cave.analyzer.bohb_learning_curves module

class cave.analyzer.bohb_learning_curves.BohbLearningCurves(runscontainer)[source]

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)[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_longest_run(c_id, result_object)[source]
get_name()[source]
plot(reset_times=False)[source]
plot_bokeh()

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