Merge branch 'refactor_base_series_annotator' of https://github.com/A…

…lex-JG3/sktime into pr/6265

extension_templates/annotation.py

@@ -59,6 +59,12 @@ class MySeriesAnnotator(BaseSeriesAnnotator):
     and so on
     """
 
+    # Change the `task` and `learning_type` as needed
+    _tags = {
+        "task": "segmentation",
+        "learning_type": "unsupervised",
+    }
+
     # todo: add any hyper-parameters and components to constructor
     def __init__(
         self,
@@ -77,8 +83,7 @@ def __init__(
         self.paramb = paramb
         self.paramc = paramc
 
-        # Change the `task` and `learning_type` as needed
-        super().__init__(task="segmentation", learning_type="unsupervised")
+        super().__init__()
 
         # todo: optional, parameter checking logic (if applicable) should happen here
         # if writes derived values to self, should *not* overwrite self.parama etc

sktime/annotation/adapters/_pyod.py

@@ -21,15 +21,17 @@ class PyODAnnotator(BaseSeriesAnnotator):
     estimator : PyOD estimator
         See ``https://pyod.readthedocs.io/en/latest/`` documentation for a detailed
         description of all options.
-    learning_type : str {"supervised", "unsupervised"}, optional (default="unsupervised")
-        The learning type of the annotator; can be supervised or unsupervised.
     """
 
-    _tags = {"python_dependencies": "pyod"}
+    _tags = {
+        "python_dependencies": "pyod",
+        "task": "anomaly_detection",
+        "learning_type": "unsupervised",
+    }
 
-    def __init__(self, estimator, learning_type="unsupervised"):
+    def __init__(self, estimator):
         self.estimator = estimator  # pyod estimator
-        super().__init__(task="anomaly_detection", learning_type=learning_type)
+        super().__init__()
 
     def _fit(self, X, Y=None):
         """Fit to training data.