update func signature

combo/models/base.py

@@ -29,8 +29,9 @@ class BaseAggregator(ABC):
 
     Parameters
     ----------
-    base_estimators: list or numpy array (n_estimators,)
-        A list of base estimators.
+    base_estimators: list, length must be greater than 1
+        A list of base estimators. Certain methods must be present, e.g.,
+        `fit` and `predict`.
 
     pre_fitted: bool, optional (default=False)
         Whether the base estimators are trained. If True, `fit`

combo/models/detector_comb.py

@@ -23,16 +23,14 @@ class SimpleDetectorAggregator(BaseAggregator):
 
     Parameters
     ----------
-    base_estimators: list or numpy array (n_estimators,)
-        A list of base detectors.
+    base_estimators : list, length must be greater than 1
+        Base unsupervised outlier detectors from PyOD. (Note: requires fit and
+        decision_function methods)
 
     method : str, optional (default='average')
         Combination method: {'average', 'maximization',
         'median'}. Pass in weights of detector for weighted version.
 
-    threshold : float in (0, 1), optional (default=0.5)
-        Cut-off value to convert scores into binary labels.
-
     contamination : float in (0., 0.5), optional (default=0.1)
         The amount of contamination of the data set,
         i.e. the proportion of outliers in the data set. Used when fitting to
@@ -69,9 +67,8 @@ class SimpleDetectorAggregator(BaseAggregator):
         ``threshold_`` on ``decision_scores_``.
     """
 
-    def __init__(self, base_estimators, method='average', threshold=0.5,
-                 contamination=0.1, standardization=True,
-                 weights=None, pre_fitted=False):
+    def __init__(self, base_estimators, method='average', contamination=0.1,
+                 standardization=True, weights=None, pre_fitted=False):
 
         super(SimpleDetectorAggregator, self).__init__(
             base_estimators=base_estimators, pre_fitted=pre_fitted)
@@ -89,10 +86,6 @@ def __init__(self, base_estimators, method='average', threshold=0.5,
 
         self.standardization = standardization
 
-        check_parameter(threshold, 0, 1, include_left=False,
-                        include_right=False, param_name='threshold')
-        self.threshold = threshold
-
         if weights is None:
             self.weights = np.ones([1, self.n_base_estimators_])
         else: