[DOC] resolved the inconsistency of double ticks for the anomaly detection module #809 (#2546)

* resolved the inconsistency of double quotes

* resolved the inconsistency of double ticks

* resolved the pre-commit errors occuring due to flake8

* Update _cblof.py

made changes in line number 19 and 145.

* Automatic `pre-commit` fixes

* Update _copod.py

made changes in line number 76

* Automatic `pre-commit` fixes

* Update _iforest.py

made changes in line number 19

* Automatic `pre-commit` fixes

* Update _kmeans.py

made changes in line number 178

* Automatic `pre-commit` fixes

* Update _merlin.py

made changes in line number 215

* Automatic `pre-commit` fixes

* Update _one_class_svm.py

made changes in line number 19

* Automatic `pre-commit` fixes

* Update _pyodadapter.py

made changes in line number 165

* Update _stomp.py

made changes in line number 125

* Automatic `pre-commit` fixes

* converted directives to single ticks

* added double ticks to code ref

* fixed link in cblof.py and some other minor changes

* Update base.py

---------

Co-authored-by: Patrick Schäfer <patrick.schaefer@hu-berlin.de>
Co-authored-by: Matthew Middlehurst <pfm15hbu@gmail.com>

Author: 3 people

aeon/anomaly_detection/series/_pyodadapter.py

@@ -40,9 +40,9 @@ class PyODAdapter(BaseSeriesAnomalyDetector):
     target time series with the same number of dimensions. The reference (or training)
     time series does not need to be clean for most PyOD models. However, knowledge in
     form of anomaly labels about the potential existing anomalies in the reference time
-    series are not used during the fitting process. Use `fit` to fit the model on the
-    reference time series and `predict` to detect anomalies in the target time series.
-    For unsupervised anomaly detection, use `fit_predict` directly on the target time
+    series are not used during the fitting process. Use ``fit`` to fit the model on the
+    reference time series and ``predict`` to detect anomalies in the target time series.
+    For unsupervised anomaly detection, use ``fit_predict`` directly on the target time
     series.
 
 
@@ -160,14 +160,15 @@ 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.
+            ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid
+            test instance.
         """
         _check_soft_dependencies(*cls._tags["python_dependencies"])
 

aeon/anomaly_detection/series/distance_based/_cblof.py

@@ -16,15 +16,16 @@ class CBLOF(PyODAdapter):
 
     This class implements the CBLOF algorithm for anomaly detection
     using PyODAdadpter to be used in the aeon framework. All parameters are passed to
-    the PyOD model ``CBLOF`` except for `window_size` and `stride`, which are used to
-    construct the sliding windows.
+    the PyOD model ``CBLOF`` except for ``window_size`` and ``stride``,
+    which are used to construct the sliding windows.
 
     The documentation for parameters has been adapted from the
-    [PyOD documentation](https://pyod.readthedocs.io/en/latest/pyod.models.html#id117).
-    Here, `X` refers to the set of sliding windows extracted from the time series
+    `PyOD documentation <https://pyod.readthedocs.io/en/latest/pyod.models.html#id117>`_
+
+    Here, ``X`` refers to the set of sliding windows extracted from the time series
     using :func:`aeon.utils.windowing.sliding_windows` with the parameters
-    ``window_size`` and ``stride``. The internal `X` has the shape
-    `(n_windows, window_size * n_channels)`.
+    ``window_size`` and ``stride``. The internal ``X`` has the shape
+    ``(n_windows, window_size * n_channels)``.
 
     Parameters
     ----------
@@ -52,21 +53,21 @@ class CBLOF(PyODAdapter):
 
     beta : int or float in (1,), default=5
         Coefficient for deciding small and large clusters. For a list
-        sorted clusters by size `|C1|, \|C2|, ..., |Cn|, beta = |Ck|/|Ck-1|`
+        sorted clusters by size ``|C1|, |C2|, ..., |Cn|, beta = |Ck|/|Ck-1|``
 
     use_weights : bool, default=False
-        If set to True, the size of clusters are used as weights in
+        If set to ``True``, the size of clusters are used as weights in
         outlier score calculation.
 
     check_estimator : bool, default=False
-        If set to True, check whether the base estimator is consistent with
+        If set to ``True``, check whether the base estimator is consistent with
         sklearn standard.
 
     random_state : int, np.RandomState or None, default=None
-        If int, random_state is the seed used by the random
+        If ``int``, random_state is the seed used by the random
         number generator; If RandomState instance, random_state is the random
-        number generator; If None, the random number generator is the
-        RandomState instance used by `np.random`.
+        number generator; If ``None``, the random number generator is the
+        RandomState instance used by ``np.random``.
 
