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qlib/examples/benchmarks/TRA/example.py
Linlang a0cef033cb update python version (#1868)
* update python version

* fix: Correct selector handling and add time filtering in storage.py

* fix: convert index and columns to list in repr methods

* feat: Add Makefile for managing project prerequisites

* feat: Add Cython extensions for rolling and expanding operations

* resolve install error

* fix lint error

* fix lint error

* fix lint error

* fix lint error

* fix lint error

* update build package

* update makefile

* update ci yaml

* fix docs build error

* fix ubuntu install error

* fix docs build error

* fix install error

* fix install error

* fix install error

* fix install error

* fix pylint error

* fix pylint error

* fix pylint error

* fix pylint error

* fix pylint error E1123

* fix pylint error R0917

* fix pytest error

* fix pytest error

* fix pytest error

* update code

* update code

* fix ci error

* fix pylint error

* fix black error

* fix pytest error

* fix CI error

* fix CI error

* add python version to CI

* add python version to CI

* add python version to CI

* fix pylint error

* fix pytest general nn error

* fix CI error

* optimize code

* add coments

* Extended macos version

* remove build package

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Co-authored-by: Young <afe.young@gmail.com>
2024-12-17 11:30:06 +08:00

39 lines
1.2 KiB
Python

import argparse
import qlib
from ruamel.yaml import YAML
from qlib.utils import init_instance_by_config
def main(seed, config_file="configs/config_alstm.yaml"):
# set random seed
with open(config_file) as f:
yaml = YAML(typ="safe", pure=True)
config = yaml.load(f)
# seed_suffix = "/seed1000" if "init" in config_file else f"/seed{seed}"
seed_suffix = ""
config["task"]["model"]["kwargs"].update(
{"seed": seed, "logdir": config["task"]["model"]["kwargs"]["logdir"] + seed_suffix}
)
# initialize workflow
qlib.init(
provider_uri=config["qlib_init"]["provider_uri"],
region=config["qlib_init"]["region"],
)
dataset = init_instance_by_config(config["task"]["dataset"])
model = init_instance_by_config(config["task"]["model"])
# train model
model.fit(dataset)
if __name__ == "__main__":
# set params from cmd
parser = argparse.ArgumentParser(allow_abbrev=False)
parser.add_argument("--seed", type=int, default=1000, help="random seed")
parser.add_argument("--config_file", type=str, default="configs/config_alstm.yaml", help="config file")
args = parser.parse_args()
main(**vars(args))