Improve error handling and type checks in preprocessing

Enhanced input validation and error messages in curvilinear and unstructured scikit-learn preprocessors, replacing assertions with explicit exceptions. Added missing backend error in Warped2DInterp. Fixed gradient caching bug in MultivaluedInterp by using correct argument key. Ensured interp_kwargs is copied before update in UnstructuredInterp.

Author: alanlujan91

src/multinterp/curvilinear/_scikit_learn.py

@@ -106,21 +106,26 @@ def __init__(
 
         feature = pp_options.get("feature", None)
 
-        if feature and isinstance(feature, str):
-            degree = pp_options.get("degree", 3)
-            assert isinstance(degree, int), "Degree must be an integer."
-            if feature.startswith("pol"):
-                pipeline.insert(0, PolynomialFeatures(degree))
-            elif feature.startswith("spl"):
-                n_knots = pp_options.get("n_knots", 5)
-                assert isinstance(n_knots, int), "n_knots must be an integer."
-                pipeline.insert(0, SplineTransformer(n_knots=n_knots, degree=degree))
-            else:
-                msg = f"Feature {feature} not recognized."
-                raise AttributeError(msg)
+        if not feature or not isinstance(feature, str):
+            msg = f"Feature must be a string ('pol' or 'spl'), got {feature!r}."
+            raise ValueError(msg)
+
+        degree = pp_options.get("degree", 3)
+        if not isinstance(degree, int):
+            msg = "Degree must be an integer."
+            raise TypeError(msg)
+
+        if feature.startswith("pol"):
+            pipeline.insert(0, PolynomialFeatures(degree))
+        elif feature.startswith("spl"):
+            n_knots = pp_options.get("n_knots", 5)
+            if not isinstance(n_knots, int):
+                msg = "n_knots must be an integer."
+                raise TypeError(msg)
+            pipeline.insert(0, SplineTransformer(n_knots=n_knots, degree=degree))
         else:
-            msg = f"Feature {feature} not recognized."
-            raise AttributeError(msg)
+            msg = f"Feature {feature!r} not recognized. Use 'pol' or 'spl'."
+            raise ValueError(msg)
 
         if std:
             pipeline.insert(0, StandardScaler())

src/multinterp/curvilinear/_warped.py

@@ -41,6 +41,9 @@ def __call__(self, *args, axis=1):
             output = self._interp_piecewise(args, axis)
         elif self.backend == "numba":
             output = self._backend_numba(args, axis)
+        else:
+            msg = f"Backend {self.backend!r} not supported for Warped2DInterp."
+            raise NotImplementedError(msg)
 
         return output
 

src/multinterp/rectilinear/_multi.py

@@ -280,15 +280,16 @@ def diff(self, axis=None, argnum=None, edge_order=1):
                 options=self.mc_kwargs,
             )
 
-        grad = self._gradient.get((axis, arg))
+        grad = self._gradient.get((axis, argnum))
         if grad is None:
-            self._gradient[(axis, arg)] = get_grad(
+            self._gradient[(axis, argnum)] = get_grad(
                 self.values,
-                self.grids[axis],
-                axis=axis,
+                self.grids[argnum],
+                axis=argnum,
                 edge_order=edge_order,
                 backend=self.backend,
             )
+            grad = self._gradient[(axis, argnum)]
 
         return MultivariateInterp(
             grad,

src/multinterp/unstructured/_scikit_learn.py

@@ -99,16 +99,20 @@ def __init__(
 
         if feature and isinstance(feature, str):
             degree = options.get("degree", 3)
-            assert isinstance(degree, int), "Degree must be an integer."
+            if not isinstance(degree, int):
+                msg = "Degree must be an integer."
+                raise TypeError(msg)
             if feature.startswith("pol"):
                 pipeline.insert(0, PolynomialFeatures(degree))
             elif feature.startswith("spl"):
                 n_knots = options.get("n_knots", 5)
-                assert isinstance(n_knots, int), "n_knots must be an integer."
+                if not isinstance(n_knots, int):
+                    msg = "n_knots must be an integer."
+                    raise TypeError(msg)
                 pipeline.insert(0, SplineTransformer(n_knots=n_knots, degree=degree))
             else:
-                msg = f"Feature {feature} not recognized."
-                raise AttributeError(msg)
+                msg = f"Feature {feature!r} not recognized. Use 'pol' or 'spl'."
+                raise ValueError(msg)
 
         if std:
             pipeline.insert(0, StandardScaler())

src/multinterp/unstructured/_scipy.py

@@ -96,10 +96,9 @@ def __init__(
             msg = f"Unknown interpolation method {method} for {self.ndim} dimensional data."
             raise ValueError(msg)
 
-        self.interp_kwargs = interp_kwargs
+        self.interp_kwargs = interp_kwargs.copy()
         if options:
-            self.interp_kwargs.copy()
-            intersection = interp_kwargs.keys() & options.keys()
+            intersection = self.interp_kwargs.keys() & options.keys()
             self.interp_kwargs.update({key: options[key] for key in intersection})
 
         self.interpolator = interpolator_class(