[ENH] Remove BaseClassifier unnecessary declarations (#1887)

* remove Theta

* remove duplicate declaration for _n_jobs and fit_time in BaseClassifier

* remove duplicate declaration for _n_jobs and fit_time in BaseClassifier

aeon/classification/base.py

@@ -57,13 +57,8 @@ class BaseClassifier(BaseCollectionEstimator, ABC):
         Class labels, either integers or strings.
     n_classes_ : int
         Number of classes (length of ``classes_``).
-    fit_time_ : int
-        Time (in milliseconds) for ``fit`` to run.
     _class_dictionary : dict
         Mapping of classes_ onto integers ``0 ... n_classes_-1``.
-    _n_jobs : int
-        Number of threads to use in estimator methods such as ``fit`` and ``predict``.
-        Determined by the ``n_jobs`` parameter if present.
     _estimator_type : string
         The type of estimator. Required by some ``sklearn`` tools, set to "classifier".
     """
@@ -74,17 +69,10 @@ class BaseClassifier(BaseCollectionEstimator, ABC):
     }
 
     def __init__(self):
-        # reserved attributes written to in fit
         self.classes_ = []  # classes seen in y, unique labels
         self.n_classes_ = -1  # number of unique classes in y
         self._class_dictionary = {}
-        self.fit_time_ = -1
-        self._n_jobs = 1
-
-        # required for compatibility with some sklearn interfaces e.g.
-        # CalibratedClassifierCV
         self._estimator_type = "classifier"
-
         super().__init__()
         _check_estimator_deps(self)
 
@@ -105,7 +93,7 @@ def fit(self, X, y) -> BaseCollectionEstimator:
             allowed and converted into one of the above.
 
             Different estimators have different capabilities to handle different
-            types of input. If `self.get_tag("capability:multivariate")`` is False,
+            types of input. If ``self.get_tag("capability:multivariate")`` is False,
             they cannot handle multivariate series, so either ``n_channels == 1`` is
             true or X is 2D of shape ``(n_cases, n_timepoints)``. If ``self.get_tag(
             "capability:unequal_length")`` is False, they cannot handle unequal
@@ -154,7 +142,7 @@ def predict(self, X) -> np.ndarray:
             other types are allowed and converted into one of the above.
 
             Different estimators have different capabilities to handle different
-            types of input. If `self.get_tag("capability:multivariate")`` is False,
+            types of input. If ``self.get_tag("capability:multivariate")`` is False,
             they cannot handle multivariate series, so either ``n_channels == 1`` is
             true or X is 2D of shape ``(n_cases, n_timepoints)``. If ``self.get_tag(
             "capability:unequal_length")`` is False, they cannot handle unequal
@@ -197,7 +185,7 @@ def predict_proba(self, X) -> np.ndarray:
             allowed and converted into one of the above.
 
             Different estimators have different capabilities to handle different
-            types of input. If `self.get_tag("capability:multivariate")`` is False,
+            types of input. If ``self.get_tag("capability:multivariate")`` is False,
             they cannot handle multivariate series, so either ``n_channels == 1`` is
             true or X is 2D of shape ``(n_cases, n_timepoints)``. If ``self.get_tag(
             "capability:unequal_length")`` is False, they cannot handle unequal
@@ -252,7 +240,7 @@ def fit_predict(self, X, y, **kwargs) -> np.ndarray:
             allowed and converted into one of the above.
 
             Different estimators have different capabilities to handle different
-            types of input. If `self.get_tag("capability:multivariate")`` is False,
+            types of input. If ``self.get_tag("capability:multivariate")`` is False,
             they cannot handle multivariate series, so either ``n_channels == 1`` is
             true or X is 2D of shape ``(n_cases, n_timepoints)``. If ``self.get_tag(
             "capability:unequal_length")`` is False, they cannot handle unequal
@@ -318,7 +306,7 @@ def fit_predict_proba(self, X, y, **kwargs) -> np.ndarray:
             allowed and converted into one of the above.
 
             Different estimators have different capabilities to handle different
-            types of input. If `self.get_tag("capability:multivariate")`` is False,
+            types of input. If ``self.get_tag("capability:multivariate")`` is False,
             they cannot handle multivariate series, so either ``n_channels == 1`` is
             true or X is 2D of shape ``(n_cases, n_timepoints)``. If ``self.get_tag(
             "capability:unequal_length")`` is False, they cannot handle unequal
@@ -376,7 +364,7 @@ def score(
             allowed and converted into one of the above.
 
             Different estimators have different capabilities to handle different
-            types of input. If `self.get_tag("capability:multivariate")`` is False,
+            types of input. If ``self.get_tag("capability:multivariate")`` is False,
             they cannot handle multivariate series, so either ``n_channels == 1`` is
             true or X is 2D of shape ``(n_cases, n_timepoints)``. If ``self.get_tag(
             "capability:unequal_length")`` is False, they cannot handle unequal