fix(families): raise ValueError for x outside support in exponential and uniform

Author: MyznikovFD

src/pysatl_core/families/builtins/continuous/exponential.py

@@ -233,9 +233,6 @@ def _base_score(parameters: Parametrization, x: NumericArray) -> NumericArray:
         The derivative with respect to λ is:
             ∂/∂λ log f = 1/λ - x   (for x ≥ 0).
 
-        For points x < 0 the density is zero; we return 0 for numerical stability
-        (though the score is technically undefined there).
-
         Parameters
         ----------
         parameters : Parametrization
@@ -248,12 +245,21 @@ def _base_score(parameters: Parametrization, x: NumericArray) -> NumericArray:
         NumericArray
             Gradient array of shape (..., 1) where the last axis corresponds to
             d(log f)/d(lambda_).
+
+        Raises
+        ------
+        ValueError
+            If any element of x is negative or non‑finite (inf or nan),
+            i.e., outside the support [0, ∞).
         """
         params = cast(_Rate, parameters)
         lam = params.lambda_
-        inside = x >= 0
-        grad = np.where(inside, 1.0 / lam - x, 0.0)
-        return grad[..., np.newaxis]  # shape (..., 1)
+        if np.any(x < 0) or np.any(~np.isfinite(x)):
+            raise ValueError(
+                f"Score is undefined for x < 0 or non‑finite x (outside support). Got x = {x}"
+            )
+        grad = 1.0 / lam - x
+        return grad[..., np.newaxis]
 
     Exponential = ParametricFamily(
         name=FamilyName.EXPONENTIAL,

src/pysatl_core/families/builtins/continuous/normal.py

@@ -251,13 +251,21 @@ def _base_score(parameters: Parametrization, x: NumericArray) -> NumericArray:
         -------
         NumericArray
             Gradient array of shape (..., 2).
+
+        Raises
+        ------
+        ValueError
+            If any element of x is non‑finite (inf or nan), i.e., outside the
+            real support of the normal distribution.
         """
         params = cast(_MeanStd, parameters)
         mu = params.mu
         sigma = params.sigma
 
-        z = (x - mu) / sigma
+        if np.any(~np.isfinite(x)):
+            raise ValueError(f"Score is undefined for non‑finite x (outside support). Got x = {x}")
 
+        z = (x - mu) / sigma
         grad_mu = z / sigma
         grad_sigma = (z * z - 1) / sigma
         return np.stack([grad_mu, grad_sigma], axis=-1)

src/pysatl_core/families/builtins/continuous/uniform.py

@@ -272,8 +272,7 @@ def _base_score(parameters: Parametrization, x: NumericArray) -> NumericArray:
             ∂/∂a log f =  1/(b - a)
             ∂/∂b log f = -1/(b - a)
 
-        For points outside the support, the gradient is set to 0 (since density is zero,
-        but the score is typically considered undefined; we return 0 for numerical safety).
+        For points outside the support or non‑finite (inf, nan), raises ValueError.
 
         Parameters
         ----------
@@ -287,15 +286,26 @@ def _base_score(parameters: Parametrization, x: NumericArray) -> NumericArray:
         NumericArray
             Gradient array of shape (..., 2) where last axis corresponds to
             [d(log f)/d(lower_bound), d(log f)/d(upper_bound)].
+
+        Raises
+        ------
+        ValueError
+            If any element of x lies outside [lower_bound, upper_bound]
+            or is non‑finite (inf or nan).
         """
         params = cast(_Standard, parameters)
         a = params.lower_bound
         b = params.upper_bound
-        width = b - a
 
-        inside = (x >= a) & (x <= b)
-        grad_a = np.where(inside, 1.0 / width, 0.0)
-        grad_b = np.where(inside, -1.0 / width, 0.0)
+        # Reject non‑finite values before support check
+        if np.any(~np.isfinite(x)):
+            raise ValueError(f"Score is undefined for non‑finite x (outside support). Got x = {x}")
+        if np.any((x < a) | (x > b)):
+            raise ValueError(f"Score is undefined for x outside support [{a}, {b}]. Got x = {x}")
+
+        width = b - a
+        grad_a = np.full_like(x, 1.0 / width, dtype=np.float64)
+        grad_b = np.full_like(x, -1.0 / width, dtype=np.float64)
         return np.stack([grad_a, grad_b], axis=-1)
 
