def __init__(self, graph, model, lr, bs, n_step, discount_factor, lr_decay,
folder_path):
self.graphs = graph
self.embed_dim = 64
self.model_name = model
self.k = 20
self.alpha = 0.1
self.gamma = 1 #0.99
self.lambd = 0.
self.n_step = n_step
self.epsilon_ = 1
self.epsilon_min = 0.05
self.discount_factor = discount_factor
#self.eps_end=0.02
self.epsilon_start = 1
self.epsilon_step = 60000
self.t = 1
self.memory = []
self.memory_n = []
self.minibatch_length = bs
if self.model_name == 'S2V_QN_1':
args_init = load_model_config()[self.model_name]
self.model = models.S2V_QN_1(**args_init)
elif self.model_name == 'S2V_QN_2':
args_init = load_model_config()[self.model_name]
self.model = models.S2V_QN_2(**args_init)
elif self.model_name == 'GCN_QN_1':
args_init = load_model_config()[self.model_name]
self.model = models.GCN_QN_1(**args_init)
elif self.model_name == 'LINE_QN':
args_init = load_model_config()[self.model_name]
self.model = models.LINE_QN(**args_init)
elif self.model_name == 'W2V_QN':
args_init = load_model_config()[self.model_name]
self.model = models.W2V_QN(G=self.graphs[self.games], **args_init)
self.criterion = torch.nn.MSELoss(reduction='sum')
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)
self.scheduler = torch.optim.lr_scheduler.LambdaLR(
self.optimizer, lr_lambda=lambda epoch: lr_decay**epoch)
self.T = 5
self.t = 1
current_path = pathlib.Path().absolute()
self.abs_folder_path = current_path / (folder_path + '/')
def __init__(self, graph, model, lr, bs, n_step):
self.graphs = graph
self.embed_dim = 64
self.model_name = model
self.k = 20
self.alpha = 0.1
self.gamma = 0.99
self.lambd = 0.
self.n_step = n_step
self.epsilon_ = 1
self.epsilon_min = 0.02
self.discount_factor = 0.999990
#self.eps_end=0.02
#self.eps_start=1
#self.eps_step=20000
self.t = 1
self.memory = []
self.memory_n = []
self.minibatch_length = bs
if self.model_name == 'S2V_QN_1':
args_init = load_model_config()[self.model_name]
self.model = models.S2V_QN_1(**args_init)
elif self.model_name == 'S2V_QN_2':
args_init = load_model_config()[self.model_name]
self.model = models.S2V_QN_2(**args_init)
elif self.model_name == 'GCN_QN_1':
args_init = load_model_config()[self.model_name]
self.model = models.GCN_QN_1(**args_init)
elif self.model_name == 'LINE_QN':
args_init = load_model_config()[self.model_name]
self.model = models.LINE_QN(**args_init)
elif self.model_name == 'W2V_QN':
args_init = load_model_config()[self.model_name]
self.model = models.W2V_QN(G=self.graphs[self.games], **args_init)
self.criterion = torch.nn.MSELoss(reduction='sum')
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)
self.T = 5
self.t = 1
def __init__(self, environment, model, lr, bs, n_step):
self.train_graphs = environment.train_graphs
self.test_graphs = environment.test_graphs
self.embed_dim = 64
self.model_name = model
self.alpha = 0.1
self.gamma = 0.99
self.lambd = 0.
self.n_step = n_step
self.decay = True # whether or not to decay this round
self.epsilon_ = 1 # starting epsilon value
self.epsilon_min = 0.01 # minimum epsilon value
self.decay_factor = 1 # overwritten by self.calc_convergence to converge in one epoch
self.memory = deque(maxlen=1) # same
self.memory_n = deque(maxlen=1) # same
self.t = 1
self.minibatch_length = bs
self.games = 0
# decide model
if self.model_name == 'S2V_QN_1':
args_init = load_model_config()[self.model_name]
self.model = models.S2V_QN_1(**args_init)
elif self.model_name == 'S2V_QN_2':
args_init = load_model_config()[self.model_name]
self.model = models.S2V_QN_2(**args_init)
elif self.model_name == 'GCN_QN_1':
args_init = load_model_config()[self.model_name]
self.model = models.GCN_QN_1(**args_init)
elif self.model_name == 'LINE_QN':
args_init = load_model_config()[self.model_name]
self.model = models.LINE_QN(**args_init)
elif self.model_name == 'W2V_QN':
args_init = load_model_config()[self.model_name]
self.model = models.W2V_QN(G=self.train_graphs[self.games],
**args_init)
self.criterion = torch.nn.MSELoss(reduction='sum')
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)
self.reset(self.games)
def __init__(self,model,lr,bs,n_step,env_name,node_max):
self.graphs = None
self.embed_dim = 64
self.model_name = model
self.node_max = node_max
self.alpha = 0.1
self.gamma = 1 #0.99
self.lambd = 0.
