mlos_viz

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

Its main entrypoint is the plot() function, which can be used to automatically visualize ExperimentData from mlos_bench using other libraries for automatic data correlation and visualization like dabl.

Submodules

Attributes

__version__

Classes

MlosVizMethod

What method to use for visualizing the experiment results.

Functions

ignore_plotter_warnings(→ None)

Suppress some annoying warnings from third-party data visualization packages by

plot(→ None)

Plots the results of the experiment.

Package Contents

class mlos_viz.MlosVizMethod(*args, **kwds)[source]

Bases: enum.Enum

What method to use for visualizing the experiment results.

AUTO[source]
DABL = 'dabl'[source]
mlos_viz.ignore_plotter_warnings(plotter_method: MlosVizMethod = MlosVizMethod.AUTO) → None[source]

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.

Return type:

None

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

Plots the results of the experiment.

Intended to be used from a Jupyter notebook.

Parameters:

  • exp_data (ExperimentData) – The experiment data to plot.

  • results_df (Optional[pandas.DataFrame]) – Optional results_df to plot. If not provided, defaults to ExperimentData.results_df property.

  • objectives (Optional[Dict[str, Literal["min", "max"]]]) – Optional objectives to plot. If not provided, defaults to ExperimentData.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.

  • kwargs (dict) – Remaining keyword arguments are passed along to the underlying plotter(s).

Return type:

None

mlos_viz.__version__[source]