OA: Templates for Anomaly Prediction #1011

src/mlpro/oa/streams/tasks/anomalypredictors/__init__.py

@@ -1 +1 @@
-from mlpro.oa.streams.tasks.anomalypredictors.basics import AnomalyPredictor
+from mlpro.oa.streams.tasks.anomalypredictors.basics import AnomalyPredictor, AnomalyPrediction

src/mlpro/oa/streams/tasks/anomalypredictors/basics.py

@@ -6,15 +6,18 @@
 ## -- History :
 ## -- yyyy-mm-dd  Ver.      Auth.    Description
 ## -- 2024-06-04  0.0.0     DA/DS    Creation
+## -- 2024-08-23  0.1.0     DA/DS    Creation
 ## -------------------------------------------------------------------------------------------------
 
 """
-Ver. 0.0.0 (2024-06-04)
+Ver. 0.1.0 (2024-08-23)
 
-This module provides ...
+This module provides basic templates for online anomaly prediction in MLPro.
+ 
 """
 
 
+from mlpro.bf.events import Event
 from mlpro.oa.streams.tasks import OATask
 
 
@@ -58,3 +61,16 @@ class AnomalyPredictor (OATask):
     """
 
     pass
+
+
+
+
+
+## -------------------------------------------------------------------------------------------------
+## -------------------------------------------------------------------------------------------------
+class AnomalyPrediction (Event):
+    """
+    ...
+    """
+
+    pass

src/mlpro/oa/streams/tasks/anomalypredictors/tsf.py

@@ -0,0 +1,76 @@
+## -------------------------------------------------------------------------------------------------
+## -- Project : MLPro - The integrative middleware framework for standardized machine learning
+## -- Package : mlpro.oa.streams.tasks.anomalypredictors
+## -- Module  : basics.py
+## -------------------------------------------------------------------------------------------------
+## -- History :
+## -- yyyy-mm-dd  Ver.      Auth.    Description
+## -- 2024-06-04  0.0.0     DA/DS    Creation
+## -- 2024-08-23  0.1.0     DA/DS    Creation
+## -------------------------------------------------------------------------------------------------
+
+"""
+Ver. 0.1.0 (2024-08-23)
+
+This module provides basic templates for online anomaly prediction in MLPro.
+ 
+"""
+
+
+from mlpro.bf.events import Event
+from mlpro.oa.streams.tasks import OATask
+
+
+
+
+## -------------------------------------------------------------------------------------------------
+## -------------------------------------------------------------------------------------------------
+class AnomalyPredictor (OATask):
+    """
+    The __init__ method should be documented in the class level docstring and the docstring itself
+    should not go beyond 100 characters length (within the dash separator). Sections inside the 
+    docstring can be seperated like the reStructuredText format.
+    
+    Parameters are documented in the Parameters section.
+    Public attributes of classes are documented inisde Attributes section.
+    Returns attronites are documented in the Returns section.
+    
+    A few examples of data types: int / str / bool / list of str / tuple of int / float / None / dict.
+    If the parameters have default values, you should add "TYPE, optional" as part of the type
+    and "The default is ...." as part of the description.
+    
+    .. _Further_formatting_information: 
+        https://numpydoc.readthedocs.io/en/latest/format.html
+    
+    Notes
+    -----
+        The content inside the section should be indented. 
+    
+    Parameters
+    ----------
+    p_arg1 : str
+        Explanation of the first parameter.
+    p_arg2 : bool
+        Explanation of the second parameter. The default is True.
+        
+    Attributes
+    ----------
+    C_MY_CONSTANT = 'My static value'
+        Explanation of the public constant C_MY_CONSTANT.
+
+    """
+
+    pass
+
+
+
+
+
+## -------------------------------------------------------------------------------------------------
+## -------------------------------------------------------------------------------------------------
+class AnomalyPrediction (Event):
+    """
+    ...
+    """
+
+    pass

src/mlpro/oa/streams/tasks/anomalypredictors/tsf/ad_based.py

@@ -0,0 +1,31 @@
+## -------------------------------------------------------------------------------------------------
+## -- Project : MLPro - The integrative middleware framework for standardized machine learning
+## -- Package : mlpro.oa.streams.tasks.anomalypredictors
+## -- Module  : basics.py
+## -------------------------------------------------------------------------------------------------
+## -- History :
+## -- yyyy-mm-dd  Ver.      Auth.    Description
+## -- 2024-06-04  0.0.0     DA/DS    Creation
+## -- 2024-08-23  0.1.0     DA/DS    Creation
+## -------------------------------------------------------------------------------------------------
+
+"""
+Ver. 0.1.0 (2024-08-23)
+
+This module provides basic templates for online anomaly prediction in MLPro.
+ 
+"""
+
+
+from mlpro.oa.streams.tasks.anomalypredictors.tsf.basics import AnomalyPredictorTSF
+
+
+
+## -------------------------------------------------------------------------------------------------
+## -------------------------------------------------------------------------------------------------
+class AnomalyPredictorAD (AnomalyPredictorTSF):
+    """
+    ...
+    """
+
+    pass

