def init_island():
np.random.seed(15)
x = init_x_vals(START, STOP, NUM_POINTS)
y = equation_eval(x)
training_data = ExplicitTrainingData(x, y)
component_generator = ComponentGenerator(x.shape[1])
component_generator.add_operator(2)
component_generator.add_operator(3)
component_generator.add_operator(4)
crossover = AGraphCrossover(component_generator)
mutation = AGraphMutation(component_generator)
agraph_generator = AGraphGenerator(STACK_SIZE, component_generator)
fitness = ExplicitRegression(training_data=training_data)
local_opt_fitness = ContinuousLocalOptimization(fitness, algorithm='lm')
evaluator = Evaluation(local_opt_fitness)
ea_algorithm = AgeFitnessEA(evaluator, agraph_generator, crossover,
mutation, MUTATION_PROBABILITY,
CROSSOVER_PROBABILITY, POP_SIZE)
island = Island(ea_algorithm, agraph_generator, POP_SIZE)
return island
def execute_generational_steps():
x = init_x_vals(-10, 10, 100)
y = equation_eval(x)
training_data = ExplicitTrainingData(x, y)
component_generator = ComponentGenerator(x.shape[1])
component_generator.add_operator(2)
component_generator.add_operator(3)
component_generator.add_operator(4)
crossover = AGraphCrossover(component_generator)
mutation = AGraphMutation(component_generator)
agraph_generator = AGraphGenerator(STACK_SIZE, component_generator)
fitness = ExplicitRegression(training_data=training_data)
local_opt_fitness = ContinuousLocalOptimization(fitness, algorithm='lm')
evaluator = Evaluation(local_opt_fitness)
ea = AgeFitnessEA(evaluator, agraph_generator, crossover,
mutation, 0.4, 0.4, POP_SIZE)
island = Island(ea, agraph_generator, POP_SIZE)
archipelago = SerialArchipelago(island)
opt_result = archipelago.evolve_until_convergence(max_generations=500,
fitness_threshold=1.0e-4)
if opt_result.success:
print(archipelago.get_best_individual().get_latex_string())
else:
print("Failed.")
def training_function(training_data, ea_choice):
component_generator = \
ComponentGenerator(input_x_dimension=training_data.x.shape[1])
component_generator.add_operator("+")
component_generator.add_operator("-")
component_generator.add_operator("*")
agraph_generator = AGraphGenerator(agraph_size=32,
component_generator=component_generator)
crossover = AGraphCrossover(component_generator)
mutation = AGraphMutation(component_generator)
fitness = ExplicitRegression(training_data=training_data)
local_opt_fitness = ContinuousLocalOptimization(fitness, algorithm='lm')
evaluator = Evaluation(local_opt_fitness)
POPULATION_SIZE = 64
MUTATION_PROBABILITY = 0.1
CROSSOVER_PROBABILITY = 0.7
if ea_choice == "age_fitness":
ea = AgeFitnessEA(evaluator, agraph_generator, crossover, mutation,
MUTATION_PROBABILITY, CROSSOVER_PROBABILITY,
POPULATION_SIZE)
else:
ea = DeterministicCrowdingEA(evaluator, crossover, mutation,
MUTATION_PROBABILITY,
CROSSOVER_PROBABILITY)
island = Island(ea, agraph_generator, POPULATION_SIZE)
opt_result = island.evolve_until_convergence(
max_generations=MAX_GENERATIONS, fitness_threshold=1e-6)
return island.get_best_individual(), opt_result
def init_island():
np.random.seed(10)
x = init_x_vals(START, STOP, NUM_POINTS)
y = equation_eval(x)
training_data = ExplicitTrainingData(x, y)
component_generator = ComponentGenerator(
x.shape[1],
automatic_constant_optimization=False,
numerical_constant_range=10)
component_generator.add_operator("+")
component_generator.add_operator("-")
component_generator.add_operator("*")
crossover = AGraphCrossover(component_generator)
mutation = AGraphMutation(component_generator)
agraph_generator = AGraphGenerator(STACK_SIZE, component_generator)
fitness = ExplicitRegression(training_data=training_data)
evaluator = Evaluation(fitness)
ea = AgeFitnessEA(evaluator, agraph_generator, crossover, mutation,
MUTATION_PROBABILITY, CROSSOVER_PROBABILITY, POP_SIZE)
island = Island(ea, agraph_generator, POP_SIZE)
return island
def evol_alg():
crossover = SinglePointCrossover()
mutation = SinglePointMutation(NumberGenerator(-1))
selection = Tournament(SELECTION_SIZE)
fitness = MultipleValueFitnessFunction()
evaluator = Evaluation(fitness)
return MuPlusLambda(evaluator, selection, crossover, mutation, 0.2, 0.4,
OFFSPRING_SIZE)
def ev_alg(full_training_data):
crossover = SinglePointCrossover()
mutation = SinglePointMutation(np.random.random)
selection = Tournament(2)
fitness = DistanceToAverage(full_training_data)
evaluator = Evaluation(fitness)
return MuPlusLambda(evaluator, selection, crossover, mutation, 0., 1.0,
MAIN_POPULATION_SIZE)
def test_evaluation_evaluates_all_individuals(single_value_population_of_4,
fitness_function):
evaluation = Evaluation(fitness_function)
evaluation(single_value_population_of_4)
