Utils

Utility functions come in all kinds and shapes. We tried to group them in a sensible way.

Training utilities

plot_exploration

 plot_exploration (exploration_results, expl_optims=True,
                   highlight_max=False, figsize=(10, 5), fontsize=18,
                   ticksize=14)

Plots exploration results. It shows the best bound obtained for every new visited state. If expl_optims=True, it also shows the proximity to the optimal relaxation.

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best_so_far

 best_so_far (m)

Returns best value up to each point along the second axis of a matrix for each row.

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arrange_shape

 arrange_shape (obj, mode='expand')

Makes all lists within a list have the same length. Can be mode ‘cut’ or ‘expand’

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plot_trainings

 plot_trainings (training_results, eval_optims=True, expl_optims=False,
                 figsize=(10, 5), fontsize=18, ticksize=14)

Plots the training results.

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get_indiv_times

 get_indiv_times (tl_eval, convergence_crit=None)

Provides convergence times from individual ratios.

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indiv_convergence_time

 indiv_convergence_time (results, max_epochs=800, tol=0.0005, T=50,
                         t_avg=20, return_diffs=False)

Similar to convergence_time but with each agent.

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convergence_time

 convergence_time (results, tol=0.0005, T=50, t_avg=20,
                   return_diffs=False)

Returns the convergence with criterion of not changing result by tol for T epochs.

Load and save data

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CPU_Unpickler

 CPU_Unpickler (file, fix_imports=True, encoding='ASCII', errors='strict',
                buffers=())

Unpickles from GPU to CPU

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checkpoint2results

 checkpoint2results (N, model, budget=None, IDs=None, episode=None)

Loads checkpoints from IDs and converts them to result format.

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load_checkpoint

 load_checkpoint (problem, budget, ID, episode)

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load_benchmark

 load_benchmark (problem, budget, method, suffix=None)

Loads benchmark data.

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save_benchmark

 save_benchmark (benchmark, problem, budget, method, suffix=None)

Saves benchmark data.

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load_model

 load_model (problem, budget, ID)

Loads agent model and parameters.

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save_model

 save_model (agent, problem, budget, ID)

Saves agent model and parameters.

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get_checkpoint_file_name

 get_checkpoint_file_name (problem, budget, ID, episode)

Returns a consistent file naming for checkpoints.

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get_benchmark_file_name

 get_benchmark_file_name (problem, budget, method)

Returns a consistent file naming for benchmarks.

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get_memory_file_name

 get_memory_file_name (problem, solver)

Returns a consistent file naming for memories.

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get_agent_file_name

 get_agent_file_name (problem, budget, ID)

Returns a consistent file naming for agents.

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get_problem_file_name

 get_problem_file_name (problem)

Returns a consistent file naming from a problem instance.

State transformations

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action_mask

 action_mask (state, basis)

Mask of the actions that can be performed. Better to use SdPEnvironment.action_mask if possible, it’s much faster.

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contained_constraints

 contained_constraints (state, basis)

Returns a mask indicating which constraints in the state are contained by larger elements of the basis in the same state, excluding themselves. Better to use the SdPEnvironment.contained_constraints method when possible (it’s faster).

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add_subgraph_size

 add_subgraph_size (subgraphs)

Takes a list of subgraphs up to any given size and adds a sublist with all the possible subgraphs of a larger size. It assumes the graph edges as minimal graph size.

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