[DOC] Fixed inconsistent backticks in docstrings aeon-toolkit#809

Author: an04shu

aeon/forecasting/__init__.py

@@ -10,4 +10,4 @@
 from aeon.forecasting._dummy import DummyForecaster
 from aeon.forecasting._ets import ETSForecaster
 from aeon.forecasting._regression import RegressionForecaster
-from aeon.forecasting.base import BaseForecaster
+from aeon.forecasting.base import BaseForecaster

aeon/forecasting/_dummy.py

@@ -24,4 +24,4 @@ def _predict(self, y=None, exog=None):
     def _forecast(self, y, exog=None):
         """Forecast using dummy forecaster."""
         y = y.squeeze()
-        return y[-1]
+        return y[-1]

aeon/forecasting/_ets.py

@@ -2,10 +2,11 @@
 
 An implementation of the exponential smoothing statistics forecasting algorithm.
 Implements additive and multiplicative error models,
-None, additive and multiplicative (including damped) trend and
-None, additive and multiplicative seasonality
+``None``, additive, and multiplicative (including damped) trend, and
+``None``, additive, and multiplicative seasonality.
 """
 
+
 __maintainer__ = []
 __all__ = ["ETSForecaster", "NONE", "ADDITIVE", "MULTIPLICATIVE"]
 
@@ -26,30 +27,30 @@ class ETSForecaster(BaseForecaster):
     """Exponential Smoothing forecaster.
 
     An implementation of the exponential smoothing forecasting algorithm.
-    Implements additive and multiplicative error models, None, additive and
-    multiplicative (including damped) trend and None, additive and mutliplicative
+    Implements additive and multiplicative error models, ``None``, additive, and
+    multiplicative (including damped) trend, and ``None``, additive, and multiplicative
     seasonality. See [1]_ for a description.
 
     Parameters
     ----------
     error_type : int, default = 1
-        Either NONE (0), ADDITIVE (1) or MULTIPLICATIVE (2).
+        Either ``NONE`` (0), ``ADDITIVE`` (1) or ``MULTIPLICATIVE`` (2).
     trend_type : int, default = 0
-        Either NONE (0), ADDITIVE (1) or MULTIPLICATIVE (2).
+        Either ``NONE`` (0), ``ADDITIVE`` (1) or ``MULTIPLICATIVE`` (2).
     seasonality_type : int, default = 0
-        Either NONE (0), ADDITIVE (1) or MULTIPLICATIVE (2).
+        Either ``NONE`` (0), ``ADDITIVE`` (1) or ``MULTIPLICATIVE`` (2).
     seasonal_period : int, default=1
-        Length of seasonality period. If seasonality_type is NONE, this is assumed to
-        be 1
+        Length of seasonality period. If ``seasonality_type`` is ``NONE``, this is assumed to
+        be ``1``.
     alpha : float, default = 0.1
         Level smoothing parameter.
     beta : float, default = 0.01
-        Trend smoothing parameter. If trend_type is NONE, this is assumed to be 0.0.
+        Trend smoothing parameter. If ``trend_type`` is ``NONE``, this is assumed to be ``0.0``.
     gamma : float, default = 0.01
-        Seasonal smoothing parameter. If seasonality is NONE, this is assumed to be
-        0.0.
+        Seasonal smoothing parameter. If ``seasonality`` is ``NONE``, this is assumed to be
+        ``0.0``.
     phi : float, default = 0.99
-        Trend damping smoothing parameters
+        Trend damping smoothing parameter.
     horizon : int, default = 1
         The horizon to forecast to.
 
@@ -79,7 +80,8 @@ class ETSForecaster(BaseForecaster):
     ETSForecaster(alpha=0.4, beta=0.2, gamma=0.5, phi=0.8)
     >>> forecaster.predict()
     449.9435566831507
-    """
+"""
+
 
     def __init__(
         self,
@@ -108,22 +110,23 @@ def __init__(
         super().__init__(horizon=horizon, axis=1)
 
     def _fit(self, y, exog=None):
-        """Fit Exponential Smoothing forecaster to series y.
+        """Fit Exponential Smoothing forecaster to series ``y``.
 
-        Fit a forecaster to predict self.horizon steps ahead using y.
+        Fit a forecaster to predict ``self.horizon`` steps ahead using ``y``.
 
         Parameters
         ----------
-        y : np.ndarray
-            A time series on which to learn a forecaster to predict horizon ahead
-        exog : np.ndarray, default =None
-            Optional exogenous time series data assumed to be aligned with y
+        y : ``np.ndarray``
+            A time series on which to learn a forecaster to predict ``horizon`` ahead.
+        exog : ``np.ndarray``, default = ``None``
+            Optional exogenous time series data assumed to be aligned with ``y``.
 
