Created
February 12, 2019 22:53
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| #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() |
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