Updated docstring comments and made methods protected

aeon/classification/dictionary_based/_cboss.py

@@ -26,22 +26,22 @@ class ContractableBOSS(BaseClassifier):
     Implementation of BOSS Ensemble from [1]_ with refinements
     described in [2]_.
 
-    Overview: Input "n" series of length "m" and cBOSS randomly samples
-    `n_parameter_samples` parameter sets, evaluting each with LOOCV. It then
-    retains `max_ensemble_size` classifiers with the highest accuracy
+    Overview: Input ``n`` series of length ``m`` and cBOSS randomly samples
+    ``n_parameter_samples`` parameter sets, evaluting each with LOOCV. It then
+    retains ``max_ensemble_size`` classifiers with the highest accuracy
     There are three primary parameters:
-        - alpha: alphabet size
-        - w: window length
-        - l: word length.
-
-    For any combination, a single BOSS slides a window length "w" along the
-    series. The "w" length window is shortened to an "l" length word by
-    taking a Fourier transform and keeping the first l/2 complex coefficients.
-    These "l" coefficients are then discretised into "alpha" possible values,
-    to form a word length "l". A histogram of words for each
+        - ``alpha``: alphabet size
+        - ``w``: window length
+        - ``l``: word length.
+
+    For any combination, a single BOSS slides a window length ``w`` along the
+    series. The ``w`` length window is shortened to an ``l`` length word by
+    taking a Fourier transform and keeping the first ``l/2`` complex coefficients.
+    These ``l`` coefficients are then discretised into ``alpha`` possible values,
+    to form a word length ``l``. A histogram of words for each
     series is formed and stored.
 
-    Fit involves finding "n" histograms.
+    Fit involves finding ``n`` histograms.
 
     Predict uses 1 nearest neighbor with a bespoke BOSS distance function.
 
@@ -51,18 +51,18 @@ class ContractableBOSS(BaseClassifier):
         If search is randomised, number of parameter combos to try.
     max_ensemble_size : int or None, default = 50
         Maximum number of classifiers to retain. Will limit number of retained
-        classifiers even if more than `max_ensemble_size` are within threshold.
+        classifiers even if more than ``max_ensemble_size`` are within threshold.
     max_win_len_prop : int or float, default = 1
         Maximum window length as a proportion of the series length.
     min_window : int, default = 10
         Minimum window size.
     time_limit_in_minutes : int, default = 0
         Time contract to limit build time in minutes. Default of 0 means no limit.
     contract_max_n_parameter_samples : int, default=np.inf
-        Max number of parameter combinations to consider when time_limit_in_minutes is
-        set.
+    Max number of parameter combinations to consider when ``time_limit_in_minutes``
+    is set.
     n_jobs : int, default = 1
-        The number of jobs to run in parallel for both `fit` and `predict`.
+        The number of jobs to run in parallel for both ``fit`` and ``predict``.
         ``-1`` means using all processors.
     feature_selection : str, default = "none"
         Sets the feature selections strategy to be used. One of {"chi2", "none",
@@ -71,10 +71,10 @@ class ContractableBOSS(BaseClassifier):
         applies not feature selectiona and yields large bag of words, e.g. much
         memory may be needed.
     random_state : int, RandomState instance or None, default=None
-        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 ``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``.
 
     Attributes
     ----------
@@ -97,8 +97,8 @@ class ContractableBOSS(BaseClassifier):
     Raises
     ------
     ValueError
-    Raised when `min_window` is greater than `max_window + 1`.
-    This ensures that `min_window` does not exceed `max_window`,
+    Raised when ``min_window`` is greater than ``max_window + 1``.
+    This ensures that ``min_window`` does not exceed ``max_window``,
     preventing invalid window size configurations.
 
 
@@ -162,7 +162,7 @@ def __init__(
         self.min_window = min_window
 
         # Calculate and initialize max_window
-        self.max_window = self.calculate_max_window()
+        self.max_window = self._calculate_max_window()
 
         self.time_limit_in_minutes = time_limit_in_minutes
         self.contract_max_n_parameter_samples = contract_max_n_parameter_samples
@@ -183,14 +183,14 @@ def __init__(
 
         super().__init__()
 
-    def calculate_max_window(self):
+    def _calculate_max_window(self):
         """Calculate max_window based on max_win_len_prop or other parameters."""
         return int(self.max_win_len_prop * 100)
 
-    def validate_window_sizes(self):
-        """Validate if the min_window size is valid against max_window.
+    def _validate_window_sizes(self):
+        """Validate if the ``min_window`` size is valid against ``max_window``.
 
-        Raises a ValueError if min_window is greater than max_window + 1.
+        Raises a ValueError if ``min_window`` is greater than ``max_window + 1``.
         """
         if self.min_window > self.max_window + 1:
             raise ValueError(
@@ -225,7 +225,7 @@ def _fit(self, X, y, keep_train_preds=True):
 
         """
         # validate window sizes
-        self.validate_window_sizes()
+        self._validate_window_sizes()
 
         time_limit = self.time_limit_in_minutes * 60
         self.n_cases_, _, self.n_timepoints_ = X.shape
@@ -240,10 +240,10 @@ def _fit(self, X, y, keep_train_preds=True):
         win_inc = max(win_inc, 1)
         if self.min_window > max_window + 1:
             raise ValueError(
-                f"Error in ContractableBOSS, min_window ="
+                f"Error in ContractableBOSS, ``min_window`` ="
                 f"{self.min_window} is bigger"
                 f" than max_window ={max_window}."
-                f" Try set min_window to be smaller than series length in "
+                f" Try set ``min_window`` to be smaller than series length in "
                 f"the constructor, but the classifier may not work at "
                 f"all with very short series"
             )