Allow parsing of nested numbers from config (facebookresearch#400)

Summary:

Nested arrays were not directly parseable from the config. E.g., [12, [2, 4], 2, [2.0, 4]] would raise an exception.

If a nested array is detected, it will check whether the desired type is either a float or an int and if so, it'll evaluate the string as a number then recursively cast.

If the desired type is neither a float nor an int, it will raise a ValueException.

Reviewed By: crasanders

Differential Revision: D64138450

aepsych/config.py

@@ -199,7 +199,7 @@ def _str_to_array(self, v: str) -> np.ndarray:
         return np.array(v, dtype=float)
 
     def _str_to_tensor(self, v: str) -> torch.Tensor:
-        return torch.Tensor(self._str_to_list(v)).to(torch.float64)
+        return torch.Tensor(self._str_to_array(v)).to(torch.float64)
 
     def _str_to_obj(self, v: str, fallback_type: _T = str, warn: bool = True) -> object:
 

tests/test_config.py

@@ -186,7 +186,9 @@ def test_nonmonotonic_optimization_config_file(self):
         self.assertTrue(
             isinstance(strat.strat_list[1].generator, OptimizeAcqfGenerator)
         )
-        self.assertTrue(strat.strat_list[1].generator.acqf is qLogNoisyExpectedImprovement)
+        self.assertTrue(
+            strat.strat_list[1].generator.acqf is qLogNoisyExpectedImprovement
+        )
         self.assertTrue(
             set(strat.strat_list[1].generator.acqf_kwargs.keys()) == {"objective"}
         )
@@ -411,6 +413,31 @@ def test_warn_about_refit(self):
         with self.assertWarns(UserWarning):
             Strategy.from_config(config, "init_strat")
 
+    def test_nested_tensor(self):
+        points = [[0.25, 0.75], [0.5, 0.9]]
+        config_str = f"""
+                [common]
+                parnames = [par1, par2]
+
+                [par1]
+                par_type = continuous
+                lower_bound = 0
+                upper_bound = 1
+
+                [par2]
+                par_type = continuous
+                lower_bound = 0
+                upper_bound = 1
+
+                [SampleAroundPointsGenerator]
+                points = {points}
+        """
+        config = Config()
+        config.update(config_str=config_str)
+
+        config_points = config.gettensor("SampleAroundPointsGenerator", "points")
+        self.assertTrue(torch.all(config_points == torch.tensor(points)))
+
     def test_pairwise_probit_config(self):
         config_str = """
             [common]
@@ -581,7 +608,9 @@ def test_pairwise_opt_config(self):
         self.assertTrue(strat.strat_list[0].model is None)
 
         self.assertTrue(isinstance(strat.strat_list[1].model, PairwiseProbitModel))
-        self.assertTrue(strat.strat_list[1].generator.acqf is qLogNoisyExpectedImprovement)
+        self.assertTrue(
+            strat.strat_list[1].generator.acqf is qLogNoisyExpectedImprovement
+        )
         self.assertTrue(
             set(strat.strat_list[1].generator.acqf_kwargs.keys()) == {"objective"}
         )