code: def hit(recs, truth, k=None):
      """
      Compute whether or not a list is a hit; any list with at least one relevant item in the
      first :math:`k` positions (:math:`L_{\\le k} \\cap I_u^{\\mathrm{test}} \\ne \\emptyset`)
      is scored as 1, and lists with no relevant items as 0.  When averaged over the recommendation
      lists, this computes the *hit rate* :cite:p:`Deshpande2004-ht`.
  
      .. math::
          \\frac{|L \\cap I_u^{\\mathrm{test}}|}{\\operatorname{max}\\{|I_u^{\\mathrm{test}}|, k\\}}
  
      This metric has a bulk implementation.
      """
      nrel = len(truth)
      if nrel == 0:
          return None
  
      if k is not None:
          nrel = min(nrel, k)
          recs = recs.iloc[:k]
  
      good = recs["item"].isin(truth.index)
      if np.any(good):
          return 1
      else:
          return 0
  
file_path: lkpy/lenskit/metrics/topn.py
imports: import logging
  import warnings
  import numpy
  import pandas
label_desc: Use image node_id 6 for calling a global function with example usage: hit(recs, truth, k) and returns: None, int, int