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Merge branch 'main' of https://github.com/you-n-g/qlib into main

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meng-ustc
2020-11-26 15:16:02 +08:00
parent e04bebde62 96e8ce920a
commit 50f6ca7df3
14 changed files with 346 additions and 276 deletions
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84
qlib/contrib/data/handler.py
View File

@@ -10,6 +10,28 @@ from inspect import getfullargspec
import copy
def check_transform_proc(proc_l, fit_start_time, fit_end_time):
new_l = []
for p in proc_l:
if not isinstance(p, Processor):
klass, pkwargs = get_cls_kwargs(p, processor_module)
args = getfullargspec(klass).args
if "fit_start_time" in args and "fit_end_time" in args:
assert (
fit_start_time is not None and fit_end_time is not None
), "Make sure `fit_start_time` and `fit_end_time` are not None."
pkwargs.update(
{
"fit_start_time": fit_start_time,
"fit_end_time": fit_end_time,
}
)
new_l.append({"class": klass.__name__, "kwargs": pkwargs})
else:
new_l.append(p)
return new_l
class ALPHA360_Denoise(DataHandlerLP):
def __init__(self, instruments="csi500", start_time=None, end_time=None, fit_start_time=None, fit_end_time=None):
data_loader = {
@@ -83,8 +105,31 @@ class ALPHA360_Denoise(DataHandlerLP):
return fields, names
_DEFAULT_LEARN_PROCESSORS = [
{"class": "DropnaLabel"},
{"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}},
]
_DEFAULT_INFER_PROCESSORS = [
{"class": "ProcessInf", "kwargs": {}},
{"class": "ZScoreNorm", "kwargs": {}},
{"class": "Fillna", "kwargs": {}},
]
class ALPHA360(DataHandlerLP):
def __init__(self, instruments="csi500", start_time=None, end_time=None, fit_start_time=None, fit_end_time=None):
def __init__(
self,
instruments="csi500",
start_time=None,
end_time=None,
infer_processors=_DEFAULT_INFER_PROCESSORS,
learn_processors=_DEFAULT_LEARN_PROCESSORS,
fit_start_time=None,
fit_end_time=None,
):
infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
learn_processors = check_transform_proc(learn_processors, fit_start_time, fit_end_time)
data_loader = {
"class": "QlibDataLoader",
"kwargs": {
@@ -95,16 +140,6 @@ class ALPHA360(DataHandlerLP):
},
}
learn_processors = [
{"class": "DropnaLabel", "kwargs": {"fields_group": "label"}},
{"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}},
]
infer_processors = [
{"class": "ProcessInf", "kwargs": {}},
{"class": "ZscoreNorm", "kwargs": {"fit_start_time": fit_start_time, "fit_end_time": fit_end_time}},
{"class": "Fillna", "kwargs": {}},
]
super().__init__(
instruments,
start_time,
@@ -168,33 +203,12 @@ class Alpha158(DataHandlerLP):
start_time=None,
end_time=None,
infer_processors=[],
learn_processors=["DropnaLabel", {"class": "CSZScoreNorm", "kwargs": {"fields_group": "label"}}],
learn_processors=_DEFAULT_LEARN_PROCESSORS,
fit_start_time=None,
fit_end_time=None,
):
def check_transform_proc(proc_l):
new_l = []
for p in proc_l:
if not isinstance(p, Processor):
klass, pkwargs = get_cls_kwargs(p, processor_module)
args = getfullargspec(klass).args
if "fit_start_time" in args and "fit_end_time" in args:
assert (
fit_start_time is not None and fit_end_time is not None
), "Make sure `fit_start_time` and `fit_end_time` are not None."
pkwargs.update(
{
"fit_start_time": fit_start_time,
"fit_end_time": fit_end_time,
}
)
new_l.append({"class": klass.__name__, "kwargs": pkwargs})
else:
new_l.append(p)
return new_l
infer_processors = check_transform_proc(infer_processors)
learn_processors = check_transform_proc(learn_processors)
infer_processors = check_transform_proc(infer_processors, fit_start_time, fit_end_time)
learn_processors = check_transform_proc(learn_processors, fit_start_time, fit_end_time)
data_loader = {
"class": "QlibDataLoader",

