Create evaluate.py

evaluate.py

@@ -0,0 +1,55 @@
+# evaluate.py
+# Purpose: small evaluation and visualization for clusters
+
+
+import numpy as np
+import pandas as pd
+from sklearn.metrics import silhouette_score
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+
+
+
+def silhouette(embs, labels):
+mask = labels >= 0
+if mask.sum() <= 1:
+return None
+score = silhouette_score(embs[mask], labels[mask])
+return score
+
+
+
+
+def cluster_stats(df_original, labels):
+df = df_original.copy()
+df['cluster'] = labels
+stats = df.groupby('cluster').agg({'customer_id':'count', 'annual_income':'median', 'spend_score':'median'})
+return stats
+
+
+
+
+if __name__ == '__main__':
+import argparse
+parser = argparse.ArgumentParser()
+parser.add_argument('--features', default='data/features.parquet')
+parser.add_argument('--emb', default='data/embeddings.npy')
+parser.add_argument('--labels', default='data/cluster_labels.npy')
+args = parser.parse_args()
+
+
+df = pd.read_parquet(args.features)
+embs = np.load(args.emb)
+labels = np.load(args.labels)
+
+
+s = silhouette(embs, labels)
+print('Silhouette score (ignoring noise labels -1):', s)
+
+
+try:
+stats = cluster_stats(df, labels)
+print(stats)
+except Exception:
+print('Could not compute descriptive stats (missing columns).')