Check type of inputs in eval, eval_dict

src/correctionlib_gradients/_base.py

@@ -127,17 +127,34 @@ def _get_result_size(self, inputs: dict[str, jax.Array]) -> int:
 class CorrectionWithGradient:
     def __init__(self, c: schema.Correction):
         self._dag = CorrectionDAG(c)
-        self._input_names = [v.name for v in c.inputs]
+        self._input_vars = c.inputs
         self._name = c.name
 
-    def evaluate(self, *inputs: Value) -> Value:
-        if (n_in := len(inputs)) != (n_expected := len(self._input_names)):
+    def evaluate(self, *inputs: Value) -> jax.Array:
+        self._check_num_inputs(inputs)
+        inputs_as_jax = tuple(jax.numpy.array(i) for i in inputs)
+        self._check_input_types(inputs_as_jax)
+        input_names = (v.name for v in self._input_vars)
+
+        input_dict = dict(zip(input_names, inputs_as_jax))
+        return self._dag.evaluate(input_dict)
+
+    def _check_num_inputs(self, inputs: tuple[Value, ...]) -> None:
+        if (n_in := len(inputs)) != (n_expected := len(self._input_vars)):
             msg = (
                 f"This correction requires {n_expected} input(s), {n_in} provided."
-                f" Required inputs are {self._input_names}"
+                f" Required inputs are {[v.name for v in self._input_vars]}"
             )
             raise ValueError(msg)
 
-        inputs_as_jax = (jax.numpy.array(i) for i in inputs)
-        input_dict = dict(zip(self._input_names, inputs_as_jax))
-        return self._dag.evaluate(input_dict)
+    def _check_input_types(self, inputs: tuple[jax.Array, ...]) -> None:
+        for i, v in enumerate(inputs):
+            in_type = v.dtype
+            expected_type_str = self._input_vars[i].type
+            expected_type = {"real": np.floating, "int": np.integer}[expected_type_str]
+            if not np.issubdtype(in_type, expected_type):
+                msg = (
+                    f"Variable '{self._input_vars[i].name}' has type {in_type}"
+                    f" instead of the expected {expected_type.__name__}"
+                )
+                raise ValueError(msg)

tests/test_base.py

@@ -110,6 +110,13 @@ def test_wrong_input_length():
         cg.evaluate(0.0, 1.0)
 
 
+def test_wrong_input_type():
+    cg = CorrectionWithGradient(schemas["scale"])
+
+    with pytest.raises(ValueError, match="Variable 'x' has type int64 instead of the expected float"):
+        cg.evaluate(0)
+
+
 def test_missing_input():
     cg = CorrectionWithGradient(schemas["scale"])