[MNT] Update scikit-learn requirement from <1.7.0,>=1.0.0 to >=1.0.0,<1.8.0 in the python-packages group (#2907)

* [MNT] Update scikit-learn requirement in the python-packages group

Updates the requirements on [scikit-learn](https://github.com/scikit-learn/scikit-learn) to permit the latest version.

Updates `scikit-learn` to 1.7.0
- [Release notes](https://github.com/scikit-learn/scikit-learn/releases)
- [Commits](scikit-learn/scikit-learn@1.0...1.7.0)

---
updated-dependencies:
- dependency-name: scikit-learn
  dependency-version: 1.7.0
  dependency-type: direct:production
  dependency-group: python-packages
...

Signed-off-by: dependabot[bot] <support@github.com>

* new validation

* Update test_rockad.py

* Update _rockad.py

---------

Signed-off-by: dependabot[bot] <support@github.com>
Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com>
Co-authored-by: MatthewMiddlehurst <pfm15hbu@gmail.com>

Author: dependabot[bot]

aeon/anomaly_detection/series/distance_based/_rockad.py

@@ -176,6 +176,8 @@ def _inner_fit(self, X: np.ndarray) -> None:
 
         if self.power_transform:
             self.power_transformer_ = PowerTransformer()
+            # todo check if this is still an issue with scikit-learn >= 1.7.0
+            # when lower bound is raised
             try:
                 Xtp = self.power_transformer_.fit_transform(Xt)
 

aeon/anomaly_detection/series/distance_based/tests/test_rockad.py

@@ -73,11 +73,6 @@ def test_rockad_incorrect_input():
     ):
         ad = ROCKAD(stride=1, window_size=100)
         ad.fit(train_series)
-    with pytest.warns(
-        UserWarning, match=r"Power Transform failed and thus has been disabled."
-    ):
-        ad = ROCKAD(stride=1, window_size=5)
-        ad.fit(train_series)
     with pytest.raises(
         ValueError, match=r"window shape cannot be larger than input array shape"
     ):

aeon/classification/sklearn/_continuous_interval_tree.py

@@ -21,6 +21,7 @@
 from sklearn.exceptions import NotFittedError
 from sklearn.utils import check_random_state
 from sklearn.utils.multiclass import check_classification_targets
+from sklearn.utils.validation import validate_data
 
 
 class _TreeNode:
@@ -374,7 +375,8 @@ def fit(self, X, y):
         """
         # data processing
         X = self._check_X(X)
-        X, y = self._validate_data(
+        X, y = validate_data(
+            self,
             X=X,
             y=y,
             ensure_min_samples=2,
@@ -464,8 +466,8 @@ def predict_proba(self, X):
 
         # data processing
         X = self._check_X(X)
-        X = self._validate_data(
-            X=X, reset=False, force_all_finite="allow-nan", accept_sparse=False
+        X = validate_data(
+            self, X=X, reset=False, force_all_finite="allow-nan", accept_sparse=False
         )
 
         dists = np.zeros((X.shape[0], self.n_classes_))

aeon/classification/sklearn/_rotation_forest_classifier.py

@@ -19,6 +19,7 @@
 from sklearn.tree import DecisionTreeClassifier
 from sklearn.utils import check_random_state
 from sklearn.utils.multiclass import check_classification_targets
+from sklearn.utils.validation import validate_data
 
 from aeon.base._base import _clone_estimator
 from aeon.utils.validation import check_n_jobs
@@ -192,7 +193,7 @@ def predict_proba(self, X) -> np.ndarray:
 
         # data processing
         X = self._check_X(X)
-        X = self._validate_data(X=X, reset=False, accept_sparse=False)
+        X = validate_data(self, X=X, reset=False, accept_sparse=False)
 
         # replace missing values with 0 and remove useless attributes
         X = X[:, self._useful_atts]
@@ -299,7 +300,7 @@ def fit_predict_proba(self, X, y) -> np.ndarray:
     def _fit_rotf(self, X, y, save_transformed_data: bool = False):
         # data processing
         X = self._check_X(X)
-        X, y = self._validate_data(X=X, y=y, ensure_min_samples=2, accept_sparse=False)
+        X, y = validate_data(self, X=X, y=y, ensure_min_samples=2, accept_sparse=False)
         check_classification_targets(y)
 
         self._n_jobs = check_n_jobs(self.n_jobs)

aeon/regression/sklearn/_rotation_forest_regressor.py

@@ -19,6 +19,7 @@
 from sklearn.exceptions import NotFittedError
 from sklearn.tree import DecisionTreeRegressor
 from sklearn.utils import check_random_state
+from sklearn.utils.validation import validate_data
 
 from aeon.base._base import _clone_estimator
 from aeon.utils.validation import check_n_jobs
@@ -168,7 +169,7 @@ def predict(self, X) -> np.ndarray:
 
         # data processing
         X = self._check_X(X)
-        X = self._validate_data(X=X, reset=False, accept_sparse=False)
+        X = validate_data(self, X=X, reset=False, accept_sparse=False)
 
         # replace missing values with 0 and remove useless attributes
         X = X[:, self._useful_atts]
@@ -222,7 +223,7 @@ def fit_predict(self, X, y) -> np.ndarray:
     def _fit_rotf(self, X, y, save_transformed_data: bool = False):
         # data processing
         X = self._check_X(X)
-        X, y = self._validate_data(X=X, y=y, ensure_min_samples=2, accept_sparse=False)
+        X, y = validate_data(self, X=X, y=y, ensure_min_samples=2, accept_sparse=False)
 
         self._label_average = np.mean(y)
 

pyproject.toml

@@ -50,7 +50,7 @@ dependencies = [
     "numpy>=1.21.0,<2.3.0",
     "packaging>=20.0",
     "pandas>=2.0.0,<2.4.0",
-    "scikit-learn>=1.0.0,<1.7.0",
+    "scikit-learn>=1.0.0,<1.8.0",
     "scipy>=1.9.0,<1.16.0",
     "typing-extensions>=4.6.0",
 ]