def _relabel()

in graspologic/cluster/divisive_cluster.py [0:0]

• 26 lines of code
• 9 McCabe index (conditional complexity)

    def _relabel(self, labels: np.ndarray, level: Optional[int] = None) -> np.ndarray:
        # re-number "labels" so that each cluster at each level recieves
        # a unique label = index of corresponding node in overall dendrogram

        # assign each cluster a new label based on its index in the dendrogram
        all_clusters = [node for node in LevelOrderIter(self)][1:]
        for indx in range(len(all_clusters)):
            all_clusters[indx]._label = indx

        new_labels = labels.copy()
        for lvl in range(1, self.height):
            uni_paths, path_inds = np.unique(
                labels[:, : lvl + 1], axis=0, return_index=True
            )
            for p in range(len(uni_paths)):
                label_inds = (labels[:, : lvl + 1] == uni_paths[p]).all(1)
                current_path = labels[path_inds[p], : lvl + 1]
                cluster = self.root
                current_lvl = 0
                # find the cluster corresponding to "current_path"
                while current_lvl <= lvl:
                    if not cluster.is_leaf:
                        cluster = cluster.children[current_path[current_lvl]]
                        current_lvl += 1
                    else:
                        break
                new_labels[label_inds, lvl] = cluster._label

        # stored to do relabeling for non-fitted data
        self._labels = labels
        self._new_labels = new_labels

        if level is not None:
            new_labels = new_labels[:, level - 1]
        return new_labels