[MNT] Update scikit-learn upper bound

aeon/anomaly_detection/series/distance_based/_rockad.py

@@ -84,10 +84,10 @@ class ROCKAD(BaseSeriesAnomalyDetector):
     >>> detector.fit(X_train)
     ROCKAD(...)
     >>> detector.predict(X_test)
-    array([0.        , 0.00554713, 0.06990941, 0.22881059, 0.32382585,
-           0.43652154, 0.43652154, 0.43652154, 0.43652154, 0.43652154,
-           0.43652154, 0.43652154, 0.43652154, 0.43652154, 0.43652154,
-           0.52382585, 0.65200875, 0.80313368, 0.85194345, 1.        ])
+    array([0.        , 0.0055471, 0.0699094, 0.2288105, 0.3238258,
+           0.4365215, 0.4365215, 0.4365215, 0.4365215, 0.4365215,
+           0.4365215, 0.4365215, 0.4365215, 0.4365215, 0.4365215,
+           0.5238258, 0.6520087, 0.8031336, 0.8519434, 1.        ])
     """
 
     _tags = {

aeon/classification/dictionary_based/_muse.py

@@ -14,6 +14,7 @@
 from joblib import Parallel, delayed
 from scipy.sparse import hstack
 from sklearn.linear_model import LogisticRegression, RidgeClassifierCV
+from sklearn.multiclass import OneVsRestClassifier
 from sklearn.utils import check_random_state
 
 from aeon.classification.base import BaseClassifier
@@ -271,8 +272,9 @@ def _fit(self, X, y):
                 class_weight=self.class_weight,
                 penalty="l2",
                 random_state=self.random_state,
-                n_jobs=self.n_jobs,
             )
+            if self.n_classes_ > 2:
+                self.clf = OneVsRestClassifier(self.clf, n_jobs=self.n_jobs)
 
         self.clf.fit(all_words, y)
         self.total_features_count = all_words.shape[-1]

aeon/classification/dictionary_based/_weasel.py

@@ -13,6 +13,7 @@
 from numba import set_num_threads
 from scipy.sparse import hstack
 from sklearn.linear_model import LogisticRegression, RidgeClassifierCV
+from sklearn.multiclass import OneVsRestClassifier
 from sklearn.utils import check_random_state
 
 from aeon.classification.base import BaseClassifier
@@ -238,12 +239,12 @@ def _fit(self, X, y):
             all_words = np.concatenate(all_words, axis=1)
         else:
             all_words = hstack(all_words)
-
         # Ridge Classifier does not give probabilities
         if not self.support_probabilities:
             self.clf = RidgeClassifierCV(
                 alphas=np.logspace(-3, 3, 10), class_weight=self.class_weight
             )
+            all_words = all_words.astype(np.float64, copy=False)
         else:
             self.clf = LogisticRegression(
                 max_iter=5000,
@@ -252,8 +253,9 @@ def _fit(self, X, y):
                 class_weight=self.class_weight,
                 penalty="l2",
                 random_state=self.random_state,
-                n_jobs=self.n_jobs,
             )
+            if self.n_classes_ > 2:
+                self.clf = OneVsRestClassifier(self.clf, n_jobs=self.n_jobs)
 
         self.clf.fit(all_words, y)
 

aeon/classification/dictionary_based/_weasel_v2.py

@@ -185,7 +185,7 @@ def _fit(self, X, y):
             n_jobs=self._n_jobs,
         )
         words = self.transform.fit_transform(X, y)
-
+        words = words.astype(np.float64, copy=False)
         # use RidgeClassifierCV for classification
         self.clf = RidgeClassifierCV(
             alphas=np.logspace(-1, 5, 10), class_weight=self.class_weight
@@ -426,7 +426,6 @@ def fit_transform(self, X, y=None):
             all_words = np.concatenate(sfa_words, axis=1)
         else:
             all_words = hstack(sfa_words)
-
         self.total_features_count = all_words.shape[1]
 
         return all_words

aeon/regression/feature_based/_catch22.py

@@ -98,8 +98,8 @@ class Catch22Regressor(BaseRegressor):
     >>> reg.fit(X, y)
     Catch22Regressor(...)
     >>> reg.predict(X)
-    array([0.63821896, 1.0906666 , 0.64351536, 1.57550709, 0.46036267,
-           0.79297397, 1.32882497, 1.12603087, 1.51673405, 0.31683308])
+    array([0.63821896, 1.0906666 , 0.58323551, 1.57550709, 0.48413489,
+           0.70976176, 1.33206165, 1.09927538, 1.51673405, 0.31683308])
     """
 
     _tags = {

aeon/regression/sklearn/_rotation_forest_regressor.py

@@ -75,6 +75,13 @@ class RotationForestRegressor(RegressorMixin, BaseEstimator):
     estimators_ : list of shape (n_estimators) of BaseEstimator
         The collections of estimators trained in fit.
 
