def main(cfg: DictConfig):
for i in range(100):
seed = i + 1000
seed_everything(seed)
sequence_length = cfg.params[0].sequence_length
hidden_layer = cfg.params[1].hidden_layer
hidden_feature = cfg.params[2].hidden_feature
activation = cfg.params[3].activation
KOSPI200Dataset.setup(train_length=train_length,
val_length=val_length,
test_length=test_length,
sequence_length=sequence_length
) # 2020.07.03 615일(개장일 기준) 이전이 2018.01.02
comet_logger = CometLogger(
api_key="SWhvV0XPkHV8tPdU8Nv67EXxU",
workspace="dldjwls3", # Optional
project_name=f"stock-candidate-{train_length}days",
experiment_name=
f'gcn_{sequence_length}_{activation}_{hidden_layer}_{hidden_feature}'
)
model = Baseline(seed=seed,
sequence_length=sequence_length,
num_feature=5,
activation=activation,
hidden_layer=hidden_layer,
hidden_feature=hidden_feature)
trainer = Trainer(max_epochs=120, gpus=-1, logger=comet_logger)
trainer.fit(model)
def main(cfg: DictConfig):
seed = 1
train_length = 500
val_length = 30
test_length = 585
sequence_length = 9
hidden_layer = 4
hidden_feature = 1
activation = 'sigmoid'
seed_everything(seed)
KOSPI200Dataset.setup(train_length=train_length,
val_length=val_length,
test_length=test_length,
sequence_length=sequence_length
) # 2020.07.03 615일(개장일 기준) 이전이 2018.01.02
comet_logger = CometLogger(
api_key="SWhvV0XPkHV8tPdU8Nv67EXxU",
workspace="dldjwls3", # Optional
project_name="stock-test",
experiment_name='test')
model = Baseline(seed=seed,
sequence_length=sequence_length,
num_feature=5,
activation=activation,
hidden_layer=hidden_layer,
hidden_feature=hidden_feature)
trainer = Trainer(max_epochs=120, gpus=-1, logger=comet_logger)
trainer.fit(model)
def main(cfg: DictConfig):
train_length = 500
val_length = 30
test_length = 585
opt = Optimizer(
config,
api_key='SWhvV0XPkHV8tPdU8Nv67EXxU',
project_name=f'stock-gcn-experiment-sequences-{train_length}days')
for experiment in opt.get_experiments():
seed = 1
seed_everything(seed)
hidden_layer = experiment.get_parameter('hidden_layer')
hidden_feature = experiment.get_parameter('hidden_feature')
activation = experiment.get_parameter('activation')
sequence_length = experiment.get_parameter('sequence_length')
KOSPI200Dataset.setup(train_length=train_length,
val_length=val_length,
test_length=test_length,
sequence_length=sequence_length
) # 2020.07.03 615일(개장일 기준) 이전이 2018.01.02
comet_logger = CometLogger(
api_key="SWhvV0XPkHV8tPdU8Nv67EXxU",
workspace="dldjwls3", # Optional
project_name=f'stock-gcn-experiment-sequences-{train_length}days',
experiment_name=
f'gcn_{sequence_length}_{activation}_{hidden_layer}_{hidden_feature}',
experiment_key=experiment.get_key())
model = Baseline(seed=seed,
sequence_length=sequence_length,
num_feature=5,
hidden_layer=hidden_layer,
hidden_feature=hidden_feature,
activation=activation)
trainer = Trainer(max_epochs=120, gpus=-1, logger=comet_logger)
trainer.fit(model)
def main(cfg: DictConfig):
for i in range(20):
train_length = 300
val_length = 30
test_length = 585 - 30 * i
seed = 1017
seed_everything(seed)
sequence_length = 10
hidden_layer = 5
hidden_feature = 12
activation = 'sigmoid'
KOSPI200Dataset.setup(train_length=train_length,
val_length=val_length,
test_length=test_length,
sequence_length=sequence_length
) # 2020.07.03 615일(개장일 기준) 이전이 2018.01.02
comet_logger = CometLogger(
api_key="SWhvV0XPkHV8tPdU8Nv67EXxU",
workspace="dldjwls3", # Optional
project_name=f"stock-rolling-simulation-{train_length}days",
experiment_name=
f'{seed}seed_gcn_{sequence_length}_{activation}_{hidden_layer}_{hidden_feature}'
)
model = Baseline(seed=seed,
sequence_length=sequence_length,
num_feature=5,
activation=activation,
hidden_layer=hidden_layer,
hidden_feature=hidden_feature)
trainer = Trainer(max_epochs=120, gpus=-1, logger=comet_logger)
trainer.fit(model)
trainer.test(model)
def val_dataloader(self):
val_dataset = KOSPI200Dataset(mode='val')
return DataLoader(val_dataset, batch_size=batch_size, shuffle=False)
def train_dataloader(self) -> DataLoader:
train_dataset = KOSPI200Dataset()
return DataLoader(train_dataset, num_workers=4, batch_size=batch_size, shuffle=True)
def prepare_data(self) -> None:
KOSPI200Dataset.load_data()
KOSPI200Dataset.process_data()
def test_dataloader(self):
test_dataset = KOSPI200Dataset(mode='val') # validation 에서의 일 수익률을 확인하고 싶어
return DataLoader(test_dataset, batch_size=batch_size, shuffle=False)