n_jobs = self.n_jobs
// account for case n_jobs < 0
if n_jobs < 0:
n_jobs = max(1, cpu_count() + n_jobs + 1)
for search_space, optimizer in zip(search_spaces, optimizers):
// if not provided with search subspace, n_iter is taken as
// self.n_iter
if isinstance(search_space, tuple):
After Change
while n_iter > 0:
// when n_iter < n_jobs points left for evaluation
n_points_adjusted = min(n_iter, n_points)
print(n_points_adjusted)
optim_result = self._step(
X, y, search_space, optimizer,
groups=groups, n_points=n_points_adjusted
