def _detach_recipe(self, recipe):
self.search_space = recipe.search_space()
stop = recipe.runtime_params()
self.metric_threshold = None
if "reward_metric" in stop.keys():
self.mode = Evaluator.get_metric_mode(self.metric)
self.metric_threshold = -stop["reward_metric"] if \
self.mode == "min" else stop["reward_metric"]
self.epochs = stop["training_iteration"]
self.num_samples = stop["num_samples"]
def _validate_metric_mode(metric, mode):
if not mode:
if callable(metric):
raise ValueError(
"You must specify `metric_mode` for your metric function")
try:
from zoo.orca.automl.metrics import Evaluator
mode = Evaluator.get_metric_mode(metric)
except ValueError:
pass
if not mode:
raise ValueError(
f"We cannot infer metric mode with metric name of {metric}. Please"
f" specify the `metric_mode` parameter in AutoEstimator.fit()."
)
if mode not in ["min", "max"]:
raise ValueError("`mode` has to be one of ['min', 'max']")
return mode