Rewrite min_post_range test to using fitted GPClassificationModel (#477)

Summary:

The test_finish_criteria test uses the MonotonicRejectionGP alongside many mocks. This seems really brittle meaning that it fails when very subtle things changes in the model (and is seemingly impossible to debug and maintain).

The min_post_range finish condition actually cares about the values of the model so we move this out of that test into its own acutally using a model we fit (GPClassificationModel) so that we can test it easier.

Reviewed By: crasanders

Differential Revision: D67066880

tests/test_strategy.py

@@ -142,10 +142,40 @@ def test_finish_criteria(self):
             self.strat.finished
         )  # not enough difference between posterior min/max
 
-        for _ in range(50):
-            self.strat.gen()
-            self.strat.add_data(np.r_[0.0, 0.0], [0])
-        self.assertTrue(self.strat.finished)
+    def test_min_post_range(self):
+        seed = 1
+        torch.manual_seed(seed)
+        np.random.seed(seed)
+        lb = [0]
+        ub = [1]
+
+        self.strat = Strategy(
+            model=GPClassificationModel(
+                lb=lb,
+                ub=ub,
+                inducing_point_method=AutoAllocator(
+                    bounds=torch.stack([torch.tensor(lb), torch.tensor(ub)])
+                ),
+            ),
+            generator=SobolGenerator(lb=lb, ub=ub),
+            min_asks=10,
+            lb=lb,
+            ub=ub,
+            stimuli_per_trial=1,
+            outcome_types=["binary"],
+            min_post_range=0.3,
+        )
+
+        loops = 0
+        while not self.strat.finished:
+            points = self.strat.gen(1)
+            response = int(np.random.rand() < points)
+            self.strat.add_data(points, torch.tensor([response]))
+            loops += 1
+
+            if loops > 50:
+                self.fail("min_post_range didn't finish even after 50 loops.")
+                break
 
     def test_max_asks(self):
         self.strat.max_asks = 50