[Doc] A fix to inconsistent ticks in Base module

aeon/base/_base.py

@@ -21,19 +21,20 @@ class BaseAeonEstimator(BaseEstimator, ABC):
 
     Contains the following methods:
 
-    - reset estimator to post-init  - reset(keep)
-    - clone stimator (copy)         - clone(random_state)
-    - inspect tags (class method)   - get_class_tags()
-    - inspect tags (one tag, class) - get_class_tag(tag_name, tag_value_default,
-                                                                    raise_error)
-    - inspect tags (all)            - get_tags()
-    - inspect tags (one tag)        - get_tag(tag_name, tag_value_default, raise_error)
-    - setting dynamic tags          - set_tags(**tag_dict)
-    - get fitted parameters         - get_fitted_params(deep)
+    - reset estimator to post-init  - ``reset(keep)``
+    - clone stimator (copy)         - ``clone(random_state)``
+    - inspect tags (class method)   - ``get_class_tags()``
+    - inspect tags (one tag, class) - ``get_class_tag(tag_name, tag_value_default
+                                        , raise_error)``
+    - inspect tags (all)            - ``get_tags()``
+    - inspect tags (one tag)        - ``get_tag(tag_name, tag_value_default
+                                        , raise_error)``
+    - setting dynamic tags          - ``set_tags(**tag_dict)``
+    - get fitted parameters         - ``get_fitted_params(deep)``
 
     All estimators have the attribute:
 
-    - fitted state flag             - is_fitted
+    - fitted state flag             - ``is_fitted``
     """
 
     _tags = {
@@ -59,14 +60,14 @@ def reset(self, keep=None):
         """
         Reset the object to a clean post-init state.
 
-        After a ``self.reset()`` call, self is equal or similar in value to
+        After a ``self.reset()`` call, ``self`` is equal or similar in value to
         ``type(self)(**self.get_params(deep=False))``, assuming no other attributes
         were kept using ``keep``.
 
         Detailed behaviour:
             removes any object attributes, except:
                 hyper-parameters (arguments of ``__init__``)
-                object attributes containing double-underscores, i.e., the string "__"
+                object attributes containing double-underscores, i.e., the string ``__``
             runs ``__init__`` with current values of hyperparameters (result of
             ``get_params``)
 
@@ -78,9 +79,9 @@ class and object methods, class attributes
         Parameters
         ----------
         keep : None, str, or list of str, default=None
-            If None, all attributes are removed except hyperparameters.
-            If str, only the attribute with this name is kept.
-            If list of str, only the attributes with these names are kept.
+             If ``None``, all attributes are removed except hyperparameters.
+             If ``str``, only the attribute with this name is kept.
+             If ``list`` of ``str``, only the attributes with these names are kept.
 
         Returns
         -------
@@ -125,15 +126,18 @@ def clone(self, random_state=None):
         Obtain a clone of the object with the same hyperparameters.
 
         A clone is a different object without shared references, in post-init state.
-        This function is equivalent to returning ``sklearn.clone`` of self.
+        This function is equivalent to returning ``sklearn.clone`` of ``self``.
         Equal in value to ``type(self)(**self.get_params(deep=False))``.
 
         Parameters
         ----------
         random_state : int, RandomState instance, or None, default=None
-            Sets the random state of the clone. If None, the random state is not set.
-            If int, random_state is the seed used by the random number generator.
-            If RandomState instance, random_state is the random number generator.
+            Sets the random state of the clone. If ``None``, the random state is not
+            set.
+            If ``int``, ``random_state`` is the seed used by the random number
+            generator.
+            If ``RandomState`` instance, ``random_state`` is the random number
+            generator.
 
         Returns
         -------
@@ -187,21 +191,21 @@ def get_class_tag(
         tag_name : str
             Name of tag value.
         raise_error : bool, default=True
-            Whether a ValueError is raised when the tag is not found.
+            Whether a ``ValueError`` is raised when the tag is not found.
         tag_value_default : any type, default=None
             Default/fallback value if tag is not found and error is not raised.
 
         Returns
         -------
         tag_value
             Value of the ``tag_name`` tag in cls.
-            If not found, returns an error if ``raise_error`` is True, otherwise it
+            If not found, returns an error if ``raise_error`` is ``True``, otherwise it
             returns ``tag_value_default``.
 
         Raises
         ------
         ValueError
-            if ``raise_error`` is True and ``tag_name`` is not in
+            if ``raise_error`` is ``True`` and ``tag_name`` is not in
             ``self.get_tags().keys()``
 
         Examples
@@ -247,15 +251,15 @@ def get_tag(self, tag_name, raise_error=True, tag_value_default=None):
         tag_name : str
             Name of tag to be retrieved.
         raise_error : bool, default=True
-            Whether a ValueError is raised when the tag is not found.
+            Whether a ``ValueError`` is raised when the tag is not found.
         tag_value_default : any type, default=None
             Default/fallback value if tag is not found and error is not raised.
 
         Returns
         -------
         tag_value
             Value of the ``tag_name`` tag in self.
-            If not found, returns an error if ``raise_error`` is True, otherwise it
+            If not found, returns an error if ``raise_error`` is ``True``, otherwise it
             returns ``tag_value_default``.
 