     Uniform = ParametricFamily(

src/pysatl_core/families/parametric_family.py

@@ -438,6 +438,11 @@ def score(self, parameters: Parametrization, x: NumericArray) -> NumericArray:
         NumericArray
             Gradient with respect to the parameters of the given parametrization.
             Shape is (..., d), where d is the number of parameters of the parametrization.
+
+        Raises
+        ------
+        ValueError
+            If any value in `x` lies outside the distribution's support.
         """
         if self._base_score is None:
             raise ValueError(

tests/unit/families/builtins/continuous/test_exponential.py

@@ -273,6 +273,21 @@ def test_score_shape(self, parametrization_name, params):
         assert grad.shape == (len(x), 1)
         assert grad.dtype == float
 
+    @pytest.mark.parametrize("bad_x", [np.inf, -np.inf, np.nan])
+    def test_score_raises_for_nonfinite_x(self, bad_x):
+        """Test that SCORE raises ValueError for non-finite x (inf, nan)."""
+        lam = 0.5
+        dist = self.exponential_family(lambda_=lam)
+        with pytest.raises(ValueError, match="Score is undefined for x < 0 or non‑finite x"):
+            dist.family.score(dist.parametrization, np.array([bad_x]))
+
+    def test_score_raises_for_x_outside_support(self):
+        lam = 0.5
+        dist = self.exponential_family(lambda_=lam)
+        x_bad = np.array([-0.1, -1.0])
+        with pytest.raises(ValueError, match="Score is undefined for x < 0"):
+            dist.family.score(dist.parametrization, x_bad)
+
 
 class TestExponentialFamilyEdgeCases(BaseDistributionTest):
     """Test edge cases and error conditions for exponential distribution."""

tests/unit/families/builtins/continuous/test_normal.py

@@ -352,6 +352,14 @@ def test_score_shape(self, parametrization_name, params):
         assert grad.dtype == float
         assert not np.any(np.isnan(grad))
 
+    @pytest.mark.parametrize("bad_x", [np.inf, -np.inf, np.nan])
+    def test_score_raises_for_nonfinite_x(self, bad_x):
+        """Test that SCORE raises ValueError for non-finite x (inf, nan)."""
+        mu, sigma = 2.0, 1.5
+        dist = self.normal_family(mu=mu, sigma=sigma)
+        with pytest.raises(ValueError, match="Score is undefined for non‑finite x"):
+            dist.family.score(dist.parametrization, np.array([bad_x]))
+
 
 class TestNormalFamilyEdgeCases(BaseDistributionTest):
     """Test edge cases and error conditions."""

tests/unit/families/builtins/continuous/test_uniform.py

@@ -248,32 +248,28 @@ def test_score_standard_parametrization(self):
         """Test SCORE for standard parametrization against analytical formula."""
         a, b = 2.0, 5.0
         dist = self.uniform_family(lower_bound=a, upper_bound=b)
-        x = np.array([1.0, 2.0, 3.5, 5.0, 6.0])
+        x = np.array([2.0, 3.5, 5.0])
         grad = dist.family.score(dist.parametrization, x)
 
         width = b - a
-        inside = (x >= a) & (x <= b)
-        expected = np.stack(
-            [np.where(inside, 1.0 / width, 0.0), np.where(inside, -1.0 / width, 0.0)], axis=-1
-        )
+        expected = np.tile([1.0 / width, -1.0 / width], (len(x), 1))
 
         np.testing.assert_allclose(grad, expected, rtol=self.CALCULATION_PRECISION)
 
     def test_score_meanWidth_parametrization(self):
         """Test SCORE for meanWidth parametrization via chain rule."""
         mean, width = 3.5, 3.0  # corresponds to a=2, b=5
         dist = self.uniform_family(parametrization_name="meanWidth", mean=mean, width=width)
-        x = np.array([1.0, 2.0, 3.5, 5.0, 6.0])
+        x = np.array([2.0, 3.5, 5.0])
         grad = dist.family.score(dist.parametrization, x)
 
