[ENH] Rework of base series annotator API (sktime#6265)

See sktime#3214 .


#### What does this implement/fix? Explain your changes.
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A clear and concise description of what you have implemented.
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Reworks and refactors the `BaseSeriesAnnotator` class:

* Removes the `fmt` and `label` attributes.
* Adds the `learning_type` and `task` attributes.
* Adds default `predict` and `transform` method.
* Adds default `predict_points` and `predict_segments` methods.
* Adds methods for converting  between dense and sparse output formats.
* Adds default methods for `predict` and `transform`.

Also carries out a move of all existing annotation estimators to the new interface.

extension_templates/annotation.py

@@ -42,16 +42,6 @@ class MySeriesAnnotator(BaseSeriesAnnotator):
 
     Parameters
     ----------
-    fmt : str {"dense", "sparse"}, optional (default="dense")
-        Annotation output format:
-        * If "sparse", a sub-series of labels for only the outliers in X is returned,
-        * If "dense", a series of labels for all values in X is returned.
-    labels : str {"indicator", "score"}, optional (default="indicator")
-        Annotation output labels:
-        * If "indicator", returned values are boolean, indicating whether a value is an
-        outlier,
-        * If "score", returned values are floats, giving the outlier score.
-
     parama : int
         descriptive explanation of parama
     paramb : string, optional (default='default')
@@ -69,6 +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 +73,6 @@ def __init__(
         est2=None,
         paramb="default",
         paramc=None,
-        fmt="dense",
-        labels="indicator",
     ):
         # estimators should precede parameters
         #  if estimators have default values, set None and initialize below
@@ -89,8 +83,7 @@ def __init__(
         self.paramb = paramb
         self.paramc = paramc
 
-        # leave this as is
-        super().__init__(fmt=fmt, labels=labels)
+        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

@@ -7,6 +7,7 @@
 
 from sktime.annotation.base._base import BaseSeriesAnnotator
 from sktime.utils.validation._dependencies import _check_soft_dependencies
+from sktime.utils.warnings import warn
 
 __author__ = ["mloning", "satya-pattnaik", "fkiraly"]
 
@@ -21,22 +22,34 @@ class PyODAnnotator(BaseSeriesAnnotator):
     estimator : PyOD estimator
         See ``https://pyod.readthedocs.io/en/latest/`` documentation for a detailed
         description of all options.
-    fmt : str {"dense", "sparse"}, optional (default="dense")
-        Annotation output format:
-        * If "sparse", a sub-series of labels for only the outliers in X is returned,
-        * If "dense", a series of labels for all values in X is returned.
-    labels : str {"indicator", "score"}, optional (default="indicator")
-        Annotation output labels:
-        * If "indicator", returned values are boolean, indicating whether a value is an
-        outlier,
-        * If "score", returned values are floats, giving the outlier score.
     """
 
-    _tags = {"python_dependencies": "pyod"}
+    _tags = {
+        "python_dependencies": "pyod",
+        "task": "anomaly_detection",
+        "learning_type": "unsupervised",
+    }
 
-    def __init__(self, estimator, fmt="dense", labels="indicator"):
+    # todo 0.31.0: remove fmt argument and warning
+    def __init__(self, estimator, fmt="deprecated", labels="indicator"):
         self.estimator = estimator  # pyod estimator
-        super().__init__(fmt=fmt, labels=labels)
+        self.fmt = fmt
+        self.labels = labels
+
+        super().__init__()
+
+        if fmt == "deprecated":
+            self._fmt = "sparse"
+            warn(
+                f"Warning from {type(self).__name__}: fmt argument will be removed in"
+                " 0.31.0. For behaviour equivalent to fmt=dense, use transform instead "
+                "of predict. In 0.31.0 the behaviour of predict will equivalent to the"
+                " current behaviour of predict when fmt=sparse.",
+                DeprecationWarning,
+                obj=self,
+            )
+        else:
+            self._fmt = fmt
 
     def _fit(self, X, Y=None):
         """Fit to training data.
@@ -77,9 +90,8 @@ def _predict(self, X):
         Returns
         -------
         Y : pd.Series - annotations for sequence X
-            exact format depends on annotation type
         """
-        fmt = self.fmt
+        fmt = self._fmt
         labels = self.labels
 
         X_np = X.to_numpy()