class_docstr: Enum for the different methods to compute label quality scores.
class_docstr_tok: ["Enum","for","the","different","methods","to","compute","label","quality","scores","."]
docstr: Returns the label-quality scores for each datapoint based on the given labels and predicted probabilities.
  
  See the documentation for each method for more details.
  
  Example
  -------
  >>> import numpy as np
  >>> from cleanlab.internal.multilabel_scorer import ClassLabelScorer
  >>> labels = np.array([0, 0, 0, 1, 1, 1])
  >>> pred_probs = np.array([
  ...     [0.9, 0.1],
  ...     [0.8, 0.2],
  ...     [0.7, 0.3],
  ...     [0.2, 0.8],
  ...     [0.75, 0.25],
  ...     [0.1, 0.9],
  ... ])
  >>> ClassLabelScorer.SELF_CONFIDENCE(labels, pred_probs)
  array([0.9 , 0.8 , 0.7 , 0.8 , 0.25, 0.9 ])
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code: def __call__(
      self, labels: np.ndarray, pred_probs: np.ndarray, **kwargs
  ) -> np.ndarray:
      """Returns the label-quality scores for each datapoint based on the given labels and predicted probabilities.
  
      See the documentation for each method for more details.
  
      Example
      -------
      >>> import numpy as np
      >>> from cleanlab.internal.multilabel_scorer import ClassLabelScorer
      >>> labels = np.array([0, 0, 0, 1, 1, 1])
      >>> pred_probs = np.array([
      ...     [0.9, 0.1],
      ...     [0.8, 0.2],
      ...     [0.7, 0.3],
      ...     [0.2, 0.8],
      ...     [0.75, 0.25],
      ...     [0.1, 0.9],
      ... ])
      >>> ClassLabelScorer.SELF_CONFIDENCE(labels, pred_probs)
      array([0.9 , 0.8 , 0.7 , 0.8 , 0.25, 0.9 ])
      """
      pred_probs = self._adjust_pred_probs(labels, pred_probs, **kwargs)
      return self.value(labels, pred_probs)
  
imports: from enum import Enum
  from typing import Callable, Dict, Optional, Union
  import numpy
  from sklearn.model_selection import cross_val_predict
  from cleanlab.internal.multilabel_utils import _is_multilabel, stack_complement
  from cleanlab.internal.label_quality_utils import _subtract_confident_thresholds
  from cleanlab.internal.numerics import softmax
  from cleanlab.rank import get_self_confidence_for_each_label, get_normalized_margin_for_each_label, get_confidence_weighted_entropy_for_each_label
label_desc: Use image node_id 1 for calling the ClassLabelScorer obj's underlying member method code with example usage: obj.__call__(labels, pred_probs) and returns: self