Seed value error in multi_runs

[soudy]

When using multi_runs, some of my classmates got the following error:

Traceback (most recent call last):
  File "C:\Users\user\AppData\Local\Programs\Python\Python39\lib\multiprocessing\pool.py", line 125, in worker
    result = (True, func(*args, **kwds))
  File "C:\Users\user\AppData\Local\Programs\Python\Python39\lib\site-packages\ndlib\utils.py", line 71, in __execute
    np.random.seed(seed)
  File "mtrand.pyx", line 246, in numpy.random.mtrand.RandomState.seed
  File "_mt19937.pyx", line 166, in numpy.random._mt19937.MT19937._legacy_seeding
  File "_mt19937.pyx", line 180, in numpy.random._mt19937.MT19937._legacy_seeding
ValueError: Seed must be between 0 and 2**32 - 1
"""

The above exception was the direct cause of the following exception:

ValueError                                Traceback (most recent call last)
~\AppData\Local\Temp/ipykernel_12920/4199588637.py in <module>
     18 
     19 # Simulation multiple execution
---> 20 trends = multi_runs(model1, execution_number=10, iteration_number=100, infection_sets=None, nprocesses=4)

~\AppData\Local\Programs\Python\Python39\lib\site-packages\ndlib\utils.py in multi_runs(model, execution_number, iteration_number, infection_sets, nprocesses)
     56 
     57             for result in results:
---> 58                 executions.append(result.get())
     59 
     60     return executions

~\AppData\Local\Programs\Python\Python39\lib\multiprocessing\pool.py in get(self, timeout)
    769             return self._value
    770         else:
--> 771             raise self._value
    772 
    773     def _set(self, i, obj):

ValueError: Seed must be between 0 and 2**32 - 1

Numpy added this check in https://github.com/numpy/numpy/commit/6b1a1205eac6fe5d162f16155d500765e8bca53c, because seeds larget than 32 bits get truncated back to 32 bit, leading to the same stream of random numbers for two different seeds. A quick and dirty fix I did was changing line 71 in ndlib/utils.py as follows:

-    np.random.seed(seed)
+    np.random.seed(seed % 2**32)

This will results in the multiple seeds leading to the same stream of random numbers, so you may want to change the way you generate seeds to fall between 0 and 2**32-1 instead.