def parse_y(self, metrics):
if not metrics:
return metrics
y_values = []
for value in metrics:
if V1Optimization.maximize(self.config.metric.optimization):
y_values.append(float(value))
else:
y_values.append(-float(value))
return np.array(y_values)
def get_bracket_suggestions(
self, bracket_iteration: int, configs=None, metrics=None
):
"""Reduce the experiments to restart."""
# Get the number of experiments to keep
n_configs_to_keep = self.get_num_runs_to_keep(
num_runs=len(configs), bracket_iteration=bracket_iteration,
)
# Order the experiments
experiments = zip(configs, metrics)
experiments = sorted(
experiments,
key=lambda x: x[1],
reverse=V1Optimization.maximize(self.config.optimization),
)
# Keep n experiments config
return [xp[0] for xp in experiments[:n_configs_to_keep]]