example.py

# Based on: https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html
from itertools import cycle, islice
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import time

# ============
# Generate datasets. We choose the size big enough to see the scalability
# of the algorithms, but not too big to avoid too long running times
# ============
n_samples = 1500
noisy_circles = datasets.make_circles(n_samples=n_samples, factor=.5,
                                      noise=.05)
noisy_moons = datasets.make_moons(n_samples=n_samples, noise=.05)
blobs = datasets.make_blobs(n_samples=n_samples, random_state=8)
no_structure = np.random.rand(n_samples, 2), None

# Anisotropicly distributed data
random_state = 170
X, y = datasets.make_blobs(n_samples=n_samples, random_state=random_state)
transformation = [[0.6, -0.6], [-0.4, 0.8]]
X_aniso = np.dot(X, transformation)
aniso = (X_aniso, y)

# blobs with varied variances
varied = datasets.make_blobs(n_samples=n_samples,
                             cluster_std=[1.0, 2.5, 0.5],
                             random_state=random_state)

test_datasets = [noisy_circles, noisy_moons, blobs, no_structure, varied, aniso]

def show_results(model):
    plt.figure(figsize=(18, 12))
    plt.subplots_adjust(left=.02, right=.98, bottom=.001, top=.96, wspace=.05,
                        hspace=.01)
    plot_num = 1

    for dataset in test_datasets:
        X, y = dataset
        X = StandardScaler().fit_transform(X)

        t0 = time.time()
        labels = model.fit_predict(X)
        t1 = time.time()

        print(labels)
        plt.subplot(2, 3, plot_num)
        colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a',
                                             '#f781bf', '#a65628', '#984ea3',
                                             '#999999', '#e41a1c', '#dede00']),
                                      int(max(labels) + 1))))
        # add black color for outliers (if any)
        colors = np.append(colors, ["#000000"])
        plt.scatter(X[:, 0], X[:, 1], s=10, color=colors[labels])

        plt.xlim(-2.5, 2.5)
        plt.ylim(-2.5, 2.5)
        plt.xticks(())
        plt.yticks(())
        plt.text(.99, .01, ('%.2fs' % (t1 - t0)).lstrip('0'),
                 transform=plt.gca().transAxes, size=15,
                 horizontalalignment='right')
        plot_num += 1

    plt.show()


from src.metaheuristic_clustering.abc import ABCClustering

abc_model = ABCClustering()
show_results(abc_model)


from src.metaheuristic_clustering.sfla import SFLAClustering

sfla_model = SFLAClustering()
show_results(sfla_model)