Trouble defining a Log Directory about deep-symbolic-optimization HOT 3 CLOSED

Hi @Sean-Reilly, if you are running with the Python interface, you are likely creating a DeepSymbolicOptimizer object, correct? You instantiate that object by passing it a path to a config file, e.g.

model = DeepSymbolicOptimizer("path/to/my_config.json")

Inside your config JSON file, in the "experiment" top-level key, you specify the path to your log directory via the "logdir" parameter, e.g.:

{
    "experiment" : {
        "logdir" : "/path/to/log/directory"
    }
}

It should then save to file when running model.train().

Relative paths are supported, but I've found absolute paths to be more stable so it doesn't depend on where you run your Python script from.

Another note is that the default value for "logdir" is "./log", so it could be that you overwrote the parameter at some point? If the above doesn't help, if you could write a minimum working example that would be helpful. Thanks!

from deep-symbolic-optimization.

I was using the DeepSymbolicRegressor function, which I’ve discovered seems to have it’s log output disabled in the __init__ file. I’ll use the DeepSymbolicOptimizer from now on to avoid this issue.

from deep-symbolic-optimization.

Ah I see. yes, for the DeepSymbolicRegressor we turned off file saving because that likely not expected behavior in an sklearn-like interface. We should add an option to turn that back on...

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