def test_minibatch_sensible_reassign_fit(): # check if identical initial clusters are reassigned # also a regression test for when there are more desired reassignments than # samples. zeroed_X, true_labels = make_blobs(n_samples=100, centers=5, cluster_std=1., random_state=42) zeroed_X[::2, :] = 0 mb_k_means = MiniBatchKMeans(n_clusters=20, batch_size=10, random_state=42, init="random") mb_k_means.fit(zeroed_X) # there should not be too many exact zero cluster centers assert mb_k_means.cluster_centers_.any(axis=1).sum() > 10 # do the same with batch-size > X.shape[0] (regression test) mb_k_means = MiniBatchKMeans(n_clusters=20, batch_size=201, random_state=42, init="random") mb_k_means.fit(zeroed_X) # there should not be too many exact zero cluster centers assert mb_k_means.cluster_centers_.any(axis=1).sum() > 10
def test_minibatch_k_means_init(data, init): mb_k_means = MiniBatchKMeans(init=init, n_clusters=n_clusters, random_state=42, n_init=10) mb_k_means.fit(data) _check_fitted_model(mb_k_means)
def test_mb_kmeans_verbose(): mb_k_means = MiniBatchKMeans(init="k-means++", n_clusters=n_clusters, random_state=42, verbose=1) old_stdout = sys.stdout sys.stdout = StringIO() try: mb_k_means.fit(X) finally: sys.stdout = old_stdout
def test_minibatch_reassign(): # Give a perfect initialization, but a large reassignment_ratio, # as a result all the centers should be reassigned and the model # should no longer be good sample_weight = np.ones(X.shape[0], dtype=X.dtype) for this_X in (X, X_csr): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, batch_size=100, random_state=42) mb_k_means.fit(this_X) score_before = mb_k_means.score(this_X) try: old_stdout = sys.stdout sys.stdout = StringIO() # Turn on verbosity to smoke test the display code _mini_batch_step(this_X, sample_weight, (X**2).sum(axis=1), mb_k_means.cluster_centers_, mb_k_means.counts_, np.zeros(X.shape[1], np.double), False, distances=np.zeros(X.shape[0]), random_reassign=True, random_state=42, reassignment_ratio=1, verbose=True) finally: sys.stdout = old_stdout assert score_before > mb_k_means.score(this_X) # Give a perfect initialization, with a small reassignment_ratio, # no center should be reassigned for this_X in (X, X_csr): mb_k_means = MiniBatchKMeans(n_clusters=n_clusters, batch_size=100, init=centers.copy(), random_state=42, n_init=1) mb_k_means.fit(this_X) clusters_before = mb_k_means.cluster_centers_ # Turn on verbosity to smoke test the display code _mini_batch_step(this_X, sample_weight, (X**2).sum(axis=1), mb_k_means.cluster_centers_, mb_k_means.counts_, np.zeros(X.shape[1], np.double), False, distances=np.zeros(X.shape[0]), random_reassign=True, random_state=42, reassignment_ratio=1e-15) assert_array_almost_equal(clusters_before, mb_k_means.cluster_centers_)
ax.scatter(X[mask, 0], X[mask, 1], c='w', edgecolor=col, marker='.', alpha=0.5) if birch_model.n_clusters is None: ax.scatter(this_centroid[0], this_centroid[1], marker='+', c='k', s=25) ax.set_ylim([-25, 25]) ax.set_xlim([-25, 25]) ax.set_autoscaley_on(False) ax.set_title('Birch %s' % info) # Compute clustering with MiniBatchKMeans. mbk = MiniBatchKMeans(init='k-means++', n_clusters=100, batch_size=100, n_init=10, max_no_improvement=10, verbose=0, random_state=0) t0 = time() mbk.fit(X) t_mini_batch = time() - t0 print("Time taken to run MiniBatchKMeans %0.2f seconds" % t_mini_batch) mbk_means_labels_unique = np.unique(mbk.labels_) ax = fig.add_subplot(1, 3, 3) for this_centroid, k, col in zip(mbk.cluster_centers_, range(n_clusters), colors_): mask = mbk.labels_ == k ax.scatter(X[mask, 0], X[mask, 1], marker='.', c='w', edgecolor=col, alpha=0.5) ax.scatter(this_centroid[0], this_centroid[1], marker='+', c='k', s=25) ax.set_xlim([-25, 25]) ax.set_ylim([-25, 25]) ax.set_title("MiniBatchKMeans")
km = MiniBatchKMeans(n_clusters=true_k, init='k-means++', n_init=1, init_size=1000, batch_size=1000, verbose=opts.verbose) else: km = KMeans(n_clusters=true_k, init='k-means++', max_iter=100, n_init=1, verbose=opts.verbose) print("Clustering sparse data with %s" % km) t0 = time() km.fit(X) print("done in %0.3fs" % (time() - t0)) print() print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels, km.labels_)) print("Completeness: %0.3f" % metrics.completeness_score(labels, km.labels_)) print("V-measure: %0.3f" % metrics.v_measure_score(labels, km.labels_)) print("Adjusted Rand-Index: %.3f" % metrics.adjusted_rand_score(labels, km.labels_)) print("Silhouette Coefficient: %0.3f" % metrics.silhouette_score(X, km.labels_, sample_size=1000)) print() if not opts.use_hashing: print("Top terms per cluster:")