comply with adaptive structure, fixed 1D test, added 4D test

Author: caenrigen

adaptive/learner/skopt_learner.py

@@ -26,24 +26,24 @@ class SKOptLearner(Optimizer, BaseLearner):
 
     def __init__(self, function, **kwargs):
         self.function = function
-        self.pending_points = list()
+        self.pending_points = set()
         self.data = OrderedDict()
         super().__init__(**kwargs)
 
     def tell(self, x, y, fit=True):
-        if x in self.pending_points:
-            self.pending_points.remove(x)
         if hasattr(x, '__iter__'):
+            self.pending_points.discard(tuple(x))
             self.data[tuple(x)] = y
             super().tell(x, y, fit)
         else:
+            self.pending_points.discard(x)
             self.data[x] = y
             super().tell([x], y, fit)
 
     def tell_pending(self, x):
         # 'skopt.Optimizer' takes care of points we
         # have not got results for.
-        self.pending_points.append(x)
+        self.pending_points.add(tuple(x))
 
     def remove_unfinished(self):
         pass

adaptive/tests/test_skopt_learner.py

@@ -25,3 +25,23 @@ def g(x, noise_level=0.1):
     for _ in range(11):
         (x,), _ = learner.ask(1)
         learner.tell(x, learner.function(x))
+
+
+@pytest.mark.skipif(not with_scikit_optimize, reason="scikit-optimize is not installed")
+def test_skopt_learner_4D_runs():
+    """The SKOptLearner provides very few guarantees about its
+       behaviour, so we only test the most basic usage
+       In this case we test also for 2D domain
+    """
+
+    def g(x, noise_level=0.1):
+        return np.sin(5 * (x[0] + x[1] + x[2] + x[3])) * (
+            1 - np.tanh(x[0] ** 2 + x[1] ** 2 + x[2] ** 2 + x[3] ** 2)
+        ) + np.random.randn() * noise_level
+
+    learner = SKOptLearner(g, dimensions=[(-2.0, 2.0), (-2.0, 2.0),
+                                            (-2.0, 2.0), (-2.0, 2.0)])
+
+    for _ in range(11):
+        (x,), _ = learner.ask(1)
+        learner.tell(x, learner.function(x))