RNG instead of state

alns/accept/tests/test_simulated_annealing.py

@@ -118,14 +118,14 @@ def test_linear_random_solutions():
     """
     simulated_annealing = SimulatedAnnealing(2, 1, 1, "linear")
 
-    state = rnd.default_rng(1)
+    rng = rnd.default_rng(1)
 
     # Using the above seed, the first two random numbers are 0.51 and 0.95,
     # respectively. The acceptance probability is 0.61 first, so the first
     # should be accepted (0.61 > 0.51). Thereafter, it drops to 0.37, so the
     # second should not (0.37 < 0.95).
-    assert_(simulated_annealing(state, Zero(), Zero(), One()))
-    assert_(not simulated_annealing(state, Zero(), Zero(), One()))
+    assert_(simulated_annealing(rng, Zero(), Zero(), One()))
+    assert_(not simulated_annealing(rng, Zero(), Zero(), One()))
 
 
 def test_exponential_random_solutions():
@@ -136,10 +136,10 @@ def test_exponential_random_solutions():
     """
     simulated_annealing = SimulatedAnnealing(2, 1, 0.5, "exponential")
 
-    state = rnd.default_rng(1)
+    rng = rnd.default_rng(1)
 
-    assert_(simulated_annealing(state, Zero(), Zero(), One()))
-    assert_(not simulated_annealing(state, Zero(), Zero(), One()))
+    assert_(simulated_annealing(rng, Zero(), Zero(), One()))
+    assert_(not simulated_annealing(rng, Zero(), Zero(), One()))
 
 
 @mark.parametrize(

alns/select/tests/test_alpha_ucb.py

@@ -58,27 +58,27 @@ def test_raises_invalid_arguments(
 def test_call_with_only_one_operator_pair():
     # Only one operator pair, so the algorithm should select (0, 0).
     select = AlphaUCB([2, 1, 1, 0], 0.5, 1, 1)
-    state = rnd.default_rng()
+    rng = rnd.default_rng()
 
-    selected = select(state, Zero(), Zero())
+    selected = select(rng, Zero(), Zero())
     assert_equal(selected, (0, 0))
 
 
 def test_update_with_two_operator_pairs():
     select = AlphaUCB([2, 1, 1, 0], 0.5, 2, 1)
-    state = rnd.default_rng()
+    rng = rnd.default_rng()
 
     # Avg. reward for (0, 0) after this is 2, for (1, 0) is still 1 (default).
     select.update(Zero(), 0, 0, outcome=Outcome.BEST)
 
     # So now (0, 0) is selected again.
-    selected = select(state, Zero(), Zero())
+    selected = select(rng, Zero(), Zero())
     assert_equal(selected, (0, 0))
 
     # One more update. Avg. reward goes to 1, and number of times to 2.
     select.update(Zero(), 0, 0, outcome=Outcome.REJECT)
 
     # The Q value of (0, 0) is now approx 1.432, and that of (1, 0) is now
     # approx 1.74. So (1, 0) is selected.
-    selected = select(state, Zero(), Zero())
+    selected = select(rng, Zero(), Zero())
     assert_equal(selected, (1, 0))

alns/select/tests/test_mab_selector.py

@@ -73,42 +73,42 @@ def test_call_with_only_one_operator_pair():
     select = MABSelector(
         [2, 1, 1, 0], 1, 1, LearningPolicy.EpsilonGreedy(0.15)
     )
-    state = rnd.default_rng()
+    rng = rnd.default_rng()
 
     for _ in range(10):
-        selected = select(state, Zero(), Zero())
+        selected = select(rng, Zero(), Zero())
         assert_equal(selected, (0, 0))
 
 
 def test_mab_epsilon_greedy():
-    state = rnd.default_rng()
+    rng = rnd.default_rng()
 
     # epsilon=0 is equivalent to greedy selection
     select = MABSelector([2, 1, 1, 0], 2, 1, LearningPolicy.EpsilonGreedy(0.0))
 
     select.update(Zero(), 0, 0, outcome=Outcome.BETTER)
-    selected = select(state, Zero(), Zero())
+    selected = select(rng, Zero(), Zero())
     for _ in range(10):
-        selected = select(state, Zero(), Zero())
+        selected = select(rng, Zero(), Zero())
         assert_equal(selected, (0, 0))
 
     select.update(Zero(), 1, 0, outcome=Outcome.BEST)
     for _ in range(10):
-        selected = select(state, Zero(), Zero())
+        selected = select(rng, Zero(), Zero())
         assert_equal(selected, (1, 0))
 
 
 @mark.parametrize("alpha", [0.25, 0.5])
 def test_mab_ucb1(alpha):
-    state = rnd.default_rng()
+    rng = rnd.default_rng()
     select = MABSelector([2, 1, 1, 0], 2, 1, LearningPolicy.UCB1(alpha))
 
     select.update(Zero(), 0, 0, outcome=Outcome.BEST)
-    mab_select = select(state, Zero(), Zero())
+    mab_select = select(rng, Zero(), Zero())
     assert_equal(mab_select, (0, 0))
 
     select.update(Zero(), 0, 0, outcome=Outcome.REJECT)
-    mab_select = select(state, Zero(), Zero())
+    mab_select = select(rng, Zero(), Zero())
     assert_equal(mab_select, (0, 0))
 
 
@@ -125,7 +125,7 @@ def test_contextual_mab_requires_context():
 
 
 def text_contextual_mab_uses_context():
-    state = rnd.default_rng()
+    rng = rnd.default_rng()
     select = MABSelector(
         [2, 1, 1, 0],
         2,
@@ -142,8 +142,8 @@ def text_contextual_mab_uses_context():
     select.update(ZeroWithOneContext(), 1, 0, outcome=Outcome.REJECT)
     select.update(ZeroWithOneContext(), 0, 0, outcome=Outcome.BEST)
 
-    mab_select = select(state, ZeroWithZeroContext(), ZeroWithZeroContext())
+    mab_select = select(rng, ZeroWithZeroContext(), ZeroWithZeroContext())
     assert_equal(mab_select, (1, 0))
 
-    mab_select = select(state, ZeroWithZeroContext(), ZeroWithZeroContext())
+    mab_select = select(rng, ZeroWithZeroContext(), ZeroWithZeroContext())
     assert_equal(mab_select, (0, 0))

alns/tests/test_result.py

@@ -36,15 +36,15 @@ def get_statistics():
         statistics.collect_objective(objective)
 
     # We should make sure these results are reproducible.
-    state = rnd.default_rng(1)
+    rng = rnd.default_rng(1)
 
     operators = ["test1", "test2", "test3"]
 
     for _ in range(100):
-        operator = state.choice(operators)
+        operator = rng.choice(operators)
 
-        statistics.collect_destroy_operator("d_" + operator, state.integers(4))
-        statistics.collect_repair_operator("r_" + operator, state.integers(4))
+        statistics.collect_destroy_operator("d_" + operator, rng.integers(4))
+        statistics.collect_repair_operator("r_" + operator, rng.integers(4))
 
     return statistics