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08fd1d3f42
* update cli.py update cli.py so that one can specify exp_manager uri in "qlib_init" and "experiment_name" in *.yaml file. * black cli.py * Resolving pre-commit-hook changes
77 lines
1.9 KiB
Python
77 lines
1.9 KiB
Python
# Copyright (c) Microsoft Corporation.
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# Licensed under the MIT License.
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import sys
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import os
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from pathlib import Path
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import qlib
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import fire
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import ruamel.yaml as yaml
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from qlib.config import C
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from qlib.model.trainer import task_train
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def get_path_list(path):
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if isinstance(path, str):
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return [path]
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else:
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return list(path)
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def sys_config(config, config_path):
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"""
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Configure the `sys` section
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Parameters
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----------
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config : dict
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configuration of the workflow.
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config_path : str
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path of the configuration
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"""
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sys_config = config.get("sys", {})
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# abspath
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for p in get_path_list(sys_config.get("path", [])):
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sys.path.append(p)
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# relative path to config path
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for p in get_path_list(sys_config.get("rel_path", [])):
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sys.path.append(str(Path(config_path).parent.resolve().absolute() / p))
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# workflow handler function
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def workflow(config_path, experiment_name="workflow", uri_folder="mlruns"):
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"""
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This is a Qlib CLI entrance.
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User can run the whole Quant research workflow defined by a configure file
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- the code is located here ``qlib/workflow/cli.py`
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"""
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with open(config_path) as fp:
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config = yaml.safe_load(fp)
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# config the `sys` section
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sys_config(config, config_path)
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if "exp_manager" in config.get("qlib_init"):
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qlib.init(**config.get("qlib_init"))
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else:
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exp_manager = C["exp_manager"]
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exp_manager["kwargs"]["uri"] = "file:" + str(Path(os.getcwd()).resolve() / uri_folder)
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qlib.init(**config.get("qlib_init"), exp_manager=exp_manager)
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if "experiment_name" in config:
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experiment_name = config["experiment_name"]
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recorder = task_train(config.get("task"), experiment_name=experiment_name)
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recorder.save_objects(config=config)
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# function to run workflow by config
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def run():
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fire.Fire(workflow)
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if __name__ == "__main__":
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run()
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