-        a, b = 2.0, 5.0
-        inside = (x >= a) & (x <= b)
-        base_grad_a = np.where(inside, 1.0 / 3.0, 0.0)
-        base_grad_b = np.where(inside, -1.0 / 3.0, 0.0)
+        _a, _b = 2.0, 5.0
+        base_grad_a = 1.0 / 3.0
+        base_grad_b = -1.0 / 3.0
         # Transform to (mean, width)
         expected_mean = 0.5 * (base_grad_a + base_grad_b)
         expected_width = -base_grad_a + base_grad_b
-        expected = np.stack([expected_mean, expected_width], axis=-1)
+        expected = np.tile([expected_mean, expected_width], (len(x), 1))
 
         np.testing.assert_allclose(grad, expected, rtol=self.CALCULATION_PRECISION)
 
@@ -283,17 +279,16 @@ def test_score_minRange_parametrization(self):
         dist = self.uniform_family(
             parametrization_name="minRange", minimum=minimum, range_val=range_val
         )
-        x = np.array([1.0, 2.0, 3.5, 5.0, 6.0])
+        x = np.array([2.0, 3.5, 5.0])
         grad = dist.family.score(dist.parametrization, x)
 
-        a, b = 2.0, 5.0
-        inside = (x >= a) & (x <= b)
-        base_grad_a = np.where(inside, 1.0 / 3.0, 0.0)
-        base_grad_b = np.where(inside, -1.0 / 3.0, 0.0)
+        _a, _b = 2.0, 5.0
+        base_grad_a = 1.0 / 3.0
+        base_grad_b = -1.0 / 3.0
         # Transform to (minimum, range_val)
         expected_min = base_grad_a
         expected_range = -base_grad_a + base_grad_b
-        expected = np.stack([expected_min, expected_range], axis=-1)
+        expected = np.tile([expected_min, expected_range], (len(x), 1))
 
         np.testing.assert_allclose(grad, expected, rtol=self.CALCULATION_PRECISION)
 
@@ -317,6 +312,17 @@ def logpdf_b(b_val: float) -> float:
         np.testing.assert_allclose(grad[0, 0], grad_a_num, rtol=1e-5)
         np.testing.assert_allclose(grad[0, 1], grad_b_num, rtol=1e-5)
 
+    def test_score_with_integer_x(self):
+        """Test that SCORE returns float gradient even for integer x."""
+        a, b = 2.0, 5.0
+        dist = self.uniform_family(lower_bound=a, upper_bound=b)
+        x_int = np.array([2, 3, 4, 5], dtype=int)
+        grad = dist.family.score(dist.parametrization, x_int)
+        assert grad.dtype == np.float64
+        width = b - a
+        expected = np.full((len(x_int), 2), [1.0 / width, -1.0 / width], dtype=np.float64)
+        np.testing.assert_allclose(grad, expected, rtol=self.CALCULATION_PRECISION)
+
     @pytest.mark.parametrize(
         "parametrization_name, params",
         [
@@ -327,12 +333,27 @@ def logpdf_b(b_val: float) -> float:
     )
     def test_score_shape(self, parametrization_name, params):
         """Test SCORE shape for all uniform parametrizations."""
-        x = np.array([1.0, 2.0, 3.5, 5.0, 6.0])
+        x = np.array([2.0, 3.5, 5.0])
         dist = self.uniform_family(parametrization_name=parametrization_name, **params)
         grad = dist.family.score(dist.parametrization, x)
         assert grad.shape == (len(x), 2)
         assert grad.dtype == float
 
+    @pytest.mark.parametrize("bad_x", [np.inf, -np.inf, np.nan])
+    def test_score_raises_for_nonfinite_x(self, bad_x):
+        """Test that SCORE raises ValueError for non-finite x (inf, nan)."""
+        a, b = 2.0, 5.0
+        dist = self.uniform_family(lower_bound=a, upper_bound=b)
+        with pytest.raises(ValueError, match="Score is undefined for non‑finite x"):
+            dist.family.score(dist.parametrization, np.array([bad_x]))
+
+    def test_score_raises_for_x_outside_support(self):
+        a, b = 2.0, 5.0
+        dist = self.uniform_family(lower_bound=a, upper_bound=b)
+        x_bad = np.array([1.0, 6.0])
+        with pytest.raises(ValueError, match="Score is undefined for x outside support"):
+            dist.family.score(dist.parametrization, x_bad)
+
 
 class TestUniformFamilyEdgeCases(BaseDistributionTest):
     """Test edge cases and error conditions for uniform distribution."""