):
pass
# Check other termination methods
return termination_impl(ga)
return new_method
@function_info
def _add_by_generation_goal(termination_impl):
"""Adds termination by generation goal to the method."""
def new_method(ga):
# If generation goal is set, check it.
if ga.generation_goal is not None and ga.current_generation >= ga.generation_goal:
return False
# Check other termination methods
return termination_impl(ga)
return new_method
@function_info
def _add_by_tolerance_goal(termination_impl):
"""Adds termination by tolerance goal to the method."""
def new_method(ga):
# If tolerance is set, check it, if possible.
try:
best_fitness = ga.population[0].fitness
threshhold_fitness = ga.population[round(ga.percent_converged*len(ga.population))].fitness
tol = ga.tolerance_goal * (1 + abs(best_fitness))
# Terminate if the specified amount of the population has converged to the specified tolerance
if abs(best_fitness - threshhold_fitness) < tol:
return False
# Fitness or tolerance goals are None, or population is not initialized
except (TypeError, AttributeError):
pass
# Check other termination methods
return termination_impl(ga)
return new_method

68
src/mutation/Mutation.py
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@ -1,72 +1,8 @@
from EasyGA import function_info
import random
from math import ceil
@function_info
def _check_chromosome_mutation_rate(population_method):
"""Checks if the chromosome mutation rate is a float between 0 and 1 before running."""
def new_method(ga):
if not isinstance(ga.chromosome_mutation_rate, float):
raise TypeError("Chromosome mutation rate must be a float.")
elif 0 < ga.chromosome_mutation_rate < 1:
population_method(ga)
else:
raise ValueError("Chromosome mutation rate must be between 0 and 1.")
return new_method
@function_info
def _check_gene_mutation_rate(individual_method):
"""Checks if the gene mutation rate is a float between 0 and 1 before running."""
def new_method(ga, index):
if not isinstance(ga.gene_mutation_rate, float):
raise TypeError("Gene mutation rate must be a float.")
elif 0 < ga.gene_mutation_rate <= 1:
individual_method(ga, index)
else:
raise ValueError("Gene mutation rate must be between 0 and 1.")
return new_method
@function_info
def _reset_fitness(individual_method):
"""Resets the fitness value of the chromosome."""
def new_method(ga, chromosome):
chromosome.fitness = None
individual_method(ga, chromosome)
return new_method
@function_info
def _loop_random_mutations(individual_method):
"""Runs the individual method until enough
genes are mutated on the indexed chromosome.
"""
# Change input to include the gene index being mutated.
def new_method(ga, chromosome):
sample_space = range(len(chromosome))
sample_size = ceil(len(chromosome)*ga.gene_mutation_rate)
# Loop the individual method until enough genes are mutated.
for index in random.sample(sample_space, sample_size):
individual_method(ga, chromosome, index)
return new_method
# Import all mutation decorators
from decorators import _check_chromosome_mutation_rate, _check_gene_mutation_rate, _reset_fitness, _loop_random_mutations
class Population:

54
src/parent/Parent.py
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@ -1,57 +1,7 @@
from EasyGA import function_info
import random
@function_info
def _check_selection_probability(selection_method):
"""Raises a ValueError if the selection_probability is not between 0 and 1 inclusively.
Otherwise runs the selection method."""
def new_method(ga):
if 0 <= ga.selection_probability <= 1:
selection_method(ga)
else:
raise ValueError("Selection probability must be between 0 and 1 to select parents.")
return new_method
@function_info
def _check_positive_fitness(selection_method):
"""Raises a ValueError if the population contains a chromosome with negative fitness.
Otherwise runs the selection method."""
def new_method(ga):
if ga.get_chromosome_fitness(0) > 0 and ga.get_chromosome_fitness(-1) >= 0:
selection_method(ga)
else:
raise ValueError("Converted fitness values can't have negative values or be all 0."
+ " Consider using rank selection or stochastic selection instead.")
return new_method
@function_info
def _ensure_sorted(selection_method):
"""Sorts the population by fitness and then runs the selection method."""
def new_method(ga):
ga.sort_by_best_fitness()
selection_method(ga)
return new_method
@function_info
def _compute_parent_amount(selection_method):
"""Computes the amount of parents needed to be selected,
and passes it as another argument for the method."""
def new_method(ga):
parent_amount = max(2, round(len(ga.population)*ga.parent_ratio))
selection_method(ga, parent_amount)
return new_method
# Import all parent decorators
from decorators import _check_selection_probability, _check_positive_fitness, _ensure_sorted, _compute_parent_amount
class Rank:

