optim: UniformFloat get neighbors

ConfigSpace/hyperparameters/uniform_float.py

@@ -110,13 +110,18 @@ def __init__(
         self.normalized_default_value = self._inverse_transform(self.default_value)
         super().__init__(name=name, default_value=default_value, meta=meta)
 
-    def is_legal(self, value: float) -> bool:
+    def is_legal(self, value: float, *, normalized: bool = False) -> bool:
         if not isinstance(value, (float, int)):
             return False
-        return self.upper >= value >= self.lower
+
+        if normalized:
+            return 0.0 <= value <= 1.0
+
+        return self.lower <= value <= self.upper
 
     def is_legal_vector(self, value: float) -> bool:
         # NOTE: This really needs a better name as it doesn't operate on vectors,
+        # rather individual values.
         # it means that it operates on the normalzied space, i.e. what is gotten
         # by inverse_transform
         return 1.0 >= value >= 0.0
@@ -216,8 +221,8 @@ def get_neighbors(
         # Generate batches of number * 2 candidates, filling the above
         # buffer until we have enough valid candidates.
         # We should not overflow as the buffer
-        while offset <= number:
-            candidates = rs.normal(value, std, size=SAMPLE_SIZE)
+        while offset < number:
+            candidates = rs.normal(value, std, size=(SAMPLE_SIZE,))
             valid_candidates = candidates[(candidates >= 0) & (candidates <= 1)]
 
             n_candidates = len(valid_candidates)

test/test_hyperparameters.py

@@ -277,7 +277,6 @@ def test_uniformfloat_is_legal():
     assert not f1.is_legal_vector(-0.1)
     assert not f1.is_legal_vector(1.1)
 
-
     with pytest.raises(TypeError):
         assert not f1.is_legal_vector("Hahaha")
 
@@ -742,9 +741,9 @@ def test_betafloat():
     b1 = BetaFloatHyperparameter("param", lower=0.0, upper=1.0, alpha=3.0, beta=1.0)
 
     # with identical domains, beta and uniform should sample the same points
-    assert u1.get_neighbors(0.5, rs=np.random.RandomState(42)) == b1.get_neighbors(
-        0.5,
-        rs=np.random.RandomState(42),
+    np.testing.assert_equal(
+        u1.get_neighbors(0.5, rs=np.random.RandomState(42)),
+        b1.get_neighbors(0.5, rs=np.random.RandomState(42)),
     )
     # Test copy
     copy_f1 = copy.copy(f1)
@@ -1186,9 +1185,7 @@ def test_uniforminteger():
     assert f1.log is False
     assert f1.normalized_default_value == pytest.approx((2.0 + 0.49999) / (5.49999 + 0.49999))
 
-    quantization_warning = (
-        "Setting quantization < 1 for Integer Hyperparameter param has no effect"
-    )
+    quantization_warning = "Setting quantization < 1 for Integer Hyperparameter param has no effect"
     with pytest.raises(ValueError, match=quantization_warning):
         _ = UniformIntegerHyperparameter("param", 0, 10, q=0.1)  # type: ignore
     with pytest.raises(ValueError, match=quantization_warning):
@@ -1881,7 +1878,12 @@ def test_betaint_legal_float_values():
         match="Illegal default value 0.5",
     ):
         _ = BetaIntegerHyperparameter(
-            "param", lower=-2.0, upper=2.0, alpha=3.0, beta=1.1, default_value=0.5,
+            "param",
+            lower=-2.0,
+            upper=2.0,
+            alpha=3.0,
+            beta=1.1,
+            default_value=0.5,
         )
 
 

test/test_util.py

@@ -79,6 +79,7 @@ def _test_get_one_exchange_neighbourhood(hp):
     num_neighbors = 0
     if not isinstance(hp, list):
         hp = [hp]
+
     for hp_ in hp:
         cs.add_hyperparameter(hp_)
         if np.isinf(hp_.get_num_neighbors()):
@@ -90,7 +91,7 @@ def _test_get_one_exchange_neighbourhood(hp):
     config = cs.get_default_configuration()
     all_neighbors = []
     for i in range(100):
-        neighborhood = get_one_exchange_neighbourhood(config, i)
+        neighborhood = get_one_exchange_neighbourhood(config, i, num_neighbors=num_neighbors)
         for new_config in neighborhood:
             assert config != new_config
             assert dict(config) != dict(new_config)
@@ -129,14 +130,14 @@ def test_random_neighborhood_float():
     hp = UniformFloatHyperparameter("a", 1, 10)
     all_neighbors = _test_get_one_exchange_neighbourhood(hp)
     all_neighbors = [neighbor["a"] for neighbor in all_neighbors]
-    assert pytest.approx(np.mean(all_neighbors), abs=1e-2) == 5.49
-    assert pytest.approx(np.var(all_neighbors), abs=1e-2) == 3.192
+    assert pytest.approx(np.mean(all_neighbors), abs=1e-2) == 5.47
+    assert pytest.approx(np.var(all_neighbors), abs=1e-2) == 2.78
     hp = UniformFloatHyperparameter("a", 1, 10, log=True)
     all_neighbors = _test_get_one_exchange_neighbourhood(hp)
     all_neighbors = [neighbor["a"] for neighbor in all_neighbors]
     # Default value is 3.16
-    assert pytest.approx(np.mean(all_neighbors), abs=1e-2) == 3.50
-    assert pytest.approx(np.var(all_neighbors), abs=1e-2) == 2.79
+    assert pytest.approx(np.mean(all_neighbors), abs=1e-2) == 3.43
+    assert pytest.approx(np.var(all_neighbors), abs=1e-2) == 2.17
 
 
 def test_random_neighbor_int():