Method transform_labels

ot/da.py:727–765

r"""Propagate source labels :math:`\mathbf{y_s}` to obtain estimated target labels as in :ref:`[27] `. Parameters ---------- ys : array-like, shape (n_source_samples,) The source class labels Returns

(self, ys=None)

            return transp_Xs

    def transform_labels(self, ys=None):
        r"""Propagate source labels :math:`\mathbf{y_s}` to obtain estimated target labels as in
        :ref:`[27] <references-basetransport-transform-labels>`.

        Parameters
        ----------
        ys : array-like, shape (n_source_samples,)
            The source class labels

        Returns
        -------
        transp_ys : array-like, shape (n_target_samples, nb_classes)
            Estimated soft target labels.


        .. _references-basetransport-transform-labels:
        References
        ----------
        .. [27] Ievgen Redko, Nicolas Courty, Rémi Flamary, Devis Tuia
            "Optimal transport for multi-source domain adaptation under target shift",
            International Conference on Artificial Intelligence and Statistics (AISTATS), 2019.

        """
        nx = self.nx

        # check the necessary inputs parameters are here
        if check_params(ys=ys):
            # perform label propagation
            transp = self.coupling_ / nx.sum(self.coupling_, axis=0)[None, :]

            # set nans to 0
            transp = nx.nan_to_num(transp, nan=0, posinf=0, neginf=0)

            # compute propagated labels
            labels = label_normalization(ys)
            masks = labels_to_masks(labels, nx=nx, type_as=transp)
            transp_ys = nx.dot(masks.T, transp)

            return transp_ys.T

    def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128):
        r"""Transports target samples :math:`\mathbf{X_t}` onto source samples :math:`\mathbf{X_s}`