Module PdmContext.Pipelines
Expand source code
from PdmContext.ContextGeneration import ContextGenerator
class ContextAndClustering():
def __init__(self,Clustring_object,context_generator_object: ContextGenerator):
"""
This class build a pipeline of Cntext generator and Clustering technique running immediately in the results of Context Generator
**Parameters**:
**Clustring_object**: The clustering technique to use to cluster the created context from Context generator
The class of the clustering technique must implement the method add_sample_to_cluster(context: Context)
**context_generator_object**: A PdmContext.ContextGeneration import ContextGenerator object
"""
self.clustering=Clustring_object
self.Contexter=context_generator_object
def collect_data(self,timestamp,source,name,value=None,type="Univariate"):
"""
Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique
**Parameters**:
**timestamp**: The timestamp of the arrived value
**source**: The source of the arrived value
**name**: The name (or identifier) of the arrived value
**value**: The value (float), in case this is None the arrived data is considered as event
**type**: the type of the data can be one of "isolated","configuration" when no value is passed
**return**: PdmContext.utils.structure.Context object when the data name match to the target name or None.
"""
contextTemp=self.Contexter.collect_data(timestamp,source,name,value=value,type=type)
if contextTemp is not None:
self.clustering.add_sample_to_cluster(contextTemp)
return contextTemp
class ContextAndClusteringAndDatabase():
def __init__(self,context_generator_object: ContextGenerator,Clustring_object,databaseStore_object):
"""
This class build a pipeline of Cntext generator and Clustering technique running immediately in the
results of Context Generator, and storing context results to database
**Parameters**:
**context_generator_object**: A PdmContext.ContextGeneration import ContextGenerator object
**Clustring_object**: The clustering technique to use to cluster the created context from Context generator
The class of the clustering technique must implement the method add_sample_to_cluster(context: Context)
databaseStore_object: Class which implement insert_record(date : pd.datetime,target: str,context : Context, metadata: str)
**databaseStore_object**: An object of database connection from PdmContext.utils.dbconnector
"""
self.clustering=Clustring_object
self.Contexter=context_generator_object
self.database=databaseStore_object
def collect_data(self,timestamp,source,name,value=None,type="Univariate"):
"""
Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique
**Parameters**:
**timestamp**: The timestamp of the arrived value
**source**: The source of the arrived value
**name**: The name (or identifier) of the arrived value
**value**: The value (float), in case this is None the arrived data is considered as event
**type**: the type of the data can be one of "isolated","configuration" when no value is passed
**return**: PdmContext.utils.structure.Context object when the data name match to the target name or None.
"""
contextTemp=self.Contexter.collect_data(timestamp,source,name,value=value,type=type)
if contextTemp is not None:
self.clustering.add_sample_to_cluster(contextTemp)
self.database.insert_record(timestamp,name,contextTemp)
return contextTemp
class ContextAndDatabase():
def __init__(self,context_generator_object: ContextGenerator,databaseStore_object):
"""
This class build a pipeline of Cntext generator and storing results to database
**Parameters**:
**context_generator_object**: A PdmContext.ContextGeneration import ContextGenerator object
**databaseStore_object**: Class which implement insert_record(date : pd.datetime,target: str,context : Context, metadata: str)
"""
self.Contexter=context_generator_object
self.database=databaseStore_object
def collect_data(self,timestamp,source,name,value=None,type="Univariate"):
"""
Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique
**Parameters**:
**timestamp**: The timestamp of the arrived value
**source**: The source of the arrived value
**name**: The name (or identifier) of the arrived value
**value**: The value (float), in case this is None the arrived data is considered as event
**type**: the type of the data can be one of "isolated","configuration" when no value is passed
**return**: PdmContext.utils.structure.Context object when the data name match to the target name or None.
"""
contextTemp=self.Contexter.collect_data(timestamp,source,name,value=value,type=type)
if contextTemp is not None:
self.database.insert_record(timestamp,name,contextTemp)
return contextTemp
Classes
class ContextAndClustering (Clustring_object, context_generator_object: ContextGenerator)
-
This class build a pipeline of Cntext generator and Clustering technique running immediately in the results of Context Generator
Parameters:
Clustring_object: The clustering technique to use to cluster the created context from Context generator The class of the clustering technique must implement the method add_sample_to_cluster(context: Context)
context_generator_object: A PdmContext.ContextGeneration import ContextGenerator object
Expand source code
class ContextAndClustering(): def __init__(self,Clustring_object,context_generator_object: ContextGenerator): """ This class build a pipeline of Cntext generator and Clustering technique running immediately in the results of Context Generator **Parameters**: **Clustring_object**: The clustering technique to use to cluster the created context from Context generator The class of the clustering technique must implement the method add_sample_to_cluster(context: Context) **context_generator_object**: A PdmContext.ContextGeneration import ContextGenerator object """ self.clustering=Clustring_object self.Contexter=context_generator_object def collect_data(self,timestamp,source,name,value=None,type="Univariate"): """ Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique **Parameters**: **timestamp**: The timestamp of the arrived value **source**: The source of the arrived value **name**: The name (or identifier) of the arrived value **value**: The value (float), in case this is None the arrived data is considered as event **type**: the type of the data can be one of "isolated","configuration" when no value is passed **return**: PdmContext.utils.structure.Context object when the data name match to the target name or None. """ contextTemp=self.Contexter.collect_data(timestamp,source,name,value=value,type=type) if contextTemp is not None: self.clustering.add_sample_to_cluster(contextTemp) return contextTemp
Methods
def collect_data(self, timestamp, source, name, value=None, type='Univariate')
-
Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique
Parameters:
timestamp: The timestamp of the arrived value
source: The source of the arrived value
name: The name (or identifier) of the arrived value
value: The value (float), in case this is None the arrived data is considered as event
type: the type of the data can be one of "isolated","configuration" when no value is passed
return: PdmContext.utils.structure.Context object when the data name match to the target name or None.
Expand source code
def collect_data(self,timestamp,source,name,value=None,type="Univariate"): """ Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique **Parameters**: **timestamp**: The timestamp of the arrived value **source**: The source of the arrived value **name**: The name (or identifier) of the arrived value **value**: The value (float), in case this is None the arrived data is considered as event **type**: the type of the data can be one of "isolated","configuration" when no value is passed **return**: PdmContext.utils.structure.Context object when the data name match to the target name or None. """ contextTemp=self.Contexter.collect_data(timestamp,source,name,value=value,type=type) if contextTemp is not None: self.clustering.add_sample_to_cluster(contextTemp) return contextTemp
class ContextAndClusteringAndDatabase (context_generator_object: ContextGenerator, Clustring_object, databaseStore_object)
-
This class build a pipeline of Cntext generator and Clustering technique running immediately in the results of Context Generator, and storing context results to database
Parameters:
context_generator_object: A PdmContext.ContextGeneration import ContextGenerator object
Clustring_object: The clustering technique to use to cluster the created context from Context generator The class of the clustering technique must implement the method add_sample_to_cluster(context: Context) databaseStore_object: Class which implement insert_record(date : pd.datetime,target: str,context : Context, metadata: str)
databaseStore_object: An object of database connection from PdmContext.utils.dbconnector
Expand source code
class ContextAndClusteringAndDatabase(): def __init__(self,context_generator_object: ContextGenerator,Clustring_object,databaseStore_object): """ This class build a pipeline of Cntext generator and Clustering technique running immediately in the results of Context Generator, and storing context results to database **Parameters**: **context_generator_object**: A PdmContext.ContextGeneration import ContextGenerator object **Clustring_object**: The clustering technique to use to cluster the created context from Context generator The class of the clustering technique must implement the method add_sample_to_cluster(context: Context) databaseStore_object: Class which implement insert_record(date : pd.datetime,target: str,context : Context, metadata: str) **databaseStore_object**: An object of database connection from PdmContext.utils.dbconnector """ self.clustering=Clustring_object self.Contexter=context_generator_object self.database=databaseStore_object def collect_data(self,timestamp,source,name,value=None,type="Univariate"): """ Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique **Parameters**: **timestamp**: The timestamp of the arrived value **source**: The source of the arrived value **name**: The name (or identifier) of the arrived value **value**: The value (float), in case this is None the arrived data is considered as event **type**: the type of the data can be one of "isolated","configuration" when no value is passed **return**: PdmContext.utils.structure.Context object when the data name match to the target name or None. """ contextTemp=self.Contexter.collect_data(timestamp,source,name,value=value,type=type) if contextTemp is not None: self.clustering.add_sample_to_cluster(contextTemp) self.database.insert_record(timestamp,name,contextTemp) return contextTemp
Methods
def collect_data(self, timestamp, source, name, value=None, type='Univariate')
-
Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique
Parameters:
timestamp: The timestamp of the arrived value
source: The source of the arrived value
name: The name (or identifier) of the arrived value
value: The value (float), in case this is None the arrived data is considered as event
type: the type of the data can be one of "isolated","configuration" when no value is passed
return: PdmContext.utils.structure.Context object when the data name match to the target name or None.