assert evaluation.eval_count == 4
for indv in single_value_population_of_4:
assert indv.fit_set
assert indv.fitness is not None
def test_evaluation_skips_already_calculated_fitnesses(
single_value_population_of_4, fitness_function):
evaluation = Evaluation(fitness_function)
single_value_population_of_4[0].fitness = 1.0
evaluation(single_value_population_of_4)
assert evaluation.eval_count == 3
for indv in single_value_population_of_4:
assert indv.fit_set
assert indv.fitness is not None
def island():
crossover = SinglePointCrossover()
mutation = SinglePointMutation(mutation_function)
selection = Tournament(10)
fitness = MultipleValueFitnessFunction()
evaluator = Evaluation(fitness)
ev_alg = MuPlusLambda(evaluator, selection, crossover, mutation,
0.2, 0.4, 20)
generator = MultipleValueChromosomeGenerator(mutation_function, 10)
return Island(ev_alg, generator, 25)
def execute_generational_steps():
communicator = MPI.COMM_WORLD
rank = MPI.COMM_WORLD.Get_rank()
data = pd.read_csv('./data/fp_2var_test9_py.csv').as_matrix()
x = data[:, 0:2]
y = data[:2]
if rank == 0:
x = init_x_vals(-10, 10, 100)
y = equation_eval(x)
x = MPI.COMM_WORLD.bcast(x, root=0)
y = MPI.COMM_WORLD.bcast(y, root=0)
training_data = ExplicitTrainingData(x, y)
component_generator = ComponentGenerator(x.shape[1])
component_generator.add_operator(2)
component_generator.add_operator(3)
component_generator.add_operator(4)
component_generator.add_operator(5)
component_generator.add_operator(6)
component_generator.add_operator(7)
component_generator.add_operator(10)
crossover = AGraphCrossover(component_generator)
mutation = AGraphMutation(component_generator)
agraph_generator = AGraphGenerator(STACK_SIZE, component_generator)
fitness = ExplicitRegression(training_data=training_data,
metric='root mean squared error')
local_opt_fitness = ContinuousLocalOptimization(fitness,
algorithm='L-BFGS-B')
evaluator = Evaluation(local_opt_fitness)
#ea = AgeFitnessEA(evaluator, agraph_generator, crossover,
# mutation, 0.4, 0.4, POP_SIZE)
ea = DeterministicCrowdingEA(evaluator, crossover, mutation, 0.4, 0.4)
island = Island(ea, agraph_generator, POP_SIZE)
archipelago = ParallelArchipelago(island)
opt_result = archipelago.evolve_until_convergence(
MAX_GENERATIONS,
fitness_threshold=FITNESS_THRESHOLD,
min_generations=MIN_GENERATIONS,
stagnation_generations=STAGNATION_LIMIT)
if opt_result.success:
if rank == 0:
print("best: ", archipelago.get_best_individual())
def create_evolutionary_algorithm():
crossover = SinglePointCrossover()
mutation = SinglePointMutation(generate_0_or_1)
variation_phase = VarOr(crossover,
mutation,
crossover_probability=0.4,
mutation_probability=0.4)
fitness = OneMaxFitnessFunction()
evaluation_phase = Evaluation(fitness)
selection_phase = Tournament(tournament_size=2)
return EvolutionaryAlgorithm(variation_phase, evaluation_phase,
selection_phase)
def main():
crossover = SinglePointCrossover()
mutation = SinglePointMutation(get_random_float)
selection = Tournament(10)
fitness = ZeroMinFitnessFunction()
local_opt_fitness = ContinuousLocalOptimization(fitness)
evaluator = Evaluation(local_opt_fitness)
ea = MuPlusLambda(evaluator, selection, crossover, mutation, 0.4, 0.4, 20)
generator = MultipleFloatChromosomeGenerator(get_random_float, 8)
island = Island(ea, generator, 25)
island.evolve(1)
report_max_min_mean_fitness(island)
island.evolve(500)
report_max_min_mean_fitness(island)
def explicit_regression_benchmark():
np.random.seed(15)
communicator = MPI.COMM_WORLD
rank = MPI.COMM_WORLD.Get_rank()
x = None
y = None
if rank == 0:
x = init_x_vals(-10, 10, 100)
y = equation_eval(x)
x = MPI.COMM_WORLD.bcast(x, root=0)
y = MPI.COMM_WORLD.bcast(y, root=0)
training_data = ExplicitTrainingData(x, y)
component_generator = ComponentGenerator(x.shape[1])
component_generator.add_operator(2)
component_generator.add_operator(3)
component_generator.add_operator(4)
crossover = AGraphCrossover(component_generator)
mutation = AGraphMutation(component_generator)
agraph_generator = AGraphGenerator(STACK_SIZE, component_generator)
fitness = ExplicitRegression(training_data=training_data)
local_opt_fitness = ContinuousLocalOptimization(fitness, algorithm='lm')
evaluator = Evaluation(local_opt_fitness)
ea = AgeFitnessEA(evaluator, agraph_generator, crossover, mutation, 0.4,
0.4, POP_SIZE)
island = Island(ea, agraph_generator, POP_SIZE)
archipelago = ParallelArchipelago(island)
opt_result = archipelago.evolve_until_convergence(max_generations=500,
fitness_threshold=1.0e-4)
if rank == 0:
if opt_result.success:
print("print the best indv", archipelago.get_best_individual())
else:
print("Failed.")
