Python sklearn.metrics.normalized_mutual_info_score() Examples

def benchmarking(gtlabels, labels):
    # TODO: Please note that the AMI definition used in the paper differs from that in the sklearn python package.
    # TODO: Please modify it accordingly.
    numeval = len(gtlabels)
    ari = metrics.adjusted_rand_score(gtlabels[:numeval], labels[:numeval])
    ami = metrics.adjusted_mutual_info_score(gtlabels[:numeval], labels[:numeval])
    nmi = metrics.normalized_mutual_info_score(gtlabels[:numeval], labels[:numeval])
    acc = clustering_accuracy(gtlabels[:numeval], labels[:numeval])

    return ari, ami, nmi, acc 

def test_pipeline_spectral_clustering(seed=36):
    # Test using pipeline to do spectral clustering
    random_state = np.random.RandomState(seed)
    se_rbf = SpectralEmbedding(n_components=n_clusters,
                               affinity="rbf",
                               random_state=random_state)
    se_knn = SpectralEmbedding(n_components=n_clusters,
                               affinity="nearest_neighbors",
                               n_neighbors=5,
                               random_state=random_state)
    for se in [se_rbf, se_knn]:
        km = KMeans(n_clusters=n_clusters, random_state=random_state)
        km.fit(se.fit_transform(S))
        assert_array_almost_equal(
            normalized_mutual_info_score(
                km.labels_,
                true_labels), 1.0, 2) 

def load_amazon():
    """Amazon dataset.

    Amazon product co-purchasing network and ground-truth communities.

    Network was collected by crawling Amazon website. It is based on Customers Who Bought
    This Item Also Bought feature of the Amazon website. If a product i is frequently
    co-purchased with product j, the graph contains an undirected edge from i to j.
    Each product category provided by Amazon defines each ground-truth community.
    """

    dataset_path = _load('amazon')

    X = _load_csv(dataset_path, 'data')
    y = X.pop('label').values

    graph = nx.Graph(nx.read_gml(os.path.join(dataset_path, 'graph.gml')))

    return Dataset(load_amazon.__doc__, X, y, normalized_mutual_info_score, 'graph',
                   'community_detection', graph=graph) 

def check_forward(self, x_data, c_data, gamma, T, y_star, y_pam):
        num_examples = len(x_data)
        x = chainer.Variable(x_data)
        c = chainer.Variable(c_data)

        loss = clustering_loss(x, c, gamma, T)

        sq_distances_ij = []
        for i, j in zip(range(num_examples), y_pam):
            sqd_ij = np.sum((x_data[i] - x_data[j]) ** 2)
            sq_distances_ij.append(sqd_ij)
        f = -sum(sq_distances_ij)

        sq_distances_ij = []
        for i, j in zip(range(num_examples), y_star):
            sqd_ij = np.sum((x_data[i] - x_data[j]) ** 2)
            sq_distances_ij.append(sqd_ij)
        f_tilde = -sum(sq_distances_ij)

        delta = 1.0 - normalized_mutual_info_score(cuda.to_cpu(c_data), y_pam)
        loss_expected = f + gamma * delta - f_tilde

        testing.assert_allclose(loss.data, loss_expected) 

def load_amazon():
    """Amazon product co-purchasing network and ground-truth communities.

    Network was collected by crawling Amazon website. It is based on Customers Who Bought
    This Item Also Bought feature of the Amazon website. If a product i is frequently
    co-purchased with product j, the graph contains an undirected edge from i to j.
    Each product category provided by Amazon defines each ground-truth community.
    """
    dataset_path = _load('amazon')

    X = _load_csv(dataset_path, 'data')
    y = X.pop('label').values

    graph = nx.Graph(nx.read_gml(os.path.join(dataset_path, 'graph.gml')))

    return Dataset(load_amazon.__doc__, X, y, normalized_mutual_info_score, graph=graph) 

def clustering_scores(self, prediction_algorithm: str = "knn") -> Tuple:
        if self.gene_dataset.n_labels > 1:
            latent, _, labels = self.get_latent()
            if prediction_algorithm == "knn":
                labels_pred = KMeans(
                    self.gene_dataset.n_labels, n_init=200
                ).fit_predict(
                    latent
                )  # n_jobs>1 ?
            elif prediction_algorithm == "gmm":
                gmm = GMM(self.gene_dataset.n_labels)
                gmm.fit(latent)
                labels_pred = gmm.predict(latent)

