Fixed incorrect code in metrics

  yt = np.asarray(y_true, dtype=int)

  yp = np.asarray(y_pred, dtype=int)

  assert np.array_equal(

      np.unique(yt), [0,

                      1]), ('Class labels must be binary: %s' % np.unique(yt))

      np.unique(yt),

      [0, 1]), ('Class labels must be binary: %s' % np.unique(yt))

  observed_agreement = np.true_divide(

      np.count_nonzero(np.equal(yt, yp)), len(yt))

  expected_agreement = np.true_divide(

      np.count_nonzero(yt == 1) * np.count_nonzero(yp == 1) +

      np.count_nonzero(yt == 0) * np.count_nonzero(yp == 0), len(yt)**2)

      np.count_nonzero(yt == 0) * np.count_nonzero(yp == 0),

      len(yt)**2)

  kappa = np.true_divide(observed_agreement - expected_agreement,

                         1.0 - expected_agreement)

  return kappa

    -------

    A numpy nd.array containing metric values for each task.

    """

    if len(y_true.shape) > 1:

      n_samples, n_tasks = y_true.shape[0], y_true.shape[1]

    else:

      n_samples, n_tasks = y_true.shape[0], 1

    if self.mode == "classification":

      y_pred = np.reshape(y_pred, (n_samples, n_tasks, n_classes))

    n_samples = y_true.shape[0]

    expected_dims = (3 if self.mode == "classification" else 2)

    if len(y_pred.shape) < expected_dims:

      n_tasks = 1

      y_true = np.expand_dims(y_true, 1)

      y_pred = np.expand_dims(y_pred, 1)

    else:

      y_pred = np.reshape(y_pred, (n_samples, n_tasks))

    y_true = np.reshape(y_true, (n_samples, n_tasks))

      n_tasks = y_pred.shape[1]

    if w is None or len(w) == 0:

      w = np.ones_like(y_true)

    assert y_true.shape[0] == y_pred.shape[0] == w.shape[0]

      if self.mode == "regression":

        y_pred_task = y_pred[:, task]

      else:

        y_pred_task = y_pred[:, task, :]

        y_pred_task = y_pred[:, task]

      w_task = w[:, task]

      metric_value = self.compute_singletask_metric(y_task, y_pred_task, w_task)

    if not y_true.size:

      return np.nan

    y_true = np.reshape(y_true, (n_samples,))

    if self.mode == "classification":

      n_classes = y_pred.shape[-1]

      # TODO(rbharath): This has been a major source of bugs. Is there a more