plot_dbscan.py

import numpy as np
import matplotlib.pyplot as plt
from sklearn.cluster import DBSCAN
from sklearn.datasets import make_blobs

from .plot_helpers import discrete_scatter, cm3


def plot_dbscan():
    X, y = make_blobs(random_state=0, n_samples=12)

    dbscan = DBSCAN()
    clusters = dbscan.fit_predict(X)
    clusters

    fig, axes = plt.subplots(3, 4, figsize=(11, 8),
                             subplot_kw={'xticks': (), 'yticks': ()})
    # Plot clusters as red, green and blue, and outliers (-1) as white
    colors = [cm3(1), cm3(0), cm3(2)]
    markers = ['o', '^', 'v']

    # iterate over settings of min_samples and eps
    for i, min_samples in enumerate([2, 3, 5]):
        for j, eps in enumerate([1, 1.5, 2, 3]):
            # instantiate DBSCAN with a particular setting
            dbscan = DBSCAN(min_samples=min_samples, eps=eps)
            # get cluster assignments
            clusters = dbscan.fit_predict(X)
            print("min_samples: %d eps: %f  cluster: %s"
                  % (min_samples, eps, clusters))
            if np.any(clusters == -1):
                c = ['w'] + colors
                m = ['o'] + markers
            else:
                c = colors
                m = markers
            discrete_scatter(X[:, 0], X[:, 1], clusters, ax=axes[i, j], c=c,
                             s=8, markers=m)
            inds = dbscan.core_sample_indices_
            # vizualize core samples and clusters.
            if len(inds):
                discrete_scatter(X[inds, 0], X[inds, 1], clusters[inds],
                                 ax=axes[i, j], s=15, c=colors,
                                 markers=markers)
            axes[i, j].set_title("min_samples: %d eps: %.1f"
                                 % (min_samples, eps))
    fig.tight_layout()