            asw_score = silhouette_score(latent, labels)
            nmi_score = NMI(labels, labels_pred)
            ari_score = ARI(labels, labels_pred)
            uca_score = unsupervised_clustering_accuracy(labels, labels_pred)[0]
            logger.debug(
                "Clustering Scores:\nSilhouette: %.4f\nNMI: %.4f\nARI: %.4f\nUCA: %.4f"
                % (asw_score, nmi_score, ari_score, uca_score)
            )
            return asw_score, nmi_score, ari_score, uca_score 

def test_pipeline_spectral_clustering(seed=36):
    # Test using pipeline to do spectral clustering
    random_state = np.random.RandomState(seed)
    se_rbf = SpectralEmbedding(n_components=n_clusters,
                               affinity="rbf",
                               random_state=random_state)
    se_knn = SpectralEmbedding(n_components=n_clusters,
                               affinity="nearest_neighbors",
                               n_neighbors=5,
                               random_state=random_state)
    for se in [se_rbf, se_knn]:
        km = KMeans(n_clusters=n_clusters, random_state=random_state)
        km.fit(se.fit_transform(S))
        assert_array_almost_equal(
            normalized_mutual_info_score(
                km.labels_,
                true_labels), 1.0, 2) 

def test_spectral_embedding_two_components(seed=36):
    """Test spectral embedding with two components"""
    random_state = np.random.RandomState(seed)
    n_sample = 100
    affinity = np.zeros(shape=[n_sample * 2,
                               n_sample * 2])
    # first component
    affinity[0:n_sample,
             0:n_sample] = np.abs(random_state.randn(n_sample, n_sample)) + 2
    # second component
    affinity[n_sample::,
             n_sample::] = np.abs(random_state.randn(n_sample, n_sample)) + 2
    # connection
    affinity[0, n_sample + 1] = 1
    affinity[n_sample + 1, 0] = 1
    affinity.flat[::2 * n_sample + 1] = 0
    affinity = 0.5 * (affinity + affinity.T)

    true_label = np.zeros(shape=2 * n_sample)
    true_label[0:n_sample] = 1

    se_precomp = SpectralEmbedding(n_components=1,
                                   random_state=np.random.RandomState(seed),
                                   eigen_solver = 'arpack')
    embedded_coordinate = se_precomp.fit_transform(affinity,
                                                   input_type='affinity')

    # thresholding on the first components using 0.
    label_ = np.array(embedded_coordinate.ravel() < 0, dtype="float")
    assert_equal(normalized_mutual_info_score(true_label, label_), 1.0) 

def test_spectral_embedding_two_components(seed=36):
    # Test spectral embedding with two components
    random_state = np.random.RandomState(seed)
    n_sample = 100
    affinity = np.zeros(shape=[n_sample * 2, n_sample * 2])
    # first component
    affinity[0:n_sample,
             0:n_sample] = np.abs(random_state.randn(n_sample, n_sample)) + 2
    # second component
    affinity[n_sample::,
             n_sample::] = np.abs(random_state.randn(n_sample, n_sample)) + 2

    # Test of internal _graph_connected_component before connection
    component = _graph_connected_component(affinity, 0)
    assert_true(component[:n_sample].all())
    assert_true(not component[n_sample:].any())
    component = _graph_connected_component(affinity, -1)
    assert_true(not component[:n_sample].any())
    assert_true(component[n_sample:].all())

    # connection
    affinity[0, n_sample + 1] = 1
    affinity[n_sample + 1, 0] = 1
    affinity.flat[::2 * n_sample + 1] = 0
    affinity = 0.5 * (affinity + affinity.T)

    true_label = np.zeros(shape=2 * n_sample)
    true_label[0:n_sample] = 1

    se_precomp = SpectralEmbedding(n_components=1, affinity="precomputed",
                                   random_state=np.random.RandomState(seed))
    embedded_coordinate = se_precomp.fit_transform(affinity)
    # Some numpy versions are touchy with types
    embedded_coordinate = \
        se_precomp.fit_transform(affinity.astype(np.float32))
    # thresholding on the first components using 0.
    label_ = np.array(embedded_coordinate.ravel() < 0, dtype="float")
    assert_equal(normalized_mutual_info_score(true_label, label_), 1.0) 

def test_spectral_embedding_two_components(seed=36):
    # Test spectral embedding with two components
    random_state = np.random.RandomState(seed)
    n_sample = 100
    affinity = np.zeros(shape=[n_sample * 2, n_sample * 2])
    # first component
    affinity[0:n_sample,
             0:n_sample] = np.abs(random_state.randn(n_sample, n_sample)) + 2
    # second component
    affinity[n_sample::,
             n_sample::] = np.abs(random_state.randn(n_sample, n_sample)) + 2

