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@you-n-g
you-n-g committed Jun 26, 2024
1 parent 55655b5 commit 2e6f82f
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Showing 7 changed files with 87 additions and 73 deletions.
8 changes: 4 additions & 4 deletions qlib/contrib/meta/data_selection/dataset.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -388,11 +388,11 @@ def _prepare_seg(self, segment: Text) -> List[MetaTask]:
train_task_n = int(len(self.meta_task_l) * self.segments)
if segment == "train":
train_tasks = self.meta_task_l[:train_task_n]
get_module_logger("MetaDatasetDS").info(f"The first train meta task: {train_tasks[0]}")
get_module_logger("MetaDatasetDS").info(f"The first train meta task: {train_tasks[0]}")
return train_tasks
elif segment == "test":
test_tasks = self.meta_task_l[train_task_n:]
get_module_logger("MetaDatasetDS").info(f"The first test meta task: {test_tasks[0]}")
get_module_logger("MetaDatasetDS").info(f"The first test meta task: {test_tasks[0]}")
return test_tasks
else:
raise NotImplementedError(f"This type of input is not supported")
Expand All @@ -405,8 +405,8 @@ def _prepare_seg(self, segment: Text) -> List[MetaTask]:
train_tasks.append(t)
else:
test_tasks.append(t)
get_module_logger("MetaDatasetDS").info(f"The first train meta task: {train_tasks[0]}")
get_module_logger("MetaDatasetDS").info(f"The first test meta task: {test_tasks[0]}")
get_module_logger("MetaDatasetDS").info(f"The first train meta task: {train_tasks[0]}")
get_module_logger("MetaDatasetDS").info(f"The first test meta task: {test_tasks[0]}")
if segment == "train":
return train_tasks
elif segment == "test":
Expand Down
2 changes: 1 addition & 1 deletion qlib/contrib/meta/data_selection/utils.py
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Original file line number Diff line number Diff line change
Expand Up @@ -54,7 +54,7 @@ def forward(self, pred, y, idx):
)
ic_all += ic_day
if len(diff_point) - 1 - skip_n <= 0:
__import__('ipdb').set_trace()
__import__("ipdb").set_trace()
raise ValueError("No enough data for calculating IC")
if skip_n > 0:
get_module_logger("ICLoss").info(
Expand Down
122 changes: 64 additions & 58 deletions qlib/contrib/model/pytorch_gru.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -37,21 +37,23 @@ class GRU(Model):
the GPU ID(s) used for training
"""

def __init__(self,
d_feat=6,
hidden_size=64,
num_layers=2,
dropout=0.0,
n_epochs=200,
lr=0.001,
metric="",
batch_size=2000,
early_stop=20,
loss="mse",
optimizer="adam",
GPU=0,
seed=None,
**kwargs):
def __init__(
self,
d_feat=6,
hidden_size=64,
num_layers=2,
dropout=0.0,
n_epochs=200,
lr=0.001,
metric="",
batch_size=2000,
early_stop=20,
loss="mse",
optimizer="adam",
GPU=0,
seed=None,
**kwargs
):
# Set logger.
self.logger = get_module_logger("GRU")
self.logger.info("GRU pytorch version...")
Expand All @@ -71,36 +73,38 @@ def __init__(self,
self.device = torch.device("cuda:%d" % (GPU) if torch.cuda.is_available() and GPU >= 0 else "cpu")
self.seed = seed

