64710c051f26a2778c03fc15ef29c4bcae01df32,official/recommendation/ncf_main.py,,evaluate_model,#Any#Any#Any#,57

Before Change

  // NumPy has an np.argparition() method, however log(1000) is so small that
  // sorting the whole array is simpler and fast enough.
  top_indicies = np.argsort(predicted_scores_by_user, axis=1)[:, -_TOP_K:]
  top_indicies = np.flip(top_indicies, axis=1)

  // Both HR and NDCG vectorized computation takes advantage of the fact that if
  // the positive example for a user is not in the top k, that index does not
  // appear. That is to say:   hit_ind.shape[0] <= num_users
  hit_ind = np.argwhere(np.equal(top_indicies, 0))
  hr = hit_ind.shape[0] / ncf_dataset.num_users
  ndcg = np.sum(np.log(2) / np.log(hit_ind[:, 1] + 2)) / ncf_dataset.num_users

  global_step = estimator.get_variable_value(tf.GraphKeys.GLOBAL_STEP)
  eval_results = {

After Change

      :ncf_dataset.num_users * (1 + rconst.NUM_EVAL_NEGATIVES)]\
      .reshape(ncf_dataset.num_users, -1)
  item_with_padding = np.concatenate(item_batches, axis=0)
  items_by_user = item_with_padding[
      :ncf_dataset.num_users * (1 + rconst.NUM_EVAL_NEGATIVES)]\
      .reshape(ncf_dataset.num_users, -1)

  tf.logging.info("Computing metrics...")

  hr, ndcg = get_hit_rate_and_ndcg(predicted_scores_by_user, items_by_user,
                                   match_mlperf=FLAGS.ml_perf)

  global_step = estimator.get_variable_value(tf.GraphKeys.GLOBAL_STEP)
  eval_results = {
      _HR_KEY: hr,

Instances