cave.analyzer.algorithm_footprint module

class cave.analyzer.algorithm_footprint.AlgorithmFootprint(runscontainer, density=200, purity=0.95)[source]

Bases: cave.analyzer.base_analyzer.BaseAnalyzer

The instance features are projected into a two/three dimensional space using principal component analysis (PCA) and the footprint of each algorithm is plotted, i.e., on which instances the default or the optimized configuration performs well. In contrast to the other analysis methods in this section, these plots allow insights into which of the two configurations performs well on specific types or clusters of instances. Inspired by Smith-Miles.

Parameters

runscontainer (RunsContainer) – contains all important information about the configurator runs

_plot()[source]
classmethod check_for_bokeh(d)
get_html(d=None, tooltip=None)[source]

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]
get_plots()[source]
plot_bokeh()

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