    # Test of internal _graph_connected_component before connection
    component = _graph_connected_component(affinity, 0)
    assert component[:n_sample].all()
    assert not component[n_sample:].any()
    component = _graph_connected_component(affinity, -1)
    assert not component[:n_sample].any()
    assert component[n_sample:].all()

    # connection
    affinity[0, n_sample + 1] = 1
    affinity[n_sample + 1, 0] = 1
    affinity.flat[::2 * n_sample + 1] = 0
    affinity = 0.5 * (affinity + affinity.T)

    true_label = np.zeros(shape=2 * n_sample)
    true_label[0:n_sample] = 1

    se_precomp = SpectralEmbedding(n_components=1, affinity="precomputed",
                                   random_state=np.random.RandomState(seed))
    embedded_coordinate = se_precomp.fit_transform(affinity)
    # Some numpy versions are touchy with types
    embedded_coordinate = \
        se_precomp.fit_transform(affinity.astype(np.float32))
    # thresholding on the first components using 0.
    label_ = np.array(embedded_coordinate.ravel() < 0, dtype="float")
    assert_equal(normalized_mutual_info_score(true_label, label_), 1.0) 

def calculate_NMI(self, query_labels, cluster_labels, **kwargs):
        return normalized_mutual_info_score(query_labels, cluster_labels) 

def _nmi(preds, targets):
  return metrics.normalized_mutual_info_score(targets, preds) 

def normalized_mutual_information(first_partition, second_partition):
    """
    Normalized Mutual Information between two clusterings.

    Normalized Mutual Information (NMI) is an normalization of the Mutual
    Information (MI) score to scale the results between 0 (no mutual
    information) and 1 (perfect correlation). In this function, mutual
    information is normalized by ``sqrt(H(labels_true) * H(labels_pred))``

    :param first_partition: NodeClustering object
    :param second_partition: NodeClustering object
    :return: MatchingResult object

    :Example:

      >>> from cdlib import evaluation, algorithms
      >>> g = nx.karate_club_graph()
      >>> louvain_communities = algorithms.louvain(g)
      >>> leiden_communities = algorithms.leiden(g)
      >>> evaluation.normalized_mutual_information(louvain_communities,leiden_communities)

    """

    __check_partition_coverage(first_partition, second_partition)
    __check_partition_overlap(first_partition, second_partition)

    first_partition_c = [x[1]
                         for x in sorted([(node, nid)
                                          for nid, cluster in enumerate(first_partition.communities)
                                          for node in cluster], key=lambda x: x[0])]

    second_partition_c = [x[1]
                          for x in sorted([(node, nid)
                                           for nid, cluster in enumerate(second_partition.communities)
                                           for node in cluster], key=lambda x: x[0])]

    from sklearn.metrics import normalized_mutual_info_score
    return MatchingResult(score=normalized_mutual_info_score(first_partition_c, second_partition_c)) 

def test_normalized_mutual_info_score(self):
        result = self.df.metrics.normalized_mutual_info_score()
        expected = metrics.normalized_mutual_info_score(self.target, self.pred)
        self.assertEqual(result, expected) 

def test_diffusion_embedding_two_components_diffusion_time_one(seed=36):
    """Test spectral embedding with two components"""
    random_state = np.random.RandomState(seed)
    n_sample = 100
    affinity = np.zeros(shape=[n_sample * 2,
                               n_sample * 2])
    # first component
    affinity[0:n_sample,
             0:n_sample] = np.abs(random_state.randn(n_sample, n_sample)) + 2
    # second component
    affinity[n_sample::,
             n_sample::] = np.abs(random_state.randn(n_sample, n_sample)) + 2
    # connection
    affinity[0, n_sample + 1] = 1
    affinity[n_sample + 1, 0] = 1
    affinity.flat[::2 * n_sample + 1] = 0
    affinity = 0.5 * (affinity + affinity.T)

    true_label = np.zeros(shape=2 * n_sample)
    true_label[0:n_sample] = 1
    geom_params = {'laplacian_method':'geometric'}
    se_precomp = SpectralEmbedding(n_components=1,
                                   random_state=np.random.RandomState(seed),
                                   eigen_solver = 'arpack',
                                   diffusion_maps = True,
                                   diffusion_time = 1.0,
                                   geom = geom_params)
    embedded_coordinate = se_precomp.fit_transform(affinity,
                                                   input_type='affinity')