20
qlib/contrib/model/catboost_model.py
View File

@@ -34,14 +34,14 @@ class CatBoostModel(Model):
def fit(
self,
dataset: DatasetH,
num_boost_round=1000,
early_stopping_rounds=50,
verbose_eval=20,
evals_result=dict(),
num_boost_round = 1000,
early_stopping_rounds = 50,
verbose_eval = 20,
evals_result = dict(),
**kwargs
):
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
["train", "valid"], col_set = ["feature", "label"], data_key = DataHandlerLP.DK_L
)
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_valid["feature"], df_valid["label"]
@@ -52,8 +52,8 @@ class CatBoostModel(Model):
else:
raise ValueError("CatBoost doesn't support multi-label training")
train_pool = Pool(data=x_train, label=y_train_1d)
valid_pool = Pool(data=x_valid, label=y_valid_1d)
train_pool = Pool(data = x_train, label = y_train_1d)
valid_pool = Pool(data = x_valid, label = y_valid_1d)
# Initialize the catboost model
self._params["iterations"] = num_boost_round
@@ -63,7 +63,7 @@ class CatBoostModel(Model):
self.model = CatBoost(self._params, **kwargs)
# train the model
self.model.fit(train_pool, eval_set=valid_pool, use_best_model=True, **kwargs)
self.model.fit(train_pool, eval_set = valid_pool, use_best_model = True, **kwargs)
evals_result = self.model.get_evals_result()
evals_result["train"] = list(evals_result["learn"].values())[0]
@@ -72,8 +72,8 @@ class CatBoostModel(Model):
def predict(self, dataset):
if self.model is None:
raise ValueError("model is not fitted yet!")
x_test = dataset.prepare("test", col_set="feature")
return pd.Series(self.model.predict(x_test.values), index=x_test.index)
x_test = dataset.prepare("test", col_set = "feature")
return pd.Series(self.model.predict(x_test.values), index = x_test.index)
if __name__ == "__main__":

23
qlib/contrib/model/pytorch_gats.py
View File

@@ -28,14 +28,12 @@ class GAT(Model):
Parameters
----------
input_dim : int
input dimension
output_dim : int
output dimension
layers : tuple
layer sizes
lr : float
learning rate
d_feat : int
input dimensions for each time step
metric : str
the evaluate metric used in early stop
optimizer : str
optimizer name
GPU : str
@@ -119,11 +117,7 @@ class GAT(Model):
seed,
)
)
if loss not in {"mse", "binary"}:
raise NotImplementedError("loss {} is not supported!".format(loss))
self._scorer = mean_squared_error if loss == "mse" else roc_auc_score
self.GAT_model = GATModel(
d_feat=self.d_feat,
hidden_size=self.hidden_size,
@@ -213,7 +207,6 @@ class GAT(Model):
losses = []
indices = np.arange(len(x_values))
np.random.shuffle(indices)
for i in range(len(indices))[:: self.batch_size]:
@@ -377,7 +370,6 @@ class GATModel(nn.Module):
self.fc_out = nn.Linear(hidden_size, 1)
self.leaky_relu = nn.LeakyReLU()
self.softmax = nn.Softmax(dim=1)
self.d_feat = d_feat
def cal_convariance(self, x, y): # the 2nd dimension of x and y are the same
@@ -396,12 +388,7 @@ class GATModel(nn.Module):
out, _ = self.rnn(x)
hidden = out[:, -1, :]
hidden = self.bn1(hidden)
gamma = self.cal_convariance(hidden, hidden)
# gamma = hidden.mm(torch.t(hidden))
# gamma = self.leaky_relu(gamma)
# gamma = self.softmax(gamma)
# gamma = gamma * (torch.ones(x.shape[0], x.shape[0]).to(device) - torch.diag(torch.ones(x.shape[0])).to(device))
output = gamma.mm(hidden)
output = self.fc(output)
output = self.bn2(output)

