mlos_viz package

mlos_viz is a framework to help visualizing, explain, and gain insights from results from the mlos_bench framework for benchmarking and optimization automation.

class mlos_viz.MlosVizMethod(value, names=<not given>, *values, module=None, qualname=None, type=None, start=1, boundary=None)

Bases: Enum

What method to use for visualizing the experiment results.

AUTO = 'dabl'

DABL = 'dabl'

mlos_viz.ignore_plotter_warnings(plotter_method: MlosVizMethod = MlosVizMethod.DABL) → None

Suppress some annoying warnings from third-party data visualization packages by adding them to the warnings filter.

Parameters:

plotter_method: MlosVizMethod: The method to use for visualizing the experiment results.

mlos_viz.plot(exp_data: ExperimentData | None = None, *, results_df: DataFrame | None = None, objectives: Dict[str, Literal['min', 'max']] | None = None, plotter_method: MlosVizMethod = MlosVizMethod.DABL, filter_warnings: bool = True, **kwargs: Any) → None

Plots the results of the experiment.

Intended to be used from a Jupyter notebook.

Parameters:

exp_data: ExperimentData: The experiment data to plot.
results_dfOptional[“pandas.DataFrame”]: Optional results_df to plot. If not provided, defaults to exp_data.results_df property.
objectivesOptional[Dict[str, Literal[“min”, “max”]]]: Optional objectives to plot. If not provided, defaults to exp_data.objectives property.
plotter_method: MlosVizMethod: The method to use for visualizing the experiment results.
filter_warnings: bool: Whether or not to filter some warnings from the plotter.
kwargsdict: Remaining keyword arguments are passed along to the underlying plotter(s).

mlos_viz package

Submodules