Merge pull request #516 from rbharath/balanced_accuracy

  return score

def balanced_accuracy_score(y, y_pred):

  """Computes balanced accuracy score."""

  num_positive = float(np.count_nonzero(y))

  num_negative = float(len(y) - num_positive)

  pos_weight = num_negative / num_positive

  weights = np.ones_like(y)

  weights[y != 0] = pos_weight

  return accuracy_score(y, y_pred, sample_weight=weights)

def pearson_r2_score(y, y_pred):

  """Computes Pearson R^2 (square of Pearson correlation)."""

  return pearsonr(y, y_pred)[0]**2

    if mode is None:

      if self.metric.__name__ in [

          "roc_auc_score", "matthews_corrcoef", "recall_score",

          "accuracy_score", "kappa_score", "precision_score"

          "accuracy_score", "kappa_score", "precision_score",

          "balanced_accuracy_score"

      ]:

        mode = "classification"

      elif self.metric.__name__ in [

      if self.compute_energy_metric:

        # TODO(rbharath, joegomes): What is this magic number?

        force_error = self.task_averager(computed_metrics[1:]) * 4961.47596096

        print("Force error (metric: np.mean(%s)): %f kJ/mol/A" %

              (self.name, force_error))

        print("Force error (metric: np.mean(%s)): %f kJ/mol/A" % (self.name,

                                                                  force_error))

        return computed_metrics[0]

      elif not per_task_metrics:

        return self.task_averager(computed_metrics)