17
qlib/contrib/model/pytorch_gru.py
View File

@@ -28,14 +28,10 @@ class GRU(Model):
Parameters
----------
input_dim : int
input dimension
output_dim : int
output dimension
layers : tuple
layer sizes
lr : float
learning rate
d_feat : int
input dimension for each time step
metric: str
the evaluate metric used in early stop
optimizer : str
optimizer name
GPU : str
@@ -112,10 +108,6 @@ class GRU(Model):
)
)
if loss not in {"mse", "binary"}:
raise NotImplementedError("loss {} is not supported!".format(loss))
self._scorer = mean_squared_error if loss == "mse" else roc_auc_score
self.gru_model = GRUModel(
d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout
)
@@ -251,7 +243,6 @@ class GRU(Model):
# train
self.logger.info("training...")
self._fitted = True
# return
for step in range(self.n_epochs):
self.logger.info("Epoch%d:", step)

17
qlib/contrib/model/pytorch_lstm.py
View File

@@ -28,14 +28,10 @@ class LSTM(Model):
Parameters
----------
input_dim : int
input dimension
output_dim : int
output dimension
layers : tuple
layer sizes
lr : float
learning rate
d_feat : int
input dimension for each time step
metric: str
the evaluate metric used in early stop
optimizer : str
optimizer name
GPU : str
@@ -112,10 +108,6 @@ class LSTM(Model):
)
)
if loss not in {"mse", "binary"}:
raise NotImplementedError("loss {} is not supported!".format(loss))
self._scorer = mean_squared_error if loss == "mse" else roc_auc_score
self.lstm_model = LSTMModel(
d_feat=self.d_feat, hidden_size=self.hidden_size, num_layers=self.num_layers, dropout=self.dropout
)
@@ -251,7 +243,6 @@ class LSTM(Model):
# train
self.logger.info("training...")
self._fitted = True
# return
for step in range(self.n_epochs):
self.logger.info("Epoch%d:", step)

