class hasMorePokemon(unittest.TestCase):
""" Test cases of hasMorePokemon """
def setUp(self):
""" Build the Trainer and Pokemon lists for use in tests """
self.trainer = Trainer()
self.pkmnWithHealth = BuildPokemonBattleWrapper()
self.pkmnWithoutHealth = BuildPokemon(pkmn = "CHARMANDER")
self.pkmnWithoutHealth.battleDelegate.currHP = 0
def hasMorePokemon(self):
""" Test the trainer actually has more Pokemon """
self.trainer.beltPokemon = [self.pkmnWithHealth.pkmn, self.pkmnWithoutHealth]
assert self.trainer.hasMorePokemon([]) is True, "Should have more Pokemon"
def noMorePokemon(self):
""" Test the trainer has no more Pokemon """
self.trainer.beltPokemon = [self.pkmnWithoutHealth]
assert self.trainer.hasMorePokemon([]) is False, "Should not have more Pokemon"
def noMoreBattleReadyPokemon(self):
""" Test the trainer has no more Pokemon """
self.trainer.beltPokemon = [self.pkmnWithoutHealth, self.pkmnWithHealth.pkmn]
assert self.trainer.hasMorePokemon([self.pkmnWithHealth.pkmn]) is False, "Should not have more Pokemon when the only Battle Ready Pkmn are out"
def setUp(self):
""" Build the Trainer and Pokemon lists for use in tests """
self.trainer = Trainer()
self.user = BuildPokemonBattleWrapper(trainer = self.trainer)
self.attackAction = BuildAttackAction()
self.actionLock = BuildActionLock(attackAction = self.attackAction)
def setUp(self):
""" Build the Trainer and Pokemon lists for use in tests """
self.trainer = Trainer()
self.pkmnOnBelt = BuildPokemonBattleWrapper()
self.pkmnWithHealth = BuildPokemon(pkmn = "BULBASAUR")
self.pkmnWithoutHealth = BuildPokemon(pkmn = "CHARMANDER")
self.pkmnWithoutHealth.battleDelegate.currHP = 0
class getAction(unittest.TestCase):
""" Test cases of getAction """
def setUp(self):
""" Build the Trainer and Pokemon lists for use in tests """
self.trainer = Trainer()
self.user = BuildPokemonBattleWrapper(trainer = self.trainer)
self.attackAction = BuildAttackAction()
self.actionLock = BuildActionLock(attackAction = self.attackAction)
def hasLock(self):
""" Check that the action is the Pkmn's Lock """
self.user.actionLock = self.actionLock
action = self.trainer.getAction(self.user, [None], None, None, None)
assert action is self.attackAction, "Should be the lock's action"
def noLock(self):
""" Check the action is from Pick Action """
self.user.actionLock = None
action = self.trainer.getAction(self.user, [None], None, None, None)
picked = self.trainer.pickAction(self.user, [None], None, None, None)
assert action.user == picked.user, "Should have the same user"
assert action.target == picked.target, "Should have the same target"
assert action.attack == picked.attack, "Should have the same attack"
def main():
dataset, learning_rate, epoch, alpha, beta, gamma, pretrain_epoch = parse_argument(
)
random.seed(9001)
dataset_config = {
'feature_file': './Database/' + dataset + '/features.txt',
'graph_file': './Database/' + dataset + '/edges.txt',
'walks_file': './Database/' + dataset + '/walks.txt',
'label_file': './Database/' + dataset + '/group.txt'
}
graph = Dataset(dataset_config)
pretrain_config = {
'net_shape': [6, 1],
'att_shape': [1],
'net_input_dim': graph.num_nodes,
'att_input_dim': graph.num_feas,
'pretrain_params_path': './Log/' + dataset + '/pretrain_params.pkl'
}
model_config = {
'net_shape': [6, 1],
'att_shape': [1],
'net_input_dim': graph.num_nodes,
'att_input_dim': graph.num_feas,
'is_init': True,
'pretrain_params_path': './Log/' + dataset + '/pretrain_params.pkl'
}
trainer_config = {
'net_shape': [6, 1],
'att_shape': [1],
'net_input_dim': graph.num_nodes,
'att_input_dim': graph.num_feas,
'drop_prob': 0.2,
'learning_rate': learning_rate,
'batch_size': 100,
'num_epochs': epoch,
'beta': beta,
'alpha': alpha,
'gamma': gamma,
'model_path': './Log/' + dataset + '/test_model.pkl',
}
print("=" * 30 + "开始进行参数预训练" + "=" * 30)
pretrainer = PreTrainer(pretrain_config)
pretrainer.pretrain(graph.X, 'net', pretrain_epoch) # walks.txt的矩阵
pretrainer.pretrain(graph.Z, 'att', pretrain_epoch)
model = Model(model_config)
trainer = Trainer(model, trainer_config)
trainer.train(graph)
train_emb = trainer.infer(graph)
train_emb = np.array(train_emb)
with open("./Log/" + dataset + "/train_emb.txt", 'w') as f:
