cave.analyzer.budgets.bohb_learning_curves module

class cave.analyzer.budgets.bohb_learning_curves.BohbLearningCurves(runscontainer)

Bases: cave.analyzer.base_analyzer.BaseAnalyzer

Visualizing the learning curves of all individual HyperBand-iterations. Model-based picks are marked with a cross. The config-id tuple denotes (iteration, stage, id_within_stage), where the iteration is the hyperband iteration and the stage is the index of the budget in which the configuration was first sampled (should be 0). The third index is just a sequential enumeration. This id can be interpreted as a nested index-identifier.

runscontainer: RunsContainer contains all important information about the configurator runs

_plot(result_object, learning_curves, hyperparameter_names, reset_times=False)

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)

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.

static get_longest_run(c_id, result_object)

get_name()

plot(reset_times=False)

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.