Make sklearn dependency optional (#20657)

keras/src/wrappers/fixes.py

@@ -1,42 +1,7 @@
-import sklearn
-from packaging.version import parse as parse_version
-from sklearn import get_config
-
-sklearn_version = parse_version(parse_version(sklearn.__version__).base_version)
-
-if sklearn_version < parse_version("1.6"):
-
-    def patched_more_tags(estimator, expected_failed_checks):
-        import copy
-
-        from sklearn.utils._tags import _safe_tags
-
-        original_tags = copy.deepcopy(_safe_tags(estimator))
-
-        def patched_more_tags(self):
-            original_tags.update({"_xfail_checks": expected_failed_checks})
-            return original_tags
-
-        estimator.__class__._more_tags = patched_more_tags
-        return estimator
-
-    def parametrize_with_checks(
-        estimators,
-        *,
-        legacy: bool = True,
-        expected_failed_checks=None,
-    ):
-        # legacy is not supported and ignored
-        from sklearn.utils.estimator_checks import parametrize_with_checks  # noqa: F401, I001
-
-        estimators = [
-            patched_more_tags(estimator, expected_failed_checks(estimator))
-            for estimator in estimators
-        ]
-
-        return parametrize_with_checks(estimators)
-else:
-    from sklearn.utils.estimator_checks import parametrize_with_checks  # noqa: F401, I001
+try:
+    import sklearn
+except ImportError:
+    sklearn = None
 
 
 def _validate_data(estimator, *args, **kwargs):
@@ -59,9 +24,6 @@ def _validate_data(estimator, *args, **kwargs):
 
 
 def type_of_target(y, input_name="", *, raise_unknown=False):
-    # fix for raise_unknown which is introduced in scikit-learn 1.6
-    from sklearn.utils.multiclass import type_of_target
-
     def _raise_or_return(target_type):
         """Depending on the value of raise_unknown, either raise an error or
         return 'unknown'.
@@ -72,7 +34,9 @@ def _raise_or_return(target_type):
         else:
             return target_type
 
-    target_type = type_of_target(y, input_name=input_name)
+    target_type = sklearn.utils.multiclass.type_of_target(
+        y, input_name=input_name
+    )
     return _raise_or_return(target_type)
 
 
@@ -86,7 +50,7 @@ def _routing_enabled():
 
     TODO: remove when the config key is no longer available in scikit-learn
     """
-    return get_config().get("enable_metadata_routing", False)
+    return sklearn.get_config().get("enable_metadata_routing", False)
 
 
 def _raise_for_params(params, owner, method):

keras/src/wrappers/sklearn_test.py

@@ -3,6 +3,9 @@
 from contextlib import contextmanager
 
 import pytest
+import sklearn
+from packaging.version import parse as parse_version
+from sklearn.utils.estimator_checks import parametrize_with_checks
 
 import keras
 from keras.src.backend import floatx
@@ -13,7 +16,45 @@
 from keras.src.wrappers import SKLearnClassifier
 from keras.src.wrappers import SKLearnRegressor
 from keras.src.wrappers import SKLearnTransformer
-from keras.src.wrappers.fixes import parametrize_with_checks
+
+
+def wrapped_parametrize_with_checks(
+    estimators,
+    *,
+    legacy: bool = True,
+    expected_failed_checks=None,
+):
+    """Wrapped `parametrize_with_checks` handling backwards compat."""
+    sklearn_version = parse_version(
+        parse_version(sklearn.__version__).base_version
+    )
+
+    if sklearn_version >= parse_version("1.6"):
+        return parametrize_with_checks(
+            estimators,
+            legacy=legacy,
+            expected_failed_checks=expected_failed_checks,
+        )
+
+    def patched_more_tags(estimator, expected_failed_checks):
+        import copy
+
+        original_tags = copy.deepcopy(sklearn.utils._tags._safe_tags(estimator))
+
+        def patched_more_tags(self):
+            original_tags.update({"_xfail_checks": expected_failed_checks})
+            return original_tags
+
+        estimator.__class__._more_tags = patched_more_tags
+        return estimator
+
+    estimators = [
+        patched_more_tags(estimator, expected_failed_checks(estimator))
+        for estimator in estimators
+    ]
+
+    # legacy is not supported and ignored
+    return parametrize_with_checks(estimators)
 
 
 def dynamic_model(X, y, loss, layers=[10]):
@@ -80,7 +121,7 @@ def use_floatx(x: str):
 }
 
