from typing import Union
from collections.abc import Callable
from deap import tools
from deap.algorithms import varAnd, varOr
from .callbacks import eval_callbacks
[docs]def eaSimple(
population,
toolbox,
cxpb,
mutpb,
ngen,
stats=None,
halloffame=None,
callbacks=None,
verbose=True,
):
"""
The base implementation is directly taken from: https://github.com/DEAP/deap/blob/master/deap/algorithms.py
This algorithm reproduce the simplest evolutionary algorithm as
presented in chapter 7 of Back2000.
population: A list of individuals.
Population resulting of the iteration process.
toolbox: A :class:`~deap.base.Toolbox`
Contains the evolution operators.
cxpb: float, default=None
The probability of mating two individuals.
mutpb: float, default=None
The probability of mutating an individual.
ngen: int, default=None
The number of generation.
stats: A :class:`~deap.tools.Statistics`
Object that is updated inplace, optional.
halloffame: A :class:`~deap.tools.HallOfFame`
Object that will contain the best individuals, optional.
callbacks: list or callable
One or a list of the :class:`~sklearn_genetic.callbacks` methods available in the package.
verbose: bool, default=True
Whether or not to log the statistics.
Returns
-------
pop: list
The final population.
log: Logbook
Statistics of the evolution.
n_gen: int
Number of generations used.
"""
logbook = tools.Logbook()
logbook.header = ["gen", "nevals"] + (stats.fields if stats else [])
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in population if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
if halloffame is not None:
halloffame.update(population)
record = stats.compile(population) if stats else {}
logbook.record(gen=0, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
# Begin the generational process
for gen in range(1, ngen + 1):
# Select the next generation individuals
offspring = toolbox.select(population, len(population))
# Vary the pool of individuals
offspring = varAnd(offspring, toolbox, cxpb, mutpb)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
# Replace the current population by the offspring
population[:] = offspring
# Append the current generation statistics to the logbook
record = stats.compile(population) if stats else {}
logbook.record(gen=gen, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
# Check if any of the callbacks conditions are True to stop the iteration
if eval_callbacks(callbacks, record, logbook):
print("Process stopped earlier due a callback")
break
n_gen = gen + 1
return population, logbook, n_gen
[docs]def eaMuPlusLambda(
population,
toolbox,
mu,
lambda_,
cxpb,
mutpb,
ngen,
stats=None,
halloffame=None,
callbacks: Union[list, Callable] = None,
verbose=True,
):
"""
The base implementation is directly taken from: https://github.com/DEAP/deap/blob/master/deap/algorithms.py
This is the :math:`(\mu + \lambda)` evolutionary algorithm.
population: A list of individuals.
Population resulting of the iteration process.
toolbox: A :class:`~deap.base.Toolbox`
Contains the evolution operators.
mu: int, default=None
The number of individuals to select for the next generation.
lambda\_: int, default=None
The number of children to produce at each generation.
cxpb: float, default=None
The probability that an offspring is produced by crossover.
mutpb: float, default=None
The probability that an offspring is produced by mutation.
ngen: int, default=None
The number of generation.
stats: A :class:`~deap.tools.Statistics`
Object that is updated inplace, optional.
halloffame: A :class:`~deap.tools.HallOfFame`
Object that will contain the best individuals, optional.
callbacks: list or Callable
One or a list of the :class:`~sklearn_genetic.callbacks` methods available in the package.
verbose: bool, default=True
Whether or not to log the statistics.
Returns
-------
pop: list
The final population.
log: Logbook
Statistics of the evolution.
n_gen: int
Number of generations used.
"""
logbook = tools.Logbook()
logbook.header = ["gen", "nevals"] + (stats.fields if stats else [])
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in population if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
if halloffame is not None:
halloffame.update(population)
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=0, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
# Begin the generational process
for gen in range(1, ngen + 1):
# Vary the population
offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
# Select the next generation population
population[:] = toolbox.select(population + offspring, mu)
# Update the statistics with the new population
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
if eval_callbacks(callbacks, record, logbook):
print("Process stopped earlier due a callback")
break
n_gen = gen + 1
return population, logbook, n_gen
[docs]def eaMuCommaLambda(
population,
toolbox,
mu,
lambda_,
cxpb,
mutpb,
ngen,
stats=None,
halloffame=None,
callbacks: Union[list, Callable] = None,
verbose=True,
):
"""
The base implementation is directly taken from: https://github.com/DEAP/deap/blob/master/deap/algorithms.py
This is the :math:`(\mu~,~\lambda)` evolutionary algorithm.
population: A list of individuals.
Population resulting of the iteration process.
toolbox: A :class:`~deap.base.Toolbox`
Contains the evolution operators.
mu: int, default=None,
The number of individuals to select for the next generation.
lambda\_: int, default=None
The number of children to produce at each generation.
cxpb: float, default=None
The probability that an offspring is produced by crossover.
mutpb: float, default=None
The probability that an offspring is produced by mutation.
ngen: int, default=None
The number of generation.
stats: A :class:`~deap.tools.Statistics`
Object that is updated inplace, optional.
halloffame: A :class:`~deap.tools.HallOfFame`
Object that will contain the best individuals, optional.
callbacks: list or Callable
One or a list of the :class:`~sklearn_genetic.callbacks` methods available in the package.
verbose: bool, default=True
Whether or not to log the statistics.
Returns
-------
pop: list
The final population.
log: Logbook
Statistics of the evolution.
n_gen: int
Number of generations used.
"""
assert lambda_ >= mu, "lambda must be greater or equal to mu."
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in population if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
if halloffame is not None:
halloffame.update(population)
logbook = tools.Logbook()
logbook.header = ["gen", "nevals"] + (stats.fields if stats else [])
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=0, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
# Begin the generational process
for gen in range(1, ngen + 1):
# Vary the population
offspring = varOr(population, toolbox, lambda_, cxpb, mutpb)
# Evaluate the individuals with an invalid fitness
invalid_ind = [ind for ind in offspring if not ind.fitness.valid]
fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)
for ind, fit in zip(invalid_ind, fitnesses):
ind.fitness.values = fit
# Update the hall of fame with the generated individuals
if halloffame is not None:
halloffame.update(offspring)
# Select the next generation population
population[:] = toolbox.select(offspring, mu)
# Update the statistics with the new population
record = stats.compile(population) if stats is not None else {}
logbook.record(gen=gen, nevals=len(invalid_ind), **record)
if verbose:
print(logbook.stream)
# Check if any of the callbacks conditions are True to stop the iteration
if eval_callbacks(callbacks, record, logbook):
print("Process stopped earlier due a callback")
break
n_gen = gen + 1
return population, logbook, n_gen