[MNT] Raise version bound for scikit-learn 1.6

aeon/base/_base.py

@@ -415,6 +415,18 @@ def __sklearn_is_fitted__(self):
         """Check fitted status and return a Boolean value."""
         return self.is_fitted
 
+    def __sklearn_tags__(self):
+        """Return sklearn style tags for the estimator."""
+        aeon_tags = self.get_tags()
+        sklearn_tags = super().__sklearn_tags__()
+        sklearn_tags.non_deterministic = aeon_tags.get("non_deterministic", False)
+        sklearn_tags.target_tags.one_d_labels = True
+        sklearn_tags.input_tags.three_d_array = True
+        sklearn_tags.input_tags.allow_nan = aeon_tags.get(
+            "capability:missing_values", False
+        )
+        return sklearn_tags
+
     def _validate_data(self, **kwargs):
         """Sklearn data validation."""
         raise NotImplementedError(

aeon/classification/base.py

@@ -26,6 +26,7 @@ class name: BaseClassifier
 
 import numpy as np
 import pandas as pd
+from sklearn.base import ClassifierMixin
 from sklearn.metrics import get_scorer, get_scorer_names
 from sklearn.model_selection import cross_val_predict
 
@@ -35,7 +36,7 @@ class name: BaseClassifier
 from aeon.utils.validation.labels import check_classification_y
 
 
-class BaseClassifier(BaseCollectionEstimator):
+class BaseClassifier(ClassifierMixin, BaseCollectionEstimator):
     """
     Abstract base class for time series classifiers.
 
@@ -66,7 +67,6 @@ def __init__(self):
         self.classes_ = []  # classes seen in y, unique labels
         self.n_classes_ = -1  # number of unique classes in y
         self._class_dictionary = {}
-        self._estimator_type = "classifier"
 
         super().__init__()
 

aeon/clustering/base.py

@@ -7,11 +7,12 @@
 from typing import final
 
 import numpy as np
+from sklearn.base import ClusterMixin
 
 from aeon.base import BaseCollectionEstimator
 
 
-class BaseClusterer(BaseCollectionEstimator):
+class BaseClusterer(ClusterMixin, BaseCollectionEstimator):
     """Abstract base class for time series clusterers.
 
     Parameters
@@ -26,10 +27,6 @@ class BaseClusterer(BaseCollectionEstimator):
 
     @abstractmethod
     def __init__(self):
-        # required for compatibility with some sklearn interfaces e.g.
-        # CalibratedClassifierCV
-        self._estimator_type = "clusterer"
-
         super().__init__()
 
     @final
@@ -132,24 +129,6 @@ def fit_predict(self, X, y=None) -> np.ndarray:
             to return.
         y: ignored, exists for API consistency reasons.
 
-        Returns
-        -------
-        np.ndarray (1d array of shape (n_cases,))
-            Index of the cluster each time series in X belongs to.
-        """
-        return self._fit_predict(X, y)
-
-    def _fit_predict(self, X, y=None) -> np.ndarray:
-        """Fit predict using base methods.
-
-        Parameters
-        ----------
-        X : np.ndarray (2d or 3d array of shape (n_cases, n_timepoints) or shape
-            (n_cases, n_channels, n_timepoints)).
-            Time series instances to train clusterer and then have indexes each belong
-            to return.
-        y: ignored, exists for API consistency reasons.
-
         Returns
         -------
         np.ndarray (1d array of shape (n_cases,))

aeon/regression/base.py

@@ -25,6 +25,7 @@ class name: BaseRegressor
 
 import numpy as np
 import pandas as pd
+from sklearn.base import RegressorMixin
 from sklearn.metrics import get_scorer, get_scorer_names
 from sklearn.model_selection import cross_val_predict
 from sklearn.utils.multiclass import type_of_target
@@ -33,7 +34,7 @@ class name: BaseRegressor
 from aeon.base._base import _clone_estimator
 
 
-class BaseRegressor(BaseCollectionEstimator):
+class BaseRegressor(RegressorMixin, BaseCollectionEstimator):
     """Abstract base class for time series regressors.
 
     The base regressor specifies the methods and method signatures that all
@@ -54,9 +55,6 @@ class BaseRegressor(BaseCollectionEstimator):
 
     @abstractmethod
     def __init__(self):
-        # required for compatibility with some sklearn interfaces
-        self._estimator_type = "regressor"
-
         super().__init__()
 
     @final

aeon/visualisation/estimator/_shapelets.py

@@ -714,6 +714,8 @@ def _get_shp_importance(self, class_id):
         # classification for the given class_id
         if isinstance(classifier, LinearClassifierMixin):
             coefs = classifier.coef_
+            if coefs.ndim == 1:
+                coefs = coefs[np.newaxis, :]
             n_classes = coefs.shape[0]
             if n_classes == 1:
                 if isinstance(self.estimator, RDSTClassifier):

pyproject.toml

@@ -50,7 +50,7 @@ dependencies = [
     "numpy>=1.21.0,<2.1.0",
     "packaging>=20.0",
     "pandas>=2.0.0,<2.3.0",
-    "scikit-learn>=1.0.0,<1.6.0",
+    "scikit-learn>=1.0.0,<1.7.0",
     "scipy>=1.9.0,<1.15.0",
     "typing-extensions>=4.6.0",
 ]