cave.analyzer.performance.plot_scatter module

class cave.analyzer.performance.plot_scatter.PlotScatter(runscontainer)[source]

Bases: cave.analyzer.base_analyzer.BaseAnalyzer

Scatter plots show the costs of the default and optimized parameter configuration on each instance. Since this looses detailed information about the individual cost on each instance by looking at aggregated cost values in tables, scatter plots provide a more detailed picture. They provide insights whether overall performance improvements can be explained only by some outliers or whether they are due to improvements on the entire instance set. On the left side the training-data is scattered, on the right side the test-data is scattered.

Creates a scatterplot of the two configurations on the given set of instances. Saves plot to file.

_plot_scatter(default: ConfigSpace.configuration_space.Configuration, incumbent: ConfigSpace.configuration_space.Configuration, rh: smac.runhistory.runhistory.RunHistory, train: List[str], test: Optional[List[str]], run_obj: str, cutoff, output_dir)[source]
Parameters

  • incumbent (default,) – configurations to be compared

  • rh (RunHistory) – runhistory to use for cost-estimations

  • test] (train[,) – instance-names

  • run_obj (str) – run-objective (time or quality)

  • cutoff (float) – maximum runtime of ta

  • output_dir (str) – output directory

classmethod check_for_bokeh(d)

Check if there is bokeh-plots in the output of this analyzer by checking the result-dictionary for the bokeh keyword.

get_html(d=None, tooltip=None) → Tuple[str, str]

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

Parameters

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

  • tooltip (string) – tooltip to be displayed in report. optional, will overwrite the docstrings that are used by default.

Returns

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

Return type

str, str

get_jupyter()

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

get_name()[source]
plot_bokeh()

This function should recreate the bokeh-plot from scratch with as little overhead as possible. This is needed to show the bokeh plot in jupyter AND save it to the webpage. The bokeh plot needs to be recreated to be displayed in different outputs for reasons beyond out control. So save all analysis results in the class and simply redo the plotting with this function. This function needs to be called if bokeh-plots are to be displayed in notebook AND saved to html-result.