298
qlib/contrib/model/pytorch_sfm.py
View File

@@ -31,7 +31,6 @@ from ...model.base import Model
from ...data.dataset import DatasetH
from ...data.dataset.handler import DataHandlerLP
class SFM_Model(nn.Module):
def __init__(self, d_feat=6, output_dim=1, freq_dim=10, hidden_size=64, dropout_W=0.0, dropout_U=0.0, device="cpu"):
super().__init__()
@@ -76,13 +75,13 @@ class SFM_Model(nn.Module):
self.states = []
def forward(self, input):
input = input.reshape(len(input), self.input_dim, -1) # [N, F, T]
input = input.permute(0, 2, 1) # [N, T, F]
input = input.reshape(len(input), self.input_dim, -1) # [N, F, T]
input = input.permute(0, 2, 1) # [N, T, F]
time_step = input.shape[1]
for ts in range(time_step):
x = input[:, ts, :]
if len(self.states) == 0: # hasn't initialized yet
x = input[:, ts,:]
if len(self.states)==0: #hasn't initialized yet
self.init_states(x)
self.get_constants(x)
p_tm1 = self.states[0]
@@ -99,65 +98,64 @@ class SFM_Model(nn.Module):
x_fre = torch.matmul(x * B_W[0], self.W_fre) + self.b_fre
x_c = torch.matmul(x * B_W[0], self.W_c) + self.b_c
x_o = torch.matmul(x * B_W[0], self.W_o) + self.b_o
i = self.inner_activation(
x_i + torch.matmul(h_tm1 * B_U[0], self.U_i)
) # not sure whether I am doing in the right unsquuze
i = self.inner_activation(x_i + torch.matmul(h_tm1 * B_U[0], self.U_i)) # not sure whether I am doing in the right unsquuze
ste = self.inner_activation(x_ste + torch.matmul(h_tm1 * B_U[0], self.U_ste))
fre = self.inner_activation(x_fre + torch.matmul(h_tm1 * B_U[0], self.U_fre))
ste = torch.reshape(ste, (-1, self.hidden_dim, 1))
fre = torch.reshape(fre, (-1, 1, self.freq_dim))
f = ste * fre
c = i * self.activation(x_c + torch.matmul(h_tm1 * B_U[0], self.U_c))
time = time_tm1 + 1
omega = torch.tensor(2 * np.pi) * time * frequency
re = torch.cos(omega)
re = torch.cos(omega)
im = torch.sin(omega)
c = torch.reshape(c, (-1, self.hidden_dim, 1))
S_re = f * S_re_tm1 + c * re
S_im = f * S_im_tm1 + c * im
A = torch.square(S_re) + torch.square(S_im)
A = torch.reshape(A, (-1, self.freq_dim)).float()
A_a = torch.matmul(A * B_U[0], self.U_a)
A_a = torch.reshape(A_a, (-1, self.hidden_dim))
a = self.activation(A_a + self.b_a)
o = self.inner_activation(x_o + torch.matmul(h_tm1 * B_U[0], self.U_o))
h = o * a
p = torch.matmul(h, self.W_p) + self.b_p
self.states = [p, h, S_re, S_im, time, None, None, None]
self.states = []
self.states = []
return self.fc_out(p).squeeze()
def init_states(self, x):
reducer_f = torch.zeros((self.hidden_dim, self.freq_dim)).to(self.device)
reducer_p = torch.zeros((self.hidden_dim, self.output_dim)).to(self.device)
init_state_h = torch.zeros(self.hidden_dim).to(self.device)
init_state_p = torch.matmul(init_state_h, reducer_p)
init_state = torch.zeros_like(init_state_h).to(self.device)
init_freq = torch.matmul(init_state_h, reducer_f)
init_state = torch.reshape(init_state, (-1, self.hidden_dim, 1))
init_freq = torch.reshape(init_freq, (-1, 1, self.freq_dim))
init_state_S_re = init_state * init_freq
init_state_S_im = init_state * init_freq
init_state_time = torch.tensor(0).to(self.device)
self.states = [init_state_p, init_state_h, init_state_S_re, init_state_S_im, init_state_time, None, None, None]
@@ -203,6 +201,7 @@ class SFM(Model):
dropout_U=0.0,
n_epochs=200,
lr=0.001,
metric = "",
batch_size=2000,
early_stop=20,
eval_steps=5,
@@ -227,14 +226,15 @@ class SFM(Model):
self.dropout_U = dropout_U
self.n_epochs = n_epochs
self.lr = lr
self.metric = metric
self.batch_size = batch_size
self.early_stop = early_stop
self.eval_steps = eval_steps
self.lr_decay = lr_decay
self.lr_decay_steps = lr_decay_steps
self.optimizer = optimizer.lower()
self.loss_type = loss
self.device = "cuda:%d" % (GPU) if torch.cuda.is_available() else "cpu"
self.loss = loss
self.device = "cuda:%d"%(GPU) if torch.cuda.is_available() else "cpu"
self.use_gpu = torch.cuda.is_available()
self.seed = seed
