def main():
parser = argparse.ArgumentParser()
parser.add_argument('-s',
'--state_size',
type=int,
default=128,
help='Size of the variable states in RUN-CSP')
parser.add_argument('-k',
'--kappa',
type=float,
default=1.0,
help='The parameter kappa for the loss function')
parser.add_argument('-e',
'--epochs',
type=int,
default=25,
help='Number of training epochs')
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('-b',
'--batch_size',
type=int,
default=10,
help='Batch size for training')
parser.add_argument(
'-m',
'--model_dir',
type=str,
help='Model directory in which the trained model is stored')
parser.add_argument(
'-d',
'--data_path',
help='A path to a training set of graphs in the dimacs format.')
args = parser.parse_args()
print('loading graphs...')
names, graphs = data_utils.load_graphs(args.data_path)
random.shuffle(graphs)
print('Converting graphs to CSP Instances')
instances = [
CSP_Instance.graph_to_csp_instance(g, is_language, 'NAND')
for g in graphs
]
# combine instances into batches
train_batches = CSP_Instance.batch_instances(instances, args.batch_size)
# construct new network
network = Max_IS_Network(args.model_dir, state_size=args.state_size)
train(network, train_batches, t_max=args.t_max, epochs=args.epochs)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-e',
'--epochs',
type=int,
default=20,
help='Number of training epochs')
parser.add_argument(
'-t',
'--t_max',
type=int,
default=25,
help=
'Number of iterations t_max for which RUN-CSP runs on each instance')
parser.add_argument('-b',
'--batch_size',
type=int,
default=64,
help='Batch size for training')
parser.add_argument(
'-m',
'--model_dir',
type=str,
help='Model directory in which the trained model is stored')
parser.add_argument(
'-d',
'--data_path',
help='A path to a training set of graphs in the dimacs graph format.')
parser.add_argument('--n_colors',
type=int,
default=3,
help='Number of colors')
args = parser.parse_args()
language = Constraint_Language.get_coloring_language(args.n_colors)
print('loading graphs...')
names, graphs = data_utils.load_graphs(args.data_path)
random.shuffle(graphs)
print('Converting graphs to CSP Instances')
instances = [
CSP_Instance.graph_to_csp_instance(g, language, 'NEQ')
for g in tqdm(graphs)
]
# combine instances into batches
train_batches = CSP_Instance.batch_instances(instances, args.batch_size)
# construct and train new network
network = RUN_CSP(args.model_dir, language)
train(network, train_batches, epochs=args.epochs, t_max=args.t_max)
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.')
args = parser.parse_args()
network = Max_IS_Network.load(args.model_dir)
print('loading graphs...')
names, graphs = data_utils.load_graphs(args.data_path)
instances = [CSP_Instance.graph_to_csp_instance(g, is_language, 'NAND') for n, g in zip(names, graphs)]
evaluate_boosted(network, instances, args.t_max, attempts=args.attempts)
def main():
parser = argparse.ArgumentParser()
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')
parser.add_argument('-m', '--model_dir', type=str, help='The model directory of a trained network')
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('-d', '--data_path', help='A path to a training set of graphs in the dimacs graph format')
args = parser.parse_args()
language = Constraint_Language.get_coloring_language(2)
print('loading graphs...')
names, graphs = data_utils.load_graphs(args.data_path)
instances = [CSP_Instance.graph_to_csp_instance(g, language, 'NEQ') for g in graphs]
train_batches = CSP_Instance.batch_instances(instances, args.batch_size)
network = RUN_CSP(args.model_dir, language=language, state_size=args.state_size)
train(network, train_batches, t_max=args.t_max, epochs=args.epochs)
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)