ecole
ecole copied to clipboard
ds4dm
Extensible Combinatorial Optimization Learning Environments
## Describe the problem or improvement suggested Implement the same Python API #49 in C++ ## Describe the solution you'd like Reward function all derive from the same ABC, so...
Add type hints for IDE completion, static check, and documentation purposes. - Investigate if Pybind need stubs; - The correct types for state function would be to define a [`Protocol`](https://docs.python.org/3/library/typing.html#typing.Protocol)...
Think about the possibility of composing environments, e.g., Node selection + branching
Replace `Exception` for `std::exception` in order to allow `std::throw_with_nested`. That would improve error messages for the users.
When the user pass a wrong action to `Environment::step`, the environment needs to be reset. However, when using the environment interactively, it would be more natural that wrong value are...
## Describe the bug Hi, I use the the observation function **ecole.observation.StrongBranchingScores** to extract strong branching scores of candidate variables in an application. I collect scores for only variables with...
## Describe the bug I am trying to run the example for branching-imitation (https://github.com/ds4dm/ecole/tree/master/examples/branching-imitation) and am getting the following error - 'ecole.core.observation.NodeBipartiteObs' object has no attribute 'variable_features' Detailed log -...
## Describe the bug 1. I cannot install Ecole with 0.8.x version via `conda-forge`. When I manually install the Ecole 0.8.x after downloading `*.tar.gz` files, the system cannot find `libecole.so.0.8`....
## Describe the bug I run the example at https://github.com/ds4dm/ecole/blob/master/examples/branching-imitation/example.ipynb and encounter this error **'ecole.core.observation.NodeBipartiteObs' object has no attribute 'variable_features'**  `--------------------------------------------------------------------------- AttributeError Traceback (most recent call last) Cell In[11],...
import ecole env = ecole.environment.Branching( observation_function=ecole.observation.NodeBipartite(),) reward_function=-ecole.reward.NNodes()) # set up an instance generator instances = ecole.instance.SetCoverGenerator(n_rows=100, n_cols=1000, density=0.05, max_coef=100) # generate ten MDP episodes for i in range(10): instance =...
