def main():
parser = argparse.ArgumentParser()
parser.add_argument('-m',
'--model_dir',
type=str,
help='Path to the trained RUN-CSP instance')
parser.add_argument('-v',
'--n_variables',
type=int,
default=100,
help='Number of variables in each training instance.')
parser.add_argument(
'--c_min',
type=int,
default=100,
help='Minimum number of clauses in each training instance.')
parser.add_argument(
'--c_max',
type=int,
default=600,
help='Maximum number of clauses in each training instance.')
parser.add_argument('-i',
'--n_instances',
type=int,
default=100,
help='Number of instances for training.')
parser.add_argument('-t',
'--t_max',
type=int,
default=40,
help='Number of network iterations t_max')
parser.add_argument('-a',
'--attempts',
type=int,
default=64,
help='Number of attempts to boost results')
args = parser.parse_args()
# create RUN_CSP instance for given constraint language
network = RUN_CSP.load(args.model_dir)
language = network.language
print(f'Generating {args.n_instances} evaluation instances')
eval_instances = [
CSP_Instance.generate_random(args.n_variables,
np.random.randint(args.c_min, args.c_max),
language)
for _ in tqdm(range(args.n_instances))
]
# train and store the network
evaluate_boosted(network, eval_instances, args.t_max, args.attempts)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-m',
'--model_dir',
type=str,
help='Path to the trained RUN-CSP instance')
parser.add_argument(
'-t',
'--t_max',
type=int,
default=100,
help=
'Number of iterations t_max for which RUN-CSP runs on each instance')
parser.add_argument('-a',
'--attempts',
type=int,
default=64,
help='Attempts for each graph')
parser.add_argument(
'-d',
'--data_path',
default=None,
help=
'Path to the evaluation data. Expects a directory with graphs in dimacs format.'
)
parser.add_argument(
'-v',
'--n_variables',
type=int,
default=400,
help=
'Number of variables in each training instance. Only used when --data_path is not specified.'
)
parser.add_argument(
'-c',
'--n_clauses',
type=int,
default=1000,
help=
'Number of clauses in each training instance. Only used when --data_path is not specified.'
)
parser.add_argument(
'-i',
'--n_instances',
type=int,
default=100,
help=
'Number of instances for training. Only used when --data_path is not specified.'
)
args = parser.parse_args()
network = RUN_CSP.load(args.model_dir)
language = network.language
if args.data_path is not None:
print('loading graphs...')
names, graphs = data_utils.load_graphs(args.data_path)
instances = [
CSP_Instance.graph_to_csp_instance(g, language, 'NEQ', name=n)
for n, g in zip(names, graphs)
]
else:
print(f'Generating {args.n_instances} training instances')
instances = [
CSP_Instance.generate_random(args.n_variables, args.n_clauses,
language)
for _ in tqdm(range(args.n_instances))
]
conflicting_edges = evaluate_boosted(network,
instances,
args.t_max,
attempts=args.attempts)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'-l',
'--language_config_path',
type=str,
help='The path to a json file that specifies the constraint language')
parser.add_argument(
'-m',
'--model_dir',
type=str,
help=
'Path to the model directory where the trained RUN-CSP instance will be stored'
)
parser.add_argument('-v',
'--n_variables',
type=int,
default=100,
help='Number of variables in each training instance.')
parser.add_argument(
'--c_min',
type=int,
default=100,
help='Minimum number of clauses in each training instance.')
parser.add_argument(
'--c_max',
type=int,
default=600,
help='Maximum number of clauses in each training instance.')
parser.add_argument('-i',
'--n_instances',
type=int,
default=4000,
help='Number of instances for training.')
parser.add_argument(
'-t',
'--t_max',
type=int,
default=30,
help=
'Number of iterations t_max for which RUN-CSP runs on each instance')
parser.add_argument('-s',
'--state_size',
type=int,
default=128,
help='Size of the variable states in RUN-CSP')
parser.add_argument('-b',
'--batch_size',
type=int,
default=10,
help='Batch size used during training')
parser.add_argument('-e',
'--epochs',
type=int,
default=25,
help='Number of training epochs')
args = parser.parse_args()
print(f'Loading constraint language from {args.language_config_path}')
language = Constraint_Language.load(args.language_config_path)
# create RUN_CSP instance for given constraint language
network = RUN_CSP(args.model_dir, language, args.state_size)
print(f'Generating {args.n_instances} training instances')
train_instances = [
CSP_Instance.generate_random(args.n_variables,
np.random.randint(args.c_min, args.c_max),
language)
for _ in tqdm(range(args.n_instances))
]
# combine instances into batches
train_batches = CSP_Instance.batch_instances(train_instances,
args.batch_size)
# train and store the network
train(network, train_batches, args.t_max, args.epochs)