    # thresholding on the first components using 0.
    label_ = np.array(embedded_coordinate.ravel() < 0, dtype="float")
    assert_equal(normalized_mutual_info_score(true_label, label_), 1.0) 

def test_diffusion_embedding_two_components_no_diffusion_time(seed=36):
    """Test spectral embedding with two components"""
    random_state = np.random.RandomState(seed)
    n_sample = 100
    affinity = np.zeros(shape=[n_sample * 2,
                               n_sample * 2])
    # first component
    affinity[0:n_sample,
             0:n_sample] = np.abs(random_state.randn(n_sample, n_sample)) + 2
    # second component
    affinity[n_sample::,
             n_sample::] = np.abs(random_state.randn(n_sample, n_sample)) + 2
    # connection
    affinity[0, n_sample + 1] = 1
    affinity[n_sample + 1, 0] = 1
    affinity.flat[::2 * n_sample + 1] = 0
    affinity = 0.5 * (affinity + affinity.T)

    true_label = np.zeros(shape=2 * n_sample)
    true_label[0:n_sample] = 1
    geom_params = {'laplacian_method':'geometric'}
    se_precomp = SpectralEmbedding(n_components=1,
                                   random_state=np.random.RandomState(seed),
                                   eigen_solver = 'arpack',
                                   diffusion_maps = True,
                                   geom = geom_params)
    embedded_coordinate = se_precomp.fit_transform(affinity,
                                                   input_type='affinity')

    # thresholding on the first components using 0.
    label_ = np.array(embedded_coordinate.ravel() < 0, dtype="float")
    assert_equal(normalized_mutual_info_score(true_label, label_), 1.0) 

def _augmented_update_medoid_ics_in_place(self, pdists, y_gt, cluster_ics,
                                            medoid_ics, loss_mult):
    for cluster_idx in range(self.n_clusters):
      # y_pred = self._get_cluster_ics(D, medoid_ics)
      # Don't prematurely do the assignment step.
      # Do this after we've updated all cluster medoids.
      y_pred = cluster_ics

      if sum(y_pred == cluster_idx) == 0:
        # Cluster is empty.
        continue

      curr_score = (
          -1.0 * np.sum(
              pdists[medoid_ics[cluster_idx], y_pred == cluster_idx]) +
          loss_mult * (1.0 - metrics.normalized_mutual_info_score(
              y_gt, y_pred)))

      pdist_in = pdists[y_pred == cluster_idx, :]
      pdist_in = pdist_in[:, y_pred == cluster_idx]

      all_scores_fac = np.sum(-1.0 * pdist_in, axis=1)
      all_scores_loss = []
      for i in range(y_pred.size):
        if y_pred[i] != cluster_idx:
          continue
        # remove this cluster's current centroid
        medoid_ics_i = medoid_ics[:cluster_idx] + medoid_ics[cluster_idx + 1:]
        # add this new candidate to the centroid list
        medoid_ics_i += [i]
        y_pred_i = self._get_cluster_ics(pdists, medoid_ics_i)
        all_scores_loss.append(loss_mult * (
            1.0 - metrics.normalized_mutual_info_score(y_gt, y_pred_i)))

      all_scores = all_scores_fac + all_scores_loss
      max_score_idx = np.argmax(all_scores)
      max_score = all_scores[max_score_idx]

      if max_score > curr_score:
        medoid_ics[cluster_idx] = np.where(
            y_pred == cluster_idx)[0][max_score_idx] 

def evaluate_clustering(y_gt, y_assignment):
    return normalized_mutual_info_score(y_gt, y_assignment) 

def my_Kmeans(x, y, k=4, time=10, return_NMI=False):
    x = np.array(x)
    x = np.squeeze(x)
    y = np.array(y)

    if len(y.shape) > 1:
        y = np.argmax(y, axis=1)

    estimator = KMeans(n_clusters=k)
    ARI_list = []  # adjusted_rand_score(
    NMI_list = []
    if time:
        # print('KMeans exps {}次 æ±~B平å~]~G '.format(time))
        for i in range(time):
            estimator.fit(x, y)
            y_pred = estimator.predict(x)
            score = normalized_mutual_info_score(y, y_pred)
            NMI_list.append(score)
            s2 = adjusted_rand_score(y, y_pred)
            ARI_list.append(s2)
        # print('NMI_list: {}'.format(NMI_list))
        score = sum(NMI_list) / len(NMI_list)
        s2 = sum(ARI_list) / len(ARI_list)
        print('NMI (10 avg): {:.4f} , ARI (10avg): {:.4f}'.format(score, s2))

    else:
        estimator.fit(x, y)
        y_pred = estimator.predict(x)
        score = normalized_mutual_info_score(y, y_pred)
        print("NMI on all label data: {:.5f}".format(score))
    if return_NMI:
        return score, s2 

def evaluate_paper_cluster(self, embedding_matrix):
        embedding_list = embedding_matrix.tolist()

