cave.analyzer.configurator_footprint module

class cave.analyzer.configurator_footprint.ConfiguratorFootprint(scenario, runs, runhistory, final_incumbent, output_dir, max_confs=1000, use_timeslider=False, num_quantiles=10, timeslider_log: bool = True)[source]

Bases: cave.analyzer.base_analyzer.BaseAnalyzer

Plot the visualization of configurations, highlighting the incumbents. Using original rh, so the explored configspace can be estimated.

Parameters
  • scenario (Scenario) – deepcopy of scenario-object

  • runs (List[ConfiguratorRun]) – holding information about original runhistories, trajectories, incumbents, etc.

  • runhistory (RunHistory) – with maximum number of real (not estimated) runs to train best-possible epm

  • final_incumbent (Configuration) – final incumbent (best of all (highest budget) runs)

  • max_confs (int) – maximum number of data-points to plot

  • use_timeslider (bool) – whether or not to have a time_slider-widget on cfp-plot INCREASES FILE-SIZE DRAMATICALLY

  • num_quantiles (int) – if use_timeslider is not off, defines the number of quantiles for the slider/ number of static pictures

  • timeslider_log (bool) – whether to use a logarithmic scale for the timeslider/quantiles

Returns

  • script (str) – script part of bokeh plot

  • div (str) – div part of bokeh plot

  • over_time_paths (List[str]) – list with paths to the different quantiled timesteps of the configurator run (for static evaluation)

get_html(d=None, tooltip=None)[source]

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

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_plots()[source]
get_static_plots() → List[str]

Returns plot-paths, if any are available

Returns

plot_paths – returns list of strings

Return type

List[str]

get_table()

Get table, if available

plot()[source]