Fixes to metrics/datasets

        os.path.join(self.data_dir, row['ids'])), dtype=object)

    return (X, y, w, ids)

  def set_shard(self, shard_num, X, y, w, ids):

    """Writes data shard to disk"""

    basename = "shard-%d" % shard_num 

    tasks = self.get_task_names()

    Dataset.write_data_to_disk(self.data_dir, basename, tasks, X, y, w, ids)

  def set_verbosity(self, new_verbosity):

    """Sets verbosity."""

    self.verbosity = new_verbosity

from sklearn.metrics import r2_score

from sklearn.metrics import mean_squared_error

from sklearn.metrics import mean_absolute_error

from scipy.stats import pearsonr

def to_one_hot(y):

  """Transforms label vector into one-hot encoding.

    score = 0.5

  return score

def pearson_r2_score(y, y_pred):

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

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

def rms_score(y_true, y_pred):

  """Computes RMS error."""

  return np.sqrt(mean_squared_error(y_true, y_pred))

      if self.name in ["roc_auc_score", "matthews_corrcoef", "recall_score",

                       "accuracy_score", "kappa_score"]:

        mode = "classification"

      elif self.name in ["r2_score", "mean_squared_error",

      elif self.name in ["pearson_r2_score", "r2_score", "mean_squared_error",

                         "mean_absolute_error", "rms_score",

                         "mae_score"]:

        mode = "regression"