cave.analyzer.local_parameter_importance module

class cave.analyzer.local_parameter_importance.LocalParameterImportance(runscontainer, marginal_threshold=0.05)[source]

Bases: cave.analyzer.cave_parameter_importance.CaveParameterImportance

Using an empirical performance model, performance changes of a configuration along each parameter are calculated. To quantify the importance of a parameter value, the variance of all cost values by changing that parameter are predicted and then the fraction of all variances is computed. This analysis is inspired by the human behaviour to look for improvements in the neighborhood of individual parameters of a configuration.

Calculate parameter-importance using the PIMP-package.

classmethod check_for_bokeh(d)
get_html(d=None, tooltip=None)

General reports in html-format, to be easily integrated in html-code. ALSO FOR BOKEH-OUTPUT.

Parameters

d (Dictionary) – a dictionary that will be later turned into a website

Returns

script, div – header and body part of html-code

Return type

str, str

get_jupyter()[source]

Depending on analysis, this creates jupyter-notebook compatible output.

get_name()[source]
parameter_importance(modus)
modus: str

modus for parameter importance, from [forward-selection, ablation, fanova, lpi]

plot_bokeh()

This function needs to be called if bokeh-plots are to be displayed in notebook AND saved to webpage.

plot_whiskers()
postprocess(pimp, output_dir)[source]