cdt/causality/graph/CGNN.py
def parallel_graph_evaluation(data, adj_matrix, nb_runs=16,
                              nb_jobs=None, **kwargs):
    """Parallelize the various runs of CGNN to evaluate a graph."""
    nb_jobs = SETTINGS.get_default(nb_jobs=nb_jobs)
    if nb_runs == 1:
        return graph_evaluation(data, adj_matrix, **kwargs)
    else:
        output = Parallel(n_jobs=nb_jobs)(delayed(graph_evaluation)(data, adj_matrix,
                                          idx=run, gpu_id=run % SETTINGS.GPU,
                                          **kwargs) for run in range(nb_runs))
        return np.mean(output)
def parallel_graph_evaluation(data, adj_matrix, nb_runs=16,
                              nb_jobs=None, **kwargs):
    """Parallelize the various runs of CGNN to evaluate a graph."""
    nb_jobs = SETTINGS.get_default(nb_jobs=nb_jobs)
    if nb_runs == 1:
        return graph_evaluation(data, adj_matrix, **kwargs)
    else:
        output = Parallel(n_jobs=nb_jobs)(delayed(graph_evaluation)(data, adj_matrix,
                                          idx=run, gpu_id=run % SETTINGS.GPU,
                                          **kwargs) for run in range(nb_runs))
        return np.mean(output)
def parallel_graph_evaluation ( data , adj_matrix , nb_runs = 16 , nb_jobs = None , * * kwargs ) : nb_jobs = SETTINGS . get_default ( nb_jobs = nb_jobs ) if nb_runs == 1 : return graph_evaluation ( data , adj_matrix , * * kwargs ) else : output = Parallel ( n_jobs = nb_jobs ) ( delayed ( graph_evaluation ) ( data , adj_matrix , idx = run , gpu_id = run % SETTINGS . GPU , * * kwargs ) for run in range ( nb_runs ) ) return np . mean ( output )
Parallelize the various runs of CGNN to evaluate a graph.
Parallelize the various runs of CGNN to evaluate a graph .