cave.analyzer.feature_analysis.feature_clustering module

class cave.analyzer.feature_analysis.feature_clustering.FeatureClustering(runscontainer)

Bases: cave.analyzer.base_analyzer.BaseAnalyzer

Clustering instances in 2d; the color encodes the cluster assigned to each cluster. Similar to ISAC, we use a k-means to cluster the instances in the feature space. As pre-processing, we use standard scaling and a PCA to 2 dimensions. To guess the number of clusters, we use the silhouette score on the range of 2 to 12 in the number of clusters

runscontainer: RunsContainer contains all important information about the configurator runs

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.

feat_analysis(output_dir, scenario, feat_names, feat_importance)

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()

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.