Algorithm comparison

Side-by-side comparison of CLARANS, FastCLARANS and scikit-learn’s KMeans on the same dataset.

"""Generate `comparison_clustering.png`.

Side-by-side comparison of CLARANS, FastCLARANS and scikit-learn's KMeans.
"""
from pathlib import Path
import time
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
from sklearn.datasets import make_blobs
from clarans import CLARANS, FastCLARANS
from sklearn.cluster import KMeans


def main():
    X, _ = make_blobs(n_samples=500, centers=3, random_state=42)
    models = [
        ("CLARANS", CLARANS(n_clusters=3, numlocal=5, random_state=42)),
        ("FastCLARANS", FastCLARANS(n_clusters=3, numlocal=5, random_state=42)),
        ("KMeans", KMeans(n_clusters=3, random_state=42)),
    ]

    fig, axes = plt.subplots(1, 3, figsize=(15, 4))

    for ax, (name, model) in zip(axes, models):
        t0 = time.perf_counter()
        model.fit(X)
        t1 = time.perf_counter()
        labels = model.labels_
        centers = getattr(model, "cluster_centers_", None)
        ax.scatter(X[:, 0], X[:, 1], c=labels, s=20, cmap="tab10", alpha=0.8)
        if centers is not None:
            ax.scatter(centers[:, 0], centers[:, 1], c="black", marker="x", s=100, linewidths=2)
        ax.set_title(f"{name}\n{(t1-t0):.3f}s")
        ax.set_xticks([])
        ax.set_yticks([])

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


if __name__ == "__main__":
    main()