Expand source code
def collect_data(self,timestamp,source,name,value=None,type="Univariate"): """ Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique **Parameters**: **timestamp**: The timestamp of the arrived value **source**: The source of the arrived value **name**: The name (or identifier) of the arrived value **value**: The value (float), in case this is None the arrived data is considered as event **type**: the type of the data can be one of "isolated","configuration" when no value is passed **return**: PdmContext.utils.structure.Context object when the data name match to the target name or None. """ contextTemp=self.Contexter.collect_data(timestamp,source,name,value=value,type=type) if contextTemp is not None: self.clustering.add_sample_to_cluster(contextTemp) self.database.insert_record(timestamp,name,contextTemp) return contextTemp
class ContextAndDatabase (context_generator_object: ContextGenerator, databaseStore_object)
-
This class build a pipeline of Cntext generator and storing results to database
Parameters:
context_generator_object: A PdmContext.ContextGeneration import ContextGenerator object
databaseStore_object: Class which implement insert_record(date : pd.datetime,target: str,context : Context, metadata: str)
Expand source code
class ContextAndDatabase(): def __init__(self,context_generator_object: ContextGenerator,databaseStore_object): """ This class build a pipeline of Cntext generator and storing results to database **Parameters**: **context_generator_object**: A PdmContext.ContextGeneration import ContextGenerator object **databaseStore_object**: Class which implement insert_record(date : pd.datetime,target: str,context : Context, metadata: str) """ self.Contexter=context_generator_object self.database=databaseStore_object def collect_data(self,timestamp,source,name,value=None,type="Univariate"): """ Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique **Parameters**: **timestamp**: The timestamp of the arrived value **source**: The source of the arrived value **name**: The name (or identifier) of the arrived value **value**: The value (float), in case this is None the arrived data is considered as event **type**: the type of the data can be one of "isolated","configuration" when no value is passed **return**: PdmContext.utils.structure.Context object when the data name match to the target name or None. """ contextTemp=self.Contexter.collect_data(timestamp,source,name,value=value,type=type) if contextTemp is not None: self.database.insert_record(timestamp,name,contextTemp) return contextTemp
Methods
def collect_data(self, timestamp, source, name, value=None, type='Univariate')
-
Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique
Parameters:
timestamp: The timestamp of the arrived value
source: The source of the arrived value
name: The name (or identifier) of the arrived value
value: The value (float), in case this is None the arrived data is considered as event
type: the type of the data can be one of "isolated","configuration" when no value is passed
return: PdmContext.utils.structure.Context object when the data name match to the target name or None.
Expand source code
def collect_data(self,timestamp,source,name,value=None,type="Univariate"): """ Call the PdmContext.ContextGeneration.ContextGenerator and pass the result to clustering technique **Parameters**: **timestamp**: The timestamp of the arrived value **source**: The source of the arrived value **name**: The name (or identifier) of the arrived value **value**: The value (float), in case this is None the arrived data is considered as event **type**: the type of the data can be one of "isolated","configuration" when no value is passed **return**: PdmContext.utils.structure.Context object when the data name match to the target name or None. """ contextTemp=self.Contexter.collect_data(timestamp,source,name,value=value,type=type) if contextTemp is not None: self.database.insert_record(timestamp,name,contextTemp) return contextTemp