37
src/survivor/Survivor.py
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@ -1,34 +1,23 @@
from EasyGA import function_info
import random
@function_info
def _append_to_next_population(survivor_method):
"""Appends the selected chromosomes to the next population."""
def new_method(ga):
ga.population.append_children(survivor_method(ga))
return new_method
# Import all survivor decorators
from decorators import *
@_append_to_next_population
def fill_in_best(ga):
"""Fills in the next population with the best chromosomes from the last population"""
needed_amount = len(ga.population) - len(ga.population.next_population)
return ga.population[:needed_amount]
ga.population.append_children(ga.population[:needed_amount])
@_append_to_next_population
def fill_in_random(ga):
"""Fills in the next population with random chromosomes from the last population"""
needed_amount = len(ga.population) - len(ga.population.next_population)
return random.sample(ga.population, needed_amount)
ga.population.append_children(random.sample(ga.population, needed_amount))
@_append_to_next_population
def fill_in_parents_then_random(ga):
"""Fills in the next population with all parents followed by random chromosomes from the last population"""
@ -41,11 +30,19 @@ def fill_in_parents_then_random(ga):
# Only parents are used.
if random_amount == 0:
return ga.population.mating_pool[:parent_amount]
ga.population.append_children(
chromosome
for i, chromosome
in enumerate(mating_pool)
if i < parent_amount
)
# Parents need to be removed from the random sample to avoid dupes.
else:
return mating_pool + random.sample(
set(ga.population) - mating_pool,
random_amount
)
ga.population.append_children(mating_pool)
ga.population.append_children(
random.sample(
set(ga.population) - mating_pool,
random_amount
)
)

80
src/termination/Termination.py
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@ -1,81 +1,5 @@
from EasyGA import function_info
@function_info
def _add_by_fitness_goal(termination_impl):
"""Adds termination by fitness goal to the method."""
def new_method(ga):
# Try to check the fitness goal
try:
# If minimum fitness goal reached, stop ga.
if ga.target_fitness_type == 'min' and ga.population[0].fitness <= ga.fitness_goal:
return False
# If maximum fitness goal reached, stop ga.
elif ga.target_fitness_type == 'max' and ga.population[0].fitness >= ga.fitness_goal:
return False
# Fitness or fitness goals are None
except TypeError:
pass
# Population not initialized
except AttributeError:
pass
# Check other termination methods
return termination_impl(ga)
return new_method
@function_info
def _add_by_generation_goal(termination_impl):
"""Adds termination by generation goal to the method."""
def new_method(ga):
# If generation goal is set, check it.
if ga.generation_goal is not None and ga.current_generation >= ga.generation_goal:
return False
# Check other termination methods
return termination_impl(ga)
return new_method
@function_info
def _add_by_tolerance_goal(termination_impl):
"""Adds termination by tolerance goal to the method."""
def new_method(ga):
# If tolerance is set, check it, if possible.
try:
best_fitness = ga.population[0].fitness
threshhold_fitness = ga.population[round(ga.percent_converged*len(ga.population))].fitness
tol = ga.tolerance_goal * (1 + abs(best_fitness))
# Terminate if the specified amount of the population has converged to the specified tolerance
if abs(best_fitness - threshhold_fitness) < tol:
return False
# Fitness or tolerance goals are None
except TypeError:
pass
# Population not initialized
except AttributeError:
pass
# Check other termination methods
return termination_impl(ga)
return new_method
# Import all termination decorators
from decorators import _add_by_fitness_goal, _add_by_generation_goal, _add_by_tolerance_goal
@_add_by_fitness_goal
@_add_by_generation_goal