observables.py: add shape verification

reservoirpy/observables.py

@@ -32,6 +32,19 @@
 from .type import Weights
 
 
+def _check_arrays(y_true, y_pred):
+    y_true_array = np.asarray(y_true)
+    y_pred_array = np.asarray(y_pred)
+
+    if not y_true_array.shape == y_pred_array.shape:
+        raise ValueError(
+           f"Shape mismatch between y_true and y_pred: 
+           {y_true_array.shape} != {y_pred_array.shape}"
+        )
+
+    return y_true_array, y_pred_array
+
+
 def spectral_radius(W: Weights, maxiter: int = None) -> float:
     """Compute the spectral radius of a matrix `W`.
 
@@ -98,7 +111,8 @@ def mse(y_true: np.ndarray, y_pred: np.ndarray) -> float:
     float
         Mean squared error.
     """
-    return float(np.mean((np.asanyarray(y_true) - np.asanyarray(y_pred)) ** 2))
+    y_true_array, y_pred_array = _check_arrays(y_true, y_pred)
+    return float(np.mean((y_true_array - y_pred_array) ** 2))
 
 
 def rmse(y_true: np.ndarray, y_pred: np.ndarray) -> float:
@@ -157,7 +171,7 @@ def nrmse(
     -------
     float
         Normalized mean squared error.
-    """
+    """     
     error = rmse(y_true, y_pred)
     if norm_value is not None:
         return error / norm_value
@@ -176,7 +190,7 @@ def nrmse(
                 f"Available methods are {list(norms.keys())}."
             )
         else:
-            return error / norms[norm](np.asarray(y_true))
+            return error / norms[norm](y_true_array)
 
 
 def rsquare(y_true: np.ndarray, y_pred: np.ndarray) -> float:
@@ -201,6 +215,8 @@ def rsquare(y_true: np.ndarray, y_pred: np.ndarray) -> float:
     float
         Coefficient of determination.
     """
-    d = (np.asarray(y_true) - np.asarray(y_pred)) ** 2
-    D = (np.asarray(y_true) - np.asarray(y_true).mean()) ** 2
+    y_true_array, y_pred_array = _check_arrays(y_true, y_pred)
+
+    d = (y_true_array - y_pred_array) ** 2
+    D = (y_true_array - y_pred_array.mean()) ** 2
     return 1 - np.sum(d) / np.sum(D)

reservoirpy/tests/test_observables.py

@@ -16,6 +16,10 @@
         (nrmse, [1.0, 2.0, 3.0], [1.5, 2.5, 3.5], {"norm": "foo"}, "raise"),
         (nrmse, [1.0, 2.0, 3.0], [1.5, 2.5, 3.5], {"norm_value": 3.0}, None),
         (rsquare, [1.0, 2.0, 3.0], [1.5, 2.5, 3.5], {}, None),
+        (mse, [1.0, 2.0, 3.0, 4.0], [1.5, 2.5, 3.5], {}, "raise"),
+        (rmse, [[1.0, 2.0, 3.0]], [1.5, 2.5, 3.5], {}, "raise"),
+        (nrmse, [1.0, 2.0, 3.0], [1.5, 2.5, 3.5, 4.2], {}, "raise"),
+        (rsquare, [1.0, 2.0, 3.0, 0.0], [1.5, 2.5, 3.5], {}, "raise"),
     ],
 )
 def test_observable(obs, ytest, ypred, kwargs, expects):