Fix seeding issues with correlated Thompson sampling (#29)

* Add regression test for #28
* Fix seeding problem in correlated Thompson sampling

Closes #28

pyrff/__init__.py

@@ -3,4 +3,4 @@
 from . thompson import sample_batch, sampling_probabilities
 from . utils import multi_start_fmin
 
-__version__ = '2.0.1'
+__version__ = '2.0.2'

pyrff/test_thompson.py

@@ -53,6 +53,30 @@ def test_correlated_sampling(self):
         assert batch.count('C') == 0
         pass
 
+    @pytest.mark.parametrize("correlated", [False, True])
+    def test_seeding(self, correlated):
+        """This is a regression test for https://github.com/michaelosthege/pyrff/issues/28"""
+        rng = numpy.random.RandomState(123)
+        samples = [
+            rng.uniform(size=200),
+            rng.uniform(size=200),
+            rng.uniform(size=200),
+        ]
+        batches = []
+        for _ in range(10):
+            batch = thompson.sample_batch(
+                candidate_samples=samples,
+                ids=["A", "B", "C"],
+                correlated=correlated,
+                batch_size=10,
+                seed=123,
+            )
+            batches.append("".join(batch))
+
+        # Assert that all batches are identical.
+        assert len(set(batches)) == 1
+        pass
+
 
 class TestExceptions:
     def test_id_count(self):

pyrff/thompson.py

@@ -61,7 +61,7 @@ def sample_batch(
         # to prevent always selecting lower-numbered candidates when >=2 samples are equal
         col_order = random.permutation(n_candidates)
         if correlated:
-            idx = numpy.repeat(numpy.random.randint(n_samples[0]), n_candidates)
+            idx = numpy.repeat(random.randint(n_samples[0]), n_candidates)
         else:
             idx = random.randint(n_samples, size=n_candidates)
         selected_samples = samples[:, col_order][idx, numpy.arange(n_candidates)]