@@ -243,11 +243,12 @@ class SFM(Model):
"\nd_feat : {}"
"\nhidden_size : {}"
"\noutput_size : {}"
"\nfrequency_dimension : {}"
"\nfrequency_dimension : {}"
"\ndropout_W: {}"
"\ndropout_U: {}"
"\nn_epochs : {}"
"\nlr : {}"
"\nmetric : {}"
"\nbatch_size : {}"
"\nearly_stop : {}"
"\neval_steps : {}"
@@ -266,6 +267,7 @@ class SFM(Model):
dropout_U,
n_epochs,
lr,
metric,
batch_size,
early_stop,
eval_steps,
@@ -284,14 +286,14 @@ class SFM(Model):
self._scorer = mean_squared_error if loss == "mse" else roc_auc_score
self.sfm_model = SFM_Model(
d_feat=self.d_feat,
d_feat=self.d_feat,
output_dim=self.output_dim,
hidden_size=self.hidden_size,
freq_dim=self.freq_dim,
dropout_W=self.dropout_W,
dropout_U=self.dropout_U,
device=self.device,
)
hidden_size=self.hidden_size,
freq_dim=self.freq_dim,
dropout_W=self.dropout_W,
dropout_U=self.dropout_U,
device=self.device
)
if optimizer.lower() == "adam":
self.train_optimizer = optim.Adam(self.sfm_model.parameters(), lr=self.lr)
elif optimizer.lower() == "gd":
@@ -299,24 +301,73 @@ class SFM(Model):
else:
raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
# Reduce learning rate when loss has stopped decrease
self.scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
self.train_optimizer,
mode="min",
factor=0.5,
patience=10,
verbose=True,
threshold=0.0001,
threshold_mode="rel",
cooldown=0,
min_lr=0.00001,
eps=1e-08,
)
self._fitted = False
self.sfm_model.to(self.device)
def fit(self, dataset: DatasetH, evals_result=dict(), verbose=True, save_path=None, **kwargs):
def test_epoch(self, data_x, data_y):
# prepare training data
x_values = data_x.values
y_values = np.squeeze(data_y.values)
self.sfm_model.eval()
scores = []
losses = []
indices = np.arange(len(x_values))
np.random.shuffle(indices)
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)
pred = self.sfm_model(feature)
loss = self.loss_fn(pred, label)
losses.append(loss.item())
score = self.metric_fn(pred, label)
scores.append(score.item())
return np.mean(losses), np.mean(scores)
def train_epoch(self, x_train, y_train):
x_train_values = x_train.values
y_train_values = np.squeeze(y_train.values) * 100
self.sfm_model.train()
indices = np.arange(len(x_train_values))
np.random.shuffle(indices)
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)
pred = self.sfm_model(feature)
loss = self.loss_fn(pred, label)
self.train_optimizer.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_value_(self.sfm_model.parameters(), 3.0)
self.train_optimizer.step()
def fit(
self,
dataset: DatasetH,
evals_result=dict(),
verbose=True,
save_path=None,
):
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
@@ -324,10 +375,10 @@ class SFM(Model):
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_valid["feature"], df_valid["label"]
save_path = create_save_path(save_path)
stop_steps = 0
train_loss = 0
best_loss = np.inf
best_score = -np.inf
best_epoch = 0
evals_result["train"] = []
evals_result["valid"] = []
@@ -335,90 +386,56 @@ class SFM(Model):
self.logger.info("training...")
self._fitted = True
# prepare training data
x_train_values = torch.from_numpy(x_train.values).float()
y_train_values = torch.from_numpy(np.squeeze(y_train.values)).float()
train_num = y_train_values.shape[0]
# prepare validation data
x_val_auto = torch.from_numpy(x_valid.values).float()
y_val_auto = torch.from_numpy(np.squeeze(y_valid.values)).float()
x_val_auto = x_val_auto.to(self.device)
y_val_auto = y_val_auto.to(self.device)
for step in range(self.n_epochs):
if stop_steps >= self.early_stop:
if verbose:
self.logger.info("\tearly stop")
break
loss = AverageMeter()
self.sfm_model.train()
self.train_optimizer.zero_grad()
self.logger.info("Epoch%d:", step)
self.logger.info("training...")
self.train_epoch(x_train, y_train)
self.logger.info("evaluating...")
train_loss, train_score = self.test_epoch(x_train, y_train)
val_loss, val_score = self.test_epoch(x_valid, y_valid)
