Merge pull request #1857 from zealseeker/master

    # If there are no nonzero examples, metric is ill-defined.

    if not y_true.size:

      return np.nan

    if self.threshold is not None and len(y_pred.shape) == 1:

      y_pred = np.expand_dims(y_pred, 0)

    if self.threshold is not None:

      y_pred = y_pred[:, 1]

      y_pred = np.greater(y_pred, self.threshold)

                                    1.0 - expected_agreement)

    self.assertAlmostEqual(kappa, expected_kappa)

  def test_one_sample(self):

    """Test that the metrics won't raise error even in an extreme condition

    where there is only one sample with w > 0.

    """

    np.random.seed(123)

    n_samples = 2

    y_true = np.array([0, 0])

    y_pred = np.random.rand(n_samples, 2)

    w = np.array([0, 1])

    all_metrics = [

        dc.metrics.Metric(dc.metrics.recall_score),

        dc.metrics.Metric(dc.metrics.matthews_corrcoef),

        dc.metrics.Metric(dc.metrics.roc_auc_score)

    ]

    for metric in all_metrics:

      score = metric.compute_singletask_metric(y_true, y_pred, w)

      self.assertTrue(np.isnan(score) or score == 0)

  def test_r2_score(self):

    """Test that R^2 metric passes basic sanity tests"""

    np.random.seed(123)