flamapy framework
Using the flamapy underlying framework
While this option required to know the underpinnings of the flamapy framework. It also allows users to use any operation available in the ecosystem instead of just the most common ones.
This interface would require to use Python, for example like this:
from flamapy.core.discover import DiscoverMetamodels
# Initiallize the dicover metamodel
dm = DiscoverMetamodels()
# Call the operation. Transformations will be automatically executed
# Use BDDConfigurations if you want to rely on BDD solver
result = dm.use_operation_from_file("PySATSatisfiable","path/to/feature/model")
print(result)
Supported operations
To run other operations that could be less tested, you have to look at their names in the GitHub repositories of the plugins. However, most of the operations are already documented with code snippets.
- Atomic Sets
- Average Branching Factor
- Commonality
- Configuration Distribution
- Configurations
- Number of Configurations
- Conflict Detection
- Core Features
- Count Leafs
- Dead Features
- Diagnosis
- Estimated Number of Configurations
- False Optional Features
- Feature Ancestors
- Feature Inclusion Probability
- Filter
- Homogeneity
- Leaf Features
- Max Depth
- Sampling
- Satisfiable
- Satisfiable Configuration
- Unique Features
- Variability
- Variant Features