cave.analyzer.feature_clustering module

class cave.analyzer.feature_clustering.FeatureClustering(runscontainer)[source]

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)
feat_analysis(output_dir, scenario, feat_names, feat_importance)[source]
get_html(d=None, tooltip=None) → Tuple[str, str]

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()

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

get_name()[source]
plot_bokeh()

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