[ENH] sklearn 1.7 compatibility

pyproject.toml

@@ -42,11 +42,12 @@ dependencies = [
   "pandas <3.0.0",
   "gradient-free-optimizers >=1.2.4, <2.0.0",
   "scikit-base <1.0.0",
+  "scikit-learn <1.8.0",
 ]
 
 [project.optional-dependencies]
 sklearn-integration = [
-  "scikit-learn == 1.6.1",
+  "scikit-learn <1.8.0",
 ]
 build = [
   "setuptools",

src/hyperactive/integrations/sklearn/_adapter/__init__.py

@@ -0,0 +1,8 @@
+# copyright: hyperactive developers, MIT License (see LICENSE file)
+
+
+from hyperactive.integrations.sklearn._adapter._sklearnadapter import _SklearnAdapter
+
+__all__ = [
+    "_SklearnAdapter",
+]

src/hyperactive/integrations/sklearn/_adapter/_sklearnadapter.py

@@ -0,0 +1,32 @@
+"""Adapter for sklearn regressors and classifiers for Hyperactive optimizers."""
+
+from sklearn.base import clone
+from sklearn.utils.validation import indexable, _check_method_params
+
+
+class _SklearnAdapter:
+
+    _required_parameters = ["estimator", "optimizer", "params_config"]
+
+    def _refit(self, X, y=None, **fit_params):
+        self.best_estimator_ = clone(self.estimator).set_params(
+            **clone(self.best_params_, safe=False)
+        )
+
+        self.best_estimator_.fit(X, y, **fit_params)
+        return self
+
+    def _check_data(self, X, y):
+        X, y = indexable(X, y)
+        if hasattr(X, "ndim") and X.ndim == 1:
+            X = X.reshape(-1, 1)
+        if hasattr(self, "_validate_data"):
+            validate_data = self._validate_data
+        else:
+            from sklearn.utils.validation import validate_data
+
+        return validate_data(X, y)
+
+    @property
+    def fit_successful(self):
+        self._fit_successful

src/hyperactive/integrations/sklearn/best_estimator.py

@@ -4,8 +4,6 @@
 
 
 from sklearn.utils.metaestimators import available_if
-from sklearn.utils.deprecation import _deprecate_Xt_in_inverse_transform
-from sklearn.exceptions import NotFittedError
 from sklearn.utils.validation import check_is_fitted
 
 from .utils import _estimator_has
@@ -47,8 +45,7 @@ def transform(self, X):
         return self.best_estimator_.transform(X)
 
     @available_if(_estimator_has("inverse_transform"))
-    def inverse_transform(self, X=None, Xt=None):
-        X = _deprecate_Xt_in_inverse_transform(X, Xt)
+    def inverse_transform(self, X=None):
         check_is_fitted(self)
         return self.best_estimator_.inverse_transform(X)
 

src/hyperactive/integrations/sklearn/hyperactive_search_cv.py

@@ -17,9 +17,10 @@
 from .checks import Checks
 from ...optimizers import RandomSearchOptimizer
 from hyperactive.experiment.integrations.sklearn_cv import SklearnCvExperiment
+from hyperactive.integrations.sklearn._adapter import _SklearnAdapter
 
 
-class HyperactiveSearchCV(BaseEstimator, _BestEstimator_, Checks):
+class HyperactiveSearchCV(_SklearnAdapter, BaseEstimator, _BestEstimator_, Checks):
     """
     HyperactiveSearchCV class for hyperparameter tuning using cross-validation with sklearn estimators.
 
@@ -77,23 +78,6 @@ def __init__(
         self.refit = refit
         self.cv = cv
 
-    def _refit(self, X, y=None, **fit_params):
-        self.best_estimator_ = clone(self.estimator).set_params(
-            **clone(self.best_params_, safe=False)
-        )
-
-        self.best_estimator_.fit(X, y, **fit_params)
-        return self
-
-    def _check_data(self, X, y):
-        X, y = indexable(X, y)
-        if hasattr(self, "_validate_data"):
-            validate_data = self._validate_data
-        else:
-            from sklearn.utils.validation import validate_data
-
-        return validate_data(X, y)
-
     @Checks.verify_fit
     def fit(self, X, y, **fit_params):
         """

src/hyperactive/integrations/sklearn/opt_cv.py

@@ -10,10 +10,11 @@
 from hyperactive.integrations.sklearn.best_estimator import (
     BestEstimator as _BestEstimator_
 )
+from hyperactive.integrations.sklearn._adapter import _SklearnAdapter
 from hyperactive.integrations.sklearn.checks import Checks
 
 
-class OptCV(BaseEstimator, _BestEstimator_, Checks):
+class OptCV(_SklearnAdapter, BaseEstimator, _BestEstimator_, Checks):
     """Tuning via any optimizer in the hyperactive API.
 
     Parameters
@@ -83,23 +84,6 @@ def __init__(
         self.refit = refit
         self.cv = cv
 
-    def _refit(self, X, y=None, **fit_params):
-        self.best_estimator_ = clone(self.estimator).set_params(
-            **clone(self.best_params_, safe=False)
-        )
-
-        self.best_estimator_.fit(X, y, **fit_params)
-        return self
-
-    def _check_data(self, X, y):
-        X, y = indexable(X, y)
-        if hasattr(self, "_validate_data"):
-            validate_data = self._validate_data
-        else:
-            from sklearn.utils.validation import validate_data
-
-        return validate_data(X, y)
-
     @Checks.verify_fit
     def fit(self, X, y, **fit_params):
         """Fit the model.