        X = []
        Y = []
        for paper in self.paper_label:
            X.append(embedding_list[paper])
            Y.append(self.paper_label[paper])

        pred_Y = KMeans(3).fit(np.array(X)).predict(X)
        score = normalized_mutual_info_score(np.array(Y), pred_Y)

        return score 

def evaluate_author_cluster(self, embedding_matrix):
        embedding_list = embedding_matrix.tolist()

        X = []
        Y = []
        for author in self.author_label:
            X.append(embedding_list[author])
            Y.append(self.author_label[author])

        pred_Y = KMeans(4).fit(np.array(X)).predict(X)
        score = normalized_mutual_info_score(np.array(Y), pred_Y)

        return score 

def evaluate_cluster(self, embedding_list):
        X = []
        Y = []
        for p in self.label:
            X.append(embedding_list[p])
            Y.append(self.label[p])

        Y_pred = KMeans(self.n_label, random_state=self.seed).fit(np.array(X)).predict(X)
        nmi = normalized_mutual_info_score(np.array(Y), Y_pred)
        ari = adjusted_rand_score(np.array(Y), Y_pred)
        return nmi, ari 

def _compute_nmi_score(labels, predictions):
  return math_ops.to_float(
      script_ops.py_func(
          metrics.normalized_mutual_info_score, [labels, predictions],
          [dtypes.float64],
          name='nmi')) 

def pam_augmented_fit(self, feat, y, loss_mult):
    pam_max_iter = 5
    self._check_init_args()
    feat = self._check_array(feat)
    pdists = pairwise_distance_np(feat)
    self.loss_augmented_fit(feat, y, loss_mult)
    print('PAM -1 (before PAM): score: %f, score_aug: %f' % (
        self.score_, self.score_aug_))
    # Initialize from loss augmented facility location
    subset = self.center_ics_
    for iter_ in range(pam_max_iter):
      # update the cluster assignment
      cluster_ics = self._get_cluster_ics(pdists, subset)
      # update the medoid for each clusters
      self._augmented_update_medoid_ics_in_place(pdists, y, cluster_ics, subset,
                                                 loss_mult)
      self.score_ = np.float32(-1.0) * self._get_facility_distance(
          pdists, subset)
      self.score_aug_ = self.score_ + loss_mult * (
          1.0 - metrics.normalized_mutual_info_score(
              y, self._get_cluster_ics(pdists, subset)))
      self.score_aug_ = self.score_aug_.astype(np.float32)
      print('PAM iter: %d, score: %f, score_aug: %f' % (iter_, self.score_,
                                                        self.score_aug_))

    self.center_ics_ = subset
    self.labels_ = cluster_ics
    return self 

def loss_augmented_fit(self, X, y, loss_mult):
        """Fit K-Medoids to the provided data.

        Parameters
        ----------
        X : array-like or sparse matrix, shape=(n_samples, n_features)

        Returns
        -------
        self
        """

        self._check_init_args()

        # Check that the array is good and attempt to convert it to
        # Numpy array if possible
        X = self._check_array(X)

        # Apply distance metric to get the distance matrix
        D = self.distance_func(X)

        num_data = X.shape[0]
        candidate_ids = range(num_data)
        candidate_scores = np.zeros(num_data,)
        subset = []

        k = 0
        while k < self.n_clusters:
          candidate_scores = []
          for i in candidate_ids:
            # push i to subset
            subset.append(i)
            marginal_cost = np.sum(np.min(D[:, subset], axis=1))
            loss = normalized_mutual_info_score(y,self._get_cluster_ics(D, subset))
            candidate_scores.append(marginal_cost - loss_mult*loss)
            # remove i from subset
            subset.pop()

          # push i_star to subset
          i_star = candidate_ids[np.argmin(candidate_scores)]
          bisect.insort(subset, i_star)
          # remove i_star from candiate indices
          del candidate_ids[bisect.bisect_left(candidate_ids, i_star)]

          k = k + 1

          #print '|S|: %d, F(S): %f' % (k, np.min(candidate_scores))

