Silhouette score functions

[VerticaDataScientist]

Silhouette Score is a metric to evaluate the performance of the clustering algorithm. It uses the compactness of individual clusters(intra-cluster distance) and separation amongst clusters (inter-cluster distance) to measure an overall representative score of how well our clustering algorithm has performed.
It can be built of 2 components:

1. silhouette_score_samples: compute the silhouette score for each sample and visualize it as a bar chart (picture)

The parameters:
- input_relation : vDataFrame
- features' list : list
- cluster_id: str

Output: it will be a column of all the scores and it will be used for visualization as shown before.

2. silhouette_score: average score of all samples. It has the same parameters as Silhouette_score_samples. The output will be a float between -1 and 1