items2vec-silhouette-scores.py

from sklearn.metrics import silhouette_samples, silhouette_score

for space_name, space in {'t-SNE': tsne_results, 
                          'original': model.wv.vectors}.items():
    for entity in ['department', 'aisle']:
        s = silhouette_score(space, df_semantic_item[entity], metric="cosine")
        print(f"Score on {space_name} space for {entity}s is {s:.4}")

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#> Score on t-SNE space for departments is -0.4912
#> Score on t-SNE space for aisles is -0.7124
#> Score on original space for departments is -0.05162
#> Score on original space for aisles is : -0.1148