code: def _calculate_tradeoff_points(
      data,
      sensitive_feature_value,
      flip=False,
      x_metric="false_positive_rate",
      y_metric="true_positive_rate",
  ):
      """Calculate the ROC points from the scores and labels.
  
      This is done by iterating through all possible
      thresholds that could be set based on the available scores.
  
      :param data: the DataFrame containing scores and labels
      :type data: pandas.DataFrame
      :param sensitive_feature_value: the sensitive feature value of the samples provided in `data`
      :type sensitive_feature_value: str or int
      :param flip: if True flip points below the ROC diagonal into points above by applying negative
          weights; if False does not allow flipping; default True
      :type flip: bool
      :return: the ROC curve points with their corresponding threshold operations
      :rtype: pandas.DataFrame
      """
      (
          scores,
          labels,
          n,
          n_positive,
          n_negative,
      ) = _get_scores_labels_and_counts(data)
  
      if n_positive == 0 or n_negative == 0:
          raise ValueError(
              DEGENERATE_LABELS_ERROR_MESSAGE.format(
                  sensitive_feature_value
              )
          )
  
      scores.append(-np.inf)
      labels.append(np.nan)
  
      # Iterate through all samples which are sorted by decreasing scores.
      # Setting the threshold between two scores means that everything smaller
      # than the threshold gets a label of 0 while everything larger than the
      # threshold gets a label of 1. Flipping labels is an option if flipping
      # labels provides better accuracy.
      i = 0
      count = [0, 0]
      x_list, y_list, operation_list = [], [], []
      while i < n:
          # special handling of the initial point
          if x_list == []:
              threshold = np.inf
          else:
              threshold = scores[i]
              while scores[i] == threshold:
                  count[labels[i]] += 1
                  i += 1
              threshold = (threshold + scores[i]) / 2
  
          # For the ROC curve we calculate points (x, y), where x represents
          # the conditional probability P[Y_hat=1 | Y=0] and y represents
          # the conditional probability P[Y_hat=1 | Y=1]. The conditional
          # probability is achieved by dividing by only the number of
          # negative/positive samples.
          actual_counts = _extend_confusion_matrix(
              false_positives=count[0],
              true_positives=count[1],
              true_negatives=(n_negative - count[0]),
              false_negatives=(n_positive - count[1]),
          )
          flipped_counts = _extend_confusion_matrix(
              false_positives=(n_negative - count[0]),
              true_positives=(n_positive - count[1]),
              true_negatives=count[0],
              false_negatives=count[1],
          )
          if flip:
              operations = [(">", actual_counts), ("<", flipped_counts)]
          else:
              operations = [(">", actual_counts)]
  
          for operation_string, counts in operations:
              x = METRIC_DICT[x_metric](counts)
              y = METRIC_DICT[y_metric](counts)
              operation = ThresholdOperation(
                  operation_string, threshold
              )
              x_list.append(x)
              y_list.append(y)
              operation_list.append(operation)
  
      return (
          pd.DataFrame(
              {"x": x_list, "y": y_list, "operation": operation_list}
          )
          .sort_values(by=["x", "y"])
          .reset_index(drop=True)
      )
  
file_path: fairlearn/fairlearn/postprocessing/_tradeoff_curve_utilities.py
imports: import numpy
  import pandas
  from sklearn.utils import Bunch
  from ._constants import LABEL_KEY, P0_KEY, P1_KEY, SCORE_KEY
  from ._threshold_operation import ThresholdOperation
label_desc: Use image node_id 5 for calling a global function with example usage: _calculate_tradeoff_points(data, sensitive_feature_value, flip, x_metric, y_metric) and returns: pd