BUGs in random_choice

[oyamad]

I tried the recently merged random_choice #391 and found the following:

1. This doesn't work with Numba v.0.36 with the following error:

   TypeError: Failed at nopython (inline calls to locally defined closures)
   sequence item 0: expected str instance, NoneType found
   

   It works with Numba v.0.37, so this should be a bug in Numba v.0.36. The problem for us is that the latest Anaconda contains Numba v.0.36.2.

2. Setting a seed with np.random.seed within a library (hidden from the user) is problematic: it seems to affect Numba's global random state.

   Suppose you have the following jit function:

   @numba.jit(nopython=True)
   def my_rand(size):
       return np.random.uniform(0, 1, size)

   Call it after calling random_choice with the same seed (with Numba v.0.37):

   qe.random_choice(np.array([0.1, 0.9]), seed=0)
   my_rand(5)

   Then it always returns the same sequence:

   array([0.71518937, 0.60276338, 0.54488318, 0.4236548 , 0.64589411])

@jstac What is your use case where you need a jitted version of this functionality? What's wrong with generating random numbers outside your jit function and then using np.searchsorted (or qe.searchsorted) inside the jit function?

[jstac]

@oyamad I need to draw one variate from a fixed discrete distribution until a stopping condition is satisfied, in a loop. It needs to be fast. I don't know the size of the sequence ex ante.

[oyamad]

@jstac I see.

Would it be possible to set a seed in your own function, removing np.random.seed from quantecon?

[jstac]

@oyamad Yes, that's certainly possible.

Regarding the numba version, thanks for picking that up. Version 0.36.2 has many problems.

So, the proposal is

1. drop the ability to set seeds in these functions (apart from externally, as you suggest)
2. replace None with 0 as the default argument for cum_sum

[oyamad]

@jstac Here's my proposal:

>>> cdf = np.cumsum([0.4, 0.6])
>>> qe.random.draw(cdf)
1
>>> qe.random.draw(cdf, 1)
array([1])
>>> qe.random.draw(cdf, 10)
array([1, 0, 1, 1, 0, 1, 1, 1, 1, 1])

Works with both Numba 0.36.2 and 0.37.0.

[jstac]

@oyamad This is beautiful. Thank you. I learned a lot from reading the code.

I fully support this. Would you mind to make a PR?