def test_island_hof(mocker):
hof = mocker.Mock()
crossover = SinglePointCrossover()
mutation = SinglePointMutation(mutation_function)
selection = Tournament(10)
fitness = MultipleValueFitnessFunction()
evaluator = Evaluation(fitness)
ev_alg = MuPlusLambda(evaluator, selection, crossover, mutation,
0.2, 0.4, 20)
generator = MultipleValueChromosomeGenerator(mutation_function, 10)
island = Island(ev_alg, generator, 25, hall_of_fame=hof)
island.evolve(10)
hof.update.assert_called_once()
hof_update_pop = hof.update.call_args[0][0]
for h, i in zip(hof_update_pop, island.population):
assert h == i
def onemax_evaluator(onemax_fitness):
return Evaluation(onemax_fitness)
def test_setting_eval_count(single_value_population_of_4, fitness_function):
evaluation = Evaluation(fitness_function)
evaluation.eval_count = -4
assert evaluation.eval_count == -4
evaluation(single_value_population_of_4)
assert evaluation.eval_count == 0
def execute_generational_steps():
communicator = MPI.COMM_WORLD
rank = MPI.COMM_WORLD.Get_rank()
x = None
y = None
if rank == 0:
df = pd.read_csv('data/combined_clean_data.csv')
df = df.dropna()
train, test = train_test_split(df, test_size=0.2, random_state=42)
columns = df.columns
x = train.loc[:, ~columns.str.contains('Damage')]
x = x.loc[:, x.columns != 'Time']
x = x.loc[:, x.columns != 'Machine'].values
y = train.loc[:, columns.str.contains('Damage')]
y = y.iloc[:, 0].values.reshape((-1, 1))
x = MPI.COMM_WORLD.bcast(x, root=0)
y = MPI.COMM_WORLD.bcast(y, root=0)
training_data = ExplicitTrainingData(x, y)
component_generator = ComponentGenerator(x.shape[1])
component_generator.add_operator(2) # +
component_generator.add_operator(3) # -
component_generator.add_operator(4) # *
component_generator.add_operator(5) # /
# component_generator.add_operator(6) # sin
# component_generator.add_operator(7) # cos
# component_generator.add_operator(8) # exponential
# component_generator.add_operator(10) # power
# component_generator.add_operator(12) # sqrt
crossover = AGraphCrossover(component_generator)
mutation = AGraphMutation(component_generator)
agraph_generator = AGraphGenerator(STACK_SIZE, component_generator)
fitness = ExplicitRegression(training_data=training_data,
metric='mean squared error')
local_opt_fitness = ContinuousLocalOptimization(fitness, algorithm='lm')
evaluator = Evaluation(local_opt_fitness)
ea = DeterministicCrowdingEA(evaluator, crossover, mutation,
CROSSOVER_PROBABILITY, MUTATION_PROBABILITY)
island = FitnessPredictorIsland(ea,
agraph_generator,
POP_SIZE,
predictor_size_ratio=0.2)
pareto_front = ParetoFront(secondary_key=lambda ag: ag.get_complexity(),
similarity_function=agraph_similarity)
archipelago = ParallelArchipelago(island, hall_of_fame=pareto_front)
optim_result = archipelago.evolve_until_convergence(
MAX_GENERATIONS,
FITNESS_THRESHOLD,
convergence_check_frequency=CHECK_FREQUENCY,
min_generations=MIN_GENERATIONS,
checkpoint_base_name='checkpoint',
num_checkpoints=2)
if optim_result.success:
if rank == 0:
print("best: ", archipelago.get_best_individual())
if rank == 0:
print(optim_result)
print("Generation: ", archipelago.generational_age)
print_pareto_front(pareto_front)
plot_pareto_front(pareto_front)