self.n_step=n_step
self.epsilon_=1
self.epsilon_min=0.05
self.discount_factor =0.999990
self.memory = []
self.memory_n=[]
self.minibatch_length = bs
self.env=envir.Environment(env_name)
self.device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
if self.model_name == 'S2V_QN_1':
args_init = load_model_config()[self.model_name]
self.model = models.S2V_QN_1(**args_init)
#self.old_model = models.S2V_QN_1(**args_init)
elif self.model_name == 'S2V_QN_2':
args_init = load_model_config()[self.model_name]
self.model = models.S2V_QN_2(**args_init)
elif self.model_name== 'GCN_QN_1':
args_init = load_model_config()[self.model_name]
self.model = models.GCN_QN_1(**args_init)
elif self.model_name == 'LINE_QN':
args_init = load_model_config()[self.model_name]
self.model = models.LINE_QN(**args_init)
elif self.model_name == 'W2V_QN':
args_init = load_model_config()[self.model_name]
self.model = models.W2V_QN(G=self.graphs[self.games], **args_init)
self.T = 5
if torch.cuda.device_count() >= 1:
print("Using", torch.cuda.device_count(), "GPUs!")
self.model.to(self.device)
#self.old_model.to(self.device)
torch.backends.cudnn.benchmark = True
else: print("Using CPU")
self.criterion = torch.nn.MSELoss(reduction='sum').to(self.device)
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=lr)
if torch.cuda.device_count() > 1:
self.model = torch.nn.DataParallel(self.model)
def __init__(self,graph,args):
self.ckpt = args.ckpt
if not args.test_only and not os.path.exists(f'{self.ckpt}/model_ckpts'):
os.mkdir(f'{self.ckpt}/model_ckpts')
self.train = graph
self.test = pkl.load(open(args.test_file, 'rb'))
self.graphs = graph
self.embed_dim = 64
self.model_name = args.model
self.k = 20
self.alpha = 0.1
self.gamma = 0.99
self.lambd = 0.
self.n_step=3
self.epsilon_=1.0
self.epsilon_min=0.02
self.discount_factor =0.999990
self.eps_end=0.02
self.eps_start=1
self.eps_step=20000
self.t=1
self.memory = []
self.memory_n=[]
if self.model_name == 'S2V_QN_1':
args_init = load_model_config()[self.model_name]
self.model = models.S2V_QN_1(**args_init)
elif self.model_name == 'S2V_QN_2':
args_init = load_model_config()[self.model_name]
self.model = models.S2V_QN_2(**args_init)
elif self.model_name== 'GCN_QN_1':
args_init = load_model_config()[self.model_name]
self.model = models.GCN_QN_1(**args_init)
elif self.model_name == 'LINE_QN':
args_init = load_model_config()[self.model_name]
self.model = models.LINE_QN(**args_init)
elif self.model_name == 'W2V_QN':
args_init = load_model_config()[self.model_name]
self.model = models.W2V_QN(G=self.graphs[self.games], **args_init)
self.criterion = torch.nn.MSELoss(reduction='sum')
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=1.e-3)
#self.optimizer_target = torch.optim.Adam(self.model_target.parameters(), lr=1.e-4)
self.T = 5
self.t = 1
if args.load_model != False:
self.model.load_state_dict(torch.load(args.load_model))
if USE_GPU:
self.criterion = self.criterion.cuda()
self.model = self.model.cuda()