+    Notes
+    -----
+    Predictions may differ slightly between scikit-learn versions. In particular,
+    scikit-learn 1.8 fixed decision-tree handling of almost constant features, which
+    can change the fitted trees used by ``RotationForestRegressor`` and therefore
+    its output, even when using the same random state.
+
     References
     ----------
     .. [1] Rodriguez, Juan José, Ludmila I. Kuncheva, and Carlos J. Alonso. "Rotation
@@ -105,7 +112,7 @@ def __init__(
         max_group: int = 3,
         remove_proportion: float = 0.5,
         base_estimator: BaseEstimator | None = None,
-        pca_solver: str = "auto",
+        pca_solver: str = "full",
         time_limit_in_minutes: float = 0.0,
         contract_max_n_estimators: int = 500,
         n_jobs: int = 1,
@@ -330,7 +337,7 @@ def _fit_estimator(
                 with np.errstate(divide="ignore", invalid="ignore"):
                     # differences between os occasionally. seems to happen when there
                     # are low amounts of cases in the fit
-                    pca = PCA(random_state=rng, svd_solver=self.pca_solver).fit(X_t)
+                    pca = PCA(random_state=rng, svd_solver="full").fit(X_t)
 
                 if not np.isnan(pca.explained_variance_ratio_).all():
                     break

aeon/regression/sklearn/tests/test_rotation_forest_regressor.py

@@ -25,23 +25,22 @@ def test_rotf_output():
     rotf.fit(X_train, y_train)
 
     expected = [
-        0.026,
-        0.0245,
-        0.0224,
-        0.0453,
-        0.0892,
-        0.0314,
-        0.026,
-        0.0451,
-        0.0287,
-        0.04,
-        0.026,
-        0.0378,
-        0.0265,
-        0.0356,
-        0.0281,
+        0.02987,
+        0.01912,
+        0.02359,
+        0.05474,
+        0.06206,
+        0.03782,
+        0.02681,
+        0.04133,
+        0.02588,
+        0.04581,
+        0.03081,
+        0.02781,
+        0.02805,
+        0.03213,
+        0.02295,
     ]
-
     np.testing.assert_array_almost_equal(expected, rotf.predict(X_test[:15]), decimal=4)
 
 

aeon/testing/expected_results/_write_estimator_results.py

@@ -1,11 +1,10 @@
 """Write expected results for estimators to file for usage in tests."""
 
-import os
 from copy import deepcopy
+from pathlib import Path
 
 from sklearn.utils import check_random_state
 
-import aeon
 from aeon.base import ComposableEstimatorMixin
 from aeon.base._base import _clone_estimator
 from aeon.datasets import (
@@ -146,14 +145,9 @@ def get_results_string(results, estimator_type):
 
         estimators = all_estimators(type_filter=estimator_type)
 
-        base = os.path.dirname(os.path.abspath(aeon.__file__))
-        write_path = os.path.join(
-            base,
-            "testing",
-            "expected_results",
-            f"expected_{estimator_type.replace('-', '_')}_results.py",
-        )
-        print("\nWriting to: " + write_path + "\n")  # noqa: T201
+        base = Path(__file__).resolve().parent
+        write_path = base / f"expected_{estimator_type.replace('-', '_')}_results.py"
+        print(f"\nWriting to: {write_path}\n")  # noqa: T201
 
         uv_results = {}
         mv_results = {}
@@ -197,7 +191,7 @@ def get_results_string(results, estimator_type):
                     deepcopy(multivariate_datasets[estimator_type][1]),
                 )
 
-        with open(write_path, "w") as f:
+        with open(write_path, "w", encoding="utf-8") as f:
             f.write(f'"""Expected results file for {estimator_type} estimators.\n\n')
             f.write("This file is automatically generated by\n")
             f.write(