     window_size : int, default=10
         Size of the sliding window.
@@ -138,15 +139,16 @@ 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
             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.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
+            ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test
+            instance. ``create_test_instance`` uses the first (or only) dictionary
+            in ``params``.
         """
         return {
             "n_clusters": 4,

aeon/anomaly_detection/series/distance_based/_kmeans.py

@@ -171,14 +171,15 @@ 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
         -------
         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.
+            ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid
+            test instance.
         """
         return {
             "n_clusters": 5,

aeon/anomaly_detection/series/distance_based/_lof.py

@@ -19,15 +19,15 @@ class LOF(PyODAdapter):
 
     The documentation for parameters has been adapted from the
     [PyOD documentation](https://pyod.readthedocs.io/en/latest/pyod.models.html#id586).
-    Here, `X` refers to the set of sliding windows extracted from the time series
+    Here, ``X`` refers to the set of sliding windows extracted from the time series
     using :func:`aeon.utils.windowing.sliding_windows` with the parameters
-    ``window_size`` and ``stride``. The internal `X` has the shape
-    `(n_windows, window_size * n_channels)`.
+    ``window_size`` and ``stride``. The internal ``X`` has the shape
+    ``(n_windows, window_size * n_channels)``.
 
     Parameters
     ----------
     n_neighbors : int, optional (default=20)
-        Number of neighbors to use by default for `kneighbors` queries.
+        Number of neighbors to use by default for ``kneighbors`` queries.
         If n_neighbors is larger than the number of samples provided,
         all samples will be used.
     algorithm : {'auto', 'ball_tree', 'kd_tree', 'brute'}, optional
@@ -36,11 +36,11 @@ class LOF(PyODAdapter):
         - 'kd_tree' will use KDTree
         - 'brute' will use a brute-force search.
         - 'auto' will attempt to decide the most appropriate algorithm
-          based on the values passed to :meth:`fit` method.
+          based on the values passed to :meth:``fit`` method.
         Note: fitting on sparse input will override the setting of
         this parameter, using brute force.
     leaf_size : int, optional (default=30)
-        Leaf size passed to `BallTree` or `KDTree`. This can
+        Leaf size passed to ``BallTree`` or ``KDTree``. This can
         affect the speed of the construction and query, as well as the memory
         required to store the tree. The optimal value depends on the
         nature of the problem.

aeon/anomaly_detection/series/distance_based/_merlin.py

@@ -210,13 +210,14 @@ 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.
+            ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid
+            test instance.
         """
         return {"min_length": 4, "max_length": 7}

aeon/anomaly_detection/series/distance_based/_stomp.py

@@ -117,14 +117,15 @@ 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.
+            ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid
+            test instance.
         """
         return {
             "window_size": 10,

aeon/anomaly_detection/series/distribution_based/_copod.py

@@ -15,8 +15,8 @@ class COPOD(PyODAdapter):
     """COPOD for anomaly detection.
 
     This class implements the COPOD using PyODAdadpter to be used in the aeon framework.
-    The parameter `n_jobs` is passed to COPOD model from PyOD, `window_size` and
-    `stride` are used to construct the sliding windows.
+    The parameter ``n_jobs`` is passed to COPOD model from PyOD, ``window_size`` and
+    ``stride`` are used to construct the sliding windows.
 
     Parameters
     ----------
@@ -69,14 +69,15 @@ def _get_test_params(cls, parameter_set="default") -> dict:
         ----------
         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.
-            `create_test_instance` uses the first (or only) dictionary in `params`.
+            ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid test
+            instance. ``create_test_instance`` uses the first (or only) dictionary
+            in ``params``.
         """
         return {}

aeon/anomaly_detection/series/distribution_based/_dwt_mlead.py

@@ -237,14 +237,15 @@ 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.
+            ``MyClass(**params)`` or ``MyClass(**params[i])`` creates a valid
+            test instance.
         """
         return {
             "start_level": 2,

aeon/anomaly_detection/series/outlier_detection/_iforest.py

@@ -16,15 +16,15 @@ class IsolationForest(PyODAdapter):
 
     This class implements the Isolation Forest algorithm for anomaly detection
     using PyODAdadpter to be used in the aeon framework. All parameters are passed to
-    the PyOD model ``IForest`` except for `window_size` and `stride`, which are used to
-    construct the sliding windows.
+    the PyOD model ``IForest`` except for ``window_size`` and ``stride``,
+    which are used to construct the sliding windows.
 