self.logger.info("GRU parameters setting:"
"\nd_feat : {}"
"\nhidden_size : {}"
"\nnum_layers : {}"
"\ndropout : {}"
"\nn_epochs : {}"
"\nlr : {}"
"\nmetric : {}"
"\nbatch_size : {}"
"\nearly_stop : {}"
"\noptimizer : {}"
"\nloss_type : {}"
"\nvisible_GPU : {}"
"\nuse_GPU : {}"
"\nseed : {}".format(
d_feat,
hidden_size,
num_layers,
dropout,
n_epochs,
lr,
metric,
batch_size,
early_stop,
optimizer.lower(),
loss,
GPU,
self.use_gpu,
seed,
))
self.logger.info(
"GRU parameters setting:"
"\nd_feat : {}"
"\nhidden_size : {}"
"\nnum_layers : {}"
"\ndropout : {}"
"\nn_epochs : {}"
"\nlr : {}"
"\nmetric : {}"
"\nbatch_size : {}"
"\nearly_stop : {}"
"\noptimizer : {}"
"\nloss_type : {}"
"\nvisible_GPU : {}"
"\nuse_GPU : {}"
"\nseed : {}".format(
d_feat,
hidden_size,
num_layers,
dropout,
n_epochs,
lr,
metric,
batch_size,
early_stop,
optimizer.lower(),
loss,
GPU,
self.use_gpu,
seed,
)
)

if self.seed is not None:
np.random.seed(self.seed)
Expand Down Expand Up @@ -130,7 +134,7 @@ def use_gpu(self):
return self.device != torch.device("cpu")

def mse(self, pred, label):
loss = (pred - label)**2
loss = (pred - label) ** 2
return torch.mean(loss)

def loss_fn(self, pred, label):
Expand Down Expand Up @@ -158,12 +162,12 @@ def train_epoch(self, x_train, y_train):
indices = np.arange(len(x_train_values))
np.random.shuffle(indices)

for i in range(len(indices))[::self.batch_size]:
for i in range(len(indices))[:: self.batch_size]:
if len(indices) - i < self.batch_size:
break

feature = torch.from_numpy(x_train_values[indices[i:i + self.batch_size]]).float().to(self.device)
label = torch.from_numpy(y_train_values[indices[i:i + self.batch_size]]).float().to(self.device)
feature = torch.from_numpy(x_train_values[indices[i : i + self.batch_size]]).float().to(self.device)
label = torch.from_numpy(y_train_values[indices[i : i + self.batch_size]]).float().to(self.device)

pred = self.gru_model(feature)
loss = self.loss_fn(pred, label)
Expand All @@ -185,12 +189,12 @@ def test_epoch(self, data_x, data_y):

indices = np.arange(len(x_values))

for i in range(len(indices))[::self.batch_size]:
for i in range(len(indices))[:: self.batch_size]:
if len(indices) - i < self.batch_size:
break

feature = torch.from_numpy(x_values[indices[i:i + self.batch_size]]).float().to(self.device)
label = torch.from_numpy(y_values[indices[i:i + self.batch_size]]).float().to(self.device)
feature = torch.from_numpy(x_values[indices[i : i + self.batch_size]]).float().to(self.device)
label = torch.from_numpy(y_values[indices[i : i + self.batch_size]]).float().to(self.device)

with torch.no_grad():
pred = self.gru_model(feature)
Expand All @@ -203,18 +207,20 @@ def test_epoch(self, data_x, data_y):
return np.mean(losses), np.mean(scores)

def fit(
self,
dataset: DatasetH,
evals_result=dict(),
save_path=None,
self,
dataset: DatasetH,
evals_result=dict(),
save_path=None,
):
# prepare training and validation data
dfs = {
k: dataset.prepare(
k,
col_set=["feature", "label"],
data_key=DataHandlerLP.DK_L,
) for k in ["train", "valid"] if k in dataset.segments
)
for k in ["train", "valid"]
if k in dataset.segments
}
df_train, df_valid = dfs.get("train", pd.DataFrame()), dfs.get("valid", pd.DataFrame())

