102 lines
3.6 KiB
Python
102 lines
3.6 KiB
Python
#####################################################
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# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2021.02 #
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#####################################################
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import inspect
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import os
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import logging
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import qlib
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from qlib.utils import init_instance_by_config
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from qlib.workflow import R
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from qlib.utils import flatten_dict
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from qlib.log import get_module_logger
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def set_log_basic_config(filename=None, format=None, level=None):
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"""
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Set the basic configuration for the logging system.
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See details at https://docs.python.org/3/library/logging.html#logging.basicConfig
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:param filename: str or None
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The path to save the logs.
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:param format: the logging format
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:param level: int
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:return: Logger
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Logger object.
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"""
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from qlib.config import C
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if level is None:
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level = C.logging_level
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if format is None:
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format = C.logging_config["formatters"]["logger_format"]["format"]
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logging.basicConfig(filename=filename, format=format, level=level)
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def update_gpu(config, gpu):
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config = config.copy()
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if "task" in config and "model" in config["task"]:
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if "GPU" in config["task"]["model"]:
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config["task"]["model"]["GPU"] = gpu
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elif "kwargs" in config["task"]["model"] and "GPU" in config["task"]["model"]["kwargs"]:
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config["task"]["model"]["kwargs"]["GPU"] = gpu
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elif "model" in config:
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if "GPU" in config["model"]:
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config["model"]["GPU"] = gpu
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elif "kwargs" in config["model"] and "GPU" in config["model"]["kwargs"]:
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config["model"]["kwargs"]["GPU"] = gpu
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elif "kwargs" in config and "GPU" in config["kwargs"]:
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config["kwargs"]["GPU"] = gpu
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elif "GPU" in config:
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config["GPU"] = gpu
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return config
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def update_market(config, market):
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config = config.copy()
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config["market"] = market
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config["data_handler_config"]["instruments"] = market
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return config
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def run_exp(task_config, dataset, experiment_name, recorder_name, uri):
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model = init_instance_by_config(task_config["model"])
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model_fit_kwargs = dict(dataset=dataset)
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# Let's start the experiment.
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with R.start(experiment_name=experiment_name, recorder_name=recorder_name, uri=uri):
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# Setup log
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recorder_root_dir = R.get_recorder().get_local_dir()
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log_file = os.path.join(recorder_root_dir, "{:}.log".format(experiment_name))
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set_log_basic_config(log_file)
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logger = get_module_logger("q.run_exp")
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logger.info("task_config={:}".format(task_config))
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logger.info("[{:}] - [{:}]: {:}".format(experiment_name, recorder_name, uri))
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logger.info("dataset={:}".format(dataset))
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# Train model
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R.log_params(**flatten_dict(task_config))
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if "save_path" in inspect.getfullargspec(model.fit).args:
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model_fit_kwargs["save_path"] = os.path.join(recorder_root_dir, "model-ckps")
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model.fit(**model_fit_kwargs)
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# Get the recorder
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recorder = R.get_recorder()
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R.save_objects(**{"model.pkl": model})
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# Generate records: prediction, backtest, and analysis
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for record in task_config["record"]:
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record = record.copy()
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if record["class"] == "SignalRecord":
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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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