5ba781c281d60ad03d0c8088400a73802dd82d03,basenji/ops.py,,per_target_r2,#Any#Any#Any#,199

Before Change

    r2: idempotent [preds.shape[-1]] tensor of r2 values for each target.
    update_op: op for updating the value given new data
  
  preds_split = tf.unstack(preds, axis=-1)
  targets_split = tf.unstack(targets, axis=-1)
  weights_split = tf.unstack(weights, axis=-1)

  r2_metrics = [
      r2_metric(p, t, w)
      for p, t, w in zip(preds_split, targets_split, weights_split)
  ]

  r2_values = [r[0] for r in r2_metrics]
  stacked_r2 = tf.stack(r2_values)
  update_ops = tf.group(*[r[1] for r in r2_metrics])
  return stacked_r2, update_ops

After Change

  tot_ss, tot_ss_update = _per_target_variance(targets, weights)
  r2 = 1. - res_ss / tot_ss

  update_op = tf.group(res_ss_update, tot_ss_update)
  return r2, update_op


def r2_averaged_over_all_prediction_tasks(preds, targets, weights):

Instances