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Merge pull request #328 from D-X-Y/fshare

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Move get_path to get_or_create_path, use the best model of SFM / TabNet
This commit is contained in:
you-n-g
2021-03-11 22:07:20 +08:00
committed by GitHub
parent e8beaa5257 e626264d5a
commit f6b019dcec
12 changed files with 47 additions and 41 deletions
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5
qlib/contrib/model/pytorch_alstm.py
View File

@@ -14,7 +14,7 @@ import logging
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
create_save_path,
get_or_create_path,
drop_nan_by_y_index,
)
from ...log import get_module_logger, TimeInspector
@@ -230,8 +230,7 @@ class ALSTM(Model):
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_valid["feature"], df_valid["label"]
if save_path == None:
save_path = create_save_path(save_path)
save_path = get_or_create_path(save_path)
stop_steps = 0
train_loss = 0
best_score = -np.inf

5
qlib/contrib/model/pytorch_alstm_ts.py
View File

@@ -14,7 +14,7 @@ import logging
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
create_save_path,
get_or_create_path,
drop_nan_by_y_index,
)
from ...log import get_module_logger, TimeInspector
@@ -220,8 +220,7 @@ class ALSTM(Model):
dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs, drop_last=True
)
if save_path == None:
save_path = create_save_path(save_path)
save_path = get_or_create_path(save_path)
stop_steps = 0
train_loss = 0

5
qlib/contrib/model/pytorch_gats.py
View File

@@ -14,7 +14,7 @@ import logging
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
create_save_path,
get_or_create_path,
drop_nan_by_y_index,
)
from ...log import get_module_logger, TimeInspector
@@ -248,8 +248,7 @@ class GATs(Model):
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_valid["feature"], df_valid["label"]
if save_path == None:
save_path = create_save_path(save_path)
save_path = get_or_create_path(save_path)
stop_steps = 0
best_score = -np.inf
best_epoch = 0

5
qlib/contrib/model/pytorch_gats_ts.py
View File

@@ -14,7 +14,7 @@ import logging
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
create_save_path,
get_or_create_path,
drop_nan_by_y_index,
)
from ...log import get_module_logger, TimeInspector
@@ -264,8 +264,7 @@ class GATs(Model):
train_loader = DataLoader(dl_train, sampler=sampler_train, num_workers=self.n_jobs, drop_last=True)
valid_loader = DataLoader(dl_valid, sampler=sampler_valid, num_workers=self.n_jobs, drop_last=True)
if save_path == None:
save_path = create_save_path(save_path)
save_path = get_or_create_path(save_path)
stop_steps = 0
train_loss = 0

5
qlib/contrib/model/pytorch_gru.py
View File

@@ -14,7 +14,7 @@ import logging
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
create_save_path,
get_or_create_path,
drop_nan_by_y_index,
)
from ...log import get_module_logger, TimeInspector
@@ -230,8 +230,7 @@ class GRU(Model):
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_valid["feature"], df_valid["label"]
if save_path == None:
save_path = create_save_path(save_path)
save_path = get_or_create_path(save_path)
stop_steps = 0
train_loss = 0
best_score = -np.inf

5
qlib/contrib/model/pytorch_gru_ts.py
View File

@@ -14,7 +14,7 @@ import logging
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
create_save_path,
get_or_create_path,
drop_nan_by_y_index,
)
from ...log import get_module_logger, TimeInspector
@@ -220,8 +220,7 @@ class GRU(Model):
dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs, drop_last=True
)
if save_path == None:
save_path = create_save_path(save_path)
save_path = get_or_create_path(save_path)
stop_steps = 0
train_loss = 0

5
qlib/contrib/model/pytorch_lstm.py
View File

@@ -14,7 +14,7 @@ import logging
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
create_save_path,
get_or_create_path,
drop_nan_by_y_index,
)
from ...log import get_module_logger, TimeInspector
@@ -226,8 +226,7 @@ class LSTM(Model):
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_valid["feature"], df_valid["label"]
if save_path == None:
save_path = create_save_path(save_path)
save_path = get_or_create_path(save_path)
stop_steps = 0
train_loss = 0
best_score = -np.inf

5
qlib/contrib/model/pytorch_lstm_ts.py
View File

@@ -14,7 +14,7 @@ import logging
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
create_save_path,
get_or_create_path,
drop_nan_by_y_index,
)
from ...log import get_module_logger, TimeInspector
@@ -216,8 +216,7 @@ class LSTM(Model):
dl_valid, batch_size=self.batch_size, shuffle=False, num_workers=self.n_jobs, drop_last=True
)
if save_path == None:
save_path = create_save_path(save_path)
save_path = get_or_create_path(save_path)
stop_steps = 0
train_loss = 0

