HybridMaxRandom/HybridMaxUncertainty ignore probability when given an integer random_state #2601

def _mix_indices(query_idx_1, query_idx_2, mix_probability=0.95, random_state=None):
    query_idx_mix = []
    i = 0
    j = 0
    while i < len(query_idx_1) and j < len(query_idx_2):
        if check_random_state(random_state).rand() < mix_probability:
            query_idx_mix.append(query_idx_1[i])
            i = i + 1
        else:
            query_idx_mix.append(query_idx_2[j])
            j = j + 1
    ...

import numpy as np
from sklearn.utils import check_random_state

# Same "random" draw every time an int seed is (re)wrapped — this is the root cause.
for _ in range(5):
    print(check_random_state(0).rand())
# 0.5488135039273248
# 0.5488135039273248
# 0.5488135039273248
# 0.5488135039273248
# 0.5488135039273248

# Compare to the correct behavior when passing an actual RandomState instance:
rng = np.random.RandomState(0)
for _ in range(5):
    print(check_random_state(rng).rand())
# 0.5488135039273248
# 0.7151893663724195
# 0.6027633760716439
# 0.5448831829968969
# 0.4236547993389047