aeon/testing/expected_results/expected_classifier_results.py

@@ -66,12 +66,12 @@
         [1.0, 0.0],
     ],
     "DrCIFClassifier": [
-        [0.5, 0.5],
+        [0.6, 0.4],
         [0.6, 0.4],
         [1.0, 0.0],
         [0.7, 0.3],
         [0.7, 0.3],
-        [0.7, 0.3],
+        [0.6, 0.4],
         [0.0, 1.0],
         [0.8, 0.2],
         [0.7, 0.3],
@@ -114,16 +114,16 @@
         [1.0, 0.0],
     ],
     "HIVECOTEV2": [
-        [0.2834, 0.7166],
-        [0.1732, 0.8268],
-        [0.8812, 0.1188],
-        [0.8812, 0.1188],
-        [0.9031, 0.0969],
-        [0.6541, 0.3459],
-        [0.0272, 0.9728],
-        [0.854, 0.146],
-        [0.6813, 0.3187],
-        [0.7571, 0.2429],
+        [0.2468, 0.7532],
+        [0.1107, 0.8893],
+        [0.9521, 0.0479],
+        [0.9042, 0.0958],
+        [0.9207, 0.0793],
+        [0.6007, 0.3993],
+        [0.0314, 0.9686],
+        [0.9207, 0.0793],
+        [0.5842, 0.4158],
+        [0.8414, 0.1586],
     ],
     "HydraClassifier": [
         [1.0, 0.0],
@@ -283,15 +283,15 @@
     ],
     "RISTClassifier": [
         [0.3333, 0.6667],
-        [0.0, 1.0],
+        [0.3333, 0.6667],
         [0.6667, 0.3333],
         [1.0, 0.0],
         [1.0, 0.0],
-        [0.3333, 0.6667],
+        [0.6667, 0.3333],
         [0.0, 1.0],
         [0.6667, 0.3333],
         [1.0, 0.0],
-        [0.0, 1.0],
+        [0.3333, 0.6667],
     ],
     "RSTSF": [
         [0.5, 0.5],
@@ -426,16 +426,16 @@
         [0.9, 0.1],
     ],
     "TemporalDictionaryEnsemble": [
-        [0.2689, 0.7311],
-        [0.0, 1.0],
+        [0.3057, 0.6943],
+        [0.1928, 0.8072],
         [1.0, 0.0],
-        [0.8656, 0.1344],
-        [0.3638, 0.6362],
-        [0.4982, 0.5018],
+        [0.6943, 0.3057],
+        [0.3855, 0.6145],
+        [0.8072, 0.1928],
         [0.0, 1.0],
-        [1.0, 0.0],
-        [0.4033, 0.5967],
-        [0.8656, 0.1344],
+        [0.8072, 0.1928],
+        [0.4985, 0.5015],
+        [0.8072, 0.1928],
     ],
     "TimeSeriesForestClassifier": [
         [0.3, 0.7],
@@ -462,13 +462,13 @@
         [0.3215, 0.6785],
     ],
     "WEASEL_V2": [
-        [0.0, 1.0],
-        [0.0, 1.0],
         [1.0, 0.0],
         [1.0, 0.0],
         [1.0, 0.0],
         [1.0, 0.0],
-        [0.0, 1.0],
+        [1.0, 0.0],
+        [1.0, 0.0],
+        [1.0, 0.0],
         [1.0, 0.0],
         [1.0, 0.0],
         [1.0, 0.0],
@@ -512,9 +512,9 @@
         [0.1, 0.2, 0.5, 0.2],
     ],
     "DrCIFClassifier": [
-        [0.2, 0.8, 0.0, 0.0],
+        [0.1, 0.9, 0.0, 0.0],
         [0.0, 0.0, 1.0, 0.0],
-        [0.3, 0.6, 0.0, 0.1],
+        [0.4, 0.5, 0.0, 0.1],
         [0.0, 0.0, 0.9, 0.1],
         [0.6, 0.3, 0.1, 0.0],
         [0.0, 0.0, 0.9, 0.1],
@@ -552,9 +552,9 @@
         [0.0359, 0.0, 0.7432, 0.2209],
         [0.9641, 0.0, 0.0359, 0.0],
         [0.0, 0.3467, 0.4683, 0.185],
-        [0.7089, 0.142, 0.0071, 0.142],
+        [0.7018, 0.1491, 0.0071, 0.142],
         [0.0, 0.1148, 0.8852, 0.0],
-        [0.0142, 0.9858, 0.0, 0.0],
+        [0.0071, 0.9929, 0.0, 0.0],
         [0.3093, 0.142, 0.4698, 0.0789],
         [0.1077, 0.0, 0.0071, 0.8852],
         [0.0, 0.0, 0.9641, 0.0359],
@@ -718,10 +718,10 @@
     "RISTClassifier": [
         [0.0, 1.0, 0.0, 0.0],
         [0.0, 0.0, 1.0, 0.0],
-        [0.3333, 0.0, 0.0, 0.6667],
+        [0.6667, 0.0, 0.0, 0.3333],
         [0.0, 0.0, 0.6667, 0.3333],
         [0.6667, 0.3333, 0.0, 0.0],
-        [0.0, 0.0, 0.6667, 0.3333],
+        [0.0, 0.0, 0.3333, 0.6667],
         [0.6667, 0.0, 0.0, 0.3333],
         [0.3333, 0.0, 0.6667, 0.0],
         [0.3333, 0.0, 0.0, 0.6667],