        # Expose labels_ which are the assignments of
        # the training data to clusters
        self.labels_ = self._get_cluster_ics(D, subset)

        # Expose cluster centers, i.e. medoids
        self.cluster_centers_ = X.take(subset, axis=0)

        # Expose indices of chosen cluster centers
        self.center_ics_ = subset

        return self 

def plot_metrics(path, dataset, ref, fraction):
    ARI = []
    NMI = []
    F1 = []
    methods = ['scABC', 'SC3', 'scVI', 'SCALE']
    for frac in fraction:
        outdir = os.path.join(path, dataset, frac) #;print(outdir)
        scABC_pred, _ = read_labels(os.path.join(outdir, 'scABC_predict.txt'))
        if os.path.isfile(os.path.join(outdir, 'SC3_predict.txt')):
            SC3_pred, _ = read_labels(os.path.join(outdir, 'SC3_predict.txt'))
        else:
            SC3_pred = None
        scVI_pred, _ = read_labels(os.path.join(outdir, 'scVI_predict.txt'))
        scale_pred, pred_classes = read_labels(os.path.join(outdir, 'cluster_assignments.txt'))
        
        ari = []
        nmi = []
        f1 = []
        for pred, method in zip([scABC_pred, SC3_pred, scVI_pred, scale_pred], methods):
            if pred is None:
                ari.append(0)
                nmi.append(0)
                f1.append(0)
            else:
                pred = reassign_cluster_with_ref(pred, ref)
                ari.append(adjusted_rand_score(ref, pred))
                nmi.append(normalized_mutual_info_score(ref, pred))
                f1.append(f1_score(ref, pred, average='micro'))
        ARI.append(ari)
        NMI.append(nmi)
        F1.append(f1)
    fraction = [ frac.replace('corrupt_', '') for frac in fraction]
    ARI = pd.Series(np.concatenate(ARI, axis=0))
    NMI = pd.Series(np.concatenate(NMI, axis=0))
    F1 = pd.Series(np.concatenate(F1, axis=0))
    M = pd.Series(methods * len(fraction))
    F = pd.Series(np.concatenate([[i]*len(methods) for i in fraction]))
    
    metrics = pd.concat([ARI, NMI, F1, M, F], axis=1)
    metrics.columns = ['ARI', 'NMI', 'F1', 'method', 'fraction']
    
    lineplot(metrics, 'ARI', dataset, False)
    lineplot(metrics, 'NMI', dataset, False)
    lineplot(metrics, 'F1', dataset, True) 

def loss_augmented_fit(self, feat, y, loss_mult):
    """Fit K-Medoids to the provided data."""
    self._check_init_args()
    # Check that the array is good and attempt to convert it to
    # Numpy array if possible.
    feat = self._check_array(feat)
    # Apply distance metric to get the distance matrix.
    pdists = pairwise_distance_np(feat)

    num_data = feat.shape[0]
    candidate_ids = list(range(num_data))
    candidate_scores = np.zeros(num_data,)
    subset = []

    k = 0
    while k < self.n_clusters:
      candidate_scores = []
      for i in candidate_ids:
        # push i to subset.
        subset.append(i)
        marginal_cost = -1.0 * np.sum(np.min(pdists[:, subset], axis=1))
        loss = 1.0 - metrics.normalized_mutual_info_score(
            y, self._get_cluster_ics(pdists, subset))
        candidate_scores.append(marginal_cost + loss_mult * loss)
        # remove i from subset.
        subset.pop()

      # push i_star to subset.
      i_star = candidate_ids[np.argmax(candidate_scores)]
      subset.append(i_star)
      # remove i_star from candidate indices.
      candidate_ids.remove(i_star)
      k += 1

    # Expose labels_ which are the assignments of
    # the training data to clusters.
    self.labels_ = self._get_cluster_ics(pdists, subset)
    # Expose cluster centers, i.e. medoids.
    self.cluster_centers_ = feat.take(subset, axis=0)
    # Expose indices of chosen cluster centers.
    self.center_ics_ = subset
    # Expose the score = -\sum_{i \in V} min_{j \in S} || x_i - x_j ||
    self.score_ = np.float32(-1.0) * self._get_facility_distance(pdists, subset)
    self.score_aug_ = self.score_ + loss_mult * (
        1.0 - metrics.normalized_mutual_info_score(
            y, self._get_cluster_ics(pdists, subset)))
    self.score_aug_ = self.score_aug_.astype(np.float32)
    # Expose the chosen cluster indices.
    self.subset_ = subset
    return self