This Python package implements a flexible multi-objective scoring system based on desirability functions and aggregation.
- Define custom scoring profiles with:
- Desirability functions for each objective
- Aggregation algorithm selection
- Optional weighting and importance factors
- Calculate individual desirability scores for each property
- Aggregate scores using the specified method
- Process data from various input formats (e.g., dictionaries, dataframes)
- Decision support systems
- Multi-criteria optimization
- Performance evaluation
- Product or candidate ranking
Create a dedicated Python environment for this package with your favorite environment manager.
conda create -n pumas python=3.9
conda activate pumas- Option 1: Install the package from the github repository:
pip install git+ssh://[email protected]/syngenta/pumas.git@main- Option 2: Install the package from the Python Package Index (PyPI):
pip install pumasExtensions to Pumas, have conditional dependencies on a variety of third-party Python packages. All dependencies are installed
A full list of conditional dependencies can be found in Pumas's pyproject.toml (stored related requirements text files).
The core installation of PUMAS support a basic scoring framework based on numerical values. To enable probabilistic scoring frameworks, to use and propagate value uncertainty, please install optional libraries with:
pip install pumas[uncertainty]The core installation of PUMAS does not include any plotting capability, and, hence, the graphical bindings are unavailable. To enable both the plotting module and the graphical binding, please install the optional libraries with:
pip install pumas[graphics]When working on the development of this package, the developer wants to work directly on the source code while still using the packaged installation.
Please install the package in development mode, including all dependencies.
git clone [email protected]:syngenta/pumas.git
pip install -e pumas/[dev]