mirror of
https://github.com/webclinic017/drift.git
synced 2026-08-07 16:17:49 +00:00
Mark Aron Szulyovszky
25b64f5a3d
* refactor(Reporting): only report the last model's results, moved wandb-related functions to `reporting` * fix(Reporting): use .mean() on axis 1 to retain the metrics, fixed get_model_name() * fix(Config): sweep file syntax * fix(Config): changed hyperparameter search method to "bayes" * chore(Sweep): adjusted sweep config based on the results we saw (removed Momentum as well) * fix(Sweep): only use classification method for now, we're not yet prepared for regression
43 lines
1.4 KiB
Python
43 lines
1.4 KiB
Python
import pandas as pd
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from typing import Optional
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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 wandb is 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: Optional[object], 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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def send_report_to_wandb(results: pd.DataFrame, wandb:Optional[object], project_name: str, model_name: str):
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if wandb is None: return
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run = wandb.init(project=project_name, config={"model_type": model_name}, reinit=True)
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wandb.run.name = model_name+ "-" + wandb.run.id
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wandb.run.save()
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mean_results = results.mean(axis = 1)
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for key, value in mean_results.iteritems():
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run.log({"model_type": model_name, key: value })
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run.finish()
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