issue 19 : A note on reproducibility

docs/tuto.rst

@@ -102,15 +102,18 @@ Because the framework uses the `random` and `numpy` modules, you can be sure
 to have the same results by running the same script several times if you
 add the following instructions before initializing problems or algorithms:
 
+
 .. code-block:: python
 
-    import random
-    import numpy
+    from moead_framework.tool.result import set_seed
 
     seed = 0
-    random.seed(seed)
-    np.random.seed(seed)
+    set_seed(seed)
 
+.. warning::
+    This approach is not safe in threaded environments.
+    By re-using a seed value, the same sequence should be reproducible from run to run as long as
+    multiple threads are not running (https://docs.python.org/3/library/random.html#notes-on-reproducibility)
 
 You can find more information at the following links:
 

moead_framework/test/test_combinatorial_algorithm.py

@@ -1,20 +1,17 @@
 import unittest
-import random
 import os
-import numpy as np
 from moead_framework.aggregation.tchebycheff import Tchebycheff
 from moead_framework.algorithm.combinatorial import Moead, MoeadDeltaNr, MoeadSPSRandom, MoeadDRA
 from moead_framework.problem.combinatorial import Rmnk
-from moead_framework.tool.result import compute_hypervolume
+from moead_framework.tool.result import compute_hypervolume, set_seed
 
 
 class AlgorithmsTest(unittest.TestCase):
     """Test implemented algorithms."""
 
     def setUp(self):
         """Init"""
-        random.seed(1)
-        np.random.seed(1)
+        set_seed(1)
         self.number_of_evaluations = 100
 
         project_path = os.path.dirname(os.path.abspath(__file__))

moead_framework/test/test_genetic_operator.py

@@ -1,20 +1,18 @@
 import unittest
-import random
-import numpy as np
 
 from moead_framework.core.genetic_operator.combinatorial import Crossover, BinaryMutation
 from moead_framework.core.genetic_operator.numerical import DifferentialEvolutionCrossover, PolynomialMutation, \
     MoeadDeOperators
 from moead_framework.problem.numerical import Zdt1
+from moead_framework.tool.result import set_seed
 
 
 class GeneticOperatorsTest(unittest.TestCase):
     """Test genetic operators."""
 
     def setUp(self):
         """Init"""
-        random.seed(1)
-        np.random.seed(1)
+        set_seed(1)
 
     def test_combinatorial_binary_mutation(self):
         """Test combinatorial binary mutation"""

moead_framework/test/test_numerical_algorithm.py

@@ -1,20 +1,17 @@
 import unittest
-import random
 import os
-import numpy as np
 from moead_framework.aggregation import Tchebycheff
 from moead_framework.algorithm.numerical import Moead
 from moead_framework.problem.numerical import Zdt1
-from moead_framework.tool.result import compute_hypervolume
+from moead_framework.tool.result import compute_hypervolume, set_seed
 
 
 class AlgorithmsNumericalTest(unittest.TestCase):
     """Test implemented algorithms."""
 
     def setUp(self):
         """Init"""
-        random.seed(1)
-        np.random.seed(1)
+        set_seed(1)
         self.number_of_evaluations = 100
 
         project_path = os.path.dirname(os.path.abspath(__file__))

moead_framework/test/test_parent_selector.py

@@ -1,23 +1,21 @@
 import os
 
 import unittest
-import random
-import numpy as np
 
 from moead_framework.aggregation import Tchebycheff
 from moead_framework.algorithm.combinatorial import Moead
 from moead_framework.core.parent_selector import TwoRandomParentSelector, OneRandomAndCurrentParentSelector, \
     TwoRandomAndCurrentParentSelector
 from moead_framework.problem.combinatorial import Rmnk
+from moead_framework.tool.result import set_seed
 
 
 class ParentSelectorTest(unittest.TestCase):
     """Test genetic operators."""
 
     def setUp(self):
         """Init"""
-        random.seed(1)
-        np.random.seed(1)
+        set_seed(1)
 
         self.number_of_evaluations = 100
 

moead_framework/test/test_solution.py

@@ -1,20 +1,18 @@
 from copy import copy
 
-import numpy as np
-import random
 import os
 import unittest
 
 from moead_framework.problem.combinatorial import Rmnk
+from moead_framework.tool.result import set_seed
 
 
 class RmnkTest(unittest.TestCase):
     """Test the 'rmnk' problem."""
 
     def setUp(self):
         """Init"""
-        random.seed(1)
-        np.random.seed(1)
+        set_seed(1)
         project_path = os.path.dirname(os.path.abspath(__file__))
         self.problem = Rmnk(project_path + '/data/instances/rmnk_0_2_100_1_0.dat')
         self.solution = self.problem.generate_random_solution()

moead_framework/test/test_tool.py

@@ -1,19 +1,16 @@
 import unittest
-import random
 import os
-import numpy as np
 from moead_framework.problem.combinatorial import Rmnk
 from moead_framework.tool.mop import get_non_dominated, population_size_without_duplicate, compute_crowding_distance
-from moead_framework.tool.result import save_population, save_population_full, compute_hypervolume
+from moead_framework.tool.result import save_population, save_population_full, compute_hypervolume, set_seed
 
 
 class ToolsTest(unittest.TestCase):
     """Test implemented algorithms."""
 
     def setUp(self):
         """Init"""
-        random.seed(1)
-        np.random.seed(1)
+        set_seed(1)
         project_path = os.path.dirname(os.path.abspath(__file__))
         self.rmnk = Rmnk(instance_file=project_path + '/data/instances/rmnk_0_2_100_1_0.dat')
         self.rmnk3D = Rmnk(instance_file=project_path + '/data/instances/rmnk_0_3_100_1_0.dat')

moead_framework/tool/result.py

@@ -1,3 +1,4 @@
+import random
 import numpy as np
 
 
@@ -43,6 +44,20 @@ def save_population_full(file_name, population):
     file.close()
 
 
+def set_seed(seed):
+    """
+    Set the seed for numpy and random packages.
+    Warning, this approach is not safe in threaded environments.
+    By re-using a seed value, the same sequence should be reproducible from run to run as long as
+    multiple threads are not running (https://docs.python.org/3/library/random.html#notes-on-reproducibility)
+
+    :param seed: integer
+    :return:
+    """
+    random.seed(seed)
+    np.random.seed(seed)
+
+
 def compute_hypervolume(solutions, ref_point):
     hypervolume_class = HyperVolume(reference_point=ref_point)