Fix missing and fragile scikit-learn imports in Keras sklearn wrappers (#21387)

* Fix sklearn imports

* remove sklearn import

* fix formatting and imports

* Remove internal API import

* Resolve review comments

Author: timovdk

keras/src/wrappers/fixes.py

@@ -34,9 +34,9 @@ def _raise_or_return(target_type):
         else:
             return target_type
 
-    target_type = sklearn.utils.multiclass.type_of_target(
-        y, input_name=input_name
-    )
+    from sklearn.utils.multiclass import type_of_target as sk_type_of_target
+
+    target_type = sk_type_of_target(y, input_name=input_name)
     return _raise_or_return(target_type)
 
 

keras/src/wrappers/sklearn_wrapper.py

@@ -172,7 +172,9 @@ def fit(self, X, y, **kwargs):
 
     def predict(self, X):
         """Predict using the model."""
-        sklearn.base.check_is_fitted(self)
+        from sklearn.utils.validation import check_is_fitted
+
+        check_is_fitted(self)
         X = _validate_data(self, X, reset=False)
         raw_output = self.model_.predict(X)
         return self._reverse_process_target(raw_output)
@@ -474,7 +476,9 @@ def transform(self, X):
             X_transformed: array-like, shape=(n_samples, n_features)
                 The transformed data.
         """
-        sklearn.base.check_is_fitted(self)
+        from sklearn.utils.validation import check_is_fitted
+
+        check_is_fitted(self)
         X = _validate_data(self, X, reset=False)
         return self.model_.predict(X)
 

keras/src/wrappers/utils.py

@@ -1,3 +1,5 @@
+import numpy as np
+
 try:
     import sklearn
     from sklearn.base import BaseEstimator
@@ -25,8 +27,8 @@ def _check_model(model):
     # compile model if user gave us an un-compiled model
     if not model.compiled or not model.loss or not model.optimizer:
         raise RuntimeError(
-            "Given model needs to be compiled, and have a loss and an "
-            "optimizer."
+            "Given model needs to be compiled, and have a loss "
+            "and an optimizer."
         )
 
 
@@ -80,8 +82,9 @@ def inverse_transform(self, y):
                 is passed, it will be squeezed back to 1D. Otherwise, it
                 will eb left untouched.
         """
-        sklearn.base.check_is_fitted(self)
-        xp, _ = sklearn.utils._array_api.get_namespace(y)
+        from sklearn.utils.validation import check_is_fitted
+
+        check_is_fitted(self)
         if self.ndim_ == 1 and y.ndim == 2:
-            return xp.squeeze(y, axis=1)
+            return np.squeeze(y, axis=1)
         return y