I would like to be able to convert back and forth between Python's standard Random and numpy's np.random.RandomState. Both of these use the Mersenne Twister algorithm, so it should be possible (unless they are using different versions of this algorithm).
I started looking into the getstate/setstate and get_state/set_state methods of these objects. But I'm not sure how to convert the details of them.
import numpy as np
import random
rng1 = np.random.RandomState(seed=0)
rng2 = random.Random(seed=0)
state1 = rng1.get_state()
state2 = rng2.getstate()
Inspecting each state I see:
>>> print(state1)
('MT19937', array([0, 1, 1812433255, ..., 1796872496], dtype=uint32), 624, 0, 0.0)
>>> print(state2)
(3, (2147483648, 766982754, ..., 1057334138, 2902720905, 624), None)
The first state is a tuple of size 5 with the len(state1[1]) = 624
.
The second state is a tuple of size 3 with len(state2[1]) = 625
. It seems like the last item in the state2 is actually the 624 in state1, which means the arrays are actually the same size. So far so good. These seem reasonably compatible.
Unfortunately the internal numbers don't have an obvious correspondence, so a seed of 0 results in different states, which makes sense because rng1.rand() = .548
and rng2.random() = .844
. So, the algorithm seems slightly different.
However, I don't need them to correspond perfectly. I just need to be able to set the state of one rng from the other deterministicly without influencing the state of the first.
Ideally, once I used the state of the first to set the state of the second, without calling any random methods, and then used the second to set the state of the first, the first state would remain unchanged, but this is not a requirement.
Currently I have a hacked together method that just swaps the 624-length list I can extract from both rngs. However, I'm not sure if there are any problems with this approach. Can anyone more knowledgeable on this subject shed some light?
Here is my approach, but I'm not sure that works correctly.
np_rng = np.random.RandomState(seed=0)
py_rng = random.Random(0)
# Convert python to numpy random state (incomplete)
py_state = py_rng.getstate()
np_rng = np.random.RandomState(seed=0)
np_state = np_rng.get_state()
new_np_state = (
np_state[0],
np.array(py_state[1][0:-1], dtype=np.uint32),
np_state[2], np_state[3], np_state[4])
np_rng.set_state(new_np_state)
# Convert numpy to python random state (incomplete)
np_state = np_rng.get_state()
py_rng = random.Random(0)
py_state = py_rng.getstate()
new_py_state = (
py_state[0], tuple(np_state[1].tolist() + [len(np_state[1])]),
py_state[1]
)
py_rng.setstate(new_py_state)
EDIT:
Doing some investigation I checked what happens to the state over 10 calls to a random function.
np_rng = np.random.RandomState(seed=0)
py_rng = random.Random(0)
for i in range(10):
np_rng.rand()
npstate = np_rng.get_state()
print([npstate[0], npstate[1][[0, 1, 2, -2, -1]], npstate[2], npstate[3], npstate[4]])
for i in range(10):
py_rng.random()
pystate = py_rng.getstate()
print([pystate[0], pystate[1][0:3] + pystate[1][-2:], pystate[2]])
['MT19937', array([2443250962, 1093594115, 1878467924, 2648828502, 1678096082], dtype=uint32), 2, 0, 0.0]
['MT19937', array([2443250962, 1093594115, 1878467924, 2648828502, 1678096082], dtype=uint32), 4, 0, 0.0]
['MT19937', array([2443250962, 1093594115, 1878467924, 2648828502, 1678096082], dtype=uint32), 6, 0, 0.0]
['MT19937', array([2443250962, 1093594115, 1878467924, 2648828502, 1678096082], dtype=uint32), 8, 0, 0.0]
['MT19937', array([2443250962, 1093594115, 1878467924, 2648828502, 1678096082], dtype=uint32), 10, 0, 0.0]
['MT19937', array([2443250962, 1093594115, 1878467924, 2648828502, 1678096082], dtype=uint32), 12, 0, 0.0]
['MT19937', array([2443250962, 1093594115, 1878467924, 2648828502, 1678096082], dtype=uint32), 14, 0, 0.0]
['MT19937', array([2443250962, 1093594115, 1878467924, 2648828502, 1678096082], dtype=uint32), 16, 0, 0.0]
['MT19937', array([2443250962, 1093594115, 1878467924, 2648828502, 1678096082], dtype=uint32), 18, 0, 0.0]
['MT19937', array([2443250962, 1093594115, 1878467924, 2648828502, 1678096082], dtype=uint32), 20, 0, 0.0]
[3, (1372342863, 3221959423, 4180954279, 418789356, 2), None]
[3, (1372342863, 3221959423, 4180954279, 418789356, 4), None]
[3, (1372342863, 3221959423, 4180954279, 418789356, 6), None]
[3, (1372342863, 3221959423, 4180954279, 418789356, 8), None]
[3, (1372342863, 3221959423, 4180954279, 418789356, 10), None]
[3, (1372342863, 3221959423, 4180954279, 418789356, 12), None]
[3, (1372342863, 3221959423, 4180954279, 418789356, 14), None]
[3, (1372342863, 3221959423, 4180954279, 418789356, 16), None]
[3, (1372342863, 3221959423, 4180954279, 418789356, 18), None]
[3, (1372342863, 3221959423, 4180954279, 418789356, 20), None]
I expect that the first item in each tuple is just the version of the algorithm they are using.
Its interesting to see that the 624 integers do not seem to change. Is this always the case?
However, I'm still unsure what the final None means in the Python version and the final 2 number are in the numpy version.
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