Expand Down Expand Up @@ -294,7 +300,7 @@ def predict(self, dataset: DatasetH, segment: Union[Text, slice] = "test"):
sample_num = x_values.shape[0]
preds = []

for begin in range(sample_num)[::self.batch_size]:
for begin in range(sample_num)[:: self.batch_size]:
if sample_num - begin < self.batch_size:
end = sample_num
else:
Expand Down
4 changes: 2 additions & 2 deletions qlib/contrib/report/data/ana.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -184,14 +184,14 @@ def plot_single(self, col, ax):
ax.set_xlabel("")
ax.set_ylabel("mean")
ax.legend()
ax.tick_params(axis='x', rotation=90)
ax.tick_params(axis="x", rotation=90)

right_ax = ax.twinx()

self._std[col].plot(ax=right_ax, label="std", color="green")
right_ax.set_xlabel("")
right_ax.set_ylabel("std")
right_ax.tick_params(axis='x', rotation=90)
right_ax.tick_params(axis="x", rotation=90)
right_ax.grid(None) # set the grid to None to avoid two layer of grid

h1, l1 = ax.get_legend_handles_labels()
Expand Down
6 changes: 3 additions & 3 deletions qlib/contrib/report/data/base.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -20,15 +20,15 @@ def __init__(self, dataset: pd.DataFrame):
----------
dataset : pd.DataFrame
We often have multiple columns for dataset. Each column corresponds to one sub figure.
We often have multiple columns for dataset. Each column corresponds to one sub figure.
There will be a datatime column in the index levels.
Aggretation will be used for more summarized metrics overtime.
Here is an example of data:
.. code-block::
.. code-block::
return
datetime instrument
datetime instrument
2007-02-06 equity_tpx 0.010087
equity_spx 0.000786
"""
Expand Down
12 changes: 9 additions & 3 deletions qlib/contrib/rolling/ddgda.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -83,7 +83,7 @@ def __init__(
loss_skip_thresh: int = 50,
fea_imp_n: Optional[int] = 30,
meta_data_proc: Optional[str] = "V01",
segments: Union[float, str]= 0.62,
segments: Union[float, str] = 0.62,
hist_step_n: int = 30,
working_dir: Optional[Union[str, Path]] = None,
**kwargs,
Expand Down Expand Up @@ -258,7 +258,7 @@ def _train_meta_model(self, fill_method="max"):
# 1) leverage the simplified proxy forecasting model to train meta model.
# - Only the dataset part is important, in current version of meta model will integrate the

# NOTE:
# NOTE:
# - The train_start for training meta model does not necessarily align with final rolling
# But please select a right time to make sure the finnal rolling tasks are not leaked in the training data.
# - The test_start is automatically aligned to the next day of test_end. Validation is ignored.
Expand Down Expand Up @@ -306,7 +306,13 @@ def _train_meta_model(self, fill_method="max"):
with R.start(experiment_name=self.meta_exp_name):
R.log_params(**kwargs)
mm = MetaModelDS(
step=self.step, hist_step_n=kwargs["hist_step_n"], lr=0.001, max_epoch=30, seed=43, alpha=self.alpha, loss_skip_thresh=self.loss_skip_thresh,
step=self.step,
hist_step_n=kwargs["hist_step_n"],
lr=0.001,
max_epoch=30,
seed=43,
alpha=self.alpha,
loss_skip_thresh=self.loss_skip_thresh,
)
mm.fit(md)
R.save_objects(model=mm)
Expand Down
6 changes: 4 additions & 2 deletions qlib/workflow/cli.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -117,8 +117,10 @@ def workflow(config_path, experiment_name="workflow", uri_folder="mlruns"):
if base_config_path.exists():
path = base_config_path
else:
logger.info(f"Can't find BASE_CONFIG_PATH base on: {Path.cwd()}, "
f"try using relative path to config path: {Path(config_path).absolute()}")
logger.info(
f"Can't find BASE_CONFIG_PATH base on: {Path.cwd()}, "
f"try using relative path to config path: {Path(config_path).absolute()}"
)
relative_path = Path(config_path).absolute().parent.joinpath(base_config_path)
if relative_path.exists():
path = relative_path
Expand Down

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