np.savetxt(f, train_emb)
def BuildPokemonBattleWrapper(pkmn="BULBASAUR", trainer=Trainer()):
""" Builds a Pokemon Battle Wrapper """
pokemon = BuildPokemon(pkmn=pkmn)
trainer.beltPokemon = [pokemon]
side = BattleSide(trainer)
wrapper = PkmnBattleWrapper(side)
wrapper.setPkmn(pokemon)
return wrapper
class getPokemon(unittest.TestCase):
""" Test cases of getPokemon """
def setUp(self):
""" Build the Trainer and Pokemon lists for use in tests """
self.trainer = Trainer()
self.pkmnWithHealth = BuildPokemon(pkmn = "BULBASAUR")
self.pkmnWithoutHealth = BuildPokemon(pkmn = "CHARMANDER")
self.pkmnWithoutHealth.battleDelegate.currHP = 0
def firstIsValid(self):
""" Test if it actually correctly aquires the first Pkmn """
self.trainer.beltPokemon = [self.pkmnWithHealth, self.pkmnWithoutHealth]
assert self.trainer.getPokemon(0) is self.pkmnWithHealth, "Should get the first Pkmn"
def firstIsFainted(self):
""" Test if accurately skips a Pokemon with no HP """
self.trainer.beltPokemon = [self.pkmnWithoutHealth, self.pkmnWithHealth]
assert self.trainer.getPokemon(0) is self.pkmnWithHealth, "Should skip Pkmn w/out health"
class chooseRandomPokemon(unittest.TestCase):
""" Test cases of chooseRandomPokemon """
def setUp(self):
""" Build the Trainer and Pokemon lists for use in tests """
self.trainer = Trainer()
self.pkmnOnBelt = BuildPokemonBattleWrapper()
self.pkmnWithHealth = BuildPokemon(pkmn = "BULBASAUR")
self.pkmnWithoutHealth = BuildPokemon(pkmn = "CHARMANDER")
self.pkmnWithoutHealth.battleDelegate.currHP = 0
def check(self):
""" Test it correctly chooses a Pkmn that is not fainted or already out """
self.trainer.beltPokemon = [self.pkmnWithHealth, self.pkmnWithoutHealth, self.pkmnOnBelt.pkmn]
assert self.trainer.chooseRandomPokemon([self.pkmnOnBelt]) is self.pkmnWithHealth, "Should get the first Pkmn"
ccsistent_loss,
'beta_W':
beta_W,
'View_num':
View_num,
'View':
layers,
'dims':
dims,
'drop_prob':
0.2,
'learning_rate':
learning_rate,
'batch_size':
2000,
'num_epochs':
1000,
'model_path':
'./Log/' + dataset_name + '/' + dataset_name + '_model.pkl',
}
model = MVModel(model_config)
trainer = Trainer(model, trainer_config)
trainer.train(graph)
acc, nmi = trainer.inferCluster(graph)
result_single = 'ccsistent_loss={:.4f}:'.format(
ccsistent_loss) + ' acc={:.4f}'.format(
acc) + ' & ' + 'nmi={:.4f}'.format(nmi)
f.write(result_single + '\n')
f.flush()
'att_shape': [200, 100],
'net_input_dim': graph.num_nodes,
'att_input_dim': graph.num_feas,
'is_init': True,
'pretrain_params_path': './Log/cora/pretrain_params.pkl'
}
trainer_config = {
'net_shape': [200, 100],
'att_shape': [200, 100],
'net_input_dim': graph.num_nodes,
'att_input_dim': graph.num_feas,
'drop_prob': 0.2,
'learning_rate': 1e-5,
'batch_size': 100,
'num_epochs': 500,
'beta': 100,
'alpha': 50,
'gamma': 500,
'model_path': './Log/cora/cora_model.pkl',
}
pretrainer = PreTrainer(pretrain_config)
pretrainer.pretrain(graph.X, 'net')
pretrainer.pretrain(graph.Z, 'att')
model = Model(model_config)
trainer = Trainer(model, trainer_config)
trainer.train(graph)
trainer.infer(graph)
def BuildTrainer(pkmn="BULBASAUR", count=1):
"""Builds a trainer with count pokmen of type pkmn """
trainer = Trainer()
pokemon = BuildPokemon(pkmn=pkmn)
trainer.beltPokemon = [pokemon]
return trainer
num_workers=0)
val_dataset = SingleImgDataset(args.val_path,
scale_aug=False,
rot_aug=False,
test_mode=True)
val_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=64,
shuffle=False,
num_workers=0)
print('num_train_files: ' + str(len(train_dataset.filepaths)))
print('num_val_files: ' + str(len(val_dataset.filepaths)))
trainer = Trainer(cnet,
train_loader,
val_loader,
optimizer,
nn.CrossEntropyLoss(),
'svcnn',
log_dir,
num_views=1)
trainer.train(30)
# STAGE 2
log_dir = args.name + '_stage_2'
create_folder(log_dir)
cnet_2 = MVCNN(args.name,
cnet,
nclasses=40,
cnn_name=args.cnn_name,
num_views=args.num_views)
del cnet
def test_trainer_init(self):
x = Trainer()
assert not x.dataloader is None
def BuildTrainer(pkmn="BULBASAUR", count=1):
"""Builds a trainer with count pokmen of type pkmn """
trainer = Trainer()
pokemon = BuildPokemon(pkmn = pkmn)
trainer.beltPokemon = [pokemon]
return trainer
