richard-churchman
5f6bbe45f7
Major bug fixes relating to the Redis implementation in Reprocessing and Search Key Cache were identified and fixed as part of the rekeying development. Included functionality to stop the Exhaustive training. Fixed the Unsupervised learning functionality in Exhaustive Training. Embedded the Mock Data CSV data into a class to make it compatible with Docker without needing to modify the Dockerfile (i.e, build). Included a new table that stores training data to enhance support capabilities for Exhaustive. Reintroduced and validated the Multitenancy functionality. Postgres is now fully deprecated in place of Redis for cache, and all implementations of the Interfaces for Postgres have been removed. No migrations exist to support this deprecation, and Postgres as a cache is now wholly unsupported. Removed the Indexing background thread, as Redis does not require it, given that it is a key-value pair database. Created a RedisCommandFlag Environment Variable to support Fire and Forget write to Redis in Invocation. Implemented functionality to Export, Peek and Download configuration environments using a secure, encrypted scheme. Created a permission to access the functionality via the user interface. Extensive database-based logging is included to provide for a comprehensive audit. Created functionality to Import the encrypted Export file inside a database transaction to ensure consistency. As Docker is now the primary deployment model, log4net instantiation has been moved to an approach that primarily uses programmatic instantiation of the ConsoleLogger and RollingFileAppender given Environment Variables. Implemented functionality to support remote \ mounted Log4Net XML Serialisation files for more advanced log4net configurations. Reintroduced access to counters via the user interface and created a permission. Took the opportunity to implement ReSharper linter suggestions. Disabled the Lock button across the whole application, with that functionality being reserved for future use. It is intended that locking happens programmatically on the basis of dependencies in future commits. Fixed several significant end-use bugs which became apparent during testing of the refactored code relating to case management and visualisations. Fixed certain other minor bugs that came up during testing. Brought documentation up to date for this commit. Brought the Docker Compose file up to date for this commit. Removed Redundant Environment Variables for this commit. Made the README file less verbose and unnecessary.
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layout, title, nav_order, parent, grand_parent
| layout | title | nav_order | parent | grand_parent |
|---|---|---|---|---|
| default | Exhaustive Adaptation Training Promoted Models Performance | 5 | Exhaustive Adaptation | Configuration |
🚀Speed up implementation with hands-on, face-to-face training from the developer.
Exhaustive Adaptation Training Promoted Models Performance
Promoted models can be manually tested via the Promoted Models Testing tab:
Clicking on the tab will present the model variables as sliders:
The sliders are set to their optimal prescription values for the purpose of classifying positive:
Moving the sliders will present the collated values of all sliders to the model to return a score:
The higher the score, the more likely the simulation is a positive case (e.g. fraud).
The intention of the manual simulation is to process several intuitive scenarios and observe the score to change equally intuitively, although keeping in mind that the model is largely bias to anomaly.
Given a score behaving counter intuitively or not flexibly enough, it can of be deactivated, and the next highest priority score will be considered promoted.