Merge pull request #361 from bouthilx/fix/balance_workload

Avoid duplicates in RandomSearch

src/orion/algo/base.py

@@ -18,6 +18,11 @@
 log = logging.getLogger(__name__)
 
 
+def infer_trial_id(point):
+    """Compute a hashing of a point"""
+    return hashlib.md5(str(list(point)).encode('utf-8')).hexdigest()
+
+
 # pylint: disable=too-many-public-methods
 class BaseAlgorithm(object, metaclass=ABCMeta):
     """Base class describing what an algorithm can do.
@@ -187,11 +192,10 @@ def observe(self, points, results):
 
         """
         for point, result in zip(points, results):
-            _point = list(point)
-            _id = hashlib.md5(str(_point).encode('utf-8')).hexdigest()
+            point_id = infer_trial_id(point)
 
-            if _id not in self._trials_info:
-                self._trials_info[_id] = result
+            if point_id not in self._trials_info:
+                self._trials_info[point_id] = result
 
     @property
     def is_done(self):

src/orion/algo/random.py

@@ -10,7 +10,7 @@
 """
 import numpy
 
-from orion.algo.base import BaseAlgorithm
+from orion.algo.base import BaseAlgorithm, infer_trial_id
 
 
 class Random(BaseAlgorithm):
@@ -57,4 +57,15 @@ def suggest(self, num=1):
         .. note:: New parameters must be compliant with the problem's domain
            `orion.algo.space.Space`.
         """
-        return self.space.sample(num, seed=tuple(self.rng.randint(0, 1000000, size=3)))
+        points = []
+        point_ids = set(self._trials_info.keys())
+        i = 0
+        while len(points) < num:
+            new_point = self.space.sample(1, seed=tuple(self.rng.randint(0, 1000000, size=3)))[0]
+            point_id = infer_trial_id(new_point)
+            if point_id not in point_ids:
+                point_ids.add(point_id)
+                points.append(new_point)
+            i += 1
+
+        return points

tests/unittests/algo/test_random.py

@@ -43,3 +43,38 @@ def test_set_state(space):
 
     random_search.set_state(state)
     assert numpy.allclose(a, random_search.suggest(1)[0])
+
+
+def test_suggest_unique():
+    """Verify that RandomSearch do not sample duplicates"""
+    space = Space()
+    space.register(Integer('yolo1', 'uniform', -3, 6))
+
+    random_search = Random(space)
+
+    n_samples = 6
+    values = sum(random_search.suggest(n_samples), tuple())
+    assert len(values) == n_samples
+    assert len(set(values)) == n_samples
+
+
+def test_suggest_unique_history():
+    """Verify that RandomSearch do not sample duplicates based observed points"""
+    space = Space()
+    space.register(Integer('yolo1', 'uniform', -3, 6))
+
+    random_search = Random(space)
+
+    n_samples = 3
+    values = sum(random_search.suggest(n_samples), tuple())
+    assert len(values) == n_samples
+    assert len(set(values)) == n_samples
+
+    random_search.observe([[value] for value in values], [1] * n_samples)
+
+    n_samples = 3
+    new_values = sum(random_search.suggest(n_samples), tuple())
+    assert len(new_values) == n_samples
+    assert len(set(new_values)) == n_samples
+    # No duplicates
+    assert (set(new_values) & set(values)) == set()