fixed default seed setting

src/NEMtropy/graph_classes.py

@@ -1022,7 +1022,7 @@ def solve_tool(
         self._solution_error()
         print("\nsolution error = {}".format(self.error))
 
-    def ensemble_sampler(self, n, cpu_n=1, output_dir="sample/", seed=42):
+    def ensemble_sampler(self, n, cpu_n=1, output_dir="sample/", seed=None):
         """The function sample a given number of graphs in the ensemble
         generated from the last model solved. Each grpah is an edgelist
         written in the output directory as `.txt` file.
@@ -2477,7 +2477,7 @@ def solve_tool(
         self._solution_error()
         print("\nsolution error = {}".format(self.error))
 
-    def ensemble_sampler(self, n, cpu_n=1, output_dir="sample/", seed=42):
+    def ensemble_sampler(self, n, cpu_n=1, output_dir="sample/", seed=None):
         """The function sample a given number of graphs in the ensemble
         generated from the last model solved. Each grpah is an edgelist
         written in the output directory as `.txt` file.

tests/test_ensemble_seed.py

@@ -0,0 +1,96 @@
+"""This test checks the default setting for the seed is working.
+When the seed is not manually set, it should be ranodmly chosen.
+"""
+
+import sys
+import os
+sys.path.append("../")
+import NEMtropy.graph_classes as sample
+import NEMtropy.matrix_generator as mg
+import numpy as np
+import unittest  # test tool
+import random
+import networkx as nx
+
+
+class MyTest(unittest.TestCase):
+
+
+    def setUp(self):
+        pass
+
+
+    def test_0(self):
+        """test with 3 classes of cardinality 1
+        and no zero degrees
+        """
+        """
+        A = np.array(
+            [
+                [0, 1, 1, 0],
+                [1, 0, 0, 1],
+                [1, 0, 0, 0],
+                [0, 1, 0, 0],
+            ]
+        )
+        e = [(0,1), (0,2), (1,3)]
+        d = [1,1,2,2]
+        print(e)
+        print(d)
+        """
+        N, seed = (10, 42)
+        A = mg.random_binary_matrix_generator_dense(N, sym=True, seed=seed)
+        # number of copies to generate
+
+        g = sample.UndirectedGraph(A)
+
+        g._solve_problem(
+            model="cm",
+            method="fixed-point",
+            max_steps=100,
+            verbose=False,
+            linsearch=True,
+            initial_guess="uniform",
+        )
+
+        x = g.x
+        # g._solution_error()
+        err = g.error
+
+        # print('\ntest 5: error = {}'.format(g.error))
+        n = 10
+        output_dir = "sample/"
+        # random.seed(100)
+        g_list = []
+        for i in range(n):
+            g.ensemble_sampler(n=1, output_dir=output_dir)
+            g_list.append(np.loadtxt("sample/0.txt"))
+
+        appo = True
+        old = g_list[0]
+        for i in range(1, n):
+            appo = appo*np.all(old == g_list[i])
+
+        # debug
+        """
+        print('original dseq',d)
+        print('original dseq sum ',g.dseq.sum())
+        print('ensemble average dseq', d_emp)
+        print('ensemble dseq sum ',np.array([d_emp[key] for key in d_emp.keys()]).sum())
+        print(d_diff)
+        print('empirical error', ensemble_error)
+        print('theoretical error', err)
+        """
+
+
+        l = os.listdir(output_dir)
+        for f in l:
+            os.remove(output_dir + f)
+        os.rmdir(output_dir)
+
+        # test result
+        self.assertTrue(not appo)
+
+
+if __name__ == "__main__":
+    unittest.main()