Quality vs k
=============

.. figure:: /_static/silhouette_vs_k.png
   :alt: Silhouette score vs k
   :figwidth: 75%
   :align: center

   Silhouette score versus number of clusters for different algorithms.

.. _code:

.. code-block:: python
   :linenos:

   """Generate `silhouette_vs_k.png` comparing CLARANS, FastCLARANS, and KMeans.
   """
   from pathlib import Path
   import matplotlib
   matplotlib.use("Agg")
   import matplotlib.pyplot as plt
   from sklearn.datasets import make_blobs
   from sklearn.cluster import KMeans
   from sklearn.metrics import silhouette_score
   from clarans import CLARANS, FastCLARANS


   def main():
       X, _ = make_blobs(n_samples=500, centers=4, cluster_std=0.60, random_state=42)
       ks = range(2, 9)
       methods = {
           "CLARANS": lambda k: CLARANS(n_clusters=k, numlocal=3, random_state=42),
           "FastCLARANS": lambda k: FastCLARANS(n_clusters=k, numlocal=3, random_state=42),
           "KMeans": lambda k: KMeans(n_clusters=k, random_state=42),
       }

       results = {name: [] for name in methods}

       for k in ks:
           for name, factory in methods.items():
               model = factory(k)
               model.fit(X)
               labels = model.labels_
               if len(set(labels)) > 1:
                   score = silhouette_score(X, labels)
               else:
                   score = float("nan")
               results[name].append(score)

       fig, ax = plt.subplots(figsize=(6, 4))
       for name, scores in results.items():
           ax.plot(list(ks), scores, marker="o", label=name)
       ax.set_xlabel("k (n_clusters)")
       ax.set_ylabel("Silhouette score")
       ax.set_title("Silhouette score vs k")
       ax.legend()

       out = "silhouette_vs_k.png"
       fig.savefig(out, bbox_inches="tight", dpi=150)
       print(f"Saved {out}")


   if __name__ == "__main__":
       main()