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(fix-sampling-utils): attribute errors and Gaussian_sampler clip function #1

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(fix-sampling-utils): attribute errors and Gaussian_sampler clip function #1

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0165668
Fix: attribute error 'np.Infinity' was removed in the NumPy 2.0 release
axreyes Oct 8, 2025
20e345b
Fix: incorrect clip target in Gaussian local sampling loop
axreyes Oct 8, 2025
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8 changes: 4 additions & 4 deletions utils/sampling_utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -160,7 +160,7 @@ def update(self, samples, scores, alpha_max, **kwargs):
return indices


def run_sampling(self, evaluator, num_samples, n_iter, minimize=False, alpha_max=1.0, early_stopping=np.Infinity,
def run_sampling(self, evaluator, num_samples, n_iter, minimize=False, alpha_max=1.0, early_stopping=np.inf,
save_path='./sampling', n_parallel=1, plot_contour=False, executor=mp.Pool, verbose=True, init_samples=None):
'''
Function to maximize given black-box function and save results to ./sampling/
Expand Down Expand Up @@ -563,7 +563,7 @@ def sample(self, num_samples, verbose=True, **kwargs):
while len(local_samples) < local_sampling:
count = local_sampling - len(local_samples)
new_samples = gaussian_mix.sample(n_samples=count)[0]
new_samples = np.clip(local_samples, self.boundaries[:,0], self.boundaries[:,1])
new_samples = np.clip(new_samples, self.boundaries[:,0], self.boundaries[:,1])

constraint_check = np.asarray(list(map(self.constraint_fn, new_samples)))
new_samples = new_samples[np.where(constraint_check)[0]]
Expand Down Expand Up @@ -677,7 +677,7 @@ def get_pairs(self, num_pairs):
distance_mat[i][j] = np.sum((samples[i]-samples[j])**2)
distance_mat[j][i] = np.sum((samples[i]-samples[j])**2)
for i in range(len(scores)):
distance_mat[i,i] = np.Infinity
distance_mat[i,i] = np.inf
pair_each_point = np.zeros(len(scores)).astype(np.int32)
id0 = 0
while(len(pairs)<num_pairs):
Expand Down Expand Up @@ -936,7 +936,7 @@ def divide(self, max_id, max_dim):
self.split_region(max_id, ids)


def run_sampling(self, evaluator, num_samples, n_iter, minimize=False, alpha_max=1.0, early_stopping=np.Infinity,
def run_sampling(self, evaluator, num_samples, n_iter, minimize=False, alpha_max=1.0, early_stopping=np.inf,
save_path='./sampling', n_parallel=1, plot_contour=False, executor=mp.Pool, verbose=True, init_samples=None):
'''
Function to maximize given black-box function and save results to ./sampling/
Expand Down