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
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. ALSO FOR BOKEH-OUTPUT.
d (Dictionary) – a dictionary that will be later turned into a website
script, div – header and body part of html-code
- Return type
Depending on analysis, this creates jupyter-notebook compatible output.
This function needs to be called if bokeh-plots are to be displayed in notebook AND saved to webpage.