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, error = evaluation
if error is not None:
result.error = f"{type(error)} {str(error)}"
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
def _mock_evaluation(individual: Individual,
predictions: Optional[Union[np.ndarray, pd.DataFrame,
pd.Series]] = np.zeros(30, ),
score: Optional[Tuple[float, ...]] = None,
estimators: List[object] = None,
start_time: int = 0,
duration: int = 0,
error: str = None) -> Evaluation:
return Evaluation(
individual, predictions,
score if score is not None else tuple(np.random.random(size=(3, ))),
estimators, start_time, duration, error)