refactor documentation to numpydoc

pyod/models/combination.py

@@ -133,28 +133,32 @@ def aom(scores, n_buckets=5, method='static', bootstrap_estimators=False,
     First dividing estimators into subgroups, take the maximum score as the
     subgroup score. Finally, take the average of all subgroup outlier scores.
 
-    :param scores: The score matrix outputted from various estimators
-    :type scores: numpy array of shape (n_samples, n_estimators)
+    Parameters
+    ----------
+    scores : numpy array of shape (n_samples, n_estimators)
+        The score matrix outputted from various estimators
 
-    :param n_buckets: The number of subgroups to build
-    :type n_buckets: int, optional (default=5)
+    n_buckets : int, optional (default=5)
+        The number of subgroups to build
 
-    :param method: {'static', 'dynamic'}, if 'dynamic', build subgroups
+    method : str, optional (default='static')
+        {'static', 'dynamic'}, if 'dynamic', build subgroups
         randomly with dynamic bucket size.
-    :type method: str, optional (default='static')
 
-    :param bootstrap_estimators: Whether estimators are drawn with replacement.
-    :type bootstrap_estimators: bool, optional (default=False)
+    bootstrap_estimators : bool, optional (default=False)
+        Whether estimators are drawn with replacement.
 
-    :param random_state: If int, random_state is the seed used by the
+    random_state : int, RandomState instance or None, optional (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`.
-    :type random_state: int, RandomState instance or None,
-        optional (default=None)
 
-    :return: The combined outlier scores.
-    :rtype: Numpy array of shape (n_samples,)
+    Returns
+    -------
+    combined_scores : Numpy array of shape (n_samples,)
+        The combined outlier scores.
+
     """
     return _aom_moa_helper('AOM', scores, n_buckets, method,
                            bootstrap_estimators, random_state)
@@ -169,28 +173,32 @@ def moa(scores, n_buckets=5, method='static', bootstrap_estimators=False,
     subgroup score. Finally, take the maximization of all subgroup outlier
     scores.
 
-    :param scores: The score matrix outputted from various estimators
-    :type scores: numpy array of shape (n_samples, n_estimators)
+    Parameters
+    ----------
+    scores : numpy array of shape (n_samples, n_estimators)
+        The score matrix outputted from various estimators
 
-    :param n_buckets: The number of subgroups to build
-    :type n_buckets: int, optional (default=5)
+    n_buckets : int, optional (default=5)
+        The number of subgroups to build
 
-    :param method: {'static', 'dynamic'}, if 'dynamic', build subgroups
+    method : str, optional (default='static')
+        {'static', 'dynamic'}, if 'dynamic', build subgroups
         randomly with dynamic bucket size.
-    :type method: str, optional (default='static')
 
-    :param bootstrap_estimators: Whether estimators are drawn with replacement.
-    :type bootstrap_estimators: bool, optional (default=False)
+    bootstrap_estimators : bool, optional (default=False)
+        Whether estimators are drawn with replacement.
 
-    :param random_state: If int, random_state is the seed used by the
+    random_state : int, RandomState instance or None, optional (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`.
-    :type random_state: int, RandomState instance or None,
-        optional (default=None)
 
-    :return: The combined outlier scores.
-    :rtype: Numpy array of shape (n_samples,)
+    Returns
+    -------
+    combined_scores : Numpy array of shape (n_samples,)
+        The combined outlier scores.
+
     """
     return _aom_moa_helper('MOA', scores, n_buckets, method,
                            bootstrap_estimators, random_state)