4
qlib/contrib/model/pytorch_nn.py
View File

@@ -19,7 +19,7 @@ from .pytorch_utils import count_parameters
from ...model.base import Model
from ...data.dataset import DatasetH
from ...data.dataset.handler import DataHandlerLP
from ...utils import unpack_archive_with_buffer, save_multiple_parts_file, create_save_path, drop_nan_by_y_index
from ...utils import unpack_archive_with_buffer, save_multiple_parts_file, get_or_create_path, drop_nan_by_y_index
from ...log import get_module_logger, TimeInspector
from ...workflow import R
@@ -176,7 +176,7 @@ class DNNModelPytorch(Model):
w_train = pd.DataFrame(np.ones_like(y_train.values), index=y_train.index)
w_valid = pd.DataFrame(np.ones_like(y_valid.values), index=y_valid.index)
save_path = create_save_path(save_path)
save_path = get_or_create_path(save_path)
stop_steps = 0
train_loss = 0
best_loss = np.inf

6
qlib/contrib/model/pytorch_sfm.py
View File

@@ -13,7 +13,7 @@ import logging
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
create_save_path,
get_or_create_path,
drop_nan_by_y_index,
)
from ...log import get_module_logger, TimeInspector
@@ -380,6 +380,7 @@ 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 = get_or_create_path(save_path)
stop_steps = 0
train_loss = 0
best_score = -np.inf
@@ -412,7 +413,10 @@ class SFM(Model):
if stop_steps >= self.early_stop:
self.logger.info("early stop")
break
self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
self.sfm_model.load_state_dict(best_param)
torch.save(best_param, save_path)
if self.device != "cpu":
torch.cuda.empty_cache()

12
qlib/contrib/model/pytorch_tabnet.py
View File

@@ -12,7 +12,7 @@ import logging
from ...utils import (
unpack_archive_with_buffer,
save_multiple_parts_file,
create_save_path,
get_or_create_path,
drop_nan_by_y_index,
)
from ...log import get_module_logger, TimeInspector
@@ -117,10 +117,7 @@ class TabnetModel(Model):
raise NotImplementedError("optimizer {} is not supported!".format(optimizer))
def pretrain_fn(self, dataset=DatasetH, pretrain_file="./pretrain/best.model"):
# make a directory if pretrian director does not exist
if pretrain_file.startswith("./pretrain") and not os.path.exists("pretrain"):
self.logger.info("make folder to store model...")
os.makedirs("pretrain")
get_or_create_path(pretrain_file)
[df_train, df_valid] = dataset.prepare(
["pretrain", "pretrain_validation"],
@@ -181,6 +178,7 @@ class TabnetModel(Model):
df_train.fillna(df_train.mean(), inplace=True)
x_train, y_train = df_train["feature"], df_train["label"]
x_valid, y_valid = df_valid["feature"], df_valid["label"]
save_path = get_or_create_path(save_path)
stop_steps = 0
train_loss = 0
@@ -207,12 +205,16 @@ class TabnetModel(Model):
best_score = val_score
stop_steps = 0
best_epoch = epoch_idx
best_param = copy.deepcopy(self.tabnet_model.state_dict())
else:
stop_steps += 1
if stop_steps >= self.early_stop:
self.logger.info("early stop")
break
self.logger.info("best score: %.6lf @ %d" % (best_score, best_epoch))
self.tabnet_model.load_state_dict(best_param)
torch.save(best_param, save_path)
def predict(self, dataset):
if not self.fitted:

26
qlib/utils/__init__.py
View File

@@ -24,7 +24,7 @@ import collections
import numpy as np
import pandas as pd
from pathlib import Path
from typing import Union, Tuple
from typing import Union, Tuple, Text, Optional
from ..config import C
from ..log import get_module_logger, set_log_with_config
@@ -276,23 +276,31 @@ def compare_dict_value(src_data: dict, dst_data: dict):
return changes
def create_save_path(save_path=None):
"""Create save path
def get_or_create_path(path: Optional[Text] = None, return_dir: bool = False):
"""Create or get a file or directory given the path and return_dir.
Parameters
----------
save_path: str
path: a string indicates the path or None indicates creating a temporary path.
return_dir: if True, create and return a directory; otherwise c&r a file.
"""
if save_path:
if not os.path.exists(save_path):
os.makedirs(save_path)
if path:
if return_dir and not os.path.exists(path):
os.makedirs(path)
elif not return_dir: # return a file, thus we need to create its parent directory
xpath = os.path.abspath(os.path.join(path, ".."))
if not os.path.exists(xpath):
os.makedirs(xpath)
else:
temp_dir = os.path.expanduser("~/tmp")
if not os.path.exists(temp_dir):
os.makedirs(temp_dir)
_, save_path = tempfile.mkstemp(dir=temp_dir)
return save_path
if return_dir:
_, path = tempfile.mkdtemp(dir=temp_dir)
else:
_, path = tempfile.mkstemp(dir=temp_dir)
return path
@contextlib.contextmanager