Feature/sklearn v1.7

.github/workflows/test.yml

@@ -84,3 +84,32 @@ jobs:
       - name: Test with pytest
         run: |
           python -m pytest src/hyperactive -p no:warnings
+
+  test-sklearn-versions:
+    name: test-sklearn-${{ matrix.sklearn-version }} python-${{ matrix.python-version }}
+    runs-on: ubuntu-latest
+
+    strategy:
+      fail-fast: false
+      matrix:
+        sklearn-version: ["1.5", "1.6", "1.7"]
+        python-version: ["3.11", "3.12", "3.13"]
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - name: Set up Python 3.12
+        uses: actions/setup-python@v5
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies for scikit-learn ${{ matrix.sklearn-version }}
+        run: |
+          python -m pip install --upgrade pip
+          python -m pip install build pytest
+          make install
+          python -m pip install scikit-learn==${{ matrix.sklearn-version }}
+
+      - name: Run sklearn integration tests for ${{ matrix.sklearn-version }}
+        run: |
+          python -m pytest -x -p no:warnings tests/integrations/sklearn/

src/hyperactive/integrations/sklearn/_compat.py

@@ -0,0 +1,104 @@
+"""
+Internal helpers that bridge behavioural differences between
+scikit-learn versions.  Import *private* scikit-learn symbols **only**
+here and nowhere else.
+
+Copyright: Hyperactive contributors
+License: MIT
+"""
+
+from __future__ import annotations
+
+import warnings
+from typing import Dict, Any
+
+import sklearn
+from packaging import version
+from sklearn.utils.validation import indexable
+
+_SK_VERSION = version.parse(sklearn.__version__)
+
+
+def _safe_validate_X_y(estimator, X, y):
+    """
+    Version-independent replacement for naive validate_data(X, y).
+
+    • Ensures X is 2-D.
+    • Allows y to stay 1-D (required by scikit-learn >=1.7 checks).
+    • Uses BaseEstimator._validate_data when available so that
+      estimator tags and sample-weight checks keep working.
+    """
+    X, y = indexable(X, y)
+
+    if hasattr(estimator, "_validate_data"):
+        return estimator._validate_data(
+            X,
+            y,
+            validate_separately=(
+                {"ensure_2d": True},  # parameters for X
+                {"ensure_2d": False},  # parameters for y
+            ),
+        )
+
+    # Fallback for very old scikit-learn versions (<0.23)
+    from sklearn.utils.validation import check_X_y
+
+    return check_X_y(X, y, ensure_2d=True)
+
+
+def _safe_refit(estimator, X, y, fit_params):
+    if estimator.refit:
+        estimator._refit(X, y, **fit_params)
+
+        # make the wrapper itself expose n_features_in_
+        if hasattr(estimator.best_estimator_, "n_features_in_"):
+            estimator.n_features_in_ = estimator.best_estimator_.n_features_in_
+    else:
+        # Even when `refit=False` we must satisfy the contract
+        estimator.n_features_in_ = X.shape[1]
+
+
+# Replacement for `_deprecate_Xt_in_inverse_transform`
+if _SK_VERSION < version.parse("1.7"):
+    # Still exists → re-export
+    from sklearn.utils.deprecation import _deprecate_Xt_in_inverse_transform
+else:
+    # Removed in 1.7 → provide drop-in replacement
+    def _deprecate_Xt_in_inverse_transform(  # noqa: N802  keep sklearn’s name
+        X: Any | None,
+        Xt: Any | None,
+    ):
+        """
+        scikit-learn ≤1.6 accepted both the old `Xt` parameter and the new
+        `X` parameter for `inverse_transform`.  When only `Xt` is given we
+        return `Xt` and raise a deprecation warning (same behaviour that
+        scikit-learn had before 1.7); otherwise we return `X`.
+        """
+        if Xt is not None:
+            warnings.warn(
+                "'Xt' was deprecated in scikit-learn 1.2 and has been "
+                "removed in 1.7; use the positional argument 'X' instead.",
+                FutureWarning,
+                stacklevel=2,
+            )
+            return Xt
+        return X
+
+
+# Replacement for `_check_method_params`
+try:
+    from sklearn.utils.validation import _check_method_params  # noqa: F401
+except ImportError:  # fallback for future releases
+
+    def _check_method_params(  # type: ignore[override]  # noqa: N802
+        X,
+        params: Dict[str, Any],
+    ):
+        # passthrough – rely on estimator & indexable for validation
+        return params
+
+
+__all__ = [
+    "_deprecate_Xt_in_inverse_transform",
+    "_check_method_params",
+]

src/hyperactive/integrations/sklearn/best_estimator.py

@@ -4,11 +4,11 @@
 
 
 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
+from ._compat import _deprecate_Xt_in_inverse_transform
 
 
 # NOTE Implementations of following methods from:

src/hyperactive/integrations/sklearn/hyperactive_search_cv.py

@@ -7,7 +7,7 @@
 
 from sklearn.base import BaseEstimator, clone
 from sklearn.metrics import check_scoring
-from sklearn.utils.validation import indexable, _check_method_params
+
 
 from sklearn.base import BaseEstimator as SklearnBaseEstimator
 
@@ -18,6 +18,8 @@
 from ...optimizers import RandomSearchOptimizer
 from hyperactive.experiment.integrations.sklearn_cv import SklearnCvExperiment
 
+from ._compat import _check_method_params, _safe_validate_X_y, _safe_refit
+
 
 class HyperactiveSearchCV(BaseEstimator, _BestEstimator_, Checks):
     """
@@ -86,13 +88,7 @@ def _refit(self, X, y=None, **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)
+        return _safe_validate_X_y(self, X, y)
 
     @Checks.verify_fit
     def fit(self, X, y, **fit_params):
@@ -141,8 +137,7 @@ def fit(self, X, y, **fit_params):
         self.best_score_ = hyper.best_score(objective_function)
         self.search_data_ = hyper.search_data(objective_function)
 
-        if self.refit:
-            self._refit(X, y, **fit_params)
+        _safe_refit(self, X, y, fit_params)
 
         return self
 

src/hyperactive/integrations/sklearn/opt_cv.py

@@ -4,14 +4,15 @@
 from typing import Union
 
 from sklearn.base import BaseEstimator, clone
-from sklearn.utils.validation import indexable, _check_method_params
 
 from hyperactive.experiment.integrations.sklearn_cv import SklearnCvExperiment
 from hyperactive.integrations.sklearn.best_estimator import (
-    BestEstimator as _BestEstimator_
+    BestEstimator as _BestEstimator_,
 )
 from hyperactive.integrations.sklearn.checks import Checks
 
+from ._compat import _check_method_params, _safe_validate_X_y, _safe_refit
+
 
 class OptCV(BaseEstimator, _BestEstimator_, Checks):
     """Tuning via any optimizer in the hyperactive API.
@@ -92,13 +93,7 @@ def _refit(self, X, y=None, **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)
+        return _safe_validate_X_y(self, X, y)
 
     @Checks.verify_fit
     def fit(self, X, y, **fit_params):
@@ -138,8 +133,7 @@ def fit(self, X, y, **fit_params):
         self.best_params_ = best_params
         self.best_estimator_ = clone(self.estimator).set_params(**best_params)
 
-        if self.refit:
-            self._refit(X, y, **fit_params)
+        _safe_refit(self, X, y, fit_params)
 
         return self