BNEngineMongo-class: Class '"BNEngineMongo"'

In ralmond/EABN: Evidence Accumulation Bayes Net Engine

Class "BNEngineMongo"

Description

A Bayes net engine hooked to a Mongo database.

Extends

Class "BNEngine", directly.

All reference classes extend and inherit methods from "envRefClass".

Activation

At the start of each iteration of the mainLoop, it checks eng$shouldHalt() method. If this returns TRUE, then execution is immediately halted. When the queue is empty, it checks the eng$stopWhenFinished() method. If this returns true, then the main loop also terminates.

The BNEngineMongo this checks the “AuthorizedApps” collection in the database to see if the current app is active and the value of the field EAsignal. The eng$activate() method sets this field to “Running”. If the field is set to “Halt”, then eng$shouldHalt() will return TRUE and the mainLoop will stop after processing the current evidence set. If the field is set to “Halt” (actually, anything other than “Running”), then eng$stopWhenFinished() will return TRUE and the mainLoop will stop when the queue is empty.

The methods eng$activate() and eng$deactivate() set and clear the EAactive flag in the “AuthorizedApps” database.

Fields

app:

Object of class character giving an globally unique identifier for the application

srs:

Object of class StudentRecordSet of NULL giving the student record set for the application.

profModel:

Object of class character giving the name of the proficiency model (for the default student record) in the warehouse manifest.

listenerSet:

Object of class ListenerSet giving a set of listeners who will listen for new statistics.

statistics:

Object of class list containing Statistic objects to be run on every update cycle.

histNodes:

Object of class character giving the names of the nodes in the proficiency model whose history will be recorded.

warehouseObj:

Object of class PnetWarehouse which stores the Bayes nets, both evidence models and student models are stored here.

waittime:

Object of class numeric giving the time in seconds the main event loop should wait before checking again for messages.

processN:

Object of class numeric giving the number of times that the main loop should run before stopping. If Inf, then the main loop will run without stopping.

dburi:

Object of class character giving the URI for the mongo database.

dbname:

Object of class character giving the name of the database to be used.

manifestDB:

Object of class MongoDB giving the collection used to store the manifest. This object may not be initialized so it should be accessed through the class-based function manifestdb().

evidenceDB:

Object of class MongoDB accessing the evidence set collection. This object may not be initialized so it should be accessed through the class-based function evidenceSets().

statDB:

Object of class MongoDB giving the statistics to use. This object may not be initialized so it should be accessed through the class-based function statdb().

histNodesDB:

Object of class MongoDB giving the history nodes. This object may not be initialized so it should be accessed through the class-based function histNodesdb().

admindbname:

Object of class character giving name admin (mongo) database, used for various listeners and the is.active() method.

adminDB:

Object of class MongoDB giving the link to the admin database. This object may not be initialized so it should be accessed through the class-based function admindb().

Methods

activate():

Sets the flag in the admin database to indicate that the process is running.

deactivate():

Clears the flag in the admin database to indicate that the process is no longer running.

shouldHalt():

This function checks the admin database to see whether or not the flag is set to cause the process to halt after processing the current record..

stopWhenFinished():

This function checks the admin database to see whether or not the flag is set to cause the process to stop when the event queue is empty.

statdb():

Returns the database contianing the statistic objects.

studentRecords():

Returns the StudentRecordSet associated with this engine.

fetchStats():

Fetches the statistics marked in the database configuration.

initialize(app, warehouse, listeners, username, password, host, port, dbname, P4dbname, profModel, waittime, statistics, histNodes, processN, ...):

initializes the class. Note that some initialization is done in the various XXXdb() functions, so these should be called instead of directly accessing the fields.

manifestdb():

Returns the MongoDB-class handle to the manifest information collection.

admindb():

Returns the MongoDB-class handle to the “AuthorizedApps” collection.

histNodesdb():

Returns the MongoDB-class handle to the hist nodes collection.

saveManifest(manifest):

Saves the current PnetWarehouse manifest to the manifestdb() collection

fetchManifest():

Retrieves the saved manifest from the manifestdb() collection.

fetchNextEvidence():

Retrieves the next EvidenceSet from the evidenceSets() collection. Returns NULL if there are not unprocessed evidence sets.

saveStats(statmat):

Saves the update statistic definitions to the statdb() collection.

setHistNodes(nodenames):

Saves the history nodes to the histNodesdb() collection.

isActivated():

Checks to see if the active flag is set.

setError(mess, e):

Added an error message to an evidence set.

evidenceSets():

Returns a MongoDB-class handle to the collection/queue of evidence sets.

getHistNodes():

Fetches the history nodes from the histNodesdb() collection.

show():

Provides a printed representation of the engine.

The following methods are inherited (from the corresponding class): evidenceSets ("BNEngine"), getHistNodes ("BNEngine"), stats ("BNEngine"), setProcessed ("BNEngine"), setManifest ("BNEngine"), activate ("BNEngine"), isActivated ("BNEngine"), saveManifest ("BNEngine"), studentRecords ("BNEngine"), saveStats ("BNEngine"), fetchNextEvidence ("BNEngine"), warehouse ("BNEngine"), show ("BNEngine"), setHistNodes ("BNEngine"), setError ("BNEngine"), fetchManifest ("BNEngine"), fetchStats ("BNEngine")

Note

The database connections are not created right away, so it is important to use the class-based functions, manifestdb(), statdb(), evidenceSets(), histNodesdb(), studentRecords(), and admindb() rather than accessing the fields directly.

Author(s)

Russell Almond

References

Almond, Mislevy, Steinberg, Yan and Williamson (2015). Bayesian Networks in Educational Assessment. Springer. Especially Chapter 13.

See Also

Classes: BNEngine, BNEngineNDB

Constituent parts: StudentRecordSet, PnetWarehouse

Setup Functions: loadManifest, setupDefaultSR, configStats, baselineHist,

Main Loop Functions: mainLoop, accumulateEvidence, handleEvidence, getRecordForUser, logEvidence, updateSM, updateStats, updateHist, announceStats,

Examples

showClass("BNEngineMongo")

ralmond/EABN documentation built on Aug. 30, 2023, 12:52 p.m.