MBDlyb is a prototype Python library resulting for design-driven model-based diagnostics. It allows for experimentation at scale by easily specifying large networks. The library formalizes and consolidates the research done in the SD2Act project (report). For more information and some high-level documentation, please visit our website.
This section briefly describes how to install MBDlyb, how to set up the database (Neo4j) which is used to store the models, and how to start MBDlyb. More documentation is available here. Pre-packaged wheel files can be found on the Releases page on Github.
MBDlyb is developed in Python and distributed as a Python wheel package. It is advised to install MBDlyb in a Python virtual environment, e.g., using venv or conda. How to create a virtual environment using venv is described below.
First create a directory for your MBDlyb installation, and open a shell (e.g., Windows PowerShell) in this directory. This can be done using the following commands:
mkdir C:\MBDlyb\
cd C:\MBDlyb\Run the following command to create a virtual environment using venv named mbdlyb_env:
python -m venv mbdlyb_envOpen a shell (e.g., Windows PowerShell) in the directory with the virtual environment, and activate the virtual environment:
cd C:\MBDlyb\
.\mbdlyb_env\Scripts\Activate.ps1The MBDlyb wheel package can be installed using the following command:
pip install <path_to_whl>This will install MBDlyb, and download and install all its dependencies.
As a prerequisite, make sure the Neo4j database engine is running.
The interactive model editor and diagnoser can be started from the command line. Open a shell (e.g., Windows PowerShell) in the directory with the virtual environment created for MBDlyb, and activate it:
cd C:\MBDlyb\
.\mbdlyb_env\Scripts\Activate.ps1MBDlyb can now be started with the following command. Make sure the database name in the --database argument matches the database created in the previous step.
python -m mbdlyb --database=example-dbNote that this command uses the default hostname, port, protocol, username and password. These can also be specified using the arguments --host, --port, --protocol, --username and --password.
Next open a webbrowser and go to http://localhost:8080/. This will open the model editor main page in which you can either open an existing model, create a new model or import an Excel/Json or Capella model.
To get a list of all command line parameters, and instructions on how to use them, run the following command:
python -m mbdlyb --helpAn example model in the form of a simple CDRadioPlayer can be found in tests/test_model.py.
MBDlyb is structured in several packages:
- mbdlyb contains the base classes use throughout modelling formalisms. Additionally, gdb contains the base classes for storing the knowledge graphs created in MBDlyb in a Neo4j graph database.
- formalisms contains different reasoning formalisms supported by the library. These classes are wrappers around the reasoning formalisms used to compute diagnoses.
- ui contains the base classes for creating a web interface to allow users to create and use their diagnostic models.
- functional contains the function-oriented modeling paradigm for diagnostics. This paradigm supports three reasoning formalisms (Bayesnet, Markov random fields and tensor networks) and the diagnostic models can be stored in a graph database. Also, a web interface has been implemented in the mbdlyb.functional.ui package. More paradigms may be added in the future.
To build MBDlyb, the Hatch project manager is required.
This can be installed by running:
pip install hatch
MBDlyb can be packaged into a wheel package with the following command:
hatch build -t wheel
Upon releasing a small feature update or bugfix, one can update the micro-version:
hatch version micro
For a larger update, one may use either hatch version minor or hatch version major.
For more information or questions about the library and the methodology supported by it, please contact Thomas Nägele.