def test_eliminate_NSGA(GaussianNB, RandomForestPipeline, LinearSVC):
GaussianNB.fitness = Fitness((3, -2), 0, 0, 0)
RandomForestPipeline.fitness = Fitness((4, -2), 0, 0, 0)
LinearSVC.fitness = Fitness((3, -1), 0, 0, 0)
eliminated = eliminate_from_pareto(pop=[GaussianNB, RandomForestPipeline, LinearSVC], n=1)
assert eliminated == [GaussianNB], "The element (3, -2) is dominated by both others and should be eliminated."
# Check order independence
eliminated = eliminate_from_pareto(pop=[RandomForestPipeline, GaussianNB, LinearSVC], n=1)
assert eliminated == [GaussianNB], "Individual should be dominated regardless of order."
def test_create_from_population(opset, GNB, ForestPipeline, LinearSVC):
GNB.fitness = Fitness((3, -2), 0, 0, 0)
ForestPipeline.fitness = Fitness((4, -2), 0, 0, 0)
LinearSVC.fitness = Fitness((3, -1), 0, 0, 0)
parents = [GNB, ForestPipeline, LinearSVC]
new = create_from_population(opset, pop=parents, n=1, cxpb=0.5, mutpb=0.5)
assert 1 == len(new)
assert new[0]._id not in map(lambda i: i._id, parents)
assert new[0].pipeline_str() not in map(lambda i: i.pipeline_str(),
parents)
def evaluate_individual(
individual: Individual,
evaluate_pipeline: Callable,
timeout: float = 1e6,
deadline: Optional[float] = None,
add_length_to_score: bool = True,
**kwargs,
) -> Evaluation:
""" Evaluate the pipeline specified by individual, and record
Parameters
----------
individual: Individual
Blueprint for the pipeline to evaluate.
evaluate_pipeline: Callable
Function which takes the pipeline and produces validation predictions,
scores, estimators and errors.
timeout: float (default=1e6)
Maximum time in seconds that the evaluation is allowed to take.
Don't depend on high accuracy.
A shorter timeout is imposed if `deadline` is in less than `timeout` seconds.
deadline: float, optional
A time in seconds since epoch.
Cut off evaluation at `deadline` even if `timeout` seconds have not yet elapsed.
add_length_to_score: bool (default=True)
Add the length of the individual to the score result of the evaluation.
**kwargs: Dict, optional (default=None)
Passed to `evaluate_pipeline` function.
Returns
-------
Evaluation
"""
result = Evaluation(individual, pid=os.getpid())
result.start_time = datetime.now()
if deadline is not None:
time_to_deadline = deadline - time.time()
timeout = min(timeout, time_to_deadline)
with Stopwatch() as wall_time, Stopwatch(
time.process_time) as process_time:
evaluation = evaluate_pipeline(individual.pipeline,
timeout=timeout,
**kwargs)
result._predictions, result.score, result._estimators, result.error = evaluation
result.duration = wall_time.elapsed_time
if add_length_to_score:
result.score = result.score + (-len(individual.primitives), )
individual.fitness = Fitness(
result.score,
result.start_time,
wall_time.elapsed_time,
process_time.elapsed_time,
)
return result