np.random.choice: probabilities do not sum to 1

This is a known issue with numpy. The random choice function checks for the sum of the probabilities using a given tolerance (here the source)

The solution is to normalize the probabilities by dividing them by their sum if the sum is close enough to 1

Example:

>>> p=[  1.42836755e-01,   1.42836735e-01  , 1.42836735e-01,   1.42836735e-01
,   4.76122449e-05,   1.42836735e-01  , 4.76122449e-05  , 1.42836735e-01,
   1.42836735e-01,   4.79122449e-05]
>>> sum(p) 
1.0000003017347 # over tolerance limit
>>> np.random.choice([1,2,3,4,5,6,7,8,9, 10], 4, p=p, replace=False)

Traceback (most recent call last):
  File "<pyshell#23>", line 1, in <module>
    np.random.choice([1,2,3,4,5,6,7,8,9, 10], 4, p=p, replace=False)
  File "mtrand.pyx", line 1417, in mtrand.RandomState.choice (numpy\random\mtrand\mtrand.c:15985)
ValueError: probabilities do not sum to 1

With normalization:

>>> p = np.array(p)
>>> p /= p.sum()  # normalize
>>> np.random.choice([1,2,3,4,5,6,7,8,9, 10], 4, p=p, replace=False)
array([8, 4, 1, 6])

Convert it to float64:

p = np.asarray(p).astype('float64')
p = p / np.sum(p)
np.random.choice([1,2,3,4,5,6,7,8,9, 10], 4, p=p, replace=False)

This was inspired by another post: How can I avoid value errors when using numpy.random.multinomial?