clustering.py

#basically like at https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html, but our data is reall
#prepre paramets
params = {'quantile': .3,
          'eps': .3,
          'damping': .9,
          'preference': -200,
          'n_neighbors': 10,
          'n_clusters': 5}
bandwidth = estimate_bandwidth(embedding, quantile=params['quantile'])
connectivity = kneighbors_graph(
        embedding, n_neighbors=params['n_neighbors'], include_self=False)
ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)
ward = AgglomerativeClustering(
    n_clusters=params['n_clusters'], linkage='ward',
        connectivity=connectivity)
spectral = SpectralClustering(
    n_clusters=params['n_clusters'], eigen_solver='arpack',
        affinity="nearest_neighbors")
dbscan = DBSCAN(eps=params['eps'])
affinity_propagation = AffinityPropagation(
    damping=params['damping'], preference=params['preference'])
average_linkage = AgglomerativeClustering(
        linkage="average", affinity="cityblock",
        n_clusters=params['n_clusters'], connectivity=connectivity)
birch = Birch(n_clusters=params['n_clusters'])
gmm = GaussianMixture(n_components=params['n_clusters'], covariance_type='full')
clustering_algorithms = (
    ('AffinityPropagation', affinity_propagation),
    ('MeanShift', ms),
    ('SpectralClustering', spectral),
    ('Ward', ward),
    ('AgglomerativeClustering', average_linkage),
    ('DBSCAN', dbscan),
    ('Birch', birch),
    ('GaussianMixture', gmm))
#now plot everything
f, ax = plt.subplots(2, 4, figsize=(20,15))
for idx, (name, algorithm) in enumerate(clustering_algorithms):
    algorithm.fit(embedding)
    if hasattr(algorithm, 'labels_'):
        y_pred = algorithm.labels_.astype(np.int)
    else:
        y_pred = algorithm.predict(embedding)
    colors = np.array(list(islice(cycle(['#377eb8', '#ff7f00', '#4daf4a',
                                             '#f781bf', '#a65628', '#984ea3',
                                             '#999999', '#e41a1c', '#dede00']),
                                      int(max(y_pred) + 1))))
        # add black color for outliers (if any)
    colors = np.append(colors, ["#000000"])
    ax[idx//4, idx%4].scatter(embedding[:, 0], embedding[:, 1], s=2, color=colors[y_pred])
    #ax[idx//4, idx%4].xlim(-2.5, 2.5)
    #ax[idx//4, idx%4].ylim(-2.5, 2.5)
    ax[idx//4, idx%4].set_xticks(())
    ax[idx//4, idx%4].set_yticks(())
    ax[idx//4, idx%4].set_title(name)

plt.tight_layout()