         Raises
@@ -292,7 +296,7 @@ def set_tags(self, **tag_dict):
         Returns
         -------
         self : object
-            Reference to self.
+            Reference to ``self``.
         """
         tag_update = deepcopy(tag_dict)
         self._tags_dynamic.update(tag_update)
@@ -307,7 +311,7 @@ def get_fitted_params(self, deep=True):
         Parameters
         ----------
         deep : bool, default=True
-            If True, will return the fitted parameters for this estimator and
+            If ``True``, will return the fitted parameters for this estimator and
             contained subobjects that are estimators.
 
         Returns
@@ -354,7 +358,7 @@ def _check_is_fitted(self):
         if not self.is_fitted:
             raise NotFittedError(
                 f"This instance of {self.__class__.__name__} has not "
-                f"been fitted yet; please call `fit` first."
+                f"been fitted yet; please call ``fit`` first."
             )
 
     @classmethod
@@ -366,14 +370,15 @@ def _get_test_params(cls, parameter_set="default"):
         ----------
         parameter_set : str, default="default"
             Name of the set of test parameters to return, for use in tests. If no
-            special parameters are defined for a value, will return `"default"` set.
+            special parameters are defined for a value, will return ``default`` set.
 
         Returns
         -------
         params : dict or list of dict, default = {}
-            Parameters to create testing instances of the class. Each dict are
-            parameters to construct an "interesting" test instance, i.e.,
-            `MyClass(**params)` or `MyClass(**params[i])` creates a valid test instance.
+            Parameters to create testing instances of the class. Each ``dict`` are
+            parameters to construct an ``interesting`` test instance, i.e.,
+            ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test
+            instance.
         """
         # default parameters = empty dict
         return {}
@@ -383,23 +388,23 @@ def _create_test_instance(cls, parameter_set="default", return_first=True):
         """
         Construct Estimator instance if possible.
 
-        Calls the `_get_test_params` method and returns an instance or list of instances
-        using the returned dict or list of dict.
+        Calls the ``_get_test_params`` method and returns an instance or ``list``
+        of instances using the returned ``dict`` or list of ``dict``.
 
         Parameters
         ----------
         parameter_set : str, default="default"
             Name of the set of test parameters to return, for use in tests. If no
-            special parameters are defined for a value, will return `"default"` set.
+            special parameters are defined for a value, will return ``default`` set.
         return_first : bool, default=True
-            If True, return the first instance of the list of instances.
-            If False, return the list of instances.
+            If ``True``, return the first instance of the list of instances.
+            If ``False``, return the list of instances.
 
         Returns
         -------
         instance : BaseAeonEstimator or list of BaseAeonEstimator
-            Instance of the class with default parameters. If return_first
-            is False, returns list of instances.
+            Instance of the class with default parameters. If ``return_first``
+            is ``False``, returns list of instances.
         """
         params = cls._get_test_params(parameter_set=parameter_set)
 
@@ -441,15 +446,15 @@ def _validate_data(self, **kwargs):
     def get_metadata_routing(self):
         """Sklearn metadata routing.
 
-        Not supported by ``aeon`` estimators.
+        Not supported by aeon estimators.
         """
         raise NotImplementedError(
             "aeon estimators do not have a get_metadata_routing method."
         )
 
     @classmethod
     def _get_default_requests(cls):
-        """Sklearn metadata request defaults."""
+        """``Sklearn`` metadata request defaults."""
         from sklearn.utils._metadata_requests import MetadataRequest
 
         return MetadataRequest(None)

aeon/base/_base_collection.py

@@ -4,19 +4,19 @@
     class name: BaseCollectionEstimator
 
 Defining methods:
-    preprocessing         - _preprocess_collection(self, X, store_metadata=True)
-    input checking        - _check_X(self, X)
-    input conversion      - _convert_X(self, X)
-    shape checking        - _check_shape(self, X)
+    preprocessing         - ``_preprocess_collection(self, X, store_metadata=True)``
+    input checking        - ``_check_X(self, X)``
+    input conversion      - ``_convert_X(self, X)``
+    shape checking        - ``_check_shape(self, X)``
 
 Inherited inspection methods:
-    hyper-parameter inspection  - get_params()
-    fitted parameter inspection - get_fitted_params()
+    hyper-parameter inspection  - ``get_params()``
+    fitted parameter inspection - ``get_fitted_params()``
 
 State:
-    fitted model/strategy   - by convention, any attributes ending in "_"
-    fitted state flag       - is_fitted (property)
-    fitted state inspection - check_is_fitted()
+    fitted model/strategy   - by convention, any attributes ending in ``_``
+    fitted state flag       - ``is_fitted (property)``
+    fitted state inspection - ``check_is_fitted()``
 
 """
 
@@ -80,26 +80,26 @@ def _preprocess_collection(self, X, store_metadata=True):
         Parameters
         ----------
         X : collection
-            See aeon.utils.COLLECTIONS_DATA_TYPES for details on aeon supported
+            See ``aeon.utils.COLLECTIONS_DATA_TYPES`` for details on aeon supported
             data structures.
         store_metadata : bool, default=True
-            Whether to store metadata about X in self.metadata_.
+            Whether to store metadata about ``X`` in ``self.metadata_``.
 
