Datasets:
jblitzar
/
github-python-corpus

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'node_limit': -1,
'time_limit': 3600.,
}
# state dimensions
# var_dim is the dimension of each candidate variable's input, i.e., the fixed dimension of matrix C_t
# Tree_t is given by concatenation of two states, for a total dimension node_dim + mip_dim
state_dims = {
'var_dim': 25,
'node_dim': 8,
'mip_dim': 53
}
if __name__ == '__main__':
# parser definition
parser = argparse.ArgumentParser(description='Parser for IL evaluation experiments.')
parser.add_argument(
'-c',
'--checkpoint',
type=str,
help='Pathway to torch checkpoint to be loaded.'
)
parser.add_argument(
'--cutoff_dict',
type=str,
help='Pathway to pickled dictionary containing cutoff values.'
)
parser.add_argument(
'--instances_dir',
type=str,
help='Pathway to the MILP instances.'
)
parser.add_argument(
'--out_dir',
type=str,
help='Pathway to save all the SCIP eval pickle files.'
)
parser.add_argument(
'-n',
'--name',
type=str,
help='Name of the MILP instance.mps.gz (containing extension) to be processed.'
)
parser.add_argument(
'-s',
'--seed',
type=int,
default=0,
help='Random seed for SCIP solver.'
)
parser.add_argument(
'--setting',
type=str,
default='sandbox',
help='Solver parameters setting.'
)
parser.add_argument(
'-v',
'--verbose',
action='store_true',
default=False,
help='Flag on verbosity.'
)
args = parser.parse_args()
# load a checkpoint path (cpu load of a gpu checkpoint)
chkpnt = torch.load(args.checkpoint, map_location='cpu')
print('Checkpoint loaded from path {}...'.format(args.checkpoint))
# read config from checkpoint: the policy parameters are inferred from the checkpoint args
checkpoint_args = chkpnt['args']
# set all random seeds
scip_seed = args.seed
instance_file_path = os.path.join(args.instances_dir, args.name) # name contains extension mps.gz
name = args.name.split('.')[0]
# set device (cpu for eval)
device = torch.device('cpu')
# get cutoff
cutoff_dict = pickle.load(open(args.cutoff_dict, 'rb'))
assert name in cutoff_dict
# setup the environment and a policy within it
env = ILEvalEnv(device=device)
if checkpoint_args.policy_type == 'TreeGatePolicy':
policy = TreeGatePolicy(
var_dim=state_dims['var_dim'],
node_dim=state_dims['node_dim'],
mip_dim=state_dims['mip_dim'],
hidden_size=checkpoint_args.hidden_size,
depth=checkpoint_args.depth,
dropout=checkpoint_args.dropout,
dim_reduce_factor=checkpoint_args.dim_reduce_factor,
infimum=checkpoint_args.infimum,