changing gridsearchQ to ensure reproducibility

quapy/model_selection.py

@@ -83,7 +83,8 @@ class GridSearchQ(BaseQuantifier):
         tinit = time()
 
         hyper = [dict({k: values[i] for i, k in enumerate(params_keys)}) for values in itertools.product(*params_values)]
-        scores = qp.util.parallel(self._delayed_eval, ((params, training) for params in hyper), n_jobs=self.n_jobs)
+        #pass a seed to parallel so it is set in clild processes
+        scores = qp.util.parallel(self._delayed_eval, ((params, training) for params in hyper), seed=qp.environ.get('_R_SEED', None), n_jobs=self.n_jobs)
 
         for params, score, model in scores:
             if score is not None:

quapy/util.py

@@ -5,6 +5,7 @@ import os
 import pickle
 import urllib
 from pathlib import Path
+from contextlib import ExitStack
 import quapy as qp
 
 import numpy as np
@@ -36,7 +37,7 @@ def map_parallel(func, args, n_jobs):
     return list(itertools.chain.from_iterable(results))
 
 
-def parallel(func, args, n_jobs):
+def parallel(func, args, n_jobs, seed = None):
     """
     A wrapper of multiprocessing:
 
@@ -44,14 +45,20 @@ def parallel(func, args, n_jobs):
     >>>      delayed(func)(args_i) for args_i in args
     >>> )
 
-    that takes the `quapy.environ` variable as input silently
+    that takes the `quapy.environ` variable as input silently.
+    Seeds the child processes to ensure reproducibility when n_jobs>1
     """
-    def func_dec(environ, *args):
+    def func_dec(environ, seed, *args):
         qp.environ = environ.copy()
         qp.environ['N_JOBS'] = 1
-        return func(*args)
+        #set a context with a temporal seed to ensure results are reproducibles in parallel
+        with ExitStack() as stack:
+            if seed is not None:
+                stack.enter_context(qp.util.temp_seed(seed))
+            return func(*args)
+    
     return Parallel(n_jobs=n_jobs)(
-        delayed(func_dec)(qp.environ, args_i) for args_i in args
+        delayed(func_dec)(qp.environ, None if seed is None else seed+i, args_i) for i, args_i in enumerate(args)
     )
 
 
@@ -66,6 +73,8 @@ def temp_seed(random_state):
     :param random_state: the seed to set within the "with" context
     """
     state = np.random.get_state()
+    #save the seed just in case is needed (for instance for setting the seed to child processes)
+    qp.environ['_R_SEED'] = random_state
     np.random.seed(random_state)
     try:
         yield