self.logger.info("train %.6f, valid %.6f" % (train_score, val_score))
evals_result["train"].append(train_score)
evals_result["valid"].append(val_score)
choice = np.random.choice(train_num, self.batch_size)
x_batch_auto = x_train_values[choice]
y_batch_auto = y_train_values[choice]
x_batch_auto = x_batch_auto.to(self.device)
y_batch_auto = y_batch_auto.to(self.device)
# forward
preds = self.sfm_model(x_batch_auto)
cur_loss = self.get_loss(preds, y_batch_auto, self.loss_type)
cur_loss.backward()
self.train_optimizer.step()
loss.update(cur_loss.item())
# validation
train_loss += loss.val
if step and step % self.eval_steps == 0:
if val_score > best_score:
best_score = val_score
stop_steps = 0
best_epoch = step
best_param = copy.deepcopy(self.sfm_model.state_dict())
else:
stop_steps += 1
train_loss /= self.eval_steps
with torch.no_grad():
self.sfm_model.eval()
loss_val = AverageMeter()
# forward
preds = self.sfm_model(x_val_auto)
cur_loss_val = self.get_loss(preds, y_val_auto, self.loss_type)
loss_val.update(cur_loss_val.item())
if verbose:
self.logger.info(
"[Epoch {}]: train_loss {:.6f}, valid_loss {:.6f}".format(step, train_loss, loss_val.val)
)
evals_result["train"].append(train_loss)
evals_result["valid"].append(loss_val.val)
if loss_val.val < best_loss:
if verbose:
self.logger.info(
"\tvalid loss update from {:.6f} to {:.6f}, save checkpoint.".format(
best_loss, loss_val.val
)
)
best_loss = loss_val.val
stop_steps = 0
torch.save(self.sfm_model.state_dict(), save_path)
train_loss = 0
# update learning rate
self.scheduler.step(cur_loss_val)
if stop_steps >= self.early_stop:
self.logger.info("early stop")
break
self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
if self.device != "cpu":
torch.cuda.empty_cache()
def get_loss(self, pred, target, loss_type):
if loss_type == "mse":
sqr_loss = (pred - target) ** 2
loss = sqr_loss.mean()
return loss
elif loss_type == "binary":
loss = nn.BCELoss()
return loss(pred, target)
else:
raise NotImplementedError("loss {} is not supported!".format(loss_type))
def mse(self, pred, label):
loss = (pred - label) ** 2
return torch.mean(loss)
def loss_fn(self, pred, label):
mask = ~torch.isnan(label)
if self.loss == "mse":
return self.mse(pred[mask], label[mask])
raise ValueError("unknown loss `%s`" % self.loss)
def metric_fn(self, pred, label):
mask = torch.isfinite(label)
if self.metric == "IC":
return self.cal_ic(pred[mask], label[mask])
if self.metric == "" or self.metric == "loss": # use loss
return -self.loss_fn(pred[mask], label[mask])
raise ValueError("unknown metric `%s`" % self.metric)
def cal_ic(self, pred, label):
return torch.mean(pred * label)
def predict(self, dataset):
if not self._fitted:
raise ValueError("model is not fitted yet!")
@@ -430,7 +447,7 @@ class SFM(Model):
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:
@@ -440,37 +457,16 @@ class SFM(Model):
if self.device != "cpu":
x_batch = x_batch.to(self.device)
with torch.no_grad():
if self.device != "cpu":
pred = self.sfm_model(x_batch).detach().cpu().numpy()
else:
pred = self.sfm_model(x_batch).detach().cpu().numpy()
pred = self.sfm_model(x_batch).detach().cpu().numpy()
preds.append(pred)
return pd.Series(np.concatenate(preds), index=index)
def save(self, filename, **kwargs):
with save_multiple_parts_file(filename) as model_dir:
model_path = os.path.join(model_dir, os.path.split(model_dir)[-1])
# Save model
torch.save(self.sfm_model.state_dict(), model_path)
def load(self, buffer, **kwargs):
with unpack_archive_with_buffer(buffer) as model_dir:
# Get model name
_model_name = os.path.splitext(list(filter(lambda x: x.startswith("model.bin"), os.listdir(model_dir)))[0])[
0
]
_model_path = os.path.join(model_dir, _model_name)
# Load model
self.sfm_model.load_state_dict(torch.load(_model_path))
self._fitted = True
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()

