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57 lines
1.7 KiB
Python
57 lines
1.7 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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from pathlib import Path
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import qlib
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import fire
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import pandas as pd
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import ruamel.yaml as yaml
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from qlib.utils import init_instance_by_config, flatten_dict
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from qlib.workflow import R
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from qlib.workflow.record_temp import SignalRecord
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# worflow handler function
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def workflow(config_path, experiment_name="workflow"):
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with open(config_path) as fp:
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config = yaml.load(fp, Loader=yaml.Loader)
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provider_uri = config.get("provider_uri")
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region = config.get("region")
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qlib.init(provider_uri=provider_uri, region=region)
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# model initiaiton
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model = init_instance_by_config(config.get("task")["model"])
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dataset = init_instance_by_config(config.get("task")["dataset"])
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# start exp
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with R.start(experiment_name=experiment_name):
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# train model
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R.log_params(**flatten_dict(config.get("task")))
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model.fit(dataset)
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recorder = R.get_recorder()
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# generate records: prediction, backtest, and analysis
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for record in config.get("task")["record"]:
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if record["class"] == SignalRecord.__name__:
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srconf = {"model": model, "dataset": dataset, "recorder": recorder}
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record["kwargs"].update(srconf)
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sr = init_instance_by_config(record)
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sr.generate()
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else:
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rconf = {"recorder": recorder}
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record["kwargs"].update(rconf)
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ar = init_instance_by_config(record)
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ar.generate()
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# function to run worklflow 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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