def test_non_tensor_input():
shape = (2, 4)
population = [[1, 2, 3, 4], [1, 2, 3, 4]]
inputs = Input(shape)
inputs.assign(population)
res = inputs.get()
assert B.is_tensor(res)
def solve_tsp(args):
population_shape, generations, cities, chart_data, distances, idx2city = args # noqa
NUM_CITIES = len(cities)
NUM_REVERSE_OPERATIONS = 4
MAX_REVERSE_PROBABILITY = 0.3
REVERSE_POPULATION_FRACTION = 0.3
MIN_REVERSE_PROBABILITY = 0.1
SHUFFLE_POPULATION_FRACTION = 0.2
population = uniform_population(population_shape)
rpi = MAX_REVERSE_PROBABILITY / NUM_REVERSE_OPERATIONS
reverse_probabilty = 1 - rpi
# Evolution model
inputs = Input(shape=population.shape)
x = inputs
for idx in range(NUM_REVERSE_OPERATIONS):
x = Reverse1D(population_fraction=REVERSE_POPULATION_FRACTION,
max_reverse_probability=reverse_probabilty)(x)
reverse_probabilty = max(reverse_probabilty - rpi,
MIN_REVERSE_PROBABILITY)
x = Shuffle(population_fraction=SHUFFLE_POPULATION_FRACTION)(x)
outputs = x
ef = EvoFlow(inputs, outputs, debug=False)
evolution_strategy = SelectFittest(mode='min')
fitness_fn = TSPFitness(distances, NUM_CITIES)
ef.compile(evolution_strategy, fitness_fn)
ef.evolve(population, generations=generations, verbose=0)
def test_graph():
"When passing OPs GF is supposed to return a graph"
i = Input((42, 1))
d1 = Dummy()(i)
d2 = Dummy()(i)
r = Dummy()([d1, d2])
assert issubclass(type(r), Dummy)
assert r.call(42) == 42 # explicitly execute the graph op
def solve_onmax_2d(args):
"""Solve the onemax problem
"""
population_shape, generations = args
population = randint_population(population_shape, 1)
inputs = Input(population_shape)
x = RandomMutations2D(max_gene_value=1)(inputs)
outputs = UniformCrossover2D()(x)
gf = EvoFlow(inputs, outputs, debug=0)
fitness_function = Sum(max_sum_value=10000000)
evolution_strategy = SelectFittest()
gf.compile(evolution_strategy, fitness_function)
gf.evolve(population, generations=generations, verbose=0)
def test_direct_2d():
NUM_EVOLUTIONS = 2
POPULATION_SIZE = 3
GENE_SIZE = 4
MAX_VAL = 10
SHAPE = (POPULATION_SIZE, GENE_SIZE, GENE_SIZE)
population = randint_population(SHAPE, MAX_VAL)
fitness_function = Sum(max_sum_value=GENE_SIZE)
evolution_strategy = SelectFittest()
inputs = Input(shape=SHAPE)
# x = RandomMutations2D(max_gene_value=1, min_gene_value=0)(inputs)
outputs = UniformCrossover2D()(inputs)
ef = EvoFlow(inputs, outputs, debug=True)
ef.compile(evolution_strategy, fitness_function)
ef.evolve(population, generations=NUM_EVOLUTIONS, verbose=0)
def test_helloworld():
"Solve the MAXONE problem"
NUM_EVOLUTIONS = 10
SHAPE = (20, 20)
MAX_VAL = 1
population = randint_population(SHAPE, MAX_VAL)
inputs = Input(SHAPE)
x = RandomMutations1D(max_gene_value=1)(inputs)
outputs = UniformCrossover1D()(x)
gf = EvoFlow(inputs, outputs, debug=0)
fitness_function = Sum(max_sum_value=SHAPE[1])
evolution_strategy = SelectFittest()
gf.compile(evolution_strategy, fitness_function)
gf.summary()
results = gf.evolve(population,
generations=NUM_EVOLUTIONS,
callbacks=[DummyCallback()],
verbose=0)
assert results
metrics = results.get_metrics_history()
# check metrics
for metric_grp, data in metrics.items():
for metric_name, vals in data.items():
assert isinstance(vals, list)
if metric_grp == 'Fitness function':
assert len(vals) == 10
assert isinstance(vals[9], float)
assert 'mean' in data
assert 'max' in data
# assert the engine solved the (Trivial) problem
max_fitness = metrics['Fitness function']['max']
assert max(max_fitness) == 1
assert max_fitness[9] == 1
assert min(max_fitness) < 1 # max sure we did improve
# check population value
population = results.get_populations()
assert (population.shape) == SHAPE
assert B.tensor_equal(population[0], B.tensor([1] * SHAPE[1]))
def test_solve_tsp():
population_shape = (150, 10)
generations = 40
cities, chart_data, distances, idx2city = tsp_setup(10)
BASELINE = 6000
NUM_CITIES = len(cities)
NUM_REVERSE_OPERATIONS = 4
MAX_REVERSE_PROBABILITY = 0.3
REVERSE_POPULATION_FRACTION = 0.3
MIN_REVERSE_PROBABILITY = 0.1
SHUFFLE_POPULATION_FRACTION = 0.2
population = uniform_population(population_shape)
rpi = MAX_REVERSE_PROBABILITY / NUM_REVERSE_OPERATIONS
reverse_probabilty = 1 - rpi
# Evolution model
inputs = Input(shape=population.shape)
x = inputs
for idx in range(NUM_REVERSE_OPERATIONS):
x = Reverse1D(population_fraction=REVERSE_POPULATION_FRACTION,
max_reverse_probability=reverse_probabilty)(x)
reverse_probabilty = max(reverse_probabilty - rpi,
MIN_REVERSE_PROBABILITY)
x = Shuffle(population_fraction=SHUFFLE_POPULATION_FRACTION)(x)
outputs = x
ef = EvoFlow(inputs, outputs, debug=False)
evolution_strategy = SelectFittest(mode='min')
fitness_fn = TSPFitness(distances, NUM_CITIES)
ef.compile(evolution_strategy, fitness_fn)
results = ef.evolve(population, generations=generations, verbose=0)
metrics = results.get_latest_metrics()
assert metrics['Fitness function']['min'] < BASELINE
def test_2d():
shape = (128, 64, 64)
population = B.randint(1, 10, shape=shape)
inputs = Input(shape)
inputs.assign(population)
assert B.tensor_equal(inputs.get(), population)
def test_call_vs_get():
shape = (128, 64)
population = B.randint(1, 10, shape=shape)
inputs = Input(shape)
inputs.assign(population)
assert B.tensor_equal(inputs.get(), inputs.call(''))