code: def _roc_auc_score(y_score, y_true):
      """Compute roc-auc score.
  
      Parameters
      ----------
      y_true : array-like, shape=[n-m+1], dtype = int
          True integer labels for each subsequence
      y_pred : array-like, shape=[n-m+1], dtype = int
          Predicted integer labels for each subsequence
  
      Returns
      -------
      F1 : float
          ROC-AUC-score
      """
      # make y_true a boolean vector
      y_true = y_true == 1
  
      # sort scores and corresponding truth values (y_true is sorted by design)
      desc_score_indices = np.arange(y_score.shape[0])[::-1]
  
      y_score = y_score[desc_score_indices]
      y_true = y_true[desc_score_indices]
  
      # y_score typically has many tied values. Here we extract
      # the indices associated with the distinct values. We also
      # concatenate a value for the end of the curve.
      distinct_value_indices = np.where(np.diff(y_score))[0]
      threshold_idxs = np.concatenate(
          (distinct_value_indices, np.array([y_true.size - 1]))
      )
  
      # accumulate the true positives with decreasing threshold
      tps = np.cumsum(y_true)[threshold_idxs]
      fps = 1 + threshold_idxs - tps
  
      tps = np.concatenate((np.array([0]), tps))
      fps = np.concatenate((np.array([0]), fps))
  
      if fps[-1] <= 0 or tps[-1] <= 0:
          return np.nan
  
      fpr = fps / fps[-1]
      tpr = tps / tps[-1]
  
      if fpr.shape[0] < 2:
          return np.nan
  
      direction = 1
      dx = np.diff(fpr)
  
      if np.any(dx < 0):
          if np.all(dx <= 0):
              direction = -1
          else:
              return np.nan
  
      area = direction * np.trapz(tpr, fpr)
      return area
  
file_path: sktime/sktime/transformations/series/_clasp_numba.py
imports: import numpy
  import pandas
  from sktime.transformations.panel.matrix_profile import _sliding_dot_products
  from sktime.utils.numba.njit import njit
label_desc: Use image node_id 6 for calling a global function with example usage: _roc_auc_score(y_score, y_true) and returns: area, np, np, np