iicd/xautodl
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

Update models

Browse Source
This commit is contained in:
D-X-Y 2021-03-29 05:04:24 +00:00
parent e637cddc39
commit b51320dfb1
7 changed files with 85 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
.latent-data/qlib

@ -1 +1 @@
Subproject commit 253378a44e88a9fcff17d23b589e2d4832f587aa
Subproject commit 968930e85f4958d16dfc2c5740c02f5c91745b97

6
README.md
View File

@ -105,11 +105,11 @@ to download this repo with submodules.
If you find that this project helps your research, please consider citing the related paper:
```
@article{dong2020autohas,
@inproceedings{dong2021autohas,
title={{AutoHAS}: Efficient Hyperparameter and Architecture Search},
author={Dong, Xuanyi and Tan, Mingxing and Yu, Adams Wei and Peng, Daiyi and Gabrys, Bogdan and Le, Quoc V},
journal={arXiv preprint arXiv:2006.03656},
year={2020}
booktitle = {International Conference on Learning Representations (ICLR) Workshop on Neural Architecture Search},
year={2021}
}
@article{dong2021nats,
title = {{NATS-Bench}: Benchmarking NAS Algorithms for Architecture Topology and Size},

10
docs/README_CN.md
View File

@ -62,13 +62,13 @@
<tr> <!-- (6-th row) -->
<td align="center" valign="middle"> NATS-Bench </td>
<td align="center" valign="middle"> <a href="https://xuanyidong.com/assets/projects/NATS-Bench"> NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size</a> </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NATS-Bench.md">NATS-Bench.md</a> </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/NATS-Bench">NATS-Bench.md</a> </td>
</tr>
<tr> <!-- (7-th row) -->
<td align="center" valign="middle"> ... </td>
<td align="center" valign="middle"> ENAS / REA / REINFORCE / BOHB </td>
<td align="center" valign="middle"> Please check the original papers. </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NAS-Bench-201.md">NAS-Bench-201.md</a> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NATS-Bench.md">NATS-Bench.md</a> </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NAS-Bench-201.md">NAS-Bench-201.md</a> <a href="https://github.com/D-X-Y/NATS-Bench">NATS-Bench.md</a> </td>
</tr>
<tr> <!-- (start second block) -->
<td rowspan="1" align="center" valign="middle" halign="middle"> HPO </td>
@ -98,6 +98,12 @@ Some methods use knowledge distillation (KD), which require pre-trained models.
如果您发现该项目对您的科研或工程有帮助，请考虑引用下列的某些文献：
```
@inproceedings{dong2021autohas,
title={{AutoHAS}: Efficient Hyperparameter and Architecture Search},
author={Dong, Xuanyi and Tan, Mingxing and Yu, Adams Wei and Peng, Daiyi and Gabrys, Bogdan and Le, Quoc V},
booktitle = {International Conference on Learning Representations (ICLR) Workshop on Neural Architecture Search},
year={2021}
}
@article{dong2021nats,
title = {{NATS-Bench}: Benchmarking NAS Algorithms for Architecture Topology and Size},
author = {Dong, Xuanyi and Liu, Lu and Musial, Katarzyna and Gabrys, Bogdan},

18
exps/trading/baselines.py
View File

@ -67,14 +67,26 @@ def extend_transformer_settings(alg2configs, name):
return alg2configs
def remove_PortAnaRecord(alg2configs):
def refresh_record(alg2configs):
alg2configs = copy.deepcopy(alg2configs)
for key, config in alg2configs.items():
xlist = config["task"]["record"]
new_list = []
for x in xlist:
if x["class"] != "PortAnaRecord":
# remove PortAnaRecord and SignalMseRecord
if x["class"] != "PortAnaRecord" and x["class"] != "SignalMseRecord":
new_list.append(x)
## add MultiSegRecord
new_list.append(
{
"class": "MultiSegRecord",
"module_path": "qlib.contrib.workflow",
"generate_kwargs": {
"segments": {"train": "train", "valid": "valid", "test": "test"},
"save": True,
},
}
)
config["task"]["record"] = new_list
return alg2configs
@ -117,7 +129,7 @@ def retrieve_configs():
)
)
alg2configs = extend_transformer_settings(alg2configs, "TSF")
alg2configs = remove_PortAnaRecord(alg2configs)
alg2configs = refresh_record(alg2configs)
print(
"There are {:} algorithms : {:}".format(
len(alg2configs), list(alg2configs.keys())

28
lib/procedures/q_exps.py
View File

@ -99,7 +99,12 @@ def run_exp(
# Train model
try:
model = R.load_object(model_obj_name)
if hasattr(model, "to"): # Recoverable model
device = model.device
model = R.load_object(model_obj_name)
model.to(device)
else:
model = R.load_object(model_obj_name)
logger.info("[Find existing object from {:}]".format(model_obj_name))
except OSError:
R.log_params(**flatten_dict(task_config))
@ -112,16 +117,29 @@ def run_exp(
recorder_root_dir, "model-ckps"
)
model.fit(**model_fit_kwargs)
R.save_objects(**{model_obj_name: model})
except:
raise ValueError("Something wrong.")
# remove model to CPU for saving
if hasattr(model, "to"):
model.to("cpu")
R.save_objects(**{model_obj_name: model})
model.to()
else:
R.save_objects(**{model_obj_name: model})
except Exception as e:
import pdb
pdb.set_trace()
raise ValueError("Something wrong: {:}".format(e))
# Get the recorder
recorder = R.get_recorder()
# Generate records: prediction, backtest, and analysis
for record in task_config["record"]:
record = deepcopy(record)
if record["class"] == "SignalRecord":
if record["class"] == "MultiSegRecord":
record["kwargs"] = dict(model=model, dataset=dataset, recorder=recorder)
sr = init_instance_by_config(record)
sr.generate(**record["generate_kwargs"])
elif record["class"] == "SignalRecord":
srconf = {"model": model, "dataset": dataset, "recorder": recorder}
record["kwargs"].update(srconf)
sr = init_instance_by_config(record)

6
lib/trade_models/quant_transformer.py
View File

@ -112,6 +112,12 @@ class QuantTransformer(Model):
def use_gpu(self):
return self.device != torch.device("cpu")
def to(self, device):
if device is None:
self.model.to(self.device)
else:
self.model.to("cpu")
def loss_fn(self, pred, label):
mask = ~torch.isnan(label)
if self.opt_config["loss"] == "mse":

29
scripts/trade/tsf-all.sh Normal file
View File

@ -0,0 +1,29 @@
#!/bin/bash
#
# bash scripts/trade/tsf-all.sh 0 csi300 0
# bash scripts/trade/tsf-all.sh 0 csi300 0.1
# bash scripts/trade/tsf-all.sh 1 all
#
set -e
echo script name: $0
echo $# arguments
if [ "$#" -ne 3 ] ;then
echo "Input illegal number of parameters " $#
exit 1
fi
gpu=$1
market=$2
drop=$3
channels="6 12 24 32 48 64"
depths="1 2 3 4 5 6"
for channel in ${channels}
do
for depth in ${depths}
do
python exps/trading/baselines.py --alg TSF-${depth}x${channel}-d${drop} --gpu ${gpu} --market ${market}
done
done