 
-@parametrize_with_checks(
+@wrapped_parametrize_with_checks(
     estimators=[
         SKLearnClassifier(
             model=dynamic_model,

keras/src/wrappers/sklearn_wrapper.py

@@ -1,14 +1,6 @@
 import copy
 
 import numpy as np
-from sklearn.base import BaseEstimator
-from sklearn.base import ClassifierMixin
-from sklearn.base import RegressorMixin
-from sklearn.base import TransformerMixin
-from sklearn.base import check_is_fitted
-from sklearn.pipeline import make_pipeline
-from sklearn.preprocessing import OneHotEncoder
-from sklearn.utils.metadata_routing import MetadataRequest
 
 from keras.src.api_export import keras_export
 from keras.src.models.cloning import clone_model
@@ -18,6 +10,28 @@
 from keras.src.wrappers.fixes import type_of_target
 from keras.src.wrappers.utils import TargetReshaper
 from keras.src.wrappers.utils import _check_model
+from keras.src.wrappers.utils import assert_sklearn_installed
+
+try:
+    import sklearn
+    from sklearn.base import BaseEstimator
+    from sklearn.base import ClassifierMixin
+    from sklearn.base import RegressorMixin
+    from sklearn.base import TransformerMixin
+except ImportError:
+    sklearn = None
+
+    class BaseEstimator:
+        pass
+
+    class ClassifierMixin:
+        pass
+
+    class RegressorMixin:
+        pass
+
+    class TransformerMixin:
+        pass
 
 
 class SKLBase(BaseEstimator):
@@ -64,6 +78,7 @@ def __init__(
         model_kwargs=None,
         fit_kwargs=None,
     ):
+        assert_sklearn_installed(self.__class__.__name__)
         self.model = model
         self.warm_start = warm_start
         self.model_kwargs = model_kwargs
@@ -119,7 +134,9 @@ def set_fit_request(self, **kwargs):
                 "sklearn.set_config(enable_metadata_routing=True)."
             )
 
-        self._metadata_request = MetadataRequest(owner=self.__class__.__name__)
+        self._metadata_request = sklearn.utils.metadata_routing.MetadataRequest(
+            owner=self.__class__.__name__
+        )
         for param, alias in kwargs.items():
             self._metadata_request.score.add_request(param=param, alias=alias)
         return self
@@ -155,7 +172,7 @@ def fit(self, X, y, **kwargs):
 
     def predict(self, X):
         """Predict using the model."""
-        check_is_fitted(self)
+        sklearn.base.check_is_fitted(self)
         X = _validate_data(self, X, reset=False)
         raw_output = self.model_.predict(X)
         return self._reverse_process_target(raw_output)
@@ -267,8 +284,9 @@ def _process_target(self, y, reset=False):
                 f" Target type: {target_type}"
             )
         if reset:
-            self._target_encoder = make_pipeline(
-                TargetReshaper(), OneHotEncoder(sparse_output=False)
+            self._target_encoder = sklearn.pipeline.make_pipeline(
+                TargetReshaper(),
+                sklearn.preprocessing.OneHotEncoder(sparse_output=False),
             ).fit(y)
             self.classes_ = np.unique(y)
             if len(self.classes_) == 1:
@@ -454,7 +472,7 @@ def transform(self, X):
             X_transformed: array-like, shape=(n_samples, n_features)
                 The transformed data.
         """
-        check_is_fitted(self)
+        sklearn.base.check_is_fitted(self)
         X = _validate_data(self, X, reset=False)
         return self.model_.predict(X)
 

keras/src/wrappers/utils.py

@@ -1,7 +1,23 @@
-from sklearn.base import BaseEstimator
-from sklearn.base import TransformerMixin
-from sklearn.base import check_is_fitted
-from sklearn.utils._array_api import get_namespace
+try:
+    import sklearn
+    from sklearn.base import BaseEstimator
+    from sklearn.base import TransformerMixin
+except ImportError:
+    sklearn = None
+
+    class BaseEstimator:
+        pass
+
+    class TransformerMixin:
+        pass
+
+
+def assert_sklearn_installed(symbol_name):
+    if sklearn is None:
+        raise ImportError(
+            f"{symbol_name} requires `scikit-learn` to be installed. "
+            "Run `pip install scikit-learn` to install it."
+        )
 
 
 def _check_model(model):
@@ -64,8 +80,8 @@ def inverse_transform(self, y):
                 is passed, it will be squeezed back to 1D. Otherwise, it
                 will eb left untouched.
         """
-        check_is_fitted(self)
-        xp, _ = get_namespace(y)
+        sklearn.base.check_is_fitted(self)
+        xp, _ = sklearn.utils._array_api.get_namespace(y)
         if self.ndim_ == 1 and y.ndim == 2:
             return xp.squeeze(y, axis=1)
         return y

pyproject.toml

@@ -34,7 +34,6 @@ dependencies = [
     "optree",
     "ml-dtypes",
     "packaging",
-    "scikit-learn",
 ]
 # Run also: pip install -r requirements.txt