         Returns
         -------
         X : collection
-            Processed X. A data structure of type self.get_tag("X_inner_type").
+            Processed ``X``. A data structure of type ``self.get_tag("X_inner_type")``.
 
         Raises
         ------
         ValueError
-            If X is an invalid type or has characteristics that the estimator cannot
+            If ``X`` is an invalid type or has characteristics that the estimator cannot
             handle.
 
         See Also
         --------
         _check_X :
-            Function that checks X is valid before conversion.
+            Function that checks ``X`` is valid before conversion.
         _convert_X :
             Function that converts to inner type.
 
@@ -138,37 +138,39 @@ def _check_X(self, X):
         Check if the input data is a compatible type, and that this estimator is
         able to handle the data characteristics.
         This is done by matching the capabilities of the estimator against the metadata
-        for X i.e., univariate/multivariate, equal length/unequal length and no missing
-        values/missing values.
+        for ``X`` i.e., univariate/multivariate, equal length/unequal length
+        and no missing values/missing values.
 
         Parameters
         ----------
         X : collection
-           See aeon.utils.COLLECTIONS_DATA_TYPES for details on aeon supported
+           See ``aeon.utils.COLLECTIONS_DATA_TYPES`` for details on aeon supported
            data structures.
 
         Returns
         -------
         metadata : dict
-            Metadata about X, with flags:
-            metadata["multivariate"] : whether X has more than one channel or not
-            metadata["missing_values"] : whether X has missing values or not
-            metadata["unequal_length"] : whether X contains unequal length series.
-            metadata["n_cases"] : number of cases in X
-            metadata["n_channels"] : number of channels in X
-            metadata["n_timepoints"] : number of timepoints in X if equal length, else
-                None
+            Metadata about ```X```, with flags:
+            ``metadata["multivariate"]`` : whether ``X`` has more than one channel or
+            not
+            ``metadata["missing_values"]`` : whether ``X`` has missing values or not
+            ``metadata["unequal_length"]`` : whether ``X`` contains unequal length
+            series.
+            ``metadata["n_cases"]`` : number of cases in ``X``
+            ``metadata["n_channels"]`` : number of channels in ``X``
+            ``metadata["n_timepoints"]`` : number of timepoints in ``X`` if equal
+            length, else ``None``
 
         Raises
         ------
         ValueError
-            If X is an invalid type or has characteristics that the estimator cannot
+            If ``X`` is an invalid type or has characteristics that the estimator cannot
             handle.
 
         See Also
         --------
         _convert_X :
-            Function that converts X after it has been checked.
+            Function that converts ``X`` after it has been checked.
 
         Examples
         --------
@@ -209,30 +211,31 @@ def _check_X(self, X):
 
     def _convert_X(self, X):
         """
-        Convert X to type defined by tag X_inner_type.
+        Convert ``X`` to type defined by tag ``X_inner_type``.
 
         If the input data is already an allowed type, it is returned unchanged.
 
-        If multiple types are allowed by self, then the best one for the type of input
-        data is selected. So, for example, if X_inner_tag is ["np-list", "numpy3D"]
-        and an df-list is passed, it will be converted to numpy3D if the series
-        are equal length, and np-list if the series are unequal length.
+        If multiple types are allowed by ``self``, then the best
+        one for the type of input data is selected. So, for example, if
+        ``X_inner_tag`` is ["np-list", "numpy3D"] and an df-list is passed, it will
+        be converted to ``numpy3D`` if the series are equal length, and np-list
+        if the series are unequal length.
 
         Parameters
         ----------
         X : collection
-           See aeon.utils.COLLECTIONS_DATA_TYPES for details on aeon supported
+           See ``aeon.utils.COLLECTIONS_DATA_TYPES`` for details on aeon supported
            data structures.
 
         Returns
         -------
         X : collection
-            Converted X. A data structure of type self.get_tag("X_inner_type").
+            Converted ``X``. A data structure of type ``self.get_tag("X_inner_type")``.
 
         See Also
         --------
         _check_X :
-            Function that checks X is valid and finds metadata.
+            Function that checks ``X`` is valid and finds metadata.
 
         Examples
         --------
@@ -275,17 +278,17 @@ def _convert_X(self, X):
 
     def _check_shape(self, X):
         """
-        Check that the shape of X is consistent with the data seen in fit.
+        Check that the shape of ``X`` is consistent with the data seen in fit.
 
         Parameters
         ----------
         X : data structure
-            Must be of type aeon.registry.COLLECTIONS_DATA_TYPES.
+            Must be of type ``aeon.registry.COLLECTIONS_DATA_TYPES``.
 
         Raises
         ------
         ValueError
-            If the shape of X is not consistent with the data seen in fit.
+            If the shape of ``X`` is not consistent with the data seen in fit.
         """
         # if metadata is empty, then we have not seen any data in fit. If the estimator
         # has not been fitted, then _is_fitted should catch this.