File size: 996 Bytes
9877759 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | from sklearn.metrics import davies_bouldin_score as dbs
from sklearn.metrics import calinski_harabasz_score as chs
def cluster_analysis(embeddings, predicted_labels, task_no):
"""
Calculate clustering quality metrics for the provided embeddings and labels.
Args:
embeddings (np.ndarray): The embeddings (n_samples, n_features) to be clustered.
predicted_labels (np.ndarray): The predicted cluster labels (n_samples,) for the embeddings.
Returns:
tuple: A tuple containing:
- Davies-Bouldin Score
- Calinski-Harabasz Score
- Silhouette Score (if more than 1 cluster exists)
"""
if len(embeddings) != len(predicted_labels):
raise ValueError("Embeddings and predicted labels must have the same length.")
# Calculating cluster analysis scores
dbs_score = dbs(X=embeddings, labels=predicted_labels)
chs_score = chs(X=embeddings, labels=predicted_labels)
return dbs_score, chs_score
|