-r2_score +test

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

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

def r2_score(y, y_pred):

  """Computes R^2 using coefficient of determination (can be negative)."""

  return r2_score(y, y_pred)

def prc_auc_score(y, y_pred):

  """Compute area under precision-recall curve"""

  assert y_pred.shape == y.shape

  model.save()

  model = TensorGraph.load_from_dir(model.model_dir)

  scores = model.evaluate(dataset, [metric], transformers)

  caffeine = 'CN1C(=O)N(C)c2ncn(C)c2C1=O'

  asprin = 'CC(=O)Oc1ccccc1C(=O)O'

  mols = np.array([caffeine, asprin])

  out = model.predict_on_smiles(mols, transformers)