Use independent discrete-optimization library #208
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n_jobs is now equal to n_jobs_non_dummy + 2 (was n_jobs_non_dummy in a previous version) Fix a bug in test_scheduling::test_optimality() and test_scheduling::test_gecode_optimality()
Make sure that the argument passed to the partial SGS function follow requirement of discrete optimisation : disjoint "completed" and "scheduled"(=ongoing) taks. Previous behaviour was leading to wrong policy results.
Make it easier to see in which if block the code pass through. MS_RCPSPSolution_Variant being a MS_RCPSPSolution, it is now clearer that the code pass first by the MS_RCPSPSolution block.
Indeed this is included in the stand-alone library discrete-optimization
This will be covered by examples in discrete-optimization proper arborescence. We move the data in examples/discrete-optimization in examples/scheduling as it is used there.
Indeed, it is already a dependency of discrete-optimization library.
If minizinc (or discrete-optimization which imports itself minizinc) is imported before skdecide, the paths to minizinc are not yet configured as it is done in skdecide.hub.__init__.py So we do it manually
- Remove depencies that are actually dependencies of discrete-optimization - Add discrete-optimization as a dependency - Update poetry.lock accordingly with poetry update NB: poetry update was taking ages (never finishing actually), until I update poetry to 1.2.
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The discrete-optimization part of scikit-decide is now managed in an independent repository.
The dependencies are updated to use the newly deployed library and the code is slightly updated to adapt to the changes in discrete-optimization 0.2.0.