code: def support_fdp(x_truth, x, eps=1e-8):
      """Computes the False Discovery Proportion for selecting the support
      of x_truth using x, namely the proportion of false positive among all
      detected positives, given by FP / (FP + TP). This is useful to assess
      the features selection or outliers detection abilities of a learner.
  
      Parameters
      ----------
      x_truth : `numpy.array`
          Ground truth weights
  
      x : `numpy.array`
          Learned weights
  
      Returns
      -------
      output : `float`
          The False Discovery Proportion for detecting the support of ``x_truth``
          using the support of ``x``
      """
      s = np.abs(x) > eps
      s_truth = np.abs(x_truth) > eps
      v = np.logical_and(np.logical_not(s_truth), s).sum()
      r = max(s.sum(), 1)
      return v / r
  
label_desc: Use image node_id 1 for calling a global function with example usage: support_fdp(x_truth, x, eps) and returns: unknown