mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-01 05:07:47 +00:00
Daniel Szemerey
1c1b8b2e54
* feat: Parametricized model selection works now. * feat: Fixed errors. Sweep generates and you can run it, but it gives an error for model.only_columns attribute. * feat: Factored the wandb management, default config managment and the model_dictionary out of the run_pipeline to a seperate file. * fix: Took out prints and fixed the mismatch of ensemble models when classifing. * fix(Models): added StaticMomentum model to the dictionary, hopefully fixed sklearn-ex RandomForestRegressor problem * fix(Dependencies): pin scikit-learn-ex's version, moved map_model_name_to_function to `models` * feat(Sweep): added `run_sweep.py` shortcut * feat(Pipeline): skip training a meta model if array is empty Co-authored-by: Mark Aron Szulyovszky <mark.szulyovszky@gmail.com>
28 lines
862 B
Python
28 lines
862 B
Python
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def launch_wandb(project_name:str, default_config:dict, sweep:bool=False):
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from wandb_setup import get_wandb
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wandb = get_wandb()
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if type(wandb) == type(None):
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return None
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elif sweep:
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wandb.init(project=project_name, config = default_config)
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return wandb
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else:
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wandb.init(project=project_name, config = default_config, reinit=True)
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return wandb
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def seperate_configs(wandb, model_config:dict, training_config:dict, data_config:dict) -> tuple[dict,dict,dict]:
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config:dict = wandb.config
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if type(wandb) is not type(None):
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for k in training_config: training_config[k] = config[k]
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for k in model_config: model_config[k] = config[k]
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# for k in data_config: data_config[k] = config[k]
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return model_config, training_config, data_config
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