Some more test fixes

                                smiles_field=self.smiles_field,

                                compound_featurizers=compound_featurizers,

                                complex_featurizers=complex_featurizers,

                                verbose=True)

                                verbosity="low")

    #Featurizes samples and transforms them into NumPy arrays suitable for ML.

    #returns an instance of class FeaturizedSamples()

from tensorflow.python.platform import googletest

from deepchem.utils import metrics

from deepchem.metrics import kappa_score 

class MetricsTest(googletest.TestCase):

  def test_kappa_score(self):

    y_true = [1, 0, 1, 0]

    y_pred = [0.8, 0.2, 0.3, 0.4]  # [1, 0, 0, 0] with 0.5 threshold

    kappa = metrics.kappa_score(y_true, np.greater(y_pred, 0.5))

    kappa = kappa_score(y_true, np.greater(y_pred, 0.5))

    observed_agreement = 3.0 / 4.0

    expected_agreement = ((2 * 1) + (2 * 3)) / 4.0 ** 2

    expected_kappa = np.true_divide(observed_agreement - expected_agreement,