How to use the aif360.metrics.utils.compute_num_pos_neg function in aif360

num_classes = 2  # binary label dataset
        dirichlet_alpha = concentration / num_classes

        # compute counts for all intersecting groups, e.g. black-women, white-man, etc
        intersect_groups = np.unique(self.dataset.protected_attributes, axis=0)
        num_intersects = len(intersect_groups)
        counts_pos = np.zeros(num_intersects)
        counts_total = np.zeros(num_intersects)
        for i in range(num_intersects):
            condition = [dict(zip(self.dataset.protected_attribute_names,
                                  intersect_groups[i]))]
            counts_total[i] = utils.compute_num_instances(
                    self.dataset.protected_attributes,
                    self.dataset.instance_weights,
                    self.dataset.protected_attribute_names, condition=condition)
            counts_pos[i] = utils.compute_num_pos_neg(
                    self.dataset.protected_attributes, labels,
                    self.dataset.instance_weights,
                    self.dataset.protected_attribute_names,
                    self.dataset.favorable_label, condition=condition)

        # probability of y given S (p(y=1|S))
        return (counts_pos + dirichlet_alpha) / (counts_total + concentration)

def num_pred_negatives(self, privileged=None):
        r""":math:`\sum_{i=1}^n \mathbb{1}[\hat{y}_i = \text{unfavorable}]`

        Args:
            privileged (bool, optional): Boolean prescribing whether to
                condition this metric on the `privileged_groups`, if `True`, or
                the `unprivileged_groups`, if `False`. Defaults to `None`
                meaning this metric is computed over the entire dataset.

        Raises:
            AttributeError: `privileged_groups` or `unprivileged_groups` 
                must be provided at initialization to condition on them.
        """
        condition = self._to_condition(privileged)

        return utils.compute_num_pos_neg(
            self.classified_dataset.protected_attributes,
            self.classified_dataset.labels,
            self.classified_dataset.instance_weights,
            self.classified_dataset.protected_attribute_names,
            self.classified_dataset.unfavorable_label,
            condition=condition)

r"""Compute the number of positives,
        :math:`P = \sum_{i=1}^n \mathbb{1}[y_i = 1]`,
        optionally conditioned on protected attributes.

        Args:
            privileged (bool, optional): Boolean prescribing whether to
                condition this metric on the `privileged_groups`, if `True`, or
                the `unprivileged_groups`, if `False`. Defaults to `None`
                meaning this metric is computed over the entire dataset.

        Raises:
            AttributeError: `privileged_groups` or `unprivileged_groups` must be
                must be provided at initialization to condition on them.
        """
        condition = self._to_condition(privileged)
        return utils.compute_num_pos_neg(self.dataset.protected_attributes,
            self.dataset.labels, self.dataset.instance_weights,
            self.dataset.protected_attribute_names,
            self.dataset.favorable_label, condition=condition)

r"""Compute the number of negatives,
        :math:`N = \sum_{i=1}^n \mathbb{1}[y_i = 0]`, optionally conditioned on
        protected attributes.

        Args:
            privileged (bool, optional): Boolean prescribing whether to
                condition this metric on the `privileged_groups`, if `True`, or
                the `unprivileged_groups`, if `False`. Defaults to `None`
                meaning this metric is computed over the entire dataset.

        Raises:
            AttributeError: `privileged_groups` or `unprivileged_groups` must be
                must be provided at initialization to condition on them.
        """
        condition = self._to_condition(privileged)
        return utils.compute_num_pos_neg(self.dataset.protected_attributes,
            self.dataset.labels, self.dataset.instance_weights,
            self.dataset.protected_attribute_names,
            self.dataset.unfavorable_label, condition=condition)

def num_pred_positives(self, privileged=None):
        r""":math:`\sum_{i=1}^n \mathbb{1}[\hat{y}_i = \text{favorable}]`

        Args:
            privileged (bool, optional): Boolean prescribing whether to
                condition this metric on the `privileged_groups`, if `True`, or
                the `unprivileged_groups`, if `False`. Defaults to `None`
                meaning this metric is computed over the entire dataset.

        Raises:
            AttributeError: `privileged_groups` or `unprivileged_groups` 
                must be provided at initialization to condition on them.
        """
        condition = self._to_condition(privileged)

        return utils.compute_num_pos_neg(
            self.classified_dataset.protected_attributes,
            self.classified_dataset.labels,
            self.classified_dataset.instance_weights,
            self.classified_dataset.protected_attribute_names,
            self.classified_dataset.favorable_label,
            condition=condition)