30
qlib/contrib/model/xgboost.py
View File

@@ -30,15 +30,15 @@ class XGBModel(Model):
def fit(
self,
dataset: DatasetH,
num_boost_round=1000,
early_stopping_rounds=50,
verbose_eval=20,
evals_result=dict(),
num_boost_round = 1000,
early_stopping_rounds = 50,
verbose_eval = 20,
evals_result = dict(),
**kwargs
):
df_train, df_valid = dataset.prepare(
["train", "valid"], col_set=["feature", "label"], data_key=DataHandlerLP.DK_L
["train", "valid"], col_set = ["feature", "label"], data_key = DataHandlerLP.DK_L
)
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_valid["feature"], df_valid["label"]
@@ -49,16 +49,16 @@ class XGBModel(Model):
else:
raise ValueError("XGBoost doesn't support multi-label training")
dtrain = xgb.DMatrix(x_train.values, label=y_train_1d)
dvalid = xgb.DMatrix(x_valid.values, label=y_valid_1d)
dtrain = xgb.DMatrix(x_train.values, label = y_train_1d)
dvalid = xgb.DMatrix(x_valid.values, label = y_valid_1d)
self.model = xgb.train(
self._params,
dtrain=dtrain,
num_boost_round=num_boost_round,
evals=[(dtrain, "train"), (dvalid, "valid")],
early_stopping_rounds=early_stopping_rounds,
verbose_eval=verbose_eval,
evals_result=evals_result,
dtrain = dtrain,
num_boost_round = num_boost_round,
evals = [(dtrain, "train"), (dvalid, "valid")],
early_stopping_rounds = early_stopping_rounds,
verbose_eval = verbose_eval,
evals_result = evals_result,
**kwargs
)
evals_result["train"] = list(evals_result["train"].values())[0]
@@ -67,5 +67,5 @@ class XGBModel(Model):
def predict(self, dataset):
if self.model is None:
raise ValueError("model is not fitted yet!")
x_test = dataset.prepare("test", col_set="feature")
return pd.Series(self.model.predict(xgb.DMatrix(x_test.values)), index=x_test.index)
x_test = dataset.prepare("test", col_set = "feature")
return pd.Series(self.model.predict(xgb.DMatrix(x_test.values)), index = x_test.index)

40
qlib/data/dataset/processor.py
View File

@@ -166,7 +166,9 @@ class MinMaxNorm(Processor):
return df
class ZscoreNorm(Processor):
class ZScoreNorm(Processor):
"""ZScore Normalization"""
def __init__(self, fit_start_time, fit_end_time, fields_group=None):
self.fit_start_time = fit_start_time
self.fit_end_time = fit_end_time
@@ -193,6 +195,42 @@ class ZscoreNorm(Processor):
return df
class RobustZScoreNorm(Processor):
"""Robust ZScore Normalization
Use robust statistics for Z-Score normalization:
mean(x) = median(x)
std(x) = MAD(x) * 1.4826
Reference:
https://en.wikipedia.org/wiki/Median_absolute_deviation.
"""
def __init__(self, fit_start_time, fit_end_time, fields_group=None, clip_outlier=True):
self.fit_start_time = fit_start_time
self.fit_end_time = fit_end_time
self.fields_group = fields_group
self.clip_outlier = clip_outlier
def fit(self, df):
df = fetch_df_by_index(df, slice(self.fit_start_time, self.fit_end_time), level="datetime")
self.cols = get_group_columns(df, self.fields_group)
X = df[self.cols].values
self.mean_train = np.nanmedian(X, axis=0)
self.std_train = np.nanmedian(np.abs(X - self.mean_train), axis=0)
self.std_train += EPS
self.std_train *= 1.4826
def __call__(self, df):
X = df[self.cols]
X -= self.mean_train
X /= self.std_train
df[self.cols] = X
if self.clip_outlier:
df.clip(-3, 3, inplace=True)
return df
class CSZScoreNorm(Processor):
"""Cross Sectional ZScore Normalization"""

4
qlib/workflow/cli.py
View File

@@ -27,9 +27,9 @@ def sys_config(config, config_path):
Parameters
----------
config : dict
configuration of the workflow
configuration of the workflow.
config_path : str
configuration of the path
configuration of the path.
"""
sys_config = config.get("sys", {})