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Pass cost_attr and cost_budget from flaml.tune.run() to the search algo (#1382)

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EgorKraevTransferwise
2024-12-04 13:50:15 +01:00
committed by GitHub
parent b9194cdcf2
commit b83c8a7d3b
1 changed files with 10 additions and 0 deletions
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10
flaml/tune/tune.py
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@@ -260,6 +260,8 @@ def run(
mlflow_exp_name: Optional[str] = None,
automl_info: Optional[Tuple[float]] = None,
extra_tag: Optional[dict] = None,
cost_attr: Optional[str] = "auto",
cost_budget: Optional[float] = None,
**ray_args,
):
"""The function-based way of performing HPO.
@@ -462,6 +464,12 @@ def run(
overwritten by the value of `n_concurrent_trials` in AutoML. When <= 0, the concurrent trials
will be set to the number of executors.
extra_tag: dict, default=None | Extra tags to be added to the mlflow runs created by autologging.
cost_attr: None or str to specify the attribute to evaluate the cost of different trials.
Default is "auto", which means that we will automatically choose the cost attribute to use (depending
on the nature of the resource budget). When cost_attr is set to None, cost differences between different trials will be omitted
in our search algorithm. When cost_attr is set to a str different from "auto" and "time_total_s",
this cost_attr must be available in the result dict of the trial.
cost_budget: A float of the cost budget. Only valid when cost_attr is a str different from "auto" and "time_total_s".
**ray_args: keyword arguments to pass to ray.tune.run().
Only valid when use_ray=True.
"""
@@ -600,6 +608,8 @@ def run(
metric_constraints=metric_constraints,
use_incumbent_result_in_evaluation=use_incumbent_result_in_evaluation,
lexico_objectives=lexico_objectives,
cost_attr=cost_attr,
cost_budget=cost_budget,
)
else:
if metric is None or mode is None: