ENH: Check input dimensions against the initialized model_

scikeras/_utils.py

@@ -4,7 +4,7 @@
 import warnings
 
 from inspect import isclass
-from typing import Any, Callable, Dict, Iterable, List, Union
+from typing import Any, Callable, Dict, Iterable, List, Mapping, Union
 
 import numpy as np
 import tensorflow as tf
@@ -107,16 +107,18 @@ def make_model_picklable(model_obj):
 
 
 def _windows_upcast_ints(
-    arr: Union[List[np.ndarray], np.ndarray]
+    inp: Union[List[np.ndarray], Mapping[Any, np.ndarray], np.ndarray]
 ) -> Union[List[np.ndarray], np.ndarray]:
     # see tensorflow/probability#886
     def _upcast(x):
         return x.astype("int64") if x.dtype == np.int32 else x
 
-    if isinstance(arr, np.ndarray):
-        return _upcast(arr)
+    if isinstance(inp, np.ndarray):
+        return _upcast(inp)
+    elif isinstance(inp, Mapping):
+        return {k: _upcast(x_) for k, x_ in inp.items()}
     else:
-        return [_upcast(x_) for x_ in arr]
+        return [_upcast(x_) for x_ in inp]
 
 
 def route_params(

scikeras/wrappers.py

@@ -5,7 +5,7 @@
 import warnings
 
 from collections import defaultdict
-from typing import Any, Callable, Dict, Iterable, List, Tuple, Type, Union
+from typing import Any, Callable, Dict, Iterable, List, Mapping, Tuple, Type, Union
 
 import numpy as np
 import tensorflow as tf
@@ -530,22 +530,45 @@ def _fit_keras_model(
                     raise e
             self.history_[key] += val
 
-    def _check_model_compatibility(self, y: np.ndarray) -> None:
-        """Checks that the model output number and y shape match.
+    def _check_model_outputs(self, y):
+        # output shapes depend on the number of classes in classification,
+        # hence we cannot just check y here, we need the user (or the
+        # data transformer) to tell us what to expect via n_outputs_expected_
+        # n_outputs_expected_ is generated by data transformers
+        # and recovered via target_encoder_.get_meta();
+        # we recognize it but do not force it to be
+        # generated to avoid forcing users to subclass
+        # generic transformers just to make them SciKeras compatible
+        n_out_expect = getattr(self, "n_outputs_expected_", None)
+        if n_out_expect and n_out_expect != len(self.model_.outputs):
+            raise ValueError(
+                "The target ``y`` seems to consist of"
+                f" {n_out_expect} outputs, but this Keras"
+                f" Model has {len(self.model_.outputs)} outputs."
+            )
 
-        This is in place to avoid cryptic TF errors.
-        """
-        # check if this is a multi-output model
-        if hasattr(self, "n_outputs_expected_"):
-            # n_outputs_expected_ is generated by data transformers
-            # we recognize the attribute but do not force it to be
-            # generated
-            if self.n_outputs_expected_ != len(self.model_.outputs):
+    def _check_model_inputs(self, X):
+        if isinstance(X, np.ndarray):
+            # Keras Model's inputs are always a list
+            X = [X]
+        elif isinstance(X, Mapping):
+            X = [X[inp_name] for inp_name in self.model_.input_names]
+        if len(X) != len(self.model_.inputs):
+            raise ValueError(
+                f"``X`` has {len(X)} inputs, but the Keras model"
+                f" has {len(self.model_.inputs)} inputs."
+            )
+        for X_in, model_in in zip(X, self.model_.inputs):
+            # check shape
+            X_in_shape = (1,) if X_in.ndim == 1 else X_in.shape[1:]
+            model_in_shape = model_in.shape[1:]
+            if X_in_shape != model_in_shape:
                 raise ValueError(
-                    "Detected a Keras model input of size"
-                    f" {y[0].shape[0]}, but {self.model_} has"
-                    f" {self.model_.outputs} outputs"
+                    f"Input {model_in.name} expected shape"
+                    f" {model_in_shape} but got {X_in_shape}."
                 )
+
+    def _check_model_loss(self):
         # check that if the user gave us a loss function it ended up in
         # the actual model
         init_params = inspect.signature(self.__init__).parameters
@@ -565,6 +588,16 @@ def _check_model_compatibility(self, y: np.ndarray) -> None:
                         " Data may not match loss function!"
                     )
 
+    def _check_model_compatibility(self, X, y) -> None:
+        """Checks that the model inputs, outputs and loss
+        match the given or expected X, y & loss.
+
+        This is in place to avoid cryptic TF errors.
+        """
+        self._check_model_inputs(X)
+        self._check_model_outputs(y)
+        self._check_model_loss()
+
     def _validate_data(
         self, X=None, y=None, reset: bool = False, y_numeric: bool = False
     ) -> Tuple[np.ndarray, Union[np.ndarray, None]]:
@@ -865,7 +898,7 @@ def _fit(
         y = self.target_encoder_.transform(y)
         X = self.feature_encoder_.transform(X)
 
-        self._check_model_compatibility(y)
+        self._check_model_compatibility(X, y)
 
         self._fit_keras_model(
             X,

tests/test_errors.py

@@ -22,14 +22,16 @@ def test_X_shape_change():
         loss=KerasRegressor.r_squared,
         hidden_layer_sizes=(100,),
     )
-    X = np.array([[1, 2], [3, 4]]).reshape(2, 2, 1)
+    X = np.array([[1, 2], [3, 4]])
     y = np.array([[0, 1, 0], [1, 0, 0]])
 
     estimator.fit(X=X, y=y)
 
+    # Calling with a different number of dimensions for X raises an error
     with pytest.raises(ValueError, match="dimensions in X"):
-        # Calling with a different number of dimensions for X raises an error
-        estimator.partial_fit(X=X.reshape(2, 2), y=y)
+        estimator.partial_fit(X=X.reshape(2, 2, 1), y=y)
+    with pytest.raises(ValueError, match="dimensions in X"):
+        estimator.predict(X=X.reshape(2, 2, 1))
 
 
 def test_unknown_param():