     The documentation for parameters has been adapted from the
     [PyOD documentation](https://pyod.readthedocs.io/en/latest/pyod.models.html#id405).
-    Here, `X` refers to the set of sliding windows extracted from the time series
+    Here, ``X`` refers to the set of sliding windows extracted from the time series
     using :func:`aeon.utils.windowing.sliding_windows` with the parameters
-    ``window_size`` and ``stride``. The internal `X` has the shape
-    `(n_windows, window_size * n_channels)`.
+    ``window_size`` and ``stride``. The internal ``X`` has the shape
+    ``(n_windows, window_size * n_channels)``.
 
     Parameters
     ----------
@@ -34,34 +34,34 @@ class IsolationForest(PyODAdapter):
     max_samples : int, float or "auto", default="auto"
         The number of samples to draw from X to train each base estimator.
 
-            - If int, then draw `max_samples` samples.
-            - If float, then draw `max_samples * X.shape[0]` samples.
-            - If "auto", then `max_samples=min(256, n_samples)`.
+            - If ``int``, then draw ``max_samples`` samples.
+            - If ``float``, then draw ``max_samples * X.shape[0]`` samples.
+            - If ``"auto"``, then ``max_samples=min(256, n_samples)``.
 
         If max_samples is larger than the number of samples provided,
         all samples will be used for all trees (no sampling).
 
     max_features : int or float, default=1.0
         The number of features to draw from X to train each base estimator.
 
-            - If int, then draw `max_features` features.
-            - If float, then draw `max_features * X.shape[1]` features.
+            - If ``int``, then draw ``max_features`` features.
+            - If ``float``, then draw ``max_features * X.shape[1]`` features.
 
     bootstrap : bool, default=False
-        If True, individual trees are fit on random subsets of the training
+        If ``True``, individual trees are fit on random subsets of the training
         data sampled with replacement. If False, sampling without replacement
         is performed.
 
     n_jobs : int, default=1
-        The number of jobs to run in parallel for both `fit` and `predict`.
-        If -1, then the number of jobs is set to the number of cores.
+        The number of jobs to run in parallel for both ``fit`` and ``predict``.
+        If ``-1``, then the number of jobs is set to the number of cores.
 
 
     random_state : int, np.RandomState or None, default=None
-        If int, random_state is the seed used by the random number generator;
+        If ``int``, random_state is the seed used by the random number generator;
         If RandomState instance, random_state is the random number generator;
-        If None, the random number generator is the RandomState instance used
-        by `np.random`.
+        If ``None``, the random number generator is the RandomState instance used
+        by ``np.random``.
 
     verbose : int, default=0
         Controls the verbosity of the tree building process.
@@ -138,14 +138,14 @@ 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
             Parameters to create testing instances of the class.
             Each dict are parameters to construct an "interesting" test instance, i.e.,
-            `IsolationForest(**params)` creates a valid test instance.
+            ``IsolationForest(**params)`` creates a valid test instance.
         """
         return {
             "n_estimators": 10,

aeon/anomaly_detection/series/outlier_detection/_one_class_svm.py

@@ -16,15 +16,15 @@ class OneClassSVM(BaseSeriesAnomalyDetector):
 
     This class implements the OneClassSVM algorithm for anomaly detection
     from sklearn to be used in the aeon framework. All parameters are passed to
-    the sklearn ``OneClassSVM`` except for `window_size` and `stride`, which are used to
-    construct the sliding windows.
+    the sklearn ``OneClassSVM`` except for ``window_size`` and ``stride``,
+    which are used to construct the sliding windows.
 
     The documentation for parameters has been adapted from
     (https://scikit-learn.org/dev/modules/generated/sklearn.svm.OneClassSVM.html).
-    Here, `X` refers to the set of sliding windows extracted from the time series
+    Here, ``X`` refers to the set of sliding windows extracted from the time series
     using :func:`aeon.utils.windowing.sliding_windows` with the parameters
-    ``window_size`` and ``stride``. The internal `X` has the shape
-    `(n_windows, window_size * n_channels)`.
+    ``window_size`` and ``stride``. The internal ``X`` has the shape
+    ``(n_windows, window_size * n_channels)``.
 
     Parameters
     ----------
@@ -43,8 +43,8 @@ class OneClassSVM(BaseSeriesAnomalyDetector):
 
         - if ``gamma='scale'`` (default) is passed then it uses
           1 / (n_features * X.var()) as value of gamma,
-        - if 'auto', uses 1 / n_features
-        - if float, must be non-negative.
+        - if ``"auto"``, uses 1 / n_features
+        - if ``float``, must be non-negative.
 
         .. versionchanged:: 0.22
            The default value of ``gamma`` changed from 'auto' to 'scale'.