src/mlpro/oa/streams/tasks/anomalypredictors/tsf/basics.py

@@ -0,0 +1,59 @@
+## -------------------------------------------------------------------------------------------------
+## -- Project : MLPro - The integrative middleware framework for standardized machine learning
+## -- Package : mlpro.oa.streams.tasks.anomalypredictors
+## -- Module  : basics.py
+## -------------------------------------------------------------------------------------------------
+## -- History :
+## -- yyyy-mm-dd  Ver.      Auth.    Description
+## -- 2024-06-04  0.0.0     DA/DS    Creation
+## -- 2024-08-23  0.1.0     DA/DS    Creation
+## -------------------------------------------------------------------------------------------------
+
+"""
+Ver. 0.1.0 (2024-08-23)
+
+This module provides basic templates for online anomaly prediction in MLPro.
+ 
+"""
+
+
+from mlpro.bf.math import Function
+from mlpro.oa.streams.tasks.anomalypredictors import AnomalyPredictor, AnomalyPrediction
+from mlpro.sl import SLAdaptiveFunction
+
+
+
+## -------------------------------------------------------------------------------------------------
+## -------------------------------------------------------------------------------------------------
+class TimeSeriesForcaster (Function):
+    """
+    ...
+    """
+
+    pass
+
+
+
+
+
+## -------------------------------------------------------------------------------------------------
+## -------------------------------------------------------------------------------------------------
+class OATimeSeriesForcaster (TimeSeriesForcaster, SLAdaptiveFunction):
+    """
+    ...
+    """
+
+    pass
+
+
+
+
+
+## -------------------------------------------------------------------------------------------------
+## -------------------------------------------------------------------------------------------------
+class AnomalyPredictorTSF (AnomalyPredictor):
+    """
+    ...
+    """
+
+    pass

src/mlpro/oa/streams/tasks/anomalypredictors/tsf/mini_batch_based.py

@@ -0,0 +1,43 @@
+## -------------------------------------------------------------------------------------------------
+## -- Project : MLPro - The integrative middleware framework for standardized machine learning
+## -- Package : mlpro.oa.streams.tasks.anomalypredictors
+## -- Module  : basics.py
+## -------------------------------------------------------------------------------------------------
+## -- History :
+## -- yyyy-mm-dd  Ver.      Auth.    Description
+## -- 2024-06-04  0.0.0     DA/DS    Creation
+## -- 2024-08-23  0.1.0     DA/DS    Creation
+## -------------------------------------------------------------------------------------------------
+
+"""
+Ver. 0.1.0 (2024-08-23)
+
+This module provides basic templates for online anomaly prediction in MLPro.
+ 
+"""
+
+
+from mlpro.oa.streams.tasks.anomalypredictors.tsf.basics import OATimeSeriesForcaster
+
+
+
+## -------------------------------------------------------------------------------------------------
+## -------------------------------------------------------------------------------------------------
+class MiniBatchManager:
+    """
+    """
+
+    pass
+
+
+
+
+
+## -------------------------------------------------------------------------------------------------
+## -------------------------------------------------------------------------------------------------
+class OATimeSeriesForcasterMB (OATimeSeriesForcaster):
+    """
+    ...
+    """
+
+    pass

test/howtos/oa/howto_oa_streams_cbap_001_KMeans_ClusterDriftDetector_InstantaneousDetection_2d.py

@@ -36,11 +36,24 @@
 the screen following the run.
 
 """
+from mlpro.bf.math import Event
+from mlpro.bf.mt import Event
 from mlpro.bf.streams.streams import *
 from mlpro.bf.various import Log
 from mlpro.oa.streams import *
 from mlpro_int_river.wrappers.clusteranalyzers.kmeans import WrRiverKMeans2MLPro
 from mlpro.oa.streams.tasks.anomalydetectors.cb_detectors.drift_detector import ClusterDriftDetector
+from mlpro.oa.streams.tasks.anomalypredictors.tsf.ad_based import AnomalyPredictorAD
+
+
+
+
+class DevindisPredictor (AnomalyPredictorAD):
+
+    def _adapt_on_event(self, p_event_id: str, p_event_object: Event) -> bool:
+
+        # Set breakpoint here...
+        pass
 
 
 
@@ -102,10 +115,11 @@ def _setup(self, p_mode, p_ada: bool, p_visualize: bool, p_logging):
         workflow.add_task(p_task=task_anomaly_detector, p_pred_tasks=[task_clusterer])
 
         # Anomaly Predictor
-        task_anomaly_predictor = ClusterDriftDetector(p_visualize=p_visualize,
-                                                              p_logging=p_logging)
-
-        workflow.adapt_on_event(p_task=task_anomaly_detector)
+        task_anomaly_predictor = DevindisPredictor( p_visualize=p_visualize,
+                                                    p_logging=p_logging)
+
+        task_anomaly_detector.register_event_handler( p_event_id = 'Anomaly',
+                                                      p_event_handler = task_anomaly_predictor.adapt_on_event )
 
         # 1.3 Return stream and workflow
         return stream, workflow