Remove CorrectionWithGradient.eval_dict

src/correctionlib_gradients/_base.py

@@ -108,16 +108,11 @@ def __init__(self, c: schema.Correction):
 
     def evaluate(self, *inputs: Value) -> Value:
         if (n_in := len(inputs)) != (n_expected := len(self._input_names)):
-            msg = f"This correction requires {n_expected} input(s), {n_in} provided"
+            msg = (
+                f"This correction requires {n_expected} input(s), {n_in} provided."
+                f" Required inputs are {self._input_names}"
+            )
             raise ValueError(msg)
 
         input_dict = dict(zip(self._input_names, inputs))
         return self._dag.evaluate(input_dict)
-
-    def eval_dict(self, inputs: dict[str, Value]) -> Value:
-        for n in self._input_names:
-            if n not in inputs:
-                msg = f"Variable '{n}' is required by correction '{self._name}' but is not present in input"
-                raise ValueError(msg)
-
-        return self._dag.evaluate(inputs)

tests/test_base.py

@@ -101,9 +101,9 @@ def test_missing_input():
     cg = CorrectionWithGradient(schemas["scale"])
 
     with pytest.raises(
-        ValueError, match="Variable 'x' is required by correction 'test scalar' but is not present in input"
+        ValueError, match="This correction requires 1 input\\(s\\), 0 provided. Required inputs are \\['x'\\]"
     ):
-        cg.eval_dict({})
+        cg.evaluate()
 
 
 def test_unsupported_correction():
@@ -146,16 +146,6 @@ def test_evaluate_scale(jit):
     assert grad == 0.0
 
 
-@pytest.mark.parametrize("jit", [False, True])
-def test_eval_dict_scale(jit):
-    cg = CorrectionWithGradient(schemas["scale"])
-    eval_dict = jax.jit(cg.eval_dict) if jit else cg.eval_dict
-    value, grad = jax.value_and_grad(eval_dict)({"x": 4.2})
-    assert math.isclose(value, 1.234)
-    assert list(grad.keys()) == ["x"]
-    assert grad["x"] == 0.0
-
-
 @pytest.mark.parametrize("jit", [False, True])
 def test_vectorized_evaluate_scale(jit):
     cg = CorrectionWithGradient(schemas["scale"])
@@ -168,19 +158,6 @@ def test_vectorized_evaluate_scale(jit):
     assert grads[1] == 0.0
 
 
-@pytest.mark.parametrize("jit", [False, True])
-def test_vectorized_eval_dict_scale(jit):
-    cg = CorrectionWithGradient(schemas["scale"])
-    eval_dict = jax.jit(cg.eval_dict) if jit else cg.eval_dict
-    x = np.array([0.0, 1.0])
-    values, grads = jax.value_and_grad(eval_dict)({"x": x})
-    assert np.allclose(values, [1.234, 1.234])
-    assert list(grads.keys()) == ["x"]
-    assert len(grads["x"]) == 2
-    assert grads["x"][0] == 0.0
-    assert grads["x"][1] == 0.0
-
-
 def test_vectorized_evaluate_simple_uniform_binning():
     cg = CorrectionWithGradient(schemas["simple-uniform-binning"])
     x = [3.0, 5.0, 11.0]  # 11. overflows: it tests clamping