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]