optics.py

import numpy as np
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
from sklearn.cluster import OPTICS

def cluster(data, min_samples=5, eps=np.inf ,reachability_plot=True, maxima_ratio=0.75, clustering_visualization=False, n_jobs=1):
        clust = OPTICS(min_cluster_size=min_samples, max_eps=eps, maxima_ratio=maxima_ratio, n_jobs=n_jobs)

        # Run the fit
        clust.fit(data)

        space = np.arange(len(data))
        reachability = clust.reachability_[clust.ordering_]
        labels = clust.labels_[clust.ordering_]

        if(reachability_plot or clustering_visualization):
                plt.figure(figsize=(10, 7))
                G = gridspec.GridSpec(2, 3)
                ax1 = plt.subplot(G[0, :])

                if(clustering_visualization):
                        ax2 = plt.subplot(G[1, 0])
                        x = np.array(data)
                        # OPTICS
                        color = ['g.', 'r.', 'b.', 'y.', 'c.']
                        for k, c in zip(range(0, 5), color):
                                Xk = x[clust.labels_ == k]
                                ax2.plot(Xk[:, 0], Xk[:, 1], c, alpha=0.3)
                        ax2.plot(x[clust.labels_ == -1, 0], x[clust.labels_ == -1, 1], 'k+', alpha=0.1)
                        ax2.set_title('Automatic Clustering\nOPTICS')

                if(reachability_plot):
                        # Reachability plot
                        color = ['g.', 'r.', 'b.', 'y.', 'c.']
                        for k, c in zip(range(0, 5), color):
                                Xk = space[labels == k]
                                Rk = reachability[labels == k]
                                ax1.plot(Xk, Rk, c, alpha=0.3)
                        ax1.plot(space[labels == -1], reachability[labels == -1], 'k.', alpha=0.3)
                        ax1.plot(space, np.full_like(space, 0.75, dtype=float), 'k-', alpha=0.5)
                        ax1.plot(space, np.full_like(space, 0.25, dtype=float), 'k-.', alpha=0.5)
                        ax1.set_ylabel('Reachability (epsilon distance)')
                        ax1.set_title('Reachability Plot')
                
                plt.tight_layout()
                plt.show()
              
        return clust

def predict(clust, data):
        return clust.fit_predict(data)