I want to write a function that randomly picks elements from a training set, based on the bin probabilities provided. I divide the set indices to 11 bins, then create custom probabilities for them.

bin_probs = [0.5, 0.3, 0.15, 0.04, 0.0025, 0.0025, 0.001, 0.001, 0.001, 0.001, 0.001]

X_train = list(range(2000000))

train_probs = bin_probs * int(len(X_train) / len(bin_probs)) # extend probabilities across bin elements
train_probs.extend([0.001]*(len(X_train) - len(train_probs))) # a small fix to match number of elements
train_probs = train_probs/np.sum(train_probs) # normalize
indices = np.random.choice(range(len(X_train)), replace=False, size=50000, p=train_probs)
out_images = X_train[indices.astype(int)] # this is where I get the error

I get the following error:

TypeError: only integer scalar arrays can be converted to a scalar index with 1D numpy indices array

I find this weird, since I already checked the array of indices that I have created. It is 1-D, it is integer, and it is scalar.

What am I missing?

Note : I tried to pass indices with astype(int). Same error.

out_images = np.array(X_train)[indices.astype(int)]

>>> a = np.eye(2)
>>> np.concatenate(a, a)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<__array_function__ internals>", line 6, in concatenate
TypeError: only integer scalar arrays can be converted to a scalar index

>>> np.concatenate((a, a))
array([[1., 0.],
       [0., 1.],
       [1., 0.],
       [0., 1.]])