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Template Classes for Anomaly Prediction
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@Devindi97
Devindi97 committed Sep 27, 2024
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76 changes: 57 additions & 19 deletions .../streams/tasks/anomaly_predictors/images/MLPro-OA_Anomaly_Predictors_class_diagram.drawio
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72 changes: 68 additions & 4 deletions src/mlpro/oa/streams/tasks/anomalypredictors/tsf/ad_based.py
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## -- yyyy-mm-dd Ver. Auth. Description
## -- 2024-06-04 0.0.0 DA/DS Creation
## -- 2024-08-23 0.1.0 DA/DS Creation
## -- 2024-09-27 0.2.0 DS Creation
## -------------------------------------------------------------------------------------------------

"""
Ver. 0.1.0 (2024-08-23)
Ver. 0.2.0 (2024-09-27)
This module provides basic templates for online anomaly prediction in MLPro.
"""


from mlpro.bf.ml import Log
from mlpro.bf.streams import Log, StreamTask
from mlpro.bf.various import Log
from mlpro.oa.streams.tasks.anomalypredictors.tsf.basics import AnomalyPredictorTSF
from mlpro.oa.streams.tasks.anomalydetectors.basics import Anomaly



## -------------------------------------------------------------------------------------------------
## -------------------------------------------------------------------------------------------------
class AnomalyPredictorAD (AnomalyPredictorTSF):
class AnomalyPredictorAD (AnomalyPredictorTSF, Anomaly):
"""
...
Parameters
-----------
p_name : str
Optional name of the task. Default is None.
p_range_max : int
Maximum range of asynchonicity. See class Range. Default is Range.C_RANGE_PROCESS.
p_ada : bool
Boolean switch for adaptivitiy. Default = True.
p_buffer_size : int, optional
p_duplicate_data : bool, optional
If True, instances will be duplicated before processing. Default = False.
p_visualize : bool, optional
Boolean switch for visualisation. Default = False.
p_logging : int
Log level (see constants of class Log). Default: Log.C_LOG_ALL
p_kwargs : dict
Further optional named parameters.
"""
C_TYPE = 'Anomaly Predictor AD'

pass

## -------------------------------------------------------------------------------------------------

def __init__(self,
p_name: str = None,
p_range_max=StreamTask.C_RANGE_THREAD,
p_ada: bool = True,
p_buffer_size: int = 0,
p_duplicate_data: bool = False,
p_visualize: bool = False,
p_logging=Log.C_LOG_ALL,
**p_kwargs):

super().__init__(p_name,
p_range_max,
p_ada,
p_buffer_size,
p_duplicate_data,
p_visualize,
p_logging,
**p_kwargs)

self.capture_anomalies = {}


## -------------------------------------------------------------------------------------------------

def get_anomaly(self, ad_anomaly):
"""
Process incoming anomaly data from the anomaly detector.
parameters
----------
ad_anomaly
Anomaly data coming from the anomaly detector.
"""

self.ad_anomaly = ad_anomaly
self.captured_anomalies.append(ad_anomaly)

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