class_docstr: Manages issues related to underperforming group examples.
  
  Note: The `min_cluster_samples` argument should not be confused with the
  `min_samples` argument of sklearn.cluster.DBSCAN.
  
  Examples
  --------
  >>> from cleanlab import Datalab
  >>> import numpy as np
  >>> X = np.random.normal(size=(50, 2))
  >>> y = np.random.randint(2, size=50)
  >>> pred_probs = X / X.sum(axis=1, keepdims=True)
  >>> data = {"X": X, "y": y}
  >>> lab = Datalab(data, label_name="y")
  >>> issue_types={"underperforming_group": {"clustering_kwargs": {"eps": 0.5}}}
  >>> lab.find_issues(pred_probs=pred_probs, features=X, issue_types=issue_types)
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docstr: Remove outlier clusters and return IDs of clusters with at least `self.min_cluster_samples` number of datapoints.
  
  
  Args:
      cluster_ids (npt.NDArray[np.int_]): Cluster IDs for each datapoint.
  
  Returns:
      unique_cluster_ids (npt.NDArray[np.int_]):  List of unique cluster IDs after
      removing outlier clusters and clusters with less than `self.min_cluster_samples`
      number of datapoints.
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code: def filter_cluster_ids(
      self, cluster_ids: npt.NDArray[np.int_]
  ) -> npt.NDArray[np.int_]:
      """Remove outlier clusters and return IDs of clusters with at least `self.min_cluster_samples` number of datapoints.
  
  
      Args:
          cluster_ids (npt.NDArray[np.int_]): Cluster IDs for each datapoint.
  
      Returns:
          unique_cluster_ids (npt.NDArray[np.int_]):  List of unique cluster IDs after
          removing outlier clusters and clusters with less than `self.min_cluster_samples`
          number of datapoints.
      """
      unique_cluster_ids = np.array(
          [
              label
              for label in set(cluster_ids)
              if label not in self.OUTLIER_CLUSTER_LABELS
          ]
      )
      frequencies = np.bincount(
          cluster_ids[
              ~np.isin(cluster_ids, self.OUTLIER_CLUSTER_LABELS)
          ]
      )
      unique_cluster_ids = np.array(
          [
              cluster_id
              for cluster_id in unique_cluster_ids
              if frequencies[cluster_id] >= self.min_cluster_samples
          ]
      )
      return unique_cluster_ids
  
imports: from __future__ import annotations
  from typing import TYPE_CHECKING, Any, ClassVar, Dict, Optional, Union, Tuple
  import warnings
  import inspect
  import numpy
  import pandas
  from scipy.sparse import csr_matrix
  from sklearn.neighbors import NearestNeighbors
  from sklearn.exceptions import NotFittedError
  from sklearn.utils.validation import check_is_fitted
  from sklearn.cluster import DBSCAN
  from cleanlab.datalab.internal.issue_manager import IssueManager
  from cleanlab.rank import get_self_confidence_for_each_label
label_desc: Use image node_id 5 for calling the UnderperformingGroupIssueManager obj's underlying member method code with example usage: obj.filter_cluster_ids(cluster_ids) and returns: unique_cluster_ids