         Returns
         -------
         self
-            Fitted BaseForecaster.
+            Fitted ``BaseForecaster``.
         """
+
         self.n_timepoints_ = len(y)
         if self.error_type != MULTIPLICATIVE and self.error_type != ADDITIVE:
             raise ValueError("Error must be either additive or multiplicative")
@@ -159,21 +162,22 @@ def _fit(self, y, exog=None):
 
     def _predict(self, y=None, exog=None):
         """
-        Predict the next horizon steps ahead.
+        Predict the next ``horizon`` steps ahead.
 
         Parameters
         ----------
-        y : np.ndarray, default = None
-            A time series to predict the next horizon value for. If None,
-            predict the next horizon value after series seen in fit.
-        exog : np.ndarray, default = None
-            Optional exogenous time series data assumed to be aligned with y
+        y : ``np.ndarray``, default = ``None``
+            A time series to predict the next ``horizon`` value for. If ``None``,
+            predict the next ``horizon`` value after series seen in ``fit``.
+        exog : ``np.ndarray``, default = ``None``
+            Optional exogenous time series data assumed to be aligned with ``y``.
 
         Returns
         -------
         float
-            single prediction self.horizon steps ahead of y.
+            Single prediction ``self.horizon`` steps ahead of ``y``.
         """
+
         return _predict_numba(
             self.trend_type,
             self.seasonality_type,
@@ -266,14 +270,22 @@ def _predict_numba(
 @njit(nogil=NOGIL, cache=CACHE)
 def _initialise(trend_type, seasonality_type, seasonal_period, data):
     """
-    Initialize level, trend, and seasonality values for the ETS model.
+        Predict the next ``horizon`` steps ahead.
+
+        Parameters
+        ----------
+        y : ``np.ndarray``, default = ``None``
+            A time series to predict the next ``horizon`` value for. If ``None``,
+            predict the next ``horizon`` value after series seen in ``fit``.
+        exog : ``np.ndarray``, default = ``None``
+            Optional exogenous time series data assumed to be aligned with ``y``.
+
+        Returns
+        -------
+        float
+            Single prediction ``self.horizon`` steps ahead of ``y``.
+        """
 
-    Parameters
-    ----------
-    data : array-like
-        The time series data
-        (should contain at least two full seasons if seasonality is specified)
-    """
     # Initial Level: Mean of the first season
     level = np.mean(data[:seasonal_period])
     # Initial Trend
@@ -326,11 +338,12 @@ def _update_states(
 
     Parameters
     ----------
-    data_item: float
+    data_item : ``float``
         The current value of the time series.
-    seasonal_index: int
+    seasonal_index : ``int``
         The index to update the seasonal component.
     """
+
     # Retrieve the current state values
     curr_level = level
     curr_seasonality = seasonality
@@ -376,29 +389,29 @@ def _update_states(
 @njit(nogil=NOGIL, cache=CACHE)
 def _predict_value(trend_type, seasonality_type, level, trend, seasonality, phi):
     """
-
     Generate various useful values, including the next fitted value.
 
     Parameters
     ----------
-    trend : float
-        The current trend value for the model
-    level : float
-        The current level value for the model
-    seasonality : float
-        The current seasonality value for the model
-    phi : float
-        The damping parameter for the model
+    trend : ``float``
+        The current trend value for the model.
+    level : ``float``
+        The current level value for the model.
+    seasonality : ``float``
+        The current seasonality value for the model.
+    phi : ``float``
+        The damping parameter for the model.
 
     Returns
     -------
-    fitted_value : float
-        single prediction based on the current state variables.
-    damped_trend : float
-        The damping parameter combined with the trend dependant on the model type
-    trend_level_combination : float
+    fitted_value : ``float``
+        Single prediction based on the current state variables.
+    damped_trend : ``float``
+        The damping parameter combined with the trend, dependent on the model type.
+    trend_level_combination : ``float``
         Combination of the trend and level based on the model type.
     """
+
     # Apply damping parameter and
     # calculate commonly used combination of trend and level components
     if trend_type == MULTIPLICATIVE:
@@ -413,4 +426,4 @@ def _predict_value(trend_type, seasonality_type, level, trend, seasonality, phi)
         fitted_value = trend_level_combination * seasonality
     else:  # Additive seasonality, if no seasonality, then seasonality = 0
         fitted_value = trend_level_combination + seasonality
-    return fitted_value, damped_trend, trend_level_combination
+    return fitted_value, damped_trend, trend_level_combination